On 2020-01-30 17:28:42, user Ayush Arpit Garg wrote:

Hi, this is really impressive work and a great follow up article from your previous Nature Materials article. Esp the aspects of the spatio-temporal control using this novel bioreactor address the critical need to modernize the electrotaxis chambers for better control and enhance our overall understanding of electrotaxis. I had a few follow up questions:

1) One of the main findings of this article was the ability of cells to rapidly sense electric fields. As a matter of fact, the time scales suggested are on the order of less than 10 sec. In this study, the EF strength was approximately 2 V/cm, do you think there is any correlation between field strength (or more accurately current density) time scales of cells to sense such fields.

2) Following up to the previous question, is there a minimum threshold of field (or current density) above which the cells can sense and respond to these external fields. The literature points out that for electrotaxis, field strength should vary between 0.5 V/cm to 10 V/cm. Can you speculate, if the cells respond to fields weaker than this threshold value.

3) In your previous article, you had shown that the leader cells have different responses to EFs compared to the cells in the bulk. The speculation was that the leader cells are indeed different as they do not have the same cell-cell junctions like the cells in the bulk. Is there any evidence suggesting that the time scales of their response might be different from cells in bulk?

4) Have you guys looked at F-actin and/or FAK changes at different time points. Is there any evidence suggesting that cells gradually time-average the net electric fields other than their ability to migrate in the direction of applied electric fields. Moreover, the force on the cell membrane components due to these external fields is what causes cell polarization leading to directed migration, so does it not in turn mean that the net force of these external fields is indeed responsible for migration?

5) This is my final question, so the final idea is to be able to control and direct cell migration for real world application. What is the long term vision in terms of clinical translation of this technology? Moreover, most cells respond to such electrotactic/galvanotactic cues, therefore, when they are applied to a complex tissue with different cell types, depending on their nature they can decide to move towards the positive or the negative electrode, have you guys though of any solutions on how to target migration of specific cells in a complex tissue.

Overall, this is a really great article with novel methodology and some very intriguing results.

Thanks!

On 2020-03-27 11:53:58, user Rui F. Oliveira wrote:

Dear Alex and co-authors,<br /> Congratulations on a very interesting study.<br /> We have recently discussed your preprint on our lab journal club and there were two issues that we would like to have your views on:<br /> 1. According to the description of the selection procedure [l.97-100: "Twenty-six females from the four top-ranked groups in each line were then paired with unsorted males to breed the next generation of polarization-selected fish. To establish control lines (n = 3), we took 26 randomly selected females from the remaining groups, and bred from those fish], it looks like your control lines do not represent either random-mating or average population lines but rather anti-polarisation selected lines, since your are removing the high polarisation females from them. If this is the case, they do not represent a traditional control line, but rather another divergent line;<br /> 2. The concern expressed above is apparently confirmed in the Fig S1 where it can be seen that the significant result in replicate 3 is due to a decrease in polarisation of the "control" line rather than to an increase in the polarisation line (i.e. in replicate 3 polarisation in F0 seems similar to F3_P and higher than F3_C), and that in the other two replicates the effect may result from both increases in the polarisation lines (i.e. F3_P > F0) and decreases in the control lines (i.e. F3_C < F0). To make this point more clear maybe it would help to statistically compare differences in polarization between F3 and F0 in polarization and control lines for each replicate.<br /> With compliments on behalf of the Oliveira Lab, <br /> Rui Oliveira <br /> (Integrative Behavioural Biology Group, Gulbenkian Institute of Science, Portugal)

On 2021-05-20 22:19:49, user ????????DB???? wrote:

This manuscript was peer reviewed and published in Biomedicines. <br /> https://doi.org/10.3390/bio...

On 2021-10-14 15:16:12, user Claire Dessalles wrote:

This article has been accepted at the journal Biofabrication, and can be<br /> cited with its new DOI. <br /> https://iopscience.iop.org/...

On 2016-02-01 15:58:33, user Sabine Fillinger wrote:

Hi Bart, interesting work! I wonder if you may correlate some events also with fungicide sensitivity/resistance. Recent work in Monilinia showed that fungicides may induce transposon movement (doi:10.1016/j.fgb.2015.10.006).

We have indications that a retrotransposition event lead to fungicide resistance in Zymoseptoria.

Maybe you should try some screening? Regards, Sabine

On 2022-05-20 03:55:55, user Jake Gratten wrote:

Response to Morton et al. (2022): model mis-specification criticism overlooks sensitivity analyses and orthogonal analyses

The core criticism of our study (Yap et al., 2021) made by Morton et al. was that the linear mixed model (LMM) framework we employed includes a questionable biological assumption – that diet and the microbiome are independent. They correctly note that diet is known to influence the microbiome (David et al., 2014; Rothschild et al., 2018), and thus, as these factors are inter-related, our model may be prone to biased inference. We acknowledge these points in relation to the specific LMM (see below) on which the critique by Morton et al. is focused. However, we respectfully disagree with their conclusion that this issue invalidates the findings reported in our paper, because their critique (1) incorrectly asserts that this result formed the basis of our conclusions, and (2) it overlooks several key analyses, including extensive sensitivity analyses that were specifically performed to test this (and other) assumptions.

Morton and colleagues focus on a single LMM analysis of ASD in their critique, in which we adjusted for sex, age and diet, the latter by fitting the top three principal components from PCA of the centre log ratio (clr)-transformed percent energy variables from the Australian Eating Survey (AES), a validated food frequency questionnaire. In this analysis, we found that 0% of the variance in ASD diagnosis was associated with the microbiome, irrespective of the microbiome features used to construct the correlation matrix describing the relationships between random effects (e.g., common species, rare species, common genes, rare genes) (Yap et al., 2021). As diet is correlated with the microbiome, it is possible that adjusting for diet in this analysis has removed variance in ASD diagnosis that may be attributable to the microbiome. In their critique, the authors present simulations purporting to show that this issue could lead to failure to detect even very large proportions of variance (in their example 83%) (Morton, Donovan, & Taroncher-Oldenburg, 2022).

Unfortunately, they fail to mention that we also performed a LMM analysis of ASD in which we did not adjust for diet (or sex or age). If there was an effect of the microbiome on ASD that had previously been removed by adjusting for diet, then this should now be “revealed” (i.e., captured by the microbiome random effect). However, we found precisely the same result as in our original analysis: that is, 0% of the variance in ASD diagnosis is associated with the microbiome (Yap et al., 2021). Based upon this analysis of the available data we believe it is unlikely that our conclusions have been biased by model mis-specification.

The authors also do not acknowledge that we performed LMM analyses of traits other than ASD, and whereas there was negligible signal for ASD, IQ and sleep problems, we found large and significant associations of the microbiome with age, sex and stool consistency. Our results for age (i.e., ~30% of the variance associated with common microbiome species) are particularly notable because they recapitulate the findings reported in a large (independent) sample of >30K adult stool metagenomes (Rothschild et al., 2020). Our LMM results for age, sex and stool consistency were also largely unaffected by adjusting for diet (Yap et al., 2021). These analyses, which were specifically included for the purpose of benchmarking the findings for ASD, provide further evidence that our methods are not prone to under-estimating the proportion of trait variance associated with the microbiome.

It is also relevant to highlight that the directionality of the causal graphs presented by Morton et al. in Figure 1 of their article (i.e., a causal effect of both the microbiome and diet on the host phenotype) are problematic, since the variance component estimates from these models might reflect cause or consequence of the focal trait. This is because microbiome taxonomic proportions change, unlike genotypes used in analogous LMM methods for estimating heritability (which are present at birth and therefore representative of causality). To demonstrate this, take as an example our analysis in which age was the dependent variable and microbiome measures were fitted as random effects (allowing capture of their interdependence). We find roughly 30% of the variance in age is associated with common microbiome species. Clearly, the way to interpret this result is that age is causal for the variance in the microbiome, not the other way round. It is equally possible that ASD influences diet and in turn the microbiome, as opposed to the opposite view espoused by Morton et al. Indeed, the wording used in their critique (i.e., “A more accurate model would have assumed an architecture that explicitly incorporates the direct influence of diet on the ASD phenotype as well as an indirect influence of diet on the ASD phenotype via the microbiome”) appears not to recognise this possibility.

Looking beyond the LMM analyses in our paper, Morton and colleagues also did not consider several other key sets of analyses on which are conclusions are based, including differential abundance testing using ANCOM (Analysis of Composition of Microbiomes) and extensive linear model analyses. In our ANCOM analysis of ASD, we find a single robustly associated species (Romboutsia timonensis) when adjusting for sex, age and dietary PCs, but this same species remains the only significant finding in analyses without covariates (Yap et al., 2021). This is entirely consistent with our LMM model findings but is not what would be expected if the microbiome was associated with a high proportion of variance in ASD diagnosis. Indeed, irrespective of how the data are analysed (e.g., sibling pairs only, excluding siblings, excluding children with recent exposure to antibiotics, and others), we find negligible evidence for association of individual species with ASD (other than R. timonensis), and no support whatsoever for taxa previously reported to be associated with ASD.

In our linear model analyses, we show that quantitative measures of the autism spectrum, including both psychometric measures (e.g., ADOS-2/G Restricted and Repetitive Behaviour (RRB) calibrated severity scores) and polygenic scores were associated with reduced dietary diversity (Yap et al., 2021). The most parsimonious interpretation of these findings is that RRBs, which are one of the core diagnostic signs of ASD, manifest in the form of more selective dietary preferences. Polygenic scores, as an immutable component of propensity to ASD-associated traits, are an important and novel aspect of our analysis, given they facilitate preliminary causal inference (noting that we were careful to avoid strong statements about causality in our paper). In contrast, other cross-sectional autism microbiome studies – whose results have been prioritised by Morton et al. – have not exploited genetic predictors for autism-related traits and so cannot distinguish between cause and consequence.

Overall, using a variety of orthogonal analytical approaches, we find a strong and consistent signal that ASD (and autistic traits) is associated with reduced dietary diversity, and that diet in turn is associated with the microbiome (Yap et al., 2021). These results are consistent with existing evidence for dietary effects on the microbiome (David et al., 2014; Rothschild et al., 2018) – as pointed out by Morton et al. – and with prior evidence (backed by clinical and lived experience) for an association of autism with diet (Berding & Donovan, 2018). We find no direct association of ASD with the microbiome, a result to which Morton and colleagues express surprise, their argument being that if ASD is associated with diet and diet influences the microbiome, then how can there be no direct ASD-microbiome association? The answer is simply that we have a finite sample, and the effect sizes are subtle. We expect that in a larger sample we might observe a direct association, but also stronger evidence that this is due to changes in diet that are related to autistic traits. This is a considerably more intuitive and parsimonious explanation for associations of the microbiome with ASD than the idea that the microbiome contributes to autistic traits, not least because there is strong evidence that ASD is a neuro-developmental condition, and expression of established ASD genes is enriched prenatally (Satterstrom et al., 2020). In this context, it is worth emphasising that the high estimated heritability of ASD (70-80%) (Bai et al., 2019) leaves relatively little room for other putative etiological causal factors (e.g., maternal immune activation). This is especially true given de novo mutations that are known to be important in ASD (Sanders et al., 2015; Sanders et al., 2012; Satterstrom et al., 2020) largely do not contribute to heritability estimates (i.e., because they are not shared by relatives) and so must consume an additional proportion of the remaining 20-30% of variance.

Morton et al.’s criticism of our study comes despite it being the largest (and therefore most statistically well-powered) to date. Our study also has the dual benefits of matching data on diet and other confounders, which are lacking in many prior studies, and deep metagenomic sequencing, compared to inferior 16S technology in most published ASD microbiome papers. We note that ours is not the first study to report negligible association of the microbiome with ASD (Gondalia et al., 2012; Son et al., 2015). We also point to a recent review in Cell on microbiome studies in animal models (including for autism) highlighting the implausibility of the high proportion of positive findings, asserting that the field suffers from publication bias (Walter, Armet, Finlay, & Shanahan, 2020). That said, we acknowledge that our study has limitations, reflecting difficulties of collecting idealised data sets. Prospective studies collecting faecal samples from infants prior to autism diagnosis are needed to further advance the field, but these are challenging both logistically and because sample size is limited by the population prevalence of ASD (~1%).

To sum up, we thank Morton et al. for their comments in relation to one specific analysis in our paper. This provides us with the opportunity to clarify the detailed analyses that we performed to reach our conclusions. Unfortunately, the critique from Morton et al. (based solely on simulations) overlooks most of our results, including sensitivity analyses that directly address their criticism. The authors suggest that our data should be re-analysed. We note that our data are available by application to the Australian Autism Biobank which allows other researchers to provide objective empirical evaluation. We are committed to transparent research and provide extensive supplementary materials and publicly available code and hope others in the research community will build upon our work.

Chloe X. Yap, Peter M. Visscher, Naomi R. Wray and Jacob Gratten <br /> (On behalf of all authors)

References<br /> Bai, D., Yip, B. H. K., Windham, G. C., Sourander, A., Francis, R., Yoffe, R., . . . Sandin, S. (2019). Association of Genetic and Environmental Factors With Autism in a 5-Country Cohort. JAMA Psychiatry, 76(10), 1035-1043. doi:10.1001/jamapsychiatry.2019.1411

Berding, K., & Donovan, S. M. (2018). Diet Can Impact Microbiota Composition in Children With Autism Spectrum Disorder. Front Neurosci, 12, 515. doi:10.3389/fnins.2018.00515

David, L. A., Maurice, C. F., Carmody, R. N., Gootenberg, D. B., Button, J. E., Wolfe, B. E., . . . Turnbaugh, P. J. (2014). Diet rapidly and reproducibly alters the human gut microbiome. Nature, 505(7484), 559-563. doi:10.1038/nature12820

Gondalia, S. V., Palombo, E. A., Knowles, S. R., Cox, S. B., Meyer, D., & Austin, D. W. (2012). Molecular characterisation of gastrointestinal microbiota of children with autism (with and without gastrointestinal dysfunction) and their neurotypical siblings. Autism Res, 5(6), 419-427. doi:10.1002/aur.1253

Morton, J. T., Donovan, S. M., & Taroncher-Oldenburg, G. (2022). Decoupling diet from microbiome dynamics results in model mis-specification that implicitly annuls potential associations between the microbiome and disease phenotypes—ruling out any role of the microbiome in autism (Yap et al. 2021) likely a premature conclusion. biorxiv. doi:https://doi.org/10.1101/202...

