On 2021-01-27 14:06:15, user Morten wrote:

Dear Dylan and co., <br /> I watched the video of yours published on Nanoporetech and read the paper with great interest! I have a few questions regarding the methods used in your study, that I hope you will take your time to respond to. <br /> 1) Was it the single Illumina reads or contigs that were taxonomically classified against the custom database? <br /> 2) What information does this approach offer not contained in the 16S rRNA amplicon approach (free of PCR bias or higher species resolution)? As I read you do not use the Illumina reads for any functional annotations? <br /> Kind regards,<br /> Morten from Denmark

On 2021-01-27 03:10:47, user Mark Konyndyk wrote:

I still think the R0 may be understated.

On 2021-01-27 03:07:54, user Mark Konyndyk wrote:

And quickly it move to Florida. Clearly the R0 was incorrect.

On 2021-01-27 00:32:01, user Fadi Abdulsater wrote:

The primers suggested in this study to detect only the B.1.1.7 are not good choice with only one mismatch in the middle of the forward primer. these primers are able to detect the Wuhan-Hu-1. you have to design primers with at least 2 mismatch at the 3'end of the primer.

On 2021-01-22 19:22:12, user alejandro wrote:

To the interested we extended this methodology to other variants, while we wait for the laboratory results.

https://www.biorxiv.org/con...

On 2021-01-20 08:25:11, user kostas wrote:

Hello and thank you for work and the interesting approach. I hope the authors understand that the first "element" behind their title, is to provide some evidence that the primers do work, and this is not totally clear to me at least in this version of the manuscript. Also as a reviewer this would be the first thing i would like to see documented.

On 2021-01-15 13:13:18, user Martin wrote:

Hello, many thanks for this work and the interesting approach. However I have doubts that these primers are able to identify the B.1.1.7 variant. The reverse primer is 100% complement to the SARS-CoV-2 reference sequence and in the forward primer there is only one mismatch in the forward primer towards the 5' end.

I'm working >15 years with real-time PCR and these primers WILL amplify the wild-type variant. There are no conditions which make the forward primer 100% specific for the new variant. The usual approach to identify a SNP is to place the SNP exactly on the 3' end of the primer (-> allel-specific PCR). In theory, this 3' mismatch cannot be extended by the Taq polymerase because there is no DNA double strand present. In reality however, you get amplification which is shifted some cycles to a higher Ct. To enhance this instability effect, a second mutation can be introduced near the 3' end of your primer.

Nevertheless, your findings are important because if this C16176T SNP is specific for B.1.1.7 there are genotyping techniques in real-time PCR which you can use to distinguish between the new variant and the "wildtype". For example, you can use two MGB-probes but with the disadvantage of blocking two fluoresence channels on your instrument only for genotyping. You can also use a molecular beacon probe with subsequent melt curve analysis.

On 2021-01-08 23:46:13, user Alejandro Marquez wrote:

Hello, honestoy I don't get how these primers can be specific for the new variant, I only found SNP variations in the middle of the primer and not in the 3'-end. Based on my experience these primers will amplify with the original and the new variant... can you explaint better...?

On 2021-01-26 23:29:47, user Edwin Alberto Pinilla Salazar wrote:

Dear Fan, below I am sending you the link of a document that correlates a lot with the research you have been carrying out and that resolves some concerns that you had, related to tests on animals and on human beings.<br /> https://www.ncbi.nlm.nih.go...

On 2021-01-26 20:23:28, user Tiago Lubiana wrote:

Now published at: https://bmcbiol.biomedcentr...

On 2021-01-26 19:39:42, user Matt wrote:

Very interesting paper! Authors, have you considered running your qpAdm result in a 3-way model with (East Asian)+Maniq+(South Asian)? Another reader of the paper has suggested to me that the time depth of divergence of the Onge from the true admixing source into South East Asian populations may lead the qpAdm results to be biased towards higher probability values for South Asian populations with high levels of ASI (ancestral South Indian) ancestry. Testing the same models with the Maniq population in place of Onge (although this population has some East Asian ancestry) might allow to test this idea. Also it would allow the use of the Onge population in the outgroup set.

On 2021-01-26 18:37:11, user Laurent Seroude wrote:

How old are the adults used? Is there also a loss of repression in old animals?

On 2021-01-26 17:00:23, user Nicholas Markham wrote:

Please find this work published at:

Markham NO, Bloch SC, Shupe JA, Laubacher EN, Thomas AK, Kroh HK, Childress KO, Peritore-Galve FC, Washington MK, Coffey RJ, Lacy DB. Murine intrarectal instillation of purified recombinant C. difficile toxins enables mechanistic studies of pathogenesis. Infect Immun. 2021 Jan 19:IAI.00543-20. doi: 10.1128/IAI.00543-20. Epub ahead of print.

PMID: 33468584.

https://iai.asm.org/content/early/2021/01/13/IAI.00543-20.long

On 2021-01-13 18:30:29, user Nicholas Markham wrote:

I'm happy this manuscript was resubmitted to Infection and Immunity and has been accepted for publication! Thank you to Borden Lacy all the co-authors!

On 2021-01-26 16:07:06, user Chris ADAM wrote:

Hello,<br /> I'm a Master student in bioinformatics working on epigenetics data. I aim to diagnose genetic diseases using machine learning. At the end of this article, it's said the code will be made available in a public repository. Has it been done yet ?<br /> Best regards,<br /> Chris

On 2021-01-26 16:04:50, user Héctor Climente wrote:

In Table 1, the number of samples for the Habib et al. dataset should read 1402, instead of 13302. This error is completely inconsequential for the results of the paper, and that number is not even referenced elsewhere in the text. Yet, it is incorrect, and might potentially confuse readers familiar with that study.

On 2021-01-26 14:33:24, user H. Chowdhury wrote:

Interesting findings using bat cell line!

On 2021-01-26 10:08:01, user Wouter De Coster wrote:

Dear authors,

Very interesting work. I wanted to bring to your attention that the 1000 genomes project samples have been resequenced to 30x and are publicly available (https://www.ebi.ac.uk/ena/b.... While obviously a lot of work to repeat your analysis this would potentially contribute to a substantially more informative study.

Best regards,<br /> Wouter

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

Dear reader,

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

Q.L. Lim

On 2021-01-25 21:39:12, user Felix Willmund wrote:

The study was recently published in Nucleic Acids Research<br /> https://doi.org/10.1093/nar...<br /> Title: The versatile interactome of chloroplast ribosomes revealed by affinity purification mass spectrometry

On 2021-01-25 13:30:04, user Al-ameen Mohammed wrote:

This is the best paper that I found explaining the Wnt / B-catenin pathway in a concise way. The paper is interesting for its relevance to the topic of B-diketones I am doing my research on.

On 2021-01-25 11:39:29, user Maria Yáñez Mó wrote:

We already published a similar approach in 2019: see Development of a quantitative method to measure EV uptake.

Toribio V, Morales S, López-Martín S, Cardeñes B, Cabañas C, Yáñez-Mó M.

Sci Rep. 2019 Jul 19;9(1):10522. doi: 10.1038/s41598-019-47023-9.

On 2021-01-24 22:49:04, user John Lisle wrote:

Unhappy news.<br /> A pragmatic regulatory approach that enables fast-cycle development within a broad structural family is essential.

On 2021-01-23 12:09:42, user Ariel Roytman wrote:

Is there a missing information in this article? <br /> I could not find the positivie control for neutralization test for non mutant variant.<br /> In addition, I would like to understand if the concetration of neutralizing antibodies was sufficient/excessive.

Without such controls there is no true meaning for those findings.

On 2021-01-24 13:28:39, user cab1967 wrote:

Most recent discussions that concluded minimal impact at the KPg boundary weren't referring to Crocodylomorpha as a whole - they were talking specifically about Crocodylia.

On 2021-01-21 18:29:53, user Roger Benson wrote:

I very much like the analysis presented in this paper. And it is clear that advances of this type have great potential to improve understanding of events in deep time. However, I think that the palaeontological understanding of the effects of the KPg on crocs is misrepresented, and there is a misinterpretation of the signal from relatively invariant standing diversity as being equivalent to an observation of ‘no extinction’ and so no turnover. For example, Mannion et al (2015) is cited as saying that effects of the KPg mass extinction were ‘minor or absent’. But in fact, Mannion et al observed substantial turnover and clearly believed that the KPg was an important event for crocodilians, as follows:<br /> “The effect of the K/Pg mass extinction on crocodylomorphs has previously been perceived as minor or non-existent19,28, with any extinction temporally staggered33. However, several non-marine groups with high biodiversity before the boundary became extinct (most notably all non-sebecid notosuchians34), and only two clades (the marine dyrosaurids and terrestrial sebecids) survived alongside crocodylians28,35. Nevertheless, the extinctions of these groups, and other non-marine crocodylomorph taxa were balanced by rapid radiations of the three surviving clades in the early Paleocene19,28,34,36, including substantial range expansions of marine dyrosaurids36,37 and terrestrial alligatoroids28 into South America.”

