Reviewer #2 (Public Review):

Pupillometry is an increasingly accessible tool for the non-invasive readout of brain activity. However, our understanding of pupil-control circuits and of the relationship between changes in pupil size and perception, cognition or action, is far from complete. Therefore, any measurements that further this understanding are of great interest to a wide audience in the field of psychology and neurobiology.

This study used pupillometry to explore the neural processing that underlie perception and dissociate those from action-related neural processing. The authors use a novel and comprehensive task design, centered on binocular rivlary, that is likely to find wider use among researchers studying the neural processes that underlie perception and action. They used a non-invasive method (pupillometry) to disscociate putative processes and circuits that might drive perceptual switching. They found changes in pupil size that are reliably different depending on the task: for example - between the conditions that require reporting a perceptual switch versus not reporting it and between rivalrous and explicit changes in the visual stimulus.

Such approaches can be very useful in deciphering which of the myriad factors that can affect pupil size are in fact active under specific, controlled conditions and thus provide a basis for guided, direct measurements of these specific brain regions.

Overall, this study is well-conceived and executed. However, I have some questions and concerns about the analyses and conclusions made from the results shown. In general, I would encourage the authors to try and include more of what we do know about neuromodulation and the cortical control of pupil pathways to frame the hypothesis and interpret the results. Further, it is unclear to me whether the constriction/dilation dissociation is tenable with the presented data and analyses.

Reviewer #2 (Public Review):

Rhabdomeric Opsins (r-Opsins) are well known for their role in photon detection by photosensory cells which are commonly found in eyes. However, r-Opsin expression has also been detected in non-photosensory cells (e.g., mechanosensors), but their function(s) in these other sensory cells is less well understood. To explore the function of r-Opsins outside the context of an eye/head (non-cephalic function) as well as to investigate the potential evolutionary path by which sensory systems that rely on r-Opsins have evolved, Revilla-i-Domingo et al. have investigated gene expression in two distinct subsets of r-Opsin expressing cells in the marine bristle worm Platynereis dumerilii : EP (eye photoreceptor) and TRE (trunk r-opsin1 expressing) cells. The authors also generate two Pdu-r-Opsin1 mutant strains in order to investigate how the loss of r-Opsin function affects gene expression and behavior.

The question of what role r-Opsins play outside of photoreceptors is an interesting one that remains poorly understood. In this manuscript, the authors demonstrate a powerful protocol for FACS sorting and sequencing different cell populations from an important evolutionary model organism.

The transcriptomic analysis presented here demonstrates that both the cephalic EP cells and the non-cephalic TRE cells express components of the photosensory transduction pathway. This observation, together with heterologous cell expression data presented demonstrating sensitivity of Pdu-r-Opsin1 to blue light, suggests that both EP and TRE cells are likely to be light sensitive. The authors also suggest that they observe "mechanosensory signatures" in the transcriptomes, which, together with the analysis of undulatory movements in headless animals, lead them to suggst that r-Opsin in TRE cells functions as an evolutionarily conserved light-dependent modulator of mechanosensation, a conclusion that is not well-supported by the data presented.

Overall, many of the conclusions drawn from the transcriptome data are inferential and based on weak evidence. Key limitations are listed below:

1) The apparent overlap between the phototransduction and mechanosensory systems has already been shown (in Drosophila for instance) and the current work adds limited information to this story, and what is added is weakened by the absence of functional and physiological analyses. This is particularly true for supporting the claims of mechanosensory signatures in these cells. For example, genes whose expression is suggested in the text as being indicative of a mechanosensory function (glass and waterwitch) are, in fact, expressed in multiple sensory cell types. Glass (gl) is a transcription factor best known for regulating the expression of phototransduction proteins in photoreceptors. The function of waterwitch (wtrw) is not fully understood, but it is broadly expressed in sensory cells in Drosophila. It would be more compelling if mechanotransduction channels like Piezo and NompC were expressed in the TREs, but there is no mention of this.

2) The suggestion that the TRE cells share similarity with the mechanosensitive mammalian inner ear is provocative, but lacks strong support. For instance, physiological characterization of the response properties of these sensory cells or identification of anatomical similarities analogous to the stereocilia upon which hair cell mechanosensitivity is based would greatly increase plausibility of this claim. Particularly for a species that diverged from mice and flies many hundreds of millions of years ago, speculation based largely on transcriptome analysis is risky. Careful validation is required as identified genes might not share a conserved function with their assigned orthologs in mice and Drosophila.

3) The current analysis lacks sufficient power to make compelling claims with regard to potential ancestral protosensory cells. The investigators are examining a single species of marine worm and doing so without detailed anatomical and functional studies of the r-Opsin-expressing cells in the worm.

4) The behavioral experiments require more functional data to interpret unambiguously. The data indicate that r-opsin1 is required for light to surpress the undulation of decapitated worms. Does this mean that the TREs are photosensors whose activity inhibits locomotion or that the TREs are light-sensitive mechanosensors ?

5) It is assumed that the TREs constitute a homogenous cell population, but this is not demonstrated. This means that the TREs could be a mixed population (for example, distinct sets of photosensors and mechanosensors) and some of the TRE-expressed genes identified could be expressed in different specific subset of TREs.

Reviewer #2:

I like this type of multimodal study, and I think that the rationale for the study is good. I am not, however, convinced about the results/conclusions provided. Here are my main points:

I don't agree with your conclusion that the mediating role of GABA changes in aging. This requires longitudinal data, the cross-sectional approach in this study can only conclude differences between groups since only 1 time point is available.

No age interaction, this is surprising to me since there are age differences?

Compensatory explanation: Is there a correlation with performance? If there isn't, the proposal of compensatory mechanisms is unclear since it is then not obvious what the compensation is for?

COVID research updates: Coronavirus antibodies last for months — if you have them. (2021). Nature. https://doi.org/10.1038/d41586-020-00502-w

Reviewer #2 (Public Review):

This study traces the detailed excitatory connections of mouse forepaw sensorimotor circuits from the spinal cord, through brainstem, thalamus, sensory and motor cortical areas, and their motor outputs. This is a welcome and important contribution, considering the technical advantages of mice for circuit cracking and the increasing number of labs studying the functions of their limbs. Although the structure and function of forelimb sensorimotor circuits have been extensively studied in primates, they have been relatively neglected in the rodent, especially compared to the enormous scope of research that has been done on the rodent vibrissae system over the past 50 years. This study uses a variety of contemporary methods to reveal important similarities and differences between the forelimb and vibrissae sensorimotor circuits.

Overall, the results do not hold major surprises, although this is itself a noteworthy result. The authors did identify a few qualitative and quantitative differences between the forelimb circuit and the parallel vibrissae-related circuit; the functional significance of these differences is as yet unclear.

The weaknesses of the manuscript are few and minor. The study would have been stronger if it had performed comparable, parallel experiments on the hand and vibrissae circuits, however the scope of the study is already ambitious and strong enough as it stands. I do have a question about the identity of the cortical L4 neurons that were recorded, and this issue should be discussed.

Reviewer #2 (Public Review):

NICEdrug.ch integrates well-established previous methods/pipelines from the same group and provides an easy-to-use platform for users to identify reactive sites, create repurposing and druggability reports, and reactive site-specific similarity searches between compounds. Case studies provided in the manuscript are quite strong and provide ideas to the reader regarding how this service can be useful (i.e., for which kinds of scientific aims/purposes NICEdrug.ch can be utilized). On the other hand, there are a few critical issues related to the current state of the manuscript, which, in my opinion, should be addressed with a revision.

Major issues:

1) Two of the most critical drawbacks are, first, the lack of quantitative assessment of the abilities of the service and its analysis pipeline. Use cases provide valuable information; however, it is not possible to assess the overall value of any computational tool/service without large-scale quantitative analyses. One analysis of this kind has been done and explained under "NICEdrug.ch validation against biochemical assays" and "Comparison of NICEdrug.ch predictions and biochemical assays"; however, this is not sufficient as both the experimental setup and the evaluation of results are quite generic (e.g., how to evaluate an overall accuracy of 0.73 without comparing it to other computational methods that produce such predictions, as there are many of them in the literature). Also, similar quantitative and data-driven evaluations should be made for other sections of the study as well.

