Reviewer #3 (Public Review):

Meyer, Benjamin et al. identified the enzyme involved in the transfer of the second GlcNAC residue on the nascent oligosaccharide in protein N-glycosylation of the thermophilic Crenarchaeon Sulfolobus acidocaldarius. Although N-glycosylation is well-known in Euryarchaeota, the enzymes involved in this process, their substrates, and the mechanisms followed to produce the mature glycan are still elusive in Crenarchaeota, a phylum belonging to TACK archaeal superphylum, which contains also Thaumarchaeota, Aigarchaeota, and Korarchaeota. The authors, by screening the data banks with the sequences of the bacterial MurG and yeast ALg13/Alg14, which catalyze the transfer of GlcNAC in N-glycosylation, identified a gene, named saci1262 and alg24 showing very low identity. The authors characterized in deep the product of this gene with a very complete approach. Firstly, the authors could demonstrate by molecular modelling that Alg24 enzyme shows a 3D structure similar to those of MurG and Alg13/Alg14, and catalytic residues similar to the latter enzyme. The functional characterization was very complete, showing that alg24 is essential in vivo, and that the recombinant Alg24 specifically uses UDP-GlcNAC and lipid-GlcNAc as donor and acceptors substrates, respectively. In addition, the enzyme was thermophilic, did not require metals for catalysis, and followed an 'inverting' reaction mechanism in which the anomeric configuration of the product is the opposite to that of the substrate. Experiments of site-directed mutagenesis demonstrated that His14 is essential for catalysis as predicted by sequence multialignments and inspection of the 3D models, while the role of Glu114, also invariant, remained obscure. Then, the phylogenetic analysis of Alg13/Alg14 on TACK archaeal superphylum, showed that Alg24 are widespread among Archaea, suggesting that N-glycosylation in Eukaryotes was inherited by an archaeal ancient ancestor. This observation fostered the hypothesis that the first eukaryotic cell originated from Asgard superphylum.

Strengths:

The main question of the work, which is the enzyme involved in the first crucial step of protein glycosylation in Archaea? is of general interest in glycobiology. Although this process, in the past believed a peculiarity of Eukaryotes, has been well studied in Euryarchaeota, it is almost unknown in TACK superphylum that, being considered the closest to the Last Eukaryotic Common Ancestor, is a very interesting matter of study. The work shows several strengths:

1) The authors unequivocally demonstrated that Alg24 is the enzyme catalyzing the transfer of the second GlcNAc unit on the nascent oligosaccharide, thereby completing the puzzle of the first step of N-glycosylation, for which only AlgH enzyme was known so far.

2) The approach used to identify Alg24, the choice of the model system, the characterization of the enzyme are absolutely excellent and set a new standard to study N-glycosylation in Archaea.

3) The identification and characterization of a novel Glycosyl Transferase is of great importance in glycobiology. GTs are elusive enzyme, difficult to purify, due to their instability and association to membranes, and to characterize because of their extreme specificity for donor and acceptor substrates. In addition, GT enzymatic assays use very expensive substrates and very laborious procedures. For this reason, characterized GT are by far less common than, for instance, glycoside hydrolases. This study is a milestone for glycobiology. GTs from thermophilic microorganisms could be interesting subject studies in general. Thermostable GT could be more easy to purify and characterize if compared to their mesophilic counterparts.

4) The knock-out in vivo of alg24 gene, was possible because S. acidocaldarius model system is one of the few Crenarchaea for which reliable molecular genetics tools can be used. These experiments, confirmed that N-glycosylation is essential in Crenarchaeota as previously shown for AlgH and AlgB.

Weaknesses:

There are not many weaknesses in this work.

1) How the characterization of Alg24 is directly connected to the evidence that N-glycosylation in Eukaryotes was inherited from an ancestral archaeal cell should be better explained.

2) The novelty of the presence of Alg13/14 and Alg24 homologues in TACK superphylum shown in this paper should be commented in comparison with the available literature.

3) The Cover Art should be revised. The 'take home message' is not clear and the phylogenetic interdependencies of the different superphyla are a bit confusing.

Reviewer #3 (Public Review):

Evolution is a historical phenomenon that plays out over time through the complex interaction of the stochastic processes of mutation and genetic drift and the deterministic process of natural selection. Biology has seen a vibrant debate over the last few decades over what this means for the repeatability of evolution, and to what degree evolutionary outcomes are shaped by the combination of necessity, chance, and historical contingency. This debate has led to intense empirical study of these factors in evolution. Reconstruction and examination of functional protein evolution has been one of the cleverest and most interesting systems used in this study. Here, the authors seek to examine the roles of chance, contingency, and necessity in the evolution of protein-protein interactions (PPI) between BCL-2 family proteins and their coregulators. They specifically look at the evolution of specific interaction between BCL-2 and BID and the more generalized interaction between MCL-1 and coregulators BID and NOXA. They authors reconstructed the last common ancestor protein of BCL-2 and MCL-1 and a series of intermediates along their respective lines of descent. They then used a very clever Phage Assisted Continuous Evolution (PACE) system to subject replicates from each time point to selection for different PPIs and examined variation in sequence variation. By looking at evolution in replicates from different time points, they were able to disentangle the effects of chance, contingency, and necessity. They found that necessity played little role in protein evolution, with little predictability between replicates of single time points and among those from multiple time points, indicating that there was no single pathway through sequence space to the selected function. They did, however, find strong and synergistic effects of chance and contingency. They did tests to demonstrate that the effects of contingency were due to epistatic interactions that affect the viability of particular historical paths. Chance, meanwhile, had effects because multiple mutations could lead down paths to the selected function. The authors conclude that history and chance must be considered when attempting to understand protein function evolution, and that the sequences of proteins with given functions reflect do not reflect necessary pathways or constrained endpoints, but particular and idiosyncratic histories. Moreover, they suggest that contingency may need to be considered as a fundamental aspect of the evolutionary process, along with mutation, drift, and selection.

Altogether, this is a wonderful and interesting manuscript that makes a substantial and material contribution to our understanding of how history and chance affect evolution. It even speaks to the nature of more fine-grained protein sequence evolution relative to neutral and adaptationist theories. The amount of work and thought that went into the research is nothing less than astonishing. Every time I found myself wondering, "but did they check this...", I found that they, in fact, did in the next section. The work is solid, and the results are robust. I do not see anything that concerns me in the nitty gritty of the actual scientific work. I do, however, think that the authors should engage the work that philosophers of science have done in the last decade or so to better develop our conceptual understanding of contingency and reconsider the meaning of their findings in light of that work.

Reviewer #3 (Public Review):

Summary

The authors have applied a comprehensive bioinformatics analysis to 31,910 prokaryotic genomes and found evidence for extracytosolic flavin transferases ("ApbE") in approximately 50% of the genomes. Moreover, they have analyzed associated gene clusters resulting in the hypothesis that five protein classes are involved in transmembrane electron transfer. Furthermore, the authors postulate that these protein classes are subject to flavinylation by ApbEs. Although the exact biochemical role of these five classes of protein remains unknown, the authors hypothesize that they might be involved in iron assimilation and respiration, at least in some cases. In this context, the authors also identified multi-flavinylated proteins and propose that these might exert a similar role as multi-heme cytochromes, for example under iron depletion; in other words, multi-flavinylated systems might replace multi-heme cytochromes if iron is limiting.

Strength & weaknesses

As is evident from the summary, the basis of the article is the bioinformatic analysis of prokaryotic genomes leading to a number of interesting hypotheses with regard to transmembrane electron transport of hitherto uncharacterized protein complexes. Thus, the proposed functions of the potentially flavinylated membrane complexes will stimulate biochemical studies to characterize the suggested involvement of flavinylated protein complexes in prokaryotes. I would consider this as the main strength of the paper that it has generated multiple challenging hypotheses to follow up experimentally.

As mentioned by the authors, about 50% of the prokaryotic genomes analyzed harbor targets for flavinylation/and the FMN transferase. However, no discussion and not even a hint is provided what these 50% of prokaryotes have in common and what distinguishes this group from the other (50%) prokaryotes. Is it lifestyle (environment), energy production, ...?

On the other hand, the presented study leaves many issues unmentioned creating the (false) impression that all it takes to transport electrons across the membrane is a series of hemes and/or flavins along the way. For example, in the discussion of the very interesting hypothesis that flavinylation might replace multi-heme cytochromes under iron deficiency, discussed on page 20 (last para), the authors mention that "flavins possess two-electron transferring properties (ref. 46)" in contrast to the heme system. If this were true than the switch from heme to flavin would also imply that the electron transport itself would have to change from one- electron to two-electron transport. It is unclear that this would be compatible with all other components of the electron transport system. On the other hand, flavins can also - under certain circumstances and in certain environments - carry out one-electron transfer processes, e. g. DNA-photolyases, flavodoxins, etc. Thus, it is conceivable that the flavins operating in the suggested systems in prokaryotes also perform one-electron transport, similar to the operating mode of heme cytochromes. It is clear that we currently lack the biochemical/physical information to know what is really going on, but at least it should be discussed more thoroughly. Equally, several other aspects of the (multi-)flavinylation should be addressed:

• What is known about the environment of the flavin(s)? - Is the flavin embedded in a protein matrix or freely accessible, in other words does it "behave" like a "free" flavin?

• How does the binding of the flavin affect the redox potential (this is very important in order to understand the direction of electron transport).

• In contrast to other covalent flavin attachments, the flavinylation addressed in the current work is reversible. Is anything known about the removal of flavins from the protein complexes in question?

• Are there any enzymes that carry out de-flavinylation? If so, how are they regulated?

• Connected to the last bullet point: Is the reversibility of flavinylation used for the overall regulation of electron transport?

I assume that most of the questions cannot be satisfactorily answered yet, but I think these issues should at least be addressed in the discussion in order to stress the need for further in depths biochemical studies that target the obvious complexity of these systems.

Reviewer #3 (Public Review):

Mutations in DHCR7, a key enzyme in cholestrol biosysnthesis have been shown to result in Smith-Lemili-Opitz syndrome. However, the mechanism by which loss of this enzyme alters brain development has not been resolved.

In this study, the authors demonstrate that DHCR7 depletion results in depletion of cholestrol in the brain and also the accumulation of the substrate 7 dehydrocholestrol. These observations are conserved in both the brain of DHCR7 knockout mice as well as patient derived iPSC differentiated in vitro.

The authors present evidence that the developmental defects in the brain are a consequence of accelerated differentiation of NSC into neural cells. These defects could be recapitulated by the addition of 7DHC metabolites on wild type cells.

Throughout the manuscript, the authors demonstrate that their findings are conserved between DHC7 k/o mice and patient derived iPSC for SLO syndrome.

To explain the mechanism underlying the cellular phenotypes described, authors propose that the accumulated 7DHC metabolites bind to and activate the glucocorticoid receptor leading to transcriptional activity.

Overall this paper attempts to provide a comprehensive mechanistic explanation for the neurodevelopmental phenotype arising from the loss of a lipid metabolizing enzyme.

Reviewer #3 (Public Review):

The regulation of the calcium pump SERCA by phospholamban has been studied extensively over many years as this system has become a focus of many biophysical approaches to study the interplay between protein dynamics, the biological function of calcium transport, and its regulation via protein-protein interactions, all of which are occurring within the environment of the sarcoplasmic membrane of heart muscle.

The authors themselves have a long track record with working on this system and the specific focus here is on the detailed mechanism of how phosphorylation of phospholamban leads to a release of its inhibitory function when bound to SERCA. Much effort has been spent on this question in the past, and the field has progressed over the years by deriving increasingly detailed structural models for SERCA-phospholamban interactions. There is now a structure from crystallography showing the interaction of the phospholamban TM domain with the SERCA TM helices and there is additional data from various biophysical methods that partially describe the conformational ensemble of the extramembrane N-terminal region of phospholamban and its interaction with SERCA. Some of that insight has distinguished between phosphorylated and unphosphorylated phospholamban, but despite much data and many simulation efforts, the exact mechanism for how phosphorylation of phospholamban alters its interaction with SERCA and thereby modulates its inhibitory functions has so far not been clearly described. This is the main goal of the present work.

There is new experimental data presented here from oriented-sample solid-state NMR experiments with the main finding of orientational shifts of the phospholamban TM helix upon binding to SERCA and upon phosphorylation. Taking advantage of this data, the main part of the study is concerned with results from computer simulations that were restrained by experimental data to develop conformational ensembles of the SERCA-phospholamban complex with and without phosphorylated phospholamban. From that, new mechanistic hypotheses are developed. While the direction of the work proposed here is promising, there are concerns about the overall approach and - as a consequence - the significance of the reported findings:

1) A main concern is the treatment of the extramembrane portion of phospholamban, which includes the serine that is being phosphorylated to relieve the inhibitory effect. Previous studies have described a helical conformation for the N-terminal segment that may be in equilibrium with a less-ordered/less-helical structure upon binding to SERCA. It is largely still not clear, however, how exactly that part of phospholamban would interact with SERCA. The idea put forth here is that a largely disordered conformation would interact with SERCA. That may be so, but it is unclear how much of that is a direct result of experimental constraints and how much could simply be a consequence of inadequate sampling. It seems that helical conformations for the N-terminal segment of phospholamban were not considered, while there is not enough discussion of why such conformations would be ruled out based on the experimental data.

2) The simulations are probably too short to fully explore the full conformational landscape of a (partially) disordered N-terminal phospholamban and it is unclear how much the experimental constraints are really limiting the conformational space in that region.

3) It is not completely clear how the present work relates to the crystal structure of the SERCA-phospholamban complex. Why were the starting structures for the SERCA-phospholamban complex initially taken from the available crystal structure (at least with respect to the TM domain of phospholamban) but then subsequently refined using much lower-resolution cross-linking data before initiating the simualtions? Is the crystal structure in significant disagreement with other experimental data considered here? More discussion and explanation is needed.

4) The main focus of the analysis of the simulation results is on the impact of phosphorylated phospholamban on the conformational sampling of SERCA. That is the key step for developing new mechanistic hypotheses. However, given that the SERCA-phospholamban complex is very large and flexible and based on the results presented, it appears that the length of the simulations may not be sufficient to fully characterize the shift in the conformational ensemble of SERCA as a function of phospholamban phosphorylation. At the minimum, some time of convergence analysis is needed to establish confidence that the difference in conformational ensembles shown most prominently in Figure 2 are indeed significant. Moreover, related to Figure 2, it is unclear whether the projection of the conformational sampling onto just two principal coordinates is sufficient for a full characterization of the conformational dynamics. It is also unclear whether the principal coordinates are the same when projecting the sampling for PLN and pPLN, if not, the comparison between the two would be further complicated.

Reviewer #3 (Public Review):

Using a combination of powerful approaches authors demonstrate large variability in the number of release sites at hippocampal excitatory synapses onto fast spiking interneurons in slices. High resolution studies of individual synapses showed highly variable amounts of Munc13-1within the AZs that have the same number of release sites. The authors further revealed a synapse size-independent variability in the number of Munc13-1 clusters per AZ and in the Munc13-1 content of individual clusters. There results support the presence of multiple independent release sites and provide insight into molecular heterogeneity of release sites.

This is a high quality study using most advanced techniques available to study molecular determinants of AZ organization. In addition to some technical issues, my main concern is conceptual: this work, although of very good quality overall, is rather incremental because it largely confirms several previous studies showing a large variability in the number of release sites per AZ in small central synapses, the association between Munc-13 and release site properties, and variability in Munc-13 content. Surprisingly only one of the three of these previous studies have been cited or discussed. My second concern is that the paper could be written more clearly - there are multiple terms used to refer to the same concepts making it difficult to follow and there is some conceptual logical fallacy in the way the results are discussed.

Reviewer #3 (Public Review):

This is an interesting paper combining several impressive techniques to argue that synaptically released glutamate is allowed to diffuse to and activate receptors at much greater distance than previously thought. iGluSnFR recordings show that glutamate released from single vesicles activates the indicator with a spatial spread (length constant) of 1.2 um, substantially farther than previous estimates based on the time course of glutamate clearance by glial transporters (PMC6725141). Similar parameters are observed with spontaneous and evoked events, large or small, or when glutamate is released via 2P uncaging. Further uncaging experiments show that both AMPARs and especially NMDARs are activated a substantial distance. AMPARs, previously thought to be recruited only within active synapses, are activated with a spatial length constant that compares quite closely with the average distance between synapses in the hippocampus. More heroic experiments and some geometric calculations show that this behavior enables neighboring synapses to interact supralinearly. The results suggest that "crosstalk" between neighboring synapses may be substantially more common than previously thought.

The experiments in this paper appear carefully performed and are analyzed thoroughly. Despite all of the quantitative rigor and careful thought, however, the authors fail to reconcile convincingly their results with what we know about neuropil structure and the laws of diffusion. There are very good data in the literature regarding the extracellular volume fraction and geometric tortuosity of the neuropil, the diffusion characteristics of glutamate and the time course of glutamate uptake. These data more or less demand that synaptically released glutamate is diluted over a much smaller spatial range than that suggested here. In the Discussion, the authors suggest that this discrepancy might reflect a simplified view of the neuropil as an isotropic diffusion medium (PMC6763864, PMC6792642, PMC6725141), whereas a more realistic network of sheets and tunnels (PMC3540825) might prolong the extracellular lifetime of neurotransmitter. I like this idea in principle, but there is no quantitative support in the paper for the claim - in fact, it seems at odds with the authors' very nice demonstration that diffusion appears to be similar in all directions (Figure 3B). I don't necessarily think a solution is within the scope of this single paper, but I would suggest that the authors acknowledge the present lack of a compelling explanation.

Reviewer #3 (Public Review):

Bifurcation between topological loading and loop extrusion is determined by DNA passing through the N-gate. For loop extrusion to occur processively, this decision needs to be made only once at the beginning. However, the authors also argue that Scc2 dissociation between rounds of ATPase cycles is required for symmetric loop extrusion. In combination, the model requires that N-gate opening is allowed only at the very beginning and cannot occur during loop extrusion, even when the cohesion loader is released. The authors should state whether this interpretation is correct and feasible given the structural data.

Loop extrusion has never been observed using yeast cohesin. It will be important to learn how the authors reconcile their model and the lack of experimental demonstration of loop extrusion in a reconstituted system.

The discrepancy in speed and the measured ATPase rate is not discussed. In vitro, loop extrusion rates are about 1000 bp per second and in vivo measurements of gamma-H2AX spreading from a double strand break, ~150kbp per min according to PMID: 32527834, which was proposed to be caused by loop extrusion (PMID: 33597753), also matches that in vitro rate. But the authors model accounts for only about 100 bp extrusion per ATPase cycle whereas the average ATPase rate is 1 per second. They do mention that the model requires 9 ATP hydrolyzed per second but do not make an attempt to explain the discrepancy.