Rothschild, D., Leviatan, S., Hanemann, A., Cohen, Y., Weissbrod, O., & Segal, E. (2020). An atlas of robust microbiome associations with phenotypic traits based on large-scale cohorts from two continents. biorxiv. doi:https://doi.org/10.1101/202...

Rothschild, D., Weissbrod, O., Barkan, E., Kurilshikov, A., Korem, T., Zeevi, D., . . . Segal, E. (2018). Environment dominates over host genetics in shaping human gut microbiota. Nature, 555(7695), 210-215. doi:10.1038/nature25973

Sanders, S. J., He, X., Willsey, A. J., Ercan-Sencicek, A. G., Samocha, K. E., Cicek, A. E., . . . State, M. W. (2015). Insights into Autism Spectrum Disorder Genomic Architecture and Biology from 71 Risk Loci. Neuron, 87(6), 1215-1233. doi:10.1016/j.neuron.2015.09.016

Sanders, S. J., Murtha, M. T., Gupta, A. R., Murdoch, J. D., Raubeson, M. J., Willsey, A. J., . . . State, M. W. (2012). De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature, 485(7397), 237-241. doi:10.1038/nature10945

Satterstrom, F. K., Kosmicki, J. A., Wang, J., Breen, M. S., De Rubeis, S., An, J. Y., . . . Buxbaum, J. D. (2020). Large-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism. Cell, 180(3), 568-584 e523. doi:10.1016/j.cell.2019.12.036

Son, J. S., Zheng, L. J., Rowehl, L. M., Tian, X., Zhang, Y., Zhu, W., . . . Li, E. (2015). Comparison of Fecal Microbiota in Children with Autism Spectrum Disorders and Neurotypical Siblings in the Simons Simplex Collection. PLoS ONE, 10(10), e0137725. doi:10.1371/journal.pone.0137725

Walter, J., Armet, A. M., Finlay, B. B., & Shanahan, F. (2020). Establishing or Exaggerating Causality for the Gut Microbiome: Lessons from Human Microbiota-Associated Rodents. Cell, 180(2), 221-232. doi:10.1016/j.cell.2019.12.025

Yap, C. X., Henders, A. K., Alvares, G. A., Wood, D. L. A., Krause, L., Tyson, G. W., . . . Gratten, J. (2021). Autism-related dietary preferences mediate autism-gut microbiome associations. Cell, 184(24), 5916-5931 e5917. doi:10.1016/j.cell.2021.10.015

On 2023-04-04 15:44:16, user Niladri Mondal wrote:

published version on<br /> https://doi.org/10.1007/s12...

On 2023-04-12 22:42:10, user Peter Frost wrote:

I am puzzled by the differences between your conclusions and those of Hawks et al. (2007):

• You concluded that the rate of human genetic evolution accelerated between ~280,000 years ago and ~1,700 years ago, with a peak acceleration at ~55,000 years ago.

• Hawks et al. (2007) concluded that the rate of human genetic evolution accelerated ~10,000 years ago and that this higher rate persisted into the time of recorded history.

As I see it, the main difference between the two studies is in the data sources. You used the Human Genome Dating database, and Hawks et al. (2007) used the HapMap SNP dataset.

Are there other methodological differences that might explain the different conclusions of these two papers?

Reference

Hawks, J., E.T. Wang, G.M. Cochran, H.C. Harpending, and R.K. Moyzis. (2007). Recent acceleration of human adaptive evolution. Proceedings of the National Academy of Sciences (USA) 104: 20753-20758. https://doi.org/10.1073/pna...

On 2020-07-02 21:34:04, user Jorrit Posthuma de Boer wrote:

The number of 2020 blood donors (55 in Table S1) does not correspond to the number of blood donors in Fig 4G (31), this means 24 donors were not incorporated. The paper does not mention a reason for this. For the exposed relatives 2 out of Table S1 or not incorporated in Fig 4G, for mild convalescent 9 and for severe convalescent 3.

A question not posed or answered by the authors is why the discrepancy between the antibody and memory T-cell response appears to increase from mild convalescent, exposed family members, to 2020 blood donors (Fig. 4G). Without a sound explanation any calculation with regards to the population immunity seems inappropriate.

The blood donors in this study donated their blood at Karolinska University Hospital in Stockholm in May 2020. At the end of April 2020 the seroprevalence in Stockholm was 7.3%. This study found a seroprevalence of 13% (4/31) as indicated in fig. 4G. However when applied to the number of donors as reported in table S1 this would have been 7.2% (4/55).

Overall (mild convalescent, exposed family members, and 2020 blood donors), the population-level immunity would have been a factor 1.35 higher, when based on SARS-CoV-2 specific memory T-cell responses (65/90), as compared to anti-body responses.

The authors claim to have found an unanticipated degree of population-level immunity against COVID-19, currently has no support, 1.35x instead of 2x more as compared to the current seroprevalence seems more inline with the presented results.

On 2019-11-21 14:42:49, user ganesha rai wrote:

Authors used a questionable LDHA inhibitor FX11 for their study. I do not see any rationale using FX11 when there are other more advanced inhibitors available. FX11 is a pan inhibitor that hits many other targets and the data generated using this small molecule is always questionable

On 2020-12-14 23:38:15, user Michael Eisen wrote:

I am skeptical of these claims, and surprised by the timing of this paper's release.

This is obviously a topic of great interest and extreme import, given the release of this preprint right as an RNA based COVID vaccine is entering wide distribution and another is on the way. The authors have raised the possibility that host retrotransposes have to potential to integrate viral RNAs into the host genome (an unsurprising result) but have not demonstrated that this occurs with appreciable frequency in infected individuals or that it has any clinical relevance.

The idea that endogenous RTs could integrate viral RNAs into the host genome is not surprising. Indeed it is seems almost certain that it happens at at least low frequency given the presence of intronless paralogs and pseudogenes. So the experimental observations that it can happen under ectopic conditions isn't of significant clinical relevance.

The real question is whether this is a rare curiosity, or if it is sufficiently common to warrant public health concern. And here the only evidence presented is the presence of chimeric (human:COVID) reads in a handful of infected cell lines and a couple of clinical samples. The problem with this observation, as the authors allude to but only passingly address, is that chimeric reads are a well-known artifact in RNA sequencing data. And several of the observations - the increased frequency of such reads with increasing viral loads, and the bias of such reads towards the most abundant viral RNAs - are exactly what you'd expect if they were artifacts. There are a number of things the authors could do to rule this possibility out and make a more compelling case, but none of them were presented here.

Given the absence of any other data to support the clinical relevance of this observation, all of the speculation about how this might impact testing, vaccination, drug screening etc.. and how this might be an adaptive strategy to store viral antigens to guard against future infections is pure speculation and should be treated as such by anyone with interest in the topic.

It is unfortunate that a paper is making highly speculative yet frightening claims of COVID integration into the human genome was released right as an RNA vaccine is being introduced to the population, and amidst well-known, and widespread, opposition to vaccination. Obviously, if the authors feel they possess evidence of clinical relevance, it is their duty to release it as expediently as possible. However, the paper makes it clear that even the authors agree they have not proven their case. Given that this is one of those rare circumstance where a scientific result has the potential to immediately impact public behavior in a way that undermines critical public health measures, I think both more thorough experiments and analyses, and more caution, were warranted.

On 2017-01-19 01:48:44, user darachm wrote:

Can you fix on scope? Does dCas9 fluor not tolerate pfa?

Would be interesting, even if not necessary in discussions about fix vs live imging, crosslink seq assays vs microscopic localization.

On 2021-07-29 20:22:05, user umueller wrote:

Box 1, left column, last paragraph: S is the selection differential, not the selection coefficient

On 2025-10-23 13:31:57, user Eugene F. Baulin wrote:

Published in Scientific Reports: https://doi.org/10.1038/s41598-025-21072-9

On 2017-06-06 08:35:46, user Arne Babenhauserheide wrote:

Firstoff: The underlying problem which makes it so hard to differenciate between honest errors and fraud is that publications are kind of a currency in science. It is not possible to make them serve a dual function — not only scientific communication but also the main currency to get a job in science — without also getting Fraud. If you want a short quotation for that, you can take Goodheart's law:

»When a measure becomes a target, it ceases to be a good measure.«

We cannot reach the best possible level of scientific communication while publications are part of the currency of science. And there is no metric which can fix this.

That said, I’m happy to see you take up changes to scientific articles! It ties into concepts I wrote a two years ago with concepts for propagating corrections: http://www.draketo.de/engli... (this is a section in a larger article about information challenges for scientific publishing)

Note however that if you have living documents and only the latest version of the document is treated as authoritative, then scientific information propagation becomes orders of magnitude more expensive. There must be a clear distinction between changes which invalidate anything others might have built upon and changes which keep all the citable information the same. As I showed in the article I linked to, there are technical measures which could reduce the cost of propagating corrections. If you make corrections easier, then these measures will become essential.

Guilt should not be the problem (and should not be part of making a change). The actual problem is that a change to a published paper incurs a cost on everyone who cited it.

Keep in mind that when you change an article, you need to inform everyone who cited it.

Journals could reduce this cost on authors by checking where the article was cited and whether the change is relevant to the reliability of the citing article. If it is, then the author of the citing article must take action. With highly cited articles, a single amendment could require hundreds of scientists to take action and amend their articles as well, if it affected the core message of the article, this could cause ripples of ever more articles to amend. There are two core ways to minimize this: Amend quickly, while the article has few citations, and ensure high quality and consequently a low rate of invalidating changes for published articles.

In the article I posted,¹ I suggested using microformats to mark amendments. Their important attribute is that they can be parsed automatically, that anyone with access to the source of a publication can automate checking for the region in which a given reference was used, and that they are not tied to any given platform. Any other method which has these properties works as well.

Keep in mind that while anyone can search through the updates, someone must do it. To make the system reliable that someone will have to be paid.

¹: Information Challenges for Scientific Publishing, section 2.3.3: Propagating corrections: http://www.draketo.de/engli...

On 2019-01-11 08:21:26, user Kisun Pokharel wrote:

Published https://www.frontiersin.org...

On 2024-12-18 17:12:24, user xPeer wrote:

Courtesy review from xPeerd.com

Summary<br /> This manuscript investigates oxaliplatin resistance in colorectal cancer (CRC), identifying the SERPINE1-based RESIST-M gene signature as a predictive marker for pro-metastatic CMS4/iCMS3-fibrotic CRC subtypes. Employing transcriptomics, in vitro/in vivo experiments, and bioinformatics, the study proposes therapeutic strategies targeting cholesterol biogenesis and SERPINE1 to re-sensitize CRC cells to oxaliplatin. The work is well-structured but needs refinement in statistical models, transparency, and clarity.

Major Revisions<br /> 1. Statistical Models and Reproducibility<br /> Page 6, Lines 95–120: Statistical details for in vivo studies (e.g., metastatic score calculation) are insufficient. Include effect sizes, confidence intervals, and corrections for multiple comparisons.<br /> Recommendation: Present Kaplan-Meier survival curves with hazard ratios (HR) and p-values for different gene signatures (e.g., RESIST-M) in relevant datasets (PETACC-3, TCGA).<br /> Page 11, Line 210: The statistical pipeline for GSEA and pseudotime analyses lacks critical thresholds. Specify adjusted p-values (e.g., FDR-corrected) for hallmark pathways.<br /> 2. Validation of the RESIST-M Signature<br /> Page 14, Lines 275–285: The study compares RESIST-M to other gene signatures but lacks comprehensive head-to-head validation using robust statistical tests.<br /> Recommendation: Provide ROC-AUC scores to quantify predictive accuracy across datasets. Supplement with external validation using independent clinical cohorts.<br /> 3. Mechanistic Insights<br /> Page 8, Lines 150–170: The link between cholesterol biosynthesis, lipid raft dynamics, and TGF-? signaling is compelling but speculative.<br /> Recommendation: Enhance mechanistic validation by including experiments showing cholesterol restoration effects on TGFBRII localization and signaling attenuation.<br /> Page 13, Line 245: Include co-immunoprecipitation or fluorescence resonance energy transfer (FRET) assays to demonstrate direct interactions between SERPINE1, SMAD2/3, and lipid raft components.<br /> 4. Ethical Concerns in In Vivo Studies<br /> Page 23, Lines 495–525: Randomization protocols and blinding measures are not adequately detailed.<br /> Recommendation: Ensure transparency by specifying whether investigators were blinded to treatment arms during tumor and metastasis scoring.<br /> 5. Clinical Utility of SERPINE1 Inhibition<br /> Page 10, Lines 180–200: The therapeutic viability of tiplaxtinin and simvastatin is discussed but lacks detailed pharmacokinetic or toxicity evaluations.<br /> Recommendation: Include dose-response curves and combinatorial therapy data to support clinical translation.<br /> Minor Revisions<br /> 1. Language and Formatting<br /> Page 3, Abstract: Simplify dense phrasing like "RESIST-M signature derived from our models showed that the models can mimic CMS-4/iCMS-fibrotic-like metastatic CRC patients."<br /> Ensure consistent nomenclature for gene/protein names (e.g., "SERPINE1" vs. "PAI-1").<br /> Improve figure legends with more descriptive captions (e.g., axes labels in Figures 4 and 5).<br /> 2. Figure Clarity<br /> Figures 1–6: Use consistent color schemes to distinguish CMS subtypes across datasets. Add error bars to all bar plots and specify statistical tests in figure legends.<br /> 3. Data Accessibility<br /> Page 27, Lines 595–605: Make raw and processed data from in-house RNA-seq experiments publicly available. Provide repository links and accession codes.<br /> AI-Generated Content Analysis<br /> Indicators:

Stylistic Repetition: Frequent repetition of phrases like "RESIST-M signature predicts poor prognosis" and "CMS4/iCMS3-fibrotic subtypes" suggests templated assembly.<br /> Simplistic Explanations: Complex mechanisms (e.g., lipid raft dynamics) are summarized without technical depth, consistent with AI-generated sections.<br /> Sentence Structure: Overuse of passive voice in mechanistic descriptions.<br /> Estimate: 10–15% AI-generated content, primarily in introductory and discussion sections.

