On 2022-01-11 10:23:10, user Frank Kirchhoff wrote:

We appreciate the interest of Nader and colleagues in<br /> our study. However, we feel that they do not reflect our results entirely<br /> correctly. As described in our paper, we ran molecular dynamics analyses for the theoretical description of the interaction. These resulted in a distance between 3.6 and 4.2 angstroms between R403 in Spike and E37 in ACE2 and a strong interaction. Both the distance range and the strength agree with recent studies (Lim et al., Scientific Reports 2020; Williams and Zhan, J. Phys. Chem. B. 2021; Laurini et al., ACS Nano, 2021). The importance of this interaction was confirmed by mutating E37 (Fig. 3). Bix and colleagues propose that the various cell types used in our study may not express integrins. This might be<br /> the case. However, it implies that the up to 50-fold enhancing effect of the T403R change on Spike-mediated infection is not dependent on integrins. Altogether, our experimental data clearly support an important role of T403R in ACE2 interaction. Our results do not exclude the possibility that this alteration may also affect the interaction with integrins. Currently, however, this is just speculative. We would be happy to provide the authors with Spike constructs allowing experimental challenging of their hypothesis.

Best regards

Fabian Zech, Christoph Jung, Konstantin M.J. Sparrer and Frank Kirchhoff

On 2020-01-20 23:30:43, user Dylan Glubb wrote:

I'm a bit surprised that the largest endometrial cancer GWAS study to date (Nat Commun. 2018 Aug 9;9(1):3166. doi: 10.1038/s41467-018-05427-7) wasn't included in the analysis.

On 2020-03-14 17:28:55, user Michael Cohen wrote:

The in-frame CGGCGGGCACGT<br /> insertion in the Covid-19 genome adds RRAR to the boundary between the <br /> S1 and S2 regions of the spike protein sequence. This insertion is <br /> followed by a serine, which thus introduces RARS that functions as a proteolytic cleavage<br /> site in some group II coronaviruses (see footnote 25 in Rota et al., 2003). <br /> If functional, addition of such a cleavage site may enhance the entry of<br /> Covid-19 viruses into cells (Kam et al., 2009).

Rota et al., 2003 https://science.sciencemag....

Kam et al., 2009 https://journals.plos.org/p...

On 2016-02-02 06:02:24, user Erin P. Price wrote:

Hi Jason and team,<br /> Congratulations on the nice paper, and for getting NASP out there!<br /> Thanks too for comparing with SPANDx. It's great to see a comparison of SNP-calling pipelines from microbial genomes.<br /> Just a couple of quick corrections to make re: some of your SPANDx comments:<br /> - As of v2.7 (which was used in your study), SPANDx can be run without a resource manager. You just need to change this preference to NONE in the scheduler.config file.<br /> - SPANDx outputs more than .nex files. The .nex files are just the input files for phylogenetic analysis for PAUP/PHYLIP/RAxML.<br /> Well done again on what looks to be a good pipeline.<br /> Regards,<br /> Erin

On 2021-05-11 04:30:37, user Patrick Chambers wrote:

"Binding affinity between RH5 and basigin is weaker than the reported values for RBD and basigin (approximately 1uM for RH5 (22, 23) compared to 185 nM for RBD (9)) indicating that this assay should be sufficiently sensitive to detect the RBD-basigin interaction."<br /> These are dissociation constants. Affinity constant is the inverse, i.e., 1/1microM = 1/1000nM = 0.001 < 0.0054 (1/185nM). So far, so good.<br /> However, other sources indicate that CD147 affinity for RBD is 100 times weaker than that for ACE2, which ranges from 5-20 nM (dissociation) or 0.002 to 0.0005 for affinity. <br /> No evidence for basigin/CD147 as a direct SARS-CoV-2 spike binding receptor (11 Jan 2021)<br /> https://doi.org/10.1038/s41...<br /> So, RH5 appears to be an inadequate positive control.<br /> CD147/Spike RBD (micrograms/mL)<br /> https://www.rndsystems.com/...<br /> ACE2/Spike RBD (nanograms/mL)<br /> https://www.rndsystems.com/...

On 2018-03-29 03:50:47, user Matt Therkelsen wrote:

How did you measure ice thickness in the tomograms? Thanks!

On 2025-03-21 13:57:33, user Rubayet Elahi wrote:

Now published in EMBO Reports https://www.embopress.org/doi/full/10.1038/s44319-025-00420-w

On 2015-07-11 20:29:52, user Gene Regulation Info wrote:

Very nice article. I can add one of the reasons why submitting preprints to arXiv is still more popular in the physics community but not in biology. Many physical journals require authors to submit already pre-formatted manuscripts. These manuscripts look very similar to their finally published versions (two columns, figures incorporated in the text, etc). Thus the manuscripts are already in a very readable format and authors do not need to do additional work to reformat them to submit to a preprint server. Biological manuscripts are much less readable and printer friendly (single column, sometimes double space formatting, and the most inconvenient thing for the reader -- figures are not integrated in the text). I believe it would help if biological journals change their submission format to more human readable. With current technologies it does not really make any difference for the journal production, but it does make a huge difference for self-archiving.

On 2017-05-16 00:37:16, user Maintenance Renegade wrote:

Uh...so what he's saying is that IQ difference correlates with observable patterns of or differences in neurological architecture? Am I getting this right? I don't understand most of this because I don't have any background or education in neuroscience.

On 2016-03-29 13:43:23, user Jonathan Jacobs wrote:

WOHOO! Hot damn! This is #madeofawesome.

On 2019-05-24 18:41:01, user Edgar Gonzalez-Kozlova wrote:

Very interesting!, I wonder if the definition of species for bacteria needs to be readjusted ?

On 2022-05-09 15:34:34, user Jenny wrote:

Very interesting pre-print! One thing I did not see was any discussion of 10X's recommendations on filtering the gtf file to only keep certain gene types: https://support.10xgenomics.... They did extensive modification of the gtf file in their build steps (https://support.10xgenomics...{files.refdata_GRCh38.version}). Are all the improvements in your pre-print on top of these modifications or as compared back to the original gtf file from Ensembl/Gencode?

On 2021-03-04 19:11:51, user James Gorley, PhD wrote:

It's perhaps not surprising the authors chose to release their paper as a preprint based on their conclusions. I'm wondering if this study took into account whether the preprint and associated publication were altered significantly? In many cases the content, data, and analysis of the preprint might be substantially different from the final published piece because of multiple revisions. Should this count as a confounder?

On 2024-01-10 09:51:21, user Todd Vision wrote:

The peer-reviewed version is available at Journal of Evolutionary Biology 36, 1525-1538, https://doi.org/10.1111/jeb.14224

On 2017-04-04 07:18:22, user Martin Smith wrote:

You should consider benchmarking on data from synthetic RNAs in addition to the human reference transcriptome. Check out www.sequin.xyz, it has free Illumina data you can access for such ventures.

On 2025-10-24 14:54:31, user Ameet Shetty wrote:

is there a supplementary data file with this article?

On 2016-08-11 05:48:19, user Bill wrote:

How is p = .017 a modest effect of location on mortality (see Table 2). Indeed, contrary to the authors claim, living at Chimp Haven or Southwest has a poor prognosis for survival.

Also with 6 variables (really only 3) and a N > 700, why adjust alpha to p < .01? Seems like the author had more than adequate power to avoid Type I error

On 2017-10-29 00:31:45, user Sergio Farias wrote:

I'm glad that scientist are studying these types of drugs. We'll see what further studies will show the research community.

On 2018-10-09 18:45:29, user Benjamin Harris wrote:

The github link does not work. It gives a 404 Error, repo doesn't exist

On 2023-05-17 05:15:51, user Michael Forrest wrote:

I've reported this relationship form before. <br /> See Figure 11 (on Page 37) of my bioRxiv preprint: https://www.biorxiv.org/con...

On 2018-02-22 08:33:02, user Guy Eyal wrote:

To run and download the human cortical neurons models in modelDB, use the password: eyal2018.<br /> Your comments and suggestions are very welcome.

On 2020-04-29 14:54:33, user Isaac Martínez wrote:

It's a very interesting approach, I'm curious to know if the code is available on github.

On 2021-02-06 18:28:13, user Uli Wellner wrote:

very interesting concept. how do you rule out the opposite theory ie duodenal / ampullary carcinoma invading the pancreas and switching to pancreatobiliary type ?

On 2021-03-14 04:26:53, user Aripuanã Watanabe wrote:

Dear Dr. Lopez-Rincon

I hope you are well

I performed an alignment test (MEGA software) with some of the primers described. There were few sequences analyzed, obtained from the GISAID database. Some sequences from Europe, USA and South America deposited in the first half of 2020.

There was a complete alignment between the primers and these sequences. Apparently they were not of the 3 variants described (B.1.1.7, B.1.351 and P.1).

I would like to ask if this type and analysis I performed is correct or if you would have any suggestions for another type of analysis?

I ask because I am considering buying these primers for testing.

Thank you very much

Best Regards,

Aripuana Watanabe

On 2020-02-28 19:47:54, user Timothy M Freeman wrote:

This article has been accepted for publication by Genome Research in the March 2020 edition and will become available online in March 2020 at http://www.genome.org/cgi/doi/10.1101/gr.255349.119.

On 2020-03-31 11:07:28, user James Kirchner wrote:

Interesting study, and a valiant effort at a hard problem.

it is worth noting, though, that the conclusion that peer review doesn't add much to the quality of published papers is vulnerable to two well-known biases (selection bias and survivor bias) which are barely mentioned.

The study can only consider papers that were posted as pre-prints, which might be better than the typical submissions that go to journals (selection bias). In other words, journals may receive lots of weak papers that are rejected during peer review, but unless those weak papers are also posted to pre-print servers, they will never be evaluated in this study. Anecdotal discussions I've had with editors suggest that a large fraction of journal submissions are "dead on arrival", implying that the editorial/peer review process is, in fact, greatly improving journal quality by keeping some of the dreck out.

In addition, the paired-sample comparisons can only consider papers that were posted as pre-prints, and were ultimately published somewhere (survivor bias). Those that were culled out by the peer-review process (or that were never submitted at all) will not appear in these comparisons.

