Reviewer #2 (Public Review):

This is a well-written paper describing the co-recruitment of p117-BCAR3 and Cas to adhesion sites for activation of lamellipodial ruffling and the subsequent ubiquitin-dependent degradation. The completeness of the description of the cycle is a major success of this article and warrants publication. I didn't find major holes in their arguments and they did document that this pathway was not universal but there were possibly analogous signaling processes with other players.

Reviewer #2 (Public Review):

Summary:

Frey et al develop an automated decoding method, based on convolutional neural networks, for wideband neural activity recordings. This allows the entire neural signal (across all frequency bands) to be used as decoding inputs, as opposed to spike sorting or using specific LFP frequency bands. They show improved decoding accuracy relative to standard Bayesian decoder, and then demonstrate how their method can find the frequency bands that are important for decoding a given variable. This can help researchers to determine what aspects of the neural signal relate to given variables.

Impact:

I think this is a tool that has the potential to be widely useful for neuroscientists as part of their data analysis pipelines. The authors have publicly available code on github and Colab notebooks that make it easy to get started using their method.

Relation to other methods:

This paper takes the following 3 methods used in machine learning and signal processing, and combines them in a very useful way. 1) Frequency-based representations based on spectrograms or wavelet decompositions (e.g. Golshan et al, Journal of Neuroscience Methods, 2020; Vilamala et al, 2017 IEEE international workshop on on machine learning for signal processing). This is used for preprocessing the neural data; 2) Convolutional neural networks (many examples in Livezey and Glaser, Briefings in Bioinformatics, 2020). This is used to predict the decoding output; 3) Permutation feature importance, aka a shuffle analysis (https://scikit-learn.org/stable/modules/permutation_importance.htmlhttps://compstat-lmu.github.io/iml_methods_limitations/pfi.html). This is used to determine which input features are important. I think the authors could slightly improve their discussion/referencing of the connection to the related literature.

Overall, I think this paper is a very useful contribution, but I do have a few concerns, as described below.

Concerns:

1) The interpretability of the method is not validated in simulations. To trust that this method uncovers the true frequency bands that matter for decoding a variable, I feel it's important to show the method discovers the truth when it is actually known (unlike in neural data). As a simple suggestion, you could take an actual wavelet decomposition, and create a simple linear mapping from a couple of the frequency bands to an imaginary variable; then, see whether your method determines these frequencies are the important ones. Even if the model does not recover the ground truth frequency bands perfectly (e.g. if it says correlated frequency bands matter, which is often a limitation of permutation feature importance), this would be very valuable for readers to be aware of.

2) It's unclear how much data is needed to accurately recover the frequency bands that matter for decoding, which may be an important consideration for someone wanting to use your method. This could be tested in simulations as described above, and by subsampling from your CA1 recordings to see how the relative influence plots change.

3)

a) It is not clear why your method leads to an increase in decoding accuracy (Fig. 1)? Is this simply because of the preprocessing you are using (using the Wavelet coefficients as inputs), or because of your convolutional neural network. Having a control where you provide the wavelet coefficients as inputs into a feedforward neural network would be useful, and a more meaningful comparison than the SVM. Side note - please provide more information on the SVM you are using for comparison (what is the kernel function, are you using regularization?).

b) Relatedly, because the reason for the increase in decoding accuracy is not clear, I don't think you can make the claim that "The high accuracy and efficiency of the model suggest that our model utilizes additional information contained in the LFP as well as from sub-threshold spikes and those that were not successfully clustered." (line 122). Based on the shown evidence, it seems to me that all of the benefits vs. the Bayesian decoder could just be due to the nonlinearities of the convolutional neural network.

Reviewer #2 (Public Review):

This work analyses the movement of the dorsal forerunner cells (DFCs) and its interaction with the extra-embryonic enveloping layer (EVL). By doing high-resolution time lapse microscopy the authors characterize the movement of the DFCc showing that they delaminate from the epithelium by apical constriction but they remain attached to the superficial EVL. By doing laser ablations they show that the movement of the DFCc depends on the attachment and vegetal displacement of the EVL. However, they show that with some frequencies some DFCc are detached from the rest of the cluster, leading to some random movement or even being left behind and differentiating into other cell types. Importantly, they investigate an additional mechanism to explain the movement of the DFCc detached cells. They show that single cells generate protrusions that connect them with the DFCc cluster forming an E-cadherin junction. This paper makes an important contribution by adding some new mode of migrations during development. Most of the conclusion are supported by the experiments.

