On 2022-04-26 11:57:49, user Markus Löbrich wrote:

As stated in this publication, we were asked by Dr. Benedetti to provide the phospho-specific RAD54 antibody used in our Spies et al publication and responded that we ran out of the badge used for the publication. Instead, we sent him a new company delivery that we just had received at this time and had not yet tested. This badge was used in the publication of Ghosh, Khalil and Benedetti and shown to be highly unspecific and useless for studying RAD54 phosphorylation. After we had sent the antibody to Dr. Benedetti, we had also tried it ourselves and confirmed its poor quality that prevents any useful studies.

Since the company could not produce a new specific badge of the phospho-specific RAD54 antibody, we took a different approach to extend our studies on the physiological significance of the RAD54-Ser572 phosphorylation. Specifically, we generated RAD54-Ser572 to Ala572 mice by knock-in techology and studied DNA double-strand break repair in fibroblasts obtained from such mice. We observed a repair defect in the RAD54-S/A mutant fibroblasts which is of the same extent and epistatic to the repair defect observed after siRNA-mediated knock-down of NEK1. This result confirms and extends our conclusion of the Spies et al paper. We are currently preparing this data for publication.

On 2022-04-26 09:49:20, user Hao Lu wrote:

We are sorry that the original RegVar domain name (http://regvar.cbportal.org/):3FU3aoLtEY896Iw2OHdUs-1lcro "http://regvar.cbportal.org/)") has expired and cannot be accessed normally for the time being. Please visit the new RegVar server at: https://regvar.omic.tech/.

On 2022-04-25 13:40:05, user Santiago Justo Arevalo wrote:

The authors declare that this manuscript is now published in Scientific reports: https://doi.org/10.1038/s41...

Please, when approppriate cite the published version of this article: https://doi.org/10.1038/s41...

On 2022-04-25 09:38:07, user Cecilia Bang Jensen wrote:

Thanks for an interesting read! Probably somebody has already pointed it out to you but if not, figure 3 b) in the paper seems to have an axis error (KSTAR and KSEA significance scores mislocated)

On 2022-04-23 21:00:06, user JABS Editor wrote:

This article was published in Journal of Applied Biological Sciences (JABS) 16(1): 89-101, 2022 with the title "GLYCOINFORMATICS APPROACH FOR IDENTIFYING TARGET POSITIONS TO INHIBIT INITIAL BINDING OF SARS-COV-2 S1 PROTEIN TO THE HOST CELL".

On 2022-04-23 07:10:22, user BenjaminSchwessinger wrote:

Here are a couple of question for your Sr33/Sr50 preprint:<br /> • Why did you not include LRR15-16 in the hybrid clones Sr50-Sr33. Figure 1D shows that these have really high entropies as well.<br /> • Could you look for AvrSr50 homologs and make them recognized by Sr33 or Sr50? Or could you look for AvrSr50 homologs (also on the fold level with AlphaFold) and test them if they are AvrSr33?<br /> • Could you do targeted/random mutagenesis of the important sites in the Sr50 LRR (based on 2D) to make Sr50 recognize AvrSr50_QCMJC?<br /> • You are talking about this NB-ARC domain – LRR interaction (latch) and how this could be involved in signalling in as far as the disruption of this interaction might trigger activation. How is this observation reflected in the AvrSr35/Sr35 structure on Biorxiv? Is this consistent? I haven’t compared this all in detail but your overall observations seems to be fairly consistent.

On 2022-04-22 15:30:30, user Micha wrote:

The final version has been published here: https://bmcbioinformatics.b...

On 2022-04-22 13:46:47, user Helene Robert Boisivon wrote:

An updated version of this preprint is now published in Frontiers in Plant Science:<br /> https://www.frontiersin.org...

On 2022-04-22 13:26:12, user Martin Rouse wrote:

I remember when K27M interacted with BRD4 (Herz et al), and K9M interacted with KDM3B...

On 2022-04-22 02:30:01, user Maria Chuvochina wrote:

Please consider correcting the newly proposed taxa names as Lutacidiplasma silvani, Lutacidiplasmataceae and Lutacidiplasmatales. Line 101: gen. nov. instead of ord. nov. Line 84: would be great to provide the GenBank ID of the type genome.<br /> Thank you!

On 2022-04-22 01:30:08, user 末永雄介 wrote:

Our paper has been published in EMBO reports! https://embopress.org/doi/f....

The name of the score was changed from PTI score to ORF dominance

On 2022-04-21 07:54:49, user Ed Green wrote:

Great resource - we were also looking at trying the Flongle for this. In your discussion you mention attempting direct bacterial colony sequencing - it might be worth looking at Octant's Octopus protocol (https://github.com/octantbi... that does 96 plex Illumina plasmid validation - they find that adding a rolling circle amplification step improves recovery of plasmid vs bacterial chromosomal DNA.

On 2022-04-20 23:51:54, user Lisa Andrews wrote:

I have a few Pictures and A Video of what I hope to be an Ivory Billed- who would I submit them to? I also have Pileated however, this one looks kinda different

On 2022-04-18 16:39:01, user Sylvie Németh wrote:

I'm concerned about Figure 6 as the top left image clearly looks like a pileated, what with the pattern of white on the head. It does look more like an ivory bill in the bottom left image, but the crest still looks like a pileated's in all the images. I don't know that "leg stance" is a consistent enough of a field mark to dismiss the obviously pileated head. I am concerned that including that woodpecker has lessened the credibility of this paper as a whole.

Figures 1/2/3 are obviously the most convincing with that big white saddle. But when watching the footage they're pulled from, it's hard to see the saddle at any other point, leading one to wonder if it is just a light trick....I want to believe, of course!

On 2022-04-17 13:45:44, user Pick Up Litter wrote:

On your trailcam footage, I believe your best evidence for the white saddle not being a "negative space" is found at the approx. 6 second mark, from the bird climbing on the right side of the main trunk. This is the point that, if you drew an outline of the bird as if the white would be negative space, you would have half a bird (a nonexisting entity, especially when compared to the previous frame which is probably the same bird). If you draw the bird as if the white is a white IB saddle, you have an IB. And the outline of the bird is slightly visible around the white. This would address that critique.

It should be noted that, in the fog footage, there are frames that can be PIWO. This can mean that there might be different birds present, or that there are angles that an IB can look much like a PIWO (probable). Since you showed the special foot adaptation, I believe you have IB images in this.

You should also add distance to the tree in your paper.

On 2022-04-16 13:25:17, user Jess Gonzalez wrote:

I saw a female ivory billed woodpecker numerous times in western North Carolina. On question about it.

On 2022-04-14 22:16:20, user Nikolas Haass wrote:

Sadly, it is not convincing at all. The 'variability' of the 'white saddle' is clearly due to lighting artefacts. Figure 1 blurry and inconclusive; Figure 2: it's a mistake not to consider foreshortening effect; Figure 3: same image as in Figure 1 and 2; Figure 4: even worse than Figure 1; Figure 6 shows what looks like a Pileated Woodpecker (judged by facial pattern and wing pattern); Figure 7: same as Figure 6; Figure 8 nicely demonstrates the lighting artefact; Figures 9 and 12 don't show sufficient detail.

On 2022-04-13 11:28:30, user Langley Respess wrote:

Thanks for posting this. Geoff Hill forwarded it to me. I am very interested in the search for the ivory bill. Let me know how I can help.

On 2022-04-12 13:45:37, user Martin Collinson wrote:

Welcome continued efforts to accuratel record the biodiversity of bottomland forests in LA and elsewhere, but I look at evidence from this paper and draw opposite conclusion, i.e. that outlook for Ivorybill must be pretty bleak. A problem with evidence in current paper, as it was for Luneau video and other records since, is a failure to properly eliminate the more likely alternative hypotheses, that these are just poor views of a common species. in this case - Pileated Woodpecker. it is fairly easy to download images of PIWO in similar poses that are pretty good matches for the plumages and postures seen in these images. I posted on twitter https://twitter.com/docmart... a composite of images from this ms with images of PIWO found in <5 minutes. Some of them I made a bit blurry to reflect the quality of the images in this paper. I believe that the images of perched birds can all relate to PIWO - at least there is no conclusive evidence that they do not. And if they could be Pileateds, they probably were.

https://twitter.com/docmartin2mc/status/1513873735488618506

On 2022-04-20 15:43:53, user Vivian Angelica Bernal Galeano wrote:

An updated version of this paper has been published in Plant physiology, check the next link: https://doi.org/10.1093/plp...

On 2022-04-20 14:07:09, user Daniel Garcia-Ovejero wrote:

Dear Aida and team,

very nice work and really interesting data. Congratulations. During the process of reading, I thought in a couple of comments that might be interesting to discuss:<br /> 1-In the human samples included, it is not specified the approximate cervical level for each one of them. Since I see that many of them show patent central canals, I was wondering if they might belong to the upper C1, or, maybe, to low medulla, in which canal patency is normal (Garcia-Ovejero et al., BRAIN, 2015, supp fig S3). It is very common to receive samples from tissue banks labelled as cervical cord that anatomically really belong to low medulla. Maybe a low mag images from your samples could help to identify this, or you could specify the cervical level of each sample in the table.<br /> 2-Some years ago (Garcia-Ovejero et al., JCN, 2013), we found a rare subpopulation of cells in the ependymal region of the rat spinal cord that was unfrequent and mostly located the lateral aspects of the ependymal lining.These cells proliferated during postnatal development and in response to injury, and formed clusters in mature ages. They could be identified by a strong expression of cannabinoid receptor 1 (cnr1, CB1). We also found them in mice. I was wondering if they could be related with the lateral populations of stem cells that you describe here or overlap somehow. Did you find expression of cnr1 in these cells?.

Thank you very much for your attention and congratulations again. And best luck with reviewers (I'm sorry that I did not received it :) )

Daniel Garcia-Ovejero, Ph.D<br /> Laboratory of Neuroinflammation<br /> Hospital Nacional de Parapléjicos<br /> Toledo<br /> Spain

On 2022-04-20 07:37:25, user Don Chan wrote:

I wonder if a specific amino acid change might be required for resistance as suggested here:<br /> https://www.biorxiv.org/con...

On 2022-04-19 22:49:23, user Joseph Wade wrote:

The following is a review compiled by graduate students participating in the Infectious Disease Journal Club, Department of Biomedical Sciences, University at Albany, SUNY:

This paper addresses the mechanism by which SARS-CoV-2 infection causes inflammation. The authors argue that SARS-CoV-2 spike protein interaction with the ACE2 receptor results in a reduction in CFTR protein levels, which in turn leads to increased inflammation in the COVID-19 airway. This is impactful because the authors identify a mechanism of inflammation caused by SARS-CoV-2 infection that was not previously appreciated. Moreover, these findings link the pathophysiology of COVID-19 and cystic fibrosis.

The paper is well written and easy to follow; the list of goals at the end of the Introduction, and the italicized conclusions at the end of each results section were particularly helpful and contributed to overall clarity. Overall, the data support the major conclusion that there is less cell-surface CFTR following Spike protein binding, leading to inflammation. Nonetheless, we felt that the title of the paper is overstated, and western blot experiments should be replicated, and quantified where appropriate.

Major comments:<br /> The paper title is overstated. Specifically, the paper does not look directly in the “COVID-19 airway”, and the paper does not determine the extent to which CFTR-mediated inflammation contributes to inflammation during infection by SARS-CoV-2. We recommend rewording the title to something like “Inhibition of CFTR signaling by the SARS-CoV-2 Spike protein leads to inflammation”.<br /> Experiments involving western blots should be repeated. Where the differences in protein levels are modest, e.g. TRADD levels in Figure 1, the authors should quantify the band intensity normalized to the control. The authors could either include replicate blots as supplementary data, or quantify all western blot data from replicates, showing variability.

Additional comments:<br /> It would be helpful if the authors could briefly describe the differences between the original Spike protein and the Beta variant in relation to why the Beta variant binds ACE2 more strongly.<br /> Figure 5 could be moved to the supplement since it reanalyzes data shown in other figures.<br /> The conclusion from Figure 6 is understated: “Thus the possibility of ACE2 being a physical bridge, direct or indirect, between Spike protein and CFTR cannot be excluded.” The data in Figure 6 make a more compelling case for an ACE2-CFTR interaction than the text suggests.<br /> The description of data for ENaC in Figure 8 should be expanded. First, it is not clear from panel A that ENaC gamma cleavage is higher in the presence of Spike protein, rather than overall higher ENaC protein levels with the same degree of cleavage. Second, there is no explanation for the lower band seen in panel B (first lane after the ladder).<br /> What is the significance of the pairs of lanes in Figure 6B? Are these replicates? Different elutions?<br /> What is the “lysate” lane shown in Figure 6B? Please also explain in the figure legend what “NRS” is.<br /> Figure 3 – what concentrations of Spike protein are used in glycoside-treated samples? The legend appears to have an error.<br /> The conclusion from Figure 7 is overstated. It does appear that the levels of CFTR at the cell surface are reduced more than total CFTR levels as a result of adding Spike protein, but the mechanism for this cannot be inferred from these data.<br /> Suggested future experiments:<br /> Test the Beta-1.315 Spike protein variant alongside the original S1S2 Spike protein for experiments shown in Figures 1, 3, and 7.<br /> Measure levels of additional cytokines/chemokines in Figure 1B.

