On 2020-06-17 21:16:44, user Miao Sun wrote:
Just little note that, this version here in bioRxiv, may be a slightly different as it looks in style and format as the final version to AJB. It is because we have several version updates (during the peer review rounds) after this initial version submitted to bioRxiv. Please use the final version published in AJB (American Journal of Botany doi: 10.1002/ajb2.1479) as reference though both versions are not fundamentally different.
On 2020-06-17 18:34:37, user Özen Karaocak wrote:
Hello authors. Is the dataset available yet?
On 2020-06-17 17:57:50, user D614G wrote:
The D614G mutation in SARS-CoV-2 Spike increases transduction of multiple human cell types
On 2020-06-17 13:13:27, user Alex Ossadtchi wrote:
Found a number of errors in mathematical expressions and in the main text. Added Monte-Carlo simulations, please, use the current version https://www.biorxiv.org/con...
On 2020-06-17 11:04:30, user Oliver Pescott wrote:
This is an interesting paper, although it is not clear to me that the literature search presented here is really an accurate way to estimate the contribution of CS in a broad sense to SDMs. Any paper using GBIF data will include citizen science-contributed data, included some datasets that are not obviously marked as CS, or may even be of a hybrid origin themselves (e.g. the data of the British Bryological Society are an amalgamation of professional and amateur data: https://www.gbif.org/datase... ). Although the authors allude to this difficulty, it does rather reduce confidence in the conclusions of the paper. What the paper really seems to be presenting is an analysis of the contribution of easily identifiable CS to SDMs, which is not the same thing as its total contribution.
On 2020-06-17 10:51:28, user Manu wrote:
It looks clear that driving mutations to splice sites is a nice approach to silence (onco)genes...<br /> Nice work! We saw the same with CRISPR/Cas9 (plosONE2019) but this approach is more elegant. Congratulations!!<br /> Manu
On 2020-06-17 10:39:45, user Markus Pfenninger wrote:
The questions "Can biodiversity adapt to increasing anthropogenic pressures?", "Is evolutionary adaptation rapid enough for the current rate of environmental change?" or similar issues are key questions in many areas of current biology. To answer these, it would be paramount to know how many different selection pressures organisms can adapt to simultaneously and at which speed these adaptations can proceed. Yet our knowledge on this vital issue is currently rather limited.
Our manuscript opens up a new approach to qualitatively and quantitatively address this important problem. The application to a natural population of a non-biting midge shows that adaptation in natural populations can be almost instantaneous, affect most of the genome and react to many selection pressures. Like several recent findings, our study thus challenges classical paradigms in population genetics.
On 2020-06-17 09:51:57, user Paul Schanda wrote:
All the SAXS data and structural models now have an accession number on the SASBDB data base:
SASDH89<br /> Mitochondrial import inner membrane translocase TIM8-TIM13 in complex with Tim23
SASDJP4<br /> Mitochondrial import inner membrane translocase TIM9-TIM10 in complex with Tim23
SASDJQ4<br /> Mitochondrial import inner membrane translocase TIM8-TIM13
On 2020-06-09 20:10:09, user Paul Schanda wrote:
The link to all the chemical-shift perturbation data has been updated. The data can be found at
http://dx.doi.org/10.17632/...<br /> https://data.mendeley.com/d...
On 2020-06-17 03:00:34, user Palermo Trapani wrote:
Gastornis: Where did the term East Med come from. Off the top of my head, I have never seen it in any academic paper. I have seen terms in the academic literature such as Anatolian-Neolithic Farmer, Iran-Neolithic, Caucus Hunter Gather, Levant Neolithic, along with WHG, EHG in for example the paper by Mathieson et al 2018 "The Genomic History of Southeastern Europe" (Figure 1). Similar terminology is used by Lazaridis et al 2016 "Genomic insight into the origin of Farming in the Near east" (Figure 1).
There was a HG population in Sicily that is the same one found in Southern Italy and is similar to other HG in Italy and Central Europe. . The Mathieson et al 2018 paper documents this and a recent paper (Catalano et al 2020) with David Reich on it confirms the findings of Mathieson et al 2018. So the basic findings of the paper that local WHG in Sicily, rather than move to other areas and continue foraging and hunting, adopted the Farming Technology of the EEF from Anatolia and the 2 are both source populations, although the Anatolian-EEF ancestry is the predominate one as documented by Raveane et al 2019 which indicates all 20 Italian regions have significant Anatolian Neolithic ancestry ranning from 56% (SItaly1 sample) to 72% (NItaly 4 Sample). As Figure 2 of that paper indicates, there are 2 samples from Sicily which would suggest 56% < Sicily1/Sicily2 Sample < 72% with respect to Anatolian Neolithic ancestry.
On 2020-06-17 02:19:25, user Left Exposed wrote:
Where can the GlypNirO software be downloaded? It doesn't google, other than the mentions in this paper. It isn't on GitHub.
On 2020-06-16 23:23:04, user Jeanette Norton wrote:
I think what this paper is missing is the comparison of potential versus gross rates of nitrification (i.e. determined by N-15 pool dilution) accomplished in several studies. That comparison adds to the interpretation of potential versus gross rates. Further there is some evidence from the kinetics of slurry assays for ranges of ammonia/ammonium concentrations where both AOA and AOB rates reach a short-term maximum. That said your conclusion that nitrification potentials do not predict actual in situ rates is totally correct and the use of nitrification potentials to predict in situ rates without modeling of substrate diffusional constraints would be inappropriate. No easy answer that applies across different soil types unfortunately.
On 2020-06-16 21:57:53, user Fraser Lab wrote:
I am posting this review on behalf of a student from a class at UCSF on peer review: https://fraserlab.com/peer_... . The student wishes to remain anonymous. I will be happy to act as an intermediary for any correspondence.
In this manuscript Moti et. al., propose a novel way of visualizing Wnt transport from the ER to the membrane using the Retention Using Selective Hook (RUSH) system. Through use of this system, they also provide insight on the involvement of filopodia used for signaling by Wnt3A.
Overall, the authors provide a very promising system for live visualization of Wnt transport inside of a producing cell. Wnts are known to be particularly difficult to tag and visualize in a live model, and this lab was able to show that their tagged Wnt3A not only transports as expected but also is still capable of signaling.
Aside from the tool they developed, the authors state that Wnt transfer between cells via actin-based filopodia. Though they do show that Wnt-positive vesicles are seen in projections, they make the strong claim that it is being transferred to a receiving cell. The images and videos show movement in the projections, but the experiments do not show that the projections are touching the neighboring cell or transferring the vesicles. In supplemental video 5B, the Wnt-positive vesicles appear to actually be migrating into the cell body as opposed to the neighboring cell, which was not discussed.
The major success of this paper is the creation of a functional RUSH-Wnt3A construct that can be used to visualize Wnt transport in the producing cell. As Wnts are very difficult to tag or manipulate, this is a great achievement and its use will strongly help further our understanding of Wnt transport.
Minor points:<br /> The authors switched between HeLa, 293T and RKO cells for different conditions. As the RKO cells were engineered with WLS knockouts, the WT RKO cells could serve as the cell line to test for RUSH-Wnt3A alone and with the Porcupine inhibitor. If this was done intentionally, the authors should state why this was done. Otherwise, using the same cells for each condition would eliminate other factors that could affect the transport of RUSH-Wnt3A. <br /> Transfection of reporter cells (STF reporter) cells with RUSH-Wnt3A for signaling assay. These results would show self-activation of Wnt signaling. Could the STF reporter cells be co-cultured with a different cell line transfected with RUSH-Wnt3A to see the activity levels of the receiving cell? This could further support filopodia, or at least cell contact, as a way of activating cell signaling.<br /> Figure 6a is missing a label for what I suspect is LGR5834DEL.<br /> Figure 6c – would like to see filopodia quantification for LGR5(FL) and a non-transfected cell.
On 2020-06-16 18:49:03, user Stefan Fedorowicz wrote:
Thank you for this study. If true, this is an extremely significant development.
On 2020-06-13 21:39:45, user Phil Scumpia wrote:
Great article. Thanks for posting the preprint. What happened with the saliva? It was collected but not reported. Was the data too variable?
On 2020-06-08 12:41:01, user OxImmuno Literature Initiative wrote:
On 2020-06-02 11:38:58, user Tobias Broger wrote:
Dear Study team. Thanks for writing this up. The study is well-conducted and interesting. <br /> I have a question/comment: did you test antibody response against other antigens? I believe it is a major limitation of the assay you used that it only detects antibodies against S1 as there appear to be a number of patients including with mild disease that mount an immune-response against S2 or N proteins that the assay from this study would miss. I think this should be discussed (and I suggest you test your serum samples with 1-2 alternative assays that cover several virus proteins, which is quick). This could change your conclusion quite a bit. Happy to chat on the phone and provide some more background if there is interest. Tobias Broger.
On 2020-06-16 18:00:13, user Quan Gu wrote:
The abstract correction: "some of the best packages". The inaccuracy to be amended.
On 2020-06-16 17:52:25, user Aaron wrote:
I'd suggest changing figure 1b (case fatality vs proportion of D or G at residue 614) to consider case fatality over time in countries with rising prevalence of G614. As is, the panel is a bit misleading as the case fatality rate for each country is going to be a product of D614 and G614 infections due to the shifting proportions of the two variants over time. Rising CFR in multiple countries with rising G614 prevalence would be more suggestive of this connection. However, it's still worth considering the fact that as prevalence of G614 has risen, so has general case load, which in and of itself can lead to overwhelmed healthcare systems and higher case fatality.
On 2020-06-16 16:42:50, user Ruchika Bajaj wrote:
Bajaj et al, https://onlinelibrary.wiley...<br /> is not cited in this paper.
On 2020-06-16 15:31:40, user David P Forsyth wrote:
Even the abstract is riddled with errors. First and foremost, vitamin E acetate has never been an ingredient in any FDA registered electronic nicotine delivery system (ENDS) e-cigarette. It has only been used to thicken THC vaping oil, usually to "cut" black market products with the intent of fooling the customer into believing it contains more THC (a common practice among many types of illegal drug dealing). It is not even possible to mix oil based vitamin E acetate with water soluble nicotine e-liquid. Nor would it be cost effective, since vitamin E acetate is more expensive than the actual ingredients in ENDS e-liquid.
CDC never linked any ENDS products to lung damage known as EVALI, although they did release an extensive list of suspected THC products, including a black market "label" known as Dank Vapes. If any of the thousands of FDA registered ENDS e-cigarette brands had been implicated in EVALI cases, the FDA would have issued immediate recalls for testing. That was not possible with illicit/unregulated and adulterated THC vaping oils.
This report repeatedly conflates THC vaping oil with "e-cigarettes" (which the FDA defines as electronic NICOTINE delivery systems). This can only be the product of ignorance or intentional misrepresentation. Stating that vitamin E acetate is a "primary component of vaping products" is patently false and discredits this report from the first paragraph. Presenting such misleading falsehoods as "science" does a disservice to public health.
On 2020-06-16 15:19:32, user Jung Choi wrote:
After a quick skim of the paper, I did not see how many samples were analyzed for each fluid/tissue.
On 2020-06-16 12:04:27, user ChrisdeZilcho wrote:
Viruses often "throw excess ballast over board”, which they acquired through natural recombination / horizontal gene transfer during virus evolution while retaining more beneficial sequences leading to fitness gain. This is dependent on the length and the site of the insert into the viral genome. Generally, the smaller the genome the higher the within-host competitive fitness. However, host switches can radically change evolutionary dynamics allowing intermediates that contain exogenous sequences, which are beneficial for the virus, to be competitive.
An interesting paper from Willemsen and Zwart published in November last year focused on wild as well as engineered viruses, for which a simulation model was developed to study the effects of genetic drift on insertion stability: “On the stability of sequences inserted into viral genomes.” https://academic.oup.com/ve...
The authors observed that many viruses “appear to be highly plastic where increases and decreases in genome size occur on a relatively short evolutionary time scale... Retroviruses with linear ssRNA(+), especially with overlapping genes, show slightly less plasticity than their DNA counterparts...Theory suggests that demography could have major implications for the loss of inserted sequences, with small population sizes, narrow bottlenecks, and short time intervals between bottlenecks resulting in high sequence stability. Hence, the stability of the inserted sequence cannot be viewed solely as a property of a genome, rather it is a phenotype and therefore depends on the environment.”
Going back to SARS-CoV-2: It is known that the new virus presents high infectivity and efficient transmission capability among the human population since it was firstly identified in contrast to its earlier counterpart CoV-1. QTQTN loss after PRRA acquisition would be expected after selection in cell lines (such as Vero E6 or others) when low MOI is used. After transmission to humans CoV-2 the QTQTN deletion may be observed in patient's isolates predominantly with low viral loads and high selection pressure (comparable to low MOI of in vitro models), although this is not discussed within the paper.
Therefore, another plausible explanation would be that due to the acquisition of the additional 4 aa of the furin-site PRRA at the S1 / S2 border through recombination or RdRP copy choice error, the virus subsequently loses 5 "non-functional" aa, which no longer provide an evolutionary advantage in the course of h-to-h transmission. So could the 12nt insertion of the furin-sequence PRRA, clearly adding function to the viral genome, but leading to an increase in genome and protein size, be accompanied by a subsequent 15nt deletion of QTQTN in order to restore the correct folding of an unstable or immunogenic spike protein?!
On 2020-06-16 11:51:59, user Jitao David Zhang wrote:
I think this paper has been published in Genome Biology, https://genomebiology.biome...
On 2020-06-16 10:03:42, user mp2766 wrote:
I would suggest to also reference another paper on multivalent clustering of ligands : ACS Nano 2019, 13, 728−736.
On 2020-06-16 04:11:28, user Elizabeth Molnar wrote:
An excellent paper, in which I am interested because of the homology of the developing spermatozoa and nervous tissues, with microtubule and mitochondria interacting with laminins and nuclear membrane receptors, axons resembling spermatid tails. I found an article which describes the importance of genes such as that studies here has in <br /> mammalian metabolism and nervous system and formation of collagen arrays, I recall work from the Blackshaw laboratory at Queensland University in the 60's and 70's on the role Luteinizing Hormone in release of spermatids from Sertoli cells, as well as Leydid steroidogenesis. Thus:
A Regulatory Loop between the Retinoid-Related Orphan Nuclear Receptor NHR-23 and let-7 family microRNAs Modulates the C. elegans Molting Cycle
Ruhi Patel, Alison R. Frand
doi: https://doi.org/10.1101/506261
Beth Molnar,
Psychiatrist,
Brisbane
On 2020-06-16 03:50:27, user Virginia Abdala wrote:
Nice work! Please note that R.W Haines is also author of the paper of 1942: The evolution of epiphyses and of endochondral bone. Biological Review 174, 267–292. You should change J.S by R. W.
On 2020-06-16 02:29:57, user Timothy Mak wrote:
Thanks to Zhangchen Zhao for spotting the following mistake in formula 10 of the paper and formula 5 of the supplementary --- |u_i^(t) - lambda| should instead be (|u_i^(t)| - lambda).
On 2020-06-15 23:44:08, user Nabina Paudyal wrote:
Very nice work highlighting the importance of Vanderwaals contact energy at specific sites to function based. Really liked how the experiments were designed based on MD simulations and in turn experiments validated the model. Wasn't sure what you meant by charge transfer in the abstract. Isn't it just Vanderwaals energy?
On 2020-06-15 15:47:42, user OxImmuno Literature Initiative wrote:
On 2020-06-15 15:05:57, user Steve Alexander wrote:
An interesting proposition - fluoxetine is metabolised to an active metabolite https://www.guidetopharmaco....<br /> Is the metabolite also active?
