On 2020-05-08 16:33:47, user Jasper Grendel wrote:

Hi, your Short_Manual.txt in your github scripts suggest to "run the command 'demo_gui' (without the ') in the commandline of matlab.". There is no 'demo-gui'-function to refer to.

On 2020-05-08 16:15:27, user Jessica Riesterer wrote:

This has been published! Please check it out as part of Methods in Cell Biology:<br /> https://doi.org/10.1016/bs....

On 2020-05-08 15:18:05, user Manish Kumar wrote:

Supp Fig. 6. Technical typo: f = 4 mm for 50x Nikon.

On 2020-05-08 15:02:21, user L0r3nz0 wrote:

Thanks for this very useful resource!<br /> Would it be possible to make the GEO repository public? At the moment the raw data cannot be downloaded: https://drive.google.com/op...

Thanks again!<br /> Lorenzo Calviello

On 2020-05-08 13:14:17, user Lucia Theodoro Freitas wrote:

https://jvi.asm.org/content... - maybe would be better take a look in this paper - they say something different and they have a consistent proof ...

On 2020-05-07 23:28:08, user clorene wrote:

WHO is collecting information about the coronavirus and spearheading efforts to find a vaccine. This preliminary report should be shared with the World Health Organization for a "peer review."

On 2020-05-07 15:43:53, user Concerned Citizen wrote:

For reference, here's some prominent virologists saying this analysis overinterprets the observed data and more analysis is required to make these claims:<br /> https://twitter.com/firefox...<br /> https://twitter.com/angie_r...<br /> https://twitter.com/trvrb/s...

On 2020-05-07 04:03:26, user GetOffMyLawn wrote:

None of this is really new news there has been two real known strains one weaker and one stronger. Seems the European one is the same that arrived in the US - the weaker version would be the one that many people had and never knew it almost like a weak or somewhat inert strain. It doesn't seem like there is any known differences in the US outside of the two?

On 2020-05-07 03:16:49, user Kristin Cunnar wrote:

Here is an interesting publication regarding a need to investigate more on Coronavirus 19 patients for fungus and bacteria. There is a paucity of information regarding fungus and Coronavirus 19 patients. Basically, unless the blood sample is drawn on day 1, there could be arguments made that any fungus or bacteria grown from a blood culture could have arisen by the environment from the Hospital instead of the Coronavirus 19 patient already arriving at the hospital with the hidden fungus and/or bacteria. Coronavirus 19 is a mycovirus. In my opnion, the Coronavirus 19 originally began as a fungal spore that got attacked by a virus which changes the genome, and can hide the fact that it is a fungus as an origin. Important to get blood sample on day 1.

https://academic.oup.com/ci...

On 2020-05-06 23:49:39, user Ian Willy Jimmy Carter wrote:

Confounder - USA bias with little done initially...so spread of clade g certain....?who has the most cases? deaths?<br /> Plenty of other clades in the world<br /> =<br /> Please find the updated summary of genetic characterization of nCoV based on : 15,290 full genomes (+326) (excluding low coverage, out of 16,423 entries)

– by GISAID and experts as of 06 May 2020.

Highlight:<br /> • 15,290 full genomes (+326) (excluding low coverage, out of 16,423 entries)<br /> o S clade 1,910 (+9): 3 USA/CT, 3 USA/UT, 3 USA/CA<br /> o G clade 9,725 (+248): 74 USA/NY, 58 USA/LA, 41 USA/UT, 28 Hungary, 16 USA/CT, 9 USA/WI, 8 USA/CA, 4 Canada, 3 Singapore, 3 USA/AK, 3 USA/NJ, 1 Poland<br /> o V clade 1,559 (+1): 1 Singapore<br /> o Other clades 2,096 (+68): 44 USA/CA, 19 Singapore, 4 Brunei, 1 Hungary

• Large submission from diverse US states including increased number of samples from Louisiana, with clade G dominance. Singapore and Brunei with strains related to India and regional circulation.

Link: https://gisaid.org/hcov-19-...

Or: https://worldhealthorg-my.s...

On 2020-05-06 17:06:30, user Skip Robinson wrote:

Has anyone seen any study that shows the Covid-19 passing all of the Koch Postulates.

On 2020-05-06 11:40:20, user Grimm wrote:

A general remark re: "we generated a codon-based nucleotide multiple sequence alignment"

Given the importance of such good ("cleaned") alignments to build on (and for testing the hypothesis put forward in the preprint), maybe you want to consider storing the alignment, as annotated NEXUS (an example) or at least aligned FASTA-format, in a data/file repository.

Rather than to just state under Data Availability "Sequence data are available from The Global Initiative for Sharing All Influenza Data (GISAID), at https://gisaid.org." Especially, since you cleaned these GISAID data for your analyses.

Here are some repositories providing a persisent doi (hence, citable) for data dumps (once can put embargoes for later release, i.e. once the paper is published) and without file format restrictions implemented by many journals for their supplements.

https://figshare.com/<br /> https://datadryad.org/<br /> https://zenodo.org/

On 2020-05-06 11:01:31, user Maju wrote:

I see contradictions between fig. 2B and fig. 3, especially when comparing England to itself, but also apparently in other countries. Am I reading something wrongly?

On 2020-05-06 10:22:27, user Saeed Hashimi wrote:

You can also find an earlier preprint showing similar data to this study.

https://www.authorea.com/us...

On 2020-05-06 04:55:14, user Alex Sloane wrote:

I feel like the paper itself is solid but the title is a bit irresponsible. It misrepresents the certainty with which increased infectivity was seen when by my reading increased infectivity was put forth as a hypothesis based on modeled interactions between protomers and the consequences those MIGHT have on transmissibility and no real evidence of increased infectivity was shown. I have family members sending me news stories referencing this article and unless I am missing something major the title of the paper is not supported by what is presented in the text. Again, unless I am somehow mistaken (it is late here and I am tired) and direct evidence of increased infectivity was demonstrated rather than inferred as a likely explanation over a number of competing hypotheses explaining the increased fitness I would love it if someone would point it out.

In the absence of that, the media deserves its share of blame for running with pre-print articles anyway but during a pandemic you can hardly fault them too much for grasping at any new information that comes out and most don't have the background to see if the title is actually supported by the text.

From where I'm standing a lot of evidence is presented that the strains containing those mutations are making up a greater proportion of infections than other strains but unless I somehow missed it I don't see any evidence regarding the strains containing these mutations being more infectious or more easily transmitted and it is just taken as given that because strains containing these mutations are overtaking other strains they must have increased transmissibility when I do not think that has either been shown or can be directly inferred from what has been presented. A number of alternative hypotheses are even presented by the authors in the text involving antibody interactions that would not necessarily affect how readily the virus is transmitted but rather it's ability to survive neutralizing antibodies once in the body. So while a quality paper I feel the title overstates what is actually shown in a way that lends itself to media sensationalism and that is my only real criticism.

On 2020-05-06 03:44:10, user Nathan wrote:

What you guys are forgetting and also the LA times is forgetting is what is posted at the very top of this article... "or be reported in news media as established information." But you guys believe what you want to.

On 2020-05-06 02:59:23, user Carlos Roberto Ferreira wrote:

It seems that this peer-reviewed study sheds more light

Emergence of genomic diversity and recurrent mutations in SARS-CoV-2<br /> https://doi.org/10.1016/j.m...

https://phys.org/news/2020-...

On 2020-05-06 02:29:48, user redape wrote:

I'm puzzled by the claim that no one is working on producing a vaccine from this version. Surely several labs are utilizing the version of the virus that is most prevalent in their region?

BTW I prefer not to use the term "Strain" as that usually refers to a genetic remodeling that entails significant hybridization or transposition...or at least the failure of a vaccine to work on the derivative clade. Since we don't have a functional vaccine yet we cannot draw this conclusion.

On 2020-05-06 00:36:13, user K Edwards wrote:

I am in computer science and technical marketing. They hand me new stuff with incomplete manuals and I turn it into communication for both IT and average people.<br /> I am astounded at the number of people commenting who can not read this paper at even a basic level I understand just enough to know they have separated out both the strain in question and the outcomes very nicely.<br /> The statistical data shows that the new strain in the same time period spreads faster. And this is key, if you looked at number at two different times you would be dealing with a ton of behavior differences. But it is clear where both strains occur in the same area following the same rules like Washington State or NY. That the new strain spreads FASTER in the same rule environment.<br /> The next thing they break down is that the prognosis by strain in the same location / time so you are dealing again with same medical capabilities etc. They also do a nice job of braking it out by age and outcome. We see a pretty clear case for the G614 strain having a larger percentage of those needing ICU is 3% for 614D vs7% 614g. I did not read deeply enough to see if they have confirmed this in any other region which would be a key follow up.<br /> And finally if you looked that details related to the new mutation we find that the D614 variation has two structures that bind 1:1 while the 614G has a new "behavior" in that while one structures binds 1:1 the the other is now binding to BOTH 1:2 While I don't know what the relative strengths of these bonds is, that would indicate some increased ability for 614G to bind. <br /> Oddly enough one of the companies doing vaccine work at one point a few weeks ago let a photographer take a picture of a screen marked "Confidential" related to the selected CV-19 vaccine candidate mappings in the same area of the Virus as they are talking about. And its working in a similar area, but the encoding was different so I couldn't make a direct comparison to see if they had targeted the CC mutation that is common to all the 614G variation. <br /> Compared to the CRAP I normally see used to prove things in the news, I would say they are on to something. That there is a descent case for causation in spread rate and mortality based on the differences in these strains.<br /> I also read Quantum / M theory for fun and this is much easier to grasp than that.

On 2020-05-05 22:16:46, user Kevin Kreger wrote:

I've been comparing estimated fatality rates using this tool: https://paroj.github.io/are...

So I did this comparison to see if India is trending higher or lower for fatality rate (because I am in India), and we seem to be more in line with China. I'm not sure if our data is good, but it is pretty clear to me that we are under testing, which would (in most cases) lead to a higher estimated fatality rate. I don't think the fatality rate is related to clades, but I hope this information (especially the plot generator link) is useful<br /> https://uploads.disquscdn.c...

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 2020-05-05 20:24:52, user George Reeves wrote:

It might be useful to call the new strain COVID-20. It does not seem to be as susceptible to control by social distancing and chloroquine as the original in China. Drug tests and vaccine studies based on the original COVID-19 may not apply to the new strain. Researchers need to consider both. Different labels for the distinct strains might help.

On 2020-05-05 20:08:57, user Robert I. Price wrote:

Classical chaos is child's play compared to Q.M. grounded stochasticism. Most biologists I have met process their subject classically. I have been staring at this situation for a bit of time and the progression of events is striking like tracking events at the most elementary of levels.

The lack of "what will happen next" predictability suggests that we might not be able to create a testing protocol before the target form mutates.

On 2020-05-05 19:24:07, user michael a zasloff wrote:

As expected, this report with its provocative title, only provides the media with more material to scare the population.<br /> As the authors say: "There was, however, no significant correlation found between D614G status and hospitalization<br /> status; although the G614 mutation was slightly enriched among the ICU subjects, this was not<br /> statistically significant (Fig. 5C)" A more appropriate title might have been: "No evidence that mutations arising in SARS-CoV2 result in more significant clinical disease..." But that would have not been picked up by the press.

On 2020-05-05 19:01:31, user IndigoRed wrote:

I barely passed my high school biology class in 1971 and am no expert at this virology thing. However, as a layman, it would seem to me that a virus that kills its hosts is not a very successful virus. A virus that infects many hosts, but is not generally fatal is a very successful virus. Just because this apparent new mutant strain may be more highly infectious than its predecessors, does not necessarily mean it's deadlier. As a layman, I do not really care how infectious the disease, but often and quickly it kills. This strain may turn out to be the best option for both humans and virus -- the virus makes us sick with its replication and people easily survive, or, better, the virus does its thing and people are asymptomatic.

On 2020-05-05 18:49:15, user Jerome wrote:

The title is misleading as transmissibility was not assessed through contact tracing or in vitro/in vivo experiment. The authors are studying prevalence and hypothesize that structural modifications might affect binding and fitness.

On 2020-05-05 14:01:38, user buzzbree wrote:

The authors appear to be extrapolating a lot of functional activity from a genomic study and went with a very splashy title. The claims made in the paper that if the virus is more transmissible the authors thought it may lead to more severe disease is surprising. As it often means the opposite- less virulent viruses commonly have more transmission from asymptomatic cases. Such as SARS2 vs the original SARS.

The argument that earlier PCR cycles for the new strain could indicate worse clinical outcomes is also a bit of a stretch as there is no evidence at what timepoint in their disease that the patients were tested- a critical piece of information.

The are no studies demonstrating that antibodies raised using the original strain do not neutralize this strain and vice versa- experiments that are not that difficult to do and would settle most of the claims made in the paper.

On 2020-05-02 05:37:21, user Bette Korber wrote:

I'm very sorry the analysis pipeline website was delayed in opening; <br /> this is because of some additional approvals needed, that I was not <br /> aware of when we submitted. Hopefully it will be approved within a few <br /> days, but likely not until after the weekend. Also, figures 2 and 3 and <br /> S3 show illustrate why we are concerned about site Spike 614; the <br /> central point is that a dramatic shift in frequency is recurring <br /> independently in many countries and regions across the globe. The Spike <br /> 614 mutations is linked to two others, to form the GISAID G clade. Such a<br /> _recurrent_ shift over the month of March in so many places globally is<br /> not readily explained by a founder virus effect, nor a sequencing <br /> error.

On 2020-05-01 23:34:33, user Aaron wrote:

The title of the manuscript seems a bit disingenuous. The authors show an increase in prevalence for G614 alongside increasing case numbers, but that is correlation, not causation. To make the jump that this is a more transmissible form of the virus would require functional studies. It seems that authors decided to go with the more sensational title rather than the more important one that this mutation didn't show any significant difference in patient outcome.

Importantly, we have millions of cases and only thousands of genomes sequenced at this point. There is likely to be some amount of bias in which genomes are being collected, with localized founder effects apt to skew proportions of mutations for a given country or region, i.e. a majority of sequences for a country/state coming from a single center.

On 2020-05-01 16:26:25, user niman wrote:

This lineage (from northern Italy - aka G clade and clade 2a) has taken over the world. In the US it is dominant in NY, NJ, MA, PA and is well established in mid-west and is now taking over the west coast. It is throughout Europe and Russia as well as Africa (including sub-Sahara) and South America.

On 2020-05-01 11:25:23, user Gan Kad wrote:

Authors, a couple of us have been discussing the observations you've made and if the conclusions you've made are the only true explanation or if there are other possibilities. Would be great to have you join us on the discussion and add your thoughts, and point out any details we might have missed. Thanks.

Spoiler alert - it seems that you have oversimplified.

https://twitter.com/Jaimede...

On 2020-05-08 11:24:36, user eseporras wrote:

Interaction information now available through IntAct: www.ebi.ac.uk/intact/query/...

On 2020-05-08 06:21:56, user Chaya Kalcheim wrote:

This preprint was significantly modified since it was posted on bioRxiv. Please view the link for the latest version published in Development.<br /> doi: Development doi: 10.1242/dev.183996

On 2020-05-07 19:51:44, user Plant Pathology Paper review wrote:

Authors of this review are graduate students in the PATH8160 CLASS of the Plant Pathology department at the University of Georgia, under the guidance of Dr. Paul Severns.

Reviewers: Burks Caroline, Costello Kathleen, Hudson Owen, Marquez Josiah, Mijatovic Jovana, Stice Shaun, Sykes Emily, Tran Sorrel, Wang Li

Remarks for the authors:<br /> There is an interesting story in this paper and the science appeared to be sound. However, the primary point of the manuscript was obscured by a disorganized presentation, especially the occurrence of methodologies outside of the Methods section. The presentation and evaluation of important data related to the hypothesis “ the minimal portion of the pathogenicity chromosome necessary to induce virulence” were almost completely overlooked by the reader due to the emphasis on methods scattered throughout all sections of the manuscript. This also made evaluation of the authors methodological approaches as they pertain to the authors hypothesis particularly challenging. Therefore, we suggest that the authors attempt to reorganize their manuscript and present the reader with a more cohesive story. Below we have identified some key passages in the manuscript that if addressed, could help improve the presentation of the authors’ study, especially with respect to the presentation of methodologies outside of the Methods section. It should also be noted that Discussion of results commonly occurred in the Results section. While these instances can be distracting, the most pressing issues originated from the presentation of methodologies.

Introduction:<br /> Consider rephrasing the hypothesis to link the methods together with the mechanistic explanation of pathogenicity. Presently, the hypothesis reads more like a statement pertaining to previous results rather than a hypothesis.

Results <br /> Lines 111-118: These are methods that are presented in the Results section.<br /> Lines 138-141: These are methods that are presented in the Results section.<br /> Line 144: Data could be presented as supplementary information. <br /> Lines 149-210: The sentences are a mixture of methods and results. If the authors moved the methods to the appropriate section and presented the results in a concise manner, it would improve the readability of a difficult section in this paper.<br /> Lines 215-225: This paragraph belongs in the methods section.<br /> Lines 286-301: This paragraph should be condensed to just the few lines of results, and the rest belongs in the methods section. <br /> Lines 361-368: This description should be in the methods section, and a summary of the results should be shown. <br /> Line 392 Section: “A partial pathogenicity chromosome is sufficient to cause disease on tomato” - If possible, add photos of the bioassay. Figure 6 appears to be possibly cut off and lacks a key. This figure appears to be a central piece of this manuscript but as presented it is difficult to interpret and could provide supporting evidence more clearly if appropriately revised.<br /> Line 412 Section: “A partial pathogenicity chromosome can turn an endophyte into a pathogen” - If possible, add photos of the bioassays to demonstrate the lack or presence of pathogenicity with the different deletions.<br /> Lines 320-323: The sentence references results in image 2A that are not represented there - HCT_△RFP#1-7 and HCT_△GFP#8-2 from △RFP#1 and △GFP#8.<br /> Lines 325-332: the 4 and 2.5 Mb extra chromosome were not represented in figure 2A<br /> Line 399-401: The loss of virulence was not represented in figure 2, perhaps rephrase this sentence. <br /> Lines 392-435: The bioassay results section lacked the presentation of statistical analysis results (e.g. p-values, z-statistics, d.f., etc.) in any portion of the text, tables, or figures that we found associated with the manuscript. <br /> Line 410: The authors may consider sharing these results as they appear to be important to their pathogenicity argument.<br /> Line 412 Section: “A partial pathogenicity chromosome can turn an endophyte into a pathogen” - If possible, add photos of the bioassays to demonstrate the lack or presence of pathogenicity with the different deletions.

Discussion:<br /> Consider rephrasing question headings into statement headings as the interpretation of the data by the authors appears to be relatively certain.<br /> Line 490: The authors might consider revising this section as it was unclear that accessing the stability of the FOI genome was an objective of this study. Perhaps the authors could work this issue into the potential limitations and future directions of their research.

