On 2020-04-20 15:50:23, user cinnamon50 wrote:

Authors

your lay summary is really nice; congrats (I have a PhD molecular biology and know how hard it is to this right)

On 2020-04-20 15:32:48, user Nikhil Gopalkrishnan wrote:

Beautiful work! How did you guys get access to negative human saliva sample? Any commercial source you can point me to?

On 2020-04-20 13:30:43, user Nicolas-Frédéric Lipp wrote:

Very interesting preprint, congrats ! I'm glad to read that lipid transport is everywhere. <br /> Just a little thing, there is confusion in the text as well as in the abstract. The protein VPS13 is frequently replaced by VSP13. Of course, it's just detail without any consequences at this stage. I wish you all the best of success for the next.

On 2020-04-20 09:40:14, user JiaHsin Huang wrote:

Warning!!! This preprint is not a full manuscript. In the current version (4/20), the authors only provide the partially descriptions about their long-read sequencing method. No results and further information support their research. I doubted that this preprint is qualified for submission in any of the peer-reviewed journal. Really disappointed to see such unfinished draft to be published in the BioRxiv.

On 2020-04-20 08:34:55, user Łukasz Sobala wrote:

Can't see the scale bar in Figure 1.

On 2020-04-20 03:28:44, user Ma. del Rocío Baños Lara wrote:

"WHO I formulation consists of 85 % ethanol (v/v), 0.725 % glycerol (v/v) and 0.125 %10 hydrogen peroxide (v/v). The isopropyl-based formulation, WHO II, contains 75 %11 isopropanol (w/w), 0.725 % glycerol (v/v) and 0.125 % hydrogen peroxide (v/v) 8"

The WHO guidelines I found state 80% of alcohol in the handrubing solution, not 85% as this work state. I tried to follow the reference 8, where the formulation was taken, but it is wrong.

https://apps.who.int/iris/b...

On 2020-04-07 00:59:07, user Dennis Couzin wrote:

The results represented in Figure 1B imply that isopropanol at concentrations considerably less than 30% also deactivate SARS-CoV-2. Figure 1B shows that a 30% concentration of WHO formulation II sufficies. In four places, the paper describes that WHO formulation as 75% isopropanol, but in one place it specifies that this one chemical is to be measured w/w. Then it is about 79% (v/v). If so, Figure IB implies that a 0.30x0.79 = 23.7% isopropanol sufficies. If the w/w specification is unintended, 0.30x0.75 = 22.5%.

On 2020-04-20 03:12:54, user Peterson Biodiversity Lab wrote:

I was asked to review this critique of our paper (Osorio-Olvera et al.), and I stated my conflict of interest clearly to the Editor of the journal where it was eventually rejected. Anyhow, if the authors of the critique are going to post it as a pre-print, then you all ought to see the reviews of it! Some pretty serious problems, I am afraid... see below. Town Peterson

REVIEW:<br /> I have read this manuscript carefully, and have a set of comments and criticisms. At the outset, however, I will point out that I am among the authors of the paper that the authors are criticizing. The authors had indeed contacted myself and my coauthors with a previous draft of this manuscript, and my coauthors and I responded with information that the authors appear to have taken, at least partially, into account.

The manuscript focuses on two facets of the Osorio et al. paper. First, they are concerned that Osorio et al. did not focus on bird species endemic to the BBS coverage region. Osorio et al., however, considered that point carefully, and included a variable in their trait analyses that summarized the proportion of the species range covered by the BBS region. Although that variable was significant in univariate analyses, its effects disappeared (i.e., became nonsignificant) in multivariate models, apparently because many of the species with low range proportions were also aquatic or migratory, which emerged as significant factors. As such, this point was taken into account by Osorio carefully.

Dallas et al., however, attempt to develop analyses of effects of incomplete range coverage via comparisons with environments represented within IUCN range polygons for each species. (I will leave aside a substantive literature that indicates that these range polygons are not appropriate or adequate range descriptors.) However, their Figure 1 betrays the main reason for the magnitude of their conclusions as regards the range coverage point: they neglected the fact that the BBS focuses on breeding distributions, and that IUCN range polygons exist for breeding and WINTERING distributions. As a consequence, the massive undersampling of environments that they show in that figure for Anthus spragueii derives from their inclusion of the wintering and migratory distributions of the species in their characterization of the species' overall (i.e., beyond BBS region) distributional area. As a large proportion of the North American avifauna is migratory, I expect that this mismatch... an error by the authors, to be specific, the derives from not knowing the avifauna that is under analysis... drives most or all of the effects that they purport to document.

An additional point in this part of the manuscript: "By including non-significant correlations, the mean abundant niche-centre relationship across all model sets becomes weak... and more species exhibit significantly positive abundant niche-centre relationships (Figure 2)." This sentence is nothing short of disingenuous. That is, including or excluding non-significant correlations cannot increase numbers of significantly positive relationships! The numbers of significant relationships cannot increase, as Osorio et al. already reported them all.

Also, the statement "Interestingly, this low empirical support is consistent with previous findings..." neglects the abundant criticism that was heaped on at least some of those previous papers by the Dallas et al. group, at least. See https://doi.org/10.1111/ele... for one example.

Dallas et al. have a second point ... "Second, while the authors train an average of 1,852 models per species to calculate MVEs, they perform no form of model selection." This statement is, again, simply not true. The Osorio et al. paper's Methods section includes a paragraph that begins, "To add a dimension of model selection to the MVE analyses..." That might be a good place to start. It is true that Osorio et al. did not use AIC as a basis for model selection but they used defendable model selection criteria that were stated explicitly, and the process involved selecting best models from among large pools of candidate models.

A minor point: "Particularly controversial rules are the “abundant-centre” and “abundant niche-centre” hypotheses" This would be far more explicit if the authors were to say "abundant RANGE-centre" versus "abundant niche-centre"

In sum, Dallas et al. present a critique of Osorio et al. They present two main criticisms, of which the first was based on mistaken inclusion of wintering ranges of species, and the second was based on what appears to be incomplete reading of the Osorio et al. methodology. Although I appreciate the attention that such a critique brings to the original work of Osorio, Martinez-Meyer, and others, I am deeply concerned about the uncareful nature of this manuscript and the points that it attempts to make.

Town Peterson, University of Kansas

On 2020-04-19 23:10:49, user Marc wrote:

Are you able to translate 2.6x10^6 TCID50 into the absolute number of genome copies of the virus?

On 2020-04-18 12:59:40, user Lennart Randau wrote:

...very promising work! Please check Figure 1C, though. The image of the second monkey with treatment and the first one without treatment are identical except for the red circles.

On 2020-04-19 20:17:01, user Marco Alonso wrote:

In one part of the discussion they ask whether medical editors could keep these editorial times short after this crisis. This should be carried out in a more complex study, as the context of quarantine can be impacting for researchers and reviewers to have greater availability of time. Similarly, has the number of articles we usually receive from other topics varied? The quarantine was able to stop other investigations, which could reduce the editorial work pressure. The last is the most worrying aspect, which is the possibility of political instrumentalization of science through articles published in journal that supply cognitive authorities. This may result in a context conducive to the increase of ethical breaches (for example, conflict of interest, invention of data, etc.), which would lead to a serious affect on trust in science.

On 2020-04-19 12:13:22, user Saurabh Gayali wrote:

Url shows error:<br /> DNS_PROBE_FINISHED_NXDOMAIN

On 2020-04-19 06:01:03, user Rajendra Kings Rayudoo wrote:

To <br /> Manish tiwari and mishra

By following paper I came to know the mutation in coronavirus the first from place to place and changes its nucleotide

So by this vaccine in one area cannot be worked to another area<br /> Is it right

On 2020-04-19 00:22:28, user Mickey Mortimer wrote:

The paper claims it includes "the largest total number of dromaeosaurids (31) used so far in a phylogenetic analysis", but Hartman et al. (2019) used at least 42 dromaeosaurids as defined here (including unenlagiines and halszkaraptorines) and was not cited at all by the authors.

Hartman, Mortimer, Wahl, Lomax, Lippincott and Lovelace, 2019. A new <br /> paravian dinosaur from the Late Jurassic of North America supports a <br /> late acquisition of avian flight. PeerJ. 7:e7247. DOI: 10.7717/peerj.7247

On 2020-04-18 19:52:52, user Andre wrote:

This is now published in a peer reviewed journal: MDPI Genes : Genes 2020, 11(4), 446; https://doi.org/10.3390/gen..., https://www.mdpi.com/2073-4...

On 2020-04-18 19:46:44, user Oliver Van Oekelen wrote:

Is the data available in a repository anywhere? Would be great to allow cooperation and speed up the impact of this data on drug discovery!

On 2020-04-13 22:50:24, user Sinai Immunol Review Project wrote:

Main findings<br /> Single-cell RNA sequencing was performed on PBMCs from two COVID-19 patients treated with Tocilizumab, an interleukin (IL)-6 receptor antagonist. One blood sample was taken from each patient within 12 hours post-treatment (severe stage of disease), and a total of three blood samples were taken 5 and 7 days post-treatment (recovery stage of disease). A total of 13,289 cells were analyzed post-quality control and compared to 55,948 previously published profiles of healthy PBMCs.

The authors identified a subpopulation of pro-inflammatory CD14+ monocyte/macrophages enriched in the severe stage of COVID-19, characterized by the upregulation of TNF, IL-6, IL-1β, and CXCL8. In addition, a cluster of plasma cells and subpopulations of effector CD8+ T cells were found to be enriched in both the severe and recovery stages. The authors attribute this finding to the fact that Tocilizumab therapy does not necessarily compromise the anti-viral immune response.

Limitations<br /> Technical<br /> Only two patients were included in this analysis. Though both patients were classified under severe COVID-19 disease, at the time of therapy, patients PZ and PW were at significantly different stages of the disease; PZ had an SpO2 < 93%, and PW presented with respiratory failure, multiple organ dysfunction, and SpO2 < 93% under high flow oxygen.

Of note, PBMCs collected from patients were compared to previously published healthy controls. However, this is not a proper control; cells collected from COVID-19 patients that had not been treated with Tocilizumab are needed. Therefore, this comparison is not sufficient to attribute significant differences in clustering patterns to Tocilizumab treatment. Moreover, without additional details concerning improvement of clinical symptoms after Tocilizumab therapy at days 5 and 7, there is a lack of data supporting the claim that these timepoints (only a week into therapy) necessarily represent a stage of recovery in COVID-19 patients.

Finally, PBMCs attributed to severe disease (presumably to define the pre-treatment stage) were collected after administration of Tocilizumab, instead of before administration to establish an untreated baseline control. In addition, PBMCs attributed to the recovery stage were collected at timepoints that were not necessarily the same for both patients. Recovery stage cells from patient PZ were collected 5 days post-treatment, whereas recovery stage cells from patient PW were collected 5 and 7 days post-treatment. Because it was not clarified, it must be assumed that the 5 and 7 day samples from patient PW were combined to represent the recovery stage for that patient, but this yields an inconsistent comparison between the two patients.

Biological <br /> Enrichment of pro-inflammatory monocyte/macrophages in patients with severe infections is expected. The study is missing a more detailed characterization of cluster 9 (identified as the group of myeloid cells unique to the severe stage) to potentially establish a novel gene program specific to COVID-19. This is furthered by the fact that despite authors' claims, cluster 9 technically belongs to both the severe and recovery stages rather than being stage-specific. It is notable that transcriptionally, the acute inflammatory response is reduced in the defined recovery stage cells.

Other findings are expected, including the persistence of plasma B cells and effector CD8+ T cells in response to a viral infection. In addition to the technical limitations aforementioned, to better assess the impact of Tocilizumab on the inflammatory response, both transcriptional and translational outcomes must be investigated. It is not sufficient to account the downregulation of pro-inflammatory cytokines and chemokines to Tocilizumab treatment without the proper controls.

Significance<br /> IL-6R antagonists, like Tocilizumab and Sarilumab, have been raised as potential therapeutics for COVID-19 as a means of reducing the hyper-inflammation seen in patients. Understanding the cellular changes that occur during and after therapy is important to determining the efficacy of such therapy and whether inhibition of IL-6 signaling compromises other adaptive immune mechanisms needed for a complete anti-viral response. This study contributes to that analysis at the single-cell level.

On 2020-04-18 19:45:23, user Chung-chih Lin wrote:

Dear Dr. Pachitarium,

I use web-based Cellpose to segment my image and it showed the error as the following figure after submssion.<br /> Even I try the images of the web page, it still showed error.<br /> Can you help me this?

Best Regards,

Chung-Chih<br /> https://uploads.disquscdn.c...

On 2020-04-18 18:09:27, user Jeremy Horst wrote:

This manuscript has been accepted for publication in Pediatric Dentistry.

On 2020-04-18 14:47:43, user S Weeth wrote:

By checking the MSDS of the buffers, we know for sure the authors made mistakes there. Another problem with the study is that the authors can't be sure when they can't isolate the RNA from the virus, it was because their methods were not compatible with the inactivation buffers or the viral RNA was destroyed by the buffers. Of course the major problem is that inactivating virus is a different concept than destroying viral RNA

On 2020-04-15 11:35:25, user Shashank Shekhar wrote:

Virulence or infectiveness depends on mainly viruse's Polymerase enzymes,and their temperatures sensitivity. In most case these polymerase start changing their tertiary 3D structure in 45deg. Celcius. Secondly, heat altered RNA chain activities are very less perfect or suited for infecting further. Third, as per your own research paper, you presented viral ineffectiveness in virulence at temperatures 60 deg.C. regimen, is not up to mark. Because, At this temp. Virulent Strain's Enzymes system and RNA chain bases concistencies are adequately altered. Fourth, if you take in consideration, along with 45-50deg. C. Temperatures ioprn in india, All the Three types of Ultra Violet radiation are so strong, that will damage /alter enzymes and viral genome(RNA), to a lavel of Gross Decrese in infecting Virulence. Kindly reply in elaborated way.

On 2020-04-18 13:47:52, user Jesus Salazar-Gonzalez wrote:

Nice work and very encouraging results for a vaccine. Regarding joncloke's comments I would like to say that this study is a first step to understand the immunity to reinfection question. Comparing the sequences of the virus from the initial infection with the reinfection strain in humans that recovered should provide clues about breakthrough variants of how broad the immunity is. Also, people who were asymptomatic and never reinfected despite possible exposue will suggest strong immunity to circulating viral variants.