On 2020-06-15 13:55:18, user Paweł Borowicz wrote:
Dear Authors,<br /> We have read your preprint with great interest during our weekly literature meeting. However, we have spotted a few things that could/should be explained/corrected.<br /> General comments:<br /> 1. Most experiments lack a statement on the number of replications performed. If they were performed only once, we would have liked to see them repeated.<br /> 2. Most experiments lack in the figure description a statement on the cells in which they were performed.<br /> 3. Most western blots lack molecular weight indication and some form of quantification.<br /> 4. When generating stably transfected cell lines, it is helpful to first generate a subcloned “mother” cell line. In this way, one can reduce the potential heterogeneity of stably transfected cell lines, which are inherent in each individual cell, (similarly to backcrossing mice in order to avoid accumulated mutations).<br /> Specific comments:<br /> 1. Fig.1 A – As only one cell is presented, it would be helpful if authors added a quantification of BCR/FES signals colocalization performed in an unbiased manner.<br /> 2. Fig.1 B – Is there any specific reason why FES is excluded from the scheme? It would be helpful for the reader, if the role of FES in the BCR signaling cascade was shown in the scheme. Also, the placing of antigen is very misleading. BCR recognition grooves are placed on top of the receptor tips (exactly like in the antibody).<br /> 3. Fig.1 C – It would be more helpful for the general audience if the results were presented in the form of a graph (or heatmap).<br /> 4. Fig.1 D – This graph shows that the FES construct lacking F-BAR domain has reduced phosphorylation. Could authors also show how that construct localizes in cells?<br /> 5. Fig.1 E – Fes protein variants loaded in each lane should be stated in the figure and not in the figure description. The statement in the figure description: “Following this, equal amounts of Fes kinase activity were added…” should be changed – “activity” cannot be added. <br /> 6. Fig.2 A – Adding a column “sample number” could help reading the table, because it is not straightforward that each row is a different sample.<br /> 7. Fig.2 C – It is not explained anywhere why some sites (in rows) are highlighted with a red color. We assume that these are associated with FES, but it should be indicated in figure description.<br /> 8. Fig.3 E – As only 6 phosphosites are presented in the figure (out of 12 claimed positive in B), could authors combine all 12 of them in the form of sequence logo?<br /> 9. Fig.3 F – These blots should be improved. They need better alignment. They seem to be strongly manipulated with contrast. Lanes which were cut and pasted together in one image should be indicated with black separating line (especially first row in the first column). The pictures do not fit the boxes (especially first row in the first column). It seems that pictures of the first sample in the second column are missing – even if there was no signal, the blot should be presented.<br /> 10. Fig.4 – Reasoning behind the experiments in Fig.4 is that FES is the only kinase responsible for the phosphorylation of the mutated pTyr sites in STS1, DOK1, PTPN18. It should be mentioned in the text that the effects observed in Fig.4 could be as well caused indirectly by FES.<br /> 11. Fig.4 B – It seems to us that it would fit better if this figure was presented first as A (also in the results section).<br /> 12. Fig.4 C – Blots showing Ptpn18 (in the IP) and Csk (in the lysates) are missing. Ptpn18 and Csk blots could be straighten.<br /> 13. Fig.4 D – Flag-Csk (IP) blot should be aligned.<br /> 14. Fig.5 – The connection with the previous figures is very weak and not obvious. Maybe it would be more suitable to put panels A and B in Fig.1, and panel C in Fig.4?<br /> 15. Fig.5 B – Was the IgM activation necessary? Is there a difference between different time points of the activation? It would be interesting to see similar experiments performed with flow cytometry to monitor changes in the surface expression of CD19. If the changes of CD19 expression are pY527 Src dependent then why are there no changes of CD19 expression in Src-flag samples? – correlation in not a causation!<br /> 16. Fig.5 C – Why were the samples stimulated for 15 min instead of 5 min as in B? Does it influence anything in the experiment? Is Fes present in the samples? If yes, its level should probably be monitored with western blot.<br /> 17. Fig.S1 B – The blots are terribly overloaded. If possible, it is advised to repeat the western blot with lower amount of sample loaded per lane.<br /> 18. Methods, Fes kinase purification, line 3 – There are some excessive punctuation marks.
On 2020-06-15 12:40:53, user Ed Rybicki wrote:
This work was a very nice proof of concept for our coproduction of human chaperones with viral glycoproteins in plants as a means of both helping them fold and be glycosylated. It has worked with HIV gp140Env, Marburg GP and other viral glycoproteins, and worked very well indeed here.
Coincidentally, it also proves that we can make coronavirus vaccine candidates in plants B-)
On 2020-06-15 06:16:38, user Yichi Su wrote:
Following my earlier question: It seems that an ideal "reporter" system should be based on a low affinity aGFP-sGFP pair that would not spontaneously drive cell-cell interaction. When the two types of cells were not natively contacting, the aGFP-sGFP would not contact due to the low affinity, and thus the transfer of sGFP would not happen; when other driving forces result in cell-cell contact, the low affinity aGFP nanobody could still lead to endocytosis of sGFP. In this way, the GFP transfer between two cell types is indeed proximity-dependent, thus should canonically report native cell-cell interactions.
On 2020-06-15 05:57:11, user Yichi Su wrote:
Question to the authors: wouldn't the expression of sGFP on "sender cells" and GFP nanobody on "receiver cells" drives the interaction between these two types of cells? I mean, even if the two types of cells were not natively contacting, then expression of this sGFP-aGFP pair on them would actually drive them to interact. In this case this system is not really a "reporter" system. It actually interferes the native state of cell-cell interaction and would bring false-positive result.
On 2020-06-15 04:33:11, user brycenesbittt wrote:
Controlling for stress is an issue here. It's possible the positive testing patients, who all knew they were positive, have stress hormones. In a followup perhaps persons could be sampled at the armpit at a drive-through testing site. The samples tested by the dogs prior to either the patient, hospital OR the researchers knowing which is which.
On 2020-06-14 21:44:40, user Heather Schamehorn wrote:
I am in North Vancouver BC Canada, I want to train a Covid sniffing dog. How can I proceed with this here?
On 2020-06-08 21:08:18, user rlsheets wrote:
You discuss that one of your researcher's samples caused a false positive due to ovulation. You argue that the male dogs may have become excited by the secreted sex hormone metabolites. Have you considered that since LH and PRL are significantly elevated in male Covid-19 patients, that they might secrete a catabolome similar to those of ovulating women? This might be another explanation as to why the dogs were confused.
On 2020-06-08 11:52:23, user N. Espuno wrote:
This is a very interesting question, thank you for sharing your work. <br /> Could you explain the reason for using the same positive samples in several trials with the same dog during the testing phase ? <br /> Best regards, <br /> Nathalie
On 2020-06-05 22:26:57, user Benny Borremans wrote:
This is nice work, a pleasure to read.<br /> I am wondering though whether the study design allows you to state that dogs can detect patients infected with COVID-19, as there does not seem to be control groups for correlates such as having any other disease, having mounted an immune response against any other infection, having stress as a result of being hospitalized. Can you state that the dogs can distinguish SARS-CoV-2 infection from other infections? <br /> Best regards,<br /> Benny
On 2020-06-15 03:42:20, user Nikki Freed wrote:
this is now published as : Pearman, W.S., Freed, N.E. & Silander, O.K. Testing the advantages and disadvantages of short- and long- read eukaryotic metagenomics using simulated reads. BMC Bioinformatics 21, 220 (2020). https://doi.org/10.1186/s12...
On 2020-06-14 19:19:07, user Pandora wrote:
If Sarscov2 can infect monkeys, which they have in US zoos, could monkeys infect humans. Could the virus originate from Monkeys like so many other viruses. HIV, Malburg, Herpes, Sv40.
On 2020-06-14 14:26:36, user Neeta Tanna wrote:
There is no supplementary materials
On 2020-06-14 08:47:42, user Ivan Ansia wrote:
Please find companion paper at https://doi.org/10.1101/202...
On 2020-06-14 08:47:04, user Ivan Ansia wrote:
Please find companion manuscript at https://doi.org/10.1101/202...
On 2020-06-14 08:34:41, user OxImmuno Literature Initiative wrote:
On 2020-06-14 01:03:59, user Alison Chaves wrote:
Hi guys, congrats for the work in the first place. This is a mechanistically quite interesting study. It makes a lot of sense. But while reading this paper I was wondering, we know that the influenza virus induces IFN and causes acute respiratory distress syndrome, but the therapy with corticosteroids does not offer clinical benefit for the patients (Here is the evidence http://tiny.cc/32gsqz):3HQgKRrDwyUSQGVlD4RdAtVYQ2w "http://tiny.cc/32gsqz)"). Of course, we know that the influenza virus does not use the ACE2 receptor in order to enter the cell. Anyway, It would be great to know how the pathology caused by the SARS-Cov-2 differs from that by the influenza virus in order to justify the counterintuitive therapy with corticoid.
On 2020-06-13 21:10:35, user Fred Kelly wrote:
The values for March 3 in Figure 1 A "Increasing G614 frequency" appear to be reversed: D614 should be 166 and G614 should be 7. As presented, fxn G would be 0.96 for that date.
On 2020-06-13 03:31:18, user Ray wrote:
Introduce both mutations into the virus and test if one can outcompete the other in cell culture. If yes repeat with model animals. Only then you can say that. Everything else is jumping on the COVID-19 train to get an easy publication. But I guess that's the only way to get money for your lab atm. Science has become a commercial product that is being milked by mass media and journals. The more scary the better it sells. This doesn't help. This just makes things worse.
On 2020-06-13 20:44:48, user Henrique dos Santos Pereira wrote:
Would anyone answer my question, please? Would the variation in frequency of these more virulent variant of the virus correspond to a trade-off virulence x transmission over time and thus also explains why velocity of death declines (mortality) while morbidity keeps accelarating in the affected populations? henrique.pereira.ufam@gmail.com
On 2020-06-13 19:37:38, user michalopoulosgk wrote:
This is an excellent paper providing very useful information about changes in gene expression that hepatocytes undergo as they enter into a proliferative state and then return to post-regenerative status at the end of regeneration. The interactions between hepatocytes and non-parenchymal cells have not been previously described in such a global scale. <br /> Previous studies not using single cell RNA approach had shown an overall change in hepatocyte transcriptomics during regeneration, but the changes of non-hepatocyte cells had not been described. <br /> I have a few questions:<br /> 1. Is there any information about cholangiocytes during the regenerative process?<br /> 2. Previous studies on the cellular topography of regeneration had shown that it proceeds from periportal (zone 1 of lobule) to pericentral (zone 3), in a gradual pattern. Is there any way to correlate the observations of the authors with changes in specific zones of the lobule?<br /> 3. Collagenase perfusion itself, by breakdown and HGF release among other things, does affect hepatocyte transriptomics immediately after perfusion. Can the autors assess to what extent this may affect the results? I do realize that is impossible to get single cell suspensions otherwise, though.
Again, this is an excellent paper.
George K. Michalopoulos<br /> University of Pittsburgh<br /> michalopoulosgk@upmc.edu
On 2020-06-13 12:24:57, user Michael Polymenis wrote:
The paper has now been published in eLife (https://elifesciences.org/a...
On 2020-06-13 09:12:00, user Gweronelorix wrote:
In fact, SUN6/ACA6 is an ancestral squamate chromosome. It is the tegu which experienced FISSION, not vice versa.
On 2020-06-13 07:17:40, user OxImmuno Literature Initiative wrote:
On 2020-06-13 07:09:54, user OxImmuno Literature Initiative wrote:
On 2020-06-13 07:07:01, user OxImmuno Literature Initiative wrote:
On 2020-06-13 00:46:37, user Dr Kendra Maas (not that kind wrote:
What/why are there dashes in E1 FIP BIP?
On 2020-06-12 16:57:13, user UAB BPJC wrote:
Review of “A flexible target-specific anti-infection therapeutic platform that can be applied to different microbial species.”
• University of Alabama at Birmingham Bacterial Pathogenesis & Physiology Journal Club
Generally, we found this paper to be well written and straightforward. The results were clear and mostly convincing. Overall, we believe that this paper provides useful technology that, if it works properly, would be extremely beneficial to the medical community.
We do have some minor issues with the paper which are as follows:
Provide a more descriptive mechanism in which the antibody construct provides mechanical stress.
We would like to see more evidence of this method working with other species of bacteria including Acinetobacter which is a highly resistant gram-negative bacterium.
More replicates are always good to see. Especially in the experiments where there are only 2.
Zoomed out view or histology scores of histology images. We would also like to see more examples of these images as they were important to the credibility of this paper.
Consistence with animal models in text. First state the use of cotton rats then shifts to saying mice in the same section without the indication of switching animal models.
Data would be more convincing if the graphs used individual data points similar to 2F. Why use in 2F and not the rest of the paper?
The supplementary section does not correctly coincide with the text in the main paper. You describe results that coincide with figure 3B, but it is written in the text as figure 4B. This happens consistently until figure 10.
In supplementary figure 5, the figure legend states that SA-IR700 was used, but the text says Tra-IR700 was used. Would provide clarity, because if SA-IR700 was used, then shouldn’t at least the WT show a decrease in CFU when treated with PIAS?
In the paper, they make the claim that they haven’t seen any resistance to PIAS. They provide one figure with 30 passages of S. aureus, but I don’t think they actually treated the bacteria as it was being passaged. Some selective pressure needs to be in place for them to actually make the claim that resistance is not occurring.
For the virus experiments, they never indicate E. coli starting CFU. In the treated groups, they are getting about 106 E. coli back. If they started with 109 E. coli, then they aren’t getting complete clearance in the viral model that they are seeing in their bacterial/fungal model. Why not use PFU for this section?
Perhaps focus more on the fact that this technology is a powerful tool that could be also used in everyday lab setting in order to remove unwanted populations from cultures. This could be added in the discussion portion of the article.
On 2020-06-12 16:19:50, user Ben King wrote:
Endochondral bone was described in ptyctodonts by Miles & Young 1977, but Gardiner (1984) disagreed
On 2020-06-12 11:19:32, user Phil Donoghue wrote:
For what it's worth we summarised records of trabecular bone in Donoghue et al. (2006, Early evolution of vertebrate skeletal tissues and cellular interactions, and the canalization of skeletal development: JEZ-MDE, v. 306B, p. 278-294) with regard to the origin and evolution of endochondral ossification. These includes records from osteostracans (Janvier, ’85), placoderms (Denison, ’78) and acanthodians (Denison, ’79).
On 2020-06-12 15:05:28, user Sinai Immunol Review Project wrote:
Main Findings<br /> The manuscript describes a versatile mouse model of SARS-CoV-2 based on adeno-associated virus (AAV)-mediated expression of human ACE2 (hACE2). The AAV-hACE2 model supported productive SARS-CoV-2 infection in the lung of mice, inducing leukocyte recruitment and mounting antibody response (IgG) against the spike (S) protein. Serum obtained from AAV-hACE2 infected mice at 7 and 14 days post-infection with SARS-CoV-2 showed neutralization activity in vitro. RNA sequencing analysis-data reveal up-regulation of cytokines and interferon-stimulated genes (ISGs) signatures, compared to control mice. Neither type I nor type II, nor type III interferons were up-regulated, indicating a resemblance with published data from the lungs of patients infected with SARS-CoV-2 (Blanco-Melo et al., 2020). To further explore the role of type I interferon signaling in SARS-CoV-2 infection, interferon-alpha receptor deficient (IFNAR-/-) and interferon regulatory transcription factors (IRF) 3 and 7 double deficient (IRF3/7-/-) mice were transduced with AAV-hACE2 and infected with SARS-CoV-2. The analysis demonstrated a decrease in interferon stimulated gene (ISG) response in the IFNAR-/- and IRF3/7-/- mice relative to wild type mice. However, there was a similar kinetics of viral clearance in type I interferon deficient and wild type mice, suggesting that viral SARS-Cov-2 replication is resistant to interferon signaling. The authors concluded the AAV-hACE2 infected SARS-CoV-2 model largely recapitulated the transcriptome changes observed in the lungs of SARS-CoV-2 infected patients.
Limitations<br /> The results shown by the authors suggest that the hACE2-AAV mouse model of SARS-CoV-2 infection generates symptoms that partly resembled human infection symptoms. However, cellular and tissue tropism of SARS-CoV-2 in infected mice or different susceptibility to the infection based on gender or age were not explored. Additionally, because no deaths of infected mice were reported, the model does not fully reflect the pathogenesis of SARS-CoV-2 as in other transgenic models. Future studies with this model should interrogate SARS-CoV-2 infection based on mouse strain, gender, and age.
Significance<br /> The hACE2-AAV mouse model partially simulated the pathology of COVID-19 with a focused and robust lung SARS-CoV-2 infection and pathology. The hACE2-AAV mouse model will allow to test patient-SARS-CoV-2 derived-viruses in mouse strains with diverse genetic backgrounds and genetic alterations, as a platform to study pathological mechanisms and therapeutic strategies to combat COVID-19 disease.
Reviewed by Martinez-Delgado, G as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.
On 2020-06-12 13:34:02, user Sinai Immunol Review Project wrote:
Main Findings<br /> The manuscript describes a versatile mouse model of SARS-CoV-2 based on adeno-associated virus (AAV)-mediated expression of human ACE2 (hACE2). The AAV-hACE2 model supported productive SARS-CoV-2 infection in the lung of mice, inducing leukocyte recruitment and mounting antibody response (IgG) against the spike (S) protein. Serum obtained from AAV-hACE2 infected mice at 7 and 14 days post-infection with SARS-CoV-2 showed neutralization activity in vitro. RNA sequencing analysis-data reveal up-regulation of cytokines and interferon-stimulated genes (ISGs) signatures, compared to control mice. Neither type I nor type II, nor type III interferons were up-regulated, indicating a resemblance with published data from the lungs of patients infected with SARS-CoV-2 (Blanco-Melo et al., 2020). To further explore the role of type I interferon signaling in SARS-CoV-2 infection, interferon-alpha receptor deficient (IFNAR-/-) and interferon regulatory transcription factors (IRF) 3 and 7 double deficient (IRF3/7-/-) mice were transduced with AAV-hACE2 and infected with SARS-CoV-2. The analysis demonstrated a decrease in interferon stimulated gene (ISG) response in the IFNAR-/- and IRF3/7-/- mice relative to wild type mice. However, there was a similar kinetics of viral clearance in type I interferon deficient and wild type mice, suggesting that viral SARS-Cov-2 replication is resistant to interferon signaling. The authors concluded the AAV-hACE2 infected SARS-CoV-2 model largely recapitulated the transcriptome changes observed in the lungs of SARS-CoV-2 infected patients.
Limitations<br /> The results shown by the authors suggest that the hACE2-AAV mouse model of SARS-CoV-2 infection generates symptoms that partly resembled human infection symptoms. However, cellular and tissue tropism of SARS-CoV-2 in infected mice or different susceptibility to the infection based on gender or age were not explored. Additionally, because no deaths of infected mice were reported, the model does not fully reflect the pathogenesis of SARS-CoV-2 as in other transgenic models. Future studies with this model should interrogate SARS-CoV-2 infection based on mouse strain, gender, and age.
Significance<br /> The hACE2-AAV mouse model partially simulated the pathology of COVID-19 with a focused and robust lung SARS-CoV-2 infection and pathology. The hACE2-AAV mouse model will allow to test patient-SARS-CoV-2 derived-viruses in mouse strains with diverse genetic backgrounds and genetic alterations, as a platform to study pathological mechanisms and therapeutic strategies to combat COVID-19 disease.
Credit<br /> Reviewed by Martinez-Delgado, G as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.