Impact:

Minimal: Core scientific claims are data-driven and original.<br /> Recommendations:

Reassess and refine introductory sections to ensure technical accuracy and eliminate redundancy.<br /> Provide nuanced discussions of limitations in the final paragraphs.<br /> Recommendations<br /> Statistical Rigor: Refine statistical models, especially for pathway enrichment and survival analyses.<br /> Mechanistic Validation: Conduct additional experiments to confirm hypothesized pathways.<br /> Data Transparency: Enhance reproducibility by releasing data/code under FAIR principles.<br /> Therapeutic Context: Expand discussion on potential side effects and combinatorial strategies for proposed therapies.

On 2019-03-17 21:14:15, user Arie Horowitz wrote:

There is no access to the supplementary files. Please upload them. Thank you<br /> Arie Horowitz

On 2021-01-26 04:19:38, user Qi Luan Lim wrote:

Dear reader,

We have published a revised version of this article in the Malayan Nature Journal 71(4): 423-437 (2019). You can contact us for more information.

Q.L. Lim

On 2022-07-18 09:58:46, user Irilenia Nobeli wrote:

NOTE FROM THE AUTHORS:<br /> We are currently investigating the implications of counting reads across overlapping features in prokaryotic genomes that may affect the results and conclusions of this manuscript. If this turns out to be the case, we will be deleting this manuscript from bioRvix. If not, we will simply upload a newer version.<br /> Please watch this space for updates.<br /> I.N.

On 2017-10-10 13:26:05, user Wenyu Zhang wrote:

Hello, would it be possible for you to share the nanopore sequencing data for Patient1 and Patient2? Thanks!

On 2021-08-15 19:28:22, user Anand Ramachandran wrote:

HELLO has been significantly reworked with new methodologies for hybrid and stand-alone variant calling. These methodologies and results are published at:

https://bmcbioinformatics.b...

On 2019-06-04 19:24:12, user Pedro Luna wrote:

Nice study, I would suggest to measure nestedness and modularity using frequency based algorithms. Also It would interesting to remove alien species from the networks and then recalculate the network descriptors to actually see what is the effect of alien species in the network structure. As you present your work you are only comparing network a with network b, your data has huge potential to make more interesting insights.

On 2016-01-14 01:47:29, user Richard Edwards wrote:

What's the output when doing fast[aq] conversion? Does it go to STDOUT? (If so, would be useful to have an option to save as a file rather than having to redirect STDOUT to a file.)

On 2020-01-01 10:05:05, user Serguey Melnikov wrote:

Interesting paper! I wonder if you had a chance to look at our recent study on a similar topic where we showed that Lokiarchaeota have many segments in ribosomal proteins that are identical to nuclear localization signals in eukaryotic ribosomal proteins: https://www.biorxiv.org/con...

On 2017-07-17 08:00:16, user Vyacheslav L. Kalmykov wrote:

Solving the biodiversity paradox has raised a new question

Not absolutely obligatory competitive exclusion of the weaker competitor in nature solves the biodiversity paradox, but at the same time it raises the question: how does evolution occurs if the fittest wins far from always?

On 2022-03-03 11:36:10, user Victoria wrote:

Exciting story! Can it be that the bacteria are simply trapped in the vicinity of the antibiotics and cannot move away?

On 2020-01-20 08:06:33, user Mazher Farid Iqbal wrote:

Respected reviewers. Kindly review it and give your valuable suggestions please,

On 2020-05-13 01:31:52, user WasteOfTime wrote:

The authors make the claim that this work is the first application of such a strategy in human cells, a claim which is invalidated by the existence of DOI:10.1093/nar/gkv1542, which demonstrates proof-of-principle of a non-homologous end-joining-based replacement of genomic sequences using two guide RNAs in both HEK293 and human induced pluripotent stem cells. The authors also do not cite this previous work in their manuscript. As it stands, this manuscript is a nice replication study, but by no means a novel method.

On 2021-01-28 15:13:00, user Laura DVR wrote:

The supplemental figures are provided under "supplementary material" and not at the end of the manuscript PDF file. I've just checked and Fig_S1 does contain the benchmarking data referred to in the text. Hopefully that clears up any confusion.

On 2021-01-27 15:29:13, user Julien wrote:

In this version, supplementary figures do not seem to be what they should be. As a result, unfortunately, there is no Figure showing superiority of the Franken pipeline compared to other clustering approaches.

On 2020-06-01 14:57:25, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2025-08-17 18:35:09, user Debal Deb wrote:

Nice photographs. And very nice of the authors to cite Debal Deb's pioneering work with Jugal.

On 2018-04-23 05:58:01, user MikeXCohen wrote:

Very nice and thought-provoking work. Indeed, others have noted that sine-wave-based analysis methods should be seen as first-pass approaches that are sufficient for identifying important features but that are probably not ideal for fine-grained analyses.

The primary limitation of the methods reviewed here is that they rely on the assumption of a one-to-one mapping between electrode and source of LFP/EEG. This assumption almost certainly does not hold in real data, as there are multiple circuits/networks that generate fields measured by a single electrode. The mixed result (i.e., the electrode time series) could have a very different and non-sinusoidal response that is not present in any of the individual sources. Multichannel recordings are the best way to disambiguous this.

Here is a simple example in MATLAB to illustrate this difficulty. Consider three independent but correlated sources. When summed onto a single electrode, the result is intruigingly nonsinusoidal:

t = 0:.001:2;

a1 = interp1(linspace(t(1),t(end),5),rand(5,1),t,'pchip')/2;<br /> a2 = interp1(linspace(t(1),t(end),5),rand(5,1),t,'pchip')/2;<br /> a3 = sin(3*pi*t-pi/8).^6;

s1 = a1 .* sin(2*pi*3*t);<br /> s2 = a2 .* (mod(t-1/4,1/3)-1/6)*8;<br /> s3 = a3 .* randn(size(t))/20;

figure(1), clf<br /> subplot(211), plot(t,s1, t,s2, t,s3), title('Source signals')<br /> subplot(212), plot(t,s1+s2+s3), title('Mixed sensor signal')

https://uploads.disquscdn.c...

A simple spectral analysis does not capture the complexities of the signals, but it does accurately describe the core common features of all sources, which is a peak at 3 Hz:

plot(linspace(0,1000,length(t)),abs(fft(s1+s2+s3)/length(t)).^2)<br /> set(gca,'xlim',[0 30])

A detailed waveform analysis, however, could lead to conclusions about "the" signal that are not properties of any individual source.

The picture starts to change if we assume that the three sources have at least somewhat different projections onto a multielectrode array, which would occur if there are subtle differences in geometry or position of the three sources (a very reasonable assumption):

% channels and forward models<br /> M = 20;<br /> l = linspace(-2,2,M);<br /> fm{1} = exp( -(l-1).^2 )';<br /> fm{2} = exp( -(l-0).^2 )';<br /> fm{3} = exp( -(l+1).^2 )';

% channels-by-time data<br /> X = bsxfun(@times,s1,fm{1}) + bsxfun(@times,s2,fm{2}) + bsxfun(@times,s3,fm{3});

figure(2), clf<br /> subplot(311), plot(t,bsxfun(@plus,X,(1:M)'/5)), set(gca,'ytick',[])<br /> title('Multichannel data')

This is not an unrealistic toy example, because I am simply assuming that each source has a shifted Gaussian projection onto the electrodes, which is what you see in laminar cortical recordings (many other forward models will work equally well). In this case, we know the geometric location/orientation relative to the different electrodes, so it's possible to separate these sources (albeit imperfectly) using a standard beamforming approach:

iX = pinv(X*X');<br /> tsr = zeros(3,length(t));

for i=1:3<br /> L = fm{i}; % "leadfield"<br /> w = (iX*L) / (L'*iX*L); % weights<br /> tsr(i,:) = w'*X; % reconstructed time series<br /> end

subplot(312), plot(t,tsr), title('Beamformed signals')<br /> subplot(313), plot(t,s1, t,s2, t,s3), title('Original source signals')

https://uploads.disquscdn.c...

In conclusion, although the authors' suggestions are useful, it seems difficult to me to trust any detailed waveform-based analysis without multivariate data to ensure that incorrect inferences are not being made due to source-level mixing.

Mike Cohen

On 2024-08-30 06:46:42, user Daniel J. Murphy wrote:

Nice work. We also found increased expression of ITPR1, PKCa and CaMKKb following acute overexpression of MYC in MEFs. See doi:10.1038/onc.2017.394

On 2020-11-14 11:55:11, user Sal Peralta wrote:

Prunella vulgar is not just a Chinese medicinal herb. The herb has a worldwide distribution and has been used medicinally across the world for millennia.

On 2020-07-28 14:01:26, user Giuseppe Guidarelli wrote:

I propose the following comments:<br /> 1) it does not seem to me that there is a demonstration that the difference in absorption between control and fungi is statistical confirmed.<br /> 2) do you think that using GM is the best way to determined the absorbed dose when the energy spectrum of radiation is unknown or non monocromatic?<br /> 3) It seems to me useless using fungi to shield radiation instead of any other material with asufficient absorption coefficient.<br /> Best regards.<br /> Giuseppe Guidarelli

On 2016-03-02 19:11:44, user Eric Klavins wrote:

Check out the current version of this article. It has my name spelled correctly :-)

On 2016-08-25 13:38:53, user Bradley Love wrote:

Thank you Tal for these comments that will help us improve the paper. Please let me know whether I misconstrue anything here.

We will make clearer that functional smoothness is actually supra voxel, so I think we are on the same page here. I agree that it's naturally to get overly focused on sub-voxel, which is why we deal with those basic limits early in the paper, then try to move on to the notion of functional smoothness, which requires similar stimuli to engender (across voxels) similar internal representations. Interesting, while functional smoothness is necessary for fMRI to be useful, smoothness across voxels is not. The matrix multiply (like a one layer neural network) shown in Figure 3 is a good example of this.

I think there has to be some degree of smoothness at the subvoxel level for fMRI to work (e.g., Figure 1), which is one reason to prefer smaller voxels. I'll think more about your ocular dominance example, but if you had an orientation column that would be fine because the change is smooth across the column. The only issue would be if the voxels were so large relative to the column that all was lost in a Nyquist- Shannon sampling sense. So, we are (trying) to say exactly what I think you are, namely all will be lost to fMRI if subvoxel smoothness is not satisfied, so yes there could be many structures that would be invisible to fMRI. We'll make this point clearer up front in the paper. We intended to make this point early on, then move on to supra functional smoothness, which is about patterns over voxels.

On 2017-03-12 09:26:53, user Assaf Grunwald wrote:

An updated version is located at preprint page 10.1101/108084, http://biorxiv.org/content/...

On 2025-06-18 10:48:15, user Oliver Stuart wrote:

This has been published in MBE: https://academic.oup.com/mbe/article/42/4/msaf079/8105428

On 2025-11-07 14:48:23, user Sofia Peressotti wrote:

Peer-reviewed version available at Advanced Functional Material: https://doi.org/10.1002/adfm.202522306

On 2014-12-08 01:07:29, user Debbie Kennett wrote:

Is there any chance you could provide a spreadsheet showing the underlying segment data for Figure S3?

On 2022-10-15 07:19:46, user Rick Webb wrote:

Fixation using glutaraldehyde and processing at room temperature can cause major artefacts in the structure of bacteria. The nucleoid, for example, looks nothing like it does in real life, its structure is grossly changed. So it would be good to see these results verified using techniques like high pressure freezing and freeze substitution where the structural preservation will be less artifactual.

On 2024-01-19 12:36:25, user Rob wrote:

Is there a link to the "A references file of 500kb of subtelomere sequences was<br /> assembled from each of the genomes" to reproduce the work?

On 2018-11-16 01:40:56, user ??? wrote:

@LischikC Do you test gRNA for slc2a15b? KO of this gene disrupt the leucophore development.

On 2024-07-16 22:58:40, user Jim T wrote:

My congratulations to the authors on this impressive work! Your estimated 300kya date for the divergence of the ancestry of Khoe-San seems like a relatively good fit for the newer dates for the emergence of the Lupemban culture. Has this possible match been considered?