Materials and methods:<br /> Cloning:<br /> Consider making a new supplemental table with all cloning vectors and constructs used.<br /> Line 539-540: these primer sequences can be in the supplemental table with all the primers, just indicate the name of the primers used in the methods section.<br /> Gene replacement in Fol:<br /> Line 554: Add the concentrations of cefotaxime and Phleomycin<br /> Fluorescence Assisted Cell Sorting:<br /> Line 568-569: For consistency, it would be clearer to refer to chromosome 14 in the methods section (opposed to “the pathogenicity chromosome”) as this is a statement of fact rather than a phenotypic interpretation of chromosome 14.<br /> Bioassays:<br /> Line 573-584: Needs a description of the number of replicated tomato plants for each deletion or horizontal chromosome transfer stain that was tested for comparisons. In addition, a description of the statistical analysis and the software that was used for analysis would be helpful for the reader to understand how comparisons were made. A quick description of the two bioassays could help the reader when viewing figure 6.<br /> Line 577: How were the spores counted?<br /> Line 579: What were the lighting conditions where the plants were kept? Consider adding a Plant cultivation paragraph specifying all environmental conditions.<br /> Line 581-584: The description of the disease index scale doesn’t have to be parenthetical., Please indicate if the disease index scale was developed for just this work or, if it was developed elsewhere a citation would be helpful. Please indicate what was used as a control for your bioassay experiments (strains, water, etc.).<br /> Line 581-584: If the authors have images for each level of the disease index, a figure with these images may be helpful for the reader to understand how the disease was scored.<br /> Horizontal chromosome transfer:<br /> Line 596: Please be consistent with the spelling of the monosporing technique throughout the paper in lines 166, 552, 560 and 596.<br /> Line 597-598: Please indicate if the antibiotic concentrations supplemented in the growing media are as previously described.<br /> CHEF electrophoresis:<br /> Please be uniform with Miracloth spelling throughout the paper in lines 159, 563,576, 594, 609, 615.<br /> Line 616-617: How long the plugs were treated with pronase E?<br /> Line 617-619: Please indicate if the buffer was changed every 24h as indicated in the Biorad CHEF-DRII protocol, since your gel was running for 260 hours (10.8 days)<br /> Ilumina single chromosome and whole genome sequencing:<br /> Reformat sentences to form a paragraph.<br /> Lines 644-649: Condense sentences in lines to one sentence to remove repetition.<br /> Lines 650-651: Please condense sentences into one sentence for brevity.<br /> Figures:<br /> Figure 1: This figure (1B) is hard to read in its current form. Larger font, a scale bar, and units on the figure would help with that. <br /> Figure 2: For the image it would be beneficial to mark the line in the tested deletion genomes where the normal reads occur to clearly denote the deletion and multiplication positions. <br /> Figure 3. Consider adding visual labeling indicating areas were the multiplication event occurred (RFP#12). Consider labeling which RFP strains were virulent / avirulent on this figure, this would allow the reader to interpret which of the SIX genes are required for virulence, and aid in interpreting the figure. <br /> Line 1000: Consider removing the word “surprising” and avoid interpreting the figure results in the legend.<br /> Figure 4: The authors should consider color coding the arrows to reflect different outcomes. The labeling in the figure legend should follow the same order as the labelled bands in the figure. If possible, all the HCT strains should be grouped together for easier comparison.<br /> Figure 5: Cropping the picture and adding arrows with specific information in the picture could be helpful to present the data. The clarity of the labeling on the axis and removal of the pictures that only add extra data (e.g.GC content graph) could be effective.<br /> Figure 6: A legend for the horizontal bars is not visible (possibly was cut off). Recommend means followed by standard error of the disease index of each strain instead of color-coded horizontal bars so that the reader does not have to put extra effort in interpreting the colors. It is understood that the 26 strains are organized by parental strains, control strains without the q arm (Strains 14-2 and 14-7), RFP and GRP strains. However, it may be helpful to clearly distinguish these groups in the figure. Showing results from statistical analysis is critical for interpretation. <br /> Line 1035-1045: There is no description of the plus and minus sign nor the white and grey boxes in table of deletion strains and genes present in the strain. Some boxes are blank and not clear to the reader if the minus sign mean absent or the blank box means absent. In addition, it is not clear if the white box means absent and the grey box means present.<br /> Line 1035-1037: The authors should consider presenting the number of tomato seedling replicates for each of the 22-deletion strain as (N = _).<br /> Line 1045: Authors indicate performing a Kruskal-Wallis test for disease index data; however, results are not reported in the results section nor in figure 6. In addition, a post-hoc test is needed (i.e. Dunn test) in order to make pairwise comparisons since the Kruskal-Wallis test only tells you if there are differences among the groups, analogous to analysis of variance (ANOVA).<br /> Figure 7: The y-axis is difficult to understand since the disease index only ranges from 0-4 and not 0-20. One possible reason for having the range from 0-20 could be related to the y-axis not representing the disease index but the number of plants in each of the disease index levels. If this is the case, we recommend labeling the legend as disease index and the y-axis as the number of plants. However, it may be better to interpret the results with means and standard error. Again, results from statistical analysis should also be included in the figure. In addition, it is not clear how each HCT strain differs. Perhaps a similar figure as figure 6 in which there is a table of all the strains, their corresponding mutations, and results of statistical group membership tests (KW-ANOVA with post-hoc pairwise comparisons).<br /> Line 1048-1057: Like in figure 6, without showing the results of the statistical analysis, the reader cannot determine if the strains described in the caption are different or the same. It is only subjective based on the colors of the bar chart.<br /> Fig 8: The authors may consider omitting this figure. The same information is presented elsewhere in the manuscript and the reader arrives at the conclusion portrayed by Fig 8 without its inclusion in the manuscript.

On 2020-05-07 16:40:40, user Greta Pintacuda wrote:

Older version of Genoppi:<br /> https://www.biorxiv.org/con...

On 2020-05-07 15:23:52, user bljog wrote:

Interesting paper and nice comparisons. Just a couple of points to bear in mind when interpreting. In table 4, for example you are comparing different pipelines but those pipelines are also being applied in different locations. The PHE pipeline is predominantly being used for sequences from England with lots of samples from Greater London, the artic pipeline is more broadly used in different labs, hence the diversity of the genomes processed will be very different making the number of singletons hard to compare. The artic pipeline uses iVar for processing so there is overlap between the two rows artic pipeline and ivar, i.e. the samples that have been processed by illumina using the artic pipeline have probably been through the same steps at the Ivar-illumina pipeline.

On 2020-05-07 15:21:51, user Jean-Michel Fustin wrote:

The data presented in this preprint is now published in Communications Biology at https://www.nature.com/arti...

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 :)

On 2020-05-07 13:00:03, user Sinai Immunol Review Project wrote:

Summary<br /> The SARS-CoV-2 pandemic has created an urgent need for widely available diagnostic tools to detect and understand host immune responses. This study introduces a fast SARS-CoV-2 detection test from swabs, based on colorimetric reverse transcriptase loop-mediated isothermal amplification (RT-LAMP) assay technology, which amplifies viral RNA. Results were validated using both qRT-PCR and total RNA sequencing. The study also introduces a new large-scale total RNA sequencing platform which enables comprehensive transcriptome profiling of the virus, host and other microbiomes. Both technologies were used to analyze samples from 735 confirmed/suspected COVID-19 patients, as well as 62 environmental swabs from NYC subway.<br /> Through a viral evolution map based on the total RNA-sequencing data, the study identified a novel subclade (A2-25563) enriched in NYC cases with likely origin in western Europe. Overall, the host transcriptome profiles revealed 216 qRT-PCR positive and 519 negative samples. Integrative analysis together with the viral load levels revealed that samples with high viral load exhibit increased expression of ACE2 (SARS-CoV-2 receptor), interferon pathway (SHFL, IFI6, IFI27, and IFIT1) and HERC6 gene and decreased expression of olfactory receptor pathway and TMPRSS-11B gene. Since ACE2 expression can be increased by pharmacologic intake of angiotensin enzyme inhibition (ACEI) or angiotensin receptor blockers (ARB), additional analysis of an observational cohort of 8,856 suspected COVID-19 patients showed a strong positive association between ACEI/ARB usage and SARS-CoV-2 infection.

Limitations:<br /> While the host transcriptome analyses were performed on individuals who tested positive and negative for SARS-CoV-2 infection and other subgroups based on viral load, the influence of other clinical characteristics/phenotypes (e.g. age, symptoms, severity) were not discussed. It would also been useful to have the characteristics of the 735 confirmed/suspected COVID-19 patients.

Significance: <br /> During this pandemic, large-scale rapid testing is key in tracking, surveillance and containment of the viral spread. To aid diagnostics, this study identified a fast LAMP assay that was applied to both naso/ oropharyngeal swabs and environmental samples and validated against gold standard qRT-PCR approach. Furthermore, total RNA-Seq profiling enabled the pursuit of multiple aspects of virus genetic features and evolution, host response, co-infections and other possible correlations between virus/host genotype and clinical phenotype. This is one of the first few studies based on sequencing data from SAR-CoV-2 patients and will serve as an important resource to the research community.

Reviewed by Myvizhi Esai Selvan as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.

On 2020-04-29 20:46:15, user Christopher Mason wrote:

working to release the data publicly soon.

On 2020-04-28 01:59:58, user Soundhar Ramasay wrote:

Excellent and humongous work. Is the RNA-seq data-sets ,specifically host transcriptome are available for download ?

On 2020-05-07 12:53:04, user Paul Macklin wrote:

Revision in the works ...

On 2020-05-07 12:27:56, user Xuhui Huang wrote:

MD simulation trajectory datasets and set-up files of this manuscript entitled "Role of 1'-Ribose Cyano Substitution for Remdesivir to Effectively Inhibit both Nucleotide Addition and Proofreading in SARS-CoV-2 Viral RNA Replication" are now available at the Open Science Framework (see the link below):

https://osf.io/8yhar/?view_...

On 2020-05-07 08:05:36, user Samuel Flores wrote:

The 23 residue peptide is a fragment of the wild type, with no substitutions. This is a natural first guess and I am pleased to see that it worked to some degree. I would have expected a problem with solubility. There are a few phenylalanines and leucines on the non-interface side of the helix. Was this a problem, and if so is there work underway to remedy it? On a second matter, I am not surprised by the diminished affinity (compared to full-length) observed by the authors. Some would be expected since some of the binding patch is outside the helix altogether. However I suspect an important contributor is a loss of curvature which may occur when the helix is removed from the rest of ACE2. Is there work ongoing in this area? We would much welcome feedback and possible collaboration.

On 2020-05-07 07:53:00, user Wiep Klaas Smits wrote:

This looks quite interesting. However, the paper does not seem to have any reference to code in a repo, or web implementation. This severely limits the uptake as a "tool" (that is suggested by giving the algorithm a name). I hope the authors will fix this.

On 2020-05-07 04:49:02, user AllisonMay wrote:

What is the state of the union (today) on trial testing/peer review of papers in connection with #glaucoma testing? Next steps?

On 2020-05-04 02:23:47, user Igor wrote:

Do we know if the eye of the mouse got young and KEPT young for its entire life, or how long did it last? Did the eye reversed back to its normal biological age?

Thanks!

On 2020-05-06 23:02:20, user Michael Hendzel wrote:

I'd like to note that this paper arose from independent projects taking place in our lab and the Hansen lab that complemented each other. We had a previous independent version of this manuscript that is also posted on BioRxiv. Given the extensive changes in authorship, communicating authors, and content, we have opted to post this as a separate submission. The original related submission from the Hendzel lab can be found here: https://www.biorxiv.org/con...

On 2020-05-06 22:29:44, user Leonardo Feldman wrote:

Could it be of interest to know if patients on IMATINIB treatment as a patient with chronic myeloid leukemia who have contracted COVID had a favorable evolution versus others?

On 2020-05-06 18:25:01, user Younes Allou wrote:

Large scale genomic analysis of 3067 SARS-CoV-2 genomes reveals a clonal geodistribution and a rich genetic variations of hotspots mutations

On 2020-05-06 15:28:18, user Charles Warden wrote:

I agree that using imputed values from a SNP chip can be a problem, and I would say medical decisions should never be made using an imputed value (whether that is from a SNP chip or lcWGS data).

I have some general notes (and opinions related to some comments), but I thought I should move those to a blog post, in order to keep the commentary more focused.

In terms of this specific paper:

1) The choice of array can affect the results. For example, while the conclusions are similar to concordance sections of this paper, the GLIPSE paper shows better performance with the Infinium Omni 2.5 compared to other SNP chips (in these interactive plots for EUR and ASW individuals, as well as the Rubinacci et al. 2020 pre-print). This should also be possible to compare with the 1000 Genomes samples, and this may be different than the GSA results?

For example, I checked the manifest files, and it looks like Infinium Global Screening array would be a “medium” density array versus a “high” density array (with the categories defined from that other study).

2) In the context of this paper, the comparison that I am interested in is not imputed SNP chip genotypes to imputed lcWGS genotypes, since I would agree that it is likely to see some (although possibly subtle) improvement for lcWGS imputations versus SNP chip imputations.

Instead, what I would like to see is directly assayed SNP chip genotypes versus imputed lcWGS genotypes. The ability to provide results without any imputations is the main reason I prefer SNP chips over lcWGS (if those were my only 2 options), so I would want to compare the SNP chip genotypes where all variants were directly measured by the SNP chip versus the lcWGS imputations.

This would mean you could only compare PRS values among probes present on the SNP chip, for example. However, it looks like you selected a CAD PRS with 1,745,179 variants (225,667 were directly typed on the GSA) and a BC PRS with 313 variants (75 were directly typed on the GSA). The BC PRS is closer to the number of variants in PRS that I have tested on myself. So, if you can find a PRS with between dozens and 1000s of variants, where 100% were directly typed on either the GSA or Omni-2.5 SNP chip (or both), then maybe that can help with providing the comparison that I would like to see?

As I mention below, maybe using public SNP chip + WGS data can help you identify a custom array where the probes were designed to cover everything for a PRS? I would guess/hope that this would be a requirement if getting FDA approval for a clinical test (and this is why I don’t consider a PRS with imputed SNP chip values to be equivalent), but maybe you can also find this for some research-level PRS (like the 23andMe diabetes example that I described in my blog post above, which I think uses 1,244 loci, even though other risk factors were more likely to predict whether you got diabetes)?

Or, removing the PRS results would be another option that would reduce concerns from myself. For example, it looks like the error rate in this paper was noticeably higher when the BC PRS used 100s of SNPs (instead of a PRS with >1 million variants). There are also factors that could cause me to prefer removing the PRS results (which I moved to the blog post).

3) I agree that batch effects (like “index hopping”) could cause down-sampling to underestimate the error rate for lcWGS (which I would guess is more of an issue for smaller libraries on higher throughput machines). In the “Experimental Overview”, it sounds like you used cell lines for 1000 Genomes individuals for the new sequencing experiments? While it is hard for me to say exactly what could cause a problem, am I correct that previous Gencove developments considered 1000 Genomes data? Is it not possible to have more independent test datasets for your estimates (for a set of ~120 individuals)? I myself am one individual from the Personal Genome Project that has public high-coverage and low-coverage WGS from different companies (along with SNP chip genotypes).

To be fair, this may be less important than some of the other points, especially if sections / content is removed. For example, if you reduced the focus to variability in technical replicates derived from 1000 Genomes subjects and different technologies (and remove the PRS application), then I don’t think this extra analysis needs to be added.

4) If the goal is to show a general principle, then maybe you could show if open-source programs like STITCH, GLIMPSE, etc. can achieve similar performance with lcWGS data? I think this would make disclosing the conflicting interests less important, even though I think that still needs to be done. This would be good to show for readers that might usually prefer to use open-source options, unless Gencove is changed to become open-source (and I think showing performance of alternative programs is common, even if that was true).

5) I think the most important issue has been fixed in the link for revision 2 (I previously had issues accessing the content in s3://gencove-sbir/, but I can see the data in https://gencove-sbir.s3.ama.... Nevertheless, in order to match the current 1000 Genomes data deposits, is it possible to deposit the data (derived from 1000 Genome subjects) into public genomics databases like the SRA? Or, if you have already done so, can you please provide accessions that don’t require an extra step (or steps) to access the data?

Summary:

I think this is an interesting topic of research, and I think lcWGS imputations can be useful for certain applications (such as relatedness and broad ancestry). However, I have concerns about the clinical utility of Polygenic Risk Scores from imputed genotypes in lcWGS data. That said, I think these results could be presented with less controversy if the PRS section and Figure 4 was removed, and that is a possible solution to some concerns that doesn’t require extra work (taking out results, rather than adding in new results).

I think testing 1000 Genomes Omni 2.5 SNP chip concordance (and/or only comparing “directly assayed” SNP chip genotypes) and potentially removing the PRS results are what I am most concerned about.

Thank you very much for putting together this pre-print. I believe that it is important to see independent presentations of results from different groups. I can also tell that a lot of work was put into this paper (with a several pages of supplemental information), so I appreciate this.

On 2020-05-06 15:19:16, user Sinai Immunol Review Project wrote:

Summary: Using publicly available scRNA-seq data of healthy human samples from 31 organs, the authors compare the gene expression levels of ACE2 and TMPRSS2 of each organ. The authors categorized each organ as susceptible to SARS-CoV infection based on its expression of ACE2 and further stratified 11 susceptible organs into three levels of risk for infection based on each organ’s TMPRSS2 expression (i.e. TMPRSS2 expression ratio >20% was defined as level 1). The authors claimed that for the first time, their scRNA-seq analysis showed the brain, gall bladder and fallopian tube as vulnerable to COVID-19 infection in addition to confirming previous molecular and clinical data implicating other organs susceptible to SARS-CoV2 (i.e. nose, heart, intestines, etc.).

Limitations: The article advances its risk stratification strategy based on a couple of naïve assumptions that are being actively contested in literature: a) ACE2/TMPRSS2-mediated viral entry is the only route used by SARS-CoV2 to infect cells and b) ACE2/TMPRSS2 expression is stable in systemic inflammatory contexts such as COVID-19. Furthermore, their risk stratification does not consider the immune contexture of each organ. For instance, while several papers have suggested that reproductive organs (i.e. testes) are also susceptible to SARS-CoV2 based on their ACE2/TMPRSS2 expression, there are no reported clinical manifestations of COVID-19 in those organs. Lastly, even though they were using scRNA-seq data available from 13 different publications, their paper does not discuss how they accounted for variations in scRNA-seq protocols as well as how they normalized each data set for comparative analysis.

Significance of the finding: Mostly confirmatory. While it is nice to see that there were clinical cases describing the adverse effect of COVID-19 for most of the eleven organs identified as SARS-CoV2 susceptible in this study, previous studies have already shown most of these organs to be susceptible to SARS-CoV2 using various approaches (including scRNA-seq analysis). Furthermore, their risk stratification strategy is simplistic as it doesn’t seem to take account of recent findings on SARS-CoV2’s alternative mechanisms of entry as well as reported clinical manifestations of COVID-19.

Review by Chang Moon as part of a project by students, postdocs and faculty at the<br /> Immunology Institute of the Icahn school of medicine, Mount Sinai.

On 2020-05-06 09:53:17, user Anna Birke wrote:

Hi all, super cool piece of science. Just some comments and feedback <br /> that came out of the Hoski Journal Club (as you will have seen on <br /> Twitter, we dedicated some time on Friday afternoon to chatting about <br /> your paper :-)):

1) in general, the paper is really well written <br /> and flows nicely, however, it would be great if there was a flow <br /> chart/diagramme at the start of the results section to give the readers <br /> an overview of the different culture conditions (continous flow, static <br /> broth and colony biofilms) and transformation experiments (strain to <br /> strain, plasmid, eDNA, gDNA to strain etc.) you carried out.<br /> 2) right at the start of the results section it says<br /> ...were obtained from broth the biofilm biomass and biofilm effluent (Figure 4a) --> should be Figure 1a. <br /> 3)<br /> the use of gDNA and eDNA was a little confusing. Maybe clarify that by <br /> gDNA you mean chromosomal genomic DNA isolated from cells and by eDNA <br /> you mean exogenous DNA that was extracted from culture supernatant (it's<br /> in the M&Ms but would be good to have that distinction in the <br /> results too). <br /> 4) one paragraph down it says that the concentration of gDNA or eDNA was 0.5 mg/ml...did you mean 0.5 ug/ml?<br /> 5) paragraph at the bottom of page 10: ...we set out to determine if this process with also occuring (no with)<br /> 6) there aren't any error bars on figure 3b? <br /> 7)<br /> it might also be a good idea to go into more detail about the strain <br /> and time-dependent differences in transformation efficiency in the <br /> discussion. So like why is the number of transformants so much higher <br /> for PAK than for PAO1? Also might be cool to culture PAK and PAO1 in <br /> seperate cultures of the course of 24 hours, take out a small volume at 5<br /> or 6 timepoints and transform to get an understanding of how <br /> transformation efficiency varies over the course of a culture. Maybe <br /> you've already done something like this, if so, it would be nice to see <br /> that in the results. <br /> 8) another set of experiments which would be <br /> really nice for the future (maybe you're already on it) is to look at <br /> the expression of genes presumably involved in transformation, <br /> especially in a time-dependent manner. <br /> 9) in the discussion it would<br /> have been really nice to hear your thought on why Pseudomonas is such a<br /> pain to transform in a lab setting if you're work clearly shows that, <br /> at least with some strains, transformation occurs naturally and also <br /> what's your hypothesis why eDNA is so inefficient at transforming (maybe<br /> also include the eDNA gDNA gel you mention in the results) if it's the <br /> source of DNA that is naturally available in a biofilm?

We hope you find these comments constructive and helpful. :-) <br /> Cheers from the Hoski Journal Club

On 2020-04-27 15:27:18, user Anna Birke wrote:

Hi all, super cool piece of science. Just some comments and feedback that came out of the Hoski Journal Club (as you will have seen on Twitter, we dedicated some time on Friday afternoon to chatting about your paper :-)):

1) in general, the paper is really well written and flows nicely, however, it would be great if there was a flow chart/diagramme at the start of the results section to give the readers an overview of the different culture conditions (continous flow, static broth and colony biofilms) and transformation experiments (strain to strain, plasmid, eDNA, gDNA to strain etc.) you carried out.<br /> 2) right at the start of the results section it says<br /> ...were obtained from broth the biofilm biomass and biofilm effluent (Figure 4a) --> should be Figure 1a. <br /> 3) the use of gDNA and eDNA was a little confusing. Maybe clarify that by gDNA you mean chromosomal genomic DNA isolated from cells and by eDNA you mean exogenous DNA that was extracted from culture supernatant (it's in the M&Ms but would be good to have that distinction in the results too). <br /> 4) one paragraph down it says that the concentration of gDNA or eDNA was 0.5 mg/ml...did you mean 0.5 ug/ml?<br /> 5) paragraph at the bottom of page 10: ...we set out to determine if this process with also occuring (no with)<br /> 6) there aren't any error bars on figure 3b? <br /> 7) it might also be a good idea to go into more detail about the strain and time-dependent differences in transformation efficiency in the discussion. So like why is the number of transformants so much higher for PAK than for PAO1? Also might be cool to culture PAK and PAO1 in seperate cultures of the course of 24 hours, take out a small volume at 5 or 6 timepoints and transform to get an understanding of how transformation efficiency varies over the course of a culture. Maybe you've already done something like this, if so, it would be nice to see that in the results. <br /> 8) another set of experiments which would be really nice for the future (maybe you're already on it) is to look at the expression of genes presumably involved in transformation, especially in a time-dependent manner. <br /> 9) in the discussion it would have been really nice to hear your thought on why Pseudomonas is such a pain to transform in a lab setting if you're work clearly shows that, at least with some strains, transformation occurs naturally and also what's your hypothesis why eDNA is so inefficient at transforming (maybe also include the eDNA gDNA gel you mention in the results) if it's the source of DNA that is naturally available in a biofilm?

We hope you find these comments constructive and helpful. :-) <br /> Cheers from the Hoski Journal Club

On 2020-05-06 09:43:04, user Jan Konvalinka wrote:

Very good and important report. There are more and more papers coming in on DDI2 which is encouraging for those of us who started to look at that strange "would -be protease" six years ago.<br /> It might be relevant to point out that very similar data have been recently published by Fassmanova et al. in Cancers: https://www.mdpi.com/2072-6...

On 2020-05-06 06:36:18, user °christoph wrote:

duplication in line 538: <br /> ...) freshly cultured freshly on the...

