Reviewer #1 (Public Review):

The study by Hendley et al takes advantage of duct-specific DBA-lectin expression to purify pancreatic ductal populations that were then subjected to scRNA-seq analysis. The ability to enrich for this relatively low abundant pancreatic cell population resulted in a more robust dataset that had been generated previously from whole pancreas analyses. The manuscript catalogs several different gene clusters that delineate heterogeneous subpopulations of three different pancreatic ductal subpopulations in mice: mouse pancreatic ductal cells, pancreatobiliary cells, and intra pancreatic bile duct cells. Additional comparisons of the resulting data sets with published embryonic and adult datasets is a strength of the study and allows the authors to subclassify the different ductal cell populations and facilitates the identification of potentially novel subpopulations. Pseudotime analysis also identified gene programs that led the authors to speculate the existence of an EMT axis in pancreatic ducts. Overall, the data analyses is strong, but the authors tend to draw conclusions that are not fully supported by the presented data.

The second half of this study focuses on three candidate proteins that were identified in the transcriptome analysis - Anxa3, SPP1 and Geminin. Crispr-Cas9 was used to delete each gene in an immortalized human duct cell line (HPDE). Deletion of each gene resulted in increased proliferation; SPP1 mutant cells also displayed abnormal morphology. Additional functional studies of the cell lines or in mouse models suggested a role for SPP1 in maintaining the ductal phenotype and Geminin in protecting ductal cells from DNA damage, respectively. Although the provided phenotypic analysis suggest important functional roles for these proteins, follow up studies will be required to fully understand the role of these genes in homeostatic or cancer conditions.

Strengths:

1) Enrichment of pancreatic ductal populations enhanced the robustness of the scRNA-Seq dataset

2) Quality of the sequencing data and extensive computational analysis is extremely good and more comprehensive than previously published datasets

3) Comparative analysis with existing mouse and human data sets

4) Use of human ductal cell lines and mouse models to begin to explore the function of candidate ductal genes.

Weaknesses:

1) There are many suppositions based on gene expression changes that are somewhat overstated.

2) The conclusion that there is an EMT axis in pancreatic ducts is not fully supported by the gene expression and immunofluorescence data

3) A good rationale for choosing Anxa3, SPP1 and Geminin for additional functional analysis is not provided. In addition, it isn't clear why Anxa3 function isn't pursued further.

4) Although extensive models (transplanted cells for SPP1 and mouse conditional KOs for Geminin) were generated, the functional analysis for each gene is preliminary; additional longer term studies will be necessary to fully understand the role of these proteins in pancreatic duct development and cancer.

Reviewer #1 (Public Review):

The authors develop a mechanistic model for inferring infectiousness profile from data on times of symptom onset in pairs of infector-infectee. The novelty of their approach lies in assuming that infectiousness of an infected individual depends also on the whether or not they have symptoms. The authors fit a data set of time of symptom onset in 191 transmission pairs to a model that assumes that infectiousness varies along the incubation period. They compare the model fit to fits from models and find that their model of differential infectiousness explains the data better than the other models considered.

This is a carefully constructed study, and the conclusions are well supported by the analysis carried out. My only concern is that the data used were obtained during the early stage of the pandemic (January to February 2020). As the pandemic was growing in most countries during this time, we are more likely to have observed shorter serial intervals. Similarly, as isolation of infected individuals would prevent them from transmitting further, longer serial intervals are likely to be under-represented in the data. Indeed, the longest serial interval in the data used was 5 days. It would be interesting to understand whether the conclusions about the proportion of onward transmissions averted by contact tracing and subsequent isolation still hold as the pandemic progresses, and we continue to observe longer serial intervals. If the authors are unable to find more recent data, this caveat should be clearly discussed.

Reviewer #1 (Public Review):

Stokes, et al. describe the effects of isoflurane on metabolism in post-natal day 7 mice, and older mice. They demonstrate that blood levels of glucose and ß-hydroxybutyrate fall quickly in response to isoflurane, and that the magnitude of the decrease increases with the length of the exposure. Mice 30 days post-natal do not exhibit these changes in response to isoflurane. The authors document the much higher circulating levels of ß-hydroxybutyrate in the post-natal day 7 mice, highlighting the importance of this substrate for supporting the energetics of the developing brain. Important control experiments, administering 100% oxygen without anesthetic to post-natal day 7 mice, as well as administering anesthetics to 30 day old mice on a ketogenic diet, did not result in significant decreases in glucose and ß-hydroxybutyrate blood levels. Remarkably, they observed significant decreases in response to very small, subanesthetic doses of isoflurane, halothane and sevoflurane in post-natal day 7 mice. Administration of bolus glucose corrects the glucose level for these mice under anesthesia, but not the level of ß-hydroxybutyrate, while administration of bolus ß-hydroxybutyrate corrects both levels.

The authors then proceed to a series of measurements in an attempt to determine a direct target of volatile anesthetics on metabolism, focusing on hepatic metabolism. This is something of a Procrustean bed, given that the there is ample evidence that volatile anesthetics affect a large number of different membrane bound processes. Nonetheless, these experiments provided valuable data demonstrating anesthetic induced decreases in fatty acid oxidation. This reviewer finds the arguments regarding impairment of the citric acid cycle a bit unconvincing: 7 and 30 day old mice exhibit the same increase in citrate and isocitrate levels, yet only the 7 day old mice show elevated lactate levels. Rather than exhibiting increased metabolic flexibility, as the authors suggest, this finding seems to argue that 7 day old mice have less metabolic flexibility. The authors demonstrate that several perturbations of fatty acid metabolism can result in depression of ß-hydroxybutyrate, leading them to focus on carnitine palmitoyl transferase-1. They demonstrate that inhibition of this enzyme produces a decrease in ß-hydroxybutyrate; however, they also find that mice with a knockout of this enzyme do not have decreased ß-hydroxybutyrate levels.

The authors are circumspect in their conclusions regarding the targets responsible for the metabolic changes observed in neonatal mice in response to anesthetics. They do correctly highlight the potential importance of these metabolic effects. It will be crucial for future research to determine whether these effects can be directly correlated to measures of cerebral function during anesthesia, e.g., EEG or evoked potentials, and to measures of neuropathological change. Of great interest to clinicians will be demonstration of whether co-administration of glucose or ß-hydroxybutyrate together with anesthetics can abrogate such changes.

Reviewer #1 (Public Review):

In this manuscript, Zilova et al. show that primary embryonic cells derived from blastula-stage Medaka and Zebrafish embryos can self-organize into retinal organoids. When aggregates of 1000-2000 primary embryonic cell are embedded in Matrigel addition, they form a neuroepithelium under the control of Rx3 which develops into a retinal organoid. The process mirrors some aspects of embryo development. Moreover, another interesting finding is that Rx3 expression is initiated in the absence of Matrigel at day 0, which indicates that the retinal fate occurs by default and is not dependent on extracellular matrix components. The authors compare the ability of cells from Mesaka and zebra fish and show that both are competent to form organoids, though each does it with the time scale of the embryo of origin. The authors show that by reducing the number of Medaka cells to aggregate (500-800 cells), Rx2 and Rx3 are expressed only in restricted regions of the small aggregates, presumably where they organize into discrete circular Rx2 and Rx3 positive neuroepithelial units that develop into structure resembling retinal epitjhelia with some diversity of retinal cell types including amacrine, ganglion, photoreceptor, bipolar and horizontal cells.

This is a novel and original piece of work that reveals the capacity of fish primary embryonic pluripotent cells to behave like mammalian embryonic stem cells and organize optic cup organoids.

Reviewer #1 (Public Review):

The complexity of the infection model developed by the authors is to be praised as it allows the dissection of host-pathogen interactions with multiple players coming together, namely human epithelial cells, endothelial cells and neutrophils, UPEC, urine, antibiotics and mechanical forces at play during bladder filling and micturition. This is truly a tour de force and should provide the authors (and other labs potentially able to recapitulate it) with an unprecedented model to study UTIs and their response to antibiotics. Notably, authors have been able to document the formation of NETs in response to UPEC infection in this model. One small caveat was the choice of antibiotics used to treat the infection in their model. Is Ampicillin really a drug of choice, both because of its inability to reach intracellular niches and it not being a drug of choice in the clinic?

Reviewer #1 (Public Review):

In their manuscript, Lawrence et al. investigate the direct effects of the microtubule-associated protein, SSNA1, on microtubule (MT) dynamics and damage using purified proteins and TIRF microscopy. Prior work on this protein showed that SSNA1 self-assembles into higher-order filaments and binds longitudinally along stabilised MTs, inducing MT branching and nucleation. In this study, they find that SSNA1 promotes templated MT nucleation, consistent with prior results, but further define the effect of SSNA1 on MT dynamics. SSNA1 overall dampens MT dynamics by reducing both growth and shrinkage rates, suppressing catastrophe frequency, and increasing rescues. The authors also quantify SSNA1 on GMPCPP over a timecourse both at single-molecule and multi-molecule concentrations. On dynamic MTs, SSNA1 recognizes the growing end and promotes end curvature, but it did not recognize the curves of taxol-stabilised MTs, leading the authors to conclude that it likely induces curvature, rather than recognizes it. Perhaps this is the mechanism by which SSNA1 prevents catastrophe, a role which the authors demonstrate for SSNA1 after both tubulin dilution or stathmin sequestration of tubulin. The most interesting part of this study is found in Figure 4, where the authors show that SSNA1 prevents MT severing by spastin and also localizes to sites of lattice damage. The authors conclude that SSNA1 is a MT stabilizing protein and a sensor of MT damage. The results on MT dynamics do not provide much insight into the mechanism of this protein, which isn't even found to colocalize with MTs in vivo (SSNA1 instead accumulates at branchpoints in neurons). The role of SSNA1 in lattice damage recognition is the highlight of this paper, and also correlates well with its in vivo localization pattern, indicating this could be a true function of this protein. This damage recognition ability could potentially be the first step that leads to SSNA1-induced MT nucleation and branching from an existing MT.