2) The second critical issue is that, in the manuscript, the emphasis should be on NICEdrug.ch, since most of the underlying computational methods have already been published. However, the authors did not sufficiently focus on how the service can actually be used to conduct the analysis they mention in the use cases (in terms of usability). Via use cases, authors provide results and its biological discussion (which actually is done very well), but there is no information on how a potential user of NICEdrug.ch (who is not familiar with this system before and hoping to get an idea by reading this paper) can do similar types of analyses. I recommend authors to support the textual expressions with figures in terms of screenshots taken from the interface of NICEdrug.ch at different stages of doing the use case analyses being told in the manuscript. This will provide the reader with the ability to effectively use NICEdrug.ch.

Reviewer #2 (Public Review):

Böhm et al. investigated the phosphorylation of the Ctf19CCAN component Ame1CENP-U by Cdk1 which forms a phosphodegron motif recognized by the E3 ubiquitin ligase complex SCF-Cdc4. They identify phosphorylation sites on Ame1 and demonstrate that phosphorylation of Ame1 leads to its degradation by the SCF with Cdc4 in a cell-cycle dependent manner. They also demonstrate that the outer kinetochore component Mtw1c shields Ame1 from Cdk1 phosphorylation in vitro. Finally, they propose a model in which at least one component, Ame1, is present in excess at S-phase in yeast to incorporate into high levels of sub-complexes for efficient inner kinetochore formation on newly duplicated centromere DNA. Then, in mitosis, phosphodegrons serve to mediate the degradation of excess Ame1 (and presumably other CCAN components) and in so doing protect against the formation of ectopic outer kinetochores.

This manuscript puts forth well-designed and thorough experiments characterizing the phosphorylation of Ame1 and its regulation by the SCF-Cdc4 complex. The writing is clear and the figures are generally easy to understand. The authors succeed in asking pertinent questions, designing experiments to answer them, and considering potential alternative explanations or confounding factors. As a whole this creates a generally convincing study regarding the phospho-regulation of Ame1. However, I also have some important concerns:

1) The authors begin the manuscript by mapping phosphorylation sites across Ctf19CCAN components but then largely narrow their experimental focus to Ame1 and to a lesser extent its binding partner Okp1. Without mutation of other components, the Ame1 mutant phenotypes are either absent or very mild. This would seem to implicate that, if this is an important process, that other targets for this quality control mechanism must exist. As it stands now, the focused investigation does not make the most compelling case for the broad conclusions that are claimed. More extensive investigation of phosphoregulation of CCAN subunits beyond Ame1 would certainly help justify the claim that phosphoregulation is used to clear excess CCAN subunits and protect against ectopic kinetochore assembly. Is there another lead from their initial mass spec work that could provide some molecular evidence that this is a general process? Failing that, the discussion could at least provide some hint at how the model could be tested in future studies.

2) The conclusion that the binding of the Mtw1 complex shields Ame1 phosphodegrons is arguably one of the most significant and interesting claims made in this paper. However, the evidence presented to support this claim seems to rely exclusively on in vitro data. Thus, this part is out of balance with other parts of the paper where some in vivo correlations are attempted/made.

3) The central model mentioned at the outset strongly predicts that the mitotic degradation of Ame1 doesn't impact its abundance at centromeres. That is not the only possibility, though, and some measurement (fluorescence of a tagged Ame1 or a ChIP on centromere DNA) of Ame1 at centromeres before and through mitosis would help instill confidence in the proposal.

Reviewer #2 (Public Review):

This study presents iteratively constructed network models of spinal locomotor circuits in developing zebrafish. These models are shown to generate different locomotor behavior of the developing zebrafish, in a manner that is supported by electrophysiological and anatomical data, and by appropriate sensitivity analyses. The broad conclusions of the study result in the hypothesis that the circuitry driving locomotor movements in zebrafish could switch from a pacemaker kernel located rostrally during coiling movements to network-based spinal circuits during swimming. The study provides a rigorous quantitative framework for assessing behaviorally relevant rhythm generation at different developmental regimes of the zebrafish. The study offers an overarching hypothesis, and specific testable predictions that could drive further experimentation and further refinement of the model presented here. The models and conclusions presented here point to important avenues for further investigation, and provide a quantitative framework to address constituent questions in a manner that is directly relatable to electrophysiological recordings and anatomical data. The study would benefit from additional sensitivity analyses, and from the recognition that biological systems manifest degeneracy and significant variability along every scale of analysis.

Reviewer #2 (Public Review):

Tu et al. submit a manuscript that evaluates the performance of the Abbott ID NOW SARS-CoV-2 test in an ambulatory cohort relative to RT-PCR tests. They enrolled 785 symptomatic patients, 21 tested positive for SARS-CoV-2 by ID NOW and PCR (Hologic) while 2 tested positive only via PCR. They also tested 189 asymptomatic individuals, none of whom tested positive by either ID NOW or PCR. The positive agreement between ID NOW and PCR was 91.3%, and the negative percent agreement was 100%. The authors also provide a review and meta-analysis of ID NOW performance across at least a dozen other named studies which is thorough and interesting. The cohort assessed in this study is small and localized. The data is undermined by sample size, with the most glaring example being the 100% negative percent agreement, which doesn't compare with the known performance of the test in broader populations.

Reviewer #2 (Public Review):

In this manuscript, Xue et al. assessed many AAV vectors and demonstrated that Thioredoxin-interacting protein (TXNIP) saves RP cones by enhancing their lactate catabolism. The results of this study were based on cone counting, IHC and reporter. While the authors focus on the cellular metabolism in the Txnip-mediated rescue effect, it is unknown whether anti-oxidative stress plays a role as well.

Reviewer #2 (Public Review):

Previously, Oon and Prehoda showed apically directed movement of aPKC clusters during polarization of the neuroblast prior to asymmetric cell division. They found that these movements required F-actin, but the distribution of F-actin has only been reported for later stages of neuroblast polarization and division. Here, the authors report pulses of cortical F-actin during interphase, followed by an apically directed flow at the onset of mitosis, a strong apical accumulation of F-actin at metaphase and anaphase, followed by fragmentation and basally directed flow of the fragments. aPKC clusters are shown to colocalize with the F-actin networks as they flow apically. The F-actin networks are also shown have partial colocalization with non-muscle myosin II, suggesting a possible mechanism for their movement. Finally, the authors solidify the results of actin inhibitor studies from their 2019 study by showing that reported effects on aPKC localization are preceded by F-actin loss as would be expected but was not previously shown. Overall, the Research Advance extends the past study by more directly showing the involvement of F-actin and myosin in the apical localization mechanism of aPKC, and by describing F-actin and myosin dynamics prior to this transition. The following concerns should be addressed.

1) The pulsatile nature of broad F-actin networks is evident during interphase, but these pulsations substantially subside upon entry into mitosis, and at this stage an apically directed flow of F-actin is the main behavior evident. This transition from pulses to flow is evident in both the movies and the kymographs of the F-actin probe. However, the authors state that the pulsations continue at the onset of mitosis and as the apical cap of aPKC matures. It is unclear whether the apical flow of aPKC and F-actin is associated with small-scale defined F-actin pulses, or small-scale random fluctuations of F-actin. The F-actin flow alone is an informative finding. The authors should consider revising their descriptions of these data (including in the manuscript title), or provide clearer examples of defined F-actin pulsations during the stage when aPKC polarizes.

2) I checked the main text, methods, figures and figure legends, but could not find listings of sample sizes. Thus, the reproducibility of the findings has not been reported.