On 2022-04-19 16:48:29, user Amita Kapoor wrote:

This has now been published in the Journal of Chormatography B<br /> https://doi.org/10.1016/j.j...

On 2022-04-19 02:34:21, user Jagdish Patel wrote:

The end of the abstract states: "the pastoralists of the Eurasian steppe spread eastward into South Asia." How the heck is South Asia "east" of the Eurasian steppes?

On 2022-04-18 19:25:05, user Daniel Himmelstein wrote:

Living manuscripts with transparent version history are hopefully the way of the future. Since 2017, we've been building an open source tool called Manubot for collaborative authoring on Git/GitHub. Our core repositories currently have 300+ stars on GitHub and have been used to author a wide range of manuscripts.

For more information, see Open collaborative writing with Manubot. In fact, I encourage the authors of this preprint to submit a pull request to the Manufesto source code to add a citation to their survey in the "Living Manuscripts" section!

On 2022-04-17 09:05:13, user Prof. T. K. Wood wrote:

Additional evidence that antitoxins are malleable and arise rapidly, see 2014 https://doi.org/10.1038/sre... where antitoxin ToxI was evolved (not cited). Also, should cite the first paper linking toxin/antitoxin systems to phage inhibition (https://journals.asm.org/do....

On 2022-04-16 15:01:31, user Kenneth P. Mitton, PhD wrote:

This manuscript was peer reviewed and published in the journal Molecular Vision in 2021.

The publication is available free at Molecular Vision. <br /> PDF link version is here:

http://www.molvis.org/molvis/v27/191/mv-v27-191.pdf

On 2022-04-15 16:50:53, user Omprakash Shriwas wrote:

Good concept, that showing directly link CMTM6 with Ribosome biogenesisis.

On 2022-04-14 18:36:40, user Elle Tigre wrote:

Great! Next, check biomarkers of the stool!

On 2022-04-14 17:26:47, user Chouheao wrote:

The neutralizing potency of these antibodies seem too low. Can they protect animal models from the infection of SARS-CoV and MERS-CoV, another 2 human betacoronaviruses which cause severe disease ?

On 2022-04-13 23:23:11, user Nathan Ewing-Crystal wrote:

Naive T cell migration between and within lymphoid organs is essential for detecting cognate antigen on antigen presenting cells; this migration is largely driven by CCL21-CCR7 signaling. This manuscript explores the WNK1 pathway, which is induced by CCR7 signaling and results in ion influx necessary for T cell migration (as shown by this group’s previous work). The authors analyze each WNK1 pathway member’s contribution to migration speed and cell volume through selective ablation and inhibition of proteins within the pathway. They further uncover a spatial enrichment of these proteins at the leading edge of migrating T-cells; localized water entry at this leading edge results in localized swelling and increasing membrane-F-actin spacing. They propose that WNK1 signaling facilitates F-actin polymerization via a Brownian ratchet mechanism.

The major success of this paper is in developing a concise model for the role of WNK1 pathway activation and subsequent ion influx in cell migration through localized water entry facilitating F-actin polymerization. Moreover, the authors decisively link chemokine signaling not only to directional decision-making but also to the physical act of cell migration.

Only minor aspects of the model require clarification; specifically, it is unclear to what extent WNK1 signaling drives directional migration in addition to physical migration (see below). Simple experiments (or reanalysis of existing ones) might be sufficient either to clarify the model or to demonstrate that the uncertainties in question will require extensive further investigation.

Overall, this paper deepens the field’s molecular understanding of chemokine signaling, specifically the extent to which intracellular structural reorganization is a necessary component of chemokine-induced migration.

Major Points:<br /> -While the data show a WNK1-dependent migration speed effect, they also suggest that this may not be the only effect of WNK1 on cell migration. If WNK inhibitors reduce T-cell shape polarization and increase circularity (supplemental Fig. 1D), is it possible that WNK regulates directional movement in addition to migration speed? Alternatively, given that migration speed is determined from timelapse images multiple seconds apart, is it possible that the measured difference in speed is actually detecting an impaired ability to maintain directional movement? An analysis of displacement vs. distance over the imaging timecourse would provide insight as to whether WNK-inhibited T-cells also display directional dysfunction. Alternatively, providing CCL21 as gradient rather than as a homogenous stimulus could enable an analysis of whether WNK signaling is necessary for accurate directional movement.

-The claim that “WNK1 pathway proteins and their activities accumulate at the leading edge of the cell” raises a few unresolved questions. Specifically, does a pre-designated leading edge accumulate WNK1/OSXR1/other WNK signaling effectors? Or do local accumulations of these proteins determine the leading edge of the cell? While a textual revision of this claim could reduce the imprecision, the question raised is interesting and worth answering. For example, when migrating cells switch direction (either spontaneously or in response to an experimentally altered chemokine gradient), do WNK1 and effector proteins begin to accumulate on the new leading edge prior to directional switch, or afterwards? If the former is the case, implying that WNK1 accumulation and signaling may drive selection of a leading edge, WNK1 may play a fundamental role not only in chemokine-driven physical migration but also in the response to chemokine gradients. If the latter is the case, implying that a leading edge recruits WNK1, this signaling pathway would instead drive migration in whatever direction has already been selected. Either way, additional analysis or context would clarify the role of WNK1 signaling in the overall response to chemokine signaling and the extent to which WNK1 drives “movement” vs. “directional migration.”

Minor Points:

-To the reader unfamiliar with in vitro migration assays, the rationale for the inclusion of ICAM - a signaling molecule that could interfere with analysis of chemokine signaling pathways - is unclear; consider providing a textual explanation for this decision.

-As stated, OXSR1 and STK39 both phosphorylate SLC12A2; is their role redundant, are both necessary? (Specifically, if the experiments in Fig 1B are repeated with OXSR1-/-/STK39+/+ and OXSR1+/+/STK39T243A/T234A, would we expect to see the same decrease in migration speed as OXSR1-/-/STK39T243A/T234A, no loss in migration speed, or an intermediate phenotype?)

-If AQP3 is the main water channel expressed on CD4 T cells, why does inhibition of this channel not change basal volume of T-cells (as with Wnk1/Osxr1 inhibition)? Does this data suggest a functional redundancy of water channels, as is suggested with ion channels?

-Consider providing an explanation or reference for the selection of CDC42/CD44 as definitive markers of the leading and trailing edge.

-In Figures 2A-C, it appears that WNK1 and downstream effectors are somewhat enriched at the trailing edge of cells relative to the middle (though to a lesser extent than at the leading edge); speculation as to the significance and cause of this enrichment would help clarify the data. Additionally, the cell expressing GFP alone appears to have a shorter distance from leading to trailing edge, which may also contribute to this effect.

-In Figure 3, plasma membrane (PM) and actin signals generated via instant structured illumination microscopy are normalized, and distances between peaks are measured. These peaks are further away at the cell’s leading edge than on the sides, leading to the interpretation that there is more distance between actin and membrane at the leading edge. However, this interpretation ignores the possibility of a thicker and/or differently distributed actin layer at the leading edge, with the densest region (corresponding to the “peak” of the normalized signal) further away from the membrane simply because the layer of actin is thicker. This scenario would produce identical normalized data despite involving no difference in the distance between the edge of the actin layer and the edge of the membrane. While figure 4 suggests that there is less membrane-proximal actin at the leading edge, the actual distance between membrane and the actin that is present remains unclear. Consider providing un-normalized data to help clarify this interpretation (with similar peak intensities between leading and non-leading edges implying similar actin layer distributions). Alternatively, consider comparing the strength of the (non-normalized) actin signal at the location of the membrane signal across cell membrane locations.

-In Figure 3B, the actin and membrane signals appear to be normalized to peak actin and peak membrane fluorescence, respectively. In contrast, in Figure 3C, both signals appear to be normalized to peak membrane fluorescence. Consider clarifying whether this is the case, and why these distinct strategies are utilized.

-Could the negative effect of hypotonic solutions on the migration speed of phenotypically normal T-cells arise from water entry through non-AQP3 water channels? (Presumably any additional entry of water through AQP3, concentrated at the leading edge, would further increase migration speed). If this is the case, wouldn’t non-specific cell-wide water entry, and subsequent diminishing of directional migration, still occur in WNK-inhibited T-cells? The use of superresolution imaging and/or membrane-proximal actin visualization in experiments involving altered tonicity could provide insight as to where hypotonicity-induced water entry is occurring. Additionally, speculation as to why the benefit of bypassing a blocked WNK pathway overcomes the deficit caused by non-leading-edge-specific water entry (if visualized) would be helpful.

-Could labeled materials (e.g. water and sodium isotopes) or depletion of individual ions (i.e. sodium, potassium, etc., one at a time) be used to further characterize the influx of ions and water proposed in this manuscript? While possibly outside of the scope of this paper, consider discussing this (or other biochemical methods) as part of a set of proposed future experiments.

Stylistic notes:<br /> -The order of the data as presented in the figures does not match the order of data as presented in the text. For example, the first half of figure 1 is discussed, followed by the entirety of figure 2, followed by the second half of figure 1. Consider revising the text to reflect the order of the data in the figures, or vice versa.

-Consider including figure 4k as a graphical abstract for the published paper (for people unfamiliar with the signaling pathway and who get confused by the acronyms). Further, on figure 4k, consider adding a visual depiction of G-actin monomer addition to the F-actin filament tips following the depiction of the membrane swelling.

-Consider stating the full names of proteins discussed before using their respective acronyms (e.g. lysine deficient protein kinase 1 for WNK1), to help readers unfamiliar with these proteins.

Note: We cannot offer expert feedback on the superresolution microscopy methods used, other than the already raised questions about the interpretation of the resulting data.

Nathan Ewing-Crystal (nathan.ewing-crystal@ucsf.edu)<br /> Catherine Kuhn (catherine.kuhn@ucsf.edu)

On 2022-04-13 20:17:21, user Chouheao wrote:

This antibody 76E1 also targets to the fusion peptide. https://www.researchsquare....

On 2022-04-13 17:30:46, user Ricardo M. wrote:

The dataset used for the analysis is actually very biased. The subscribers that have both parents in the databank are also way more likely to have many other family members tested in their pursuit of certain genealogical questions, when compared to an average subscriber. Even though the analysis excludes parents (I believe also descendants) during computation of each individual's phasing, all the other family members would be available for IBD data, and common sense indicates the group selected is likely to have more matches and closer matches than the databank in general. Their average of 4.31 close matches (>400 cM) taking into account their complete database suggests several first cousin or second cousins, which is not common sense expectation for most users. How does that average of close matches for this dataset compare to a random sampling of the entire database? Could they have adjusted the results using the same Table 2 to adjust their results to such a scenario and what results would they have seen?<br /> I also note that competitor's 23andme whitepaper is interestingly more detailed in the actual methodology of their phasing algorithm than this preprint, which is interesting as this is actually a longer "paper". Wonder if it is accepted for publication.

On 2022-04-12 19:25:18, user Gary McDowell wrote:

This paper investigates the general phenomenon of bias in journal publication of terms of which experimental methods are used. This includes looking at whether extra experiments using animal models are being requested by reviewers of journal articles. The authors take anecdotal reports of an increased reliance on animal-model validation of experimental results in peer review and measure the prevalence of the phenomenon using a survey.

One caveat is that the survey is shared through social media and private channels, introducing a bias in audience receiving it, and also that those most likely to respond will be those personally motivated to do so. In addition, the sample size is somewhat small. However, the authors account for this in their analysis and as the purpose of the study is to start to assess whether the phenomenon exists, this is not a major concern.

It would be interesting to see whether there are overlaps in the specific groups identified from the survey results, but I appreciate the sample sizes are small and any results would be purely indicative of further areas for exploration at this stage. For example, are there any trends looking at peer review experience vs number of requests for animal model experiments (is it roughly linear, suggesting it has been occurring at a steady rate over people’s careers and could be static, or are people who are perhaps earlier in their career seeing a higher rate, indicating a possible increase)? Are the reviewers in the sample who request animal models giving different responses/comments to those who do not request them (there seems to be a 50/50 split)?

I was a little confused by the differing frequencies in Figure 2 and Figure 3 (and there may be a figure legend missing for Figure 3, or both charts are in Figure 2 in which case that figure legend needs expanding) - more people responded that they never request animal experiments vs those who say they request 0 or N/A in the frequency chart. Is there a reason for this?