“Early Stone Age (ESA) archaeology is effectively absent from the rainforest zone, with the early Middle Stone Age (MSA) Lupemban industry representing the earliest sustained archaeological signature. Uranium-series dates of approximately 265 ka BP for the Lupemban at Twin Rivers (Zambia), although queried, suggest a precocious late Middle Pleistocene dispersal of early Homo sapiens into the equatorial rainforest belt.” - Taylor 2021

https://royalsocietypublishing.org/doi/full/10.1098/rstb.2020.0484

On 2019-12-25 06:35:28, user chetan arya wrote:

Our little contribution to understand how nature designs and evolves novel systems to counteract man-made mess of environment. Evolution of new robust enzymes which can perform very basic but essential chemical reactions under extreme conditions is the theme of this study. Breaking a stable ubiquitous amide bond such as DMF is well studied in chemistry field but is an unknown phenomenon in biochemistry field. Being a recalcitrant and well used organic solvent, there is nothing organic in DMF when it comes to pollution. We found a microbial system which can denitrify DMF for their food source. Most important enzyme of DMF metabolic pathway, which is a hydrolase is proven novel at every levels. Complete different spectrum of sequence, structure, catalytic activity and mechanism. So please find more about this interesting enzyme through this paper.

On 2015-11-23 14:38:43, user Janet wrote:

Very interesting work. However, at least some of the results are<br /> surprising based on our current knowledge on disease genes.

The claim " (...) 2,557 LoF-intolerant genes for which human disease<br /> phenotypes have not yet been identified." might not be correct.

On one hand, the pathogenicity of a gene is not always caused by<br /> genetic variants. A paradigmatic example would the huntingtin gene<br /> (HTT), currently annotated in your list as non-pathogenic<br /> (clinvar_path = 0).

On the other hand, relying on Clinvar and HGMD resources to make an<br /> evaluation of the gene association to diseases produces a picture<br /> that is far from complete. A more exhaustive assessment of this<br /> issue, employing other of the currently freely available<br /> genotype-phenotype resources would produce different results.

To provide just an example, according to DisGeNET<br /> (http://www.disgenet.org/) "http://www.disgenet.org/)") 850 of the genes with pLI >= 0.9 and<br /> clinvar_path = 0 are reported by at least one expert-curated<br /> resource (such as UniProt) as associated to disease. Furthermore,<br /> more than 2,000 genes have evidences from the literature of being<br /> associated to human diseases.

In the following link you can access the highest score disease<br /> associations of some of the LoF-intolerant genes

http://disgenet.org/web/Dis...

On 2016-05-28 23:16:41, user wzrd1 wrote:

A small sample (you do have an interesting notion of small) size study can ascertain if one should initiate a larger study.<br /> Or inform one that a larger study isn't necessary.

On 2021-04-20 03:32:07, user Karthik Raman wrote:

Interesting. Nice to see network features being used as well. We found some specific network features (beyond the regular centrality measures) to be fairly powerful: https://journals.plos.org/p...

On 2022-06-14 08:57:51, user Joachim Goedhart wrote:

The authors observe fluorescence in cells and biological tissue. The fluorescence is attributed to proteins and these are named human fluorescent protein I and II (HFP1, HFP2).

However, there is no evidence that the fluorescence originates from a protein. The source of the emitted signal can be any (auto)fluorescent molecule (e.g. riboflavin).

The labels in figure 3&4 are too small to read and prevent evaluation of those results.

On 2021-03-19 15:31:16, user Octavio Martínez de la Vega wrote:

This preprint has been published as an article in Plants, please see https://www.mdpi.com/2223-7...

On 2017-02-06 00:10:15, user bung_lu_su wrote:

I was always under the assumption that Hannibal was a sociopath which has been linked to high IQ

On 2022-03-25 12:45:09, user Daniel Campbell-Meiklejohn wrote:

This manuscript has been published under a new title and in a revised form:

Livermore, J.J.A., Holmes, C.L., Moga, G. et al. A single oral dose of citalopram increases interoceptive insight in healthy volunteers. Psychopharmacology (2022). https://doi.org/10.1007/s00...

On 2025-10-14 15:45:20, user Diana Duarte wrote:

Thanks for the interest in this manuscript, a peer reviewed version is available at https://molhort.biomedcentral.com/articles/10.1186/s43897-025-00176-w

On 2023-05-17 05:59:59, user Mayra Calderon wrote:

Great paper! I really liked the clarity of the figures and the step-by-step explanation of each method used, as well as the confirmation of background information through the different methodologies. Figure 1, specifically, was very well put together, setting the foundation for the rest of the experiments and the preliminary data used in subsequent experiments and figures.

It was also fascinating to see the different drug assay experiments conducted using both in vivo and in vitro methods, as well as the use of different model organisms such as mice and humans. The advancement of combination drug therapy in targeting cancer cells and affecting cell proliferation is very promising, not just for LUAD but for different types of cancers, as mentioned in the paper.

Although the research conducted was clear and thoroughly explained, the figures were a bit confusing to understand and could have benefitted from supplementary figures or further clarification of their intended meaning. Some figures did not mention the significance of the data, while others did. It was challenging to differentiate which sets of data were significant and which ones were not, as some contained "ns" (not significant) and others were left blank. Additionally, figures like Figure 4a included significance markers without an explanation of why the data was considered significant in the first place. Figures such as Figure 7a and Figure 7c were also confusing in terms of what determined their level of significance. However, the results were explained clearly, and the interpretation of the data sets, despite the challenges, was possible based on the written results of each figure.

On 2020-03-31 22:08:38, user Sinai Immunol Review Project wrote:

Summary and key findings: The authors reported a human monoclonal antibody that neutralizes SARS-CoV-2 and SARS-Cov which belong to same family of corona viruses. For identifying mAbs, supernatants of a collection of 51 hybridomas raised against the spike protein of SARS-CoV (SARS-S) were screened by ELISA for cross-reactivity against the spike protein of SARS-CoN2 (SARS2-S). Hybridomas were derived from immunized transgenic H2L2 mice (chimeric for fully human VH-VL and rat constant region). Four SARS-S hybridomas displayed cross-reactivity with SARS2-S, one of which (47D11) exhibited cross-neutralizing activity for SARS-S and SARS2-S pseudotyped VSV infection. A recombinant, fully human IgG1 isotype antibody was generated and used for further characterization.

The humanized 47D11 antibody inhibited infection of VeroE6 cells with SARS-CoV and SARS-CoV-2 with IC50 values of 0.19 and 0.57 ug/ml respectively. 47D11 mAb bound a conserved epitope on the spike receptor binding domain (RBD) explaining its ability to cross-neutralize SARS-CoV and SARS-CoV-2. 47D11 was shown to target the S1B RBD of SARS-S and SARS2-S with similar affinities. Interestingly, binding of 47D11 to SARS-S1B and SARS2-S1B did not interfere with S1B binding to ACE2 receptor-expressing cells assayed by flow cytometry.

Limitations: These results show that the human 47D11 antibody neutralizes SARS-CoV and SARS-Cov2 infectivity via an as yet unknown mechanism that is different from receptor binding interference. Alternative mechanisms were proposed but these as yet remain to be tested in the context of SARS-CoV2. From a therapeutic standpoint and in the absence of in vivo data, it is unclear whether the 47D11 ab can alter the course of infection in an infected host through virus clearance or protect an uninfected host that is exposed to the virus. There is a precedent for the latter possibility as it relates to SARS-CoV that was cited by the authors and could turn out to be true for SARS-CoV2.

Relevance: This study enabled the identification of novel neutralizing antibody against COV-that could potentially be used as first line of treatment in the near future to reduce the viral load and adverse effects in infected patients. In addition, neutralizing antibodies such as 47D11 represent promising reagents for developing antigen-antibody-based detection test kits and assays.

On 2023-01-23 18:11:04, user Jinoh Kim wrote:

A link to the accepted paper: https://www.molbiolcell.org...

On 2019-03-05 23:02:30, user Theo Sanderson wrote:

The values provided for the F1-score seem to be a factor of 2 lower than those calculated using the harmonic mean of the precision and the recall.

On 2019-08-05 00:56:10, user CwbyLA wrote:

Was this paper ever published?

On 2018-02-15 12:47:51, user Ed wrote:

a simple suggestion: in the figures, consider changing excited colour to red and inhibited to blue. I know it is a simple transfer function, but at least to me, your choice of colours is counterintuitive.

On 2014-09-15 18:33:49, user Matt MacManes wrote:

For interested parties- the data (minus the reads) are available here: https://www.dropbox.com/sh/...

On 2024-08-22 15:51:55, user Jack M wrote:

I have two comments:

1. A recent paper claims that "certain cancers can use ketone bodies as an alternative energy source to sustain tumor fitness, such as pancreatic tumors". The results in this paper, even without a glutamine inhibitor, would disagree with that statement. Maybe you can mention the conflicting approaches to ketogenic diet in the discussion?

2. Have you looked into the work of Mukherjee et al ? They report a very similar effect with ketogenic diet and DON in late stage experimental glioblastoma. Perhaps this intervention could be a more universal therapy?

Thank you for this very promising work exploring diet-drug combinations!

On 2020-03-30 12:04:26, user Aaron wrote:

It's a nice paper with an elegant model. My concern is its all based on the assumption that everything controlling their HSF1-reporter (not a HSR reporter directly) affects the HSR. i.e. that HSF1 is the be all and end all. We've shown in bat cells that multiple HSP's are upregulated (i.e. the HSR) in the absence of high HSF1/2 levels meaning there are other regulators of the HSR independent of HSF1/2. Surely this is the case in other cells too and may explain some of their different responses between stressors.

On 2020-12-28 15:25:57, user Sadik wrote:

Gap-free rice genome! Overlapping repeats classes filtering in the method section caught my attention and wanted to learn the how-to's in suppl. methods.

On 2019-11-29 11:40:58, user Lydia Maniatis wrote:

"In<br /> both experimental datasets, drifting gratings were presented at <br /> locations that overlapped with the receptive fields of the recorded V1 <br /> neurons."

I can't find where the authors describe how they ascertained <br /> the boundaries of the recorded neurons "receptive fields." This is a <br /> serious gap in methods, a clear obstacle to any potential replication <br /> attempt.

In general, there doesn't appear to be an agreed-on <br /> definition of the receptive field concept; it is always and only defined<br /> on the basis of the method each team uses to determine it (whatever it <br /> is) in the context of particular projects. Thus, the paper contains not only a methodological, but also a theoretical gap.

On 2017-02-20 10:11:25, user SamGG wrote:

Very interesting work.

IMHO, the filtering step stated as "discarding PSMs matching to <br /> irrelevant peptides in the complete search" should be detailed. I didn't<br /> understand what it consists in.

The article from Bourgon, Gentleman and Huber PNAS 2010 should be read by every people interested in FDR. The filter applied before the FDR computation must be independent from the statistical test on which the FDR computation will be computed. Filtering before FDR sounds as hacking FDR as it reduces the exploration space before computing FDR. But if filtering and statistical testing are clearly independent, such a succession of steps makes sense.

Every mass spectrometrist should also (re)read J. Cottrell's blog. The http://www.matrixscience.co... post (as stated in this article) but also the end of http://www.matrixscience.co....

Conflict of interest: none.

On 2021-12-21 19:45:19, user Alizée Malnoë wrote:

The manuscript by Nies et al. demonstrates how changing pulse amplitude modulation (PAM) parameters can affect non-photochemical quenching (NPQ) and photosystem II yield (?PSII). Using in silico simulations of PAM experiments, the authors illustrate how NPQ and ?PSII are affected by varying: i) the delay between measurement of maximal fluorescence Fm and the onset of the actinic light (or between turning off AL and measurement of Fm’’ in the dark), ii) the intensity of the actinic light, iii) the frequency of the saturating pulses, iv) and their duration. Nies et al. finish by validating their in silico model, and suggesting that scientists using PAM must provide the details of all of the parameters listed above in the methods section of any publications to allow their experiments to be accurately reproduced and modeled. We enjoyed this manuscript, however, we have some comments and suggestions for improvement, listed below.

Major comments<br /> - We suggest moving part of the model validation section of the results, shown in Figures 8 (and 9), to the start of the manuscript. This rearrangement would show the reader that the mathematical model used in the in silico simulations can accurately reproduce experimental data, before the parameter-dependent changes to NPQ and ?PSII are simulated. In the current arrangement, the reader needs to have prior knowledge that the changes in NPQ and ?PSII shown in the simulations are accurate, before the herein updated model has been validated.<br /> - Fig8. Regarding the validation of the mathematical model by comparing to experimental PAM measurements with different SP durations, or different delays of AL onset from Fm measurement, with the simulated data: what is the rationale for choosing these, how about testing the other parameters such as AL intensity and frequency of SP? Please comment on the impact of the different parameters on e.g. the NPQ measurement and rank them by stronger to lower effect based on your simulations and experiments. Also a historical perspective/physiological relevance of delaying the SP from actinic onset would be welcome! How about giving recommendation to researchers in the field to have Fm determination/SP right at onset of illumination, with no delay, to prevent further confusion (and similarly have the final SP in AL on, followed by AL off with no delay).<br /> - Line 326. Regarding the use of another model of photosynthesis, we found this very interesting and suggest that a comparison of the simulations generated by the two mathematical models using the same set of parameters be included as a main or supplemental figure, and its description be included in the results section. The GitLab link (line 330) doesn’t specify which exact file to look at.<br /> - Line 127. “We have used 500 µmol s–1 m–2 as the default light intensity of AL.” For simulations, an intensity of 500 µmol m–2 s–1 was used, but for experiments (line 152) “The intensity of red AL was set at approx. 457 µmol m–2 s–1”. We understand that matching the actinic light during the experiment to 500 µmol m–2s–1 cannot be possible, alternatively we suggest that the simulations be carried out at 457 µmol m–2 s–1 for sake of consistency. Importantly, is 457 µmol m–2 s–1 the value given by the manufacturer for the chosen setting, and did you measure it to confirm its value? (depending on instrument calibration, usage and age, the light output can be different than set)<br /> - Line 204, 205. “The calculated steady-state NPQ values are higher for SP intensities below 3000 µmol s–1 m–2”, according to Fig.5, it seems that the threshold is rather 2000, than 3000 (or 4000).<br /> - Fig7. To test the “actinic effect” of SP duration, we would suggest to perform a simulation with AL=100 µmol m–2 s–1 AL and/or AL=0 to check whether SP themselves can induce NPQ. According to Fig8A (experimental), it seems that at 0.8s, NPQ is indeed slightly higher than with shorter SP duration.<br /> - Line 370, a necessary addition would be to list here, or write a template of, what you suggest for minimum information is needed as standard for the community. It could be similar to Table 2, and needs to include duration of AL on, off and AL quality.