Reviewer #2 (Public Review):

This work tests the ability of a kinase inhibitor to increase bone mass in a mouse model of osteoporosis. The inhibitor, which targets SIK and other kinases, was shown previously by these investigators to increase trabecular bone mass in young intact mice. Here they show that it increases trabecular, but not cortical, bone in oophorectomized mice and that this is associated with increased bone formation and little or no effect on bone resorption. In contrast, postnatal deletion of SIK2 and SIK3 increased both bone formation and resorption, suggesting that the inhibitor targets other kinases to control resorption. Indeed, the authors confirm that the inhibitor effectively suppressed the activity of CSF1R, a receptor tyrosine kinase essential for osteoclast formation. The authors also provide some evidence of unwanted effects of the inhibitor on glucose homeostasis and kidney function.

Overall, the studies are performed well with all the necessary controls. The effects of the inhibitor on CSF1R inhibition are convincing and provide a compelling explanation for the net effects of the compound on the skeleton.

1) The ability of the inhibitor to increase trabecular but not cortical bone mass will likely limit its appeal as an anabolic therapy. Indeed, the authors show that PTH, but not the inhibitor, increases bone strength. However, this limitation is not addressed in the manuscript. In addition, the mechanisms leading to these site-specific effects were not explored.

2) The mechanisms by which YKL-05-099 increases bone formation remain unclear. The authors point out that their previous studies indicate that the compound stimulates bone formation by suppressing expression of sclerostin. However, YKL-05-099 increased trabecular bone in the femur but not spine of intact mice and did not increase cortical bone in intact or OVX mice. In contrast, neutralization of sclerostin increases trabecular bone at both sites in intact mice as well as increases cortical bone thickness. These differences do not support the idea that YKL-05-099 increases bone formation by suppressing sclerostin.

3) The authors repeatedly state that the kinase inhibitor uncouples bone formation and bone resorption. However, the authors do not provide any direct evidence that this is the case. Although the term coupling is used to refer to a variety of phenomena in skeletal biology, the most common definition, and the one used in the review cited by the authors, is the recruitment of osteoblasts to sites of previous resorption. The authors certainly provide evidence that the kinase inhibitor independently targets bone formation and bone resorption, but they do not provide evidence that the mechanisms leading to recruitment of osteoblasts to sites of previous resorption has been altered. The resorption that takes place in the inhibitor-treated mice likely still leads to recruitment of osteoblasts to sites of resorption. Thus coupling remains intact.

4) The results of the current study nicely confirm previous findings by the same authors, demonstrating the reproducibility of the effects of the inhibitor. They also provide a compelling explanation for the net effect of the inhibitor on bone resorption (it stimulates RANKL expression but inhibits CSF1 action). While this latter finding will likely be of interest to those exploring SIK inhibitors for therapeutic uses, overall this study may be of limited appeal to a broader audience.

Edara, Venkata Viswanadh, William H. Hudson, Xuping Xie, Rafi Ahmed, and Mehul S. Suthar. “Neutralizing Antibodies Against SARS-CoV-2 Variants After Infection and Vaccination.” JAMA, March 19, 2021. https://doi.org/10.1001/jama.2021.4388.

Reviewer #2 (Public Review):

The authors analyze diminishing-return (beneficial mutations likely having a small effects for genotypes of high fitness) and increasing-costs epistasis (deleterious mutations likely having large effects for genotypes of high fitness). A framework is proposed where the fitness of genotype after a mutation at a single locus can be estimated from (i) the additive effect at the locus and (ii) a component determined by the fitness of the original genotype at the locus, referred to as "global epistasis". The concept of locus-specific global epistasis is new, even if variants of global epistasis have been discussed in published work. The manuscript shows that the locus specific assumption is empirically justified and it provides applications to a study of yeast.

Regression effects (diminishing returns and increasing costs epistasis) are quantified under the assumption that epistasis can be considered noise (idiosyncratic epistasis). The result is expressed in terms of Fourier representation for the fitness of a genotype, and the proof depends on a locus-specific analysis of correlations derived from the Fourier representation. In particular, the author clarify under what circumstances one can expect the regression effects. Several conclusions are very precise, and numerical results are provided as a complement to the analytical work.

The second part of the manuscript concerns historical contingency. Absence of contingency means that the expected fitness effect of new mutation for a genotype is independent of previous substitutions. A condition for minimal contingency in provided, and a new model (The Connected Network model, or CN-model) which satisfies is introduced.

A somewhat puzzling point is that the authors emphasize that their proposed frame workexplains diminishing-return and increased-costs epistasis. Diminishing return has been described as a "regression to the mean effect" of sorts in Draghi and Plotkin (2013) for the NK model, and it was argued that a similar regression effect applies to a broad category of fitness landscapes in Greene and Crona (2014). Moreover, "increased-costs epistasis" is likely to apply broadly as well with a similar argument also for landscapes that fall outside the category discussed by in the manuscript (an example is in the Recommendation section). On the other hand, a major strength of the manuscript is that it provides a superior quantitative precision, and some quantitative understanding for when one can expect diminishing returns and increased costs epistasis (that should be emphasized more in my view).

From a conceptual point of view, the locus specific framework, as well as the historical contingency discussion are valuable contributions. The fact that the author could construct a model (the CN model) that satisfy their minimal contingency condition is very interesting as well.

The weakness of the manuscript is the presentation of the work, especially for a general audience. More context and background, explanations of quantitative results and references would help. There are also a few cases of unclear claims and confusing notation (SSWM seems to be assumed without that being stated, the notation for Fourier coefficients is unclear in some cases) and the text has some other minor issues. Fortunately, a limited effort (in terms of time) would resolve the problem, and also improve the prospects for high impact.

The impact of reopening schools on SARS-CoV-2 transmission in England. (n.d.). LSHTM. Retrieved 10 March 2021, from https://www.lshtm.ac.uk/newsevents/news/2021/impact-reopening-schools-sars-cov-2-transmission-england

Perra, N. (2021). Non-pharmaceutical interventions during the COVID-19 pandemic: A review. Physics Reports. https://doi.org/10.1016/j.physrep.2021.02.001

COVID-19 Living Evidence. (2020, October 23). Weekly update of COAP As of 23.10.2020, we have indexed 80588 publications: 8902 pre-prints 71686 peer-reviewed publications Pre-prints: BioRxiv, MedRxiv Peer-reviewed: PubMed, EMBASE https://t.co/RaDy1Wm4Hq https://t.co/FYRaYPe8oG [Tweet]. @evidencelive. https://twitter.com/evidencelive/status/1319578431848353793

BDI-pathogens/covid-19_instant_tracing. (n.d.). GitHub. Retrieved 13 February 2021, from https://github.com/BDI-pathogens/covid-19_instant_tracing

Wang, P., Nair, M. S., Liu, L., Iketani, S., Luo, Y., Guo, Y., Wang, M., Yu, J., Zhang, B., Kwong, P. D., Graham, B. S., Mascola, J. R., Chang, J. Y., Yin, M. T., Sobieszczyk, M., Kyratsous, C. A., Shapiro, L., Sheng, Z., Huang, Y., & Ho, D. D. (2021). Antibody Resistance of SARS-CoV-2 Variants B.1.351 and B.1.1.7. Nature, 1–9. https://doi.org/10.1038/s41586-021-03398-2

Ashraf, B. N. (2020). Economic impact of government interventions during the COVID-19 pandemic: International evidence from financial markets. Journal of Behavioral and Experimental Finance, 27, 100371. https://doi.org/10.1016/j.jbef.2020.100371

David R Tomlinson 💙. (2021, March 5). Truth. 💙 @FreshAirNHS @theRCN @TheBMA @mancunianmedic @DrLindaDykes @Chakladar_A @KGadhok https://t.co/Ke2C84KuaT https://t.co/C469qvrSoK [Tweet]. @DRTomlinsonEP. https://twitter.com/DRTomlinsonEP/status/1367962251211202566

Reviewer #2 (Public Review):

This well-conceived and well-presented work has both originality and substance, and contributes important new ideas to the Hh signaling field with wonderful clarity.