One aspect that struck me as most interesting was the request for validation in animal models for work carried out in human systems is very similar to an anecdotal phenomenon from my own experience as a developmental biologist working with Xenopus, and from anecdotal discussions from colleagues, that there is a high request for validation of frog work in tissue-culture systems, or mouse systems. I have heard similar criticisms as those indicated in the boxes. There is also a less similar anecdotal phenomenon in mass spectrometry of requiring Western Blot validation of results, which is a far less sensitive technique. Based on this, could the authors comment on whether the phenomenon they describe could be evidence of larger phenomena, for example, the need to validate across multiple models, or even simply the perception that peer reviewers should just be requesting more experiments? Could there be a phenomenon that is mouse-centric, noting the prevalence of comments about mice? Or is this truly a phenomenon that may be limited to their field? You mention in the discussion that this appears to be a new type of bias, and so I’m curious as to whether that is the case given other “added experiment in other models” phenomena that may exist, but maybe have not been systematically studied and reported. This also would be interesting to compare with the discussion on conservatism bias, because there may be some different questions as to whether the bias is based on innovation, or based on more-rarely-used models, or is based on a drive to add experiments/replicate experiments in multiple systems, all of which have slightly different nuances. The prevalence of mouse models in science and whether there is a bias towards mice would be a question to ask that aligns with the point about the bias of having more enthusiasm for applications addressing someone’s own area of research that the authors mention.

The discussion is extremely comprehensive and the article raises a number of very important points for further consideration by the community, and adds new interesting thoughts to the process of peer review from a particular perspective, and is very interesting to read. The results should be taken as quite preliminary and indicative but are a great insight into an interesting topic that has largely been focused on anecdotal complaints.

It does not appear that survey respondent names or identifying information were collected, nor information on the journals in question gathered. What could have been useful is being able to determine whether certain journals have a prevalence for this behavior, as particular targets of further work. However, on the basis of this work, one thing that could potentially be very interesting is partnering with journals that people are likely to publish with, presenting them with this information and asking them to distribute a follow-up survey to their reviewers and people who have submitted manuscripts in the past. This would hopefully be very useful for the journals, and perhaps anonymous data could be passed across in a data collection agreement from a number of journals to investigate the phenomenon, without necessarily naming the specific journals in the results. This is purely as an idea to potentially partner with journals who are interested in reform, if one desired outcome of this work is to address this problem explicitly.

This review has been undertaken with a view to using the FAST principles (Iborra et al., OSF Preprints, 2022, https://osf.io/9wdcq/) for preprint peer review.

COI: I have no conflicts to disclose.

On 2022-04-12 13:11:41, user Mattia Deluigi wrote:

We appreciate that the authors have now mentioned the backmutated rNTSR1 crystal structure and added additional panels.

However, we remain of the opinion that the backmutated crystal structure should also be included in Figs 2e (ligand) and 2g (TM7-ECL3) in the main text.

This is because the authors aim to compare the cryo-EM approach with crystallography, and the backmutant structure features better electron density for the ligand and a better model for the TM7-ECL3 region than the non-backmutant structure shown in the current manuscript, including residue W339/334 in ECL3, which is discussed by the authors, and F/Y7.28.

Furthermore, by showing the backmutant in these main Figs, one would better assess the differences in the TM7-ECL3 region between rat and human NTSR1 than in the current Fig 2g, including F/Y7.28 and W339/334, which are crucial for ligand design.

In addition, one would immediately realize that the electron density supported well the placement of the chlorine moiety of the ligand, which is not apparent from the current Fig 2e.

Additional suggestions:

1) In line 159, it appears that ref 19 has been misplaced, as the back-mutated construct rNTSR1-H4bmx mentioned in that sentence is described in ref 17 (together with all the crystal structures) and not in ref 19. A more appropriate position for ref 19 would be in line 156 after “to acquire numerous thermostabilizing mutations”.

2) The correct names of the crystallized constructs are rNTSR1-H4x and rNTSR1-H4bmx. In the current version of the manuscript, the “x” and/or “1” are often missing, or “NTRS” is written instead. In the legend of Fig 2g, “ECL2” should be corrected to “ECL3” or “TM7-ECL3 region”.

Kind regards,

Mattia Deluigi, Christoph Klenk, and Andreas Plückthun

On 2022-04-12 09:51:55, user Ben Clifton wrote:

The 160-fold increase in Kd for mAb114 UCA is a really stunning result but I found it quite mysterious from a protein evolution point of view. The affinity-matured version of the mAb114 VH domain seems to be one of the training sequences from the UniRef90 database (https://www.uniprot.org/uni.... Although mAb114 is only one sequence out of ~100 million, the language model is also highly parameterized, so it's not totally unreasonable to think that one highly relevant sequence could influence the results. Are the mutations that increase affinity still predicted even if mAb114 VH is removed from the training set? At any rate, it may be necessary to revise line 169 ("despite having no knowledge of the matured form").

On 2022-04-12 07:06:06, user gezmi wrote:

Great work! It would be interesting to see the predictions underlying Table 3 (predicted vs experimental binding affinity) instead of a correlation coefficient. (Also Table C2 and C5).

On 2022-04-11 19:41:33, user Chen wrote:

Very interesting preprint. Supplemental tables 3 and 4 are missing.

On 2022-04-11 15:48:08, user Stuart MacGowan wrote:

We followed this up in a paper now published in PLOS Computational Biology (https://doi.org/10.1371/jou... where we collected experimental data to verify these predictions.

It turned out we were right about the variants we thought would inhibit binding and protect against infection!

But, we were wrong about the predicted affinity enhancing variants, which turned out to inhibit or have little effect on binding. This has clear implications for where research should be focussed on improving these binding predictors in the future.

Some other neat results in the new paper too.

On 2022-04-11 04:35:25, user Joshua Mylne wrote:

This is now published in Plant Communications, see https://doi.org/10.1016/j.x...

On 2022-04-10 11:06:58, user uv wrote:

More credit could have been given to the CATCH method. this is basically exactly the same enrichment method

On 2022-04-10 10:59:21, user Yutaro Ueoka wrote:

Thank you for interesting article.<br /> Fig4a and Fig4b seems to be completely same graph. mistake?

On 2022-04-09 21:06:27, user Bruce Corliss wrote:

The link to the github repository has been changed to: https://github.com/uva-peir....

On 2022-04-08 10:59:36, user Amos Bairoch wrote:

The paper indicates "Caco-2 and HT29-MTX cells (ATCC)" but the HT29-MTX cell line is not distributed by ATCC. If you are using HT29-MTX you need to indicate where you really got it from. Alternatively if you are using HT-29-MTX-E12 distributed by ECACC (see the relevant Cellosaurus entry (https://web.expasy.org/cell... you need to indicate that and not just HT29-MTX.

On 2022-04-08 06:37:30, user Nikolas Haass wrote:

now published: J R Soc Interface 19: 20210903<br /> PMID: 35382573; doi: 10.1098/rsif.2021.0903

On 2022-04-07 15:41:49, user mcmorais@utmb.edu wrote:

Another excellent contribution to the dengue biology field from the Lok lab. Congratulations!

On 2022-04-07 12:12:22, user Malte Jochum wrote:

This preprint is now published as a paper in Biology Letters here: https://royalsocietypublish...

On 2022-04-07 10:53:22, user Michael Tress wrote:

Note that UniProtKB have now updated the TAFAZZIN display isoform as a result of this paper.

On 2022-04-06 12:20:47, user Wouter De Coster wrote:

Dear authors,

Thank you for the interesting work, although these regions are not something that specifically crossed my research path before. I noticed the link to the repository doesn't work (yet, maybe marked as private)?

Cheers from Antwerp,<br /> Wouter

On 2022-04-06 07:33:18, user David Smith wrote:

X- ray and gamma-ray radiation interacts with either the bond between the innermost electrons in an atom (photoelectric absorption), with the electrons individually without regard to their presence in an atom (Compton scattering) or, if the energy is high enough, with the high electric field in the immediate vicinity of the nucleus to convert to an electron positron pair. None of these mechanisms cares about the chemical bonds between the atoms, so there can be no meaningful difference between 1 g/cm2 of melanin and 1 g/cm2 of pretty much any other organic compound. (This is not necessarily the case for charged particle radiation, like the deuterons in reference [33]). The chemical formula of melanin is C18 H10 N2 O4, which has no high-Z elements in it that could enhance x- and gamma-ray attenuation, as noted (with no reply) by VGR Subramanian. I'm elaborating here for future readers. In the current version of the article the authors back off slightly from their claims, but they still ignore the basic physical mechanisms that imply that organic material is organic material as far as x- and gamma- radiation shielding is concerned. Whether it is living material, melanin, etc. cannot make it perform better than any other random slab of CHNO compounds. To suggest otherwise would require a standard of proof high enough to justify overturning 125 years of settled physics on the interaction of radiation with matter.<br /> -David Smith<br /> University of California, Santa Cruz

On 2022-04-05 14:22:41, user Igor Zivkovic wrote:

This preprint is published, and a link to the publication is forthcoming.

On 2022-04-05 14:10:29, user Alizée Malnoë wrote:

The manuscript “The role of LHCBM1 in non-photochemical quenching in Chlamydomonas reinhardtii” by Liu et al. aims to elucidate how LHCBM1 is involved in non-photochemical quenching (NPQ) in Chlamydomonas reinhardtii. The Chlamydomonas mutant lacking LHCBM1 (npq5) displays a low NPQ phenotype. The authors found that the antenna size and LHCSR3 accumulation are not responsible for the lower NPQ phenotype in npq5. They also artificially acidified the lumenal pH to protonate LHCSR3 for NPQ induction and found that npq5 NPQ is still low. They propose that absence of LHCBM1 could alter the association of LHCSR3 with the PSII supercomplex or that LHCBM1 interacts with LHCSR3 which would enhance its quenching capacity. This work enriches the knowledge about the impact of lack of LHCBM1 on antenna size, PSII function, LHCSR1 and 3 proteins accumulation and NPQ capacity during a 48-h high light treatment.

Major comments<br /> - Fig. 1, it is stated that LHCBM1 (Type IV) does not accumulate but Fig. S1 and S2 show that Type IV accumulates between 7 and 9% of the total amount of LHCBMs. Could you comment on this accumulation, and whether it increases in high light? <br /> - Statistical test between WT and npq5 for all the data represented in bar graphs would be required to state that differences are significant. Consider showing all data points in addition to error bars.<br /> - Consider providing Fo and Fm values for all fluorescence measurements (and D1 accumulation) to discuss whether a possible increased Fo in npq5 in HL is due to disconnected antenna and/or loss of functional core complexes.<br /> - Discussion, discuss why there is much less LHCSR1 in the npq5 mutant? Is this regulation at the transcriptional or post-translational level? It could be that LHCBM1 interacts with LHCSR1 preventing its degradation. In the abstract, and discussion, as no interaction data is shown, consider toning down statements regarding LHCBM1 interaction with LHCSR3. <br /> - Discussion, discuss whether similar results were observed for the npq4 mutant (increased antenna size in HL and less functional PSII observed in npq5). Is this phenotype linked to the low NPQ capacity or specific to the lower level of LHCBM1 in the npq5 mutant?

Minor comments<br /> - Please give a title to summarize the first result in Fig. 1. In the result section, consider making the section subtitles more informative about the main result from that section (e.g., “Photoprotection capacity after high-light acclimation” could be “NPQ induction is severely hampered in npq5 after high-light acclimation”).<br /> - Page 3, method, actinic light of 1,500 μmol photons.m-2.s-1 (and saturating pulses of 12,000 μmol photons.m-2.s-1) are used for the fluorescence measurements. Comment on the choice of such a high light intensity (and high intensity pulses, actinic effect?). Why not use a light intensity closer to the HL treatment? Also state length of dark-acclimation prior to each type of measurement.<br /> - Immunoblot method, state dilutions used and secondary antibody type and dilution used.<br /> - Page 4, it is stated that “the level of the other LHCBMs is similar to that of WT (CC-425)” by referring to Fig. S1 and S2. It is difficult to see in Fig. S1 that the level of other LHCBMs is similar in npq5 and WT as the data is represented in percentage of total LHCBMs. Could you also represent the amount of the different LHCBMs in npq5 normalized to WT in a bar graph? For the Type III and Type II/I LHCBMs accumulation, a dilution series might be best for quantification to ensure that CP47 antibody signal is not saturated.<br /> In Fig. S1, npq5 should be italic. In Fig. S2, one of the labels is likely wrong: there are two 48h time points. By biological replica, is it meant three independent batches of grown and treated cells? What is the fourth band right below the upper band detected by the LHCBM5 antibody at 48h? <br /> - Fig. 5, please enlarge panels B and C to match panel A.<br /> - Fig. 6, specify in the legend that 24h and 48h refer to the HL treatment. <br /> - Discussion, “and confirmed here by analysis of the [npq5] mutant” (not npq4)

Jingfang Hao and Pierrick Bru (Umeå University) - not prompted by a journal; this review was written within a preprint  journal club with input from group discussion including Alizée Malnoë, Aurélie Crepin, Fadime Demirel, Jack Forsman and Domenica Farci.