Reviewer #2 (Public Review):

Scharmann et al. present a study of sex-biased gene expression as a function of sexual dimorphism in leaf tissue in the genus Leucadendron. Comparative studies of sex-biased expression across clades are still relatively rare, and this analysis tests some core findings of a recent paper (Harrison et al. 2015). Overall, I like the analysis and think it could be a valuable addition to the literature on sex-biased genes. This is particularly true given the difficulty of cross-species expression comparisons and the paucity of them in plants.

However, there are some critical differences between the Harrison paper and the one here, and I think it would be helpful if the authors present them early in the text. Specifically, Harrison et al. (2015) was primarily focused on gonad tissue, which in animals is the site of the vast majority of sex-biased genes. In contrast, the authors here focus on vegetative (leaf) tissue, which is analogous to animal somatic tissue. None of the patterns that Harrison et al. (2015) observed and report from the gonad were evidence in the somatic tissue they assessed. Also, by looking at gonadal tissue, Harrison et al. (2015) focused on the tissue that produces gametes, which are thought to be subject to some of the strongest sexual selection pressures. The fairest comparison would be flower tissue in plants, so I am unsure how much of the Harrison results would be expected to hold up in leaf samples. This doesn't mean the authors should do the analyses they present, just that they should be a little more upfront about what they might reasonably expect to find.

There is also a conflation at times in the paper between sexual dimorphism, which the authors can quantify in their leaf samples, and sexual selection. I explain this in more detail below, but to summarize here, I think the expectations for the relationship between sex-biased gene expression and sexual selection versus sexual dimorphism are somewhat distinct.

Finally, I am a little concerned that the low numbers of sex-biased genes, expected from leaf tissue, offer limited power for some of the tests the authors want to do. Harrison et al. (2015) had hundreds of sex-biased genes from the gonad, and this power made it possible to detect subtle patterns. The authors have a few dozen sex-biased genes, and this makes it difficult to know whether their negative results are the result of low statistical power. That they find clear associations between pre-sex-biased genes and rates of evolution is quite impressive given this low power.

Reviewer #2 (Public Review):

The paper by Lauer et al provides further insight into the factors that might determine why RO1 applications from AAB (African American Black) principal investigators appear to fare worse than their white counterparts. Their work is derived from an earlier analysis published by Hoppe et al that found 3 factors determined funding success among AAB PIs. These included decision to discuss at study section, impact score, and topic choice. The latter, topic choice (community and population studies) appeared to represent more than 20% of the variability in funding gaps. This raised the question of whether there was reviewer bias at study sections. In the Lauer paper, after controlling for several of these variables, the authors found that the topic choice of AABs (ie. preferred topics) were indeed important in respect to funding, but they uncovered the fact that the topic choices occurred more frequent in ICs that had lower funding rates. Thus the authors conclude that the disparity between AAB and white investigator RO1s is very dependent on topic choice which ultimately ends up in larger ICs with lower funding percentiles.

Overall the paper is relatively straightforward and could be important as It provides some additional data to consider. It is in fact basically a re-analysis of the Hoppe paper, but that is reasonable since that paper left many unanswered questions. Its implications however are less clear, and these raise additional questions of importance to the extramural scientific community as well as IC leadership.

Overall the reader is left with the unsettling question: Can we just wish away these disparities based on IC funding rates? (Figure 1).

1) Why would topic choice of community engagement or population studies fare worse at an Institute such as AI rather than at GM if both have the relatively same proportion of preferred topics, and both have relatively high budgets compared to other institutes. Is there one or more ICs that drive the correlations between IC funding and preferred topics or PIs?

2) Since only 2% of all PIs are AAB does that represents another issue of low frequency relative to the larger cohort?

3) It would be valuable to know if community engagement or population studies in total do worse than mechanistic studies. The authors do admit that preferred topics of AABs in general fare worse(Figure 2, Panel B).