On 2021-01-20 09:55:56, user Eduardo Puértolas wrote:

Interesting study on how Crocodylomorpha was affected by the K/Pg extinction event, I encourage the authors to continue with this work and line of research. The approach to the problem is novel and promising, but I recommend to the authors to take a look at Puértolas-Pascual et al. (2016). In this work, based on fossil occurrence data, we already observed that Crocodylomorpha were severely affected by the crisis, at least in Europe.

Puértolas-Pascual, E., Blanco, A., Brochu, C. A., & Canudo, J. I. (2016). Review of the Late Cretaceous-early Paleogene crocodylomorphs of Europe: extinction patterns across the K-PG boundary. Cretaceous Research, 57, 565-590.

On 2021-01-24 12:48:14, user alejandro wrote:

Please cite this paper

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

On 2021-01-24 10:51:46, user Jack Ashby wrote:

A very significant contribution to the field, both specifically for the thylacine and the potential for the method to be used for other species. I am no statistician, so I'll leave comments on that to others.

I couldn’t spot why the graphs show a most likely extinction date of after 2000, but the wording of the results suggest a greater emphasis on the 1990s? That post-2000 blue line in Fig 1 isn’t discussed in the text (although I think I follow where it’s come from reading the SI, but it should be explained in the main paper.

Could there be any mention of weighting for effort, or at least greater discussion of the notion that some regions are more densely populated/travelled than others, adding a potential bias to the spacing of records. At first glance of the maps, the sightings aren’t obviously correlated with population centres (quite the opposite, and for example, it’s notable that the heavily farmed Midlands are bare of sightings, while the heavily farmed northwest is not (particularly relevant for the pre-1936 sightings when farmers would<br /> have be extra vigilant, and before the protection came in)). I appreciate this is not a statistical approach like the rest of the paper, but it is something<br /> my eye was drawn to.

The significance of the value of the database, the fact that it will be updated with new records and its public-availability could be better highlighted.

I don’t think you have explained the rubric that was used to determine the quality ratings, which obviously have a big implication on your results. Was there a definition of each rating for each observation-type? Without knowing what specific criteria you used to rate the<br /> sightings, this analysis is not repeatable, and hard to unpick.

On 2021-01-24 10:42:27, user Ibrahima GUEYE wrote:

Very pleasant article with a rigorous scientific approach. Two peptides (P9 and P10) emerge due to their ability to completely inhibit SARS-CoV-2 without pulmonary cytotoxicity. However, I have two concerns. First: the two algorithms used; namely Agadir (to assess the helical content) and Kolaskar (to assess the antigenicity) are not applicable for sequences containing homotyrosine (hY). P9 and P10 contain it. They were forced to measure the helical content by experimental method without having to give empirical data. (disciples of Claude BERNARD may be). Therefore for the antigenicity it will require clinical studies. Second: what about kidney toxicity?<br /> Médecin-commandant Ibrahima GUEYE<br /> Spécialiste en Médecine d'urgences et de catastrophes <br /> Master 1 Biomathématique et Bioinformatique <br /> 3 PRINTING <br /> Number theory (Erdös-Straus conjecture)<br /> Médecin-chef CMIA Ziguinchor<br /> https://orcid.org/0000-0002...

On 2021-01-24 09:19:58, user fischmidtlab wrote:

Wonderful story that underscores the importance of biparatopic approaches for passive immunization!

Two questions:<br /> 1. Do you know whether Spike E484D is a genuine escape mutant (i.e. prevents neutralization with C121)? Demonstrating escape mutants with wt SARS-CoV-2 in these short infection models would be an important finding.<br /> 2. Did you get the chance to test performance of CoV-X2 against the more dramatic E484K mutant with charge reversal observed in vitro (Weisblum et al.) and in the recent variants B.1.351 (South Africa) and P.1 (Brazil)?

Great work, looking forward to see more of this!

On 2021-01-24 05:42:44, user Samuel Flores wrote:

Where is text S1?

On 2021-01-24 05:36:21, user Pengyi Yang wrote:

This paper is now published in Molecular Systems Biology doi: 10.15252/msb.20199389

On 2021-01-23 13:40:41, user Drezen wrote:

The revised version of this paper, after reviewing, has been published in Communications Biology https://doi.org/10.1038/s42...

On 2021-01-22 22:22:26, user Researcher_111 wrote:

The research question of this paper is interesting, but I wished that the sample sizes were more than n=2 per treatment.

On 2021-01-22 19:08:45, user alejandro wrote:

1) If you go to the reference 12 ( we will put a link in the bibliography ) you will see there is a section for the P.1 Variant

https://www.cdc.gov/coronav...

P.1 lineage (a.k.a. 20J/501Y.V3)

The P.1 variant is a branch off the B.1.1.28 lineage that was first<br /> reported by the National Institute of Infectious Diseases (NIID) in Japan in<br /> four travelers from Brazil, sampled during routine screening at Haneda airport<br /> outside Tokyo…

Reference 17 should be removed, thank you for your comment

On 2021-01-22 13:42:42, user Gonzalo Bello Bentancor wrote:

I detected several errors in the description of the P.1 variant in the introduction section.

1) "Recently, a new variant SARS-CoV-2 from has been identified by scientist and clinicians in Brazil, and named P.1 variant or 20J/501Y.V3 Nextrain clade (12, 16, 17)".<br /> The references 12 and 17 do not describe the P.1 variant, but the B.1.1.7 lineage (ref 12) and a second B.1.1.28 clade circulating in Brazil (designated P.2) that harbors the E848K mutation but is not the P.1 variant (ref 17).

2) "An although the rate of infection and mortality of the P.1 variant is still unknown it has been identified in different areas in Brazil including the Amazonia and Rio de Janeiro, as well internationally in Japan (16, 17)."<br /> The variant P.1 was not detected in Brazil outside the state of Amazonas and the strain described in Rio de Janeiro in ref 17 was the P.2 variant.

3) "One reinfection has been linked to P.1 (E484K mutation) (22)."<br /> Reference 22 described a case of reinfection with the P.2 variant. The only case of reinfection with P.1 described so far was described here: https://virological.org/t/s...

Although both P.1 and P.2 variants evolved from the B.1.1.28 lineage, they correspond to independent emerging variants.

On 2021-01-22 17:50:16, user Fraser Lab wrote:

This work reports two atomic models of the main protease (Mpro) from SARS-CoV-2 using serial femto-second crystallography (SFX). The goal of the paper was to use this structural information to assist drug repurposing efforts against SARS-CoV-2. To accomplish this, the authors used computational docking and molecular dynamics simulations to investigate the molecular basis for binding of three previously reported Mpro inhibitors. There is a substantial (and growing) body of work describing the structure, function and inhibition of the main protease from SARS-CoV-2, but the claims of how the current results will become part of that effort are overstated in the manuscript.

The major result of this paper is the efficient use of the new macromolecular femtosecond crystallography setup at LCLS-II: two high resolution structures were reported from <5 hours of instrument time (1.9 and 2.1 Å). The major weaknesses of this paper stem from both the disconnected nature of the primary results and the rigor of some of the analyses. Specifically: 1) the reported structures have little relevance to drug repurposing efforts (the stated goal of the paper), 2) the differences between prior conventional structures and the analysis of the molecular dynamics simulations lack rigour, by lack of comparisons of electron densities and estimations of convergence/significance 3) the implications of the new Mpro models and/or the molecular dynamics simulations for inhibitor design are not articulated, 4) the compounds highlighted have been reported as promiscuous covalent inhibitors.

Some of the results described in this manuscript may be of interest to the SFX community, particularly if revised to more solidly compare to existing data. However, to maximize the relevance to the wider structural biology and SARS-CoV-2 research communities, the manuscript should be revised significantly.

Elaborating on point 2 above:

1. The manuscript does not present a fair comparison of structural information obtained from SFX versus traditional crystallography. Electron density maps are only presented for the SFX structures (e.g. Fig. 3B). Claims of structural differences would be stronger if the structural comparison was performed using crystals grown from the same conditions and with data processed/refined/modeled in a consistent way. Isomorphous FO-FO electron density maps would be particularly helpful.

2. The authors restrict their comparison to a single previously reported structure of Mpro (e.g. 6WQF). They should extend this analysis to the ~240 previously reported structures of Mpro from SARS-CoV-2. The majority of these structures were determined using cryo-crystallography and have ligands bound, however the comparison would still be informative, and is required for their claims of novelty. In particular, active site flexibility upon ligand binding has previously been characterized (e.g. Figure 1C of https://www.nature.com/arti..., Figure 4 of https://www.nature.com/arti... - it would be helpful to know whether the structural differences reported by Durdagi et al match those previously reported.

3. The structural differences involve alternative side conformations of non-catalytic residues in the active site (e.g. Figure 2B and D). Importantly, no specific link is articulated between the alternative conformations identified and Mpro function or inhibition. The authors suggest that the new structures will help modeling efforts (e.g. P5 L20, P11 L5). The authors present modeling efforts in this manuscript. How have the structural differences identified by the authors helped their modeling efforts (compared to the previously available structural information)? There are obvious computational analysis controls here that are missing.