On 2022-04-05 10:35:27, user Marin Burek Kuntic wrote:

Why do data in your control groups (WB and other experiments) don't have any standard deviation?

On 2022-04-02 01:13:51, user Gordon Smyth wrote:

The Figshare repository associated with this article now has a DOI, which is<br /> https://doi.org/10.6084/m9....

On 2022-04-01 20:33:17, user Joel McManus wrote:

Please see the McManus Lab's response to the commentary from Byeon, Leppek and Barna<br /> https://drive.google.com/file/d/11PmIIZ4xiq1CYVWAx1imA71Qdpv08DmD/view?usp=sharing

On 2022-02-27 23:32:57, user Maria Barna wrote:

Please see the latest response to the Akirtava et al. preprint from the Barna lab:https://drive.google.com/fi...

On 2022-02-12 04:05:27, user S. Ecret wrote:

This is a really well written and clear preprint. Given the subject matter, it is also written very fairly with regards to the critique of the labs whose work is being re-assessed. Well done to all those involved. I sincerely hope that the concerned labs will respond in a productive and open-minded manner.

I am not qualified to discuss most of the technical aspects of this preprint but had a suggestion that might be, if nothing else, fun to do with the Infernal/R-scape work:

(Writing from memory so please excuse any mistakes in names)

The authors aptly show that the Hox9a hTL shoes no evolutionary signal for a conserved RNA structure. They also discuss inconsistencies with cryo-EM data with regards to the P4-ES9S interaction.

Infernal/R-scape cannot natively work on trans interactions - but perhaps by stitching the P4 and ES9S sequences from the already studied ~550 genomes together with a synthetic, fixed stem loop sequence, Infernal/R-scape could be used to assess the likelihood of a evolutionary signal for the trans interaction? The idea would be that a P4 and ES9S sequence from a single genome would be stitched together and then subjected to assessment after alignment.

I'm not sure what the Eddy or Rivas groups would say about such a use of their tool..

On 2022-02-11 19:22:10, user Joel McManus wrote:

Tom Dever's group has a paper in press from an independent study with Rachel Green (ref 30 in our preprint) that also shows Hox gene 5' UTRs are much shorter in developing mouse somites and don't include the putative IRESes. They also found the putative Hoxa9 IRES has promoter activity. On a positive note, they show Hox genes (including Hoxa9) have conserved functional uORFs. Exciting stuff!

On 2022-04-01 13:22:29, user Rohit Satyam wrote:

Hi authors. Congratulations for such a great articles. Would you mind answering a query related to this article that I posted to save space here: https://github.com/Ghosn-La....

On 2022-04-01 11:49:27, user Prof. T. K. Wood wrote:

Beautiful work; congratulations! As always in the anti-viral field (including toxin/antitoxin systems), the evidence for these systems as "killing" and "programmed cell death" circuits is weak (based on over-expression) and perhaps should not be so readily embraced.

On 2022-04-01 05:23:55, user PZM Diagnostics wrote:

Published in Archive of Pathology and Laboratory Medicine https://doi.org/10.5858/arp...

On 2022-03-30 10:17:29, user GP wrote:

don't miss on relevant literature:<br /> https://www.frontiersin.org...

On 2022-03-28 21:55:53, user Fraser Lab wrote:

In this paper, the author uses innovative SVD rotation methods to analyze previous serial crystallography datasets with the aim of delineating retinal isomerization in bacteriorhodopsin (bR). This analysis reveals novel intermediate states that lend insight into how the transition takes place as well as the role played by residues in bR’s active site. The author hypothesizes that a non-specific Coulomb attraction force triggers sampling of multiple cis conformations and single bond rotations in retinal, but the selection of the 13-cis conformation is achieved by the stereochemical constraints in the protein environment. The author lays out the major questions of the paper very clearly in the introduction and the results and discussion sections remain well-focused on answering those questions. The mathematical and computational methods presented here demonstrate how they can be used to tease out the intermediates from “multi” datasets (as shown by the author in his other publications). We share the author’s frustration that “never-observed dataset reconstituted from a collection of experimental datasets does not match the well-established crystallographic template of PDB” - there are interesting new directions in joint refinement across datasets that are considered siloed by the PDB - and this paper contributes well to that frontier. Overall, the paper is well written and the methods section is particularly instructive in the general approach, but lacking details that would allow us to follow the exact implementation used for this paper.

Major Points:

1. Overall: no statement on data/code availability is given. Even if proprietary software is used, it is essential to know what options are used and to have a detailed protocol to reproduce the work.

2. The author comments on how timepoints from the Kovacs et al drive the vectors of interest (U10, U14, U17), yet it’s challenging to differentiate the insights gained from the SVD over FO-FO maps from Kovacs. We suggest a quantitative comparison showing signal-to-noise or goodness of fit which may help the reader interpret the benefits gained from SVD.

3. While the methods section is quite detailed in describing how SVD is performed and how the Ren rotations retain the orthonormal properties of the SVD, the details of how the angles for rotations are chosen are not given. The author says “SVD analysis presented in this paper employs rotations extensively” but what angles of rotations were sampled? Which were the good rotations and which were the bad rotations? A read-through of the earlier papers of the author (Ren 2019, Ren 2016) did not provide answers for these questions either (with large portions of the text in the methods sections being nearly identical between these papers) We infer this is using the software “Dynamix” based on the Acknowledgements. While it is understandable that decisions on what rotations need to be done are subjective, it is imperative that the author guides the reader through these decisions to help in reproduction/application of this method. Without these details it is difficult to know, for example, whether the oscillatory vectors are related to particular choices in the SVD procedure or are relatively without bias.

4. Majority of datasets are clustered in the current SVD iteration - did another Ren rotation not separate Kovacs datasets according to an interesting piece of metadata? This seems like an opportunity to rotate the SVD and further demonstrate the utility of the Ren rotation method.

5. The author shows the oscillatory behavior of 5 pairs of components in figure S3. But how did he arrive at these 5 pairs? Did other pairs not show such kind of oscillatory behavior?

6. The author goes into fine details of the intermediate conformations of retinal and shows differences in distances in the range of less than an angstrom. Is there a goodness-of-fit metric that can be used by the author to show that the atomic positions determined by the author are indeed the best/only possible positions? What if there is another intermediate conformation of retinal that fits equally well into the densities but does not have the S-shaped creasing? How do these conformations navigate the need to fit to density without being too high energy?

Minor points:

1. Labeling of “inboard” and “outboard” terms can be done in main Figure 1 rather than referring reader to S1

2. Figure 2(b): overall trend is useful, might there be an easier way to visualize? Some sort of 1D - distance plot of integrated intensities?

3. Panel C in figure 2 is cited extensively in the text. Rather than mentioning the sub-panels as the 1st, 2nd, 3rd panel, etc., the author could just split panel c into d,e,f, etc.

4. Figure 2(c) - lines are difficult to visualize. They could be made bold. The author could also reduce the panel to show only atomic displacement and torsion angle and move the rest to supplement if necessary, so that the two main plots could be made bigger for easier interpretation.

5. Figure 1(c) and Figure 3(a) & (b) are very difficult to interpret. The author can zoom in further and use translucent volume rendering rather than mesh

6. Figure 3(d) - The author could label as “helix A, B, …, G” so that it’s immediately obvious what’s shown in figure, otherwise a nice illustration of motion

7. Figure 3(e) retinal could be color coded or more easily separated from amino acids to make it easier and faster to interpret.

8. The maps of I’ (Fig S6), I (Fig S7), J’ (Fig S9) and J (Fig S10) are contoured at different sigma levels presumably due to the Fo contributions differing based on the reconstitution procedure. Is there any trend in the reconstitution that would guide us to calibrate what sigma levels to be equivalent across datasets, or is this purely visual?

Alexander Wolff, Ashraya Ravikumar, James Fraser (UCSF)

On 2022-03-28 16:52:33, user Robert Flight wrote:

The links for the packages should be:

R: https://github.com/MoseleyB...

Python: https://github.com/MoseleyB...

On 2022-03-25 15:14:03, user Wenjing Ma wrote:

Our work has been accepted by Briefings in Bioinformatics here: https://doi.org/10.1093/bib....

On 2022-03-25 13:52:01, user Nikolaj Koch wrote:

and also if you used the one with a C or U at postion 25

On 2022-03-25 13:43:20, user Nikolaj Koch wrote:

Hi guys,

could specify were you got your tRNA structure from in Figure 2a since the base 25 is a C in your case but for mazei or barkeri wildtype you have a U there.

Best,<br /> Niko

On 2022-03-25 13:13:16, user Teun Dekker wrote:

While it is difficult to judge the content based on an abstract, Prof. Bruce has kindly shared a preprint with us. The paper shows that Desmodium releases volatiles, as we also demonstrate. However, it is quite apparent from their data that it does not do so constitutively: the various extracts that were used were highly variable in release rates, which is typical for induction. The high levels of green-leaf volatiles reported, which were absent in our samples, are also a telltale sign of damage. In the paper, maize also released some volatiles, indicating some above or below ground herbivore induction too. But, without the critical data on volatile release following controlled herbivore damage (i.e. a positive control) or internal standards, it is difficult to assess the results.

In contrast, our carefully collected data shows that when unchallenged, neither Desmodium, nor maize release volatiles. We have analyzed hundreds of samples from various geographic locations (Sweden, Tanzania, Uganda, South Africa), from the greenhouse and the field, using different soils and microbial augmentations, using entrainments as well as SPME. All these samples, without exception, show that undamaged Desmodium does not release the ‘push’ volatiles constitutively (monoterpenes and sesquiterpenes), while Melinis does constitutively release a diversity of terpenes in very large quantities. As we know, plants generally do not release volatiles constitutively. Some do, often through glandular trichomes (e.g. Melinis), which Desmodium lacks. Upon induction Desmodium indeed releases volatiles, but it is low compared to induced maize and Melinis. Between brackets, the above also shows that differences in methodology (SPME versus entrainments) cannot explain the differences between the studies, which was perhaps implied by Prof. Bruce’s comment.

However, most significant is that our large number of field collections demonstrate that Desmodium rarely and minimally releases volatiles in the field, even though most plants showed some herbivore damage. In contrast, maize releases substantial amounts and a large diversity of terpenes. To be at the basis of reducing Lepidoptera pest pressure in maize, Desmodium would have to reliably and at high levels release volatiles in the field, whether induced or constitutively. Ironically, if pest reduction would depend on volatiles, maize seems to do a much better job. Indeed, our data also show that whole Desmodium plants did not repel S. frugiperda from ovipositing on maize plants.

Our research provides the first experimental evidence that Desmodium does not constitutively release volatiles, and only minimally and unreliably in the field, in response to herbivory. This is very different from what has been suggested over the last two decades (in the absence of data), and implies that the pest suppressive role of Desmodium is due to other mechanisms. We provide such an alternative mechanism: Desmodium is a catch crop that truncates larval development and kills insects. Such observations have been made in the somewhat grey literature, with even vertebrates found to be captured by Desmodium. Obviously, how the mechanism exactly plays out in the field under various conditions needs further detailed field studies. Knowing the exact mechanisms of pest suppression is critical to further improve the maize intercropping system, and to rationally translate such sustainable pest suppressive intercropping methods in other cropping systems. We are keen on teasing out the interplay of mechanisms together with other researchers, particularly at icipe, who designed the system in the first place.

Teun Dekker

On 2022-03-22 17:39:51, user Toby Bruce wrote:

The finding, reported here, that Desmodium intortum "did not release volatiles constitutively at all” is not what we found. I am writing on behalf of our research team from the International Centre of Insect Physiology and Ecology (icipe) and Keele University. We have data in press (https://www.frontiersin.org/articles/10.3389/fevo.2022.883020/abstract) identifying constitutively emitted D. intortum volatiles. Furthermore, these volatiles were found to elicit electrophysiological responses from Spodoptera frugiperda antennae and were repellent to moths in bioassays. We used air entrainment for sampling of volatiles not SPME but still it is surprising that the study reported here found no constitutive emission of D. intortum volatiles. While we disagree about the volatiles, the finding of low larval survival of S. frugiperda caterpillars on D. intortum is in agreement with studies conducted at icipe.

On 2022-03-25 09:46:45, user JohnD wrote:

Interesting work, congrats! <br /> Findings would benefit being compare to pre-existing work, including first studies describing engineering of a human bone marrow organoids (https://www.pnas.org/doi/10..., https://www.pnas.org/doi/10....

On 2022-03-24 22:51:01, user Xinwei Cao wrote:

Hi Miguel, congratulations on such a nice and expansive study. I quickly glanced through your manuscript and am intrigued by supplementary Figure 12. It looks to me that in some cell types Yap (transcript?) level correlates extraordinarily well with total transcript level (if I understood those panels correctly) whereas in others there is no correlation at all. This pattern is not seen with Myc or Mtor. I am wondering if these data suggest that the correlation between Yap and hypertranscription is selective for cell-type, whereas Mtor (and maybe Myc) is more generic. Furthermore, YAP protein activity/stability is highly regulated, which may also affected your analysis, which I assume is based on transcript levels. For the ChEA data analysis, I wonder whether YAP's preferential binding for enhancers (versus Myc for promoters) may have an effect on the result.

