On 2022-09-13 18:32:44, user ABHINAV JAIN wrote:

Thanks for providing Ingres tool for GRN.<br /> I am wondering did you guys also compared it with the other existing tools? If yes which one and how was your experience?

Thanks

On 2022-09-13 16:39:13, user Joachim Goedhart wrote:

Great work, I enjoyed reading it.<br /> One suggestion is to split the graph with the 'Bleaching profile' (figure 1D) according to the laser line that was used for excitation. I don't think it is fair to compare FPs that are excited with different laser lines as the illumination intensity of the lines may differ (as far as I can tell the power of the lines was not measured). It would also simplify the comparison between different spectral classes.

On 2022-09-13 14:09:31, user Stephan Emmrich wrote:

Dear Reader,<br /> Please note that the two bioRxiv manuscripts with doi:<br /> https://doi.org/10.1101/859454<br /> https://doi.org/10.1101/202...

Have been published as one paper in EMBO J 2022, please check out<br /> https://www.embopress.org/d...

Thanks,<br /> Stephan

On 2022-09-13 13:34:43, user Miles Markus wrote:

In this interesting paper, earlier studies are referred to in which it was concluded that the first Plasmodium vivax malarial relapses early in life are genetically homologous; and that parasites which gave rise to sequential recurrences in a particular patient with P. vivax malaria were hypnozoite-associated meiotic siblings. The previous authors’ conclusions might or might not be correct. An alternative possibility is that some or all of these recurrences are/were, in fact, hypnozoite-unrelated in that they were recrudescences (not relapses), homologous recurrences being highly suggestive of a clonal merozoite (perhaps non-circulating) origin. SEE (try clicking on the link below): Markus, M.B. 2022. Theoretical origin of genetically homologous Plasmodium vivax malarial recurrences. Southern African Journal of Infectious Diseases 37 (1): 369. https://doi.org/10.4102/saj...

On 2022-09-08 22:06:06, user Walter S Leal wrote:

I read this preprint with vivid interest and took the opportunity here to comment on a couple of issues.

I like the approach of performing repellency tests with a higher throughput assay but having an exit hands-on-cage (real-world) assay. A few relevant issues occurred to me, which the authors may want to consider:

1) It might be more appropriate to report repellency in terms of protection (per EPA & WHO guidelines). Also, it seems that 75% repellency is too low. A good repellent should provide approximately 100% protection for a couple of hours.

2) As you know, the core principle of scientific publication is to provide enough information that a qualified person can perform the work. Several qualified researchers could test whether the newly discovered repellents are indeed more effective than DEET. However, the names of the new repellents, their sources, and chemical characterization (if newly synthesized) were not disclosed. {Is there a SI file I missed?]

3) Suppose there is an issue of intellectual property. In that case, this issue should be addressed first, and then the work should be considered for publication with a complete list of compounds tested, particularly those claimed to be more effective than DEET.

Other minor issues for your consideration:<br /> 1) In the introduction, the sentence referring to the discovery of picaridin needs to be re-phrased. Picaridin was developed before insect ORs were discovered, let alone the co-receptor Orco.

2) Insect ORs are not G-protein coupled receptors, as implied in the introduction.

Thank you for sharing a preprint in BioRxiv. It is a remarkable development.

Walter Leal

On 2022-09-07 14:26:19, user Feng Yang wrote:

I am the corresponding author of the original study. [Journal name redacted to follow bioRxiv's policy] rejected this Preprint based on our Concerns on their concern. Unfortunately, I do not know how to publish the PDF file of our response (it does not fit BioRxiv since our PDF file does not contain additional experimental data). I am pasting it below. We welcome open discussion based on solid experimental data and are looking forward to more independent studies in this area.<br /> Re: On the therapeutic potential of MAPK4 in triple-negative breast cancer <br /> Feng Yang<br /> Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas<br /> * Corresponding Author: Feng Yang, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030. Phone: 713-798-8022; Fax: 713-790-1275; E-mail: fyang@bcm.edu<br /> Boudghene-Stambouli et al. recently published “On the therapeutic potential of MAPK4 in triple-negative breast cancer” in BioRxiv concerning our Nature Communications publication, “MAPK4 promotes triple negative breast cancer growth and reduces tumor sensitivity to PI3K blockade.”, published 11 January 2022 (1). We want to reply to their comments as follows.<br /> Boudghene-Stambouli et al. essentially detected a similar MAPK4 protein expression pattern (Our report (1) vs. Boudghene-Stambouli et al., Fig. 1c) in the human TNBC cells, when using the same commercially available antibody AP7298b. However, they claimed, “We failed to detect a specific ERK4 band in any of the cell lines, including Hs578T cells transfected with human ERK4 cDNA.” They then used their own “validated custom polyclonal ERK4 antibody that we use routinely in our laboratories” to produce a different MAPK4 expression pattern (Boudghene-Stambouli et al., Fig. 1c). They provided a siRNA knockdown for the “validation” of their antibody. In this case, Boudghene-Stambouli et al. largely ignored our previous publications using the commercially available AP7298b to successfully confirm the overexpression, knockdown (up to five independent shRNAs), and knockout of MAPK4 in many human cancer cell lines and in “normal” cells (1-4). AP7298b can also detect a purified GST-MAPK4 fusion protein in the GST pulldown assays and the purified Flag/His-tagged wild-type and mutated MAPK4 proteins in the in vitro kinase assays (2). It should be noted that instead of our extensive validation of AP7298b using many MAPK4-overexpressing, knockdown (up to five independent shRNAs), and knockout cells as well as purified MAPK4 proteins (overexpressed/purified from both prokaryotic and eukaryotic cells), Boudghene-Stambouli et al. only used a single siRNA to “validate” their un-named custom antibody. Besides, they did not confirm HA-MAPK4/Erk4 overexpression in their Hs578T cells (Boudghene-Stambouli et al., Fig. 1c). Please note, due to the sensitivity of different antibodies, even if an HA-positive western blot is provided, it may not confirm significantly increased ectopically overexpressed MAPK4 expression over the endogenous MAPK4. Finally, their custom antibody detected many non-specific bands compared to AP7298b (Boudghene-Stambouli et al., Suppl. Fig. 1c, which was included in their submission recently rejected by [Journal name redacted to follow bioRxiv's policy] after peer-review). Therefore, we have concerns over Boudghene-Stambouli et al.’s concern on MAPK4 protein expression levels in the MAPK4-high TNBC cell lines that we used in our study (1).<br /> It is well-known that mRNA and protein abundances may not correlate well in biological systems. Therefore, Boudghene-Stambouli et al.’s concern about the variation of MAPK4 mRNA expression across the cell lines will not carry that much weight. We also noticed that Boudghene-Stambouli et al. used our reported 5’ primer but a modified 3’ primer for their qPCR data in Fig. 1a. We wonder whether they have performed qPCR using our reported 5’ and 3’ primers to detect MAPK4 expression (3), and what were the results? Besides, although we have not systematically examined MAPK4 mRNA expression in human TNBC cell lines as we did for human prostate cancer cell lines (3), we did qPCR confirmed MAPK4 expression in MDA-MB-231, SUM159, as well as the non-small cell lung cancer H1299 cells. Besides, Zheng et al. independently showed MAPK4 mRNA and protein expression in HCC1937 and MDA-MB-231 cells (5), two of the TNBC cell lines concerned by Boudghene-Stambouli et al. Without knowing the quality of Boudghene-Stambouli et al.’s RNA-seq data, we could not comment on their Fig. 1b data.<br /> Another concern of Boudghene-Stambouli et al. is their failure to verify our reported MAPK4-AKT signaling axis, a conclusion drawn from their Fig. 2 data. Without providing their data, the corresponding author Dr. Meloche has communicated with me about this issue. At that time, I provided the following answer. “I am not sure if you did a transient transfection in the 293 cells. Unlike MK5, phosphorylation of AKT is subjected to many more direct and indirect regulations in the cells. It is hard to imagine that you can easily detect MAPK4 phosphorylation of cell endogenous AKT in the transiently transfected 293 cells. It can be a hit and miss, especially if you do not carefully monitor cell confluency. I think that we only reported data from the stable 293T cells overexpressing MAPK4 or MAPK4 phosphorylating a co-transfected AKT in 293T cells. In the latter case, we suspect that these ectopically overexpressed AKT are less susceptible to endogenous cellular posttranslational modifications and more susceptible to the regulation of overexpressed MAPK4. Again, unless you can’t repeat our data, such as MAPK4 phosphorylating a co-transfected AKT in 293T cells, I do not see a common ground for our debate here either.” Now I see the experimental data, and Boudghene-Stambouli et al. did perform a transient transfection and tried to detect phosphorylation change of endogenous AKT, which we have already expressed concern about in our previous personal communications. Interestingly, as a positive control for their Fig. 2 data, Boudghene-Stambouli et al. showed MAPK4 enhanced the phosphorylation of an ectopically overexpressed but not endogenous MK5, raising concern about this so-called positive control per se. We are also unsure how much MAPK4 was overexpressed compared to endogenous MAPK4 (Western blots on GFP could not provide that information) nor the nature of the seemingly increased AKT T308 phosphorylation in the MAPK4 transfected 293 cells (Boudghene-Stambouli et al., Fig. 2).<br /> I want to finish this discussion using what I wrote to Dr. Meloche in another email. “Without detailed information from your side, it is hard for me to guess what happened. I want to emphasize several technical details that may help. 1. Please collect cells at about 50%-70% confluency. If your lab collected cells at very high confluency, please try this. 2. We have been using Dox-inducible knockdown and overexpression approaches. We typically maintain the cell culture without Dox induction and do a couple of days (such as three days) induction just before the experiments. 3. If you use a non-induction system as we did in some of our studies, please ensure that you only use the engineered cell lines at early passages. You can do this by freezing down many vials from a very early passage and only using the thawed-out cells for minimal additional passage(s). The cancer cells in culture may adapt to the cellular “stress” from long-term MAPK4 overexpression or knockdown.”<br /> We welcome open discussions based on solid experimental data. We will do our best to help if any group meets technical difficulty in repeating our data under the reported experimental conditions. We have validated our MAPK4-AKT signaling in more than 20 human cancer cell lines (Ref. (1-3), and unpublished data), and additional independent reports also confirmed MAPK4 phosphorylates/activates AKT in human cancer cells (5, 6). We welcome and are looking forward to more independent studies in this area.<br /> References <br /> 1. Wang W, et al. MAPK4 promotes triple negative breast cancer growth and reduces tumor sensitivity to PI3K blockade. Nat Commun. 2022;13(1):245.<br /> 2. Wang W, et al. MAPK4 overexpression promotes tumor progression via noncanonical activation of AKT/mTOR signaling. The Journal of clinical investigation. 2019;129(3):1015-1029.<br /> 3. Shen T, et al. MAPK4 promotes prostate cancer by concerted activation of androgen receptor and AKT. The Journal of clinical investigation. 2021;131(4).<br /> 4. Cai Q, et al. MAPK6-AKT signaling promotes tumor growth and resistance to mTOR kinase blockade. Sci Adv. 2021;7(46):eabi6439.<br /> 5. Zeng X, et al. MAPK4 silencing together with a PARP1 inhibitor as a combination therapy in triplenegative breast cancer cells. Molecular medicine reports. 2021;24(2).<br /> 6. Tian S, et al. MAPK4 deletion enhances radiation effects and triggers synergistic lethality with simultaneous PARP1 inhibition in cervical cancer. J Exp Clin Cancer Res. 2020;39(1):143.

On 2022-09-07 10:19:53, user Scott Hayes wrote:

Firstly I would like to congratulate the authors on their study. I really love the inventiveness of the experimental set up! The results are really unexpected. The finding that phosphate starvation proceeds ABA mediated drought responses is interesting on a mechanistic basis and will likely have direct implications for crop management practices. The field-to-lab experimental pipeline looks really effective and I look forward to more people taking up this approach.

I do have a couple of points that I believe the authors could discuss in more depth. In their ridge trials, the geometry of the soil may play a role. Phosphate starvation induces a lot of lateral roots close to the soil surface, to maximise phosphate capture. In the ridge set-up, lateral roots are restricted to only a single plane. Is it possible that this contributes to the PSR in ridge-grown plants? It is also possible that increased rooting depth under mild-drought treatment also reduces phosphate uptake. If the authors see a reduction in PSR gene expression after re-watering ridge plants, this may help to rule out geometric explanations. It should be noted that the pot experiments do already indicate this to some extent.

I am also not so sure about the authors’ conclusion on why PSR is induced at mild drought but suppressed under severe drought “Under severe drought conditions, given the circumstantial evidence, our observations would support the notion that PSR induction is suppressed by a relative increase in Pi concentration due to a decrease in leaf water content (Fig. 4).” I would argue an alternative, more straight forward hypothesis, that plants simply prioritise drought stress over this level of phosphate starvation when drought is severe. Would the authors expect to see a recovery of the PSR if Pi levels dropped even lower?

Once again, thanks to the authors for their very through-provoking study!

I look forward to hearing from you,

Scott

On 2022-09-06 09:30:00, user Prof. T. K. Wood wrote:

The seminal discovery of phage inhibition by the toxin/antitoxin Hok/Sok system should be mentioned herein given Hok/Sok was discovered 15 years earlier compared to those cited here and provided the mechanism that was confirmed by the Laub group 26 years later (ref 6). See doi: 10.1128/jb.178.7.2044-2050.1996 and https://journals.asm.org/do....

On 2022-09-05 08:53:44, user Kristian Unger wrote:

Congratulations to this great work! Have you made the software already available somewhere?

On 2022-09-04 13:55:42, user Shrishti Singh wrote:

Cheers to the team on this work.! This is a highly-reproducible way to make contrast agents.

On 2022-09-02 03:31:28, user Milind Watve wrote:

The manuscript received an unusual response from a reputed journal to which it was communicated on 11th Feb 2022.<br /> Our correspondence with the editor as under. Name of the journal and editor is not revealed following the policy of BioRxiv.

Fri, Aug 12, 6:17 PM

to Milind

Dear Dr. Watve,

I am writing with the difficult news that we have not been able to secure an Academic Editor to handle your manuscript "Hyperglycemia in type 2 diabetes: physiological and clinical implications of a brain centered model" (MS number ----------). Additionally, we have been unable to secure feedback from peer reviewers. We have therefore reluctantly decided that we must return your manuscript to you without review.

I recognize that this decision will be frustrating -- it is our desire to provide every suitable manuscript the opportunity for review and evaluation by experts in the research community -- and I sincerely apologize that we have not been able to do so in this case. We have exhausted the pool of potential (journal name) Academic Editors qualified to handle your manuscript but have not been able to secure a commitment to handle the submission. We have also invited a number of peer reviewers with relevant expertise, but we have not been able to secure the reviews required to support an editorial decision. We are withdrawing your manuscript from consideration to prevent further delays in the assessment of your submission, and so that you can move forward immediately if you choose to submit your work elsewhere.

Again, I am very sorry not to have more positive news for you. I wish you the best in finding an alternative venue for this work.

Best regards,

Editor-in-Chief

Milind Watve milindwatve@gmail.com<br /> Sun, Aug 14, 10:23 AM

to --------- bcc: Akanksha

Dear ---------,<br /> I understand the agonies of editors. No issues. But I have one request. <br /> I would like to have your consent to post this letter in the public domain. It is very likely to be a remarkable event in the history of science and students of the history and philosophy of science need to have access to this information. How people in a field react to a paper challenging an existing dogma is a very important question in the history and philosophy of science and making this letter public is extremely essential. Therefore I want to append it to the preprint, as well as write an article about it on my blog on which I have often written about problems in science and science publishing. Link here if you want to view it (https://milindwatve.in/)<br /> Awaiting your response.

milind<br /> (Dr. Milind Watve)<br /> https://milindwatve.in/

Journal name ><br /> Sun, Aug 14, 10:24 AM

to me

Dear Milind Watve

Thank you for contacting --------. We will reply to your query as soon as we are able.

In the meantime, please take a look at the following links for more information about our processes:

We appreciate you reaching out and will be back in touch shortly.

All the best,

Milind Watve milindwatve@gmail.com<br /> Mon, Aug 29, 9:20 PM

to --------

Dear Editor,<br /> This is to inform you that since I did not get any reply from you for over two weeks, I am assuming that you have no objection if I publish your letter in any appropriate context, in a respectful manner.

milind<br /> (Dr. Milind Watve)

On 2022-09-02 01:38:59, user Matthew Templeton wrote:

What similarities are there to the Myrtle rust (Austropuccinia psidii) genome, given that both organisms have broad host ranges and very large Tn-rich genomes?

On 2022-09-01 16:43:00, user Rath R. Weird wrote:

Updated info on the first author:<br /> Vadim Molodtsov Rospotrebnadzor Russian Federation<br /> https://sysbiomed.ru/team/m...

On 2022-08-31 13:08:23, user Iratxe Puebla wrote:

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Amrita Anand, Richa Arya, Aurora Cianciarullo, Luciana Gallo, Dipika Mishra, Sanjeev Sharma, Ryman Shoko and Rajan Thakur. The comments were synthesized by Ehssan Moglad.

The study conducted by Doyle et al. aimed to test the lipid phosphatidylinositol 4-phosphate (PI4P) transfer activity of the human ORP5 protein via orthogonal targeting of different sites of membrane contact, namely, between the plasma membrane (PM) and the mitochondrial outer membrane.

Major comments

Figure 1: The idea behind the experiment is great, however, there are some questions about the data presented:

• Recommend using better representative images, the morphology of the cells in panels B and C appears distorted, are these cells undergoing some death? This would make interpretation of the data difficult.
• In Figure 1B, the control experiment should be done with a known mitochondrial marker to test if the construct works as expected.
• P14P measurement using the P4M probe is unclear from the images and quantification provided. Please provide a multi-plane image of the probe showing its distribution on the PM and at the ER-PM or ER-mito contact site.
• Please provide an additional graph to show the relative change of PI4P at the PM compared to the rest of the cell or respective contact site.
• For a better comparison, recommend showing the normal (control) distribution of PI4P in the images.

Results: ‘As expected, we did not see the accumulation of PI4P at these contact sites (see graph in Fig. 1C), presumably due to SAC1 activity in the ER. Instead, the fluorescence of PM PI4P seemed to decline’: Please indicate whether this result is statistically significant.

Figure 2: The experiment with the FKBP-PI4KC1001 construct is not discussed in the text. Also, further clarification would be helpful for the results presented in panel B. In +SAC1mito, it is showing accumulation after Rapa treatment, please discuss why PI4P is not showing accumulation.

'The rationale was that without inhibition of PI4P synthesis, observing reductions in PM PI4P catalyzed by transport of PI4P out of the PM would require a rate that exceeded synthesis, which may not be possible through reduced flux at the much smaller surface area of induced PM-mitochondria contact sites, compared to ER-PM contact sites (compare Figs. 1C and D). We also imaged by Total Internal Reflection Fluorescence Microscopy (TIRFM) to more sensitively detect changes in PM PI4P with the high-affinity PI4P biosensor, P4Mx2.': Recommend revising the fragment for clarity.

Figure 3: The shape of the cells across figure 3 varies substantially, can some text be added to discuss why this is the case?

Figure 3: The authors have already shown in a previous paper that SAC1 predominantly acts only in the 'cis' configuration. However, induced coupling of overexpressed ORP between ER-PM and mito-PM using rapamycin might bring these membranes closer than usual or cause the formation of more membrane contact sites. Thus, there may be some possibility for SAC1 to act in 'trans'. Alternatively, there could be indirect changes in PM PI4P due to increased activity of endogenous ORP5 at these induced contact sites. To address this:

• Would it be possible to confirm if there was an increase in the number/size of contact sites by checking for mapper expression and localization when ORP5 constructs are expressed and coupled with Rapamycin? Lipid binding mutants of ORP5 could also be used to show that those lipid binding mutants do not cause a depletion upon coupling with Rapamycin.
• For experiments where SAC1 (Fig 3) was overexpressed along with FRB::FKBP-ORP5-ΔTMD, please show control conditions where the SAC1 alone was expressed without the ORP5. Also, in a control condition where lipid binding mutants of ORP5 are expressed along with SAC1, there should be minimal effects on PM PI4P depletion compared to WT ORP5. Adding these controls will further confirm prior observations and substantiate the effects of FRB:: FKBP-ORP5-ΔTMD expression, and rule out any potential artifacts from overexpression of just SAC1.

A major confound across all the experiments is the activity of endogenous ORP5 that is not measured. Is it possible to perform experiments (such as in Figure 3) where the endogenous ORP5 is downregulated using siRNA or shRNA and a siRNA/shRNA-resistant version of ORP5 is overexpressed in this background? There could be potential compensatory effects from other ORP5, and this would require simultaneous knockdown of multiple ORPs.

Minor comments

Figure 1: It could be helpful to start Figure 1 using a scheme of the two hypotheses on how ORP5 regulates PI4P levels at the plasma membrane. This will help easily assess the data presented in the Figures for and against the hypotheses.

Figure 1A: It is difficult to see the co-localization in the current color scheme. It would be helpful to use a different color combination, provide zoomed-in images, or use pointers to highlight.

Figures 1C and D: Please specify in the legend the timepoint when rapamycin was added and the subcellular membrane the measurements were made from.

In discussion: 'In principle, this observation does not demonstrate lipid transfer (though it is compatible with it). Although tethering at a site of membrane contact seems to facilitate access of PI4P to SAC1, ORP5 could simply be presenting the lipid to the phosphatase, as opposed to depositing PI4P into the membrane for subsequent hydrolysis by SAC1. If ORP5 works in such a presentation mode, it is not clear to which membrane the resulting PI lipid is released: either back into the PM, or into the tethered membrane. In other words, lipid transfer is not necessarily part of the reaction'. Are there ways in which this can be tested? Suggest proposing some future experiments in the text.

On 2022-08-31 07:56:29, user Dr. Jaimini Sarkar wrote:

This study @biorxivpreprint has isolated bioactive phytochemical from mangrove - Sonneratia apetala, effective against human pathogenic bacteria.The study also shows that the potency of the extract varies with geographical location of the plant.

On 2022-08-30 11:26:14, user Nándor Lipták wrote:

Dear Authors,

Unfortunately, knockout lethal phenotypes are quite common in mice. We collected the most promising rescuing methods in a review last year, you may find it useful for your present preprint or future experiments:

10.33549/physiolres.934543<br /> https://www.ncbi.nlm.nih.go...

On 2022-08-29 14:32:38, user Victoria Hewitt wrote:

Thanks to the hard work of Alex Whitworth and the co-authors listed here and contributions from Madeleine J Twyning finally officially published at https://www.life-science-al...

On 2022-08-29 12:05:44, user Manuel Ruedi wrote:

This is a very fine new piece of evidence that the Myotis radiation is both quick... and complexe. I have a single comment regarding the place of M.brandtii within the Old World clade, rather than within the New World (as evidenced elsewhere, incl. in large phylogenies using 1610 UCE to recover that topology): The branch linking brandtii to the few other Old W taxa is very short, so that the root of the whole Myotis tree is very fragile. The authors used distant Vespertilionids to place this root (instead, they could have used Kerivoulinae or Muriniae representatives, i.e. the sister-group of Myotinae, which would have been more effective in placing this root of Myotis). Also because they used only few Old World species, they gave little chance for that group to represent its full diversity.<br /> But what is clear from this brilliant study is that the brandtii lineage appears more basal to the New World radiation than previously reported.