On 2020-06-06 12:45:38, user OxImmuno Literature Initiative wrote:
On 2020-06-02 13:39:48, user insta gramm wrote:
In my opinion a time 0h time point is needed to claim virus replication, as it seems like the amount of virus only decreases after 2 days. Compared to the control, there is virus so it should work (the virus probably reached peak by 2 days), but please don't let this be another paper like the rhinovirus "mouse model" one where the amount of viruses only decreases with time.
https://pubmed.ncbi.nlm.nih.gov/18246079/
Need to confirm that this is not injected virus degrading over time.
On 2020-06-12 04:23:12, user UMAMAHESWARI RAJAGOPALAN wrote:
This report should be removed as the claim made by this report is utterly false. The method, called statistical interferometery is developed solely by Kadono et. al. and his laboratory of Saitama university and his lab has extensively published in different journals. I am a principal collaborator in this research. Authors claim of proposing the method is absolutely stealing the rights of the original inventor.
On 2020-06-11 20:27:30, user Seth Blackshaw wrote:
I am delighted to see this work in print, which will be a huge benefit for anyone using PCRP reagents. I was wondering if you could clarify one important point. You state that you used both hybridoma supernatant and purified mAbs for this work, but don't state which assays used which. We have generally had much more robust results using purified PCRP mAbs, and this may affect some of the results.
On 2020-06-11 19:54:33, user Dave wrote:
From the manuscript:
The updated SPLITREADER source code, split in two parts (SPLITREADERbeta2.5_part1.sh and SPLITREADER-beta2.5_part2.sh) as well as the 4 following<br /> processing scripts are available on (https://github.com/baduelp/....
Note that the source code you're referring to doesn't appear to be present in the repo at https://github.com/baduelp/...
On 2020-06-11 18:25:10, user Megan Hagenauer wrote:
Useful analysis. We were also struck by how much the cell type signature database matters when performing deconvolution analyses. The question of whether to include cell type estimates as covariates when performing differential expression analyses is one that I still waffle over (our results definitely provided pretty lackluster guidance) - your simulations suggest a clear benefit. If you have the time, I would love to see whether you find a similarly clear benefit of including cell type estimates as covariates while performing differential expression (DE) in your simulations if you vary the signature database (currently you use Darmanis' data as the signature database for deconvoluting mixtures of Darmanis' data - what happens if you use a different signature database? e.g., the IP and CA datasets included in your Multibrain database?). That might provide a closer approximation to what we typically encounter when performing DE analyses on transcriptional profiling data from macro dissected data.
On 2020-06-11 14:27:49, user AspiringPolymath wrote:
By your definition of proteome complexity, water fleas and a great number of other multicellular organisms are quantitatively more complex than humans. I'm not sure that is a mathematically accurate view of biological complexity that can be applied to single cells.
On 2020-06-11 12:30:24, user Bartek Makower wrote:
There is no reference to which company the IFNg ELISpot assay used was purchased from. Or was it develped inhouse?
On 2020-06-11 12:22:09, user Roberto Marano wrote:
Nice article. I enjoy the reading so I wish to provide a few suggestions for improvements (if you wanna take them :) <br /> -68-69: substitute ref 17&18 with more recent references<br /> -103, change rpm to rcf or g units<br /> -130: change 16S rDNA to 16S rRNA (that's the name of the gene even if it's in DNA form)<br /> -343-345: I personally disagree. unless you can prove they are associated in your case, don't speculate it. intI1 is everywhere and could theoretically be associated to everything it co-occurred with if you frame it that way.
Good luck!<br /> R
On 2020-06-10 21:08:44, user Jean Muller wrote:
Dear authors,<br /> As we are developing AnnotSV, we have discovered the preprint of your new method for ranking SV and we take this opportunity to make a few comments that we hope will be of any help. As many others, we clearly share your view that the integration of multiple methods such as SV annotator, phenotype driven predictor and SV impact predictors will refine the SV characterisation and help to take medical decisions. Please find below our comments:<br /> 1. We appreciate the inclusion of AnnotSV in your evaluations, however we have noticed some shortcuts that we wished to highlight here as it does not reflect how AnnotSV is ranking SVs. First, only v1 has been evaluated and published, while v2 that includes a temptative ranking method is not yet evaluated properly and is provided as is. Moreover, AnnotSV provides a phenotype-driven score calculated with Exomiser that is really useful for clinical interpretation, even if that score is not yet integrated in the ranking.
You wrote that « AnnotSV ranks SVs into five classes based on their overlap with known pathogenic or benign SVs and genes associated with disease ». This is true for class 1 and 5 but also misleading as it gives the impression that there is no way for AnnotSV to discover novel pathogenic SV. As you can see on our documentation (see https://lbgi.fr/AnnotSV/ranking) our class 4 includes critical genes annotations such as the pLI (from ExAC), Haploinsufficiency and triplosensitivity scores from ClinGen, as well as enhancer and promoter elements. You should then of course consider this while comparing SV calls and we are surprised that this performs only barely better than chance but one cannot fully exclude this of course :D
In line with that, could you explain in more details the following part of your manuscript: « To compare the performance of AnnotSV and SV impact predictors on novel variants, we created a dataset of DECIPHER SVs that do not overlap dbVar SVs used by AnnotSV, and we recorded the prediction accuracy of each method (Fig. 5b). ». Are predictions evaluated both on benign and pathogenic SV or as guessed only on pathogenic ones? If we understood well the method section, the DECIPHER dataset used here contains “pathogenic”, “likely pathogenic”, “benign”, or “likely benign” SV, but not VOUS (Variant Of Unknown Significance) SV. You then removed SV that do overlap dbVar pathogenic SVs. How many SV are still present in your resulting DECIPHER dataset (for each size range and impact category of SV?).
The StrVCTVRE method is trained both on sets of pathogenic and benign SV. In particular, putatively benign SV were collected from gnomAD-SVs, in which the « gnomAD rare unlabeled » corresponds to SVs with a global MAF < 1% and with no individuals homozygous for the minor allele. We understand all the selection you did but these SV cannot be associated without any doubt to benign SV. Indeed, a significant part of these rare SV could be presumably heterozygous pathogenic SV responsible for recessive diseases. Thus our guess is that this category should be removed from the training.
Will you make your training datasets available (benign and pathogenic SV from each data sources)? This would be very useful to compare and evaluate upcoming programs.
Once again, this is a very nice work and we look forward for your method and will consider integrating your score to our tool. Hope you will find those comments of any use.
Best regards,<br /> Véronique Geoffroy and Jean Muller
On 2020-06-10 20:08:16, user Charles Warden wrote:
I believe there is a typo in the title for this article:
"PhiX spik-in" --> "PhiX spike-in"
On 2020-06-10 19:41:25, user Guangyang Yu wrote:
where is the supplementary figures to this paper?
On 2020-06-10 18:25:05, user Simon Drescher wrote:
To add some more literature which is not mentioned in the manuscript:
A review about asymmetric phospholipids: Huang and Mason, 1986 BBA 864, 423-470; "Structure and properties of mixed-chain phospholipid assemblies".
And some original work about the SDPC including x-ray studies and freeze-fracture electron micrographs: Hui et al. 1984 Biochem. 23, 5570-5577; "Acyl Chain Interdigitation in Saturated Mixed-Chain Phosphatidylcholine Bilayer Dispersions".
Finally, McIntosh et al. 1984 Biochem. 23, 4038-4044; "New Structural Model for Mixed-Chain Phosphatidylcholine Bilayers" - a whole paper about SDPC using x-ray.
Hence, the biophysical properties of SDPC are known for >35 years ...
On 2020-06-08 13:55:57, user Simon Drescher wrote:
SDPC should be 1-stearoyl-2-decanoyl-sn-glycero-3-phosphatidylcholine instead of "2-decyl" - since two ester bonds are in SDPC.
Futher, I think the relevant literature is not mentioned in this article, for example:
Lewis et al. 1994 Biophys. J. 66, 207-216; "EnigmaticThermotropic Phase Behavior of Highly Asymmetric Mixed-Chain Phosphatidylcholines That Form Mixed-lnterdigitated Gel Phases" describing the the phase behavior of SDPC, the lipid used in this study.<br /> and
Lewis et al. 1994 Biophys. J. 67, 197-207; "Studies of Highly Asymmetric Mixed-Chain Diacyl Phosphatidylcholines that Form Mixed-Interdigitated Gel Phases: Fourier Transform Infrared and 2H NMR Spectroscopic Studies of Hydrocarbon Chain Conformation and Orientational Order in the Liquid-Crystalline State"<br /> ...to name only two.
Finally, statements such as "It is not currently known what biophysical properties membranes composed of asymmetric lipids will display" are not true, since these properties are already known. Also, the fact that asymmetrical phospholipids show chain interdigitation is not new.
On 2020-06-10 18:03:53, user robustel wrote:
This is fantastic work that I'm extremely excited about and looking forward to testing it. It is an excellent idea, and it is clear that the round trip times are substantially shortened in this tempering scheme relative to REST2, and thus that the REHT method has tremendous potential. I would love to see additional analyses that might explain why the base temperature replicas of Trp-cage and HIS-5 appear to be so different with REHT and REST2, as in addition to providing more confidence for early adopters of the REHT method, this might also reveal something very interesting about the nature of entropic barriers in sampling protein folding and IDPs.
It is perplexing that REHT and REST2 are not resulting in the same distributions in the base replica when using the same force field, and examining the free energy surfaces provided, it is not clear if this is just the result of increased sampling in REHT finding additional minima relative to REST2, or REST2 being stuck in different minima. It seems as though the free energy surfaces in the base replica of REHT simulations may be somewhat "flattened". This raises some concern that the tempering scheme here may not produce exactly the same equilibrium ensemble at the base temperature. Perhaps the water is not completely relaxed in the base replica using the given exchange frequency, and therefore a different distribution, where water is at a slightly different temperature, or is in different meta-stable states, is being sampled?
I would be curious to see the following additional information in the final manuscript: <br /> 1) A comparison of the temperature, kinetic energy and potential energy distributions of water in the base replicas of Trp-cage and HIS- 5 for REST2 and REHT. <br /> 2) A comparison of the hydration shell properties in base replicas of Trp-cage and HIS-5 for REST2 and REHT.
In Figure S6, the free energy distributions of the base replica for Trp Cage folding are substantially different with simulations run using the same Force Field. The results do not show that REHT recovers the same distribution as REST2 with faster convergence, but that the shape of native basin is substantially different: it is broader and flatter and the free energy barriers are very different:
The author's explain this with the following "The predicted free energy barrier by the REHT method (~2 Kcal/mol) matches closely with the suggested free energy barrier of ~ 2.1 Kcal/mol,30,34 while a larger barrier of about ~ 6 Kcal/mol is observed in REST2 simulations. ", which cites an experimental study and a computational study using a different force field. Regardless of agreement to experiment or other studies with different force fields, unless the conformational space sampled is substantially different in the case of the REHT AND REST2 simulations (which does not appear to be the case), and one or both are not converged, the authors should recover the same distributions with both methods. Additionally, the figure of 6Kcal/mol for REST2 appears to be extracted from a 2D surface of RMSD vs. Rg, whereas the 1D distribution of RMSD produces a barrier of ~2Kcal/mol for REST2 and ~1Kcal/mol for REHT. 2Kcal/mol is in better agreement with the barrier from a 1-D contact based reaction coordinate from the cited 2011 shaw study which uses the a99SB*-ILDN force field, which is quite similar to a14SB.
Also perplexing is the dramatic difference between the Rg distributions obtained from REHT and REST2 for His-5 using the same force field. Again, unless the conformational space sampled is substantially different in the case of the REHT and REST2 simulations, this implies that one or both of the simulations are not well converged, in which case the comparison to experimental data for unconverged simulations is somewhat irrelevant, as simulations run with the same force field with sufficient sampling should converge to the same distributions. In the final manuscript, I would be very curious to see comparisons of 2D free energy surfaces (just contours as these are easier to compare than the 3-D plots presented in the main text) of Rg vs. SASA and Rg. vs Helical content for the base replica of the REHT and REST2 simulations. This should help make it clear how similar the regions of the conformational space being sampled are, and if indeed REST2 is simply stuck in local minima of phase space, or if the two methods are somehow resulting in different distributions, different rates of discovery for different types of states in the base temperatures.
Is it possible that on the time-scales used in this study, based on the nature of the exchange schemes and higher temperature water in REHT, that REHT more readily samples extended states of IDPs in the base temperature replica, while REST2 more readily samples compact states at lower temperatures? Given enough time, we should recover the same distribution, but a bias in the types of states explored in early simulations times would in-and-of itself be an interesting result. and it would also be very interesting to attempt to understand these differences.
Paul Robustelli<br /> Dartmouth College
On 2020-06-10 11:20:52, user Ben King wrote:
This paper looks at the Guiyu problem, and confirms your argumentation.
On 2020-06-10 08:00:52, user Karl Kadler wrote:
Nice paper that reconfirms findings by David Hulmes and Barbara Brodsky (nee Doyle) in the 1970s that it is dipoles formed by 100 residues occurring in pairs of unlike charge<br /> which are responsible for the 1D stagger between molecules.
On 2020-06-10 05:43:29, user Y. wrote:
@ All authors, excuse me, could you please double check the ORCID id for the first author, I think it is wrong.
On 2020-06-10 03:41:44, user Paul Gordon wrote:
Hi, thanks for posting this. I'm wondering if you will be depositing the P1 sequence in GISAID? I did not see this information anywhere in the manuscript.
On 2020-06-10 01:59:44, user Mr TX wrote:
The experiment of your paper was not in strict compliance with our product user manual, significant irregularities occurred in the experimental operation, and your team did not take rigorous experimental procedure, your team obtained abnormal data results consequently. Meanwhile, your team have not sought to communicate with our technical department to find the reasons behind the findings, and just simply published the paper that contained erroneous and inaccurate data, which caused a significant adverse impact to our company. Having said that, we hope you can revise the current experiment design, follow the required procedure and rigorous approach to repeat the scientific experiment as soon as possible, modify or withdraw this paper, to ensure a rigorous scientific spirit. <br /> We send you a Clarification letter, please reply as soon as possible
On 2020-06-01 18:31:10, user Thomas Weitzel wrote:
Thank you for your comment. Indeed this is a mistake and the correct phrase is: "The overall sensitivity values of RapiGen, Savant, and Bioeasy tests were 62.0% (CI95% 51.0–71.9), 16.7% (CI95% 10.0–26.5), and 85.0% (CI95% 75.6–91.2), respectively, with specificities of 100%."
On 2020-05-31 20:07:59, user Ron wrote:
Please note, there is a mistake in the abstract. Above the words 'and 85.0%' should appear Savant, and not Liming Bio.
On 2020-06-09 23:25:07, user Mike Lin wrote:
Good stuff! A similar scheme is used inside GLnexus. It's for a specialized purpose only, not as useful as your general purpose-tool, but I appreciate if you see fit to mention it. https://www.biorxiv.org/con...
GLnexus imports gVCF files into a persistent database backed by RocksDB (http://rocksdb.org), a key/value storage library providing efficient scans of ordered key ranges, parallel bulk load, storage compression, and online defragmentation. During import, each gVCF record is assigned to a 30-kilobase reference genomic range "bin" according to its position. The records in each bin are serialized to a binary message and stored in RocksDB using the key <bin genomic="" range,="" participant=""> (Figure 2A). To retrieve records near a certain genomic range, GLnexus asks RocksDB for an ordered scan of keys beginning with the relevant bin(s), thus efficiently "slicing" the cohort gVCF corpus. A binary search index stored alongside each bin further expedites locating the gVCF records which directly overlap the query range. (Records overlapping multiple bins are repeated in each bin, and the query logic hides duplication while scanning multiple bins.)
On 2020-06-09 23:21:24, user Philip wrote:
Line 136-139- In areas where this was not applicable due to the lack of organized road connections as commonly seen in semi-urban and rural areas, the polio-vaccination micro-plan was adopted, using the polio house markings as a guide.... was lifted from Mshelbwala et al 2017 https://onlinelibrary.wiley.... Authors should acknowledge that.
On 2020-06-09 20:59:54, user MS wrote:
A good screening tool for peak of outbreaks where most samples are anticipated positive thus few negatives require conf or in ED departments where a positive is used to triage but a negative is followed up with more sensitive method before cohorting
On 2020-06-09 20:51:18, user JSRosenblum wrote:
Please someone review this paper that knows that XMD8-92 is a bromodomain inhibitor, and that bromodomain inhibitors have numerous profound biological activities. Please... There are way better ERK5 inhibitors available, BAY-885 (which you can get for free from SGC!) and AX15836...
On 2020-06-09 20:36:07, user PatriciaS wrote:
Very interesting take on the importance of Inate Immunity players such as Neutrophils in the early onset of Leishmaniasis.