On 2020-05-06 06:27:32, user David Posada wrote:

Dear Daniele et al,

I am a bit confused, how is it possible to use methods for *clonal* deconvolution –which assume no recombination and infinite-site models– in a virus that recombines and with multiple mutations at individual sites?

On 2020-04-27 18:04:28, user Alex Crits-Christoph wrote:

I thank the authors for submitting this work. I was surprised that there was no citation of:

https://academic.oup.com/ci...

Which appears to be one of the first works on measuring intra-population variation in SARS-CoV-2. In particular, many of the conclusions from that work seem difficult to reconcile with the work of these authors:

"However, very few intra-host variants were observed in the population as<br /> polymorphism, implying either a bottleneck or purifying selection <br /> involved in the transmission of the virus, or a consequence of the <br /> limited diversity represented in the current polymorphism data. Although<br /> current evidence did not support the transmission of intra-host <br /> variants in a person-to-person spread, the risk should not be overlooked"

On 2020-05-06 05:18:53, user Kajoli Banerjee Krishnan wrote:

It would be interesting to see how rapidly the ETC measure of the Cov-1 pair falls as the segment length is increased from 25 to 5000 (with reference to Figure 5) and whether the pattern is similar across many random segments.

On 2020-05-06 00:23:21, user Steve Gothard wrote:

Please check the dates in table 5, some dates are listed as December 24, 2020

On 2020-05-05 23:48:55, user Maha Mansoor wrote:

where did you chose your forward and reverse primers ? in picture 2 https://uploads.disquscdn.c...

On 2020-05-05 20:42:00, user Taekjip Ha wrote:

Thank you very much for sharing your interesting manuscript!<br /> We used your preprint as one of the journal club papers in the Single<br /> Molecule & Single Cell Biophysics course for graduate students of Johns<br /> Hopkins University during the Covid-19 lockdown. Students also practiced peer<br /> reviews as the final assignment. I am submitting their formal reviews here <br /> and hope you find them useful.

Taekjip Ha

Reviewer 1.

The authors develop an ?-hemolysin nanopore-based sequencing by synthesis assay<br /> which can be used to interrogate the kinetic properties of single DNA<br /> polymerases. Their method is novel and addresses the problem of increasing the<br /> throughput of polymerase screening methods. Previous techniques only allowed<br /> kinetics of polymerases to be screened one at a time. This new method is a<br /> clever integration of existing nanopore sequencing technologies that addresses a<br /> longstanding problem in development of specialized polymerases in biotechnology.<br /> The paper is interesting to read and not especially difficult for someone<br /> outside of the field to understand.

Each polymerase-pore complex could be uniquely tagged with a circular barcode<br /> template, allowing the assay to be multiplexed and scaled up to accommodate 96<br /> complexes at once. Convincing proof of concept data is shown highlighting the<br /> ability of the method to distinguish between barcodes, as well as the stability<br /> of the circular template. The title and abstract are appropriate, concise, and<br /> clearly lay out the aims of the paper. Introductory figures showing assay design<br /> and low throughput tests are very well presented and easy for the reader to<br /> follow. Low throughput tests show clear clustering, in both two-dimensional<br /> plots and PCA, of data obtained from each tested polymerase which could be used<br /> to distinguish and characterize them. Later in the paper, however, there are<br /> confusing inconsistencies between what is stated and what is shown in the data.

Figure 3a shows how each kinetic parameter is defined by the voltage trace. Only<br /> four of the five kinetic parameters are shown: dwell time, tag release rate, tag<br /> capture rate, and full catalytic rate. Tag capture dwell time (TCD) is not<br /> shown, yet it is featured in the principle components analysis and is shown to<br /> have a relatively high coefficient for some polymerases. How this parameter is<br /> defined by the trace and how it differs from dwell time is not clearly addressed<br /> in the main text of the paper. This figure (3a) and the subsequent analysis<br /> could be improved by explaining how each parameter is calculated and how they<br /> differ to clear up any ambiguity. Explanations of how each parameter correlates<br /> to polymerase fidelity, processivity, speed, etc. may also help convince the<br /> reader of the utility of their method. This is done well for some but not all of<br /> the described parameters.

Figure 5 shows the distribution of counts associated with each of 96 unique<br /> circular barcodes over three polymerases. RPol1 is associated with relatively<br /> few read counts which are not much higher from background off-target signal from<br /> RPol33. The uneven distribution of barcode counts is attributed to the low<br /> processivity of polymerase 1. Later (figure 6), in the 96-plex screen of<br /> polymerase mutants, less than twenty mutants in the screen have detectable<br /> barcode counts and those that do have few counts. This observation is again<br /> thought to be due to poor processivity of the polymerases. Polymerase fidelity<br /> very likely also plays a role in the ability of the assay to identify<br /> polymerases. Since barcode assignment is alignment based, and nanopore<br /> sequencing platforms are known to have a relatively high error rate as well, one<br /> can imagine that a more error-prone polymerase will also escape detection. There<br /> is no benchmarking data to define a polymerase detection threshold. It is clear<br /> that the efficacy of the method decreases for polymerases with lower fidelity<br /> and processivity, but what might be designated as ‘low’ is never defined. What<br /> subset of polymerases make it through this new screening process and what are<br /> their defining kinetic characteristics? How widely applicable would this method<br /> be for identifying desired features in polymerase variants? What kinds of<br /> polymerases would be expected to be missed by the screen?

There are some minor inconsistencies in the data that should be addressed.<br /> Supplemental table 5 shows the calculation of the proportion of mapped reads in<br /> the low throughput 3-plex experiments. The number of total raw reads used to<br /> calculate the 67% CBT mapping as described by the main text is 418, the value<br /> for RPol1 alone rather than a sum of the total read values for all three<br /> columns. Similarly, the text states that 20 polymerase variants were identified<br /> in the screen while figure 6a shows only 17 polymerases were associated with<br /> barcode counts.

The method described in the paper is conceptually strong and should be very<br /> helpful in identifying polymerases with desirable kinetic properties when<br /> coupled to mutagenesis screens. It has the potential to be improved upon as<br /> nanopore sequencing technology is further developed and the error rate that is<br /> currently innate to the platform is decreased. It is likely that general<br /> improvements to nanopore sequencing itself would greatly decrease false positive<br /> rates in the described method. This technique could also be more applicable if<br /> its points of failure were addressed and proper thresholds defined. The higher<br /> false positive rate observed in RPol2 (supplemental figure 11a) is more likely<br /> to be a fault of the polymerase fidelity rather than a characteristic of the<br /> barcode set. What kind of polymerase misincorporation rate is permissible to<br /> still allow confident barcode assignment? At what point does polymerase<br /> processivity become an issue and cause ambiguity in barcode identification?<br /> There appears to be a set of kinetic parameters that must be met in order for<br /> differences in polymerases to be resolved by this assay. Clearly defining what<br /> it is good at and what it is going to miss is essential before it can be used<br /> reliably for screening.

Reviewer 2.

Summary<br /> In this article, the authors expand upon their previously published system of singlemolecule<br /> nanopore sequencing-by-synthesis and investigate whether it can be scaled-up to be<br /> used as a screening method downstream of polymerase directed evolution experiments. The<br /> major advancement in this paper is that as a screening tool for polymerases, it also has the<br /> capability to provide detailed kinetic information on each of the polymerases, something that<br /> prior methods struggled to do. As a proof-of-principle, the authors simultaneously screen 96<br /> polymerases with 96 barcodes and extract kinetic data from their single-molecule profiling.<br /> This work has multiple merits. Notably, although the general framework is the same, the<br /> authors have made a series of changes to improve their system since their previously published<br /> work, that played a role in allowing them to make multiplexed measurements. The authors also<br /> creatively pull a variety of kinetic parameters from their single-molecule voltage traces that<br /> allow them to easily separate different polymerases after principle component analysis.<br /> On the other hand, the work has a couple of issues, detailed below, with regards to<br /> controls and clarity that would be helpful if addressed.<br /> Major Issues<br /> 1. The authors utilize DNA bases that are tagged to generate unique signals for recognition<br /> when captured and blocking the nanopore. From the principle component analysis<br /> tables (Supplementary Table 4a-c), it appears that the polymerases vary quite a bit with<br /> regards to processing different bases. At present, it is unclear whether these kinetic<br /> differences are being caused by differences between structures of the bases, or whether<br /> they are caused by differences between structures of the tags. One control would be to<br /> repeat one set of experiments with the tags shuffled between the bases and observe<br /> how reproducible the results are. This would give the reader a sense of how much<br /> measurements are being affected by the tags used for this technique.<br /> 2. For the experiment in Fig. 5, the authors end up showing that barcodes can be identified<br /> with a false positive rate of 13%. This is with a pilot experiment of 96 barcodes. From<br /> this data, it suggests that this technique would be difficult to scale-up any further, which<br /> may limit its usefulness – in fact even 96 barcodes may already be pushing the limit.<br /> From reading the paper, it is unclear if what is dominating this problem is the length of<br /> the barcode (i.e. limited sequence divergence due to 32-nt), or if nanopore sequencing<br /> accuracy is still a limiting factor. It would be great to see a small pilot experiment with<br /> longer barcodes to see if this could allow for improved accuracy, or some in silico<br /> statistical modeling extrapolating from their current data (e.g. length of barcode x<br /> required to accurately separate number of polymerases y with a false positive rate of z).

quite flexible, it still is unaddressed whether this repeated jostling of the tag<br /> (linked directly to the base) would affect kinetic measurements. Overall, it would be nice<br /> to see some measurements compared or benchmarked against a more well-established<br /> technique side-by-side (e.g. single-molecule optical trap), just to see if the data matches<br /> up or not. Notably with a parallel technique, you can also do the control of tagged vs.<br /> untagged nucleotides, thus unambiguously determining the potential effect of a tag on<br /> polymerase kinetics.<br /> Minor Issues<br /> 1. In the abstract the authors mention they “develop a robust classification algorithm that<br /> discriminates kinetic characteristics of the different polymerase variants.” It is unclear<br /> what this is referring to in the paper. If it is simply the principle component analysis then<br /> saying “develop” may be a bit overreaching.<br /> 2. Rather than referring to prior publications this publication should have in the<br /> supplement and/or methods the exact nucleotide + tag combinations used in this paper.<br /> 3. It is unclear after reading the methods why there are three separate PCA tables per<br /> polymerase in the supplement.<br /> 4. It is unclear what is the difference between tdwell and tag capture dwell from the written<br /> descriptions in the paper. Highlighting the difference visually in Fig. 3a (as was done<br /> with the rest of the kinetic variables) would help the reader clearly understand exactly<br /> what is being measured.<br /> 5. A table of the 96 barcodes used for Fig. 5/6 should be added to the supplementary<br /> materials.<br /> 6. The numbers in Supplementary Table 5 do not add up correctly – the authors should<br /> take a look again and make sure the correct numbers are present.<br /> 7. In Fig. 2 the authors experimentally calculate BMPI cut-offs for 3 different barcodes and<br /> get 0.8, whereas in Supplementary Fig. 8 the authors do an in-silico calculation for BMPI<br /> cut-off and still get 0.8. One would imagine that increasing the number of barcodes<br /> would require a stricter BMPI cut-off. Some sort of commentary on this, or perhaps<br /> reanalysis of the multiplexed data with a stricter BMPI cut-off could be helpful.<br /> 8. In Supplementary Fig. 12 the authors show a protein gel of their pore-polymerase<br /> conjugates. The bands show that post-linking, there is still a decent amount of nonlinked<br /> polymerase. In the methods there is no mention of a size exclusion purification<br /> step post-conjugation. Are the authors loading a mixed population onto their chips? This<br /> needs to be clarified.<br /> 9. In Supplementary Table 7 the tag capture dwell (TCD) variable missing.

Reviewer 3.

In the study titled Multiplex single-molecule kinetics of nanopore-coupled<br /> polymerases, Palla et al. developed and demonstrated the use of a<br /> single-molecule sequencing technology for the high-throughput identification of<br /> DNA polymerases with desired kinetic properties. Nanopore sequencing reactions<br /> were carried out on complementary metal-oxide-semiconductor (CMOS) chips, each<br /> of which contains over 30,000 individually addressable electrodes, thereby<br /> allowing sequencing reactions to be carried out on each chip in a multiplex<br /> fashion. Each DNA polymerase was coupled to an ?-hemolysin pore and bound to a<br /> 51 bp circular barcoded ssDNA template (CBT). The template is bound to a primer,<br /> thus enabling the incorporation of the appropriate nucleotides by the polymerase<br /> into the ssDNA template. Since each ssDNA template is circular, multiple<br /> iterations of the barcoded region can be observed during the sequencing of each<br /> template. Furthermore, each of the four nucleotides are uniquely tagged. When a<br /> nucleotide is being incorporated into the template ssDNA, the tag attached to<br /> the nucleotide is captured in the nanopore, thereby decreasing the conductance<br /> through the pore. Such a decrease in conductance is measured by an analog to<br /> digital converter (ADC) placed parallel to the sequencing circuit, and the<br /> recorded ADC values are then converted into a fraction of open channel signal<br /> (FOCS). Because the four tags are different from each other, the corresponding<br /> FOCS generated differ from each other as well, and can thus be used to<br /> distinguish the nucleotides from each other. Using a software, the FOCS is<br /> converted into raw reads. Then, using a barcode classification algorithm, each<br /> qualified raw read is compared to any template of the experimenter’s choice.<br /> Aligning a raw read to the correct template will more likely generate a higher<br /> barcode match probability index (BMPI) value for that read, while aligning a raw<br /> read to an incorrect template will more likely generate a lower BMPI value for<br /> that read. As such, for each sequencing experiment, the average BMPI value<br /> (derived from comparing raw reads to a template) can be used to identify the<br /> template to which the polymerase is bound. And if each polymerase-template pair<br /> is unique, the average BMPI value can then be used to identify the polymerase as<br /> well. Lastly, the authors defined a set of five kinetic parameters that can be<br /> measured during the course of a sequencing reaction. Because different<br /> polymerases are likely to differ from each other with respect to these kinetic<br /> parameters, comparison of the parameters between polymerases can help identify a<br /> polymerase with the desired properties.

To develop their nanopore sequencing technology, the authors first showed that<br /> the BMPI value can be used to identify a CBT. Thereafter, the authors showed<br /> that, after a polymerase is loaded with a particular CBT, the loaded CBT will<br /> not get replaced by another CBT that is present in the same reaction volume,<br /> thereby demonstrating the potential for multiplexing this sequencing platform.<br /> Then, as stated above, the authors defined five kinetic parameters that can be<br /> measured during sequencing. Using Principle component analysis (PCA), the<br /> authors showed that these kinetic parameters differ between polymerases, thus<br /> indicating the ability of this platform to distinguish polymerases based on<br /> these parameters. To demonstrate the multiplex potential of their platform, the<br /> authors conducted multiplex experiments in which different sets of CBTs were<br /> loaded onto three different polymerases. These pore-polymerase-CBT conjugates<br /> were then pooled prior to loading onto the CMOS chip. Notably, these experiments<br /> showed that CBTs can be identified in a pooled format. Finally, as a practical<br /> demonstration of the capability of the platform to identify, in a multiplex<br /> format, polymerases with properties of interest, the authors generated 96<br /> polymerases, each of which was then loaded with a unique CBT. In this multiplex<br /> reaction, the authors identified four polymerases that are potential candidates<br /> for further development for use in DNA amplification methods.

Here are some thoughts I had while going through the preprint:

1. The authors state that, in their pooled 3-plex sequencing experiment, about<br /> 67% of the raw reads (n = 418) were identified as any of the three barcodes used<br /> in the experiment. In Supplementary Table 5, it can be seen that, for total<br /> RPol-CBT, [the percent of raw reads with BMPI > 0.8] = [the number of raw reads<br /> with BMPI > 0.8] / [the total number of raw reads]. That is, 66.9% = 280 / 418.<br /> However, the table shows that the total number of raw reads for the RPol1-CBT1<br /> alone is 418. If this is the case, it is unclear to me how the total number of<br /> raw reads for all three RPol-CBTs (RPol1-CBT1, RPol2-CBT2, and RPol3-CBT3) can<br /> be 418 if that of RPol1-CBT1 alone is already 418.

2. On p19, line 1, I believe that “Experiments 1 and 3” should say “experiments<br /> 1 through 3”, since in all three of these experiments, the raw reads were<br /> compared to the correct template, as noted in the legend below the figure<br /> (Supplementary Figure 6b).

3. In Supplementary Figure 6a, the color-coding legend indicates that the<br /> barcode region of the ssDNA template is highlighted in grey. However, nothing in<br /> the ssDNA sequence was highlighted in grey.

4. The data presentation for Supplementary Figure 6b along with the associated<br /> text description are a bit confusing too me. It is stated that, in experiments<br /> 1-3, the reads were compared to the correct templates, while the reads in<br /> experiment 4-5 were compared to the incorrect templates shown in Supplementary<br /> figure 6a. In this part of the study, the three pore-polymerase-CBT conjugates<br /> (RPol1:CBT1, RPol2:CBT2, and RPol3:CBT3) were first individually assembled, and<br /> then pooled and loaded onto the CMOS chip. Assuming that this has been done for<br /> each of the five experiments indicated in Supplementary Figure 6, then there is<br /> really no universally correct template (e.g., comparing CBT1 to the raw reads of<br /> a pooled experiment would only yield higher BMPI values for a third of the reads<br /> (i.e., only for RPol1:CBT1-derived raw reads). Are the raw reads from experiment<br /> 1, 2, and 3 compared to CBT1, CBT2, and CBT3, respectively? This wasn’t<br /> specified anywhere in the text.

5. Regarding Figure 6a, the authors stated that, out of all of the 96<br /> polymerases screened in this multiplex experiment, 20 polymerases were<br /> identified as having detectable activity (p23, bottom). However, as depicted in<br /> Figure 6a, there are only 17 polymerases for which the associated barcodes were<br /> counted (i.e., there are only 17 yellow bars). Thus, it is unclear to me where<br /> the number “20” is derived from.

6. In the PCA analysis in Supplementary Figure 11, the authors tried to map the<br /> sequencing data derived from the multiplex experiment back to those derived from<br /> the singleplex experiments involving the same three polymerases. The sequencing<br /> data set for the second barcode set (CBT33-64) could not be mapped back well,<br /> and it was stated that this might be due to the high false positive rate of<br /> barcode identification for that barcode set. That being said, as indicated in<br /> Supplementary Table 6, the false positive rate for RPol1:CBT1-32 and<br /> RPol2:CBT33-64 are 11.94% and 16.06%, respectively. Thus, if the author’s claim<br /> is true, the inability to map back is due to a 16.06% – 11.94% = 4.12%<br /> difference in the false positive rate. It is unclear to me if a 4.12% difference<br /> in false positive rate would really lead to such a dramatic difference in the<br /> ability to map back. Also, it is unclear if this higher false positive rate<br /> arose due to polymerase (RPol2), the templates (CBT33-64), both, or neither.<br /> Logically, it seems unlikely that the rate would be due to the CBTs since it is<br /> unlikely that the middle third of the set of 96 CBTs would just happen to give<br /> higher false positive rates in comparison to the other two thirds. An easily<br /> accomplished comparison between two polymerases would be to load both<br /> polymerases with the exact same set of CBTs, and then compare the derived false<br /> positive rate for each polymerase. Then, one can repeat the experiment but using<br /> a different CBT set. This will help narrow down whether the observed false<br /> positive rate is due to the polymerase or the CBTs themselves.

7. Regarding Figure 5, it is unclear to me the exact differences between 5a and<br /> 5b. I see that the data presentation is a little different, but I’m not sure if<br /> both figures are necessary here given that both deal with the same three<br /> polymerases as well as the same set of 96 CBTs.

8. It is stated that the surface of each individual CMOS chip contains 32,768<br /> electrodes (p30) and that the chip contains thousands of pores (p4). Now, as<br /> mentioned in the measurement setup (Figure 1a legend), the measurement setup<br /> requires two electrodes (a counter electrode and a working electrode). Given<br /> this, it is unclear to me what proportion of those 30,000-some electrodes are<br /> working or counter electrodes. I believe that clarification on this would help<br /> the reader get a better sense of the number of pore-polymerase-CBT conjugates on<br /> each individual CMOS chip, and thus, a better sense and appreciation of the<br /> multiplex scale.

9. On p30, under the section Pore-polymerase-template complex formation,<br /> “SpyCather” should say “SpyCatcher” (i.e., a “c” is missing).

On 2020-05-05 19:48:55, user Sinai Immunol Review Project wrote:

Title <br /> Disparate temperature-dependent virus – host dynamics for SARS-CoV-2 and SARS-CoV in the human respiratory epithelium

V’kovski et al., bioRxiv/2020.04.27.062315 [https://doi.org/10.1101/202...]

Keywords<br /> • Upper and lower respiratory tracts <br /> • Temperature<br /> • Interferons

Main Findings<br /> The anatomical distance between the upper and lower respiratory tracts, as well as their different ambient temperatures (32-33°C and 37°C, respectively) could influence viral replication and subsequent immune responses, as previously reported in studies with influenza and rhinovirus infection [1, 2]. By employing an in vitro model of SARS-CoV-2 and SARS-CoV infection of human airway epithelial cells (hAEC) originated from three different donors, this study aimed to investigate the effect of temperature on virus-host innate immune response dynamics in the epithelial lining of the human respiratory tract. <br /> The authors show comparable SARS-CoV and SARS-CoV-2 replication at 37°C. However, when assessing viral replication efficiency at 33°C, SARS-CoV-2 infection resulted in 100-fold higher viral titers compared SARS-CoV, or SARS-CoV-2 replication at 37°C. Data in transcriptional signature after infection revealed that at 37°C, SARS-CoV-2-infection triggered earlier and greater changes in the transcriptional program compared to infection at 33°C. Among the differentially expressed genes (DEGs) after SARS-CoV-2-infection, the authors identified immunological mediators, such as chemokines and cytokines (CXCL10, CXCL11, TNFSF13B, and CCL5/RANTES), Interferon Stimulating Genes (ISG, such as OASL), as well as type I and III interferon genes. The authors tested SARS-CoV-and SARS-CoV-2 replication sensitivity to exogenous type I or type III interferon (IFN-I/III), and found a drastic reduction in viral load after hAEC infection.