On 2022-08-28 23:09:38, user Peter Hickey wrote:

Where can this software be downloaded? I could not find a link in the paper or via Google.

On 2022-08-28 11:13:55, user David Curtis wrote:

I recently investigated the performance of different predictors of pathogenicity and what I found was that some approaches worked well for some gene/disease combinations but less so for others - there was no universal best method which consistently out-performed the others. Also, the problem with using ClinVar/HGMD for validation is that you may end up only dealing with the kinds of variants that people judge to be pathogenic.

My paper is here:<br /> Exploration of weighting schemes based on allele frequency and annotation for weighted burden association analysis of complex phenotypes. David Curtis. Gene 2022 30;809:146039. doi: 10.1016/j.gene.2021.146039. Epub 2021 Oct 22.<br /> https://pubmed.ncbi.nlm.nih...

On 2022-08-28 09:00:20, user Iratxe Puebla wrote:

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Ruchika Bajaj and Gary McDowell. Review synthesized by Bianca Melo Trovò.

This study demonstrates the utility of an L-Methionine analog - ProSeMet - to tag and enrich proteins which have residues that are methylated in vivo, ex vivo and in vitro. Furthermore, the study demonstrates that this can be used in combination with mass spectrometry to identify these sites. Overall this is a useful, well-verified and well-described approach that will be helpful for future identification and investigation of methylation sites.

Major comments

It would be helpful if the manuscript could additionally discuss the reversibility of methylation generally, and the reversibility of the modification of protein residues by the alkyne group specifically, in the discussion, and whether that has any implications for their results. It may be that the dynamics of methylation and demethylation vary between the two; or it may be that they are the same - either way, that may affect how they suggest others use this method and interpret its results.

Perhaps related to the question of reversibility, it would be helpful if the manuscript would comment on whether these are “true” methylation sites or not; i.e. whether they consider all these methylation sites to be functional. Trying to determine this would be an interesting direction for future work, but for this study a reflection on whether these novel functional methylation sites are simply capable of being methylated, or are likely to be methylation sites that are meaningful biologically, would be helpful.

Results, ProSeMet competes with L-Met to pseudo methylate protein in the cytoplasm and nucleus: the manuscript claims that ProSeMet is not incorporated into newly synthesized proteins but rather converted to ProSeAM and used by native methyltransferases. There does appear to be some reduction in the labeling with ProSeMet on cycloheximide treatment in Figure 2D - could this suggest that it is incorporated into newly synthesized proteins as well as being converted to ProSeAM? If not, could the manuscript explain why not? This experiment clearly shows that in contrast to AHA labeling, there is still use of ProSeMet as a substrate when translation is inhibited; however, it is not clear how this demonstrates that it is not incorporated at all into newly synthesized proteins. If methyl has been incorporated in previously present proteins, perhaps this can be clarified in the text.

Results, ProSeMet competes with L-Met to pseudomethylate protein in the cytoplasm and nucleus: the conclusion that “Cell fractionation of the cytosolic and nuclear compartments followed by SDS-PAGE fluorescent analysis revealed no fluorescent labeling of the L-Met control” is correct but may be overstated as there appears to be some background in the cytosolic fraction.

Minor comments

Introduction: Recommend including a mention to ProSeMet's permeability.

Introduction, Figure 1: the last step with CuAAC and N3 labeling in the description of the Chemoenzymatic approach for metabolic MTase labeling is not clear. Please, add the description in the legend.

Results, Figure 2D: the image suggests an overloaded gel, consider using an alternative gel image.

Supplementary Material, Fig. S1: the data with L-met is only shown with T47D stacks.

Supplementary Material, Fig. S3: please add the control for the no treatment condition.

Results, Fig. 2A ‘ incubating for 30 m in L-Met free media’: Please confirm that the length of incubation was 30 minutes.

Results, Enrichment of pseudo methylated proteins used to determine breadth of methyl proteome: Please provide some description for the SMARB1-deficient G401 cell line. Why smarb1 deficient?

Results, Figure 3: Please define BP, MF, HP, NES, and label the x and y axes in panel D.

Results, ProSeMet-directed pseudo methylation is detectable in vivo: Please, clarify if the administration was oral.

Comments on reporting

Results, ProSeMet competes with L-Met to pseudo methylate protein in the cytoplasm and nucleus: Please verify the quantity reported: 5µg on SDS-PAGE gel seems low.

Results, ProSeMet-directed pseudo methylation is detectable in vivo: the manuscript reports that “mice starved prior to ProSeMet injection had increased ProSeMet labeling in the heart, whereas mice fed prior to ProSeMet administration had increased labeling in the brain and lungs”. The error bars are large, it would be helpful to show the individual real data points for the graphs in Figure 4.

Results, Figure 4C: please report the mathematical expression used to calculate the relative fluorescence.

Supplementary Material, Fig. S7: please provide more details on the antibody employed.

Suggestions for future studies

Future studies could investigate the biological functionality of the novel methylation sites - but this is a great proof of principle.

On 2022-08-28 06:38:09, user Omri Wurtzel wrote:

The peer-reviewed version of the manuscript is found, open access, in the following link:<br /> https://doi.org/10.15252/em...

On 2022-08-27 15:52:20, user Mark A. Hanson wrote:

The first version of this article was accidentally missing its Acknowledgements section. This has been rectified in v2. To ensure this information is present regardless of manuscript version, we would like to additionally post this information here:

We would like to thank Samuel Rommelaere, Jean-Philippe Boquete, Emi Nagoshi, Lukas Neukomm, Kausik Si, and Anzer Khan for helpful discussion. We would also like to thank Brian McCabe, Mariann Bienz, Barry Ganetzky, Steven Wasserman and Lianne Cohen, the Vienna Drosophila Resource centre, and the Bloomington Drosophila Stock Centre for fly stocks requested over the course of this research. This research was supported by Sinergia grant CRSII5_186397 and Novartis Foundation 532114 awarded to Bruno Lemaitre.

On 2022-08-27 06:56:43, user Iratxe Puebla wrote:

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Claudia Molina Pelayo, Demetris Arvanitis, Pablo Raneo-Robles, Sónia Gomes Pereira. The comments were synthesized by Vasanthanarayan Murugesan.

In this preprint, Hughes et al. describe the interaction between the ER protein PERK and the mitochondrial protein ATAD3A. During ER stress, PERK phosphorylates elF2a leading to reduced global protein synthesis. The authors show that increased interaction between PERK and ATAD3A during such stress attenuates elF2a phosphorylation locally around mitochondria, resulting in continued translation of mitochondrial protein despite a reduction in global protein translation. The authors present multiple lines of evidence to support this claim and the experiments were well performed. The findings may have important implications for the understanding of mitochondrial protein synthesis and the interactions between mitochondria and the ER.

The following suggestions were raised:

Experiments

The manuscript would benefit greatly by measuring protein translation explicitly showing that mitochondrial protein translation is retained despite a reduction in global protein synthesis under certain conditions. That would help determine whether mitochondrial protein translation is protected under certain conditions driven by ATAD3 expression.

The specificity of ATAD3A towards PERK activation requires further experimental validation. Some specific suggestions are:

• Changes in activation of other pEIF2a kinases, such as GCN2 or PKR, could be measured to discard their involvement.
• In Figure S2, protein levels of ATF6 should accompany changes in spliced XBP1.
• ATF4 levels, a downstream marker of the signaling pathway, could be measured.

Manuscript

Recommend providing more details about the experimental protocol when treating cells with ER stressors. Different treatment durations are found throughout the manuscript (30min, 1h, 8h…). More information would be helpful in understanding the election of those time points for different experiments.

In Figure 2, recommend including the blots for the downstream targets ATF4, GADD34 and CHOP at the 30 minutes time point, where the upstream activation starts.

In Figure 2, the differences shown in the representative images for p-eIF2a and ATF4 appear milder than what is shown in the graph. In particular when compared with the interpretation of blots in Fig. S2. It is suggested to include all the blots used for quantification in Figure 2 in a supplemental figure so it can be clear how overexpressing/downregulating ATAD3A has a meaningful effect on this signaling pathway.

Figure 2B shows 5 different (phospho)proteins using the same loading control blot. This approach would require stripping of the membrane after each blotting, can this be specified in figure legends and in the Materials & Methods. Was the membrane stripped after each blot or were different membranes used? If different membranes were used, please indicate so and present the individual beta-actin blots corresponding to each protein as a supplemental figure.

In Figure 3A, arrows indicating the contact sites between ER and the mitochondria would be helpful in highlighting the colocalization of the two proteins. Please also provide scale bars for the images.

In Figure 3D, the #contacts per mitochondria, it is important to specify the area of images analyzed. It is unclear that n=45 images from 3 separate experiments refers to 45 images per experiment or a total of 45 images pooled from 3 experiments. Please clarify.

Recommend discussing the limitation of experiments using a single siRNA for loss-of-functions studies and experiments using cell culture.

On 2022-08-26 18:08:12, user Guido Lenz wrote:

Nice work - how does this relate to the reduction in variance of colonies with the growth of these colonies? https://doi.org/10.1158/000...

On 2022-08-26 14:15:53, user Anthony Gitter wrote:

The manuscript refers to a Methods section, which is not included in the full text. Could the authors please update their manuscript to include the Methods?

On 2022-08-26 13:40:17, user Matt Higgins wrote:

We were extremely interested to see these impressive structures of a chimeric Sec translocon, held in a state ready for post-translational translocation through interaction with Sec62/63, bound to eight different inhibitors. Particularly noteworthy to us is that the inhibitor mycolactone binds in a different location in this study when compared with our previous structure of the same inhibitor bound to a ribosome-bound translocon, primed for co-translational translocation (Gerard et al Molecular Cell 79 406-15).

The authors speculate that “Our data suggest that the density feature previously assigned as mycolactone is unlikely to be mycolactone.” While it is true that our previous structure has a resolution of ~5A in the region of the map attributed to mycolactone, and therefore also true that we cannot unambiguously place mycolactone in this density, we remain confident that this density is mycolactone for the following reasons:

(i) Our procedure involved incubation of microsomes with mycolactone at a concentration of ~0.3µM (compared with 100µM used by Itskanov) before detergent/digitonin treatment and purification of ribosome-associated Sec complexes. A similar sample was prepared without mycolactone. When these two protein complexes were studied by cryo-electron microscopy, the Sec translocon adopted a substantially different conformation when mycolactone-bound compared with free. The only difference between these two samples was the presence of mycolactone, indicating that this structural difference is due to mycolactone binding.

(ii) We confirmed the presence of mycolactone in our mycolactone-bound purified using mass spectrometry of a sample taken immediately before addition to grids for structural analysis.

(iii) Analysis of the electron density for the mycolactone-bound translocon did not reveal any density feature in the mycolactone-bound sample in the location of the binding site observed by Itskanov. Therefore ribosome-bound, mycolactone-bound translocon is different from Sec62/63-bound, mycolactone-bound translocon.

(iv) The only additional density feature observed in the ribosome-bound, mycolactone-bound translocon is that which we have attributed to mycolactone and molecular dynamics simulations confirm that mycolactone is stable in this binding site.

It is therefore our view that we did not misattribute the electron density into which we have placed mycolactone. Instead, it is our view that the difference between these two structures is likely to be genuine and mechanistically interesting.

There are possible technical differences which could account for the different binding sites observed when comparing our structure with that of Itskanov:

• While we added mycolactone to the Sec translocon while still in the native membrane environment of microsomes, and then extracted the mycolactone-bound complex, Itskanov added to mycolactone to translocon after its purification and integration into a non-lipid peptidisc. Our model for how mycolactone reaches its binding site in our system relies on translocon “breathing” within the physiological situation of a lipid bilayer, and mycolactone itself being present in this bilayer. It is not known if the translocon within a peptidisc is able to undertake similar “breathing”, nor how highly hydrophobic mycolactone may interact with this material.

• While we used native canine microsomes, Itzkanov et al used a hybrid translocon, comprised of human transmembrane regions and yeast extracellular regions. It is not known if this hybrid translocon is functional for translocation, or whether the translocation of model substrates by it is inhibited by mycolactone.

• There is also a large difference in mycolactone concentration used in the different studies. Mycolactone is effective at sub-nM concentrations on live cells. To provide sufficient molar ratios of mycolactone in concentrated microsomes, we used ~300nM in our studies, while Itskanov used the much higher concentration of 100µM mycolactone. It would be interesting to know whether this was the minimal concentration required for them to see binding, indicating a lower affinity binding site, or was simply the concentration selected.

While there are technical differences which might account for the different binding sites observed, there is also the far more interesting possibility that both studies have correctly identified binding sites for mycolactone and that this inhibitor acts differently in post-translational and co-translational translocation.

The Sec translocon can act through either a post-translational (involving Sec62/63) or a co-translational (involving ribosomes) mechanism. McKenna, Simmonds and High have previously shown (PMID 26869228) that mycolactone-mediated blockade is different in these two systems. While mycolactone shows a broad effect, preventing co-translational translocation of a wide range of substrates, it has a more restricted effect during post-translational translocation, only affecting translocation of a subset of substrates. Together with the differences in mycolactone binding between these two structures, this suggests the intriguing possibility mycolactone might have two different binding sites; perhaps one site which occurs during co-translational translocation where mycolactone is stably wedged into the cytosolic side of the lateral gate (Gerard et al), and one site which operates in post-translational translocation and is more easily overcome by signal peptide binding (Itskanov et al). Future studies will be required to test this intriguing possibility.

Sam Gerard, Matt Higgins and Rachel Simmonds

On 2022-08-15 09:45:59, user Richard Zimmermann wrote:

Congratulations to all authors for both making this heroic effort and this brilliant manuscript, which will undoubtedly further advance the already ongoing attempts to develop Sec61 channel inhibitors into therapeutics in human medicine (reference 28). Interestingly, the manuscript reports that two of the inhibitors (Eeyarestatin 1 and Mycolactone) are different from the other tested inhibitors in "further penetrating the cytosolic funnel of Sec61". Notably, Eeyarestatin 1 as well as Mycolactone were previously shown to enhance Sec61-mediated Ca2+ leakage from the ER in human cells, which can lead to apoptosis and, therefore, appears as medically relevant. In my opinion, it´s a pity that this aspect was not discussed in the manuscript.

On 2022-08-25 21:28:06, user Gregory S. Paul wrote:

In the process of assessing the underwater swimming performance of Spinosaurus, Sereno et al. arrived at a specific gravity of 0.83 to help restore the body density of the crocodile like headed, sail backed dinosaur. Such a value is almost certainly too low for a large nonavian theropod, being in the area of some of the lowest density flying birds and derived pterosaurs as detailed and/or estimated in Larramendi et al. (2021, Paul 2022). Having less extensive air-sac complexes, including reduced forelimbs, images of swimming large ratites indicate their neutral (midbreath) SGs approach 0.95. Lacking pneumatic limb elements, large theropod dinosaurs should have been even denser. Spinosaurus was less pneumatic than most giant theropods, but more so than typical large land mammals which have NSGs just below 1.0 – most swimming nonaquatic mammals keep their heads sufficiently above water partly by the upwards thrust of active swimming and are at risk of drowning if they become too tired. Estimating the NSG of atypical Spinosaurus is difficult, but it would have been significantly higher than that of ratites and perhaps a little below that typical of terrestrial mammals.

Highly aquatic animals that use fairly conventional tetrapod limbs and/or sculling tails for swimming tend to be as dense as or denser than water, with some able to bottom walk including capybaras and hippos, the latter being too dense to surface swim and thus sporting a specific gravity of perhaps 1.1 (Larramendi et al. 2021). Highly aquatic crocodilians are in the area of 1.0. Flightless marine penguins are moderately pneumatic and buoyant. After using their highly hydrodynamically modified and powerful flippers to propel themselves to sometimes considerable depths, the buoyancy helps penguins return to the surface at a pace that minimizes both risk of the bends and energy expenditures when oxygen reserves are depleted (Sato et al. 2002). Those extreme circumstances do not apply to Spinosaurus. and its being pneumatic enough to be a little less dense than water also does not fit the standard adaptations for flipperless tetrapods that pursue prey underwater in shallows as crocodilians sometimes do. Nor does the large rigid, high drag sail. And the deep tail with its very tail slender neural spines assigned to the taxon is more similar to the display structures of nonaquatic basilisks than large bodied tail scullers (Sereno et al.).

This paleoartist remains skeptical of Spinosaurus restorations with very reduced hindlimbs, that not yet being verified by any sufficiently complete, well-articulated spinosaur specimens that meet the paleoreconstruction criteria needed to verify a configuration that is so extraordinary for a theropod dinosaur.

Larramendi A., Paul G. S. & Hsu, S. (2021). Review and reappraisal of the specific gravities of present and past multicellular organisms, with an emphasis on vertebrates, particularly pterosaurs and dinosaurs. Anat. Rec. 304:1833-1888.

Paul, G. S. (2022). The Princeton Field Guide to Mesozoic Sea Reptiles. Princeton University Press, Princeton.

Sato, K. et al. (2002). Buoyancy and maximal diving depth in penguins: do they control inhaling air volume? J. Exp. Biol. 205:1189-1197.

On 2022-08-25 15:11:03, user Sam Nooij wrote:

I would like to thank the authors for sharing an updated version (v3) of such a fascinating manuscript. I enjoyed reading it and I have a couple of questions/points of feedback that I would like to share.<br /> 1. Do I understand correctly that in lines 133-134 the authors state that higher percentages of read recruitment to donor MAGs suggest bacterial colonisation? This seems to me very indirect evidence and not a justifiable conclusion based on this observation alone.<br /> 2. In figure 1, Canada is shown slightly bigger than the other countries. Is this because the donors and patients from the current study are also from Canada? I could not find this information in the text.<br /> Furthermore, I like that the authors made a distinction between industrialised and less industrialised countries. Would it be possible to change the colours slightly (e.g. make the red magenta) to make it easier for colour blind people to see?

Also, the blue and purple rows are somewhat difficult to distinguish for me. Is this intentional, as the donor and post-FMT recipient microbiota are supposed to be similar?<br /> 3. I find it interesting that the authors found that only 16 and 44% of donor microbial genomes were detected in all donor metagenomes, suggesting that only a minority of bacteria is stably present over longer periods. (Lines 180-182.) Have the authors considered doing similar analyses on these genomes to find if they are HMI and LMI bacteria?<br /> 4. I find the comparison made between short-read taxonomy and donor population detection (lines 184-188) a little difficult to follow. Would it be possible to rephrase this part or add a little explanation of what exactly is compared?<br /> 5. Lines 190-194 are also fascinating! Even with such small numbers, it is striking to see that more bacteria colonise from pills that from colonoscopic transfer. Could the authors speculate or provide additional info on why this may be the case?<br /> 6. From the final conclusion (lines 431-435) I gather that FMT or similar microbiota therapeutics are unlikely to (temporarily?) cure IBD. Do the authors have suggestions as to what might work better, and would they like to share their perspectives on promising new treatment options?<br /> 7. And finally, what do the ellipses in supplementary figures 2 and 3 represent? I suppose they show some sort of area around each cluster centroid. A few extra words of explanation in the figure captions would be nice. This information is also not easily found in the analysis scripts. (And by the way, it is wonderful that the authors share all code and instructions on how to reproduce the analyses!)

On 2022-08-25 09:42:44, user Didier Mazel wrote:

Very interesting observations. I was wondering if you had checked the effect of introduced secondary structure on the translation of synonymous genes, in line with what we observed (DOI:

10.1002/bit.26450 and DOI:

10.1371/journal.pgen.1000256) ?

On 2022-08-24 20:22:59, user Paul Carini wrote:

Nice paper! I wonder if the extreme mis-estimation of growth rates by DNA or protein SIP could be explained by exuded substances used to form biofilms in soil. DNA and protein are both used to construct extracellular matrices in biofilms. Biofilms are also thought to be an important component of soil microbial communities. DNA or protein that makes it into a biofilm would presumably be labeled by stable isotopes. This could be an example of non-growth related activity that would incorporate a label. Just a thought on an otherwise cool paper.

On 2022-08-23 17:41:37, user Mario wrote:

The paper has now been published in Nuture Communications:<br /> https://doi.org/10.1038/s41...

On 2022-08-23 12:13:34, user Kenneth De Baets wrote:

Interesting - it might be worth applying these methods also to the Jurassic belemnite dataset we compiled for disparity analyses: https://doi.org/10.1016/j.p...

On 2022-08-23 09:17:39, user Deon de Jager wrote:

Hi Juraj et al.,

Really interesting work! How did you deal with reference genomes that are not well assembled? E.g. the common eland genome (Taurotragus oryx) has >4 million scaffolds, none of which are larger than 50 KB. In your PSMC pipeline you state you only used contigs at least 100 KB in length?

On 2022-08-22 09:04:56, user Gaëlle Hogrel wrote:

Here is the final published version: https://www.nature.com/arti....

It contains new data about the relation between TIR-SAVED assembly and TIR NADase activity.

Graphical abstract and quick description of the work are available on this Twitter thread: https://twitter.com/GaelleH...

On 2022-05-30 09:55:28, user Malcolm White wrote:

We just became aware that one panel in extended data figure 3 was duplicated - with the same technical replicate shown twice. This error will be remedied in the published version.

On 2022-08-21 23:53:52, user Michael McLaren wrote:

An HTML version of the manuscript can be read at https://mikemc.github.io/di...

On 2022-08-19 20:54:50, user Stephanie Wankowicz wrote:

Summary: In this paper the authors set out to develop new methods for refinement of models into cryo–EM density maps. There are three primary interrelated contributions:

-Assigning “responsibility” for different regions of the map to a model and then fitting GMM as a real space B-factor. This is a new way to model atomic B-factors, since it is done in real space, compared to reciprocal space in most other software.<br /> -Sampling an ensemble based on those B-factors. The major success of this paper was that the authors created a new ensemble method that samples within the B-factors to improve the fit of hundreds of cyro-EM maps, demonstrating that their method is robust and can be done in a high throughput manner.<br /> -Refinement procedures for composite maps based on smoothing of responsibility. The examples all seem to be from individual maps with different levels of resolution across the map, not from true composite maps (calculated from different masking procedures for example). This part was very confusing for us to follow and although there are methodological links to the B-factor assignment/ensemble modeling parts of the paper, it might be better explained in a separate manuscript.

Major comments:<br /> 1. The introduction only briefly discusses B-factors and doesn’t lay out what is distinct about this method. For a contrast, sampling is discussed with references and contrast:<br /> “ The sampling itself is usually based on either molecular dynamics (MD)4,9, minimisation10, normal mode analysis and/or gradient following techniques11,12, or Fourier-space based methods2.”<br /> Similarly, B-factor refinement should be discussed. The way Phenix and Refmac handle it (real vs. reciprocal space), the limitations that the GMM addresses, etc.

1. With regard to sampling, there are other methods that are now similar for generating ensembles (the EMMI work from Vendruscolo and Bonomi for example). It would be useful to contrast the limitations of those methods and how this method is distinct. For example, this method seems likely to be much more computationally simple to run. It would also be good to benchmark against examples of those ensemble methods in terms of RMSF/inferred B-factors.