On 2020-06-09 14:06:41, user Donald R. Forsdyke wrote:
GENERAL DIFFERENCES IN mRNAs DEPENDENT ON GENOMIC CONTEXT<br /> To be attacked by a CD8 T lymphocyte, a virus-infected host cell must synthesize proteins from which short peptides (p) can associate with MHC proteins (pMHC complexes; MAPs). This response should occur rapidly. Thus, its timing would not necessarily correlate with the eventual abundance or half-life of the corresponding mature proteins, but it might correlate positively with early translation rates of their mRNAs. <br /> .<br /> These features would fit the co-translational (“DRiP”) view advocated by Daouda et al. (1), where "accuracy and efficiency" of synthesis is an important variable. However, the same features would also fit the view that the quantity of a mature protein synthesized shortly after infection would suffice to act as peptide source (2).<br /> .<br /> Daouda et al. trained an artificial neural network to recognize differences between potential host "source transcripts" (large segments of mRNAs that flank short segments that encode MAPs), and potential host "non-source transcripts" (large segments of mRNAs that do not encode MAPs). The differences between the mRNAs proved to be better predictors of<br /> peptide-generating proteins than differences between the corresponding amino acid<br /> sequences. Indeed, mRNA "sequences located far upstream and downstream" of the<br /> short peptide-encoding region were "informative regarding MAP presentation."<br /> .<br /> Although the second embedded layer of the CAMAP machine-learning model treated<br /> individual codons as "words," it is not clear to what extent CAMAP output was<br /> influenced by that input, or whether the predictive model was influenced more by<br /> aspects of mRNA sequences that relate to their individual genomic contexts. For<br /> example, mRNAs of genes in GC-rich isochores differ from mRNAs of genes in GC-poor isochores. The codon RNY rule (i.e. purine, any base, pyrimidine) in GC-rich isochores predicts codons of general form GNC, whereas in GC-poor isochores the general form would be ANT.<br /> .<br /> Other global genomic pressures that might differ between genes include fold-pressure (that<br /> relates to mRNA stem-loop configurations that would include segments of dsRNA), and compliance with Chargaff’s second parity rule (3). Furthermore, there is the R-loading rule of non-mitochondrial mRNAs. Because Rs pair poorly with Rs, this would suggest an evolutionary pressure to prevent self-RNAs pairing with each other and forming segments of dsRNA long enough to trigger the early synthesis of interferons and hence new MHC proteins (3, 4). R-loading differences between potential host mRNAs might have affected their CAMAP scores.<br /> .
1.Daouda T, et al. (2020) Codon arrangement modulates MHC-I peptides presentation. Biorxiv: June 4. https://doi.org/10.1101/202...<br /> .<br /> 2.Forsdyke DR (2015) Lymphocyte repertoire selection and intracellular self/not-self discrimination:historical overview. Immunology and Cell Biology 93, 297-304.<br /> .<br /> 3.Forsdyke DR, Mortimer JR (2000) Chargaff's legacy. Gene 261, 127-137.<br /> .<br /> 4.Rosa FM, Cochet MM, Fellous M (1986) Interferon and major histocompatibility genes:<br /> a model to analyze eukaryotic gene regulation. Interferon 7, 47-87.
On 2020-06-09 12:13:18, user Alex Mielke wrote:
Review<br /> While the idea behind this preprint – using modelling approaches to determine what an appropriate null model for behavioural variation between chimpanzee/orang-utan communities would be – is nice, the simulations fail in several ways in replicating the generative process that underlies the original Whiten et al 1999 paper. It is simply an insufficient null model because it ignores any information about ape behaviour except the number of cultural traits produced by that paper. The model simply does not approximate the system that it claims to simulate. It also feels like a strawman is attacked here by singling out one paper and ignoring everything else that is known about these species and their behaviour in the wild. It is telling that apart from the Whiten et al 1999 paper and van Schaik et al 2001 paper, almost no other papers on tool use in wild apes is cited. In the following, I will detail where the simulations fail to convince. This is often due to a complete lack of explanation as to why certain choices were made, making it impossible to replicate if one would start from scratch. There also seems to be a lack of humility in terms of what simulations can and cannot do.<br /> I will focus my criticism largely on the modelling approach and less on the concept of ‘socially-mediated reinnovation’, even though there is certainly enough to be said on the topic. The subheading for each paragraphs summarises the argument made. Mainly, this boils down to the following: the simulations ignore what we know about chimpanzee/orang-utan behaviour in general but also about the Whiten et al paper specifically. The simulations ignore that apes show hundreds of behaviours and exist in thousands of communities, and Whiten et al randomly picked subsets of both – picking 7 other communities might have led to having 70 or 90 cultural behaviours. By pre-selecting 64 behaviours as their only choice, the authors forego the conclusion of their simulations. They also ignore the fact that not all the behaviours in Whiten et al are the same in form, function, complexity, usage etc. The concept of ‘innovation’ seems so broad to be meaningless, because individuals can ‘reinnovate’ behaviours they already used in the past and seem to never be exposed to the behaviours of others until they innovate, at which point they suddenly take social information into account. Innovation rates seem excessively high. Innovation as defined here is meaningless for social behaviours. In the simulations, this leads to the fact that the rule that defines ‘innovation’ is essentially the same as one that would define ‘copying’. The simulations cover the entire range of possible results if one tweaks the parameters right; the authors then focus only on the simulation that contained the observed value in Whiten et al and claim that the parameters for that simulation were biologically the most meaningful, without giving any indication as to how this was decided.
Material and Methods<br /> Highly unnatural demographics and ignorance of the consequences of failed learning and of known learning biases towards specific group members<br /> First off, it is hard to see how the description of an oranzees life here follows ‘realistic demographic features’, as is stated by the authors. Citing the Hill et al 2001 paper here is somehow odd, because even in the best field site in that study, half of individuals died before 25 years of age. In most field sites, risk of death is highest in the first year of life, and continues to be high before individuals reach adulthood; in many sites, few males especially reach 25 years of age. We also have a good idea about inter-birth intervals in these species. This is non-trivial because individual survival will depend on mastering socially learned skills. Presumably, copying does not evolve mysteriously out of the blue – it is useful when the costs of failing to perform a task correctly are high. The model, and I would say the theory underlying it, ignore that an ape who fails to learn a skill correctly faces immense costs. Also, the weird age distribution of communities means that the number of individuals from whom an individual can learn is skewed: there is ample evidence that younger primates learn more, and they use older individuals and higher-ranking individuals as models (e.g. Kendal et al 2015, Horner et al 2010). Individuals will obviously learn more from their mother than any other group member, especially early on – that effect alone renders the simulations meaningless, because an individual will have all the skills it needs by age 8 and then just apply them. So, if learning probability in the simulations is based on the frequency a behaviour is observed in the population, treating all potential models evenly and not weighting the impact of potential models by their age (e.g. remove infants and juveniles) biases the outcome of the results. Any theory and model that is based solely on the frequency of behaviours in the population fails to account for all of these well-known effects.
Faulty assumptions of base likelihoods of behaviours and ignorance to the generative process underlying Whiten et al<br /> I think the most fundamental mistake encoded in the simulations is that they completely fail to understand the process that generated the Whiten et al 1999 results, and rather set up a process that is designed to create exactly the same number of behaviours just to make a point. I could make a random model with each individuals having a 30% chance of showing each of 65 behaviours, and there would obviously be some solutions that could look similar to the Whiten et al results, but that would not mean that the model at all captures any underlying processes. Simulation studies are only useful in as much as they can actually represent the probabilities underlying the original study, especially in this case where the simulation is specifically design to invalidate one existing study. Whiten et al 1999 did not select 65 behaviours out of 65; they selected 65 behaviours out of the several hundred observed chimpanzee behaviours in each site (Nishida et al 2010). They never claimed that these were the only behaviours in which variation could occur, and in fact adding more field sites since then has brought to light many other variants of existing behaviours, as well as entirely new behaviours. Non-tool use behavioural variation is not even included. Also, that study used a very small subset of randomly selected field sites. Everything else aside, sampling out of 65 behaviours means that the number of ‘customary’ etc behaviours is bound in a certain range, which becomes quite obvious in the supplementary, where even random selection leads to similar results as the Whiten et al 1999 study. This does not seem to make the authors suspicious. Essentially, any model would have to explain not why there is variation in 7 group in 65 behaviours, but how likely it is that a random selection of 7 groups (out of thousands) with hundreds of different behaviours shows the patterns observed here. For example, while all the communities in the Whiten et al study drag branches during displays, there is no a priori reason to believe that this is a chimpanzee universal. Also, just because the Whiten et al study does not include group-specific gesture use does not mean it does not exist. The impact of this decision becomes obvious once the genetic parameter is included: if the 65 variable behaviours are a subset of several hundred genetically or environmentally fixed behaviours, then the genetic and environmental parameters would have fundamentally different functions in the simulations.
Ignoring that most problems in the wild can be solved in hundreds of different ways<br /> The other problem with this generative process is one that is also apparent in all experimental studies in captivity, when testing whether apes learn tool use socially: usually, in those experiments, apes have a limited number of different ways of performing an action – often 2 options. However, this is not the case in wild populations. There are usually a large number of options with equal success likelihood that are NOT used; the more detailed the analysis, the more possible options there are. This becomes apparent, for example, in the use of bark pieces of different sizes for termite fishing in neighbouring communities in Gombe (Pascual-Garrido 2019). Chimpanzees in all sites could use a whole lot of tools to fish for termites, comb their hair etc: sticks of different sizes, bark of different sizes, parts of leaves, full leaves, their fingers etc. There are hundreds of different ways to groom someone. Many of these options are not used by any of the groups in the Whiten et al 1999 paper, without good reason, which again speaks against the a priori reduction to 64 behaviours as highly artificial. Again, the generative process for the original paper includes a random selection of field sites that happen to result in 65 solutions. Adding an 8th field site would have added e.g. 10 more behaviours. By ignoring this, the base likelihood of each behaviour in the simulations is off, and the result of the simulations more or less decided before any model is run.
Reinnovation is meaningless for social behaviours and embedded behaviours in sequences<br /> Next point: for social behaviours, re-innovation is a rather pointless concept. A display is not successful if nobody in the group understands what the displayer wants to express – even though individuals could incorporate a fantastical number of potential elements into their displays. Play elements that nobody else knows will not lead to successful play. Hand-clasp grooming does not work if only one individual does them. Courting a female by building a ground nest, as some chimpanzee males do, only works if the female gets the idea. This is as if I would re-invent the handshake – what is the point if nobody understands its meaning? This is completely putting aside that the 65 behaviours in Whiten et al largely ignore social traditions and communication, and focus heavily on tool use, which was the best studied at the time. Apes have probably in access of 100 different play elements in each group (Nishida et al 2010; Petru et al 2009), and it can easily be expected that innovation and social transmission occurs in this context (Perry 2011). One non-tool use example in Whiten et al 1999, rain dancing, cannot conceivably be reinnovated by one individual – what would that even look like, given that it is a coordinated action of several individuals with no discernible physical function? Many of the described behaviours in Whiten et al 1999 are not simple behaviours that occur in a vacuum, but action sequences with several elements that have to be fulfilled in the exact right order and are embedded in sequential behaviour patterns; for example leaf clipping. The generating process of the simulation ignores this and reduces behaviours to independent, on/off instances that fulfil their function outside of a wider context.
Artificial number of states and artificial assumptions about the use of behaviours<br /> The lack of detail in the Whiten et al study also plays an important role. For example, even though ‘drag branch’ is a common behaviour in all field sites, detailed analysis will likely find that there are different ways of dragging a branch, as has been found for other behaviour on the list (e.g. digging for honey Estienne et al 2017). But for the simulations here, this means that achieving the wanted ‘state’ of an individual is directly bound to doing a behaviour exactly like the partner. Also, the basic assumption of the simulations (there are 8 different grooming/play/courtship behaviours that all have the same outcome) is thoroughly misleading, because this is again not the case for the generating process underlying Whiten et al: The 64 behaviours on that list largely fulfil different functions, so instead of simulating 8 categories leading to 64 behaviours, the simulations would have to address 64 ‘states’ that need to be fulfilled. Just because many of them are used to acquire food does not mean that they all serve the same function. Categorising the behaviours as done in the simulations also ignores that even though the form of behaviours might be similar, function can differ drastically. For example, two behaviours that would fall under ‘display’, are branch dragging and drumming. Both are used in displays, but at different times and have different messaging functions, and drumming is also used in some field sites for long-distance communication in other contexts. Function and context might be specific to one sex or age-group: drumming in juveniles, for example, is often part of play; female chimps will slap the ground in displays rather than drum, even though drumming is sometimes observed.
Almost no decision in the simulation process is justified<br /> In general, it would be fantastic if there was even a basic description of why any of the simulations was designed as described here; many of the choices seem to be arbitrary and could not be replicated by someone engaging in the same activity.
‘Innovation probability’ is meaningless for social behaviours<br /> The innovation probability of social behaviour, psocial, seems to have no correspondence in the real world, and it is unclear to the reader why this way of calculating the necessity to innovate was chosen. This again highlights the inability of this framework to account for social traditions. Chimpanzees groom every day, play every day, display regularly, etc; they also observe others do these behaviours, and are the recipient, long before they actively take part in social interactions in their group. Also, from a modelling perspective, it is not clear here what is being innovated: for example, if an individual already has one ‘play’ behaviour but none of the other categories, is the play behaviour potentially reinnovated? Why are these social categories treated as fulfilling one overwhelming urge for ‘social’? I would understand if individuals would be assigned a random number of behaviours in each category, and would have to reinnovate if these do not match those of other group members (seems biologically much more plausible), but the way this process is described here seems meaningless. Also, the state of an individual’s social behaviours cannot exist outside of the state of other group members.
Group members’ needs are not independent<br /> The same is true for food: all group members at a certain time point would obviously be exposed to the same need and availability of food resources, so why is the simulation assuming independence of these things?
Socially-mediated innovation<br /> Individuals lack memory and the concept of ‘innovation’ used here is meaningless<br /> Now we come to some of the most irritating decisions taken in the modelling process, and it feels hard for the reader to understand why they were taken. These seem to completely ignore anything we know about social learning or the life history of animals. Let’s start with the first one: what is the meaning of ‘innovation’ if innovation can happen every month over and over again? If I read this right, each individual can ‘reinnovate’ the same behaviour multiple times in their life? That seems nonsensical – individuals create a repertoire of skills that they apply when necessary. They don’t ‘reinnovate’ nutcracking every time they are hungry, this renders the idea of innovation meaningless. If they already have one ‘play’ behaviour, and their state tells them to play, they use that behaviour. Essentially, the simulation pretends that these behaviours are consistently in flux in a population and an individual, but we know that they are not in wild ape groups. Also, the concept of innovation makes sense for zoo-based apes who are exposed to a new tasks, but is completely nonsensical for wild individuals: for example, by the time any chimp starts nut cracking, they will have observed several millions of strikes by their mother and other group members – are we to believe that they did not in any way take this information into account when acting? They will observe these actions by others while they themselves are in a sensitive period for learning the skills. This is fundamentally incomparable to throwing some stones into a zoo enclosure and hoping that an adult chimpanzee will potentially bang them on a nut. That reinnovation is potentially possible does not rule out the most individuals in a community do not in fact reinnovate. For example, I could easily re-invent the handshake; that does not mean that I initially learned the form and function of handshakes by myself.
The simulation rules used are undistinguishable from copying <br /> The second confusing aspect of the modelling approach described here is, that it would look essentially look the same if copying was described; it is unclear for the reader how these two models would differ from each other in real life. This does not mean that you rule out copying – it means that you are essentially modelling the same effect and give it a fancy name. The assumed difference in the simulations between socially mediated reinnovation (I find a pattern, I check whether this pattern fits the group pattern, I adopt the pattern) and copying (I check the group pattern, I adopt the group pattern) is the order of action and social information. As they are here modelled in the same step, there is no difference. The frequency of ‘innovation’ for any behaviour depends on the frequency of occurrence in the group. That is the same for copying – I can only copy a behaviour that I can observe, and the more I observe them, the more likely I am to copy them. Let us say I am looking for a way to crack nuts. There are three different ways of cracking nuts in my community. I choose to use one of those ways. This is particularly pertinent if the frequency of most behavioural options for a state is zero in the population, and no real ‘innovation’ (new solution) occurs. In the example run in the additional information, many behaviours seem to have one fixed choice in the population. I am not ‘reinnovating’ anything – I make use of the information that is present in the group, which I have observed my entire life. What the authors call ‘innovation’ from an individual perspective is not an ‘innovation’ from an information perspective – in my opinion, this it is thoroughly misnamed, because it assumes that individuals only incorporate social information after they have found an individual solution, which seems wasteful. It is therefore unclear why ‘socially mediated reinnovation’ is supposed to be a simpler explanation for copying. The S factor indicates that sometimes, individuals do not copy faithfully; that does not provide any evidence that the rule described here differs from copying. There is also no accounting for the fact that we do not know at which rate chimpanzees and orang-utans innovate at all; the assumption of the modelling approach seems that each individual innovates whatever they need all the time, but this stands in complete contrast to the fact that chimpanzee communities seem to spend decades doing things the same way. I would urge the authors to somehow indicate why they think that their approach is not simply modelling exactly the same process that everyone else would call ‘copying’, except that they switch whether individuals first observe and then do, or first do and then observe. Because, the latter is meaningless for long-lived animals with long infancy.
Results<br /> The simulations cover the full range of possible values, and the authors simply pick the one they like best<br /> I am not going to go into much detail for the results, because I am not sure what they are supposed to show given the problems raised above. Just relating to Figure 1: It is clear from this figure that a) the result of the simulation is dramatically influenced by the ‘genetic variability’ parameter, which seems artificial and not anchored in any real-world research, even when ignoring the problems of preselecting a subset of behaviours raised above; you can basically achieve any distribution between 0 and 64 by varying this parameter, so some of them necessarily will cover the value from Whiten et al 1999. B) On top of that, the variation for each of the combinations of environmental and genetic factors is huge, all of them cover a range of 20-30 cultural traits (about half the possible values). So, again, what does it mean in this case that the value described in Whiten et al falls into this category? Every other value does as well under some conditions, and the authors simply pick a subset and argue that this is the one they were looking for all along. For example, the manuscript says that there is a good match for alpha_e = 0.8 and alpha_g = 0.2. What does this mean biologically? Is there any indication that this in any way represents that actual circumstances of these chimpanzee communities in the original paper? Is this a better representation of chimpanzee communities than the ones with alpha_g = 0 or alpha_g > 0.5, and what are the criteria to make this decision? If we assume that these chimpanzee communities share several hundred behaviours that were not included in the original 65 possibly cultural behaviours, then alpha_g for chimpanzees is probably very large; the picture is distorted by just using one specific subset. It is repeatedly stated that this simulation represents ‘realistic values for genetic propensity and ecological availability’; it seems a bit cartoonish to reduce genetics and ecology to one value each and call that ‘realistic’. Obviously models need to abstract, but then this should be presented as what it is.