On 2022-03-24 17:28:30, user Amanda Walton wrote:

Hello, your research intrigues me. To think that worker bees and queen bees are not only distinct in their roles within the colony and their biology, but also display diverging gut microbiomes specific to the ecological niche to which it resides and thus a varying metabolism is quite fascinating. I do have some questions concerning quality control of your methods, though. What was the PCR target region, and how many PCR cycles were run? Minimizing the number of rounds of PCR reduces the sequencing error rate and fraction of chimeric sequences. I did see that you used universal primers to amplify the V4 region of the 16sRNA gene. Likewise, your methods section mentions quality filtering with VSEARCH, but other specifics are limited. I appreciate you considering both the dominant and rare taxa appropriately, and your handling and preparation of samples prior to DNA extraction was spot on. Further, OTU abundance was calculated by mapping the total quality-filtered paired reads to the final clusters, with the resulting abundances normalized by the total 16s rRNA gene copy numbers as estimated through qPCR, and differences in copy numbers across samples were evaluated with an aligned Rank Transformation of a Factorial Model with the R package ARTool. This method was explained well and is thorough. What about measuring sequencing coverage of the communities? Also, I did not see any mention of internal standards at the PCR step or the DNA extraction step. While I did see that resulting sequence reads were quality-trimmed, what was the total number of sequences obtained per sample and how many after quality filtering (and what criteria are used for quality filtering)? Thank you in advance, as I look forward to hearing back from you.

On 2022-03-24 16:12:37, user Kanji Kajangi wrote:

Figure 6: Microrheological properties of bladder cancer cells. Storage G' (filled points) and loss modulus G" (empty points) plotted as a function of oscillation frequency. <br /> Black: untreated. Red: treated with 5 uM cytochalasin D. <br /> The oscillation amplitude was 50 nm, and the frequency varied from 1-100 Hz for single cells- (A, D, G) and monolayers (B, E, H) and 1-250 Hz for the spheroids (C, F, I). <br /> Lines represent fits of the power-law function to the obtained data.

On 2022-03-24 11:26:05, user kamounlab wrote:

Reading notes from Forderer et al. Sr35 resistosome. See Twitter thread. https://twitter.com/KamounL...

On 2022-03-23 22:47:26, user Trey Baker wrote:

The commentary is sorted into a question and answer format for ease of writing.

1. What is the PCR target region, primers used, # of cycles? The PCR target region was V4 and 30 cycles were run. The primers used were never listed. The article also failed to provide the temperatures and duration of the cycles.

2. Does the bioinformatics method include steps to minimize PCR errors and chimeras? The “quality screening” in mothur should be explicit as to the filtering procedures.

3. Given that microbial communities are uneven and dominated, does the analysis focus on the dominant taxa, and consider the rate taxa appropriately? The relative abundance of C. diff was shown in supp fig 2, but no data or analysis of dominate taxa was given.

4. How are samples taken and handled before DNA extraction? Mice were given antibiotics and orally given C. difficile spores, then cecal samples were removed from their corpses. The cecal samples were then ground in a mortar and stored at -80 degrees.

5. Are OTU abundances corrected for variance in 16S copy number and for variances in genome size? No.

6. Is sequencing coverage of the communities measured and it is complete? No coverage measures were mentioned. # of sequence per sample not given. No rarefaction curves shown.

7. Does the PCR step contain internal standards? the DNA extraction step? the sequencing step? No, none of the three steps contained internal standards.

8. Is the sampling strategy well described and appropriate for the microbiome of interest? Yes, a description can be found in question 2, and it is appropriate for the microbiome.

9. Can you see total number of sequences obtained per sample and how many after quality filtering (and what criteria are used for quality filtering)? No and filtering steps were not described in detail.

10. 16S profile is normalized. Does the study also use some method to quantify total bacterial numbers, like qPCR or culture? The study measures bacterial metabolites as a quantification method.

Additional Comments:<br /> The experiment lacked an obvious control group.<br /> The method of chimera screening was not stated, only the database used.

On 2022-03-23 17:36:36, user Joram wrote:

This has now been published in Metabolites: https://doi.org/10.3390/met...

On 2022-03-23 16:24:04, user Sam Lord wrote:

Did the authors not see our reviews? The statistics are wrong in Fig 5, but the published version made ZERO effort to fix this problem. Very disappointing.

On 2022-03-23 14:34:58, user Jonathon Corral wrote:

The methods to this experiment are very well thought out but can use clarification in certain areas to aid in the reproducibility and accuracy of the science behind your data collection. The DNA extraction, amplification and sequencing steps utilize many internal standards and controls to ensure that each step is accurately producing the desired outcomes. The PCR amplification step utilizes 35 cycles which is quite high and can lead to the introduction of more errors within the DNA that is amplified therefore reducing the number of cycles to 25 would be beneficial in avoiding this possibility. The sequencing step on an Illumina MiSeq platform using the MiSeq Reagent Kit v3 300PE is normalized but fails to convey the number of reads that are taken per sample as well as what program is being used to randomly draw each sample. Next, further explanation of how the number of overlapping random OTUs between all pairs of compartments are calculated and why was this step repeated a 10,000 times. Often data sets that have a very large sample size can produce data that appears to be normally distributed but is actually not therefore it is important to determine the effective sample size sample of 10000 to determine the normality of the collected data. A Shapiro wilks test for normality will be useful determining the normality of your data. For statistical analysis, a chi squared test is used to determine the significance of the highest number of random overlapping OTUs among the 10,000 permutations against the number of observed overlapping OTUs for that pair of compartments but there is no mention of an analysis of Alpha, Beta or gamma diversity among the sampled microbiome as well as a designation of goods coverage. To further analyze the soil microbiome, a quantification of the total bacteria community can be done using qPCR or culturing techniques in order to determine the abundance of the bacteria and their mechanisms used to manipulate the community’s ecology.

SHSU3594

On 2022-03-23 14:02:25, user Marcus wrote:

The first comment I have pertains to the antibiotic treatment that the ABX mice received. In the study the mice were given water that contained four different antibiotics. Is this combination of antibiotics truly representative of what is given to human patients. Do human patients receive this strong antibiotic treatment for the same two week duration? The next comments I have apply to the 16S rDNA sequencing methodology. For stool collection, I would provide more details about if the mice were sacrificed in order collect stool samples or if stool was simply collected from the cage. As mice are known to be coprophagic. Adding an internal standard step for the DNA extraction step would help confirm that there are no errors with the DNA extraction method. I would also include the forward and reverse primers and primer sequences that were used for your study. The number of sequences per sample and total number of sequences is a detail that should also be found within this section. If and how PCR errors and chimeras were addressed is another addition to consider. With this, I would include the specific program for this step (i.e. ChimeraSlayer). I would include whether or not there was a step conducted to address the sequencing coverage of your samples. Inclusion of PCR details such as the number cycles, temperature, and time is a detail that should be included. Clarification of how taxa diversity and abundance were decided would be beneficial for the reader. Were Operational Taxonomical Units, OTUs, created for the calculation of alpha and beta diversity. If so, what was the similarity threshold set at for forming OTUs. There should also be inclusion if OTUs abundance was corrected for 16S copy number and genome size.

On 2022-03-23 08:32:38, user Julieta Alfonso wrote:

Great article, congratulations. Could you also share the supplementary tables? Thank you.

On 2022-03-23 05:55:17, user Sebastian Juarez Casillas wrote:

Addressed to the writers,<br /> Overall, the research is extremely strong and interesting, but there are some things that might improve the paper before publication as well as some personal questions to learn more about the choices in the methods. Grammar throughout the paper can be improved, this can easily be done by a third-party program such as Grammarly or within Word program. Beta diversity was explained in detail and was well received but I was unable to find data bout alpha diversity. I will propose the following questions to personally learn more about the research as well as they may aid in expanding on the data: <br /> Were there any internal steps to preserve the samples? <br /> How were the singletons and doubletons dealt with? <br /> Were the patients screened for other diseases that may impact the gut microbiome? Such as diabetes, inflammatory bowel disease, or irritable bowel syndrome.<br /> How were the methods for sequencing the V4 hypervariable region of the 16s rRNA gene adapted? <br /> How many cycles were there in the qPCR step?<br /> I hope these questions aid in the progress of the research,

Best Regards, <br /> Sebastian Juarez

On 2022-03-23 03:50:42, user sanduni ranasinghe wrote:

SHSU5394

Overall, I enjoyed reading this manuscript by Li and Cui et al. As a researcher with a broad and acute interest in river microbiome, this paper addresses its finding in a more transparent manner. It is impressive to see a detailed method section along with data analysis that shows a high reproducibility of your work.

Abstract - Abstract addresses the research question of interest. It also summarizes the important findings of the study. I suggest adding the names of the abundant and rare taxa found in water and sediments into the abstract.

Introduction - The introduction is well written using literature available under this topic. This clearly discusses the previous research that points towards the question of interest.

Methods -

The sampling method is well described. I suggest including the time that samples were collected because this can have a certain impact on the physiological properties of water. Storage of samples in liquid nitrogen is a good approach. Missing information about internal standards used during extraction is a limitation. Details on how the template DNA is quantified after extraction is absent. A negative control or an internal standard that was used to monitor PCR errors is not mentioned in the methodology. I appreciate describing the parameters and criteria used for pre-processing of sequences in detail which improves the reproducibility of your work. Making your raw sequence data accessible to others is commendable. I would suggest you include details about internal standards used in the sequencing step.

Defining rare and abundant taxa criteria is appreciated as many studies do not mention that. I would like to see the number of sequences obtained per sample which would also clarify why the sequence data were normalized at this depth of 27,890 sequences per sample. Also, I would suggest you mention the usage of R 4.0.4 for the analysis at an earlier statement under the data analyses section where beta diversity analysis is described. Then the reader will get a better understanding that beta diversity analyses were done using R rather than finding it at the end of the data analysis part.

Results-

The major findings in terms of the whole, dominant, and rare communities are clearly visualized using boxplots and Venn diagrams. I would suggest you include a table that shows alpha diversity indices (Chao 1, observed OTUs) of each sample along with coverage.

Another major suggestion I would like to propose is presenting your results (at least dominant phyla) using a higher taxonomic level like class/family or genus other than phylum level (if possible). This would give more insight to researchers in understanding the microbiome diversity of the thermokarst lakes which can be useful for future research.

On 2022-03-23 02:45:56, user Eduardo Gutierrez wrote:

Overall, this article is clear and concise. The study has a logical pathway and contains experiments that correlate well with the research question being investigated. Considering this, this study is easy to follow and flows well. It does not take much time to understand the basis of this article. <br /> The methods and materials section of this article mainly focuses on 16S ribosomal RNA sequencing in order to create a bacterial profile within the gut microbiome of endurance runners in Japan. By focusing on quality control, I would suggest adding which specific primers were used for PCR. I would also recommend lowering the amount of PCR cycles in order to minimize error and chimeras. I also suggest that there should be more detail in the sampling collection subsection by using the information from the results section. In order to make sure that PCR, DNA extraction, and sequencing occurred with minimal error, I would also suggest having internal standards. For example, using individual strains as a control can help validate the process of 16S profiling. Gluconeogenesis is a highlight within this study and a well-known physiological process within animals. However, I would suggest adding a brief section explaining the process in order to further help the reader understand the material given. Bacteroides uniformis is another focal point in this study. Understanding the background of how this bacteria is normally can also help build a foundation and further strengthen the study.<br /> Overall, this study is well done and opens up potential topics of using certain bacteria to evaluate athletic performance levels in humans. This study can also lead into using certain bacterias for therapeutic/rehabilitative purposes.

On 2022-03-22 23:17:06, user Brent wrote:

This method section conveys about using BLAST nucleotides in order to identify species of bacteria on the ISS, they mention using Metagenomics to read the 100-bp. Another point that interested me, was that they didn’t identify the variable region they’re targeting in the experiment. There was also no mention of the standard filtering of the sequence, what did the team use in this experiment? There was also no variance of the genome size mentioned in the article. The sample retrieving method is mentioned, they extracted the bacterial samples from the vacuum filters and dust collected from the ISS, as well from the astronaut’s skin to characterize. However, what steps were taken to ensure no contamination was introduced within the samples retrieved? The article doesn’t mention how the bacterial samples were kept in sterile environments before shotgun-sequencing. They do mention the method used to quantify the Antibiotic resistance bacterial genes, subculturing the samples retrieved from the space station then comparing them with the same species of bacteria pre- and post-launch for uniqueness. I would suggest adding how the team collected the samples before sequencing, to give the reader insight on what steps were taken to isolate the bacterial genes. If they’re mentioned in another article, I would provide a link to the article within the methods section. I really enjoyed this paper, it's fascinating to learn about how microorganisms originating from earth can survive beyond the stars.