4. The catalytic histidine is modeled with a flipped side chain in 7CWB compared to 7CWC, however, this difference is not mentioned in the manuscript. Are the authors confident in their modeling? Looking at previously reported apo coordinates (e.g. 6WQF, 6YB7), both conformations have been modeled. The conformation modeled in 7CWB seems more compelling, based on the ability of His41 to H-bond to Cys145, and the H-bond with Wat441. The authors claim that Wat441 (W5) plays a crucial role in catalysis (P5 L28). Flipping His41 disrupts the Wat441 H-bond network, however this is not mentioned. If the authors are confident in this difference, then they should summarize their reasons and the implications for Mpro function and inhibition.

5. A substantial and growing body of work (e.g. https://scripts.iucr.org/cg..., https://scripts.iucr.org/cg..., https://pubmed.ncbi.nlm.nih..., https://www.pnas.org/conten... deals with radiation damage in X-ray crystallography. Carefully designed experiments, coupled with advances in detector technology, mean that problems associated with radiation damage can be mitigated, even at room temperature. Indeed, the authors of a recent paper reporting the room temperature model of Mpro (Ref 27 in Durdagi et al - https://www.nature.com/arti... explicitly state their efforts to mitigate radiation damage: “We grew large crystals that could be used on a home source to ensure minimal radiation damage.” If Durdagi et al are suggesting that radiation damage was a problem with previously reported data, then they need to present evidence to support their claim. One option would be to collect cryogenic and room temperature data using identical crystallization conditions, then calculate isomorphous difference maps to test for radiation damage (clearly, careful experimental design is required, and problems of non-isomorphism may be encountered). Without this experiment, claims of issues with radiation damage at room temperature are not supported by evidence and should be removed.

6. Differences in dynamics were identified with simulations performed using 7CWB and 7CWC, despite the starting coordinates being almost identical. This strongly suggests that the simulations have not achieved equilibrium sampling. Perhaps the 7CWC model was simulated with the four residue addition at the N-terminus, and this can explain the differences? This should be mentioned in the main text.

7. Differences are also highlighted between the dynamics identified from simulations of 7CWB and a previously reported cryo model (6WQF) and room-temperature model (6Y2E). As with the previous point, these coordinates are almost identical, and if simulations of the same coordinates produce different results, how can the authors be confident that simulations with different compounds will produce useful results?

James Fraser and Galen Correy (UCSF)

On 2021-01-22 13:08:36, user harshit vaish wrote:

Here it is.<br /> https://febs.onlinelibrary.wiley.com/doi/10.1111/febs.15551

On 2021-01-22 11:46:16, user Iosif Koutagiar wrote:

I would like to notice the absence of my name (Iosif Koutagiar) from the list. Unfortunately there are some greek people with a not typical greek surname.

On 2021-01-22 10:52:05, user Pietro Roversi wrote:

The paper is out in an improved form at https://doi.org/10.1016/j.s...<br /> Thanks to the referees for their constructive criticism!

On 2021-01-22 09:02:28, user Richel Bilderbeek wrote:

As an extensive user of PureseqTM [4], I disagree with the 'accurate prediction'<br /> part of the title. For example, the nonsense protein sequence '!@#$%^&*()'<br /> is predicted to have a topology '0000000000' (i.e. it does not reside<br /> in the membrane) [1]. Due to this, I predict the selenocysteins in<br /> selenoproteins to be ignored as well [2]. The TRDD1_HUMAN <br /> protein with (indeed, very short sequence) 'EI' is predicted to<br /> have topology '[nothing]', instead of 00 [3].

I put these comments here, as I cannot reach the developers of PureseqTM<br /> via the PureseqTM email address, GitHub, nor their personal email address,<br /> since May 2020.

I posted bug reports at:

* [1] https://github.com/PureseqT...<br /> * [2] https://github.com/PureseqT...<br /> * [3] https://github.com/PureseqT...<br /> * [4] I wrote an R package to call it, called pureseqtmr

On 2021-01-22 08:31:43, user Xiao Huang wrote:

A beautiful story showing the longevity benefits of synNotch controlled CAR-T cells in solid tumor treatment. Congrats @KoleRoybal !!!

On 2021-01-22 03:01:42, user Ahmed wrote:

There is a typo in the abstract "uesad" I think it should be used!

On 2021-01-22 01:55:15, user Zhiyong Liu wrote:

We have used genetic approaches to show that, in the presence of cochlear outer hair cell (OHC) damage, adult cochlear supporting cells can be transformed into OHC-like cells by simultaneous expression of Atoh1 and Ikzf2. We are looking forward to receiving comments and suggestions about how to further move forward. Thanks

On 2021-01-22 01:39:54, user Alex wrote:

Hi, this was an insightful analysis, looking forward to reading more of your (all’s) work. My question is whether there was any discrepancy in the phase III trials in the UK vs. other nations? Is this exploration by deletion signature seen in any other viruses in circulation? Thanks!

On 2021-01-21 20:40:27, user Andrew Whittaker wrote:

A humble non virologist here but can we reconstruct Ratg13? I thought that maybe an even more useful exercise than reconstructing sars Cov 2 since we got plenty of that going around!

On 2021-01-21 20:28:12, user Nenad Bartoniček wrote:

Great work! However, the authors should clarify that 5' kits from 10X do not target 5' UTRs, but target 5' end of a 300-400 bp fragment that contains polyA.

On 2021-01-21 18:35:37, user ahmed wrote:

Office National Statistics Coronavirus (COVID-19) Infection Survey data is regularly updated<br /> and available here including estimates of COVID-19 cases to 02 January for England, regions of<br /> England and by cases compatible with the new variant (VOC 202012/01)...

On 2021-01-08 23:14:39, user Martin wrote:

Hello there, I think that it should be possible to fool the virus to mutate to its next variation more quickly in the lab to see what it transforms into next. By doing this you should be able to jump one step ahead to modify the vaccine to work against the next mutation. I think of virus' DNA/RNA as a computer program that must go down a certain route i.e. it has routines & subroutines when it is attacked in certain ways. By getting the virus to change to its next mutation quicker, we can get one or two jumps in front of it to produce a new vaccine to stop it before it mutates in the real time world situation.

On 2021-01-20 22:00:30, user Giorgio Gilestro wrote:

Can you please elucidate why you used Wuhan NCIB 43740568 as a control? That strain is basically extinct and it is not the one you are trying to protect from. You should have used something that carries D614G at the very least.

Also, the data clearly show a descending trend in almost all of the sera. Now, 16 is a very small number considered the amount the samples you certainly must have but even then, a decrease in 12 out of 16 is not something I would dismiss so easily: those are potentially 12/16 = 75% of vaccinated subject that will sport lower titre and, potentially, higher transmission capabilities.

On 2021-01-20 13:48:47, user Yaniv erlich wrote:

The paper has several problems:<br /> 1. The median calculation is off. According to Supp Table 1, the median is 0.76x and NOT 0.79x. That means that 50% of the vaccinated people loss nearly quarter of the titer activity in the presence of B.1.1.7.

1. The paper says in the abstract that "[t]he immune sera had equivalent neutralizing titers to both variants". Again, this is not correct. Twelve out of sixteen individuals have a titer ratio below one when comparing B.1.1.7 activity to Wuhan strain. A conservative a-parametric test (coin-toss) show that the reduction is statistical significant (p<0.05). It is wrong to say that they have "equivalent" titers.

2. The authors mention that 0.79x (sic) reduction is not biological significant. They do not report their pre-registered hypothesis about biological significance and do not contextualize this sentence. At which level they think that the reduction is alarming and why losing almost quarter of the activity is OK?

In addition, the important thing is that the variant already have some gains. It might not escape but it might poised to escape. All of these subtle points are not communicated when tens of millions and Governments all over the world consider this vaccine as the main exit strategy.

On 2021-01-20 19:38:42, user Michael Heskett wrote:

Nice work! Figure 6 appears to be missing from this draft.

On 2021-01-20 19:09:14, user Cerith Jones wrote:

This preprint has now been peer reviewed and published in Microbial Genomics (Microbiology Society) and is available, open access, here: https://www.microbiologyres...

On 2021-01-20 18:24:44, user Maxwell Neal wrote:

A peer-reviewed, revised version of this paper has been published by Nucleic Acids Research at https://doi.org/10.1093/nar....

On 2021-01-20 17:53:06, user Patrick Jodice wrote:

*error in abstract* should be 54,700 km of surveys, not 54.7 km. 50 km would not have been much of an effort : ) <br /> Apologies to my coauthors for not catching that prior to submission.

On 2021-01-20 16:51:21, user disqus_HVx5VNpDm7 wrote:

Published in FEBS Letters journal (https://doi.org/10.1002/187...

On 2021-01-20 13:28:17, user Bjarni Halldórsson wrote:

The relationship between the ACAN VNTR and height was also noted by us here: https://www.biorxiv.org/con...