4) Another concern is that the data are up to 2015; it has now been five years and things have changed dramatically at NIH and in society. There are now many more multiple PI applications including AABs that may not be the contact PI yet are likely to be in a preferred topic area.

5) There is nothing in the discussion about potential resolutions to this very timely issue; In other words we now know that the disparity in funding is such that AAB RO1s do worse than white PIs because they are selecting topics that end up at institutes with lower funding rates. Should the institutes devote a set aside for these topic choices to balance the portfolio of the IC and equal the playing field for AABs? Are there other alternative approaches?

Reviewer #2 (Public Review):

Thank you for the opportunity to review the short report "Regional sequencing collaboration reveals persistence of the T12 Vibrio cholerae O1 lineage in West Africa" by Ekeng and colleagues. The authors report an analysis of 46 new Vibrio cholerae genomes in context of 1280 published genomes. The goal of their analysis was to establish a recent snapshot of VC population genomics in West Africa and assess the occurrence importations of new lineages. From their analysis, they infer that the recent cases were endemic.

Overall, this report presents findings from a region with little genomic surveillance, and as such these data are valuable for the understanding of endemic cholera in the region. The authors' analysis is technically sound, and the figures are well constructed. However, the depth of the analysis is relatively shallow, even for a short report, and the conclusions drawn from the data appear more subjective then based on the analysis at hand. These weaknesses could be addressed by a more in-depth analysis and clarification of the points below. Last, I did appreciate that this study was conducted in the context of a regional training. This could be an effective model for future analyses of regional importance. I feel like that wasn't the main focus of the report. If they were to shift their focus, I would want to know:

1) Where did the isolates come from (e.g., cholera treatment centers, hospitals, or broader active surveillance)?

2) Do they conduct environmental sampling and could this be part of future efforts?

3) Who attended the training? Were they members from regional ministry of health labs, academic institutes etc?

4) Were the attendees laboratorians, bioinformaticians, clinicians etc?

5) Was there an effort to analyze the data, particularly the bioinformatics portion, locally or did the rely 100% on the collaborators at JHU? If the latter, then I don't think this is a good sustainable model for ongoing genomic epidemiology. If the prior, then were local or regional computing resources used? 6) Are they continuing to sequence isolates after the training?

Reviewer #2 (Public Review):

Halliday et al. sampled plant communities and foliar fungal diseases along an elevation gradient in Swiss Alps, to test the potential relationship between environment, plant communities and diseases in the context of climate change. The authors confirmed that elevation can affect diseases by both abiotic and biotic factors, and, host community pace-of-life was the main driver for diseases along elevation. The topic is important and new, the study is well-designed, and the analysis is reasonable.

Reviewer #2 (Public Review):

Despite the fact that reverse transcription was discovered 50 years ago, there are still some black boxes regarding RT spatiotemporal activity. Recent studies elsewhere and here indicate that RT can occur in the nucleus, revising the "dogma" that RT occurs exclusively in the cytoplasm of infected cells. However, it is still debated whether this concept can be extended to all HIV target cells and which RT processes can start and finish in the nucleus. The authors also performed several experiments designed to show that uncoating (loss of capsid) occurs in the nucleus. The authors deserve credit for developing and applying complicated imaging technologies. However, live imaging data comes from pseudo-viruses, which have low infectivity, so high amounts of virus have been used to obtain some of the results. This is a limitation, and I have some reservations about the conclusions and the generalization of the results, and also about the lack of statistics for the CLEM-ET studies, probably owing to the complexity of the technique (detailed below). In addition, despite using state-of-the-art CLEM-ET, it is possible to visualize only structures with strong fluorescence and recognizable structures. I therefore wonder how can the authors can conclude that only the forms that still have a conical or partial conical shape are the most important to follow? It is possible that more flexible CA structures can access the nucleus and that the authors neglect them owing to limitations of the technology. Immuno-gold CA labeling could solve this issue, and the authors have the technologies required to perform these experiments.

Reviewer #2 (Public Review):

This manuscript builds upon some important thought-leading work within the Ras field that the authors have published in recent years. They have previously demonstrated how changing the protein expression levels of KRAS can modulate the number of Ras-driven tumours that are observed and posited that this suggests an optimal level of Ras signalling that is neither too stressing nor too insufficient to promote tumourigenesis.