2. When you refer to the TEMPy-REFF models in each case study are they always ensemble models using segmentation?

3. How are the weights for each focus map decided for when creating a composite map? Stated in ‘combining focused maps into a single overall composite map, with optimal weights of the focused maps.’ (page 3)<br /> We think that more information on how you are generating ensembles belongs in the results section which will help clarify the paper. Some additional specifics we think would make this section strong include: Are the ensembles being created for different segments of the model (based on map segmentation) or the entire model? When creating an ensemble, what is the input model? Has it already gone through iterations of the map to model fitting? How are ensemble models represented? Please provide examples and discuss how you would like these models interpreted.

4. Please clarify how b-factors are represented in your ensemble models and input into maps. Furthermore, in the discussion you state ‘We address this challenge using B-factor estimation. We find, as previously shown by us and others, that an ensemble of equally-well fitted models represents this local variability better than a single model.’ (page 16). However, it is unclear how the b-factors integrate with the ensemble model to represent local resolution. Please clarify which part of your model correlates with local resolution.

5. On average, how many models were included in an ensemble? Please provide a graph of CCC values versus number of models in an ensemble for more examples (ie more than SI Figure 7). How are you thinking about the trade-off between a more complex model versus a small gain in CCC? How deterministic is this procedure? Can you repeat and compare at least one dataset? If you generate multiple ensembles starting from the same structure - do you get the same number of models out and are they similar?

6. If we understand the calculations correctly, the increase in CCC comes from those models being refined independently, not collectively (which makes the increase all the more impressive). Does this suggest the ensemble captures both precision and accuracy (as discussed here: https://pubmed.ncbi.nlm.nih... and therefore the sampling allows escaping of local minima in a clever way. Are there other examples like the His alternative conformation that can help speak to this?

7. When assigning responsibility for a part of the map that may be able to similarly explain two parts of the model, how does the method decide which part of the model should fit in that segment of the map?<br /> Please provide more insight on the interpretation of uncertainty of discrete positions of different sidechains as described in the sentence ‘ensemble adopting either (bottom inset), or uncertainty in the exact side chain confirmation (bottom inset) of two residues (Y76 and L78)’. How is uncertainty measured? Is the RMSF similar or comparable to what would be inferred by B-factors? Please compare the numbers you are reporting to other traditional refinement softwares such as REFMAC and Phenix. It’s unclear whether this is capturing anharmonic motions in a really different way or just sampling the B-factor harmonic component.

Minor comments:<br /> 1. In Figure 1a, please provide more description about what you are representing with the blue and orange circles in the responsibility estimation.<br /> 2. How does your method represent very high resolution structures with low b-factors but high numbers of alternative conformers (specifically looking at PDBs: 7A4M, 7A5V of Apoferritin and GABA receptor).<br /> 3. In Figure 5a, please clarify how you are normalizing the B-factor.<br /> 4. Please deposit output models in Zonodo or some other public repository.<br /> 5. What does SMOCf stand for? Please introduce this briefly in the results.

Review by Stephanie Wankowicz & James Fraser

On 2022-08-19 16:37:23, user Duncan Sproul wrote:

Interesting pre-print. Assuming I understand the approach correctly, it is based on the number of reads observed at each location along the genome in an asynchronous population of cells. Therefore, I was wondering how the modelling approach deals with variation in this sequencing depth due to technical factors - eg varied representation of sequences due to library preparation or mappability in the genome?

On 2022-08-19 12:59:37, user Stuart Atkinson wrote:

The clicable links in the references are all (I think) wrong.

On 2022-08-18 12:02:23, user Pieter Bots wrote:

First of all, I welcome such a thorough investigation of new initiatives. I hope this will be done more in the future with other initiatives as well. Though, I personally do have several queries with respect to the SciCV effectiveness and this preprint.

With respect to the academic age, in addition to the time take off for parental leave etc. another omission in the manuscript is that in some labs / countries it's common to not publish during their PhD and some labs / countries where it's mandatory to publish to pass their viva which is not taken into account in the academic age. In addition to this, the academic age does not take into account that some students might publish during their BSc or MSc or are included as co-authors on papers during their BSc or MSc. All of these affects the academic age metric without affecting the applicants ability or suitability to be awarded funding. The academic age also does not take into account any time spent in institutions where bullying and harassment has occurred affecting someone's productivity and I would argue this could effectively decrease someone's academic age. In addition the academic age metric could be misused by students and delaying their PhD publications to the last year of their PhD to decrease their academic age and make it come across as if they've got more potential compared to PhD students who didn't do this or weren't advised to do so. So to me the academic age just appears to be another metric that does not (accurately) reflect the applicants capabilities or even career stage.

One of my main concerns with respect to the narrative CV and another query with respect to the preprint is related to the section in 'Interviews' on the narratives. <br /> (1) "The interviews indicate that applicants disliked the amount of time needed to author narratives". Based on my own personal experience (the narrative CV for an EPSRC grant caused major anxiety and was the single task that prevented me from submitting the grant proposal) I suspect that potential applicants with anxiety, caring responsibility, dyslexia, etc. will struggle a lot more with completing this task and as it did in my case prevent them from submitting their grant proposal. So did the authors attempt to investigate this, what is the authors opinion on this? Would including people that were not able to submit grant applications in the interviews and survey change the conclusion that the "SciCV was a relevant and successful initiative"?<br /> (2) "redundancy and the use of boastful language in narratives were also criticised by some reviewers." To me this comes across as if the narrative CV will benefit those most with the best support or understanding how to 'play the system': the politicians, not the applicants with most potential to complete a research project successfully.

Finally, my last query about the preprint is related to the final paragraph in the discussion: "Not surprisingly, SciCV alone had only a limited effect on the adherence to DORA-conformity during evaluation. ... Other funding organisations experimenting with new, text-based CV format such as the Science Foundation Ireland report similar findings [10], highlighting the need for additional accompanying measures such as clear guidelines or training." With (such) a limited effectiveness of the SciCV/narrative CV, I am curious how the authors come to the conclusion that the "SciCV was a relevant and successful initiative". Also, is it worth requiring applicants to put in significantly more effort creating such document for the submission for grant applications with limited effectiveness, and when even then still additional measures are needed for the process to be fair? The authors suggest such measures to be clear guidance and training, which, frankly, assessors and reviewers could easily ignore and let their personal (implicit or explicit) biases guide their assessment or review. So to me, the narrative CV, albeit well intended (and before trying to write one myself I thought this was/could be an amazing intervention) comes across as trying to make life of the reviewers and assessors more easy while creating significantly more work for the applicants, potentially preventing them from even submitting a grant application. Has the narrative CV/SciCV been compared to other interventions that create less work for the applicant and force reviewers/assessors to adhere to DORA and remove any chance for biases to guide assessments/reviews, such as double blind review (e.g.: https://smallpondscience.co...

Best wishes,

Pieter

On 2022-08-17 18:44:46, user N Chin wrote:

Hi, can you expand more on the section "Filtering redundant pathway"? I'm confused on how to implement it. Thank you.

On 2022-08-17 15:39:08, user Martin R. Smith wrote:

Congratulations on a very careful and detailed study. You might be interested to further explore the distribution of trees in tree space mappings using distances other than the Robinson-Foulds, which is known to mis-represent spatial relationships within trees: see Smith 2022a, Syst. Biol, https://doi.org/10.1093/sys... . It might also be possible to reconcile some of the discordance by removing rogue taxa -- see Smith 2022b, Syst. Biol, https://doi.org/10.1093/sys... / R package "Rogue".

On 2022-08-17 11:37:05, user Iratxe Puebla wrote:

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Ruchika Bajaj, Pablo Ranea-Robles and Michael Robichaux. Review synthesized by Michael Robichaux.

The manuscript reports findings from new knockout human cell lines for the mitochondrial release factors mtRF1 and mtRF1a. The work contributes new insight into mitochondrial protein translation and mechanisms related to mitochondrial disease. A specific role is demonstrated for the release factor mtRF1 in the translation of COX1, a mitochondrial respiratory protein. The manuscript also identified a compensatory role for the mitochondrial ribosome-associated quality control (mtRQC) pathway when mitochondrial translation termination is impaired.

Overall the experiments and results presented in the manuscript are supportive of the conclusions described in the text. These findings are impactful toward understanding mitochondrial translation termination.

Major comments

In the results section related to Figure 1d, an increase in reactive oxygen species (ROS) is measured using the mitoSOX probe. Considering that mitoSOX measures superoxide accumulation in the mitochondria, please consider specifying in the text that the ROS measured is of mitochondrial origin. In addition, since mitoSOX labeling may be affected by changes in mitochondrial membrane potential or mitochondrial shape and size, please consider adding an experimental condition using a membrane-potential-responsive, redox-insensitive probe. Finally, please clarify the results presented in Figure 1d with more technical detail. What do the n-values signify? Technically, how is ROS production measured?

For Figure 2b-d, in gel activity for complex I and IV are measured; please provide further technical details for these experiments. Please describe what kind of activity is being measured and how it is measured. Also consider adding a density graph of these gel data for clarification of the results.

Referencing Figure 2b-c, specifically, it is stated in the Results section that “mtRF1 loss does not affect complex I..”; however, the figure shows an increase in activity of ~20% for the mtRF1-/- condition. Please consider rephrasing or clarifying this point.

For Figure 3, there is a possible discrepancy in these results that may need to be addressed. For example, the difference of the relative intensity of ND6 between the WT vs. mtRF1a-/- conditions shown in Fig 3a is significantly less than what is quantified for the same comparison in the bar graph in Fig 3e. It is possible these analyses were performed differently; if so, please report this.

Minor comments

It is stated in the first Results section: “In the absence of mtRF1a, cells tend to produce more reactive oxygen species (ROS)...”, which is vague, please rewrite more technically since it is describing the quantitative data in Fig 1D. From this same section, the final statement: “Thus, both release factors are critical for mitochondrial function and cellular growth” is perhaps too conclusive based only on the results from Figure 1.

Related to Fig 1c: consider converting the graph to a log scale, which may help illustrate the difference in growth rates between conditions. In addition to measuring cellular growth, please also consider measuring/counting mitochondria and examining cell morphology changes, which may be easy, additive experiments to include here.

In the Results section related to Fig. 2a-d, the respiratory chain complexes are presented with no context. Consider mentioning these complexes in the Introduction or contextualizing them better in this section.

In this same Results section please add appropriate citations for “Figure 2g” when referencing results related to that figure panel.

A portion of the results text related to Figure 4a-b states: “With the exception of MTND6 (mRNA encoding for ND6), all of the mt-mRNAs arise from the polycistronic transcript synthesized from the heavy strand. If the loss of mitochondrial RFs would affect mitochondrial transcription, one would expect an overall decrease in all mitochondrial transcripts. However, as we observe a selective decrease in specific transcripts in the individual knockouts, we conclude that it is more likely an issue of RNA stability rather than synthesis.” This may be more appropriate to include in the Discussion section.

In the Results section related to Figure 5, please again consider properly citing the figures when describing the results presented in those figures and panels.

While Figure 6 is an informative model figure, please consider explaining the model with respect to results in the manuscript.

Comments on reporting

Please consider adding more detail in the Methods section about the statistical analyses performed in this study. In addition, other statistical tests may be needed for some group comparisons (e.g., two-way ANOVA for the data in Fig. 5d).

For all the western blot data presented in the manuscript, please consider adding the full blot scans to the supplemental material.

Referencing Supplementary Table S1, please consider adding validation references for the antibodies used in this study. This is of great benefit to other researchers.

Suggestions for future studies

Future studies may test the effect of the combined ablation of the mtRF1 and mtRF1a release factors.

On 2022-08-16 12:05:17, user Estrela wrote:

Dear readers,<br /> Our paper has been accepted for publication in Cell&Bioscience journal.<br /> Please check here the published version: https://rdcu.be/cTFcA

Best reagards,<br /> Estrela Neto

On 2022-08-16 05:38:19, user Martin R. Smith wrote:

A small note on the RF distance: this doesn't count "the number of operations required to convert one tree into another" (that would be an edit distance such as SPR / TBR), but the number of splits in one tree but not another. It also has a number of shortcomings (see Smith 2020, https://doi.org/10.1093/bio... ) – potentially a more resilient metric would produce a more consistent signal, and paint a more complete picture, in your table 6?

On 2022-08-16 05:21:15, user Martin R. Smith wrote:

Very interesting to see the early origin of these important proteins. For what it's worth, you might be able to squeeze yet more resolution out of your Rogue taxon analysis using the information theoretic approach implemented in the R package "Rogue"; see Smith 2022, Syst Biol, https://doi.org/10.1093/sys...

On 2022-08-15 12:23:10, user Anshu Bhardwaj wrote:

Really appreciate the authors acknowledging the comments and adding Singh et al for 'Numtogenesis'.

On 2022-08-15 11:25:40, user Biró Bálint wrote:

Dear All,

Thank you very much for your comments. As you have correctly pointed out some of the references have been mixed up. This has been corrected and we uploaded a new version of our manuscript which would be available hopefully very soon.

Best regards,<br /> Authors

On 2022-08-13 17:57:32, user Rajender Singh wrote:

Dear Authors, <br /> Lopez et al. is not the right reference as you have stated in your manuscript in the line 'The sequences in the nuclear genome with mitochondrial origins are called numts and their integration process itself is called numtogenesis (Lopez et al., 1994).'

You should replace this with other suitable references, which I am mentioning here;

Migration of mitochondrial DNA in the nuclear genome of colorectal adenocarcinoma. PMID: 28356157

Single molecule mtDNA fiber FISH for analyzing numtogenesis. PMID: 28322800

Numtogenesis as a mechanism for development of cancer. PMID: 28511886

I hope you will take a note of my comment.

Thanks.

Dr. Rajender Singh<br /> Senior Principal Scientist and Professor

On 2022-08-13 06:04:09, user Prash wrote:

Keshav Singh's lab are the proponents of this. may we request you to replace Lopez et al with https://scholars.uab.edu/di.... #Numtogenesis

On 2022-08-13 03:57:29, user Keshav wrote:

Please also see another paper for the term <br /> numtogenesis<br /> https://scholars.uab.edu/di...

On 2022-08-13 03:45:59, user Keshav Singh wrote:

Please note the term numtogenesis was coined by Singh et al 2017 and not by Lopez et al 1994.<br /> Thanks <br /> Keshav Singh

On 2022-08-15 09:25:19, user Iratxe Puebla wrote:

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Ashley Albright, Samuel Lord, Arthur Molines and Sónia Gomes Pereira. Review synthesized by Iratxe Puebla.

The paper studies the involvement of aneuploidy in promoting chromosomal instability and suggests the aneuploid state of cancer cells as a point-mutation independent source of genome instability. The paper reports a considerable amount of data. We outline below some suggestions regarding presentation and the analyses reported:

‘mis-segregation in otherwise pseudo-diploid human cells’ - Please provide some explanation for the term ‘pseudo-diploid’.

‘suggesting that dormant replication origins’ - Please provide a sentence clarifying the meaning of ‘dormant replication’.

‘Cells activate dormant origins in response to reduced fork rate and stalled forks to ensure that the genome gets fully replicated in time’ - Please provide a reference to support this statement.

Figure 3

Recommend re-arranging the order and position of the panels for greater clarity.

‘Interestingly, we found a positive correlation between S phase length and frequency of abnormal mitoses (mean S phase length in control: 603,3 ± 55,4; aneuploid: 728,7 ± 46,2) (Fig. 3c).’ - Figure 3C shows that the cells that have an abnormal mitosis had a slightly longer S phase on average, however there is no correlation analysis done or an analysis around "frequency of abnormal mitosis", recommend revising the sentence.

Figure 3C - Cells with a longer S phase (or cell cycle in general) will receive more light before reaching mitosis. Is it possible that the correlation mentioned is due to photo-toxicity? Longer S phase -> more photo-toxicity -> abnormal mitosis. Recommend adding a control to account for the potential phototoxicity of the imaging.

Figure 4 - Panels C and D show that, among the cells that have foci, the number of foci is increased, either by aneuploidy or by the drugs. However, it is unclear from the data if the number of cells with foci also increases. Would it be possible to plot the % of cells with more than 1 foci for each condition? (as in Figure 4G). Also, C and D are aggregates of multiple experiments, it would be good to show the data per replicates.

‘there was a sub-population of senescent cells in the aneuploid sample (Fig. 5a)’ - Was senescence tested in the normal (euploid) population too (at the same passage)? Is that the sample named as "control" in the figure legend?

‘in aneuploid cycling cells was comparable to that of the controls for at least 3 generations by live-cell imaging (Fig. 6a-c)’ - Suggest clarifying here what the control is, in addition to naming it in the figure legend.

Comments on analyses/reporting

In various figures (including Figures 1H,J,L,N,O; 2C,G,E; 3I; 4C,D; 5H,I; 6H,I,J), there is a concern about the statistical approach to calculate p-values based on multiple measurements or cells within each sample. The t-test assumes that each measurement is independent, and multiple cells within the same sample are not independent. Recommend either not reporting p-values or averaging together the values from each sample and calculating the p-value using those sample-level means. For more information, see https://doi.org/10.1371/jou... and https://doi.org/10.1083/jcb...

For each bar graph throughout the paper, recommend reporting the value of n, in the figure itself, the figure legend, or in the text. Using Figure 1C as an example, this reports a doubling in the number of cells with greater than 10 errors, but the significance of that would vary depending on the number of cells analyzed. Some plots in panels c and f have no error bars, and it would be useful to report the number of experiments.

Almost every figure features representative images. The manuscript includes a massive amount of data already, but it may be relevant to show additional images in the supplement in cases where representative images are used in figures.

Data analysis for RNAseq ‘results were filtered only based on p-value’ - Please clarify why the False Discovery Rate was not taken into the filtering step.

On 2022-08-15 09:10:34, user Iratxe Puebla wrote:

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Luciana Gallo, Lauren Gonzalez, Claudia Molina, Arthur Molines, Srimeenakshi Sankaranarayanan and Sanjeev Sharma. Review synthesized by Iratxe Puebla.

The manuscript studies the role of the long-coding RNA lncRNA H19 in cellular senescence. The results show that H19 levels decline as cells undergo senescence and repression of H19 is triggered by the loss of CTCF and prolonged activation of p53. The loss of H19 leads to increased let7b-mediated targeting of EZH2. The mTOR inhibitor rapamycin maintains lncRNA H19 levels throughout the cellular lifespan preventing reduction of EZH2 and cellular senescence.

The reviewers found the methodology appropriate but raised some comments and suggestions about the paper as outlined below:

Introduction ‘H19 is a highly conserved, maternally expressed imprinted gene and encodes a 2.3 kb long non-coding RNA (lncRNA). It is located immediately downstream of the neighboring gene IGF2.’ - An additional reference to the expression pattern/levels of lncRNA H19 across 'normal' tissues/developmental stages would be useful to provide immediate insight into the contexts where H19 is important and note the conditions where its levels are altered.

‘To characterize the role of H19 in the cellular senescence of somatic cells, we examined H19 expression during replicative senescence of human cardiac fibroblasts’ - The data on changes in expression of H19 with age/culture time is very interesting. Suggest providing some comments on the choice of experimental systems for each experiment and why HCF cells were used to study replicative senescence while other experiments were completed in skin samples.

Figure 1

Figure 1a - Please indicate in the legend how far apart or what are the passage numbers for 'early' and 'late' passages for the cell culture experiments. Is the reduction in H19 gradual or does it sharply decrease after a certain number of passages? What biological meaning would either of these observations have and how does it relate to mouse data in vivo?

Supplementary Figure 1 shows a sharp drop between PD 20 and PD 50. Would it be possible to provide a finer analysis of H19 levels across many cell passages?

Figure 1b - Recommend using the same normalization in a) and b). In a) levels are normalized to the first condition "early" while in b) levels are normalized to the second condition "old".

Figures 1d and g - Please provide further information on how Cumulative population doublings were measured and clarification for the numbers on the Y axis.

‘decreased the lifespan of cells (Figure 1d; Figure 1-figure supplement 1c)’ - Figure 1d measures cells' doubling time, not lifespan. If lifespan is being inferred from doubling time, please provide some clarification on how this is being done. There are fewer cells after 15 days but it does not mean that cells are dying, it could be that they are growing slower. Please also provide details for the methodology followed to obtain the data in this panel.

Figure 2

‘CTCF mRNA and protein levels decreased in the late passage cells (Figure 2a and b), and CTCF knockdown in early passage cells induced premature senescence characterized by increased SA-β-gal staining and reduction in proliferation (Figure 2-figure supplement 2a). In contrast, treatment with rapamycin mitigated CTCF depletion, which is consistent with the effect of rapamycin maintaining H19 levels (Figure 2a and b). Furthermore, the regulatory link between CTCF and H19 is supported by decreased H19 expression in CTCF-targeted cells (Figure 2c).’ - CTCF knockdown and rapamycin treatment can affect many pathways, recommend toning down this conclusion. In Supplemental Figure 2a, the % of positive cells in the siNeg condition is significantly higher than in Figure 1e (close to 50% in Sup Fig 2a vs 30 % in Fig 1e). Recommend providing some comments on the variability of the control value as that level of variability can confound the conclusions. For example, the siCTCF condition is lower than the siNeg control condition when compared with the value from Sup Fig 2a but not when compared with the value from Fig 1e.

Figure 2d - Remove "presentation last saved just now" from the panel.

‘a stress-dependent downregulation of CTCF through proteasomal degradation of CTCF protein in endothelial cells (51)’ - The paper cited here discusses epithelial cells, should the reference to endothelial cells be updated?

Figure 3 - Please provide further clarification regarding acute stress or prolonged activation of p53. What are the timescales? How do these relate to replicative senescence seen with aging or as cells at late passages?

‘Together these results confirm that activation of p53 is responsible for the downregulation of H19 as part of DNA damage response’ - Please provide further clarification regarding the reference to DNA damage. Is this an inference from the statement about "activation of p53 is crucial for establishing senescence as part of DDR"? p53, like CTCF and mTOR, can play different roles.

‘Given the mounting evidence suggesting the role of lncRNA H19 as a competing endogenous RNA (ceRNA) or miRNA sponge (60–62), we speculated that H19 might mediate the senescence program by regulating miRNA availability. To determine which miRNAs are directly regulated by lncRNA H19 during senescence, we evaluated miRNA expression profiles in control and H19 targeted cells (Figure 4a).’ - Can some further clarification be provided for this claim, if H19 is acting as a miRNA sponge, it wouldn't affect its overall levels, but rather its ability to bind its target genest? Based on the data presented, the link between let7b and H19 appears to be more related to let7b expression than sequestration. Consider removing the fragment or revising it to clarify the mechanistic link drawn between H19 and let7b. To show that H19 is acting as a sponge in this system, it may be necessary to mutate the complementary sequence and check whether let7b's activity increases (i.e. its target genes are down-regulated).

‘Among the top miRNAs upregulated in H19 depleted cells were members of the let7 family; specifically, let7b expression was significantly upregulated (Figure 4b’ - Suggest adding some more information about the other miRNAs that are affected.

Figure 4f ‘Senescence-associated secretory’ - Please clarify why SERPINE mRNA level is considered instead of IL-6 as in Figure 1f.

‘suggests the loss of EH2 results in a general decrease in PRC2 activity’ - should EH2 read EZH2?