Minor comments<br /> - Line 46. “Allow”, should be “allows”<br /> - Line 75. “Groups but also” should be “groups experimentally, but also”<br /> - Line 115. Replace higher by vascular.<br /> - Line 140. 26C is higher than standard temperature for Arabidopsis growth (22C), what’s the rationale for choosing this temperature?<br /> - Line 150. Define Fv and explain if the 5s of far red light is turned on at the very beginning of the experiment i.e. before time 0.<br /> - Line 153. “default settings (10)”, specify “set at value of” 10. We suggest writing a small table with these parameters (see major comment).<br /> - Line 161. Which leaf did you choose, younger or older? This information is important to state, see differences with leaf age for example in Bielczynski et al. Plant Phys (2017) doi: 10.1104/pp.17.00904.<br /> - Line 173-174. We suggest that the SP time points are moved to the methods section.<br /> - Line 185-186. “In the upper panel….derived NPQ and ?PSII”, this whole sentence can be removed as it should be clear from the figure legend.<br /> - Line 211. “Far more” how many did you look at?<br /> - Fig. 6. “6A and 6A”. Should be “6A and 6B”<br /> - Line 234. “Switching on AL with the first SP in light-triggered after 1 s” suggest rewording as it was unclear what light-triggered means.<br /> - Line 241. The observed effect is likely due to the total conversion of zeaxanthin to violaxanthin for long periods of dark-adaptation. <br /> - Line 243. Suggest changing “whereas” should be “however” as it is clearer.<br /> - Line 256. Define PMST.<br /> - Line 264-268. We suggest moving this block of text to the discussion section.<br /> - Line 264. “AL is another important information” should be “AL is another important piece of information”<br /> - Fig. 8B and 8D. As the simulated curves seem to all overlap, and often in this study we look for fine nuances between data, we think it would be beneficial to read the simulated curve superimposed on top of the experimental data allowing a fair comparison and analysis between the two types of data. Displaying the same graphs at a larger scale would help to read them.<br /> To help in this, we propose Figure 8 to be divided in two figures, since Fig. 8A-D is related to “SP experiment” while Fig. 8E-H is related to the “delay experiment”. This would allow the size of the panels to be increased to help the reader interpret the data.<br /> - Fig. 8F and 8H. Plot titles “Delay NPQ/?PSII Sim lation”, should be “Delay NPQ/?PSII Simulation”<br /> - Fig. 9 seems to be redundant as the reader should be able to observe the difference between the two independent experiments by comparing Figure 8A and 8E. We therefore suggest that Fig. 9 be removed.<br /> - Line 286. “Measurements are” should be “measurements have been”<br /> - Line 289-303. We suggest moving this block of text to the introduction section<br /> - Line 324. Replace “many” by “all”!<br /> - Line 351. “Agreements” should be “agreement”<br /> - Line 361-372. We feel that the points made in this block of text have already been made earlier in the manuscript and repeated several times. Therefore this block of text can probably be omitted as it is redundant.<br /> - General comments concerning the figures: we suggest adding dark/light bars to the top of most plots in Figures 3B-C, 4B-C, 6A-D, 7A-B, 8A-H; as it would improve the readability/interpretation of the plotted data. Fig. 2-8, figure identifier letters are presented in a different font style than the rest of the text, throughout the document. While we recognize them to be hyperlinks, we think font style should be uniform.

Jack Forsman and Andre Graca (Umeå University) - not prompted by a journal; this review was written within a preprint  journal club with input from group discussion including Alizée Malnoë, Jingfang Hao, Maria Paola Puggioni, Pierrick Bru, Aurélie Crepin, Wolfgang Schröder.

On 2024-09-17 10:34:10, user balli wrote:

The authors state in the intro... "only one clinical study has been published...."<br /> Please look at Jebsen et al 2019 J Med Case Rep / Spicer et al 2021Clin Cancer res.<br /> Also, using short peptides restric their use for intratumoral administration, please provide evidence.<br /> What about immunogenicity and potential ADA responses by longer peptides, please adress.<br /> Given that BOP peptides are strong inducers of ICD, why was immunodefect mouse model used ?

On 2017-05-02 14:16:16, user Peter Civán wrote:

Dear Jae and Michael,

Thanks for this interesting paper! I’m glad the debate goes on and people are trying to make sense out of the contradictions.

You made several good points here and I totally agree that the genomic window size of the CLDGRs is critical for clustering patterns that are based on genetic distance.

However, the situation is not as simple as “the smaller genomic windows provide more correct genealogy”. Surely, we know that sh4 and prog1 coding sequences are fixed in all cultivated rice, so if we focus on a genomic window narrow enough (e.g. just the coding sequence, or in an extreme case just the FNP), we will inevitably recover a monophyletic O. sativa group (or better say paraphyletic O. rufipogon). The question is, how far from the gene can we go and collect genealogically informative signal (undisturbed by recombination)? Neither I nor you have answered this question.

Consider the situation on the attached figure. Keep in mind that the “domestication gene” can be a very ancient allelic variant that emerged in wild populations long before the domestication, and also keep in mind that wild rice populations are quite dynamic (in terms<br /> of recombination and glacial-interglacial movement). Then we can imagine a situation where we have multiple combinations of alleles (Xa–Xe) with different genetic backgrounds within the wild population. Let’s imagine two independent domestication events, leading to two cultigens (I and II). The allele Xd is selected in both cases and fixed in both cultigens. If we focus on the narrow window, we recover monophyly of the cultigens. If we focus on the<br /> large window, we recover polyphyletic cultigens. In this particular cartoon, the latter would be correct.

I cannot be sure that this is indeed the case of indica and japonica, because I did not identify the entire haplotypes with their recombination points (the quality of the data just doesn’t allow that). I think both of us may be over-interpreting the selective sweep analysis a bit. Maybe we need to focus on other kinds of data and methods (your recent coalescence analysis is one example). Maybe our paper (Civan and Brown. Origin<br /> of rice (Oryza sativa L.) domestication genes. Genet Resour Crop Evol. In press) will bring some new insights, and hopefully, there will be more stuff coming from me and Terry soon.

https://uploads.disquscdn.c...

On 2018-04-05 10:27:10, user Edward Pegler wrote:

In f3 stats for Iran_C and Iran_BA, you show Balkan Neolithic populations paired with Iran_N as a better fit than Anatolian Mesolithic or Neolithic populations with Iran_N (e.g. S3.4 and S3.5). I understand that both are considered as 'Anatolian agriculturists' but is the fit difference significant and should this be discussed in the paper or supplement?

On 2018-07-29 01:32:58, user Smita Krishnaswamy wrote:

Please see and cite SAUCIE: https://www.biorxiv.org/con...

On 2016-12-15 16:15:29, user David wrote:

I´ve served as paid reviewer at Rubriq and Peereviewers.com and it was good experience

On 2020-04-04 13:06:46, user Warinner Group wrote:

As the senior author of this study, I would like to clarify that we obtained permission from the Mongolian Office of Professional Inspection and from the National University of Mongolia to conduct this study and to analyze the skeletal material included in the study. Our permit number is A 0109258, MN DE 7 643

On 2021-05-11 07:17:03, user Alvaro Crevenna wrote:

The paper is really nice, would you share the supplementary material as well please? ;-)

On 2024-07-19 19:57:28, user Karen Menuz wrote:

Author's note: this manuscript was divided into two articles. One is the linked Frontiers article in 2021. Other data is a portion of the data found in a 2nd article in 2024: https://www.frontiersin.org/journals/cellular-neuroscience/articles/10.3389/fncel.2024.1390557/full

On 2020-04-03 15:34:56, user Sinai Immunol Review Project wrote:

COMPARATIVE PATHOGENESIS OF COVID-19, MERS AND SARS IN A NON-HUMAN PRIMATE MODEL

Keywords: SARS-CoV2, cynomolgus macaque, SARS-CoV

Main findings:<br /> This work assesses SARS-CoV-2 infection in young adult and aged cynomolgus macaques. 4 macaques per age group were infected with low-passage clinical sample of SARS-CoV-2 by intranasal and intratracheal administration. Viral presence was assessed in nose, throat and rectum through RT-PCR and viral culture. SARS-CoV-2 replication was confirmed in the respiratory track (including nasal samples), and it was also detected in ileum. Viral nucleocapsid detection by IHC showed infection of type I and II pneumocytes and epithelia. Virus was found to peak between 2 and 4 days after administration and reached higher levels in aged vs. young animals. The early peak is consistent with data in patients and contrasts to SARS-CoV replication. SARS-CoV-2 reached levels below detection between 8 and 21 days after inoculation and macaques established antibody immunity against the virus by day 14. There were histopathological alteration in lung, but no overt clinical signs. At day 4 post inoculation of SARS-CoV-2, two of four animals presented foci of pulmonary consolidation, with limited areas of alveolar edema and pneumonia, as well as immune cell infiltration. In sum, cynomolgus macaques are permissive to SARS- CoV-2 and develop lung pathology (less severe than SARC-CoV, but more severe than MERS-CoV).

Limitations:<br /> Even though cynomolgus macaques were permissive to SARS-CoV-2 replication, it is unclear if the viral load reaches levels comparable to humans and there wasn’t overt clinical pathology.

Relevance:<br /> The development of platforms in which to carry out relevant experimentation on SARS-CoV-2 pathophysiology is of great urgency. Cynomolgus macaques offer an environment in which viral replication can happen, albeit in a limited way and without translating into clinically relevant symptoms. Other groups are contributing to SARS-CoV2 literature using this animal model (1), potentially showing protection against reinfection in cured macaques. Therefore, this platform could be used to examine SARS-CoV2 pathophysiology while studies in other animal models are also underway (2,3).

References:<br /> 1. Bao L, Deng W, Gao H, Xiao C, Liu J, Xue J, et al. Reinfection could not occur in SARS-CoV-2 infected rhesus macaques. bioRxiv. 2020 Mar 14;2020.03.13.990226. <br /> 2. McCray PB, Pewe L, Wohlford-Lenane C, Hickey M, Manzel L, Shi L, et al. Lethal Infection of K18-hACE2 Mice Infected with Severe Acute Respiratory Syndrome Coronavirus. J Virol. 2007 Jan 15;81(2):813–21. <br /> 3. Bao L, Deng W, Huang B, Gao H, Ren L, Wei Q, et al. The Pathogenicity of 2019 Novel Coronavirus in hACE2 Transgenic Mice. bioRxiv. 2020 Feb 28;2020.02.07.939389.

On 2025-02-05 21:51:35, user Evan Saitta wrote:

This preprint has now been formally published: https://elifesciences.org/articles/46205

On 2020-03-23 12:31:56, user UAB Journal Club wrote:

The Bacterial Pathogenesis and Physiology Journal Club<br /> The University of Alabama at Birmingham<br /> Spring 2020

Review of “Filamentous bacteriophage delay healing of Pseudomonas-infected wounds”<br /> Bach et al., BioRXiV

Summary<br /> In this manuscript, the authors follow up on their earlier work implicating the filamentous Pf phage in Pseudomonas aeruginosa virulence, with a study focused on the effects of that phage on wound healing in both mice and humans. They establish clear correlations between the presence of Pf phage and delayed healing, identifying preliminary mechanistic explanation for this effect (impaired keratinocyte migration and alteration of monocyte/macrophage wound response).

We have found this paper to be well-written and the results and methods to be clearly described. While we would like to offer some suggestions to the authors and pose questions that have come forth, overall, we find the authors’ conclusions convincing.

Minor Comments<br /> 1) The paragraph in the Results section entitled “Pf phage enhance in vitro Pa biofilm formation” describes results that seem to be a replication of previously published findings (see Rice et al. 2008 ISME J 3:271–282). Please at least acknowledge this.

2) The authors to not indicate how much Pa each mouse is inoculated with. Include in figure legends or methods.

3) The authors should include scale bars on all confocal images

4) The authors did not define the term “epithelial gap”. The manuscript would benefit from this inclusion.

Major Comments<br /> 1) The authors establish that soluble factors produced by Pf-exposed macrophages impact keratinocyte migration (Fig. 3), but are not able to replicate these effects with purified components. This is reported as “data not shown”, but we think this is a very important result. We would like to see these data, and even if the authors are not able to establish which of the soluble factors are important in this effect (experiments which would greatly increase the impact of this paper, but are, admittedly, challenging), would like to have more discussion of what those factor(s) might be and how the authors intend to solve this problem.