On 2021-01-20 12:09:56, user S Ramasubramanian wrote:

It's very promising to see non injectable intra nasal single dose vaccine for covid-19, which will be boon to india..hope to see it in use very soon..

On 2021-01-20 02:07:12, user Filip Fratev wrote:

It is not so collegial to not cite previous studies on the topic and to present the results in a way that make the impression that this is the first in silico study about these mutations. Furthermore, it is not clear on the basis of which method these conclusions were drawn, just higher flexability? I can't see any numbers. There are also elementary questions such as why SB14 FF, which creates much larger fluctuations, and not SB19 FF was used? <br /> There are many ambiguities and for me this is a routine study.

On 2021-01-20 00:56:04, user Masami Fujiwara wrote:

This is an interesting topic. However, use of time as covariate is tricky. For example, if the last observations in a season of late and early reproducers are different by one month, then the duration over which survival is estimated is different by one month. If the annual survival rate is 0.7 then the monthly survival is 0.97. There can be 3% difference in their survival by changing the duration by one month. It is plausible that late reproducers have higher survival rate, but it is plausible that they all have the same survival rate, but you are simply estimating survival over shorter period (11 months vs. 12 months). I recommend you develop a model accounting for differences in time unit.

On 2021-01-19 23:27:45, user Sean wrote:

Can you explain why your paper has not been published?

On 2021-01-19 22:40:05, user Fraser Lab wrote:

This manuscript by Heyne et al., studies the binding affinity and fitness landscape of three trypsin homologs (Bovine trypsin, Bovine α-Chymotrypsin, Human Mesotrypsin) to the inhibitor BPTI, to get at the question of how mutations affect protein-protein interactions (PPI) of cognate and non-cognate enzymes and inhibitors. Importantly, the three trypsin homologs have widely different binding affinities for BPTI, with the cognate BPTI being the tightest. The authors use their previously developed yeast surface display platform to express BPTI variants and measure relative binding affinities to the trypsin homologs, and further define the fitness landscape of each interaction.

While cognate PPIs typically show high binding affinities, non-cognate PPIs often exhibit much weaker affinities even despite sharing structural similarity, underscoring that sequence plays a large role in the strength of an interaction. To understand how difference in sequence affects relative binding affinities, the authors chose to generate single and double mutants of the binding interface of BPTI, affinity sort BPTI/protease variants, and normalize the NGS-based enrichment values. Binding landscapes of BPTI in the presence of all three homologs showed that single and double mutations largely lead to a destabilization effect, with mutations to the cognate protein leading to on average a greater magnitude of destabilization than non-cognate. Perhaps unsurprisingly, mutations to the lowest affinity binder MT/BPTI show the greatest frequency of affinity-enhancing mutations and their positions are nicely shown structurally. We wonder, how often do mutations affect residues interacting with sequence differences between the non-cognate homologs? Are there trends where the differences in starting residue explain the difference?

Beyond single mutations, coupling energies were determined for double mutations of BPTI and defined as being additive, having positive epistasis, or having negative epistasis. For all three homologs, positive epistasis was the second most common effect of double mutations, suggesting an increase in fitness. Several questions which come to mind (what is a potential explanation for this result, especially in the case of BT/BPTI? Is there something unique about the positions in which negative or positive epistasis dominates coupling? How do specific affinity-enhancing single mutations structurally compare to double mutations which promote fitness? are there certain explanations for epistasis that are pervasive across positions, e.g charge swaps, or small-large substitutions?).

Overall, this study expands upon previous work to target a fundamental question of how sequence mutations affect binding affinities, and they do so in an excellent protein-protein interaction system. The major weakness is a lack of greater dissection of specific BPTI mutations and their chemical interactions with the associated enzyme for both destabilizing and affinity-enhancing interactions. We are also excited by the possibility for future papers to mutate the interface from the perspective of the trypsin homologs and to look at epistasis across the interface.

Minor comments:<br /> “we employed a recently developed by our group strategy “ - we employed a strategy recently developed by our group<br /> Change color scheme to be more color blind sensitive <br /> Label axis scales of figure 1C <br /> Show the full heat maps for figure 3 in the supplement <br /> Were double mutations analyzed for the YSD assay? Is there a heat map of this?<br /> Label figure 4 plots with homolog names<br /> Change color scale bar on fig 1 completely or at least so it's more intuitive with the other figure scales bars

James Fraser and Gabriella Estevam (UCSF)

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 2021-01-19 19:53:09, user Vatsan Raman wrote:

We thank the reviewer for their thoughtful evaluation, commentary, and suggestions provided. The final published version in PNAS fully addressed all their comments.

A detailed point-by-point response was submitted to the journal.

We substantially revised the manuscript and added eight new supplementary figures to address concerns and clarify key points.

These salient changes are summarized below:

1. We extensively employing statistical tests to validate our conclusions on conservation of allosteric hotspots, relationship between centrality score and location of allosteric hotspots, and ranking of dead variant based on rescuability.
2. We have provided detailed clarification of differences between the disrupt-and-restore experiment (Fig. 1) and rescuability experiment (Fig. 2), and harmonized the data representation in both figures
3. We demonstrate that our conclusions on regions of importance for allosteric signaling is robust to changes in the quantitative thresholds defining a hotspot and classifying a residue as a dead variant.
4. We validate the molecular dynamics simulations by demonstrating agreement in small-angle X-ray scattering profiles from MD ensembles with experimental data.

On 2021-01-19 17:07:01, user Martin Styner wrote:

Overall very nice work. The only potential worry I have is that sulcal depth is used by FreeSurfer (the software that generates the cortical surfaces and measurements) to establish the location-wise correspondence across subjects. It is thus expected that sulcal depth measurements have a significantly higher bias and methodologically-induced tighter distribution as compared to cortical thickness and surface area, which might explain in part the observed effects. It would be important to see whether other, comparatively unbiased cortical folding measures (such as local gyrification index) can confirm the patterns observed here.

On 2021-01-18 18:15:16, user mauromanassi wrote:

Nice work! You may want to have a look at Pizzagalli et el 2020 https://www.nature.com/arti...

On 2021-01-19 16:53:03, user antonio_j_p_rez wrote:

It has been already publish in Briefings in Bioinformatics:<br /> https://academic.oup.com/bi...

We hope that some of the proposed regulators can be useful as a putative target in the fighting against SARS-CoV-2.

On 2021-01-19 15:51:02, user Martin R. Smith wrote:

This is an interesting article; I wonder whether there is a good justification for your choice of the Robinson–Foulds distance? In particular, its discrete nature and narrow range makes me a little nervous of using confidence intervals, particularly if generated from random trees (most of which will exhibit a near-maximum RF value). I have explored some alternative measures, which might be more robust in this context, at https://doi.org/10.1093/bio... (Smith 2020, Bioinformatics)

On 2021-01-19 09:39:52, user Andreas Ritter wrote:

Dear colleagues,<br /> Congratulations for this very nice pre-print.

Me and my lab worked quite some time on a similar topic.<br /> Since Miyamoto et al. showed in 2015 that KIF2C could be moderatly involved in the regulation of ciliogenesis, we tried to corroborate their data. <br /> We used siRNA, GFP-MCAK overexpression constructs, stable CRISPR dCas9 i/a system and looked for ciliated cells (with starvation/without starvation), cilia length, cilia stability, Cilia MT-modifications etc., but we can’t find a single parameter which was significantly changed in HeLa or hTERT RPE-1 cells on any of the above-mentioned conditions.

Do you have a quantification, how many folds you overexpressed KIF2C in these cells?

Did you verify these results in other cell lines?

Is it possible that SCFFbxw5 also stabilizes KIF2A on the basal body, which is known to have a huge impact on ciliogenesis / cilia length?

Thanks again for sharing these interesting results.

Best regards,<br /> Andreas Ritter

On 2021-01-18 22:57:41, user Charles Warden wrote:

Hi,

Thank you for posting this pre-print.

I think Bowtie1 can work well with miRNA-Seq data, but you have to trim out the adapter first.

I don't believe that I saw a step in the methods describing adapter trimming before alignment. Did I miss something?

If not, would it be possible to either add comparisons to show the effect of trimming, or possibly replace the Bowtie1 statistics include an upstream trimming step? If you are starting with 51 bp or longer reads, then I think that could be relevant.

Thank You,<br /> Charles

On 2021-01-18 14:35:44, user Paul Macklin wrote:

Published at:

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

On 2021-01-07 18:55:53, user Paul Macklin wrote:

In press at Scientific Reports (2021)

On 2021-01-18 08:53:01, user The_Zman wrote:

There's an error in the liganddock.xml protocol file, there's a missing closing tag for <scorefxns>

On 2021-01-18 02:17:34, user Steven Hart wrote:

I'm not exactly seeing how this is any different than what I published in 2014....<br /> https://pubmed.ncbi.nlm.nih...