On 2020-04-05 18:02:46, user Matthew Katona wrote:

There are 8 known strands. Data on if reinfection doesnt occur across all strands or is reinfection same strand only?

On 2020-04-18 07:12:17, user Sushil Dubey wrote:

The article is now published in elife <br /> https://elifesciences.org/a...

On 2020-04-17 23:49:40, user Danushka Weerasekera wrote:

This preprint underwent a major revision since it was last posted on bioRxiv and a link to the published article in BioMed Research International with a revised title as "The antler cycle and fecal testosterone of male sambar deer Rusa unicolor unicolor at the Horton Plains National Park in Sri Lanka" is forthcoming.

On 2020-04-17 23:18:54, user Charles Warden wrote:

Thank you for posting this preprint.

However, I think there may be some sort of formatting issue in Figure 5B. I was not sure what the categories were supposed to be, but they look like errors due to the presence of symbols like "&" and "!".

I was admittedly skimming the paper, but I think these are supposed to relate to RNP reassortants? I also only see a description for the "A)" section in the legend for Figure 5.

On 2020-04-17 19:12:10, user KR wrote:

I was wondering if you had any thoughts about why the bat coronavirus RBD doesn't bind nor enter the cells expressing the bat ACE2 protein?

On 2020-04-17 18:50:57, user Bárbara Bitarello ????️‍???? wrote:

This new version has corrected Equation 5. That is the only change.

On 2020-04-16 15:38:55, user Bárbara Bitarello ????️‍???? wrote:

Hi everyone, there's a typo in Equation 5 and I will be uploading a new version soon!

On 2020-04-10 05:10:40, user Luca Pagani wrote:

Great work Barbara and Iain! Re the combination of ancestry specific PRS, check out our recent work on the same idea: https://www.nature.com/arti...

On 2020-04-17 18:17:15, user Paul Macklin wrote:

v2 preprint under revision now.

On 2020-04-17 18:02:48, user Kailash Chandra wrote:

Zoological Survey of India under MOEFCC Govt of India is taking initiative to work on the pandemic Coronavirus vaccine.<br /> My best wishes and support to the whole team.

On 2020-04-17 16:40:11, user Petra Bauer wrote:

Discussed in our lab meeting journal club: bHLH11 interacts with bHLH subgroup IVc TFs, but negatively regulates them to inhibit iron uptake, works possibly via recruiting TOPLESS-related corepressors - interesting assays for inhibitory effects on promoter activation.<br /> Please add the references for transcription factor studies cited.

On 2020-04-17 13:57:57, user Liz Miller wrote:

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

In this work the authors explore the departure of vacuolar cargo from the Golgi complex in coordination with cisternal maturation in S. cerevisiae. Building up on previous work (Casler et al. 2019), they have developed a regulatable vacuolar cargo which forms aggregates at the endoplasmic reticulum (ER) and can be solubilized upon the addition of a ligand. Once soluble in the ER lumen, the cargo enters in the secretory pathway, reaching the vacuole as the final destination in a Vps10 dependent manner. Transport dynamics can be followed using 4D microscopy, whereby co-expression with fluorescent protein markers allows them to follow the entry and departure of the vacuolar cargo coincident with the different stages of Golgi cisternal maturation, pre-vacuolar endosomes (PVE) and vacuole. Using this strategy the authors have followed the kinetics of transport and maturation with excellent time resolution. One of the main findings of this work is that the vacuolar cargo departs from the Golgi complex half-way through cisternal maturation. The departure of vacuolar cargo coincides with the arrival of GGA adaptors, required for Vps10 dependent traffic from the Golgi complex en route to the vacuole. Subsequent analysis of the traffic kinetics towards the PVE and vacuole revealed that the majority of the vacuolar cargo reaches the PVE less than 10 minutes after solubilization, whereas arrival to the vacuole takes between 30 to 60 minutes. Interestingly, the transfer of material between these organelles seem to follow two dynamics; a gradual transfer of cargo for several minutes or an abrupt transfer of material accompanied by decrease in coincident signal with the endosomal marker Vps8. Based on these observations, the authors propose that the traffic between PVE and vacuole occurs via a series of kiss-and-run interactions between organelles over a lengthy period of time. Overall, this regulatable vacuolar cargo represents an exciting new tool to scrutinize the secretory pathway in yeast, with an undoubtedly great potential when combined with genetic and EM techniques.

Following our group discussion, we have some brief comments:

1. We noticed that only a fraction of the cargo seems to reach the vacuole, if the fluorescence intensity is compared before the addition of the ligand and in later stages at the vacuole. This is to be expected since traffic of vacuolar cargo is receptor mediated, and can therefore be expected to saturate as a wave of cargo reaches the Golgi, resulting in excess vacuolar cargo lost through extracellular secretion. Of course, photobleaching will also account for some of the loss in fluorescence intensity. It would be interesting to see a quantification of the fraction of cargo reaching the vacuole, which would be indicative of the capacity of the Golgi cisterna to deal with incoming material from the ER.

2. We would have liked to see a figure directly comparing cargo and Apl2 kinetics, as shown for cargo and Gga2 in figure 5c. We appreciate that Apl2 itself is distinct from the GGAs, so this experiment would largely serve to emphasize the distinct route that vacuolar cargo would take from Apl2-dependent cargo.

3. The appearance of Sec7 seems accelerated when GGA adaptors are deleted, and we noticed that vps10∆ seems to have a similar effect. Is the dynamic arrival and disappearance of Apl adaptors affected by this change in maturation dynamics? It would be interesting to hear the authors speculation on this and its relevance to Golgi maturation dynamics.

4. It would be interesting to know the kinetics of AP-3 and AP-3 dependent cargos in the context of the cisternal maturation kinetics provided here. Perhaps this will be the next study!

On 2020-04-17 13:53:15, user Domenica wrote:

We have used the following datasets

1. Series15_HealthyLungBiopsy_1
2. Series15_HealthyLungBiopsy_2
3. Series15_COVID19Lung_1
4. Series15_COVID19Lung_2

but we had a big problem with

1. Series15_COVID19Lung_1
2. Series15_COVID19Lung_2

samples.<br /> They were mapped on the last human genome version from ENSEMBL with STAR+RSEM

The quality of data is not good and impossible to get any type of mapping. The majority of reads is represented by reads with this sequence:

GATCGGAAGAGCACACGTCTGAACTCCAGTCACTCTCGCGCATCTCGTATGCCGTCTTCTGCTTGAAAAAAAAAAGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG

That is the Illumina adapter plus a string of A and G. Once you eliminate these reads what is still inside are viral reads and very few human reads that are insufficient for statistical analysis. Please, can you eplain the reason of these results on samples of patients?

On 2020-04-15 15:39:30, user Viera wrote:

Thanks for data sharing! Were all datasets sequenced in one batch? I can not find info about batches.

On 2020-04-08 23:13:17, user Federico Giorgi wrote:

Truly awesome work! Did you use paired reads or single ended? It is not written in the methods :-) EDIT: it's single ended, I found it on your SRA upload https://trace.ncbi.nlm.nih....

On 2020-04-06 09:58:12, user Jorge wrote:

Dear authors,

Congratulations on this work, very helpful.<br /> Could you please provide stat values for the RNA-seq data of SARS-cov-2 in NHBE cells?

Many thanks<br /> Jorge

On 2020-04-17 13:35:48, user Chris Gisriel wrote:

Excellent work! A suggestion: The scope of the current introduction seems a bit narrow, and focuses only on a previous paper from the same group. The authors might consider expanding the introduction to reference the other work performed in identifying Chl f/d molecules found in both photosystems during FaRLiP in highly similar organisms, especially Chl d in PSII, of course. There is a lot of recent spectroscopic and structural data to suggest Chl f sites which may well be applicable, too. The context feels incomplete without this.

On 2020-04-17 12:31:23, user UAB Bacteriology Journal Club wrote:

Review of “Boosting Toll-like receptor 4 signaling enhances the therapeutic outcome of antibiotic therapy in pneumococcal pneumonia” Casilge et al.

University of Alabama at Birmingham<br /> Bacterial Pathogenesis and Physiology Journal Club

Summary<br /> This manuscript is well written. The authors showing that in TLR4 agonist MPLA can be improved Streptococcus pneumoniae (Spn) clearance with a combination treatment of sub-inhibitory doses of amoxicillin in a mice model. Moreover, the authors found that MPLA enhances host immune response for invasive Spn via inducing the pro-inflammatory cytokines and up-regulating the granulocyte related genes. This paper is very reasonably demonstrated, but we have a few comments and clarifications.

Major Comments<br /> • The authors analyzed Spn titer on lung and spleen and analyzed gene expression or protein expression by RT-PCR or ELISA assay in lung or blood, respectably. Why you didn’t check Spn titer on blood and gene expression on spleen?

• The authors used 30μg AMX for “high-dose AMX monotherapy”. However, some of the other experiments 350 μg used. If the authors have a reason, please explain in the manuscript. It will be helpful to understand.

• The authors used one antibiotic “AMX”. How are other antibiotics? The title of this manuscript is “antibiotic therapy”. If you can, please check other antibiotics also. It will give a more powerful story.

• How is the AMX-resistant Spn (β-lactam resistant strains) strains and other pathogens? It clearly lowers the effective MIC of AMX in these experiments, but we wonder whether that extends to the possibility of overcoming at least moderate levels of AMR. These experiments are probably beyond the scope of this paper, but I would be interested to hear the authors’ opinion on whether this might be the case.

• The authors used three different mice. However, the authors did not explain why different mice were chosen and used. If the authors have some reasons, please explain in the manuscript.

Minor Comments<br /> • In Fig. 2, it would be very helpful to include treatment labels on each panel (A to E)

• In Fig. 3B and 3C, please explain which gene used for normalization. In Fig. 3D, this panel is difficult to read. It might be more clearly expressed as a table, or possibly by using color instead of greyscale. Please broadly explain the function of genes in microarray data.

• In Fig 4A, the time point is not matched with Fig 4B. Moreover, What is the difference between “untreated”, “uninfected”, “naïve”, and “reference”??

On 2020-04-17 12:02:30, user Kisun Pokharel wrote:

Now published in Genes: https://doi.org/10.3390/gen...

On 2020-04-17 10:22:59, user Rajesh G wrote:

Great article. We would like to discuss this research paper feasibility on API 5500 triple quadruple. Please share our contact details or skype id so that we can discuss further.

On 2020-04-08 04:46:46, user Andrei Drabovich wrote:

Hi. Most of your theoretical peptides have 1-2 miscleavages. How these peptides are useful at all for SRM or PRM methods?

On 2020-04-17 01:20:14, user Ksenia Krasileva wrote:

Dear Ethan and Madhav,

We read your pre-print in our lab journal club and collected comments that we hope might be useful: https://docs.google.com/doc...

Do not hesitate to contact me if you have questions or comments.

-Ksenia

On 2020-04-16 22:29:08, user Maximus wrote:

Hi, I would like to use short read sequencing to count the abundance of the various SARS2 transcripts. I need to build a transcriptome file containing the sequence of the most abundant SARS2 transcripts. In addition to the full-length genome, I would like to include transcript sequences of the sgRNAs. Would I be able to use table S2 or S3 for this purpose? From my understanding, these tables should contain the information required to construct an accurate transcriptome for the virus.

On 2020-04-16 17:42:31, user igor_t_ru wrote:

from supplementary materials - NC_019725 Temperate-Confident Escherichia phage ADB-2

from publication <br /> https://www.ncbi.nlm.nih.go...

Escherichia phage ADB-2 was isolated from a chicken fecal sample. It is a<br /> virulent phage and shows effective inhibition of Escherichia coli <br /> strains.

it is just example. I see many problematic LifeStyle assignments.

On 2020-04-16 12:20:55, user Mukesh Mahajan wrote:

A recent article on "Paratope prediction and its application to ab-ag docking" is really very nice work by Bonvin group. This article will significantly guide researchers about structural understanding of HV regions in the antibodies. However, I was unable to understand how to select the probable residues involved in the ab-ag interaction from the probability plot (output)?

On 2020-04-16 10:49:14, user Darren Martin wrote:

I think that we maybe need to find more viruses that connect to the tree in the branch that separates SARS-CoV2 from the MRCA node of SARS-CoV2 and RATG. Without the genome sequences of these missing relatives we're not going to get very far wrt figuring out what actually happened.

On 2020-04-16 08:54:22, user Ilario De Toma wrote:

why for the SPIKE protein you do not find ACE2 for example?

On 2020-04-14 19:35:51, user Jeff Law wrote:

Excellent work! I wanted to work with the protein sequences, but I wasn't sure how to map the names provided here to UniProt IDs. So I extracted the translated protein sequence from the "DNA Sequences" zip file, and also listed the corresponding Uniprot ID(s). Available here: https://docs.google.com/spr...

On 2020-04-14 09:09:14, user eseporras wrote:

Interaction data now available at the IntAct database: https://www.ebi.ac.uk/intac...

On 2020-04-13 08:00:30, user Tartaglia Lab wrote:

This work is interesting and we find quite useful that the authors shared it. Thanks!

In our work (https://www.biorxiv.org/con... we studied protein interactions with SARS-CoV-2 RNA using advanced computational approaches.

Just focusing on the RNA binding proteins present in the two studies, we found a significant overlap of genes such as Janus kinase and microtubule-interacting protein 1 JAKMIP1 (Q96N16), A-kinase anchor protein 8 AKAP8 (O43823) and A-kinase anchor protein 8-like AKAP8L (Q9ULX6), which in case of HIV- 1 infection is involved as a DEAD/H-box RNA helicase binding protein (among others).

It is very curious that our list of protein- RNA binding partners contains elements identified also in this protein-protein network analysis. Yet, it must be mentioned that ribonucleoprotein complexes evolve together and their components sustain each other through different types of interactions.

On 2020-04-12 18:49:21, user Jan Baumbach wrote:

Great work! Thanks! We used the data in our drug repurposing candidate prediction platform CoVEx: https://exbio.wzw.tum.de/co...

On 2020-04-09 14:06:37, user paul Brear wrote:

Nice work, I think it should be CSNK2A1 interacting with the N protein in the figures of the maps . You currently have CSNK2A2

On 2020-04-08 23:23:29, user Geoff wrote:

Was scanning the citations and noticed it seems like citations 21 and 33 are referencing the same paper (in review). But just 33 has the DOI

On 2020-04-07 19:50:23, user Doug Selinger wrote:

Nice work! Notably, indomethacin is one of the compounds of interest mentioned here. My collaborators and I just released a preprint with data supporting the antiviral activity of indomethacin against SARS-CoV-2.