Reviewer #1 (Public Review):

The authors interrogated an underexplored feature of CRISPR arrays to enhance multiplexed genome engineering with the CRISPR nuclease Cas12a. Multiplexing represents one of the many desirable features of CRISPR technologies, and use of highly compact CRISPR arrays from CRISPR-Cas systems allows targeting of many sites at one time. Recent work has shown though that the composition of the array can have a major impact on the performance of individual guide RNAs encoded within the array, providing ample opportunities for further improvements. In this manuscript, the authors found that the region within the repeat lost through processing, what they term the separator, can have a major impact on targeting performance. The effect was specifically tied to upstream guide sequences with high GC content. Introducing synthetic separator sequences shorter than their natural counterparts but exhibiting similarly low GC content boosted targeted activation of a reporter in human cells. Applying one synthetic separator to a seven-guide array targeting chromosomal genes led to consistent though more modest targeted activation. These findings introduce a distinct design consideration for CRISPR arrays that can further enhance the efficacy of multiplexed applications. The findings also suggest a selective pressure potentially influencing the repeat sequence in natural CRISPR arrays.

Strengths:

The portion of the repeat discarded through processing normally has been included or discarded when generating a CRISPR-Cas12a array. The authors clearly show that something in between-namely using a short version with a similarly low GC content-can enhance targeting over the truncated version. A coinciding surprising result was that the natural separator completely eliminated any measurable activation, necessitating the synthetic separator.

The manuscript provides a clear progression from identifying a feature of the upstream sequences impacting targeting to gaining insights from natural CRISPR-Cas12a systems to applying the insights to enhance array performance.

With further support, the use of synthetic separators could be widely adopted across the many applications of CRISPR-Cas12a arrays.

Weaknesses:

The terminology used to describe the different parts of the CRISPR array could better align with those in the CRISPR biology field. For one, crRNAs (abbreviated from CRISPR RNAs) should reflect the final processed form of the guide RNA, whereas guide RNAs (gRNAs) captures both pre-processed and post-processed forms. Also, "spacers" should reflect the natural spacers acquired by the CRISPR-Cas system, whereas "guides" better capture the final sequence in the gRNA used for DNA target recognition.

A running argument of the work is that the separator specifically evolved to buffer adjacent crRNAs. However, this argument overlooks two key aspects of natural CRISPR arrays. First, the spacer (~30 nts) is normally much longer than the guide used in this work (20 nts), already providing the buffer described by the authors. This spacer also undergoes trimming to form the mature crRNA. Second, the repeat length is normally fixed as a consequence of the mechanisms of spacer acquisition. At most, the beginning of each repeat sequence may have evolved to reduce folding interactions without changing the repeat length, although some of these repeats are predicted to fold into small hairpins.

Prior literature has highlighted the importance of a folded hairpin with an upstream pseudoknot within the repeat (Yamano Cell 2016), where disrupting this structure compromises DNA targeting by Cas12a (Liao Nat Commun 2019, Creutzburg NAR 2020). This structure is likely central to the authors' findings and needs to be incorporated into the analyses.

Many claims could better reflect the cited literature. For instance, Creutzburg et al. showed that adding secondary structures to the guide to promote folding of the repeat hairpin enhanced rather than interfered with targeting. Liu et al. NAR 2019 further showed that the pre-processed repeat actually enhanced rather than reduced performance compared to the processed repeat. Finally, the complete loss of targeting with the unprocessed repeat appears represent an extreme example given multiple studies that showed effective targeting with this repeat (e.g. Liu NAR 2019, Zetsche Nat Biotechnol 2016).

Relating to the above point, the vast majority of the results relied on a single guide sequence targeting GFP. While the seven-guide CRISPR array did involve other sequences, only the same GFP targeting guide yielded strong gene activation. Therefore, the generalizability of the conclusions remains unclear.

Reviewer #1 (Public Review):

This manuscript is a meta-analysis of literature, predominantly that from evolutionary psychology. The background seems well-explained, and the discussion and literature review well-written. The authors have done an impressive job of collating and synthesising a truly vast amount of literature that (as they demonstrate) is often pretty ambiguous in its results. The results are well-presented and well-reasoned, without overstating the evidence. The entire manuscript is clear and easy to read and follow. Table 1 makes it particularly easy to follow. I appreciate their emphasis that the various hypotheses about sexual dimorphism are not mutually exclusive, and that this study does not seek to explicitly test either one of them.

There is enough evolutionary anthropology inserted here to see that the authors have a passing familiarity with it, although I would encourage them to dig much more deeply into this literature in framing their work. In short, there is a tension between evolutionary psychology and evolutionary anthropology that can be very fruitfully explored with the results of this analysis, and the authors only scratch the surface of this at the end of the manuscript.

Something that seems crucial here, and in this literature more generally, is the likelihood that men have a number of different effective strategies. The background and discussion do a good job of discussing the various possibilities, and how combinations of possibilities that include both female choice or male-male competition could explain human mating behavior. However, it does not really dig into what the implications might be for how multiple, distinct strategies could impact different aspects of the data. What comes to mind is orangutans, in which the large, masculine males appear to obtain mating opportunities primarily through female choice, while the smaller males that have not developed the large body sizes and facial flanges may obtain additional mating opportunities through sexual coercion. In a large sample or meta-analysis like this, a combination of strategies in human males that are at odds with one another, yet both highly effective, may have results that tend to cancel one another out - is there any evidence of this? Getting more into the primate literature here could be useful.

The authors point out that there could be a strong confounding effect with the way testosterone operates developmentally. Testosterone during adolescence translates well to the development of masculine characteristics, but does not necessarily predict testosterone later in life (hence, the expression of masculine features may not actually relate well to circulating testosterone that could be at least partially drive male-male competition). The authors did an excellent job of discussing these potential confounding effects, but I would have found potential issues like this (and like the one above) to be presented usefully in a table that lays out the different potential confounding issues, and then discusses what the predictions should be in the meta-analysis results for each one.

The meta-analysis seems well-designed, and the methods appropriate. However, it did feel a bit like data mining with so many different variables run against one another. I do not think this is actually the case, and the authors do justify each of their decisions. In fact, one of the main outcomes of this work is that they show how few of these parameters actually relate strongly to one another. However, the authors might want to be aware that this study could be read as data-mining because of the search for significance amongst so many different variables, and offset this with explicit discussion and framing up front that they intend to examine how effective the various study parameters actually are at uncovering the relationships they seek to uncover. This is something the authors discuss very articulately at the end, but I would appreciate seeing this up front as one of the goals of the paper.

Reviewer #1 (Public Review):

Summary: The study by Steenkiste focuses on the formation of adaptor protein complexes at sites of integrin receptor adhesion in the modulation of in vitro membrane ruffling and cell movement. The authors are studying the role of BCAR3 (also termed AND34 or NSP1) protein regulation by post-translational mechanisms (ubiquitin degradation and tyrosine phosphorylation). This is one of many adaptor proteins localized to adhesion sites. Studies are being performed on MCF10A or Hela cells to knockdown (siRNA) or over-express tagged protein constructs. By proteomics, a new phosphorylation site was identified (BCAR3 Y117). Mutagenesis showed that BCAR3 Y117 is important for enhancement of in vitro cell movement under conditions where the cullin-5 E3 ligase has also been reduced by siRNA expression.

Opinion: The authors provide support for a "co-regulatory" model whereby the recruitment of BCAR3 to adhesions acts in part to modulate another adaptor protein tyrosine phosphorylation, p130Cas. This is associated with enhanced cell migration. The data presented are generally supportive of the conclusions and consistent with previous studies of BCAR3 and p130Cas. However, an unresolved issue is why cell phenotypes are dependent on cullin-5 knockdown or otherwise investigated by BCAR3 mutant over-expression. Cul5 loss can alter multiple aspects of cell signaling and the transient knockdown or inducible over-pression assays are a limited primary means of investigation. As multiple protein domains and post-translational modifications modulate the BCAR3-p130Cas complex, the authors did not establish a strong mechanistic linkage between newly-identified BCAR3 Y117 phosphorylation, SOCS6 binding, and a CUL5-dependent cell phenotype. Additionally, some of the experimental conditions (+/- EGF in growth media) are difficult to connect to EGF receptor activation and or signaling.

Reviewer #1 (Public Review):

In the current manuscript, Frey et al. describe a convolutional neural network capable of extracting behavioral correlates from wide-band LFP recordings or even lower-frequency imaging data. Other publications (referenced by the authors) have employed similar ideas previously, but to my knowledge, the current implementation is novel. In my opinion, the real value of this method, as the authors state in their final paragraph, is that it represents a rapid, "first-pass" analysis of large-scale electrophysiological recordings to quickly identify relevant neural features which can then become the focus of more in-depth analyses. As such, I think the analysis program described by the authors is of real value to the community, particularly as it becomes more commonplace for labs to acquire multi-site in vivo recordings. However, to maximize its utility to the community, several aspects of the analysis need clarification.

Reviewer #1 (Public Review):

Pulgar et al. describe an interesting mechanism explaining how directed motion of group of cells maintain their migratory path as a group of cells. Incomplete delamination allows here to maintain coordinated cell movements amongst the DFC. The story is self-contained, logical, well-written and just in general very nice. The mechanism described belongs to the so-called mechanical drag which is a new type of multicellular locomotion and may be a general feature involved in many morphogenetic systems.