Reviewer #3 (Public Review):

The manuscript entitled "Biosynthesis system of Synechan, a sulfated exopolysaccharide, in the model cyanobacterium Synechocystis sp. PCC 6803" is a scientifically sound manuscript and is of interest for a broad scientific audience. It provides interesting and valuable new insights and many experiments were performed. However, there are some points which must be addressed to make the manuscript more consistent and easier to grasp.

• Title: I would suggest to change the title, since Biosynthesis system is not a common term.
• Abstract: Cyanbobacteria are not unique in having sulphated polysaccharides. What is about Carrageenan's and also exopolysaccharides from Porphyridium strains (see current publications on that). If it means that amongst bacteria the cyanobacteria are the only ones, this should be clearly stated.
• Would avoid to use may utilize the polysaccharides... Please be more specific or delete this.
• Lane 32: Can really every bacterium produce several EPS? This should be carefully evaluated.
• Lane 34: The applications named are very broad and not specific, what are the real applications there?
• Lane 49: again uniquely?
• Lane 56: the sulphated polysaccharides are used for colony and biofilm... This sentence must be rephrased and corrected.
• Lane 84: bubbling culture etc. I can´t find any detailed explanation on the cultivation systems, what is essential for the methods part. Please add volume, light source and principle of illumination (inside outside etc.). Please rephrase the sentence that the light was generated by fluorescent lamps.... They were used for illumination.
• Lane 97: GTs can not be screened by disruption, it is their function what is screened.
• Figure: would suggest to use A) instead of A,
• Table S1: What does Importance mean in the table, would suggest to change that towards a more specific value/information
• Lane 232: to see the transcriptome... this should be rephrased
• The description of the different EPS is a bit confusing, since it is only described that the WT contains several sugars, which are then given in table 1. The deletion strain shows a different composition. This should be explained a bit straighter. Why is ribose given in table 1, if there is no ribose observed? In general, the whole manuscript needs correction of the English language to make it clearer in some aspects. Also, the structure of the manuscript might be reworked a bit, since it is a bit confusing in some parts. Especially the effect of the different deletions should be given clearly and straight. Also, the complexity of the manuscript will be easier to grasp by some rearrangements of the results. The current complexity might come from having all supplement figures already in the manuscript, but it also comes from sometimes complex sentences, as well as jumping a bit in between the topics. But finally, this is a really valuable and interesting study.

Reviewer #3 (Public Review):

In this work, the authors used in vitro binding and liposome fusion assays to study how Sec17 and Sec18 regulate SNARE-driven fusion. In previous studies, it was found that deletion of the C-terminal layers of the Qc SNARE involved in yeast vacuole fusion blocks fusion but the inhibition can be partially bypassed by addition of Sec17 and Sec18. This work extended the finding and showed that Sec17 and Sec18 can even restore fusion when two Q SNAREs are C-terminally truncated and the third chain bears point mutations. The authors conclude that HOPS and membrane anchored Rabs first promote the tethering of vacuole membranes. Subsequently, HOPS promotes membrane docking - the initial assembly of the SNAREs, likely through the SM protein in the HOPS complex. Then Sec17 and Sec18 kick in to activate the zippering of membrane-proximal regions of SNAREs. This function seems to require interactions of Sec17 with HOPS. The findings are unexpected and raise important questions.

Reviewer #3:

The prevalent treatment options for LSCC are limited in efficacy. Through genetic inactivation of Usp28 in a novel lung cancer mouse model, and chemical inhibition of Usp28 in induced LSCC in mice and human LSCC xenograft tumors, the authors demonstrated the specific dependency of LSCC (but not LADC) on the protein deubiquitinase Usp28. The authors also showed that loss of Usp28 by either means leads to depletion of the oncoproteins c-Myc, p63 and c-Jun in LSCC. Finally, the authors described a novel small molecule that is specific for Usp25/28 among a group of assessed deubiquitinases. Based on these results, the authors suggested chemically targeting USP28 as a potential therapeutic option for human LSCC patients.

Strengths: The presentation of the work is clear, concise and easily readable. The data presented largely supports the authors' conclusions on the role of USP28 in LSCC tumorigenesis and that inhibition of USP28 is a viable therapeutic option for LSCC treatment. The generation of the KFCU mice model that can give rise to both LADC and LSCC concurrently is interesting and presents a valuable tool for the wider cancer community.

Weakness: The manuscript can benefit from a deeper analysis of the relationship between FBW7 and USP28 in patient cohorts. A comparison of the activity/efficacy of FT206 to existing USP28 inhibitors will also be helpful.

Reviewer #3 (Public Review):

This paper demonstrates the additional utility that can be extracted from short-read genome resources such as the genomes from the 1000 Genomes Project by leveraging variant discovery in long-read platforms. These genotyped variants can be used for eQTL studies, or to identify potential signatures of selection. Thus, low-coverage population-scale sequencing datasets such as the 1000 Genomes data can still be of use when coupled with other datasets.

One of the challenges I have with this manuscript however is clearly understanding the novel aspects of the reported results in the context of previous work in this field. Initially, it is unclear how many of the genotyped variants are already in the 1000 Gnomes dataset, this should be clearly reported. Comparisons of LD to nearby SNPs does not take into account that the SV discovery in the 1000-genomes project was done separately from the SNP calling. Thus, while it is suggested as presented that most of these variants were previously intractable, this is insufficiently explored. Additionally, discussion of low LD with SVs is well documented in 1KG and elsewhere. Subsequently, the eQTL analyses are "broadly consistent" with previously reported eQTL analyses from both the 1000 genomes project and GTEx, but no direct comparison is performed. If the overall goal is to point out that using additional datasets can identify new variants that can be genotyped, it is important to perform comparisons to other population-scale datasets such as HGDP and SGDP (Almarri et al Cell, Hseih et al Science, etc). In these cases, higher coverage sequencing allowed discovery of variants which could then be genotyped, similar to this paper's assertion that long-read sequencing provided a new discovery set for subsequent genotyping. Indeed, the two highly stratified variants selected for follow up are reported in gnomAD. The paper mostly focusses on the identification of highly stratified loci. Again, comparison to previously reported highly stratified loci (1KG, Sudmant et al 2015, and Almarri 2020, Hseih et al) is necessary here.

Furthermore, while the analyses of the IGH hapotype are clearly presented and interesting, as noted in the manuscript, these have already been identified. The authors mention that this locus was already identified but suggest it was "not further examined," due to "stringent filtering" however this locus was reported as one of 11 "high frequency introgressed regions" thus this description seems to mischaracterize Browning et al's recognition of the importance of this locus. The strongest part of the manuscript is the ABC modelling of the IGH haplotype elucidating the putatively extremely strong selective signatures at this locus. More focus on these results and the importance of following up and fully understanding such loci would benefit the manuscript. Broadly, this paper is well written and clearly presented however would be very much strengthened by placing it more broadly in the context of previous work and focusing more on the novel modelling analyses of specific loci that are performed.

Reviewer #3 (Public Review):

The article by Sprenger et al. uses the power of yeast genetics to generate mutants of the ESCRT-III subunits, and study their impact on the formation of a functional ESCRT-III complex. By using functional FP tags of subunits Snf7, Vps2 and Vps24, and of the CPS cargo, they essentially follow recruitment of subunits to the vacuolar membrane, formation of Class E compartments and sorting of CPS as readouts of the endosomal ESCRT-III function. They found that recruitment of Vps2, Vps24 and Snf7 is unaffected by deletions of other subunits (Did2, Ist1, Vps60), supporting the view that Vps2-Vps24 and Snf7 form an initial subcomplex.

To decipher molecular interactions between Vps2-Vps24 and Snf7 subunits, they use point mutants to replace well-chosen hydrophobic residues in two subunits by cysteines, and cross-link them to probe the interactions of those residues in the functional case. They also change hydrophobic residue pairs into charged residue pairs to replace the hydrophobic interaction by an electrostatic interaction, and restore functionality (only when both mutants were used).

Overall, it is an elegant study, with very clear and well executed experiments, and which give strong support to a so far hypothetical architecture of the Vps2-Vps24-Snf7 as a double-strand filament, one of which is Snf7 only, and the other is an alternative repeat of Vps2 and Vps24.

Reviewer #3 (Public Review):

In this manuscript, Takaine et. al. leveraged their QUEEN ATP biosensor to ask an interesting and important question: how and why cells maintain high and stable ATP concentrations in Saccharomyces cerevisiae.

The strength of their approach is to obtain single-cell quantification of ATP concentration over time. They use the technology to demonstrate the importance of the AMP kinase, and two other proteins involved in ATP synthesis/homeostasis (the adenylate kinase, ADK1, and the transcription factor, BAS1) in the maintenance of stable and high levels of ATP.

The main novelty of their findings with respect to ATP homeostasis is the detection of sudden, transient decreases in ATP concentration in mutants. The main claim in the title and abstract of the paper is that "High and stable ATP levels prevent aberrant intracellular protein aggregation". In our opinion, the data do not yet support this claim.

Essential issues:

1) The most important missing experiment, which would be required to support the title, is to image both ATP levels and protein aggregation events in the same cell. The current dataset shows that the mutants under study have both decreased ATP levels and suggest that these levels are less stable, and finally that complete ATP depletion leads to protein aggregation, but it is not possible to extrapolate these observations to the current conclusions.

2) The second most important issue is a lack of statistics with respect to spontaneous drops in ATP concentration. A couple of examples are shown, but it should be possible to obtain data for hundreds of cells. Do the examples in figure 2 represent 90% of cells? 1% of cells? 1/1000? We need to be given a more complete sense of the penetrance of these effects.

Reviewer #3 (Public Review):

Cellular quiescence, the reversible exit from the cell cycle, is essential for long-term cell survival. One feature of quiescent cells is transcription inactivation and this paper examines gene reactivation during quiescence exit and the accompanying changes to chromatin structure. Using a variety of genome-wide analyses, including 4tU-seq, ChIP-seq, and MNase-seq, the authors show that transcription occurs within minutes of quiescence exit, and for most genes, the initial rate of transcription exceeds that of normal cycling cells. Moreover, this work shows that gene repression during quiescence, and activation upon quiescence exit, are associated with distinct chromatin organization, particularly over promoters. Finally, the authors uncover a role for the RSC chromatin-remodeling complex in establishing a chromatin organization that facilitates normal gene expression during quiescence exit. To support the above findings, the authors generated an impressive amount of sequencing datasets that robustly support their findings and will undoubtedly be of great use to many yeast transcription researchers. Although more transparent and consistent bioinformatic analyses of these data would better communicate the findings, this work enhances our understanding of gene expression changes during the transition between key cell states and thus will be of interest to a broad spectrum of readers ranging from molecular to developmental biologists.

Reviewer #3 (Public Review):

In this manuscript, authors seek to resolve conflicting models for corepressor function using the elegant synthetic auxin response system. Auxin signaling is governed by a de-repression paradigm and is ideally suited to interrogate co-repressor function - in this case, the TOPLESS (TPL) co-repressor. Several contradicting models have been put forward for the mechanism of TPL-mediated gene repression, ranging from a requirement for protein oligomerization for activity, interaction with distinct partners, and even which regions of the protein are required for repressive activity. Leydon et al use the yeast-based synthetic auxin response system to interrogate these models using a single reporter locus, allowing for straight-forward examination of TPL function.

Reviewer #3 (Public Review):

In this manuscript, the authors aim to elucidate the evolutionary history of the paired NLRs Pik-1/Pik-2 in rice. They ask two primary questions:

(1) When (in evolutionary history) did the paired Pik-1/Pik-2 locus arise and when was the integrated domain integrated into the locus?

(2) Has the binding affinity of the integrated domain changed over evolutionary time?

The authors convincingly demonstrate that the integrated domain is undergoing positive selection, that its integration is ancient (~15MYA) and that inferred ancient alleles bind modern AVR-PikD with poor affinity. The subsequent biochemistry experiments and structural analyses identify which residues are important for interactions with AVR-PikD and which allelic combinations induce autoimmunity.

The biochemical work, while interesting in and of itself for identifying the interacting residues and interactions between domains, was less informative about the evolution of the NLR-effector interaction, and most of the work did not advance our understanding of the questions listed above. The most emphasized biochemistry finding was that of reduced binding affinity of ancestral Pik-1 integrated domain. Specifically, the authors demonstrate that modern AVR-PikD has poor affinity with the ancient Pik-1 integrated domain. From this result the authors infer that ancestral Pik-1 likely bound a different effector. But it was not clear how the authors ruled out binding to an ancient AVR-PikD? I was confused as to why the authors excluded this possibility. Perhaps the authors contend that the absence of the Avr-PikD in other modern blast lineages indicates Avr-PikD is unique to modern rice-infecting M. oryzae. But this modern absence does not preclude Avr-PikD in the ancestral population. Furthermore, changes in binding over time would be the effective null hypothesis in the scenario of coevolving NLR and effector. Their finding seems consistent with expectations of coevolution, a phenomenon that has been widely reported in interactions between NLRs and effectors. The novelty in this manuscript stems from the synthesis of molecular evolution analysis with ancestral state reconstruction and testing.

Overall this manuscript is exemplary in its integration of biochemical and evolutionary analyses to study plant-pathogen coevolution. While the findings are unsurprising, future emulation of this type of data integration will likely lead to significant insight into the coevolution of plants and their pathogens.

Reviewer #3 (Public Review):

In this study, the authors present a high-resolution single-cell transcriptomic atlas of the pancreatic ductal tree. Using a DBA+ lectin sorting strategy murine pancreatic duct, intrapancreatic bile duct, and pancreatobiliary cells were isolated and subjected to scRNA-seq. Computational analysis of the datasets unveiled important heterogeneity within the pancreatic ductal tree and identified unique cellular states. Furthermore, the authors compared these clusters to previously reported mouse and human pancreatic duct populations and focused on the functional properties of selected duct genes, including Spp1, Anxa3 and Geminin. Overall, the results presented here suggest distinct functional roles for subpopulations of duct cells in maintenance of duct cell identity and implication in chronic pancreatic inflammation. Finally, such detailed analysis of the pancreatic duct tree is relevant also in the context of cancer biology and might help elucidating the transition from pancreatitis to pancreatic cancer and/or different predisposition to cancer.

The study is very well done, with careful controls and well-designed experiments.

Reviewer #3 (Public Review):

The new models proposed here provide some potentially useful alternatives to estimating the generation time, serial interval, and the relative infectiousness of pre-symptomatic infections. The framing of the paper seems very focused on improving fits to the transmission pair data, however, and I think it would be more impactful to consider the implications of poor estimation of pre-symptomatic transmission and the generation time. I think this shift in focus could also help strengthen the narrative of the paper, which wavers between focusing on model fitting and the importance of implications for contact tracing.

I was a bit lost in the application of the models to the contact tracing example. The definition of the contact elicitation window (lines 142-144), where identification of contacts would occur up to x days prior to contact symptom onset, makes sense theoretically in this model comparison setting, but it is hard to translate these findings to real-world application. Are there any implications that could be useful for informing contact elicitation strategy (e.g., for how many days after time of infection or symptom onset could contact tracing have a measurable benefit in preventing onward transmissions?)

Lines 147-151: Given that the impact on onward transmission events is so dependent on the contact tracing assumptions, I would recommend stating the assumptions explicitly here, reporting the results in relative terms as compared to a single model, or both.

How different are the variable infectiousness model results from parameter estimates from the original studies that reported the transmission pairs data?

Can the authors comment on the plausibility of the infectiousness distribution in their new proposed models? While better model fitting certainly provides a measurable improvement to leveraging existing data, I'm not aware of studies that support the discontinuous assumptions about infectiousness made here.

Assuming alpha means the same thing across the models, why is the 95% credible interval so large for the Feretti model? In general, the model parameters should be more clearly explained for this model.

Reviewer #3 (Public Review):

Volatile anesthetics (VA) are thought to cause developmental defects in newborns and the authors previously studied the metabolic consequences of VA on newborn mice. Surprisingly, they found VA exposure rapidly and dramatically dropped circulating levels of the ketone beta-hydoxybutyrate (BHB). Newborn mice use ketones as energetic substrates (compared to glucose in weaned animals) so perturbing ketone metabolism could underpin some of the detrimental side-effects of VA. Therefore, the authors sought to determine why VA cause this drop in ketone availability in newborns.

The authors first found that multiple VAs rapidly (half maximal effect occurs in ~10min) and at subanesthetic doses decrease BHB levels from ~2mM to <1mM in newborn but not in older (older than P19) mice. Extended VA exposure (>60min) also caused a decrease in circulating glucose. BHB levels could be rescued by IP injection prior to anesthesia. Why do VAs cause this effect? Ketones are known to be produced by fatty acid oxidation in the liver. The authors therefore indirectly assessed fatty acid oxidation by measuring levels of acylcarnitines (an intermediate metabolite in fatty acid oxidation) in newborn livers after VA treatment and found lower levels of acylcarnitines consistent with lower levels of fatty acid oxidation in the liver upon VA treatment. Pharmacologically inhibiting fatty acid oxidation could also drop BHB levels in newborn plasma as well. Thus, the authors provide compelling evidence that VA exposure blocks fatty acid oxidation and ketogenesis in the liver of newborns and this underlies the drop in BHB in the circulation.

The authors next asked why VAs decreased fatty acid oxidation. VAs are thought to inhibit the electron transport chain (ETC) which would cause redox imbalances (particularly in the NAD/NADH ratio) that could lead to altered TCA cycle metabolic activity that could potentially impact fatty acid oxidation. The authors therefore indirectly tested this hypothesis by measuring TCA cycle intermediates and did by VA exposure altered newborn liver levels of several TCA cycle metabolites including citrate. Citrate is metabolized by the enzyme ACLY to generate cytosolic Ac-CoA which is used by the enzyme ACC to produce malonyl-CoA, an intermediate in lipid synthesis. Malonyl-CoA is also known to inhibit the production of acylcarnitines and fatty acid oxidation. Therefore, the higher levels of citrate in VA exposed livers prompted the authors to determine if VA exposure specifically in neonates increased malonyl-CoA and if this blocked fatty acid oxidation and ketogenesis. The authors measured malonyl-CoA in newborn livers and observed an increased upon VA exposure. ACC inhibitions have been developed and the authors found that ACC inhibition (which presumably would prevent malonyl-CoA formation) could partially rescue the drop in BHB brought on by VA exposure in newborns. Thus, this study delineates how altered fatty acid oxidation and ketogenesis in the liver underlies the drop in BHB elicited upon VA exposure and opens the door to future studies determining if the drop in BHB contributes to newborn sensitivity to VAs and future studies elucidating exactly how VA exposure alters the TCA cycle and citrate metabolism to block fatty acid oxidation.

Reviewer #3 (Public Review):

Zilova et al. investigate cell differentiation in aggregates made from cells of early medaka and zebrafish embryos upon culture in defined media. Using reporter lines and immunostaining, they find evidence for retinal differentiation and morphogenesis in these aggregates, the extent of which depends on the size of the aggregates. This dependence of patterning and morphogenesis on aggregate size indicates that these processes are at least partially controlled by cell-cell interactions in the population. The authors also perform experiments with cells from genetic mutants that indicate similar genetic control of retinal morphogenesis in aggregates and intact fish embryos.