On 2020-04-04 06:09:35, user Reema Verma wrote:

Another group of researchers from ICGEB, New Delhi have also proposed Valproic Acid as an inhibitor of RNA dependent RNA polymerase of CoV2. They have put it on Preprintand OSF servers.

On 2020-04-03 13:15:47, user Spencer wrote:

If anyone is interested in following up on drug leads, we've digitized (parts of) table 1: https://docs.google.com/spr... (added pubchem links, drug brand names, etc)

Feel free to reuse!

On 2020-04-02 17:59:16, user ricky wrote:

Are there followup studies to define the interaction maps either:<br /> -using less-pathogenic strains of coronaviruses (like the seasonal cold)<br /> -using SarsCov2 proteins in cells from a species that does not get severe disease (such as bats)

On 2020-04-16 07:52:49, user Rob ter Heine wrote:

It would be interesting to know the following things to be able to better translate this research into practice:<br /> 1. In vivo nelfinavir is highly bound (>99%) to plasma proteins (HIV Med. 2006 Mar;7(2):122-8). The experiments were performed in cell culture medium, most likely containing less proteins and, thus, a higher unbound fraction. What was the protein binding of nelfinavir in this medium? The translation of the IC50 to the in vivo situation needs to be done with a correction for difference in protein binding.<br /> )2. In vivo nelfinavir is metabolized to its M8 metabolite.which is hydroxylated nelfinavir (Br J Clin Pharmacol. 2008 Apr; 65(4): 548–557_ . This drug shows in vivo activity against HIV and reaches sufficiently high concentrations to add to the therapeutic effect of nelfinavir. Does the M8 metabolite also inhibit sars-cov-2? If so, this needs to be accounted for the translation of these findings to the in vivo situation.

On 2020-04-16 02:13:23, user surechaurasiya wrote:

I looked for expression of ACE2 in lung tissues on both GTEx and HPA as soon as COVID-19 made headlines around the world.But could not see any expression. Very little mRNA may be coming from immune cells. You guys have done great job by bringing it up. Good luck!

On 2020-04-06 21:46:05, user Steve Scully wrote:

These datasets are based on a very small number of healthy individuals. It appears from other publications that ACE2 becomes over expressed in diseased cardiac and cardiovascular tissues including the lung alveolar smooth muscle. I suggest doing this analysis on a bigger data sets including TCGA.

On 2020-04-06 20:35:17, user Steve Scully wrote:

Am I correct that all the findings from this paper are based on a “draft” of the human proteome from a very small sample of individuals in 2014 and 1 cell line?

On 2020-04-15 16:24:20, user Marcos Escosa wrote:

Good morning,<br /> Ursolic acid is a candidate agent for patients with refractory malignant gliomas. Combination treatment of temozolomide and ursolic acid synergistically enhance cytotoxicity and senescence in TMZ-resistant glioblastoma cells. Ursolic acid is a naturally derived pentacyclic triterpene acid that exerts broad anticancer effects, and shows capability to cross the blood-brain barrier. <br /> Regrds,<br /> Dr. Marcos Escosa

On 2020-04-15 14:43:16, user Азат Онгарбаев wrote:

Interesting. However in case with Allplex™ 2019-nCoV RT-QPCR it is not clear what volume of the Internal Control (IC) do you add, and when you do it?

On 2020-04-15 14:14:10, user Federico Marini wrote:

Thanks Qi - and sorry for missing our your comment so long time!<br /> We did include your tool, IDEA, in the Supplementary Table where the comparison is done.<br /> See the "live" version of the comparison here: https://federicomarini.gith...

On 2020-04-15 09:12:04, user Mounia Chami wrote:

Great work,

You forgot to cite this paper

Localization and Processing of the Amyloid-β Protein Precursor in Mitochondria-Associated Membranes.

Del Prete D, Suski JM, Oulès B, Debayle D, Gay AS, Lacas-Gervais S, <br /> Bussiere R, Bauer C, Pinton P, Paterlini-Bréchot P, Wieckowski MR, <br /> Checler F, Chami M.

J Alzheimers Dis. 2017;55(4):1549-1570. doi: 10.3233/JAD-160953.

PMID:27911326Free PMC Article

On 2020-04-15 08:16:23, user BAJ wrote:

pls have a look at https://github.com/C3BI-pas.... I will be submitting a version very soon to biorxiv...

On 2020-04-15 07:43:45, user Alejandro Manzano Marín wrote:

This paper describes an independently evolved co-obligate symbiosis in Periphyllus aphids, but fails to provide compelling evidence for A. urticata and M. carnosum. You can read my thorough review in PubPeer: https://pubpeer.com/publications/51CF51BF8B00F33721655DB14425F4#1. In brief, previous evidence does not support a fixed status for Serratia symbiotica in the last two aphid species. There was an inadequate experimental design and a mixture of different antibiotic treatments for main figure (omitting a significant reduction in fecundity for curing a facultative S. symbiotica in A. pisum). More and geographically distant populations should have been included given their results and previous evidence for the presence of S. symbiotica in M. carnosum and A. urticata. Insufficient manual curation of annotations for inferring metabolic complementarity led to incorrect assumptions of co-dependency. Lastly, there is an incorrect interpretation for a separate bacteriome for S. symbiotica in A. urticata.

On 2020-04-15 04:21:01, user Yong Jia wrote:

To all readers of this article, at this stage, we want to stress that the potential effect of the R408I mutation on vaccine development should be treated with caution, as it still needs to be verified by clinical tests.

On 2020-04-14 19:26:10, user Suraj Kannan wrote:

Hi! Our group has recently used a similar concept of entropy to study maturation processes in cardiomyocytes, and iPSC-CM maturation. I have several comments and questions about your study - 1) I'm pretty sure Strober et al. is a bulk RNA-seq dataset, not single cell, so I'm not sure you can use it as an assessment of single cell entropy. This is particularly true given how heterogeneous CM differentiations are at the single cell level. 2) I'm still not fully sure why entropy appears to increase over the course of differentiation and decrease in reprogramming to pluripotency in your system. You state that this entropy metric can be seen as a measurement of state order, but if anything, shouldn't more differentiated cells have more order and therefore lower entropy? This is the principle that underlies not only our entropy score approach, but several other entropy-based single cell approaches.

On 2020-04-14 17:01:13, user Cheng Huang wrote:

To clarify, Vero cells are incompetent in type I IFN production due to defect in type I IFN genes. However, these cells are actually competent in IFN response when treated with IFNs. IFN treatment of cells that are competent in IFN production will lead to the production of endogenous IFNs, which may make the results more complicated. Thus, Vero cells are commonly used in evaluation of the antiviral activity of IFNs to many viruses, including SARS-CoV (please refer to the references cited in the manuscript).

On 2020-04-12 12:44:46, user Dacquin Kasumba wrote:

Vero cells are known to be IFN-I incompetent. Why choose this cell line for your study and how do you explain the "remarkable" sensitivity against SARSCov2 in an cell line that is not sensitive to type-1 IFN?

On 2020-04-14 15:51:20, user Sinai Immunol Review Project wrote:

Main Findings <br /> - Study profiled nine tissues from cynomolgus monkey (NHP model with no COVID19 infection) by scRNAseq and analyzed ACE2 and TMPRSS2 expression findings to existing human scRNA datasets. Also conducted scATACseq on kidney tissue specifically to map chromatin accessibility relevant to above genes. <br /> - ACE2 and TMPRSS2 predominantly detected in ciliated, club cells and type2 <br /> alveolar cells, as well as proximal tubule cells of kidney, and cholangiocytes in liver (agreeing with human data). Some differences exist in ACE2 and TMPRSS2 expression between human and NHP samples, especially in liver but also in lung. <br /> - Correlation analyses show immune-modulatory genes such as TMEM27, IDO2, DNAJC12, and ANPEP co-expressed with ACE2 upregulation in kidney. Also, IL6R gene expression correlated well with ACE2 in proximal tubule cells (agreeing with human data). <br /> - scATACseq of kidney cells determined open chromatin regions with discrete ACE2 peaks in proximal tubule cells S3, and TF motifs for these regions were enriched in STAT1/STAT3 and IRF1 binding sites.

Limitations <br /> - Variations in ACE2/TMPRSS2 gene expression between NHP and human tissues could arise due to differences in digestion and/or processing protocols hence biasing reads. Will be important to analyze protein expression for relevant genes in NHP tissue to confirm such differences. <br /> - Study requires ex vivo co-culture experiments with kidney tubule epithelial cells and COV-2 to ascertain IL6R signaling axis is linked to ACE upregulation and/or impacts downstream alarmin/PAMP release.

Significance <br /> - Single-cell NHP profiling is relevant and useful considering monkeys are a preferred model for studying the effectiveness of drug treatments and of vaccines against COVID-19 <br /> - scATACseq results with IRF1/STAT1 accessibility suggests a link between paracrine interferon signaling + IL6 and enhanced ACE2 expression in kidney that can exacerbate COVID-19 severity due to increased viral entry and dissemination. <br /> - Findings supports other reports of cytokine-induced tissue damage in kidneys of COVID-19 patients, as well as provide additional support for anti-IL6R strategies such as Tocilizumab.

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

On 2020-04-14 15:37:58, user Benjamin Branoff wrote:

A peer-reviewed version of this manuscript has been accepted and is available here: https://www.tandfonline.com...

On 2020-04-14 07:03:59, user Torsten Seemann wrote:

This paper uses "Snippy" for variant calling, which I wrote, on FASTQ data I and others produced. The authors may not realise that the reads are not WGS but amplicon sequencing and need appropriate primer trimming to avoid false SNPs.

On 2020-04-13 21:41:12, user Aaron wrote:

I could be mistaken, but I believe that the authors actually mean Washington state (home of University of Washington) rather than Washington DC.

On 2020-04-14 04:40:17, user Leo Wang wrote:

Why did you use LNCaP-AR but not LNCaP parental cells (adenocarimoma) as a control to NCI-H660.

On 2020-04-14 02:06:35, user cm wrote:

For reasons I described here, I believe the authors should make it a priority to measure the concentration of baicalin required to inhibit viral replication:

https://chrismasterjohnphd....

In brief:

Pharmacokinetic studies indicate that 400 mg baicalein taken twice a day separated by 12 hours would lead to a steady state of the plasma concentration of baicalin that exceeds the EC50 of baicalein for viral replication. But a similar dosing schedule of baicalein would lead to maximal plasma concentrations about 16-fold lower than the EC50 for baicalein itself.

The pharmacokinetic data suggest that a safe dose of the molecule would only be effective if baicalin has similar activity as baicalein.

Although it required the IC50 of baicalin against the 3CL protease was 128 times the IC50 of baicalein, they did not test the EC50 against viral growth for baicalin.

When comparing baicalein to the crude extract, the IC50 against the enzyme was 800 times higher for the extract, while the EC50 against viral replication was only 1.5 times higher for the extract.

It is entirely possible that baicalin would be more effective against viral replication than its activity against the enzyme would suggest, and the results with the crude extract provide clear precedent for that.

Given the pharmacokinetics, it is imperative to test and report the EC50 of the glycosylated metabolite, baicalin, against viral replication.

My argument is in more detail here:

https://chrismasterjohnphd....

On 2020-04-13 21:24:43, user Mutasem Taha wrote:

The supplementary table of this preprint raises doubts about the findings, for example, Enoxacin is given a high index of 1.18 while its very close analogue Norfloxacin is considered inactive and given index of 0.07. Similarly, Prothionamide is considered potent with an index of 1.14 while its close analogue ethionamide is totally inactive according to the study with activity index of 0.12. <br /> The preprint considered Nitazoxanide to be inactive (index 0.6) although it is currently in clinical trials as treatment for COVID-19 (https://clinicaltrials.gov/... and was reported to have anti SARS-Cov-2 IC50 of 2.1 microM in a peer reviewed paper (Cell Research (2020) 30:269–271; https://doi.org/10.1038/s41....

On 2020-04-13 19:32:56, user Barry J Barclay wrote:

I have been thinking about a possible molecular mechanism of Leflunomide/Teriflunomide antiviral activity. DHODH is a mitochondrial enzyme and the first step in the synthesis of pyrimidine nucleotides. There is some evidence that the COVID-19 virus binds to the outer mitochondrial membrane and evades the intrinsic MAVs system by doing so. Additionally the virus has a high uracil content so DHODH inhibition will slow RNA polymerase due to UTP depletion and preventing viral RNA replication. Lastly one of the side effects of the drugs is immunosuppression but ameliorated by deoxcytidine that allows for the biosynthesis of pyrimidine deoxyribonucleotides and allowing for DNA synthesis in mitochondria and maintaing their normal antiviral, bioenergetic and metabolic function.

On 2020-04-13 11:54:38, user Barry J Barclay wrote:

Very nice paper and and a significant contribution to the health and safety not only of patients but of front-line healthcare workers as well until we have a vaccine. Need accelerated approvals and distribution ASAP.

On 2020-04-13 17:39:45, user Charles Warden wrote:

Thank you for putting together this paper.

While it seems like it isn't essential for the main results, there were a couple things that I think you could note in Table 1:

1) The bumphunter function is used for the DMR analysis in minfi, which is described in Jaffe et al. 2017.

2) COHCAP (Warden et al. 2013) is another option for both site and region level analysis. It would be mostly like the summarization for IMA (which is listed), although newer updates allow refinement of region boundaries through a clustering step (in the Bioconductor package).

Since I am the author for point 2), I realize that I am not completely unbiased. However, I thought this might be worth mentioning, if you are not aware of that program.