SHSU5394

On 2022-03-22 23:10:37, user Adalia wrote:

The target region of PCR is mentioned and the primers are given for qPCR, but there is no mention of the 16S primers or number of cycles carried out. The number of cycles done can introduce errors if it is too high, so a cycle number of around 20-25 is ideal. Did you use universal primers or were there specific ones used? Were any primer pads added to prevent hairpin formation? There is also no mention of a negative control used in the PCR step, or any other internal standards to prevent error. Were there any internal standards steps taken for PCR, extraction, or sequencing? The coverage of alpha and beta diversity is there and is sufficient, but there is also no statistical analyses of gamma diversity of the microbiomes. Gamma diversity is a measure of the overall diversity for the different ecosystems within a region.There is a mention of quality filtering for chimera removal, which is very good. Despite the coverage of chimera filtering, operational taxonomic units are not explained. Did you cluster your OTUs besides the mention of clustering of chimera and singleton ASV reads? If so, why is it not mentioned in the methods? The Good’s coverage was also not mentioned at all. Was any Good’s coverage calculations done? Or was there any other coverage done, such as chao1 or Shannon’s? Given that this study does use qPCR, that does help to better understand the microbiome as it quantifies the bacteria given instead of just stating the bacteria that is present. The sampling methods are well explained, but I do question if the fecal samples are accurate in determining the microbiome if you were unable to collect them after the race due to dehydration. Is one sample taken before the race sufficient enough to be able to ascertain the microbiome completely, or would there be missing information that could be important? What did you filter for effective sample size? I think it would be helpful to go more in depth with the quality filtering that was done. Is a 50% completeness for MAGs high enough? When you mention normalizing the abundance for sequencing depth, how was this done?<br /> The flow of the paper is good, but the explanations in the methods section could be better. For example, the DNA sequencing steps are not thoroughly explained in this paper, and instead are referenced in other papers, making it difficult to know exactly what was done with the samples prior to extraction without having to go digging for the other papers.. Some questions on extraction can be made, specifically when it comes to the fecal samples. For an accurate depiction of the microbiome of the horses, I believe samples should have been taken before and after the races to understand any differences that might be seen prior to the races instead of what is just present before the race. #SHSU5394

On 2022-03-22 18:03:56, user Emily wrote:

Hello authors,

Thank you for posting this excellent article on the gut microbiome in adult gars. I do, however, have a few comments and questions on your study.

First, you mention that you are studying the GMB of the fish. You describe this acronym as the gut emicrobiome; however, when I researched this acronym, I was unable to find anything. Is this a typo? Where could I find this information?

Second, I was confused about the sample collection of the gar. How did the fishermen catch the fish, specifically what bait was used? Would this affect the gut microbiome of the gar? What food was fed to the fish grown in the lab? Were there significant differences in the GMB based on the collection method? Following that, you state that you squeeze the GMB; I am confused about how you obtained the feces. How did you squeeze the gut microbiome? Would this affect a change in the location? Would you be able to elaborate further on this?

Next, while investigating your methods sections, I found some missing information in your PCR step. To improve your study and help end the replication crisis, I would add the PCR cycle number and temperature for this amplification. Thank you for including the variable region and specific primers you used.

Finally, I read your sections on bioinformatics and phylogenetic analysis and had a suggestion. When I first read the paper, I could not find your Good’s coverage and how you clustered the OTUs. I would move the information on how many clustered sequences and the similarity percentage right after removing the chimeras. Moreover, I was curious about an internal standard for sequencing and clustering analysis. I suggest adding a known strain you grew in the lab to the sample as to confirm that your sequences and binning are correct. Another suggestion would be to elaborate on why you adjusted the identity percentage to 99% with a coverage of 70%. Did this help with the phylogenetic analysis? Is this a quality control for the phylogenetic analysis similar to chimera removal?

Overall, this study highlighted the gut microbiome of tropical gar and allowed for further research questions to be asked. I appreciate the amount of information on the method section and implore you to add my suggested feedback. Thank you for your time.

SHSU5394

On 2022-03-22 15:48:58, user Prescott Deininger wrote:

Geof, It is really important that you not ignore passive expression of L1 elements. Any method (like RT-PCR or most hybridization approaches) that does not distinguish between L1 elements expressed from their own promoter and those that are included in other transcripts through passive transcription from other promoters, is almost certainly not measuring real L1 mRNA expression.

On 2022-03-22 14:32:36, user Stephanie wrote:

Excuse me, I can't seem to find the Supplementary Materials?

On 2022-03-22 00:48:53, user Logan Townsend wrote:

this is a great piece of work and something that we pursued. We published some results in Gcgr-/- mice suggesting impaired adrenergic signalling (10.1096/fj.201802048RR) but thought it was probably indirect. We did experiments in adipocytes and saw NO effect on lipolysis or anything else even with very high doses of glucagon, completely supporting your work, but we never published it. Looking forward to this coming out!

On 2022-03-21 23:12:35, user Megan wrote:

Hello authors,<br /> Thank you for submitting this interesting preprint over the effects of the microbiome in response to parasitic infection in Daphnia species to a public domain. I have a few questions on your methods and results.<br /> The detailed sampling and processing section was great, but I was interested in why only one sampling site used and collected on a singular day. Do you think using more sampling sites and/or over a longer period would give greater insights? Was there a particular reason this site was chosen?<br /> For dissection and sample preparation, there was no mention of how the samples were stored following collection. Were the samples immediately processed after collection or were they stored? If so, how were they stored?<br /> For pre-processing of the reads, how was coverage measured for the samples? Did you include steps to minimize PCR errors or chimeras? Did you correct for variance in 16S copy number and variance in genome size? The samples were rarefied to an even depth of 170,000 reads. Were any other methods used to quantify total bacterial numbers such as qPCR or culture methods?<br /> For results, what is the purpose of the horizontal spread of the dot points in figure 2? In figure 3, could the data points be clustered on the PCOA plots?<br /> Overall, this article was very insightful to the host-parasite-bacterial community interaction with potential for more broad applications in the significance of the host-associated microbiota. I really hope these comments/questions help.

SHSU5394

On 2022-03-21 23:05:13, user Megan wrote:

Hello authors,<br /> Thank you for submitting this interesting preprint over the effects of the microbiome in response to parasitic infection in Daphnia species to a public domain. I have a few questions on your methods and results.<br /> The detailed sampling and processing section was great, but I was interested in why only one sampling site used and collected on a singular day. Do you think using more sampling sites and/or over a longer period would give greater insights? Was there a particular reason this site was chosen?<br /> For dissection and sample preparation, there was no mention of how the samples were stored following collection. Were the samples immediately processed after collection or were they stored? If so, how were they stored?<br /> For pre-processing of the reads, how was coverage measured for the samples? Did you include steps to minimize PCR errors or chimeras? Did you correct for variance in 16S copy number and variance in genome size? The samples were rarefied to an even depth of 170,000 reads. Were any other methods used to quantify total bacterial numbers such as qPCR or culture methods?<br /> For results, what is the purpose of the horizontal spread of the dot points in figure 2? In figure 3, could the data points be clustered on the PCOA plots?<br /> Overall, this article was very insightful to the host-parasite-bacterial community interaction with potential for more broad applications in the significance of the host-associated microbiota. I really hope these comments/questions help.

On 2022-03-21 23:11:00, user Soso wrote:

This paper has so much information and all the data looks great. However, I do have a few questions and suggestions on the methods and materials section of your paper.

First, fecal sample collection was mentioned but not the process of how the samples were collected. Was the feces collected after it passed from the monkey or was there some process to collect upper GI samples? The difference in collection methods may affect the presence of certain bacterial species, such as anaerobic species.

Second, I did not see internal controls for DNA extraction, PCR, or the sequencing process. Were there any internal controls used to verify the results of each step? So, DNA extraction for example, known bacterial species grown in the lab can be included at specific concentrations and used to calculate how much DNA should be extracted from this known species, this way we can check to make sure the DNA extraction process worked as expected.

Third, were there any steps taken to minimize PCR errors (such as ambiguous bases, etc.) and chimeras? It would be beneficial to mention these steps and what programs were used to minimize them in the materials and methods section, for example ChimeraSlayer is often used for removing chimeras. Also, it was mentioned that analysis was performed on samples after rarefaction to 10000 sequences/sample, but how many sequences/sample were there before this rarefaction?

Last, in the Microbiota analysis section for 16S amplification, the link for the earth microbiome project does not seem to work for me. I apologize if this is an error on my end, but it says the page cannot be reached. Is there another way I can find this information? It may be beneficial to also include PCR steps and cycles you followed in the methods and materials section so that if anything ever happens to the link and it no longer works, you still have the steps you followed in the paper.

Overall, I really enjoyed your paper. I hope you find these questions and comments helpful!

SHSU5394

On 2022-03-21 13:45:45, user todd primm wrote:

fascinating study. you should adjust your counts of your known strains by their 16S copy numbers to reflect true abundance.

On 2022-03-21 04:02:34, user Andrew Bell wrote:

First, well done on achieving such high resolution, and largely noninvasively. Now we can start to see evidence of what is really going on in the cochlea. Your work raises a whole lot of issues, but I’ll just mention a few key findings. I hope you find these comments helpful.

1. In the abstract a fairly provocative statement is that the data is not explained by current theories. In my view, I don’t think this is quite right, as the motions you reveal appear to be the result of simple resonance between the rows, an idea first raised in my PhD thesis and then in several associated publications. Perhaps the most germane are Bell & Fletcher (2004), The cochlear amplifier as a standing wave, JASA 116, 1016; https://doi.org/10.1121/1.1766053 and Bell (2012), A resonance approach to cochlear mechanics, PLOS One, https://doi.org/10.1371/journal.pone.0047918. Both papers set out a scheme whereby the three rows of OHCs work together to establish a resonant element which gives rise to a standing wave between the rows. Tuning thus depends largely on the row spacing, not the stiffness of the BM. The OHCs are stimulated virtually instantaneously by the fast pressure wave (OHCs are pressure sensors, for which I’ve made a case elsewhere), not the conventional travelling wave. In this way, I think most of your findings can be accommodated, as set out below.

2. In your Introduction you say that a special sort of phasing is required in order to amplify the travelling wave. This is not necessary if you look at it in terms of resonance. As Bell (2012) broadly explains, the travelling wave is simply the observed result of what happens in response to a graded bank of highly tuned resonators that are almost simultaneously excited by a fast pressure wave. The delay observed is then simply Q/pi cycles, where Q is the tuning sharpness. In other words, I suggest you may be looking at things back-to-front causally: it is the resonance that gives rise to an apparent TW, not that the TW is a causal entity that, through very careful phasing, is able to amplify BM motion and give rise to a large peak! That is, the OHCs don’t amplify motion at all; instead they are pressure transducers which, via electromotility, vibrate in response to the sound pressure surrounding them (the OHCs contain pressure-sensitive ion channels). I’ve published a number of papers on this, and I’m happy to discuss the idea with you in more detail if you wish. In brief, I am suggesting that, if we look at cochlear mechanics differently, the TW is an epiphenomenon of a tuned bank of active elements. The elements are local oscillators – there doesn’t need to be global coupling in order to propagate a TW.

3. I am suggesting that each triplet of OHC1, OHC2, and OHC3 act together like a guitar string arranged radially. However, unlike a string, there is a fluid connection between the rows (a squirting wave) so that the wave travels at a particularly low velocity. Applied to your observations, at a BF of 46 kHz the wave traverses OHC1 to OHC3 (a distance of about 30 um) in 1/46000 of a second – that is, a speed of about 1 m/s. As an explanation, so-called squirting waves have such low phase velocities, and anatomically are well suited to act in the space between the TM and reticular lamina, as Bell & Fletcher (2004) describe. Electromotility of the OHCs causes squeezing in that space, generating squirting waves.

4. At their tuned frequency (BF), the amplitude of vibration is largest, and that is consistent with a resonating element that is tuned to that frequency. Thanks to your high resolution, we can see the activity of each of the three OHCs. In Figure 5c there seems to be a larger amplitude of vibration for RL3 than RL1; another radial profile at a different level (Figure 7c) shows that the amplitudes are about equal. Given the intricate geometry, I think that the findings are generally consistent with a radial standing wave with the OHCs at the antinodes.

5. Now, about the phases. The 3 OHCs seem to have about the same phase, and this is consistent with a standing wave between them. A standing wave is a wave that oscillates in time but whose profile of peak amplitude does not move in space. My papers suggest that OHC2 acts in antiphase to OHC1 and OHC3, an arrangement which is closer to a xylophone bar than a guitar string. In other words, each OHC sits at an antinode, and the result is a full-wavelength standing wave. Your OCT device sees all the OHCs vibrating at the same amplitude, but doesn’t see the wave moving backwards and forwards between them. Other phase arrangements may be possible, but the full wavelength case is probably the simplest. For a guitar string, there is only 1 antinode and 2 nodes, so if this applied in the cochlea, all the work would be done be OHC2 (we wouldn’t need 3 rows of OHCs).