These biases are unavoidable, and while they don't invalidate a study of this kind, they do limit the inferences that should be drawn. In particular, the inference that peer review doesn't improve the quality of science is inconsistent with our everyday experience that merely the expectation that our work will be subject to peer review forces us to be more careful than we might have been otherwise. How many of us have had to tell a grad student, "look, I know you *believe* that, but you the data don't *prove* it so it won't get through peer review"?

The checklist approach used in this study has obvious limitations as well, which the authors briefly discuss but have been largely lost in the subsequent media commentary. This study focuses on whether certain formal items have been documented, but not on the essential question of whether a paper makes sense: do the data justify the conclusions? I have reviewed dozens of papers that suffered from gross methodological, logical, and even mathematical errors. These were detected and corrected through the peer-review process, but none of these issues would appear in the checklists used in this study.

Should preprints "be considered valid scientific contributions"? They are contributions (and potentially valuable ones), but their validity has been vetted only by the authors themselves, who have obvious conflicts of interest. By contrast, peer-reviewed papers have at least been vetted by (in the best case) independent editors and reviewers as well.

Neither process is perfect. But there is a reason by bioRxiv now displays a banner saying<br /> ______________________ <br /> "BioRxiv is receiving many new papers on coronavirus SARS-CoV-2. A reminder: these are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information." <br /> ______________________ <br /> There are crucial differences between the peer-reviewed literature and the blogosphere, whether or not those differences can be detected by studies like this one.

On 2022-11-02 18:28:17, user Paul Robbins wrote:

I have some concerns about the attribution of many of the gene alterations that were proposed to have resulted from RNA editing. When the changes were evaluated in our tumor samples it appears that multiple changes resulted from incorrect mapping of the sequence reads. For example, the PHF2 variants, which occurred at the first 2 bases of intron 16, corresponded to the first 2 bases of exon 17. These are visible in IGV when soft clipping is turned off, and the first 5 bases that were proposed to be intronic also matched the first 5 bases of exon 17, which is indicative of the problem of mapping RNA-seq reads properly. This was not true of all of the changes, as the G>A changes in NEIL1 mapped to exonic sequences, but we also have transcriptome data from matched normal tissue samples from 1 of our patients where expression of these variants was observed. This is another issue that has not to my knowledge been adequately addressed, as normal tissues express ADAR, indicating that these changes may not be tumor-specific. Finally, there is the issue of the high error rate of reverse transcription, which may not occur equally at all sites. This is a problem that is not easy to resolve, unless these changes are directly probed, which would seem like a good way of potentially validating these changes.

On 2021-03-25 02:01:48, user Charles Warden wrote:

Hi,

Thank you for posting this preprint.

If I look at the provided FPKM values for GSE113957, they are all small for NM_021804 (ACE2|ACEH|-|Xp22.2|protein-coding).

I noticed a reference in the paper for downloading counts from GREIN, but I believe the values for ENSG00000130234 also tended to be small (mostly 0 and 1, with a maximum of 20).

If I look in the GTEx portal, there are some tissues with low ACE2 expression, but others that I think would end up with higher counts than reported in this study? I think that is also similar to the ACE2 FPKM expression in various tissues from NCBI Gene with double digit FPKM values in some tissues and essentially 0 expression in other tissues.

So, these are my questions:

a) Can you reproduce your finding in other datasets / tissues?

b) Is it possible that there might be some sort of confounding and/or not-optimal technical components?

For example, I thought the scale for the Chen et al. 2020 paper using GTEx data looked noticeably different for ACE2?

With the help of another individual, I think the Muus et al. 2021 paper might also provide references to some data that might be relevant (with both scRNA-Seq and bulk RNA-Seq).

Best Wishes,<br /> Charles

On 2021-10-24 02:45:04, user Ashlyn Blevins wrote:

This is an exciting study!!! I am very much looking forward to reading more of your work and about airDNA. This could be a great new way of biomonitoring. I am not very familiar with ecology field work and would like to know more about how you took your samples. The orange rings in figure 1 look quite large. Did you just walk around with your sampling apparatus, use a drone, etc? I also noticed that two samples of mole rat DNA were excluded due to cross contamination of tubing from prior use. Was the tubing in this experiment cleaned or changed between each sampling area or would the filters you used catch everything? Thanks for any insight - I am really looking forward to learning more! :-)

On 2021-10-25 08:35:03, user CDSL JHSPH wrote:

Dear Dr. Clare et. al.,

It is my pleasure to review your paper! Thank you for contributing to global terrestrial biomonitoring and ecological analysis. Using airDNA as a biomonitoring tool under natural settings show great potential. The decline in biodiversity throughout the world urges the development of non-invasive techniques that could offer rapid and accurate results. Your study successfully reveals the power of airDNA sampling at distance, and from my perspective, the new technique would truly revolutionize terrestrial biodiversity surveys.

My question would be, do you anticipate any difference of conducting airDNA sampling between zoo setting and real natural environment? As in the zoo, animals are gathering together and each kind of them have their own enclosures. While in the natural environment, animals will move more freely as there will be no space limit. Also, we know that some endangered species actually live under extreme conditions. For example, the weather might be extreme. Will the study results be influenced by extreme weathers?

Moreover, I am wondering if we need more intermediate steps to shift from zoo setting to actual field as zoo setting can evoke less complex behaviors and is still involved by human. Since we are aiming at non-invasive technique, what future steps could be done?

All in all, I think the paper makes significant contribution to the biomonitoring field, the methodology is quite convincing. I am just curious about the actual application of airDNA in the wild and the future plans regarding this study.

Thank you for your work and I am looking forward to future outcomes on this topic!

On 2022-12-16 16:49:43, user yuangaowang wrote:

From the author: the eccDNA methods has been published in Nature Protocol, more description and troubleshooting are included, please see:<br /> https://www.nature.com/arti...

On 2018-05-31 15:41:10, user Juan Mata wrote:

A great new method from the Lilley lab to investigate the RNAinteractome: cheaper, easier and more comprehensive (no need to purify polyadenylated RNA)

On 2020-02-14 09:50:30, user Thomas Höfer wrote:

This is a previous version of a paper that has appeared in eLIFE:<br /> https://elifesciences.org/a....

On 2020-06-23 07:26:55, user A scientist wrote:

This study has glaring methodological issues which should be raised during peer-review. It is unsurprising that the EPIC array detects more differential methylation since these results were not corrected for multiple comparisons. It is also unclear why an array-based method is still being used for comparison rather than reduced representation bisulfite sequencing (RRBS) or other sequencing techniques when these are widely available.

On 2024-11-18 13:40:44, user Min-Ho Nam wrote:

This manuscript has been published in Advanced Science.<br /> DOI: 10.1002/advs.202403245

On 2025-03-17 18:50:48, user Catherine Douds wrote:

Thank you so much for sharing this work, it’s a really great paper. My biggest criticism is that the methods section does not clearly define how the Ribo-seq and RNA-seq were aligned. Did you align with a different transcript or for each dataset? Or was there one transcript all data were aligned to? I’d love to see a more detailed methods section in the final version.

On 2019-11-26 00:18:45, user José H. Oliveira wrote:

Figure 4 is missing.

On 2023-01-26 09:44:19, user Juri Rappsilber wrote:

We discussed your manuscript in our lab and enjoyed its balanced tone and inquisition of an important aspect of the search. Thank you for sharing your search parameters for xiSEARCH and as a developer of that tool I second your choice of parameters completely. However, to answer the question “STY or not STY?” your current search experiment seems insufficient to us for reasons detailed below, and we would like to suggest a number of controls and changes. When allowing either KSTY or KGVL you disregard an important element of link assignment based on the often-incomplete MS data. Indeed, the MS2 data often do not unambiguously assign the linked residue by neighbouring backbone fragments and the likely link site is chosen based on the predefined chemistry of the linker. Fig 2G is a nice example of this. Depending on what you define as chemical preference of the crosslinker, you either report S or G. In all those cases, any residue is equally likely based on the MS data and your linker definition determines the reported site. If STY are disregarded and the abundant GVL are pre-defined, obviously, they will be reported. To put this to an extreme, why do you not disregard K as a link possibility and only allow GVL? We would like to suggest a couple of changes to your experiment:<br /> (1) To determine if STY are targets of NHS crosslinkers, only evaluate sites that were reported unambiguously based on MS data, i.e, with neighbouring fragments. Maybe run a completely indiscriminate crosslinker and look for enriched amino acids. Open modification search analyses linear peptides which differ from crosslinked peptides in not having a second reaction step in a spatially confined setting. The first reaction of a crosslinker brings it into the protein, the second reaction takes place with a spatially proximal residue of appropriate chemistry. The second step can only be assessed by looking into crosslinked peptides.<br /> (2) To determine if the data support STY as well or as poorly as the chemically meaningless GVL, define a linker KSTYGVL to let STY and GVL compete. Note that xiSEARCH currently will take the more C-terminal residue in any ambiguity window and thus have some random component in link site assignment. If an ambiguity window contains a K near STY, xiSEARCH has a preference setting for K, though. Consequently, if a STY is reported in favour of a nearby K, there must be supporting MS2 evidence in the spectrum for this report. <br /> (3) Do the GVL linker experiment (no KSTY). I would expect you to find many crosslinked peptides, nearly all the same peptide pairs as with K or KSTY. <br /> (4) Do KYST versus KGVL and match the YST with the GVL peptides where the are the identical peptide matches and compare the score. Plot a 2D scatter plot with score(YST) versus score(GVL). My expectation is that you will have many equal scoring peptides (ambiguity window based on insufficient MS2 data) and many cases where the score(YST) will be larger. <br /> As a final note, link site assignment is currently an open issue of crosslinking, and you are right to point this out. Curiously, speaking to Jan Kosinski as a modeller, he did not care about link sites at all. Our own work on using photo-crosslinking (where link site assignment is even more problematic because of the wider reactivity) did not show difference in protein structure model quality using link sites with a +/- 5 residue scatter (PMID: 26385339). So far, it seems to be unclear if link site precision offers any structural value. I hope it does… albeit it might not, considering the length of NHS crosslinkers and the flexibility of proteins. From a mass spectrometry side, I would, nonetheless, prefer to see link sites reported with a measure of precision.

On 2016-11-26 13:53:07, user Sharif A. Mukul wrote:

Published article:

Pavel, M.A.A., Mukul, S.A., Uddin, M.B., Harada, K., Khan, M.A.S.A. 2016. Effect of stand characteristics on tree species richness in and around a conservation area of Bangladesh. Journal of Mountain Science, 13: 1085-109, doi: 10.1007/s11629-015-3501-2.