AssayResult: 1.5

AssayResultAssertion: Abnormal

Approximation: Exact assay result value not reported; value estimated from Figure 3e.

AssayResult: <1

AssayResultAssertion: Abnormal

Approximation: Exact assay result value not reported; value estimated from Figure 3e.

AssayResult: 1

AssayResultAssertion: Abnormal

Approximation: Exact assay result value not reported; value estimated from Figure 3e.

AssayResult: 3

AssayResultAssertion: Abnormal

Approximation: Exact assay result value not reported; value estimated from Figure 3e.

AssayResult: 1

AssayResultAssertion: Abnormal

ControlType: Abnormal; empty vector

Approximation: Exact assay result value not reported; value estimated from Figure 3e.

AssayResult: 27

AssayResultAssertion: Normal

StandardDeviation: 14

ControlType: Normal; wild type PALB2 cDNA

Approximation: Exact assay result and standard deviation values not reported; values estimated from Figure 3e.

AssayGeneralClass: BAO:0000450 fluorescence microscopy

AssayMaterialUsed: CLO:0003684 HeLa cell

AssayDescription: HeLa cells were treated with PALB2 siRNA and transfected with peYFP-PALB2 expressing PALB2 variants (or empty vector), followed by exposure to 2 Gy of γ-IR. Six hours after irradiation, cells were subjected to immunofluorescence for RAD51 foci (where foci formation serves as marker of normal DNA damage repair function).

AssayReadOutDescription: The number of RAD51 foci per cyclin A-positive cells expressing the indicated YFP-PALB2 constructs.

AssayRange: foci/cell

AssayNormalRange: Not reported

AssayAbnormalRange: Not reported

AssayIndeterminateRange: Not reported

ValidationControlPathogenic: 0

ValidationControlBenign: 0

Replication: Three independent experiments with 50 cells per condition

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

AssayResult: 5.3

AssayResultAssertion: Normal

StandardErrorMean: 0.46

HGVS: NM_024675.3:c.1010T>C p.(Leu337Ser)

Reviewer #2:

This study reports a new cell line model for Dyskeratosis congenita, generated by introducing a disease-causing mutation, DKC1 A386T, into human iPS-derived type II alveolar epithelial cells (iAT2). The authors found that the mutant cells failed to form organoids after serial passaging and displayed hallmarks of cellular senescence and telomere shortening. Transcriptomics analysis for the mutant cells unveiled defects in Wnt signaling and down-regulation of the downstream shelterin complex components. Finally, treating the mutant cells with a Wnt agonist, a GSK3 inhibitor CHIR99021 can rescue these defects and enhance telomerase activity. Overall, the study is well designed and executed. Data presented are generally clear and convincing. The new model presented here can be of great interests in the field to study the effects of DC disease causing mutants in diverse cell types.

AssayGeneralClass: BAO:0000450 fluorescence microscopy

AssayMaterialUsed: CLO:0003684 HeLa cell

AssayDescription: HeLa cells were treated with PALB2 siRNA and synchronized to G1/S phase by double thymidine block. Cells were then transfected with peYFP-PALB2 expressing PALB2 variants (or empty vector) and irradiated with 2 Gy. Four hours after irradiation, cells were subjected to immunofluorescence for RAD51 foci (where foci formation serves as marker of normal DNA damage repair function).

AssayReadOutDescription: The number of RAD51 foci per cyclin A-positive cells expressing the indicated YFP-PALB2 constructs was scored and presented as percentage change relative to the wild type mean RAD51 foci number per cell.

AssayRange: %

AssayNormalRange: Not reported

AssayAbnormalRange: Not reported

AssayIndeterminateRange: Not reported

ValidationControlPathogenic: 1

ValidationControlBenign: 3

Replication: Three independent experiments, each with 225 cells per condition

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

AssayResult: 38

AssayResultAssertion: Abnormal

PValue: < 0.0001

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

AssayResult: -96

AssayResultAssertion: Abnormal

PValue: < 0.0001

ControlType: Abnormal; empty vector

AssayResult: 0

AssayResultAssertion: Normal

ControlType: Normal; wild type PALB2 cDNA

AssayResult: -34

AssayResultAssertion: Abnormal

PValue: < 0.0001

AssayResult: -11

AssayResultAssertion: Indeterminate

PValue: Not reported

AssayResult: -4

AssayResultAssertion: Indeterminate

PValue: Not reported

AssayResult: -14

AssayResultAssertion: Indeterminate

PValue: Not reported

AssayResult: -56

AssayResultAssertion: Abnormal

PValue: < 0.0001

AssayResult: -6

AssayResultAssertion: Normal

PValue: Not reported

AssayResult: -25

AssayResultAssertion: Abnormal

PValue: < 0.01

AssayResult: -31

AssayResultAssertion: Abnormal

PValue: < 0.0001

AssayResult: -16

AssayResultAssertion: Normal

PValue: Not reported

AssayResult: -10

AssayResultAssertion: Normal

PValue: Not reported

AssayResult: -21

AssayResultAssertion: Indeterminate

PValue: < 0.01

AssayResult: -20

AssayResultAssertion: Indeterminate

PValue: < 0.05

AssayResult: 8

AssayResultAssertion: Indeterminate

PValue: Not reported

AssayResult: -29

AssayResultAssertion: Abnormal

PValue: < 0.0001

AssayResult: -98

AssayResultAssertion: Abnormal

PValue: < 0.0001

AssayResult: -36

AssayResultAssertion: Abnormal

PValue: < 0.0001

AssayResult: 3

AssayResultAssertion: Indeterminate

PValue: Not reported

AssayResult: -32

AssayResultAssertion: Abnormal

PValue: < 0.0001

AssayResult: 85.76

AssayResultAssertion: Indeterminate

PValue: 0.0445

Comment: Exact values reported in Table S3.

HGVS: NM_024675.3:c.110G>A p.(Arg37His)

Reviewer #2 (Public Review):

In the manuscript Li and colleagues explored the mechanisms that potentially regulated the transcoelomic metastasis of ovarian cancer. By using the in vivo genome-wide CRISPR/Cas9 screen in human SK-OV-3 cell line after transplanted in NOD-SCID mice, the authors identified that IL-20Ra was a potential protective factor preventing the transcoelomic metastasis of ovarian cancer. SK-OV-3 cells with higher expression of IL-20R have lower metastatic potential in vivo. On the contrary, a mouse cell line ID8 with lower IL20Ra expression metastasized aggressively, which could be reversed by over expressing IL-20Ra in the cells. In human, the metastasized ovarian cancers had lower expression of IL-20Ra than the primary tumors. Mechanistically, the authors hypothesized that IL-20 and IL-24 produced by peritoneum mesothelial could act on tumor cells through the IL-20Ra/IL-20Rb receptor to promote the production of IL-18. IL-18 could drive the macrophages into M1 like phenotypes, which in turn controlled the transcoelomic metastasis of the cancer. The in vivo phenotypes in this study were consistent with these hypotheses. The role of IL-20Ra in this setting is potentially interesting and novel.

Virological. ‘Recombinant SARS-CoV-2 Genomes Involving Lineage B.1.1.7 in the UK - SARS-CoV-2 Coronavirus / SARS-CoV-2 Molecular Evolution’, 17 March 2021. https://virological.org/t/recombinant-sars-cov-2-genomes-involving-lineage-b-1-1-7-in-the-uk/658.