6. Taking together all the above, I hope you may appreciate that if we had a ringing xylophone bar between OHC1 and OHC3 then an OCT device would see all the OHCs vibrating at the same amplitude and the same phase. It would require special techniques to detect the standing wave, and I wonder if your device has that capability. This would provide convincing evidence in favour of a resonance model.

7. Note that in my papers I regard the phase lag at resonance to reflect the group delay of the resonators. For a linear resonator, the group delay amounts to Q/pi cycles. It is interesting to look at the group delays you recorded in Figure 8f-h and Figure 10e,f. At BF (resonance) they show a phase lag of 2–3 cycles. So if one considers these delays to derive from a linear resonator (not strictly true, but perhaps not too far off), then the associated Q values would be pi times 2 or 3, which is about 6–9. Such Q values are roughly the same as those measured otoacoustically for the gerbil.

In summary, I suggest it is possible to interpret your findings using a different causal chain, the inverse of what you have done. That is, the causal chain may involve the direct electromotility of OHCs in response to sound pressure, and not that OHCs have to very carefully amplify atomic-scale BM motions to create a traveling wave – and this approach simplifies cochlear mechanics enormously. The alternative view is that the BM may just be

a supporting membrane for an array of tuned elements, which are independently excited by the fast pressure wave. Indeed, it is interesting that the ITER team (Khanna and colleagues) adopted this view more than 30 years ago. They said that “The present observations suggest that the outer hair cells vibrate mechanically along their axes in response to acoustical stimulation.” (p.188) https://doi.org/10.3109/00016488909138336. It is perfectly possible to look at your data in a different, but internally consistent, way.

I hope this helps us move towards the truth of the matter. Best wishes for your publication. Andrew Bell.

On 2022-03-21 00:08:22, user Marc K wrote:

Nice analysis! Would have been great to see what the authors make of the biochemical interactions and structures reported over the last couple of years for some of the very ancient metazoan Bcl-2 proteins (from sponge, hydra and trichoplax), and how that fits with their conclusions.

On 2022-03-19 08:47:28, user Chao Li wrote:

A great job! But it seems that the supplementary materials have not been uploaded.

On 2022-03-18 18:20:42, user Bartholomew Roland wrote:

This is fantastic work. I can't wait to see this published.

On 2022-03-18 18:01:26, user Sylvain Loyen wrote:

Will you release any Y-DNA information? Y-DNA haplogroup distribution tables per cluster? For deeper analyses, anonymized lists of samples with their locations and subclades?

On 2022-02-06 14:39:34, user Patrick O'Keefe wrote:

Is the ancient DNA sequence data from the paper available publicly at ENA or some other site?

On 2022-03-18 09:04:32, user Martin R. Smith wrote:

An interesting study. A small thought on tree comparison: whilst the normalized RF distance is no doubt picking up the general trend in Fig. 2, I wonder whether the more subtle distinction between IB and IC in Fig. 8 might be worth exploring with a less crude tree distance metric (e.g. Quartet distance or information-theoretic RF distance, see Smith 2020, Bioinformatics, https://doi.org/10.1093/bio... for a review) – perhaps a less problematic metric might show more clearly whether there is a pattern in the distance to neighbouring trees?

On 2022-03-17 19:32:20, user Stewart Loh wrote:

The authors may wish to compare their results to our recent study of WT and I40V LECT2 (DOI: 10.1016/j.jbc.2021.100446). The differences are subtle to be sure, but significant for the zinc-free protein.

On 2022-03-17 17:00:47, user Joram wrote:

This work is now published in Frontiers in Digital Health: https://doi.org/10.3389/fdg...

On 2022-03-17 13:00:06, user Tandem team (Marc Graille Lab) wrote:

A revised version of our BIORXIV/2021/472726 manuscript has been published in the journal Acta Crystallographica <br /> Section D under the following title : "The X-ray crystallography phase problem solved thanks to AlphaFold and RoseTTAFold models : a case study report.".<br /> Here is the link : https://doi.org/10.1107/S20...

Unfortunately, it is not in open access (too expensive) so if you are interested, ask the contact author.

On 2022-03-16 09:06:46, user yael grunwald wrote:

This paper has been accepted to The Plant Cell and can be viewed at https://doi.org/10.1093/plc...

On 2022-03-15 17:19:15, user Wendy JM Huang's Lab at UC San wrote:

This article has been peer reviewed and published on Cell Reports with a modified title "RORγt phosphorylation protects against T cell-mediated inflammation". See article on:<br /> https://www.cell.com/cell-reports/fulltext/S2211-1247(22)00256-X

On 2022-03-15 10:42:31, user sagar khare wrote:

Thank you, Roberto and James for taking the time to engage with our work and for these helpful comments! We are revising the manuscript in the light of these comments (among others). We will post a detailed response to your comments in time.

Re. why we used AF2 when exptl structures are available: we generated these models in early December and posted this preprint on Dec 13 before any experimental data were available. When the first structures came out in early Jan, we performed a comparison with our models and submitted to journal in mid-January. (what can I say, that's the pace of scientific publishing :))

On 2022-03-04 02:13:39, user Roberto Efraín Díaz wrote:

This manuscript describes a computational pipeline to predict how antibody (and other “therapeutic entities”) binding varies to different SARS-CoV-2 Spike protein variants. The goal of this pipeline is to be able to generate structural models of novel SARS-CoV-2 variants in complex with existing therapeutics (nanobodies, antibodies, etc) and (re)design antibodies to maintain or improve antibody binding interactions.

The authors initially develop a set of computed structural models (CSMs - an acronym/term that we find unnecessary and confusing, wouldn’t “model” suffice?) of Omicron Spike protein bound to ACE2 and therapeutic entities using experimentally-determined structures optimized by Rosetta FastRelax or predicted structures from AlphaFold2.

The major confusion we have with this paper is the utility of generating predicted structures from AlphaFold2 when there are numerous experimentally-determined structures available on the PDB as starting models for mutagenesis and Rosetta optimization. The justification for using AF2 is not outlined clearly in the manuscript, and the data do not suggest that AF2 provides any obvious benefits over using Rosetta alone. The majority of the paper uses a consensus approach to score predictions based on different procedures. However, we could see a compelling argument for using AF2 in future VOC (or new viruses) that are even more diverged than Omicron. If this is the type of future application the authors imagine, presenting consensus scores as the sum of scores instead of the individual scores for each procedure undermines the potential comparative utility of having both experimental and predicted models in this analysis (a potential strength of the manuscript).

The major limitation of this study is that it is purely computational and while the anecdotal evidence that certain clinically relevant antibodies retain (or lose) effectiveness against Omicron is a good validation, the rescue mutant studies will likely have little resonance with the community until validated experimentally. Perhaps there is a greater argument to be made based on some of the deep mutational scanning data that is emerging. The presentation of energetic changes as either “stabilizing” or “destabilizing” without accompanying quantitative values or calibration to experiment obscures the magnitude of de/stabilization caused by each amino acid substitution.

Overall, this manuscript is an interesting exploration of how researchers can utilize computational structural biology to (re)design therapeutics in real-time to address the ongoing SARS-CoV-2 pandemic. The manuscript would benefit from providing stronger justification of why predicted structures can be essential for understanding how host-pathogen protein:protein interfaces evolve in an evolving pandemic - and from learning how to make those predictions stronger.

Major Points<br /> 1. The justification for generating CSMs with and without position coordinate restraints should be explained.<br /> 2. Consensus Scoring currently obscures the Rosetta Energy Unit value for each CSM and does not help the reader better understand why 4 CSMs are needed to accomplish the outlined goal of the paper. <br /> 3. Is it conceivable that the degree of (de)stabilization is related to the magnitude of the REU value, i.e. 1.4 REU vs 3 REU?<br /> 4. L309-311: Citation 26 proposes that Omicron may have developed into immunosuppressed chronically infected COVID-19 patients, but this is a hypothesis and not currently supported by data. I would remove this statement or cite primary literature supporting this claim such as Kemp, S.A., Collier, D.A., Datir, R.P. et al. SARS-CoV-2 evolution during treatment of chronic infection. Nature 592, 277–282 (2021). https://doi.org/10.1038/s41....<br /> 5. Figure 2A: why is the cryoEM structure overlaid with the predicted structure? The structures look nearly identical, so unclear what this representation adds. If positional differences between experimental and predicted structure is important, color residues in AF2 structure by RMSD.<br /> 6. Figure 2B-C: if you’re trying to highlight changes in the extensive interaction network between WT and Omicron, show as an overlay.<br /> 7. Figure 3: this figure does not make sense.<br /> 8. Figure 2, 4-10: Represent WT and Omicron comparisons as an overlay in one panel instead of two structures in side by side panels. This will be easier for reader to visualize the differences.

Minor Points<br /> 1. Refer to Wuhan-Hu-1 as WT, by NCBI number, or other identifier that does not use the city name, so as to reduce stigma.<br /> 2. L121: The definition of “cocktail” as “two or more bound proteins in a single multi-protein complex.” The term “multi-protein” implies two or more proteins, either protomers of the same protein or monomers of different proteins. Suggested edits would be “two or more proteins bound to an oligomeric protein complex (ACE2 trimer).”<br /> 3. L132: Define pLDDT earlier than line 136.<br /> 4. L152-154: Why were N- and C-termini included in both experimental and predicted models if they are not necessary and will skew the overall Calpha RMSD?<br /> 5. L251-252: Recommend adding a figure/table showing sequence consensus between Omicron and other variants only for AA sites mutated in Omicron<br /> 6. Figure 2A: In the inset, it is unclear why Res 387-389 are annotated if the authors want to focus on Res 364-376. I would show the full residue range 364-376 as sidechains and label each residue. It seems that there are additional residues 477-478 shown as sidechains in the full image to represent mutations found in omRBD. This is unclear from the text and figure legend. I recommend removing the inset. <br /> 7. If using a yellow-to-green spectrum coloring scheme, include a scale bar on the image. Also, yellow to green is a difficult color spectrum to visually interpret, and this is made more difficult given that the backbone is colored green.

We review non-anonymously, James Fraser and Roberto Efraín Díaz (UCSF)

On 2022-03-15 03:40:03, user Ji-Long Liu wrote:

This manuscript has now been published in eLife (https://elifesciences.org/a....

On 2022-03-14 14:39:44, user Alizée Malnoë wrote:

The manuscript by Cuitun-Coronado et al. investigates the effects of circadian and diel cycles on the photosynthetic processes of the liverwort Marchantia polymorpha. The authors clarified three points: 1) they confirmed that M. polymorpha does display circadian regulation of several photosynthetic parameters; 2) they showed that light-dark cycles mask circadian cycles for these parameters; 3) by using a pharmacological approach, they showed that chloroplast translation is necessary for the circadian regulation of photosynthesis.

The study fills a gap of knowledge in the circadian regulation of photosynthetic activity across evolution, by providing an additional link to what was known in cyanobacteria, microalgae, and vascular plants. It is original and well-conducted. The interpretation and the discussion of the results is very convincing, and the conclusions are carefully drawn and backed up by the data.

Major comments<br /> - Page 7, lines 173-176: it is stated that the differences in period length observed between PAM and DF measurements might be due to differences in light intensity. How about light quality, as the PAM measurements were performed under blue light, vs. mixed red/blue for DF?

• Blue light, which was used in both types of experiments, has been shown to impact circadian rhythms in plants through the action of cryptochromes (e.g. Chaves et al., Annu. Rev. Plant Biol., 2011). Also, BL is responsible for chloroplast relocation responses, which is mediated by phototropins (e.g. see Kong et al. Plant and Cell Physiology, 2013). In Arabidopsis it can also impact plastid transcription through the intermediate of the blue-responsive plastid sigma factor SIG5 (Tsunoyama et al, FEBS Lett. 2002) and it is regulated by BL photoreceptors cryptochromes (Onda 2007, Plant Journal). Can you comment on these potential effects? Has their involvement in Marchantia polymorpha been investigated? Did they motivate your use of blue light for these experiments?

• Page 13, lines 316-323, please discuss the lack of effect on circadian rhythm by rifampicin: at first it may appear odd that translation has an effect but transcription doesn’t. Could it be due to rifampicin only inhibiting PEP and transcription by NEP compensates?

• Page 14, lines 354-363: <br /> 1) Is there a reason why you performed light curves with increasing light intensity up to 789 µmol m-2 s-1 if you end up using only the data with light intensity closer to growth conditions (108 µmol m-2 s-1)? <br /> 2) Could the 20 minutes of dark-adaptation every two hours and increasing light intensity up to 789 µmol m-2 s-1 have affected the circadian rhythms of plants that were supposed to be at constant LL? This experimental setup could be considered as fluctuating light condition. Please discuss whether this may have an effect on the observed results.