This work is a nice example of morphogenesis of differentiated cell types upon dissociation and re-aggregation of early embryonic cells. The similar behaviour of aggregates from evolutionarily distant species reported in the manuscript underscores the generality of the findings. Organoid formation from teleost cells recapitulates species-specific timescales and is therefore faster than organoid generation from mammalian cells which constitutes a potential technical advantage of this system. The major advance of this work lies in providing a clear example that organoids consisting of early neural and retinal cells can be formed in non-mammalian species. Such an approach can open up new avenues for describing basic principles of cell differentiation and pattern formation during embryogenesis, and can thereby be useful to the community.

While the reported observations are highly interesting, the level of quantitative analysis currently does not fully support all of the author's interpretations and conclusions.

1) The authors variably interpret their observations as the result of self-assembly or self-organization. At the moment, the data does not allow distinguishing whether the observed phenomena result from cells following largely cell-autonomous differentiation paths and come together through cell sorting, or whether dissociation and aggregation generates a condition that leads to (spatially restricted) retinal differentiation in cells that would not normally adopt this fate. I would say that the first scenario is consistent with self-assembly, while the second one is more self-organized in the sense that the new cell-cell interactions resulting from the aggregation result in emergent cellular behaviours. A first step to distinguish between these possibilities would be to quantitatively demonstrate that aggregation biases cell differentiation towards neural and retinal fates at the expense of other cell types, compared to the intact embryo. The examples shown in Figure 2 and 3d seem to indicate an overrepresentation of neural cells, but it would be good to see a quantitative comparison to the embryo.

2) The authors use the term "primary embryonic stem cells" for the early embryonic cells that they aggregate. I find this problematic as some cells in this population may already have lineage bias and not have true multi-lineage potential. I also understand there is a difference between the cells that are used in this study and the teleost embryonic stem cells referenced in lines 49 and 50, in the sense that the latter were established as true self-renewing cell lines. But correct me if I have missed something here.

3) The authors claim that their system is highly reproducible. Unfortunately, they do not give an indication of the success rate of aggregate formation in figure 1. Figure 4 shows the most complex patterns, but I realize that there is quite a bit of variability in between the aggregates - they are just as likely to have one or two Rx2-expressing areas (panel b). I also could not find information how many aggregates show the patterns in panels e and f, and from how many aggregates the data in panels g - i has been collected.

Reviewer #3 (Public Review):

In the manuscript, "Dynamic persistence of UPEC intracellular bacterial communities in a human bladder-chip model of urinary tract infection" by Sharma, et al., the authors develop a bladder-on-chip model and provide evidence that this is a useful model for mimicking in vivo infections. The focus is on intracellular infection structures created by uropathogenic Escherichia coli (UPEC) seen in experimental mice infections and "real" human infections; such structures have been most extensively characterized in mouse models for obvious reasons. The authors focus on three key aspects: development of a structure known as an intracellular bacterial community (IBC), the neutrophil response to infecting UPEC, and the bacterial response to antibiotic treatment. There is a minor point about the ability to apply mechanical stretch to the model to mimic bladder filling and voiding.

In my assessment, key strengths of this work are:

1) Integration of both epithelial and vascular endothelial cell types, allowing for multiple fluid spaces and studies of neutrophil migration

2) Ability to apply mechanical stretch to the entire system to mimic changes in bladder volume

3) Extensive microscopic characterization of the model (a key feature enabled by this system) including live microscopy, immunostaining, and electron microscopy

I believe there is one key underlying issue with this paper: as a report on the technical development of a new system / device / technique, the authors have what amounts to a very strong hypothesis, namely that their new system is a good model for the in vivo infection. This leads to a general bias in both the presentation and, in my opinion, the interpretation of the data, to make the system sound "as good as possible". Key manifestations of this bias and overinterpretation include:

1) The immediate interpretation of all intracellular structures as IBCs.

2) The immediate interpretation of all data in Figure 2 as neutrophil swarms and NETs.

3) Some odd behaviors in response to ampicillin, which should not penetrate host cells and has been shown using the same cell types to not affect intracellular UPEC.

4) The claim that a 10% linear change in dimension is "physiologically relevant" and "a significant proportion" of that seen in vivo.

To clarify point 1 (which applies as well to point 2), IBC is an abbreviation for "intracellular bacterial community", and these were first described in mice. There has been very sparing molecular characterization of IBCs, which makes a morphological classification very tricky - I believe the field generally thinks that IBCs refer to a specific structure that is formed (at least) in mice and humans in vivo. Somewhat similar structures have been seen previously in vitro but rightfully are more carefully described with different terms or as "structures resembling IBCs". I think similar care needs to be taken with this model as well.

Overall, the authors have done quite a complete job in characterizing their model and have good data to argue for a morphological similarity to key steps that have been previously described to happen in vivo. I believe they get ahead of themselves both in data interpretation and in the writing of the manuscript, which leads to some oddness where it seems the authors begin to talk as if their model has already been validated. This occurs throughout the manuscript in the use of the IBC abbreviation and also largely in the section on neutrophil responses (in particular swarms and NETs). There are occasional sentences where the appropriate care is taken (i.e. that the data is being collected to argue that the structures seen are indeed NETs), but this is interspersed with writing that is assuming the point is already proven (for example, see lines 286 (appropriate) and 287-289 (not); and 471-476 (appropriate), 477-479 (not), 481-483 (appropriate)).

Regarding the ampicillin data, the odd behaviors are:

1) Apparent elimination of intracellular UPEC (particularly for large collections)

2) Apparent indifference for some intracellular UPEC (they continue to grow)

3) Ampicillin is generally thought to not cross host membranes, and in Blango & Mulvey 2010 it does not affect UPEC harbored within 5637 cells. The authors collect #1 and #2 under "dynamic heterogeneity" and then claim in the discussion that they can "realistically model antibiotic treatment regimens". Given these discrepancies listed above, I do not believe they can yet support this claim.

Finally, the ability to apply mechanical stretch is only used in one pilot experiment at the end, producing a suggestive result (that UPEC burden increases when a duty cycle of stretching and relaxing is used). This is a key advantange of their model that gets a proportionately larger share of attention in the introduction and discussion. It also may provide an explanation for the ability of ampicillin to enter the host cells, or to access intracellular bacteria (through vesicular uptake during contraction, as UPEC themselves are thought to do).

Reviewer #3 (Public Review):

By means of in vitro reconstitution, the authors find that the microtubule associated protein Sjögren's Syndrome Nuclear Autoantigen 1 (SSNA1), know to form fibrils binding longitudinally along microtubules, modulates microtubule instability by reducing dynamicity and inducing rescues, prevents catastrophes in absence of free tubulin or in presence of the tubulin-sequestering protein stathmin, inhibits microtubule severing of spastin and detects spastin-induced damage sites. SSNA1, thus, is revealed as a very potent microtubule stabilizing factor.

The reconstitution of microtubule dynamics is sound and well performed, and the parameters of dynamicity are thoroughly analyzed. The observed intensity of SSNA1 fluorescence demonstrates that the proteins do not bind uniformly along microtubules. Consequently, the rates of microtubule dynamics are not affected globally. Instead the observed rates are affected at different times for individual microtubules and, importantly, directly correlate with locally accumulating SSNA1. The authors thus validly conclude that nucleotide state recognition is not the primary mechanism of SSNA1 localization and activity. Clues towards the mechanism of SSNA1 activity are provided by the observation that SSNA1 detects spastin-induced damage sites, indicating that SSNA1 binds to partial, open microtubule structures and then stabilizes them, which is consistent with cryo-electron-tomograms available in the literature. To me it is not clear, however, if SSNA1 localize-to and act-on distinct sites of microtubule damage exclusively, or if these sites rather serve as positions of initiation or nucleation of cooperative SSNA1 binding, which the kymographs and movies seem to suggest.

The presented observations nicely explain how the microtubule severing enzyme spastin, which directly interacts with SSNA1 and thus recruits it to the very sites of immediate damage, promotes regrowth of microtubules and increases their number and mass in vivo. The manuscript would benefit from further investigation-into and quantifications-of the "progressive accumulation" of SSNA1 on the dynamic microtubules, which, thus far are presented only by way of representational example.

Reviewer #3 (Public Review):

Magnusson et al., do an excellent job of defining how the repeated separator sequence of Wild Type Cas12a CRISPR arrays impacts the relative efficacy of downstream crRNAs in engineered delivery systems. High-GC content, particularly near the 3' end of the separator sequence appears to be critically important for the processing of a downstream crRNA. The authors demonstrated naturally occurring separators from 3 Cas12a species also display reduced GC content. The authors use this important new information to construct a synthetic small separator DNA sequence which can enhance CRISPR/Cas12a-based gene regulation in human cells. The manuscript will be a great resource for the synthetic biology field as it shows an optimization to a tool that will enable improved multi-gene transcriptional regulation.

Strengths:

• The authors do an excellent job in citing appropriate references to support the rationale behind their hypotheses.
• The experiments and results support the authors' conclusions (e.g., showing the relationship between secondary structure and GC content in the spacers).
• The controls used for the experiments were appropriate (e.g., using full-length natural separator vs single G or 1 to 4 A/T nucleotides as synthetic separators).
• The manuscript does a great job assessing several reasons why the synthetic separator might work in the discussion section, cites the relevant literature on what has been done and restates their results to argument in favor or against these reasons.
• This paper will be very useful for research groups in the genome editing and synthetic biology fields. The data presented (specially the data concerning the activation of several genes) can be used as a comparison point for other labs comparing different CRISPR-based transcriptional regulators and the spacers used for targeting.
• This paper also provides optimization to a tool that will be useful for regulating several endogenous genes at once in human cells thus helping researchers studying pathways or other functional relationships between several genes.

Opportunities for Improvement:

• The authors have performed all the experiments using LbCas12a as a model and have conclusively proven that the synSeparator enhances the performance of Cas12a based gene activation. Is this phenomenon will be same for other Cas12a proteins (such as AsCas12a)? The authors should perform some experiments to test the universality of the concept. Ideally, this would be done in HEK293T cells and one other human cell type.

Reviewer #3 (Public Review):

As the authors lay out, there are a number of theoretical perspectives that expect that male features that are sexually dimorphic and, hence, vary in their levels of "masculinity" (or perhaps less sex-anchored, vary along a male-female dimension) within human males, to have been under sexual selection historically (if not now), which may in part explain their sexual dimorphism. The target article examines associations between a number of such traits that have been examined-bodily strength and muscularity, facial masculinity, vocal pitch, 2nd to 4th digit ratio (2D:4D), height, and testosterone levels-with measures of mating "success" (e.g., sexual partner number) and reproductive outcomes (e.g., reproductive success). With traits keyed such that more positive values reflect greater "maleness," virtually all associations with putative fitness components were found to be positive, though not all associations had confidence intervals that do not cross the zero-point (i.e., not all are "significant").

The strongest associations were with body masculinity. Specific measures included strength, body shape, and muscle or non-fat body mass (though the associations are not broken down by indicator type). In the mating domain, the overall correlation was .13 (.14 in the behavior domain, perhaps most related to mating "success"). In the reproductive domain, the mean correlation was .14, and .16 in high fertility samples (a subset of which may represent natural fertility populations). Especially when strength (e.g., grip strength) was used as the measure of body masculinity, these associations are likely underestimated, due to imperfect validity of the masculinity/muscularity indicator.

Associations with voice pitch were, on average, nearly identical to those involving body masculinity: .13 overall in the mating domain and .14 overall in the reproductive domain. But due to smaller sample size, the confidence interval around the correlation in the reproductive domain included zero.

The next grouping of traits, in terms of strength of association, contains facial masculinity and testosterone levels. There, associations were .09 and .08 in the mating domain and .09 and .04 in the reproductive domain, respectively. Once again, not all confidence intervals were exclusively above zero.

Associations with both 2D:4D and height were weaker: .03 and .06 in the mating domain and .07 and .01 in the reproductive domain, respectively.

I offer a few observations.

First, the meta-analysis, to my mind, offers some interesting data. We need to be aware of its limitations. Many samples are drawn from WEIRD populations (Henrich et al., 2010). It remains unclear to what extent fertility and reproductive success in these samples, even when drawn from high fertility populations, reflect processes that would have operated in ancestral human groups. It makes sense that some of these features may well have been variably associated with fitness components in ancestral populations, but potential key moderator variables (e.g., pathogen prevalence, level of paternal provisioning, level of intergroup violence, degree of female choice [vs. arranged marriages]) may not be available to examination here. To the extent moderation exists, mean levels in this meta-analysis are less meaningful (though not meaningless), as we do not know whether the distribution of moderators in this sample of samples is representative of populations of interest. (E.g., due to advances in modern medicine, these samples may be much healthier than ancestral populations in which these features were subject to selection.) And that is just a partial list of caveats we need to keep in mind. Nonetheless, with those limitations kept in mind, these findings are interesting to reflect upon.

Second, the associations of course do not tell us what processes drive them. They are correlations. Indeed, we do not know whether the traits themselves were directly implicated in the processes leading to their associations with fitness outcomes. (2D:4D surely wasn't-it's a marker of other causal variables-but its associations are among the weakest seen here.) It makes some sense that the stronger the associations, the more likely the trait in question was directly causally implicated in these processes. And again, that may be particularly true of body masculinity, as associations with it may be underestimated due to fallible indicator validity. But even then, we cannot rule out other mediating traits. Perhaps more muscular men exhibit greater confidence and gain leadership roles more readily than less muscular men, giving them an edge in intrasexual competition or intersexual choice due to associated behavior or status. Or maybe they ultimately gain greater control of resources, giving them advantages in competition for mates or provisioning of offspring. This is not to deny that muscularity may well have been (and be) under sexual selection; but it may have been selected along with other traits rather than the direct target of selection itself.

Third, then, we do not know what intrasexual or intersexual selection processes may have been involved historically, even if these traits have directly been under sexual selection. To what extent are these associations due to advantages in intrasexual competition? To what extent might they be due to female preferences and choice? Naturally, as the authors note, these processes are not mutually exclusive. After all, in lekking species, males compete with one another for a symbolic spatial position, which, because it represents the outcome of the competition, leads to mating success via female choice. Still, we might be interested in knowing what processes led to the associations found, and how they speak to sexual selection and mating processes in humans.

Once again, however, the associations reported are interesting to reflect upon. And they could, either directly or indirectly (by stimulating additional research), lead to better answers to issues raised above. One key outcome that relatively little data currently speak to, for instance, is mortality rate of offspring. As the authors note, men who are more successful with respect to mating effort may invest lower amounts of parental investment in offspring. In theory, then, their greater offspring number could be offset to an extent by lower survival rates. In the relatively few data the authors aggregated from the literature, that was not clearly the case. But more data may be needed, especially with respect to the strongest predictors of mating success, and especially in more traditional societies.

Paternal investment in offspring, however, need not pay off just in terms of offspring survival rates; paternal provisioning may permit greater rates of reproduction via shortening of interbirth intervals in traditional societies. The data here show that, at least with respect to body masculinity, more masculine men have greater mating success and greater reproductive success. Yet the data do not necessarily tell us that the female partners of these men have greater reproductive success. More masculine men's rates of offspring production could be spread over more female mates than that of less masculine men. Knowing whether female partners of more masculine men benefit reproductively by mating with masculine men is pertinent to addressing whether the reproductive success of masculine men has been mediated, in part, by female mate choice.

la non réponse n'est donc pas acceptable https://www.legifrance.gouv.fr/codes/article_lc/LEGIARTI000031367338/

Reviewer #3 (Public Review):

This manuscript sheds new light on the regulation and function of a signaling network comprised of the adaptor molecules Cas and BCAR3. The data presented in the manuscript are generated through rigorous experimentation, frequently with the use of multiple approaches to arrive at the stated conclusions.

Minor concerns:

1) Figure 3e. The authors state that "SOCS6 binds BCAR3 and Cas independently" (bottom of page 7). However, while they show that the EE BCAR3 mutant binds to SOCS6 under conditions when it does not bind to Cas, they do not show the reciprocal interaction in this paper. Their previous paper (J Cell Sci 2014) suggests that SOCS6 binding to Cas may be independent of BCAR3 but neither that paper nor the current manuscript explicitly examine that. Unless there is direct evidence that SOCS6 can bind to Cas in the absence of BCAR3, perhaps it would be more accurate for the authors to limit their conclusion by saying that "SOCS6 binds to BCAR3 independently of Cas."

2) Figure 8a and c. Without showing a Western blot to address total pools of phosphorylated Cas, it is not clear whether the depletion in pY165 is targeted to the pool of Cas present in adhesions or to a diminution in phosphorylation of the total pool of Cas in the cell. At a minimum, the authors would need to clarify that phosphorylation at Y165 of Cas in the pool of Cas that is localized to adhesions is reduced in the presence of Y117F, R177K, or the EE mutant of BCAR3.

Reviewer #3 (Public Review):

In this manuscript, the authors use high-resolution live imaging to investigate how progenitor cells travel through an embryo to a distant site for differentiation and organ formation. The test case is the movement of dorsal forerunner cells (DFCs) in the zebrafish embryo, which give rise to a transient organ called Kupffer's vesicle that functions to establish the left-right body axis. DFCs are derived from enveloping layer (EVL) cells ~5 hours post-fertilization (hpf) and then move towards the vegetal pole of the embryo. They ultimately end up in the tailbud where they differentiate into epithelial cells to form Kupffer's vesicle between 10-11 hpf. Live imaging convincingly shows that EVL cells undergo apical constriction and delaminate from the EVL layer to form DFCs. Some DFCs remain connected to the EVL via ZO-1 enriched tight junction-like apical attachments. The authors propose that spreading of the EVL layer 'drags' the underlying DFCs towards the vegetal pole via these apical attachments. Supporting this model, EVL and DFCs co-migrate with the same speed and directionality, and perturbation of an actomyosin ring network in the yolk syncytial layer (YSL) disrupts movement of both EVL and DFCs. Between 8-9 hpf DFCs detach and are uncoupled from the EVL. The authors show that E-cadherin is necessary for DFC-DFC adhesion, and additional imaging experiments show that DFCs can extend long protrusions that 'capture' detached DFCs to facilitate clustering. Taken together, these data suggest an interesting drag mechanism for guiding progenitor cell movements, however the results presented do not fully demonstrate this mechanism, and alternative mechanisms were not thoroughly tested.