On 2015-08-27 12:48:09, user Karl Broman wrote:

The revised version just adds the supplemental figure and table.

On 2021-01-13 01:19:51, user Ross Wang wrote:

This preprint has been updated as a newer version and has been published at J. Am. Chem. Soc. A link between the preprint and the paper will be forthcoming

On 2020-06-09 08:00:41, user Nikolas Haass wrote:

Now published: Pigment Cell Melanoma Res, 2020 Jun 8. Online ahead of print. PMID: 32511882; doi: 10.1111/pcmr.12903

On 2023-11-23 09:17:21, user Julio Rodríguez wrote:

Peer-reviewed version published here: https://advances.in/psychol...

On 2019-10-02 00:00:24, user Bradley wrote:

Interesting article. I have a few questions: Does CCN not form a C-mannosylation as with other TSP1 domains, and if so could this contribute to the differences from the canonical TSP1 fold observed? How certain can you be that the refold has not shuffled the disulfides? If I were reviewing this paper, that's what I would want to know as far as validation. <br /> Any comment on why MAD was performed over MR? Maybe mention in methods?

On 2021-01-19 22:24:48, user Tauras wrote:

Very interesting work and a cool idea! However, I don't see much detail about the PRSs used. Knowing the trait heritability, variance explained by the PRS, and seeing a leave-k-out cross-validation would be very helpful in evaluating the the results. For example, maybe TB doesn't display a signal because there is little genetic variance or the PRS explains a small proportion of the genetic variance.

Furthermore, there's no discussion about population turnover which we know plays a major role in changing allele frequencies at the time scales that are being considered. Immigration into Europe during the early Neolithic may explain these effects, especially in how some PRSs appear discontinuous (the pre-neolithic line doesn't meet the post-neolithic line). Changes during the neolithic/modern times may also be driven by migration. I'd be very hesitant to say "adaptation" when it can be more parsimoniously explained by gene flow. Maybe it's possible to explicitly model ancestry proportions as well as time in the linear models?

On 2016-09-17 19:36:02, user Alan Kay wrote:

A revised and peer reviewed version of this paper is available at http://journal.frontiersin....

On 2021-08-19 14:35:18, user Meng Wang wrote:

We have recently reported that the Tn5-based epigenomic profiling methods, especially Stacc-seq and CoBATCH, are prone to open chromatin bias (https://www.biorxiv.org/content/10.1101/2021.07.09.451758v1). Rather than directly address this bias issue, the authors of Stacc-seq argued in this preprint that FC-I normalization (normalizing by input/IgG control) was better than FC-C (normalizing by background) for Stacc-seq etc. data analysis. Based on this, they claimed that our results had “a major analysis issue”. However, the truth is that we had already used both FC-I and FC-C normalization methods and both showed clear open chromatin bias for Stacc-seq and CoBATCH. The fact that our analyses demonstrating that CUT&Tag (5% FPR) showed much lower FPR than Stacc-seq (30% FPR) or CoBATCH (50% FPR) indicated that the high FPRs were not due to “artificially enhanced the relative enrichment of potential open chromatin bias”, but an intrinsic problem of Stacc-seq and CoBATCH. In our opinion, the preprint has several problems, which are detailed below.

1. The preprint ignored the fact that we had already used both FC-I and FC-C normalization methods. The authors assumed that we only used FC-C for Stacc-seq etc. (Fig. 1A in Liu et al.). However, in fact we used both FC-C and FC-I in our analyses. In Fig. 1c, d and Fig. S2 of our manuscript (Wang et al.), methods labeled with “with IgG” were results from FC-I normalization, and methods without such label were results from FC-C normalization. Importantly, results from both normalizing methods showed clear open chromatin bias for Stacc-seq and CoBATCH (Fig. 1c,d and Fig. S2 in Wang et al.).

2. The results of global H3K27me3 enrichment at the Polycomb targets in this preprint (Fig. 1C) was contradictory to their claim that using FC-C would cause “complete loss or dramatic reduction of enrichment at true targets for datasets generated by Tn5-based methods”. Fig. 1C of this preprint showed a clear H3K27me3 enrichment around the TSS of Polycomb targets compared to adjacent regions when using FC-C. The difference between results from FC-I and FC-C is caused by the y-scale. The fold change is a relative measurement, so the y-scale of different normalization methods is not directly comparable. If they set the y-scale of FC-C to 0~2, the enrichment pattern would be highly similar to that using FC-I.

3. The genome browser snapshots of several loci in a large scale (low resolution) could not demonstrate that the results from FC-I and FC-C normalization are globally different. This preprint provided several example loci (Fig. 1B and Fig. 2 in Liu et al.) to show that using FC-C would cause “complete loss or dramatic reduction of enrichment at true targets for datasets generated by Tn5-based methods”. However, showing browser view of very large regions are misleading as the resolution is too low. For genome browser display, the look of the signal track patterns depends on y-scale, x-scale and windowing and smoothing function. When viewing a very large region, the signals are sampled and aggregated by genome browser and are not the raw signals. Thus, the patterns may not reflect the real situation. Indeed, when zoomed-in to check these regions, we found the peak patterns from FC-I and FC-C normalization are highly similar. In addition, examples from several loci could not reflect the global pattern. The global enrichment shown in Fig. 1C of this preprint did not support their conclusion, as discussed in point 2.

In summary, our original analysis has already included the normalization method suggested by the authors of this preprint. Results from both normalization methods supported that Stacc-seq and CoBATCH had high open chromatin bias. In fact, the results from this preprint also support our conclusions. In Fig. 2 of this preprint, regardless whether FC-I, FC-C or RPKM were used, the discrete peaks from Stacc-seq etc. were more similar to ATAC-seq peaks, but were totally different from ChIP-seq peaks.

Meng Wang and Yi Zhang<br /> Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, Massachusetts 02115, USA

On 2017-06-27 00:00:17, user Perseus Smith wrote:

Love it!

On 2020-02-23 11:17:23, user YIGUO ZHANG wrote:

This paper has been published in Antioxidants (Basel), 9 (1) 2019 Dec 19, entitled "<br /> Unification of Opposites Between Two Antioxidant Transcription Factors Nrf1 and Nrf2 in Mediating Distinct Cellular Responses to the Endoplasmic Reticulum Stressor Tunicamycin"<br /> PMID: 31861550 DOI: 10.3390/antiox9010004

On 2020-07-03 11:46:18, user Nick Pullen wrote:

It looks to me like Fig 15 a,b are the same as c,d

On 2024-10-19 19:13:36, user sam wrote:

It would be good to see how altered the phosphoproteome is after FACS. Thats a long time for the cells and a lot can change in phosphopatterns in a matter of seconds .

On 2016-07-13 06:03:33, user Chris Carter wrote:

A related paper using the same set of autism genes showed that many are related to barrier function (blood/brain, skin, intestinal) and to control of the respiratory cilia that sweep particles from the airways.

The Barrier, airway particle clearance, placental and detoxification functions of Autism susceptibility genes <br /> Neurochemistry Interational 99,42-51, 2016 http://www.ncbi.nlm.nih.gov...

On 2020-04-01 06:15:19, user Artem Nedoluzhko wrote:

Thank you for the interesting preprint, In my opinion, several words about sampling (i.g. about Second Fram Expedition) should be added. It will make the manuscript more informative. In another case, Figure 2C looks unclear for people, who don't know about the heroic history of Norwegian Arctic Expeditions. Good luck!

On 2016-01-13 20:43:25, user Natalie Davidson wrote:

In Figure 5b, you state that the green dots represent the removal of the UMIs. This would indicate that removing known PCR duplicates make the log-linear fit of TPM vs Molarity worse. Should the colors be switched in the plot?

On 2015-05-27 10:57:33, user kamounlab wrote:

This paper was cited in a Note added in proof in Suledo et al. New Phytologist 2015 http://onlinelibrary.wiley....

"Since this article was accepted, a commentary by Wu et al. (2015) was submitted for publication indicating that, despite findings that auto-active mutants of tomato NRC1 are able to induce programmed cell death in N. benthamiana (Gabriëls et al., 2007) and N. tabacum (this paper), mining of the N. benthamiana genome revealed no orthologue of NRC1. However, an NRC gene family has been identified in this plant species and at least one member of this family, NRC3, is involved in Pto-mediated cell death."

On 2016-05-28 15:00:29, user Marc RobinsonRechavi wrote:

The link given in the PDF to the availability (https://www.cs.bham.ac.uk/ szh/software.xhtml) does not seem to work. (Posted following tweet https://twitter.com/antifre... )

On 2019-04-17 13:17:39, user Daniel Louis Kiss wrote:

The final peer-reviewed manuscript is available through FEBS Letter's free access program.<br /> https://twitter.com/FEBS_Le...

On 2022-08-13 03:49:12, user Keshav wrote:

Please also see <br /> https://genomemedicine.biom...

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 2021-12-23 08:11:40, user Gabriel Netsari wrote:

Hi, <br /> Both rs2032640 and rs546062461 are absent from IAM samples, therefore it is impossible to know if they are E-L19 or E-M81. Furthermore, IAM.05 is B-L1388 according to YSEQ Cladefinder.

Cordially.

On 2022-10-14 22:25:37, user charlesqzhou wrote:

Dear bioRxiv members, please note that the this preprint is now published in Interface Focus at https://royalsocietypublish...

On 2022-12-16 18:15:51, user Marco Gabrielli wrote:

Comment #2 by taylor.reiter:<br /> "k-mer signature differences "<br /> Would you be willing to briefly describe the size of k-mer used for this? I could imagine very different results for k-mer size of 4 (tetranucleotide abundances) vs. 21 or 31 (which are generally genus or species specific)

Response:<br /> K-mer-based eukaryotic identification tools use usually small k-mer abundances (generally 5-6, depending on the tool). Larger sizes would provide information too geni-specific probably confounding a demarkation between the two superkingdom. We will clarify this in the revised manuscript.

On 2021-11-24 04:45:24, user Tianyu Liu wrote:

Hi, do you have plan to upload your codes for Polarbear to github? Thanks a lot.