On 2020-04-13 15:43:51, user Sinai Immunol Review Project wrote:

Main Findings

In this study, Campbell et al. investigate the immunogenic potential of SARS-CoV-2-derived epitopes in the context of eliciting CD-8 T cell responses. Using pVAC, a computational toolkit developed by the Parker Institute for Cancer Immunotherapy to predict cancer neoantigens presented by human leukocyte antigen (HLA) class I, they analyzed the binding affinity of 9360 HLA-I alleles to 9-mer epitopes derived from all 11 viral proteins spanning the SARS-CoV-2 proteome. They identified 6,748 epitope – HLA-I pairs, which consisted of 1,103 unique 9-mer epitopes and 1,022 HLA class I alleles, with a predicted binding affinity Kd < 500 nM. Each viral protein was predicted to generate immunogenic epitopes (12 to 684 epitopes, with a median of 31) and some of these epitopes could bind to multiple HLA-I alleles (1 to 55 alleles, with a median of 3). The study tested 2987 HLA-A, 3707 HLA-B and 2666 HLA-C alleles, of which majority of viral epitopes were predicted to bind HLA-B alleles (295 HLA-A, 614 HLA-B and 113 HLA-C). Of note, 10 of the 30 experimentally validated immunogenic SARS-CoV HLA-I epitopes were identified among current SARS-CoV-2 predicted epitopes. Together, this work shows that a diverse set of HLA alleles can potentially bind to a wide variety of viral peptides, suggesting the presence of strong cytotoxic T cell (CTL) responses against SARS-CoV-2 in COVID-19 patients and serves as a valuable resource for SARS-CoV-2 epitope selection and experimental validation efforts, which would guide the design of T-cell based vaccination strategies against COVID-19.

Limitations

A limitation of this study is that it identifies only 9-mers peptides with <500 nM binding affinity, but it does not look for 8-mer or 10-mer peptides and excludes peptides with >500 nM affinity but within statistical rank-cutoff of 2%, which could still generate an immune response to the virus. Another limitation is that the authors did not evaluate epitope - HLA-II pairs, even though HLA class II is also important in the context of viral immune responses. Studies have shown that convalescent sera of recovered COVID-19 patients contain neutralizing antibodies, which can be used as an early treatment for recipient patients or as a prophylactic for at-risk or exposed patients. Generation of these antibodies require viral detection by B cells, aided by antigen presentation by HLA II molecules to CD4 T cells, which stimulates B cells to differentiate into antibody-producing plasma cells. Thus, further study could provide insight into the ability to potentially initiate potent antibody responses by identifying longer peptides and their affinity for class II variants.

Significance

This study predicts 6,748 unique pMHC complexes across the SARS-CoV-2 peptidome and global variety of HLA alleles, highlighting that a wide variety of patients can generate a CTL-mediated response to infection. These results can help explain the heterogeneity of COVID-19 disease phenotypes and can be used to monitor CD8+ T cell responses across patients with many different haplotypes. Since SARS-CoV-2 proteome is quite large to be screened experimentally for immunogenic epitopes in an unbiased fashion, these in silico findings guide the selection of immunogenic epitopes for functional validation of SARS-CoV-2 epitopes, which can be used in vaccine development and pre-clinical treatment studies. Moreover, all data generated in this study is publicly available for further investigation, which is critical for clinical translation in the evolving pandemic.

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

References<br /> Casadevall A, Pirofski LA. The convalescent sera option for containing COVID-19. J Clin Invest. 2020;130(4):1545–1548. doi:10.1172/JCI138003<br /> Hundal J, Kiwala S, McMichael J, et al. pVACtools: A Computational Toolkit to Identify and Visualize Cancer Neoantigens. Cancer Immunol Res. 2020;8(3):409–420. doi:10.1158/2326-6066.CIR-19-0401

On 2020-04-13 15:39:42, user Morgan Carter wrote:

We have contributed a Pre-review of this paper on PREreview2:<br /> https://www.prereview.org/p...

On 2020-04-13 14:59:39, user Sinai Immunol Review Project wrote:

Main Findings (Immunological) <br /> - Analyzed nasal airway epithelial transcriptome data from 695 asthmatic and healthy children (GALA II study, no COVID-19) to determine WGCNA networks. TMPRSS2<br /> gene was contained in a set of three highly correlated networks <br /> exhibiting strong enrichments for IL13-induced mucus secretory cell <br /> genes and canonically Type-2 inflammation pathways. ACE2 gene was correlated with interferon response and cytotoxic immune signaling eigengene enrichment. <br /> -TMPRSS2 upregulated and ACE2 downregulated upon rIL13 treatment in muco-ciliary air-liquid interface (ALI) cell culture; supporting transcriptome analyses. Also, scRNAseq data from tracheal airway epithelial cultures chronically stimulated with IL-13 also showed congruent changes in TMPRSS2 and ACE2. <br /> - Used metagenomic analysis from dataset to find 18 asymptomatic children with four different coronaviral sequences (CoV; OC43, JKU1, 229E, NL63). Compared 11 subjects with highest CoV infection to 571 CoV-naïve subjects, with 37 rhinovirus (HRV)-infected subjects serving as ‘basal/normal respiratory virus’ comparators. <br /> - Induction of cytotoxic immune response was considerably higher in CoV-infected subjects, compared to HRV-infected individuals. IL10, IL1B, IFNG, IFNA2, STAT1, and ACE2 upregulation shared for CoV or HRV infections. IL6 upregulated and genes associated with CD8 T cells, DCs and NK cells specifically enriched in CoV infections. <br /> - Identified eQTLs for ACE2 and TMPRSS2 using GALA II dataset and used multi-variable modeling to estimate the relative contribution of these factors to population variation in these genes (not reviewed here).

Limitations <br /> - Dataset is large, but having asthmatic young subjects as part of GALA II study might skew WGCNA analyses to enrich for T2 biomarkers and secretory phenotype genes. Will be important to replicate analyses in another independent non-COPD/allergy/asthma dataset and reconcile ACE2+TMPRSS2 correlation with T2 emergence. <br /> - Provides rationale for increased cytotoxic responses in CoVID-19 cases, but no explanation for ACE2 and TMPRSS2 anti-correlative trends in epithelia upon T2 inflammation. <br /> - Will need ALI experiments with IL13 addition in CoV-2 infection models to ascertain changes in ACE2/TMPRSS2 to be consistent with existing patient data for COVID-19.

Significance <br /> - Study strongly suggests airway epithelial TMPRSS2 expression is highly upregulated upon Type 2 inflammation, specifically by IL13 stimulation of epithelia. Also, ACE2 expression (and therefore viral infectivity) is inextricably linked to interferon response and could sabotage initial inflammation. <br /> - Changes in immune contextures observed in asymptomatic CoV+/HRV+ subjects suggest remodeling of airway epithelia even after viral resolution, which can impact future infections with COVID-19 and outcomes. <br /> - Results showing IL10, IL1B, IL6 enrichment and cytotoxic programs (CD8, NK, DC) in CoV+ subjects supports other concurrent studies showing their importance in cytokine storm and points to broader conserved inflammatory pathways worth targeting in COVID-19.

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

On 2020-04-13 14:58:48, user Michael wrote:

This paper may describe great new tools for cell imaging.<br /> However, it needs a real abstract that explains this. Essentially, a topic sentence like, "We have engineered new versions of Fucci to improve cell cycle live imaging. They are X times brighter, Y times more stable. Modifications are A and B."

On 2020-04-13 13:07:00, user Sinai Immunol Review Project wrote:

Title: On the interactions of the receptor-binding domain of SARS-CoV-1 and SARS-CoV-2 spike proteins with monoclonal antibodies and the receptor ACE2<br /> Correa Giron et al., bioRxiv [@doi: 10.1101/2020.04.05.026377]<br /> Keywords<br /> • Monoclonal antibodies<br /> • Binding affinities<br /> • RBD/ACE2 interaction

Main FindingsThis study combined a theoretical approach, with structural and computational methods to identify antibody epitopes on the ACE2 (angiotensin-converting enzyme 2) receptor-binding-domains of the Spike (S) proteins of SARS CoV1 and SARS CoV2. The authors used constant-pH Monte Carlo (MC) simulations and the PROCEEDpKa method, to determine binding affinities of four SARS-Cov monoclonal antibody fragments (CR3022, 80R, m39, F26G29) to SARS-Cov2, as well as the titratable amino acids involved in the interactions. They also compared antibody binding affinities to the binding affinity of S RBD of SARS-Cov1 and 2 to the ACE2 receptor to ensure that antibody binding affinities were higher compared to those of S RBD/ACE2. They identified CR3022 as the Ab having the highest affinity with SARS-Cov2 RBD. Based on their simulations they were able to suggest some 3 amino acid modifications on CR3022, to enhance its binding affinity to SARS-Cov2 RBD. The computational analysis of interactions between SARS-CoV-1 S RBD proteins and the S RBD-specific neutralizing mAbs (80R, F26G19, m396, CR3022) recapitulated previous experimental results suggesting that the approach was valid. The only mAb with measured affinity for the SARS-CoV-2 S RBD protein by BLI assay was CR3020, which also exhibited the highest theoretical binding affinity determined in the present study.

Limitations<br /> Simulations were performed on S RBD proteins alone. However, given that the S protein is trimeric, structure modifications and/or conformational changes in the S protein trimer could alter antibody binding affinity. In addition, while CR3022 has been shown to bind S RBD, it did not neutralize SARS-Cov2 in vitro. The CR3022 modifications suggested by the authors could enhance the binding affinitys and the neutralizing potential of CR3022, but this is currently hypothetical and remains to be tested.

Significance<br /> These results are in agreement with previous experimental studies where CR3022 was shown to bind SARS-Cov2 RBD (10.1101:2020.01.28.923011, 10.1126/science.abb7269) and confirm the validity of the approach. A human monoclonal antibody against SARS-Cov2 RBD could be used as therapeutics to treat patients with severe COVID-19, by inhibiting ACE2/RBD interaction.

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

On 2020-04-13 10:12:00, user George Sangster wrote:

Many mitochondrial genomes have been duplicated in the phylogenies because the GenBank accessions AND the curated RefSeq sequences (which are identical to the original sequences on GenBank) have been used.

For instance,

NC_025650 (Diptychus maculatus) is the exact same sequence as KM659026.<br /> https://www.ncbi.nlm.nih.gov/nuccore/Nc_025650

NC_035994 (Schizothorax curvilabiatus) is the exact same sequence as MF804977.<br /> https://www.ncbi.nlm.nih.gov/nuccore/nc_035994

NC_021420 (Gymnocypris namensis) is the exact same sequence as KC558498<br /> https://www.ncbi.nlm.nih.gov/nuccore/nc_021420

On 2020-04-13 04:06:25, user Eric Beck wrote:

Published version of this preprint:<br /> Beck, B. R., Shin, B., Choi, Y., Park, S., & Kang, K. (2020). Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Computational and Structural Biotechnology Journal.<br /> https://www.sciencedirect.c...

On 2020-04-12 17:46:52, user Geoffrey Thomson wrote:

Just a heads up. In your Supplemental Table S9 and S10 where you have concatenated the headers of your FASTA sequences a number of rows have been cut-off before the end. (e.g. the ">orange1.1g010129m" row in Table S9)

On 2020-04-12 14:03:50, user Savannah Est wrote:

Would just like to start off saying this study is hugely impactful and exciting! I really like the idea of using plant extracts to deliver drugs--makes sense from a regulatory perspective. A couple of comments:

1. I think when you do large screens, clear data representation can be difficult due to the high number of data points. I really like what you did with Figure 1c by selecting a group of well performing candidates and clearly labeling the portion of the graph that is the desirable outcome. I would like to see this also for Figure 1b. Perhaps you can include the data for all of the candidates in the supplemental information or in an entirely separate figure and highlight the zone of interest and do a "zoom in figure" (sort of like the diagram in Fig. 1a). I think this would make the data more clear.

2. I understand this is meant to be a platform delivery candidate for oral delivery of protein drugs, but in the context of diabetes/disease states, are there significant changes to the gut that may render this system less effective? I don't know the answer to this but since you did not test in any disease models, it would be interesting to discuss. It may not be in the scope of the current study, but I would love to see the insulin/pelargonidin capsule given to both wild type and diabetes mice and compare the AAC as well as presence of inflammatory factors.

Thank you!

On 2020-04-12 03:23:40, user Caroline de Gorter wrote:

Although I'm an amateur, I did hear that bats who carry the virus do not get sick themselves, due possibly to their very high metabolism. Researching metabolism on-line, I discovered - among others - an article by Heather S. Smallwood et al, called Targeting metabolic reprogramming by influenza infection for<br /> therapeutic intervention concerning 'ordinary' IAV influenza metabolism, which stated that: "Influenza infection increases metabolic flux, distinct from TLR activation, inducing glucose<br /> and glutamine dependence to sustain host cell viability" (https://www.ncbi.nlm.nih.go....<br /> The article goes on to say: "We tested viability of infected<br /> NHBE with glucose and glutamine restriction and found IAV infection significantly reduced<br /> viability in this context."

I realize that IAV is not a corona virus and that I am a mere amateur sleuth. Still, the fact that obese humans are among the higher risk groups for developing full Acute Respiratory Distress Syndrome after contracting SARS-CoV-2, this more or less confirmed the research results named in the above-mentioned article for me.

I am furthermore very interested to know if hormones can in any way help fight Covid-19. Hormones determine metabolism to a point, don't they? I have a hyperthyroid condition (no medication needed thankfully), which means I can eat all I want and not put on weight. I also have a very low fat percentage (-20%) and a fast metabolic rate. All governed by my thyroid hormonal imbalance.

Enough said. I am just trying to help and, as a total outsider to any science whatsoever, am probably barking up the wrong trees completely. Still, outsiders sometimes see connections where specialists might not care to look.

Good luck with your no-doubt very excellent and highly skilled work. I hope you can help save human kind, although human kind does not always help to save the world. ;-)

On 2020-04-12 00:00:45, user Aaron wrote:

The authors have presented no data supporting the title of the article: "Human ACE2 receptor polymorphisms predict SARS-CoV-2 susceptibility." Regardless of these potential alterations in ACE2-Spike interactions, there are many other aspects at play that will determine an individual's susceptibility to infection (i.e. expression levels of required proteins for viral entry, genotype of S protein, immune function, lung health, etc.). Without genetic data from patients showing a higher than expected proportion of patients with these polymorphisms, claims of susceptibility cannot be made.

On 2020-04-11 20:05:31, user Steven Christiansen wrote:

What is the PDB structure for the tmprss2 structure? I would like to have access to it. I'm a graduate student.

On 2020-04-11 19:17:20, user Vitaly Ganusov wrote:

This review was done by students and instructor of the course at UTK MICR 607: Journal club in computational biology (Spring 2020).

Major Comments

It is still unclear how different the proposed model is with other models that the authors cited in the introduction. The model that includes 128 versions seems to be comprehensive. A better description/discussion is needed how novel the model is. Perhaps there is no need for the novelty of the model, but application to novel data (or in a novel way).