Reviewer #3 (Public Review):

The authors study the leaf transcriptomes of males and females in 10 species of Leucadendron and infer genes expressed significantly differently between males and females (sex-biased genes, hereafter SBGs). Most SBGs in Leucadendron leaves evolved recently, suggesting that SBGs turnover (evolution and reversion) is very high because the genus is ancestrally dioecious since >10My. Using species in which the genes orthologous to SBGs are not sex-biased, the authors show that SBGs have high rates of expression evolution already before becoming SBGs. This suggests that most SBGs evolved under drift and the majority of SBGE (sex-biased gene expression) is evolving neutrally. This is confirmed by the estimated small proportion of SBGs evolving under adaptation (about 20% of SBGs have 5 fold higher expression divergence compared to polymorphism divergence, a mark of relatively recent adaptation). Also, SBGs are more tissue specific (less pleiotropic). Finally, the percentage of SBG is not correlated to the intensity of morphological dimorphism. All these findings go against the classical view that SBGE is driven by sex-specific selection for sexual dimorphism.

The analyses are very cautious with well designed controls and randomizations.

The results support well the conclusions.

This study puts forward the role of drift in sex-biased gene expression, offering a new interpretation of this common evolutionary phenomenon.

Reviewer #3 (Public Review):

This analysis focuses on funding success for a set of NIH R01-mechanism grant applications submitted between 2011 and 2015, with a focus only on those which had white and Black Principal Investigators (PIs). It is presented as a follow-up to the previously published paper from Hoppe and colleagues in 2019, uses the same population of applications and relies on the same analysis of application text to cluster these applications by topic. The authors set out to determine how success rates associated with the application's proposed topic may be determined by the success rates associated with the Institute or Center within the NIH to which the application had been assigned for potential funding. This is a critical and important investigation that is of high potential impact. The scholarship of the Introduction and Discussion, however, fails to convey this to the reader. There are many recent publications in the academic literature that address why a disparity of funding to AA/B investigators, and a disparity of funding of topics that are of interest to AA/B investigators, are such critical matters for the NIH to identify and redress. Similarly, the Discussion and Conclusions sections do not suggest any specific actions that may be recommended by these findings, which is an unfortunate oversight that limits the likely impact of this work.

The significance of this work is limited by a number of methodological choices that are unexplained or have not been justified and therefore appear to be somewhat arbitrary. While it can be necessary to draw category lines in an investigation of this type, it is necessary to provide some indication of what would happen to the support for the central conclusions if other choices had been made. This includes the exclusion of multi-PI applications if the Black PI was not the contact PI, the definition of AA/B-preferred ICs as the top quartile (particularly given the distribution of success rates within this quartile), the definition of AA/B-preferred topics as the 15 word clusters that accounted for only half of the AA/B applications, and the ensuing inclusion of only 27% of the AA/B applications. Arbitrary choices to use only a subset of the data raise questions about what the conclusions would be if the entire dataset of grants assigned across all of the ICs, and on all of the topics, was used.

A fundamental limitation to this manuscript is that the authors are relying on an indirect logic of analysis instead of simply reporting the success rates for applications with AA/B and white PIs within each IC. The primary outcome deployed in support of the central conclusion is a reduction of the regression coefficient for the contribution of PI race to award success and an elimination of statistically significant contribution of research topic preferred by AA/B applicants to the award success once IC success was partialed out. The former analysis is interpreted in imprecise terms instead of simply reporting what magnitude of effect on the white/Black success rate gap is being described. And the latter analysis appears to show a continued significant effect of PI race on award success even when the IC success rate is included. The much more intuitive question of whether award rates for white and AA/B applicants differ within each IC has not been addressed with direct data but the probit model outcome suggests it is still significantly different. This gives the impression that the authors have conducted an unnecessarily complex analysis and thereby missed the forest for the trees- i.e. even when accounting for IC award rates there is still a significant influence of PI race.

The manuscript is further limited by atheism omission of any discussion of how and why a given grant is assigned to a particular IC (this is exacerbated by incorrect phrasing suggesting the applicant "submits an application to" a specific IC) and any discussion of the amount of the NIH budget that is assigned to a given IC and how that impacts the success rate. This is, at the least, necessary explanatory context for the investigation.

Reviewer #3 (Public Review):

In this study, the authors use a combination of fluorescent and electron microscopy to visualize the trafficking of HIV-1 viral particles during infection. The goal was to determine the components of nuclear HIV-1 virions during infection, and specifically to determine the degree to which reverse transcribed DNA in the nucleus associates with capsid protein and other fluorescent markers of infectious virions, such as fluorescently labeled integrase, which is increasingly used by many labs to track HIV-1 particles in the nucleus. The strengths of the manuscript lie in the imaging approaches used to quantitatively measure colocalization between viral DNA, CA and IN during infection, which are rigorous and well executed. Tomograms of high pressure frozen/plastic substituted samples showing apparently intact capsid cores at and within the nucleus are the most significant outcomes of the study and represent a significant technical achievement. These images provide some of the most compelling evidence to date that cores an enter the nucleus intact, despite previous studies suggesting that capsid disassembly occurs in the cytoplasm or at the nuclear pore.

The weaknesses of the manuscript lie in the use of HeLa based target cells in all but one of the figures. Although the results in primary cells are generally consistent with the results observed in HeLa cells, differences between HeLa and primary cells have been noted in other studies, and the manuscript would have been significantly improved with more extensive use of primary cells throughout. Additionally, the numerous other recent recent studies that have demonstrated that reverse transcription and uncoating complete in the nucleus, including works from the Pathak, Diaz-Griffero, Di Nunzio, Dash and Campbell labs, among others, reduce the potential impact of these studies. The Di Nunzio lab, in particular, has recently published a nearly identical system for labeling the reverse transcribed HIV-1 genome during infection. However, differences in approach prevented that study from being able to conclude that intact capsids exist in the nucleus (or made such conclusions open to alternative interpretations). In contrast, the CLEM-ET studies in this manuscript unambiguously show intact cores in the nucleus, and this is an advance for the field, in addition to being a substantial technical achievement. Nevertheless, the prior studies in this area, published last year, do impact the novelty of the observations made in this manuscript.

In the aggregate, this manuscript adds to a growing body of work suggesting that models of HIV-1 infection that have dominated the field for years should be reconsidered. As recently as 2 years ago, the idea that even a small amount of CA protein remained associated with the viral replication complex in the nucleus was somewhat heretical. While old models die hard, and some in the field are likely to debate how "intact" the capsid cores observed in this manuscript actually are, the idea that intact or nearly intact cores can enter the nucleus is increasingly difficult to deny in light of data provided here. This raises questions regarding how the HIV-1 core, which exceeds the generally accepted size limitation of nuclear pore complexes by 50%, based on the width of a capsid core, can enter the nuclear environment in an intact or nearly intact state (an issue that is addressed in the recent (and cited) Zila et al. bioRxiv paper, but not here).

Reviewer #3 (Public Review):

This manuscript by Koiwai et al. described the single-cell RNA-seq analysis of shrimp hemocytes and was submitted as a Resource Paper in eLife. In this study, they identified 9 cell types in shrimp hemocytes based on their transcriptional profiles and identified markers for each subpopulation. They predicted different immune roles among these subpopulations from differentially expressed immune-related genes. They also identified cell growth factors that might play important roles in hemocyte differentiation. This study helps to understand the immune system of shrimp and maybe useful for improving the control of the pathogen infections. The analysis of the data and interpretation is overall good but there are also some concerns:

1) The number of UMI and genes detected per cell after mapping to the in-house reference genome does not appear to be presented, and the similarities or differences between the three replicated samples are not discussed, as well as the low number of genes detected per cell (~300 in this study) .

2) The correlation between the morphology and the expression of marker genes demonstrated in Figure 6 is questionable. Cells of the same size could express totally different genes. On the other hand, cells that are different in size can express nearly identical genes. The evidence presented in this manuscript is not enough to support a correlation between cell size and gene expression. Therefore, the author would either need to provide more evidence to support this correlation, or not make such correlation.

3) There are many spindle-shaped cells in Figure 6B, but none of them appeared in Figure 6C and D after sorting, and the reason for this is unclear.

4) The hemocyte differentiation model in Figure 7 is not supported by any experimental data.

Reviewer #2 (Public Review):

In this manuscript, Lamers et al wanted to characterise the previously reported adaptation of SARS-CoV-2 to non-human (Vero) cells. Vero cells are commonly used by laboratories to grow experimental stocks of some viruses as these cells permit high titres of many viruses, they lack the ability to produce type I interferons (cytokines which could interfere with downstream assays), and their non-human nature means soluble factors in virus stocks are less likely to impact experiments in human cells. However, a number of reports have recently been published describing that growth of SARS-CoV-2 in Vero cells leads to loss of the SARS-CoV-2 Spike protein multibasic cleavage site (MBCS). This apparent adaptation to the Vero cell-line leads to a virus compromised in its ability to enter, and therefore replicate in, human cells, meaning that experimental results obtained in human cells using the Vero-adapted SARS-CoV-2 may not fully reflect the situation occurring with authentic SARS-CoV-2. It is therefore important for the research community to understand SARS-CoV-2 adaptation to laboratory cell-lines/conditions and to have propagation methods that are suitable for maintaining the authenticity of clinical virus isolates.

The major finding of Lamers et al in this manuscript is that human cell-lines (e.g. Calu-3) and primary human organoid systems can be used to propagate clinical isolates of SARS-CoV-2 to high titres without the acquisition of 'laboratory adaptations'. To get to this finding, the authors carefully study the adaptation of a representative SARS-CoV-2 isolate in Vero cells, monitoring plaque size phenotypes and performing whole-genome deep sequencing to identify adaptive variants that appear in the viral Spike gene. These variants (including newly-described substitutions as well as deletions around the MBCS) are validated for their impact on viral infectivity in human and Vero cells using pseudovirus assays, fusion assays, and western blot assays, and their role in affecting the entry route of SARS-CoV-2 is dissected using pathway-specific inhibitors (such as camostat and E64D) and cell-lines with/without TMPRSS2 (an important protease for Spike cleavage). Importantly, using these assays and tools, the authors can make solid and well-reasoned arguments as to why SARS-CoV-2 adapts to Vero cells, and thus why certain culture conditions and cell substrates lead to a loss of SARS-CoV-2 genetic stability. Using similar tools, this also allows the authors to carefully study whether any adaptations occur when SARS-CoV-2 stocks are passaged in human cell substrates (such as Calu-3 or primary human organoids), and study culture conditions in Veros (such as expression of TMPRSS2) that prevent changes in SARS-CoV-2.

The data in this manuscript are thorough and well-presented. Importantly, the conclusions are strongly supported by the data, particularly the overall take-home message that human cell substrates can be used to efficiently propagate SARS-CoV-2 isolates without introducing cell culture adaptations. However, beyond this simple message, the manuscript also provides new mechanistic insights into the reasons for such viral adaptations in the Vero cell system, and identifies previously undescribed adaptations in the MBCS region that will be valuable for other researchers to take note of. The authors also describe a methodological workflow to produce SARS-CoV-2 in human cells that highlights a buffer-exchange step to remove potentially interfering human cytokines/debris, and which will be useful for other researchers.

Overall, the manuscript makes a clear and important contribution to the SARS-CoV-2 field and will be of interest to active researchers who are studying this virus experimentally.

Reviewer #3 (Public Review):

In some species, supporting cells (SCs) of the cochlea can replace hair cells and thus restore hearing. In the mouse, neonatal SCs can also produce hair cells; however, this property is lost during early postnatal life. This study sought to test whether forced expression of two transcription factors normally associated with OHC development, Atoh1 and Ifzh2, can induce adult mammalian supporting cells to take OHC-like properties. Using Cre-dependent expression in mice, the authors showed that co-expression of Atoh1 and Izfh2 could induce a small number of adult SCs to express the OHC-specific gene, Prestin. This conversion was significantly enhanced when existing OHCs were ablated, in this case using a Prestin-DTR mouse model generated by the authors. A detailed phenotypic analysis combined with single cell RNA-sequencing (scRNA-seq) supports the idea that Atoh1/Izfh2 can partially convert adult SCs into OHC-like cells. However, the conversion is not complete, with immature bundles and a gene signature that resembles P1 OHCs (and sometimes E16 OHCs) more than P7/P30 OHCs or P60 SCs. Accordingly, the new OHCs are not sufficient to restore hearing in the Prestin-DTR mouse model. Together, these data encourage optimism that adult SCs can be steered along the OHC path, though clearly more manipulations will be needed to produce mature, functional OHCs.

The main weakness of the study is the scRNA-seq analysis, which depends on very small sample sizes. Suggestions to improve upon the analysis are listed under Specific Recommendations.

Reviewer #3 (Public Review):

Pettmann et al. aimed at significantly improving the accuracy of SPR-based measurements of low affinity TCR-pMHC interactions by including a 100% binding control (injecting of a conformation-specific HLA-antibody) in the surface plasmon resonance protocol. Interpolating with the information of saturated pMHC binding on the chip The authors arrive at KDs for low affinity binders that are significantly higher than the previously reported constants. If correct, this has considerable ramifications for the interpretations of the results obtained from functional assays measuring the T cell response towards pMHCs featured in a titrated fashion. Unlike what was put forward by earlier reports, the authors conclude that the discriminatory power of TCRs is far from perfect, as T cells still respond to low affinity pMHC-ligands without a sharp affinity threshold. This is also because they managed to detect T cells responding to even ultra-low affinity ligands if provided in sufficient numbers.

The body of work convinces in several regards:

(i) It is exceedingly well thought out and introduces a quality of analytical strength that is absent in most of the literature published thus far on this topic.

(ii) At the same time theoretical arguments are bolstered by a large body of experimental "wet" work, which combines a synthetic approach with cellular immunology and which appears overall well executed.

(iii) The data lead to hypotheses in the field of T cell antigen recognition in general and in the theatre of autoimmunity, cancer and infectious diseases.

There are a few aspects that may limit the impact of the study. I have listed them below:

(i) The study does not provide kinetic data for the low affinity ligand-TCR binding but rather argues from the position of affinities as determined via Bmax. This limits somewhat the robustness of the statements made with regard to kinetic proofreading.

(ii) Thresholds for readouts were arbitrarily chosen (e.g. 15% activation). It appears such choices were based on system behavior (with the largest differences observed among the groups) but may have implications for the drawn conclusions.

In summary, the work presented contributes to demystifying the link between TCR-engagement and (membrane proximal) signaling. It also provides a fresh perspective on the potential of TCR-cossreactivity.

Reviewer #3 (Public Review):

Sun et al have assembled, modified, and applied a series of existing gene editing tools to tissue-derived human fetal lung organoids in a workflow they have termed "Organoid Easytag". Using approaches that have previously been applied in iPSCs and other cell models in some cases including organoids, the authors demonstrate: 1) endogenous loci can be targeted with fluorochromes to generate reporter lines; 2) the same approach can be applied to genes not expressed at baseline in combination with an excisable, constitutively active promoter to simplify identification of targeted clones; 3) that a gene of interest could be knocked-out by replacing the coding sequence with a fluorescent reporter; 4) that knockdown or overexpression can be achieved via inducible CRISPR interference (CRISPRi) or activation (CRISPRa). In the case of CRISPRi, the authors alter existing technology to lessen unwanted leaky expression of dCas9-KRAB. While these tools have previously been applied in other models, their assembly and demonstrated application to tissue-derived organoids here could facilitate their use in tissue-derived organoids by other groups.

Limitations of the study include:

1) is demonstrated application of these technologies to a limited set of gene targets;

2) a lack of detail demonstrating the efficiency and/or kinetics of the approaches demonstrated.

While access to human fetal lung organoids is likely not available to many or most researchers, it is probable that the principles applied here could carry over to other organoid models.

Reviewer #3 (Public Review):

Moncla et al. investigated the transmission of mumps virus in Washington, USA during an outbreak in 2016-2017. They sequenced viral genomes from infected individuals in Washington and elsewhere within the United States and used phylogenetic approaches to understand the origins and patterns of spread exhibited by the virus during the outbreak. They observe a large fraction of cases in individuals who are part of the Marshallese community, and identify a link to a similar outbreak in the Marshallese community in Arkansas. They develop a method for determining the role of the Marshallese community in the Washington outbreak that is robust to sampling bias and size. This method is well thought-out and presented and demonstrates that the outbreak in Washington state was sustained by transmission within this particular community. This paper provides a thoughtful approach to dealing with sampling issues that are often overlooked in phylogenetic studies. By consulting with a public health professional from within the affected (Marshallese) community, the authors are able to contextualize their results and demonstrate the underlying issues that may have contributed to mumps spread within the state.

Working with public health advocates from affected communities is exceptionally important for long term public health impact, and this paper sets an example that should be followed by others in the pathogen genomics field. The methodology used to determine mumps transmission patterns in Washington is sound and the conclusions are well explained. However, some additional context on the issues and potential pitfalls of source-sink analyses based on phylogenetic inference would help improve this already solid paper. Specifically:

1) The authors seem to assume a somewhat random sample throughout Washington state. They state that given a low sampling proportion they do not expect to have captured infection pairs, which seems reasonable. However, they then go onto assume that their sample is primarily comprised of samples from long, successful transmission chains. This is a reasonable assumption if there is no major difference in accessibility of samples from long transmission chains and shorter ones (for example, decreased access to healthcare). Could this impact the assumption of sampling primarily from long transmission chains? It seems from the data collected in this outbreak that this was not the case for mumps in Washington but addressing this assumption clearly (and potential ways to interrogate it) could make their methodology more applicable to other pathogen studies.

2) There are many examples of phylogenetic analyses that have led to conclusions about pathogen sources and sinks that were later shown to be wrong because of oversampling or other sampling biases. The authors address unequal sampling between clades, but additional contextualization of the problem and how this approach is different may help strengthen the methodology presented in the paper.

3) The authors present compelling evidence that the mumps outbreak in Washington state was sustained by the Marshallese community, and state that mumps did not transmit efficiently among the general Washington populace. That said, there were several other mumps outbreaks in the United States in the same 2016-2017 time period. Was there something different about Washington state that prevented mumps transmission outside of the Marshallese community? Were there no other close-knit communities (universities, prisons, other cultural communities, etc.) affected? It just seems surprising that the Marshallese community was the only community sustaining transmission at a time where many different types of communities were affected across the United States.

Reviewer #3 (Public Review):

The authors investigated pupillary response looking at the changes corresponding to perceptual events (spontaneous or physical changes) and contrasting them with requirements of over reporting (changes were reported or ignored). They demonstrate that the former is associated with a rapid constriction and re-dilation, whereas the latter shows an opposite effect with dilation being followed by re-constriction. The particular strength of the work is in no-report conditions using on OKN-based inference about timing of perceptual events that allowed for this dissociation to be observed, whereas manual report conditions allowed for a direct comparison with prior work. The analysis and control experiments are very thorough showing that reported results are unlikely to be explained other factors such as saccades or blinks.

The study makes a significant contribution but proposing a no-report paradigm for identifying perceptual events that should work for any multistable display. The fairly rapid pupil constriction event could provide an easy to detect and temporally reliable marker of perceptual switches, expanding ways the multistability data is collected. The same approach could also be useful for no-report studies of visual awareness in general.