On 2020-06-12 15:05:28, user Sinai Immunol Review Project wrote:

Main Findings<br /> The manuscript describes a versatile mouse model of SARS-CoV-2 based on adeno-associated virus (AAV)-mediated expression of human ACE2 (hACE2). The AAV-hACE2 model supported productive SARS-CoV-2 infection in the lung of mice, inducing leukocyte recruitment and mounting antibody response (IgG) against the spike (S) protein. Serum obtained from AAV-hACE2 infected mice at 7 and 14 days post-infection with SARS-CoV-2 showed neutralization activity in vitro. RNA sequencing analysis-data reveal up-regulation of cytokines and interferon-stimulated genes (ISGs) signatures, compared to control mice. Neither type I nor type II, nor type III interferons were up-regulated, indicating a resemblance with published data from the lungs of patients infected with SARS-CoV-2 (Blanco-Melo et al., 2020). To further explore the role of type I interferon signaling in SARS-CoV-2 infection, interferon-alpha receptor deficient (IFNAR-/-) and interferon regulatory transcription factors (IRF) 3 and 7 double deficient (IRF3/7-/-) mice were transduced with AAV-hACE2 and infected with SARS-CoV-2. The analysis demonstrated a decrease in interferon stimulated gene (ISG) response in the IFNAR-/- and IRF3/7-/- mice relative to wild type mice. However, there was a similar kinetics of viral clearance in type I interferon deficient and wild type mice, suggesting that viral SARS-Cov-2 replication is resistant to interferon signaling. The authors concluded the AAV-hACE2 infected SARS-CoV-2 model largely recapitulated the transcriptome changes observed in the lungs of SARS-CoV-2 infected patients.

Limitations<br /> The results shown by the authors suggest that the hACE2-AAV mouse model of SARS-CoV-2 infection generates symptoms that partly resembled human infection symptoms. However, cellular and tissue tropism of SARS-CoV-2 in infected mice or different susceptibility to the infection based on gender or age were not explored. Additionally, because no deaths of infected mice were reported, the model does not fully reflect the pathogenesis of SARS-CoV-2 as in other transgenic models. Future studies with this model should interrogate SARS-CoV-2 infection based on mouse strain, gender, and age.

Significance<br /> The hACE2-AAV mouse model partially simulated the pathology of COVID-19 with a focused and robust lung SARS-CoV-2 infection and pathology. The hACE2-AAV mouse model will allow to test patient-SARS-CoV-2 derived-viruses in mouse strains with diverse genetic backgrounds and genetic alterations, as a platform to study pathological mechanisms and therapeutic strategies to combat COVID-19 disease.

Reviewed by Martinez-Delgado, G as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.

On 2021-03-05 03:09:59, user ShengwenCalvinLiPhD wrote:

Large scale field production of organic farming, duplicated with 30 years of field studies, demonstrates the Effective control of a viral disease with a high transmission rate through selective predation

On 2024-09-24 16:44:20, user The Fehr Lab wrote:

These authors have done a fabulous job at creating new macrodomain inhibitors, which is extremely appreciated. However, having a major conclusion and an implication that Mac1 inhibitors are not antiviral based solely on negative data is misleading. We have published that a macrodomain inhibitor can inhibit virus replication (PMID: 38592023) and will have another story that will soon be available in BioRxiv that describes even more Mac1 targeting compounds that inhibit virus replication. There are some notable problems with the compound described here that could explain its lack of antiviral activity that should be taken into account. Again, I think the novel chemistry identified in this paper is exceptional, but more cautiousness should be taken before making broad claims that this is not a good drug target. Based on genetic data Mac1 and another recent paper on bioRxiv ( https://doi.org/10.1101/2024.08.08.606661 ), it appears that Mac1 is a suitable target for antiviral development, and we are continuing to work to see that dream come to fruition.

On 2025-04-18 17:51:29, user Franck Dumetz wrote:

I really like your work and I'm very interested to use your trained model on the organisms I work with. I did not do the experiment to measure reactivity on my organisms. I was wondering if the method use to determined the base reactivity mattered? I'm not set yet wether I'll be using DMS-MaP-seq or SHAPE-MaP-seq or if I will use ONT.

On 2020-05-02 21:24:55, user Bill Denney wrote:

The article linked to at the end of the sentence here does not refer to niclosamide. Is the a typographical error or are you drawing a more subtle comparison to parvinium? "Although niclosamide suffers a pharmacokinetic flaw of low adsorption, further development or drug formulation could enable an effective delivery of this drug to the target tissue"

On 2024-07-21 00:09:37, user Meet Zandawala wrote:

Manuscript title: TRP? regulates lipid metabolism through Dh44 neuroendocrine cells

Summary: This manuscript from Youngseok Lee lab examines the role of TRP gamma channel in regulating metabolic physiology. Specifically, it focuses on the regulation of lipid metabolism via DH44 neuroendocrine cells. It is a follow-up on the work from the same lab where they showcased the importance of TRP gamma in DH44 cells in regulating post-ingestive food selection (Dhakal et al 2022: https://doi.org/10.7554/eLife.56726 ). Overall, this work adds to the growing body of work on DH44 neuroendocrine cells which appear to be crucial internal metabolic sensors. We have a few major comments and suggestions on the preprint which could help clarify the mechanisms by which TRP gamma regulates lipid metabolism.

1. TRP gamma mutants exhibit higher TAG and protein levels compared to controls. Inhibition of DH44 neurons using Kir2.1 recaptiulates the phenotype of increased TAG however protein levels are unaffected. Since these manipulations are not restricted to the adult stage, it is not possible to rule out developmental defects. It would be beneficial to also include the fly weight for these manipulations to see if their size is altered by these manipulations. Also, is there any impact on developmental timing?
2. The experiments implicating the role of AMPK in DH44 neurons are quite interesting. However, the link between TRP gamma activation, AMPK and DH44 signaling is missing. How is DH44 release altered when TRP gamma is knocked down specifically in DH44 neurons?
3. The author rescue the increased TAG levels in TRP gamma mutants by driving UAS-TRP expression using DH44-GAL4. However, they also able to rescue the phenotype by expressing UAS-TRP in DH44-R2 expressing cells. As far as we are aware, DH44 and DH44-R2 represent two independent populations. This raises some questions. What is the identity of the DH44-R2 cells which normally express TRP? What is the importance of having TRP gamma in both the source (DH44 cells) and the target (DH44-R2 cells) to regulate lipid homeostasis? Wouldn’t modulation of DH44 release alone be sufficient to regulate lipid homeostasis?
4. DH44 is released as a hormone from both the PI neurons in the brain and endocrine cells in the VNC ( https://link.springer.com/article/10.1007/s00018-017-2682-y ). Neither this or the previous study on TRP gamma in DH44 neurons examined the presence or absence of TRP gamma in DH44 neurons the VNC. It is not clear if the DH44-GAL4 used in this study targets the DH44 neurons in the VNC.
5. General comment about structure: The manuscript could benefit if additional context was provided for some of the experiments. The experiments using metformin are interesting and a valuable addition. However, since the link between metformin and DH44 signaling was not explored, the rationale for conducting these experiments is not quite clear. Is the rescue of TAG levels with metformin in TRP gamma mutants DH44-dependent or is metformin directly acting on the fat body? Metformin treatment in DH44 > TRP RNAi flies can clarify this.
6. The manuscript would benefit from having a model which includes all the components in this inter-organ pathway (TRP gamma, DH44 neurons, gut etc).

Minor comment:<br /> 1. Stock numbers for fly strains have not been provided.

Signed by,<br /> Meet Zandawala <br /> Jayati Gera<br /> (Zandawala lab members)

On 2024-08-18 14:42:36, user David Ron wrote:

That mutations abolishing kinase activity (e.g., GCN2 Lys619Ala) also render RAF inhibitors unable to activate the ISR in cells is a powerful argument that GCN2's kinase activity links application of RAF inhibitors to ISR activation.<br /> That the same mutations also abolish GCN2's responsiveness to RAF inhibitors in vitro is less helpful - a dead donkey does not respond to the buggy whip, whether applies its back or to its head. <br /> In this vein, interpreting the inability of RAF inhibitors to activate GCN2 when the gatekeeper residue Met802 is mutated (Figure 9) is key to the conclusion that these agents exert their effect on GCN2 by engaging its kinase active site ATP binding pocket.<br /> I may have missed it, but is there evidence that the Met802 mutations retain reasonable kinase activity? The Met802Phe is not so helpful in this regard as it may lack 'headroom' for further activation.<br /> It might be helpful to measure ISR activation in GCN2? cells, reconstituted with GCN2 Met802 mutants in response to histidinol. Preservation of a response to histidinol in face of loss of responsiveness to RAF inhibitors would lend strong support to the authors conclusion.

On 2025-01-06 16:49:33, user Mark Gasser wrote:

Now published at Journal of Microbial Biotechnology (Wiley) https://doi.org/10.1111/1751-7915.70064

On 2020-09-13 05:14:59, user Jin Yu wrote:

Inchworm stepping of Myc-Max heterodimer protein diffusion along DNA, Biochemical and Biophysical Research Communications, 2020 https://doi.org/10.1016/j.b...

On 2017-08-23 15:05:42, user Dorothy Bishop wrote:

This is a great use of Biobank data. Very little is known about the impact of trisomy X, especially in adulthood, and the data presented here on fertility and menopause are important. My only question concerns whether there is information on whether any of these women were aware of the trisomy. The paper notes that some of the Turner syndrome cases had a prior diagnosis, but does not mention this for trisomy X. If not, do we know whether Biobank would be likely to have recorded this information? Thanks.

On 2018-05-15 14:13:21, user Matthew Figley wrote:

You might consider citing our work that ALS-linked mutations in PFN1 alter stress granule dynamics - http://www.jneurosci.org/co...

On 2021-11-24 21:56:04, user Luis Carretié wrote:

This paper has been accepted for publication in Cerebral Cortex. The final version includes several changes suggested during the review process that are not present in this BioRxiv (initial) version.

On 2018-12-16 16:42:36, user David Freedman wrote:

WHO PHEIC ended in November 2016

On 2021-04-14 16:19:46, user Christophe Leterrier wrote:

This preprint seems to be missing a Methods section that is referred to multiple times within the text, as well as supplementary tables. It would be good to upload a complete manuscript as a new version, and/or use the supplementary files uploading option. Thank you

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

This study focused on the role of TPP1 in degrading fibrillar ?-amyloid in microglia. In the course of a follow-up study, we were unable to reproduce the experiments showing differences in degradation of fibrillar ?-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 ?-amyloid fibrils or culturing of the microglia.