On 2021-01-18 00:52:37, user Sidney wrote:

The authors may want to take a look at the paper Alves et al. 2020, Global Ecology and Biogeography.

On 2021-01-17 22:08:49, user Peter Cabal wrote:

if this current vaccination program fails, this drug might stop/slow down this endemic pandemic

On 2021-01-17 08:27:43, user SDN wrote:

Peer-reviewed version here, DOI: 10.1021/acssynbio.0c00318

On 2021-01-17 06:24:27, user hannah wrote:

Hi may I know, any treatment for infected plant or I just burn it and bury dip?

On 2021-01-16 19:09:46, user Nicholas Markham wrote:

McAllister et al. have generously posted their excellent C. difficile physiology manuscript on bioRxiv. This careful investigation of how selenophosphate synthetase governs metabolism exemplifies the power of CRISPR-Cas9-mediated bacterial gene deletion and restoration. Thank you to the authors for sharing their work. It has introduced me to Strickland metabolism, and I expect the reviews will be positive. I wonder if referees will ask for more discussion on what molecular mechanisms are responsible for the difference in phenotype between plasmid complementation and gene editing. They might wish to see how protein levels are similar or different. It’s understandable one wouldn’t speculate on how atmospheric hydrogen makes a striking difference in phenotype, but I’m very curious to think about how this variable affects the whole field!

On 2021-01-16 17:58:54, user xuboniu wrote:

Nice work! This story is very good, and it is consistent with our previous work (Tendon Cell Regeneration Is Mediated by Attachment Site-Resident Progenitors and BMP Signaling. Curr Biol. 2020 Sep 7;30(17):3277-3292), which also showed tendon cell ablation in zebrafish disrupted muscle morphology.

On 2021-01-16 15:31:58, user John Doe wrote:

Great study! I did noticed however that the studies above only included a percentage of 1rm to fatigue (failure) but not a rest time between sets. My question is, what should the rest time between sets look like, or does this even matter. For example, if I were to perform a set of 12 reps at 80% 1rm to fatigue (failure) I know that my second set would be much lower in rep count that my first if I use only 30 seconds rest. In order to provide a higher volume what should this maximum time be, and does this really matter? Some help here would be so appreciated. Thanks, Michelle.

On 2021-01-16 10:15:47, user Phil Carl wrote:

This article has now been peer-reviewed and published in the Western Ocean <br /> Journal of Marine Science in 2018. The correct citation of this paper <br /> is:<br /> Laboute P, Borsa P (2018) A feeding aggregation of Omura‘s whale <br /> off Nosy Be, Mozambique Channel. Western Indian Ocean Journal of Marine Science 17, 93-97. doi : 10.1101/311043

On 2021-01-16 06:35:25, user Mikhail V. Kozlov wrote:

We, the authors of the paper criticized in this preprint (Kozlov, M.V., Sokolova, I.V., Zverev, V., Egorov, A.E., Goncharov, M.Y. & Zvereva, E.L. 2020. Biases in estimation of insect herbivory from herbarium specimens. Scientific Reports, 10, 12298; doi: 10.1038/s41598-020-69195-5), address this comment to scientists who are interested in obtaining historical data on insect herbivory by analyzing herbarium specimens. The manuscript by Meineke et al. (doi: 10.1101/2020.09.01.278606) was submitted to Scientific Reports, and we were invited to submit our response to the same journal. Several days ago we were informed that Scientific Reports, based on the results of peer review, declined to publish our discussion. Although this decision was disappointing, we were pleased to learn that one of the reviewers found our counter-arguments “stronger, more complete and more statistically supported” than the criticism by Meineke and her co-authors. Furthermore, another reviewer, despite appreciating “the desire to find some way to exploit the potentially rich source of information in herbaria and other natural history collections”, warned that “if we do so before we have a firm grasp of potential errors and biases we may end up slowing the development of science, as spurious results stimulate even more papers and grant applications based upon false or very shaky scientific premises”. At the same time, reviewers also criticized some details of our response to Meineke et al., and we plan to account for their insightful comments at the next stage of our discussion. We kindly ask the readers of this preprint remain critical to its content and refrain from citing it until its revised version, accompanied by our response, is published in a peer reviewed journal. In conclusion, we thank Emily Meineke and her colleagues for initiating this discussion, which hopefully will attract the attention of scientists to the pitfalls and caveats associated with the use of herbivory data collected from herbarium specimens in global change research. Mikhail V. Kozlov (mikoz@utu.fi), on behalf of all co-authors.

On 2021-01-16 04:04:18, user Anshul Kundaje wrote:

Could you please update the preprint with a link to the code and the models

On 2021-01-15 17:24:31, user Richard Stopforth wrote:

Dear Haizhang and colleagues,

First of all, congratulations on this work and very interesting data.

I wanted to comment that I have previously developed an assay capable of detecting soluble immune complexes, by measuring SHIP-1 recruitment to human FcgRIIb (https://pubmed.ncbi.nlm.nih.... Although employing a different system (detection of inhibitory as opposed to activatory FcgR signalling), I think this paper is worthy of a reference, if only to highlight its differences or limitations.

Your observation that FcgRIIA is non-responsive to soluble immune complexes is very interesting, as I found FcgRIIA (H131) to pair very effectively with FcgRIIb in the activation of SHIP-1 recruitment to FcgRIIb in response to soluble immune complexes. This is presumably caused by the licensing of inhibitory signalling by activatory FcgRIIA-inhibitory FcgRIIb co-ligation. Altogether, this possibly indicates that activatory FcgRs may have differing roles in the regulation of activatory or inhibitory signalling in response to soluble immune complexes? In relation to this, I was wondering if you co-expressed your FcgRIIb/c and FcgRIIa constructs in your reporter cells, whether you could crosslink these receptors (i.e. with anti-human FcgRIIa/b, AT-10) and detect any differences in comparison to both 1) individual receptors alone and 2) soluble immune complexes?

Kind regards,

Richard Stopforth

University of Southampton<br /> R.J.Stopforth@soton.ac.uk

On 2021-01-15 16:09:07, user Oliver Pescott wrote:

The statement in the discussion that "model-averaging is an empirical shrinkage estimator and it is quite easy to show using simulation that under conditions of small to moderate signal-to-noise ratio, model-averaging, which averages over incorrectly specified models, outperforms OLS or ML estimates of the correctly specified model" is very interesting. Is it possible to provide a reference for this, or to give some indication of what is considered to be a "small to moderate signal-to-noise ratio"?

On 2021-01-15 11:30:54, user Wiep Klaas Smits wrote:

Interesting work! I think you may find the following reference relevant for your analysis: https://pubmed.ncbi.nlm.nih...

On 2021-01-15 01:02:38, user James Rule wrote:

Hi, this paper looks great, and I am looking forward to it coming out. I just have some quick feedback on synonymizing some of the Phocini Genera (Halichoerus, Phoca, Pusa) into Phoca.

You state that this was done "in the absence of striking morphological data"; However, the morphology of these Genera, particularly in the skull and bony ear region, are starkly different. Therefore, I do not think this is warranted.

This was one of the reasons why these names have been preserved in the most recent review of pinniped taxonomy (https://doi.org/10.1111/j.1....

Feel free to get in touch if you want to discuss the morphological differences further.

On 2021-01-14 16:44:28, user kmorano wrote:

Hey all, excited to have this up but N.B. - small error in typesetting to be corrected shortly, SBD-gamma in abstract should be SBD-beta!

On 2021-01-14 16:28:49, user Simon Zinkhan wrote:

Our manuscript has since been accepted by the Journal of Controlled Release. Find the updated version here: https://www.sciencedirect.c....<br /> Thank you for showing an interest in our research!

On 2021-01-14 15:01:40, user Lamya Ghenim wrote:

Our preprint has been published and a link will be forthcoming.

On 2021-01-14 13:35:30, user Lamya Ghenim wrote:

our preprint has been published and a link will be forthcoming. lamya Ghenim

On 2021-01-14 13:33:59, user Alex wrote:

Interesting discovery highlighting the interplay between epigenetic proteins and metabolic reprogramming. Great wok by Jamia Millia Islamia University researchers!

On 2021-01-14 07:07:41, user Alessandro Stern wrote:

Very interesting preprint! However, where can I download the supplementary table files? Couldn't find them anywhere? Many thanks in advance!

On 2021-01-13 23:31:25, user Ross Wang wrote:

This preprint has been updated and has been published by J. Am. Chem. Soc. https://pubs.acs.org/doi/10...<br /> A link between the preprint and the paper will be forthcoming.

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 2021-01-13 01:04:18, user Ross Wang wrote:

This preprint has been updated and has been published by J. Am. Chem. Soc. https://pubs.acs.org/doi/10...<br /> A link between the preprint and the paper will be forthcoming.

On 2021-01-13 19:38:50, user Moh wrote:

Is there any codes available for this methods in Python or R?