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 PARP inhibitor Olaparib for 48 h inhibits end-joining mediated by PARP and sensitizes cells to DNA damage; cell survival is measured by FACS 24 h after Olaparib washout

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

AssayRange: %

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

AssayAbnormalRange: PARPi resistance levels ≤30% of wild type

AssayIndeterminateRange: Not reported

ValidationControlPathogenic: 12

ValidationControlBenign: 9

Replication: 2 independent experiments

StatisticalAnalysisDescription: Not reported

AssayResult: 26.03

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 11.42

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

AssayResult: 24.27

AssayResultAssertion: Abnormal

ReplicateCount: Not reported

StandardErrorMean: Not reported

Comment: Exact values reported in “Supplementary Data 1” file; result for this variant not reported in “Source Data” file.

AssayResult: 96.22

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 16.7

Comment: Exact values reported in “Source Data” file. Discrepancy in “Supplementary Data 1” file: nucleotide reported as c.3191A>G.

AssayResult: 15.23

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 6.42

Comment: Exact values reported in “Source Data” file. Discrepancy in “Source Data” file: protein reported as Q899X.

AssayResult: 52.23

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 6.33

Comment: Exact values reported in “Source Data” file. Discrepancy in “Source Data” file: protein reported as I1037R.

AssayResult: 74.36

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 1.89

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

AssayResult: 87.27

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 1.3

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

AssayResult: 17.29

AssayResultAssertion: Abnormal

ReplicateCount: 3

StandardErrorMean: 6.81

ControlType: Abnormal; empty vector (set 5)

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

AssayResult: 7.86

AssayResultAssertion: Abnormal

ReplicateCount: 3

StandardErrorMean: 2.39

ControlType: Abnormal; empty vector (set 4)

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

AssayResult: 34.03

AssayResultAssertion: Abnormal

ReplicateCount: 3

StandardErrorMean: 10.86

ControlType: Abnormal; empty vector (set 3)

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

AssayResult: 12.78

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 3.65

ControlType: Abnormal; empty vector (set 2)

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

AssayResult: 10.93

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 0.78

ControlType: Abnormal; empty vector (set 1)

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

AssayResult: 100

AssayResultAssertion: Normal

ReplicateCount: 38

StandardErrorMean: 0

ControlType: Normal; wild type

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

AssayResult: 102.22

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 8.29

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

AssayResult: 21.7

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 1.42

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

AssayResult: 55.4

AssayResultAssertion: Not reported

ReplicateCount: 4

StandardErrorMean: 13.29

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

AssayResult: 17.5

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 7.75

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

AssayResult: 102.7

AssayResultAssertion: Not reported

ReplicateCount: 3

StandardErrorMean: 12.82

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

AssayResult: 94.47

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 0.99

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

AssayResult: 13.87

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 1.32

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

AssayResult: 93.44

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 2.24

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

AssayResult: 9.67

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 1.31

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

AssayResult: 109.07

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 9.27

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

AssayResult: 98.64

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 5.5

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

AssayResult: 102.88

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 20.71

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

AssayResult: 16.6

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 4.35

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

AssayResult: 103.21

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 8.98

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

AssayResult: 108.27

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 16.12

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

AssayResult: 98.43

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 14.96

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

AssayResult: 102.57

AssayResultAssertion: Not reported

ReplicateCount: 3

StandardErrorMean: 11.51

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

AssayResult: 103.83

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 3.67

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

AssayResult: 87.51

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 4.4

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

AssayResult: 56.67

AssayResultAssertion: Not reported

ReplicateCount: 4

StandardErrorMean: 12.4

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

AssayResult: 85.13

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 15.04

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

AssayResult: 108.56

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 19.59

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

AssayResult: 10.42

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 0.01

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

AssayResult: 99.69

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 3.09

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

AssayResult: 12.35

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 1.48

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

AssayResult: 14.79

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 7.81

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

AssayResult: 84.41

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 1.42

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

AssayResult: 25.09

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 3.48

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

AssayResult: 97.37

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 5.14

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

AssayResult: 12.77

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 5.34

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

AssayResult: 78.91

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 6.86

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

AssayResult: 8.41

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 1.95

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

AssayResult: 24.31

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 5.23

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

AssayResult: 14.78

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 9.34

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

AssayResult: 81.17

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 9.32

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

AssayResult: 91.11

AssayResultAssertion: Not reported

ReplicateCount: 3

StandardErrorMean: 17.74

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

AssayResult: 26.39

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 1.11

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

AssayResult: 94.54

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 19.94

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

AssayResult: 86.26

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 4.22

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

AssayResult: 7.73

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 2.25

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

AssayResult: 29.04

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 7.24

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

AssayResult: 115.45

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 11.81

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

AssayResult: 78.3

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 11.75

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

AssayResult: 86.54

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 11.96

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

AssayResult: 87.96

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 10.31

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

AssayResult: 78.2

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 7.31

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

AssayResult: 103.53

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 7.06

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

AssayResult: 19.46

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 1.75

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

AssayResult: 64.92

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 8.7

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

AssayResult: 11.06

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 2.4

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

AssayResult: 117.58

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 0.81

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

AssayResult: 10.68

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 0.32

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

AssayResult: 23.96

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 7.6

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

AssayResult: 120.54

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 11.09

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

AssayResult: 74.18

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 6.49

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

AssayResult: 95.74

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 14.87

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

AssayResult: 83.96

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 9.89

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

AssayResult: 94.84

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 20.56

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

AssayResult: 17.43

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 5.19

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

AssayResult: 108.51

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 17.71

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

AssayResult: 67.82

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 10.97

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: 9.68

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 3.44

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

AssayResult: 115.71

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 3.09

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

AssayResult: 11.28

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardDeviation: 1.24

StandardErrorMean: 0.87

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

HGVS: NM_024675.3:c.104T>C p.(L35P)

AssayResult: 113.2

AssayResultAssertion: Normal

ReplicateCount: 30

StandardErrorMean: 13.9

Comment: This variant had normal function (75-125% of wildtype peak current, <1% late current, no large perturbations to other parameters). These in vitro features are consistent with non-disease causing variants. (Personal communication: A. Glazer)

HGVS: NM_198056.2:c.1038G>T p.(Glu346Asp)

AssayGeneralClass: BAO:0010044 targeted transcriptional assay

AssayMaterialUsed: BTO:0000773 lymphoblastoid cell line derived from control individuals or individuals with germline TP53 variants

AssayDescription: Comparative transcriptomic analysis using RNA-Seq to compare EBV cell lines of wild type and pathogenic TP53 in the context of genotoxic stress induced by doxorubicin treatment. p53 RNA levels were evaluated and expressed as a percentage of the mean levels obtained for the three wild-type TP53 individuals.

AdditionalDocument: PMID: 23172776

AssayReadOutDescription: The p53 mRNA levels were expressed as a ratio of the normal values obtained for 3 TP53 wild-type control individuals.

AssayRange: UO:0000187 the p53 RNA levels were evaluated and expressed as a percentage of the mean levels obtained for three wild-type TP53 individuals.

AssayNormalRange: N/A

AssayAbnormalRange: N/A

AssayIndeterminateRange: N/A

AssayNormalControl: wild type TP53

AssayAbnormalControl: LFS patient cells

ValidationControlPathogenic: 8 Individuals with dominant-negative TP53 missense variants, 10 Individuals with null TP53 variants, and 13 Individuals with other TP53 missense variants

ValidationControlBenign: 3 patients with wild type TP53

Replication: experiments were performed in triplicates.

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 #2 (Public Review):

Panigrahi and co-authors introduce a program that can segment a variety of images of rod-shaped bacteria (with somewhat different sizes and imaging modalities) without fine-tuning. Such a program will have a large impact on any project requiring segmentation of a large number of rod-shaped cells, including the large images demonstrated in this manuscript. To my knowledge, training a U-Net to classify an image from the image's shape index maps (SIM) is a new scheme, and the authors show that it performs fairly well despite a small training set including synthetic data that, based on Figure 1, does not closely resemble experimental data other than in shape. The authors discuss extending the method to objects with other shapes and provide an example of labelling two different species - these extensions are particularly promising.