On 2018-04-30 19:29:22, user Elizabeth Rucks wrote:

Hi Kevin,<br /> We have fully characterized and successfully adapted the<br /> Inc-APEX2 approach to map the inclusion membrane protein interactome, and we<br /> are happy to see our work is gaining some recognition, and that others are<br /> finding utility in the system!

As we have been working on and publishing on this topic and<br /> discussing it in various formats since 2014, we have validated both the APEX2<br /> and BioID (BirA*) proximity labeling systems to help us answer questions about<br /> the chlamydial-host interactome. We have found pros and cons to both systems.<br /> Interestingly, we have encountered many of your same protein hits, including<br /> those that you label ‘high confidence proteins’, and we have discounted many of<br /> those protein hits as background. My best advice is to take the time to<br /> carefully go through the controls for these types of studies.

Good luck,

Lisa Rucks

lisa.rucks@unmc.edu

On 2023-08-22 15:13:28, user Davide Trotti wrote:

This paper has now been accepted and published in iScience. Here is the URL link to the manuscript<br /> https://www.cell.com/iscien...<br /> Thanks<br /> D

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... "https://pubmed.ncbi.nlm.nih.gov/35647923/)") 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 2021-02-24 06:19:18, user AHMAD KASSEM wrote:

My colleagues and I have recently chosen your paper for our journal club. This paper was excellent to illustrate the use of Mass spectrometry in biomedical research. I think the new TIFF method is promising and could lead to a breakthrough in the realm of single-cell proteomics. Your group has done an excellent job in the overall presentation of data; however, I have a couple of suggestions that might strengthen this paper. Firstly, fig 3. (d) presents the pair-wise correlation of the ten cells using the TIFF method, but it has no control. I suggest you could show the pair-wise correlation for the ten cells analyzed with the standard method. Secondly, all the bar-graphs and violin plots except for fig.5 (d) were not analyzed for statistical significance. The lack of statistical significance analysis made it trickier to interpret the data, so I would suggest adding them wherever possible. Finally, your group mentioned the importance of applying both sc proteomics and sc-RNA sequencing to get the optimal results. I believe that including other methods such as RNA-seq or even traditional biochemical methods (i.e., Western blot or Flow cytometry) to provide further evidence to the results obtained by the TIFF method could dramatically increase the creditability of your results.

On 2025-08-26 09:31:03, user Constant VINATIER wrote:

Feedbacks about your preprint : https://doi.org/10.1101/2025.08.02.668124

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

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

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

On 2016-02-23 02:24:06, user Adam Roddy wrote:

Unless I'm missing something what is termed chi in this paper is equivalent to what we more normally call ci/ca. It's unclear to me why there needs to be a different variable name; this only obfuscates the message.

Also, on line 284 is referenced "The ratio of xylem repiration [sic] to transpiration capacity..." What is "xylem respiration"? Are xylem not predominantly dead cells? Or are you referring to the parenchyma?

On 2022-05-22 20:08:31, user Garth Kong wrote:

Sorry for the confusion, CITE-Viz is up now!

On 2024-07-16 19:03:57, user Zehua Zeng wrote:

* Published Article: OmicVerse: a framework for bridging and deepening insights across bulk and single-cell sequencing

* Link: https://www.nature.com/articles/s41467-024-50194-3#Ack1

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

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

On 2025-10-30 16:29:52, user Maël Doré wrote:

This research is now published in Global Ecology and Biogeography. DOI: https://doi.org/10.1111/geb.70127 .

On 2017-10-28 16:48:02, user Lionel Christiaen wrote:

Student #7<br /> Prior to this paper, it was known that transcription factors (TFs) only stay bound for a few seconds before dissociating from DNA, and that low affinity binding sites are necessary for TFs to distinguish between binding sites with a similar sequence. The authors wanted to answer the question of how these brief TF contacts could allow for transcription from low affinity sites. They hypothesized that multiple low affinity sites could act together to “trap” TFs and create “microenvironments” with high TF concentration that would allow for transcription. To address this question, they used the svb locus in Drosophila which contains multiple distinct enhancers that have low affinity binding sites for Ubx. Techniques they used included: super-resolution confocal microscopy, FISH, immunofluorescence, and live imaging of specially prepped (~4x expanded) transgenic embryos.<br /> The authors first found that Ubx was localized to distinct regions of the nucleus that did not overlap with unrelated TFs, which suggested that Ubx is not localized by a general mechanism that limits the distribution of all TFs. They also showed that Ubx did no co-localize with repressive chromatin and it only partially overlapped with active transcription sites, providing evidence that Ubx has specificity in its localization and is not found at all active loci. Since these results were found in fixed embryos, the authors wanted to confirm it wasn’t just an artifact of the fixation process, so they performed live imaging of Ubx using a Halo tag and a fluorescent dye ligand, JF635. They found the same results, in which Ubx was localized to specific regions at high concentration, and they further showed that its localization was dependent on DNA binding by mutating the homeodomain. They next wanted to determine if the regions of high Ubx concentration co-localized with sites of active svb transcription using FISH. Indeed, they found that Ubx was enriched at sites of svb transcription, but it was not enriched at sites of active transcription driven by a synthetic enhancer, providing more evidence that its localization has specificity. They next wanted to determine if binding site affinity affected the localization of Ubx by either changing a low-affinity site to a high-affinity site, or by removing multiple low-affinity sites. They found that the switch to a high-affinity site led to decreased enrichment of Ubx microenvironments, and the removal of low-affinity sites resulted in active transcription only occurring in regions with high Ubx concentration. This inverse correlation between affinity and Ubx concentration led them to conclude that binding site affinity determines the enhancer’s response to local Ubx concentration. Lastly, they found that the Ubx co-factor, Hth, was co-enriched around active transcription sites, suggesting that high concentrations of TFs and their co-factors are required for transcription. The authors concluded that clustered binding sites for the same TF, cooperative interactions between TFs and their co-factors, and clustering of enhancers could all result in increased local concentration of TFs by acting as a “trap” to increase their time near low-affinity enhancer sites.

Technically innovative with their use of methods to expand embryos, as well as their use of Halo-tagged Ubx and the dye, JF635, for live imaging.

Major<br /> Fig. 2 A/B don’t seem to match up.

How did they quantify [Ubx] at svb loci in fig 3 f, h, j, l, n, p, r when they used lacZ reporters for the above images?<br /> Why use lacZ reporters rather than FISH of endogenous svb?<br /> There seems to be large variability in their results (i.e. enrichment of Ubx at TALEA driven enhancers of 0.02 ± 0.63), so how significant are any of these results?

Maybe check other Ubx regulated loci with FISH to see if the same concepts hold true.

On 2017-06-08 11:52:14, user Alexander J. Shackman wrote:

"Genetic correlations were also found for each of the factors—but, importantly, not with the general<br /> neuroticism factor—with obesity (anxiety/tension rg = 0.49" ---- missing neg sign

On 2019-08-17 19:10:13, user Hurley Li wrote:

Data leakage problem in your model!!!

The design of your adjacency matrix and the way you split the train/test set will cause a huge data leakage problem in your training, because your train / test set is created independently for gene_adj and gene_adj.transpose(copy=True), and therefore the edges from the test set in gene_adj is actually included in the training set of gene_adj.transpose(copy=True).

Same problem goes for the train / test set between gene-disease matrix and disease-gene matrix. The validation edges from gene-disease matrix are actually used for training in disease-gene matrix, and vise versa.

Could you please clarify?<br /> Thanks!

On 2021-06-03 13:52:31, user Chris wrote:

Abbreviating AUROC as AUC is awkward - AUPR is also an AUC metric (that has different characteristics to AUROC).

On 2023-03-04 04:52:18, user UTK - Journal Club 603 wrote:

Summary. <br /> The bacterium Bordetella bronchiseptica causes respiratory infections, atrophic rhinitis, and kennel cough. B. bronchiseptica is widely transmitted via respiratory droplets between animals. This study investigates the dynamics of how B. bronchiseptica induces an immune response and, more specifically, on how eosinophils may provide long-term immunity against Bordetlla spp. after B. bronchiseptica infection. Using RNA sequencing data, animal disease models, microscopy, and cytokine analysis, the report confirms that eosinophils play a larger role than previously thought in generating a more robust adaptive immune response.

Positive feedback. <br /> There are several good things about the paper. Eosinophils are an understudied and potentially misunderstood immune cell type, and the paper sheds light on their role in a bacterial infection (while it is typical to think of eosinophils to be important in worm infections). The use of mutated Bordetella btrs and RB50 strains was a significant strength of the paper. In comparison to the immune response in the murine models, they provided solid evidence collectively. In addition, the repetition of screening methods for the effects of the mutant on immune suppression provides stronger evidence of immunological variation. Using two strains of mice (Balb/c and B6 with two different mutants) is also a strength.

Major Concerns

The authors do not talk about the specificity of the GATA-1-deficient mice for depletion of eosinophils. It has previously shown that GATA-1 regulates basophil development and function of basophils (https://www.pnas.org/doi/10... "https://www.pnas.org/doi/10.1073/pnas.1311668110)"). Perhaps as a confirmatory experiment, the authors should perform eosinophil depletion with mAbs as was previously published (https://www.jacionline.org/... "https://www.jacionline.org/article/S0091-6749(05)04018-2/fulltext)"). This will allow to confirm that eosinophils are directly involved in the process of bacterial control and better establish causality.<br /> When looking at lymphocytes in the lung, authors do not discriminate between cells in the lung vasculature vs. lung parenchyma. This may be important to determine which cell population is actually in the lung and involved in bacterial control. How intravascular staining could be used to detect cells in the blood vs. tissue is described here: https://www.nature.com/arti... <br /> The authors need to add figure/panels on the gating strategies for detecting T cells and B cells, along with histograms for major panels. <br /> When determining if eosinophils are required to promote a TH17 microenvironment, Figure 7D shows a possible false positive - i.e., detection of IL17 in naive lungs. These tissues should not have IL-17.<br /> Measurement of immune responses are not numerically consistent between different panels. For example, Fig 34D states 200 million T cells to be detected which is likely impossible. Please check ALL numbers and make them correct.<br /> In most cases it would be useful to measure Ag-specific immune response. Is IFNg+ T cells detected specific to the bacteria?<br /> Side note: B. bronchiseptica rarely infects humans. It is a clinical concern in animals such as canines, felines, livestock, and mice. iBALT formation may correlate to tissue damage within the lungs of humans. I understand that the infection with B. bronchiseptica may provide resistance to B. pertussis, but vaccinations are already in place to provide resistance. Do these vaccines stimulate a similar initial response as RB50 and RB50?btrs?