The ability to decompose pupillary response into two components - perception and over manual response - will also be useful for studying neural correlates of spontaneous perceptual switches, as it could help to better understand switch-time activity in various frontal and parietal regions. Here, also some regions are associated with active response, whereas other with perception, distinction that could be potentially better understood based on the idea that only the former involves noradrenaline-affected processing. My main worry methodologically is the under and overestimation of mean switch rate via OKN (figure 1C). OKN estimates are all within .4-.8 range, whereas for self-report rates differ from 0.2 to over 1. Further analysis would be helpful. I think it would be helpful if the authors elaborated on what kind of switches went unreported (or, conversely, what kind of events led to false alarms): switches before very short dominance phases (could be to fast to report via key presses), to return transitions, etc.

Reviewer #3 (Public Review):

Opsin proteins are ancient light-sensitive molecules found in photoreceptor cells throughout the animal kingdom. Recent discoveries including those made in the current paper have revealed that besides r-opsins, some classes of photoreceptor cell also express genes that are found in mechanosensory cells, and that r-opsins have both light-dependent and light-independent effects on mechanical force transduction or motion. A question remains as to whether or not: 1) a protosensory cell of animals existed which contained both photoreceptor and mechanoreceptor-like features and, 2) whether the original function of opsin included light-dependent mechanosensory features? The authors consider three competing hypotheses for the cellular evolution of photoreceptor and mechanosensory function. Two of the hypotheses envision either photo- or mechanosensory function for opsins evolving first, the third imagines them evolving simultaneously. The authors note that the majority of what we know about rhabdomeric opsins comes from studying the eye photoreceptors of the fruit fly, Drosophila melanogaster. But might this kind of photoreceptor have functions that are derived compared to the ancestral photoreceptor cell? To investigate this question, the authors turn to the non-model system, Platynereis dumerilii, which has both head and non-head photoreceptors. Here the authors use 1) a fluorescent cell sorting method to perform RNA profiling of eye and trunk photoreceptor cells of a mutant marine worm and find evidence of co-expression of photo- and mechanosensory genes in photoreceptor cells. They also compare the genes that are expressed in Platyneris photoreceptors with genes expressed in Drosophila JO (hearing organ in flies), Zebrafish lateral lines and mouse IEH (inner ear hair) cells, and again they find some commonly-expressed genes. 2) The authors use cell culture to express the opsin, demonstrate that it interacts with G-alphaq, and that it's peak sensitivity is in the blue range. 3) They use in situ hybridization to validate the RNA-seq and detect select enriched transcripts in the photoreceptor tissues. 4) They use a new method, which should be widely useful to other researchers, to detect undulation behavior of the opsin mutant vs. wildtype worms and show that the mutant worm behavior is perturbed in altered light cycles. Taken together, the authors suggest that an ancient light-dependent function of opsin was linked to mechanosensation and that light-independent mechanosensory functions of opsins evolved secondarily. The interpretation is somewhat reasonable given the available data but does not yet entirely rule out other possibilities (see below).

This paper is a tour-de-force and a really impressive collection of experiments which examines the function of r-opsin in Platyneris. There's lots of innovation here from the use of fluorescent cell sorting and cell-specific RNA-Seq on a non-model system to the deep-learning based approach to examining behavior. Overall, the authors' interpretation of their data seems reasonable however I do believe a even stronger case could be made that what we are talking about is shared ancestry vs. recent recruitment if the authors made phylogenetic trees of the numerous TRE genes that are enriched between Drosophila JO and mouse IEH cells. If a significant number of these genes were true orthologs vs. paralogs across all three species then this would provide stronger evidence of an ancient light-dependent mechanosensory function for r-opsin. GO enrichment terms, while intriguing and suggestive, don't go far enough into the weeds. Also, I think the estimate of there being only 12 genes involved in making a photoreceptor cell able to detect light is probably an underestimate, as this ignores, for example, the understudied molecular machinery required for chromophore metabolism and transport. At the very least, the work should help inspire vigorous debate between vision and auditory neuroscience communities (which do not usually converse with one another) to more carefully consider the ways in which their systems overlap and why.

Reviewer #3:

The authors hypothesized lower GABA levels in older adults would influence cortico-cortical phase relationships more than cortico-muscular phase relationships during performance of a bimanual motor task. To this end, they evaluated the mediating role of endogenous bilateral sensorimotor cortex GABA content in relation to behavioral performance and patterns of interhemispheric and cortico-muscular electrophysiological phase coherence during a bimanual motor control task. The central finding was that the mediating influence of right M1 GABA on the relationship between cortico-cortical electrophysiology and behavior diverged between the younger and older groups, with lower endogenous GABA concentrations potentially benefitting bimanual motor performance in young adults and hindering performance in older adults. The result was specific to right M1 GABA, raising questions about hemispheric asymmetry, and behavioral performance differed substantially between groups, possibly influencing the sensitivity of the analyses of the electrophysiological phase relationships. Moreover, several earlier studies suggest endogenous M1 GABA content relates to cortico-muscular excitability measurements, other than phase synchrony, and it is unclear what distinguishes phase synchrony from these other measurements. The behavioral, MRS, and electrophysiological methods employed are fairly well-established and are combined in a novel manner. The Bayesian moderated mediation analysis represents a new approach to evaluating relationships between these measures under the moderating influence of age. The central questions concerning the roles of cortical endogenous GABA in bimanual control, and in age-related changes in motor control more generally, are important for determining the neural computations underlying flexible and precise behavior.

1) The total number of finger taps within the 2000 ms transition epoch likely differed between groups and could influence the ISPC measures. It would be helpful to rule out this possibility by examining relationships between ISPC measures and the total number of taps.

2) The differences between right and left M1 are somewhat surprising and merit further attention, particularly given the cortico-cortical ISPC results. The interpretation provided in the discussion (lines 607-618) is not particularly satisfying since this asymmetry is a critical feature of a key result. Can the authors leverage their own data to provide further insight into why RM1 GABA+ may be more likely to exhibit a relationship than LM1 GABA+? Would analyzing the behavioral data separately for the left and right hands provide further insight? Does the non-dominant hand lag behind the dominant hand, and/or is it more susceptible to errors?

3) There were some general issues concerning the GABA+ data:

a. Figure 2a suggests an interaction in the pattern of variance in the GABA+ data between the Young and Older groups for the LM1 and RM1 voxels. Is this interaction in variance significant, and if so, what might this mean for the M1 GABA+ results? Specifically, Young show greater variance for LM1, and Older show greater variance for RM1. Also, Young appear to show considerably lower variance for RM1 than LM1. However, the data in Figure 2 supplement 2 suggest that variance in the Young is similar between LM1 and RM1. Do these numbers accurately reflect the data depicted in Figure 2a?

b. It would be helpful to show the difference spectra in Figure 2 supplement 1b with separate plots for Young and Older.

c. Figure 2, supplement 1a: Was the LM1 voxel more dorsal and medial than the RM1 voxel?

4) The authors interpret the decrease in failure and increase in error rate across the task in the Older group as an indication of a loss of precision over time. Alternatively, might this pattern also arise because these participants are becoming faster at correcting their errors (i.e. within 2000 ms), avoiding trials from being categorized as a failure? More generally speaking, it would be helpful if the authors provided additional information about the cumulative error rate trials and what behavior looked like on these trials.

5) The authors should provide further justification for the assignment of age as the moderator and GABA+ as the mediator in their statistical model. Conceptually, it seems these factors could be reversed.

6) Several studies have established relationships between transcranial magnetic stimulation measures of cortico-muscular excitability and endogenous GABA+ content in the dominant M1. The manuscript would benefit from further discussion of the relationship of the phase connectivity measurements used here in comparison to these other previous studies.

7) It is not clear that data or analysis code are available.

Reviewer #3 (Public Review):

This study provides a concept of circuit organization of a pathway from the brainstem to the primary somatosensory (S1) and motor (M1) cortices through the thalamus to control the hand/forelimb movements. Previous studies reveal detailed circuit organization of ascending somatosensory pathways in the whisker system. In contrast, much less is known about circuit organization of another ascending pathway controlling the hand/forelimb movements, although it is known that there are some similarities and differences between two different somatosensory systems.

This paper revealed detailed circuit organization of the ascending pathways including the lemnisco-cortical and corticocortical pathways to control the hand/forelimb movements. The strength of this study is to use a variety of sophisticated techniques, such as optogenetics, trans-synaptic viruses, both anterograde and retrograde viruses, mouse genetics, and electrophysiology, to map the neural circuits in details. The circuit was revealed by electrophysiology together with optogenetics, which is very convincing. In addition, the detailed circuit organization revealed by this study will provide an important information for future behavioral studies. The weakness is the limitation of trans-synaptic viruses. For example, pseudorabies viruses move between multiple neurons, so to interpret the results may be complicated. Although behavioral analyses have not been performed in this study, it is beyond the scope of this study and future study will follow up the behavioral analyses.

Reviewer #3 (Public Review):

In this manuscript, Böhm et. al. aim to understand how precise kinetochore assembly is tied to cell cycle progression in budding yeast. In this work, the authors identify CDK phosphorylation sites concentrated in the N-terminus of Ame1, a protein of the COMA complex, and set out to characterize the role these phosphorylation sites may play protein function at the kinetochore. Although phospho-null Ame1 does not affect cell viability, expressing an Ame1 mutant that lacks the phosphorylated domain results in cell death. Interestingly, overexpression of the phospho-null Ame1 mutant accumulates to a higher level than the wild type protein leading the authors to hypothesize that these phosphorylation sites function as phosphodegrons in the Ame1 protein. Through molecular modeling and genetic analysis, the authors determine that Ame1 is a substrate of the SCF E3 ubiquitin ligase and is likely recognized by the Cdc4 F box protein. The authors go on to convincingly show that phosphorylation of what is referred to as the "CDC4 phosphodegron domain" is phosphorylated in a step-wise manner that is cell cycle dependent and that the phosphorylated Ame1 protein specifically is degraded in mitosis. In addition to Ame1 phosphorylation, the authors show that Ame1 degradation depends on whether Ame1 is bound to the Mtw1c (binding prevents degradation), which only happens at a fully assembled kinetochore. Based on these observations, the authors propose a model in which the phosphodegron motif functions to degrade any molecules of the COMA complex that are not incorporated into the kinetochore and in this way prevents kinetochore assembly at ectopic regions of the chromosome.

Reviewer #3 (Public Review):

Computational models, provide a way to understand emergent network function, and at their best provide a canvas for experimentalists to probe hypotheses regarding function. In this manuscript, Bui and colleagues provide a set of iterative models to describe the locomotor development of larval zebrafish at key developmental stages. These include coil, double coil, and swimming behavior that leads to 'beat-and-glide' behavior. During development, the model steadily moves from gap junction mediated connectivity to more complex synaptic-based network models. In my opinion, this is a very interesting foundation that can be used as a catalyst for future research for experimentalists or to develop more involved models. Like any model it is possible to be critical of the assumptions made. But I expect that it will not be static and be revised over the years. It is important to realize that these sets of models are unique in that they strive to provide models for motor control of a single species across development. The zebrafish is an excellent example since genetic models are widely used, development is swift, and there is active research to understand the physiology of locomotion.

Strengths and weaknesses:

The key strength of this manuscript is the detailing of a set of related models detailing the motor output of the larval zebrafish across key stages of development. The models should form a basis for future research. It also a first of its kind - I don't know of similar models focusing on development of locomotor function. The main weakness is the reliance on assumptions of model connectivity. But I suggest that if the model is treated as a basis for the community to refine and validate it will be incredibly useful.

Reviewer #3 (Public Review):

In this revised manuscript (Oon and Prehoda), the authors performed additional live-imaging experiments and recorded aPKC and actin dynamics simultaneously in larval neuroblasts. They also provide evidence that aPKC polarization is lost upon F-actin disruption by Latrunculin A treatment. These are great improvements. The pulsatile dynamics of actin and myosin II showed in the manuscript are compelling. Images presented in this manuscript are of high-quality and impressive.

However, the pulsatile apical myosin network in delaminating neuroblasts in Drosophila embryos was reported previously (An Y. et al., Development, 2017). This important and relevant paper should be cited in the introduction of the current manuscript. Therefore, the finding on the pulsatile actomyosin in larval brain neuroblasts reported in this manuscript is not a total novel discovery. Another major concern is that Lat-A did not specifically disrupt actomyosin pulsatile movements, as it generally disrupts the F-actin network. So these experiments only strengthened the link between the F-actin network and Par polarity (which was already demonstrated in Kono et al., 2019; Oon 22 and Prehoda, 2019). Low doses of Cytochalasin D are known to disrupt myosin pulses still allowing the assembly of the actomyosin network (Mason et al., Nature Cell Biology 2014). The author should treat neuroblasts with low doses of CytoD to only disrupt actomyosin pulses, not the entire F-actin network, and examine the effect on Par polarity. It is also worthwhile to knockdown sqh to disrupt apical pulsatile actin dynamics. Besides, most of the concerns previously raised by the reviewer were not addressed in the revised manuscript.

Reviewer #3 (Public Review):

In this study, Tang and colleague report that the multikinase inhibitor YKL-05-099 increases bone formation and decreases bone resorption in hypogonadal female mice with mechanisms that are likely to involve inhibition of SIKs and CSFR1, respectively. The authors also report that postnatal mice with inducible, global deletion of SIK2 and SIK3 show an increase of bone mass that is associated to both an augmentation of bone formation and bone resorption.

The paper provides novel and interesting information with potentially highly relevant translational implications. The quality of the data is outstanding and most of the authors' conclusions are supported by the data as shown.

Reviewer #3 (Public Review):

The proposed model is a variation on existing probabilistic fitness landscapes with a number of novel ingredients that are crucial for explaining the observed patterns. The model successfully accounts for the experimental results and makes new predictions, some of which are confirmed by the analysis of existing data. It also provides a coherent picture of the dynamics of adaptation that matches experimental observations. Overall, this is a conceptually deep and potentially highly influential study.

I see only one major issue that requires clarification. This concerns the distinction between the directed mutation scheme (leading to Eqs.(3,4) in the main text) and the symmetric version (Eqs.(5,6)).

Reviewer #3 (Public Review):

Yang et al. build on earlier studies from the Zheng lab and show in tissues that (i) the Hedgehog (Hh) co-receptor Ihog mediates homophilic interactions that enable cytoneme bundling and (ii) that Ihog-Hh interactions are stronger than and displace Ihog-Ihog interactions during signaling, consistent with biochemical and cell-based studies of relative affinities of Ihog for itself or Hh. These studies are bolstered by modeling and experiments showing co-localization of Dally and Dlp, which presumably supply the heparan sulfate chains needed to promotes homo- and hetero-philic interactions involving Ihog. I found the studies convincing, interesting, and an important extension of biochemical/cellular work on Ihog to tissue behavior.

AssayResult: 0.01 µM: 70; 0.08 µM: 50; 0.8 µM: 40; 8 µM: 15

AssayResultAssertion: Abnormal

Approximation: Exact Olaparib concentrations and assay result values not reported; values estimated from Figures 4a and 4c.

AssayResult: 0.01 µM: 50; 0.08 µM: 35; 0.8 µM: 25; 8 µM: 10

AssayResultAssertion: Abnormal

Approximation: Exact Olaparib concentrations and assay result values not reported; values estimated from Figures 4a and 4c.

AssayResult: 0.01 µM: 60; 0.08 µM: 55; 0.8 µM: 40; 8 µM: 15

AssayResultAssertion: Abnormal

Approximation: Exact Olaparib concentrations and assay result values not reported; values estimated from Figures 4a and 4c.

Comment: This variant was used as an abnormal control in other assays in this publication, but it was not specifically designated as a control in this assay.

AssayResult: 0.01 µM: 65; 0.08 µM: 50; 0.8 µM: 30; 8 µM: 20

AssayResultAssertion: Abnormal

Approximation: Exact Olaparib concentrations and assay result values not reported; values estimated from Figures 4a and 4c.

AssayResult: 0.01 µM: 50; 0.08 µM: 40; 0.8 µM: 20; 8 µM: 15

AssayResultAssertion: Abnormal

Approximation: Exact Olaparib concentrations and assay result values not reported; values estimated from Figures 4a and 4c.

AssayResult: 0.01 µM: 90; 0.08 µM: 80; 0.8 µM: 75; 8 µM: 35

AssayResultAssertion: Normal

ControlType: Normal; wild type PALB2 cDNA

Approximation: Exact Olaparib concentrations and assay result values not reported; values estimated from Figures 4a and 4c.

AssayGeneralClass: BAO:0003009 cell viability assay

AssayMaterialUsed: CLO:0036938 tumor-derived cell line

AssayDescription: Transient expression of wild type and variant mCherry-tagged PALB2 cDNA constructs in Trp53 and Palb2-null mouse cell line; exposure to increasing concentrations of PARP inhibitor Olaparib for 5 days inhibits end-joining mediated by PARP and sensitizes cells to DNA damage; cell survival is determined by measuring fluorescence intensity after staining with a cell viability reagent.

AssayReadOutDescription: Percent cell survival after treatment with Olaparib

AssayRange: %

AssayNormalRange: Olaparib resistance levels comparable to that of cells expressing wild type PALB2; no numeric threshold given

AssayAbnormalRange: Not reported

AssayIndeterminateRange: Not reported

ValidationControlPathogenic: 0

ValidationControlBenign: 0

Replication: Not reported

StatisticalAnalysisDescription: Not reported

AssayResult: 5

AssayResultAssertion: Normal

StandardErrorMean: 0.58

HGVS: NM_024675.3:c.101G>A p.(Arg34His)

AssayGeneralClass: BAO:0003061 reporter protein

AssayMaterialUsed: CLO:0009454 U-2 OS cell

AssayDescription: U2OS cells were treated with PALB2 siRNA and synchronized to G1/S phase by double thymidine block. Cells were then co-transfected with peYFP-PALB2 expressing PALB2 variants (or empty vector), pCR2.1-mRuby2LMNAdonor, and pX330-LMNAgRNA, which generates mRuby2-Lamin A/C fusion if HDR is successful.

AssayReadOutDescription: Mean relative percentages of mRuby2-positive cells over the YFP-positive population relative to the wild type condition.