The major strength of the study is the extensive use of live imaging and analysis of dynamic events. The study provides a nice cellular mechanism in the process they described. The molecular mechanism would be the only weakness of the study.

An overall very exciting study.

Reviewer #1 (Public Review):

*A summary of what the authors were trying to achieve.

The study takes advantage of the interesting plant genus Leucadendron to compare gene expression between male vs. female in species with more or less sexual dimorphism. This question was addressed in a somewhat comparable manner in only one previous paper by Harrison et al. 2015 across six bird species. The overarching question is the role of natural selection in sexual dimorphism.

*An account of the major strengths and weaknesses of the methods and results.

-Beside the genus-wide comparison of whole transcriptomes across related species, which makes in itself a strong dataset, the major strength of the analysis is the phylogenetic framework that allows the authors to track the evolution of sex bias through several tens of million years of evolutionary history. Despite ancestral dioecy in the genus, very few genes show consistent sex bias across several species, with sex-bias being mostly species-specific. Two striking negative results will be of special interest to the community : 1) species with more pronounced sexual dimorphism at the morphological level do not tend to exhibit more pronounced sex-biased gene expression 2) the few genes that do show sex-biased expression were apparently recruited among those with the highest expression variance to begin with, strongly suggesting that sexual selection has not been the main force driving their expression divergence.

-In my view, the main limitation of the work is the use of leaf rather than reproductive tissues, making the comparison to other studies less straightforward to interpret. It is especially important that the expectations for somatic vs gonadic tissues be made a lot clearer in the text. Also, the fact that a single leaf phenotype is measured (specific leaf area) seems arbitrary : one could imagine sexual dimorphism on many other characteristics, yet they are not considered here. The text on p.324 mentions "striking convergence in aspects of morphological dimorphism across the genus", but there is no way for the reader to appreciate the extent of this convergence. Finally, it would be useful to at least make some mention of the sex-determination system in these species, since the expectations would differ if some of the sex-biased genes were linked to sex chromosomes.

*An appraisal of whether the authors achieved their aims, and whether the results support their conclusions.

The analysis is mostly sound, but I am a bit concerned by the arbitrary threshold used to define SBGE. The text on p.305 says that "This result is extremely robust to the choice of threshold", but 1) the results are not reported so it is impossible for the readers to evaluate the basis of this assertion and 2) it is not clear whether robustness of the other results has been evaluated at all. This aspect clearly deserves more attention.

*A discussion of the likely impact of the work on the field, and the utility of the methods and data to the community.

This work will be of interest to the community, as rapid rates of expression evolution would generally be interpreted as the consequence of sex bias, whereas the phylogenetic analysis presented here instead supports the idea that the expression of genes that end up being sex biased were instead intrinsically less constrained to begin with.

Reviewer #1 (Public Review):

This manuscript by Lauer et al follows up on previous articles that ask the question whether there are funding disparities at the National Institutes of Health for African American or Black (AAB) investigators. The investigators breakdown the analysis by race, topic of proposal, and NIH institute-Center (IC) to which an application was assigned. They conclude that the most important factor in determining funding is the Institute assignment with lower funding rates related to the funding capacity of a particular Institute (e.g National Eye Institute vs Minority Health and Health Disparities). The present study is a welcome addition to this debate since if biases do exist, NIH needs to address these. The strengths of this manuscript are the detailed breakdown of the available data in order to evaluate for biases, the availability of data for multiple years (2011-2015) and the consideration of alternate explanations (e.g new applications vs resubmissions; single vs multi PI, etc). A weakness of the data is that if their conclusion is that Institute assignment was the main determinant of funding rates, why wasn't the approach for Institute assignment discussed? Are there possible biases in this assignment besides keyword searches? There is also the question of whether there is circular logic operating here. The Minority Health and Health Disparities received the most AAB applications but had one of the lowest funding rates. Wouldn't this Institute be expected to be one in which AAB applicants would try to direct their application to? This manuscript is sure to generate additional discussion on this topic which is an important step in trying to address the issue of potential funding disparities. However as the authors point out the fact that only 2% of the applications submitted to the NIH were from AAB investigators is of concern.

Reviewer #1 (Public Review):

Ekeng et al. have sequenced and analyzed 46 Vibrio cholerae whole genome sequence data. The authors demonstrated a predominant lineage (T12) where all isolates from 2018-2019 fall. Their analysis suggest continuous transmission through repeated reintroduction of the same lineage back into the population. The work is interesting and the conclusions of this paper are mostly well supported by data. The present study reinforce the need of more genome sequence data and a strong surveillance network to interpret the data.

This is a successful model of regional coordination to have genomic surveillance data from a region where surveillance data was inadequate. The manuscript should be modified to focus on strengthening of genomic surveillance further.

Reviewer #1 (Public Review):

Halliday et al. developed a framework to disentangle the total effect of environment on disease into a direct effect and indirect effects by environment-induced change of host community and by modifying the relationships between host community and disease.

Applying this framework, the authors studied the direct and indirect effects of elevation on plant leaf disease in the Swiss Alps. They focused on host community structures as mediator of indirect effects. Host community structures were measured by host species richness, phylogenetic diversity, and community pace of life. One important finding is that the positive effect of host community pace-of-life on disease weakened as elevation increased, suggesting an important, but less appreciated, mechanism on how elevation can indirectly influence plant disease. However, since the major findings were based on the analyses with elevation but not specific environmental variables, it does not have that strong implications about the influence of global climate change on disease as the authors stated.

The developed framework on environmental effects on disease, the well-designed filed study and the large-scale dataset would all make this paper an important contribution to the field.

Overall, the statistical analyses were reasonable. However, accurate interpretations of some results would require more clarifications on the analyses.

Reviewer #1 (Public Review):

The authors of this paper seek to understand how HIV infects cells. HIV is a retrovirus that harbors a core of RNA nucleic acid in complex with important replication enzymes such as reverse transcriptase. After infection, reverse transcriptase converts the RNA into DNA, which is then integrated into the chromosome. The authors used advanced imaging techniques to visualize the DNA that is made by reverse transcription. They used fluorescent readout markers of proteins to also look at the viral proteins that are brought into the cell and track with the viral DNA during the virus infection.

From this work the authors conclude that reverse transcription is completed in the cell nucleus, that intact or nearly intact cores are the substrate for nuclear import, and that virus core uncoating likely occurs in the nucleus, immediately preceding the integration step. Moreover, by using electron tomography, they drill down to the sub-micron level to glean an ultrastructural view of the viral complexes that are performing these important HIV infection steps. Some of these complexes appear to be novel, and thus the work will be of interest to other scientists in this field.

Weaknesses of the study include insufficient control samples for some of the experiments and also clarifying some of the approaches used and some of their interpretations of the data (detailed below). The authors of this paper could have also done a better job of citing papers published by other scientists who came up with very similar conclusions and/or used very similar techniques.

Reviewer #1 (Public Review):

The centrality of RAS proteins in human malignancies has long been established, but many issues regarding their regulation and functions remain unresolved. The results of this paper provide strong supporting evidence for an emerging model that posits that activated KRAS can only be tolerated by cells up to a certain point, after which the stress it imposes outweigh its transforming potential. These restrictions impose limits on the amount of KRAS expressed in tumor cells and are also consistent with the frequent coupling of KRAS mutations with loss of the tumor suppressor p53, as the latter relieves the stress signals induced by KRAS.

Reviewer #1 (Public Review):

Summary and Strength:

Single-cell RNA sequencing is the most appropriate technique to profile unknown cell types and Koiwai et al. made good use of the suitable tool to understand the heterogeneity of shrimp hemocyte populations. The authors profiled single-cell transcriptomes of shrimp hemocytes and revealed nine subtypes of hemocytes. Each cluster recognizes several markers, and the authors found that Hem1 and Hem2 are likely immature hemocytes while Hem5 to Hem9 would play a role in immune responses. Moreover, pseudotime trajectory analysis discovered that hemocytes differentiate from a single subpopulation to four hemocyte populations, indicating active hematopoiesis in the crustacean. The authors explored cell growth- and immune-related genes in each cluster and suggested putative functions of each hemocyte subtype. Lastly, scRNA-seq results were further validated by in vivo analysis and identified biological differences between agranulocytes and granulocytes. Overall, conclusions are well-supported by data and hemocyte classifications were carefully performed. Given the importance of aquaculture in both biology and industry, this study will be an extremely useful reference for crustacean hematopoiesis and immunity. Moreover, it will be a good example and prototype for cell-type analysis in non-model organisms.

Weaknesses:

The conclusions of this paper are mostly well supported by data, but some aspects of data analysis QC and in vivo lineage validation need to be clarified.

1) It is not a trivial task to perform genome-wide analyses of gene expression on species without sufficient reference genome/transcriptome maps. With this respect, the authors should have de novo assembled a transcriptome map with a careful curation of the resulting transfrags. One of the weaknesses of this study is the lack of proper evaluation for the assembly results. To reassure the results, the authors would need to first assess their de novo transcripts in detail and additional data QC analysis would help substantiate the validity.

2) The authors applied SCTransform to adjust batch effects and to integrate independent sequencing libraries. SCTransform performs well in general; however, the authors would need to present results on how batch effects were corrected along with before and after analysis. In addition, the authors would need to check if any cluster was primarily originated from a single library, which could be indicative of library-specific bias (or batch effects).