Minor concerns

The paper did not give sufficient context for some of the employed models. Comparing BALB/c and C57BL/6 to the eosinophil-deficient EPX/MBP, for example, two mice models were used: BALB/c and eosinophil-deficient EPX/MBP. Prior to conducting study on eosinophils, I am unsure of the meanings of the acronyms, however I understand why they were used.<br /> Would experiments with infection and then antibiotic treatment be informative?<br /> Eosinophils have been shown to play a role in TB in mice, e.g., PMID: 34347010, 35905725. Perhaps this should be mentioned.<br /> The author should increase the resolution of the figures used in the paper, some axes labels are very tiny and impossible to read. The current state could lead to confusion or misinterpretation of the data provided.<br /> Throughout the paper, somewhat inappropriate language is employed. For instance, the term novel and the opening sentence of the abstract. Check if the author can also adjust the usage of these terms when describing findings. A less biased observation is the result.<br /> With a computer and printout, microscopy images proved tough to observe. For improved processing, the writers should raise the exposure of their photos. While discussing exposure, the writers should modify figures to make them more accessible to colorblind readers.<br /> The authors should quantify the relevance of the graphs' statistical significance. The values would aid the reader in comprehending which facts are significant.<br /> Some figures are difficult to comprehend because they contain too many or too few data points. Figure 5C is a case of insufficient specificity. The reader cannot grasp the data in the CD4+IL17+ graph. <br /> In many places conclusions are reached only by looking at mRNA levels. Can these be confirmed with ELISA?<br /> Statement that pathogens evolve to suppress immunity lacks evidence. Some pathogens may actually want inflammation for transmission, e.g., Mtb.<br /> Selection of differentially expressed genes should be corrected for false positive detection, e.g., using FDR (e.g., https://en.wikipedia.org/wi... "https://en.wikipedia.org/wiki/False_discovery_rate)") <br /> The author should add extra identifiers to Figure 6's figures. There are two D's on display. This makes it difficult to explain and read in the figure explanation. Figure 6's micrograph likewise lacks a visible or understandable scale bar. These photos may be difficult to decipher for the reader.<br /> For all the microscopy, the author should state that the images were taken with the same exposure.<br /> The authors should be more thorough in distinguishing that lungs are complex organs, with many results in different results where the organism tries to colonize.

On 2019-05-04 18:37:34, user L. T. Fang wrote:

Detailed documentation of the somatic mutation analysis can also be found at https://sites.google.com/vi.... That page is continuously maintained and updated to reflect the latest finding and version updates with regard to the somatic mutation "ground truth."<br /> Comments are welcome here.

On 2023-08-09 23:47:50, user Ashraya Ravikumar wrote:

Review of "Predicting Relative Populations of Protein Conformations without a Physics Engine Using AlphaFold2"

The development of machine learning algorithms, most notably Alpha Fold 2 (AF2), have improved the speed, quality and accuracy of protein structure prediction. A next challenge is to use these approaches to predict alternate conformations and the effects of sequence variants on structure. Considering the ubiquity of functionally significant fold-switching and order-disorder transitions, developing the ability to predict these alternate conformations has the potential to inform the discovery of new drug targets. Similarly, the conformational equilibrium of drug receptors relates to their affinities for drugs, highlighting the importance of predicting the relative population of different conformations.

Previous research has found that subsampling the input multiple sequence alignments in AF2 and increasing the number of predictions was able to sample alternative structures of the same target protein, even capturing different fold-switching states of known metamorphic proteins. Prior work has also generated conformational ensembles through reducing the max_seq:extra_seq parameter values and used these ensembles as starting points for molecular dynamics simulations to sample more conformations of interest such as cryptic ligand binding pockets.

Here, the authors use a similar approach of MSA subsampling to discover alternate conformations and their relative populations of certain proteins purely using the AF2 pipeline without the need for extensive MD simulations. They demonstrate how subsampling MSA by modulating the max_seq:extra_seq parameters can generate ensembles of protein conformations whose relative populations correlate with experimental knowledge. They test AF2’s capacity to predict differences in conformer populations with two example proteins–Abl1 tyrosine kinase core and granulocyte-macrophage colony-stimulating factor (GMCSF). With Abl1, they found that AF2 can qualitatively predict the effects of mutations on active state populations of kinase cores with up to eighty percent accuracy. They also found that their method predicted most of the activation loop intermediate states in the active-to-inactive transition of the kinase core, performing comparably to predictions obtained from multi-microsecond MD simulations. Despite the paucity of sequence data for GMCSF compared with Abl1, they were able to predict the extent of variation in backbone dynamics among GMCSF variants, which allowed them to conclude that AF’s prediction engine could decode population signals from relatively scarce data. Overall, the results are very interesting and encouraging and the manuscript is well written. We have the following points which we feel, if addressed, could make this manuscript stronger.

Major points:

1. The MSA subsampling approach that the authors have adapted in this work has been used by others previously (as cited by the authors themselves), albeit with some modifications. So it is important to see if the existing methodologies, for instance the DBSCAN based clustering and MSA subsampling by Wayment-Steele et al., are able to predict these relative state populations of variants. Also, the optimization of max_seq:extra_seq requires quite a bit of pre-existing experimental information. How is this method to be applied for a relatively new system? The authors could also provide some guidelines on how the max_seq:extra_seq numbers to be sampled are chosen and in general comment about the hyper-parameter space in their approach and how it compares to other schemes/approaches.
2. Apart from the large change in A-loop from active to inactive state in Abl kinase, the other important structure change involves the ????C helix moving out (as shown in Reference 22 cited in the preprint). The authors have not discussed this aspect. The snapshots shown from enhanced MD does not seem to show this change either (upon visual examination of the snapshots shown in the figures). Hence, the biological relevance of the MD simulation becomes questionable. Does the AF2 subsampled ensemble reflect the change in the helix position?
3. The authors haven’t performed statistical analyses on the RMSD comparisons or the CSP comparisons of GMCSF to claim the differences to be significant or not. For example, the authors say their approach has worked “as the range of the distribution of RMSDs of residues 80-90 and 110-125 is significantly larger for most of the mutations tested at both of these sites”. What is this distribution of RMSD compared against? Are these differences statistically significant?
4. Given that GMCSF has very limited sequence data in MSA to start with, does MSA subsampling actually help? The authors could try doing predictions using the traditional AF2 pipeline and compare those distributions against their approach.

Minor points:

1. Although the authors are right in looking for only the ground and I2 states in Abl kinase predictions, it will be interesting to explore if there were any predictions that matched the I1 state and if not, to speculate why more extensively
2. The data on some of the max_seq:extra_seq optimizations discussed for Abl kinase is missing. For example, 512:8 or 8:1024
3. There is no citation provided for the single and double mutants whose relative ground state populations were tested for Abl Kinase.
4. The nature of these mutations on Abl Kinase is not discussed. Are some of these mutations pathogenic or drug-resistant? It will be interesting to correlate the nature of mutation with its structural effects.The authors could provide more introduction of how these mutations were identified and add more discussion on trends.
5. What is the rationale behind choosing the PCs mentioned by the authors for Abl kinase enhanced sampling?
6. Why have the authors not shown the RMSD distribution of Distance 2 in Figure 4C?
7. How were the mutations on the histidine triad of GMCSF chosen? <br /> Sebollela et al. 2005 (https://pubmed.ncbi.nlm.nih... "https://pubmed.ncbi.nlm.nih.gov/16027123/)"), which is not cited in this paper specifically but cited in one of the papers (Cui et al. 2020 - https://doi.org/10.1021/acs... "https://doi.org/10.1021/acs.biochem.0c00538)") that this paper cites, substitutes H15 with alanine to demonstrate a decrease in heparin affinity
8. For the GMCSF system, do the authors see a relationship between the plDDT scores and the extent of RMSD?
9. Prior work that uses AF2 to sample conformational ensembles has seen that AF2 is able to predict more diverse conformations when the protein is not part of AF2’s training dataset. Was GMSCF part of the training dataset? If yes, how would the author’s approach vary for a protein that is not part of the training dataset?
10. Some of the figures are not informative/important enough to be main figures. For example, Figure 2 is mainly the AF2 pipeline, Figure 5 is just a pictorial representation of Supplementary Table S1. Also, Figures 6 and 7 could be combined into a single figure.
11. The CSP data for H15N is not shown in Figure 9B whereas its RMSD is shown in Figure 9C
12. The cut-off values used for jackhmmer not mentioned.
13. Residues are being addressed as codons in some places in the text
14. The authors may also want to include a few sentences contrasting their approach with this recently posted work: https://www.biorxiv.org/con... in the introduction or discussion.

Review written by Ashraya Ravikumar and Sonya Lee with input from other Fraser Lab members at UCSF

On 2022-09-30 22:28:04, user MIT Microbiome Club wrote:

How good were the different models at predicting the *sign* of the interactions? (Minor)

On 2024-08-30 17:42:18, user David Cerný wrote:

The in-text citations are not properly linked to the corresponding bibliography entries. It would be great to post a v2 that fixes this problem.

On 2018-06-09 22:51:19, user Anna Maria Musti wrote:

This paper represents an innovative experimental approach, combining live imaging with light- and cryo-electron microscopy to resolve the functional ultrastructure of tunneling nanotubes and to demonstrate their specific identity. Hopefully, we will soon learn about molecular and signaling networks involved in nanotubes biogenesis!!

On 2018-06-10 22:32:33, user Chiara Recchi wrote:

Finally the difference between filopodia and TNTs has been clearly demonstrated! Amazing images and movies, what a solid beautiful work! Well done, congratulations!

On 2017-08-30 13:56:57, user Qin Jerry wrote:

Dear authors,<br /> I don't think I understand after reading this paper. So can you give me a address where I can download the raw data of this article? I want to practise to processing data following your procedure. Thanks!

On 2018-06-11 14:33:50, user Edgar Buhl wrote:

It is really important to compare results produced in the lab to what is actually happening out there in the wild, especially when studying pests. A study like this is thus really helpful in determining which parameters are important in order to adapt lab protocols!