Discussion<br /> I just want to very specifically point out this statement: ‘More generally, the results of our models suggest caution when deriving individual-level mechanisms from population-level patterns (see also (Acerbi et al. 2016; Barrett 2019)).’ However, the same thing is also true the other way round. This paper obviously produces some population-level patterns, and under certain circumstances and when one abstracts everything one knows about primates, they look like they might be similar to the ones reported in the wild. That does not mean that the individual-level process that was used to generate the data was biologically meaningful or represents the system you want to study; as described above, there are many unexplained decisions taken by the authors, and they fail to convince this reader that their choices are replicable and accurately describe chimpanzee or orang-utan behaviour.
On 2020-06-09 11:26:10, user David Gahan wrote:
Regrettably, the link to this article from theGuardian.com is under a link 'peer reviewed paper' but this article in biorxiv specifically says 'preprint'. Mustn't misuse terms!
On 2020-06-06 01:35:56, user azhao wrote:
The title of this report gives an impression that this report has discovered the origin of the novel Croronavirus that causes COVID-19 disease. But it does not: "For these reasons, we cannot rule out an origin for the clade of viruses that are progenitors of SARS-CoV-2 that is outside China, and within Myanmar, Lao PDR, Vietnam or another Southeast Asian country". The title could be more specific to the work. which is using "a phylogeographic Bayesian statistical framework to reconstruct virus transmission history between different bat host species and virus spatial spread over evolutionary time".
Under "Ancestral hosts and cross-species transmission" section: "The phylogenetic reconstructions for α-CoVs in China suggest an evolutionary origin within rhinolophid and vespertilionid bats (Fig. 2A)". And later, "Chinese β-CoVs likely originated from vespertilionid and rhinolophid bats (Fig. 2B)". So both α-CoVs and β-CoVs are likely originated from the same two kinds of bats?
Near the end of Discussion section, "Importantly, the closest known relative of SARS-CoV-2, a SARS-related virus, was found in a Rhinolophus sp. bat in this region(20)", What is "this region"? There is no context to show the name of "this region".
A question about Figure 1.B Map of China provinces, why the CoVs sequences for Shanghai and Jiangsu are not available?
On 2020-06-09 11:18:46, user BlackWinny wrote:
An excellent paper that should even be distributed (translated into vernacular languages if needed) as early as in junior high schools, without waiting to enter high school and university.
On 2020-06-09 08:00:41, user Nikolas Haass wrote:
Now published: Pigment Cell Melanoma Res, 2020 Jun 8. Online ahead of print. PMID: 32511882; doi: 10.1111/pcmr.12903
On 2020-06-08 18:33:25, user Alpesh Pansheriya wrote:
Herd immunity is baseless in country like India.
On 2020-05-31 16:02:24, user Sinai Immunol Review Project wrote:
Pre-existing and de novo humoral immunity to SARS-CoV-2 in humans
Ng, K., Nikhil, F., Cornish, G., Rosa, A., et al.,; bioRxiv 2020.05.14.095414; doi: https://doi.org/10.1101/202...
Keywords
• Humoral immune response<br /> • Human endemic coronaviruses<br /> • COVID-19
Main findings
Using a novel flow-cytometry-based assay, Ng et al. analyzed anti-SARS-CoV-2 S-protein binding antibodies in sera collected from COVID-19 patients between 2 to 43 days after symptom onset (SARS-CoV-2+; pilot cohort n=35, extended cohort n=135; i.e. n=170 total), from SARS-CoV-2-negative individuals with recent PCR-confirmed human endemic coronavirus infection (SARS-CoV-2-/HCoV+; n=34) or SARS-CoV-2-negative donors of unknown HCoV-status (SARS-CoV-2-; n=30 unspecified and n=50 pregnant donors; i.e. n=80 total) as well as from both SARS-CoV-2- and HCoV-negative healthy controls (SARS-CoV-2-/HCoV-; n=31). With the exception of 6 patients in the pilot group, presumably sampled prior to seroconversion, the majority of COVID-19 sera at later collection time points contained uniformly high levels of concurrent IgG, IgA, and IgM antibodies binding to HEK293T cells transiently transfected with the SARS-CoV-2 S-protein. Of note, anti-S IgG, but not IgA or IgM, binding antibodies could also be detected, albeit at lower levels, in 8 of 95 samples from SARS-CoV-2-/HCoV+ and SARS-CoV-2- donors both by FACS assay and using a standard anti-SARS-CoV-2 S-protein ELISA for validation. Importantly, in accordance with a recent preprint by Premkumar et al. (https://doi.org/10.1101/202... none of these samples were found to be cross-reactive against the highly specific S1 protein or anti-receptor-binding domain (RBD) as determined by respective ELISA assays. Additionally, 7 of these samples (n=3 anti-S IgG-positive and n=4 anti-S IgG-negative by FACS assay) were confirmed by specific ELISA to also react with the SARS-CoV-2 N-protein, suggesting that a total of 12/95 SARS-CoV-2-negative donors with previous or unknown HCoV history produced cross-reactive IgG binding antibodies against conserved SARS-CoV-2 epitopes. This observation was additionally corroborated by similar findings in a second cohort of SARS-CoV-2- patients with unknown HCoV history (n=50), revealing that approximately 10% of these donors had pre-existing antibodies that were cross-reactive against conserved SARS-CoV-2 proteins. Notably, according to the authors, FACS-based anti-S IgG detection, in particular at earlier time points prior to seroconversion, was seemingly more sensitive compared to standard ELISA assays, and simultaneous expression of anti-S IgG, IgA, and IgM binding antibodies, exclusively limited to COVID-19 patients, was found to persist for at least 43 days. Finally, Ng et al. demonstrate that anti-S IgG binding antibody-containing sera from donors with recent HCoV infections were equally capable of neutralizing lentivirus-pseudotyped SARS-CoV-2 as serum samples obtained from seroconverted COVID-19 patients, suggesting that cross-reactive neutralizing antibodies may interfere with SARS-CoV-2 target cell entry likely mediated by the alternative receptor CD147 (unfortunately, no data shown). Furthermore, levels of neutralizing antibody in SARS-CoV-2-/HCoV+ sera correlated particularly robustly with anti-S IgG binding antibodies detected by FACS assay.
Limitations
A general limitation of this preprint by Ng et al. is that extended data repeatedly referred to throughout the manuscript is not accessible. It is therefore impossible to assess some of the information provided in text, e.g. about potential advantages of the FACS assay developed here over conventional ELISA assays or regarding ACE2 and CD147 expression on HEK293T cells as well as potential implications for SARS-CoV-2 cellular entry. Furthermore, in addition to the relatively small sample size in Figure 1 (cf. n=2-5 donors per panel), the exact number of donors enrolled per study group as well as of specimens used per individual experiment is somewhat unclear and should be specified: according to the methods section, a total of n=114 SARS-CoV-2 negative donors with recent or unknown HCoV infection were included; yet, throughout the manuscript, the authors repeatedly refer to n=95 donors with diagnosed/potential HCoV-infection as well n=50 pregnant donors of uncertain HCoV-status. Similarly, numbers for both the SARS-CoV-2 negative, potentially HCoV-positive, and the COVID-19 donor groups displayed in Figure 2a-d may seem misleading within the general context of the manuscript (n=35 COVID-19 displayed vs. n=170 COVID-19 mentioned in text, n=64 potentially HCoV-positive donors displayed vs. n=114 mentioned in text). This also seems to be the case for some of the SARS-CoV2-negative donors testing positive for pre-existing cross-reactive antibodies. Given that this is one of the central observations in this preprint, one would expect the respective numbers mentioned in the manuscript to match the data displayed in Figure 2a (n=8 by FACS assay vs. n=5 on display). Moreover, information on clinical course and symptoms of COVID-19 patients would be of particular interest with respect to potential differences in antibody subtype composition related to disease severity as well as in the context of sample timing and viral persistence as based on qPCR detection (in addition to limited information on two donors of interest already discussed in the manuscript). Similarly, to confirm that simultaneous detection of IgG, IgA, and IgM co-expression is indeed highly specific for active/convalescent COVID-19 as suggested by the authors, this should be validated in donors with current HCoV infection or in those recently recovered (i.e. earlier than 3-9 months post HCoV diagnosis as was the case here) to exclude sample bias due to waning HCoV humoral immunity with time in comparison to the very recent COVID-19 patients studied here. Given that the clinical course of HCoV infections might be similar to mild COVID-19 it is critical to determine whether the observations about concurrent expression of IgG, IgA, and IgM will reliably discriminate between active HCoV and SARS-CoV-2 infection. Finally, neutralizing capacity of cross-reactive IgG antibodies eventually needs to be confirmed using live SARS-CoV-2 to better assess the potential for antiviral protection.
Significance
In this preprint, Ng et al report the flow-cytometry-based detection of pre-existing binding antibodies against conserved SARS-CoV-2 epitopes in around 10% of SARS-CoV-2 negative donors with confirmed or potential previous history of HCoV infections. Notably, binding antibody-containing sera from the same donors were found to possess neutralizing activity comparable to samples obtained from seroconverted COVID-19 patients, suggesting that pre-existing cross-reactive antibodies may interfere with SARS-CoV-2 entry into target cells, likely via alternative receptors such as CD147. Assessing the potential role of both pre-existing binding and neutralizing antibodies in healthy donors in the context of COVID-19 pathogenesis will be of particular importance. The observations made here are also highly relevant for the design and development of reliable diagnostic assays as well as of potential vaccine candidates, and should therefore be further explored in ongoing research on potential coronavirus therapies and prevention strategies.
This review was undertaken by V. van der Heide as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.
On 2020-06-08 12:44:05, user OxImmuno Literature Initiative wrote:
On 2020-06-08 12:28:54, user OxImmuno Literature Initiative wrote:
On 2020-06-08 12:23:39, user OxImmuno Literature Initiative wrote:
On 2020-06-08 09:10:54, user David Curtis wrote:
The number and range of highly significant results obtained is very striking, especially compared to what we found when we applied similar methods to a much large schizophrenia case-control sample (Curtis et al., 2018). It’s concerning that there is a significant excess of synonymous and benign non-coding variants in cases, which is not really what I would expect. The results are critically dependent on whether the covariates included adequately control for any systematic biases in the dataset. It might be helpful if at least the summary statistics for the variant counts in each category were presented.
Curtis, D., Coelewij, L., Liu, S.-H., Humphrey, J., Mott, R. (2018) Weighted Burden Analysis of Exome-Sequenced Case-Control Sample Implicates Synaptic Genes in Schizophrenia Aetiology. Behav. Genet. 43, 198–208.
On 2020-06-08 09:03:05, user Mónika Bokor wrote:
The paper https://www.biorxiv.org/con... is modified and published as Int. J. Mol. Sci. 2020, 21(11), 3997; https://doi.org/10.3390/ijm....
On 2020-06-08 01:59:00, user Kurganov Erkin wrote:
I would like to ask several questions regarding the channel kinetics:<br /> 1. How authors explain the vivid activation of hTRPA1 in the presence of 100 mkm Ca. (In Figure 2 A,B and C, 100 mkm Ca activates hTRPA1, but conductance and open probability differs largely with and without inhibitors experiments.<br /> 2. Which conductance is the basic conductance at 10 mkm Ca? What other factors are involved in such big difference conductance?<br /> 3. Did others check other divalent or monovalent cations on channel activation? If yes, then do they activate hTRPA1 in the absence of agonist? Is there any difference with and without ankyrin repeat? If no, then do they think that there should be calcium binding site apart from ankyrin repeat?
On 2020-06-06 13:03:25, user OxImmuno Literature Initiative wrote:
On 2020-06-06 13:01:05, user OxImmuno Literature Initiative wrote:
On 2020-06-06 12:55:04, user OxImmuno Literature Initiative wrote:
On 2020-06-06 12:52:40, user OxImmuno Literature Initiative wrote:
On 2020-06-06 12:49:52, user OxImmuno Literature Initiative wrote:
On 2020-06-03 14:03:52, user Sinai Immunol Review Project wrote:
Main findings<br /> In this pre-print, Zhang et al. hypothesized that one possible mechanism used by SARS-CoV-2 to participate in efficacious viral spread is an evasion of the immune response by interfering with antigen presentation by infected cells via MHC-I. Of note, this behavior has been previously characterized in other viruses, including HIV-1 and HSV.
The authors identified ORF8 as one such possible mediator of this mechanism. Interestingly, an overexpression of ORF8 in HEK293T cells resulted in a significant down-regulation of MHC I (HLA-A2) expression, as determined by flow cytometry. Using GFP as a molecular marker and HIV-1-derived Nef protein as a positive control, the authors evaluated MHC-I heavy chain and β2M expression as a functional readout of overall MHC-I expression on the control and ORF8-expressing 293T cells. Flow cytometric analysis on these cells, as well as other cell lines that were similarly tested (human fetal colon, human bronchial epithelial, and human liver), showed significant down-regulation of MHC-I, in response to ORF8 overexpression via plasmid transfection. The authors confirmed that 293T cells could harbor ORF8 production by exposing ACE2-expressing 293T cells to a SARS-CoV-2 strain. Importantly, sequences of ORF8 from SARS-CoV-2 and SARS-CoV-1 showed the least homology, suggesting that this may be a potentially novel immune evasion mediator.
To determine the cellular pathways underlying this relationship between ORF8 and MHC-I, the authors used small molecule inhibitor analysis to determine that ORF8-mediated down-regulation of MHC-I expression is facilitated by lysosomal degradation. The authors performed western blot analyses following in-tact lysosomal extraction to show that an enrichment of MHC-I protein is seen in the lysosomes of 293T cells that had been transfected with the ORF8 plasmid. In fact, confocal microscopy of the ORF8-expressing cells showed a co-localization of the MHC-I proteins with LAMP1, a marker for lysosomal membranes. In fact, the authors demonstrated that ORF8 expression also co-localized with MHC-I. Subsequent co-immunoprecipitation experiments confirmed binding of ORF8 with MHC-I complexes in these compartments.
The authors further elucidated this mechanism by determining the route of the endolysosomal pathway used by this mechanism. A series of co-localization/confocal microscopy experiments demonstrated significant co-localization of ORF8 with ER components (CALNEXIN staining) and lysosomes (LAMP1 staining), but not the Golgi or early endosomal vesicles. The inability to counteract the ORF8-mediated down-regulation of MHC-I via knockdown of vesicle-trafficking-related proteins supported this finding. Importantly, the authors also evaluated MHC-I protein ubiquitination as a cause of reduced surface expression; however, knockdown of ER-associated protein degradation genes did not reverse the phenotype. The authors identified, instead, that selective knockdown of autophagy-associated proteins restored MHC-I expression, implicating a role for autophagy in this process.
Finally, the authors tested the potential role of this relationship between ORF8 and MHC-I in immune evasion. Using SSp-1, a predicted potential SARS-CoV-2 epitope, the authors pulsed 293T cells (transfected with empty vector or the ORF8 plasmid) with SSp-1 and exposed these cells to SSp-1-specific CTLs via sensitization of HLA-A2 healthy donor PBMCs with autologous DCs pre-pulsed with SSp-1. The killing assay showed reduce elimination of target ORF8-expressing 293T cells by these CTLs. The authors performed similar subsequent experiments by using CTLs isolated from a recovering COVID-19 patient (selected from among other patients, based on ability for their CTLs to secrete IFN-ɣ, when exposed to S and N protein-derived peptides). When exposed to 293T cells that were pulsed with the same peptide mixture, the SARS-CoV-2-specific CTLs eliminated ORF8-expressing 293T cells at a lower efficacy.
Limitations<br /> First, many of the experiments involved use of a cell line (HEK293T) that had been transfected with a plasmid to overexpress ORF8. It remains to be seen whether productive infection with a SARS-CoV-2 strain of cells that naturally express human ACE2 results in the same down-regulation of MHC-I.
Of note, the knockdown siRNA experiments investigating the role of the autophagy pathway in ORF8-mediated down-regulation of MHC-I could be bolstered with the use of additional controls for the Western blot analyses, specifically cellular and lysosomal membrane proteins. (Moreover, to provide context, the authors could have also included a list of hits produced by the mass spectrometry analysis.)
Second, the authors do not provide a kinetics analysis that includes an assessment of overall cell death of the transfected or infected 293T cells. Though it is unlikely that we would see significant cell death in the in vitro experiments described in this report (given that the 293T cells were usually transfected by a plasmid), the kinetics of cell turnover may have an impact on the overall contribution of ORF8-mediated down-regulation of MHC-I to successful immune evasion.
Third, the T cell experiments could have been better supplemented by analyses of MHC-I and ORF8 expression by infected cells and non-infected cells of these patients by using FACS-sorted epithelial cells collected from BAL. Moreover, the non-reactive T cells from COVID-19 patients could have been used as controls, alongside the reactive T cells that were used in this study to better assess killing in a controlled experimental design.
Finally, without an in vivo experiment that demonstrates the aforementioned observations, it is difficult to assess whether additional signaling pathways active during viral infection, such as interferon signaling, may influence the relationship between ORF8 and MHC-I.