In this manuscript they use urethane to induce lung tumours in mouse models that have either normal or high levels of KRAS expression (also higher oncogenic stress). They are also able to modulate the associated oncogenic stress levels by the presence (higher stress) or deletion (lower stress) of p53. Urethane normally generates Q61 KRAS mutations, biochemical analysis by other groups has previously shown that these mutations are more active than G12 mutations. Following urethane induction, they observe an improved competence to support tumorigenesis in the high KRAS model when p53 is removed. They also observe a shift towards G12 mutants under genetic conditions where oncogenic stress is higher (higher KRAS expression, presence of p53). ie. stress compensators (p53 loss or weaker activating mutation) permit promotion of tumourigenesis in the high KRAS model. The converse was also observed. Loss of p53 (lower stress) resulted in higher mRNA levels of G12 mutants - suggesting that the weaker mutant increases protein expression/cancer signalling to occupy the new oncogenic stress headroom that has been created. Some support for the hypothesis that these effects are mediated by differences in Ras signalling amplitude between the different mutants was provided by analysing the expression of three key Ras gene targets. As predicted, higher expression (signalling output) was seen in Q61 vs Q12 mutants and when p53 was deleted.

Strengths:

The mouse model conditions provide a suitable range of options to allow the hypothesis to be tested. The data are all internally consistent and broadly support the general conclusions.

Weaknesses:

The mRNA data are interpreted as evidence for changes in protein expression and Ras signalling activity - there is no formal evidence that this is the case.

The similarity in G12/13 mutations between the KRAS normal and high KRAS mice in Figure 2C is unexpected. The authors focussed on the potential for higher G12/13 mutant expression in the KRAS normal mice to explain this. It is also intriguing how there wasn't a more complete switch to Q61 in the high KRAS tumours when p53 was deleted. Whilst the Ras signalling dosing/oncogenic stress nexus are a reasonable explanation, the model/methods are a snapshot in time and don't have the resolution to fully understand the detail of what is going on here.

This study represents a solid contribution supporting an important model and will stimulate future work to understand Ras variant cancer contributions.

Reviewer #2 (Public Review):

In this manuscript Koiwai et al. used single cell RNA sequencing of hemocytes from the shrimp Marsupenaeus japonicus. Due to lack of complete genome information for this species, they first did a de novo assembly of transcript data from shrimp hemocytes, and then used this as reference to map the scRNA results. Based on expression of the 3000 most variable genes, and a subsequent cluster analysis, nine different subpopulations of hemocytes were identified, named as Hem1-Hem9. They used the Seurat marker tool to find in total 40 cluster specific marker transcripts for all cluster except for Hem6. Based upon the predicted markers the authors suggested Hem1 and Hem2 to be immature hemocytes. In order to determine differentiation lineages they then used known cell-cycle markers from Drosophila melanogaster and could confirm Hem1 as hemocyte precursors. While genes involved in the cell cycle could be used to identify hemocyte precursors, the authors concluded that immune related genes from the fly was not possible to use to determine functions or different lineages of hemocytes in the shrimp. This is an important (and known) fact, since it is often taught that the fruit fly can be used as a general model organism for invertebrate immunologists which obviously is not the case. Even among arthropods, animals are different. The authors suggest four lineages based upon a pseudo temporal analysis using the Drosophila cell-cycle genes and other proliferation-related genes. Further, they used growth factor genes and immune related genes and could nicely map these into different clusters and thereby in a way validating the nine subpopulations. This paper will provide a good framework to detect and analyze immune responses in shrimp and other crustaceans in a more detailed way.

Strengths:

The determination of nine classes of hemocytes will enable much more detailed studies in the future about immune responses, which so far have been performed using expression analysis in mixed cell populations. This paper will give scientists a tool to understand differential cell response upon an injury or pathogen infection. The subdivision into nine hemocyte populations is carefully done using several sets of markers and the conclusions are on the whole well supported by the data.

Weaknesses:

One obvious drawback of the paper is first the low number of UMIs. A total number of 2704 cells gave a median UMI as low as 718 which is very low. Especially shrimp no. 2 has an average far below 500 and should perhaps be omitted. Therefore, one question is about cell viability prior to the drop-seq analysis. The fact of this low number of UMIs should be discussed more thoroughly.