3) Hem6 cells lack specific markers and some cells in this cluster are scattered throughout the other clusters (Fig. 1 & 2). Based on the pattern, it is possible that these cells are continuous subsets of other clusters. It would be good if the authors could group these cells with Hem7 or other clusters based on transcriptomic similarities or by changing clustering resolution. Additionally, they may also be a result of doublets, and it is unclear whether doublets were removed. Hem6 cells require additional measures to fully categorize as a unique subset.

4) The authors took advantage of FACS sorting, qRT-PCR, and microscopic observation to verify in silico analyses and defined R1 and R2 populations. While the experiments are appropriate to delineate differences between the two populations, it is not sufficient to determine agranulocytes as a premature population (Hem1-4) and granulocytes as differentiated subsets (Hem5-9). To better understand the two groups (ideally nine subtypes), additional in vivo experiments would be essential. For example, proliferation markers (BrdU or EdU) could be examined after FACS sorting R1 and R2 cells to show R1 cells (immature hemocytes) are indeed proliferating as indicated in the analyses.

5) FACS-sorted R1 or R2 population does not look homogeneous based on the morphology and having two subgroups under nine hemocyte subtypes may not be the most appropriate way to validate the data. The better way to prove each subtype is to use in situ hybridization to validate marker gene expressions and match with morphology.

Reviewer #1 (Public Review):

This manuscript, which follows on from a recent eLife paper documenting the relevance of the multi-basic cleavage site (MBCS) in the spike (S) protein of SARS-CoV-2, shows that growing SARS-CoV-2 on relevant epithelial cell lines or differentiated stem cell-derived culture systems prevents the emergence of MBCS mutations than impact on properties of S that contribute to cell tropism and the viral entry mechanism.

The paper builds on the authors previous work and that of others, and in some respects the results are not surprising. Nevertheless, the paper sets out a number of important findings. 1) That SARS-CoV-2 grown in Vero cells rapidly acquire MBCS mutations, where as virus grown in airway epithelial cells or Vero-TMPRSSR2 cells do not; 2) that deep sequencing is necessary to see mutations that are not apparent in consensus sequence reads, 3) that factors such as the addition of fetal calf serum can influence the selection of mutant phenotypes and 4) that cultures derived from differentiated stem cells can provide reproducible systems for virus culture. Together, the work sets out clear guidelines for the production of SARS-CoV-2, and potentially other viruses, avoiding the pitfalls that can arise from growing viruses in permissive transformed cell lines.

The data and manuscript are clearly presented, and my concerns are minimal. Overall, the paper will make a useful addition to the SARS-CoV-2 literature and will be of value to researchers working not just of SARS-CoV-2 but on many other viruses.

Reviewer #1 (Public Review):

Mature mammalian hair cells in the cochlea do not regenerate after damage. The outer hair cells of the cochlea, which function to amplify sound, are particularly susceptible to damage. Ectopic activation of two key transcription factors for outer hair cell formation, Atoh1 and Ikzf2, in damaged adult cochlea is sufficient to convert supporting cells into hair cells expressing Prestin, which is an essential protein mediating outer hair cell functions. Although there is no functional recovery in these transgenic mice based on auditory brainstem response, this study paves the way for future design of models for hearing recovery. The main concern is the identity of the OHC-like cells drawn from the small sample size in the scRNA-seq experiments.

Reviewer #1 (Public Review):

The presented manuscript takes a comprehensive and elaborated look at how T cell receptors (TCR) discriminate between self and non-self antigens. By extending a previous experimental protocol for measuring T cell receptor binding affinities against peptide MHC complexes (pMHC), they are able to determine very low TCR-pMHC binding affinities and, thereby, show that the discriminatory power of the TCR seems to be imperfect. Instead of a previously considered sharp threshold in discriminating between self and non-self antigen, the TCR can respond to very low binding affinities leading to a more transient affinity threshold. However, the analysis still indicates an improved discrimination ability for TCR compared to other cell surface receptors. These findings could impact the way how T cell mediated autoimmunity is studied.

The authors follow a comprehensive and elaborated approach, combining in vitro experiments with analytical methods to estimate binding affinities. They also show that the general concept of kinetic proofreading fits their data with providing estimates on the number of proofreading steps and the corresponding rates. The statistical and analytical methods are well explained and outlined in detail within the Supplemental Material. The source of all data, and especially how the data to analyze other cell surface receptor binding affinities was extracted, are given in detail as well. Besides being able to quantify TCR-pMHC interactions for very low binding affinities, their findings will improve the ability to assess how autoimmune reactions are potentially triggered, and how potent anti-tumour T cell therapies can be generated.

In summary, the study represents an elaborated and concise analysis of TCR-pMHC affinities and the ability of TCR to discriminate between self and non-self antigens. All conclusions are well supported by the presented data and analyses without major caveats.

Reviewer #1 (Public Review):

The authors demonstrate applications including fluorescent marking of membranes with GFP or monomeric RFP, reporter alleles for convenient assessment of differentiation status based on fluorescence, and targeted gene knockout. They also demonstrate conditional gene knockdown and induction with tight control achieved by engineering a protein destabilizing domain. The design of the constructs is clever and imparts the ability to leverage iterative FACS to enrich successfully targeted cells, particularly useful when targeting alleles that are not actively expressed by the progenitors. The work is well done and clearly presented.

Reviewer #1 (Public Review):

In this study, Moncla et al. used genomic data to analyse a mumps outbreak in Washington, in order to draw inferences about the epidemiological factors driving the outbreak. Some important strengths of the analysis include sophisticated sequencing and modeling techniques to reconstruct chains of transmission during the outbreak, which support the conclusions that the mumps virus was introduced several times in Washington from other North American regions during the outbreak, and that the Washington Marshallese community was particularly at risk of mumps infection and transmission during this time. Limitations of the analysis include potential for sampling bias, where the sample may not be entirely representative of mumps outbreak cases, and a sample size that is too low to allow sufficient statistical power to assess the impacts of age and vaccination status on transmission. The work has potential public health impacts in terms of identification of a vulnerable community and points to social networks as the primary risk factor for potential future respiratory virus outbreaks. The analysis methods could be potentially applied for the phylodynamic analysis of other infectious disease outbreaks.

Reviewer #1 (Public Review):

It is difficult to overestimate the importance of this paper. The full connectome of the Drosophila central complex is both the beginning and the end of an era. It provides the first comprehensive dataset of arguably the most enigmatic brain region in the insect brain. This endeavor has generated ground truth data for years of functional work on the neural circuits the connectome outlines, and constitutes an unparalleled foundation for exploring the structure function relations in nervous systems in general. This will be of great importance far beyond work on the Drosophila brain, and will have far reaching implications for comparative research on insect brains and likely also smoothen the path toward understanding navigation circuits in vertebrate nervous systems. Based on presented data, the paper develops overarching ideas (at exquisite detail) of how sensory information is transformed into head direction signals, how these signals are used to enable goal direction behavior, how goals are represented, and how internal state can modulate these processes. The connectome enables the authors to base these ideas and their detailed models on actual biological data, where earlier work was forced to indirectly infer or speculate. While significantly going beyond models of central-complex function that existed previously, the authors have to be much credited for incorporating huge amounts of existing knowledge and data into their interpretations, not only work from Drosophila, but also from many other insects. This makes this paper not only an invaluable resource on the connectome of the Drosophila central complex, but also a most comprehensive review on the current state of the art in central-complex research. This unifying approach of the paper clearly marks a reset of central-complex research, essentially providing a starting point of hundreds of new lines of enquiry, probably for decades to come.

Given the type and amount of data presented, the paper is clearly overwhelming. That said, it also clearly needs to be presented in the way it was done, mostly because no single aspect of the function of this neuropil makes as much sense in isolation as it makes sense when viewed in conjunction of all its other functions. The complexity of the neural circuits discussed is clearly reflected in the enormous scope of the paper. Nevertheless, the authors have done a fantastic job in breaking the circuits and their function down into digestible bits. The manuscript is very systematic in its approach and starts with sensory pathways leading to the CX, covering the clearly delineated head direction circuits and then moving on to the more complex and less understood parts, always maintaining a clear link between structure and function. As function is necessarily based on previous work, including that from other species, the results part is interwoven with interpretation, but this is clearly necessary to keep the text readable. The authors have made considerable efforts to provide additional introductions and summaries whenever needed, almost creating nested papers embedded within the overall paper.

The figures are equally overwhelming as the text at first sight, but when taking the time to digest each one in detail, they present the data in a rich and clear manner. The figures are often encyclopedic and will serve as reference about the central complex for years. The summary graphs that are presented in regular intervals are welcome resting places for the reader, helping to digest all the detailed information that has preceded or that will follow.

The analysis performed in the paper is excellent, comprehensive and should set the standard for any future work on this topic. Also, the text is very honest about the limits of the conclusions that can be reached based on this kind of data, which is important in generating realistic and feasible hypotheses for future experiments.

Reviewer #1 (Public Review):

Brascamp and colleagues address pupil-size changes around perceptual switches in perceptual multistability. Several previous studies have found pupil dilation around or after the switch and some have found pupil constriction, though the latter was typically less robust. Moreover, while most previous studies included some controls for the effect of reporting and for the physical stimulus change, to my knowledge, so far, no study has fully crossed the factors report/no-report and endogenous/exogeneous switch. In the present study, this gap is filled using a binocular-rivalry stimulus and an OKN-based no-report paradigm. This allows the authors to isolate the constriction component from the dilation component and interestingly they find the constriction more robustly tied to the perceptual switch, while the dilation component is mostly related to the response. Experiments are soundly conducted and analysed and results are interpreted with appropriate care. Since the results challenge frequent interpretations as to why perceptual switches in multistability may cause pupil-size changes, the paper is of high relevance to the fields of pupillometry and multistability, but also to other areas where pupillometry is used as index of perceptual and cognitive processes. I only have some minor questions and requests for clarification with regard to result presentation and interpretation.