The authors show that their network can reproduce results of manual segmentation with bright field, phase and fluorescence input. Performance on fluorescence data in Fig. 1 where intensities vary so much is particularly good and shows benefits of the SIM transformation. Automated mapping of FtsZ show that this method can be immediately useful, though the authors note this required post-processing to remove objects with abnormal shapes. The application in mixed samples in Fig. 4 shows good performance. However, no Python workflow or application is provided to reproduce it or train a network to classify mixtures in different experiments.

Performance was compared between SuperSegger with default parameters and MiSiC with tuned parameters for a single data set. Perhaps other SuperSegger parameters would perform better with the addition of noise, and it's unclear that adding Gaussian noise to a phase contrast image is the best way to benchmark performance. An interesting comparison would be between MiSiC and other methods applying neural networks to unprocessed data such as DeepCell and DeLTA, with identical training/test sets and an attempt to optimize free parameters.

INSTALLATION: I installed both the command line and GUI versions of MiSiC on a Windows PC in a conda environment following provided instructions. Installation was straightforward for both. MiSiCgui gave one error and required reinstallation of NumPy as described on GitHub. Both give an error regarding AVX2 instructions. MiSiCgui gives a runtime error and does not close properly. These are all fairly small issues. Performance on a stack of images was sufficiently fast for many applications and could be sped up with a GPU implementation.

TESTING: I tested the programs using brightfield data focused at a different plane than data presumably used to train the MiSiC network, so cells are dark on a light background and I used the phase option which inverts the image. With default settings and a reasonable cell width parameter (10 pixels for E. coli cells with 100-nm pixel width; no added noise since this image requires no rescaling) MiSiCgui returned an 8-bit mask that can be thresholded to give segmentation acceptable for some applications. There are some straight-line artifacts that presumably arise from image tiling, and the quality of segmentation is lower than I can achieve with methods tuned to or trained on my data. Tweaking magnification and added noise settings improved the results slightly. The MiSiC command line program output an unusable image with many small, non-cell objects. Looking briefly at the code, it appears that preprocessing differs and it uses a fixed threshold.

Reviewer #2 (Public Review):

The influenza A genome is made up of eight viral RNAs. Despite being segmented, many of these RNAs are known to evolve in parallel, presumably due to similar selection pressures, and influence each other's evolution. The viral protein-protein interactions have been found to be the mechanism driving the genomic evolution. Employing a range of phylogenetic and molecular methods, Jones et al. investigated the evolution of the seasonal Influenza A virus genomic segments. They found the evolutionary relationships between different RNAs varied between two subtypes, namely H1N1 and H3N2. The evolutionary relationships in case of H1N1 were also temporally more diverse than H3N2. They also reported molecular evidence that indicated the presence of RNA-RNA interaction driving the genomic coevolution, in addition to the protein interactions. These results do not only provide additional support for presence of parallel evolution and genetic interactions in Influenza A genome and but also advances the current knowledge of the field by providing novel evidence in support of RNA-RNA interactions as a driver of the genomic evolution. This work is an excellent example of hypothesis-driven scientific investigation.

The communication of the science could be improved, particularly for viral evolutionary biologists who study emergent evolutionary patterns but do not specialise in the underlying molecular mechanisms. The improvement can be easily achieved by explaining jargon (e.g., deconvolution) and methodological logics that are not immediately clear to a non-specialist.

The introduction section could be better structured. The crux of this study is the parallel molecular evolution in influenza genome segments and interactions (epistasis). The authors spent the majority of the introduction section leading to those two topics and then treated them summarily. This structure, in my opinion, is diluting the story. Instead, introducing the two topics in detail at the beginning (right after introducing the system) then discussing their links to reassortments, viral emergence etc. could be a more informative, easily understandable and focused structure. The authors also failed to clearly state all the hypotheses and predictions (e.g., regarding intracellular colocalisation) near the end of the introduction.

The authors used Robinson-Foulds (RF) metric to quantify topological distance between phylogenetic trees-a key variable of the study. But they did not justify using the metric despite its well-known drawbacks including lack of biological rational and lack of robustness, and particularly when more robust measures, such as generalised RF, are available.

Figure 1 of the paper is extremely helpful to understand the large number of methods and links between them. But it could be more useful if the authors could clearly state the goal of each step and also included the molecular methods in it. That would have connected all the hypotheses in the introduction to all the results neatly. I found a good example of such a schematic in a paper that the authors have cited (Fig. 1 of Escalera-Zamudio et al. 2020, Nature communications). Also this methodological scheme needs to be cited in the methods section.

Finally, I found the methods section to be difficult to navigate, not because it lacked any detail. The authors have been excellent in providing a considerable amount of methodological details. The difficulty arose due to the lack of a chronological structure. Ideally, the methods should be grouped under research aims (for example, Data mining and subsampling, analysis of phylogenetic concordance between genomic segments, identifying RNA-RNA interactions etc.), which will clearly link methods to specific results in one hand and the hypotheses, in the other. This structure would make the article more accessible, for a general audience in particular. The results section appeared to achieve this goal and thus often repeat or explain methodological detail, which ideally should have been restricted to the methods section.

Reviewer #2 (Public Review):

In this study, Fraccarollo and colleagues describe the existence and higher prevalence of subpopulations of immature monocytes and neutrophils with pro-inflammatory responses in patients with acute myocardial infarction. CD14+HLA-DRneg/low monocytes and CD16+CD66b+CD10neg neutrophils correlate with markers of systemic inflammation and parameters of cardiac damage. In particular in patients positive for cytomegalovirus and elevated levels of CD4+CD28null T cells, the expansion of immature neutrophils associates with increased levels of circulating IFNg. Mechanistically, immature neutrophils regulate T-cell responses by inducing IFN release through IL-12 production in a contact-independent manner. Besides, CD14+HLA-DRneg/low monocytes differentiate into macrophages with a potent pro-inflammatory phenotype characterized by the release of pro-inflammatory cytokines upon IFNg stimulation.

This very interesting study provides new insights into the diversity and complexity of myeloid populations and responses in the context of cardiac ischemia. It is technically well performed and the results sufficiently support the conclusions of the study.

Strengths

The authors provide a detailed analysis of the phenotype and function of two subpopulations of CD14+HLA-DRneg/low monocytes and CD16+CD66b+CD10neg neutrophils in the context of acute myocardial infarction (AMI). Extensive phenotyping of these immune populations at different time-points after the onset of the disease provides strong correlations with multiple parameters of inflammation and severity of the disease. Hence, these subpopulations emerge as biomarkers of heart ischemic diseases with predictive potential. Using in vitro approaches, the authors support these correlations with mechanistic analyses of the inflammatory and immunomodulatory function of these populations. Finally, the authors use mouse models of ischemia-reperfusion injury to mimic the conditions observed in the AMI patients and supporting the pro-inflammatory role of immature neutrophils in this disease.

Weaknesses

The associations between immature neutrophils, IFNg, and CD4+CD28null T cells found in AMI patients positive for cytomegalovirus are not well supported by the mechanistic findings observed in vitro. Here, the induction of IFNg production by immature neutrophils is restricted to CD4+CD28+ T cells but not CD4+CD28null T cells.

The experimental data obtained from mouse models of AMI to support their findings in humans would require a more extensive study. Causality between the expansion of these immature populations and the course of the disease is missing. Also, although expected, substantial differences are found between equivalent subpopulations in mice and humans thus limiting the relevance of the mouse data.

Reviewer #2 (Public Review):

PKC-theta is a critical signaling molecule downstream of T cell receptor (TCR), and required for T cell activation via regulating the activation of transcription factors including AP-1, NF-kB and NFAT. This manuscript revealed a novel function of PKC-theta in the regulation of the nuclear translocation of these transcription factors via nuclear pore complexes. This novel perspective for PKC-theta function advances our understanding T cell activation. The manuscript provided solid cellular and biochemical evidence to support the conclusions. However, nuclear pore complexes regulate the export and import essential components of cells, it is not clear whether PKC-theta selectively regulates the translocation of above transcription factors, or also other components, and whether regulates both import and export. It is essential to provide more substantial evidence to support the conclusion.