The authors find that there is a difference between prediction of the CH68 model and their model with multiple rounds of replication of DNA. However, the previous model also assumes this and it is unclear which specific differences between the models make the predictions different. There should be a limit of the new model which becomes the old (CH68) model if it exist (and absence of such limit discussed).

Experimental data are not well explained. Because fitting models to data is important in this work, data must be explained, citing the data is insufficient. Do these datasets have the marker frequency distribution? If so, how was that obtained and what are potential errors in that process? If the authors generated marker frequency distribution, this needs to be explained. Also, the pattern is the data with clearly defined peaks seem too good to be true. How exactly mapping of DNA segments to the genome was done needs to be explained.

How exactly models are fitted to data? Using least squares does not seem to be appropriate. Perhaps use of likelihood methods is needed.

There are better ways of comparing model fits to data, e.g., using AIC. Also the figures 2 should include fits of 2 models (the new model and the CH68 model), so the quality of the model fits can be also visually compared.

The model predicts very sharp peak and sharp changes in the slope in marker frequency with position. This is an ideal scenario. In reality, there is heterogeneity in the population thus likely to result in "smoothened" curves. Perhaps this deserves discussion (or can be addressed in the work by allowing for distribution of some model parameters).

Estimates of model parameters should include confidence intervals.

Minor comments

It would benefit the reader if the figure legends are shown together with figures.

Numbering lines in the paper would be useful to make specific references to the text.

Adding numbers of data points in the graphs with data (e.g., Figure 2) could be useful.

The text states that marker frequency is determined by comparing the number of DNA copies per replicating cell vs. non replicating cell. How would one determine which cell in the population is replicating and which is not? This is not possible for bulk populations.

Table S1 is missing from the supplement.

The last sentence of the 3rd paragraph in the Introduction mentions that your model is significant because it uses ‘genome-wide parameters of replication’. What exactly do you mean by genome-wide? Does the replication speed not differ slightly along different parts of the genome due to hitone content, methylation %, and coiling?

The first paragraph in the Results mentions that your variables are dimensionless. Why did you choose to make the variables this way? What is the advantage? How is something within a biological system dimensionless. Please provide a clearer explanation.

There is a grammatical error in the second paragraph of the “Replication in E.coli with two origins’ section. “Third, one of the explanation”; explanation should be plural for this phrase to make sense.

On 2020-04-11 15:30:26, user Sinai Immunol Review Project wrote:

Title: Fatal toxicity of chloroquine or hydroxychloroquine with metformin in mice

Summary:<br /> Chloroquine (CQ) and hydroxychloroquine (HCQ) have been presented as potential therapeutic strategies for acute respiratory syndrome, a serious complication sometimes observed in COVID-19 patients. The drug was rapidly pushed to clinical testing and approved within weeks as a recommended COVID-19 treatment by the National Health Commission of the People's Republic of China[1]. However, CQ or HCQ, sometimes used in combination with anti-diabetic drug metformin, may have serious toxic side-effects that have not been appropriately addressed.<br /> The authors, who study metformin with CQ or HCQ as potential anti-cancer drugs in mice, investigated their toxicity in mice. They found that the combination was lethal in at least 40% of nude mice, tumour-bearing or not. Similar results were obtained in non-immunocompromised mice. They observed that nude mice treated with HCQ+metformin presented an increased number of autophagosomes in the heart, liver and kidneys. Lactate dehydrogenase (LDH) and creatine kinase (CK) were also elevated in treatment groups compared to control vehicle-treated mice.

Critical analysis:<br /> The authors of this article performed studies exclusively in mice. They note that the lethal doses of CQ or HCQ are similar to those in humans with allometric scaling. However, the side-effects of the combinations observed in mice must yet be confirmed in humans. The exact etiology behind the increased lethality in treated mice was not discussed. As both metformin and QC or HQC exert hypoglycemic effects, together with observed side effects such as cardiomyopathy and retinopathy induced by QC and HQC, point to exacerbated adverse side effects caused by the combined treatment. The dose of 500mg twice per day of CQ recommended in China can rapidly approach a dangerous dose in humans[1]. Indeed, CQ poisoning has already been reported in Nigeria and resulted in at least one death in Arizona, USA.

Relevance to current epidemic:<br /> Given that metformin is widely used as an anti-diabetic drug, the article acts as a word of caution for individuals and physicians who have been taking and prescribing these drug combinations prior to adequate clinical trials. The clinical relevance of treating COVID-19 patients with CQ+metformin or HCQ+metformin is yet to be confirmed or rejected.

References:<br /> 1. Wong et al. Caution and clarity required in the use of chloroquine for COVID-19, The Lancet, April 02 2020

By Maria Kuksin

On 2020-04-10 02:14:31, user Brian Hanley wrote:

I talked to a colleague who runs an ER in South Texas. He says, "Lots of data on patients in this area on plaquenil and metformin." He says it's obvious this does not happen in humans. Not at doses used in medicine. Another example of mouse model not corresponding to human model.

On 2020-04-09 17:50:06, user ZephirAWT wrote:

This study used flawed allometric scaling with body surface area and administered lethal doses of each compound to the mice. This had nothing to do with any supposed drug interaction. The LD50 for metformin in mice via ip administration is 247 mg/kg (they gave them 250 mg/kg) and for CQ it is 66 mg/kg (they gave them 60 mg/kg). These doses are not equivalent to the therapeutic doses that humans use (5 mg/kg) that it shouldn’t come as a surprise that these mice died.

And guess what? Just a few months before coronavirus outbreak hydroxychloroquine did show an excellent results just for prophylaxis of diabetes - actually much better than many super-duper modern (and expensive) drugs, like Canagliflozinfrom SGLT2 group of antidiabetics. So I wouldn't definitely take hydroxychloroquine interaction with metformin way too seriously. After all, we have fifty years experience with hydroxychloroquine and nobody still raised connection of adverse effects with diabetes lethality the less, whereas world is full of diabetes - which speaks for something.

On 2020-04-07 06:37:09, user Kerstin Brand wrote:

Metformin is given orally for good reason, the plasma concentration is never reaching the immense peaks you create with the intraperitoneal application. Sorry, your experiment was unfurtunately not planned well, and the data is therefore only applicable for patients taking metformin intraperitoneally.

On 2020-04-06 23:36:23, user itellu3times wrote:

Thank you for the warning, millions of people on metformin, which is otherwise considered one of the safest drugs with few interaction problems.

On 2020-04-06 19:53:41, user Valerie wrote:

Thank you for sharing your research! Have you researched (or have plans to) how CQ/HCQ react with other diabetes drugs apart from Metformin (e.g., Glimepiride, Vildagliptin, etc.)?

Diabetics are anyway at higher risk of complications from Covid-19, so it's concerning if CQ/HCQ which have shown some early signs of working cannot be used for that patient group.

On 2020-04-06 15:17:40, user Johnathon wrote:

This is concerning but the numbers look off. HCQ treatment for Covid-19 lasts about 6 days. This study says they gave it to the mice for 4 weeks. Secondly the amount of HCQ they gave the mice is 60mg/kg. HCQ dosage for a male of 80kg is about 400mg/day which is about 5mg/kg or 12 times less. Similarly the amount of metformin they gave to the mice is 250mg/kg, the dosage of metformin for said male is about 1000mg/day which is about 12mg/kg or 20 times less. In both cases the amount of medication they gave the mice is way more than a human would take and they gave it to the mice for a lot longer (4 times longer) than a covid-19 treatment would last for. I agree further study is needed and that you can't always conclude something about humans from studying mice but these numbers in the mice are much greater than the amount of medication a human would be treated with and for a much longer period of time.

On 2020-04-06 00:37:55, user Jimmy Chang wrote:

At what kind of concentrations in blood?<br /> Are you sponsored by any pharma?

On 2020-04-05 13:51:05, user Abdeljabar wrote:

CQ and HCQ are used for many years ... without toxicity in the treated patients with malaria or lupus ...<br /> The combination that need to be discussed and verified is CQ/HCQ with pneumonia antibiotic instead of confusing with metformin <br /> The molecular mechanism how CQ/HCQ Inhibit RNA polymerase via Zinc to stop virus réplication support the used of CQ/HCQ combined to pneumonia antibiotic ... Pr Raoul from France prouve that ....

On 2020-04-11 14:16:05, user Sinai Immunol Review Project wrote:

Title: Inhibition of SARS-CoV-2 infection (previously 2019-nCoV) by a highly potent pan-coronavirus fusion inhibitor targeting its spike protein that harbors a high capacity to mediate membrane fusion

Keywords: SARS-CoV2, S protein, fusion inhibitor, hACE2

Main findings:<br /> Members of the coronavirus family rely on fusion with the host’s cell membrane during infection. The spike (S) protein plays an essential role in this step (1). This work sheds light on the particularities of the S protein from SARS-CoV2 and compares it to previously existing coronaviral S proteins. SARS-CoV2 S protein showed stronger affinity for hACE2 compared to SARS-CoV. Previous work by this group had established the potential of a previous version of the peptide (EK1) in reducing the infectivity of several coronavirus by blocking the S protein interactions with the host cell (2). In this work, authors present an improved EK1C4 version of this peptide. In vitro experiments on human cells showed EK1C4-mediated reduction of the infectivity of SARS-CoV-2 and other coronavirus. Administration of EK1C4 in prophylaxis or shortly after exposure to the HCoV-OC43 coronavirus protected newborn mice from virus-induced lethality.

Limitations:<br /> This work suggests that the furin cleavage site from CoV2-S could be related to its increased infectivity compared to CoV-S. However, a previous report suggested that the enhanced cell-to-cell fusion mediated by this furin cleavage might not necessarily translate into enhanced viral infectivity (3). The mutation of this site, or inhibition of furin in 293T cells expressing SARS-CoV or CoV-2 S proteins, could inform if this mechanism is actually involved in SARS-CoV2 pathogenesis, and potentially suggest additional routes for therapeutic intervention. <br /> The in vivo model used to address the protective potential of EK1C4 is based on the coronavirus HCoV-OC43. Further work should aim to establish the potential of EK1C4 for prevention of SARS-CoV-2 replication in more relevant animal models (e.g. Rhesus Macaques or hACE2 mice). It is, however, acknowledged, that experimentation in vitro included both SARS-CoV2 and the same HCoV-OC43 used in the in vivo infection model.<br /> Even though the safety of the unmodified EK1 peptide in mice had been previously examined (2), the potential side effects and pharmacokinetics/dynamics of EK1C4 in the in vivo setting are not addressed in this report.

Relevance:<br /> The development of prophylactic drugs against viral infection is a paramount element of epidemic control, specially in the time before a vaccine can be readily available. Similar strategies have been followed in the past in response to threats of this nature (4,5). This work’s relevance is limited by the absence of relevant animal models of SARS-CoV-2 infection. Further pharmacological analyses would also be necessary to ensure the safety of EK1C4 in animal models before a potential safety analysis in human subjects.

1. Heald-Sargent T, Gallagher T. Ready, set, fuse! the coronavirus spike protein and acquisition of fusion competence. Vol. 4, Viruses. Multidisciplinary Digital Publishing Institute (MDPI); 2012. p. 557–80.
2. Xia S, Yan L, Xu W, Agrawal AS, Algaissi A, Tseng CTK, et al. A pan-coronavirus fusion inhibitor targeting the HR1 domain of human coronavirus spike. Sci Adv. 2019 Apr 1;5(4):eaav4580.
3. Follis KE, York J, Nunberg JH. Furin cleavage of the SARS coronavirus spike glycoprotein enhances cell-cell fusion but does not affect virion entry. Virology. 2006 Jul 5;350(2):358–69.
4. Jones JC, Turpin EA, Bultmann H, Brandt CR, Schultz-Cherry S. Inhibition of Influenza Virus Infection by a Novel Antiviral Peptide That Targets Viral Attachment to Cells. J Virol. 2006 Dec 15;80(24):11960–7.
5. Ahmed A, Siman-Tov G, Hall G, Bhalla N, Narayanan A. Human antimicrobial peptides as therapeutics for viral infections. Vol. 11, Viruses. MDPI AG; 2019.

On 2020-04-11 13:12:49, user Sergey Morozov wrote:

The paper is based on original well-planned study results of in-vitro anti-viral (SARS-CoV-2) activity of a derivative of 5-fluorouracil, Carmofur, an FDA-approved antineoplastic drug. The data are very actual and may impact clinical practice in case the results are confirmed in animal models and in RCTs. Besides antiviral activity shown by the authors, known anti-metabolic effects of 5-fluorouracil and its derivates may also be of help in severe COVID-19 cases to decrease inflammatory activity. The study seems to be methodologically correct. The text requires minor language polishing.

On 2020-04-11 09:40:27, user drc007 wrote:

Just checked PDB and found no record for 7BUY.

On 2020-04-11 10:26:37, user Jubin Rodriguez wrote:

This paper seems to be recommending the use of amantadine etc. for direct COVID-19 treatment without first testing it out in human RCTs. This is irresponsible. A important point that needs to be discussed is why these drugs have not been used so far in treatment protocols relating to SARS or other coronavirus infections?

On 2020-04-10 16:28:45, user Jubin Rodriguez wrote:

Also, amantadine has been shown to be ineffective against SARS-CoV-1 by this study: https://wwwnc.cdc.gov/eid/a...