Generally, the aforementioned experiments could have compared the results of using SARS-CoV-2 ORF8 with using SARS-CoV-1 ORF8 to further demonstrate the specificity of this mechanism to SARS-CoV-2 infection (in addition to the evidence that SARS-CoV-2 and SARS-CoV-1 ORF8 proteins are the least homologous [Fig. 1]).
Significance<br /> In summary, this report provides a direct mechanistic link between SARS-CoV-2 infection and immune evasion that involves a role for ORF8, a seemingly SARS-CoV-2-specific protein, and MHC-I expression. The results warrant further investigations in an in vivo model to validate the proposed relationship between ORF8 expression in infected cells and MHC-I down-regulation and the subsequent impaired ability for CTLs to kill these infected targets.
This review was undertaken by Matthew D. Park as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.
On 2020-06-03 12:04:05, user Sinai Immunol Review Project wrote:
Main findings<br /> In this pre-print, Zhang et al. hypothesized that one possible mechanism used by SARS-CoV-2 to participate in efficacious viral spread is an evasion of the immune response by interfering with antigen presentation by infected cells via MHC-I. Of note, this behavior has been previously characterized in other viruses, including HIV-1 and HSV.
The authors identified ORF8 as one such possible mediator of this mechanism. Interestingly, an overexpression of ORF8 in HEK293T cells resulted in a significant down-regulation of MHC I (HLA-A2) expression, as determined by flow cytometry. Using GFP as a molecular marker and HIV-1-derived Nef protein as a positive control, the authors evaluated MHC-I heavy chain and β2M expression as a functional readout of overall MHC-I expression on the control and ORF8-expressing 293T cells. Flow cytometric analysis on these cells, as well as other cell lines that were similarly tested (human fetal colon, human bronchial epithelial, and human liver), showed significant down-regulation of MHC-I, in response to ORF8 overexpression via plasmid transfection. The authors confirmed that 293T cells could harbor ORF8 production by exposing ACE2-expressing 293T cells to a SARS-CoV-2 strain. Importantly, sequences of ORF8 from SARS-CoV-2 and SARS-CoV-1 showed the least homology, suggesting that this may be a potentially novel immune evasion mediator.
To determine the cellular pathways underlying this relationship between ORF8 and MHC-I, the authors used small molecule inhibitor analysis to determine that ORF8-mediated down-regulation of MHC-I expression is facilitated by lysosomal degradation. The authors performed western blot analyses following in-tact lysosomal extraction to show that an enrichment of MHC-I protein is seen in the lysosomes of 293T cells that had been transfected with the ORF8 plasmid. In fact, confocal microscopy of the ORF8-expressing cells showed a co-localization of the MHC-I proteins with LAMP1, a marker for lysosomal membranes. In fact, the authors demonstrated that ORF8 expression also co-localized with MHC-I. Subsequent co-immunoprecipitation experiments confirmed binding of ORF8 with MHC-I complexes in these compartments.
The authors further elucidated this mechanism by determining the route of the endolysosomal pathway used by this mechanism. A series of co-localization/confocal microscopy experiments demonstrated significant co-localization of ORF8 with ER components (CALNEXIN staining) and lysosomes (LAMP1 staining), but not the Golgi or early endosomal vesicles. The inability to counteract the ORF8-mediated down-regulation of MHC-I via knockdown of vesicle-trafficking-related proteins supported this finding. Importantly, the authors also evaluated MHC-I protein ubiquitination as a cause of reduced surface expression; however, knockdown of ER-associated protein degradation genes did not reverse the phenotype. The authors identified, instead, that selective knockdown of autophagy-associated proteins restored MHC-I expression, implicating a role for autophagy in this process.
Finally, the authors tested the potential role of this relationship between ORF8 and MHC-I in immune evasion. Using SSp-1, a predicted potential SARS-CoV-2 epitope, the authors pulsed 293T cells (transfected with empty vector or the ORF8 plasmid) with SSp-1 and exposed these cells to SSp-1-specific CTLs via sensitization of HLA-A2 healthy donor PBMCs with autologous DCs pre-pulsed with SSp-1. The killing assay showed reduce elimination of target ORF8-expressing 293T cells by these CTLs. The authors performed similar subsequent experiments by using CTLs isolated from a recovering COVID-19 patient (selected from among other patients, based on ability for their CTLs to secrete IFN-ɣ, when exposed to S and N protein-derived peptides). When exposed to 293T cells that were pulsed with the same peptide mixture, the SARS-CoV-2-specific CTLs eliminated ORF8-expressing 293T cells at a lower efficacy.
Limitations<br /> Many of the experiments involved use of a cell line (HEK293T) that had been transfected with a plasmid to overexpress ORF8. It remains to be seen whether productive infection by a SARS-CoV-2 strain of cells that naturally express human ACE2 results in the same down-regulation of MHC-I.
Furthermore, without an in vivo experiment that demonstrates the aforementioned observations, it is difficult to assess whether additional signaling pathways active during viral infection, such as interferon signaling, may influence the relationship between ORF8 and MHC-I. Lastly, the authors do not provide a kinetics analysis that includes an assessment of overall cell death of the transfected or infected 293T cells. Though it is unlikely that we would see significant cell death in the in vitro experiments described in this report, given that the 293T cells were usually transfected by a plasmid, the kinetics of cell turnover may have an impact on the overall contribution of ORF8-mediated down-regulation of MHC-I to successful immune evasion.
The CTL experiments could have been better supplemented by analyses of MHC-I and ORF8 expression by infected cells and non-infected cells of these patients by using FACS-sorted epithelial cells collected from BAL.
Significance<br /> In summary, this report provides a direct mechanistic link between SARS-CoV-2 infection and immune evasion that involves a role for ORF8, a seemingly SARS-CoV-2-specific protein, and MHC-I expression. The results warrant further investigations in an in vivo model to validate the proposed relationship between ORF8 expression in infected cells and MHC-I down-regulation and the subsequent impaired ability for CTLs to kill these infected targets.
This review was undertaken by Matthew D. Park as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.
On 2020-06-06 08:58:50, user Luis Toledo wrote:
KU60019 at 10microM also inhibits ATR...
On 2020-06-05 23:40:58, user Allison Hamilos wrote:
Please note that the companion paper, "Dynamic dopaminergic activity controls the timing of self-timed movement," is available on bioarxiv at: https://doi.org/10.1101/202...
On 2020-06-05 19:42:24, user Alexandra Beliavskaia wrote:
Dear authors, the preprint text mentions supplementary data, but there is no supplementary data linked to the preprint itself. Would you be so kind to point me to where one can find it? Thank you!
On 2020-06-05 18:25:50, user Marianna K wrote:
It doesn't seem like you looked at IgA antibody response at all? This would be very valuable data for the vaccine development community if you have frozen patient samples remaining!
On 2020-06-02 16:05:36, user Esmeralda R. wrote:
This is a great article!<br /> It is a good news to know that everyone carries a highly neutralizing antibody. At least now, we know that the vaccine developed in the future may work very well. <br /> I hope Dr Nussensweig and team may be able to develp further a very efficient vaccine against SARS-Cov2. <br /> People from my company, Real Gramas are all excited about this vaccine to be lauched soon!
On 2020-06-01 16:40:10, user OxImmuno Literature Initiative wrote:
On 2020-06-05 17:41:32, user Laurent Seroude wrote:
Manuscript has been peer-reviewed and has been accepted for publication in Genome on June 5th 2020.
On 2020-06-05 16:46:53, user Mariana Mateos wrote:
The final version of this manuscript is now published and freely available at https://www.frontiersin.org/articles/10.3389/fmicb.2020.01080/full
On 2020-06-05 15:20:13, user David Melville wrote:
I should have cited: Loftus, A. F., Hsieh, V. L. & Parthasarathy, R. Modulation of membrane rigidity by the human vesicle trafficking proteins Sar1A and Sar1B. Biochem. Biophys. Res. Commun. 426, 585–589 (2012). They found some evidence of SAR1A dimerization with their study as well.
On 2020-06-05 15:18:06, user David Melville wrote:
Final version here:<br /> doi: 10.1074/jbc.RA120.012964<br /> "Small sequence variations between two mammalian paralogs of the small GTPase SAR1 underlie functional differences in coat protein complex II assembly"
On 2020-06-05 14:14:13, user Shoaib Ahmed wrote:
hi, <br /> very interesting article. After directly involve in the field and following the research of other gps, what do you this is the possible mechanism of installation of these MODs at specific location in different mRNA substrates? Why are mods helping some viruses while working as antiviral in other cases? Especially if they are being installed at specific sequence motif or secondary structures?<br /> Thank you.<br /> Dr. Choudhary S Ahmed<br /> Chemistry, UCONN
On 2020-06-05 12:56:48, user Patrick Lemaire wrote:
Since the last version of this preprint, we have improved the software <br /> and modified the access to both software tutorials and data. All details<br /> can be found here: http://www.crbm.cnrs.fr/en/...
On 2020-06-05 11:38:46, user Patrick Lemaire wrote:
ASTEC software and datasets: Updated Access.
Following further developments, we are happy to share below updated access to the data and software presented in the preprint. The information below and more can also be found on the ASTEC webpage: http://www.crbm.cnrs.fr/en/...
We hope that you will like these tools and data and that they will be useful to your own work.
Patrick Lemaire and co-authors.
Software and Tutorials:
Github repositories:
The software, standard parameters files and tutorials can be found in the Github repository: https://github.com/astec-se....
"astec-2019-published" is the repository dedicated to this paper with a fixed and autonomous (full version, python and C codes) ASTEC software package. This include all the codes and libraries necessary to install the software with examples as standard parameters files and a tutorial to test it. A complete documentation is also provided to guide the users.
"astec" is the repository for for the ongoing development, enhancement and storage the package development process. Only the python codes are included in this repository. The standards parameter files or tutorial provided in the astec-2019-published repository may have to be adapted to more recent versions of the code.
Standard parameters. We provide a standard example parameter file that can be initially used to setup each ASTEC step on either the test set we provide (see below) or to initiate a project on personal data.
Tutorial and test dataset. We provide a tutorial to test each step of ASTEC, a set of 20 timepoints from ASTEC-Pm1 with a downsized resolution to 1μm3. The ASTEC pipeline on this dataset can be run a standard workstation (4 threads; < 1 mn for the fusion and segmentation of a timepoint).
Jupyter notebook. Provides examples on how to use the outputs of ASTEC can be found in the Github repository: https://github.com/leoguign...
Datasets
The ASTEC imaging, segmentation or geometric informations for two wild-type Phallusia mammillata embryos (ASTEC-Pm1 and ASTEC-Pm2) can be downloaded from Figshare (https://figshare.com/s/765d.... These datasets include:
– Raw images: The four views for some time points of the movie Astec-Pm1 are shared to test the fusion algorithms or evaluate the quality of the acquisition obtained with the MuViSPIM light-sheet microscope. The format of the images is .hdf5 files that can be easily read with an ImageJ/FIJI plug-in.
– Fused images: complete sequence of fused images are shared for ASTEC-Pm1 and -Pm2.
– Segmented images: complete sequence of segmented images (voxelic representation) are provided for the entire ASTEC dataset.
– Meshed segmented images: a meshed version of the segmented images (.obj format), which can be uploaded into the interactive Morphonet web tool (morphonet.org), is provided for the complete dataset.
– Geometric properties: Properties measured after spatial registration of the movies are shared in both .pkl and .xml format for the complete dataset.
Note : For each embryo, all files are compressed into a single archive file (tar.gz). A tool provided in the astec-2019-published Github repository can be used to convert .inr format files into the more common .mha files.
On 2020-06-05 11:35:53, user sang wrote:
Can you provide a clearer figure 2B. I can't see the gene names.
On 2020-06-05 09:29:21, user Pei-Hui Wang wrote:
This work has been published on Journal of Medical Virology ( https://doi.org/10.1002/jmv... ).
On 2020-06-05 09:01:12, user James Wells wrote:
Seems like a versatile platform for cell scalability issues surrounding cell therapy in general. This technology can be further enabled with microfluidics for high-throughput. Excellent article!
On 2020-06-05 03:11:48, user N.Mallikarjunarao1 wrote:
This work leads to new drug design in curbing covid .
On 2020-06-04 20:44:40, user Esmeralda R. wrote:
Great paper!<br /> I hope it gets published soon after peer review. <br /> Many scientists are seeing the involvement and relation of the SARS-CoV2 to ACE2. <br /> Real Gramas<br /> Grama Esmeralda
On 2020-06-05 02:59:22, user Naito Motohiko wrote:
from Author M.Naito
Dear the readers who are unfamiliar with math
Thank you for reading my preprint manuscirpt.<br /> The first half of the result of my manuscript discuss about oncology with mathematical method. Thus, It would be very difficult to understand it as well as boring. <br /> if you feel so, I recommend to read my manuscript with skipping the first two chapters, and supposing these two chapters are mathematically true. Doing above mentioned, I consider this can be read as well as other papers.<br /> Of cause, mathematical discussions are welcomed as well as oncological one.
Best Regards M. Naito
On 2020-06-05 00:32:01, user Clive Delmonte wrote:
It would be really exciting to have images of double stranded DNA alone, and then bound to antibodies and other species. Such images could resolve structural disputes which have been prominent in this field for decades.
On 2020-06-04 00:10:15, user klim wrote:
This is amazing and really impressive work! Why is the cold FEG energy spread asymmetric?
On 2020-06-04 21:37:51, user Torsten Seemann wrote:
The github URL cited in the paper leads to 404 NOT FOUND. Perhaps the repo is still "private" ?<br /> https://github.com/tgolubch...
On 2020-06-04 11:23:56, user Raúl G. Saraiva wrote:
Thanks for sharing your work. I couldn't access the sequences of CbAEs to better understand these proteins. How do you comment on your results based on our previous assessment of the nature of Chromobacterium sp. Panama's anti-DENV activity (see https://journals.plos.org/p... )? Did you perform loss-of-function assays to establish CbAEs as the causative agent of the antivrial activity of your chromobacterium?
On 2020-06-01 14:29:26, user OxImmuno Literature Initiative wrote:
On 2020-06-04 11:10:15, user Stefan wrote:
I carefully read your manuscript and like your method. I hope this can significantly improve RNA structure analysis.
Here are some thoughts of mine:
1) looking at the 71bp sequence in your supp Figure 2, I count 24 A's, i.e. 4 more than in your Figure 1C. Why are those 4As in the closing stem missing? This also applies to Panels A and B in Figure 2. Furthermore, if the structure in 2A should correspond to the MFE structure sin supp2, there seems to be an offset in the white circles, which I think denote A's
2) regarding Figure 1G: the three drawn structures together make up ~60%. I wonder how heterogeneous the remaining 40% are? Could you maybe classify them by their level 1 shape (https://bibiserv.cebitec.un... to report a more coarse grain overview?
3) For better orientation, it would be nice to have extra ticks on every tenth nucleotide in Figure 2C, as you used for supp2.
4) I don't understand your sentence "Both bound and unbound TPP structures had similar modifications profiles (Supp. Fig. 7) indicating preformation of structure in the absence of the ligand." Doesn't "bound" imply ligand presence?
5) Am I right, if I understand you dAMI scores for multiple individual molecules as something similar to covariance for a set of ortholog sequences? Regarding Figure 2E: you nicely discuss findings for boxes I to III. Looking at the 3' end of the sequence, I see a lot of red. Wouldn't that imply further structuredness at the molecules 3' end in both states? The squiggle plots in 2C draw those areas as unpaired regions, but is this supported by crystallography? I am also trying to wrap by head around the red rectangle between positions 7 and 61 for the unbound state, but cannot see how they would form a stem.
On 2020-06-04 10:07:18, user FakeIoannidis wrote:
How is this different from False discovery rate (FDR)-based methods? what is really new?
On 2020-06-04 07:44:19, user Billy Bostickson wrote:
Any Updates from the authors? <br /> Perhaps they need to refocus the paper on RaTG13 to enhance the validity of the findings?
On 2020-06-04 05:28:38, user imroze khan wrote:
Our latest manuscript on how carcass scavenging reverses the fitness effects of female interference competition #chemicalwarfare in flour beetles, with interesting implications for the evolution of competitive interaction, chemical ecology and spread of disease!
On 2020-06-04 04:38:11, user Shinya Fushinobu wrote:
We included several amendments according to suggestions from Reviewers and Editor of PLOS ONE. So this preprint is obsolete. Pls follow this link.<br /> https://doi.org/10.1371/jou...
On 2020-06-02 00:46:54, user Shinya Fushinobu wrote:
On 2020-06-04 04:37:07, user Victoria wrote:
Was MATLAB used for this study and, if so, is the code available for MDS or PCA?
On 2020-06-04 03:06:08, user Ron Conte wrote:
They say the S-enantiomer was 60% more effective than the R-enantiomer. Yes, but it was (by my quick calculation) only 17.6% more effective than the mix of R- and S-HCQ that is used as medicine. Is that not correct?
On 2020-06-04 02:52:13, user Abhijit Chakraborty wrote:
Knock-down of the TIM/TIPIN complex promotes apoptosis in melanoma cells<br /> https://www.oncotarget.com/...
On 2020-06-03 18:50:14, user Min Xu wrote:
Nice work on the interaction of rabbit hemorrhagic disease virus RdRp complex and nucleolin. It provides new insight to the relationship of virus replication and nucleolin.
On 2020-06-03 18:40:45, user Sandra Newell wrote:
3 June 2020
Review of “Regulation by the Pitcher Plant Sarracenia purpurea of the Structure of its Inquiline Food Web” by A. M. Ellison, N. J. Gotelli, L. A. Bledzki, and J. L. Butler, preprint on BioRxiv.