Figure 5 - What happens to CDKN2A levels when H19 is depleted or overexpressed? Can the H3Kme3 antibody binding data be supported with expression data for CDKN2A? It may be relevant to see whether it follows the expectation that loss of H19 reduces EZH2 expression and increases p16 expression.

Figure 6 - Please provide some brief clarification for what the solid and dashed lines represent in the model.

‘More importantly, prolonged treatment with mTOR inhibitor rapamycin maintains lncRNA H19 levels by preventing the loss of CTCF expression and activation of p53, thus preventing the induction of senescence.’ - There is a question as to whether the experiments presented support this statement, suggest reframing the fragment. The strongest mechanistic experiments in the study are those regarding let7b, because they use the mimic to "rescue" its function.

Supplementary Figure 1d - It is nice to see authors tested 2 different siRNAs for H19 and these showed the same effect in Panel d. Can some discussion be provided for why overexpression of H19 leads to an increase in senescence markers and reduced proliferation.The outcomes of siRNA experiments may not sufficiently support the correlation between H19 levels and senescence induction. This is an example where both excess H19 and reduced levels of H19 have the same effect and it is a very important result. Would it be possible to titrate the expression of H19 to achieve different levels of overexpression and then analyze senescence markers under these conditions? It may also be possible to generate a siRNA-resistant overexpression construct to rescue the effects seen with siRNA-mediated depletion of H19.

Supplementary Figure 5 - Recommend updating the presentation to more clearly highlight the decrease in binding as mentioned in the main text.

Methods

‘10g of plasmid DNA was transfected’ - should this read 10 micrograms?

‘ΔΔCT method’ - Please clarify the control for calculating relative mRNA levels.

‘Cells were incubated with EdU stain (100mM Tris (pH8.5), 1mM CuSO4, 1.25 μM Azide Fluor 488, and 50mM ascorbic acid) at room temperature for 30 mins. Cells were washed with PBS twice and imaged using EVOS FL Auto microscope (Thermo Fisher)’ - Please report the duration that the cells were incubated with EdU in culture before the cells were fixed and EdU incorporated in the DNA was stained.

On 2022-08-14 23:37:12, user Isaac Larkin wrote:

Could you add a plot of homopolymer length/frequency distribution in the genome, and maybe a map/table of the longest homopolymers, as a supplemental figure? That will be useful as a point of comparison to the homopolymer basecalling accuracy of the latest nanopore basecallers, since long homopolymers are the primary remaining systematic source of error in nanopore sequence data.

On 2022-08-13 07:43:06, user Isaac Larkin wrote:

I couldn’t find the Oxford Nanopore flow cell/pore/kit/basecaller versions used, or stats about the ONT read length/quality distributions, either in the preprint or the linked GitHub repo. Those should definitely be specified in the methods section. Also, I don’t see the sequence data for HG00621 at the hpgp-data GitHub repo the preprint says it’s located at. In the repo’s README, I only see links for sequence data corresponding to samples HG01109, HG01243, HG02080, HG03098, HG02055, HG03492, HG02723, HG02109, HG01442 and HG02145. I think the repo’s README needs to be updated to include links to the relevant (HG00621) sequence datasets, in the same way as is displayed for the samples above.

On 2022-08-14 20:38:52, user Ricardo M. Biondi wrote:

With my colleague Alejandro E. Leroux we have written an extended commentary with our opinion on the work, which can be found in the link: https://www.qeios.com/read/....<br /> In short, we find that the authors do not properly cite previous work, notably the Gao and Harris paper (2006) that reaches similar conclusions. In addition, the introduction fails to acknowledge even basic issues. For example, the classical PKCs are constitutively phosphorylated by PDK1 without growth factor signaling. Akt/PKB becomes phosphorylated by PDK1 in a PI3-kinase dependent manner but also has been described to become phosphorylated by PDK1 in a PI3-kinase INDEPENDENT manner. In contrast to what Levina et al. indicate in the introduction, a model to explain PDK1 phosphorylation of substrates must take into consideration that some substrates are phosphorylated in a PIP3-independent manner! <br /> For a detailed commentary on the results section, again I recommend that you go to the qeios link above. Most of the hard biochemistry in the paper is dedicated to describing the dimer that must be formed along the very very slow process of trans-autophosphorylation in vitro. The hard-core biochemical studies are based on a fusion of PDK1 to PIF. It is difficult to understand what useful information can be obtained from those "dimers"... PIF binds with high affinity to PDK1: what would be the sense of crosslinking GST-PIF to PDK1? would you obtain any information about the GST/ PDK1 heterodimers??? If the model was correct, PIFtide should inhibit trans-autophosphorylation. The authors did not do this control experiment. But it was done previously: this was NOT observed in the paper by Frödin et al (2000). So the dimer model with an important hydrophobic motif binding to the PIF-pocket in the neighbour molecule is very likely incorrect. Finally the authors claim autoinhibition by the PH domain and release of this autoinhibition by PIP3. I have not yet seen any convincing data to support the existence of an autoinhibited PDK1. Please, refer to the qeios link for further details. In short, I believe that the conclusion of this part of the work is also not supported by their data nor by 25 years of careful work by different laboratories.

On 2022-08-11 15:38:04, user Kirk Overmyer wrote:

This pre-print has been published and the final paper is now available online at The Plant Cell.

https://academic.oup.com/pl...

https://doi.org/10.1093/plc...

On 2022-08-11 06:49:50, user Miguel Cañedo wrote:

Really nice work, congratulations! I wonder if adaptation has any "cost" for the population. Are there any trade-offs? Maybe the adapted populations would be outcompeted by non-adapted populations under certain conditions?

On 2022-08-10 23:02:59, user Moritz Oberlander wrote:

I was a little bit disappointed that you did not show the proteolysis of C-terminal domain at TTMV-ly1 homologs as well, at least one or two with 99% identity; for instance:

541 KWGGDLPPMSTITNPTDQPTYVVPNNFNETTSLQNPTTRPEHFLYSFDERRGQLTEKATK TTMV-ly1: French children

541 KWGGDLPPMSTITNPTEQPTYVIPNNFNETTSLQNPTTRPEHFLYSFDERRGQLTEKATK safia 523-10: Tanzania children

541 KWGGDLPPMSTITNPTDQPTYVIPNNFNETTSLQNPTTRPEHFLYSFDERRGQLTEKATK xz029-anello-1: China children

C-terminal domain changes at the TTMV-Ly1 homologs only in positions 557.aa and 563.aa

I know that anelloviruses are “orphans” but they may have some “siblings". I think it’s important for an infectious study, scaling up VLP production, and to avoid a misleading degradation of the C-terminal domain at the TTMV-Ly1.

On 2022-07-27 18:58:54, user Moritz Oberlander wrote:

There is an opportunity to discuss the infectious study in the paper J Galmès, 2013 and her French thesis: https://tel.archives-ouvert... with TTMV-Ly1,-Ly2,-Ly3 virus particles in lung epithelia or embryonic kidney cells. It would be interesting to compare them but it seems to me, there is not significant difference in the infection of the TTMV-Ly1, Ly2, Ly3 viral particles (Figure 45).

I looked at the TTMV-Ly2 because it has two characteristic repeats in the 5’-noncoding region, the second repeat was probably created by the insertion of about 66nts (insert: gccggaaaaccacataatttgcatggctaaccacaaactgatatgctaattaacttccacaaaaca). I searched (Blast) for a homologues direct lineage of TTMV-Ly2 (to exclude a recombination) based on this insert and found several homologues of Ly-2. I wanted to see if they hold a similar spike structure as well. Some lineage-homologs seem to be similar but safia-668-2 and 314-17 may show more changes in spike region of Ly2:

YTGANLPGDTTQIPVADLLPLTNPRINRPGQSLNEAKITDHITFTEYKNKFTNYWGNP TTMV-ly2 2979nts<br /> YAPGNPPTTPDNIQIADLIPLTNPRENKPGMSLNEAKIGGKTDFNNYKTNYKNYWGNP safia-367-10 2991nts<br /> YAPGNPPTTPDNIQIADLIPLTNPRENKPGMSLNEAKIGGKTDFNNYKTNYKNYWGNP safia-692-0 2992<br /> YAPGNPPTTPDNIQIADLIPLTNPRENKPGMSLNEAKIGGKTDFNNYKTNYKNYWGNP safia-418-10 2941<br /> YAPGNPPTTPDNIQIADLIPLTNPRENKPGMSLNEAKIGGKTDFNNYKINYKNYWGNP safia-569-10 2849<br /> YAPGNPPTTPDNIQIADLIPLTNPRENKPGMSLNEAKIAAKTDFNKYKTNYKNYWGNP safia-388-14 2991<br /> YAPGPPIPTAENLKVGDLIPLTNPRDNVSGESFFEQQTTTHETWKQYFTNYKKHWGNI safia-668-2 2977<br /> YAPGPPIPTAENLKVGDLIPLTNPRDNVSGESFFEQQTTTHETWKQYFTNYKKHWGNI safia-314-17 2977

FNKHIQEHLDMILYSLKSPEAIKNEWTTENMKWNQLNNAGTMALTPFNEPIFTQIQYN ly2<br /> FNVEIQEHIQDILYSLKSPEAIKSAWTTENMTWKQLDNAGQMALTPFNEPIFTQIQYN safia 367-10<br /> FNVEIQEHIQDILYSLKSPEAIKSAWSTENMTWKQLDNAGQMALTPFNEPIFTQIQYN safia-692-0<br /> FNVEIQEHIQDILYSLKSPEAIKSAWTTENMTWKQLDNAGQMALTPFNEPIFTQIQYN safia-418-10<br /> FNVEIQEHIQDILYSLKSPEAIKSAWTTENMTWKQLDNAGQMALTPFNEPIFTQIQYN safia-569-10<br /> FNVEIQEHIQDILYSLKSPEAIKSAWTTENMTWKQLDNAGQMALTPFNEPIFTQIQYN safia-388-14<br /> FNVHTSEHLEDLLYSLKSPEAIAKKALENENKTDLKWSELDNAANMALTPFDQPIFIP safia-668-2<br /> FNVHTSEHLEDLLYSLKSPEAIAKKALENENKTDLKWSELDNAANMALTPFDQPIFIP safia-314-17

In the phylogenetic tree, the TTMV5-TGP96 (no the second insert) is the closest homolog to TTMV-Ly2:<br /> YTGTNPPSDTSQIKVADIIAVTDKKNNKPGESYHDQQTTSNKNWQQYFENYQQFWGNP TTMV5-TGP96

It seems to be difficult to find a direct lineage for TTMV-Ly1 or its homologs with similar structure and discuss that......

On 2022-08-10 17:32:04, user Misha Skliar wrote:

The revised manuscript is now published in the Journal of Extracellular Vesicles: https://onlinelibrary.wiley.... Compared to the bioRxiv manuscript, the JEV paper includes additional experimental results, such as proteomic analysis of plasma EVs isolated by asymmetric depth filtration. We also added the comparison with a multistep precipitation-purification sequence for plasma EV isolation. The published paper provides a mechanistic explanation of high purity and yields achieved with the developed method. The proposed mechanism is tested by demonstrating the size selectivity for synthetic samples and the selectivity by the elasticity of captured nanoparticles, which we analyzed by capturing rigid and soft nanoparticles (latex beads and pre-isolated EVs) using the developed method.

On 2022-08-10 05:03:34, user Peter Hickey wrote:

Could I suggest please making the link to the software (https://github.com/FredPont... more prominent. I couldn't find it anywhere in the preprint until I clicked on a hyperlink in a table in the appendix.

On 2022-08-09 17:55:17, user SCrosby wrote:

10x has told us they would rather the samples be cryopreserved vs methanol fixed since it yields better cell quality, higher UMI and gene counts, and lower ambient RNA in the sample. The cells are generally easier to handle (wash, filter, etc) after thawing.<br /> I would be curious it hear the authors' comment!<br /> Seth Crosby

On 2022-08-09 15:01:20, user Uri Ben David wrote:

Response to “Revisiting the effects of Cas9 on p53-inactivating mutations reveals sex-biased genome editing by CRISPR-Cas9”.

Authors: Oana M. Enache, Veronica Rendo, Rameen Beroukhim, Todd R. Golub and Uri Ben-David

A couple of years ago we reported Cas9-induced p53 signaling in cancer cell lines (ref 1). Here, Guo and Xiong address the possibility that this finding is affected by cell line sex biases (ref 2). In their preprint, they are trying to make 3 points related to our paper. We will address each of these points separately.

1) TP53 mutations also shrink and not only expand upon Cas9 introduction.

To study the trend of p53-inactivating mutations to expand or shrink following Cas9 introduction, we performed an analysis of pre-existing subclonal mutations (Fig. 3d in ref1). As mentioned in our paper several times, we deliberately restricted this analysis to pre-existing mutations with 0.02<af<0.48 or="" 0.52<af<0.98="" in="" the="" parental="" cell="" line.="" the="" reason="" for="" the="" focus="" on="" subclonal="" mutations="" in="" this="" analysis="" is="" that="" the="" tendency="" of="" mutations="" to="" expand="" or="" shrink="" can="" only="" be="" tested="" in="" subclonal="" events,="" as="" clonal="" events="" can="" only="" shrink="" and="" not="" expand,="" whereas="" non-detected="" events="" can="" only="" emerge="" but="" not="" shrink.="" inclusion="" of="" such="" clonal="" mutations="" would="" therefore="" bias="" the="" analysis.="" we="" found="" a="" highly="" significant="" trend="" for="" subclonal="" inactivating="" tp53="" mutations="" to="" expand="" following="" cas9="" introduction="" (fig.="" 3d="" in="" ref1),="" and="" tp53="" ranked="" 1st="" among="" all="" genes="" in="" this="" respect="" (fig.3e="" in="" ref1).="" in="" contrast,="" guo="" and="" xiong="" used="" different="" selection="" criteria="" for="" inclusion="" and="" exclusion="" of="" mutations.="" two="" of="" the="" shrinking="" mutations="" identified="" in="" their="" fig.="" 1a="" (in="" ovk18="" and="" c2bbe1)="" are="" clonal="" mutations="" (with="" af="" of="" ~0.5="" or="" ~1="" in="" the="" parental="" population).="" we="" argue="" that="" it="" is="" improper="" to="" include="" clonal="" mutations="" in="" this="" analysis,="" and="" it="" is="" clearly="" wrong="" to="" report="" them="" as="" “tp53="" inactivating="" subclonal="" mutations”="" (legend="" to="" fig.="" 1a="" in="" ref2).="" the="" third="" mutation="" that="" they="" identified="" as="" shrinking="" (in="" a2780)="" was="" also="" not="" analyzed="" by="" us,="" since="" it="" is="" a="" known="" snp="" that="" is="" pretty="" prevalent="" in="" the="" population="" (="">1% in gnomAD (ref3); see Supplementary Data 3 and our exclusion criteria described in the Methods section of ref1). We therefore think that it is a mistake to consider this mutation as an ‘inactivating TP53 mutation’ as well.<br /> Importantly, if one were to include the clonal inactivating mutations that Guo and Xiong have added to our analysis in their Fig. 1a2, then there is no justification for the exclusion of mutations that were not detected at all (AF~0) in the parental cell line but were present in the Cas9-expressing cell line, such as the mutation observed in the cell line SNU1 (Fig. 3c in ref1). However, this event was excluded in Fig. 1a of ref2. Similarly, if one were to include known SNPs in the analysis, then there is no reason to exclude the one in the cell line JHH7, which emerged from AF=0 to AF=1 (and was excluded both from our original analysis and from Fig. 1a2). In other words, the inclusion criteria for Fig. 1a of ref2 are inconsistent. <br /> Lastly, if we add the clonal mutations to the analysis (but exclude the known SNPs), there is still a significant trend for the expansion of TP53-inactivating mutations (p=0.03 in a one-tailed McNemar test for directionality). Guo and Xiong’s statement that they found “significantly shrinking inactivating subclonal mutations of TP53 in Cas9-cells, which means Cas9 also selects against TP53 inactivating mutations” (Abstract of 2) is therefore misleading. (We note that Guo and Xiong report that “four inactivating mutations from four cell lines were shrinking (P=0.039)”, but their manuscript does not provide any information about the statistical test that was applied to calculate significance.)

2) There is a potential sex-bias in our results.

We did not test whether any of our results were affected by a potential sex bias. Given that p53 has an effect on X chromosome inactivation, we cannot rule out the possibility that sex may affect p53 signaling following Cas9 introduction. However, sex representation in our cell line cohort was very balanced, and Cas9-induced p53 activation and selection were found in both male and female lines. Of the 43 TP53-WT lines used for the gene expression analyses, 21 were female, 21 were male, and one was of unknown sex; of the 122 TP53-mutant lines, 62 were female, 59 were male, and one was of unknown sex. Moreover, we used TP53-WT cell lines from both sexes (3 male lines, 2 female lines, 1 of unknown sex) to validate p53 activation following Cas9 introduction, and detected p53 pathway activation in both the male and the female lines (Fig. 2 and Extended Data Fig. 2 in ref1). Of the 10 cell lines in which a TP53 mutation was found to emerge or expand (Fig. 2c,d in ref1), 6 were female and 4 were male. Therefore, there is no evidence for any sex bias in these results.<br /> While Guo and Xiong raise an interesting hypothesis, they do not provide any real evidence that any of our results were indeed affected by sex bias. Instead, they make a few anecdotal statements on the matter:

a) “The largest fold-change of p53 activation was observed in a female cell line (BT159)”.<br /> This is meaningless, as we tested the mRNA expression in 165 cell lines and protein expression in 9 cell lines. Guo and Xiong do not report any systematic comparison of the expression changes between male and female cell lines (although all of the data necessary for such analysis are available in our original paper).

b) “There were more DNA damage foci in MCF7, which is a female cell line”. This assay was performed in only 3(!) cell lines, precluding any meaningful interpretation of sex bias. We also note that Cas9-induced p53 activation was actually mild in MCF7, compared to other male and female cell lines (Fig. 2e in 1), further weakening this particular anecdotal claim.

c) “The largest TP53-inactivating subclonal mutations expanding or shrinking (293T, HCC1419, and OVK18) is seen in female lines”. This claim does not hold true if OVK18 is removed from the analysis. Moreover, according to Fig. 1a of ref2, 2 out 4 shrinking mutations and 4 out of 10 expanding mutations are actually seen in male lines, so the trend of mutations to expand or to shrink seems to be pretty sex-balanced.

d) In the final paragraph of their manuscript, Guo and Xiong state that “We think the possible sex-biased effects of Cas9 may provide a possible reason for their failure to detect p53 activation in Cas9-expressing HCT116 (male) cells." This is factually wrong. We found significant activation of p53 in HCT116 cells transduced with Cas9, as is clearly shown in Extended Data Fig. 2d and<br /> 2e of ref1.<br /> We note that the majority of the manuscript by Guo and Xiong (Fig. 1b-d, Supplementary Fig. S1-S4, Supplementary Table S1) is an analysis of sex bias in CRISPR screens, which does not directly pertain to our paper. Sex biases in CRISPR screens may have nothing to do with the Cas9-induced p53 signaling that we observed. Moreover, we compared CRISPR to shRNA screens and found significant differences associated with p53 mutation status (Fig. 5 in ref1). Guo and Xiong do not discuss this at all, nor do they provide any evidence that this analysis was affected by cell line sex bias.

3) TP53 mutation status of some cell lines is inaccurate in our paper.

The Supplementary Note of 2 reads: "We found that 11 cell lines (RERFLCAI, SISO, SNU761, COV644, COLO684, HS294T, G292CLONEA141B1, D283MED, G401, SJSA1, and SNU1041) used as TP53-WT (Fig. 5a and Supplementary Data 5 in ref.1) by Enache et al. actually have non-silent TP53 mutations (Supplementary Table S2), although this should not affect their conclusions."

There are 698 cell lines in Supplementary Data 5 and Fig. 5a, and we clearly did not validate the TP53 mutation status of each individually, but rather followed established annotations. There are several ways to classify TP53 mutation status in cell lines, and mutation calling algorithms constantly evolve. As described in our Methods section (ref1), we followed the annotations by Giacomelli et al. (ref4), which are based on the CCLE cell line annotations (ref5), according to which all of the 11 cell lines listed above are TP53-WT. These annotations have since been updated, however, and in the version downloaded by Guo and Xiong (22Q2, https://depmap,org/portal/), these cell lines are now classified as TP53-mutant. Importantly, exclusion of these cell lines has no effect on the outcome of the single analysis in which they were used (Fig. 5a in 1; p=8.8x10-6 instead of the original p=2.7x10-5; one-tailed t-test). Therefore, the slight discrepancy between the annotations used by us and those used by Guo and Xiong is irrelevant to the points that they raise.

In summary, we thank Guo and Xiong for raising the intriguing possibility that sex may affect the cellular response to Cas9, in particular in the context of p53 pathway activation. However, this question remains open for now, as more research and data analysis are needed to determine whether this speculation is correct.

References<br /> 1. Enache, O. & Rendo V. et al. Cas9 activates the p53<br /> pathway and selects for p53-inactivating mutations. Nat Genet 52, 662-668 (2020).

1. Guo M. & Xiong Y. Revisiting the effects of Cas9 on<br /> p53-inactivating mutations reveals sex biased genome editing by CRISPR-Cas9. This preprint.

2. Karczewski K.J. et al. The mutational constraint spectrum<br /> quantified from variation in 141,456 humans. Nature 581, 434-443 (2020).

3. Giacomelli, A. O. et al. Mutational processes shape the<br /> landscape of TP53 mutations in human cancer. Nat Genet 50, 1381–1387 (2018).

4. Barretina, J. et al. The Cancer Cell Line Encyclopedia enables<br /> predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).

On 2022-08-05 04:42:49, user Eda Yildirim wrote:

We are excited that our paper is now published @Nature Communications https://www.nature.com/arti.... Congrats to members of the Eda Yildirim Lab (@YildirimLabDuke) @DukeCellBiology @DukeMedSchool.

On 2022-08-04 09:15:32, user Erin Schuman wrote:

Interesting! Please also check out our very related work (sadly not cited by Yang et al.,) in which we show the exchange of ribosomal proteins (RPs) in neurons, using dynamic SILAC, and show that oxidative stress stimulates the exchange of some nascent RPs on mature ribosomes. <br /> https://www.nature.com/arti...

On 2022-08-04 06:09:03, user Iratxe Puebla wrote:

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Richa Arya, Samuel Lord, Arthur Molines and Sónia Gomes Pereira. Review synthesized by Richa Arya.

In the manuscript titled “Nanog organizes transcription bodies” Kuznetsova et al. discuss how the transcriptional bodies are assembled. They show that Nanog and Sox19b cluster before the transcription actually starts and initiate the formation of transcription bodies.

The following comments and suggestions were raised to help strengthen the manuscript:

1. Result: RNA Pol II transcription localizes to two transcription bodies when zebrafish genome activates…

The heading of the section is a bit confusing. Does it refer to the transcription of RNA pol II or transcripts of RNA pol II?

Within the result section: ‘…as 64-cell stage…’ Would be good for the reader to clarify that this is division number 6 to make it clearer that it is way earlier than what is reported in the previous sentence.