On 2020-04-10 16:05:48, user Jubin Rodriguez wrote:

I find it odd that coronavirus sequences from bat RaTG13 and pangolin have not been included here despite them being the closest (known) phylogenetic relatives to SARS-CoV-2

On 2020-04-11 01:56:22, user Thomas N Seyfried wrote:

The authors present interesting information on a possible role of hydrogen sulfide suppression in GBM. The authors found that a HFD fed ad libitum suppressed hydrogen sulfide and enhanced body weight and GBM growth in mice. It is not clear, however, if the authors used a HFD or a KD for their analysis. I was unable to find information on the composition and content of fats, proteins, and carbohydrates in their HFD. We previously published papers showing that unrestricted feeding of the high fat ketogenic diet enhanced growth, inflammation, and angiogenesis in the CT-2A tumor. (DOI:10.1038/sj.bjc.6601269; DOI:10.1186/1743-7075-4-5). The CT-2A tumor was one of the tumors used in the author’s study. The CT-2A tumor cells express markers of neural stem cells and are growth inhibited by calorie restriction and calorie restricted ketogenic diets when grown in vivo (doi:10.1042/AN20100002; DOI 10.1002/ijc.23492). The authors referenced our recent paper (#46) where we stated that unrestricted feeding of ketogenic diets was not included in our study, as consumption of high-fat ketogenic diets fed in unrestricted amounts can cause insulin insensitivity and weight gain” (https://doi.org/10.1186/174... https://doi.org/10.1038/s42.... The author’s findings support this previous research showing that the unrestricted feeding of their HFD accelerates GBM growth. Previous research showed that HFD-induced CT-2A growth acceleration was linked directly to levels of blood glucose: the higher the blood glucose, the faster the growth. There was no information presented in the author’s manuscript on blood glucose measurements in the mice fed the HFD. Many studies have shown that it is hyperglycemia, not obesity by itself, that accelerates GBM growth in humans (http://dx.doi.org/10.1016/j... DOI: 10.1200/JCO.2008.19.1098; DOI 10.1007/s00066-014-0696-z; https://doi.org/10.1227/01.... DOI 10.1007/s11864-015-0356-2; https://doi.org/10.1038/s42.... Glucose is the major driver of the Warburg effect that is expressed in all GBM. Consequently, it becomes difficult to interpret the author’s findings without additional information. The high-fructose syrup obesity diet also enhances intestinal tumor growth in mice through multiple mechanisms that should also be discussed (DOI: 10.1126/science.aat8515).<br /> Thomas N. Seyfried<br /> Boston College

On 2020-04-10 17:37:28, user Sinai Immunol Review Project wrote:

Key Findings<br /> The authors of this study describe a common respiratory viral antigens microarray that can be used to assess the cross-reactivity of antibodies against the novel SARS-CoV-2 antigens and common human coronaviruses. Sixty-seven (67) antigens from across subtypes of coronaviruses, influenza viruses, adenoviruses, respiratory syncytial viruses and other viruses, were printed onto microarrays, probed with human sera, and analyzed. Five (5) serum samples collected before the SARS-CoV-2 outbreak were tested as part of a study that monitored acute respiratory infection cases. Four out of five (4/5) sera samples showed high IgG seroreactivity across the 4 common human coronaviruses. All sera showed low IgG seroreactivity to SARS-CoV-2.

The S1 domain of the spike protein is suggested to be subtype specific as it demonstrated very low cross-reactivity among the novel coronaviruses (SARS-CoV-2, SARS-CoV and MERS-CoV) and between the novel coronal viruses and common human coronaviruses. In contrast, the S2 domain of the spike protein and the nucleocapsid (NP) protein showed low levels of cross-reactivity between the coronavirus subtypes, suggesting the presence of more conserved antigenic domains. Therefore, the authors conclude that the S1 domain is an ideal candidate for virus-specific serologic assays.

Importance<br /> This study provides insights into the potential cross-reactivity of common human coronavirus antibodies for SARS-CoV-2 antigens. Understanding cross-reactivity is useful because it can help in the development of sensitive and specific serological assays that can test for COVID-19. Additionally, studies such as this can help inform vaccine development and indicate which antigens would be optimal to target.

Limitations<br /> This study is limited by a small sample size (n=5) of serum samples that all came from students who were part of the same college resident community at The University of Maryland. A greatest limitation is the lack of serum samples from COVID-19 patients. Additionally, the MFI of 2019-nCov from these naïve samples (in particular sera 4) is not zero. This indicates that while relatively low, there is some cross-reactivity of common human coronavirus antibodies for SARS-CoV-2 antigens, including the S1 domain. A larger sample size and samples from SARS-CoV2 infected patients may be needed to confirm the sensitivity and specificity of the assay.

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

On 2020-04-10 13:24:08, user Sami S wrote:

I just read the paper: while the analysis is fine, they have used only one strain of sequenced genome from “India” and compared it to one each from US and other places. It is ignoring the fact that the virus is spreading from people traveling from many different places so it’s completely biased and shouldn’t be taken as any sign of “lower severity”.

On 2020-04-05 15:08:13, user Shuvechha Chakraborty wrote:

In the paper it is mentioned that miR-27b targets the spike glycoprotein region which harbours the mutation A930V (24351C>T). The sequence of miR-27b that targets this region is different than what is found in the updated miRBase database. Is there any reason this sequence is used rather than the updated one?

On 2020-04-10 12:43:19, user Dave O'Connor wrote:

In our hands, swabs collected into VTM have an RT-LAMP sensitivity of between 10^3-10^4 copies / µL (https://openresearch.labkey... which operationally misses about 30% of clinical samples that have also tested by qRT-PCR. There simply isn't any RT-LAMP product generated below this threshold. The approach in this paper will be really useful if this sensitivity issue can be addressed, but is an important caveat until sensitivity is improved.

On 2020-04-08 17:31:52, user Mikolaj Slabicki wrote:

NOTE: This protocol has not been validated with clinical samples. To facilitate collaborations<br /> with interested parties to jointly advance the fight against the current coronavirus pandemic, we have set up a public forum on www.LAMP-Seq.org.

On 2020-04-10 12:05:50, user Guest wrote:

Are the sequences of antibodies reported in this paper (Table 1) available somewhere or upon request?

On 2020-04-10 11:22:42, user Eric Xu wrote:

We have deposited both structures and maps to PDB and EMDB but the process will take a few days. As such, we have submitted a revision today, mainly to include pdb files and map files for both structures, as supplemental materials associated with this BioRxiv submission. In this case, we hope everyone can download the structure and map as soon as they want. Hopefully, the revision will be released soon, by the end of today.

On 2020-04-10 04:03:52, user Patrick Sexton wrote:

Just as a minor point of note, our manuscript (your reference 20; full reference https://doi.org/10.1021/acs... was the first to apply this analysis to GPCRs. In our study, the method allowed us to understand the differences in conformational dynamics between adrenomedullin 1 (AM1) and adrenomedullin 2 (AM2) receptors that is critical for the distinct observed receptor phenotypes. It also revealed co-ordinated motions of the ECD and G protein. We are currently using this method to extensively to study many GPCR complexes.<br /> It works best on high quality, high resolution data.

On 2020-04-10 03:06:23, user Sinai Immunol Review Project wrote:

Summary/Main findings: <br /> Lon et al. used a bioinformatic analysis of the published SARS-CoV-2 genomes in order to identify conserved linear and conformational B cell epitopes found on the spike (S), envelope (E), and membrane (M) proteins. The characterization of the surface proteins in this study began with an assessment of the peptide sequences in order to identify hydrophilicity indices and protein instability indices using the Port-Param tool in ExPASy. All three surface proteins were calculated to have an instability score under 40 indicating that they were stable. Linear epitopes were identified on the basis of surface probability and antigenicity, excluding regions of glycosylation. Using BepiPred 2.0 (with a cutoff value of 0.35) and ABCpred (with a cutoff value of 0.51), 4 linear B cell epitopes were predicted for the S protein, 1 epitope for the E protein, and 1 epitope for the M protein. For structural analysis, SARS-CoV assemblies published in the Protein Data Bank (PDB) acting as scaffolds for the SARS-CoV-2 S and E amino acid sequences were used for input into the SWISS-MODEL server in order to generate three-dimensional structural models for the assessment of conformational epitopes. Using Ellipro (cutoff value of 0.063) and SEPPA (cutoff value of 0.5), 1 conformational epitope was identified for the S protein and 1 epitope was identified for the E protein, both of which are accessible on the surface of the virus. Finally, the Consurf Server was used to assess the conservation of these epitopes. All epitopes were conserved across the published SARS-CoV-2 genomes and one epitope of the spike protein was predicted to be the most stable across coronavirus phylogeny.

Critical Analysis/Limitations:<br /> While this study provides a preliminary identification of potential linear and conformational B cell epitopes, the translational value of the epitopes described still needs extensive experimental validation to ascertain whether these elicit a humoral immune response. The conformational epitope analyses are also limited by the fact that they are based off of predicted 3D structure from homology comparisons and not direct crystal structures of the proteins themselves. Additionally, since there was not a published M protein with a high homology to SARS-CoV-2, no conformational epitopes were assessed for this protein. Finally, while evolutionary conservation is an important consideration in understanding the biology of the virus, conservation does not necessarily imply that these sites neutralize the virus or aid in non-neutralizing in vivo protection.

Relevance/Implications:<br /> With further experimental validation that confirms that these epitopes induce effective antibody responses to the virus, the epitopes described can be used for the development of treatments and vaccines as well as better characterize the viral structure to more deeply understand pathogenesis.

On 2020-04-10 03:01:43, user Xin Wang wrote:

Our paper has already been published online https://www.cell.com/iscien...

On 2020-04-09 23:44:12, user Sinai Immunol Review Project wrote:

Title: <br /> Susceptibility of ferrets, cats, dogs and different domestic animals to SARS-coronavirus-2<br /> The main finding of the article: <br /> This study evaluated the susceptibility of different model laboratory animals (ferrets), as well as companion (cats and dogs), and domestic animals (pigs, chickens and ducks) to SARS-CoV-2. They tested infection with two SARS-CoV2 isolates, one from an environmental sample collected in the Huanan Seafood Market in Wuhan (F13-E) and the other from a human patient in Wuhan (CTan-H). <br /> Ferrets were inoculated with either of the two viruses by intranasal route with 105 pfu, and the viral replication was evaluated. Two ferrets from each group were euthanized on day 4 post infection (p.i.). AT day 4 p.i., viral RNA and infectious viruses were detected only in upper respiratory tract (nasal turbinate, upper palate, tonisls, but not in the trachea, lungs or other tissues. Viral RNA and virus titer in the remaining ferrets were monitored in nasal washes and rectal swabs on days 2, 4, 6, 8 and 10 p.i. Viral RNA and infectious viruses were detected in nasal washes until day 8 p.i. One ferret in each group developed fever and loss of appetite on days 10 and 12 p.i., however, viral RNA was practically undetactable. These two ferrets showed severe lymphoplasmacytic perivasculitis and vasculitis in the lungs and lower antibody titers compare to other 4 ferrets. <br /> Cats. Five subadult 8-month-old domestic cats were inoculated with CTan-h virus and three uninfected cats were placed in a cage adjacent to each of the infected cats to monitor respiratory droplet transmission. Viral RNA was detected in the upper respiratory organs from all infected cats and in one out of three exposed cats. All infected (inoculated and exposed) cats developed elevated antibodies against SARS-CoV2. Viral replication studies with juvenile cats (70-100 days) revealed massive lesions in the nasal and tracheal mucosa epithelium and lungs of two inoculated cats which died or were euthanized on day 3 p.i., and infection in one out of three exposed cats. These results indicated SARS-CoV2 could replicate in cats, that juvenile cats were more susceptible that adults, and theat SARS-CoV2 could be transmit via respiratory droplets between cats. <br /> Dogs and others. Five 3-month-old beagle dogs were inoculated and housed with two uninoculated beagles in a room. Two virus inoculated dogs seroconverted, but others including two contact dogs were all seronegative for SARS-CoV2 and infectious virus was not detected in any swabs collected. Viral RNA was not detected in swabs from pigs, chickens, and ducks inoculated or contacted. These results indicated that dogs, pigs, chickens, and ducks might have low or no susceptibility to SARS-CoV2.<br /> Critical analysis of the study: <br /> This manuscript describes the viral replication and clinical symptoms of SARS-CoV2 infection in ferrets, and the SARS-CoV2 infection and transmission in cats. Clinical and pathological analysis was not performed in cats, therefore the correlation of virus titer with symptoms severity in the adult and juvenile cats could not be determined. <br /> The importance and implications for the current epidemics:<br /> SARS-CoV-2 transmission to tigers, cats and dogs has been previously reported. It should be noted that this manuscript did not evaluate the transmission from cats to human. Nevertheless, it clearly showed higher susceptibility of ferrets and domestic cats to SARS-CoV-2. This data strongly indicates the need for surveillance of possible infection and transmission of SARS-CoV-2 by domestic cats.

On 2020-04-04 13:24:25, user Jade Hawke wrote:

Cats aren't spreading Covid-19 to humans, but they can catch it from you, and give it to other cats. There is no evidence it will go from cat to human. Please don't harm your cat, or cats you see roaming.

On 2020-04-03 23:35:32, user David E. Shellenberger wrote:

The popular media are misconstruing the study. For an intelligent discussion of the research, see this piece:

"Coronavirus can infect cats — dogs, not so much:<br /> But scientists say it’s unclear whether felines can spread the virus to people, so pet owners need not panic yet."<br /> https://www.nature.com/arti...

"There is no direct evidence that the infected cats secreted enough coronavirus to pass it on to people, she says."

"Saif says that none of the infected cats showed symptoms of illness, and that only one out of the three felines exposed to infected animals caught the virus."

On 2020-04-09 23:40:33, user Sinai Immunol Review Project wrote:

Title: <br /> SARS-CoV-2 proteome microarray for mapping COVID-19 antibody interactions at amino acid resolution<br /> The main finding of the article: <br /> This study screened the viral protein epitopes recognized by antibodies in the serum of 10 COVID-19 patients using a new SARS-CoV-2 proteome peptide microarray. The peptide library was constructed with 966 linear peptides, each 15 amino acids long with a 5 amino acid overlap, based on the protein sequences encoded by the genome of the Wuhan-Hu-1 strain. <br /> To investigate crossreactivity between SARS-CoV-1 and SARS-CoV-2, they tested rabbit monoclonal and polyclonal antibodies against SARS-CoV-1 nucleocapsid (N) in the microarray. Antibodies against SARS-CoV-1 N displayed binding to the SARS-CoV-2 nucleocapsid (N) peptides. Polyclonal antibodies showed some crossreactivity to other epitopes from membrane (M), spike (S), ORF1ab and ORF8. This suggests that previous exposure to SARS-CoV-1 may induced antibodies recognizing both viruses. <br /> Screening of IgM and IgG antibodies from 10 COVID-19 patients showed that many antibodies targeted peptides on M, N, S, Orf1ab, Orf3a, Orf7a, and Orf8 from SARS-CoV-2, while immunodominant epitopes with antibodies in more than 80 % COVID-19 patients were present in N, S and Orf3. It is shown that the receptor binding domain (RBD) resides on S protein and RBD is important for SARS-CoV-2 to enter the host cells via ACE2. Among six epitopes on S protein, structural analysis predicted that three epitopes were located at the surface and three epitopes were located inside of the protein. Furthermore, some IgM antibodies from 1 patient and IgG antibodies from 2 patients bound to the same epitope (residue 456-460, FRKSN) which resided within the RBD, and structural analysis determined that this epitope was located in the region of the RBD loop that engages with ACE2.<br /> Critical analysis of the study: <br /> In addition to the limitations mentioned in the manuscript, it would have been informative to do the analysis over the course of the disease. The pattern of antibody recognition, especially on S protein, and the course of antibodies of different isotypes recognizing the same peptide might correlate to the clinical course in these patients. It would alos have been informative to analyze the presence of cross-reactive antibodies from pateints previously exposed to SARS-CoV-1. <br /> The importance and implications for the current epidemics:<br /> This study identified linear immunodominant epitopes on SARS-CoV-2, Wuhan-Hu-1 strain. This is a valuable information to design vaccines that will elicit desirable immune responses.