AssayRange: %

AssayNormalRange: Not reported

AssayAbnormalRange: <40%

AssayIndeterminateRange: 41%-77%

ValidationControlPathogenic: 1

ValidationControlBenign: 3

Replication: Three independent experiments, each with n > 300 YFP-positive cells per condition

StatisticalAnalysisDescription: One-way ANOVA followed by Dunnett's post hoc analysis

AssayResult: 5

AssayResultAssertion: Abnormal

PValue: < 0.0001

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

ControlType: Abnormal; empty vector

AssayResult: 100

AssayResultAssertion: Normal

ControlType: Normal; wild type PALB2 cDNA

AssayResult: 34

AssayResultAssertion: Abnormal

PValue: < 0.0001

AssayResult: 68

AssayResultAssertion: Indeterminate

PValue: < 0.0001

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 90

AssayResultAssertion: Normal

PValue: Not reported

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 46

AssayResultAssertion: Indeterminate

PValue: < 0.0001

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 23.6

AssayResultAssertion: Abnormal

PValue: < 0.0001

AssayResult: 82

AssayResultAssertion: Normal

PValue: Not reported

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 53

AssayResultAssertion: Indeterminate

PValue: < 0.0001

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 41

AssayResultAssertion: Indeterminate

PValue: < 0.0001

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 95

AssayResultAssertion: Normal

PValue: Not reported

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 90

AssayResultAssertion: Normal

PValue: Not reported

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 83

AssayResultAssertion: Not reported

PValue: Not reported

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 77

AssayResultAssertion: Indeterminate

PValue: < 0.01

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 81

AssayResultAssertion: Not reported

PValue: Not reported

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 38

AssayResultAssertion: Abnormal

PValue: < 0.0001

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 5

AssayResultAssertion: Abnormal

PValue: < 0.0001

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 36

AssayResultAssertion: Abnormal

PValue: < 0.0001

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 85

AssayResultAssertion: Not reported

PValue: Not reported

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 58

AssayResultAssertion: Indeterminate

PValue: < 0.0001

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 86.74

AssayResultAssertion: Not reported

PValue: 0.1836

Comment: Exact values reported in Table S3.

HGVS: NM_024675.3:c.11C>T p.(Pro4Leu)

AssayGeneralClass: BAO:0002805 cell proliferation assay

AssayMaterialUsed: CLO:0037317 mouse embryonic stem cell line

AssayDescription: Stable expression of wild type and variant PALB2 cDNA constructs in Trp53 and Palb2-null mouse cell line containing DR-GFP reporter; exposure to cisplatin for 48 h induces interstrand-crosslink DNA damage; cell survival is measured by FACS 24 h after cisplatin washout

AssayReadOutDescription: Relative resistance to cisplatin represented as cell survival relative to wild type, which was set to 100%

AssayRange: %

AssayNormalRange: Cisplatin resistance levels comparable to that of cells expressing wild type PALB2; no numeric threshold given

AssayAbnormalRange: Cisplatin resistance levels comparable to that of cells expressing empty vector; no numeric threshold given

AssayIndeterminateRange: Not reported

ValidationControlPathogenic: 2

ValidationControlBenign: 2

Replication: 2 independent experiments

StatisticalAnalysisDescription: Not reported

AssayResult: 128.59

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 14.72

Comment: Exact values reported in “Source Data” file.

AssayResult: 19.43

AssayResultAssertion: Abnormal

ReplicateCount: 5

StandardErrorMean: 4.42

ControlType: Abnormal; empty vector

Comment: Exact values reported in “Source Data” file.

AssayResult: 100

AssayResultAssertion: Normal

ReplicateCount: 6

StandardErrorMean: 0

ControlType: Normal; wild type PALB2 cDNA

Comment: Exact values reported in “Source Data” file.

AssayResult: 84.05

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 16.48

Comment: Exact values reported in “Source Data” file.

AssayResult: 97.73

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 5.41

Comment: Exact values reported in “Source Data” file.

AssayResult: 19.53

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 8.56

Comment: Exact values reported in “Source Data” file.

AssayResult: 119.03

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 6.12

Comment: Exact values reported in “Source Data” file.

AssayResult: 37.28

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 11.28

Comment: Exact values reported in “Source Data” file.

AssayResult: 111.51

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 7.63

Comment: Exact values reported in “Source Data” file.

AssayResult: 80.44

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 9.06

Comment: Exact values reported in “Source Data” file.

AssayResult: 27.29

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 6.53

Comment: Exact values reported in “Source Data” file.

AssayResult: 102.2

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 12.81

Comment: Exact values reported in “Source Data” file.

AssayResult: 112.08

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 4.1

Comment: Exact values reported in “Source Data” file.

AssayResult: 87.4

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 0.88

Comment: Exact values reported in “Source Data” file.

AssayResult: 100.97

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 7.27

Comment: Exact values reported in “Source Data” file.

AssayResult: 20.08

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 6.84

Comment: Exact values reported in “Source Data” file.

AssayResult: 89.72

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 7.95

Comment: Exact values reported in “Source Data” file.

AssayResult: 93.33

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 11

Comment: Exact values reported in “Source Data” file.

AssayResult: 83.16

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 0.2

Comment: Exact values reported in “Source Data” file.

AssayResult: 26.03

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 11.42

Comment: Exact values reported in “Source Data” file.

AssayResult: 72.7

AssayResultAssertion: Not reported

ReplicateCount: 3

StandardErrorMean: 9.73

Comment: Exact values reported in “Source Data” file.

AssayResult: 97.61

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardDeviation: 0.97

StandardErrorMean: 0.68

Comment: Exact values reported in “Source Data” file.

HGVS: NM_024675.3:c.10C>T p.(P4S)

AssayResult: 28.5

AssayResultAssertion: Abnormal

ReplicateCount: 21

StandardErrorMean: 7.6

Comment: This variant had partial loss of function of peak current (10-50% of wildtype) and a >10mV loss of function shift in Vhalf activation, therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

HGVS: NM_198056.2:c.1045G>A p.(Asp349Asn)

AssayGeneralClass: BAOCL:20:0010044 targeted transcriptional assay

AssayMaterialUsed: CL:2000001 peripheral blood mononuclear cell from patients

AssayDescription: Comparative transcriptomic analysis using reverse transcription to compare peripheral blood mononuclear cells of patients with wild type or pathogenic TP53 variants in the context of genotoxic stress induced by doxorubicin treatment. Ten biomarkers corresponding to p53 targets were measured to determine a functionality score.

AdditionalDocument: PMID: 23172776

AssayReadOutDescription: In the treated condition, the peak height of each of the 10 p53 target genes was measured and divided by the sum of the heights of the three control genes. This value was then divided by the same ratio calculated in the untreated condition. In the assay, the mean of the 10 values defines the p53 functionality score. The final p53 functionality score is the mean of the scores obtained in RT-MLPA and RT-QMPSF assays.

AssayRange: An arbitrary functionality score was calculated from the induction score of the 10 p53 targets.

AssayNormalRange: >7.5

AssayAbnormalRange: <5.5

AssayIndeterminateRange: Between 5.5 and 7.5 is associated with an intermediate effect.

AssayNormalControl: wild type TP53

AssayAbnormalControl: LFS patient cells

ValidationControlPathogenic: 8 individuals had seven distinct TP53 variants which could be considered as likely pathogenic or pathogenic based on their ClinVar classification or their truncating nature.

ValidationControlBenign: 51 individuals had no detectable germline TP53 variant

Replication: at least two wells were seeded per patient (treated and untreated) and duplicates or triplicates were performed whenever possible.

StatisticalAnalysisDescription: Differentially expressed genes between doxorubicin-treated and untreated cells were arbitrarily defined using, as filters, a P<0.01 and fold-change cutoffs >2 or <2, for up and down regulation, respectively. The resultant signal information was analyzed using one-way analysis of variance (ANOVA, P= 0.001), assuming normality but not equal variances with a Benjamani–Hochberg correction for multiple comparisons using three groups: controls, null, and missense mutations.

SignificanceThreshold: P=0.001

Comment: statistical analysis and P value from previous publication.

Reviewer #3 (Public Review):

The authors aimed to develop a 2D image analysis workflow that performs bacterial cell segmentation in densely crowded colonies, for brightfield, fluorescence, and phase contrast images. The resulting workflow achieves this aim and is termed "MiSiC" by the authors.

I think this tool achieves high-quality single-cell segmentations in dense bacterial colonies for rod-shaped bacteria, based on inspection of the examples that are shown. However, without a quantification of the segmentation accuracy (e.g. Jaccard coefficient vs. intersection over union, false positive detection, false negative detection, etc), it is difficult to pass a final judgement on the quality of the segmentation that is achieved by MiSiC.

A particular strength of the MiSiC workflow arises from the image preprocessing into the "Shape Index Map" images (before the neural network analysis). These shape index maps are similar for images that are obtained by phase contrast, brightfield, and fluorescence microscopy. Therefore, the neural network trained with shape index maps can apparently be used to analyze images acquired with at least the above three imaging modalities. It would be important for the authors to unambiguously state whether really only a single network is used for all three types of image input, and whether MiSiC would perform better if three separate networks would be trained.

Reviewer #3 (Public Review):

The authors sought to show how the segments of influenza viruses co-evolve in different lineages. They use phylogenetic analysis of a subset of the complete genomes of H3N2 or the two H1N1 lineages (pre and post 2009), and use a method - Robinson-Foulds distance analysis - to determine the relationships between the evolutionary patterns of each segment, and find some that are non-random.

1) The phylogenetic analysis used leaves out sequences that do not resolve well in the phylogenic analysis, with the goal of achieving higher bootstrap values. It is difficult to understand how that gives the most accurate picture of the associations - those sequences represent real evolutionary intermediates, and their inclusion should not alter the relationships between the more distantly related sequences. It seems that this creates an incomplete picture that artificially emphasizes differences among the clades for each segment analyzed?

2) It is not clear what the significance is of finding that sequences that share branching patterns in the phylogeny, and how that informs our understanding of the likelihood of genetic segments having some functional connection. What mechanism is being suggested - is this a proxy for the gene segments having been present in the same viruses - thereby revealing the favored gene segment combinations? Is there some association suggested between the RNA sequences of the different segments? The frequently evoked HA:NA associations may not be a directly relevant model as those are thought to relate to the balance of sialic acid binding and cleavage associated with mutations focused around the receptor binding site and active site, length of NA stalk, and the HA stalk - does that show up in the overall phylogeny of the HA and NA segments? Is there co-evolution of the polymerase gene segments, or has that been revealed in previous studies, as is suggested?

The mechanisms underlying the genomic segment associations described here are not clear. By definition they would be related to the evolution of the entire RNA segment sequence, since that is being analyzed - (1) is this because of a shared function (seems unlikely but perhaps pointing to a new activity), or is it (2) because of some RNA sequence-associated function (inter-segment hybridization, common association of RNA with some cellular or viral protein)? (3) Related to specific functions in RNA packaging - please tell us whether the current RNA packaging models inform about a possible process. Is there a known packaging assembly process based on RNA sequences, where the association leads to co-transport and packaging - in that case the co-evolution should be more strongly seen in the region involved in that function and not elsewhere? The apparent increased association in the cytoplasm of the subset of genes examined for the single virus looks mainly in the cytoplasm close to the nucleus - suggesting function (2) and/or (3)?.

It is difficult to figure out how the data found correlates with the known data on reassortment efficiency or mechanisms of systems for RNA segment selection for packaging or transport - if that is not obvious, maybe you can suggest processes that might be involved.

Reviewer #3 (Public Review):

This is a well-executed study with interesting and novel findings. The main strength is the combined use of well-executed flow cytometry studies in human patients with MI and in vitro experiments to suggest a role for immature neutrophils in infarction. The main weakness is the descriptive/associative nature of the data. What is lacking is in vivo experimentation documenting the proposed pro-inflammatory role of immature neutrophils. This limits the conclusions. The following specific concerns are raised:

Major:

1.In some cases, conclusions are not supported by robust data. For example, the authors conclude that CD14+HLA-DRneg/lo monocytes play a crucial role in post-infarction inflammation based exclusively on in vitro experiments. Moreover, conclusions regarding the pro-inflammatory role of immature neutrophils are based on in vitro data and associative studies.

2.Immature neutrophils have a short lifespan. Information on the fate of immature neutrophils in the infarct is lacking. The in vivo mouse model may be ideal to address whether immature neutrophils undergo apoptosis or mature within the infarct environment

3.The rationale for selective assessment of specific genes and for the specific neutrophil-lymphocyte co-culture system is unclear. In neutrophils, the basis for selective assessment of some specific genes (MMP9, IL1R1, IL1R2, STAT3 etc), vs. other inflammatory genes known to be expressed at high levels by neutrophils is not explained. Similarly, the rationale for the experiment examining interactions of CD10neg neutrophils with T cells is not clear. Considering the effects of neutrophils on macrophage phenotype and on cardiomyocytes, study of interactions with other cell types may have made more sense.

4.The concept of CMV seropositivity is suddenly introduced without a clear rationale. The data show infiltration of the infarcted heart with immature neutrophils and CD14+HLA-DRneg monocytes. One would have anticipated more experiments investigating the (proposed) role of these cells in the post-infarction inflammatory response, rather than comparison of CMV+ vs negative patients.

Reviewer #3 (Public Review):

This paper from He, Y. et al examines how PKC-theta in activated T cells controls RanBP2 nuclear pore subcomplex formation and nuclear translocation of NFkB, NFAT and AP1 family transcription factors. He, Y et al systematically pull apart a molecular mechanism showing that: 1) T cell receptor-activated PKC-theta localises to the nuclear envelope and associates with RanGAP1, 2) PKC-theta deficiency reduces nuclear localisation of import proteins and AP1-family transcription factors in mature mouse T cells and Jurkat cell line, but not primary mouse thymocytes 3) RanGAP1 is phosphorylated by PKC-theta and that phosphorylation of RanGAP1 on Ser504/Ser506 facilitates RanGAP1 sumoylation and is needed for association with other RanBP2 complex components and 4) that wildtype but not Ser504/506 mutant RanGAP1 can rescue nuclear translocation of transcription factors in RanGAP1 knockdown cells.

A key strength of this work is that, for many key results, multiple methods for validating findings are used e.g. immunoblots of subcellular fractionation + confocal microscopy to show failure of c-Jun into the nucleus in Prkcq-/- mature T cells (Fig 3 G-H). Furthermore, although the majority of the molecular work takes advantage of the more tractable Jurkat cell line for dissection of molecular mechanism, a number of key points are validated in primary mouse or human T cells such as PKC-theta dependent TCR induced association of RanGAP1 with the nuclear pore (Fig 3D-E) and multiple methods of gene deletion were used e.g. siRNA, knockout mouse model and stable CRISPR deletion. The validation of a functionally meaningful phospho-site on the RanGAP1 protein is valuable for further understanding the biology of this protein.

Immune receptor control of nuclear transport machinery has not been extensively studied but, as is highlighted by this study, is increasingly being understood as an important step in immune receptor control of transcription factor function. The molecular mechanism that is uncovered here is novel and interesting to the immunological community as it links TCR signalling to an indirect mechanism for regulating localisation of multiple key transcription factors for the T cell immune response.

There are some concerns listed below. Addressing these concerns would add clarity to the manuscript and support some stated or implied conclusions.

1) The data on the role of PKC-theta driven RanBP2 subcomplex translocation of AP1 transcription factors is largely limited to within 15 min of T cell activation. The broad statements of the paper e.g. line 427 - "PKC-theta plays an indispensable role in NPC assembly" imply that PKC-theta is essential for this process during long-term T cell receptor activation; however, whether PKC-theta deletion has long term impact on nuclear translocation after these first 15 minutes is not established. The demonstration that the RanGAP1 mutant is not able to induce IL-2 production over 24 hrs (Fig 6D) does support the model that a longer-term requirement for RanGAP1 phosphorylation on Ser504/506 is important for translocation and functional AP1 transcriptional outcomes in this system, but from the data presented it does not necessarily follow that PKC-theta is the only regulator of this beyond the 15 min of activation shown here. It is well established that AP1 transcription factors increase in expression for multiple hours after T cell activation and if PKC-theta deletion impact is not long lasting this could mean PKC-theta is important for the kinetics of AP1 translocation but not necessarily for final functional outcome after a longer period of stimulation as is implied here.

2) It has been shown in the published literature the impact of PKC theta deletion on in vivo immune responses has been varied, with studies showing clearance of murine Listeria, LCMV, HSV. The manuscript currently lacks discussion around how the formation of a largely functional immune response in these contexts fits in with the strong defect in nuclear translocation of multiple important T cell transcription factors that they show here.

Reviewer #3 (Public Review):

This manuscript characterizes the additive genetic variance-covariance of behavioural traits and cortisol level in a captive Trinidadian guppy population, in particular to test for the genetic integration of behavioural and physiological stress responses.

The experimental design, trait definitions and statistical analyses appear appropriate. The main weakness of the study is a lack of clarity on the definition of genetic integration and the statistical ways to characterize, confirm or reject genetic integration (in particular, what defines and how to test for a "single major axis of genetic variation"?).

The additive genetic variation-covariation is correctly estimated. The presence of additive genetic correlations and the eigen decomposition of G seem to support genetic integration, but the lack of clear predictions makes the the conclusion not completely clear. Another minor conclusion, that "correlation selection in the past has likely shaped the multivariate stress response" is not directly supported by the results as the argument ignores the possible role of other evolutionary forces (in particular mutational input which is likely to be pleitropic for behaviour and hormone levels).

The nature of genetic (co)variation in behaviour and physiology is poorly known because most quantitative genetic studies of behavioural and physiological traits are still univariate, while it is clear that selection and evolution are better understood as multivariate processes. In addition to presenting some fresh results on the topic, this manuscript provides a mutivariate framework that could be applied in other populations. In particular, eigen decomposition of genetic variance-covariance matrices is not new but its application to the study of stress response integration is original and promising. As the authors mention, such methods could help improve health and welfare in captive animal populations via indirect artificial selection against stress, which is quite an original and stimulating idea.

Reviewer #3 (Public Review):

Calcium-permeable AMPA receptors (CP-AMPARs) have been shown to have important roles in modulating many aspects of neuronal function. They are distinguished from calcium-impermeable AMPARs (CI-AMPARs) by a property known as inward rectification and block by relatively selective polyamine compounds; this relative lack of selectivity has led to caveats in the interpretations of the roles of CP-AMPARs. The authors here demonstrate that complete block of CP-AMPARs, with no apparent effect on CI-AMPARs, can be achieved by intracellular application of the polyamine NASPM. Importantly, the authors provide evidence that this block is apparently not affected by the presence of auxiliary subunits, one of the key caveats regarding prior interpretations of the effects of polyamines and the roles of CP-AMPARs. The authors hypothesize that this new approach, use of intracellular NASPM, can provide greater clarity regarding the role of CP-AMPARs in future.

The approach is sound, the experiments are performed appropriately, the data provided is robust, the presentation is clear, the analyses including statistics are appropriate, the immediate interpretations are therefore fully supported, and the overall manuscript outstanding. The authors appropriately used both a heterologous expression system as well as in vitro neuronal preparation to address their hypotheses. The use of intracellular NASPM to unambiguously distinguish CP-AMPARs from CI-AMPARs has the potential to be transformative in future interpretations about the role of CP-AMPARs, so these findings are very relevant and highly impactful to the field.