Altogether, these results suggest a temporal, temperature-dependent gene expression profile that is inversely associated with SARS-CoV-2 replication which may account for the high replicative capacity of SARS-CoV-2 in the upper airways.

Limitations<br /> There is considerable variability between donors with regards to viral replication at 37°C and 33°C (specially in Figure 1a), and it is unclear how many times experiments were performed. <br /> The observation that temperature influences both viral replication and innate immune responses is essentially correlative. Further functional studies evaluating whether the temperature-dependent viral replication is a direct consequence of a delayed interferon response will be required. The variability between samples may also explain the fact that authors didn’t detect any DEGs in hAEC infected with SARS-CoV.<br /> It was recently proposed that ACE2 can be an ISG [3] induced specifically by IFNa, favoring SARS-CoV-2 cell entry. It would be interesting to indicate the level of ACE2 expression after IFNs exogeneous treatment of hAEC in the different culture temperatures.<br /> Moreover, it would be important to perform immunofluorescence analysis showing colocalization of ACE2 and SARS nucleocapsid protein.

Significance<br /> This study provides insights about the impact of microenvironmental temperature on virus-host interactions. Enhanced replication of SARS-CoV-2 at 33°C, may influence its replication in the upper respiratory tract and transmissibility in comparison to SARS-CoV.

Credit<br /> Reviewed by Alessandra Soares-Schanoski as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.

References

1. Holwerda, M., et al., Determining the Replication Kinetics and Cellular Tropism of Influenza D Virus on Primary Well-Differentiated Human Airway Epithelial Cells. Viruses, 2019. 11(4).
2. Foxman, E.F., et al., Temperature-dependent innate defense against the common cold virus limits viral replication at warm temperature in mouse airway cells. Proc Natl Acad Sci U S A, 2015. 112(3): p. 827-32.
3. Carly G.K. Ziegler, S.J.A., Sarah K. Nyquist, Ian M. Mbano, Vincent N., et al., SARS-CoV-2 receptor ACE2 is an interferon-stimulated gene in human airway epithelial cells and is detected in specific cell subsets across tissues. Cell, 2020.

On 2020-05-05 18:59:09, user Taekjip Ha wrote:

Replication stress causes replication forks to stall, which results in single end double strand breaks (DSBs). Double strand breaks can be repaired through homologous recombination (HR) or non-homologous end joining (NEHJ), however it is currently unclear how the final repair pathway is decided, what order the repair proteins are recruited to the DSB, and where along the DNA relative to the DSB the repair proteins bind. In vivo evaluation of these processes, particularly the spatial and temporal recruitment of repair proteins, has been limited due to the resolution constraints of fluorescence microscopy. In their manuscript titled “Super-resolution visualization of distinct stalled and broken replication fork structures”, Whelan et al. utilize multicolor single molecule super resolution microscopy, dSTORM, to gain understanding of seDSB repair pathways and recruitment of repair proteins. Using this technique, the authors are able to resolve individual stalled replication forks, induced DSBs, and recruited repair proteins from HR and NEHJ pathways. They demonstrate specific labeling of DSBs, and are able to characterize protein recruitment to stalled RF versus DSBs.

Topoisomerase I, Ku, and MRE11 all colocalize with DSBs induced by CPT treatment of cells. Interestingly, the authors find that Ku and MRE11 are both recruited to DSBs at approximately the same time however Ku dissociates within 1 hour whereas MRE11 remains associated to upwards of 4 hours. Evaluation of the positioning of Ku and MRE11 on the DNA relative to the DSB indicates that these two proteins are not interacting with each other, rather Ku is localized close to the DSB while MRE11 is further away from the DSB on the nascent DNA. In contrast, RAD51 and RAD52 colocalize to stalled replication forks but not DSBs. Upon conversion of stalled replication forks to DSBs (through Veliparib PARP inhibition), Ku and MRE11 colocalization to the DNA increases. These results identify two distinct groups of first responder proteins that are mutually exclusive, with one group recruited to stalled replication forks (RAD52, RAD51, and RECQ1) and the other group recruited to seDSBs (MRE11, Ku).

Major Issues<br /> 1. It would be helpful, particularly for readers not in this field, if a cartoon schematic comparing HR and NEHJ and the proteins from each pathway that are discussed in this paper was included in the introduction.<br /> 2. In multiple results figures (1A, 1E, 3A, 3B, 4A), control samples have non-random (greater than 1) colocalization values. What explains this occurrence? Is there an alternate method of analyzing or portraying the data to clarify this (or text explanation)? <br /> 3. Is it possible that the regressed strand of DNA can bind TUNEL? Recognizing that TUNEL labeling is relatively broad, is there another detection method to more specifically identify seDSBs? <br /> 4. Figure 4: The authors conclude that RAD51 and RAD52 both are first responders to stalled replication forks, however they only include images demonstrating colocalization of RAD51 with DSB, Ku, MRE11, and RECQ1.

Minor Issues<br /> 1. In the introduction, the authors abbreviate single-end double strand breaks as “seDSBs” however throughout the rest of the paper “DSBs” is used, which is a bit confusing. If the authors are indeed talking about the same type of double strand break, it would be beneficial to have consistent abbreviations throughout the paper.<br /> 2. Figure 3 A,B and Figure S4: Treatment time for cells differ depending on drug used (NCS for 1hr, HU for 4 hours). However, the control is exposed to the drug solvent for one time point. Does exposure of control cells to the drug solvent induce different results based on time of exposure?<br /> 3. Page 19, second paragraph: colocalization percentages of Ku and MRE11 in the text do not match the corresponding figure (Fig. 3 D, E) <br /> 4. This work utilizes U-2 OS cells, which is a cancer cell line. a cancer cell line for this work. How do the results compare to RF stalling, DSB formation and repair in a non-cancerous cell line?<br /> 5. Figure 5B demonstrates increased colocalization of DSB, Ku, and MRE11 with naDNA upon Veliparib treatment after CPT. It would be interesting to evaluate how colocalization of RAD51 and RAD52 with naDNA changes with these treatments. The model proposed in panel C indicates that these proteins dissociate upon double strand break formation, which would be strengthened by the inclusion of a similar experiment in B but for RAD51/RAD52.

On 2020-05-05 18:53:52, user Taekjip Ha wrote:

Thank you very much for sharing your interesting manuscript!<br /> We used your preprint as one of the journal club papers in the Single<br /> Molecule & Single Cell Biophysics course for graduate students of Johns<br /> Hopkins University during the Covid-19 lockdown. Students also practiced peer<br /> reviews as the final assignment. I am submitting their formal reviews here <br /> and hope you find them useful.

Taekjip Ha

Reviewer 1.

Summary<br /> Overall I enjoyed this methods development paper and thought the technique<br /> showed promise for future application. I think this work is suitable for<br /> publication after some minor revisions, mainly expanding the discussion of<br /> interesting results and considering any remaining experimental limitations, or<br /> lack thereof.

This paper characterizes the technique ABEL-FRET, which combines Anti-Brownian<br /> ELectrokinetic trapping and single-molecule FRET (smFRET) to achieve long-time<br /> imaging of freely diffusing biomolecules. The introduction describes smFRET as a<br /> molecular ruler and points out that current methods are restricted to either<br /> immobilization of molecules (which can aberrantly impact structure or function)<br /> or diffusive molecules (which limits imaging time). ABEL-FRET is situated as a<br /> method to get the best of both worlds. The authors show that their version of<br /> ABEL-FRET is more efficient than existing smFRET modalities utilizing confocal<br /> or TIRF microscopy—nearing the shot-noise limit of theoretical photon counting<br /> precision, and resolving single base pair differences in dsDNA. Illustrating the<br /> potential uses of their setup, the authors then use ABEL-FRET to examine three<br /> example systems. Example systems include: the spontaneous switching of Holliday<br /> junction isomers, ssDNA binding kinetics of the bacterial recombinase RecA, and<br /> the kinetics of single-stranded DNA-binding protein (SSB) sliding on ssDNA. This<br /> paper’s kinetic results are largely in agreeance with previously published data<br /> obtained using immobilization smFRET. Returning to their Holliday junction and<br /> SSB models, the authors propose their method is also amenable to hydrodynamic<br /> profiling—providing conformational and binding stoichiometric information.

The main contribution of this paper is that it makes a previously proposed<br /> method a novel reality and performs an initial characterization of its precision<br /> in mostly DNA centered assays. The major strengths of this paper include:<br /> clarity in explaining the methodology, comparing findings to the existing field<br /> of knowledge as confirmation of technical accuracy, and writing style. The<br /> weaknesses of this paper lie predominantly in the lack of an expanded discussion<br /> which may have answered many questions that arose.

Major comments:<br /> This paper proposes that the transient event indicated by a black arrow in<br /> Figure 3d may be a new dynamic state of RecA. The presented data is not strong<br /> enough to fully support this claim or rule out the possibility that the<br /> transient event represents an optical aberration or noise. Theoretically one<br /> could put an arrow in any transient peak and propose a new state. To solidify<br /> this claim, more experimental replicates could be collected to see if this peak<br /> persists (indicating a real event) or disappears as background noise. If<br /> sufficient replicates were already tested and the event was present in all, then<br /> it would helpful to see the new state indicated on multiple representative<br /> traces to prove its constancy. The number of experimental replicates could also<br /> be explicitly stated on this figure or the S12b graph moved into the main figure<br /> to support this claim. As the proposition of a potentially new RecA state would<br /> contribute greatly to the existing field of knowledge, it warrants further<br /> discussion or obvious proof in the text. Since this ABEL-FRET technique is a<br /> major technological upgrade from existing methods, any new information collected<br /> from it should be thoroughly validated to prove its reliability. Maybe<br /> information in the supplement should be added as new figures or more explicitly<br /> presented.

A similar major point concerns the use of ABEL trapping and its potential<br /> electrokinetic impacts on charged biomolecules. Since this paper focuses on<br /> negatively charged DNA and positively charged DNA-interacting proteins, it would<br /> benefit from references or control experiments showing that the applied voltages<br /> do not change endogenous binding dynamics. This concern was addressed in<br /> Supplemental Note 3, but it is not obvious from the one sentence mention in the<br /> main text. Although it is understandable that not all concerns can be addressed<br /> in the main text, expanding the discussion of any controls which answer common<br /> questions gains added favor for innovative methods.

Minor comments:<br /> Overall this paper was clear in word choice and grammar. Minor comments are just<br /> more questions that popped up while reading which could easily be addressed in<br /> the discussion without the need for further experimentation:

-In this microfluidic device setup, is diffusion in the z-axis an issue at all?<br /> Are biomolecules able to diffuse in and out of focus at any point? Would such<br /> diffusion impact FRET efficiency background noise?

-The discussion states that this method should be compatible with any<br /> FRET-labeled biomolecules, have dynamics of other proteins been tested yet (i.e.<br /> those not focused around DNA or DNA binding)? How would things change if<br /> flexible proteins (more susceptible to voltage changes) are trapped and imaged?<br /> Are there restrictions to what biomolecules can be profiled using this method?

-Additionally, have any FRET fluorophore pairs other than Cy3-Cy5 been tested<br /> with this technique? Since it seems as if confocal microscopy was used here,<br /> could this technique be optically limited compared to other forms of single<br /> molecule imaging that rely on higher resolution microscopes? Does this matter<br /> for measurements of hydrodynamic profiling?

Reviewer 2.

Although single-molecule Förster resonance energy transfer (smFRET) has been<br /> used widely since its introduction over two decades ago, there is still room to<br /> tweak and improve this method for additional biological applications. Many<br /> smFRET methods rely on tethering molecules to a surface, which can disrupt their<br /> activity or function. Additionally, immobilization eliminates the possibility of<br /> interrogating hydrodynamics concomitantly with distance information provided by<br /> FRET. However, without surface immobilization, tagged molecules diffuse in and<br /> out of the detection volume rapidly, preventing long observation times. One<br /> promising method to overcome the limitations inherit in immobilizing molecules<br /> for smFRET is Anti-Brownian Electrokinetic (ABEL) trapping. In an ABEL trap, a<br /> single molecule’s position is monitored in real time, and its Brownian motion is<br /> cancelled out by applying electrokinetic force, keeping the molecule within the<br /> field of view for an extended amount of time. This allows of longer observation<br /> times, without the need to tether the molecule of interest. In this work, the<br /> authors extend the possible observation time of ABEL-FRET, achieve high<br /> resolution by obtaining high precision FRET efficiency measurements, and are<br /> able to combine hydrodynamic measurements with smFRET.

The authors achieve a longer sampling time than has previously been reported;<br /> they are able to observe a FRET pair within the ABEL trap for up to ten seconds,<br /> an exciting advancement in the field. Additionally, their high precision FRET<br /> efficiency measurements allow them to achieve single base pair resolution when<br /> observing double stranded DNA labelled on either end with a FRET pair. Due to<br /> the long observation times and the fact that this technique is tether-free, they<br /> are also able to profile the hydrodynamics of molecules caught in the ABEL trap.<br /> The paper is well written, the logic is sound and clearly spelled out, and most<br /> proper controls are included.

Although current events prevent most of us from performing new experiments in<br /> the lab, there are several points it would be worthwhile for the authors to<br /> address. First, what, if any, effect does the ABEL trap have on protein<br /> hydrodynamics? Although the authors demonstrate that increasing the<br /> electrokinetic force applied by the trap does not impact the kinetics of<br /> Holliday junctions, it would be reassuring to see the same validation performed<br /> with a tagged protein. Several proteins with different charge states would be<br /> preferable, to confirm that diffusion is not significantly altered by the forces<br /> necessary to contain a protein within the trap. If the authors have data<br /> speaking to this question, it would be worthwhile to include; if not, they might<br /> speculate on why they are not concerned about electrokinetic effects on<br /> proteins. Similarly, are charged ions, such as Mg2+ used in the Holliday<br /> junction experiments, affected by the ABEL trap? Could the electrokinetic forces<br /> applied affect the local concentration of these small molecules, influencing the<br /> biological processes being observed? More discussion of this would be<br /> beneficial.

My second issue relates to data interpretation. The authors state that with<br /> their high resolution, it is possible to detect additional transient states that<br /> have been missed by previous methods. The data supporting this come from<br /> experiments to validate their technique by investigating RecA-ssDNA<br /> nucleofilament dynamics. The authors convincingly reproduce past experiments<br /> that have identified three different conformations. In addition, they argue, the<br /> resolution of their experiment allows them to identify more transient states<br /> that have gone undetected in the past (shown in Fig 3d, Fig S12b). Although it<br /> is possible that these additional FRET efficiency peaks are indeed newly<br /> discoverable states, due to the low number of occurrences observed, it is<br /> difficult to distinguish them from noise. Until it is possible to reproduce<br /> these results with a larger sample size, or via an independent method, we should<br /> be cautious in our interpretation of the additional peaks.

The remaining questions and limitations do not, however, detract from the<br /> significance of the technical advancements this paper introduces. The increased<br /> resolution and ability to couple smFRET measurements with hydrodynamics are<br /> important steps forward in realizing the potential of smFRET. It will be<br /> exciting to see what interesting biology can be uncovered with this improved<br /> technique.

Reviewer 3.

Wilson and Wang present a technique for acquiring single molecule Förster<br /> resonance energy transfer (smFRET) measurements that avoids the potential<br /> confounds of established smFRET techniques by using Anti-Brownian ELectrokinetic<br /> (ABEL) trapping to capture free molecules in solution. They demonstrate the<br /> ability of this technique to measure sub-nanometer distances on dsDNA species<br /> and detect changes in DNA conformational states on a millisecond timescale with<br /> the same fidelity as traditional tethered smFRET techniques and with enhanced<br /> precision. The authors highlight the inherent ability of their ABEL-FRET<br /> technique to constantly sample molecular charge and diffusion, which allows them<br /> to temporally pair FRET signal and diffusion kinetics in order to profile<br /> molecular species in three-dimensional space. Through these pilot experiments,<br /> Wilson and Wang showcase the utility of a unique single-molecule imaging<br /> technique that generates measurements comparable to those of tethered smFRET<br /> while providing the added benefit of hydrodynamic profiling.<br /> The primary justification for ABEL-FRET, as framed by the authors in their<br /> introduction, is the ability of the technique to circumvent the potential<br /> confounds introduced by traditional smFRET techniques, which either immobilize<br /> molecules by covalent tethering or lack the temporal longevity needed to probe<br /> the conformational dynamics of free molecules in solution. The major challenges<br /> arising from tethered smFRET, as emphasized by the authors, are 1) shortcomings<br /> in signal detection precision caused by a field of view limited to the<br /> molecule-coverslip interface, 2) an inability to extract diffusion information<br /> due to covalent tethering and, 3) the potential of covalent tethering to<br /> introduce biochemical consequences on conformation or function. The presented<br /> data unquestionably supports the ability of ABEL-FRET to capture molecules on a<br /> much longer timescale than with contemporary untethered techniques. The authors<br /> provide good evidence supporting the ability of ABEL-FRET to make detection<br /> measurements with greater precision than tethered smFRET (Fig 1b) and devote a<br /> significant number of experiments to showing the benefits of hydrodynamic<br /> profiling afforded uniquely by ABEL-FRET (Fig 4). While the aforementioned<br /> improvements are alone enough to justify the utility of ABEL-FRET for measuring<br /> single molecule conformational dynamics, the ability of ABEL-FRET to avoid the<br /> potential biochemical pitfalls of molecular tethering is never directly tested.<br /> Taking this into consideration, an introduction that places more emphasis on the<br /> optical and diffusion limitations of tethered smFRET, rather than the<br /> biochemical limitations, would better position and highlight the strengths of<br /> ABEL-FRET that are directly supported by the data. Likewise, more discussion<br /> could be devoted to speculating or explaining why ABEL-FRET signal detection<br /> allows for such highly precise FRET efficiency measurements, as this finding is<br /> striking and strongly justifies the utility of ABEL-FRET over previous smFRET<br /> techniques. <br /> In experiments probing the conformational states of DNA species in the<br /> presence of RecA (Fig 3 and Fig S12) the authors provide data showing the<br /> distribution of observed FRET states (Fig S12b). While these results are<br /> interpreted as three separate populations, thus conformations, and the existence<br /> of a “minor state” is highlighted, more replicates are needed to separate these<br /> populations in order to fully support this interpretation. Though more data is<br /> needed to confirm RecA binding states, the experiments exploring the binding<br /> conformations of RecA and SSB provide good foundational data that can serve as<br /> models or examples for reference in future studies of interactions where the<br /> binding conformations/dynamics are unknown. <br /> The authors demonstrate well the ability of ABEL-FRET to make highly precise<br /> measurements for as long as seconds and can extract the same conformational<br /> populations as tethered smFRET from FRET efficiency measurements. These<br /> strengths validate ABEL-FRET as a technique comparable to its contemporaries;<br /> however, the ability to simultaneously extract smFRET and diffusion information<br /> from ABEL-FRET highlights the uniqueness and justifies the necessity of this<br /> technique in molecular profiling. The authors elegantly demonstrate the ability<br /> to uncover more conformational populations than previously identified with their<br /> own smFRET measurements alone by applying an orthogonal diffusion axis to their<br /> FRET efficiency measurements. In doing so, they provide direct evidence for two<br /> separate biological phenomena and simultaneously demonstrate the unique<br /> capabilities of ABEL-FRET.<br /> Wilson and Wang provide a linear and logical introduction to their new<br /> single-molecule profiling technique, ABEL-FRET. They validate the technique’s<br /> ability to produce conformational data on par with its contemporaries while also<br /> demonstrating that ABEL-FRET performs with greater temporal longevity than free<br /> molecule smFRET and better optical precision than tethered smFRET. The authors<br /> go on to show the unique ability of ABEL-FRET to integrate both FRET and<br /> diffusion information in order to unveil conformational populations before<br /> unresolved with traditional smFRET. This paper presents exciting new technology<br /> with evident utility and great promise.

On 2020-05-05 18:47:07, user Taekjip Ha wrote:

Thank you very much for sharing your interesting manuscript!<br /> We used your preprint as one of the journal club papers in the Single<br /> Molecule & Single Cell Biophysics course for graduate students of Johns<br /> Hopkins University during the Covid-19 lockdown. Students also practiced peer<br /> reviews as the final assignment. I am submitting their formal reviews here <br /> and hope you find them useful.

Taekjip Ha

Reviewer 1.