‘...productive transcription starts in two transcription bodies in the nucleus. These transcription bodies are isolated, large, long-lived, and appear at a predictable time during development…’. At this stage the study has reported localization data but not activity data (this is included later). The formation of clusters (which is what is detected) might suggest but cannot conclude about the activity of the enzyme or whether RNA is actually being produced.

1. Figure 1

‘…the percentage of nuclei with at least one Pol II (Ser5P or Ser2P) cluster is indicated…’. The injection is happening at the 1-cell stage. Then observations are made at the 64/128/256 cell stage. Are all the nuclei labelled at these stages? or only a subset? Recommend providing some clarification about the percentages reported? Are they a consequence of the embryo being mosaic, some cells containing the label injected at the 1-cell stage and some not? Or is it biological noise? Or a combination of both? Is this the ratio of (# of cells with puncta)/(total # of cells) or is it (# of cells with puncta)/(# of cells containing some labelled Pol II)?

'C. Tracks of transcription bodies at 64-, 128-, and 256-cell stage. The presence of Pol II Ser5P, Ser2P, or both, is indicated by red, blue, and white circles, respectively. Time on the x-axis in minutes after mitosis.’. The sample size seems too small, can some clarification be provided to help with the interpretation of this data:

In the first plot, one embryo is 64 cells. Even taking in consideration the fact that the embryo might be a mosaic with 50% of the cells labelled and that one can not image all of the cells due to the thickness of the sample, it should leave a few cells imaged per embryo (5-10 cells). It would be good if one experimental replication was made of multiple embryos injected in parallel. So, with all these considerations, the 20-ish tracks displayed on the first plots seem like a small number. If one experimental replicate is 3-4 embryos and 5 cells can be imaged per embryo then around 20 tracks would be the result of 1 replicate (vs 3 as indicated in the methods). If 10% of the cells can be imaged at 256-cell stage, with 3 replicates each made of multiple embryos, it would give more than 60-ish tracks.

‘For wt, N=3, n=111; for mir430 mutant N=3, n=72…’. Please clarify what the two n refer to in the figure legend?

The methods state "A minimum of 3 biological and 3 technical replicates was generated for each experiment. The number of experimental replicates (N) as well as the number of measured nuclei (n) are reported for each conducted experiment individually in the respective figure legend." - Recommend including a shorter but similar clarification in the figure legend.

1. Result: Transcription factors cluster prior to, and independent of transcription

'and visualized each transcription factor in combination with the initiating form of RNA Pol II (Figure 2A, and Movies S4-6…’ Suggest adding a clarification about when zygotic translation starts in zebrafish and whether translation starts before transcription in zygotes.

‘We conclude that transcription factors cluster prior to, and independent of transcription elongation.’ From the data it should be possible to estimate a mean delta T from TF clustering to Pol II clustering, it may be relevant to report such a number.

1. Figure 2: Pairwise non-parametric Wilcoxon tests: There is a concern about the use of a pairwise test as the two conditions CTRL and amanitin are two different conditions.

2. Result: RNA accumulation results in dissociation of transcription factor clusters

‘...the appearance of RNA Pol II Ser5P (initiation) clusters was also delayed in the absence of transcription elongation (Figure 2E)…’. Suggest calculating the delta in time between TF cluster appearance and Pol II cluster (as suggested above). It appears the "delay" in the apparition of the Pol II puncta is the delay observed for the TF, which would indicate that with or without transcription Pol II joins the TF cluster at the same time.

‘…while accumulation of RNA causes them to dissolve…’. Is this based on the observation that inhibition of transcription results in a longer cluster lifetime? RNA accumulation might promote clusters to dissolve, but whether it is the "cause" of their dissolution has not been tested. Recommend reframing the fragment to avoid conclusions about RNA accumulation.

1. Result: Nanog organizes transcription bodies

'…cycle (Figure 3A)…'. There is a concern about comparing Nanog and Sox apparition time if they are not observed within the same embryo / nuclei. The present data are convolved by variations between embryos and between nuclei, recommend providing some clarification and looking at the time difference between each TF and the corresponding Pol II cluster.

‘...Nanog RNA Pol II Ser5P could still be detected…’. Suggest re-phrasing this part as "to determine if RNA Pol II Ser5P could still be detected in the absence of Nanog".

1. Figure 3.

‘In C-D, the percentage of nuclei with the indicated pattern is indicated…’. Suggest some further clarification about the percentages reported. In C does this indicate that 9 % of the cells form Sox clusters in absence of Nanog? And in D that 27 % of cells form Pol II clusters in absence of Nanog? If that is the case, recommend discussing it as it might impact the conclusion that Nanog is "required" for Pol II clustering.

'Pairwise non-parametric Wilcoxon tests were performed, ns indicates P > 0.05…'. Reconsider the use of pairwise tests, as noted above.

1. Figure 4 - ‘percentages indicate how often the shown phenotype is observed. For D and E, N ≥ 3 and n ≥ 18.’ - Please clarify how these percentages are calculated. Is this the percentage of nuclei with the described phenotype per embryo? Or the percentage of embryos with at least one nucleus with the depicted phenotype? In Figure 1 the percentage for Pol II in WT at 128-cell stage is 80%. Figure 4 reports 100%, is it evaluating the same thing? If it is preferable not to write exactly N and n values for all the conditions, maybe these could be shown in the figure itself.

2. Result: Nanog DBD as well as IDR are required to organize transcription bodies

‘In this study we analyzed the assembly of two transcription bodies…’. Recommend placing this under a separate Discussion/conclusions section.

‘…and RNA accumulates, transcription factor clusters disassemble.’ It is not clear that the statement is supported by the data, consider reframing the fragment.

1. STAR METHODS

Please provide additional details about the different aspects of methodology. Also consider depositing the custom scripts to a public platform such as github or zenodo where these materials can be publicly accessed and referenced, supporting reproducibility.

‘Preparation of embryos for use in live-cell microscopy….At 16- to 32-cell stage…’. In the movies (or at least their legends), the embryos shown are at the latter cell stages. Would it be possible to clarify whether later staged embryos were prepared and how? If the approach involved waiting until the desired development stage was achieved, please indicate so.

‘Image pre-processing with Noise2Void… The network was trained on and applied to the raw spinning disk confocal data in full 3D with both color channels being present…’. Are there specific parameters that should be specified? How many stacks / movies were used for training? How was it evaluated that the training was sufficient?

‘Signal normalization…..The denoised and max-projected 2D image data was normalized…’. Please report the details of the process e.g what was the normalization?

‘Determining developmental stage and mid-point between interphases……This method is very reliable as the inter-nuclear distances in these early stages are highly stereotypic…’. Was this method previously described and/or used? If so, please provide references.

On 2022-08-03 21:21:03, user smartalec wrote:

page 8: "The identity of potential drivers of SCLC metastasis on chromosome 16p, the top gain (Supplementary Fig. S7B), remains unknown, but genomic gain of 16p13.3 has been associated with poor outcome in prostate cancer (48) and this region contains the PDK1 gene, coding for a component of the PI3K/AKT pathway." Its not PDK1 which lives on Chr2. The correct gene is PDPK1.

On 2022-08-03 20:51:43, user Fred Maxfield wrote:

This study focused on the role of TPP1 in degrading fibrillar β-amyloid in microglia. In the course of a follow-up study, we were unable to reproduce the experiments showing differences in degradation of fibrillar β-amyloid in microglia from wild-type and Tpp1(-/-) mice. We do not understand the reason for the difference, which may be the result of subtle differences in the preparation of β-amyloid fibrils or culturing of the microglia.

On 2022-08-03 10:50:51, user Iratxe Puebla wrote:

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Ruchika Bajajand Michael Robichaux. Review synthesized by Michael Robichaux.

The manuscript presents a cryo-electron microscopy focused study of a recombinant type V-K CRISPR-associated Cas12k transposon recruitment complex from Scytonema hofmanni that is DNA-bound and includes a complete R-loop formation. In addition to mapping the assembly and interactions within this transposon complex, the study also details the discovery of ribosomal protein S15 as an essential component for the transposition activity of the complex. The work presented in this manuscript may contribute to the development of new programmable CRISPR-associated genome-engineering tools in eukaryotic cells.

Major comments

• The figures in the manuscript are generally well-organized and clear. In particular, the 2D diagram of the Cas12k-TnsC complex in Figure 1A is a useful figure panel; however, please consider refining the diagram for readability by replacing the current nucleotide sequence rearrangement with simpler shapes or graphics.

• For the structural complex models in Figure 2, please consider adding annotations that highlight both the completed R-loop as well as the 122॰ angled confirmation of the PAM distal to proximal DNA, which are both features that are highlighted in the Results section text.

• The title for the “TniQ nucleates TnsC filament formation” Results section and the title for Figure 4 are both possibly overstated since these mechanistic conclusions are based solely on transposition assay results.

• In the discussion, please consider revising the language used to describe the mechanism of transposon complex assembly (the model in Figure 7) to better justify a rationale for proposing a “cooperative” assembly mechanism that is based on the data in this manuscript, which is a structural assessment of the whole complex and its sub-complex interactions.

Minor comments

• In the first section of Results section, consider adding a description of the recombinant system used to purify the protein complex used for cryo-EM as done for the Figure 1 legend (“V-K CRISPR-associated transposon system from Scytonema hofmanni (Strecker et al., 2019)”).

• For Figure S1B, the orientation map is not clear, an adjustment to the color contrast may improve the clarity of this panel.

• For the cryo-EM data in Figures S2, please better define the TnsC oligomer organization (i.e., hexameric, variable). Also for Figure S2, please consider improving the image contrast for the angular distribution images in panel B.

• For Figure S3, both the incomplete R-loop and the missing Cas12k-sgRNA + TsnC contacts described in the text for this non-productive complex structure are not evident or identifiable in the models presented in the figure. Please consider annotations or descriptions in the figure legend.

• For Figure S4, please consider defining all rotations and dispositions that make up the conformational rearrangements in the RuvC domain, as described in the Results section text.

• For Figure 2, please consider adding a 2D diagram of the current complex structure in comparison to previously-reported structural models.

• The organization of Figure 3 is too busy, please consider re-formatting for clarity.

• For Figure S8, please consider including a “zoomed-out” image of the Cas12k+S15 structure.

• In the concluding paragraph of the Discussion section, please elaborate more on how the findings from this work may impact the “genome engineering application of CRISPR-associated transposons”.

Comments on reporting

• As outlined in Figure S1, 75K particles were used for the final cryo-EM reconstruction of the Cas12k-TsnC recruitment complex. Please consider discussing the structural elements or discrepancies of the other classified particles.

• Table S2 and S3 appear to be missing.

• In the “TniQ recognizes tracrRNA and R-loop” Results section, please specify which TniQ and tracrRNA mutations reduced transposition activity.

Suggestions for future studies

Please consider future studies that address the relevance of this transposon complex structure to physiological processes via cell-based assays.

On 2022-08-02 22:02:47, user Tania Gonzalez wrote:

The miRNA-seq data was deposited to NCBI GEO under accession GSE184860. Link here: https://www.ncbi.nlm.nih.go...

On 2022-08-01 19:15:07, user Donald R. Forsdyke wrote:

GENOME-WIDE STRUCTURE POTENTIAL COPIED TO PRE-mRNA INTRON REGIONS

Exons encode proteins and, following Szybalski’s transcription direction rule, their transcribed DNA strands are purine-loaded to impair base parity (A>T, G>C). Since the stems of stem-loop structures require parity (Chargaff’s second parity rule) this decreases the false alerting of immune mechanisms by double-stranded self RNAs (1-3). With their base parity less impaired than exons, structural constraints on introns should be less than those on exons. Thus, with more flexibility in the ordering of their bases, introns would seem to have a higher potential for structure. Indeed, Rangan et al. (4) report that yeast “introns include more non-random secondary structure elements compared to coding regions.” What could these structures be doing? They suggest there might have been evolutionary selection for regulatory elements:

The widespread presence of structured elements in S. cerevisiae introns raises the possibility that similar motifs and stable secondary structures play a role in introns in higher-order eukaryotes, perhaps forming regulatory elements in human pre-mRNA.

While this may be partially correct, it should be noted that eukaryotic pre-RNA structures reflect evolutionary pressures acting directly on both the DNA genomes from which RNAs are transcribed and the RNAs themselves (to ensure their proper functioning). The natural editing of RNA transcripts (pre-mRNA to mRNA) would have included both intron removal (as discussed by the authors) and the removal (or modification) of any exon structures that had primarily operated at the DNA level but were unacceptable at the RNA level. Thus, the structural landscapes of mature mRNAs reflect evolutionary pressures affecting both their cytoplasmic functions (e.g., protein-encoding and purine-loading) and the few (if any) remaining nuclear (DNA) level structural functions that happen to be acceptable (i.e., are neutral) at the mRNA level.

It is a laudable goal to detail accurate RNA structures as they might operate within living cells (4). However, genome-level structure functions should not be forgotten. For this purpose, it is of fundamental interest to compare genes under negative Darwinian selection (slow mutation rate) with those under positive Darwinian selection (high mutation rate). In the latter case, protein-encoding functions in yeast should more effectively out-compete nucleic acid structure functions for a place in exons (3).

Indeed, when genes under positive selection pressure were examined in higher-order eukaryotes, using computational methods similar to some the authors employ (5), it was found that the base order required for structured stem-loops was more constrained in exons (i.e, less structure). However, the base order required for local intron structures was less constrained (much more structure). Stem-loop potential in introns was conserved more than in exons (6).

In other words, a dispersed genome-wide potential for structure - such as might be required to support recombination – seemed to have been diverted from exons to neighboring introns (7). If they have not already done so, I encourage the authors in their future work to compare intron structure landscapes in two classes of yeast genes – those conserved under negative selection and those subject to positive Darwinian selection.

1. Bell SJ, Forsdyke DR (1999) Deviations from Chargaff’s second parity rule correlate with direction of transcription. Journal of Theoretical Biology 197, 63-76.
2. Barak M, et al. (2020) Purifying selection of long dsRNA is the first line of defense against false activation of innate immunity. Genome Biology 21, 26.
3. Forsdyke DR (2021) Functional constraint and molecular evolution. Encyclopedia of Life Sciences 2, 1-11.
4. Rangan R, et al. (2022) RNA structure landscape of S. cerevisiae introns. BioRxiv: (Here) (July 23).
5. Reuter JS, Mathews DH (2010) RNAstructure: software for RNA secondary structure prediction and analysis.BMC Bioinformatics 11, 129.
6. Forsdyke DR (1995) Conservation of stem-loop potential in introns of snake venom phospholipase A2 genes: an application of FORS-D analysis. Molecular Biology & Evolution 12, 1157-1165.
7. Forsdyke DR (2016) Exons and introns. In: Evolutionary Bioinformatics. 3rd edition. Springer, New York, pp. 235-252.

On 2022-08-01 17:29:01, user Pooja Asthana wrote:

Summary:<br /> In this paper, the authors have employed Microcrystal electron diffraction (MicroED) to identify the positions of hydrogen atoms in hen egg-white lysozyme. The major success of the paper results from the continued improvement of MicroED data collection procedures: 35% of hydrogens contained in the structure can be visualized with visible hydrogen bonding networks and hydrogens on water molecules. They were able to locate more hydrogen atoms in the protein backbone than in the side chains owing to more rigidity of the backbone structure. They observe density for acidic side chain residues and their negatively charged side-chain carboxyl groups which are thought to be poorly resolved in single particle cryo-EM at moderate resolution. This observation is tantalizing, but incomplete. By our eye in Fig 2c the “right oxygen” in Asp18” is lower signal than the “left”. Presumably this indicates that the side chain is not protonated - and it is possible that there may be opportunities here to test the strength for “for negatively charged atoms at lower scattering angles” based on such differences in signal. Such an analysis would be very interesting (and potentially using different truncations of the data, truly test this model). The last section of the paper describes that the inter-nuclei distances are more accurate to determine the hydrogen bond lengths than the center of mass of electron clouds, which agrees with the analysis in Molprobity (Williams et al, Protein Science 2018). Comparison to X-ray (which occurs a bit in the discussion) and neutron data (e.g. https://pubmed.ncbi.nlm.nih... on this point would be very interesting. Further missed opportunities include comparisons of the h signal strength to detection by neutron/X-ray and whether there are any trends that would connect with hydrogen-exchange measurements.<br /> Overall, the paper is concise and focuses on the observations enabled by the new data collection improvements, but misses opportunities to connect to other analyses on lysozyme (perhaps the best system to make such comparisons in!)

Following are some minor points that we would like to mention:

Minor points:

Abstract line 18: instead of “informing” it should be “information”

Line 61: “Here, hydrogen atoms were identified by omitting them from the model and inspecting the peaks in a calculated Fo–FC difference map following refinement in Servalcat based on crystallographic refinement routines implemented in REFMAC5 (Murshudov et al., 2011; Yamashita et al., 2021). Since resolution is a local feature in cryo-EM, the accuracy of hydrogen identification varies across the map.”<br /> There is some ambiguity in the way we read this. By “here” do the authors mean in the previous single particle EM work to high resolution outlined in the preceding sentences or their current manuscript? If they mean the preceding papers suggest starting the paragraph, “In those works,”; if they mean the current manuscript, the statement about resolution varying across the map needs a more full and nuanced explanation.

Line 91: “Lowering the total exposure also reduces the effects of radiation damage that can affect the structural integrity of the protein and the ability to localize hydrogen atoms”<br /> It would be interesting to test the radiation damage directly here, but maybe prohibitive across 16 crystals?

Line 110: ‘‘Nevertheless, these results are the most complete hydrogen bonding network visualized to date by macromolecular MicroED’’ <br /> The authors did a nice comparison of all the hydrogen bonds based on X-ray, MicroED and neutron diffraction. It would be worth mentioning if they were able to identify any new hydrogen bond position or network which was not previously reported, this would further connect to the other methods as mentioned above.

Line 127-128: ‘‘Interestingly, whereas the Asp52 and Gly54 N-H distances are close to the idealized positions, the difference peak for the Asn44 N-H is located at an almost equal distance shared between the donor and Asp52 carbonyl acceptor’’<br /> Which idealized positions are the authors referring to? idealized from the neutrons or X-ray or both? There may be settings in Phenix that allow this to be controlled (although REFMAC is used here).

Line 182/183: ‘‘The number of observations for some 183 bond types is insufficient for a rigorous statistical analysis’’<br /> The authors can mention which bond observations are significant and the observed bond length elongation. For example, C-H2 has the highest number of observations (99) with a deviation of 15. However, there is no mention for the apparent elongation of bond length in this case.

Figure 2d: Label the contour level for the 2 additional water molecules w1079 and w1005.

Figure 3: Legend needs explanation for MicroED curve too.

Supplementary Table 1: We understand that the overall crystal quality statistics are weak in the case of MicroED. However, the low completeness after merging 16 datasets is not entirely understood and perhaps deserves some comment here. Is there a preferred orientation on the grid that leads to a systematic problem in filling reciprocal space?

Pooja Asthana and James Fraser (UCSF)

On 2022-07-29 13:28:13, user Iratxe Puebla wrote:

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Richa Arya, Joseph Biggane, Luciana Gallo, Arthur Molines, Sónia Gomes Pereira. Review synthesized by Vasanthanarayan Murugesan.

In this preprint, the authors describe a novel pathway that maintains protein homeostasis in cells recovering from heat stress termed stress-induced protein disaggregase activation pathway (siDAP). siDAP induces the DNAJA1+DNAJB1-Hsp70 protein disaggregase and targets aggregates of tightly misfolded proteins. This pathway is distinct from more-known ubiquitin-dependent quality control and works in sequence with it. Further, the authors show that this pathway is compromised in aging cells. The authors have provided a wealth of convincing data to support the claims made.

The following items were raised:

Major comments

Manuscript

It is recommended to revise the manuscript to better integrate the data and the text. The paper provides extensive data to support the study claims, but further background material for the experiments in the introductory or results section would support interpretation e.g., concepts required to understand the final two figures are not discussed in the introduction.

Reducing the number of supplementary figures may make the manuscript easier to follow and help in tightening the narrative.

Experiments

Results ‘Immediately after HS, DNAJA1 and DNAJB1 rapidly relocalized to nucleoli’ - It is unclear from the DAPI stain what happens to the nucleolus at 0h after HS. It seems to be present in some cells but not all. Could a marker of the nucleolus be used and/or some clarification included?

Results ‘This suggests that predominantly newly synthesized DNAJA1 and DNAJB1 molecules drive the assemblage of the DNAJA1+DNAJB1-Hsp70 disaggregase in cells after HS’ - Fig S5D shows that B1 forms puncta after HS even in CHX treated cells, which suggests that protein synthesis is not needed. Can some clarification be added for this fragment.

Results ‘diffuse GFP fluorescent signal (cyan) indicating that protein aggregates were largely absent’ - The presence of aggregates or puncta before HS cannot be ruled out, the puncta or aggregate could be too small to be resolved. Recommend commenting on this.

Results ‘Blocking Hsp70 activity by VER-155008 also caused DNAJA1+DNAJB1 scaffolds to persist up to 24h after HS, presumably due to their continuous association with the aggregates (Figure 2D).’ - The HSP70 aggregates look different after treatment with VER, they look more like the A1/B1 puncta than in the DMSO condition, it may be worth commenting on this.

In Figure 6, the distinction between biological aging and replicative aging could be stated more clearly. Cell lines derived from donors of different biological ages form siDAP puncta and recover from heat shock. However, the cells lose this ability when cultured in dishes at passage 12 or 18 irrespective of biological age. Hence it is not clear if passaging cells mimics biological aging with regard to protein homeostasis.

Minor comments

Figure 1H: Recommend including some comments on why the size of HSF is more at 0 hr, and commenting on whether HSF-1 depletion changes HSP70 levels.

Figure 2 (B-D) - The size of cells in U vs 0 hour appear different, the 0 hr cells look bigger. Suggest adding a scale bar and clarification on whether the magnification is the same.

In Figure S4/S5, it is hard to infer the state of the nucleolus during stress with DAPI staining and subsequently the localization of DNAJA1 and DNAJB1 to the nucleolus is not clear.

In Figure S4D, it is shown that CHX doesn’t affect the formation of puncta but the text states that newly synthesized DNAJA1 and DNAJB1 are required for the assembly of DNAJA1-DNAJB1-HSP70. Please provide some clarification for this contradiction.

In Fig S8, statistical analysis of different siDAP induction is suggested.

In Fig 3, please provide clarification for the choice of experiments in CHX-treated cells for testing the effect of VER-155008.

In Fig 5, the caption mentions cells with/without VER-155008 treatment which cannot be seen in the figure.

‘In fact, we found that human cells can tune the activation of siDAP according to the level of protein damage sustained after HS’ - It may be informative to check if the cytotoxicity levels differ from HS at 39ºC and at 42ºC.

In Fig 6, quantification of PLA^Dt, similar to Fig 1F is suggested. Please also report the conditions used for heat shock in these experiments, 42oC for 2 hrs?

‘Moreover, siDAP was fully active in all fibroblast lines tested (Figure 6A; Figure S22A and B). Similar to immortalized HeLa cells, primary dermal fibroblasts only induced the DNAJA1+DNAJB1 JDP scaffold after HS (Figure S22C)’ - May be worth mentioning that the apparently higher intensity of the fluorescence signal in the cells derived from aged subjects. The fluorescence signal per cell looks much greater in 70 yo (Fig. S22 only) and 75 yo (Figs. 6 and S22). The next few lines discuss the relevance of decreased fluorescence (representative of loss of siDAP induction) with serial passaging/replicative age. However, upon HS, siDAP signal seems to go up with chronological age, but then in the replicative aging experiments, siDAP is lost quickly.