On 2020-05-05 21:45:40, user Arnaldo Guerrero wrote:

It seems to me that, considering that any spike mutation would affect drastically the production of a working vaccine, this report about the mutation of the Covid-19 virus is most important to be looked up by the peer medical/scientific community. As it is right now, the "medical-ridden" media act with disdain when asked about these mutations reported. The main reason given is that it is a Pre-report that has not been evaluated by said medical/scientific community. I propose that the key here is prioritization. This type of information could signify the making of decisions that are necessary in order to obviate the wasted time that its non-reporting could create, since we are talking about the making of vaccines that takes years to do so. Also, it could prevent any premature governmental action in the name of economics without consideration of the health of their citizens. Make haste then, so that lives can be saved. Thank you.

On 2020-05-21 17:41:37, user Research Explained wrote:

Our team wrote a general public friendly summary of this study and its strengths and weaknesses. Check it out at: https://www.researchexplain...

On 2025-11-04 18:29:17, user Leonardo Salmena wrote:

This study has now been published https://rdcu.be/eOaYe

On 2024-06-06 12:00:30, user Elli wrote:

I am unable to see the supplementary tables the preprint refers to, where can I find these?

On 2024-10-17 14:05:57, user Christina Warinner wrote:

It is very nice to see the authors statistically confirm on a large number of samples that oral bacteria contribute to the thanatomicrobiome of archaeological teeth. They may want to note that this pattern has been previously observed and reported twice before: <br /> Mann AE, Sabin S, Ziesemer KA, Vågene Å, Schroeder H, Ozga A, Sankaranarayanan K, Hofman CA, Fellows-Yates J, Salazar Garcia D, Frohlich B, Aldenderfer M, Hoogland M, Read C, Krause J, Hofman C, Bos K, Warinner C. (2018) Differential preservation of endogenous human and microbial DNA in dental calculus and dentin. Scientific Reports 8:9822. DOI: 10.1038/s41598-018-28091-9<br /> Vågene AJ, Campana MG, Robles García N, Warinner C, Spyrou MA, Andrades Valtueña A, Huson D, Tuross N, Herbig A, Bos KI, Krause J. (2018) Salmonella enterica genomes recovered from victims of a major 16th century epidemic in Mexico. Nature Ecology and Evolution 1-9. DOI:10.1038/s41559-017-0446-6.

On 2019-03-08 10:46:51, user David Gfeller wrote:

EPIC: Not a great acronym, since EPIC is a published method for cell fraction predictions developed in my lab (https://elifesciences.org/a... "https://elifesciences.org/articles/26476)"), and that we are also actively developing HLA ligand predictors (MixMHCpred and MixMHC2pred).

On 2021-04-26 05:23:48, user YU QIAO wrote:

A great and very interesting study. The writing is concise while clearly articulates the logic. It’s very effective to use both knockout and over/ectopic-expression of fascin throughout the paper; it makes the information more comprehensive and the reasoning more solid. Personally, I really like the in vivo study with PFKFB3 knockdown, especially the use of a fascin OE background. Additionally, it is very appealing to target fascin, but I believe that for the last section, more data from an in vivo study can make the paper stronger. Also, it might be worthwhile to test other fascin inhibitors, to note the potential side effects of each, and to put the inhibitors with combination with, for example, other chemo- or radio-therapy. A general comment on the figures: the varieity of colors makes it hard to comprehend some of the figures, and some colors (e.g. Fig1.G-H) are too light or too similar to each other and cause confusion. The color choice for control and OE is not red-green blind friendly. In Figure 5, the representation in C and D is a bit confusing; it could be more clear to show r and p values in a different way, or in different colors. For Figure 6, it will be more effective to show only one or two representative mice/samples per group, as there is some repeated information and the figures are too small. However, overall, it’s a really beautiful study, and I greatly appreciate your work.

On 2020-05-13 18:29:44, user Sinai Immunol Review Project wrote:

Title Bulk and single-cell gene expression profiling of SARS-CoV-2 infected human cell lines identifies molecular targets for therapeutic intervention<br /> Wyler et al. biorXiv [@doi: 10.1101/2020.05.05.079194]

Keywords<br /> • scRNAseq<br /> • Interferon-Stimulated-Genes (ISGs)<br /> • HSP90

Main FindingsWyler et al. performed bulk and single cell RNA sequencing of three human cell lines at different time points after infection with SARS-CoV-1 or SARS-CoV-2. The cell lines used were H1299 and Calu-3, both epithelial lung cancer cell lines, and Caco-2, a colorectal adenocarcinoma cell line. Permissiveness to SARS-CoV-1/2 was different among cell lines: H1299, which express low ACE2 levels, produced less viral RNA and lower yield of infectious virus than Caco-2 and Calu-3.

Bulk RNA-sequencing showed important differences in host transcriptome responses between the Caco-2 and Calu-3 cell lines. Caco-2 cells exhibited an increase in ER stress genes. In contrast, Calu-3 exhibited a strong induction of Interferon-Stimulated-Genes (ISGs), such as IFNB1, CXCL10, HLA-B, HLA-C. This ISG induction was 2-fold higher for SARS-CoV-2 infection compared to SARS-CoV-1. scRNAseq from Calu-3 cells confirmed the differential ISGs expression. Sars-CoV-2 induced higher expression of IFIT1 and IFIT2 than SARS-CoV-1. Only a cluster of SARS-CoV-2 infected cells showed strong IFNB1 induction. RNA velocity analysis, which can measure the amount of nascent RNA, showed that the induction of ISG was short and transient during viral infection, and preceded Nf-kB signaling target genes activation (IL6, TNF, NFKB1A). A minor increase of ACE2 expression was also noted.

To detect subtler transcriptomic changes not related to the IFN response, the authors analyzed scRNAseq from H1299 cell line, which seem less permissive to infection. HSP90 expression correlates with the amount of viral SARS-CoV-2 RNA, but not with SARS-CoV-1 RNA. A similar induction was found in Calu-3 scRNAseq at early time point. Chemical blocking of the HSP-90 pathway in Calu-3 cells upon viral infection led to a strong reduction of viral replication and expression of the pro-inflammatory genes IL1B and TNF, but interestingly, not of IFIT-2.

Limitations<br /> Although increased transcription of ER stress genes was identified in Caco-2 cells, the authors did not report changes in HSP90 expression in this cell line. This could further indicate whether HSP90 induction is a lung-specific mechanism and could explain COVID19 pathology. Moreover, the relevance of these findings would benefit from the confirmation of HSP90 upregulation in more physiological systems such as primary cells or tissue derived from patients. Furthermore, validation of the role of HSP90AA1 and specificity of 17-AAG using HSP90AA1 knock-out cell would further strengthen these results.

The authors correlate the low susceptibility of H1299 with lower expression of ACE2, but scRNAseq data of H1299 indicates that the majority of cells are infected. Therefore, it is unclear what factors are responsible of H1299 relative resistance to infection.<br /> The authors state that the lack of ISGs induction in Caco-3 could be due to a reduced expression of Pattern Recognition Receptors (PRRs) is this cell line. There might be other differences between cells lines that would explain the contrasting results, rather than the PRR expression. To confirm the role of RNA sensors, the authors could perform targeted experiments, such as genetic deletion of PRR pathway for example. <br /> Figure 1 and Suppl Figure 1 reference in the text seems to have been mixed (S1D wrongly referred as Fig 1B, S1C as 1C, etc.)

Significance<br /> Although this model uses epithelial cancer cell lines, it is of great interest to understand the effect of SARS-CoV-2 infection on lung epithelial cells. Indeed, this study identifies a potential drugable target (HSP90) against SARS-CoV-2 infection, although these findings remains to be confirmed in primary tissues, animal models, or patients. The effect of blocking HSP90 identified here is of important clinical relevance, as it decreases viral replication and the production of cytokines that could be involved in the ARDS pathogenicity.<br /> Analysis of patients with severe COVID-19 showed impaired IFN-I responses compared to mild or moderate cases1, and, together with studies using animal models2, suggest a central role for the dysregulation of IFN-I signaling in COVID-19 pathology. Activation of IFN signaling upon SARS-CoV-2 infection has been observed in lung epithelial organoids3, but other studies indicate a lack of robust IFN type I/III signaling upon SARS-CoV-2 infection compared to influenza A or RSV infection4. The possible discrepancies between the results presented in this preprint and other studies could be explained by different experimental settings, such as the use of different time points, cell lines or MOIs, and warrant further investigations.

References<br /> 1. Impaired type I interferon activity and exacerbated inflammatory responses in severe Covid-19 patients | medRxiv. Accessed May 12, 2020. https://www.medrxiv.org/con...<br /> 2. Boudewijns R, Thibaut HJ, Kaptein SJF, et al. STAT2 signaling as double-edged sword restricting viral dissemination but driving severe pneumonia in SARS-CoV-2 infected hamsters. bioRxiv. Published online April 24, 2020:2020.04.23.056838. doi:10.1101/2020.04.23.056838<br /> 3. Ravindra NG, Alfajaro MM, Gasque V, et al. Single-Cell Longitudinal Analysis of SARS-CoV-2 Infection in Human Bronchial Epithelial Cells. Microbiology; 2020. doi:10.1101/2020.05.06.081695<br /> 4. Blanco-Melo D, Nilsson-Payant BE, Liu W-C, et al. SARS-CoV-2 launches a unique transcriptional signature from in vitro, ex vivo, and in vivo systems. bioRxiv. Published online March 24, 2020:2020.03.24.004655. doi:10.1101/2020.03.24.004655<br /> 5. Zheng H-Y, Zhang M, Yang C-X, et al. Elevated exhaustion levels and reduced functional diversity of T cells in peripheral blood may predict severe progression in COVID-19 patients. Cell Mol Immunol. 2020;17(5):541-543. doi:10.1038/s41423-020-0401-3

Credit<br /> Reviewed by Emma Risson and Bérangère Salomé as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.

On 2024-09-10 18:32:52, user Thomas Sorger wrote:

Please note that the date of the second reference (2003) is incorrect. The correct date is 1983:<br /> 2. Armstrong E. Relative brain size and metabolism in mammals. Science 220: 1302–1304 (1983).<br /> Tom Sorger

On 2019-06-26 02:23:03, user John F. Allen wrote:

Remarkable paper, explaining much. I've long wondered why plastid DNA encodes NAD(P)H dehydrogenase subunits only in land plants.