Summary of Evaluation:

Here, Janissen et al. describe a novel mechanism by which viral RNA-dependent<br /> RNA polymerases (RdRp) undergo induced template switching during RNA synthesis.<br /> These template switching reactions can be intermolecular, resulting in<br /> homologous recombination, or intramolecular, resulting in copy-back synthesis.<br /> Typically, RNA-analogues introduced as antivirals result in chain termination or<br /> lethal mutagenesis, but non-single-molecule experiments may have inappropriately<br /> classified instances of template switching as termination and would not have<br /> been detected. Therefore, by utilizing a single-molecule approach, the authors<br /> are able to analyze RdRp pauses, backtracking, and copy-back synthesis, which<br /> they ultimately determine can be induced by the addition of a<br /> pyrazine-carboxyamide antiviral nucleotide with an unconfirmed mechanism.<br /> Overall, the paper makes a compelling argument for viral RdRp backtracking and<br /> recombination induction as a third mechanistic class of antivirals, although a<br /> few components of the experimental design and conclusions may require further<br /> experimentation. The use of a single-molecule approach to probe the in vitro<br /> dynamics of RdRp synthesis, though previously described, proves powerful in<br /> elucidating how reversals and recombination, particularly of EV-A71 RdRp, may<br /> occur. Further, the data suggests that the recently approved antiviral T-1106<br /> may be acting through this recombination mechanism, which has not been<br /> previously described. <br /> The article benefits from well-structured and balanced figures that successfully<br /> convey the data at hand in a straight-forward manner. Although occasionally<br /> verbose (discussion) and short at others (conformational dynamics results), the<br /> paper’s writing successfully conveys the importance of the findings and supports<br /> the findings with appropriate literature references. The work itself tells a<br /> fairly complete story with logical transitions and progression between<br /> experiments and conclusions. The paper is overall of high quality, though<br /> further controls and validation may be necessary to fully substantiate some<br /> claims as detailed below. Though not necessarily field-defying, the paper<br /> introduces the possibility of novel mechanisms of antiviral therapeutics that<br /> could serve to push human health forward and is deserving of high recognition.

Summary of Data:

To elucidate the mechanism by which template switches occur, the authors first<br /> utilized a magnetic tweezers assay to determine the elongation or retraction of<br /> an RNA-RNA complex that served as a read-out of RdRp synthesis or<br /> backtracking/reversal, respectively. Using the RdRp from EV-A71, a virus more<br /> prone to recombination than the RdRp of their previous work (poliovirus, PV),<br /> the authors show instances of RdRp backtracking and magnetic bead retraction,<br /> which they conclude is due to a template switching mechanism that leads to<br /> copy-back RNA synthesis and formation of defective viral RNA products. Utilizing<br /> an EV-A71 RdRp variant analogous to a previously described mutation in PV RdRp<br /> that impairs recombination, the authors showed that while replication was not<br /> impaired, the EV mutant showed a 100-fold decrease in viral titer in an assay<br /> that required recombination for successful viral replication. The mutant virus<br /> was also highly attenuated in a mouse model relative to WT, in agreement with<br /> previous work that PV RdRp requires recombination to cause disease in a mouse<br /> model. <br /> Mutant EV-A71 RdRp showed increased pause probability and pause duration, but<br /> decreased reversal probability compared to WT, which suggested a decreased<br /> ability to backtrack in the mutant. The orthologous PV mutant showed similar<br /> results. Using molecular dynamics simulations, the authors showed that the<br /> EV-A71 mutant had a smaller RNA-binding channel compared to WT, with the mutant<br /> channel more closely resembling the PV RNA-binding channel size. The dynamics<br /> data corroborates a mechanism by which EV-A71 RdRp, but neither its mutant nor<br /> PV RdRp, has a binding channel large enough to accommodate copy-back RNA<br /> synthesis. <br /> Finally, the authors utilized the antiviral ribonucleotide T-1106, a drug with<br /> inconsistent mechanistic understanding, in cell-based viral recombination<br /> experiments. The WT EV-A71 RdRp showed increased pausing, pause duration, and<br /> reversal probability in the presence of T-1106. In the recombination assay,<br /> T-1106 increased recombination in WT EV-A71 but not the recombination defective<br /> mutant. The authors also show that the mutant RdRp does not lead to viral<br /> resistance to T-1106.

Major Issues:

• In the single molecule assay, the retraction of the magnetic bead is<br /> attributed to copy-back synthesis. Though a plausible mechanism, the main<br /> evidence of this mechanism is of similar kinetics between elongation and<br /> copy-back. While a valid assumption given the data shown, further validation is<br /> required to definitively say that copy-back synthesis is occurring. The most<br /> obvious way to validate this is through the determination of the RNA products.<br /> Though it may be difficult to detect RNA products in these single molecule<br /> experiments, this information is crucial to confirm that copy-back synthesis is<br /> indeed occurring, especially since this mechanism is invaluable to conclusions<br /> drawn throughout the paper.

• The in vitro experiments in this paper exclusively look at intramolecular<br /> template switching (though this must be further validated as stated above).<br /> However, most if not all of the cell-based assays exclusively assay for<br /> intermolecular recombination (luciferase donor assay). Though the correlation<br /> between the two types of recombination are believable, validating that<br /> intermolecular recombination trends hold in vitro and that intramolecular trends<br /> hold in the cell-based assays is a crucial control. Without this data, the<br /> mechanistic conclusion of copy-back and recombination sharing an intermediate is<br /> jeopardized.

Minor Issues:

• The paper would benefit from greater elaboration on the effects of defective<br /> viral genomic products on viral replication to provide context for the activity<br /> of the purported new antiviral mechanistic target. What is known about defective<br /> viral genomic products?

• In the T-1106 assays, a 400µM T-1106 concentration is the only concentration<br /> that significantly increased recombination. This is not elaborated in the paper.<br /> Would you not expect higher concentrations to also have increased recombination?

• The flow of the paper is occasionally interrupted by terse, short sentences<br /> and the occasional grammatical error. Luckily, this is a bioRxiv and these are<br /> easily fixed prior to peer review.

Reviewer 2.

Summary: <br /> Janissen et al describe a third mechanistic class of antiviral ribonucleotides<br /> that utilize RdRp template-switching reactions, an interesting topic that is<br /> highly relevant today and can be especially appreciated in the context of the<br /> recent COVID-19 pandemic. They first demonstrate the need for new broad-spectrum<br /> antiviral therapies and identify viral polymerases as a powerful target, placing<br /> special emphasis on RNA-dependent RNA polymerases (RdRp). The currently approved<br /> antiviral nucleotides fall into two functionally distinct mechanistic classes;<br /> they are either chain terminators that stop nucleic acid synthesis or lethal<br /> mutagens which increase mutational load on the viral genome. However, these<br /> often have off target effects which lead to the emersion of a new class of<br /> antiviral nucleotides known as the favipiravir (T-705) class which requires the<br /> cellular nucleotide salvage pathway. Within this class, the nucleoside analog,<br /> T-1106, has high efficacy but its mechanism of action is unknown which prevents<br /> FDA approval. This work is an expansion of a previous study that used a magnetic<br /> tweezers approach to illustrate that pausing and backtracking of the elongating<br /> Poliovirus (PV) RdRp was enhanced by incorporation of T-1106 into the nascent<br /> RNA. They noted that traditional polymerase elongation assays would have missed<br /> the backtracked state, which they believe provides evidence for a third<br /> mechanistic class of antiviral ribonucleotides that rely on RdRp mediated inter-<br /> (homologous recombination) or intramolecular (copy back RNA synthesis) template<br /> switching. <br /> In hopes to elucidate this mechanism, Janissen et al hypothesized that T-1106<br /> induced backtracking generates a free 3’ single-stranded RNA end, which<br /> functions as an intermediate for template switching and results in a reduction<br /> of viral replication. In this work they 1) characterized the recombination prone<br /> Enterovirus (EV) RdRp in their magnetic tweezers system, 2) developed a<br /> recombination deficient EV RdRp (Y276H), 3) briefly analyzed the structures of<br /> WT and mutant PV and EV RdRps in silico, and 4) explored the effect of T-1106 on<br /> the WT and mutant RdRps. They used the same magnetic tweezers approach is in the<br /> previous study to demonstrate that EV RdRp pauses similarly to what was seen for<br /> PV RdRp, which is inversely correlated with nucleotide concentration. Their more<br /> interesting finding was that unlike PV RdRp, EV RdRp displays reversals. They<br /> proposed a probable reversal mechanism in which EV RdRp pausing leads to<br /> backtracking that produces a free single stranded 3’ RNA end which can serve as<br /> a primer for copy-back RNA synthesis as observed by a decrease in bead height.<br /> To connect reversals with recombination they generated a recombination deficient<br /> RdRp mutant, Y276H, which is orthologous to the known PV mutant, Y275H.<br /> Replication, plaque formation, and genome amount of virus titer were determined<br /> to be similar for WT and Y276H EV RdRp. They confirmed that this mutant was<br /> recombination deficient and showed that oral inoculation of EV Y276H resulted in<br /> attenuation of virulence in hSCARB2 mice compared to WT. Y276H had increased<br /> pausing, decreased processivity, and decreased reversals which were still<br /> pause-dependent. This finding was puzzling but was proposed to be due to<br /> increased stability of Y276H on the free 3’ RNA end, rendering it unavailable<br /> for reversals. Similar results were observed for the PV RdRp Y275H. In hopes to<br /> explain why EV RdRp can reverse but not PV RdRp and the impacts of the mutations<br /> they superimposed the structures and conducted molecular dynamics simulations.<br /> They concluded that PV had the smallest RNA channel which was similar to EV<br /> Y276H and that EV WT RdRp had the largest channel, enabling it able to undergo<br /> copy-back RNA synthesis. Finally, they explored the effect of T-1106 on EV RdRp<br /> template switching, which was shown to increase dwell time, reduce processivity,<br /> increase pause probability and duration, and increase reversal probability, all<br /> of which were claimed to be reflective of intramolecular template switching.<br /> They concurrently assessed T-1106’s effect on PV RdRp which was reflective of<br /> intermolecular template switching. They therefore concluded that antiviral<br /> ribonucleotides lead to increased backtracking and recombination, in which<br /> recombined products are not replication-competent and thus lead to a decrease in<br /> virulence. Most of the claims in this paper are substantiated by data, however,<br /> there are some major and minor flaws outlined below that need to be addressed<br /> prior to acceptance. A revised version of this paper would be suitable. <br /> General Feedback:<br /> Overall, this paper provides compelling evidence that RdRps display pausing and<br /> backtracking behavior. Their magnetic tweezers platform allows for single<br /> molecule analysis of the RdRps, which to my knowledge, has not been done before<br /> besides through their previous paper. The existence of a third mechanistic class<br /> of antiviral ribonucleotides is substantiated by their data, however, they only<br /> briefly addressed T-1106. The majority of the paper is spent characterizing EV<br /> RdRp in their magnetic tweezers system, with only one figure dedicated to T-1106<br /> effects. It may be more beneficial to split this paper in two, with one paper<br /> focusing on characterizing EV RdRp and comparing it to PV RdRp and the other<br /> determining the effects of T-1106, especially considering the T-1106 experiments<br /> that must be done to confirm its viability as an antiviral. Additionally, to<br /> convince the existence of an entire third mechanistic class, the favipiravir<br /> class of antiviral ribonucleotides should all be analyzed in their system. In<br /> general, the paper has an acceptable and organized flow, with only minor<br /> adjustments necessary (see minor issues). The experiments appear reproducible<br /> and robust. Their work in PV and EV RdRp recombination is mainly confirmatory,<br /> however, their platform allows for analyzation of this process at the<br /> single-molecule level which reveals novel insight into the mechanism. There are<br /> a few major issues that need to be addressed, outlined below, to support this<br /> finding and complete the paper. The discussion of the paper is also repetitive<br /> and should be edited to be more concise.

Major Issues:

The article did not discuss the intrinsic ability of RdRps to undergo template<br /> switching, which they extensively showed in their assays. If recombination and<br /> copy-back synthesis are intrinsic why would these be a valuable target as an<br /> antiviral? Is there a specific level or cut off where too much recombination<br /> becomes detrimental? I would like to see an assay in which they determine the<br /> level of recombination necessary to decrease virulence.

I would like to see more virulence studies, why didn’t they treat the hSCARB2<br /> mice with T-1106? This should be conducted to directly address T-1106 efficacy<br /> both in the context of WT and Y276H EV RdRp treated mice.

While the single molecule experiments demonstrate copy-back synthesis<br /> (intramolecular template switching) the cell-based experiments exclusively<br /> quantify homologous recombination (intermolecular template switching). This<br /> paper should contain an experiment that directly quantifies copy-back synthesis<br /> in a cellular context. Since copy back RNA synthesis should generate hairpins,<br /> RNA seq could be conducted to determine if sequences with hairpin-forming<br /> properties are enhanced in cells infected with EV RdRp and treated with T-1106<br /> compared to WT.

The magnetic tweezers approach was also unable to directly quantify<br /> intermolecular template switching. If possible, another template could be<br /> introduced to assay if pausing, and thus no change in bead height, becomes<br /> indefinite, which could indicate that the RdRp has left the initial template.

They claim that T-1106 has no effect on EV Y276H RdRp, but they show a<br /> significant reduction of recombination at higher doses (100-fold, figure 6H), so<br /> the data does not substantiate their claim.

Bar graphs are no longer an acceptable form of data presentation, these figures<br /> should be converted to dot plots to show data variability and illustrate<br /> replicates.

Is there a way to generate a mutant that has a greater propensity for<br /> recombination? If so, this would allow for a direct analysis of whether<br /> increasing recombination leads to decreased virulence. Another way to address<br /> this question would to be comparing PV and EV virulence, especially in a mouse<br /> model, since EV RdRp is more recombination prone.

Minor Issues:

This pausing-backtracking phenomenon was shown in both their previous work and<br /> in this paper, however, it was not confirmed through the use of other methods.<br /> Confirming the pausing phenomenon through other methods would be beneficial,<br /> perhaps using nanopore sequencing and/or single molecule tracking of the RdRp in<br /> cells to elucidate kinetic rates and interaction dynamics.

The structure and dynamics information seems out of place and is not very<br /> informative. This figure may be better suited at the beginning of the paper, or<br /> may not be needed at all, to describe the structural differences between PV and<br /> EV RdRps, and the greater propensity for EV RdRp recombination. It could later<br /> be mentioned that the mutants display pore sizes similar to PV RdRp which could<br /> be shown in a supplementary figure. These data show a smaller channel width for<br /> Y276H compared to WT EV RdRp. How could a smaller channel width affect<br /> backtracking ability, especially since PV and EV RdRps both display backtracking<br /> ability? How could this relate to the function of an antiviral ribonucleotide,<br /> does the nucleotide interfere with pore interactions? These questions are not<br /> adequately addressed and would contribute to the paper. Additionally, would a 4<br /> Å difference be sufficient to yield the PV RdRp unable to accommodate a three<br /> stranded intermediate at the time of initiation?

They did not hypothesize as to why 400 uM T-1106 concentration had the optimal<br /> response in their recombination assay. This should be addressed.

Determining the molecular basis for the reduced recombination capabilities of<br /> the recombination deficient RdRps would be beneficial but may be the grounds for<br /> a separate paper. For example, how might the Y275(6)H mutation be stabilizing<br /> the polymerase and reducing recombination?

Recommendation: <br /> I recommend revision of this article before acceptance in which the major and<br /> minor issues are addressed.

Reviewer 3.

The search for efficacious anti-viral therapeutics has become prominent in<br /> light of the recent coronavirus outbreak, with the RNA polymerase being a common<br /> target. A recent class of pyrazine carboxamide antiviral nucleotide and its<br /> analogs have shown promise, but there is ambiguity in the mechanism of action.<br /> It has been shown to increase backtracking during elongation for the poliovirus<br /> (PV) RNA-dependent RNA polymerase (RdRp), which may free the nascent 3’ end and<br /> allow for a template switch and recombination, producing inviable viral genome.<br /> This study used the more recombination-prone enterovirus (EV) RdRp to establish<br /> this connection between backtracking and recombination using a magnetic tweezers<br /> platform.<br /> The magnetic bead in this assay is tethered to a surface with ssRNA, and<br /> annealed to it is a template with a hairpin serving as a primer for the RdRp. As<br /> the RdRp polymerizes, the annealed RNA is displaced from the tethered RNA. At<br /> forces of >8pN, this causes the tethered RNA to lengthen, which is monitored by<br /> observing the height of the bead. This simple and highly informative assay<br /> showed that the EV RdRp is able to reverse, likely from the freed nascent 3’ end<br /> annealing to and elongating off itself and allowing reannealing to the template<br /> RNA. <br /> They then generated an EV mutant (Y276H) orthologous to the recombination<br /> deficient Y275H PV mutant and used a clever cellular assay with a gene construct<br /> reporting on recombination ability to show it is also recombination deficient,<br /> and also less deadly. This assay leaves non-recombined genomes unable to produce<br /> virus and expressing luciferase, and recombinants are viable and have low<br /> reporter output.<br /> To connect recombination ability and reversals, this mutant was tested for its<br /> ability to backtrack using the magnetic bead assay, and while it showed<br /> increased pausing, it showed decreased backtracking and reversals. To test if<br /> this was due to stabilization of the 3’ nascent RNA freed with the WT, they<br /> evaluated each RdRp in an in vitro RNA synthesis assay and found the mutant had<br /> a slower nucleotide incorporation rate. This same assay was performed with the<br /> PV WT and mutant RdRp to show similar results, but they importantly note that PV<br /> does not undergo reversals.<br /> Next the authors looked to the structure and dynamics of the PV and EV WT and<br /> mutant enzymes for insight into the mechanism of backtracking and recombination.<br /> They did not find obvious differences in crystal structure between EV and PV or<br /> between EV WT and mutant model. From here they did a lot of molecular dynamics<br /> simulations that I don’t fully understand, but they essentially tracked the<br /> distance between two residues within the RNA tunnel of the EV WT, EV mutant, and<br /> PV WT RdRps. Interestingly, the average distance was largest in the EV WT RdRp,<br /> smallest for PV WT, and the EV mutant was in between (but closer to PV). This is<br /> good suggestive data to show for the implications of RNA tunnel width for<br /> reversal ability, but they make no bold claims.<br /> In their last set of experiments, the authors again used the magnetic bead<br /> assay to assess EV RdRp movement but with the T-1106 drug. Unlike the<br /> recombinant deficient mutant, the drug caused a decrease in pausing and an<br /> increase in reversals. When the cellular recombination assay was applied with<br /> the WT EV RdRp and with the T-1106 drug was administered, there was an increase<br /> in recombinant-proficient plaque formation and a decrease in<br /> recombination-dependent reporter protein output. When the drug was applied to<br /> the mutant RdRp in the recombination assay, there was no activity to suggest<br /> that recombination was taking place.<br /> In supplementary Figure 6 the authors tested the sensitivity of each the EV WT<br /> and mutant RdRp to a titration of T-1106 concentrations. This was a great assay<br /> to perform, as it shows that even as the virus accumulates mutations in the<br /> polymerase the drug remains proficient. However, the sensitivity of the mutant<br /> to the drug is surprising since the drug was shown to cause no significant<br /> increase in the recombination ability of the mutant polymerase. While not stated<br /> explicitly, this could be addressed in the model posed in figure 6K as the<br /> aborted RNA synthesis.<br /> The model proposed from these data shows a logical conclusion drawn about how<br /> the drug is functioning on the polymerase, and the data were overall extremely<br /> well articulated. The experiments were mostly well described and straightforward<br /> while also being innovative and informative. This could be valuable information<br /> in drug development and testing for anti- RNA viral therapeutics.

Major points<br /> • Figure 2B and F: why is the mutant about equal to WT in the plaque assay, but<br /> has a significantly higher survival rate in vivo? You mention this is consistent<br /> with PV, but propose no reason.<br /> • Figure 3G and 4G: Mechanistically, why does a decreased rate of nucleotide<br /> incorporation correspond to an increase in polymerase stability?<br /> • Figure 6H: I would have liked to see the magnetic bead assay for the T-1106<br /> drug applied to the EV RdRp mutant.<br /> • Supplementary Figre 6: How is it that the mutant can still be so sensitive to<br /> the drug? It should maybe be discussed that the T-1106 drug is inhibiting some<br /> other property of the enzyme that leads to recombination as well as normal<br /> function.

Minor points<br /> • Figure 4: Why was the magnetic bead assay performed for the PV WT and mutant<br /> RdRp?<br /> • Why do you think PV RdRp doesn’t undergo reversals? Perhaps something to do<br /> with the RNA tunnel width?<br /> • Figure 5: What are the next experiments that should be done to explore the<br /> structure/dynamics? Is the tunnel width a potential factor in reversal ability?<br /> • There should be a sentence clarifying that the cellular recombination assay<br /> used in Figure 6 is the same as the one in Figure 2.

On 2020-05-05 18:33:30, user Taekjip Ha wrote:

Thank you very much for sharing your interesting manuscript!<br /> We used your preprint as one of the journal club papers in the Single<br /> Molecule & Single Cell Biophysics course for graduate students of Johns<br /> Hopkins University during the Covid-19 lockdown. Students also practiced peer<br /> reviews as the final assignment. I am submitting their formal reviews here <br /> and hope you find them useful.

Taekjip Ha

Reviewer 1.

Summary:<br /> In this study, the authors describe the development of a tool that can be used<br /> to observe and measure single-moleculeCap-dependent and Cap-independent<br /> translation, concurrently, in live cells. The authors spend a considerable<br /> portion of themanuscript on controls to rule out ribosome run-through from the<br /> first ORF to the second, swapping tags, and addressingfluorescent bleed through,<br /> which is appreciated. They also present novel measurements including translation<br /> site localizationand diffusion, ribosome occupancy, and elongation rates. The<br /> translation elongation measurements are particular striking giventhat an<br /> analogous single-molecule experiment has not been demonstrated previously.<br /> Overall, this study is elegant in its useof the bicistronic construct and has<br /> potential applications in studying endogenous eukaryotic IRES elements, such as<br /> incircRNAs.

Given that, there are certain points of clarification that should be addressed<br /> or expanded upon in the manuscript.