Discussion ‘There is some evidence to suggest that cellular surveillance systems that usually keep protein aggregation in check deteriorate during aging….’ - There may be some conflation of biological aging and "replicative aging". There seemed to be conflicting results when looking at differently biologically aged samples, which may affect interpretation of whether replicative aging in a dish recapitulates aging processes.

Methods Cell culture - Please provide further information about the age and other details for the 6 primary fibroblast cell lines.

Recommend increasing the size of the microscopic image panels in several figures to better highlight the features mentioned.

On 2022-07-29 11:32:47, user Iratxe Puebla wrote:

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Ruchika Bajaj, Rasmus Norrild and Akihito Inoue. Review synthesized by Bianca Trovò.

Antibody-based technologies for the detection and quantification of analytes in complex biological samples present challenges regarding the infrastructure and chemical modifications involved. There is therefore interest in developing alternative biosensor platforms that leverage split luciferase enzymes. Single-component luminescent biosensors can be more easily produced and work in both homogenous and immobilized assay formats. The manuscript reports the design of BAT, a single-component, NanoLuc-based, Binding Activated Tandem split enzyme biosensor for the detection of the SARS-CoV-2 spike protein in multiple assay formats.

The reviewers praised the efficiency and practical value of the reporter system described, as the reporter protein works even in crude bacterial cell lysate, as well as the novelty of the mechanism of action for the reporter system. A few comments and suggestions raised are outlined below.

Major comments

• In the introduction, the manuscript mentions the single-component, NanoLuc-based, Binding Activated Tandem split enzyme (BAT) biosensor, which is said to “not rely on a large conformational change in the binding module or competition with a tethered decoy as with other single component platforms”. The manuscript argues for the uniqueness and generality of the BAT approach based on a mechanism that does not rely on conformational change. Further references and explanation for the mode of action would be helpful to support the argument. Given the lack of conformational change in the binding module, can an explanation be included for what causes the split components to come closer and reconstitute again?

• Results, Design and optimization of SARS-CoV-2 biosensor “S-BAT”: the explanation of Figure 1a and Figure S1f provided in the context of the model for the mechanism of BATs could be strengthened with crystal diffraction data to validate the hypothesis, especially for clarifying how steric hindrance occurs when binding with the antigen (although this will not elucidate any conformational change happening in LCB1 upon binding).

• Results, Design and optimization of SARS-CoV-2 biosensor “S-BAT”: the manuscript reports a full mutational analysis, or deep mutational scanning (DMS) leading to the generation of “a point mutant in the S-BAT binding module at Asp30 (“S-BAT*”), designed to ablate salt bridges formed with Lys417 and Arg403 in the Spike receptor binding domain (RBD)”. Would it be possible to have this mutation motivated in the manuscript, and why was it chosen over other possible mutations in that context?

• Results, Design and optimization of SARS-CoV-2 biosensor “S-BAT”: the statement that “the absence of a hook effect at super-stoichiometric concentrations of Spike binding sites to sensor copies supports a predominantly cis activation mechanism” is a strong point but further clarification on this point is recommended, for example, further context on the Hook effect, and what would have been expected if trans activation was the major mode of action.

• Discussion: the manuscript has shown different versions of the same assay, so a discussion on advantages of one version over the other would be important.

Minor comments

• Results, Design and optimization of SARS-CoV-2 biosensor “S-BAT”: “In addition to having high thermal stability, rigidity, and no disulfide bonds to complicate purification” - Please clarify which protein these qualities refer to.

• Results, Design and optimization of SARS-CoV-2 biosensor “S-BAT”: “This suggests that cis activation is likely the predominant source of signal in the assay, but we cannot rule out the contribution of a trans activating mechanism. In the trans mechanism, simultaneous binding to multiple protomers in a single Spike might increase the effective concentration, driving activation.” - Please provide further comments on the cis and trans mechanisms.

• Results, S-BAT is functional in multiple assay formats: “Adsorption-based immobilization is advantageous in that it requires no chemical modification to the protein reagent”, recommend reporting the efficiency of this step and how much signal is left after the washing steps.

• Methods, Cloning: the manuscript reports that all BAT constructs were subcloned using NcoI and BamHI restriction sites via Gibson assembly. Restriction site cloning and Gibson assembly seem to be two orthogonal methods, suggest providing further information on the cloning procedure.

• Methods, Recombinant Protein Production: "The un-cleaved BAT sensors were concentrated to ∼1.0 mL, and the concentration was calculated from the A280 value”. It is unclear if this was done using highly pure imidazole or if the signal was subtracted from Imidazole? A280 quantification is known to be difficult with Imidazole present.

• In Figure 1c: “Performance (Signal to Noise (S/N) multiplied by the magnitude of signal change (S-N))” please provide the mathematical expression for this analysis

On 2022-07-27 14:46:46, user Karen Lange wrote:

Overall this is a very thorough study looking at the roles of Septin9 and ARHGEF18 in ciliogenesis. I especially like Figure 3 where the authors compare the severity of partial knock down of SEPTIN9 with siRNA vs in the knock-out CRISPR cell line. The complete lack of cilia in Figure 3J/K is very striking. The experiments using centrin-fused RhoA and ARHGEF18 are very creative. It is impressive how long the cilia are in Figure 5D! I find it interesting that expressing the Centrin-GFP-DHPH construct doesn’t cause the same long cilia phenotype in the Septin9 KO cells. This observation is based on the images shown in the panels because I do not see cilia length measured in Septin9KO + Centrin-GFP-DHPH.

I appreciate the use of scatter plots to highlight the variability/reproducibility of the data. I assume the different colors relate to different replicates, but this is not clearly stated.

I have a few specific comments with regards to Figure 7:

I found it difficult to see the 3 RPGRIP1L puncta. I think if the panels were more zoomed in or larger it would help to better highlight the shift from 3 to 2 puncta in the Septin9 KO cells.

In the Septin9 KO cells RPGRIP1L and TCTN2 staining was not observed at the transition zone. This observation is not surprising since the Septin9 KO cells do not have cilia (Figure 3J/K). As such, I do not believe that the conclusion that Septin9 “mediates the localization of transition zone components to the ciliary base” is founded. Septin9 could have a function early in ciliogenesis that is not necessarily specific to mediating the localization of transition zone components. Perhaps this could be better resolved in the Septin9 siRNA knock down where short cilia are present.

The data showing that expression of constitutively active RhoA restores RPGRIP1L and TCTN2 at the transition zone (7I,7J,7M,7N) is very nice and consistent with this construct restoring cilia in the Septin9 KO. However, I think the data in 7M and 7N would be clearer if you included the Sept9 KO data on this graph.

One last point – Figures 7A, 7B, and the inset on 7C state RPGRIP1 while the text only refers to RPGRIP1L. The Materials and Methods do not mention which antibody was used so I am not clear which protein was the focus of this study. Similarily, I think it is a typo in 7F and the Figure 7 legend where it says TCTN1.

On 2022-07-26 16:54:39, user DrDiagnosis wrote:

The authors should take a look at https://doi.org/10.1002/mp....

On 2022-07-26 16:45:31, user Andy Villunger wrote:

Hi Sebastian, this is highly interesting stuff, but I think the title is a bit of a stretch and makes too strong a claim right now. Using the BH3 swap mutants is nice, but does not tell you much about the activity of the full-length (endogenous) protein. You may be aware that the "BH3-only" version of BCL-G is barely expressed in most cells and the version with BH2 & BH3 domain is much more abundant (PMID_23059823). So, studying the BH3 domain in isolation appears somewhat artificial to me. Also, in the CT26 xenotransplant experiment you show would benefit from overexpressing a BH3_mut version of BNIP5 and some data that documents it is really impaired BAK-dependent cell death that gives the growth advantage.....there could be many alternative explanations. So, comparing CT26 lacking Bax or Bak ± BNIP5 wt vs. mut would be a strong experiment to test your hypotheses. Happy to chat more.....

On 2022-07-26 09:48:35, user Iratxe Puebla wrote:

The study reports single-cell intracellular pH (pHi) measurements in different cell lines to measure spatiotemporal pHi dynamics during cell cycle progression. The manuscript reports an increase in pHi at the G2/M transition, decreased pHi at the G1/S boundary, S/G2 boundary, and prior to division, and increases during mid-S phase and G2, and suggests that pHi dynamics are necessary for cell cycle progression.

The reviewers praised the topic of the study, measuring intracellular pH during the cell cycle and looking at the heterogeneity between cells are both important questions. However, there were some questions raised about the methodology as well as the interpretation of the data, as outlined below.

Comments about methodology

The pH sensor used in the study has been used previously but the single-cell level use requires new types of control and validations. It would be relevant to report:

• What is the measurement error?
• How efficient is the permeabilization protocol?
• How homogeneous is the expression of the sensor? How does the expression level impact the pHi readout?

These technical parameters could explain the heterogeneity in pHi reported in Figures 1,2,3, and they are relevant to understand if the fluctuations reported are relevant biologically or at the level of technical variability.

Recommend providing additional details on the methodology for single cell pHi measurements, to ensure the experiments can be fully reproduced. Please report sample sizes.

There is apparent intra-cellular heterogeneity present within each cell. The text should highlight whether the cytoplasm is heterogenous in pH. The study uses a single ROI per cell to measure intracellular pHi, however, if the cytoplasm is heterogeneous as some images show, the location of the ROI can influence the readout. It is recommended to use image analysis tools to segment cells and use the whole signal rather than a selected portion.

There are concerns about the statistical analysis for several figures (including Figs 2I, 3I, and 5), in particular regarding the calculation of p-values based on multiple measurements or cells within each sample. The t-test and ANOVA assume that each measurement is independent, while multiple nuclei within the same sample are not independent. Recommend not reporting p-values or averaging together the values from each sample and then calculate the p-value using those sample-level means. For more information, see https://doi.org/10.1371/jou... and https://doi.org/10.1083/jcb...<br /> The median is used as the reporter of the populations, the context for this choice is unclear. There are concerns about reporting standard deviation to estimate the spread around the median.

Specific comments

Introduction ‘In normal cells, intracellular pH (pHi) is near neutral (∼7.2)...’ - Could the text specify the type of cells the statement relates to, does it apply to all eukaryotes, mammalian cells, or even more specific and only demonstrated for human cells?

Results ‘single-cell standardization is performed using buffers of known pH containing the protonophore nigericin (Fig. 1A, see methods for details).’ - The experiments use two pH extremes (~6.7 and 7.7 per the Materials & Methods)) and assume a linear relationship of the emission ratio between these extremes. Is this linear relationship verified? The supplementary Fig S2. shows an increase in signal across just two points. Suggest presenting an analysis of the biosensor across 4-5 different pH points to demonstrate linearity and dynamic range within the first set of figures. Plotting the ratio as well as the fluorescence intensities of individual channels across these pH ranges would also be relevant.

Figure 1

• Is there an explanation for the signal from the nucleus? It seems initially more acidic than the cytoplasm and then it does not change as much as the cytoplasm during the nigericin treatment. Is this due to bad permeabilization?
• ‘B) NL20, C) A549, and D) H1299’ - Please indicate which cells are normal and which cancerous.
• ‘E-G) Histograms of single-cell pHi in E) NL20 (n=173, 3 biological replicates), F) A549 (n=424, 4 biological replicates), and G) H1299 (n=315, 3 biological replicates).’ - The distributions are aggregates of 3 or 4 biological replicates. What do the distributions look like in each replicate? Are the differences between conditions visible? If the E, F and G histograms are generated using data pooled from different replicates, recommend separating the replicates and presenting the distributions separately for each replicate experiment.

‘We next measured single-cell pHi in individual NL20-mCh-pHl (Fig. 1B), A549-mCh-pHl (Fig. 1C), and H1299-mCh-pHl (Fig. 1D) cells’ - From the methods: "Individual Regions of Interest (ROI) are drawn for each cell in each condition (initial, high pH nigericin, and low pH nigericin), and mCherry aggregates are removed using thresholding holes." From the cells in the image, it appears that the cytoplasmic signal is not homogenous and suggests that the choice of ROI will affect the reading for each cell. In this condition, to do single cell measurements, it is recommended to use the signal from the entire cell (cytoplasm) rather than using an ROI.

‘Representative pHluorin and mCherry channels and single-cell standardization lines can be found in Fig. S2’ - The pH probe appears to be comprised of a straight fusion between the pH sensitive GFP (pHluorin) and pH insensitive mCherry. One would expect that the ratio of GFP to mCherry is only determined by pH (and not by expression level or excitation intensity). A question arises around the dynamic range (shown in fig. S2) being different between the different cell lines. For instance, the ratios observed for pH=7 and pH=7.8 are 3 and 8 for NL20, 3 and 5 for A549, and 0.5 and 2 for H1299. Can an explanation be provided for the differences between cell lines? Were the single cell measurements verified with a dye (BCECF/SNARF/SNAFL)? Was the permeabilization protocol validated?

‘(NL20-mCh-pHl) (Fig. 1E; 7.42±0.07).’ - The first sentence of the results section indicates "In normal epithelial cells, pHi is near neutral (∼7.2), while cancer cells have a constitutively increased pHi (pHi>7.4)." According to this statement, the NL20 cell line has a pHi corresponding to cancer cells, can this be clarified?

‘These data show the advantages of measuring single-cell pHi under physiological culture conditions that match population averages, but also provide pHi distributions lost at the population level.’ - The single cell data reveal the heterogeneity, can further explanation be provided for the advantage gained by these data over bulk measurements?

‘These data also show that pHi is heterogeneous even in clonal, genetically identical, cell lines, suggesting pHi may be a biomarker for non-genetic cell phenotype’ - The data show heterogeneity, but do not address how much and what the source of heterogeneity is. It would be helpful to: report the error on the measurement, compare the spread of pHi to something else to get a sense of the normal level of noise in the measurement. Could this be compared to the spread of mCherry intensity, to check if there is more spread in pHi than in expression level of the construct.

Independent measurement of the heterogeneity of the pH (e.g. with another probe/dye) would shed some light. The heterogeneity (or spread) of basal biosensor distributions could be compared against the distributions achieved after nigericin treatment - to bring out the differences in biological heterogeneity versus measurement error. The results could then further elaborate on whether the biological heterogeneity has relevance in the regulation of cellular processes.

‘pHi in physiological environments’ - Can some clarification be provided for how prior studies did not follow physiological conditions, while the current set up would provide such physiological conditions?

‘We synchronized H1299-mCh-pHl cells using Palbociclib’ - The study uses H1299 line in most figures hereafter, A549 line in some while not the NL-20 lung cells, can some justification be provided for the selection of cell lines for specific experiments.

‘In this representative replicate, we observed single-cell pHi significantly decreased between 0 and 4 h, significantly increased between 4 and 8 h, decreased between 8 and 12 h, and increased again between 12 and 24 h (Fig. 2D).’ - It is not clear whether these data are consistent with the other replicates (Figure S3). For example, another replicate shows a consistent decrease of pHi between 0-4h and 4-8h, which is not the case for the example shown in the main figure. Can some clarification be added about discrepancies between replicates. In Figure S3 the different time points were statistically compared to their previous time point, can the same statistical analysis be applied to the replicate in Figure 2?

Figure 2

‘Box and whisker plots of F) cyclin E1, G) cyclin A2, and H) cyclin B1 immunoblot data across 3 biological replicates’ - There is a concern about the use of boxplots for n=3 as they summarize the data into 5 statistics (2x whiskers, Q1, Q3 and the median): www.nature.com/articles/nme.... It is recommended to show the individual data with a dotplot.

‘Figure 2I. Violin plots of raw pHi across 3 biological replicates’ - A superplot is recommended for identifying the biological vs. technical replicates: https://doi.org/10.1083/jcb.... The significance should be determined based on n=3 (not on the pooled technical replicates).

‘Cyclin immunoblots and pHi agreed across 3 biological replicates, and additional blots are shown in Fig. S3.’ -The replicates from Fig. S3 and Fig. 2 do not appear to show a clear behavior. For example at 4h, two replicates show a decrease while the third shows an increase in pHi. Could some clarification be added for this?

‘When pHi measurements on Palbociclib-treated cells were compared over three biological replicates, we found that pHi significantly decreased at the G1/S transition (4 h, 7.75±0.15) and in late S phase (12 h, 7.69±0.09), significantly increased at G2/M (24 h, 7.82±0.11) (Fig. 2I), and then significantly decreased once more at the end of the experiment in asynchronous cells (36 h, 7.67±0.10) (Fig. 2I).’ - The population in Fig 1 shows a large spread from around 7.4 to 8. This emcopasses all the distribution shown in Fig 2 and if the individual time points are undersampled, small fluctuations are expected in the mean and the median. Can some comment be provided about the potential influence of undersampling on the fluctuation? If the fluctuations were due to undersampling they would be random and could explain why the replicates are not in very good agreement. Also, can some clarification be added about how many cells were measured in each time point.

Figure 3 - The various replicates provided here and in Sup. Fig. 4 show variability. For example, only the replicate in the main figure shows a decrease at 4h and 12h. The third and fourth replicates are in good agreement and for those pHi stays roughly the same and then drops between 12h and 24h. Should this be reflected in the text?

From the values on the y axis for each time point and replicate, it seems that the sample size varies between replicates. There is the risk of undersampling, and also that if one replicate contains much more cells than others, it would dominate the distributions once the data are pulled together. Can the sample size for each time point and replicates be reported?

‘and decreases at 12 h and 24 h (Fig. S5B-C)’ - The text previously reported that pHi increased between 12 and 24h for H1299 cells, here it reports that there is a decrease at 24h. Please provide a clarification.

‘we established a time-lapse approach to track pHi dynamics over an entire cell cycle in a single cell.’ - This is a robust approach to detect pH changes over time.

‘we selected prophase as a “normalization point” for each individual dividing cell’ - Recommend referring to "synchronization point" instead of ‘normalization’.

Figure 4

The paper shows that synchronization alters baseline pHi. Could a similar experiment be completed without synchronization?

‘A) Representative stills of Video S1 of a dividing H1299-mCh-pHl cell at indicated time (h)’ - It would be good to compare this to the metastatic cells used to establish how much of the pHi fluctuations observed during the cell cycle are "cancer" related.

‘Furthermore, the pHluorin increases observed over time in dividing cells are not correlated with increased mCherry fluorescence, which indicates pHluorin increases are not due to increases in biosensor expression (Fig. S8B-C).’ - It is great that this measurement was completed. However, from the plots provided Sup. Fig. 8 B and C, in dividers and non-dividers, it looks like the two signals (mCh intensity and pHluorin) are well correlated (first a decrease for a few hours, then it rises until 10h then it decreases). Could this indicate that the readout is influenced by protein concentration / expression? Suggest plotting the two signals vs each other’s on a scatter plot and formally testing for correlation.

Figure 5

For the FUCCI reporter, plotting mVenus and mCherry intensities normalized between the max and min value for each cell allows clear identification of transition between phases. It may be helpful to present example single cell traces from 5-10 cells for each treatment, to more clearly appreciate the cell cycle phase transitions and their durations on panels F,G and H.

‘D) Single-cell pHi of H1299-FUCCI cells treated with EIPA and SO859 (E+S, n=233) to lower pHi, untreated (CRL, n=267), or treated with ammonium chloride (NH4Cl, n=202) to raise pHi (see methods for details)‘ - Please clarify how or when delta pHi was calculated for data in Fig. 5D.

‘previous work in lower- order organisms’ - "lower-order" has a negative connotation, please consider re-phrasing to include the species or at least family of organisms.

Discussion - Recommend further discussion about altered progression through cell cycle phases at different pHi and how it could be altered in cancer cells. Is increased intracellular pH in cancer cells related in any way to their increased proliferation? If so, which cell cycle steps are affected? High intracellular pH seems to elongate all phases except the M phase.

Methods

‘Multiple Z-planes were collected with the center focal plane maintained using a Perfect Focus System (PFS).’ - Please report whether pH was analyzed on a projection, a single z-slice, each z-slice?

Single-cell pHi measurements - Please provide additional detail for the protocol for the single cell pHi measurement. Include information on whether the work involves single image, stacks, projections, etc, and the size and location of the ROIs. Please also provide further context for the "mCherry aggregates", does this mean the construct is cleaved and the mCh aggregate? Does the GFP aggregate too?

NIS Analysis Software//GraphPad Prism - Please report the version of the software used.

‘Individual Regions of Interest (ROI) are drawn for each cell in each condition’ - Could the ROI on a few of the cells be drawn and highlighted in the main figures to show the size and location of the ROI?

‘8% laser power for GFP; 700 ms exposure time and 10% laser power for TxRed; and 100 ms exposure time and 5% laser power DAPI’ - Please report the exact wavelengths used to excite the fluorophore, (e.g. 8% power of a 488 laser (GFP excitation)).

Supplementary figures

Figure S3 panel A - Should the calibration slope be the same for every cell? Can some explanation be provided for why some cells have a steeper slope than others?

Figure S4 - Replicates appear to show different trends in pHi and Cyclins, which makes it difficult to interpret the data.

Figure S8 panel A - This plot shows correlation between the two quantities, they both rise and fall at the same time. Can some clarification be provided.

On 2022-07-26 03:42:41, user MarkD wrote:

For the most part. this paper treats gene annotations as an opaque collection that is used regardless of the specific type of gene.

It is especially fascinating that RefSeq has genes that don't have exons.

On 2022-07-22 22:26:51, user Guillermo Gómez wrote:

Amazing findings! exNef was my PhD thesis and in those days (2005) I insisted that Nef by itself was able to cause severe immune depression independent of viral replication. Here in bioRXiv we published in Dec 2021 that ORF8 is the "Nef" of COVID-19, ie, a superantigen with enough abilities (at least in silico) to cause disease. Happy to see that research about viral toxins are still alive!

On 2022-07-22 15:49:08, user Still Too Slow wrote:

Lines 25/26: 75.6 ± 0.6 mmol C/L day; 0.06 g/L "day" - the "day" should be hour

Also the mmol C is not intuitive. I spent way too long working out the rates to figure out why the rates and concentrations weren't meshing.

On 2022-07-21 15:49:11, user Marie-Cecile Caillaud wrote:

eLife Sciences Publications, Ltd

https://doi.org/10.7554/eLi...

Manuscript has been accepted

Acceptance Date 2022 Jul 9

Publication: eLife

This paper has been accepted for publication so its publisher has pre-registered a Crossref DOI.

This persistent identifier and link (10.7554/eLife.73837) can already be

shared by authors and readers, as it will redirect to the published

article when available

On 2022-07-21 14:39:58, user Benjamin Buchmuller wrote:

Very exciting work! Reminds me of Giehr et al. (NAR, 2018) "Hairpin oxidative bisulfite sequencing" https://dx.doi.org/10.1093/... I feel that their work should be cited and discussed in the manuscript.