Reviewer: Sandra J. Newell, Professor Emerita, Department of Biology, Indiana University of Pennsylvania, (sjnewell@auxmail.iup.edu) or (sandra.newell1@gmail.com)
The authors present a valuable contribution to the pitcher plant literature in the controlled experiment testing whether the pitcher plant affects the inquilines’ community structure.
However, to state that “the plant usually is considered simply as an inert container, not as an interacting part of the food web” ignores a long history of speculation about the relationship between the plant and its inquilines.
Much earlier research focused on characteristics of a pitcher and their effects on inquilines. The interactions are apparent when the pitcher opens, and obligate inquilines seek their host plant. For example, Istock et al. (1983) concluded that pitcher-plant mosquitoes were likely attracted to pitchers by a volatile chemical cue. Ovipositing pitcher-plant mosquitoes are also attracted to younger and larger pitchers (Mogi and Mokry 1980; Bradshaw 1983; Heard 1994), and to pitchers with lower levels of nutrient enrichment (Hoekman et al. 2007). After colonization, abundance of various inquilines changes with leaf age (Fish and Hall 1978; Nastase et al. 1995). Trzcinski et al. (2003) found that abundance of mosquitoes, midges, and sarcophagid flies was greater in larger pitchers, and Heard (1994) suggested that the oviposition preference for larger pitchers was because they were less likely to dry out. The authors might want to expand their discussion to consider why pitcher-plant insects were reluctant to oviposit in pitchers with intact tubes. Can the differences in community structure between pitchers with and without tubes be attributed entirely to oviposition decisions?
Microclimate created within the pitcher also influences the inquiline community, and the question then is how much is microclimate derived from the pitcher characteristics and behavior. Rango (1999) found greater abundance of both mosquitoes and midges in pitchers with greater volume of water. Water volume and temperature also influenced the development and survival of inquilines (Kingsolver 1979). Similarly, Hamilton and Kourtev (2011) demonstrated that pitcher-plant mosquitoes were more abundant in pitchers with higher relative water volume, and both mosquitoes and midges were more abundant in pitchers with lower pH. Fish and Hall (1978) measured pH of pitcher fluid and its relation to pitcher age and inquilines. Pitchers may have some control over the pH within the pitcher fluid, although Sarracenia certainly less so than Nepenthes (Adlassnig et al 2011). The authors rightly point out that the pitcher influences oxygen levels in the aquatic habitat. Hamilton (2010) discusses how the various aspects of microclimate and pitcher characteristics may interact to influence inquilines. The authors could perhaps expand their discussion of the mechanisms underlying the differences between pitchers with and without tubes (and the tube controls). Can the authors speculate as to why Fletcherimyia had higher mortality in the tubes? Can the differences in community structure between pitchers with and without tubes be attributed entirely to the mortality of the flies?
The question of whether inquilines are commensals or mutualists has been much debated (Adlassnig et al 2011). Inquilines obviously benefit from the association with pitchers, which constitute their habitat. Much of the debate has centered around the extent to which the plant benefits from the inquilines. That discussion has focused on the extent to which inquilines promote digestion of prey, making nutrients more available to the plant. Plummer and Kethley (1964) and Lloyd (1942) review the early history of absorption of nutrients by pitchers, and Juniper et al. (1989) devote several chapters to nutrition and digestion. The authors of the manuscript rightly point out, there is evidence for a mutualistic relationship between the plant and its obligate mosquito and midge inquilines. The authors also rightly point out the important role of bacteria in this process. Juniper et al. (1989) argued for a minimal role of microbes in digestion. However, recent research supports the mutualistic relationship between plant and microbes in the context of prey digestion (Luciano and Newell 2017; Young et al 2018). Also, Luciano and Newell (2017) used a controlled experiment to demonstrate that acid phosphatase activity was higher in pitchers than in samples incubated outside the pitcher, demonstrating that the pitcher and its microbes are involved in digestion of prey. While the authors have focused their study on the macrobial components of the community, I would expect the microbial community within the tubes to be different from that of the pitcher per se. The size of the perforations in the tube controls would certainly allow microbes free passage in and out of the tube.
The Nepenthes reference in the Introduction (“increasing prey capture efficiency (Thornham et al. 2011)”; should it be 2012?) does not seem relevant. The only stated effect of the pitcher on inquilines is “reducing fungal colonization.” A better reference here would be Kitching and Pimm (1985), in which they consider length of inquiline food chains with regard to characteristics of Nepenthes pitchers.
With regard to the statistical analyses, more information would be helpful. The section on Statistical Analysis lists standard tests. It’s not clear how and why a response surface design is used; additional information about the response surface design is necessary. In the Results section each p-value should be accompanied by the type of test being reported. Also, the authors need to indicate when the second experiment was performed in the Methods section.
The strength of this paper lies not in its rejection of a straw-man hypothesis (i.e., pitchers as inert containers) but in the use of a controlled experiment rather than the correlational analyses of earlier work. A second strength is the focus on community structure (i.e., food web saturation) as opposed to behavior or abundance of individual species. I look forward to seeing the published paper.
Literature Cited<br /> Adlassnig, W., M. Peroutka, and T. Lendl. 2011. Traps of carnivorous pitcher plants as habitat: composition of the fluid, biodiversity and mutualistic activities. Annals of Botany 107: 181-194.
Bradshaw, W. E. 1983. Interaction between the mosquito Wyeomyia smithii, the midge Metriocnemus knabi, and their carnivorous host Sarracenia purpurea. Pages 161-189 In: J. H. Frank and L. P. Lounibos (eds.), Phytotelmata: Terrestrial Plants as Hosts for Aquatic Insect Communities. Plexus, Medford, NJ.
Fish, D. and D. W. Hall. 1978. Succession and stratification of aquatic insects inhabiting the leaves of the insectivorous pitcher plant, Sarracenia purpurea. American Midland Naturalist 99: 172-183.
Hamilton, R., IV. 2010. Observations of pitcher plant (Sarracenia purpurea L.) phytotelm conditions from two populations in Jackson Bog, Stark County, OH. Ohio J. of Sci. 110(5): 98-103.
Hamilton, R., IV, and P. S. Kourtev. 2011. Seasonal dynamics of three inquiline species in isolated island populations of Sarracenia purpurea L. in Lake Michigan. Terrestrial Arthropod Reviews 4: 237-253.
Heard, S. B. 1994. Imperfect oviposition decisions by the pitcher plant mosquito (Wyeomyia smithii). Evolutionary Ecology 8: 493-502.
Hoekman, D., C. Terhorst, A. Bauer, et al. 2007. Oviposition decreased in response to encriched water: A field study of the pitcher-plant mosquito, Wyeomyia smithii. Ecological Entomology 32: 92-96.
Istock, C. A., K. Tanner, and H. Zimmer. 1983. Habitat selection by the pitcher-plant mosquito, Wyeomyia smithii: Behavioral and genetic aspects. Pages 191-204 In: J. H. Frank and L. P. Lounibos (eds). Phytotelmata: Terrestrial Plants as Hosts for Aquatic Insect Communities. Plexus, Medford, NJ.
Juniper, B. E., R. J. Robins, and D. M. Joel. 1989. The Carnivorous Plants. Academic Press, London.
Kingsolver, J. G. 1979. Thermal and hydric aspects of environmental heterogeneity in the pitcher plant mosquito. Ecological Monographs 49: 357-376.
Kitching, R. L. and S. L. Pimm. 1985. The length of food chains: Phytotelmata in Australia and elsewhere. Proc. Ecol. Soc. Aust. 14: 123-140.
Lloyd, F. E. 1942. The Carnivorous Plants. Dover Publications Inc., NY (republished 1976).
Luciano, C. S. and S. J. Newell. 2017. Effects of prey, pitcher age, and microbes on acid phosphatase activity in fluid from pitchers of Sarracenia purpurea (Sarraceniaceae). PLoS ONE 12(7): e0181252. https://doi.org/10.1371/jou...
Mogi, M. and J. Mokry. 1980. Distribution of Wyeomyia smithii (Diptera, Culicidae) eggs in pitcher plants in Newfoundland, Canada. Tropical Medicine 22(1): 1-12.
Nastase, A. J., C. de la Rosa, S. J. Newell. 1995. Abundance of pitcher-plant mosquitoes, Wyeomyia smithii (Coq.) (Diptera: Culicidae) and midges, Metriocnemus knabi (Coq.) (Diptera: Chironomidae), in relation to pitcher characteristics of Sarracenia purpurea L. American Midland Naturalist 133: 44-51.
Plummer, G. L. and J. B. Kethley. 1964. Foliar absorption of amino acids, peptides, and other nutrients by the pitcher plant, Sarracenia flava. Botan. Gaz. 125(4): 245-260.
Rango, J. J. 1999. Summer phenology of aquatic insect communities inhabiting the leaves of the northern pitcher plant, Sarracenia purpurea L. Northeastern Naturalist 6(1): 19-30.
Trzcinski, M. K., S J. Walde, and P. D Taylor. 2003. Colonisation of pitcher plant leaves at several spatial scales. Ecological Entomology 28: 482-489.
Young E. B., J. Sielicki, J. J. Grothjan. 2018. Regulation of hydrolytic enzyme activity in aquatic microbial communities hosted by carnivorous pitcher plants. Microbial Ecology 76: 885-898.
On 2020-06-03 15:36:28, user Muller Lab wrote:
In the discussion:
"In this study, we demonstrate that RDV and its parent nucleoside GS-441524 are active against SARS-CoV-2 in a physiologically relevant cell line and that RDV exerts substantially higher antiviral activity in primary human airway cultures."
This sentence would suggest that RDV is superior to its parent, the nucleoside GS-441524 in primary human airway cultures.
But why then, does it say in table I:
"GS-441524 was not tested for antiviral potency nor RDV-NTP levels in HAE cultures"
Looking through the paper, I see no data on direct RDV vs GS-441524 in HAE (human airway epithlial cells). What am I missing?
On 2020-06-03 15:34:41, user Dr. Sarah Signor wrote:
I also cannot find much in the documentation about exactly how retrocopies are detected - for example any mRNA transcript could become a retrocopy, presumably, do you check for retrocopies with all exons, some adjacent exons, exon skipping, etc.?
On 2020-06-03 15:31:50, user Dr. Sarah Signor wrote:
The use of dimorphic in this context is really confusing to me, could the authors perhaps define what they mean by this in the manuscript?
On 2020-06-03 14:45:33, user Caroline Wright wrote:
Thanks for this paper - I enjoyed reading it, and was pleased to see you came to the same conclusions as us about the need for validation prior to clinical action for rare pathogenic variants called on microarrays. Also a really important point about the different biases in different ethnic groups.
We looked at your supplementary data (table 3), and it seems you actually got a fairly similar proportion of false positives in the very rare category of variants as we did in our paper (https://www.biorxiv.org/con..., despite using a more modern array. For the 108 variants not in gnomAD, only 25% validated with Sanger sequencing:
On 2020-06-03 14:02:01, user Peter Rogan wrote:
It's not entirely suprising that LINE elements contribute to gene regulation. Insertion of a fully length L1Heg into the beta globin cluster coincided with reversal of order of expression of the primordial embryonic and fetal genes (https://doi.org/10.1093/oxf.... However, most LINE elements are defective, and throughout their sequence accumulate mutations rapidly and are highly polymorphic. Are you suggesting that these sequence changes are not neutral? The paper should address this issue.
On 2020-06-03 12:36:36, user Иван Азаров wrote:
Hello! Thank you for your tool!<br /> It's very intresting possibility to use CPM modelling.<br /> Do you plan develop your code?<br /> Do users have possibility modify full energy function, which used inside CPM models, to add your own parts?
On 2020-06-03 11:57:56, user N.Mallikarjunarao1 wrote:
At molecular level covid spike protein interactions with ACE2 are explored
On 2020-06-03 09:30:54, user OxImmuno Literature Initiative wrote:
On 2020-06-02 14:05:55, user Stefania Di Blasio wrote:
This is a pre-print of an article published in Nature Communications, on June 2nd, 2020. The final authenticated version is available online at: https://doi.org/10.1038/s41...
On 2020-06-02 09:21:09, user Marcos wrote:
Published in Nucleic Acids Research <br /> https://academic.oup.com/na...
On 2020-06-02 08:13:49, user Joseph Lorent wrote:
The full peer-reviewed version of this article is now available online at:<br /> https://www.nature.com/arti...
On 2020-06-02 07:57:10, user J zhang wrote:
very interesting study, this article has shown the resistant isolates carrying TR and Qoi, BMC resistance mutations. but how can you explain the resistant isolates with only TR, no Qoi, BMC mutations? another things, is that clear how fast fungi develops resistance to DMI and QoI?(also dependent when these fungicides applied in the field.) TR might develop and come from hospital and get mutation in QoI resistance later in the environment if patient can spread fungi to natural, which now is not sure yet, but patient can cough out fungi to plates, this is true.
when QOI was applied in the field? mixed with DMI or later than DMI, or before the application of DMI? if QoI applied early, they should find strains with mutations only BenA or cytB or both, but they did not, probably they did not screen all with QoI plates. I assume QoI applied early than DMI, If that, TR still can develop in patient, if patient inhaled the fumigatus with only mutations only BenA or cytB.
Since the ag and clinical routes are fading now, I think to exclude clinical routes totally is till a challenge.
On 2020-06-02 07:32:38, user Mercè Campillo wrote:
Human Olfactory Receptor Mutation Database (hORMdb) and interactive web application (http://lmc.uab.cat/hORMdb):XAAHiCjqSSWFkIIK4Ywc7eNq1U8 "http://lmc.uab.cat/hORMdb)") for the management and visualization of this mutational database.
On 2020-06-02 06:02:15, user OxImmuno Literature Initiative wrote:
On 2020-06-02 01:34:41, user Saul Newman wrote:
There are many odd things embedded in this paper. The first is that, despite enormous resources and an un-scoopable result, the treated sample size (N=6 biological reps) is incredibly small compared to the measured population.
It is unclear why, given access to almost 600 rodents and enough resources to measure their methylation profiles, only six biological replicates were included. Most scientists with this level of resources would seek to replicate such an amazing result, to exclude error or contamination. Given the rather huge claims made here, therefore, it seems highly unusual that only 18 rats in total were actually used for the experiment, and that the experiment was not replicated.
That is, given the researchers have access to a huge number of rats of known age, and are claiming to reverse aging, why would you fail to make absolutely sure of it by measuring more rats? It is not like they lack the resources, and the experimental intervention (injecting plasma into rats) is painfully simple.
Several obvious controls are also missing from the experimental design.
It is also striking that the PDF of this preprint has three times as many downloads as abstract views, and thirty times as many PDF downloads as HTML views. This pattern is completely unlike the organic download patterns of other preprints.
Given that any (non-automated, human) viewer has to view the abstract to reach and click the PDF download, this suggests somebody has written a bot-scraper to help their download count.
It could be suggested that this preprint is not targeted at sound, replicated science but rather represents a carefully calculated bid for good press for "Nugenics" and "Elixir" sales.
On 2020-06-02 01:22:18, user Alina wrote:
“When BLAST searching using 100-bp windows along the RmYN02 genome, we find no single viral genome as the top hit, instead the top hits are found sporadically in different viral strains of the SARS-CoV lineage.” Is this normal for newly characterized SARSr-CoVs?
On 2020-06-02 00:20:01, user Michael Hall wrote:
The URL to the repository goes to a 404 Not Found page: https://github.com/WGS-TB/D...
On 2020-06-01 23:17:45, user Ben Curran wrote:
What constitutes an outlier in the datasets used? Both the mouse and TCGA datasets.
On 2020-06-01 19:24:40, user Andrew Read wrote:
Hi, author Andrew Read here.<br /> There is a minor error in the labelling and legend for figure 3c (2h and ev need to be switched). We are working on getting the correction submitted, but until then please be aware.<br /> Take care and please reach out with any other feedback :)
On 2020-06-01 17:48:07, user Kendra Sirak wrote:
Genotype data for the newly reported 184 individuals in this study are now available at: https://reich.hms.harvard.e...
On 2020-06-01 16:37:19, user OxImmuno Literature Initiative wrote:
On 2020-06-01 16:34:22, user OxImmuno Literature Initiative wrote:
On 2020-06-01 14:57:25, user OxImmuno Literature Initiative wrote:
On 2020-05-31 18:46:24, user Sinai Immunol Review Project wrote:
Main Findings <br /> - Study profiled immune dysregulation in peripheral blood of COVID-19 patients (n=71) along with recovered donors (RD; n=25) and healthy subjects (HD; n=37) using high dimensional cytometry. Integrated correlation mapping of clinical features (CRP/Ferritin etc) with immune phenotype was carried out, and findings in specific cellular compartments were parsed against pre-existing conditions or treatment modalities.
CD8 T cell profiling indicated an increase in effector memory 2 (EM2) CD45RA- CD27- CCR7+, and CD45RA+ effector memory (EMRA) CD45RA+ CD27- CCR7- CD8 T cell populations as well as increased frequencies of PD1+ CD39+ non-naïve T cells in ~2/3rd of COVID-19 patients compared to HD. This was associated with higher proliferation and HLA-DR, CD38 expression. FlowSOM clustering indicated similarly that ~60% subset of COVID-19 patients had increased TEMRA-like and activated CD8 T cell populations, while the CD45RA-CCR7+ central memory (CM)-like clusters were decreased compared to HD.
Most major CD8 T cell populations in RDs were comparable to HDs but non-naïve CD8+ T cells in RD expressed higher PD1. High-dimensional tSNE mapping indicated subtle differences in CX3CR1 and Tbet expression.