2) Data regarding replicates, statistics and methods were improperly analyzed, nor discussed, or not reported in certain sections:

a. For Fig. 3 there are not enough independent replicates for the experiments in panels C and F to do the reported statistical tests.

b. No information is given for the number of replicates for the experiments shown in Fig. 4. Please ensure that at least 3 independent replicates of each experiment are performed.

c. Broad consideration of appropriate statistical analysis should be assessed. Statistical analyses for repeated measures data should be considered whenever data are in time-course, one-way ANOVA is not appropriate for these data. Additionally, the citation of which post-hoc analysis was used for all ANOVA should be included in figure legends. Replicate (N, population number) should be included in all reporting of statistics within figure legends (for example, replicate number and how data are combined is not explained in Figure 4B). ANOVA should be used to assess three-group comparisons, rather than paired or unpaired student t-test (e.g. Figures 2 and 3).

d. Methods for the phage uptake experiments are lacking detail (e.g. Flow protocol, stains used for Live/dead, etc.)

e. In the results section title “Pf phage alter macrophage cytokine production profiles,” it is unclear that the sample being tested is the supernatant of the BMDMs treated with Pf or LPS. While this is explained in the figure legend, the manuscript would benefit from clarifying the experimental procedure in the text of the results section.

3) The authors discuss briefly the need to know whether Pf phage are found in Pseudomonas isolated from other infection sites, but we would argue this is an important piece of information for interpreting the human wound dataset.

a. Are Pf phage actually more abundant in wound isolates than in other infectious strains? Are Pf phage common in environmental isolates of P. aeruginosa? While an exhaustive survey is probably beyond the scope of this paper, a good baseline could be obtained using the author’s established qPCR method on a variety of P. aeruginosa strains available from culture collections.

b. Are Pf phage present in mucoid strains of P. aeruginosa associated with other pathologies? (a discussion point which my broaden the application of these results

Figure-specific comments:

1) Figure 1: 1B, 1G, and 1H would benefit from inclusion of objective scoring in addition to the representative images as this is a major foundation of the study, allowing for statistical comparisons.1D and 1E both include day 13 timepoint but with different N’s. This is concerning and should be mentioned in the legend if they are from separate experiments.

2) Figure 2: it would benefit the conclusions to see a time course of uninfected wounds day 1-13 compared to the day 1-13 infection model. Additionally, there is no representative image for the PF4 treated wound, an important piece of data considering the normalization to wound area on Day 1. We suggest adding the data which show no alteration in inflammatory cell counts in Pf phage treated groups is important enough to include in the main figure 2 rather than in supplemental figures. This is an important foundation to build that point Pf4 phage is impairing re-epithelialization of the wound rather than altering inflammatory responses.

3) Figure 3: 3C is lacking a positive control. It is reported that there were only two experiments assessed which would not be properly assessed by ANOVA. Clarification of replicates and sample size or how statistics were used in either figure legend or broadly in the methods would clarify. It is unclear why representative data are shown in Fig. 3B. Panels 3C and 3F represent the same output data measures and thus the axes should be the same as to not distort the data. Inclusion of a positive control to 3C would resolve this.

4) Figure 4: The authors do not specify the selection for further analysis of certain cytokines/chemokines from the larger dataset from Figure 4B. For examples, VCAM1 was selected while TNFa was not, despite a clear reduction in TNFa levels in the Pf-treated group. A brief mention of reasoning for selection would improve understanding of results. It is mentioned that many of these factors were tested but the data was not shown. If these data are negative, it would be valuable to include these in a table form with the limit of detection listed for each within the supplemental (e.g. TNFa <7pg/mL). There is a panel for TGFa (which was not discussed in results) but not the TNFa which was. Verify data shown and clarify in discussion.

5) Figure 5: Panels do not match the reference/legend (e.g. i.e. H in figure, I in caption)

On 2025-09-08 02:07:05, user Peter Hickey wrote:

Could you please add the Supplementary Material to the bioRxiv submission?

On 2025-05-13 06:13:38, user Vojtech wrote:

Now published here<br /> https://doi.org/10.1099/ijsem.0.006552

On 2016-07-21 10:11:49, user robince wrote:

A toolbox implementing the methods in Matlab and Python is available at:<br /> https://github.com/robince/...

Code for all the analysis and figures presented in the manuscript is at:<br /> https://github.com/robince/...

Thanks for reading,

Robin

On 2018-07-19 17:21:52, user jvkohl wrote:

Vaccines have been linked from quantized energy theft to adaptations in viruses via single amino acid substitutions that increase virulence. See: http://www.sciencemag.org/c... Substitutions Near the Receptor Binding Site Determine Major Antigenic Change During Influenza Virus Evolution Koel et al. (p. 976) show that major antigenic change can be caused by single amino acid substitutions.

On 2022-04-21 19:02:36, user Owen Hamill wrote:

The complete reference site is below

https://doi.org/10.1007/s11...

On 2024-02-07 22:51:02, user fmorandi wrote:

The study associated with GSE193141 has now been published (https://link.springer.com/a... "https://link.springer.com/article/10.1007/s11357-023-00986-0)"). Please cite it.

On 2020-03-06 06:21:41, user Sven Gould wrote:

From Knopp et al. 2020:<br /> "It has been speculated that N-terminal targeting sequences evolved from antimicrobial peptides (AMPs) (Wollman, 2016), as both share similarities in terms of charged amino acid residues, the ability to form amphiphilic ?-helices, and because they are frequently identified in host-endosymbiont relationships (Mergaert et al., 2017). One example regarding the latter is Paulinella chromatophora, whose chromatophore origin is independent from that of the Archaeplastida and much younger (Nowack, 2014). Two types of NTSs were identified that target nuclear-encoded proteins to the chromatophore, but both are not related to the simultaneously identified AMPs (Singer et al., 2017), which argues against an AMP-origin of the NTS in Paulinella. The concept is also not compatible with the origin of phenylalanine-based plastid targeting and Toc75.

The components of the Toc and Tic machinery share a mixed pro- and eukaryotic ancestry (Jarvis and Soll, 2001, Day and Theg, 2018). Toc75, the ?-barrel import pore in the outer membrane, is of prokaryotic origin and a member of the Omp85 superfamily (Day et al., 2014). Some bacterial Omp85's recognize their substrates through a C-terminal phenylalanine (Robert et al., 2006) and evidence is emerging that the POTRA domains of Toc75 act as binding sites for the NTS (O'Neil et al., 2017). If we recall that the phenylalanine-based motif is retained in rhodophytes and glaucophytes (Wunder et al., 2007), we can conclude that the pNTS did not evolve from AMPs but rather adapted in evolution and traces back to a recognition signal for the cyanobacterial Omp85 that evolved into Toc75 (Sommer et al., 2011). The ancestral character of phenylalanine-based plastid-targeting was lost with the origin of the Chloroplastida and we suggest simultaneously to the expansion of the Toc75 family, with significant consequences for the green lineage."

On 2016-03-02 04:48:47, user Arox Kamng'ona wrote:

Interesting article, how about the total number of microbial genes in our body’s microbial communities vs the total number of genes in our human genome? are we still outnumbered by a 100-fold margin?

On 2017-02-24 16:43:11, user Mia Bengtsson wrote:

Thanks for sharing this very interesting and informative piece of work! I very much enjoyed the read and several questions and concerns that I had about preprints (from an author perspective) were answered. I have some comments that I hope can be useful:

lines 31-69, on the "Landscape of preprint servers": Could there be more examples worth mentioning here than PeerJ preprints and BioRxiv? The journal Biogeosciences for example uses a model where submitted papers are published in "Biogeosciences Discussions" while they are in review with Biogeosciences, is this not also a form of preprint?

lines 58-59: "It is only possible to transfer a PeerJ Preprint for submission to PeerJ." This could be interpreted as if you can only publish in PeerJ after submitting a preprint to PeerJ Preprint, which is not what is meant I think.

lines 213-216: it was a bit unclear to me initially what this example served to illustrate, perhaps due to the following sentence. Were some of the image manipulated works retracted due to the preprint, before the final publication?

lines 236-237: relative novelty?

line 254: That preprints can be revised was news to me, this seems like it could be a major difference to peer-reviewed work where you are usually not even allowed to correct typos once the accepted article has gone online. Perhaps expand on the implications of this?

line 292-314: This touches on one of my major open questions about preprints currently. The examples online two different strategies on how to coordinate preprint submission with traditional journal submission: Either you submit preprint first, wait for comments that can be integrated into new version which is then submitted to journal (slow). Alternatively you submit preprint and journal version at the same time and use both preprint comments and reviewer comments to improve the MS for a future submission. I would tend to do the latter due to time constraints, but I am concerned that incorporating preprint comments parallell to reviewer comments could confuse (or worse: offend) journal reviewers and editors that are not familiar with the preprint model. Imagine you want to make changes to MS not called for by reviewers, will the editor feel compelled to send out the MS for another round of review thus slowing down the publication process? I guess this should not be an issue if changes are justified well in cover letter (perhaps linking to preprint comments?) and as long as editor is sensible and competent. Nevertheless the issue has crossed my mind when considering submitting preprints parallell to journal submissions.

line 329: "mSpehereDirect." what is up with the "." here?

Figure 1a: For a second I was confused by the grey bars until I realised they were indicating the border between years. Will perhaps be more visually clear when year 2017 is included? (very minor issue).

I'm looking forward to see future versions of this manuscript, preprint or published in journal!

On 2017-03-01 19:33:55, user Crovata wrote:

D. Piffer, the removed reference to the Y-DNA haplogroup-population expansion in source by H. Rindermann (2012), i.e. the correlation between the general cognitive ability (GCA) and the Y-DNA haplogroup-population expansion does seem a bit sketchy and vague, however there's an empirical need for a specific research on this matter.

Quote:

1) "Changes in allele frequencies can also occur via population expansion" it points to possible evolutionary correlation between allele frequencies and specific Y-DNA haplogroup, possibly even mtDNA haplogroup (although less possible).

2) "furthermore the contemporary distribution of these three haplogroups is positively associated with the variation in cognitive ability among contemporary European nations" it points to the note above and need for a more specific and focused research on the allele frequencies/IQ in European nations/populations with substantial/mainly Y-DNA haplogroups which are not Indo-European R1a and R1b.

In your previous research on alleles associated with higher educational attainment and higher IQ and their correlation to specific nation/population, specifically "Estimating the genotypic intelligence of populations andassessing the impact of socioeconomic factors andmigrations" (2015) quote "the relationship between the 4 SNPs g factor and IQ is due to natural selection on a specific phenotype and not the result of aspurious correlation arising from genome-wide evolutionary processes such as random drift or migrations... comes from the finding that the rank of sub-continental genotypic scores of intelligence did not perfectly match measures of genetic distances obtained from neutral markers and was an independent predictor of IQ".

In your "Factor Analysis of Population Allele Frequencies as a Simple, Novel Method of Detecting Signals of Recent Polygenic Selection: The Example of Educational Attainment and IQ" (2013) concluded that, quote, "frequencies of alleles associated with higher educational attainment... the results are similar across the HapMap and 1000 Genomes data sets: East Asian populations (Japanese, Chinese) have the highest average frequency of “beneficial” alleles (39%), followed by Europeans (35.5%) and sub-Saharan Africans (16.4%)... IQ increasing alleles were highly correlated with frequencies 14 of educational attainment alleles... The extracted factor reached highest values among East Asians (around 1-1.5), Europeans have a slightly lower factor score (0.1-0.4), and Africans obtained the lowest (negative) factor score (-1.4/-1.6)... The results show that this evolutionary process, which was already far advanced at the time when modern humans spread across the globe approximately 65,000 years before present, has continued in modern human populations after that time. It invalidates theories that<br /> assume, explicitly or implicitly, that human cognitive evolution has ended with the first appearance of physically modern Homo sapiens".

I could not read the work by H. Rindermann (2012), but in your previous research (2013), as well "Correlation of the COMT Val158Met polymorphism with latitude and a hunter-gather lifestyle suggests culture–gene coevolution and selective pressure on cognition genes due to climate" (2013), Sardinians had PC1 -0.59 i.e. Met 0.36 with IQ 90, in both with both lowest scores among listed European nations, with comparison to Italians (Tuscany) PC1 0.14 i.e. Met 0.46 with IQ 97.

As can be seen from Distribution of European Y-chromosome DNA ("http://www.eupedia.com/europe/..."), there's significant Y-DNA difference between Tuscany and Sardinia; R1b 52.5-18.5%, I2 1.5-37.5%, which positively correlates to the remark above from yours new research (2017) that the Neolithic Indo-Europeans (R1) had higher cognitive ability and thus more complex and advanced social organization, culture and tools, compared to Mesolithic Europeans (I2), which eased their conquer and expansion of Europe from Pontic–Caspian steppe.

Here will make only a slight digression, the so-called Cro-Magnon (EEMH), one at least 13,000 year old from Switzerland belonged to I2a, generally had, currently for unknown reasons, slightly larger brain capacity (1,500-1,600 cc). Although "EQ was the same for Cro-Magnons as it is for us", it would be interesting, if possible, to find out their allele frequency and compare it to current populations with high frequency of I2a (Scandinavia, Balkan (Dalmatia, Herzegovina), Sardinia).

As for correlation between GCA factors and Y-DNA haplogroup-population expansion will do a simple and rough sketch between researches from 2013 and Y-DNA hgs from YTree (https://www.yfull.com/tree/):: "https://www.yfull.com/tree/):")

Pygmy and Bushmen (with lowest factors and IQ 54) significantly belong to the oldest Y-DNA hgs A and B, thus it confirms the "Pygmy vs non Pygmy data set", as the "non Pygmy" belong to the hg CT. CT branches into DE, and majority of African people (with second-lowest factors and IQ 71) belong to hg E, while Ainu, Ryukyuans and some Tibetans who belong to hg D are not included in this data, but there were significant cultural and social differences (hunter gatherer vs farmer etc) between hg D (Jomon) and O (Yayoi) people from mainland Japan, and thus should be done a regional research in this aspect.