On 2022-07-20 09:54:59, user Herranz Lab wrote:

Amazing work!<br /> I was trying to check some of the Supplementary Table data, but I can't seem to be able to download it. Are you planning on making them available/downloadable too?<br /> Thanks

On 2022-07-19 18:14:29, user T Sawaya wrote:

I'm wondering if you could publish the disaggregated data for immunity against BA.4 and BA.5 separately. In the abstract, you mention this vaccine providing neutralizing antibody titers against BA.4 but in the figures, BA.4 and 5 figure together. I wonder if that means the Novavax vaccine isn't protective against BA.5 or if it was just a typo in the abstract on your end. Either way, it would be helpful to see antibodies against BA.4 and BA.5 separately and if that is not possible, have an explanation as to why they were aggregated.

On 2022-07-18 12:50:30, user Marc RobinsonRechavi wrote:

Dear authors,

I noticed that sequencing data was generated in this study, but there is no declaration of availability. Can you please deposit the data and provide availability in the manuscript?

Best regards<br /> Marc

On 2022-07-18 10:31:38, user Prof. T. K. Wood wrote:

There is little doubt that S. pneumoniae, as probably all bacteria, make persister cells, but Fig. 5 shows there is primarily cell death still occurring at 25 hr, not persistence being studied. Arguably, the rate of dying has decreased but persistence is not reached. Also, there is little credible evidence that TAs are involved in persistence (line 343).

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

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

On 2022-07-17 12:24:43, user Tiago Lubiana wrote:

Hey, the link to e DUB Portal in the abstract seems to be broken, I cannot click it at least

On 2022-07-16 17:09:16, user carrental wrote:

Very cool and fresh story. How comes that the C-terminal fragment of the receptor enters the mitochondria?

On 2022-07-16 07:41:26, user Iratxe Puebla wrote:

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Richa Arya, Luciana Gallo, Lauren Gonzalez, Sam Lord, Dipika Mishra, Arthur Molines, Mugdha Sathe, Ryman Shoko, Ewa Maria Sitarska. Review synthesized by Ehssan Moglad.

Study conducted by Chieh-Ren Hsia et al. which looked at nuclear deformation in confined migration and its effect in chromatin organization and function.

Major comments

Results ‘To distinguish between true changes in chromatin modifications and effects of physical compression of the nuclear content due to deformation, we normalized the heterochromatin mark intensity to the euchromatin mark intensity in each cell.’ - The results are normalized to H3K9ac, with the assumption that its levels do not change during migration/confinement. Has this assumption been confirmed? For example, by normalizing both H3K27me3 and H3K9ac to total H3 instead - and showing that K27me3 increases with confined migration while H3K9ac doesn't.

Results ‘Increased heterochromatin formation should result in an increased ratio of heterochromatin marks to euchromatin marks, whereas physical compression of chromatin would increase both marks, and thus not alter their ratio…’ - Can some comments be provided on what the meaning would be for heterochromatin to "increase" and euchromatin to not change? There are two ways in which heterochromatin could "increase" - either the portion of the genome in heterochromatin could increase (which would mean the portion in euchromatin would decrease), or the portion of the genome in heterochromatin could stay the same but K27me3 levels could be higher in those regions (which might not affect euchromatin levels). One way to distinguish between these would be to stain for K36me3 as the "euchromatin" marker instead of K9ac - because K36me3 and K27me3 are mutually exclusive.

Figure 1 <br /> - Could the effects seen be due to cells spending different amounts of time in the channels? Do all cells migrate at a similar speed? <br /> - Panels D, F, I: it is unclear if the cells shown in the plot for the change in heterochromatin marks are all that migrated or only those that show the difference. Suggest including a dot plot to also show individual data. Can some clarification be provided for how to interpret that controls "before" in 1D and 1F are statistically different?

Counts in Fig S2A-D are sometimes very low (same applies to Fig 1I, Fig 2B,C,E.), it may be nice to compare some more cells.

Results ‘Although the effect was less pronounced than in the ≤2×5 μm2 confined channels (Fig. 1C-F)’ - Can the normal size of these cells be reported ? Also the size of nuclei. is it bigger than the pore size?

There are concerns about the statistical analysis related to SEM and p-values based on multiple measurements or cells within each sample. The t-test and ANOVA assume that each measurement is independent, and multiple cells within the same sample are not independent. Suggest to either not report p-values or average together the values from each sample and calculate the p-value using those sample-level means. For more information, see https://doi.org/10.1371/jou... and https://doi.org/10.1083/jcb...

Minor comments

Results ‘custom-made polydimethylsiloxane (PDMS) microfluidic devices with precisely defined constrictions that mimic interstitial space’ - The manuscript report the size of the channels, and notes that it mimics interstitial spaces, it would be helpful to also report the size range for interstitial spaces in vivo.

Figure IH: Are these the same cells as in the reference (cells in which vertical confinement is sufficient to induce a nuclear response)? Are 5 um channels squeezing the nucleous?

“significantly larger increase in heterochromatin than cells migrating through the 10-μm tall channels (Fig. 1H, I), demonstrating that the observed effect is primarily attributed to the confinement and not the migration process per se” - There is a statistical difference between the confined migration and non-confined migration groups, but there is also a statistically significant increase in heterochromatin in the non-confined migration group compared to baseline (and with larger sample sizes than in the confined group), so it may be worth commenting on the possibility of the effect of migration alone.

“Cells maintained CMiH even after completing at least one round of mitosis, without any trend of reversion in their heterochromatin levels (Fig. 2C; Fig. S4A, B), suggesting that the epigenetic modifications were inheritable through DNA replications” - This is an intriguing concept, however, it is unclear whether the cells that migrated did so before or after dividing. To support the claim about inheriting CMiH, it would be relevant to see heterochromatin levels in a mother cell increase after it squeezes through a channel, then the daughter cell (which doesn't squeeze through a channel) having a higher heterochromatin level than the "before" cells. That's not possible with immunofluorescence, maybe the GFP-HP1a could be useful for such a live-imaging approach? Otherwise, if all these "mitotic cells" divided after squeezing through a channel, that could be stated in the text, legend, and/or methods. Alternatively, the conclusion could be nuanced/toned down.

Figure 3 - The number of samples analyzed in some cases appears small. Suggest showing the data as dot plots to allow interpretation of the sample sizes for each group and the differences between the groups.

On 2022-07-16 04:46:40, user Hui Zheng wrote:

Little is known about whether and how Vitamin C specifically targets SARS-CoV-2 infection and its underlying molecular mechanisms, although some reports suggest that vitamin C may be beneficial in the treatment of SARS-CoV-2 infection. This study was finished through repeated verification with the help of many scientists. We sincerely hope that this easy and flexible strategy is useful to restrict the large-scale spread of SARS-CoV-2 in the population and to reduce the disease severity of COVID-19 in the early stage of SARS-CoV-2 infection.

On 2022-07-15 13:55:40, user Anirudh .Shanbhag wrote:

The article has been published in ACS Synthetic biology entitled 'Validated In Silico Population Model of Escherichia coli'. Link to the article is as follows:<br /> https://pubs.acs.org/doi/10...

On 2022-07-14 15:54:12, user Qian Zhu wrote:

I am author of the smfishHMRF package (part of Giotto) that is used in one of your comparisons in Figure 6. I am highly doubtful about the results your presented of Giotto in Figure 6 and same of SpaGCN. I believe much of the results you are seeing is due to the selection of genes to find spatial domains than having to do with the underlying method. We also do not rule out improper usage of our package in this comparison. We will share our findings with you in a separate thread.

On 2022-07-14 06:25:43, user André Rendeiro wrote:

Very interesting study. There is no methods section on the acquisition of microscopy images and quantification of nuclear features (Fig1c). Could the authors elaborate on how nuclear dispersion is measured?

On 2022-07-13 22:14:10, user JKlumel wrote:

1. Does the half-life of the fusion protein is expected to be 4-8 hours similar to YTI enhanced IgG bound to FcRn receptor?
2. In classical situation FcRn orchestrates processing of IgG-opsonized immune complexes (IC) in concert with classical Fcγ receptors to present the antigenic peptides to APCs and ensure clearance of the IC. How the fusion protein opsonized immune complexes are going to be recognized by to APCs and cleared when the Fc is silenced? There will be no risk of neutrophil extracellular trap induced collateral damage resulting from excessive attempts to remove the Fc silenced IC?
3. In studies with recombinant ACE2 it was shown that rACE2 in concentrations close to physiological range in plasma could enhance SC2 infection, while high concentrations achieved inhibitory effects on the infection. Is there a risk that nasally administered fusion protein shall get in plasma in physiological range?
4. Is there a risk of IgG suppression in the mucosa of the upper respiratory tract due to FcRn binding to the fusion protein?
5. Could the invention be financially competitive product in comparison to other nasal prophylactic agents: hypertonic saline, polysaccharides, povidone iodine, nitric oxide?

On 2022-07-13 13:46:46, user Iratxe Puebla wrote:

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Oana Nicoleta Antonescu, Ruchika Bajaj, Sree Rama Chaitanya and Akihito Inoue. Review synthesized by Ruchika Bajaj.

This study has characterized the function of Hero proteins in improving the recombinant expression of TAR DNA-binding protein in E. coli and restoration of enzymatic activity of firefly luciferase during heat and stress conditions. This study may be useful for future applications of Hero proteins in life sciences research. Please see below a few points offered as suggestions to help improve the study.

• In introduction, 3rd paragraph, in context with “amino acid composition and length of Hero proteins”, please elaborate on the effect of these two factors on the function and stability of hero proteins.
• The manuscript refers to “cis and trans” terms on several occassions. Please explain these terms in context with the association of Hero protein with the target proteins.

• Introduction - A paragraph describing the origin of Hero proteins and the differences between the types of Hero proteins in the introduction section would be helpful for readers to understand the background on these proteins. For example, please explain the background on naming these proteins as Hero 7, 9, 11 etc. The genes SERF2, C9orf16, C19orf53, etc are mentioned in the plasmid construction section in the Material and methods. Please provide a brief explanation for the relationship between these genes and Hero proteins.

• Please add more details in the Material and methods section, especifically in western blotting and the luciferase assay, to support the reproducibility of these experiments.

• Figure 1A. Please explain the role of each component (for example factorXa) either in the text or the legend.
• Figure 1B: Please add clarification regarding the normalization of lanes by total protein concentration.
• Fig 1C. Please provide an explanation for the higher order bands in the western blot. The western blot using anti-FLAG antibodies shows non-specific bands. Alternative tags or antibodies or detection methods may be used, for example, GFP tag and in-gel fluorescence can be used to check the expression.
• Figure 1D and 1E, the error bars are high. Suggest checking the data and providing the mathematical expressions used to calculate relative yields.
• Figure 2D and E, the error bars are high, access to the raw data behind the graphs may aid interpretation. An explanation for the choice of temperatures 33 C and 37 C would be helpful. Is there any relation between the choice of temperature and the Tm of the protein? The protein is directly being treated at high temperature, similar experiments with cell-based assays would be helpful to understand the effect of the Hero proteins on the stability of Fluc. Would it be possible to report the mathematical expressions used to calculate “Remaining Fluc activity”. Recommend indicating n if these activities are calculated per mg of the protein. Please explain if the reduction in activity is due to loss of protein or loss of luminescence activity from each molecule of the protein.
• Figure S1, access to the raw data would be helpful to understand the signal to noise ratio for activity.
• Figure 2 and 3 show similar experiments with wild type and mutants, it may be possible to combine the figures (for example, to avoid the redundancy in Figure 2C and 3A).
• Figure 3D and G, access to the raw data would be helpful to interpret the signal and noise ratio especially given the low values.
• Figure 4, Can some further discussion be provided for the reason for higher residual activity for SM and DM than wild type? Tm experiments during stress conditions (heat shock and freeze thaw cycles) may be helpful to define the stability of Fluc and Fluc mutants.
• Figure 5: Suggest including an explanation for choosing Proteinase K -among other proteases- for these experiments.
• The residual activity is different in Figure 4 and 5, which could be due to different stress conditions. Please include some discussion about possible explanations.
• In section “Hero proteins protect Fluc activity better in cis than in trans”, ‘When the molarity of recombinant GST, Hero9, and Hero11 proteins was increased by 10-fold...’ does molarity refer to the concentration of protein ?
• In the first paragraph of the discussion, “physical shield that prevents collisions of molecules leading to denaturation” and “maintaining the proper folding” is mentioned. Is it the hypothesis for the mechanism behind the stability provided by Hero proteins? Can further discussion on this be provided, along with a relevant reference.
• In the discussion section, it is mentioned that “Hero may be reminiscent of polyethylene glycol (PEG)”. Please provide further explanation for why hero proteins are correlated with PEG in this fragment.
• A discussion on why specific Hero proteins may be better for specific target proteins may be helpful.
• In the second paragraph, of the Discussion “Hero protein can behave differently depending on the client protein and condition” and “important to test multiple Hero proteins to identify one that best protects the protein of interest” are mentioned. Suggest adding further discussion of these points, for example around any alternatives or computational predictions or simulations to test individual Hero proteins for specific client proteins.

On 2022-07-13 12:08:26, user Pietro Parisse wrote:

A revised version of the manuscript has been published in Nanomedicine NMB https://www.sciencedirect.c...

On 2022-07-12 20:32:14, user Xing Jian wrote:

It is not clear to me which “case” patient sample from the Coriell Institute was used, and where the whole-exome sequencing data was obtained. These information should be presented in the published version.

On 2022-07-12 15:56:36, user Sara Paver wrote:

This is a neat study - I look forward to reading it in more detail. I think your introduction might benefit from incorporating the work of Salazar and colleagues (https://sfamjournals.online...

On 2022-07-10 17:36:37, user Ashraya Ravikumar wrote:

Summary:

In this paper, the authors address the important question of how Aminoacyl-tRNA synthetases (AARSs) have evolved. A key attribute of AARSs is that they have specialized to transfer specific amino acids to their cognate tRNAs, with minimal cross-reactivity. Although there are two major classes of AARSs (Class I and II), they focus specifically on Class I AARSs (since they could not perform a stable phylogenetic analysis on Class II). To this end, they have employed structure based sequence alignment of HUP domains of different Class I AARSs, based on which they built phylogenetic trees and performed ancestral sequence reconstruction. They make interesting, but counterintuitive, observations on the evolutionary trajectory of AARSs in comparison to the timeline of emergence of amino acids themselves. Specifically, they note that AARSs which charge amino acids that emerged later in time appear as early branches in the phylogenetic tree and vice versa. They also observe that one of the AARS ancestor (Anc-all-minus) had a wide substrate binding pocket that did not confer amino acid sidechain selectivity, but rather selected for L-configuration ɑ-amino acids. Based on these results, the authors propose a new model of evolution of Class I AARSs called generalist-maintaining (GM), where the early ancestor with non-specific/generalist activity is maintained and as amino acids emerged later in time, became starting point for the evolution of specialized AARSs. Overall, the paper is concise and self-sufficient. The conclusions drawn by the authors are significant and well supported by data. There are a few minor points that we want to bring to the attention of authors, which could improve the manuscript further.

Minor points:

The authors discuss one set of ancestral reconstruction throughout the paper. Were there any alternatives generated by the software used? If yes, on what basis was this particular reconstruction chosen? Perhaps, if there are alternatives, the authors could build the ancestry based on them and see if it yields similar results. If it does, it could make the conclusions from this paper more robust.<br /> The authors mention in a single sentence in Methods - “Ancestral states were inferred using codeml from the PAML package”. Since this is one of the most important steps in this work, some explanation about how this was done and any parameter choices or tuning can be useful.<br /> On Page 7, they say “The anticodon binding domains are thought to have emerged later, in agreement with our analysis, which indicated that the anticodon domains of Class I AARSs relate to at least three separate evolutionary emergences (Table S1)”. The three emergences of anticodon domains is not clear from Table S1. If we are to go by the different H groups according to ECOD, there are only two in the anticodon binding domain column in Table S1. Some clarity on this will be helpful<br /> Since the authors frequently compare the specialization of AARSs with the emergence of amino acids, a schematic showing the order of appearance of amino acids will be more illustrative than making the readers refer to multiple papers. Potentially using real amino acids in Figure 4 would be more clarifying than A,B,C,D,E?<br /> The phylogenetic trees shown in Figure S4 are key to this work. The authors could make the Raxml tree (since this was used for ancestral reconstruction) a main figure to guide the reader about this important part of the paper.<br /> The expansions of many abbreviations used throughout the paper haven’t been given (for example - HUP, HIGH, ECOD)<br /> The importance of outgroups in ancestral reconstruction is remarked upon twice “due to the lack of a suitable outgroup, making ancestor reconstruction intractable.”... “Using Clade 2 as an outgroup, we succeed in the construction” - but would benefit from a sentence or two (and reference) explaining this necessity to readers from non-phylogenetic backgrounds.<br /> The discussion of the structural/functional characteristics of the inferred ancestors could be expanded. For example: “Further, the inferred Anc-All-minus pocket bears no hallmarks of a Val activating enzyme,” it is unclear which hallmarks you are referring to and what is different in the ancestor. Predicted AlphaFold2 structures of the ancestors might help here, especially aligned with substrate bound/docked structures of extant AARSs.

Ashraya Ravikumar and James Fraser (UCSF)

James Fraser had a long scientific relationship and personal friendship with the late Dan Tawfik and objectivity (which is always something in the eye of the beholder) may be particularly impacted here.

On 2022-07-10 01:25:17, user Charles Warden wrote:

Hi,

Thank you very much for posting this preprint.

I think this provides a number of useful references and comparisons.

I also have a few comments:

1) While rMATS and MISO are popular programs, I don't think they are necessarily my preferred starting point for splicing analysis.

For example, I might consider the following as starting points for splicing analysis:

a) QoRTS + JunctionSeq (an extension of DEXSeq with a separate dispersion estimate for junctions versus exons)<br /> b) Custom analysis of the tab-delimited splice junction counts (SJ.out.tab) from STAR.

Perhaps b) is too open-ended. However, if you labeled the junctions like genes or transcripts, you could calculate Count-Per-Million (CPM) values and perhaps use traditional differential expression methods (DESeq2, edgeR, limma-voom, etc.).

Unlike a), there would not really be specialized splicing visualization for b) without additional coding.

However, both of these strategies do not fit the description of "alternative splicing events (ASEs) are conceptualized as binary choices". Instead, each junction is treated like a feature.

So, I can see how that can complicate comparisons. Nevertheless, limma-voom and DESeq2 are used as part of this package. If I look at the ASE-methods.R code and the supplemental methods, I think the main difference is the "Treatment:Isoform" interaction variable.

In other words, if you had 2 groups with 3 replicates and 2 junctions as part of splicing event, am I understanding the that the difference between SpliceWiz and what I would have otherwise expected is that you now have 12 count measurements (2 junctions) instead of 6 count measurements (1 junction)?

So, if I am understanding everything, I am not sure if you have comments with respect to why a strategy more like JunctionSeq (or what I might write with custom code for individual junctions) is not preferable and not provided as an option within SpliceWiz?

2) I thought Figure S4 was interesting and useful.

However, the retained intron counts are quite different, as also mentioned in the text: "Of the 15,188 extra retained introns annotated by SpliceWiz, 15,144 (99.7%) of these would have been excluded due to this extra criterion."

Identifying additional differential intron retention events might be helpful, but I also wonder if perhaps this also corresponds to additional false positives. For example, I am also not sure how often there might be pre-mRNA and/or pseudogenes where no introns are spliced (not a single retained intron event). For example, if there are confounding factors not sufficiently modeled in simulated data, then I would expect estimates of accuracy could be considerably different than in actual data.

3) As a minor point, there is a typo in the description for Figure 5: "Heirarachical" instead of "Hierarchical".

Thanks Again,<br /> Charles

On 2022-07-09 18:08:54, user Brian Stevenson wrote:

Please note that observation of punctate patterns of DNA in Borrelia burgdorferi has been published before. Similar patterning was also observed in relapsing fever Borrelia. The pattern observed here should not have been a "surprise" (line 100).

References that should be cited are:

Jutras et al., 2012, EbfC (YbaB) is a new type of bacterial nucleoid-associated protein and a global regulator of gene expression in the Lyme disease spirochete, J. Bacteriol 194:3395-3406,<br /> pubmed.ncbi.nlm.nih.gov/225...

Kitten and Barbour, 1992, <br /> The relapsing fever agent Borrelia hermsii has multiple copies of its chromosome and linear plasmids, Genetics 132:311-324, <br /> pubmed.ncbi.nlm.nih.gov/142...<br /> (this paper is currently cited for a different reason)

On 2022-07-09 16:30:13, user K.R. Caspar wrote:

You might want to consider this paper, which previously discussed and dismissed the idea of self-domestication in social bathyergids:

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

On 2022-07-08 10:20:41, user Kees Jalink wrote:

Please note that this work has in the mean time been published in largely unaltered form in Scientific Reports, 2021 Oct 20;11(1):20711; DOI: 10.1038/s41598-021-00098-9.

For clarity, we also share the reviewer comments and our responses to that with you:

Reviewer Comments:

Reviewer 1<br /> This is a very elaborate and interesting study proposing a dynamic genetic screen based on FRET-FLIM and which allows for a more refined understanding of the impact of gene knockouts on cellular signaling and metabolic processes compared to the simple cell viability and colony formation readouts widely used in the past (and also currently). The authors used an innovative FRET-FLIM sensor (created by them) expressed in stable cell lines to monitor changes in the levels of cyclic AMP by modulating phosphodiesterase-induced breakdown via treatment with silencing RNA (siRNA) oligonucleotides.

I only have a few comments regarding the text of the article, as follows.

1. It is very difficult to understand from the abstract the purpose of the paper, at least from the standpoint of the general FRET enthusiast with no specific knowledge regarding the biological application described. (i) Specifically, in the second paragraph, the agonist of what receptor are the authors referring to? What is the connection between the” 22 different phosphodiesterases (PDEs)” and the baseline levels of cAMP. Is that what the sentence refers to? It is hard to guess from the current sentence structure.<br /> (ii) The authors used “HeLa cells stably expressing our FRET-FLIM sensor.” Precisely what sensor does “our” stand for? (iii) The rest of the paragraph does not seem much easier either, especially given the numerous undefined acronyms. All these questions are fully addressed in the body of the paper, though not in the abstract.<br /> The last 75% of the abstract has been completely rephrased to address hopefully all of the reviewers concerns. Changed part is indicated with track-changes.

2. On page 3, the authors state: “FLIM is a robust and inherently quantitative method for FRET detection which requires no additional calibrations or correction parameters.” That is not entirely correct, for a couple of reasons: (i) FLIM generally requires separate knowledge of the donor lifetime in the absence of FRET. <br /> The reviewer is right. We condensed the text so much that we cut some corners. We have now rephrased that claim, and mentioned that the donor lifetime is a necessary calibration. See page 3.