On 2021-01-13 16:52:01, user José L Medina-Franco wrote:

A peer-reviewed version of this paper has been published in http://dx.doi.org/10.3390/p... Juárez-Mercado K, Prieto-Martínez F, Sánchez-Cruz N, Peña-Castillo A, Prada-Gracia D and Medina-Franco J. 2021. Expanding the Structural Diversity of DNA Methyltransferase Inhibitors PHARMACEUTICALS, 14, 17.

On 2021-01-13 08:46:01, user Ivan Molineris wrote:

If I understood well the annotation "ENSEMBL" used in this paper correspond to the _comprehensive_ GENCODE annotation. What about _basic_ GENCODE annotation?

On 2021-01-12 19:41:37, user Patrick O'Connell wrote:

When studying SLAMF7 signaling the method used to activate or block the receptor is critical. Adding anti-SLAMF7 clone 162.1 to cell culture will BLOCK SLAMF7 signaling, not activate it. To activate the receptor you must cross-link the antibodies to a plate (which can be troubling w/ adherent cells like macrophages). We have seen similar IFN responses, but the opposite of those here. When we add the antibody in a soluble form we see results similar to those here, suggesting you are indeed blocking the receptor and not activating it.

Reference: SLAMF7 Is a Critical Negative Regulator of IFN-α–Mediated CXCL10 Production in Chronic HIV Infection. Patrick O’Connell, Yuliya Pepelyayeva, Maja K. Blake, Sean Hyslop, Robert B. Crawford, Michael D. Rizzo, Cristiane Pereira-Hicks, Sarah Godbehere, Linda Dale, Peter Gulick, Norbert E. Kaminski, Andrea Amalfitano, Yasser A. Aldhamen. The Journal of Immunology January 1, 2019, 202 (1) 228-238; DOI: 10.4049/jimmunol.1800847

On 2021-01-12 18:18:55, user Paige wrote:

Your online resource supplement 1 does not contain the information regarding your fusion primers as is indicated in your paper:<br /> "For the second PCR step, 1 µl of<br /> the product obtained from the first PCR was used and amplification was performed using fusion<br /> primers with individual tags per sample (see Electronic Supplemental Material, Online Resource 1,<br /> section 2.3)"

On 2021-01-12 17:25:55, user Fraser Lab wrote:

Summary:<br /> This manuscript by Huss, P., et al, is major technological step forward for high throughput phage research and is a deep dive into the deep mutational landscape of a portion of the T7 Phage receptor binding protein (RBP). The author develop a new phage genome engineering method, ORACLE, that can generate a library of in any region of the phage genome. They apply ORACLE to do a deep mutational scan of the tip domain of T7 RBP and screen for enrichment in several bacterial. The authors find that different hosts give rise to distinct mutational profiles. Exterior loops involved in specialization towards a host appear to have the highest differential mutational sensitivity. The authors follow up these general scans in the background of phage resistant hosts. They find mutations that rescue phage infection. To demonstrate the utility of the approach on a clinically relevant task, the authors apply the library to a urinary tract associated clinical isolate and produce a phage with much higher specificity, creating a potentially powerful narrow scope antibiotic.

Overall, the ORACLE method will be of tremendous use for the phage field solving a technical challenge associated with phage engineering and will illuminate new aspects of the bacterial host-phage interactions. It was also quite nice to see host-specialization validated and further explored with the screens done in the background of phage resistance mutations. The authors do a tremendous job digging into potential mechanisms when possible by which mutations could be altering fitness. We especially appreciate how well identity of amino acids tracks host specialization within exterior loops.

We have no major concerns about the manuscript but have some minor comments to aid interpretation. There are also some minor technical issues. We think this manuscript will be of broad interest, especially for those in the genotype-phenotype, phage biology, and host-pathogen fields.

Minor comments:

P5L20: In the introduction to the ORACLE section the authors mention homologous recombination then they mention using 'optimized recombination' that is done with recombinases. This contrast should be mentioned somewhere perhaps to highlight the benefit of having specific recombinases.

P6L16: Using Cas9 to cut unrecombined variants is clever... Cool! This is a real 21st Century Dpn1 idea.

P6L27 The authors state that there is a mild skew towards more abundant members after ORACLE. Why might this be? In iterations more abundant members simply become even more abundant? To be clear this isn't a substantial limitation and it's common to see these sorts of changes during library generation. Just curious. Overall looks like a fantastic method.

P7L6: Authors mention ORACLE increases the throughput of screens by 3-4 orders of magnitude. How many variants can one screen? Is this screen of a little over 1k variants at about the threshold of the assay?

P8L7: The authors assign functional scores based on enrichment and normalize to wild type. Is a FN=1 equivalent to wild type?

P9L5: Awesome!

P10L7: Authors mention R542 forms a hook with a receptor. There should be a citation here.

P10L21: For N501, R542, G479, D540 there are wonderful mechanistic explanations. However, for D520 there is not. Any hypothesis for why this is distinct from the others? Are there other residues that behave similarly? I feel it would be really helpful to have a color scale that discriminates between FN 1 (assuming wild type) and enriched/depleted w/in figure 3A.

P12L4: Authors note residues that are surface exposed yet intolerant to mutations in the previous paragraph. Authors also calculate free energy changes with Rosetta and state free energy maps pretty well with tolerant. What is the 93% based on? Perhaps a truth/contingency table would be useful here to discriminate compare groupings. What residues are in the 7% others. Can the energy scores help understand the mechanisms behind the mutations better?

P12L7: Authors state substitutions predicted to stable and classified intolerant could indicate residues necessary for all hosts. What about those that fall outside of the groupings? Unstable residues can also be necessary.

P14L22L Authors mention comparing systematic truncations, however they do not present any figure. This should be in a figure to aid in looking at the data and would surely be helpful to people in the phage field. A figure should be included here especially because this is one of the main discussion topics at the end of the manuscript.

P16L2: The authors did the selection in the background of a clinically isolated strained and discuss 3 variants that were clonal characterized. Was this library sequenced similar to before?

Figures:<br /> Barplots needs significance tests.

Figure 2C-E ; Fig 3A. All figures are colored white to red. With this color scale it's hard to appreciate which variants are neutral vs those that are enriched. A two or more color scale would be more appropriate. Log-scaling might be wise to get a better sense of the dynamic range that is clearly present in fig2F.

FIg 4F: Needs a statistical test between bar plots.

Fig6A-C: These figures have tiny symbols that represent the architecture at an insertion position. It's probably easier to look at if the same annotations from Fig 4B or C for architecture were used.

Fig6D: needs tests for significance

Supp fig 4E: This figure is the first evidence that the physics chemistry of amino acids w/in surface exposed loops determine host specificity. This is followed up by Figure 4D and E. I would consider moving this to one of the main figures.

Supp fig 5: A truth table could be useful here to test for ability to classify based on rosetta compared to FD. It looks like here that the tolerant residues have a distinct pattern

Why are these colored white to red? Perhaps

Minor typo:<br /> P7L11: relationships not 'relationship'

Reviewed by James Fraser and Willow Coyote-Maestas (UCSF)

On 2021-01-12 16:13:53, user Sui Huang wrote:

Very nice finding - supporting the old idea of "cancer attractors": that cancer is not so much due to mutational (or epigenetic) innovation of new abnormal phenotype, but actually a dysregulation in cell type determination and of tissue homeostasis that leads to the INAPPROPRATE OCCUPATION of a PRE-EXISTING cell type attractor - that is there, but must not be accessed in normal conditions.

On 2021-01-11 23:01:21, user Adrian (Abbas) Salavaty wrote:

The peer reviewed and final version of this article has been published in Patterns. See the following links.<br /> https://doi.org/10.1016/j.p...<br /> https://pubmed.ncbi.nlm.nih...

On 2021-01-11 15:09:51, user Jane Giller wrote:

I have been looking for specific information regarding the mutation of the spike protein in the faster spreading SARS-CoV-2 virus and whether this renders any immunity the host had,from prior infection, redundant? I may be asking the question in the wrong way but thus far I have found no evidence or research that says this is the case. Can you possibly shed some light on this or link to some research on this, please?

On 2021-01-11 14:55:00, user Ariane wrote:

Hello,<br /> We tried the online tool and the one downloaded from GitHub. We didn't get the same results. Are they 2 different versions (and which one would be better)? Or do you use specific parameters on the online tool?

On 2021-01-11 12:03:54, user YC Foo wrote:

Hello! Many thanks for making this available here! May I know if it would be possible to gain access to the Supplemental Tables mentioned here in the article, specifically Sup. Table 2? I'm afraid I've not been able to locate them from the text. Many thanks!

On 2021-01-11 04:28:40, user Tudor wrote:

Almost everyone in this comments section is sadly conflating anti-vaxx rethoric and a published scientific paper, from a reputable group, from reputable institutions. Not only does the paper not address vaccines in any way, but it's absolutely irrelevant whether it feeds anti-vaxxers, flat-earthers or radical relligious nuts. What is relevant is whether the science presented stands to scrutiny and how high the bar needs to be set.<br /> There should absolutely be no discussion of retraction or censorship, instead we should constructively suggest corrections or additional experimental data to address the flaws. Ideally, there should be other groups challenging this by doing their own experiments. The hypothesis is plausible and there is a plethora of data on the genome integration of ARN viruses (not retroviruses), but almost no data on which eRT got them there and how. <br /> Hopefully some peer-reviewers somewhere are looking at this constructively and scientifically and not hysterically, like some here.