To hg CF belong the majority of non-African populations. It branches into C (established branch) and F (semi-established branch as it mostly branches and includes the majority of Eurasian people). The indigenous people of Australia mostly belong to C1, while of Papua New Guinea, Micronesia also significantly belong to C1, as well K and M (F's sub-branches), and have lower factors and IQ 82. However, to C2 belong Siberian-Tungusic, Mongolian and some Turkic speaking people in North and East Asia who have a relatively high factors and IQ 100 (Mongolia), which could indicate admixture with O and N populations (see below).

The Middle East and Southeastern Europe, compared to other parts of Europe mostly of Indo-European Y-DNA haplogroup origin as seen from "https://jakubmarian.com/averag..." (2012), has slightly lower IQ in ME between 82.5-92 while SE between 82-98. The ME and SE are characterized by F's subranch IJ, i.e. J in ME and I in SE, with the exception of Albania (lowest IQ in the region with 82) and Montenegro (IQ 86) with also high frequency of Y-DNA haplogroup E. Roughly, from west Croatia to south Greece and east Romania, the frequency of R1a-R1b i.e. R1 haplogroup is lower compared to other parts of Europe: Croatia (R1 32.5% - IQ 98), Bosnia and Herzegovina (19.5% - 93), Serbia (24% - 90), Montenegro (17% - 86), Albania (25% - 82), Bulgaria (28% - 93), Romania (29.5% - 91). It is indicative that beside genetic heritage, isolation, selection, social organization, historical-cultural events, such low IQ is also due lower modern standard and education, but as you pointed in your research, neverthless specific population has limited full potential according to its genetic potential. As mentioned above, these nations should be included in future allele/IQ data sets for better understanding of the topic and simple empirical evidence.

India and Pakistan have low factors and IQ 82 and 84 respectively. Although in both Pakistan and most of India there's high frequency of Y-DNA R1a (Aryans arrival), they are significant admixture of haplogroups L, H, O and J, depending on the ethnic group and region, especially for India. The data set for India could be empirically misleading for conclusion as included Keralite from South India, where hg-O is 0%, and Kachari from Northeast India, where hg-O is 79.7% - two totally different populations and historical-geographical regions.

Seemingly, to the K2 haplogroup branch belong the populations with highest factor and IQ. It branches into K2b and K-M2335. From K2b branches Kn2b1 i.e. M and S, which are high in populations of Papua New Guinea, but they show low factor and IQ 82.5, which beside selection/isolation and climate pressure, could be influenced (personal remark) by small amounts of DNA of an extinct human species which was recently reported. Another K2b branch is P from which branch Q and R.

However, according to yours remark, quote, that the "Native Americans have much lower factor scores than East Asians, despite their high genetic resemblance... implies that the selective pressures for higher IQ continued after the split" (2013) is empirically vague, at least from genealogical perspective, as Native Americans mostly belong to Y-DNA hg-Q while most of the East Asians belong to the Y-DNA hg-O, i.e. in the data set there's no Asian nation with mostly or significant frequency of Y-DNA hg-Q to compare the factor scores with those of the Native Americans with mostly Y-DNA hg-Q. Thus it should be done a research and comparrison between Native Americans and nation/population who live in Siberia, like Kets (93%), who are especially interesting due to possible relation between Yeniseian languages and Na-Dene languages, Selkups (66%), Siberian Yupik (39%), Nivkhs (35%), Chukchi (33%), Tuvinians (38%), or Turkmen in Golestan (42%) and Jawzjan (31%). As for Inuits high Met frequency and IQ compared to average of all Native Americans, it seemingly favors "cold climate pressure", but there exist too many differences in Met data among Native Americans regional populations, 0.013-0.55, indicating other or various reasons, or it can be easly counter-argued by "warm climate pressure" of Mayan (0.55) i.e. lack of the same among other Native Americans (<0.55) who live in "warm climate". Also note that Q is sister branch, at least was 31,900 years ago, of R with high GCA.

The majority of European population belong to the R1 i.e. R1b or R1b branches. It is interesting that specific populations like Basque, who are mainly R1b, had low (-0.30) PC1 factor, which could indicate some older heritage which predated Indo-European genealogical (R1b) assimilation. R1a and R1b are the youngest haplogroups, around 22,800 years old.

K-M2335 branches into NO i.e. N and O. They are a few thousand years older than R. To N belong populations of Northern Eurasia like Yakut, Saami and Finns, while to O belong populations of East Asia like Chinese, Koreans, Japanese, and both groups had high(est) factors and IQ. If it is argued that those populations have inherited highest average factors, then the argument for "cold climate pressure" in Yakut people (N) and Inuit people (Q) is a bit vague.

To conclude - it is complex topic and each Y-DNA haplogroup does and does not necesarilly indicate specific alleles/IQ rather than human evolutionary expansion Out of Africa, along with it other events like social selection, isolation drift, climate pressure and so on. However, it needs further research and confirmation on empirical data and specifical regional levels, as Y-DNA (and mtDNA, need additional research to confirm it) could indicate (there's a need for an average factor of its own) cognitive ability of specific genealogical group of Homo sapiens sapiens throughout history of human evolution and expansion.

On 2018-03-08 03:41:17, user Seth Bordenstein wrote:

Nice work and useful method for RNA enrichment. We targeted DNA of Wolbachia from insect homogenates. 98%+ of the nucleotides captured were Wolbachia. Link to pub: https://www.ncbi.nlm.nih.go...

On 2021-05-11 16:48:45, user Maria Belen Carbonetto wrote:

Thanks for sharing this article in a pre-print mode.<br /> I have a question for the authors, have you added the SynMock plasmids directly into samples before DNA extracction, or after?<br /> thanks!

On 2021-06-22 23:53:26, user Maulik Patel wrote:

I am very proud to post this manuscript from my lab. It represents more than 3 years of work on a project initiated and executed by an extremely talented graduate student James Held in the lab. We would love to receive helpful feedback on the manuscript! Thank you.

On 2025-02-28 12:53:50, user Prof. T. K. Wood wrote:

l 339: Paris is a toxin/antitoxin system and TAs are the most-prevalent phage inhibition system; this should be noted.

l 365: The seminal identification of of TAs and phage inhibition (1996) should cited:<br /> doi: 10.1128/jb.178.7.2044-2050.1996

On 2025-05-27 07:16:30, user Christian Panse wrote:

see also SIB in-silico talk https://youtu.be/acDiXq2xbOw?si=IKYyG2b_kRTTf_vk

On 2022-05-28 14:29:51, user Gene Warren wrote:

I didn't see when the sera from patients hospitalized during the delta wave was collected. I'm guessing it was during their hospitalization, but I'm not sure, and if it was instead collected during the study period I'm curious what the time elapsed since their hospitalization was.

On 2024-08-21 16:31:36, user DUPA- Preprint Review wrote:

The manuscript by Siyi Gu and colleagues presents an unbiased approach using well-established APEX2 proximity labeling proteomics and targeted pharmacological experiments that demonstrate different CCR5 chemokine receptor ligands-based induction of distinct receptor signaling responses and trafficking behaviors, including intracellular receptor sequestration, which offers a potential therapeutic strategy for inhibiting CCR5 functions a repertoire of CCR5 functional diversity. The study reveals the molecular basis for receptor sequestration, including information that can be exploited to develop actionable patterns for developing chemokine-based CCR5 targeting molecules that promote retention of the receptor inside the cell. This is particularly noteworthy because CCR5 plays a crucial role in the immune system and is important in numerous physiological and pathological processes such as inflammation, cancer, and HIV transmission. The work has great scope and importance as an alternative therapeutics strategy to address inflammation, cancer, and HIV transmission, and the experiments are well-designed and sound. The manuscript is well-written, clear, and has a reasonable and logical flow.<br /> We have some minor comments and a few methodological suggestions that would improve the quality of the manuscript.

1. The authors have used the widely accepted APEX proximity labeling technique; however, they have not included a mock or vehicle-treated control to accurately examine the interacting proteins in the different ligand stimulated conditions. This would compensate for any proteins that interact with CCR5 in the absence of a ligand.
2. The method section sometimes lacks critical information (please read all and add relevant details when needed, which is important for the nonexperts). For example, Fig 1 has missing details on the negative control or vehicle control to compare time-resolved ligand-dependent trafficking along with some agonists, partial agonists, antagonists, and inverse agonists.
3. The effect of the different ligands on CCR5 functionality and trafficking should be assessed in the physiologically relevant cell line to determine whether the findings from this paper hold true and can be used for therapeutics. Often, using overexpression systems leads to the observation of phenomena that are absent when the receptor is expressed at native levels.
4. The degradation assays to show CCR5 localization to the lysosome are interesting and relevant but it would be useful to see the whole blot so that the reader can view these as low molecular weight products.
5. Suppose authors can add time-resolved live confocal microscopy for the trafficking of CCR5 under different treatment conditions and show the colocalization. This would add to the impact of the study.

On 2020-08-29 08:39:36, user Alex wrote:

The final article has now been published in Nature Communications. The final authenticated version is available online at: https://dx.doi.org/10.1038/....

On 2017-09-22 17:11:00, user Elisabeth Bik wrote:

There seems to be an inadvertent duplication in Figure 4 - maybe the authors could check?<br /> https://uploads.disquscdn.c...

On 2018-02-15 15:27:40, user Francisco Franco Arenas wrote:

This could start a revolution in how we understand memory not only in unicelurar species but also in more developed ones.

On 2020-03-10 02:15:46, user El Güero wrote:

Legend to figure 3 needs correcting, only mentions panel A and B whereas figure runs to panels A to D.

On 2024-10-03 07:45:33, user Michiel wrote:

Interesting article on relevant topic. The recent article Identification, Elucidation and Deployment of a Cytoplasmic Male Sterility System for Hybrid Potato is a additional useful resource on this topic: https://www.mdpi.com/2079-7737/13/6/447

On 2017-05-08 12:38:25, user Viktor Müller wrote:

Dear Authors,

In what we termed the 'Microbiome Mutiny Hypothesis', we posited that some members of the microbiome might, as an adaptive strategy, switch from mutualistic/commensal to virulent lifestyle when the health of the host declines, e.g., due to old age (https://biologydirect.biome... "https://biologydirect.biomedcentral.com/articles/10.1186/s13062-014-0034-5)"). The evolutionary logic behind this assumption is that when the survival of the host is expected to be reduced, the optimal strategy for residual transmission shifts towards short-term exploitation.

The age-dependent effect of the microbiome that you elegantly demonstrated, might partly be explained as an instance of the 'microbiome mutiny'. Would your data (or your model system) allow the validation of this possibility? In the frame of the observed phenomenon, the Microbiome Mutiny Hypothesis would apply, if the negative health effect of an 'aging microbiome' could, at least partly, be attributed to bacterial species that are present also in young fish, but change their gene expression towards a more virulent phenotype in older animals. In contrast, the lack of a microbiome mutiny would imply that the species responsible for the negative health effect in old fish are virulent already in young fish, but their population is kept under control by the immune system of the host and/or the resilience of the microbiome until the host ages. The question is whether the effect of aging can be fully explained by the change in the composition of the microbiome, or it involves changes also in the 'behaviour' of the persisting species? E.g., is there a way to distinguish whether the increase in the expression of virulence-associated bacterial genes occurred by the increasing abundance of species that were expressing these genes at a stable per capita level, or it involved (also) an increase in per capita expression in some bacteria? The latter would provide evidence for the Microbiome Mutiny Hypothesis.

We would be happy to continue this discussion either here or in private.

Best regards,

Viktor Müller

On 2020-07-15 22:09:42, user Fabiano Menegidio wrote:

Genome available in a public database?

On 2018-02-08 09:52:52, user Taimoor wrote:

Nice work! Not in the cancer field, but we see an opposite effect in stromal cells: Lack of E-cadherin heightens sensitivity to IGF-1 stimulation (Akt, PI3k activation etc.). Cannot disclose more now, but nevertheless found this manuscript very interesting.

On 2019-06-03 02:25:23, user Wenming Xiao wrote:

We replaced this article with the pre-peer review version. Peer reviewed version will be published on a journal soon. Apologize for confusion!

On 2020-02-20 21:07:22, user Kyla Linn wrote:

Review of the manuscript by Quévreux et al. “Interplay between the paradox of enrichment and nutrient cycling in food webs”

Summary.

The authors provide a mathematical model to explain the effects of nutrient cycling on the paradox of enrichment. The study addressed three specific questions based around the effects of nutrient cycling on the stability of food webs. The study used three models, depicted in Figure 2, to address the questions presented in the paper. As a result, the study determined that nutrient cycling can have both stabilizing and destabilizing effects on the dynamics of the food web and species biomass.

Comments.

Figure 1. The legend mentions a pathway that uses arrows on the figure. However, there are no arrows on the figure. It seems like the figure is lacking some essential portions as it did not add to the understanding of the paper.

Figure 2. Should have NC, C, and SC defined in the legend. The legend should tell us a difference between the different sized arrows and the thickness of the lines. Is SC giving any additional insights into the model? If so, this is not clear.

Nutrient cycling is not something that has been ignored. It might just have not been used to look at food web stability. Bronk DA, Glibert PM, Ward BB (1994) Nitrogen uptake, dissolved organic nitrogen release, and new production. Science 265:1843–1846 .

Duration of nutrient recycling, how often does this happen? Does it happen in scale of dynamics that they are looking at based on stability? Does this recycling happen in this time scale? Some estimates from field studies could be useful here.

Table 1. How much of recycling is there? Is this a tough estimate to make? Is this why d and delta are just between [0,1]? Is the linear scale appropriate to be used? Perhaps log scale with some very small values are more realistic.