Minor comments<br /> - Page 2, lines 27-28, "the circadian regulation of several measures of photosynthetic biochemistry (delayed fluorescence, the rate of photosynthetic electron transport, and non-photochemical quenching of chlorophyll fluorescence)": please rephrase this sentence as it seems that the circadian regulation has an effect on the measurements themselves while they are tools to see something happening at photosynthetic levels. We suggest replacing it with "circadian regulation affects photosynthesis performances, detectable by several photosynthetic parameters such as delayed fluorescence, the rate of photosynthetic electron transport, and non-photochemical quenching of chlorophyll fluorescence". Same can be applied in Pages 4-5, lines 98-99.

• Page 4, lines 84-90, this sentence is very long and difficult to read, please rephrase and split it into at least two sentences for better readability and clarity.

• Page 5, lines 114-118, discussion might be a better place for this.

• Page 5, line 118, please replace "Features of PAM fluorescence" with "Features of chlorophyll fluorescence as measured by PAM" as the first indicates a specific method to measure an intrinsic phenomena (Chl fluo) and it is not the only one for Chl fluo measurements.

• Page 5, line 123, please replace "PAM metrics" with "prompt fluorescence" for consistency with DF, named just before.

• Page 6, lines 126-127, to be consistent with the nomenclature, "LL" should be "low light", not "constant light". Consider replacing LL with CLL ("constant low light") since the plants are actually exposed to a lower light compared to the standard growth light described in materials and methods. Also, please include in the description of the light conditions “LD” together with its full name (which is directly mentioned before in line 138 without its full name "light-dark cycles").

• Pages 6-7, lines 152-154, this sentence is more technical and it does not seem necessary to understand and interpret the results described later. Please consider moving this sentence in Material and methods to keep the results section more readable.

• Page 7, lines 167-172, please provide a reason why photosynthetic activity was measured only under LL and not also in the other two conditions described for DF.

• Page 8, lines 196-199, reading the text it seems you used only T24 and T28. Please list all the T lengths (T20, T22, T24 and T28) as reported in Figure 3 legend.

• Page 10, line 254, please add a short sentence to briefly comment the results obtained with the rifampicin treatment.

• Page 13, lines 316-318, "Perhaps inhibiting the expression of these proteins prevents or reduces PSII repair, which disrupts circadian cycles of the rate of electron transport by interfering with the electron transport process". Please rephrase this sentence, as it is written it can be interpreted as defective PSII repair disrupts circadian cycles.

• Page 13, line 335, describe conditions of its natural habitat “such as”.

• Page 14, lines 341, in the growth conditions you describe that the standard light conditions used were 100 μmol photons m-2 s-1 of white light. Is there a reason why for the DF experiment described in this manuscript you decided to use 60 µmol m-2 s-1?

• Page 18, legend of Figure 1: line 421, “thallus” should be “thalli”; “light/cycles” should read “light/dark cycles”.

• Line 430, define FFT-NLLS.
• Please move the definition of RAE from lines 433-434, to its first occurrence line 430.

• Line 433, define SEM.

• Page 19, line 470, please write SEM instead of s.e.m., for consistency with the legend of Figure 1.

• Figure 1: the figure is very big and can create confusion, please consider to split it in two figures, e.g. a-f Figure1 and g-k Figure 2.

• In the legend, include the abbreviations for the light conditions and correct "ligh/cycles" with "light-dark cycles" as reported in the text.

• Panel g. Please better describe this panel, especially the colours meaning in the clock.

• Figure 1 (a,c,e) 2 and Figure 4: the alternance of white and light grey bands to mark the 12h periods can be very easily misinterpreted as light/dark cycles. To clarify and display the constant light conditions at a glance, please consider using another means to evidence them, such as dashed lines.

• Figure 4: similarly to Figure 1, this figure is very large and hard to read. Consider splitting it.

• Please write the concentrations of lincomycin and rifampicin in the relevant panels, as the different shades are difficult to distinguish and understand in panels a-d, g-j, m-p and s-v.
• Panels f-l and r-x: please use the same scale on the y axis, or, if not possible, bring attention of the reader to the different scale in the figure legend. At first, we were puzzled by the ‘no-treatment’ not starting from similar amplitudes.

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

On 2022-03-14 09:27:36, user Martin R. Smith wrote:

Congratulations on a great study! I'm very interested to see how the different distance metrics behave in real life settings. <br /> Just a note on the tree space analysis: interpreting clustering based on 2D PCoA plots can be misleading, particularly when using RF distances – see Smith 2022, https://doi.org/10.1093/sys... . There may be some value in checking that the 2D mappings have not introduced distortions, and indeed that trees plotted as nearby are in fact similar based on the original distances.

On 2022-03-11 09:45:26, user Katarzyna Walczewska-Szewc wrote:

It is excellent work. For sure, it helps to fill some gaps in the KATP-related research. I wonder why authors have not referred to the previous work on this topic while describing their numerical MD model and results (e.g. Walczewska-Szewc and Nowak,2020). That would state for the perfect background - what was done and what still needs to be done.

On 2022-03-09 16:25:10, user Maxime Bellefroid wrote:

The peer-reviewed manuscript has now been published and is available at the following link : https://doi.org/10.1093/nar...

On 2022-03-09 10:30:55, user Marc Joiret wrote:

This paper has been accepted for publication in the peer-reviewed journal 'Physical Review E'. There were three rounds of reviewing. You can find the reviewed published version at https://journals.aps.org/pr...

On 2022-03-09 01:12:42, user Khem wrote:

Hi James, great work, congratulations!!!<br /> Please can you also upload the supplementary tables, i couldnt find them. I am curious to know, how did you find P. copri from 16S data (Kang et al)? or did you find in shotgun data from other studies?

On 2022-03-08 23:22:04, user Anson Call wrote:

Very interesting! The github link isn't working though - would love to see your repo!

On 2022-03-08 13:56:33, user Eugene M. Obeng wrote:

Line 1165: Do you intend to say Y510 instead of Y501?

On 2022-03-08 02:07:09, user Jimin wrote:

Live demo of chromatin architecture prediction:<br /> https://nyu.c-origami-previ...

On 2022-03-08 02:03:59, user Jimin wrote:

Link to the project website:<br /> https://tanjimin.github.io/...

On 2022-03-07 06:19:12, user Juri Rappsilber wrote:

Hi there, we discussed your manuscript in our journal club and got stuck on the following:<br /> How can the alpha and beta peptides overlap in mass in one of your figures, given that the alpha peptide is defined as the larger-mass peptide.<br /> How is the score calculated?<br /> How is the site score calculated?<br /> How is the site called if there are multiple alternatives?<br /> How are the data reported when there is not a single site identified?<br /> Performance evaluation is done on self-made synthetic data! The danger of biased assessment is rather large. Also, the synthetic data is - in the best case - a reflection of the current (imperfect) understanding of how SDA-crosslinked peptides fragment.<br /> Please show your matches on the structure of condensin and plot a distogram. My expectation is that at 1% FDR you will have a substantial number of long-distance links. Which is why we went to <1% in our biological paper where number of links did matter not, but biological insight.<br /> Best wishes,<br /> Juri

On 2022-03-05 09:04:54, user Sourya Subhra Nasker wrote:

A note for the readers-<br /> This pre-print article has been peer-reviewed and published in Bioscience Reports by Portland Press. The link to the article will be forthcoming.

On 2022-03-04 09:15:15, user GKI wrote:

In the introduction we read:.

“Our results suggest that both the Huns and the“ immigrant nucleus ”of the Varos originated in present-day Mongolia and their origins can be traced back to Xiongnus. On the other hand, the "immigrant core" of the conquering Hungarians came from an earlier mixture of the Mansi, the early Sarmatians and the late Xiongnus. "

This is somewhat at odds with the statement on page eight.

The Proto-Ugric peoples emerged from a mixture of Mezovsky and Nganasan populations in late Bronze. In the Iron Age, the Mansi parted ways, and the proto-invaders mingled with the early Sarmatians between 643-431 BC and the Early Huns between 217-315 BC.

I ask the authors to resolve this contradiction!

On 2022-03-03 19:11:05, user Brett Chrest wrote:

Misleading conclusion: "We showed that mRNA vaccine (Pfizer) changes mitochondria by downregulation of cytochrome c resulting in lower effectiveness of respiration (oxidative phosphorylation) and lower ATP production."

• Respiratory function nor ATP production were not actually accessed.

Given the highly dynamic nature of the mitochondria, is is not always clear that a reduction in Cyt c is pathologic. Given the fact that intensity hardly changed (and also not statistically significant) in normal astrocytes when incubated with mRNA, the concern for alternations is weak. As seen in Fig 4., this was seen once again seen in Fig 5A.

*This point above should be highly emphasized in the abstract since this paper is already being circulated online in order to discredit the vaccine and intensify hesitancy.*

The entire justification for the mRNA being in direct contact with brain cells/tissue is unclear and not based on scientific data:<br /> "COVID-19 mRNA has been recovered from the cerebrospinal fluid [11], suggesting it can cross the blood–brain barrier (BBB)"

• Citation 11 links to a 2004 paper on SARS-CoV replication in the respiratory tract of mice.
• Dr. Abraham Alahmad, who studies the BBB, discussed this idea back in 2021 (links below); the biodistribution to the brain was incredibly low in mice and unlikely to have any meaningful effect in humans. The mechanistic justification for directly exposing brain cells to, what is estimated to be, an entire dose of the vaccine is flawed.<br /> https://twitter.com/scienti...<br /> https://scientistabe.wordpr...

The manuscript highly contradicts the methodology of the paper:<br /> "Also, mRNAs are short-lived molecules. The vaccine’s messenger does not stay inside the cell indefinitely, but is degraded after a few hours, without leaving a trace."

• Why was mRNA incubated for 96 hours, or even 24 hours? The justification here for how in vitro incubation of the mRNA for 96 hours translates to human physiology is lacking.

Highly misleading abstract sentence:<br /> "mRNA vaccine produce statistically significant changes in cell nucleus due to histone alterations."

• It should be emphasized that this was only seen in cancer cell lines, NOT normal brain cells. Additionally, changes in astrocytoma intensity were only seen under supraphysiological incubation of 96 hours.

On 2022-03-03 11:36:46, user Susana and Joao wrote:

Hi David,

Fantastic work! Neat and straight to the point.

Quick question, when we see in figure 1 the formation of two smaller clusters before emerging a larger and single one, can we say these are initial LLPS formation steps or two well-formed smaller condensates that fuse into a bigger one?

It's interesting to see in figure 2 that lower concentration GSY condensates diffuse from left to right in the box, while higher concentration condensates remain fixed (don't diffuse) once formed. Is this an MD artefact, or does it represent larger bodies' diffusion hindrance?

Also, in figure 2 B), for 2M concentrations, we see forming two initial condensation spots (dark blue). In contrast, this is not so visible for other concentrations, and the system seems to transit straight to a single cluster. Is there a concentration lower and upper limit to form these initial smaller clusters? Have you found this behaviour to depend much on the initial seed number? It does depend on the force-field, as seen in FigS1.

Minor typos.

fig 4: y-axis legend of panel C - missing (

figS1 - have the starting colours be a bit darker. The first line is too light.

figS2 - we would suggest having the Ncontacts on the same scale for all the panels to facilitate comparison

Cheers,<br /> Susana Barrera-Vilarmau and João MC Teixeira

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 2022-03-03 11:18:55, user Synechocystis wrote:

Interesting manuscript. I especially like the fact that inactivation of FtsH overcomes a PSII assembly defect. This is in line with earlier work in cyanobacteria that showed that inactivation of the thylakoid FtsH2 protease led to enhanced accumulation of PSII assembly intermediates and variant PSII complexes. For example, Komenda et al (2008) JBC 283: 22390 and Boehm et al (2011) JBC 286: 14812

On 2022-03-02 17:46:25, user Clare wrote:

Could you please upload your supplemental tables?

On 2022-03-02 11:38:18, user Marcel Schilling wrote:

The link in the abstract points to https://github.com/chaoning..., not https://github.com/chaoning....

On 2022-03-01 23:32:21, user eman ali rashad wrote:

This article published now. https://doi.org/10.1080/165...

On 2022-03-01 16:30:25, user Waylon J Hastings wrote:

Great work. I appreciate the use of real data to support thermodynamics based theories of aging. I would suggest adding a legend to Figure 3 and Figure 5 that that clarify the meaning of the differently colored lines prior to journal submission.

On 2022-03-01 13:25:37, user JHeckenhauer wrote:

This preprint has been published. A link will be forthcoming

On 2022-03-01 08:12:01, user Jian-Huan Chen wrote:

The preprint has been published in Genomics, Proteomics & Bioinformatics. <br /> https://www.sciencedirect.c...<br /> Please cite the update-to-date published version if possible. <br /> Thank you!

On 2022-03-01 07:08:40, user Mark Fisher wrote:

Interesting work, but where is the supplementary information? I can't seem to find it.