Major comments:

1. In the section titled “IRES and CAP translation sites stretch out as<br /> ribosomes load”, the authors show evidence thatCap-only and IRES-only<br /> translation sites “stretch out as ribosomal content increases”. However, in a<br /> different section of themanuscript where ribosome occupancy is measured, it is<br /> shown that Cap+IRES translation sites have more ribosomes per RNAmolecule than<br /> Cap-only or IRES-only translation sites. However, the “stretching” measurements<br /> do not reflect this difference:Figure 3C/D show that the single modes of<br /> translation have a greater average stretch than dual-mode translation<br /> sites.Additionally, the authors make no indication that the Cap and IRES sites<br /> should counteract each other in any way. The authorsdo not adequately address<br /> this disconnect.
2. In the discussion, the authors state “One of the most interesting<br /> observations with our biosensor was that Cap translationactually enhances that<br /> of the IRES, but not the other way around”. In Figure 6F, the authors measure<br /> fluorescent intensitiesof translation sites under stress conditions and show<br /> that Cap-only translation decreases while IRES-only translationincreases in the<br /> presence of cellular stress. In the caption for Figure 6F, the authors state<br /> “Cap + IRES intensitiesrepresent the Cap translation intensity in spots with<br /> both Cap and IRES intensities”. The data in the corresponding “CAP-IRES”panels<br /> show that the Cap intensities differ greatly (increases, especially in the<br /> presence of arsenite) when IRES translationis active. Does this not indicate<br /> that IRES translation enhances that of the upstream Cap-dependent ORF?
3. In the Results section for RNA diffusion measurements, one inconsistency<br /> that the authors should address is that Cap-onlyand IRES-only sites display<br /> indistinguishable MSDs. The authors state “this overall trend suggests the<br /> mobility of ourbiosensor is mainly dictated by the degree of translation rather<br /> than the type of translation”. However, in the ribosomeoccupancy experiments, it<br /> is shown that Cap-only translation sites contain almost triple the number of<br /> ribosomes as comparedto IRES-only. This is a clear difference in the “degree of<br /> translation” but does not agree with the MSD data.

Minor comments:<br /> 1. Under stress conditions, Figure 6D shows that Cap-only translation sites<br /> decrease in intensity while IRES-only translationsites increase in intensity.<br /> Presumably, the following analysis should be obtainable with the same data set.<br /> What is the“stretching” measurement at these sites? Given statements by the<br /> authors, Cap-only translation sites should be more compactunder stress<br /> conditions compared to Cap-only translation sites without stress. The inverse<br /> should be true for the IRES-onlytranslation sites. <br /> 2. There is no description of the method used to measure the distance for RNA<br /> stretching. From the illustration in Figure 3A,it appears that the measurement<br /> is made from the center of each fluorescent spot to the center of the other, but<br /> an explicitdescription of the method would be appreciated.

Reviewer 2

Peer review of the preprint, “Quantifying the spatiotemporal dynamics of IRES<br /> versus Cap translation with single-molecule resolution in living cells”<br /> Koch, A. et al. investigate the unknown single molecule dynamics of viruses<br /> hijacking host cells using internal ribosome entry sites (IRES). In order to<br /> determine the dynamics between IRES and Cap mediated translation, Koch, A. et<br /> al. developed a novel method in which the kinetics of IRES and Cap mediated<br /> translation can be visualized in real-time. They developed a bicistronic<br /> biosensor containing two separate open reading frames with repeated epitopes.<br /> Each of these open reading frames are differentially translated either in a Cap<br /> or IRES mediated manner. Depending on which open reading frame is translated,<br /> different fluorophore labeled antibodies will bind to the epitope<br /> co-translationally and on the emerging nascent chain. As a result, the biosensor<br /> will be decorated with different fluorophores depending on which open reading<br /> frame is being translated. From this data, Koch, A. et al. determined the mode<br /> of translation depending on which fluorophores are observed to colocalize with<br /> the transcript. Using this new technique, the authors demonstrated that two open<br /> reading frames can be simultaneously translated, and two different manners of<br /> translations can be visualized on a mRNA. Normally, two to three times more<br /> ribosomes are recruited to Cap mediated translation sites as compared to IRES<br /> mediated translation sites; however, during oxidative and ER stress, IRES<br /> mediated translation is favored. Both Cap and IRES mediated translation sites<br /> are stretched out with increasing ribosome load and both sites have similar<br /> mobilities, spatial distributions and elongation rates. Additionally, the<br /> authors also suggest that upstream translation can positively impact downstream<br /> translation. <br /> The authors ingeniously combine common techniques used in ensemble experiments,<br /> such as bicistronic transcript, with nascent chain tracking to develop a method<br /> to visualize different modes of translation in real-time in vivo with single<br /> molecule resolution. This technique was used to understand the dynamics of IRES<br /> mediated translation, but this method also has broad applications. The technique<br /> developed by Koch, A. et al. seems promising and exciting. In general, the<br /> article is well written, and I recommend this work to be published; however, a<br /> few clarifications and improvements are needed to enhance the clarity and<br /> development of the text before the work can be published. <br /> The abstract concisely explains the importance, goals, methods and conclusions<br /> of the work. The introduction nicely explains the aims of the paper and<br /> importance of the novel technique developed as well as the importance of<br /> determining the mechanism by which viruses use IRES to hijack the cell’s<br /> translational machinery. Koch, A. et al. also provide context for which the work<br /> has been done, such as previous ensemble experiments. The ensemble experiments<br /> lacked the spatial temporal resolution needed to determine the kinetics and<br /> dynamics of IRES translation in real-time; yet, the authors satisfy this gap in<br /> knowledge using a new method. However, the authors did not provide a comparison<br /> of the data collected in the ensemble experiments and the data collected in this<br /> work using the new technique. It would be important to understand if the<br /> previous ensemble experiments support the data collected using this new<br /> technique. This could provide further support and verification for the new<br /> technique. <br /> The authors provide an adequate amount of background needed to understand the<br /> importance and context of an experiment. The experiments and results are clearly<br /> described. However, there are a few points that need clarification or further<br /> explanation to determine the validity and reasoning of the experiments and<br /> conclusions, including why were lysine demethylase KDM5B or kinesin like protein<br /> Kif18b used in the open reading frame as opposed to other proteins or why did<br /> the open reading frames not encode for the same protein, but with different<br /> tags? It would have been better for both open reading frames to encode for the<br /> same protein with different tags, so that the length of open reading frame from<br /> the 5’ Cap to the first stop codon would be roughly the same size as the length<br /> of the open reading frame from the IRES site to last stop codon. This may have<br /> helped clarify and provide a fair comparison between the amount of stretching on<br /> the different translational sites and the number of ribosomes at each<br /> translation site. This would also eliminate the open reading frame size as a<br /> possible contaminating factor. This may also explain the different ratio of<br /> ribosomes recruited to the Cap and IRES translation sites when the original tag<br /> and switch tag were used in Figure 5. When the switch tag was used, the ratio of<br /> ribosomes recruited to the Cap versus IRES translation sites was 2.8, but when<br /> the original tag was used, the ratio of ribosomes recruited to the Cap versus<br /> IRES translation sites was 2.1. This could be due to the different open reading<br /> frame lengths including the 24X SunTag-Kif18b being longer at 8kb and thus<br /> allowing more space on the translation site for ribosomes as compared to the 10x<br /> flag-KDM5B’s translation site length at 5kb. Additionally, in Figure 3, the<br /> authors try to answer a difficult question by measuring the distance from<br /> actively translating ribosomes to the 3’ end of the transcript to determine how<br /> the translation sites stretch with increasing ribosome load; however, the<br /> authors do not account for the different lengths of the translation sites.<br /> Understandably, it’s difficult to measure the distance of translation site<br /> stretching. It could be useful to place stem loops labeled with fluorophore<br /> tagged antibodies or a dCas9 labeled with a fluorophore before the IRES site, so<br /> that more precise measurements of the translation site stretching can be<br /> obtained, if feasible. <br /> The authors suggest that IRES and Cap mediated translation sites stretch out<br /> with increasing ribosomal load as shown in Figure 3D. Yet, there is an outlier<br /> in the general trend when the Cap translation site is examined on Cap + IRES<br /> translation sites in Figure 3C (top plot). As the ribosome load increases, the<br /> Cap translation site stretches from 130 nm to 150 nm, but then retracts to 144<br /> nm. It is true that the general trend is that as the ribosome load increases,<br /> the translation site stretches, but this outlier should be acknowledged.<br /> Additionally, clarification or an explanation should be provided to explain why<br /> single mode translation sites, shown in Figure 3D are stretched out longer than<br /> the translation sites in the IRES + Cap translation sites, shown in Figure 3C.<br /> Additionally, the authors should address possible reasons why the Cap<br /> translation site is not two to three times more stretched than the IRES<br /> translation site given that two to three times more ribosomes are recruited to<br /> the Cap translation site.<br /> Additionally, the authors should address the precision of the technique and<br /> data, meaning how they analyzed the data when more than one ribosome was on a<br /> translation site. The authors should address how they analyzed the data when<br /> more than one fluorophore was present at single location. Did the authors<br /> measure the photobleaching steps at that location or did the authors take the<br /> average distance from a group of nearby fluorophores to measure the distance<br /> from the actively translating ribosomes to the 3’end of the transcript? It may<br /> be the case that a group of fluorophores or ribosomes may not be resolved at one<br /> location, if so, how did the authors analyze this data. The authors should<br /> acknowledge or address a limitation in the experimental design that the<br /> technique relies on upon measuring the intensity of the fluorophore labeled<br /> antibodies binding to a nascent chain that has potentially many epitope binding<br /> sites as the ribosome translates the transcripts. The longer time the ribosome<br /> translates the transcript, the more epitopes appear on the nascent chain. As a<br /> result, a higher intensity on a translation site does not always mean more<br /> ribosomes. It could mean that a ribosome has translated more of the transcript<br /> resulting in a longer nascent chain with more epitopes and possible fluorophore<br /> labeled antibodies binding to the nascent chain resulting in an increase in<br /> signal intensity. <br /> Koch, A. et al. provide proper controls to determine the total amount of<br /> transcripts in the cell by labeling transcripts at the 3’ end. However, it would<br /> behoove the authors to provide a few additional control experiments or<br /> explanations. It would be beneficial for the authors to provide an explanation<br /> of the choice and amount of tags. SunTags, specifically v1 SunTag, are known to<br /> aggregate1 which may negatively impact the data or the conclusions drawn from<br /> the data. Similar experiments can be performed with different tags as a negative<br /> control to verify that the choice of tags does not influence the data. The<br /> number of tags in each open reading frame are different, which may affect the<br /> amount of fluorophore labeled antibodies that bind to the nascent chain and<br /> could affect the observed intensity. A control experiment should be performed to<br /> account for the number of epitope tags in each reading frame and the resulting<br /> intensity, before the amount of translation or ribosomes can be determined and<br /> compared at the different translation sites. The authors do address this concern<br /> in Figure 5 by using the original and switch tag. Additionally, the authors<br /> should verify that adding MS2 stem loops to the 3’ end of transcript does not<br /> affect the stability, localization or translation of the transcript. The authors<br /> provide a control experiment to determine that ribosome is not continually<br /> translating through two open reading frames and that IRES can independently<br /> recruit ribosomes. The authors also suggest that upstream translation can<br /> enhance downstream translation of non-overlapping open reading frames. This is<br /> explained though simulations, but it would improve the authors’ credibility if<br /> this conclusion can also be verified experimentally by using a negative control,<br /> such as removing the 5’ Cap from the transcript and determining the number of<br /> ribosomes recruited or translated on the transcript, if feasible and the<br /> transcript is stable. <br /> Finally, the authors beautifully explained how physiological conditions, such as<br /> oxidative or ER stresses, during a viral infection could affect IRES and Cap<br /> mediated translation. The authors determined that IRES mediated translation was<br /> enhanced as compared to Cap mediated translation. If feasible, it would be<br /> beneficial to conduct the same stretching experiments under oxidative and ER<br /> stress conditions to further support the conclusion and provide a fair<br /> comparison to the data under normal conditions.<br /> Overall, the article is well written; however, the article’s layout can be<br /> improved to further clarity and develop main points in the paper. Initially, the<br /> authors suggest that that are three times more Cap mediated translation events<br /> as compared to IRES mediated translation events. Then the authors explain the<br /> biophysical properties of the translation sites as well as the elongation rate<br /> at these sites. Next, the authors suggest that two to three times more ribosomes<br /> are recruited to the Cap mediated translation site as compared to the IRES<br /> mediated translation site as shown in Figure 5. However, the authors reference<br /> this last point throughout the beginning of the paper. I suggest that the<br /> authors discuss and present the data in Figure 5 earlier in the paper such as<br /> after Figure 1. This would improve the flow and logical progression of a key<br /> point in the paper and would also provide an explanation as to why the authors<br /> chose to present the data in Figures 3 and 4. Additionally, Figure 5 would also<br /> support the data provided in Figure 1. <br /> In general, the authors elegantly describe a novel technique and its application<br /> in this article. This novel technique has potential to advance the field by<br /> providing single molecule analysis in real-time in living cells. The conclusions<br /> and findings of Koch, A. et al. are significant and important for determining<br /> the dynamics between IRES and Cap mediated translation. I look forward to<br /> reading the work when its published.

Reference <br /> 1. Tanenbaum, M. E.; Gilbert, L. A.; Qi, L. S.; Weissman, J. S.; Vale, R.<br /> D., A protein-tagging system for signal amplification in gene expression and<br /> fluorescence imaging. Cell 2014, 159 (3), 635-646.

On 2020-05-05 15:44:12, user Sinai Immunol Review Project wrote:

Main findings: The author’s data suggest that a receptor-binding domain-based (RBD) vaccine for SARS-Cov2 could be safe and effective. Similar to SARS-CoV-1, SARS-CoV-2 requires expression of the cellular receptor angiotensin-converting enzyme 2 (ACE2) to infect cells. The spike (S) protein mediates entry of CoV-2 engages ACE2 through its receptor-binding domain (RBD), an independently folded 197-amino acid fragment of the 1273-amino acid S-protein promoter. <br /> The authors immunized mice using RBD fusion protein, mixed with adjuvant. Blood were collected at day 0(before injection), day 5 and day10 and analyzed neutralization of viral entry. The data suggested that antibodies to the RBD domain of SARS-CoV-1 potently neutralize SARS-CoV-1 S-protein-mediated entry in rat, and the presence of anti-RBD antibodies correlates with neutralization in SARS-CoV-2 convalescent sera.<br /> Antibody dependent enhancement (ADE) can contribute to the pathogenicity of viral infection a major concern observed with other viruses but not with SARS-Cov-2 to date. Importantly, the RBD-elicited antibodies may mediate ADE less efficiently than those recognizing other neutralizing S-protein epitopes. Furthermore, anti-RBD anti-serum did not promote ADE at serum dilutions and under a condition in which is the ZIKV ADE could be observed.

Relevance: The study highlights a potential strategy for SARS-CoV-2 vaccine, using RBD protein to promote neutralization and protection. The work demonstrated limited evidence of ADE, that has been shown to contribute to pathogenesis in zika and dengue.

Limitation: Data show that the RBD alone is sufficient to elicit a potent neutralizing antibody response. It remains to be determined whether trimer-based vaccines, or trimer/RBD combinations, can be more effective, and whether they carry additional safety concerns.

Credit:<br /> Reviewed by Zafar Mahmood as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.

On 2020-05-05 10:25:54, user Vasilis Promponas wrote:

It is questionable whether two of the genomes analysed in this work lack an ORF7b gene. An NCBI tBLASTn search using as query sp|P0DTD8|NS7B_WCPV against MT050493.1, MT012098.1 gives clear 100% identical hits (27736-27864 frame +1; 27740-27868 frame +2) followed by an in-frame stop codon.

On 2020-05-05 04:01:34, user American Woman wrote:

How can this information be used in consideration of canned food meant to be eaten cold, such as fruit. With such a tremendous multitude of workers infected, it's almost certain the virus will make its way into food at the manufacturing plant before it ends up sealed in cans and glass jars. Can the virus, like food, remain 'preserved' and ready to infect upon opening? Are the current heating methods used in commercial canning enough to deactivate this particular virus? I hope someone gives me a good, detailed, science-based answer that does not include phrases such as 'not likely,' 'low risk' or 'there's no evidence of.' I'm looking for someone to reassure me there is NO risk, but also to explain why. Thank you, everybody, and hope you stay safe out there!

On 2020-05-04 22:23:30, user Chung-Wen Lin wrote:

Thanks a lot for the study. Does the project's github repository closed due to some reasons? I got a 404 page not found error.

[update] Here is the correct url: https://github.com/jnoms/vi...

On 2020-04-30 10:53:20, user Hiroki U wrote:

Great work! How can I access to the dataset containing positions of non-canonical junctions?

On 2020-05-04 21:38:47, user Daniel Himmelstein wrote:

My review of version 2 of this preprint is available online. The TLDR summary is that the approach and utility of SciScore are important advances, however the lack of data and code availability for the study limits its impact.

On 2020-05-04 20:36:26, user Mansoor Siddiqui wrote:

A really good study with intriguing findings. I have a small suggestion, since protein palmitoylation regulates microneme secretion in merozoites (https://doi.org/10.1021/acs... and gliding motility in Toxoplasma gondi (10.1016/j.molbiopara.2012.03.006.) authors must treat the viable merozoites with palmitoylation inhibitor 2-BP and asses its effect on motility in merozoites.

On 2020-05-04 20:10:54, user Sanne wrote:

I noticed that the binding affinity of 47D11 for SARS-CoV in the ELISA binding curves is almost the same for Secto and SB1 (EC50 0.018 and 0.02 ug/mL), whereas the binding affinity for SARS-CoV-2 is lower for Secto than for SB1 (EC50 0.15 and 0.03 ug/mL). <br /> However this difference in affinity is not noticeable from the binding kinetics experiment, it is even the other way around: there is a difference in affinity between Secto and SB1 for SARS-CoV (Kd 0.754 and 16.1 nM) and no significant difference between Secto and SB1 for SARS-CoV-2 (Kd 10.8 and 9.56 nM). Is there an explanation for these seemingly contradictory observations? Or is it wrong to draw these comparisons between the ELISA binding curves and the kinetic binding assay?

On 2020-05-04 15:30:32, user Yasunori Watanabe wrote:

Please note that the published version can be found here:

https://science.sciencemag....

(DOI: 10.1126/science.abb9983)

On 2020-05-04 14:02:05, user Chad Yost wrote:

There is no genetic evidence to suggest that there was a human population bottleneck between 50–100 Ka, yet you write this manuscript from the position that there was. Your results aside, papers like this continue to perpetuate the myth of the Toba catastrophe hypothesis by ignoring recent studies and citing egregiously out-of-date works that lack empirical evidence. I suggest you digest the recent literature that directly address the Toba catastrophe hypothesis as well as those that reconstruct effective population size for modern humans over the past 200 ka. It’s hard to fully appreciate your findings when your background sources are cherry-picked to dramatize your narrative.

On 2020-05-04 12:04:56, user silvoLab wrote:

Thanks for the full-fledged evolutionary analysis. We show RNA editing of the viral transcriptomes (with a much more limited evolutionary analysis) here: https://t.co/JEpyMayhTQ

A few comments:<br /> - we also found that APOBEC-mediated editing is more evident than ADAR-mediated one at the evolutionary scale. Yet, ADAR-mediated editing is much higher at the transcript level (quasispecies). We are still wondering the reason for this.

• the only APOBECs that target RNA are APOBEC1 (plenty of evidence), APOBEC3A (good evidence, but its target specificities could be tight), and APOBEC3G (so far only in vitro evidence). No other AID/APOBEC has ever been linked to RNA editing.

• re’ the sequence context of the mutations: while APOBEC3G sequence context preference is quite different (CCC) and APOBEC3A could be a possible match (TC), we think APOBEC1 has a sequence context preference that matches the mutations on the coronavirus transcripts/genomes almost perfectly ([AU]C).

• we also observe the strand-bias in APOBEC-mediated editing at the transcript level. In our manuscript we come up with a model to explain it. Long story short.. we think most APOBEC-mediated editing comes quite late, after replication and before packaging.

On 2020-05-04 11:08:10, user Maciej Śmiałek wrote:

https://www.mdpi.com/1422-0...

Peer-reviewed full article visible withing this link

On 2020-05-03 20:52:07, user Jean-Yves BOULAY wrote:

This paper may interest the author: this article investigates the molecular modules system proposed by Professor Sergei Petoukhov. This study describes numerous phenomena of symmetry in the distribution of the amino acids in the genetic code table. These phenomena consist to arithmetical arrangements of sets of modules numbers, or/and protons numbers which are counted in each of the 20 amino acids used by the standard genetic code. These arithmetical phenomena are by configurations of multiples of prime numbers also.<br /> https://www.researchgate.ne...<br /> some examples :<br /> Without the rebel group (see paper):<br /> In the genetic code table, the total modules sum of the columns 1and 2 (126 + 112) and the total modules sum of<br /> the lines 3 and 4 (122 + 116) are identical! And the sum of the right chequered configuration is also the same.<br /> Also, the total modules sum of the columns 3 and 4 (114 + 158) and the total modules sum of the lines 1 and 2<br /> (144 + 128) are identical! And the total modules sum of the second chequered configurations is also the same.<br /> https://uploads.disquscdn.c...

On 2020-05-03 18:11:50, user jung wrote:

Isn't it amazing? Bravo to awesome Doc. Sean Ekins. Repurposing Pyramax for the Treatment of Ebola Virus Disease: Additivity of the Lysosomotropic Pyronaridine and Non-Lysosomotropic Artesunate.