(ii) One has to fully separate the donor emission from acceptor emission, which is usually done achieved band-pass filters (it is also done, though not very often, using spectral resolution). This is not unlike what is done in intensity-based measurements, in which, at least one is attempting to unmix the donor and acceptor signals or at least apply some post-measurement corrections for bleed through (caused by spectral overlap between donor and acceptor emission). The fact that FLIM researchers often choose to ignore this kind of corrections may not be interpreted, in my view, as an advantage of FLIM. Please consider adjusting the text.<br /> We could not agree more! In particular in view of our own contributions to obtain truly quantitative sensitized emission FRET (van Rheenen et al, BJ 2004), we are keenly aware of the dangers of spectral overlap. That is the reason that in this study we used our Epac-SH189 FRET sensor which has dark (i.e. non-emitting; Y145W mutation; Klarenbeek et al, PLoSOne 2015 ) acceptors. Given the high QY of the donor, the lifetime of this sensor has ignorable contribution of acceptor emission even if a large spectral window is selected. We did not attempt unmixing approaches, because our pilots had indicated near-identical lifetimes when the emission was taken just from the part of the spectrum that is exclusively occupied by mTurquoise. This information is presented towards the end of the introduction on page 5, in the paragraph where we present a more specific outlook to the contents. It has been rephrased in part for better emphasis.

1. The results shown in Figures 6 and 7 are fascinating. To let the reader more easily follow the story, could the authors insert “agonist” or “antagonist” as necessary within the following sentences? “We first stimulated HeLa cells with 40 nM isoproterenol which caused a rapid rise in cAMP levels and subsequently added propranolol at 60 nM concentration which caused a sharp decline following the stimulation. Finally, 25 µM forskolin…”<br /> We have adapted the text (page 14) and legend of figure legends (page 15) according to these suggestions.

Once again, in my evaluation, this is an excellent, detailed, and rich in information study worthy of publication in a high caliber journal.

Reviewer 2<br /> The manuscript submitted by Harkes et al. on the topic of FRET-FLIM HCS with siRNA screen to monitor dynamically the change of cAMP concentration using a FRET biosensor is well written with interesting results and shows the high potential of this method. In order to increase its impact and for clarification I have few queries:<br /> [1] I found the introduction of interest but previous work in the scope of HCS FLIM is missing. I suggest to add references of several groups working in this direction (French, Tramier, Esposito...).<br /> In our introduction we sought to emphasize work that is geared towards screening of fast dynamic changes in lifetime in living cells, which implies imaging with very high photon fluxes and with methods that do not waste photons unnecessarily, so as to avoid unnecessary cell damage. We agree with the reviewer that this does not acknowledge much of the work of those who contributed to high-content and high-speed FLIM imaging, in particular from pioneers like Drs French, Tramier, Esposito, but also e.g. Gerritsen, Ameer-Beg and others. We have now rewritten that part (page 4) and added 4 references to just a few of the very relevant contributions.

[2] For lifetime analysis, authors have used biexponential fit with two fixed lifetimes 3.4 and 0.6 ns with a final determination of mean lifetime using the different preexpo factors. I'm not sure that this approach is the more appropriate. First, what means these two fixed lifetimes? is it pertinent with the biosensor under study? this is not really discussed in the manuscript.<br /> Second, if finally you use a mean parameter for the concentration curve fitting, why not using a mean analysis such as the mean arrival time? This parameter is now directly calculated in the FALCON version and seems pertinent because HyD have very low noise. From my point of view, this will increase the sensitivity and the speed of the measurement. In any case, this has to be discussed.<br /> In fact, we had given this quite a bit of thought but for brevity, it did not make it to the final draft. In brief, we performed 2-component fits because these fitted much better than single-component fits. The lifetimes of 3.4 and 0.6 ns were selected because these were the dominant components in large numbers of global (i.e. whole-image) fits. We expected a dominant component of 3.4 ns, which is that of the Epac sensor in its low-FRET configuration. The low lifetime, 0.6 ns, is in fact significantly below the resting-state lifetime of the cells (minimally 1.9 ns in some cells) but it is the second dominant Tau in the two component fits and the phasor analysis also indicates a intercept below 1 ns. Our data were thus collected and exported (data reduction) for those two components, enabling us to analyze both effects on long as well as on short lifetime contributions upon PDE knockdown.<br /> In preparing figures for the manuscript, we noted no clear advantage of separately presenting data of both lifetimes and their amplitudes, so we decided to extract the weighted mean lifetime. This has now been described more completely in the Methods section (page 6). For the reviewer, we also note that we are not fond of using the FALCONs mean photon arrival time because 1st, unlike the fitting, it proved to be quite sensitive to environmental (background) light, and 2nd, there is a small bug in our version of the software which sometimes causes erroneously high lifetimes when recalculating old data with the ‘mean photon arrival time’ option. 3rd, this method has the same number of free fit parameters as a single component fit. We experienced that the result with this method had less pixel to pixel variation compared to a single component fit.

[3] When showing screen results, only fitting parameters are presented in figures. Is it too difficult to present few curves in which differences can be shown? This will increase the understanding of the reader before to present the statistics.<br /> We thank the reviewer for this excellent suggestion. Two panels have now been added to the boxplot in Fig. 4

[4] From my point of view, details regarding how to add drugs in multi-well plate has to be presented. Is it pipeting ? and in this case it does not really make High Content automated approach. Moreover, how is managed the focus since probably you loose it during pipeting... or is it more automated device that you use in the context of multiwell plate. In addition, how is selected the FOV? how to manage the human choice? This has to be detailled and discussed.<br /> We have extended the text in Materials and Methods to cover all of these aspects, see page 6, 7. In brief, in this study we present only data from studies where stimulus addition and mixing where done manually, although we have also implemented automated addition of stimuli (3 channels). For the protocol involving rapid sequential agonist-antagonist stimulation we found that, at least with our equipment, automated addition of stimuli depended too much on diffusion, and therefore results were more variable than with manual mixing. For keeping in focus, we routinely used the Leica hardware focusing option, AFC.

One last important issue: in revising our manuscript, we noted that the reported sequences for siRNAs used for PDE8A accidentally had become mixed up. We now corrected those entries in Supplementary Table S1. This correction does not affect any of the experiments, microscopy data or interpretations whatsoever, it solely affects one row in that table. <br /> We expect that with those changes, we have adequately addressed all issues raised by the reviewers. We feel that the manuscript has significantly improved in the review process and we are indebted to both reviewers for their time and thoughtful comments.

Sincerely, on behalf of all authors, <br /> Kees Jalink

On 2022-07-07 16:03:17, user Kathrin Liszt wrote:

Hi,<br /> in your methods at "Affinity based Cas9-Mediated Enrichment" you describe to collect the supernatant which must have around 740 µL volume that includes your DNA sample that need to be washed then with the Ampure XP beads. How much beads did you add for that wash. How did you do the wash with the Ampure XP beads?<br /> Regards,<br /> Kathrin

On 2022-07-07 10:16:54, user Prof. T. K. Wood wrote:

Congratulations. Never was any credible evidence that anti-phage systems like toxin/antitoxin and CBASS systems, etc. kill cells; just wild claims without evidence. Note the the first TA system found to inhibit phage by transcription shutoff should be cited (Hok/Sok, https://journals.asm.org/do... ) since it was discovered 25 years before ToxIN.

On 2022-07-06 17:55:48, user Iratxe Puebla wrote:

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Ruchika Bajaj, Sree Rama Chaitanya Sridhara and Wei Chen. Review synthesized by Bianca Melo Trovò.

Genetic transcription happens through individual Transcription Factors (TFs) whose binding events can, in some systems, temporally correlate with the stochastic firing of transcriptional bursts. The determinant of bursting is however unclear, specially whether the DNA binding kinetics solely contributes to that. The study develops an imaging-based synthetic recruitment assay called CRISPRburst in order to measure the TFs impact on bursting kinetics. The authors find that the association of TFs with specific protein partners determines their bursting output, and train a model to predict the kinetic signatures of all human TFs.

Major comments

The manuscript reports that “the maximal intensity per transcription site (TS) is likely limited by physical constraints of the transcription machinery as a limited number of RNA polymerase molecules can be loaded per gene due to polymerase velocity and spacing”. It is recommended to describe how this limitation correlates with the value of active fraction, or could be part of further analysis of this functional data.

‘Functional characterization of TFs using an imaging-based synthetic recruitment assay’ section: “If the frequency and duration of active periods were solely defined by TF binding” [...] “TFs recruited via dCas9 would all exhibit similar active fractions”. This prediction appears to rely on the assumption that the binding rate is the same for all TFs, which is usually not the case.

‘Functional characterization of TFs using an imaging-based synthetic recruitment assay’ section: Given that the TFs that do not bind to the LTR also show high correlation, it is unclear how the correlation for the 6 factors that directly bind LTR justifies that dCas9 recruits TFs in a similar way to the physiological conditions. What is the explanation for the high correlation coefficient for the TFs that do not bind LTR? There is a question as to whether the dCas9 system represents the physiological conditions because the DNA binding kinetics for each TFs are distinct, and different from that for PYL1 binding to ABI1. It would be expected that those different DNA-binding kinetics also contribute to the frequency, duration, or intensity of bursting. Some clarification could be provided around this point.

‘Interactions with co-activators are more predictive of TF kinetic specificity than IDR features’ section “This model was unable to classify TFs into kinetic classes (Figure 3B, right), demonstrating that TF-cofactor interactions play a greater role in specifying kinetic function than IDR sequence content”: Given that TFs interact with cofactors through their transactivation domains, which are intrinsically disordered, why do the TF-cofactor interactions not lead to correlation between IDRs and the kinetic function? Could the protein-protein interactions besides IDR-cofactor (e.g. cofactor-cofactor interactions) play a role in the kinetic function? Do the cofactors cluster into the different kinetic function groups?

Minor comments

Introduction ‘differ in features typically used to classify TFs, such as DNA binding domain homology’: it may be worth making a mention in the introduction to what other binding partners TFs interact with.

First paragraph of results ‘CRISPRburst, an inducible dCas9-mediated recruitment platform to study transcription kinetics’: What is the binding strength of PYL1 to ABI1? How does that compare to the typical TF-DNA binding strength?

Figure 1C: “3) Live cells are imaged 16 h post-recruitment.” This is the end time point. Are there time-dependent data available?

Figure 1 F, G: The error bars are high. Can further information be provided in the legend on how these error bars were calculated (biological vs technical replicates)?

Figure 1, ‘An average of 220 cells were analyzed per TF’ Does this imply that 220 transcription sites were scored? Considering each imaged cell has single integration of the reporter gene?

‘In total, the LTR-MS2 cell line stably expresses 1) the LTR-MBS reporter gene’: Is there information on where in the genome the reporter gene is integrated? And does it impact the transcription bursts? (considering the role of (epi)genetics in the transcriptional outcome as rightly reinforced by the data related to Fig.4).

Functional characterization of TFs using an imaging-based synthetic recruitment assay: Please provide a description for the Krüppel associated box.

“Upon recruitment, 28 TFs generate an increase in reporter active fraction”. It would be helpful to provide further clarification on how the reporter active fraction is defined and how the criteria "ratio > 1.30" was determined. A mathematical equation may also aid the description.

‘0.64 to 3.04 for active fraction and 0.68 to 1.64 for intensity (Figure 1F-G, S1E) ‘: It may be helpful to divide the active fraction (0.64 to 3.04) into different categories, for example, 3.04 - 2.5, 2.5-2.0 etc. to check whether these categories are correlated to function.

Regarding intrinsically disordered regions (IDRs) in the Results section ‘Bursting kinetics define distinct TF classes’: Can further clarification be provided in the main text for the meaning of cumulatively longer IDRs.

“these findings suggest that while the biophysical properties of IDRs may tune the amplitude of TFs’ effects, they likely do not solely encode TF kinetic specialization”: does this include post-translational modifications? If so, are there any relevant examples or illustrations?

In the section ‘TF families exhibit broad kinetic diversity’ section, “the family-defining KRAB domain does not bind DNA but recruits cofactors, consistent with the idea that DNA binding domains provide little information on kinetic specialization (Figure S6B)”. It may be relevant to discuss potential solutions to these issues in the Discussion section.

Discussion section “Our study centered on the simple HIV promoter thus provides a robust conceptual framework to investigate more complex systems, e.g. how TFs synergize with one another, interact with core promoter motifs, or communicate to promoters from distal enhancers”: all the future directions mentioned here are very relevant and exciting. Could the discussion of these items be expanded e.g., how do developmental cues drive TF kinetics or bursts?

Methods section: Are there any anomalies observed in the subcellular localization of the TFs when tagged with PYL1 or under the ABA treatment?

Comments on reporting

The manuscript reports a partial least-squares multivariate regression model in which a predictive weight to each possible interactor was assigned. Can further description and a related equation be provided for this model?

Fig. 3: Can further context be provided for the choice of SEM instead of SD which may provide a better representation of data variability?

On 2022-07-05 22:26:50, user Prof. T. K. Wood wrote:

Would be great to see the supplemental information. Also, would be interesting to quantify the 100S fraction over time.

On 2022-07-04 10:14:10, user 吴豪达 wrote:

Thanks for the development of bioRxiv!

The paper was published in Frontiers in Immunology (https://urldefense.proofpoi... ).

On 2022-07-02 08:02:09, user Aram P. wrote:

Hi all. Thanks for Your work. I want to mention two possible errors in Your paper.<br /> 1. The paper say that Late Armenia cluster do have a partial continuity with Early Armenia cluster. The estimated proportion is 50%. That's looks good. But the other 50% can't be from Steppe as You state. Because Late Armenia cluster is shifted to Near East. So it's more likely that the extra 50% is from South not from Steppe <br /> 2. The other issue I see is the place of modern Kurdish samples on the PCA. Near Lybians.

Thanks in advance for Your attention.

On 2022-06-07 03:30:39, user Sean Dugaw wrote:

I share Davidski’s criticisms regarding the geographic labels. In addition, I would add that Egypt is not ever considered part of the Levant. I understand why you have grouped the populations of Egypt and the Levant together, however a label which includes both terms seems called for.

On 2022-05-19 13:21:33, user R. Rocca wrote:

Authors, the labels in the ENA Browser completely mismatch the BAM files. For example, the row with the label ID R11563 has a link to 1554.bam. Most, if not all the labels are wrong. If they are used for future research, there will no doubt create a lot of erroneous results.

On 2022-05-18 22:14:12, user Davidski wrote:

Hello authors,

Unfortunately, there are some serious problems with the geographic concepts in your preprint:

• your Steppe region includes a large swath of Eastern Europe that is mostly forest and forest steppe. Only about a third or less of this region is actually a steppe (the Pontic-Caspian steppe). Calling this region Eastern Europe would be more useful and in tune with geographic conventions.

• what you call Eastern Europe is not generally, by itself, known as Eastern Europe, especially since the fall of the Iron Curtain. That is, Czechia, Hungary and Slovakia (often along with Poland) are nowadays more commonly described as East Central Europe.

• what you call SE Central Europe is actually much of the Balkans, and thus straight up Southeastern Europe.

Honestly, calling Bulgaria Central Europe, while, at the same time, calling Czechia (inc. Bohemia) Eastern Europe just doesn't look right.

On 2022-07-01 14:28:11, user Andrea wrote:

Interesting hypothesis.<br /> But since I noticed that the changes occur close to the binding site (see https://aquaria.app/SARS-Co... ): Have you considered that the increased PPAR mimicry could be of functional importance? -- A quick Google search brought e.g.: https://www.frontiersin.org...

On 2022-07-01 10:23:35, user Iratxe Puebla wrote:

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Ruchika Bajaj, Bobby Hollingsworth, Gary McDowell and Michael Robicheaux. Review synthesized by Michael Robicheaux.

The preprint manuscript by Trendel et al., “Translational Activity Controls Ribophagic Flux and Turnover of Distinct Ribosome Pools”, presents a dataset that examines the lifecycle of human ribosomes, and their constituent subunit proteins, in response to translational inhibition using proteomics and cryo-EM approaches. The study focuses on the fate of 80S monosomes, which are shown to be inactive and to form a dynamic pool separate from active polysomes and nascent ribosomal subunits.

General comments

• The manuscript is well-written and organized, and the methodology is thorough and detailed.

• The effort to validate mass spectrometry quantitative measurements, particularly the peptide sum normalization (PSN), is commendable. The description of total sum normalization and its weaknesses in this methodology is well articulated. This work will be useful for others working on similar problems in quantitative mass spectrometry.

• The described pulse-SILAC methods are quite successful at monitoring protein stability in response to different perturbations; however, the statements in favor of ribosome subunit decay through ribophagy/selective autophagy require further support. Since ribosome component decay can be due to a variety of additional pathways (see cited reference #17, An et al., 2020), it may be necessary to soften the conclusions regarding ribophagy. Additional pulse-SILAC experiments in cell lines that lack key autophagy components (e.g., ATG12/FIP200 KO cells) could be considered to directly test the ribophagy model.

• There are questions as to whether the cryo-EM processing supports the conclusions stated in the manuscript. Specific comments regarding this are provided below. In addition, additional processing detail in the flowcharts presented within the supplemental data would be helpful to better understand processing choices (e.g., D classes that move forward for additional analysis/classification/refinement).

• It would be relevant to discuss how the proteomic half-life measurements compare to those published by Li et al. 2021 (Mol Cell), which use a different method (cyclohexamide chase).

• The manuscript reports significant differences in the half-lives of the 40S/60S ribosomal subunits vs 80S/polysome fractions (Fig 1E), and states that these make up separate ribosomal pools without free exchange. However, it should be considered as an alternative that the decay rate of assembled ribosomes could be much less than the unassembled group so that the pool of free components becomes gradually depleted. In this case, exchange could still occur with a decreasing rate as the pool of free ribosome proteins are degraded faster than assembled ones. It would also be relevant to discuss the possibility that nascent 40S and 60S subunits form 80S monosomes in an alternative “life cycle” pathway.

Specific comments and suggestions

• In paragraph 1 of the Introduction, please specify the context of “serum withdrawal” as the stimulus for idle 80S ribosome accumulation. Is this from cell culture or some other system?

• In paragraph 1 of the Introduction, the sentence, “Degradation of ribosomal complexes, especially under nutrient-poor conditions, is mediated by ribophagy, a selective form of autophagy [14–17]” could be more nuanced as it does not describe other non-autophagic ribosomal degradation pathways, such as those described in cited reference #17 (An et al., 2020).

• In the “A Highly Robust Normalization Procedure...” Results section, the manuscript states that the intensive ribosomal purification methods lead to high variability in the mass spectrometry measurements. Based on this, have alternative methodologies been considered for ribosome purification?

• In panel E of Figure 1, the color scheme makes the data difficult to differentiate, could also consider separate figures for the large and small subunit datasets.

• In the “Protein Half-Lives in Polysome Profiling Fractions...'' Results section, “On average ribosomal proteins of the small subunit had 3-fold longer half-lives within the 80S fraction compared to the 40S fraction (p=5.2E-8, Wilcoxon ranksum test), whereas large subunit proteins had 4.6-fold longer half-lives within the 60S fraction compared to the 80S fraction (p=1.0E-10).” Are the “60S” and “80S” fractions mixed up at the end of the sentence?

-In the “The Monosome Fraction Predominantly Contains Inactive 80S Ribosomes...” Results section, the manuscript reports, based on their cryo-EM data (Fig. 2), that 80S monosomal complexes are idle and distinct from polysomal 80S complexes. This conclusion of a single ribosome state would need supportive evidence. From the initial particle stack (>1 million) that yielded <60k high-resolution particles after classification: were there other low-resolution class averages or heterogeneous particles that may represent actively translating ribosomes? Conclusions about ribosome activity from less than 5% of the total pool of ribosomes could be due to the conformational plasticity of translating ribosomes. In a different paper (Brown et al., eLife. 2018), several structures/states of the ribosome come out of a smaller dataset. Furthermore, a structure of comparable resolution from the polysome fraction appears necessary to support the conclusion that the 80s monosome complex is functionally distinct. The same comparative data is recommended for conclusions drawn from the cryo-EM structural analysis of arsenite treated 80S particles (Fig .S6).

• In the “The Monosome Fraction Predominantly Contains Inactive 80S Ribosomes..” Results section, this section introduces ribosomal P-stalk proteins, their plasticity and role in active ribosomes, which are concepts that could be included in the Introduction section of the manuscript.

• In Figure 2, it is unclear from the figure legend if the 80s monosome density in panel B is from the low-salt treated preparation in panel A or from a different prep.

• In the “Inhibition of Translation Produces Inactive 80S Ribosomes...” Results section, recommend revising the text to reframe the conclusion as "supports our model".

In the “An Increased Pool of Inactive 80S Ribosomes..” Results section, recommend toning down the claims about decay rates which may require control experiments in cells lacking key autophagy proteins, such as ATG12.

• In the “An Increased Pool of Inactive 80S Ribosomes...” Results section, consider reframing the conclusions from the previous study (Trendel et al. 2019) to indicate that ribophagy is the predominant mechanism of ribosomal protein turnover in response to arsenite treatment. The prior study did not examine ribosomes treated with arsenite when autophagy was blocked. Additional quantitative tests for flux into lysosomes (Lyso-IP, Ribo-Keima shift assay) should be considered to support that ribophagic flux, specifically, eliminates proteins from ribosomal pools. Based on this comment, the inclusion of ribophagy in Fig. 5 and the statements in the final paragraph of the Discussion may require revision.

• In the “An Increased Pool of Inactive 80S Ribosomes...” Results section, the manuscript describes proteomic data in response to increasing concentrations of arsenite. The effects of these treatments on polysome profiles could be useful future experiments.

• In the “Constrained Conformational Plasticity...” Results section, there are questions about this analysis due to the small size of the final particle stack for both proteins. An alternative analysis pipeline is to mix the particles from both datasets for the simultaneous analysis of all pooled particles, from which the number of particles in a given state can be quantified.

• In the “Distinct Pools of Ribosomal Subunits...” Discussion section, the discussion of inactive 80S complexes potentially re-entering the polysome “assembly line” is quite interesting to consider in terms of its dynamics and follow-up experiments that would test this theory (including subcellular localization).

In the “Distinct Pools of Ribosomal Subunits...” Discussion section, the manuscript posits that the degradation of newly synthesized ribosomal subunits is not energetically favorable; however, it should be considered that intrinsically disordered proteins, such as transcription factors, can be produced and quickly degraded in oscillatory patterns (e.g. see https://pubmed.ncbi.nlm.nih.... A quality control pathway that would eliminate immature or nascent ribosomal subunits is conceivable.

• Consider depositing all EM data in EMPIAR and relevant structures in EMDB/PDB, and depositing the mass spectrometry raw data in ProteomeXchange or similar database. A data availability statement could be added with relevant accession links and IDs.

• It would be helpful to build a tool to browse protein-level half lives and re-analyze raw data (e.g., tidy script depositing in Github or similar).

On 2022-07-01 02:26:15, user Sciency wrote:

This is spectacular work. I have a feeling I'll be referencing it a bunch. I have two small questions.<br /> - You used the Reef Life Survey Database, and for each sampling location, one of the parameters was whether that location is within 10km of a reef? I'm not yet familiar with the database, but it sounds like it covers more than reefs and reef-adjacent areas? (assuming anything further than 10km wouldn't count as reef-adjacent)

• I'm not super-sure the phrasing of "hypothesize that MPAs counterbalance (mediate) the effect of coral reefs, human density, and thermal stress on species richness" is fully clear. In the diagram, I see that a coral reef increases species richness by 54% if the reef is within an MPA, and by only 15% if the reef is not in an MPA. So, to me, this would not be MPAs "mediating" the effects of the reefs. Maybe enhancing or supporting? Mediating usually has the connotation of making a negative effect less negative or a positive effect less positive, no?

Thanks for the paper. Beautiful work.