Reviewer #3 (Public Review):

Gill et al. presents an extensive analysis of information/data collected as part of a pertussis vaccine study conducted in Zambia (the basis for an earlier publication, Gill et al., CID 2016). As part of the initial study, the investigators collected serial NP samples from mother/infant pairs at sequential follow-up clinic visits and analyzed them by PCR for the presence of IS481 and, in some cases, ptxS1. The results from these assays were evaluated in conjunction with clinical information on potential manifestations of respiratory illness in the infants and mothers. The authors found important patterns of PCR Ct values, which might not have been considered positive on a single sample PCR from a single patient PCR in a US clinical microbiology lab. Together, however, representing a collection of serial samples from study subjects, they strongly support the proposal that asymptomatic infections occurred in these study subjects. The authors used multiple approaches, including determining a mathematical "Evidence For Infection" or EFI to analyze the data from individual subjects and infant/mother pairs. From the collective data and analytical approaches, the authors provide a compelling case for infections with B. pertussis that are not associated with significant clinical symptoms. This possibility has certainly been considered previously, but not possible to address in the absence of the enormous amount of quantitative data and analysis provided from this prospective study. Another important point made from these data is that PCR Ct values can be useful in other than an all-or-nothing (positive or negative) decision, as is done appropriately with single patient samples submitted to clinical microbiology labs for PCR analysis.

Reviewer #3 (Public Review):

In the manuscript entitled "Allosteric communication in Class A 1 b-lactamases occurs via cooperative 2 coupling of loop dynamics", Galdadas et al. aim to use a combination of nonequilibrium and equilibrium molecular dynamics simulations to identify allosteric effects and communication pathways in TEM-1 and KPC-2. They claimed that their simulations revealed pathways of communication where the propagation of signal occurs through cooperative coupling of loop dynamics. This study is highly relevant to the field as allosteric regulation is believed to be a major signal transduction pathway in several drug-targeted proteins. A better understanding of these regulations could increase the efficacy and specificity of drugs.

Reviewer #3 (Public Review):

The paper by Eyal Ben-David and colleagues reports an elegant single cell experiment in a genetic outcross of C. elegans to show where specific genetic regulation of gene expression could be seen at the level of individual cells. This is the first, to my knowledge, genetic mapping experiment at the single cell level in a complex organism. One neat trick was use the transcript sequencing data for genotyping each individual cell. Another above-and-beyond-the-call-of-duty feature was the permutation tests to set FDR levels, which ended up being similar to Benjamini-Hochberg.

There is complex single cell processing to analyse this data. It could be more clear how complex this analysis is: quite complex models are used to both (a) cluster the cells into cell types across each individual and (b) model the resulting eQTLs. (c) somewhat more routinely, a HMM is used to gentoype but from the single cell transcript data, which is cute. Personally I think more should be made in the main text of the methods, highlighting the complexity of the models (there is at least one parameter this reviewer did not understand why was in the model!). However, a variety of bulk to single cell or single cell to previous experiment data shows that they seem to have discovered correct eQTLs.

A particular focus was on single cell neuronal eQTLs; this plays to the unique "named cell" aspect of C. elegans and this dataset, and did not disappoint. they found a fair number and one that they highlighted had the (rare) antagonistic effect between cell lines, something much discussed or theorised might exist in some cell types - here it is in all its glory. Backing up this was evidence that the single cell neuronal QTL data cannot be seen by "pan neuronal" analysis.

Overall this is an excellent paper; it clarifies much of which has been theorised or discussed, while in many ways (in my view) hiding its methodological sophistication in the main text.

Reviewer #3 (Public Review):

This paper presents an extensive study on providing a large dataset CEM500K, pre-trained models for electron microscopy data. This dataset is provided by the authors as an unlabeled dataset for supporting generalization problems like transfer learning.

Strengths:

— The motivation problem is well defined as the lack of large and, importantly, diverse training datasets of supervised DL segmentation models for cellular EM data.

— A large and comprehensive dataset, CEM500K, including both 2D and 3D images is designed by the authors to overcome this issue.

— The experimental results present the efficiency and prominent role of this dataset in training DL.

Concerns:

— Some of the claims have not been well supported by proofs/references/examples. As an example, the following claim "The homogeneity of such datasets often means that they are ineffective for training DL models to accurately segment images from unseen experiments" would be more valuable if some examples are provided by the authors.

Reviewer #3 (Public Review):

In this manuscript, the authors studied how cholinergic neurons in the medial septum contribute to the acquisition of spatial memory. The question that is addressed is that of the requirement for the appropriate timing of cholinergic neurotransmission in memory formation. The main finding is that in mice optogenetic stimulation of cholinergic neurons in the medial septum slowed acquisition of a spatial memory task when the stimulation was applied at the goal location, but not during navigation toward the goal location. Stimulation at the goal location also reduced the rate of hippocampal sharp-wave ripples (SWRs), which the authors point to as a possible explanation of the observed learning deficit.

The task-phase specific manipulation of the MS cholinergic neurons is a good and appropriate approach. The effect on learning in the Y-maze task after goal location specific stimulation is both clear and convincing. The lack of a behavioral effect with navigation-only stimulation may be due to ACh levels already being high during this task phase (as the authors suggest). It would have been nice if the authors had also used inhibition to address the importance of timing of ACh neuromodulation.

The authors used prolonged excitatory optogenetic stimulation that lasted anywhere from several seconds (e.g. at goal without reward or running towards goal) to over 30 seconds (e.g. at goal with reward). There are several potential issues with this stimulation protocol:

— From Figure 1B, it appears that the light-induced increase of mean spike frequency is sustained for quite some time after the light is turned off. The sustained activity will make the manipulation in the behavioral task less temporally specific (and thus less task-phase specific). To assess the possible impact of the sustained activation on the findings in the paper, it should be quantified (i.e. duration of sustained activity, dependence on duration of prior light stimulation) - ideally in awake animals (i.e. under the same conditions as the behavioral experiments). Supporting data to better support this conclusion could be provided in a later study (with a link provided to this study), with this caveat appropriately discussed here.

— Prolonged light stimulation could lead to non-specific side-effects. Importantly, the authors controlled for this by performing the same light-stimulation protocol in animals that did not express ChR2. Although non-specific effects of light stimulation were found for theta power, the effects on learning and SWR rate at the goal location could not be explained by non-specific light effects. These data add confidence to the main findings. Still, the number of control animals is low (n=2) and increasing the sample size would make these control experiments more robust. This potential caveat should be mentioned.

— Because the time that animals spent at the goal location is much longer than the travel time to the goal location, the behavioral difference between the "navigation" and "goal" groups could be due to the duration of optical stimulation. The authors point out that the "throughout" group has overall the longest stimulation duration, but an "intermediate" behavioral performance, which would suggest that stimulation duration is not the determining factor.

Unfortunately, the statistical analysis that the authors performed is inconclusive (i.e. the throughout group is not different from either "navigation" or "goal" groups). However, if duration is an important factor, the hypothesis would be that days-to-criterion for "throughout" condition is larger than "goal" condition (i.e. H0: throughout<=goal and H1: throughout>goal). Authors could test this directly (rather than H0: throughout=goal and H1: throughout≠goal). Bayes Factor analysis could help to assess the confidence in H0 rather than concluding that there is a lack of evidence due to low sampling.

Even so, the authors' argument could be weakened if long-term stimulation has reduced efficacy (as suggested by the authors on page 18). To exclude this possibility, changes in the long-term stimulation efficacy should be quantified, e.g. by quantifying the stability of light-induced firing of ACh neurons with the same stimulation protocol as used in awake animals, and/or by checking whether the stimulation-induced reduction of SWR rate gets smaller across trials within a day. Supporting data to better support this conclusion could be provided in a later study (with a link provided to this study), with this caveat appropriately discussed here.

The main novelty of the study is that specific stimulation of cholinergic neurons in the medial septum when animals reach the goal location results in a learning deficit. The reduction of SWRs upon cholinergic stimulation was shown before, but the authors now show that this reduction coincides with and may provide an explanation of the delayed learning. However, the link between the effect of the stimulation on SWRs and the behavioral deficit is indirect and not extremely convincing. This caveat should be discussed and conclusions tempered accordingly. Specific points related to this that should be discussed are described below.

— First, the analysis of SWR rate is performed in a separate set of experiments as in which the behavioral effect is assessed. This makes it difficult to more directly relate the change of SWR rate to the learning deficit.

— Second, the reduction of SWR rate is not absolute and SWRs are still present at lower rate. The data in Figure 4E indicate that for some animals the average SWR rate with stimulation is higher than for other animals without stimulation.

— Third, the Y-maze task used by the authors tests the acquisition of spatial reference memory and bears similarities to the inbound phase of the continuous spatial alternation task in 3-arm mazes. In Jadhav et al. (2012), the inbound phase was not sensitive to selective SWR disruption. These prior data would be an argument against a causative role of the reduction of SWR rate in the observed behavioral deficit.

— Fourth, while the authors briefly discuss other possible causes (e.g. effects on plasticity), they do not appear to consider non-hippocampal contributions or possible interference with reward-related dopamine signaling.

Reviewer #3 (Public Review):

In the paper entitled "Stress Resets Transgenerational Small RNA Inheritance" Houri-Ze'evi L, Teichman G et al examine the interaction between multiple heritable phenotypes by knocking down a heritable GFP reporter and examining its interaction with other stresses, such as starvation and high temperature, which cause transgenerationally heritable phenotypes. They demonstrate that exposing worms to stresses inhibits the transgenerational silencing of the GFP reporter strain they use. They further demonstrate that deletion of genes involved in the MAPK pathway, the skn-1 transcription factor and the putative H3K9 methyltranferase met-2 eliminate the differential response in the F1 and F2 generations after exposure to stress and the GFP reporter silencing. They also sequence the small RNAs in the P0 and F1 generation with and without the added stresses.

All in all, the authors have expanded the mechanistic understanding of how heritable small RNAs are influenced by environmental conditions. I think that the conservation of several of the known regulators of epigenetic inheritance appearing in this study reflects how the regulators of non-genetic inheritance are beginning to converge on a few central pathways. The bit about MET-2 is still a bit premature as it's link to SKN-1 and regulated small RNAs is not completely fleshed out here, but I'm sure future studies will help delineate how this putative methyltransferase is communicating with SKN-1 on a more mechanistic level. Future studies examining how and why the MAPK pathway is so critical in this inheritance paradigm will be interesting.

Reviewer #3:

The authors present the algorithm clearly by comparing it to the most popular SMLM clustering algorithms and showing its robustness in varying density SMLM data, which is a big problem in the field. The presented experimental test on 3D LAMP-1 SMLM data also contributes to the robustness of the paper.

While reading the manuscript, I missed a comparison with another graph-based SMLM clustering algorithm published previously by Khater et al. in relation to accuracy and computation speed, which is particularly important to demonstrate the advantages of StormGraph. The approach should also be included in Table 1. I also think that a direct comparison in terms of accuracy and computation speed is crucial.

During the review process, a similar paper has been posted to bioRxiv dated 22. December, https://www.biorxiv.org/content/10.1101/2020.12.22.423931v1.full so the authors could not be aware of this work; however, it would be nice if the authors could comment on this work.

Reviewer #3:

The use of frequency tagging to analyze continuous processing at phonemic, word, phrasal and sentence-levels offers a unique insight into neural locking at higher-levels. While the approach is novel, there are major concerns regarding the technical details and interpretation of results to support phrase-level responses to structured speech distractors.

Major concerns:

1) Is the peak at 1Hz real and can it be attributed solely to the structured distractor?

• The study did not comment on the spectral profile of the "attended" speech, and how much low modulation energy is actually attributed to the prosodic structure of attended sentences? To what extent does the interplay of the attended utterance and distractor shape the modulation dynamics of the stimulus (even dichotically)?

• How is the ITPC normalized? Figure 2 speaks of a normalization but it is not clear how? The peak at 1Hz appears extremely weak and no more significant (visually) than other peaks - say around 3Hz and also 2.5Hz in the case of non-structured speech? Can the authors report on the regions in modulation space that showed any significant deviations? What about the effect size of the 1Hz peak relative to these other regions?

• It is hard to understand where the noise floor in this analysis - this floor will rotate with the permutation test analysis performed in the analysis of the ITPC and may not be fully accounted for. This issue depends on what the chosen normalization procedure is. The same interpretation put forth by the author regarding a lack of a 0.5Hz peak due to noise still raises the question of interpreting the observed 1Hz peak?

2) Control of attention during task performance

• The authors present a very elegant analysis of possible alternative accounts of the results, but they acknowledge that possible attention switches, even if irregular, could result in accumulated information that could emerge as a small neurally-locked response at the phrase-level? As indicated by the authors, the entire experimental design to fully control for such switches is a real feat. That being said, additional analyses could shed some light on variations of attentional state and their effect on observed results. For instance, analysis of behavioral data across different trials (wouldn't be conclusive, but could be informative)

• This issue is further compounded by the fact that a rather similar study (Ding et al.) did not report any phrasal-level processing, though there are design differences. The authors suggest differences in attentional load as a possible explanation and provide a very appealing account or reinterpretation of the literature based on a continuous model of processing based on task demands. While theoretically interesting, it is not clear whether any of the current data supports such an account. Again, maybe a correlation between neural responses and behavioral performance in specific trials could shed some light or strengthen this claim.

Additional comments:

• What is the statistic shown for the behavioral results? Is this for the multiple choice question? Then what is the t-test on?

• Beyond inter-trial phase coherence, can the authors comment on actual power-locked responses at the same corresponding rates?

Reviewer #3 (Public Review):

In this manuscript, Filipowicz and Aballay present a nice story that characterizes a new learned behavioral phenotype prompted by intestinal distention during infection with the bacterial pathogen E. faecalis. The authors show that distention of the anterior portion of the intestine by E. faecalis induces an aversive behavioral response. Importantly, the authors show that this aversive learning response is controlled by multiple sets of neurons, including some that express the GPCR NPR-1 and others that express the ion channels TAX-2/4. The authors nicely showed that TAX-2 expression in ASE neurons was sufficient for pathogen avoidance, but not other chemosensory neurons. Next the authors examined the mechanism of aversive learning following ingestion of E. faecalis, showing that AWB and AWC neurons are required. Finally, the authors show that two proteins that could be mechanoreceptors in the intestine (GON2 and GTL-2) are required for pathogen avoidance. Together these data characterize important mechanisms of pathogen avoidance and an aversive learning response.

I have one issue for the authors to consider. The title of the manuscript emphasizes the role of TRPM channels in mediating the learned pathogen avoidance response. Demonstrating that the site of action of the TRPM channels is the intestine could further strengthen this exciting finding.

Reviewer #3 (Public Review):

The authors have developed a new culture method to expand adult lung cells in vitro as 3-D organoids. This culture system is different from previous organoid cultures which include either bronchiolar, or alveolar, lineages. Rather, the authors attempted to preserve both lineages over long-term passaging. The 3-D cultured organoids can be dissociated and re-plated as 2D monolayers, which can be either cultured immersed in medium or in air-liquid interface (ALI) conditions, exhibiting a different bias towards alveolar and airway lung cell types respectively. The 2D monolayer cultures can be infected by COVID-19 virus and showed a progressive increase in virus load, which was distinct from iPSC- derived alveolar type 2 (AT2) cell and bronchiolar epithelial cell culture control infections. Through bioinformatics analysis, the authors were able to show that their monolayer cultures acquired similar immune response features to an in vivo COVID infection dataset, indicating that this culture system may be suitable for modeling COVID infection in vitro. It is particularly interesting that the bioinformatics analyses suggested that this adult human lung organoid system, with both airway and alveolar phenotypes, showed greater resemblance to the transcriptional immune response of severely COVID-infected lungs than either cultured cell type alone. This aspect of the manuscript strongly suggests that the authors' approach of developing a mixed lung organoid model is an extremely good one.

However, the data presented in figures 2 and 3 cast serious doubts over the long-term reproducibility of the organoid system. That individual organoids contain both airway and alveolar lineages has not yet been convincingly demonstrated (Fig 2). In addition, bulk RNAseq experiments illustrate that the overall cell composition of the cultures drifts significantly during long-term passaging (Fig 3). Due to this variability, the organoids' ability to act as a suitable model for viral infections that would be amenable to drug screening approaches is also questionable.

Reviewer #3 (Public Review):

The paper presents results of a serological survey done on 10,000+ employees and workers associated with CSIR labs in India during August-September 2020. The survey finds 10.14% seropositively. In addition, correlations are drawn between seropositivity and biological and lifestyle factors. A follow up study is also done on a subset of employees found seropositive and antibodies are found to survive even after six months in most.

Strengths: This is a one of the two surveys with a pan-India footprint, making it a valuable addition to understanding of Covid-19 pandemic evolution in the country. It also finds good inverse correlation between seropositivity and (i) blood group O, (ii) vegetarian diet, (iii) smoking, and (iv) use of private transport. While (iv) is obvious, (ii) and (iii) are a little surprising. It suggests a deeper study is required to understand the reasons behind it.

Weaknesses: While it is a pan-India survey, the population is not quite representative of general population of the country. CSIR labs are mostly in cities, and most of the employees use private transport. So the results cannot be generalized to the country as a whole. Restricting to people using public transport would be a better representation, although it still would not be fully representative.

The data collection and analysis are done meticulously, and provide some new insights into differential impact of Covid-19 virus on people.

Reviewer #3 (Public Review):

The article by Hesse, Owenier et al entitled "Single-cell transcriptomics 1 defines heterogeneity of epicardial cells and fibroblasts within the infarcted heart" will be of interest to the readers of heart regeneration, as it helps in understanding how epicardial cells contribute to heart regeneration following myocardial Infarction. Hesse, Owenier et al. investigate the role of epicardial stromal cells(EpiSC) after arterial ligation induced myocardial infarction (MI) in mouse. They perform single-cell RNASeq (scRNASeq) on isolated, FAC sorted, epicardial stromal cells, activated cardiac stromal cells (aCSC) from infarcted hearts and control cardiac stromal cells (CSC). The authors find 11 cell clusters of EpiSC. They confirmed the spatial localization of the different clusters by in situ hybridization and performed Gene ontology studies to understand the biological processes affected by those clusters. They found that those clusters fall into three major functional groups, as follows: 1) Wt1 expressing and cardiogenic factor expressing, 2) chemokine expressing and HOX genes expressing and 3) cardiogenic factor expressing. Interestingly there are two identified groups which express different cardiogenic factors 1) Wt1 positive with cardiogenic factors MESP1, WNT11, ISL11, TBX5, GATA 4,5,6 and the other group 3) Wt1-with Nkx2.5, BMP2 and BMP4. Authors show that multiple clusters are enriched in Hif1a, Hif1a related genes and glycolysis related genes which are known to be downstream of Wt1 cells. To further understand the hierarchical development of the EpiSC cells, the authors performed pseudo time-series analysis using RNA Velocity analysis on Wt1 reporter mice and find three different groups. Interestingly Wt1+ cells did not convert into other cell types. They further performed ligand receptor analysis to find interactions between different cell types. The authors implemented scRNAseq for aCSC and find cell clusters ECM rich cells, fibroblasts, interferon expressing cells, and cycling fibroblasts/myofibroblasts. They further compared the transcriptional profiles of EpiSC with the aCSC. They found gene sets, which are specific for EpiSC, and genes that are specific for aCSCs. Specifically, they found that Hif1a, glycolysis responsive genes, and cardiac contractile proteins were highly expressed in EpiSCs. Furthermore, the authors showed that the transcriptional profile of EpiSC, aCSC and CSC are different.