Details about how quality control (QC) was performed would be needed, for example the cutoff values for number of UMI per cell, and also one important information showing the quality is the proportion of mitochondrial genes. The clustering into nine subpopulations seems solid, however the determination of lineages based upon the pseudo time analysis with cell-cycle related genes is not that strong. The authors identify four lineages, all starting from hem1 via hem2-Hem3- Hem4 and then one to Hem5, another through part of Hem 6 to Hem 7, next through part of Hem 6 to Hem 8 and finally through part of Hem 6 to Hem 9. Referring to Figure 3 - supplement 3, it seems as if Hem6 could be subdivided into two clusters, one visible in B and C, while another part of Hem & is added in D. Also, the data in figure 3 - supplement 1 showing expression of cell cycle markers do not convincingly show the lineages. Cluster Hem 3 and 4 seems to express much fewer and lower amount of these markers compared to cluster Hem6 - Hem9.

It is also clear (from figure 5 - supplement 1) that there are more than one TGase gene and the authors would need to discuss that fact related to differentiation.

While the part to determine subpopulations is very strong, the part about FACS analysis and qRT-PCR is weaker than the other sections, and doesn't add so much information. Validation of marker genes and the relationship between clusters and morphology shown in figure 6 is not totally convincing. It seems clear that both R1 and R2 contains a mixture of different cell types even if TGase expression is a bit higher in R1. A better way to confirm the results could be to do in situ hybridization (or antibody staining) and show the cell morphology of some selected marker proteins in a mixed hemocyte population. FACS sorting is very crude and does not really separate the shrimp hemocytes in clear groups based on granularity and size. This may be because the size of hemocytes without granules vary a lot. You need cell surface markers to do a good sorting by FACS. Another minor issue is the discussion about KPI. There are a huge number of Kazal-type proteinase inhibitors in crustaceans and it is not clear from this data if the authors discuss a specific KPI-gene, and there is a mistake in referring to reference 65 which is about a Kunitz-type inhibitor.

In summary, this paper is a very important contribution to crustacean immunology, and although a bit weak in lineage determination it will be of extremely high value.

Reviewer #2 (Public Review):

The goal of this study is to devise a means of promoting adult mouse auditory sensory cell development from supporting cells (SCs), as occurs naturally in birds and fish following sensory cell death. Previous studies indicated that activating Atoh1, an early acting transcription factor that specifies sensory cell fate during embryogenesis, was not sufficient for such regeneration. The authors hypothesized that adding a second transcription factor, Ikzf2, which maintains outer hair cell (OHC) fate, would synergize with Atoh1 and push adult SCs to differentiate as OHCs. They tested this hypothesis by over-expressing both Atoh1 and Ikzf2 in supporting cells after killing the endogenous OHCs in adult cochleae. The authors showed that the induced cells first express the general HC marker, Myo6, and only later become Prestin-positive, much as occurs during normal development. Unfortunately, these induced OHC-like cells had abnormal stereocilia and did not restore auditory (ABR) thresholds. Moreover, there was a loss of IHCs (the primary auditory receptors) suggesting that much more is needed to induce a real OHC and to protect IHCs than simply inducing the two selected transcription factors. Single-cell RNAseq (scRNA-seq) results showed that the induced OHC-like cells are enriched for HC genes and depleted for SC genes, but overall are most similar to neonatal HCs as defined in published scRNA-seq data from other groups. Overall, the scRNA-seq data did not offer a clear path forward, other than to identify and test additional transcription factors that might push the induced cells to the next stage. Nevertheless, the extent of SC transformation is impressive and has not been seen in previous approaches. This is an important contribution to our understanding of the control of OHC gene expression and differentiation contributed by two important transcription factors.

Reviewer #2 (Public Review):

The paper revisits the question of ligand discrimination ability of TCRs of T cells. The authors find that the commonly held notion of very sharp discrimination between strongly and weakly binding peptides does not hold when the affinities of the weak peptides are re-measured more accurately, using their own new method of calibration of SPR measurements. They are able to phenomenologically fit their results with a ~2 step Kinetic Proofreading model.