Reviewer #1 (Public Review):

Strengths and Weaknesses. The authors did quite a lot to establish gene expression and function of the annelid's trunk cells and compare them to photoreceptors of the annelid's eye. They isolated the cells with FACS and characterized gene expression in detail, they knocked down r-opsin with TALEN in the trunk and found a significant difference in a crawling response, and they express the opsin in cell culture to confirm wavelength and G-protein sensitivity. As a potential link between light sensitivity and mechano-sensitivity, they report r-opsin function and light intensity influence expression of atp2b2, a gene that modulates neuronal sensitivity in other organisms. Wavelength and G-protein activation data are valuable because I can think of few or no other organisms in the entire group of lophotrochozoan animals, where this level of experimental manipulation could be done. In short, a strength of this manuscript is the detailed characterization of the trunk receptor cells, which express r-opsins. The authors have brought much evidence to the claim that these TRE cells have both light and mechano-sensitive gene expression and function. Based on these findings in an annelid worm, I believe the paper is a significant advance, and of interest to a broad audience by adding to a growing set of discoveries of similar hybrid sensory cells.

If a hybrid mechano/photo-receptor is indeed an ancient cell type in bilaterians, this would bring many evolutionary implications for sensory biology. However, in these evolutionary interpretations is where I find a weakness of the manuscript. Namely, with only a handful of species shown thus far to have the hybrid cell type - and many differences in detail about these cell types in different organisms - we can not yet make firm conclusions about whether the multi-functional cells were ancestral. I believe other interpretations are equally valid (and still interesting) and should be given more consideration. Namely, it seems possible that photo- and mechan- sensory processes "joined forces" (e.g. through separate co-option events) in new cell-types, multiple times during evolution. The current manuscript loosely indicates ancestral multi-functionality is more parsimonious. However, no detail is given about that. I suppose the authors mean a single origin of hybrid cell types requires fewer evolutionary transitions than multiple origins. However, such a parsimony count does not count the transitions requiring loss of phototransduction in mouse hearing and do not count transitions to loss of mechanosensitivity in eye photoreceptor cells.

substitutes

Replacement

Barriers

A lot more barriers for suppliers to jump through

unintended consequences

Reviewer #1:

The authors have acquired a substantial multimodal dataset and have used careful statistical approaches throughout. The data are acquired and analysed using appropriate methods.

Overall, this is an impressive body of work that aims to answer an interesting question. However, a number of questions over the methods and interpretation make the authors' conclusions difficult to justify.

When comparing between older and younger adults it would also be helpful to know the amount of grey matter in the voxels of interest. It might be expected that older adults might have more atrophy and therefore lower GABA+, than younger adults and this should be controlled for in the statistical models. The authors have put assumptions into their quantification, which are reasonable but are still assumptions. It would be helpful to directly test for a difference in grey matter fraction in the voxel between the two groups, and include this in the model if necessary.

The authors then look at behaviour, where they use a previously described task which consists of bimanual tapping, with switching between two patterns. The results are complex as there are a number of behavioural metrics, and no clear pattern emerges. While older adults produced more errors in continuation, they also produced more fully correct switching transitions. Older subjects were slower than younger adults in all trials. While this task produces a very rich dataset, which is helpful for analysing complex behaviour, it is not clear how each metric should be interpreted in terms of the underlying neural mechanisms, and how they can be usefully combined, could be given.

In terms of connectivity, the authors found no significant group X task difference between in-phase and anti-phase conditions. They therefore look at the groups and tasks separately. They show different changes in connectivity between age groups in different frequency bands, for example between left and right M1 in the alpha/mu and beta, between EMG and left M1 in the theta band. I am not sure that describing EEG-EMG connectivity as cortico-spinal is strictly accurate - there may be a number of other factors in this -corticomuscular would seem to be more precise. The frequency bands used are not typical, and it would be helpful to have an a priori explanation of which are being tested and why - as well as details about correction for multiple comparisons across these bands.

Finally, the authors bring their GABA, behaviour and connectivity metrics together in a number of mediation analyses. They demonstrate a relationship between cortico-cortical connectivity and behaviour, which is mediated by age.

The authors describe their finding of higher GABA+ in the occipital cortex as a posterior-anterior gradient, which I think is not justified by the results - there could be a number of other reasons for this, for example that different functional networks have different GABA+ levels, which is not related to their anatomical position. With only three voxels it is difficult to make a general claim such as this, and this should probably be reworded.

The authors state that higher GABA+ indicated neural system integrity and better functioning in the older group. This seems to be rather over-interpreting their results - there are many other metrics of integrity and functioning that have not been assessed here. I would suggest rewording.

Consequence: finding a different market from a safe one to where you can male a higher profit

Insentive to decrease the supply

Competition

trying to make money by saying that this 'might' be a soultion but not actually caring if it was or not

physical money cost on society

Reviewer #1 (Public Review):

This paper presents the exciting statement that increasing viral loads within a community can be used as an epidemiological early-warning indicator preceding increased positivity. It would be interesting to support this claim to present both Ct and positivity on the same graph to demonstrate that indeed, declining Ct can be used as an early marker of a COVID-19 epidemic wave. Percentage of positive test data should not only include the ones obtained in the present study but should be compared with "national data" as the present study design includes a bias in patients selection that might not reflect the "true" situation at the time. Only with this comparison, we could claim that the present study design could predict COVID-19 epidemic waves. A correlation of Ct with clinical evidence to rank the confidence of positive results is also included and further support the high specificity of the RT-PCR for detecting SARS-CoV-2 (99.995%).

In a serological investigation, it was observed that some of these RT-PCR-positive cases do not appear to seroconvert and that possible re-infections might occur despite the presence of anti-spike antibodies. Although, reported on few individuals and therefore to be taken with extreme caution, this add some piece of information to the current unknown of the serological response of COVID-19 patient and would be of uttermost importance in the context of the current vaccination campaign.

Reviewer #1 (Public Review):

Sensorimotor integration is required for the accurate execution of volitional movements, but the neural circuits underlying sensorimotor integration are still not fully understood. The whisker system of the rodent has emerged as one model of sensorimotor integration with many recent studies focused on the synaptic organization of the underlying circuitry. Here, Yamawaki et al report results regarding the synaptic organization of the ascending sensory pathways related to mouse forelimb somatosensory and motor cortex. Using anatomical and functional approaches, they elucidate the circuitry from the cuneate nucleus through thalamus to forelimb S1 and M1. This work complements recent studies in the mouse of other aspects of the forelimb sensorimotor pathways and leads to informative comparisons to the circuit organization of the whisker system. The studies are well executed and well explained. The use of multiple approaches compensates for the limitations of each individual technique, although some limitations such as any effects of viral tropism are difficult to overcome. Overall, this work contributes to a better understanding of the wiring diagram of sensorimotor circuits in the mouse.

Joint Public Review:

Worker bees perform specialised tasks: young workers nurse larvae, older ones forage for either nectar or pollen. Behaviours - including these specialist ones - arise when a stimulus (nectar, pollen or larvae) exceeds a certain 'response threshold' of the organism. This threshold can be modulated by neuropeptides to alter behaviour.

The study first shows that response thresholds to task-related stimuli differ among nurse bees, nectar and pollen foragers. Pollen foragers are most responsive to sucrose and pollen, and nurse bees most responsive to chemical stimuli of larvae. Then, taking a proteomic approach, they identify a neuropeptide, Tachykinin related protein (TRP), to be expressed in a task-specific pattern: low in nurse bees and highest in the nectar foragers.

This work provides valuable resource information on the abundance of brain neuropeptides in two species of bees. The study is exceptional in its breath of techniques used and the addition of manipulative experiments which are difficult to do in honey bees. Through their studies the authors identify a neuropeptide that modulates response thresholds of bees.

The study would have been exceptional if the authors had included studies on the expression of the tachykinin receptor. The level of tachykinin expression increases between nurse bees and foragers, but does not involve changes in spatial expression (Takeuchi et al., 2004 ref. 56). So, it is likely that the specificity of the effects of tachykinin are due to differences in the spatial expression of the receptor.

Reviewer #1 (Public Review):

The authors developed a very interesting tool, named NICEdrug.ch, used it to identify drug metabolism and toxicity, and finally predicted druggability of disease-related enzymes and reposition drugs. Comprehensive integration effort based on publicly available datasets and several previous methods developed by the authors (e. g. BridgeIT, BNICE.ch, ATLAS of Biochemistry) results with a resource named NICEdrug.ch. The idea is interesting and addresses a very important problem in the field. The manuscript is clearly written, provides enough analysis of overall challenges and an overview of the most important results. Also, it presents figures that are remarkable.

Reviewer #1 (Public Review):

Kinetochores are huge protein assemblies on chromosomes which are used as attachment point for microtubules and allow microtubules to pull chromosomes into daughter cells during cell division. The proteins that form the kinetochore are well known, but the temporal regulation of the assembly of all these proteins into functional kinetochores is less understood.

In this paper the authors have identified phosphorylation sites in the 'CCAN' of budding yeast, the 'inner', i.e. chromatin-proximal, part of the kinetochore. They characterize in detail the function of phosphorylation of Ame1 (CENP-U in humans), which is part of CCAN. The data support the idea that a cluster of phosphorylation sites in Ame1 is phosphorylated by mitotic CDK1 and serves as phospho-degron for the E3 ligase SCF/Cdc4.