There were some grammatical errors, text needs additional proof-reading. For example see, Line 266, “50 species got extinct”. Line 265, “run a for 900”.

Holling’s paper or other relevant papers/books should cited in equation 5.

Figure 3. Boxes of each subgraph would help as the x axis numeration looks like 3000 rather than 300 and 0. The graphs when printed in black and white (or viewed by a color-blind person) are indistinguishable.

What is different in Figure 3 of “species persistence” and in Figure 5 of “fraction of species”? Also, in figure 5, how do you define stabilized or not and how do you define the threshold?

The overall novelty of this work needs to be clearly stated.

Is the cited consumption efficiency of 0.45 correct? It seems for some predators (e.g., lions), this is probably much lower.

Line 564: Is the hypothesis that “positive effects of biodiversity on ecosystem’s stability due to nutrient recycling” scientific? (i.e., can it be falsified?)

Line 583: perhaps the need to consider nutrient recycling by ALL ecologists is overstated. There are many things to consider in complex systems, and what to consider depends on the question.

On 2017-05-26 08:36:19, user Martin Johnsson wrote:

Very interesting! I may have missed it, but I couldn't find the sequencing coverage of your samples -- this seems like a quite important piece of information (i.e. with regard to your discussion of high versus low coverage sequencing).

On 2022-10-09 23:27:46, user jiarong wrote:

I am glad to see to see updated manuscript w/ the contamination in the negative set (microbial + plasmid) removed in the simulated Refseq data, addressing my previous comments (https://www.biorxiv.org/content/10.1101/2021.04.12.438782v1#comment-5490721473. I have a few more comments:<br /> - I am concerned that there are unknown prophages in the microbes in the mock community data that could significantly skew the precision lower. The contaminant screening that is used in the simulated Refseq data might help here too.<br /> - From my experience on VirSorter2, the optimal score cutoff for highest F1 could change a lot with different datasets such as environment types, eg. soil samples generally requires higher cutoff. Thus this SOP (https://www.protocols.io/vi... "https://www.protocols.io/view/viral-sequence-identification-sop-with-virsorter2-5qpvoyqebg4o/v3)") is the recommended way to run VirSorter2.

On 2017-01-14 11:14:49, user Jean Manco wrote:

I1534 was reported as Y-DNA haplogroup R1b1a2 by Mathieson et al 2015, but as R1a by Sergey Malyshev, who found the sample negative for CTS11468 and many other R1b specific SNPs. No doubt he would be willing to supply details, as posted here in 2015: http://www.anthrogenica.com...

On 2018-05-13 08:33:55, user David Escors wrote:

Any feedback or review to our manuscript will be more than appreciated.<br /> Thank you

On 2021-12-01 14:45:16, user Firoz K. Bhati wrote:

hey i have gone through this manuscript since i also had some exposure of this field i have a question to ask, did you check the expression of OCT-1 and OCT-3 in these 3 cell lines?. these transporter involve in the influx of metformin in the cells. the progesteron is an inhibitor of these transporter, so my question is, it might be possible that the effect of metformin is reversed because these transporters were inhited by progesteron.Please Check this article<br /> https://www.ncbi.nlm.nih.go...

On 2024-12-03 10:34:33, user Alejandro Gómez Mejia wrote:

This preprint has been reviewed and published in mSphere: DOI: https://doi.org/10.1128/msphere.00430-24

On 2022-11-17 20:19:36, user Rohit Farmer wrote:

This paper describes HDStIM. HDStIM is a method for identifying responses to experimental stimulation in mass or flow cytometry that uses a high dimensional analysis of measured parameters and can be performed with an end-to-end unsupervised approach.

Check out ColabHDStIM an easy to use web-like-interface to test/run HDStIM: https://github.com/rohitfar...

On 2025-08-26 09:28:21, user Constant VINATIER wrote:

Feedbacks about your preprint : https://doi.org/10.1101/2025.07.25.666761

About registration: <br /> We could not find any information about the pre-registration of your study in the pre-print. Pre-registration involves documenting the hypotheses, methods, and/or analyses of a scientific study prior to its conduct (10.1073/pnas.1708274114; 10.1038/s41562-021-01269-4). If your study was pre-registered, we strongly encourage you to include the registration number in the pre-print, ideally in the abstract make this important information easy to retrieve, as this practice enhances transparency and reproducibility. If the study was not pre-registered, this should be acknowledged as a limitation. For future studies, we recommend pre-registering on an appropriate repository.<br /> About Protocol Sharing: <br /> We did not find the protocol for your study. If you have one, we encourage you to share it as supplementary material or deposit it in a publicly available repository such as the Open Science Framework ( https://osf.io ) or Zenodo ( https://zenodo.org/) "https://zenodo.org/)") . You can then include a statement in the Methods section indicating that your protocol is openly available (e.g., 'The protocol for this study is available at (link)/ in the supplementary'). Sharing your protocol will help readers better understand your study and enable them to reproduce it if they wish to test it.<br /> About the Statistical Analysis Plan Sharing: <br /> We did not find the Statistical Analysis Plan (SAP) for your study. If you have one, we encourage you to share it as supplementary material or deposit it in a repository such as the Open Science Framework ( https://osf.io ). You can then include a statement in the Methods section indicating that your protocol is openly available (e.g., 'The SAP for this study is available at (link)/ in the supplementary'). Sharing your SAP will help readers better understand your study and enable them to reproduce it if they wish to test it.<br /> About Deviations and/or changes<br /> We could not find any information about potential deviations or changes to the protocol in your pre-print. Since such deviations are common, if this applies to your study, we strongly encourage you to include a subsection titled Changes to the Initial Protocol in the Methods' section and discuss these changes as a potential limitation of your results. If any deviations occurred during your study, please specify them in this new subsection.<br /> About Data sharing / FAIR Data<br /> We found insufficient information about your data sharing approach. Data should be findable, i.e. data are to assigned a globally unique and persistent identifier (for instance there is a DOI assigned to the dataset, or data are registered or indexed in a searchable resource). Data should also be accessible, i.e. data are retrievable by their identifier and can be accessed following an open, free, and universally implementable protocol. As your data id not sensitive data, we encourage you to share it openly on a data sharing repository (Dryad, etc.) and include the Digital Object Identifier (DOI) in the Methods section. If you want more information about good practices of data sharing, visit https://www.go-fair.org/ <br /> About Code sharing<br /> We could not find any information about your (statistical) code. Sharing code is important for enhancing transparency and reproducibility, especially since it does not contain sensitive information. We encourage you to openly share it on a code sharing platform (Github, Codepen, CodShare, etc.) and include the Digital Object Identifier (DOI) in the Methods section. If you want more information about Code sharing https://fair-software.nl/ .

On 2020-07-05 21:05:30, user Kevin Olivieri wrote:

What an exciting paper! It is pretty amazing to see this series of SNPs to each appear at the same frequency in 1000 Genomes. Also, it shows up at a high frequency in the 5 Siberian individuals in the NBCI's dbSNP. Again each SNP appears at the same frequency.

The gene most of the variants appear in is LZTFL1, which has been implicated in lung cancer and bronchial epithelial cell differentiation (https://www.nature.com/arti... "https://www.nature.com/articles/onc2015328)"). Another study showed its expression correlated with disease in bovine respiratory syncytial virus (https://journals.plos.org/p... "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0186594)"). I would be excited to see any follow up study for the role of these variants in Covid-19 infection.

On 2016-03-15 16:28:05, user Patricia Barlow-Irick wrote:

How Psychology, Ethology and the Foundations of Neuroscience are working together to solve animal training problems (unknownst to them).

On 2020-06-23 20:37:04, user Ray wrote:

Can I just ask.. as this progresses, will knowledge become "obsolete" in the form that instead of having to "read up" on something, you can just trigger a "memory" of said information being relayed from the BMI

On 2021-05-26 16:14:34, user Milka Kostic, PhD wrote:

Dear authors,

Thank you for sharing your preprint with the community. I enjoyed reading this manuscript and happy to share some thoughts below.

Kind regards,

Milka

GENERAL COMMENTS ON PREPRINT BY Frost, Rocha and Ciulli

In this preprint, Frost, Rocha and Ciulli report results of how cells respond to compounds previously described as inhibitors of von Hippel–Lindau (VHL) protein as measured by quantitative mass spectrometry (MS)-based proteomics. VHL is a well-studied protein that serves as a substrate recruiting/recognition subunit of Cullin RING E3 ligase CRL2VHL. The best characterized substrate of VHL is HIF1a, a hypoxia-inducible transcription factor that is under strict control of VHL and oxygen levels. Under normal oxygen conditions, HIF1a is hydroxylated on a proline residue and this modification enhances binding with VHL, resulting in HIF1a polyubiquitination and proteasomal degradation. However, under hypoxic conditions, which often occur in solid tumors, HIF1a is not hydroxylated and is therefore stabilized and able to up-regulate cancer-promoting gene transcription. However, in some conditions, upregulating HIF1a could be beneficial (if interested google Roxadustat). Therefore, agents that inhibit VHL-HIF1a complex formation under normoxic (normal O2) conditions emerged as of interest in drug discovery. Additionally, VHL is one of the most frequently hijacked E3 ubiquitin ligase in the context of targeted protein degradation and PROTAC (Proteolysis Targeting Chimera) development. Thus, VHL ligands are currently of very high interest.

The same lab has previously developed and characterized several VHL-HIF1a complex disruptors, including VH032 and VH298. Here, they take an important step to examine how these compounds affect cellular proteome. They also examine proteomic effects of proline hydroxylation (PHD) inhibitor (IOX2) - PHD is the enzyme that hydroxylates HIF1a under normoxic conditions - and benchmark everything against proteomic effects of hypoxia. The most interesting insight to emerge from these experiments is that treatment with VHL-HIF1a complex disruptors (aka inhibitors of Protein-Protein interactions (iPPIs)) leads to increase in levels of only two proteins: AMY1 (Amylase 1) and VHL. So, setting amylase aside, treatment with VHL “inhibitors” (more formally inhibitors of VHL-HIF1a binding) increases levels of VHL.

This is an unexpected and interesting finding, and the authors follow up to show that the effect is time-dependent, that the effects are at the level of protein not mRNA, that negative controls don’t have the same effect, and that this increase in VHL level is likely due to compound-induced increase in VHL protein stability. Importantly, the authors dive deeper into what happens to VHL ad HIF1a levels upon treatment with VH298, and how the effects change upon prolonged treatment and by increasing concentration of VH298.

The observations can be summarized as: it is complicated! More specifically:

Short treatment with VHL298 leads to disruption of VHL-HIF1a complex and increases levels of HIF1a (seen before and here)<br /> Prolonged treatment with VHL298 stabilizes VHL and increases VHL levels, and increased VHL level reduce HIF1a levels (seen here)<br /> Therefore, pharmacology of VHL ligands, even in the context of their use for PROTAC development may very well be complicated and future efforts would need to take this into account.

Overall, these are interesting results, and in my view the main implications of this work are importance of: (1) performing robust quantitative proteomics experiments as a part of validating/characterizing effects of small molecules; (2) not underestimating complexity of small molecule mechanism of action; and (3) accounting/examining potential differences of acute vs. prolonged treatments and acute vs. prolonged effects when characterizing and validating small molecules.

On 2023-08-09 11:08:59, user Alexis Sperling wrote:

This is now published:https://doi.org/10.1098/rso...

On 2017-02-25 16:43:40, user Vladimir Seplyarskiy wrote:

http://www.biorxiv.org/cont...

On 2020-08-11 20:25:20, user Clifton Ricana wrote:

Accepted manuscript found on PLoS Pathogens: https://journals.plos.org/p...

On 2023-03-19 04:39:50, user Melolontha hippocastani wrote:

https://pubpeer.com/publica...

I believe the author's data preprocessing methods are problematic.

In the paper, the author stated that "all the TPM was standardized with log2(TMP + 0.001)", but upon reviewing the author's processed data provided in the database, I found that the author did not process the data according to the actual situation of each dataset. For example, in the Braun et al. KIRC cohort (PMID:32472114), the supplementary material of the paper provided TPM data that was transformed by upper quartile normalization and then log2 transformed . However, in the data frame provided by the authors, the already standardized data was once again transformed with log2(TMP + 0.001), which is incorrect. Furthermore, since the supplementary material provides data that has been standardized by quartiles, it cannot be reversed to the unnormalized TPM data. Therefore, the correct approach would be to apply for the raw data stored in EGA and re-quantify the data.

In addition, different datasets used different reference genomes(hg19 or hg38), and the author should have indicated this or re-quantified all datasets.

Additionally, the author's labeling of the treatment types for Braun's data is also incorrect, as Checkmate025 is a mixed cohort where patients receive either immune checkpoint inhibitor therapy or mTOR-targeted therapy. The author simply labeled this cohort as an ICI cohort in the table.

In summary, the author should carefully review the data provided.

On 2020-06-08 01:59:00, user Kurganov Erkin wrote:

I would like to ask several questions regarding the channel kinetics:<br /> 1. How authors explain the vivid activation of hTRPA1 in the presence of 100 mkm Ca. (In Figure 2 A,B and C, 100 mkm Ca activates hTRPA1, but conductance and open probability differs largely with and without inhibitors experiments.<br /> 2. Which conductance is the basic conductance at 10 mkm Ca? What other factors are involved in such big difference conductance?<br /> 3. Did others check other divalent or monovalent cations on channel activation? If yes, then do they activate hTRPA1 in the absence of agonist? Is there any difference with and without ankyrin repeat? If no, then do they think that there should be calcium binding site apart from ankyrin repeat?