It is a very carefully researched and thorough paper. The conclusions seem to be supported by the data and fundamental for our understanding of the T cell immune response with potentially very high impact in many scientific and applied fields. The calibration method could be of potential use in other cases where low affinities are an issue.

As a non-expert in the details of experimental technique, it is somewhat difficult to understand in detail the Ab calibration of the SPR curve - which is a central piece of the paper. The main question is - what are the grounds (theoretical and/or empirical) to expect that the B_max of the TCR dose response curve will continue to be proportional to the plateau level of the Ab. Figure 1D does suggest that, but it would be hard to predict what proportionality shape the curve will take for lower affinity peptides. Given that essentially all the paper claims rest on this assumption, this should explained/reasoned/supported more clearly.

On the theoretical side - I think the scaling alpha\simeq 2 in Figure 2 is indeed consistent with a two-step KPR amplification. However, there are some questions regarding the fitting of the full model to the P_15 of the CD69 response. As explained in the Supplementary Material the authors use 3 global and 2 local parameters resulting in 37 (or 27) parameters for 32 data points. To a naive reader this might look excessive and prone to overfitting. On the other hand, looking at Figure S8 shows the value ranges of lambda and k_p are quite tight. This is in contrast to gamma and dellta that look completely unconstrained.

Finally, one of the stated advantages of the adaptive proof-reading model is that it is capable of explaining antagonism. It is hard to see how a 'vanilla" KPR model is capable of explaining antagonism.

Reviewer #2 (Public Review):

There is now a considerable body of knowledge about the genetic and cellular mechanisms driving the growth, morphogenesis and differentiation of organs in experimental organisms such as mouse and zebrafish. However, much less is known about the corresponding processes in developing human organ systems. One powerful strategy to achieve this important goal is to use organoids derived from self-renewing, bona fide progenitor cells present in the fetal organ. The Rawlins' lab has pioneered the long-term culture of organoids derived from multipotent epithelial progenitors located in the distal tips of the early human lung. They have shown that clonal cell "lines" can be derived from the organoids and that they capable of not only long-term self-renewal but also limited differentiation in vitro or after grafting under the kidney capsule of mice. Here, they now report a strategy to efficiently test the function of genes in the embryonic human lung, regardless of whether the genes are actively transcribed in the progenitor cells. The strengths of the paper are that the authors describe a number of different protocols (work-flows), based on Crisper/Cas9 and homology directed repair, for making fluorescent reporter alleles (suitable for cell selection) and for inducible over-expression or knockout of specific genes. The so-called "Easytag" protocols and results are carefully described, with controls. The work will be of significant interest to scientists using organoids as models of many human organ systems, not just the lung. The weaknesses are that they authors do not show that their lines can undergo differentiation after genetic manipulation, and therefore do not provide proof of principle that they can determine the function in human lung development of genes known to control mouse lung epithelial differentiation. It would also be of general interest to know whether their methods based on homologous recombination are more accurate (fewer incorrect targeting events or off target effects) than methods recently described for organoid gene targeting using non homologous repair.

Reviewer #2 (Public Review):

In this manuscript, Moncla et al. undertake a large sequencing and phylogenetic study to investigate the underlying epidemiology of the 2016-2017 Washington State Mumps epidemic. The authors generate 110 sequences and include 166 novel sequences in their analysis. This data set represents over a quarter of the publicly available Mumps genomes from North America.

They then apply a mixture of phylogenetic methods and intuitive data analyses to uncover, that i) Mumps was imported into Washington at least 13 times. ii) A disproportionate amount of transmission occurred in the Marshallese community in WA with limited transmission in the non-Marshallese community. iii) These heterologous transmission dynamics might be explained by historical and current health disparities within the community, but are not due to low vaccination coverage.