On 2020-05-03 13:14:01, user Karishma wrote:

Hello

I read this paper and found it really good for my model plant. I want to make stable mutant CRISPR/Cas9 plants using the Golden Gate cloning system. I have several question which I did not get and writing below<br /> 1. For a particular gene you selected 2 oligonucleotide is this correct?<br /> 2. If 1 is correct, so can I use 4 oligonucleotide (for the knockout of same gene which is having Bpi1 sites)? And then by using Bsa1 enzyme can further ligate in the recipient vector for transformation

Kind regards<br /> Karishma

On 2020-05-03 04:06:41, user Peddi likhitha wrote:

Good review sir, i accept with your views of integrating different technologies for sustainable Bt cotton. Congratulation s and all the best for your publication.

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 2020-05-02 18:25:04, user Kate Sawatzki wrote:

Thanks for doing this analysis, I've been running similar comparisons ad hoc as species come up. Can you expand more on why you see ferret come up as very low risk when they've already been shown to be highly susceptible with moderate to severe disease in vivo? The inoculum was not sufficiently different from those in cats and pigs to merit a dismissal, in my opinion. I think a more complete explanation of current knowledge/literarure is appropriate to understand the potential limitations of these analyses.

On 2020-04-21 09:34:41, user Rajendra Kings Rayudoo wrote:

To <br /> Head of the paper<br /> By the above reading are you suggesting that mammals around' us are susceptible to infection and can mediate them to humans

Like the tiger nadia in Bronx zoo ny tested positive

Is there any possibility of other than<br /> ace 2receptor to function for covid protein to contact

On 2020-05-02 14:41:09, user UAB BPJC wrote:

Review of Le et al. “Peptidoglycan editing provides immunity to Acinetobacter baumannii during bacterial warfare”

University of Alabama at Birmingham Bacterial Pathogenesis and Physiology Journal Club

Summary: In this manuscript, the authors performed a series of in vitro biochemical and in vivo experiments to demonstrate that the opportunistic human pathogen A. baumannii secretes non-canonical D-Lysine and incorporates it into its peptidoglycan capsule to resist PG-targeting type VI secretion systems of other bacterial competitors. At the same time, this expression of D-Lysine makes A. baumannii susceptible to host enzymes and therefore less pathogenic.

Overall, we found this manuscript to be thorough, intriguing, impactful, and well-written. We have attached a few comments and suggestions to further strengthen this manuscript:

Major Comments:

1. The conclusion that “D-Lys production is detrimental to pathogenesis, at least in part due to DAO activity” seems like an overinterpretation of the authors’ results. The authors show that racK mutants are defective in pathogenesis and show that they are more susceptible to DAO in vitro, but without testing pathogenesis in a mouse lacking DAO, they cannot conclude for certain (even “in part”) that DAO is involved in vivo.

Minor Comments:

1. The authors point out that one of the notable features of pathogenic A. baumanii is its resistance to antibiotics, which was the impetus for their examination of its cell wall structure. However, later in their paper they demonstrate very clearly that RacK (and therefore presumably D-Lys) is not present in human clinical strains. Are non-clinical isolates more resistant to antibiotics? If PG modification is not important for antibiotic resistance in clinical strains, what factors are?

2. The manuscript suggests D-Lys is detrimental to pathogenesis based on increased killing by neutrophils and increased dissemination when D-Lys is absent. However, it was also stated that D-Lys is incorporated into PG when A. baumannii grows as a biofilm. Since biofilms are a major mechanism for maintaining chronic infections, can D-Lys incorporation be seen as detrimental to pathogenesis in general? Would it be more accurate to say that D-Lys is representative of a different form of pathogenesis, or are disease symptoms mainly due to planktonic A. baumannii? If you treated A. baumannii (wild type and RacK knockout) biofilms with DAO, would you still see increased killing of the D-Lys producing strain compared to the RacK knockout?

3. Strain UPAB1 is introduced with no explanation of what it is and why it is interesting or important. This should be clarified.

4. In several places, the authors refer to the “last common ancestor of A. baumanii” when they should say the “last common ancestor of the Acb-complex”, or possibly the “last common ancestor of the genus Acinetobacter”.

5. Is there a possible explanation for why this phenomenon is specifically seen during stationary phase rather than a population response to attack by a competitor?

6. Figure images seem pixilated in many instances. Image quality is low. This makes some figures hard to read (e.g. the graph in Fig 2B and all of Fig 3). These can be better visualized on the electronic version of the document but in any printed version are hard to interpret.

Figure-Specific Comments:

Fig. 3: Although probably not necessary, it could be beneficial to perform a competition assay with A. baumannii and P. aeruginosa lacking T6SS functionality just to confirm again that T6SS is important for killing of A. baumannii.

Panel A could be broken into separate panels to make the legend easier to understand.

Fig. 4: Were growth rates assessed for the two strains? the increased burden may be due to increased growth rate of the mutant strain. Additionally, based on the data shown in the top row of panel A, the comparisons should not be statistically different (SD error bars overlapping and skewed distribution of data within each group).

The authors tested the DAO effects on wild-type and mutant A. baumannii strains. It would be better to include two following experiments:

1. H2O2 itself survival test to confirm the H2O2 tolerance of the mutants

2. Macrophage survival test. Even if DAO is the major component against A. baumannii, it would be better to include the macrophage survival test with your WT and mutant strains.

Fig. 5: Include Fig. S7 as a panel.

On 2020-05-02 12:05:45, user Fco Cabrera wrote:

Hi! Daniel Blanco, I have a new analysis with these database, great efforts are still needed. Ferret data not shown any pro-inflammatory response, i guess this is very weird, did you see inflammation in lungs or some clinical trait?.

On 2020-04-22 16:16:26, user Ji wrote:

I guess A549 cell was not infected by SARS-CoV2 in your system, since A549 does not express ACE2 and TMPRSS2. <br /> Maybe that is the reason why there seemed little gene expression change in SARS-CoV2-infected A549 compared to Influenza or RSV?

On 2020-04-22 06:09:42, user Thanden wrote:

How come A549 is used for this study? According to several papers this cell line is not susceptible to SARS-CoV-2 infection and not capable of forming CPEs - so not a big surprise that a very muted response is seen??

On 2020-05-02 07:55:18, user Michael Lai wrote:

Very interesting finding. 'do you see the same thing with SARS-Cov-1?

On 2020-05-02 07:15:51, user Ian Willy Jimmy Carter wrote:

Our experience at 56C for 30 minutes - did nothing...the virus still grew in cell culture as if nothing happened. This was recommended by BGI on the MGI platform for extractions ...didnt like that platform either.

On 2020-05-02 01:51:05, user Paul Wolf wrote:

The big question right now is what was the intermediate animal that tranferred covid-2 from bats to humans. This study was about ferrets, which is one possible vector, since this is how the scandalous gain of function research works. Once a virus spreads on its own throughout a ferret population, it has adapted to the ferret which is apparently similar to the human system.

I wonder why pangolins weren't included in this study? Is there some reason to believe covid-2 was transmitted by cats and ducks? The pangolins receptors are apparently a close match to covid-2's, which is where this theory came from, that it came from a seafood market that sold them. But it's not known whether pangolins can actually be infected with covid-2.

On 2020-05-02 01:42:50, user Gizaw M Wolde wrote:

I appreciate the effort the authors have taken for the work. However, the finding is NOT convincing at all to draw such a conclusion ! First of all, not all transgene are driven by CaMV35s. For example, to date there are more than 33 and 20 different Transgenic corn and soybean varieties, respectively, where the transgenes are likely to be driven by different promoters. So why authors mainly focused on CaMV35s to make such broad sense conclusion that the corn being produced in Ethiopia are non-GMO ? Very confusing indeed. The authors aslo hardly presented enough data for a manuscript based on their preferred promoter, i.e. CaMV35s, used for detecting transgenes in the suspected crops (maize and Soybean). Does the varieties used in the study are really different varieties or just same but being cultivated in different places? What is the justification that they are indeed different cultivars ?

On 2020-05-02 00:26:51, user millimeters wrote:

Peer-reviewed article is available at https://scirange.com/abstra...

On 2020-05-01 17:36:54, user Peter W. Mullen wrote:

It appears above that Dr. Lazic's first name is misspelled (the "n" is missing).

On 2020-05-01 17:26:00, user Ivan Shabalin wrote:

The paper describes an interesting idea, but the structural analysis and the predictions of the effects of the mutations are unsubstantiated and should be significantly revised.

For example, the presented analysis of the first mutation - K26R - has the following red flags:

• Figure 3b shows sugar labelled as "mannose", whereas the first sugar in glicosilated mammalian proteins should be N-acetylglucosamine (GlcNAc, or NAG), as it is in the crystal structures used for the analysis.

• The claim "We predict that K26R would abrogate stabilizing polar<br /> contacts with N90, impairing coordination of the glycan (Figure 3b) and lead to an increase in the affinity of the virus to the ACE2 receptor" seems completely baseless. First, K and R are positively charged residues with similar lengths; I'm not convinced this mutation would necessarily abrogate the contact with NAG. Second, why would R form the predicted interaction with D-30? why not with Glu23, which is closer? Third, the following claim is very questionable "At the same time, R26 is now primed to establish backbone and side chain interactions with ACE2 D30 which then is poised to build a salt-bridge with CoV-2 RBD K417". In what way the proposed interaction R26-D30 would make D30 more prone to forming the salt bridge with K417? If anything, it would only decrease the strength of the bridge be being a "charge competitor". And, the salt bridge D30-K417 already exists in one of the structures they analyzed (6LZG).

Similarly, there are issues in the analysis of the second mutation:<br /> - "The T27A mutant (Figure 3c) removes side chain-backbone and backbone-backbone interactions between T27 and E30 likely increasing the local dynamics of helix α1". First, there is a typo - it is E23. Second, the stability of a helix is mostly set by the C=O...N bonds between main-chain atoms, and the elimination of a side chain H-bond is unlikely to have a significant effect on the shape of the helix. Then, "This would allow the N-terminus of α1 to bend slightly and accommodate the unique CoV-2 RBD receptor binding-ridge loop that more intimately contacts ACE2 compared to its SARS-CoV counterpart" sounds very much unsubstantiated. To draw these conclusiong, MD simulations might need to be not perfomed.

Clearly, the analysis of other mutations is also questionable.

On 2020-05-01 15:08:14, user Prakash katakam wrote:

My review article might be useful further..... http://www.iglobaljournal.c...

On 2020-05-01 15:03:36, user James Mallet wrote:

This is a very useful manuscript that estimates parameters of gene flow and effective population size of these species that are known to hybridize occasionally. It's a pity it is not published! However, I disagree with the central idea of using a completely neutral hypothesis to explain the initial phase of speciation! If you do that, of course you must have "minimal contact" in order for divergence to occur! It couldn't have occurred in sympatry under the neutral model. I think what it does show is that gene flow is still continuing today, and the two alternatives are either (a) speciation initiated in allopatry, or (b) it occurred by means of a slow accumulation of selected differences in sympatry/parapatry, with gene flow likely declining slowly through time. I don't think this paper can distinguish the two.

On 2020-05-01 12:58:33, user Sinai Immunol Review Project wrote:

Title: <br /> Genomic determinants of pathogenicity in SARS-CoV-2 and other human coronaviruses<br /> The main findings of the article: <br /> In this study, potential genomic determinants of pathogenicity of the high case fatality rates (CFR) coronavirus strains were identified using integrated comparative genomics and advanced machine learning methods. <br /> A total 3001 coronavirus genomes were compared. Of those, 944 belong to viruses that infect humans, including both viruses with low CFR (NL63, 229E, OC43 and HKU1) and those with high CFR (MERS, SARS-CoV-1 and SARS-CoV-2). The full genomes of these coronaviruses were aligned to detect high-confidence genomic features that were predictive of the high-CFR. Eleven regions were detected in total, and the nucleocapsid (N) protein and the spike (S) glycoprotein were significantly enriched with those predictive regions. Further analysis revealed that 4 out of 11 differences identified were reflected in the protein alignment, 3 of which resided in the N phosphoprotein and one in the S glycoprotein. The deletions and insertions found in N proteins mapped to monopartite nuclear localization signals (NLS), one bipartite NLS and a nuclear export signal (NES). They resulted in the accumulation of positive charges in the monopartite NLS, bipartite NLS and NES of SARS-CoV-1 and SARS-CoV-2, whereas in MERS-CoV, positive charges accumulated primarily in the first of the two monopartite NLS. The accumulation of positive charges is known to result in the enhancement of these signals, thus implying that the localization pattern of the N proteins differs between high-CFR and low-CFR strains, and this may be a factor contributing to the increased pathogenicity of high-CFR strains. The analysis of S protein revealed a unique 4 amino acid insertion upstream of the heptad repeat region in all high-CFR viruses but not in any of the low-CFR ones. The structural analysis of SARS-CoV confirmed that this insertion increased the length and flexibility of the region connecting the fusion peptide and the first heptad repeat, likely affecting the membrane fusion process of the high-CFR coronaviruses. <br /> By the alignment of all 3001 collected coronavirus strain sequences, they also identified a unique insert in the RBD and the RBM in the cases of SARS-CoV and SARS-CoV-2, and within the subdomain that binds to DPP4 in the case of MERS-CoV. This unique insert was also found in the most proximal zoonotic strains before the zoonotic strains jumped to human. The unique insertions occurred independently in three high-CFR strains and resulted in a larger hydrophobic surface and additional interactions, which might contribute not only to the zoonotic transmission of the high-CFR CoV strains to humans but also their high CFR.<br /> Critical analysis of the study: <br /> This study provides predictions on unique sequences present in coronaviruses of high pathogenicity, and in highly related zoonotic species. Functional analysis of the unique elements is not carried out. <br /> There is an inconsistency related to description of positive/negative charged proteins between figure legends of 2 and 3, and the main text.<br /> The importance and implications for the current epidemics:<br /> The identification of unique sequences presents in the high-CFR but not in the low-CFR human coronaviruses can be experimentally tested to investigate the importance of each unique motive in the pathogenicity and transmissibility of human coronaviruses. This information will be useful to identify viruses with a potential to cause future pandemic, and to develop novel therapeutic strategies.

On 2020-05-01 09:08:57, user Matthias Witschel wrote:

Niclosamide is known to be poorly bioavailable and is a potent unselective mitochondrial uncoupler (doi.org/10.1038/s41419-017-...:b80vQ2MyAX0hlPjW_-7pLpWkxOQ "doi.org/10.1038/s41419-017-0092-6)"). Selective toxicity to the tapeworms in the gut is likely mainly due to low uptake and translocation in the host. A recent clinical study (doi: 10.1371/journal.pone.0198389) showed for Niclosamide a "maximum plasma concentration ranged from 35.7–82 ng/ml" at non-toxic doses (in contrast to the 1979-paper cited in this publication), which is significantly below the IC50 required for inhibiting SARS-CoV-2 shown in this paper. Therefore, this limitation should be considered before promoting any further clinical studies against SARS-CoV-2, even though Niclosamide is a registered drug.

On 2020-04-21 14:05:52, user Senad Hasanagic wrote:

In principle, mTORC1 INHIBITS autophagy. That is the principle of anti-tumorigenic effects of mTOR inhibitors. So not clear how SARS COV 2 inhibits autophagy by lowering levels of mTORC1 and why in your model mTORC1 promotes autophagy contrary to its well-known biological function.

On 2020-04-30 23:49:31, user Michael Thorpe wrote:

Is there a similar analysis for other viruses Eg influenzas??

On 2020-04-30 23:30:13, user Sinai Immunol Review Project wrote:

Main findings<br /> In this report, the authors provide an evaluation of available animal models, based on their ability to recapitulate characteristics of COVID-19 as seen in humans. As of now, both non-human primate models and transgenic hACE2 mice have limitations. An immediate type I IFN response, for one, has been notable in these models, even though the dysregulated inflammatory response seen in SARS-CoV-2 infection in humans is significant in its delayed IFN response.

Here, the authors describe the efficiency of SARS-CoV-2 viral replication and viral load in wild-type (WT), Ifnar1-/-, and Il28r-/- C57BL/6 mice. Results showed that wild-type mice that do not bear the human ACE2 receptor can be infected with SARS-CoV-2, "although inefficiently and likely transiently." Comparison with mice without the type I IFN receptor and the type III IFN receptor showed that all strains presented with a mild lung pathology that did not resemble COVID-19 bronchopneumonia; notably, though, Ifnar1-/- mice did present with increased levels of inflammatory infiltration, compared to the WT strain, suggesting that the active and functional IFN response in WT mice restricts pathogenesis. Hence, the disease is not fully replicable in mice.

The authors then propose that Syrian hamsters may be a better model, given previous reports that they are highly susceptible to SARS-CoV-2. WT, STAT2-/- (loss of type I and III IFN signaling), and Il28r-/- (loss of type III IFN signaling) hamsters were intranasally inoculated with SARS-CoV-2. WT hamsters carried high viral loads and infectious titers in the lungs, and histological analysis revealed necrotizing bronchiolitis, massive immune infiltration, and edema. These findings reflect the histopathological characteristics of human bronchopneumonia seen in COVID-19.

Interestingly, while viral RNA levels were not significantly different in the lungs of these hamsters, titers of infectious virus in the lung and viremia were highest in the STAT2-/- hamsters, which suggests STAT2 plays a role in deterring viral dissemination. In fact, these hamsters had the highest levels of viral RNA in the spleen, liver, and upper and lower GI tracts. Still, Il28r-/- hamsters exhibited bronchopneumonia and lung inflammation, actually to a greater extent than STAT2-/- hamsters, suggesting that type III IFN indeed plays an important, protective role in restricting pathogenesis and the inclusion of a type I IFN exacerbates bronchopneumonia. Though, IL-6, IL-10, and IFN-ɣ were not remarkably high in the serum of infected hamsters, IL-6 and IL-10 were elevated in the lungs of infected STAT2-/- and Il28r-/- hamsters.

Limitations<br /> Without comparing the aforementioned three murine strains to a transgenic hACE2 mouse strain as a "control", it is difficult to properly assess the significance of viral replication and load in the non-transgenic mice. A similar limitation can be identified in the experiments involving the Syrian hamsters. A transgenic model with hamsters would provide additional evidence to suggest that viral replication in these animals in the absence of a human version of the ACE2 receptor is not happenstance and is truly sufficient to reflect COVID-19 disease. Or, alternatively, a transient knockout of the ACE2 receptor in hamsters before challenging them with SARS-CoV-2 to assess for signs of reduced viral infection may be appropriate.

Another experiment that is absent in this study is the use of antibodies against SARS-CoV-2 components required for viral entry as a means of immunizing Syrian hamsters before challenging them with the virus. This, followed by immunofluorescence or immunohistochemistry and flow cytometry for ACE2-expressing cells, would provide further validity to the concept of SARS-CoV-2 actually invading and replicating inside ACE2+ cells in Syrian hamsters, which has been described for SARS-CoV-1, but not yet for SARS-CoV-2.

Significance<br /> Ongoing studies have identified notable limitations to the use of mice to recapitulate human COVID-19 following SARS-CoV-2 infection. Lung pathology is not significant. Moreover, viral dissemination to extrapulmonary organs, including the GI tract or the liver, and viremia are not observed. Therefore, to address this unmet need for an improved animal model for SARS-CoV-2 infection, the present study proposed the use of golden Syrian hamsters, which have been previously described to better reflect SARS-CoV-1 infection than mice. The histopathological findings do provide convincing evidence to suggest that these animals may indeed be superior than mice to also study SARS-CoV-2 infection and COVID-19, but the role of STAT2 - as suggested by the experiments involving Syrian hamsters - remains to be further elucidated. The precise cellular mechanism for viral dissemination throughout the vasculature and to other peripheral organs is not clear, so the supposed dual impact of STAT2 deletion in mice or in hamsters is still phenotypic observation.

Reviewed by Matthew D. Park as part of a project by students, postdocs, and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.

On 2020-04-30 18:50:08, user Marine Ecology Lab wrote:

THIS PREPRINT HAS BEEN PEER-REVIEWED AND PUBLISHED IN "NORTHEASTERN NATURALIST" IN 2020:<br /> Scrosati, R.A. 2020. Effects of intertidal elevation on barnacle recruit density and size in wave-exposed habitats on the Atlantic Canadian coast. Northeastern Naturalist 27: 186-194.<br /> http://pdfpapers.weebly.com...

On 2020-04-30 18:25:29, user CopernNik wrote:

Can't find it in Github yet.

On 2020-04-29 16:58:42, user Valentina Peona wrote:

It's sounds like a great tool! Though the github repository is not working. I hope you upload the tool soon, eager to test it :)

On 2020-04-30 16:08:54, user PZM Diagnostics wrote:

The article is published at https://www.mdpi.com/2673-3...

On 2020-04-30 16:08:25, user Janet Smith wrote:

Seems like there was a very short (2-4 weeks?) time between recovery from the Original infection and second challenge. Has there been follow up of serum antibody titre in both original (non-challenged) cohort and the challenged cohort? And/or has a later challenge been attempted?

On 2020-04-30 14:37:23, user hellarch wrote:

Curious to see immunostaining of autopsy samples from fatal cases

On 2020-04-30 13:33:12, user Jianping Liu wrote:

This reprint was published in Virus Research 2020 Apr 2:197956 after peer review. doi: 10.1016/j.virusres.2020.197956.

On 2020-04-30 11:46:20, user Lee Henry wrote:

The authors would like to thank Dr. Manzano-Marín for his post-publication review. While the author raises important points, we believe his concerns are based on misinterpretations of the methodology and results.