On 2021-01-04 08:07:16, user Blue Horizon wrote:

Removing this paper is neither the wise nor scientific way of handling it (censorship never is). Rationally and evidence-based tearing it apart is. If neccessary explain in relatable terms to the public why this is irrelevant and/or nonsensical. Yes this appears to be a highy speculative low quality piece of work. But let's just assume for a second it was a valid assumption and the sars-cov2 virus could integrate into the host genome. First of all this would not be a sensational result, other viruses are doing this for a long time and not always with negative consequences for the host. Second: if this was the case it would be an additional argument in favor of vaccination because the apparent problem would be the virus integrating into the genome, not the (in itself harmless) spike protein alone (as in the vaccines in question). And the longer you let the virus roam and replicate (naturally) in the host, the higher the likelihood of it reverse transcribing into the host genome. Also the main scare-argument of anti-vaxxers is the speculative claim of potential carcinogenicity<br /> while there is no evidence of such in regards to this specific process of sequence integration.

On 2020-12-31 14:32:19, user Berry Van Rossum wrote:

Dear authors, while not being an expert in this field, thus I am probably mistaken here, but think I see a misalignment in the base-pair coding in example fig1c. The fifth one, being T in the human chromosoom, links to a G in the 92nt mapped SARS-COV-2 genome. Again, I am no expert in this field, but maybe something to verify? With highest regards, Berry van Rossum, Organic Chemist, The Netherlands.

On 2020-12-28 09:41:45, user Alberto Villena wrote:

At the end of the Results, they say: “In summary, our results show induced LINE-1 expression in cells stressed by viral infection or exposed to cytokines, SUGGESTING a molecular mechanism for SARS-CoV-2 retro-integration in human cells.”

And then, in the first paragraph of the Discussion: “In this study, we showed evidence that SARS-CoV-2 RNAs CAN be reverse-transcribed and integrated into the human genome...”

How it is possible to go from “suggesting” to “can”?

I would suggest that recurrent PCR positive results would also come from stress granules that accumulated mRNA when produced in excessive amounts, which is a typical result of viral infection and replication. Stress granules are quite resistant and may remain in the tissues for long times.

On 2020-12-22 16:14:17, user Melissa Booth@The Science Comm wrote:

This paper should be removed from this PRE-PRINT (non-peer-reviewed) server. The experiments used in this study do NOT address the question that the authors’ are posing about integration of genomic material from SARS-CoV-2 into host (human) cells.

Regardless of the authors’ intent, the premature posting of this study appears to be pandemic band-wagoning, or worse, scientific grandstanding. The intent of pre-print servers is to make the iterative nature of human scientific endeavor more inclusive and transparent. However, the information ecosystems that we all share are polluted by social media algorithms and malicious actors which propagate misinformation faster than facts. As scientists, we must consider how we interact with contemporary information systems.

As a microbiologist and molecular geneticist, it is clear to me that the experiments in this PREPRINT (not peer-reviewed) paper, do not support the claims in the title.

Chimeric sequence reads that the researchers found in the sequence databases are likely artifacts from the preparation of the RNA libraries. These chimera artifacts are a common phenomenon in total RNA sequence library construction from complex samples whether the source material is from humans, soil, sewage, ocean water, etc., and these artifacts end up in sequence databases. The other lab bench experiments DO NOT show that SARS-CoV-2 RNA is reverse transcribed to DNA, transported into the nucleus and then integrated into the host genome under NORMAL conditions. However, there are assays that could answer the question about viral integration. The authors could collect samples from infected patients that are still shedding virus but are non-infectious (as they mention in their summary) and perform Southern Blot analysis to determine if viral sequences have indeed been integrated into the host genome. And ultimately, IF the researchers find integration under normal conditions, the next questions follow: Are infected cells persistent? Do these integrated elements propagate in host cells? These are the questions that get to their hypothesis about purported viral integration elements being responsible for persistent detection of virus in non-infectious, post-COVID patients.

P.S. If the authors do not have clearance/desire for human clinical research, how about looking for integration in minks or other animals that are currently suffering from SARS-CoV-2 infections?

On 2021-01-11 03:30:48, user Daniel Isenegger wrote:

Would be good if this paper compared promoter activity relative to ZmUBI not 35S in monocots.

On 2021-01-10 19:22:53, user David Dubnau wrote:

This paper is certainly not "science". It is clear that the usually excellent and appropriately minimal bioRix gate-keeping procedures slipped up with this paper. In this era of skepticism toward science and discrediting of rational thinking, it is doubly important to maintain standards. I hope this does not happen again.

On 2021-01-10 03:06:19, user Nir wrote:

BioRxiv, please remove this manuscript. It is pseudo science, and have no place in the archives.

On 2021-01-10 11:19:03, user Roberta Creti wrote:

Reading also at your article in PNAS brought me 12 years ago back (Creti R, Baldassarri L, Montanaro L, Arciola CR. The Alpha-like surface proteins: an example of an expanding family of adhesins. Int J Artif Organs. 2008 Sep;31(9):834-40. doi: 10.1177/039139880803100912. PMID: 18924096). Glad to know this topic has new life thanks to your work!

On 2021-01-10 10:20:45, user Stefano Campanaro wrote:

Dear Francisco Zorrilla, Kiran R. Patil and Aleksej Zelezniak,<br /> I read your preprint and I really appreciated it. However, I would like to mention that we have recently demonstrated the feasibility of reconstructing the GEMS starting from Metagenome Assembled Genomes for hundreds of species and a series of microbial communities associated with the anaerobic digestion environment. Our paper was recently published in "Metabolic Engineering" with the title "Revealing metabolic mechanisms of interaction in the anaerobic digestion microbiome by flux balance analysis" (DOI: 10.1016/j.ymben.2020.08.013). The procedure used is similar to the one you reported and based on assembly with Megahit, binning with Metabat2, quality evaluation with checkM, GEMs reconstruction using CarveMe, evaluation of the GEMs using Memote. Additionally, we performed an additional series of analyses and verifications using other software. Without diminishing the importance of your study, on behalf of my co-authors, I think it could be interesting for you to compare the procedure reported in your preprint and the results obtained with our one.<br /> Thanks a lot.<br /> Sincerely<br /> Stefano Campanaro<br /> Associate Professor<br /> Department of Biology University of Padova

On 2021-01-09 19:44:10, user Miha Kosmač wrote:

Hi Izzy and Rafa,

I've really enjoyed reading your manuscript. If it's not too computationally expensive I think it could be a really nice way of assigning known cell types with considerable certainty. <br /> I guess the one thing I am a bit iffy on is the paragraph on the canonical CD4 marker genes. FOXP3 is a generally accepted marker of Tregs (a particular and usually very small subset of CD4 T cells). PTPRC (a.k.a. CD45) is used as a pan-immune cell marker and IL7R (CD127) is used as a marker of memory B cells as well as an exclusion marker for Tregs (CD3+ CD4+ CD25+ CD127-). In flow cytometry there is usually a hierarchy of how the markers are used for cell assignment, which I suppose is lost when people use them in scRNA-seq. <br /> I think the question of marker specificity is a very complex problem in cell biology. It is always dependent on what cell types are being compared and specific markers in absolute terms are very rare. <br /> I thought I'd share my thoughts on this particular aspect of the manuscript. Thank you for both the publication of the framework and the code.

Best wishes,<br /> Miha

On 2021-01-09 14:43:25, user Arlin Stoltzfus wrote:

Nice work. I have some questions. The main argument of the paper is that a case of extreme parallelism is caused by extreme non-uniformity of rates of mutation, rather than by extreme non-uniformity of fixation probabilities caused by fitness differences. (1) Why is there no measurement of the rate of the A289C mutation with and without the enhancing context? (2) Why does the title of the paper refer to synonymous sequences? Saying that synonymous sequences facilitate parallel evolution is a very strange way of reporting extreme parallelism caused by a mutational hotspot.

On 2021-01-09 10:40:02, user UKBadri wrote:

A very positive news indeed in these gloomy times as countries race to vaccinate their residents based on their local priorities and hats off to Pfizer BionTec for prioritising this as they must be very busy right now. Interestingly I picked up the preprint from the press release probably aimed at the wider world including the investors. It would have been helpful if the authors had further expanded the field implications of their work on the potential implications on the ;progress of the pandemic and in our efforts to protect the population as we face the strengthened foe - the more transmissible variant - Dr P Badrinath, PH Physician & Epidemiologist, Suffolk, UK .<br /> Disclaimer: These are my personal views and do not represent that of my employer.