These conclusions are supported by a wide array of carefully controlled phylogenetic methods. The authors explore the sensitivity of their findings to sampling bias. Additionally, the conclusion that transmission occurred disproportionally within the Marshallese community is supported by multiple implementations of the structured coalescent as well as, more coarse but intuitive methods such as the rarefaction analysis and the "descendent" analysis in Figure 4. The "descendent" analysis complements the structured coalescent models and highlights how tips that are close to internal nodes inform the "state" of those unsampled ancestors. Each internal node represents an unsampled ancestor, and if transmission rates are higher in one population, then samples from that population are more likely to be close to those ancestors. The approach captures these processes; however, calling downstream tips "descendants" is unfortunate, as it is unknown if the tips that have "descendants" are direct ancestors of their "descendants" in the transmission chain. Inferring transmission dynamics from divergence trees is difficult, and variants of this approach are likely to be useful in other systems.

The finding that transmission disproportionally occurred in the Marshallese community leads the authors to propose several possibilities for why this may be. The authors should be commended for reaching out to Marshallese health advocates in this process and including the community in their study. This context is a major strength of the study.

Both the data generation and data analysis are achievements that advance our understanding of the epidemiology of Mumps. As can be seen in the tree in Figure 1 the 2016-2017 epidemic in North America was seeded by at least two divergent lineages that appear to have all contributed to the same outbreak. The large number of sequences contributed by this study will help future work uncover the dynamics that drive Mumps epidemics at larger scales. The findings also highlight how large outbreaks can persist in highly vaccinated populations and how an array of phylogenetic approaches can be employed to uncover the underlying population heterogeneity behind an outbreak. To have both of these achievements in the same manuscript sets this work apart.

Reviewer #2 (Public Review):

In this incredibly detailed effort, Hulse, Haberkern, Franconville, Turner-Evans, and coauthors painstakingly and patiently reveal the connectivity of central complex neurons within one "hemibrain" EM-imaged connectome of a fruit fly. This is best read as one of a series of such detailed papers including Scheffer et al., 2020 (which introduces the dataset) and Li et al., 2020 (which focuses on the mushroom body).

The authors achieve two major goals. First, they present a full account of all neurons (by type) present in the central complex and the connections between them (including to and from regions outside the central complex). By necessity, this work only examines such connections within a single animal from whose brain the hemibrain volume was imaged. Nonetheless, the relatively conserved morphology of fly neurons (at the scale of which regions they form arbors within) allows the authors to confidently relate their neurons to known examples from genetically labeled lines imaged at the light level. (And in some cases, they are able to show that some neurons with similar morphology can then be further subdivided into different types on the basis of their connectivity). Importantly, the hemibrain dataset contains both sides of the central complex, allowing for a complete analysis.

Secondly, the authors contextualize the observed connectivity patterns within the known functions of the central complex (particularly navigation and sleep/arousal). Appropriately and importantly, they offer detailed explanations for how the circuitry observed can support these functions. In some cases, particularly in their discussion of the fan body, they show how the connectivity patterns can support multiple variations of models of path integration (and more broadly how its architecture supports vector computation in general). These analyses make their central complex connectome a useful map - there is little doubt that it will inspire many future experiments in the fly community.

The only limitations of this work are rooted in the nature of the source material: it's only one animal's brain and because it's EM-based there's often no way to know whether a given cell type (if new) is even excitatory or inhibitory (though, notably, the authors take care to note where this is the case and to offer alternate interpretations of the circuit function). Synaptic strength is another relative unknown (not to mention plasticity rules or modulatory influences). For EM-based connectomes, the number of synapses made between two neurons is considered the basis for determining whether or not they are meaningfully connected. However, this precise number can vary as a function of how complete the reconstructions are (generally, as proofreading progresses, more synapses are found). This work improves on prior hemibrain studies by carefully demonstrating that it is possible to set a threshold on the relative fraction of synaptic contributions within a region in order to identify meaningful connections. (That is, they find that as the number of synapses discovered increases, the relative contribution remains relatively constant).

This is a massive work. There are 75 figures, not including supplements, and numerous region and neuron names to keep track of (not to mention visualize). It is impossible to read in a single sitting. So for the purposes of this public review, I highly recommend to any reader that they first find the region of the paper they're interested in and skip to that to view in side-by-side mode. The "generally interested" reader is best served by reading through the Discussion, which has more of the structure-function analyses in it and then referring to the Results as their curiosity warrants.

Scheffer et al., 2020 is available here: https://elifesciences.org/articles/57443#content Li et al., 2020 is available here: https://elifesciences.org/articles/62576#content

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.