These data add an important knowledgebase to the understanding of the transcriptional landscape of the Epicardial stromal cells and would help identifying specific pathways/transcriptional genes which are activated during myocardial infarction.

Reviewer #3 (Public Review):

The differences in signaling and responses in the three different T cell receptor transgenics are shown by several different means. These include Nur77 and CD5 expression as markers for the strength of signaling, the frequency of calcium fluxes and length of signaling-induced pauses in movement, using 2 photon microscopy of thymic slices (comparing selecting and non-selecting thymus), time course of induction of markers of positive selection signaling, the time course of "arrival" of CD8 single positive cells and CCR7+ cells in the post-natal thymus, and a time course of development of SP thymocytes after injection of EdU. Each of these methods is fairly convincing on its own, but added up, they are very convincing.

The only issues that I could take issue with are about how we define self-reactivity. Because it is not feasible to measure the affinities for self peptides on MHC (due to low affinity and the fact that we mostly don't know what they are), the authors have to rely on surrogate markers, the upregulation of CD69 and of Nur77. These are widely accepted in the field, so they are as good a surrogate as is possible at this time.

Similarly, 3 transgenic strains are taken as examples of high, medium and low self-reactivity. Two of the strains are positively selected on H2Kb, one on Db, one on Ld. Therefore, the experiments cannot be genetically controlled in the same manner. On balance, I accept that there aren't too many other ways to do the experiment, and that all the main points are supported by other types of experiment.

The most interesting aspect of the work consists of analysis of gene expression by RNASeq from cells from each of the three TCR transgenic mice from early positive selection, late positive selection, and mature CD8 SP. Perhaps unsurprisingly, the more strongly self-reactive cells showed increased expression of genes involved in protein translation, RNA processing, etc. However, genes associated with lower self-reactivity were enriched for lots of different ion channels. These included calcium, potassium, sodium and chloride channels. One of these was Scn4b, part of a voltage gated sodium channel previously shown by Paul Allen's lab to be involved in positive selection. These types of genes were associated with the stage of development before selection, and were retained through selection in the weakly self-reactive thymocytes. Other ion channel genes that typically came on at the end of selection were also upregulated earlier in the lower self-reactivity cells, and may be involved in allowing long-term signaling for these cells to undergo the whole positive selection program.

Reviewer #3 (Public Review):

The authors present here a very interesting and thorough systems biology study of S. cerevisiae involving 22 steady state conditions with different growth rates and nitrogen sources. Proteomics and transcriptomics data, as well as intracellular amino acid concentrations, are gathered in a study that, if only for the sheer amount of data, is quite unique.

The authors use differential expression analysis, clustering algorithms and correlations to divide the genes and proteins studied into a small number of groups whose behaviour can be generally categorized. For a starter there is a small group (~10%) that map to central carbon metabolism and seems to be regulated by cues not covered in this study (growth rate and metabolic parameters involving amino acid and nucleotide availability). The rest of genes (90%) seem to have their transcript and protein levels heavily determined by growth rate and/or amino acid metabolism. For different growth rates, the expression of these genes and corresponding proteins seemed to be very correlated, and dependent on the availability of translation and transcription machinery (RNA polymerase and ribosomes). For different nitrogen sources, gene expression seemed dependent on amino acid and nucleotide availability.

These general rules are insightful and can provide a much more informative way to analyze multiomics data sets, by e.g. accounting for expected over/under expressions due to growth rate changes. Indeed, the authors attempt this for two cases: a distantly related yeast (S. pombe) and a human cancer cell model. While they are able to show that most transcript variation for S. pombe seems to be due to growth rate changes, the rest of the inferences do not seem very informative.

In general, while the findings are interesting and seemed to be mainly supported by the evidence, the manuscript is complicated to read. Evidence is scattered throughout the manuscript and needs to be gathered and compiled by the reader to check the results. Some of the writing is remiss: Figures 6A and 6C have the same caption and different graphs. It is also not clear how the differential expression calculations in Figure 1C were done: what are the two conditions being compared? Figure 7 encapsulates what is learnt from this paper but needs a more informative caption describing the full metabolic lesson learnt.

In summary, the data presented here is a golden data set that will make a great contribution to science, the general rules are interesting and seemed to be supported by the data, but to be more useful to readers the writing of the paper could be made clearer.

Reviewer #3 (Public Review):

The combination of Cre and Flp recombinase dependent system is powerful in manipulating specific intersectional neurons and has been successfully used in many systems. However, the system cannot express target genes sufficiently in some neurons, e.g., the LepRbVMH neurons. This paper solved this problem by developing a novel AAVs system, in which two AAVs were used, the "Driver" AAV permits Flp dependent expression of tTA, and the "Payload" AAV permits TRE-driven and Cre dependent expression of target gene. Because there two AAVs used, it is also expected to increase the capacity to incorporate more transgenes into the AAV system. The novel system to manipulate the intersectional neurons described in this work is an important addition to the current tools. It should be an excellent resource for the neuroscience community.

This paper is nicely written and compared the previous intersectional approach of AAV-EF1α-Con/Fon-hChR2(H134R)-EYFP with their novel tTARGIT approach in labelling LepRbVMH neurons. The data convincingly demonstrated that the tTARGIT system can label many more cells. Small caveats include the author co-injected AAV-hSYN-Flex(Lox)-hM3Dq-mCherry as an injection site marker with AAV-EF1α-Con/Fon-hChR2(H134R)-EYFP, the serotypes of these AAVs were not reported. It is well known that different serotypes of AAVs infect different types of neurons with a different efficiency. Furthermore, the combination of the different AAV might affect each other's infection, leading to low expression of one type of AAV. The titres of AAVs also make a big difference to many AAVs, which were not reported in this paper. These information are important for other investigators if they would adopt the tTARGIT system in their own research.

Reviewer #3 (Public Review):

Miskolci et al have investigated if it is possible to measure the natural fluorescence of two important co-enzymes (NADH/NADPH and FAD) in living cells to determine their metabolic status. This tests the hypothesis that changes to the relative ratio of NADH/NADPH to FAD+ reflect a shift between glycolytic and oxidative phosphorylation in living macrophages. To investigate this they have used 2-photon FLIM to measure intensity and fluorescence lifetime of NAD/NADPH and FAD+ in mouse macrophages in vitro and zebrafish macrophages in vivo in a tail injury model. By comparing their measures of NAD(P)H and FAD+ from macrophages responding to different injury or infection cues and comparing this to a maRker of inflammation (TNF-alpha) they argue that there is a reduced redox state indicative of glycolytic metabolism in pro-inflammatory macrophages.

The adoption of label free imaging techniques to measure metabolic processes in cells in vivo is a valuable and important development that, although not novel to this work, will help researchers to probe cell biology in situ. FLIM using time correlated single photon counting (TCSPC) allows an accurate and robust measure of the relative state of a molecule that shows changes in its fluorescent lifetime as a consequence of changing chemical state. Although Stringari et al (doi.org/10.1038/s41598-017-03359-8) were the first to describe the utility of wavelength mixing FLIM for measuring NAD(P)H and FAD+ levels in zebrafish, they did not focus on macrophages which is the focus of this work.

The results from this work are interesting, as they argue that it is possible to determine cell metabolism in cells within living animals without a need to use a genetically encoded sensor and they argue that pro-inflammatory macrophages in zebrafish appear to have a lower redox state, which may reflect a more glycolytic metabolism. This assumption is not tested but rather inferred based on the measures of fluorescence intensity and lifetime of endogenous NADH/NADPH and FAD coupled with a small metabolic sampling of injured tissue. This lack of corroboration for a the supposed difference in metabolism between pro-inflammatory and non-inflammatory macrophages is a weakness of the paper and makes it hard to accept the conclusion that the redox state may reflect different metabolic profiles. A biosensor for NADH/NADPH (iNap) has been demonstrated to be a sensitive tool for measuring NADPH concentration in vivo in zebrafish during the injury response (Tao et al (doi: 10.1038/nmeth.4306) and it would be intriguing to know how similar the response is of this biosensor to the label free measurements described using FLIM. This is additionally relevant as the authors also note that in mouse macrophages cultured in vitro, they observe an opposite redox response which is well supported by the literature and a variety of different methods. Why the zebrafish macrophages should show a different redox state to mouse macrophages is not clear and an alternative explanation is that the measures used do not directly reflect the metabolic profile of the cells. One further caveat to the chosen method of using fluorescence lifetime to measure the redox state of NADH/NADPH is that lifetime of NADH is affected by which proteins it is bound to. This is not accounted for in the method used for calculating the redox ratio used for defining the redox state and could potentially alter the interpretations of relative NADH/NADPH levels in a cell. The authors acknowledge this, but do not consider whether this would affect the conclusions they arrive at from their measures of NAD(P)H intensity and fluorescence lifetime in macrophages.

Reviewer #3 (Public Review):

This analysis is enormous in scope. That said, approximately half the glomeruli were either truncated or had very fragmented ALRNs. The authors may wish to reserve use of the term "full" in the title ("....a full olfactory connectome") until a subsequent paper.

ALRN-ALRN connectivity seems very interesting. It would be helpful to provide more information about this in the text (line 148 or so). The information in Fig. 3D is hard for non-specialists to interpret. Does the connectivity show any patterns? Is it stereotyped? Do the connections make functional sense?

One intriguing finding is the "shortcuts" between the olfactory and motor systems that could be used for behaviors that are hard-wired or require fast responses. These may be particularly relevant to thermosensory and hygrosensory input, but can the authors say anything about what kind of olfactory information flows through these shortcuts? For example, the ALRNs that respond to wasp odorants have been identified. Please note that most readers do not know what kind of odorants project to individual glomeruli, e.g. "DC4" .

Fig. 8C It's hard to know how confident to be of the neurotransmitter assignments here. It would be helpful to provide in the text a statement about what assumptions these assignments are based on. In the same vein, line 380 refers to "a neurotransmitter prediction pipeline". Some kind of reference should be provided here.

line 522 "This suggests that thermo/hygrosensation might employ labeled lines whereas olfaction uses population coding to affect motor output." This brings up the question of whether very narrowly tuned ORNs such as the one signaling geosmin show any differences in connectivity from broadly tuned ORNs.

lines 94-96 Graph traversal model. Some more discussion of this model and its underlying assumptions would be helpful. Are the results influenced by the lack of some of the glomeruli from the dataset?

Fig. 7D Can the authors provide more discussion of the possible functional significance of the two uPN types?

Reviewer #3 (Public Review):

Schrieber et al. studied the effects of biparental inbreeding in the dioecious plant Silene latifolia, focusing specifically on traits important for floral attractiveness and pollinator attraction. These traits are especially important for dioecious species with separate sexes as they are obligate outcrossers. The authors find that inbreeding mostly decreases floral attractiveness, but that this effect tended to be stronger in the female flowers, which the authors suspect to result from the trade-off with larger investment in the sexual functions in the female plants. The authors then go on to couple the changes in visual and olfactory floral traits to pollinator attraction which allows them to conclude or at least speculate that differences in pollinator behavior are mostly driven by the changes in olfactory traits. The study is robust in its broad and well-balanced sampling of populations, rigorous and in large part meticulously documented experimental designs and linking of the effects on mechanisms to ecological function. The hypothesis are clearly stated and the study is able to address them mostly convincingly. However, some of the aspects of the decisions the authors made and possible caveats need to be addressed and elaborated on.

A major caveat, in my opinion, is that while the authors find stronger effects of inbreeding on pollinator visitation rates in the plants from the North American (Na) origin, these plants were tested in an environment that was foreign to them, which could have important consequences for the results of this study. This is specifically because the main pollinator Hadena bicruris moth is completely absent from the populations in Na, and yet, was the main pollinator observed in the pollinator attraction experiment. As this pollinator is also a seed predator, the Na populations are released from the selection pressure to avoid attracting the females of this species and thus risking the loss of seeds and fitness. In fact, some of the results suggest that the release from the specialist pollinator and seed predator in Na has led to increase in the attractiveness of the female flowers based on the higher number of flowers visited in the outcrossed females compared to outcrossed males in the plant from the Na origin and the similar, though not statistically significant, pattern in the olfactory cue. While ideally this pollinator attraction experiment should be repeated within the local range of the Na plants, this is of course is not feasible. Instead I suggest the problem should be addressed in the discussion explicitly and its consequences for the interpretation of the results should be considered.

The incorporation of the VOC data in the actual manuscript was quite limited and I found the reasoning for picking only the three lilac aldehydes (in addition to the Shannon diversity index) for the univariate statistical tests insufficient. How much more efficient was the effect of the lilac aldehydes compared to the other 17 compounds deemed important in the previous study? While the data on this one aldehyde matches the pollinator attraction results, having one compound out of 70 (or out of 20 if only considering the ones identified important for the main pollinator) seems, perhaps, fortuitous lest there is a good reason for focusing on these particular compounds.

Sampling time of VOCs is reported ambiguously. Was it from 21:00 to 17:00 the next day or in fact from 9pm to 5AM (instead of 5 pm as reported)? Please be more specific in the text as this is quite important. If sampling tubes were left in place during the daytime, some of the compounds could have evaporated due to heating of the tubes in the summer. It would also be important to mention whether all of the headspace VOCs were sampled on the same day and whether there could be variation in i.e. temperature.

Considering the experimental setup for the pollinator attraction observations and the pooling of the data at the block level (which I think is the right choice) it seems possible the authors were more likely to get a result where pollinator behavior matches the long-distance cue, the VOCs. Short-distance cues such a subtle difference in flower size would perhaps not be distinguished with the current setup. I would be interested to know if the authors agree, and if so, mention this in the discussion.

Reviewer #3 (Public Review):

Using high fat diet (HFD)-fed male mice and a variety of experimental approaches, the authors demonstrated the efficacy of xanthohumol (XN) and tetrahydro-xanthohumol (TXN) in attenuating weight gain and hepatic steatosis independently of calorie intake and identify inhibition of PPARγ as a mechanism. A strength of the study design was the incorporation of the test compounds into isocaloric, ingredient matched high-fat diet (HFD) formations and inclusion of a LFD control group. A weakness of the study, although minor, is that the dose of compound consumed will vary between mice and from day-to-day depending on how much food each animal consumes. The lower dose of XN (LXN, given as 30 mg/kg of diet) was found ineffective compared to the higher dose of XN (HZN, 60 mg/kg of diet) and TXN (30 mg/kg of diet) was most effective in attenuating weight gain and reversing HFD-induced liver steatosis. TXN almost completely suppressed hepatic lipid vacuole accumulation and showed greatest reduction in liver mass relative to body weight. TXN increased fasting plasma triglycerides compared to all other groups, but explanation is uncertain. Fecal excretion of TAG between groups was similar and therefore could not explain the decreased weight gain or improved liver phenotypes in XN- or TXN-treated groups. Whole body energy metabolism suggested that XN and TXN supplemented mice were more physically active then HFD-fed mice. HXN and TXN supplemented mice showed less accumulation of subcutaneous and mesenteric fat mass, but these groups had somewhat higher levels of epididymal fat mass.

After 16 weeks on diets, RNAseq performed on murine liver tissues. Compared to HFD group, TXN group had 295 differentially expressed genes (DEGs), HXN group had 6 DEGs, and LFD group had 212 DEGs. TXN supplementation upregulated 6 and down regulated 25 KEGG pathways. SVM was used to identify signature genes that significantly differentiated HFD and TXN group transcriptomes. Of 13 identified genes, 8 showed significant, differential hepatic expression between TXN and HFD groups. Of these 8 genes, 3 genes (Ucp2, Cidec, Mogat1) were identified as known target genes of PPARγ with roles in lipid metabolism. qPCR of liver tissues was used to verify these RNAseq results.

XN or TXN were shown to inhibit murine preadipocyte 3T3-L1 differentiation and adipogenesis and lipid accumulation in a dose dependent manner. In a second dose escalating experiment, TXN or XN were shown to block the ability of rosiglitazone (RGZ), a PPARγ agonist, to promote adipogenesis of 3T3-L1. These data suggested that XN and TXN may interfere or compete with binding of RGZ to the PPARγ receptor. qPCR of 3T3-L1 cells confirmed that TXN or XN could inhibit gene expression of RGZ-induced PPARγ target genes (Cd36, Fabp4, Mogat1, Cidec, Plin4, Fgf21) and further supported the hypothesis that TXN and XN are PPARγ antagonists. To further test this idea the authors performed a competitive PPARγ TR-FRET binding assay and showed that XN and TXN could displace a labelled pan-PPARγ ligand in a dose-dependent manner. Finally, molecular docking experiments confirmed the putative binding pose and position of XN/TXN and estimated the relative binding affinities of various ligands for PPARγ. XN and TXN may serve as scaffolds for the development of more potent therapeutics in structure-activity relationship (SAR) studies. Overall, this work contributes compelling preclinical data to support future clinical investigations to determine dosing, efficacy, and safety of XN and TXN as therapeutics for diet-induced NAFLD.

Reviewer #3 (Public Review):

The manuscript "HPF1 and nucleosomes mediate a dramatic switch in activity of PARP1 from polymerase to Hydrolase" by Rudolph et al. studies the effect of HPF1 on the steps of the catalytic reaction of PARP1. They use various PARP1 activators i.e. free DNA and varied forms of core nucleosomes to quantify reaction rates in the presence and absence of HPF1, using several assays. The main point of the manuscript is the observation that in the presence of HPF1, PARP1 is converted to an NAD+ hydrolase, which releases free ADPr, instead of its normal activity to produce ADPr polymers. The PARP1 hydrolase activity has been described previously, but they now show that HPF1 increases it substantially under the conditions that they tested. The authors also describe their independent identification of HPF1 residue E284 as a residue that is essential for Ser modification, confirming previous structural and biochemical work from Ivan Ahel's group. Although the assays are well performed and controlled and yield important quantitative information that was missing in the field, the main result of the hydrolase activity of PARP1 is hard to reconcile with current knowledge of HPF1 effects in cell-based experiments.

(A) Model of the device without any stress/strain (left) and Finite element analysis model of the wearable device, not deformed (right). The model to the right exhibits the components inside the device. (B-C) The model shown being stretched 60%, vertically and horizontally respectively, show the maximum strain of the chip being 0.01%. This is much less than the normal failure strain for silicon (1%). (D) This figure shows the FMEA model being stretched 30% vertically and horizontally. The maximum strain in the chip components is below 0.004%. (E) Figure shows sensing performance of device when being stretched using an ECG. No significant effects from the mechanical stretching where evident in the results.