CD4 T cell analyses indicated similar increases in EM2/3 and EMRA populations, but no major changes in cTfh cells in COVID-19 patient blood when compared to controls. FlowSOM analyses showed decreases in CD45RA+CCR7+ naïve CD4 T cells but also cTfh-like cells in another subset of COVID-19 patients. In a subgroup of COVID-19 patients and most RD patients, activated cTfh were increased suggesting ongoing or residual germinal center interactions and links to humoral immunity.
Luminex assays of plasma and paired stimulated-PBMC supernatant showed a subset of COVID-19 patients to have higher CXCL9, CXCL10, CCL2 and ILRA compared to HD. Eosinophil or T cell chemokines such as Eotaxin and CCL5 were decreased in COVID-19 plasma.
A subset of COVID-19 patients (~2/3) had lower class-switched (IgD-) and non-class-switched (IgD+) memory B cells, increased frequencies of CD27+CD38+ plasmablasts, and higher proliferation in certain memory B cell subsets. There was no clear association between increased plasmablast frequency and cTfh activation status. Here, COVID-19 subjects segregated into 2 groups: one with high plasmablast responses and low CD4 activation, and a second with lower plasmablasts and higher CD4 T cell activation. Clustering COVID-19 patients according to global B cell response resulted majorly in 2 distinct EMD clusters (matching aforementioned categories), and the remainder 30% clustered with HD/RD controls.
Study observed stable tSNE distributions of immune response (CD8, CD4 and B cell differentiation) over days of hospitalization, but subpopulation analyses indicated a sustained CD8 T cell proliferative response (not CD4, nor plasmablasts) in COVID-19 patients over 7 days in clinic. Using a previously-described dataset to define typical HD-like stability and then mapping variation over time, the study additionally showed ~60% of patients had increases in HLA-DR+CD38+ non-naive CD4 T cells temporally, and ~42% patients displayed sustained plasmablast responses.
Data clustering based on immune features overlaid with disease severity score over time revealed “immunotype” groups with similar composite signatures, and an UMAP-based meta-analysis showed ‘stepwise’ increase in principal features with progressive disease. These computational approaches underlined 3 archetypes of COVID-19 patients – an immunotype 1, comprised of CD4 T cell activation/proliferation and Tbet+ plasmablasts and linked to disease severity , an immunotype 2 of CD8 T cell EM/EMRA activation and CD138+ki67+ plasmablasts, and finally an immunotype 3 with undetectable T/B cell responses which may represent ~30% of COVID19 patients.
Limitations <br /> - Whether all of the observed changes in the T and B cell compartment, including potential germinal center responses, are due to SARS-COV-2 directed responses is unclear and could be verified using either COV-2-tetramers, COV-2-specific ELISAs, or TCR/BCR sequencing analyses. Associative studies of changes in myeloid compartment would also be extremely valuable here.
It should be noted that the COVID-19 cohort included various pre-existing comorbidities, as well as different treatments with HCQ, steroids or remdesivir. Care was taken to parse many of these variables, but readers should not interpret interventional efficacy from this data.
As stated by the authors, the model used to define ‘immunotypes’ may benefit from adding additional immune features, such as comprehensive serum cytokine measurements as well as viral titers. Additionally, information on how/if these immunotypes associate with specific COVID-19 symptoms and complications (eg- endothelial dysfunction or myeloid activation) might be enlightening.
Significance <br /> - This study presents extensive longitudinal profiling of T and B cell compartments in COVID-19 patients and convalescent donors, as well as provide useful correlation-frameworks for defining ‘immunotypes’ or ‘immune trajectory archetypes’ in this disease. Basically, COVID-19 manifests in many different ways immunologically.
Importantly, this study demonstrates some patients displayed dramatic changes in T cell or B cell activation over 1 week in the hospital, but there were also other patients who remained stable lymphocyte-wise. A deeper dive into characteristics of these ‘lympho-stable’ COVID-19 patients would be critical, especially in terms of the myeloid cell correlates and differential inflammatory cascades.
There was no clear link between plasmablast responses and follicular CD4 T cell dynamics, suggesting there might not be extensive T cell-dependent humoral immunity in COVID-19. However, more carefully-controlled studies honing on this question are needed to comprehensively resolve this.
Some ‘lympho-stable’ COVID-19 patients have high and ever-increasing CD8, CD4 T proliferation response, potentially as an emergency mechanism to keep numbers up<br /> amidst lymphopenia. The observed activation in almost all non-naïve CD8<br /> subtypes suggests potential bystander activation and/or emergency <br /> proliferation globally in addition to the antigen-driven activation of <br /> SARS-COV-2-specific CD8 T cells.
The durable lymphocytic response in COVID-19 contrasts with the short transient peak characteristic of other viral infections (influenza, HIV, yellow fever etc). This incessant T cell proliferation in COVID-19 might give rise to anergy/exhaustion, and might be linked to the delayed Type I/III IFN dynamics in SARS-COV and also SARS-COV-2. Hence, analyzing the temporal pattern of COVID-19 remains critical.
Reviewed by Samarth Hegde as part of a project by students, postdocs and faculty at the <br /> Immunology Institute of the Icahn School of Medicine, Mount Sinai.
On 2020-06-01 14:54:27, user OxImmuno Literature Initiative wrote:
On 2020-06-01 14:47:53, user Jose Artur Brito wrote:
Although a very interesting piece of work, It is not clear from the manuscript that the first time the trissulfide moiety was observed in SQR was in the structure of Acidianus ambivalens (PDB entries 3H8I and 3H8L).<br /> The Biochemistry paper describing that structure (https://pubs.acs.org/doi/10..., which in fact was the first structure of an SQR ever to be described/published, clearly identifies a trissulfide bridging the redox active cysteines.<br /> Reading the present form of this manuscript, one gets the feeling the the trissulfide was initially observed by Marcia et al. or in the human form of SQR. That is clearly not the case. Just to get the record straight.
On 2020-06-01 14:47:20, user OxImmuno Literature Initiative wrote:
On 2020-06-01 14:43:52, user OxImmuno Literature Initiative wrote:
On 2020-06-01 14:35:21, user OxImmuno Literature Initiative wrote:
On 2020-06-01 14:32:10, user OxImmuno Literature Initiative wrote:
On 2020-06-01 14:20:23, user OxImmuno Literature Initiative wrote:
On 2020-06-01 10:48:04, user Justin Byrne wrote:
The Network Ecology research group at Newcastle read through this paper today for our journal club. We were pleased to see an ecological paper making use of the full capabilities of dada2 and deseq2 on a fungal dataset.
We thought that good work had been done in the experimental design to control for the effects of environmental gradients, although would have preferred if there was an attempt to verify how successful this was. We generally empathised with the difficulties of selecting sample sizes before knowing the degree of variability in sample diversity, and how the costs of sequencing and sample preparation can limit the size of these kinds of studies. It appears that the study size was appropriate for the simple diversity metrics; but, as the authors highlight themselves, may struggle to identify consistent features of their networks.
Overall, we struggled with the strong and coherent message provided by the initial results, that becomes less clear by the inclusion of these ecological networks. However, we strongly feel that it is important for this work to be conducted, addressing how networks can and cannot be integrated into biomonitoring.<br /> We would have liked more information about how the technical replicates, negatives, and positives were incorporated into the analysis. We also would like to know how samples were normalised and pooled prior to sequencing to ensure that equal concentrations of DNA were combined for sequencing. Furthermore, given that deseq2 allows for normalising sample abundance by read depth, it would be interesting to have more discussion of the benefits and drawbacks of using read abundance as an input to biodiversity metrics.
We were very interested by the results that suggested that differences in network links resulted from rewiring rather than taxa turnover. Given recent work - Co‐occurrence is not evidence of ecological interactions, F. Guillaume Blanchet (2020) – has looked at potential pitfalls for inferred networks, we would be interested to see if it becomes revisited in the discussion in the final article.
Thank you for an interesting, informative, and convincing paper.
On 2020-06-01 10:00:04, user Jules Ruis wrote:
For more information about Fractals see www.fractal.org
On 2020-06-01 07:14:28, user S.Akshay wrote:
This is one of the most important clocks as our ultimate biological destiny is defined by our proteome and regulated by our transcriptome. Congratulations to David and Bjorn.
On 2020-06-01 04:19:59, user Lachlan Coin wrote:
Hi authors, nice work! However this statement: " However, prokaryotic transcriptomes have not been characterized on the genome wide level by native RNA-seq approaches so far as prokaryotic RNAs lack a poly(A) tail, which is required to capture the RNA and feed it into the nanopore. " is not correct. We did something similar in K. Pneumoniae : https://doi.org/10.1093/gig...
On 2020-06-01 00:20:05, user Stefanie Ickert-Bond wrote:
On 2020-05-31 21:10:36, user Simon Anders wrote:
Dear Authors
may I ask a question about your Supplementary Figure 5?
I have tried to understand your estimate of sensitivity and specificity, as shown in your Figure 1e. You state a sensitivity of 95.6%, i.e. you detected 66 of your 69 positive samples, and a specificity of 99.2%, i.e., you correctly called negative for 131 of your 132 negative samples (69*0.992=66 and 132*0.992=131), and you state that these values were found when using a decision threshold of 11,140 quantiflour units.
However, if I draw a line at 1,1*10^5 in your Supplementary Figure 5, I see a larg part of the dark red dots (i.e., of the positive samples) below the threshold (i.e., as false negatives). And if I lower the threshold to get most positive samples above it, I would get very many false positives (gray dots above the threshold). So, this Supplementary Figure 5 cannot be what gives rise to your ROC curve in FIgure 1e. Then, what is it? And do you also have the plot of quantiflour results versus qPCR result that gives rise to the ROC curve?
I'm probably just reading your plot wrongly, but I can't figure out how these two figures don't contradict each other.
Thanks in advance for an explanation.
Simon
On 2020-05-31 19:52:18, user geomcnamara@earthlink.net wrote:
I hope the authors move fig S6 to the main text.<br /> Reference 29 "Protein-PAINT" should be cited in introduction and "independent validation" should be switched to credit their peer reviewed publication first.<br /> This paper is reminiscent of Fluorescent Speckle Microscopy (FSM, pre-super-resolution microscopy era).<br /> Note: mNeonGreen obsoleted by AausFP1 (6/2019) https://www.biorxiv.org/con... <br /> which is ~2x brighter than mNG and ~5x brighter than EGFP. Nice interactive chart on FP performance at https://www.fpbase.org/chart (I suggest starting with Y-axis Brightness, X-axis emission wavelength). One amino acid change (T203Y) would result in a bright yellow, likely similarly narrow spectra and likely very nice AausFP1green->AausFP1(T203Y)yellow spectral overlap for FRET.<br /> Triple FP ... record brightness published is V6 (Venus6, Nguyen ... Vogel 2015 PLoS One, pubmed 23152925) - Steve Vogel may have made "V8" (earlier multimers may be found in our thousand proteins of light review and data table, inside the PubSpectra zipo downloadable at https://works.bepress.com/g... ).. Some readers may also find of some use my suggestions on 'binary Tattletales' https://works.bepress.com/g... (pre-AausFP1).
On 2020-05-31 04:24:12, user JS wrote:
Human equivalent of the effective dose is rather low, 43mg/day for an 80kg adult.
It is quite unfortunate that this preprint has not progressed to publication, and to human trials.
On 2020-05-30 23:29:03, user Peter Frost wrote:
The authors conceive hair color as a 1-dimensional trait, and selection on hair color is conceived as being directional -- from dark to light. In reality, selection has diversified hair color in Europeans, pushing it in many different directions. Ancestral humans had only one allele for hair color. Present-day Europeans have over 200.
It would be difficult to model this kind of selection. Whenever a novel allele for hair color arose through mutation, it would have risen rapidly in population frequency until its novelty wore off. An equilibrium would then develop with the other hair colors, until another novel allele arose. At that point, the existing colors would lose ground. Any single color would thus show a gradual decline over time as the total number of colors increased.
On 2020-05-30 23:12:32, user Gabor Balazsi wrote:
Very interesting to see this work now in mammalian cells after achieving it in yeast! Congratulations! It may be worth mentioning and citing this other paper where we established decoupled mean-noise control in CHO cells: https://www.nature.com/arti...
On 2020-05-30 14:20:59, user Halima Benbouza wrote:
Just for information. At a diagnosis lab in Algeria ( CAC-Batna) they have tested an RT-PCR, with samples without extracting RNA, couple weeks ago and the results were the same as those performed when using RNA extraction kits.
On 2020-05-30 13:57:34, user Donald R. Forsdyke wrote:
Experiments with cultured susceptible cells have led the authors to conclude that “suramin inhibits binding or entry” (1) This early onset inhibition by suramin of infections by SARS-CoV-2 would probably occur when viruses attach to the ACE2 receptors and enter cells. An impressive result (their figure 3) is that suramin works if added before, or at the time of,<br /> adding virus. However, if added one hour later there is no inhibition. They correctly note that suramin is known to interact with a wide variety of proteins (2). These would include some in the unheated calf serum used for their cell cultures, so could implicate complement components that can be activated by way of the lectin pathway (3). The growing evidence for involvement of this pathway in SARS-CoV-2 pathology has recently been summarized (4).
.<br /> 1. Da Silva CSB, Thaler M, Tas A, Ogando NS, Bredenbeek PJ, Ninaber DK, Wang Y, Hiemstra PS, Snijder EJ, Van Hemert MJ (2020) Suramin inhibits SARS-CoV-2 infection in cell culture by interfering with early steps of the replication cycle. bioRxiv: https://doi.org/10.1101/202....<br /> .<br /> 2. Wiedemar N, Hauser DA, Mäser P. (2020) 100 years of suramin. Antimicrobial<br /> Agents and Chemotherapy 64, e01168-19.<br /> .
On 2020-05-30 11:32:34, user Tibor Páli wrote:
Dear Authors, quite interesting findings! I don't know if you are aware that some time ago we were working on a 16kDa gap-junctional protein that is highly homologous to the subunit c of V-ATPase (in fact, it can substitute for it in a hybrid enzyme). We found that as purified from the hepatopancreas of Nephrops norvegicus, the 16-kDa proton channel proteolipid contains an endogenous divalent ion binding site that is occupied by Cu2+! https://doi.org/10.1016/j.b...
On 2020-05-30 01:37:51, user MAYA DEWI DYAH MAHARANI wrote:
New Normal is actually a harmonization between the ecological, economic and social dimensions. And world leaders should implement a sustainable development policy
On 2020-05-29 23:18:04, user Miguel wrote:
Here the problem protandim NRF2 is Way more effective then pb125 and joes other NRF2 Product Gnmx .
On 2020-05-29 17:17:49, user Omar Homero Pantoja Ayala wrote:
Macromolecules, like proteins, do not contribute significantly to cell osmolarity, therefore, the changes in volume must be related to the activity of other transporter(s) that may be under circadian control.
On 2020-05-29 16:37:34, user Thomas Perkmann wrote:
Dear authors,
Many thanks for sharing this exciting work. In the publication, the negative samples of the specificity cohort display median values of 2.2 -2.4 AU/ml. In the manufacturer's IFU, there is a LOD of 3.8 AU/ml (i.e., the device does not give values below 3.8 AU/ml). How were these low values measured?
Thanks for answering this question.
Best regards Thomas Perkmann
On 2020-05-29 15:04:35, user GallipoliLab wrote:
Very nice work. I went back to our RNA-seq and metabolomics data (doi: 10.1182/blood-2017-12-820035) and all very consistent. We completely missed this dependency somehow. Good that you uncover it. I suppose would this work in combination with quizartinib? One would not expect it
On 2020-05-29 14:53:17, user Jan Lötvall wrote:
Could the authors comment here to explain. The last thing we want, as scientists including the authors, is that this, or any research, is highjacked by conspiracy theorists to twist truth. I will e-mail the authors, suggesting that they comment here and specifically respond to Ken's detailed comments.
On 2020-05-29 12:51:56, user Matteo Brilli wrote:
Supplementary Table 2 contains accessions to full genomes not orfs used. Why is ORF3b not present (well, not annotated) in the reference genome?
On 2020-05-29 11:15:25, user M Irfan Qureshi wrote:
Excellent Article!<br /> It complement many assumption expressed in the article below:<br /> Qureshi, M. Irfan and Asim, Mohammad, Probing Occurrence and Possible Roles of New Insert in Spike Glycoprotein of SARS-CoV-2 (May 15, 2020). Available at SSRN: https://ssrn.com/abstract=3... or http://dx.doi.org/10.2139/s...
On 2020-05-29 03:01:55, user Paul wrote:
Paper has been accepted!<br /> Please cite this article as:<br /> Logel, D.Y., Jaschke, P.R., A high-resolution map of bacteriophage øX174<br /> transcription, Virology (2020), doi: https://doi.org/10.1016/j.v....
On 2020-05-28 19:20:08, user John Vanek wrote:
When did you conduct the search? Also, how well did your search string pick up papers that didn't include higher classification in their title/keywords? For example, papers that identified lizards, snakes, or turtles, but not "reptile."? Was there a reason for limiting the web of science search to the first 300 results?
(“island rule” OR “island effect” OR “island syndrome” OR island*) AND (gigantism OR dwarfism OR “body size” OR weight OR SVL OR snout-vent length OR length OR size) AND (mammal* OR bird* OR avian OR amphibia* OR reptile*)
Either way, looks like a herculean effort! Great job and I look forward to digging in!
On 2020-05-28 16:29:38, user Jason G Smith wrote:
There are other examples of defensins enhancing viral infection of which you should be aware (PMID 28622386, 21672195). The literature is more extensively reviewed in PMID 28715972.