On 2024-10-06 09:17:57, user Phil Jones wrote:

This article has now been published:<br /> https://www.nature.com/articles/s41588-024-01891-8

On 2024-01-11 10:11:41, user Beth wrote:

Fetal sex is not mentioned in this study. Could you look at sex differences? Was fetal sex controlled for when looking at the effect of gravidity? Was there an equal balance of male/female placentas in the primi and multigravida groups? If not, this could confound the identified differences between these two groups.

On 2021-08-03 14:27:30, user Hypo wrote:

Why was Illustration g removed?

It was a chart of Percent of CD8 in donor R6 Showing the change in Non Spike to Spike from Post infection to Post Vaccine.

It was a topic of intense discussion among people I know and they found it very interesting.

On 2020-07-19 16:44:56, user Casey Burridge wrote:

"Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) emerged in December 20191,2 and is responsible for the COVID-19 pandemic3". You have referenced this yet I cannot find evidence of this anywhere in that document. In fact, has any study conclusively proved that Sars-Cov-2 causes COVID-19?

On 2020-10-17 00:16:03, user Matthew Maxwell wrote:

Hi, Amazing package! I was wondering if Nebulosa has the ability to generate UMAP plots that are split by Seurat metadata parameters such as experimental conditions or orig.ident?

On 2018-07-28 04:20:23, user Frank Stahl wrote:

Amazing Work!

On 2022-10-20 14:55:33, user pp crawler wrote:

A comment can be found on Pubpeer.

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

On 2021-06-21 16:36:33, user Philip Cook wrote:

Now published in Nature Methods:

https://www.nature.com/arti...

On 2023-10-18 18:45:43, user Vanessa Staggemeier wrote:

Moura et al. evaluate the loss of suitability areas for non-flying mammals in the Caatinga in two future periods (2060-2100) under climate change effects and what would be the expected changes in the biotic composition of communities.

The authors employed an interesting approach with restrictions on species dispersal in the models and the results contribute to predicting the effects of climate change in this biome.

We see the importance of focusing on biotic changes and % of range loss, but it is our belief that adding the final predictions for each species in the supplementary material, in terms of maps and range shifts (direction of shift), it would be worth and informative because this information is important for managers and decision makers (those who manage conservation units but also to the researchers working on specific taxa).

We also think that including a more detailed discussion about some species that have been modelled in other previous studies could enrich the work and make some of the results obtained here clearer. For example, why species with a wide distribution such as Callicebus barbarabrownae would lose their entire area of suitability in 2060? Other studies, such as Barreto et al. 2021 and Gouveia et al. 2016 found different results, could you attribute this to the methodological choices?

We think the words used in the bibliographic review were not wide enough to include studies with mammals in the Caatinga because some important references are out of the included papers. The chosen words are mainly related to the biome or region. Maybe another approach would be to review occurrence records in a systematic way looking for articles with species names (as keyword) based on a preliminary list of mammals.

Including latitude and longitude in the maps it would be more informative and including political division of states could help to subside discussion for specific regions of Caatinga.

We wrote this comment during a meeting to discuss preprint papers that occurred by September, but I was able to post it just now.

I saw that the paper was accepted yesterday, so I am not sure if our suggestions/questions will have some worth to the authors (feel free to reply or not), but we decided to contribute with them anyway.

Many congratulations for your article! Although we think that some points could be different, we are sure that article is a nice contribution to understand potential effects of climate change in Caatinga :)

Comment written at the Laboratório de Ecologia Vegetal, Evolução e Síntese (LEVES) at the Universidade Federal do Rio Grande do Norte, RN, Brasil. Joined this meeting: Vanessa Staggemeier, Hercília Freitas, Víctor de Paiva, Yan Gabriel, Alexander Chasin, Rhuama Martins, Vitoria Alves, Jose Nilson dos Santos, Rafael Rocha dos Santos, Maria Luiza and João Paulo Câmara.

References<br /> 1) Barreto, H. F., Jerusalinsky, L., Eduardo, A. A., Alonso, A. C., Júnior, E. M. S., Beltrão-Mendes, R., ... & Gouveia, S. F. (2021). Viability meets suitability: distribution of the extinction risk of an imperiled titi monkey (Callicebus barbarabrownae) under multiple threats. International Journal of Primatology, 1-19.

2) Gouveia, S. F., Souza-Alves, J. P., Rattis, L., Dobrovolski, R., Jerusalinsky, L., Beltrão-Mendes, R., & Ferrari, S. F. (2016). Climate and land use changes will degrade the configuration of the landscape for titi monkeys in eastern Brazil. Global Change Biology, 22(6), 2003-2012.

On 2019-03-26 10:19:43, user Maximilian Krause wrote:

the correct code repository link is https://github.com/adnaniaz...

On 2020-07-18 00:50:12, user Alok Sharma wrote:

A comprehensive tool to do single-cell RNA-seq data clustering. It can also perform, outlier detection, estimating number of clusters, and batch correction. This is a python package

On 2019-11-30 05:14:44, user Jesse CM wrote:

Love ants and love efforts to disentangle the drivers of biodiversity gradients, but I'm not always sure eusocial insects are the best starting point for testing methods that use SAD and intraspecific spatial aggregation. I realize that a major reason for the paper is to demonstrate a new method, but it seems to me that it would be worthwhile to point out that a similar study of lycosid spiders or plethodontid salamanders might not necessarily show the same patterns.

Ants are eusocial, colonial organisms, so changes in m^2-level SAD's along a gradient would seem to warrant at least a brief mention that it's likely related to a change in colony size. I think this doesn't necessarily detract from the techniques discussed in this paper, but I was surprised not to see this aspect of the biology of the study organism discussed at all.

Specifically, as the authors say, "... changes in species richness would be expected to be closely linked to changes in total numbers of individuals but not changes in species relative abundances or their spatial distributions." [but] "...if higher energy decreased competitive exclusion then changes in richness could be linked to changes in the relative abundance of species rather than the total number of all individuals."

The above statements are straightforward to interpret for asocial organisms that are approximately the same size and trophic level, but when applied to eusocial organisms that have varying colony sizes (and worker sizes that vary over 2 - 3 orders of magnitude and have different trophic strategies), they are a bit more difficult to interpret. Is a worker ant an "individual" for the "more individuals hypothesis"? Or is a colony a more appropriate "individual"?

Somewhat related: it might also be useful to mention why the conclusions of Sanders et al. 2007 seem to be so different from the conclusions of the present paper regarding the relationship between "abundance" and elevation, since they use the same dataset. The use of number of m^2-level incidence data as a proxy for abundance (rather than raw number of individuals m^-2) in Sanders et al. 2007 seems to make sense in light of ant natural history, however the present paper suggests that including m^2-level individual counts leads to different conclusions with respect to abundance, elevation, and species richness. This is certainly interesting, but I think the paper would be improved by framing the conclusions in light of Sanders et al. 2007's methods for quantifying abundance, and a quick acknowledgement of ant natural history.

A useful augmentation of this discussion might be to include an analysis of the same dataset as presence-absence data in the supplemental materials (as the authors suggest is possible).

On 2020-10-02 10:07:12, user Anon wrote:

The authors of these papers are spreading misleading information on their research using the national media of Bangladesh. They are claiming BioRxiv as a leading us-based journal.

On 2020-03-28 13:18:15, user Euan Pyle wrote:

Hi, this is nice work! I noticed at one point you say "Thus, lipid-protein interactions are likely crucial in the activity of the majority of membrane proteins but actual functional data to support this hypothesis is scarce." which is true! Although, we published a few papers finding a functional role for protein-lipid interactions if you are convinced by the argument we present:

https://www.cell.com/cell-c...

https://pubs.acs.org/doi/ab...

On 2020-05-07 14:45:27, user Liz Miller wrote:

This paper was the subject of the Miller lab journal club and, following a lively discussion of the findings, we offer the following comments.

In this paper, Jiménez-Rojo and colleagues investigate roles of sphingolipids (SL) and ether lipids (EL) in cell physiology using a powerful genetic screen, lipidomics and biophysical experiments. A genome-wide CRISPRi screen identified genes conferring hyper-sensitive and resistant phenotypes in the context of SL depletion. These genes fell into 3 major families relating to sterol synthesis (hyper-sensitive mutants), glycerolipid synthesis (both hyper-sensitive and resistant mutants) and vesicular trafficking (hyper-sensitive mutants). Lipidomics analysis of SL-depleted cells showed an increase in ether lipid (EL) species, confirming an EL/SL relationship observed in the CRISPRi screen. Authors also show similar physico-chemical properties of EL and SL, and how they interact with the GPI-anchor protein cargo receptor TMED2, proposing roles of these lipids in anterograde and retrograde traffic. Overall, this study not only provides insight into cellular adaptations during SL depletion, but also gives a biophysical explanation behind evolutionary selection of EL as a GPI anchor base in mammals, evolving from ceramide in yeast.

We have some brief comments that arose during the group discussion:

Blocking SL synthesis through inhibition of SPT with myriocin changes the lipidome significantly, including ER membrane composition. Changes in the latter are known to trigger ER stress and UPR. We were wondering if the UPR was monitored, and whether ER stress might represent a confounding interaction in the genetic analysis. Trafficking genes may arise as sensitizing mutations because of an exacerbated UPR in addition to more specific lipid effects.

Sterol synthesis-related genes were enriched in hyper-sensitive mutants, similar to EL synthesis genes. Knowing that sterols also rigidify membranes, it would be interesting to see if these species were also upregulated upon myriocin treatment to compensate for SL loss, similarly to EL.

The authors propose that upon reduced retrograde traffic, SM18 is redirected to the endo-lysosomal pathway and provide some evidence for this. However, reduced COPI traffic was not demonstrated directly in a similar manner to that of Contreras et al (2012) using exotoxin A. We wondered if perturbations in retrograde traffic might indirectly affect GFP-GPI transport under SL depletion (Figure 5)?

We also were curious if the increased GPI-AP traffic under myriocin and sgAGPS treatment observed in Figure 5 E&F is still TMED2-dependent. Could the altered lipidome have changed the transport pathway for GFP-GPI?

Lastly, we were wondering how the presence of additional TMDs might affect the MD simulations of p24-lipid interactions, especially considering that p24 oligomerization is important for their function and stability.

Thank you for sharing your work on BioRXiv and we hope our comments are of some use/interest to the community :)