In response to the fixed status of S. symbiotica in A. urticata and M. carnosum, the data in Henry et al 2015 and Monnin et al 2020 are based on different primer pairs and therefore are not comparable. Henry et al 2015 used primers to detect facultative S. symbiotica, but these did not always amplify Serratia in co-obligate lineages due to sequence divergence (as noted in Henry et al 2015). To account for this, we used two sets of primers in Monnin et al 2020. These primers included ones that detect phylogenetically diverse Serratia lineages. This allowed us to screen the samples (including stored samples from Henry et al 2015) and confirm the infections status in both aphids was ubiquitous. The total number of A. urticata and M. carnosum populations surveyed for Serratia was 7 and 16 respectively, both were sampled in the UK and Netherlands, collected over 9 years, including those in Oxford and London (~96km apart).

The caption of Figure 1 clearly indicates the data are a synthesis of two separate curing experiments, and directs readers to the results of the individual experiments in the supplementary material. These were conducted on populations of aphids from two different countries, and despite one having a longer antibiotic treatment (+2 days), the results are comparable and show exceptionally large effect sizes in A. urticata and M. carnosum, compared to A. pisum. It was not possible to follow the G1 offspring of co-obligate aphid lineages, as they do not develop to maturity.

For both annotation and detection of pseudogenes we used the program DFAST (Tanizawa et al., 2018) followed by manual curations. Our analysis and subsequent inspection of the ribD gene indicated that the second domain was severely truncated and was therefore marked as a pseudogene - this was incorrectly annotated on NCBI but has since been amended. Our annotations also indicated that the murF gene was pseudogenized. The uncertainty around the functionality of the murF gene in Buchnera was mentioned in our discussion, and we look forward to further studies on gene expression profiles to confirm its role in the symbiosis.

Lastly, the numerous FISH images we took of A. urticata indicated it contains a single smaller bacteriome. The location and shape of the bacteriome was not central to our story but rather that it appears as a much smaller structure in the younger less integrated co-obligate of A. urticata compared to the more ancient association found in Periphyllus aphids.

Tanizawa et al (2018) Bioinformatics 34:1037–1039

On 2020-04-30 08:22:53, user Stephanie Kath-Schorr wrote:

This preprint is lacking crucial citation of previous works (Kath-Schorr group)! Even the title is similar to our recent publication in Angewandte Chemie (C. Domnick, F. Eggert, C. Wuebben, L. Bornewasser, G. Hagelueken, O. Schiemann*, S. Kath-Schorr* "EPR distance measurements on long non‐coding RNAs empowered by genetic alphabet expansion transcription" doi:10.1002/anie.201916447 !

On 2020-04-29 19:21:27, user Joseph Christian Daniel wrote:

In the 2019 paper "Viral Metagenomics Revealed Sendai Virus and Coronavirus Infection of Malayan Pangolins (Manis javanica)" it was mentioned 11 dead pangolins and from 2 of them Coronaviridae families were identified. But in the current paper, "Are pangolins the intermediate host of the 2019 novel coronavirus (2019-nCoV) ?" it was mentioned that "In March of 2019, we detected Betacoronavirus in three animals from two sets of smuggling Malayan pangolins (Manis javanica) (n=26) intercepts by Guangdong customs [11]." Which number is correct?

On 2020-04-29 19:10:11, user Sinai Immunol Review Project wrote:

Keywords: SARS-CoV-2, COVID-19, ACE2, TMPRSS2, TROP2, Liver, scRNA-seq

Main findings: The authors performed scRNA-seq on human liver tissue and identified an epithelial progenitor cell type that co-express ACE2 and TMPRSS2. ACE2 receptor was highly expressed in cholangiocyte-biased liver progenitors (EPCAM+) which also had a high expression for TROP2 gene. TMPRSS2 was expressed by both TROP2high and TROP2int liver cells. The authors reported that these cholangiocyte-biased progenitor cells had a high expression of TROP2 that correlated with high levels of ACE2 and TMPRSS2 in liver. This suggests that SARS-CoV-2 can infect TROP2high cells via ACE2 and TMPRSS2, contributing to liver dysfunction by compromising the ability of the human liver to regenerate cholangiocytes. The authors also note that ACE2 and TMPRSS2 positive cells are absent in human fetal liver.

Critical Analyses:<br /> 1. Normal tissue from hepatocellular carcinoma patients are tumor-adjacent tissues from these patients and might not be truly ‘normal’.<br /> 2. Tissues from liver cancer patients may have an altered phenotype from healthy controls.<br /> 3. ACE2 expression may be variable and inducible in other cell types in the context of inflammation.

Relevance: <br /> The apparent only cell population in liver that expresses ACE2 and TMPRSS2 are EPCAM+ liver progenitor cells, making them suitable hosts for viral infection and potentially resulting in liver dysfunction. This cell population of liver progenitors with a cholangiocyte fate bias may favor SARS-CoV-2 entry into liver, affecting cholangiocyte precursors, thus leading to liver comorbidities in COVID-19 patients.

Reviewed by Divya Jha, PhD and edited by Robert Samstein, MD PhD, as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.

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 2020-04-29 12:34:20, user Ural Yunusbaev wrote:

Is the 225Mb honeybee genome small enough for the Tapestry?

On 2020-04-29 09:55:46, user Darren Martin wrote:

Thank you for sharing this Matthew. You should maybe also consider the possibility that it is Rat3G that has acquired the spike encoding region via recombination from a currently unsampled coronavirus lineage (as opposed to SARS-CoV2 having acquired this region from a pangolin virus). IMHO the data strongly supports this alternative explanation. Maybe verify this for yourself using, for example, the VisRD method. VISRD is one of the few available methods (which do not include Simplot, RIP or eyeballed phylogenetic trees) for actually identifying which of the four or more related sequences that are needed to yield a recombination signal is most likely the actual recombinant.

On 2020-04-29 06:44:37, user Kijeong Kim wrote:

Haplotracker: a web application for simple and accurate mitochondrial haplogrouping using short DNA fragments

On 2020-04-29 05:37:00, user Jack wrote:

There is a typo I think:<br /> Although many servers incorrectly characterized αS as being probabilistically ordered in at least a single stretch of residues, the RaptorX sever provided a correct prediction that the protein was entirely disordered

On 2020-04-28 22:48:17, user Perseus Smith wrote:

Define Prakriti?

On 2020-04-28 20:27:16, user Matthew Bogyo wrote:

This work is now published at Cell Reports Medicine : DOI:https://doi-org.stanford.id...

On 2020-04-28 14:40:28, user Andi WIlson wrote:

Great paper.

From your TE-mediated remodelling in P. citrichinaensis figure, it appears you are suggesting that its "original" MAT locus looked like that of the homothallic species P. capitalensis where both MAT1-1 and MAT1-2 information is harboured at a single location. It is then suggested that a transposable element mediated the transfer of the MAT1-2 information to a different scaffold.

However, from the figure showing the evolution of thallism in this group of fungi as well as and statements elsewhere in the manuscript (eg: the reconstruction suggests that homothallism of P. capitalensis and P. citrichinaensis does not originate from the same evolutionary event), I think it is more likely that the "original" locus harboured only MAT1-1 information, with MAT1-2 information having been introduced into the genome elsewhere (probably during sexual recombination with a MAT1-2 individual).

Can you comment on this?

On 2020-04-28 14:19:39, user Michael Sewell wrote:

I can't find the supplementary figures to this paper

On 2020-04-28 14:02:08, user David Fajardo wrote:

There is a peer review version of this preprint already published in PLOS ONE: https://journals.plos.org/p... This version include a lot of supplementary information and additionally I chose the open peer review option so anybody can read the evaluation of reviewers and my response.

On 2020-04-28 13:15:19, user Łukasz Płóciennik wrote:

We are pleased to introduce a research article entitled “Detection of epistasis between ACTN3 and SNAP-25 with an insight towards gymnastic aptitude identification”. We follow up on experiments originally performed by Luigi Galvani in 1782 and 1786 but from a genetics perspective. We demonstrate that a gene involved in the differentiation of muscle architecture – ACTN3 and a gene, which plays an important role in the nervous system – SNAP-25 interact. The results obtained from the analyses revealed a remarkable crosstalk between ACTN3 – SNAP-25 polymorphisms. We found that for ACTN3 * SNAP-25 moderation effect, the homogenous derived genotype carriers exhibit the lowest chance of classification to the athlete group – gymnasts. Our observation of rs1815739 * rs362584 interaction may be applicable in advanced talent identification procedures. The ACTN3 – SNAP-25 interaction has never been reported before but based on the model and empirical evidence presented here, the two genes are undeniably interlinked.

PS. Please, look in on Supplementary Material and explore the graphical abstract developed by Prof. Marek Jóźwicki (Department of Architecture and Design, <br /> Academy of Fine Arts, Gdańsk, Pomorskie Voivodeship, Poland).

On behalf of the authors.

On 2020-04-28 12:54:53, user PUCEAT wrote:

The authors comment in their article about our paper by Neri et al entitled " Human Pre-Valvular Endocardial Cells Derived From Pluripotent Stem Cells Recapitulate Cardiac Pathophysiological Valvulogenesis" published last year In Nature Communications. They raised two main concerns about our study:<br /> 1: that our protocol of stem cells differentiation was not described in detail. This is a wrong statment as the protocol is described as a bench protocol as mentioned by reference 65 in the article which refers to the protocol exchange website :DOI:10.1038/protex.2019.008<br /> 2: that our procol requires cell sorting: indeed as pluripotent stem cells are by nature an heterogeneous and non-synchronized cell population , it is not possible to generate just with a few growth factors a pure population of any cell type without cell-sorting. Our step by step protocol that recapitulates developmental pathways leads to a quite pure population of endocardial cells and then of valvular cells. This is specifically shown by our single-cell-RNA seq data.<br /> We would recommend the authors to use the same approach if they want to claim that they might have obtained a genuine and pure population of valvular cells

On 2020-04-28 09:15:02, user Me Too wrote:

Interesting study, lot of work done. Could the authors motivate why they only chose 1 distance, ie 10cm? It would've been great to see several different distances and the likely inverse relation with infection rate. <br /> Eg., would it not be interesting to see that infection would always occur no matter the distance as long as you're in the same room?

On 2020-04-27 19:08:33, user gwern wrote:

This could use improvement. The discussion of 'missing heritability' is approximately a decade out of date: height PGSes are 10x larger than the 5% quoted, and there is no missing heritability for height or BMI (see either https://www.gwern.net/docs/... or https://www.biorxiv.org/con... ). The loss of predictive power in transracial GWASes doesn't mean what it's taken to mean, as it's driven by changes in LD & allele frequency and the causal alleles appear to largely remain the same. The discussion of the microbiome, as illustrated by figure 1, is also wrong: as noted in the paper itself about the heritability of microbiomes, host genetics *cause* microbiomes, and further, the microbiome is caused *by* environment and health, and is deeply confounded. Despite being so heavily reverse-caused & confounded, Rothschild (https://www.biorxiv.org/con... - cited for the least interesting parts of it?) shows that microbiome correlates very modestly at best, like BMI at 16%, which upper bound on the causal effect is consistent with the minimal effects we see in many of the human microbiome experiments. And if the microbiome hardly causes even BMI, it's not going to explain much of more distant traits (not, again, that there is any 'missing heritability' problem left these days for the microbiome to explain, and why would microbiome effects not fall into the shared or non-shared-environment components?).

On 2020-04-27 18:17:30, user Pooja Saxena wrote:

Wondering about the low mutation comment and wanting to refer to supp. table 3. Please add a link to it.

On 2020-04-27 17:25:27, user marius w wrote:

Great read! Of very minor note, it seems that the citation Ferron et al. is divided into 2 citations (2018a and 2018b), while it seems to be the same paper, probably a formating issue

On 2020-04-27 13:14:33, user Sinai Immunol Review Project wrote:

Main Findings <br /> - Study uses scRNAseq to profile viral-entry genes in intestinal cell types and show confirmatory result of predominant ACE2 expression in CD26+EPCAM+ mature enterocytes. The transcriptomics also map TMPRSS2, TMPRSS4 and ST14 protease gene expression across mature enterocytes and other enteric cells. They additionally show ACE2 protein localizing to the brush border of the intestinal epithelium. <br /> - Researchers use VSV chimera reporter virus with SARS-CoV-2 S protein to assay viral tropism in culture and demonstrate active replication in human duodenal enteroids as well as apical mature enterocytes (ACE2+) in monolayer system. <br /> - Used HEK293 with ectopic expression of proteases to show that TMPRSS2 or <br /> TMPRSS4 co-expression with ACE2 enhanced viral infectivity by enhancing endocytosis. CRISPR-Cas9 based deletion or pharmacological inhibition of TMPRSS proteases in human primary enteroids limited viral replication, more so upon TMPRSS4 deletion. <br /> - Further, a co-culture system designed with ACE2/TMPRSS2/4 expressing host cells and S-protein expressing donor cells, to mimic enteroid cell population interactions. Importantly found that TMPRSS2 can influence viral entry in trans through paracrine interaction, unlike TMPRSS4 that functions in cis. <br /> - SARS-CoV-2 chimera virus were very unstable in colonic fluids, compared to rotaviruses that were stable in gastric and colonic fluids. Supporting these data, CoVID-19 patient fecal sample analysis also consistently showed viral RNA load, but no infectious particles.

Limitations <br /> - The studies use pseudotyped-VSV reporter virus instead of WT virus, which could potentially have an impact on tropism. It will be important to replicate some of the <br /> co-culture studies using active SARS-CoV-2 virus as confirmation. Similarly, ectopic expression and co-culture experiments can be strengthened by replicating studies in primary cells. <br /> - Study mentions lack of infectious particle in fecal samples, but no conclusive data presented to support this important point.

Significance <br /> - TMPRSS4 important for influenza virus tropism, but not previously shown to be important in CoV-2 infectivity in the gut context. <br /> - Co-culture experiments imply that paracrine interaction of TMPRSS2 (from goblet cells for example) on neighboring TMPRSS4/ACE2 expressing enteric cells can drive synergistic increase in viral infectivity in gut epithelia. Whether such gene expression distribution and protein interactions exist in other tissue contexts (lung, kidney) remains to be seen. <br /> - Fecal-oral transmission of SARS-CoV-2 has not been broadly observed,<br /> but intestinal infection of ACE2++ cells has been reported clinically; <br /> this study reconciles these paradoxical observations by demonstrating <br /> that despite active infection and replication in enterocytes the virus <br /> might not remain intact in colonic fluids. Conversely, observed SARS-CoV-2 stability in small intestine might play a role in clinically presented GI pathologies.

Reviewed by Samarth Hegde as part of a project by students, postdocs and faculty<br /> at the Immunology Institute of the Icahn School of Medicine, Mount <br /> Sinai.

On 2020-04-27 11:24:04, user Vijay Veer wrote:

In India another source major was people returned from middle East after Umra (Haz). Southi Arabia and other gulf countries got the virus from European counties including Italy. Other wise this analyse seems highly relevant.

On 2020-04-27 08:42:12, user Dr. Mukesh Thakur wrote:

Readers, title does not mean the chronology of entry of coronavirus in India. Undoubtedly, the first coronavirus case reported in India from Thrissur, Kerala and the patient traveled to Kochi, from Kunming via Calcutta on January 24th, and diagnosed positive for SARS-CoV-2 on January 30th 2020. Kindly read the complete article to understand the spread of virus in India and their genetic assignment. Pls contact for any clarification (8171051282)

Dr Mukesh Thakur, Sci, ZSI.

On 2020-04-27 10:46:58, user véronique laroulandie wrote:

fig14 - please add to the legend "adapted accourding to Masset el al. 2016".

On 2020-04-27 07:47:44, user Dr William Davies wrote:

The authors may be interested in our recent paper which showed that Stoml3 was one of very few genes whose expression was altered in female mouse brain following steroid sulfatase inhibition: https://www.ncbi.nlm.nih.go...

On 2020-04-26 20:46:01, user Keith Robison wrote:

Interesting concept and execution. But the background on coronavirus has a glaring error: coronaviruses are positive sense RNA viruses and never use reverse transcriptase in their lifecycle.

On 2020-04-26 18:23:33, user Jennifer Uhrlaub wrote:

I am commenting to say THANK YOU! I used this paper as a guide and was able to make a titered stock of SARS-CoV-2 within one week of receiving a vial from BEI. I can't say my final protocols match these exactly (because that's never the case) but without them it would have taken me much longer.

On 2020-04-26 18:23:03, user Pratibha Bhalla wrote:

The contents of this preprint have been published in two parts in two different journals as cited below. The second one was published in the Scientific Reports, has been linked to the article as detailed with the article. The first part was published in the journal Gene. Interested users may see the publication by using the details as given below. <br /> Bhalla, P., Vernekar, D. V., Gilquin, B., Coute, Y., Bhargava, P. (2019) Gene 702, 205-214. PMID: 30593915<br /> https://doi.org/10.1016/j.g....

On 2020-04-26 17:44:04, user Sinai Immunol Review Project wrote:

Main findings:<br /> To elucidate mechanisms of viral replication within human hosts, the authors used computational approaches to analyze the structural properties of SARS-CoV-2 RNA and predict human proteins that bind to it. They compared 2800 coronaviruses and 62 SARS-CoV-2 strains using CROSS (Computational Recognition of Secondary Structure) and CROSSalign algorithms, which predict RNA structure using sequence information and evaluate structural conservation, respectively. From these structural comparisons, they found that the spike S protein that interacts with the human receptor angiotensin-converting enzyme 2 (ACE2) is highly conserved amongst coronaviruses.

The study also identified over 100,000 human protein interactions with SARS-CoV-2 utilizing catRAPID, an algorithm that determines binding potentials of proteins for RNA using secondary structure, van der Waals, and hydrogen bonding contributions. They found that the 5’ of SARS-CoV-2 is highly structured and has a strong propensity to bind to human proteins with known involvement in viral RNA processing. Amongst the proteins they identified can bind to 5’, there was significant enrichment in proteins associated with HIV infection and replication, including ATP-dependent RNA Helicase (DDX1), A-kinase anchor protein 8-like (AKAP8L), and dsRNA-specific Editase 1 (ADARB1).

Limitations:<br /> The study was based off of computational structural predictions and subsequent gene ontology enrichment analyses. Computational predictions are less accurate than experimental observations, and while their predicted binding partners prompt interesting hypotheses, they must be experimentally confirmed. Furthermore, the gene ontology annotations used to create their candidate list are based off prior studies and inherently miss novel biological implications. Targeted biochemical and structural studies that can be built off their identified targets will be essential for elucidating viral-host complexes and informing potential targeted drug designs.

Significance:<br /> Their computational approach demonstrated findings confirmatory of other studies in many respects. Their structural analysis findings of the high conservation of the spike S protein amongst analyzed coronaviruses and SARS-CoV-2 strains suggest that the spike S evolved to specifically interact with host ACE2, supporting that the human engineering of SARS-CoV2 is very unlikely and implicating spike S as a potential therapeutic target. Their RNA-protein interaction predictions suggest several relevant host-virus interactions that warrant further investigation. If proved experimentally, their identified links to proteins studied in the context of HIV and other viruses may be relevant for the repurposing of existing antiviral drugs for SARS-CoV-2.

Review by Michelle Tran as part of a project by students, postdocs, and faculty at the Immunology Institute of the Icahn School of Medicine at Mount Sinai.

On 2020-04-26 14:01:28, user Marko Hyvonen wrote:

Hello, <br /> Interesting manuscript. A quick comment on Figure 1. Looks like the patient data for panels C and D are identical. Could you check and perhaps amend?<br /> Marko

On 2020-04-26 13:17:24, user Amartya Sanyal wrote:

This article is now published in BMC Bioinformatics. Please cite: <br /> Khalil AIS, Khyriem C, Chattopadhyay A, Sanyal A. Hierarchical discovery of large-scale and focal copy number alterations in low-coverage cancer genomes. BMC Bioinformatics. 2020;21(1):147. Published 2020 Apr 16. doi:10.1186/s12859-020-3480-3. <br /> PMID: 32299346.<br /> https://rdcu.be/b3N08

On 2020-04-26 12:46:08, user Michal wrote:

A great, very interesting work (and beautiful figures)! Could authors please make the GitHub repository public (https://github.com/saezlab/... is 404 at the moment). Also, was it the intent of authors to share the reviewers credentials in the preprint (it might have - just checking)?

On 2020-04-26 12:13:27, user Swarkar Sharma wrote:

We are open to collaboration to validate various hypothesis functionally as well as with higher computational resources. together we can make it a better and effective study.

On 2020-04-26 08:32:14, user Mengqi Ji wrote:

This paper will appear in Nature Machine Intelligence soon.<br /> For the code and data, please refer to: https://github.com/mjiUST/V...

On 2020-04-25 16:41:39, user Anna N. wrote:

FYI, you have a typo in the accession numbers<br /> "KY182964" should be "KU182964.1" Bat coronavirus isolate JTMC15

On 2020-04-25 15:20:04, user Tom Bruin wrote:

The most important and challenging part of this study is to isolate the osteocyte and rigorously identify their presence and purity.<br /> Unfortunately, there are no date demonstrating that what they are indeed measuring are just osteocytes. These are bone chunks, which will have vascularization, osteoblasts, neural cells; etc. The evaluation the present of the remnant bone is insufficient to draw conclusions that they have osteocytes.. <br /> Without this clear and convincing evidence, the rest of the analysis may be flawed.

On 2020-04-25 13:17:54, user David Sherman wrote:

Nice work and glad to see you had success with various megaenzymes, including PikAIII. We found significant value in our recent paper <br /> DOI:10.1021/acssynbio.0c00038 using Orbitrap proteomics to confirm presence of the key biosynthetic enzymes. Nice analysis of phosphopantetheinylation of the PCP/ACP. Any sense of IVTT system to handle proteins that express poorly or are insoluble from traditional E. coli production methods?

On 2020-04-25 09:50:22, user Swarkar Sharma wrote:

This is interesting, we have overlapping findings. Please visit https://www.biorxiv.org/con....