The authors show phosphorylation of these CDK1 consensus sites in vivo and their phosphorylation by CDK1/Clb2 in vitro. Genetic experiments and molecular dynamics simulations support the idea that phosphorylation sites on Ame1 can serve as phospho-degron for SCF/Cdc4. Even the non-phosphorylatable mutant of Ame1 is stabilized in an SCF mutant background, though, suggesting that this phospho-degron is not the only way in which SCF influences kinetochore protein levels.

Mutants in the characterized phosphorylation sites do not impair budding yeast growth. This suggests that the degron characterized in this paper may be important for fine-tuning, but is not essential for the proper execution of mitosis. The observations overall add to prior evidence that kinetochore assembly can be regulated by phosphorylation and/or ubiquitination.

Interestingly, the authors find that phosphorylation of Ame1 by CDK1 in vitro is impaired when Ame1 binds Mtw1, another kinetochore protein. The fact that Mtw1 seems to shield these sites from phosphorylation leads the authors to put forward an interesting model: they propose that cell cycle-dependent phosphorylation and SCF-dependent degradation of kinetochore subunits allows for excess subunits during kinetochore assembly in S-phase (which will speed up assembly) while depleting any excess subunits after assembly, when the kinetochore needs to be functional.

This is an interesting model. The in vivo evidence is still limited, though. For now, it remains unknown whether the phosphorylation status of kinetochore-bound and free Ame1 is indeed different, whether more soluble Ame1 exists in S-phase, whether too early degradation of Ame1 (or possibly other kinetochore proteins) indeed impairs kinetochore assembly, or whether a failure to remove the soluble pool after assembly leads to mitotic defects. It is an attractive proposal, though, that can now be further explored experimentally.

In addition to the specific characterization of Ame1 sites, the paper also includes comprehensive data on CCAN phosphorylation sites obtained by mass spectrometry which can serve as basis for future studies.

Reviewer #1 (Public Review):

The manuscript is somewhat readable but the many acronyms for the cell types in model and biology make it difficult to follow. Is there a reason why the biological neuron names cannot be used in the model? The presentation of data in figures can be more powerful. In many cases, the data in figures and the supplemental videos show apparently different results. This can be an artifact of how the videos were made and if yes, these can be improved. Tail tip coordinates can be plotted to show the behaviors in much better detail.

Especially for beat and glide swimming, the points regarding burst firing, inhibition, etc. have not been robustly made.

Reviewer #1 (Public Review):

The study presents relatively high and robust sensitivity of Abbott ID NOW for the detection of SARS-CoV-2 (COVID-19) in an ambulatory population, utilizing the RT-PCR methodology as a comparative correlation. The study was well designed and enrolled both symptomatic and asymptomatic populations to provide sufficient statistical power for the comparative analysis of the methodologies, as well as to represent accurately the patient populations. This is a useful and timely study that has a great impact in clinical setting for the rapid detection of COVID-19.

Reviewer #1 (Public Review):

The goal of this manuscript is to develop gene-agonistic approaches for promoting cone survival in retinal degenerative diseases. Based on their previous studies, the authors tested a total of 20 genes by subretinal delivery using an AAV vector which utilized a cone-specific promoter. Most of these genes augmented glucose utilization. Interestingly, only Txnip showed a positive result by prolonging cone survival (tested up to 50 days in rd1 retina). Txnip therapy also appears to be effective in rd10 and rho-/- retina. Additional strength of this study is the use of Txnip C247S allele that blocks its association with thioredoxin. Furthermore, additional work on how Txnip may contribute to cone survival by better utilization of lactate for energy is well presented though the conclusion on "heathier" mitochondria require additional data. This manuscript is potentially of great interest. The data are extensive and biological implications of the study are clear. However, the broad conclusions with respect of Txnip therapy for RP (or even AMD) are less than justified based on the data. Two weaknesses are apparent: the first is related to the method of quantification using whole mount retina, and the second related to the duration of the study. Immunostainings of retinal sections (and even TEMs) are critical to elucidate the structure of surviving cone photoreceptors (specially in the absence of rods) and their relationship to other cells (e.g., RPE, bipolar cells, glia). Similarly, Prusky's OMR can't be equated to visual acuity. The authors need to show cone structure/function at P50 and beyond (how long do the cones survive?) in rd1 and other models before claiming the potential benefit of Txnip for retinal and macular degeneration.

Reviewer #1 (Public Review):

Oon and Prehoda report pulsatile contraction of apical membrane in the process of Par protein polarization in Drosophila neuroblasts. This explains how/why actin filament was required to localize/polarize Par complex. Specifically, using spinning disc confocal microscopy with high temporal resolution, they found the directed actin movement toward the apical pole, which nicely correlates with concentration of aPKC. They also show that myosin II is involved in this pulsatile movement of actin filament. This very much resembles the observation in C. elegans embryos, and nicely unifies observations across systems. Although descriptive in nature, I think this is an important observation and indicates a universal mechanism by which cells are polarized. I think this is a well executed study.

Reviewer #1 (Public Review):

The primary objective of this manuscript was to examine if multi-kinase inhibitor YKL-05-099 can inhibit salt inducible kinases (SIKs) with the goal to examine a new class of bone anabolic agents for the treatment of osteoporosis. They found that YKL-05-099 was successful in increasing anabolism and, surprisingly, decreasing bone resorption, leading them to investigate why this inhibitor differed from the effects of deletion of SIK2 and SIK3. They found that YKL-05-099 also inhibited the CSF1 (M-CSF) receptor, thus, inhibiting osteoclast activity. This is an interesting manuscript but there are some flaws in the conduct of the experiments and in the analyses which lessen its impact. Nevertheless, it opens the way for another possible oral therapeutic for osteoporosis.

Reviewer #1 (Public Review):

One of the most consistent and thus surprising patterns revealed by experimental evolutionary studies is the observation of a very predictable pattern of increase in fitness of replicate populations. The fitness increase tends to be very rapid at the beginning and then slows down but continues to increase for tens of thousands of generations (e.g. the Lenski LTEE). The studies from the Desai group specifically two: one by Kryazhmisky et al and one by Jonnson et al further established that the pattern of decrease in the fitness gain is due to really counterintuitive patterns of global epistasis. In particular it is not due to the evolution running out of adaptive mutations but rather to the fact that the same adaptive mutations are less beneficial on fitter backgrounds (Kryazhmisky et al). Johnson et al further found that the fitter backgrounds are more fragile with deleterious mutations being more deleterious on fitter backgrounds. All of this is rather bizarre at first glance as the microscopic epistasis is known to be highly idiosyncratic.

This paper, along with one by Lyons et al (Nat Ecol Evol 2020), resolves this paradox and shows that the observed pattern of global epistasis is in fact directly dependent on microscopic epistasis being widespread, involving multiple loci - with most parts of the organisms being connected in an "everything affecting everything" pattern, and being idiosyncratic. The Lyons et al paper focused on the data showing the epistasis is in fact idiosyncratic - their key observation - and provided an intuition for why such widespread idiosyncrasy would result in the observed pattern of global epistasis. Although neither set of authors seems to use this term, this should fit the notion of the Anna Karenina principle: "All happy families are alike; each unhappy family is unhappy in its own way." That is, in order for the right things to happen, most things need to go right, but in order for things to fail, anyone of many such things can go wrong. The more adapted systems are more fragile and more difficult to improve, because in both cases it is easier to disrupt what is already working.

The Reddy and Desai paper takes this notion and develops a very simple and transparent quantitative theory of this principle that generates specific quantitative predictions about the dynamics of adaptation that we, as a field, will spend considerable time now testing. The work has the potential to become a seminal paper in the field.

Reviewer #1 (Public Review):

This study builds upon previous findings by the authors and others that the Hedgehog (Hh) co-receptor Ihog not only binds Hh to trigger Hh signal transduction, but also engages trans-homophilic interactions in cell-cell adhesion. Using experimental manipulation and mathematical modeling, the authors assessed the role of Ihog trans-homophilic binding in stabilizing cytoneme structure and the relative strengths of Ihog-Ihog and Hh-Ihog binding. These findings led to a model whereby the weaker Ihog-Ihog trans interaction promotes direct membrane contacts along cytonemes and that Hh-Ihog binding releases Ihog from trans Ihog-Ihog complex. The studies are well designed and executed, and the findings are convincing.

AssayGeneralClass: BAO:0003061 reporter protein

AssayMaterialUsed: CLO:0036938 tumor-derived 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; I-SceI endonuclease introduces a double-stranded break in the reporter construct and efficient repair results in GFP expression, which is detected by flow cytometry

AssayReadOutDescription: Homology directed repair (HDR) activity fold change, measured as GFP-positive cells and normalized relative to wild type PALB2 (set to 5.0) and the p.Y551X truncating variant (set to 1.0).

AssayRange: scaled score

AssayNormalRange: >4.4

AssayAbnormalRange: ≤1.7 for "deleterious" variants and ≤2.4 for "hypomorphic"variants

AssayIndeterminateRange: >2.4-<4.4

ValidationControlPathogenic: 7

ValidationControlBenign: 4

Replication: At least 2 independent clones per variant, each analyzed in duplicate

StatisticalAnalysisDescription: Not reported

AssayResult: 5

AssayResultAssertion: Normal

StandardErrorMean: 0

ControlType: Normal; wild type PALB2 cDNA

AssayResult: 0.5

AssayResultAssertion: Abnormal

StandardErrorMean: 0.03

AssayResult: 0.5

AssayResultAssertion: Abnormal

StandardErrorMean: 0.05

AssayResult: 0.6

AssayResultAssertion: Abnormal

StandardErrorMean: 0.15

AssayResult: 0.6

AssayResultAssertion: Abnormal

StandardErrorMean: 0.07

Comment: This variant was reported as c.2145_2146delT p.(Asp715Glufs2), however the numbering implies the deletion of two nucleotides. The deletion of TA, c.2145_2146delTA, gives the reported protein change (p.(Asp715Glufs2), and was assumed to be the intended variant. Use this evidence with caution.

AssayResult: 0.8

AssayResultAssertion: Abnormal

StandardErrorMean: 0.14

AssayResult: 0.8

AssayResultAssertion: Abnormal

StandardErrorMean: 0.13

AssayResult: 1

AssayResultAssertion: Abnormal

StandardErrorMean: 0

AssayResult: 1.5

AssayResultAssertion: Abnormal

StandardErrorMean: 0.16

AssayResult: 1.7

AssayResultAssertion: Abnormal

StandardErrorMean: 0.34

AssayResult: 1.7

AssayResultAssertion: Abnormal

StandardErrorMean: 0.84

AssayResult: 2.4

AssayResultAssertion: Abnormal

StandardErrorMean: 0.22

Comment: This variant is reported as a potential hypomorphic variant.

AssayResult: 3

AssayResultAssertion: Indeterminate

StandardErrorMean: 0.32

AssayResult: 3.6

AssayResultAssertion: Indeterminate

StandardErrorMean: 0.28

AssayResult: 3.6

AssayResultAssertion: Indeterminate

StandardErrorMean: 0.01

AssayResult: 3.6

AssayResultAssertion: Indeterminate

StandardErrorMean: 0.11

AssayResult: 3.7

AssayResultAssertion: Indeterminate

StandardErrorMean: 0.13

AssayResult: 3.8

AssayResultAssertion: Indeterminate

StandardErrorMean: 0.18

AssayResult: 3.9

AssayResultAssertion: Indeterminate

StandardErrorMean: 0.04

AssayResult: 4

AssayResultAssertion: Indeterminate

StandardErrorMean: 0.1

AssayResult: 4

AssayResultAssertion: Indeterminate

StandardErrorMean: 0.32

AssayResult: 4

AssayResultAssertion: Indeterminate

StandardErrorMean: 0.07

AssayResult: 4

AssayResultAssertion: Indeterminate

StandardErrorMean: 0.16

AssayResult: 4.1

AssayResultAssertion: Indeterminate

StandardErrorMean: 0.56

AssayResult: 4.4

AssayResultAssertion: Indeterminate

StandardErrorMean: 0.02

AssayResult: 4.4

AssayResultAssertion: Indeterminate

StandardErrorMean: 0.21

Comment: This variant was reported as c.398C>G p.(Ser133Thr), however the given allele from reference sequence is incorrect and does not match the actual sequence at the given position. The opposite nucleotide change, c.398G>C, gives the reported protein change (p.(Ser133Thr)), and was assumed to be the intended variant. Use this evidence with caution.

AssayResult: 4.4

AssayResultAssertion: Indeterminate

StandardErrorMean: 0.02

AssayResult: 4.4

AssayResultAssertion: Indeterminate

StandardErrorMean: 0.09

AssayResult: 4.4

AssayResultAssertion: Normal

StandardErrorMean: 0.39

AssayResult: 4.5

AssayResultAssertion: Normal

StandardErrorMean: 0.23

AssayResult: 4.5

AssayResultAssertion: Normal

StandardErrorMean: 0.16

AssayResult: 4.6

AssayResultAssertion: Normal

StandardErrorMean: 0.09

AssayResult: 4.6

AssayResultAssertion: Normal

StandardErrorMean: 0.57

AssayResult: 4.6

AssayResultAssertion: Normal

StandardErrorMean: 0.57

AssayResult: 4.6

AssayResultAssertion: Normal

StandardErrorMean: 0.32

AssayResult: 4.7

AssayResultAssertion: Normal

StandardErrorMean: 0.13

AssayResult: 4.7

AssayResultAssertion: Normal

StandardErrorMean: 0.32

AssayResult: 4.7

AssayResultAssertion: Normal

StandardErrorMean: 0.1

AssayResult: 4.7

AssayResultAssertion: Normal

StandardErrorMean: 0.1

AssayResult: 4.7

AssayResultAssertion: Normal

StandardErrorMean: 0.92

AssayResult: 4.7

AssayResultAssertion: Normal

StandardErrorMean: 0.06

AssayResult: 4.7

AssayResultAssertion: Normal

StandardErrorMean: 0.11

AssayResult: 4.8

AssayResultAssertion: Normal

StandardErrorMean: 0.23

AssayResult: 4.8

AssayResultAssertion: Normal

StandardErrorMean: 0.79

AssayResult: 4.8

AssayResultAssertion: Normal

StandardErrorMean: 0.24

AssayResult: 4.8

AssayResultAssertion: Normal

StandardErrorMean: 0.67

AssayResult: 4.9

AssayResultAssertion: Normal

StandardErrorMean: 0.29

AssayResult: 4.9

AssayResultAssertion: Normal

StandardErrorMean: 0.93

AssayResult: 4.9

AssayResultAssertion: Normal

StandardErrorMean: 0.44

AssayResult: 4.9

AssayResultAssertion: Normal

StandardErrorMean: 0.71

AssayResult: 4.9

AssayResultAssertion: Normal

StandardErrorMean: 0.01

AssayResult: 4.9

AssayResultAssertion: Normal

StandardErrorMean: 0.08

AssayResult: 4.9

AssayResultAssertion: Normal

StandardErrorMean: 0.04

AssayResult: 4.9

AssayResultAssertion: Normal

StandardErrorMean: 0.49

AssayResult: 4.9

AssayResultAssertion: Normal

StandardErrorMean: 0.57

AssayResult: 4.9

AssayResultAssertion: Normal

StandardErrorMean: 0.27

AssayResult: 4.9

AssayResultAssertion: Normal

StandardErrorMean: 0.32

AssayResult: 4.9

AssayResultAssertion: Normal

StandardErrorMean: 0.1

AssayResult: 5

AssayResultAssertion: Normal

StandardErrorMean: 0.6

AssayResult: 5

AssayResultAssertion: Normal

StandardErrorMean: 0.08

AssayResult: 5

AssayResultAssertion: Normal

StandardErrorMean: 0.15

AssayResult: 5

AssayResultAssertion: Normal

StandardErrorMean: 1.01

AssayResult: 5

AssayResultAssertion: Normal

StandardErrorMean: 0.58

AssayResult: 5

AssayResultAssertion: Normal

StandardErrorMean: 0.32

AssayResult: 5

AssayResultAssertion: Normal

StandardErrorMean: 1.21

AssayResult: 5.1

AssayResultAssertion: Normal

StandardErrorMean: 0.56

AssayResult: 5.1

AssayResultAssertion: Normal

StandardErrorMean: 0.7

AssayResult: 5.1

AssayResultAssertion: Normal

StandardErrorMean: 1

AssayResult: 5.2

AssayResultAssertion: Normal

StandardErrorMean: 0.39

AssayResult: 5.2

AssayResultAssertion: Normal

StandardErrorMean: 0.1

AssayResult: 5.2

AssayResultAssertion: Normal

StandardErrorMean: 0.33

AssayResult: 5.2

AssayResultAssertion: Normal

StandardErrorMean: 0.7

AssayResult: 5.3

AssayResultAssertion: Normal

StandardErrorMean: 0.84

AssayResult: 5.3

AssayResultAssertion: Normal

StandardErrorMean: 0.46

AssayResult: 5.4

AssayResultAssertion: Normal

StandardErrorMean: 0.13

AssayResult: 5.6

AssayResultAssertion: Normal

StandardErrorMean: 0.16

AssayResult: 5.8

AssayResultAssertion: Normal

StandardErrorMean: 1.15

AssayResult: 5.8

AssayResultAssertion: Normal

StandardErrorMean: 0.1

AssayResult: 6

AssayResultAssertion: Normal

StandardErrorMean: 0.13

AssayResult: 6.2

AssayResultAssertion: Normal

StandardErrorMean: 1.39

AssayResult: 6.4

AssayResultAssertion: Normal

StandardErrorMean: 0.45

AssayResult: 6.4

AssayResultAssertion: Normal

StandardErrorMean: 0.96

AssayResult: 6.5

AssayResultAssertion: Normal

StandardErrorMean: 1.55

AssayResult: 6.6

AssayResultAssertion: Normal

StandardErrorMean: 0.05

AssayResult: 6.6

AssayResultAssertion: Normal

StandardErrorMean: 0.8

AssayResult: 6.9

AssayResultAssertion: Normal

StandardErrorMean: 0.35

AssayResult: 7.1

AssayResultAssertion: Normal

StandardErrorMean: 0.43

AssayResult: 7.2

AssayResultAssertion: Normal

StandardErrorMean: 0.01

AssayResult: 7.3

AssayResultAssertion: Normal

StandardErrorMean: 0.08

AssayResult: 7.5

AssayResultAssertion: Normal

StandardErrorMean: 0.61

AssayResult: 7.7

AssayResultAssertion: Normal

StandardErrorMean: 0.15

AssayResult: 7.7

AssayResultAssertion: Normal

StandardErrorMean: 0.15

HGVS: NM_024675.3:c.100C>T p.(Arg34Cys)

Reviewer #1:

In this manuscript, Fernandez et al examine the impact of defective telomere length maintenance on type II alveolar epithelial cells, which are thought to be central to the pathogenesis of pulmonary fibrosis in dyskeratosis congenita (DC) and related telomere biology disorders. Murine models have been used to address how telomere dysfunction in AT2 cells drives pulmonary fibrosis however these models have limitations. Therefore, the investigators' study of human AT2 cells/organoids derived from induced pluripotent stem cells (iAT2 cells) in the presence and absence of a known DC pathogenic variant provides an exceptional model. In addition, the investigators use expression profiling to uncover decreased canonical WNT signaling in iAT2 cells with telomere dysfunction and then demonstrate rescue of telomere dysfunction and iAT2 cell growth with the addition of a GSK3 inhibitor, a canonical WNT agonist. The data appear to be of high quality, the approaches and interpretation appropriate, with some noted exceptions below. Given the importance of the problem (dysfunctional telomere-induced pulmonary fibrosis) and the apparent benefit of GSK3 inhibition of iAT2 cell growth and telomere dysfunction, which extends the work published by this group previously on intestinal organoids (might enhanced canonical WNT signaling more broadly affect other tissues with telomere-induced senescence?), this work is significant.

A few aspects of the studies dampen the ability to draw certain conclusions. For example, the authors use iPSCs that are 5 vs 25 passages after introduction (or not) of the DKC1 A386T mutation for the generation of iAT2 cells. They then show iAT2 DKC1 mutant organoids generated from the later passage iPSCs have an apparent growth defect as early as Day 50 but that those generated from the earlier passage iPSCs do not at Day 70 [with caveats the images are of different quality (comparing Fig. 1B and Fig. S3D) and quantitative data (similar to Fig. 1C) are lacking for the iAT2 organoids generated from the early passage iPSCs]. They argue that progressive telomere shortening is the cause of the growth defects. If this is the case, then the iAT2 cells generated from the earlier passages should eventually show growth defects with progressive telomeres shortening, which was not shown.

The telomere length analysis of the iAT2 cells at Day 50 and Day 70 are not markedly different, and neither the % p21 + nor TIF+ cells is shown for Day 50. Therefore, the conclusion that it is the accumulation of short uncapped telomeres in the DKC1 mutant iAT2 cells that alters gene expression and induces senescence at Day 70 ignores the extent of these changes at Day 50.

The statement that CHIR99021 (when present in the medium from Day 49-70) rescued the growth defect seems generous; the effect is partial and the assay is for organoid formation efficiency only. Moreover, it is most likely prohibiting the further accumulation of senescent cells rather than rescuing cells that were not previously growing.

It is striking that prolonged CHIR99021 treatment (ie, through to Day 70) resulted in increased telomerase activity, and more so in mutant compared to wild type cells. First, how reproducible was this effect? I appreciate that the authors have not explored this for this manuscript, however, TERT expression does not rescue DKC1 mutants but TERC does. Were TERC levels increased? Also, given this robust increase, it is striking that no difference is detected in TeSLA assays given the proportion of very short detected telomeres that would presumably be substrates for telomerase. It is noteworthy that, in the protocol to derive iAT2 cells, CHIR99021 is present in the media prior to Day 28. This raises the question of whether there is rescue of telomerase in the cells exposed to CHIR99021 in the interval of iAT2 specification?

AssayGeneralClass: BAO:0003009 cell viability assay

AssayMaterialUsed: CLO:0003684 HeLa cell

AssayDescription: HeLa cells were treated with PALB2 siRNA followed by transfection peYFP-PALB2 expressing PALB2 variants (or empty vector) and exposed to olaparib (2.5 µM) for 3 days. Nuclei were stained with Hoechst 33342 and measured as an indicator of cell viability.

AssayReadOutDescription: Cell viability expressed as percentage of survival in olaparib-treated cells relative to vehicle (DMSO)-treated cells

AssayRange: %

AssayNormalRange: Not reported

AssayAbnormalRange: Not reported

AssayIndeterminateRange: Not reported

ValidationControlPathogenic: 1

ValidationControlBenign: 3

Replication: At least 3 independent experiments, each performed in triplicate

StatisticalAnalysisDescription: Kruskal–Wallis test with Dunn's multiple comparison post-test

AssayResult: 5

AssayResultAssertion: Abnormal

PValue: < 0.0001

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

AssayResult: -98

AssayResultAssertion: Abnormal

PValue: < 0.0001

AssayResult: 48

AssayResultAssertion: Abnormal

PValue: < 0.0001

Approximation: Exact assay result value not reported; value estimated from Figure 1D.

ControlType: Abnormal; empty vector

AssayResult: 100

AssayResultAssertion: Normal

ControlType: Normal; wild type PALB2 cDNA

AssayResult: 106

AssayResultAssertion: Not reported

PValue: > 0.9999

Comment: Exact values reported in Table S3.

AssayResult: 108.6

AssayResultAssertion: Not reported

PValue: > 0.9999

Comment: Exact values reported in Table S3.

AssayResult: 64.45

AssayResultAssertion: Abnormal

PValue: < 0.0001

Comment: Exact values reported in Table S3.

AssayResult: 84.49

AssayResultAssertion: Indeterminate

PValue: 0.0058

Comment: Exact values reported in Table S3.

AssayResult: 92.43

AssayResultAssertion: Not reported

PValue: > 0.9999

Comment: Exact values reported in Table S3.

AssayResult: 88.66

AssayResultAssertion: Not reported

PValue: 0.727

Comment: Exact values reported in Table S3.

AssayResult: 96.63

AssayResultAssertion: Not reported

PValue: > 0.9999

Comment: Exact values reported in Table S3.

AssayResult: 97.59

AssayResultAssertion: Not reported

PValue: > 0.9999

Comment: Exact values reported in Table S3.

AssayResult: 94.36

AssayResultAssertion: Not reported

PValue: > 0.9999

Comment: Exact values reported in Table S3.

AssayResult: 98.94

AssayResultAssertion: Not reported

PValue: > 0.9999

Comment: Exact values reported in Table S3.

AssayResult: 87.19

AssayResultAssertion: Not reported

PValue: 0.341

Comment: Exact values reported in Table S3.

AssayResult: 98.25

AssayResultAssertion: Not reported

PValue: > 0.9999

Comment: Exact values reported in Table S3.

AssayResult: 57.61

AssayResultAssertion: Abnormal

PValue: < 0.0001

Comment: Exact values reported in Table S3.

AssayResult: 109.2

AssayResultAssertion: Not reported

PValue: > 0.9999

Comment: Exact values reported in Table S3.

AssayResult: 95.47

AssayResultAssertion: Not reported

PValue: > 0.9999

Comment: Exact values reported in Table S3.

AssayResult: 97.77

AssayResultAssertion: Not reported

PValue: > 0.9999

Comment: Exact values reported in Table S3.

AssayResult: 103.5

AssayResultAssertion: Normal

PValue: > 0.9999

Comment: Exact values reported in Table S3.

AssayResult: 100.7

AssayResultAssertion: Not reported

PValue: > 0.9999

Comment: Exact values reported in Table S3.

AssayResult: 102.6

AssayResultAssertion: Not reported

PValue: > 0.9999

Comment: Exact values reported in Table S3.

AssayResult: 77.32

AssayResultAssertion: Indeterminate

PValue: 0.0002

Comment: Exact values reported in Table S3.

AssayResult: 82.22

AssayResultAssertion: Indeterminate

PValue: 0.004

Comment: Exact values reported in Table S3.

AssayResult: 96.97

AssayResultAssertion: Not reported

PValue: > 0.9999

Comment: Exact values reported in Table S3.

AssayResult: 102.1

AssayResultAssertion: Normal

PValue: > 0.9999

Comment: Exact values reported in Table S3.

AssayResult: 101.6

AssayResultAssertion: Normal

PValue: > 0.9999

Comment: Exact values reported in Table S3.

AssayResult: 109.7

AssayResultAssertion: Not reported

PValue: > 0.9999

Comment: Exact values reported in Table S3.

AssayResult: 109.4

AssayResultAssertion: Not reported

PValue: > 0.9999

Comment: Exact values reported in Table S3.

AssayResult: 107.5

AssayResultAssertion: Not reported

PValue: > 0.9999

Comment: Exact values reported in Table S3.

AssayResult: 100.5

AssayResultAssertion: Not reported

PValue: > 0.9999

Comment: Exact values reported in Table S3.

AssayResult: 103.3

AssayResultAssertion: Not reported

PValue: > 0.9999

Comment: Exact values reported in Table S3.

AssayResult: 108.7

AssayResultAssertion: Not reported

PValue: > 0.9999

Comment: Exact values reported in Table S3.

AssayResult: 106.8

AssayResultAssertion: Not reported

PValue: > 0.9999

Comment: Exact values reported in Table S3.

AssayResult: 94.01

AssayResultAssertion: Not reported

PValue: > 0.9999

Comment: Exact values reported in Table S3.

AssayResult: 92.68

AssayResultAssertion: Not reported

PValue: > 0.9999

Comment: Exact values reported in Table S3.

AssayResult: 92.03

AssayResultAssertion: Not reported

PValue: > 0.9999

Comment: Exact values reported in Table S3.

AssayResult: 93.06

AssayResultAssertion: Not reported

PValue: > 0.9999

Comment: Exact values reported in Table S3.

AssayResult: 86.49

AssayResultAssertion: Not reported

PValue: 0.3376

Comment: Exact values reported in Table S3.

AssayResult: 76.21

AssayResultAssertion: Indeterminate

PValue: 0.0001

Comment: Exact values reported in Table S3.

AssayResult: 85.76

AssayResultAssertion: Indeterminate

PValue: 0.0445

Comment: Exact values reported in Table S3.