Reviewer #2 (Public Review):

This paper addresses a fundamental question regarding the evolution of the stress response, specifically that the action of natural selection on the stress response should promote the functional integration of its behavioral and physiological components. Therefore, the authors predict that genetic variation in the stress response should include covariation between its component behavioral and physiological traits. The results are intrinsically interesting and seem to provide a critical proof of principle that, if confirmed, will prompt future follow up research. However, there are some fundamental conceptual and experimental design issues that need to be addressed, in order to assess the conclusions that can be drawn from the results presented here.

Conceptual issues:

1) The authors selected multiple behavioral measures of the stress response but only considered the glucocorticoid response as a physiological trait. In my view this has several problems:

A) Although, for historical reasons and because they are easier to measure, glucocorticoids have been perceived as a stress hormone, the fact is that they respond not only to threats to the organism (i.e. stressors) but also to opportunities (e.g. mating). In other words, glucocorticoids are produced and released whenever there is the need to metabolically prepare the organism for action. Therefore, glucocorticoids are probably not the best physiological candidate to look for phenotypic integration with stress behaviors, since they must have also been selected to be produced and released in other ecological contexts. In this regard it would have been interesting to measure the phenotypic integration of cortisol also with behaviors used in non-threatning but metabolically challenging ecological opportunities (e.g. mating), and to investigate the occurrence of an eventual trade-off (or of a "phenotypic linkage") between these two sets of traits (stress traits vs. mating traits).

B) Sympathetic activation is a key component of the physiological stress response in vertebrates. It is thus odd not to consider the sympathetic response in a study that has the main aim of studying the evolution of a phenotypically integrated stress response. I understand that the sympathetic response in guppies is more difficult to study than measuring cortisol, but this technical challenge can certainly be overcome (e.g. techniques for measuring cardiac response to threat stimuli have been recently developed for other challenging model organisms, such as fruit flies; e.g. https://www.biorxiv.org/content/10.1101/2020.12.02.408161v1); or if not, then an alternative model organism should have been used to address this question.

2) Typically, in vertebrates the behavioral response to a stressor has a passive (e.g. freezing) and an active (i.e. fight-flight) component. It would be very interesting to assess if these two components are phenotypically integrated with each other and each of them with the physiological response. Unfortunately, the authors did not use behavioral measures of each of these two components. Instead they have extracted 3 spatial behaviors from an open field test (time in the central part of the tank in an open field test (OFT); relative area covered; track length) and emergence latency in an emergence from a shelter test. It is not clear how each of the measured behaviors captures these two key components of the behavioral stress response. For example, a fish that freezes in the central part of the tank when it is introduced in the OFT will have a high time in the middle score and eventually a high relative area covered, but relatively low track length. However, if it darts towards the tank wall and freezes there, the result would probably be low time in the middle and low relative area covered. Thus, a fish that has spent approximately the same time in freezing may show very different behavioral profiles according to the variables used here. This could be avoided if explicit measures of fleeing and freezing behavior have been used. Given that the authors have video-tracked the fish, I suggest they can still extract such measures (e.g. angular speed is usually a good indicator of escape/fleeing behavior; and a swimming speed threshold can be validated and subsequently used to detect freezing behavior from tracking data) from the videos. The fact that variables of these two types of behavioral responses to stress have not been used in this study may explain to a large extent why the authors came to the conclusion that, "the structure of G is more consistent with a continuous axis of variation in acute stress responsiveness than with the widely invoked 'reactive - proactive' model of variation in stress coping style".

3) The authors used a half-sib breeding design, which is the golden standard in evolutionary quantitative genetics. However, and this is not a specific critique of the present study but a general problem of this field, the extent to which estimates of G obtained with breeding designs reflect the G that would be obtained by actually sampling a natural population is questionable, because these designs create artificially structured populations with higher levels of outbreeding and concomitantly also with higher genetic variation than what is usually found in nature. This problem can be illustrated by analogy using the example of heritability estimates, which are typically lower when obtained from selection studies by comparing the generation after selection to the one before selection (aka realized heritability), than when computed from artificial breeding designs.

Methodological issues:

4) The authors considered the OFT, ET and ST testing paradigms to be behavioral assays that allow the characterization of the behavioral components of the stress response in guppies, because all these paradigms involve capturing and transferring the focal fish to a novel environment (tank) and in social isolation. Undoubtedly these procedures must have induced stress, however the stressor was not standardized because it consisted in the capture and transfer, and these may have varied from fish to fish (btw are there measures of handling time for each fish? And how to measure "handling intensity"?). In my view a standardized stressor, such as a looming stimulus (e.g. Temizer et al. 2015 Current Biology 25: 1823-34; Bhattacharyya et al. 2017 Current Biology 27, 2751-2762; Hein et al. 2018 PNAS 115: 12224-8), should have been used such that the behavioral measures could have been linked to the stressor in a more controlled way.

5) Moreover, the authors have measured the "stress behaviors" and cortisol in response to two different stressors: the handling described above and the confinement and social isolation for the GC response. This is not the best experimental design, because the behavioral and physiological expression is expected to be linked and to be flexible, as shown by the data on cortisol habituation to repeated stressor exposure. Thus, when the goal of the study is to characterize the co-variation between traits it is critical to standardize the stimulus that triggers their expression in the two domains (behavioral and physiological) and behavior and physiological measures should have been obtained in response to the same stressor stimulus for each individual. In principle, the failure to do so will artificially decrease the observed co-variation between traits, due to environmental differences (i.e. test contexts and their specific stressors).

Reviewer #2 (Public Review):

This study compares the pharmacology of intracellular polyamine blockers for Ca-permeable (CP-AMPAR) and Ca-impermeable (CI-AMPAR) AMPA receptors in the absence/presence of auxiliary subunits. Spermine is a widely used polyamine blocker to identify CP-AMPARs in native tissue, but the blocking action of spermine varies depending on which auxiliary subunits are associated with the CP-AMPARs. Hence, spermine has limitations. The goal of the present work was to identify if other polyamine blockers would be more efficient than spermine in identifying CP-AMPARs.

The authors studied CP- and CI-AMPARs in heterologous cells (HEK293T) and in primary cerebellar stellate interneurons from mice lacking the GluA2 subunit. They primarily used electrophysiology to assay channel block by various polyamines. While the technology is standard, the experiments are carried out in a rigorous manner and encompass numerous controls and variations on appropriate constructs (GluA2-containing and GluA2-lacking AMPARs and various prominent auxiliary subunits - TARPs, cornichons, and GSG1L).

The main conclusion of the work is that 100 uM NASPM fully blocks CP-AMPAR regardless of the associated auxiliary subunit. This conclusion is strongly supported by experiments including testing various auxiliary subunits in the defined conditions of HEK293T cells as well as recording and demonstrating that NASPM fully blocks AMPAR-mediated currents in stellate cells lacking GluA2 subunits.

I have no major criticisms of the work.

Reviewer #2 (Public Review):

In the current study Gill et al present a retrospective analysis of NP swabs of mother infant pairs taken longitudinally in Zambia. They use qPCR CT values to quantify the amount of IS431 in each sample to detect pertussis infection. They find strong evidence for asymptomatic pertussis infection in both mothers and infants, validating previous work identifying the role of asymptomatic transmitters in populations. This is a tremendously important study and is conducted and analyzed very well. The manuscript is well written, and I heartily recommend publication. Excellent work, well done.

Comments:

This study was done in a population with wP vaccine, I wonder if that's part of the reason many of the CT values are high. Can the authors speculate what this study would look like in a population having received aP for a long period? I'd appreciate more discussion around vaccination in general.