On 2021-01-08 15:00:42, user kunikoinoue wrote:

The conclusion of this study is not strong with only in vitro binding<br /> assay. The key problem is that they only use one mutation 501Y. The UK <br /> B117 variant has 8 mutation including another key mutation 69-70del: which was found in strains that eluded the immune response in some immunocompromised patients.

To understand in clinical how severe COVID variant escape from <br /> antibodies, Please look at UK paper published on Biorxiv 1-2 week <br /> before . COVID variant spike deletion H69/V70 and D796H mutations <br /> conferred reduced susceptibility to the convalescent plasma (CP) and <br /> sera from multiple donors.

Curiously, the authors need investigate How many vaccinated people in their July Trial <br /> were infected COVID including B117?

On 2021-01-08 13:52:05, user Rodrigo L. O. R. Cunha wrote:

This piece of work explores further the effect of hypervalent tellurium compounds on cells, complementing another of our group's recent contribution, where we showed the antitumor activity on an experimental melanoma model (DOI: 10.1016/j.bmc.2019.03.032). These compounds have also neglected in vivo acute toxicity, and the cellular feedback we observed herein may be related to this!

On 2021-01-07 17:11:28, user Krishna Aluri wrote:

Review of manuscript: Insight into the autosomal-dominant inheritance pattern of SOD1-associated ALS from native mass spectrometry Jelena Cveticanin, Tridib Mondal, Elizabeth M. Meiering, Michal Sharon, and Amnon Horovitz

This review was done as part of the SfN Reviewer Mentor Program (Mentor: Dr. Antonio Vicente Ferrer-Montiel, Mentee: Krishna C. Aluri

The manuscript is an interesting application of double mutant type cycle analysis in combination with native mass spectrometry. The manuscript provides insights into effect of preferential association of wild-type and mutant monomers and their effects the disease epidemiology. This study expands our current understanding of various studies that showed correlation of physicochemical properties of mutants to ALS epidemiology. In my view, this type of analysis can be applied to other autosomal- dominant diseases and can be valuable. The manuscript can be accepted with some minor revisions.<br /> Revisions: <br /> 1. Rephrase abstract sentence “The disease has an autosomal-dominant inheritance pattern”. ALS has both autosomal and recessive forms even though majority is autosomal dominant the sentence should reflect it. <br /> 2. The authors did not include the statistical analysis section in the methods (what method was used for calculating correlation coefficient? What software was used?) <br /> 3. Figure 5A and Figure S2 only showed error bars for coupling constant measurements, it will be informative to see error bars for disease duration and age of onset

Additional Questions:

1. The authors mentioned that most of the studies observed correlation between monomer stability and disease duration. Is there a correlation between the monomer stability and coupling constant? It would be interesting to if such correlation exists. In other words, does the mutants with less monomer stability have higher coupling constants?
2. The authors noted that there is a positive correlation between age of onset and coupling constants, but they did not evaluate the correlation of coupling constant and overall lifetimes of the ALS patients as performed in previous studies

On 2021-01-07 08:47:51, user Andreas Brune wrote:

An interesting and important study - thanks for sharing the preprint!

The high abundance of Bathyarchaeia in an insect-feeding newt is particularly intriguing. Note that Friedrich et al. (2001) (and others thereafter) already detected Bathyarchaeia in termite guts. The genomes were published last year (https://doi.org/10.7717/pee... they are not methanogens but potential acetogens (https://doi.org/10.1101/202....

Best wishes, <br /> Andreas

On 2021-01-07 08:14:42, user Prof. T. K. Wood wrote:

As always for these type of systems, there are no compelling data for invoking "death" throughout the manuscript. Overexpression does not equate to physiological relevance.

On 2021-01-07 02:36:36, user Raghu Parthasarathy wrote:

As I noted for the first version: Isn't it very well known that, for example, smelling perfume across a room is not due to diffusion -- far too slow for any reasonable diffusion coefficient -- but rather due to convection and other active flows? <br /> There may be a convection-related effective D, but no one would ever think that the Stokes-Einstein D (Eq. 36) describes motion of odors in air.

On 2021-01-01 06:20:38, user Raghu Parthasarathy wrote:

Isn't it very well known that, for example, smelling perfume across a room is not due to diffusion -- far too slow for any reasonable diffusion coefficient -- but rather due to convection and other active flows?

On 2021-01-06 18:56:32, user turnersd wrote:

The web app doesn't look to provide a data download/export capability, and the supplemental material is listed as closed access: https://zenodo.org/record/4...

On 2021-01-06 17:52:08, user Сергей Владимиров wrote:

Good day! Have you isolated the virus SARS-CoV-2 ? Are there any publications on virus isolation? Thank you!

On 2021-01-06 15:24:17, user Paul Robustelli wrote:

I enjoyed reading your paper.

I was struck by the fact that among the MD simulation details reported, you do not explicitly state the protein force field you used in the text. What protein force field did you use?

On 2021-01-05 19:12:54, user Johanna N. wrote:

Hi

I have two minor questions/comments regarding the method section:<br /> 1) For how long did you incubate the cells with EdU? I assume it's rather a short time.<br /> 2) You state that you normalize cell=level data by using median and MAD from "empty wells". Do you maybe rather mean from "untreated" wells?

On 2021-01-05 09:29:51, user Lillian Kofod wrote:

Thank you for your good work. It is so important to understand the indoor environment and how it infect our health. We need more nature into the indoor living to become more healthy.

On 2021-01-05 00:58:07, user Charles Warden wrote:

Hi,

Thank you for posting this pre-print.

I see that this pre-print has both supplementary material and a link to code:

https://www.biorxiv.org/con...

https://github.com/OSU-BMBL...

However, the section for "Supplementary Materials" says "Supplementary Data are available at Science Advance online".

Is this intentional (perhaps part of an automatic submission from a journal?), or should the section say something else?

Best Wishes,<br /> Charles

On 2021-01-04 22:06:22, user Emanuel Goldman wrote:

The description of fomite transmission is incomplete. The authors write,

"At 4 DPI, donors were euthanized, and sentinel animals (2 animals per cage) were placed into the contaminated cage". What is the time interval between removal of the donors and entry of the sentinels? If it was done within an hour or two, airborne transmission cannot be excluded.<br /> Emanuel Goldman (author of reference 47) egoldman@njms.rutgers.edu

On 2021-01-04 21:17:35, user drjenniferomanilay wrote:

Our revised paper was just accepted for publication at the Journal of Immunology!

On 2021-01-04 19:04:26, user Macacamulatta wrote:

I don't see Fig. 8 in the pre-print.

On 2021-01-04 17:24:36, user Johanna N. wrote:

This is a nice manuscript with important conclusions. As a Cell Painting person, I am of course delighted to see that Cell Painting might provide more benefit than the L1000.

I'd like to point out one small caveat: I would have found it beneficial if more of the assays would have been annotated. It seems that the largest part is annotated as "unknown", which makes the subfigure less informative.

On 2021-01-04 15:07:33, user Дмитрий Карабанов wrote:

Karabanov D.P., Pavlov D.D., Bazarov M.I., Borovikova E.A., Gerasimov Yu.V., Kodukhova Yu.V., Smirnov A.K., Stolbunov I.A. Alien species of fish in the littoral of Volga and Kama reservoirs (Results of complex expeditions of IBIW RAS in 2005–2017) // Transactions of IBIW RAS. 2018. Issue 82(85). P. 67-80.

On 2021-01-04 07:58:33, user Robert wrote:

Dear Gabriele, thanks for sharing this pre-print! I assume (because it's a preprint...) you also want to discuss your findings, and I would like to enter that discussion.

First and foremost, I would like to thank you for posting your findings! I was not aware of the geometric relationship you highlight, and also you share data about kinetic measurements of enzymes in your manuscript. You provided a graphical interface to some calculations, that I am sure is helpful for people doing that calculations regularly. Thanks!

I would like to hook into the geometric relationship first: you present it as a viable alternative other fitting algorithms. Could you elaborate how it is different from conventional linear fitting, i.e. applying the relationship often-presented-as "y = a*x + b"?

Further, the Lineweaver-Burk plotting is known for introducing errors of variable size into the parameters, depending on the location of measurement points; this can be greatly shown in your geometric elucidation, because the tangens function will react very sensitively to some points, and less sensitive to other combinations. Good reviews of the Lineweaver-Burk "problems" are for example found by Athel Cornish-Bowden (and discussed together with other plot forms).

I fear that your algorithm will produce -- for some experimental set-ups, not for all! -- parameters with very large deviation from the "true" ones. That is why non-linear regression (i.e. direct fitting of the data to the actual function, without any linearization steps) is actually a good thing.

What I value very much is that you present (probably new?) data about aldehyde dehydrogenase and xanthine oxidase. You also share the raw data, which I value even more! Could you also provide some methodological details about how you generated those values? Being two-substrate enzymes, e.g. the concentration of the non-varied substrate would be interesting, but of course also the exact enzyme identity, etc. You might find the STRENDA guidelines a helpful read, they present things to report to make the enzymology field more reproducible: https://www.beilstein-insti...

Looking forward to enter a discussion with you, Robert