On 2020-04-09 22:21:59, user Maxence Nachury wrote:

Thanks Ed and Tamara! Swapnil and I are excited to get feedback. Great questions for future work, we can't wait to get back to the bench to address them.

On 2020-04-08 19:10:49, user GiganteD wrote:

The Caspary lab did a virtual journal club of this pre-print. We would like to share our mock review. We hope it invites further discussion of this paper and its interesting model.

Here, the authors are looking at possible mechanisms for GPCR ciliary exit; may be receptor-dependent, may be context-dependent – there’s no *one way* out of cilia, but the authors address one potential mechanism = ubiquitination

Comments:<br /> 1. What is the impact of mutating lysine residues to arginine in the GPCRs? Do these substitutions influence protein function/activity?

1. Would the inclusion of SAG in the experiment of Figure 3G cause an even greater ciliary localization of Smo. Perhaps a syngergistic or additive effect. Alternatively, if there is no change, it would show that just blocking ubiquitination is sufficient for Smo ciliary enrichment.

2. Perhaps future experiments would involve the targeting of ubiquitin ligase to increase ubiquitination of GPCRs, possibly in the absence of ligand stimulation.

3. Figure 4 and its conclusion seemed a little suggestive, assumptions about kinetics leading to a possible mechanism; unclear if it’s strong or direct enough to be conclusive at this point. Moreover, the authors talk about BBS5 being in the cilium by 10 minutes, but the 10min timepoint in 4C seems to be an outlier.

4. Is it thought that only when the cilium is saturated with beta-arrestin or reached a certain threshold can its function be achieved? How could beta-arrestin sense how much other beta-arrestin is around?

5. In Figure 4D it would have been interesting to continue looking at Smo after it reached maximal ciliary fluorescence at 20 min, at least for the length of the experiment, to see if it started to go back down, possibly indicating the removal of Smo from cilia after pathway activation. Perhaps that is a ubiquitin mediated process.

6. The early conclusion SAG induced increases in cilia ubiquitin was mediated by GPR161 was confusing, given that SAG is a SMO ligand. However, this conclusion is nicely supported by Figure 3D.

Overall, these data are well combined with the recent pre-print from the Pazour lab (Desai et al 2019 bioRxiv, in review) has very nicely shown that Smo is ubiquitinated prior to being activated and if you disrupt that, Smo stays in the cilium and doesn’t get removed. Together, Smo as a GPCR just functions differently in relation to ubiquitination and ciliary enrichment.

Remaining questions after this manuscript:<br /> • How does the BBSome recognize ubiquitinated-GPCRs? Is it direct via ubiquitin, a cytoplasmic determinant on the GPCR, some combination of these, or indirect via an unknown intermediary?<br /> • Do UbK63 and beta-arrestin act differently in cilia compared to their functions in/at the plasma membrane?<br /> • Is ciliary exit directly coupled to degradative sorting? Can activated/ubiquitinated GPCRs removed from the cilium be “reset” and reused?<br /> • What route is taken by Ub-GPCRs out of cilia: endocytosis, diffusion in plasma membrane, other?<br /> • How are “unwanted” proteins that accidentally get into cilia recognized and removed – do they also get ubiquitinated?

On 2020-04-09 20:02:29, user Timokratis KARAMITROS wrote:

We thank the reader for the feedback. We believe that the attempted validation of our results has several methodological issues, such as the construction of wrong psudogenomes. The isolated phenomena in this work are well supported by a high number of independent, non-duplicated, high-quality reads. Sequencing artefacts, no matter how "strange" they are, cannot happen multiple times in the same position and -at least in one case- in two biological replicates. Since this manuscript is under review, we will provide further details on our methods in the revised version. Any further clarifications can be provided via direct communication with the corresponding author, as usual.

On 2020-04-03 01:37:22, user Alex Crits-Christoph wrote:

I thank the authors for submitting this work - I think intrapatient data and variation can help us understand viral evolutionary dynamics and it is essential to start this work on this sars-cov-2 virus.

However, I have analyzed the data for the proposed recombination events and found that it is likely a sequencing artifact. I think it is important to consider paired read information - if these reads were from true viral genome sequences, they should be connecting to read pairs flanking the proposed inversion site. However, they uniformly either do not have pairs, are in the reverse orientation, or are precisely overlapping with the pairs. While this is a strange sequencing artifact, it does not seem like these reads were from recombined viral genomes as proposed. <br /> Analysis available here:

https://github.com/alexcrit...

On 2020-04-09 18:53:07, user Janine Brunner wrote:

The work and additional results are now published in eLife and accessible under <br /> https://doi.org/10.7554/eLi...

New affiliations:

Janine Brunner<br /> VIB-VUB Center for Structural Biology<br /> Pleinlaan 2<br /> 1050 Brussels<br /> Belgium <br /> janine.brunner@vub.be

Stephan Schenck<br /> VIBVIB-VUB Center for Structural Biology<br /> Pleinlaan 2<br /> 1050 Brussels<br /> Belgium<br /> stephan.schenck@vub.be

On 2020-04-09 18:24:10, user Pablo Iglesias wrote:

Nice work. You might want to check the following:<br /> http://www.biomedcentral.co...<br /> It showed, experimentally, that in Dictyostelium cells, the actin-crosslinking protein Dynacortin contributes to cell polarization during chemotaxis. By cross-linking and possibly stabilizing actin polymers, dynacortin also contributes to cortical viscoelasticity, which may be critical for establishing cell polarity.

On 2020-04-09 15:16:59, user Sinai Immunol Review Project wrote:

Main Findings:<br /> Human ACE2 was previously identified as the host receptor for SARS-CoV-2. Despite ACE2 being expressed in both lung alveolar epithelial cells and small intestine enterocytes, respiratory problems are the most common symptom after viral infection while intestinal symptoms are much less frequent. Thus, the authors here investigate the biology behind the observed protection of the intestinal epithelium from SARS-CoV-2. Human defensin 5 (HD5), produced by Paneth cells in the small intestine, was shown to interact with human ACE2, with a binding affinity of 39.3 nM by biolayer interferometry (BLI). A blocking experiment using different doses of HD5 coating ACE2 showed that HD5 lowered viral spike protein S1 binding to ACE2. Further, a molecular dynamic simulation demonstrated a strong intermolecular interaction between HD5 and the ACE2 ligand binding domain. To test HD5 inhibitory effect on S1 binding to ACE2, human intestinal epithelium Caco-2 cells were preincubated with HD5. Preincubation strongly reduced adherence of S1 to surface of cells. HD5 was effective at a concentration as low as 10 µg/mL, comparable to the concentration found in the intestinal fluid.

Limitations:<br /> The study focuses exclusively on intestinal cells. However, HD5 could have been tested to block ACE2-S1 binding in human lung epithelial cells as a potential treatment strategy. It would be useful to know whether HD5 could also prevent viral entry in lung cells.

Relevance:<br /> This work provides the first understanding of the different efficiency of viral entry and infection among ACE2-expressing cells and tissues. Specifically, the authors show that human defensin 5 produced in the small intestine is able to block binding between S1 and ACE2 necessary for viral entry into cells. The study provides a plausible explanation on why few patients show intestinal symptoms and suggests that patients with intestinal disease that decrease defensins’ production may be more susceptible to SARS-CoV-2. It also indicates that HD5 could be used as a molecule to be exogenously administered to patients to prevent viral infection in lung epithelial cells.

Review by Erica Dalla as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

On 2020-04-09 14:50:15, user Seth Blackshaw wrote:

Another major problem are the numbers of putative Muller-derived cones shown here. There seem to be more cones than Muller glia. What is the evidence that Muller glia are proliferating to give rise to cones? If Muller glia are transdifferentiating to cones, and depleting the number of glia in the process, how does the retina not just fall apart?

On 2020-04-09 14:40:05, user Seth Blackshaw wrote:

Two big problems here. First, GFAP minipromoters are not reliable tools for conducting lineage analysis. This needs to be done by labeling Muller glia prior to infection, preferably by using an inducible, cell-specific Cre trangene. Second, no evidence for the existence of cells that are in a transitional state between Muller glia and neurons is included. This is readily done using scRNA-Seq.

On 2020-04-09 07:00:32, user Joachim Goedhart wrote:

In this manuscript, a circular permutated GFP (cpGFP) was inserted into the G protein-coupled receptor GPR68 to be able to monitor its activity. A similar strategy has previously been used to generate probes for GPCR activity. The activation of GPR68 results in a fluorescence increase in the cpGFP. Among the activating stimuli that were used are fluid shear stress and extracellular acidification.

One word of caution is related to the pH sensitivity of cpGFP, since the fluorescence intensity of the cpGFP itself is sensitive to pH. The pKa of cpGFP is generally in the physiological range, unlike the pKa of GFP which is below this range. Moreover, the pKa of the different states (activated/deactivated) can be different. For a detailed study on the effect of pH see: https://doi.org/10.1371/jou...

Although iGlow could be a magnificent probe for detecting GPR68 activity, the effect of intracellular pH on the fluorescence intensity of the probe needs to be carefully examined.

On 2020-04-09 03:03:20, user Jaworski JP wrote:

Bruce and coleagues have performed a very good work in order to sort a botleneck step that is related to the nucleic acid extraction procedure for the molecular detection of SARS-2-CoV, the causative agent of COVID-19 pandemic. They tested 150 clinical samples from patients infected with SARS-2-CoV (samples previously with defined as positive by CDC RT-qPCR) in paralel, with and without nucleic acid extraction step previous to the RT-qPCR. They found that in their hands 143/ 150 samples tested positive when the extraction was performed according to WHO recommendations. Interistingly, 138/ 150 tested positive when RT-qPCR was run directly from preheated (95C) samples (NPswabs). The high sensitivity observed for the DIRECT RT-qPCR suggests it could be used as a screening test in order to save time and resources (specially scarce reagents).<br /> COMMENTS<br /> 1) page 4, second conclusion of testing clinical samples section: the authors state that RNA extraction did not augment diagnostic sensitivity; however, if author consider best conditions for both proceducers, the RTqPCR using extracted RNA from non pre-heated samples detected 143/150, compared to 138/150 using the directed RT-qPCR. So, keeping optimal conditions for each procedure (it is non pre-heat for RTqPCR with extraction and pre-heat for direct RTqPCR) the extraction procedure augments diagnostic sensitivity (considering equivalent input). This is also in agreement with the higher CTs observed for individual samples tested by DIRECT RTqPCR during the factibility and setting up steps (preliminary estimation of analytical sensitivity?). <br /> 2) It would be great if the authors could add data to better describe the performance of the direct RTqPCR, specially its robustness (repeatibility and reproducibility) analytical sensitivity and specificity. More importantly, diagnostic specificity.

On 2020-04-08 19:18:30, user Sinai Immunol Review Project wrote:

Summary of Findings:<br /> -The authors utilize homology modeling to identify peptides from the SARS-CoV-2 proteome that potentially bind HLA-A*02:01.<br /> -They utilize high-resolution X-ray structures of peptide/MHC complexes on Protein<br /> Data Bank, substitute homologous peptides with SARS-CoV-2 peptides, and calculate MHC/SARS-CoV-2 peptide Rosetta binding energy.<br /> -They select MHC/SARS-CoV-2 complex models with highest binding energy for further study and publish models in an online database (https://rosettamhc.chemistr...).

Limitations:<br /> -The authors only utilize computational methods and predicted SARS-CoV-2 peptides must be validated experimentally for immunogenicity and clinical response.<br /> -Due to computational burden and limited availability of high resolution X-ray structures on PDB, authors only simulate 9-mer and 10-mer peptide binding to HLA-A*02:01.<br /> -Since the authors compare select existing X-ray structures<br /> as a starting point, backbone conformations that deviate significantly between test and template peptides are not captured. Furthermore, Rosetta modeling protocols do not capture all possible structures and binding energy scoring does not fully recapitulate<br /> fundamental forces.(1,2)

Importance/Relevance:<br /> -The authors identify and publish high-scoring SARS-CoV-2 peptides that may direct<br /> a targeted, experimental validation approach toward a COVID-19 vaccine.<br /> -The authors utilize Rosetta simulation to further filter results from NetMHCpan 4.0,<br /> supporting machine learning prediction with structural analysis.<br /> -The authors develop RosettaMHC, a computationally efficient method of leveraging<br /> existing X-ray structures for identification of immunogenic peptides.

References:<br /> 1.Bender, B. J., Cisneros, A., 3rd, Duran, A. M., Finn, J. A., Fu, D., Lokits, A. D., . . . Moretti, R. (2016). Protocols for Molecular Modeling with Rosetta3 and RosettaScripts. Biochemistry, 55(34), 4748-4763. doi:10.1021/acs.biochem.6b00444<br /> 2.Alford, R. F., Leaver-Fay, A., Jeliazkov, J. R., O'Meara, M. J., DiMaio, F. P., Park, H., . . . Gray, J. J. (2017). The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design. J Chem Theory Comput, 13(6), 3031-3048. doi:10.1021/acs.jctc.7b00125

Review by Jonathan Chung as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

On 2020-04-08 19:06:39, user Zhenia Shauchuk wrote:

Hi, very interesting article! Briefly went through it and would like to ask about one aspect: could you control geometry of the actin-actin crosses while estimating MyoV % switching? From Fig. 3 and Fig. 4 it looks like the motor was traveling on the top filament and at the crossing other filament was bellow.

Sincerely

On 2020-04-08 16:36:30, user Trudy Oliver wrote:

Very happy to see this large and powerful resource for SCLC community!

I am admittedly biased, but a key reference not highlighted is that MYC was originally shown to correlate with non-NE fate, drive NEUROD1 expression in an in vivo model, and to promote unique drug sensitivities in vivo in Mollaoglu et al, Cancer Cell, 2017

https://doi.org/10.1016/J.C...

MYC Drives Progression of Small Cell Lung Cancer to a Variant Neuroendocrine Subtype with Vulnerability to Aurora… http://disq.us/t/2o9f2tr

On 2020-04-08 13:26:50, user Scott Nichols wrote:

Excellent findings! A quick questions and two comments from our lab meeting discussion:

Question -The integrin-blocking antibody was assayed for adhesion phenotypes on a fibronectin (Mammalian origin?) substrate. Does it also disrupt filopodial attachment to uncoated glass slides, on which Capsaspora is presumably attaching to its endogenously secreted substrate? Just wondering if Capsaspora integrins 'normally' adhere in the same way as animal integrins.

Comment 1. The vinculin staining is certainly punctate, but doesn't seem particularly co-localized with integrin in most places on the filopodia. It is possible that the vinculin that isn't co-localized with this integrin could be bound to attachments that use other integrins. Might be nice to show the co-localization quantitatively. Were there no co-precipitates in the IP's?

Comment 2. We were all very curious to see some low-exposure images to see what the integrin and vinculin staining looked like in the cell body. Any idea why there might be high levels in the cytosol of the cell body (or perhaps it is also membrane localized there?). Is it punctate or diffuse, localized with actin filaments?

Comment 3. "Vinculin" in Capsaspora is definitely a VIN-family member, but isn't a vinculin orthology. Vinculin, alpha-catenin, alpha-catenin-like and alpha-catulin proteins in animals are all more closely related to each other than any are to non-animal family members. It might be worth clarifying this distinction.

Anyway, really cool paper and finding. Just wanted to let you know the questions that came up.

On 2020-04-08 10:08:05, user MITSAKA DIMITRA wrote:

Amantadine is used by patients with PD .In countries with high<br /> COVID-19 mortality, a retrospective study of the amantadine group <br /> patients comparing to a non amantadine group wil be an in vivo <br /> experiment that will reveal the efficacy of amantadine to COVID-19

On 2020-04-08 06:25:28, user ChiaHsin Liu wrote:

Could you check the Supp.Table 5 ?<br /> The MHC-I coverage should be divided by 24 not 23 because I see 24 MHC-I you consider.<br /> Another question, you select "VAAIFYLITPVHVMS" of orf1ab according to the high coverage of MHC-I. So that I check the data and I find "AAIFYLITPVHVMSK", just 1 site shift and has identical class 1 core, should be in the same ranking. How you choose between them?

On 2020-04-08 05:18:36, user Alex Alexandrov wrote:

Great paper! Check out similar work in yeast with a different twist on the interpretation! https://www.ncbi.nlm.nih.go...

On 2020-04-08 02:22:00, user Al Grim wrote:

Nice work that contributes to the attribution toolkit. One observation for the Campylobacter exercise: Attributable sources often come with clusters of spatially/temporally auto-correlated bacterial genomes. It would be crucial to compare your results after removing these irrelevant, but rather influential auto-correlations (within batch/farm/region/country/year correlations).

On 2020-04-08 02:06:32, user Sinai Immunol Review Project wrote:

Title: Virus-host interactome and proteomic survey of PMBCs from COVID-19 patients reveal potential virulence factors influencing SARS-CoV-2 pathogenesis

Keywords: PBMC – virulence factors – interaction network – nsp9 – nsp10 – NKRF

Main findings:<br /> The authors identified intra-viral protein-protein interactions (PPI) with two different approaches: genome wide yeast-two hybrid (Y2H) and co-immunoprecipitation (co-IP). A total of 58 distinct PPI were characterized. A screen of viral-host PPI was also established by overexpressing all the SARS-CoV-2 genes with a Flag epitope into HEK293 cells and purifying each protein complex. Interacting host proteins were then identified by liquid chromatography and tandem mass spectrometry. 251 cellular proteins were identified, such as subunits of ATPase, 40S ribosomal proteins, T complex proteins and proteasome related proteins, for a total of 631 viral-host PPI. Several interactions suggesting protein-mediated modulation of the immune response were identified, highlighting the multiple ways SARS-CoV-2 might reprogram infected cells. <br /> Subsequently, the authors compared global proteome profiles of PBMCs from healthy donors (n=6) with PBMC from COVID-19 patients with mild (n=22) or severe (n=13) symptoms. 220 proteins were found to be differentially expressed between healthy donors and mild COVID-19 patients, and a pathway analysis showed a general activation of the innate immune response. 553 proteins were differentially expressed between the PBMC of mild and severe COVID-19 patients, most of them (95%) being downregulated in severe patients. Functional pathway analysis indicated a defect of T cell activation and function in severe COVID-19. There was also evidence suggesting reduced antibody secretion by B cells. Together, these results suggest a functional decline of adaptive immunity. A FACS analysis of PBMC from severe patients indicated higher levels of IL6 and IL8 but not IL17 compared to mild patients.<br /> Finally, the authors focused on NKRF, an endogenous repressor of IL8/IL6 synthesis that was previously identified as interacting with SARS-Cov-2 nsp9,10,12,13 and 15. Individually expressed nsp9 and nsp10 (but not nsp12, nsp13, nsp15) induced both IL6 and IL8 in lung epithelial A459 cells, indicating that nsp9 and nsp10 may be directly involved in the induction of these pro-inflammatory cytokines. The authors finally argue that nsp9 and nsp10 represent potential drug targets to prevent over-production of IL6 and IL8 in infected cells, and reducing the over-activation of neutrophils.

Limitations:<br /> First, the authors seem to have forgotten to include the extended data in the manuscript, and their proteomic data does not seem to be publicly available for the moment, which limits greatly our analysis of their results.<br /> While this work provides important data on host and viral PPI, only 19 interactions were identified by Y2H system but 52 with co-IP. The authors do not comment about what could lead to such differences between the two techniques and they don’t specify whether they detected the same interactions using the two techniques. <br /> Moreover, the PBMC protein quantification was performed comparing bulk PBMC. Consequently, protein differences likely reflect differences in cell populations rather than cell-intrinsic differences in protein expression. While this analysis is still interesting, a similar experiment performed on pre-sorted specific cell populations would allow measuring proteome dynamics at a higher resolution.<br /> Finally, the authors did not discussed their results in regards to another SARS-CoV-2 interactome of host-viral PPI that had been published previously [1]. This study reported 332 host-virus PPI, but no interaction of viral proteins with NKRF was found. Some interactions were found in both studies (eg. N and G3BP1, Orf6 and RAE1). However, the time point used to lyse the cells were different (40h previously vs 72h here), which could explain some of the differences.

Significance<br /> The identification of many interactions between intra-viral and host-virus PPI provides an overview of host protein and pathways that are modulated by SARS-CoV-2, which can lead to the identification of potential targets for drug development. <br /> In the model proposed by the authors, nsp9 and nsp10 from SARS-Cov-2 induce an over-expression of IL6 and IL8 by lung epithelial cells, which recruits neutrophils and could lead to an excess in lung infiltration. Nsp9 has been shown to be essential for viral replication for SARS-Cov-1 [2], and shares a 97% homology with nsp9 from SARS-Cov-2 [3]. Further, nsp9 crystal structure was recently solved [3], which can help to develop drug inhibitors if this protein is further confirmed as being important for the virulence of SARS-Cov-2.

1. Gordon DE, Jang GM, Bouhaddou M, et al. A SARS-CoV-2-Human Protein-Protein Interaction Map Reveals Drug Targets and Potential Drug-Repurposing. bioRxiv. March 2020:2020.03.22.002386. doi:10.1101/2020.03.22.002386
2. Miknis ZJ, Donaldson EF, Umland TC, Rimmer RA, Baric RS, Schultz LW. Severe acute respiratory syndrome coronavirus nsp9 dimerization is essential for efficient viral growth. J Virol. 2009;83(7):3007-3018. doi:10.1128/JVI.01505-08
3. Littler DR, Gully BS, Colson RN, Rossjohn J. Crystal Structure of the SARS-CoV-2 Non-Structural Protein 9, Nsp9. Molecular Biology; 2020. doi:10.1101/2020.03.28.013920

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

On 2020-04-03 12:14:32, user pedrobeltrao wrote:

Could you please share the list of interactions as a supplmentary file as well ? or provide a link in the comments section to a file containing this information?

On 2020-04-07 22:30:02, user Anita Bandrowski wrote:

You state:<br /> "Distribution of Materials: Mouse strains are available from MMRRC."<br /> Is there any way to know the RRID of the MMRRC mice? That would help to track them down easily.

On 2020-04-07 20:51:11, user Sinai Immunol Review Project wrote:

An orally bioavailable broad-spectrum antiviral inhibits SARS-CoV-2 and multiple 2 endemic, epidemic and bat coronavirus

Sheahan et al. 2020

Main Findings: β-D-N4 30 –hydroxycytidine (NHC, EIDD-1931) is an orally bioavailable ribonucleoside with antiviral activity against various RNA viruses including Ebola, Influenza and CoV. NHC activity introduced mutations in the viral (but not cellular) RNA in a dose dependent manner that directly correlated with a decrease in viral titers. Authors show that NHC inhibited multiple genetically distinct Bat-CoV viruses in human primary epithelial cells without affecting cell viability even at high concentrations (100 μM). Prophylactic oral administration of NHC in C57BL/6 mice reduce lung titers of SARS-CoV and prevented weight loss and hemorrhage. Therapeutic administration of NHC in C57BL/6 mice 12 hours post infected with SARS-CoV reduced acute lung injury, viral titer, and lung hemorrhage. The degree of clinical benefit was dependent on the time of treatment initiation post infection. The authors also demonstrate that NHC reduces MERS-CoV infection titers, pathogenesis, and viral RNA in prophylactic and therapeutic settings.

Caveats: Most of the experiments were conducted using MERS-CoV, and SARS-CoV and a few experiments were conducted using other strains of CoV as opposed to SARS-CoV-2. The authors note the core residues that make up the RNA interaction sites (which constitutes the NHC interaction sites) are highly conserved among CoV and because of this conservation their understanding is that NHC can inhibit a broad-spectrum of CoV including SARS-CoV-2.

In addition, the temporal diminishing effectiveness of NHC on clinical outcome when NHC was used therapeutically is concerning. However, the longer window (7-10 days) for clinical disease onset in human patients from the time of infection compared to that of mice (24-48 hours), may associate with increased NHC effectiveness in the clinic.

Importance: Prophylactic or therapeutic oral administration of NHC reduces lung titers and prevents acute lung failure in C57B\6 mice infected with CoV. Given its broad-spectrum antiviral activity, NHC could turn out to be a useful drug for treating current, emerging and future corona virus outbreaks.

By: Luisanna Pia and PhD, Konstantina Alexandropoulos

On 2020-04-07 20:22:52, user Rasmus Møller wrote:

Can you share the Data S1 excel sheet? It doesn't seem to appear under Supplementary Material. It is very interesting that you see an up regulation of IL-6 and other cytokines produced by the epithelium. I would have thought they were mostly expressed by infiltrating macrophages and T cells. I am very curious to see if you have CCL20 included in your panel as the CCL20-CCR6 axis is crucial in Th17 recruitment and several clinical findings show an influx of Th17 cells in severe COVID-19 cases.

On 2020-04-07 19:40:03, user Donna K. McCullough wrote:

Hello! A grad student journal club recently read your preprint and had a few questions about the article. The comments are attached below.

Summary:<br /> The authors seek to understand if an unusual mode of diatom movement (circular run-and-reversal) is an optimization or ‘normal’ run-and-tumble movement in other microbes. Using a Langevin mathematical model, the authors aim to show that this diatom movement is an evolutionary product of optimized diatom foraging strategies for silica. In this way, the authors aim to relate the evolution of microbial movement to the ecology of searching behavioral strategy through the connection of observed movements and modeled behavior based on optimized search strategy.

Comments:

Major:<br /> The figures seem to require more explanation than is given in the figure legends’ text. I would consider adding more exact information to point out all relevant points as well as review the acronyms utilized in the figures even if original acronyms are in the text.

The figure 3 of this paper (Son, K., Brumley, D. & Stocker, R. Live from under the lens: exploring microbial motility with dynamic imaging and microfluidics. Nat Rev Microbiol 13, 761–775 (2015). https://doi-org.proxy.lib.u... ) shows some very similar movement patterns to what you evaluate here. It may be of use to look at this.

Figure 1a would be a lot more understandable if the microorganisms in the right-most boxes were categorized in the figure so an easier comparison could be made between ‘normal’ microbial movement and the movement particular to the model organism of this study.

If the ‘circular run-and-reversal’ movement pattern in currently known literature supports the ‘searching behavioral strategy’, is this supporting a rule for ecology or an exception?

You mention a “tracing technique” (line 78), but you never clarify what tracing technique is actually used.

There was little mention of the dSi concentrations after the introduction. Were these dSi concentrations relevant for the organism? Was the organism actually starved for dSi in these concentrations? Was something else in the media limiting at certain parts of the experiment. More mention of the growing conditions and relevancy of those conditions would help with the evaluation of these results in an ecological context.

Minor:

A few typographical/grammatical errors occur in the script such as in line 21,120, 282, etc.

On 2020-04-07 18:19:25, user Rasmus Møller wrote:

Has a human RNA virus ever been shown to encode a miRNA? I'd be surprised if SARS-CoV-2 encodes 194.

On 2020-04-07 17:15:49, user Anita Bandrowski wrote:

Dear Authors,

We, the methods review project, have reviewed your methods section for reagent identifiably and for compliance with NIH rigor guidelines. <br /> The automated report can be found at the cos.org/

We found the you did not mention the following rigor guidelines: blinding, randomization, power analysis, or sex as a biological variable.

The follwowing cell line was used: <br /> 293T cell line: "293T cells were transfected with a mixture of luciferase reporter ( firefly luciferase)"<br /> To better identify this research resource please check the following RRID, and include this if it is correct:<br /> Suggestion: DSMZ Cat# ACC-635,<br /> RRID:CVCL_0063. n2t.net/RRID:CVCL_0063

On 2020-04-07 16:39:29, user Rasmus Møller wrote:

How much active replication do you get in your primary monocytes? What titers does SARS-CoV-2 reach in those cells?

On 2020-04-07 11:29:26, user Romain Koszul wrote:

Thanks @GauthierJeremy and JM Drezen for the nice collaboration and congrats for the paper.