Reviewer #2 (Public Review):

This manuscript by Galdadas et. al. used a combination of equilibrium and non-equilibrium simulations to investigate the allosteric signaling propagation pathway in two class-A beta-lactamases, TEM-1 and KPC2, from allosteric ligand binding sites. The authors performed extensive analysis and comparison of the simulated protein allostery pathway with know mutations in the literature. The results are rigorously analyzed and neatly presented in all figures. The conclusions of this paper are mostly supported by previous mutational data, but a few aspects of simulation protocol and data analysis need to be validated or justified.

Line 293, by "comparing the Apo_NE and IB_EQ simulations at equivalent points in time" and perform subtraction "from the corresponding Ca atom from one system to another at 0.05, 0.5, 1, 3, 5ns". It is not clear to me why those time points were chosen? Have authors attempted at validating whether or not the signal from the ligand-binding site has had enough time to propagate across the allosteric signaling pathway? If one considers that the ligand is a spatially localized signal, it requires time to propagate. This is in contrast with the Kubo-Onsager paper cited by authors in which the molecule is responding to a global perturbation such as an external field. However, a local perturbation on one side of the protein will need time to propagate to the other side of the protein (30 angstroms away in this case). A simple and naive example is to map out all the bus stops on one's route. 800 simulations between the first and second stop will not be able to provide the locations of other stops. Since authors have used this "subtraction technique" on several other proteins, it would be nice to clarify how this approach works on mapping out signaling propagation perturbed by local ligand binding/unbinding and how to choose the time points for subtraction.

Another question is whether tracing the dynamics of Calpha alone is enough. As we have seen from the network analysis papers, Calpha sometimes missed some paths or could overemphasize others. The Center of the mass of residue has been proposed to be a better indicator of protein allostery. Authors may wish to clarify the particular choice of Calpah in this study.

In Figure5, the authors seem to use Pearson correlation to compute dynamic cross-correlation maps. Mutual information (M)I or linear MI have advantages over Pearson correlations, as has been discussed in the dynamical network analysis literature.

Reviewer #2 (Public Review):

In this manuscript, Dahlen et al. aimed to agnostically investigate the association between ABO and RhD blood group and disease occurrence for a large number of disease phenotypes using large-scale population-based Swedish healthcare registries. Using 2 large subject cohorts, they convincingly demonstrate that beyond the known associations between ABO, infectious diseases and thrombosis, there are other associations with very different diseases. This paper is purely epidemiological with no biological data to explain the observed associations. The clinical phenotypes are derived from hospital coding and probably lack precision, especially in terms of diagnostic certainty.

Reviewer #2 (Public Review):

eQTLs can vary between cell types. To capture this in an organism as complex as a mammal looks daunting and expensive if eQTLs have to be mapped a single cell type at a time. However, here the authors propose a 'one pot' method where whole animals are dissociated and the cell types deconvoluted based on a robust set of markers. Thus in a single experiment, eQTLS can be mapped in tens of cell types at once - here they identify 19 major cell types but in the case of the nervous system break it down with even more specificity, down to individual cells.

They test their method in C. elegans which is ideal for this - the lineage is invariant, there are extensive sets of cell type specific markers, and they can exploit their previously published method called ceX-QTL to generate massive pools of segregants using an elegant genetic trick.

Overall I was extremely impressed with the clarity of writing, the care of data analysis, and I honestly found that every analysis I was looking for had been done. They highlight some beautiful findings, most striking of which was the opposing regulation of nlp-21 in two neurons, a perfect example of the resolution this can achieve.

Reviewer #2 (Public Review):

In their manuscript "CEM500K - A large-scale heterogeneous unlabeled cellular electron microscopy image dataset for deep learning", the authors describe how they established and evaluated CEM500K, a new dataset and evaluation framework for unsupervised pre-training of 2D deep learning based pixel classification in electron microscopy (EM) images.

The authors argue that unsupervised pre-training on large and representative image datasets using contrastive learning and other methods has been demonstrated to benefit many deep learning applications. The most commonly used dataset for this purpose is the well established ImageNet dataset. ImageNet, however, is not representative for structural biases observed in EM of cells and biological tissues.

The authors demonstrate that their CEM500K dataset leads to improved downstream pixel classification results and reduced training time on a number of existing benchmark datasets a new combination thereof compared to no pre-training and pre-training with ImageNet.

The data is available on EMPIAR under a permissive CC0 license, the code on GitHub under a similarly permissive BSD 3 license.

This is an excellent manuscript. The authors established an incredibly useful dataset, and designed and conducted a strict and sound evaluation study. The paper is well written, easy to follow and overall well balanced in how it discusses technical details and the wider impact of this study.

Reviewer #2 (Public Review):

Landemard et al. compare the response properties of primary vs. non-primary auditory cortex in ferrets with respect to natural and model-matched sounds, using functional ultrasound imaging. They find that responses do not differentiate between natural and model-matched sounds across ferret auditory cortex; in contrast, by drawing on previously published data in humans where Norman-Haignere & McDermott (2018) showed that non-primary (but not primary) auditory cortex differentiates between natural and model-matched sounds, the authors suggest that this is a defining distinction between human and non-human auditory cortex. The analyses are conducted well and I appreciate the authors including a wealth of results, also split up for individual subjects and hemispheres in supplementary figures, which helps the reader get a better idea of the underlying data.

Overall, I think the authors have completed a very nice study and present interesting results that are applicable to the general neuroscience community. I think the manuscript could be improved by using different terminology ('sensitivity' as opposed to 'selectivity'), a larger subject pool (only 2 animals), and some more explanation with respect to data analysis choices.

Reviewer #2 (Public Review):

Hay et al. investigated the effect of optogenetic activation of MS cholinergic inputs on hippocampal spatial memory formation, which extended our current knowledge of the relationship between MS cholinergic neurons and hippocampal ripple oscillations.

The authors showed that optogenetic stimulation at the goal location during Y maze task impaired the formation of hippocampal dependent spatial memory. They also found that opto-stimulation at the goal location reduced the incidence of ripple oscillations, while having no effect on the power and frequency of theta and slow gamma oscillations.

Interestingly, the authors reported different results compared to previously published work by applying the analytical methods developed by Donoghue et al. (Donoghue et al., Nat Neurosci, 2020). They showed that optogenetic activation of MS cholinergic neurons during sleep not only reduced the incidence of hippocampal ripple oscillations, but also increased the power of both theta and slow gamma oscillations, which is contradict to both decreased or no change of theta and gamma power by previous reports (Vandecasteele et al., 2014, Ma et al., 2020). These results are valuable to the community of hippocampal oscillation studies.

Reviewer #2 (Public Review):

In humans, extreme stresses, such as famine, can trigger multi-generational physiological responses through altered metabolism. In C. elegans, environmental stresses, such as heat shock, can similarly promote changes in gene expression and physiology. In addition, researchers observed more than two decades ago that dsRNA triggers can silence gene expression transgenerationally. This manuscript by Houri-Zeevi et al., entitled "Stress resets ancestral heritable small RNA responses", seeks to tie these two observations in C. elegans together mechanistically, showing that environmental stress (heat shock, high osmolarity, or starvation) can alter the small RNA populations in adults and their progeny, affecting their gene expression levels. The authors used a GFP reporter as a proxy for exo-siRNA levels in various experimental paradigms. P0 animals were fed dsRNA targeting the GFP transgene, and their F1 progeny were subjected to one of the environmental stresses. The GFP expression levels of P0, F2, and F2 adults were measured, showing that the stressed F1 and their F2 progeny have increased de-silencing of the GFP transgene compared to controls. The authors also performed small RNA sequencing on these populations, showing that a subset of small RNAs become "reset" or decreased after stress, while a different subset was increased. Additionally, the p38 MAPK pathway, SKN-1 TF, and MET-2 H3K4me1/2 HMT were shown to be required for the stress-dependent changes in transgene de-silencing. The manuscript is well-written and contains some very interesting and convincing results that should be of broad interest to the fields of stress biology and RNAi.

Reviewer #2: