On 2020-07-07 11:35:42, user Ludwika Zofia Fortuna wrote:
Basing on what I read so far I must thank you for no weird text layouts. You can read the paper smoothly and find paragraphs easily. Thank you for your research and I will be waiting for further insights about the significance of Rab46 in endothelial cells functioning ????
On 2020-07-07 10:58:11, user Aram P. wrote:
Dear Anahit Hovhannisyan et al.
Thanks for the interesting paper. I agree that there is no genetic evidence for Balkanian origin of Armenians. I also think that modern Armenians retained a substantial amount of genes from Neolithic period. But the question of mass migration after Bronze Age is disputable.<br /> When five years ago first ancient DNA from modern Armenia was published I started to analyze them (using both online tools and academic data) and I came to the conclusion that they are different from modern Armenians. Thus some events occured in Armenia after LBA and EIA period. Which would be after 850 BC. To have an idea with what type of genetic events we are dealing with we must have more precise definition of terms.
For instance those samples from modern Armenia almost certainly do NOT represent the genetic situation in all Armenian Highland at LBA-EIA period. They represent the Lchashen Metsamor culture which was mostly restricted to modern Armenia, parts of Kars and Igdir regions in Turkey. On the other hand we know that there was a distinct Urartean culture in Van region which rapidly expanded in Middle Iron Age period. We don't have samples from ancient Van but some indirect data suggests that they will be different from ancient samples from modern Armenia. Another factor that almost certainly increased heterogeneity in Arm. Highland in MLBA period is the well known expansion of Hittite culture from Anatolia to western parts of historic Armenia. We also see a Steppe shift in MLBA samples from modern Armenia. Visible in Your D stats Z scores.
Based on this data we can say that during MLBA period Arm. Highland became quite heterogenous. Structured by regions. And this heterogeneity started probably at EBA period. We can't use samples from Eastern Armenia to describe the situation in all Armenian Highlands. We need samples from Eastern Turkey to better understand those regional structures.<br /> Recent paper from Skourtanioti et al. fully confirms this idea of regional structurisation. We have Kur Araxian period samples from Arslantepe EBA ( Malatya region ) and they are very different from Kur Araxian samples (EBA) from modern Armenia. Surprisingly they are much more western and slightly more southern shifted than modern Armenians. While EBA samples from modern Armenia are more eastern (CHG) shifted. Which suggests a clinal distribution of ancient BA populations in Armenian Highlands.
My conclusion is that there wasn't any massive migration into Armenian Highlands at Iron Age. After LBA Arm. Highland remained relatively isolated like Haber concluded. The extra western (Sardinian like) shift that modern Armenians have is not the result of massive migration but due to intra-Highland homogeneisation of divergant populations that were already living in historic Armenia at LBA-EIA period. Thus genetic isolation is not always equal to genetic continuity.<br /> The names of this regional populations are well known. Biaina, Etiuni (Lchashen-Metsamor), Diaukhi, Arme Shubria. Their political unification started with Urartu and was continued in Yervandid (Orontid) Armenia period. For much better understanding of this events we need ancient samples not only from Eastern Turkey but also samples from Urartean, Yervandid and Artashesids periods in modern RA to understand how genetic profile changed during this periods.
On 2020-07-01 11:02:58, user Arthur Asatrian wrote:
Hi authors. Thanks for interesting paper. But I want to note some issues.<br /> The two paper on events in East Africa that You mention are from 2014 and 2015. Thus before samples from Levant and other parts of Near East was available. This is the main reason why they used Sardinia as a source of West Asian input in East Africa. Today such samples from Levant are available and Sardinia is not anymore relevant. Also there is no much mystery about this Iron Age migration. They were the Ethiosemites like Pickrell et al. suggests.
Conclusions<br /> Based on these analyses, we can propose a model for the spread of west Eurasian ancestry in southern and eastern Africa as follows. First, a large-scale movement of people from west Eurasia into Ethiopia around 3,000 y ago (perhaps from southern Arabia and associated with the D’mt kingdom and the arrival of Ethiosemitic languages) resulted in the dispersal of west Eurasian ancestry throughout eastern Africa.
On 2020-06-25 06:14:18, user Davidski wrote:
Hello authors,
Thanks for the preprint, but this is surely a more accurate outline of the population history of the Armenian Highland:
https://www.sciencedirect.c...
Chalcolithic era migrations from the Pontic-Caspian steppe and/or the North Caucasus introduced steppe ancestry to the Armenian Highland, bringing at least some of its populations (ie. from the Areni-1 Cave) closer genetically to those of Eastern Europe
population expansions during the Early Bronze Age associated with the Kura-Araxes cultural phenomenon, which may have originated in what is now Armenia, resulted in a resurgence of indigenous Caucasus hunter-gatherer (CHG) ancestry across the Caucasus and its spread to many other parts of West Asia
another significant pulse of admixture from Eastern Europe appeared in the Armenian Highland during the Middle Bronze Age and it persisted, or at least its influence did, throughout the Late Bronze Age and into the Iron Age
it's not yet clear what happened in the Armenian Highland during the Iron Age in terms of significant genetic shifts, due to the lack of ancient human samples from the region dating to this period, but it's still possible that the speakers of proto-Armenian arrived there from the Balkans at this time
the present-day Armenian population is the result of the processes described above, as well as later events, such as those associated with the Urartian and Ottoman Empires.
On 2020-07-07 09:23:25, user Colin_Brown wrote:
Research on indomethacin, both citing this, called for here:<br /> www.bmj.com/content/368/bmj... <br /> and here:<br /> https://medicalupdateonline...
On 2020-07-07 09:21:30, user Milind Gore wrote:
In recent publication on T and B cell response in recovered patients vs un exposed individuals, The paper demonstrated that neg individuals did not have antibodies to Covid 19 as against IgA, IgM and IgG antibody response. Surprisingly portion was few negative individuals showed T cells both CD4 and CD8 Most probably these were against M and N proteins. This means that non Covid but alpha and Beta HuCoV have cross reactive response, This is useful information. In a very old study in Netherlands, PCR positivity of ILI had 0.5 % children between 5-10 yr age had alpha and beta corona but at that itme V NAb response was demonstrated in 75% children. I think this is simple answer to this
On 2020-07-03 17:59:45, user itellu3times wrote:
Great work, at this point the precise parameters can't matter as much as the confirmation that T-cell immunity occurs at all. Certainly questions remain about cross-immunity with past - and future - corona viruses, and the period for which the memory is maintained.
On 2020-07-02 21:34:04, user Jorrit Posthuma de Boer wrote:
The number of 2020 blood donors (55 in Table S1) does not correspond to the number of blood donors in Fig 4G (31), this means 24 donors were not incorporated. The paper does not mention a reason for this. For the exposed relatives 2 out of Table S1 or not incorporated in Fig 4G, for mild convalescent 9 and for severe convalescent 3.
A question not posed or answered by the authors is why the discrepancy between the antibody and memory T-cell response appears to increase from mild convalescent, exposed family members, to 2020 blood donors (Fig. 4G). Without a sound explanation any calculation with regards to the population immunity seems inappropriate.
The blood donors in this study donated their blood at Karolinska University Hospital in Stockholm in May 2020. At the end of April 2020 the seroprevalence in Stockholm was 7.3%. This study found a seroprevalence of 13% (4/31) as indicated in fig. 4G. However when applied to the number of donors as reported in table S1 this would have been 7.2% (4/55).
Overall (mild convalescent, exposed family members, and 2020 blood donors), the population-level immunity would have been a factor 1.35 higher, when based on SARS-CoV-2 specific memory T-cell responses (65/90), as compared to anti-body responses.
The authors claim to have found an unanticipated degree of population-level immunity against COVID-19, currently has no support, 1.35x instead of 2x more as compared to the current seroprevalence seems more inline with the presented results.
On 2020-07-02 18:22:09, user Mazda Sabouri wrote:
There was a previous study suggesting that 40-60% of people had some degree of T-cell immunity to this virus as a result of cross immunity. Seems like these 40-60% are the ones who didn't develop antibodies in this study. If true, it means we started this pandemic quite close to herd immunity. The antibodies may go away after a few weeks, but those T-cells stick around. If we get to the point where 60-80% of people have T-cell immunity we should be out of the woods. Asymptomatic spread is actually quite rare according to the WHO. And people with T-cell immunity seem to be asymptomatic when infected.
On 2020-07-01 10:35:01, user Merit Melin wrote:
How many of the COVID-19 patients had T cell responses but not antibodies? Short answer is three (Figure 4G). Nine family members of COVID-19 patients had T cell responses but not antibody responses. As excellent as the T cell work in this paper is, I would be critical of drawing generalized conclusions suggesting that those with mild or asymptomatic symptoms would develop cellular immune responses in the absence of humoral immunity. The timing of sample collection is critical for the sensitivity of the antibody tests, and it is known that it will take two to three weeks, sometimes even longer for antibodies to develop following COVID-19 infection. The kinetics of antibody and T cell responses may differ – T cell responses may be detectable sooner than IgG antibodies. It would be important to disclose the information on the timeline of sample collection for the serological and T cell analyses of the subjects in each group, relative to onset of symptoms or exposure.
On 2020-06-30 08:09:04, user Fridtjof lund-johansen wrote:
Fantastic paper!! More information on antibody measurement would be good. Fig. S8 is a bit hard to interpret. What is the specificity and sensitivity of antibody measurement? This is fundamental to support the claim that 50% exposed individuals have T cells but no antibodies.
On 2020-07-06 21:42:49, user thomas_carroll wrote:
Congratulations on the interesting approach and findings here! I too would hope to see a more comprehensive set of hits (and their Z-scores/FDRs) make it into the public domain, especially as this paper moves towards publication. The findings described here will be especially powerful in integrative analyses with data from other approaches- and these integrative approaches would be best served by access to a more complete results list (or the raw data needed to recreate such a list).
Interested to see how our understanding of entry proteases like TMPRSS2/CTSL continues to evolve, perhaps with future screens in other cell lines!
On 2020-06-30 08:15:24, user OxImmuno Literature Initiative wrote:
On 2020-06-18 16:54:12, user marius w wrote:
This look like a very nice paper. But would you mind also sharing the full list of hit genes? Along with enrichment scores and other relevant metrics? A supplementary table would be the best format. Not having access to this is a bit upsetting, and I hope this not on purpose..
On 2020-07-06 20:52:19, user Albert D Donnenberg wrote:
Many of the genes in this segment are chemokine receptors. Important actors in inflammatory responses. Sounds like real biology.
On 2020-07-05 21:05:30, user Kevin Olivieri wrote:
What an exciting paper! It is pretty amazing to see this series of SNPs to each appear at the same frequency in 1000 Genomes. Also, it shows up at a high frequency in the 5 Siberian individuals in the NBCI's dbSNP. Again each SNP appears at the same frequency.
The gene most of the variants appear in is LZTFL1, which has been implicated in lung cancer and bronchial epithelial cell differentiation (https://www.nature.com/arti.... Another study showed its expression correlated with disease in bovine respiratory syncytial virus (https://journals.plos.org/p.... I would be excited to see any follow up study for the role of these variants in Covid-19 infection.
On 2020-07-05 15:33:01, user OddWing wrote:
I am curious--if this is a major risk factor, why the number of deaths in Bangladesh is comparatively low? (According to this paper, Bangladesh has the highest % of people with this risk factor)<br /> To put this in numbers: <br /> 1. Bangladesh has approximately half the population of the USA (fact)<br /> 2. ~130,000 people have died from COVID-19 in the USA. Should we have seen half that, i.e. 65,000 deaths in Bangladesh? (I understand there are other risk factors)<br /> 2. 63% of the people in Bangladesh has this sequence, compared to 4% of the people in the Americas. Why isn't the death rate 16X in Bangladesh compared to the USA's rate?<br /> (Bangladesh has an official count of 2000 as of today. Even allowing for undercounting it is nowhere near 65K)
On 2020-07-05 07:15:48, user Fernando Mendez wrote:
The result is very interesting. I do strongly suggest, however, a change at least in the title "The major genetic risk factor for severe COVID-19...".This is the risk factor with the highest statistical significance in the populations for which the samples taken (from Spain and Italy) are representative. We don't know what the genetic factors are in other populations that are not appropriately represented in the GWAS, including Africans, Indigenous Americans, Melanesians, East Asians,... you get it.
On 2020-07-05 06:44:04, user Yousuf Akhond wrote:
Mortality due to COVID-19 is Bangladesh is 1.25% of the confirmed cases which is far less (lesser if you consider the undetected positive cases) than those in the people of European descent. So, that observation does not explain your theory. Higher mortality in the people of Bangladeshi origin in UK could be explained by other factors: lack of Vit D due to dark skin, poor health compared to the natives, weather to which they are not adapted to, being originated from a particular region of Bangladesh etc. One area that should be investigated for the relative resistance in Bangladeshi people in Bangladesh is the possible cross-protection due to previous infection by other viruses (most of the people I know here in Bangladesh suffered from a cold during ~January-March 2020 - with cold, cough and diarrhoea).
On 2020-07-04 13:20:20, user David Curtis wrote:
rs11385942 is very much commoner in South Asians than Europeans:
https://gnomad.broadinstitu...
Given this fact, how sure can we be that the GWAS hits you cite are not simply an artefact due to failure to properly control for population stratification? The second study you cite has not published the results you rely on in a peer-reviewed journal - the citation just points to a description of the initiative.
When I see hits like this which arise from a variant with markedly different allele frequencies in different populations my natural reaction is to suspect an artefact.
On 2020-07-04 04:18:30, user Dialog2Debate wrote:
If that is so, then why don't we find a lot more Asians and fewer Europeans with severe COVID? How does this square with the disease risk factors for COVID of diabetes, obesity and age?
On 2020-07-04 00:49:55, user Jacques René Giguère wrote:
But African descent in Europe and Americas had higher cases and mortality despite not having interbred with Neanderthal?
On 2020-07-06 18:00:57, user FARAH EMYLIA ZULKIFLI wrote:
hello..may i ask what is the journal of this article called Targeted Proteomics for the Detection of SARS-CoV-2 Protein?
On 2020-07-06 16:09:36, user OxImmuno Literature Initiative wrote:
On 2020-07-06 15:54:20, user OxImmuno Literature Initiative wrote:
On 2020-07-06 15:50:28, user OxImmuno Literature Initiative wrote:
On 2020-07-06 15:09:37, user Wim Van den Ende wrote:
Dear authors<br /> Two questions, if I may:<br /> 1. Are the TMPRSS2/SPIKE models you produced here available as pdb files for the scientific community?<br /> 2. 6vsb shows many important gaps, like the gap between S810 and T827. How were these gaps filled? The accuracy of this process may greatly influence the outcome of the modelling, especially for the positioning of K811 and K814.<br /> Looking forward to your reply<br /> Best wishes<br /> Wim Van den Ende
On 2020-07-06 14:10:38, user odin wrote:
I think this paper should be discussed in the recent paper: Diogo, R. (2020). Cranial or postcranial‐dual origin of the pectoral appendage of vertebrates combining the fin‐fold and gill‐arch theories?. Developmental Dynamics.
On 2020-07-06 07:59:14, user Gregor Sturm wrote:
Great manuscript - the deep learning approach for deconvolution really seems promising!
I have a few comments:
1) In the discussion, you state
We therefore believe that efforts to increase the similarity between simulated training data and the target bulk RNA-seq data could increase Scaden's performance further.
Have you evaluated differences between using 10x and Smartseq2 scRNA-seq data for training? I would expect bulk RNA-seq samples simulated from Smartseq2 to be much closer to genuine bulk than when using 3' or 5' scRNA-seq.
There are comprehensive Smartseq2 datasets available from Immune cells in various cancer types, e.g. 10.1016/j.cell.2017.05.035, 10.1038/s41591-018-0045-3, 10.1038/s41586-018-0694-x
2) mRNA content can vary greatly between cell-types. For instance, Macrophages tend to have a lot more mRNA than T-cells (See, for instance, the scaling vectors of the quanTIseq method: https://github.com/icbi-lab...
As far as I can tell, you normalize scRNA-seq data by per cell before simulating bulk samples, getting rid of these differences. In order to achieve a greater similarity between simulated and genuine bulk, I would suggest addressing these differences, e.g. by using a more sophisticated normalization strategy.
3)
This raises the possibility that Scaden trained on scRNA-seq data might reliably deconvolve other bulk omics data as well, such as proteomic and metabolomic data
This is a pretty bold speculation, given that it is known that the correlation between bulk RNA-seq and bulk Proteomics data is only around 0.5. I would love to see that tried out, though!
Cheers, <br /> Gregor
On 2020-07-06 07:57:38, user mike marchywka wrote:
I saw you picked up my tweet regarding this paper. The situation with vitamin K seem to be unappreciated. I cited your work in more detail here, ( I think this comment was removed but not idea why ),
On 2020-07-06 01:13:30, user Brent Lengel wrote:
From the study's methodology:
"...***We acknowledge that this study was conducted with untrained individuals and not transgender athletes.*** Thus, while this gave us the important opportunity to study the effect of the cross-hormone treatment alone, and as such the study adds important data to the field, it is still uncertain how the findings would translate to transgender athletes undergoing advanced training regimens during the gender-affirming intervention. It is also important to recognize that we only assessed proxies for athletic performance, such as muscle mass and strength. Future studies are needed to examine a more comprehensive battery of performance outcomes in transgender athletes. Given the marked changes in hemoglobin concentration in the current study, it is possible that gender-affirming treatment also has effects on endurance performance and aerobic capacity. Furthermore, since the TM and to some extent also the TW demonstrated progressive changes in muscle strength, and strikingly some of the TW individuals did not lose any muscle mass at all, follow-ups longer than 12 months are needed to better characterize the long-term consequences and individual responsiveness to gender affirming interventions. Future studies should also include age- and body size-matched cisgender control groups undergoing the same assessment timepoints without the therapies."
On 2020-07-05 21:33:08, user Billy Bostickson wrote:
Does this mean that SARS-COV-2 contains artefacts of insect cell expression systems or baculovirus viral particles or does it mean it may have partly evolved via recombination events involving viruses in insects and bats in a cave for example? Just can't understand how insect sequences could be found in a virus which supposedly emerged from bats.
On 2020-06-30 06:20:44, user Ivan van Ogre wrote:
Should we be worrying about mosquitos?
On 2020-07-05 17:52:49, user Dude Dujmovic wrote:
This is an important paper. I would like to see clinical study on NK cells and severity of COVID-19.
On 2020-07-05 16:20:10, user David Curtis wrote:
If you want to evaluate the performance of this method you should compare it with SCOREASSOC, which was pretty successful at identifying genes for Alzheimer's disease.
https://www.biorxiv.org/con...<br /> (It's published now in Annals of Human Genetics.)
On 2020-07-05 15:49:18, user Bryan Tegomoh wrote:
How long was the chronic exposure to IL-6?
On 2020-07-05 14:49:30, user Xavier Le Goff wrote:
This article has been published in Eur Phys J E Soft Matter. 2020 May 29;43(5):31.<br /> PMID: 32474823
On 2020-07-05 11:55:57, user OxImmuno Literature Initiative wrote:
On 2020-07-05 11:45:04, user OxImmuno Literature Initiative wrote:
On 2020-07-05 11:37:04, user OxImmuno Literature Initiative wrote:
On 2020-07-05 07:51:16, user Davidski wrote:
Hello authors,
Thanks for the preprint, but unfortunately I have to say that the qpAdm models you chose don't make much sense.
The Fatyanovo people are basically identical to the samples from the main Sintashta cluster, so you ought to be modeling their ancestry as something like Yamnaya/Globular Amphora, or, more precisely as early Corded Ware/Globular Amphora.
On 2020-07-05 05:13:04, user Ciro Leonardo Pierri wrote:
FYI: The ms linked here above has been accepted for publication in a more completr form on Cell Mol Life Sci: https://link.springer.com/a...
On 2020-07-04 12:23:14, user tit992 wrote:
The published version on Hindawi - Ecam https://www.hindawi.com/jou...
On 2020-07-04 09:20:51, user Antonio Cassone wrote:
We detected and reported on D614G mutation of SARS-2-CoV genome in Italian isolates , proposing the same interpretation about its functional value (enhanced virus transmission) based on biostrctural S1 change( ; see Benvenuto et al. Evidence for Mutations in SARS-CoV-2 Italian Isolates Potentially Affecting Virus Transmission J.Med.Virol., 2020, Jun 3:10.1002/jmv.26104. doi: 10.1002/jmv.26104.) We congratulate the Authors for providing direct evidence supporting D614G their and our own interpretation.
On 2020-07-04 06:51:13, user lin white wrote:
Why so many studies talk about human arg1 is expressed? I can not repeat the result. So strange!
On 2020-07-03 15:13:59, user Urulibranstika ivan wrote:
arg1 should not be expressed in healthy human, without saying represent normal bone healing marker
On 2020-07-04 05:48:44, user zhiyanle wrote:
If the authors got to know the first patients in Europe and South-Asia, they should find out that the patients came from China (some of them are Chinese). That is, the virus re Beijing’s new wave is rooted in PRC.
On 2020-07-04 00:05:45, user Petri Dish: A Science Comedy P wrote:
Upon reading the article, I totally get what they mean by “T cell targeted”, ie that the PLGA-R particles induce Tregs via APCs. But this is a very misleading use of the term “targeted”, especially in a manuscript that also uses “targeted” to mean what it typically does in the context of nanoparticles — targeted for uptake by a cell population, usually though surface modification with a targeting moiety. I feel it would behoove the authors to change their language surrounding “T cell targeting” to avoid confusion in an otherwise very nice paper.
On 2020-07-03 21:13:30, user Tanaya Bose wrote:
First report to measure diffusion of proteins in eukaryotic, prokaryotic and in vitro buffer condition.
On 2020-07-03 16:41:10, user Laura Sanchez wrote:
Dear Eldjárn et al, this preprint was discussed in a lab meeting and we would like to offer the following for review. Thank you for posting this very interesting manuscript. Best, The Sanchez Lab
The manuscript by Eldjárn et al. describes the development of a computational method to enable large-scale linking of gene cluster families (GCFs) and molecular families (MFs). This method uses multiple complementary scoring functions, combining both feature-based and correlation-based approaches, which, the authors state, allows for more effective prioritization of valid links between GCFs and MFs than using the individual scoring functions. The manuscript provided a very nice summary which integrated information from many different fields to set up the problem they were trying to address in the introduction. However, the utility of this method could not be tested as the documentation was absent from the repository links and the manuscript could benefit from a concrete example (actual metabolite linked to a specific BGC) to more effectively show its advantages over current techniques. Below is a list of major and minor critiques for this preprint.
Major:
Figure 1 could be re-done to better visually demonstrate the problem the authors are trying to address. For instance, a box around the higher scoring population would be helpful for the reader to understand the problem as the numbers in the figure legend are difficult to correlate to the visual. The purpose/conclusion for Figure 1B is unclear. Moreover, Figure 1B is unrelated to 1A and out of order in terms of where IOKR was discussed in the manuscript.
Figure 2 shows a problem with the range of the expected value and variance (which varies with GCF and MF size) before standardizing the correlation score, yet, there is no chart to show how this changes after the correlation score is standardized. The authors should consider adding charts to show how this changes after the score is standardized, as well as an interpretation to help the reader understand why this change was necessary. For example, it is unclear whether a yellow (high) or blue (low) score is more desirable and it is unclear what the ideal distributions would look like. Additionally, the scales on the charts are inconsistent and make them difficult to interpret since they utilize the same color gradient. We suggest labeling the scale high-low rather than numbered if the values are not comparable. The authors should consider inverting the Y axis so it starts with zero at the bottom and increases as you move up the chart.
It is unclear how Figure 1 and Figure 2 are related. Is Figure 2 explaining how the problem in Figure 1 was fixed? If so, the authors should consider combining the two figures such that Figure 1a and Figure 2 are combined and Figure 1b is presented later in the text along with the section discussing IOKR framework.
The authors should consider using more concise language to help communicate the utility and limitations of this method more effectively. For example, the authors should use the term “bacteria” instead of “microbe” because the databases feeding into the program are heavily biased toward bacterial metabolites, and fungal metabolites are not well represented nor are they in the three datasets tested in the manuscript. It would also be worth considering giving the new score introduced in this manuscript a name, and referring to it by that name throughout the paper to avoid confusion.
This manuscript could benefit from a concrete example (actual metabolites linked to specific BGCs). A firm example with compound names linked to a specific gene cluster would help the reader evaluate how well the method performs compared to traditional methods. The manuscript does evaluate the performance of this technique using “verified hits”, but the identity of those hits and how they were verified remains unclear (unless the verification was the original report, which was also somewhat unclear). For example, the BGC listed at the top of Figure 6 (BGC0000137) encodes rifamycin, a commonly known bacterial metabolite. The authors should consider revealing the identity of at least one verified metabolite and providing a list of “hits” for the BGC encoding that metabolite with associated scores. This would allow readers to more effectively evaluate the new scoring method and determine what a “good score” looks like. A good choice for an example would be a metabolite/ BGC pair where a link was observed using this method and not other methods..
We appreciated that the authors discussed the data dependent limitations. They were apparent when reading the manuscript, and although they were briefly addressed in the discussion section, they might be more thoroughly discussed. The authors should consider drawing attention to biases toward specific organisms or metabolite classes in the databases feeding into the program, and discuss how those biases might affect the results and limit the usefulness of this scoring method to specific applications.
In Figure 6, for BGC0001228, it appears as though IOKR alone provided a higher score for the verified link than the combined score. Can the authors comment on the features of the data that led to this anomaly, and provide suggestions on how to determine the best scoring method?
We are excited at the prospect of using this tool. At the time we accessed the paper for discussion on 6/23, NPLinker did not have any documentation in the github repository so we were not able to evaluate how well it functions. The authors should provide comprehensive documentation. Additionally, a figure outlining how to use NPLinker to analyze a real dataset would be helpful, either in the manuscript or documentation.
Minor:
The metabolite used as an example in the introduction (C35H56O13) has a very unique molecular formula which is easy to link to a BGC (if the metabolite product is known), for example, NP Atlas only returns one hit for this molecular formula. The authors should consider picking an example that would have multiple hits.
The bottom of section 2.2 states “In the case of Figure 1, the standardised scores<br /> are 0.0 and 2.65, favoring the right-hand pair”, but in Figure 1 the two scenarios are positioned vertically, not horizontally.
The caption for Figure 5 says verified links are colored green, but in the figure they are red.
On 2020-07-03 12:45:06, user Dieter Egli wrote:
Linking this to our previous study:<br /> Inter-homologue repair in fertilized human eggs?<br /> https://www.biorxiv.org/con...
On 2020-07-03 12:43:47, user Dieter Egli wrote:
Linking the two studies:<br /> Reading frame restoration at the EYS locus, and allele-specific chromosome removal after Cas9 cleavage in human embryos<br /> https://www.biorxiv.org/con...
On 2020-07-03 11:46:18, user Nick Pullen wrote:
It looks to me like Fig 15 a,b are the same as c,d
On 2020-07-03 09:09:18, user Tim Fenton wrote:
Nice work on an interesting question! Did you look at PIK3CA mutation spectrum when stratifying the breast cancers by whether they were classed as APOBEC hypermutated or not? In Figure 4B you clearly show more PIK3CA mutations in this group and I would predict that they are nearly all E545K and/or E542K mutations as opposed to H1047 mutations? When we looked by tumour type, those cancer types with strong enrichment for the APOBEC mutational signatures (bladder, cervix, HPV+ HNSCC) almost exclusively harboured (presumably APOBEC-induced) helical domain mutations. Breast cancer is much more mixed though and seems to be a bit of an outlier, in that even the HER2+ subtype, which shows stronger enrichment for APOBEC mutation signature than the other subtypes (regardless of A3B genotype), still displays a lot of the non-APOBEC related kinase domain (H1047) mutations (see Fig 4 in Henderson et al Cell Reports 2014 (https://www.sciencedirect.c.... It would be interesting to know whether you still see H1047 mutations even in the APOBEC hypermutated breast cancers, since that would suggest a particularly strong selection pressure for these kinase domain mutations in breast cancer, not seen in bladder or cervix, for example.
On 2020-07-03 07:22:26, user H. Etchevers wrote:
Very interesting work! However, do not neglect other, earlier lineage tracing concerning the sources of pericytes in the developing embryo, that could enrich the interpretation of your study:
In the forebrain, face, neck and truncus arteriosus, vSMC derive from the cephalic neural crest (Etchevers et al., 2001, Jiang et al., 2000, Le Lievre and Le Douarin, 1975). vSMC of the heart septum (Waldo et al., 1998) and the proximal cardiac artery (Bergwerff et al., 1998, Etchevers et al., 2001) are also neural crest-derived, whereas vSMC of the coronary veins and arteries originate from the myocardium and epicardium respectively (Mikawa and Gourdie, 1996, Perez-Pomares et al., 2002, Vrancken Peeters et al., 1999).
(also see Arima 2012 DOI: 10.1038/ncomms2258 and Maeda 2016 DOI:10.1016/j.ydbio.2015.10.026)
On 2020-07-03 06:49:53, user Wolfgang Jarolimek wrote:
The paper is technically very well executed. The data are nicely presented and described. The results and in particular the conclusions are not convincing. It is claimed that the antibody inhibits lysyl oxidase like 2 activity, but already the initial reports show that it is a very weak inhibitor. The authors did not try to show its primary activity and therefore, all results and conclusions could derive from effects other than inhibition of LOXL2 activity. Given that the reported effects are opposite to the literature, it may be more likely that the problem is in the antibody and not the superiority of the primary cell system. This should be discussed.
On 2020-07-03 06:10:16, user Yogesh Taparia wrote:
Can the same be done using the minion form Oxford nanopore? Sanger sequencing is such a bottleneck to getting on with the set of experiments to follow.
On 2020-07-02 20:30:49, user Paul Gordon wrote:
Thanks for posting this. I tried to find CNP0001111 in the sequence database pointed to in the manuscript, but there are no matching records. Is the data under embargo, or was there a typo? Thanks for any clarification you can provide.
On 2020-07-01 13:44:03, user Helene Banoun wrote:
Hello
Your work is consistent with a hypothesis that I have formulated
"How can we explain the temporal evolution of the pandemic?
An analysis of the curves of the epidemic at the late stage shows the evolution towards the benignity of the virus throughout the world. There is a prolonged increase in new cases with a steady decrease in severe cases and deaths.
Cross-immunity with common cold coronaviruses has been suggested. This would involve viral sequences coding for the spike protein but also and importantly for non-structural proteins that could interact with the cellular immune response (CD4+ and CD8+).
The mutations in the viral RNA sequence observed during Covid-19 also concern regions involved in the interaction of the virus with cells of the host immune system. It appears that the emerging virus has adapted to the host immune system by altering its transmissibility and/or virulence. The virus adapts by natural selection to the immune system of its host (the human population); it is the sum of these individual adaptations that produces the overall evolution of the virus during the epidemic. This hypothesis is consistent with the Theory of Evolution, which often helps to solve puzzles in biology."
On 2020-07-02 20:26:44, user Matt Grainger wrote:
I really enjoyed reading this paper. I have a few questions/ comments.
You talk about subgroup analysis, but from what I can see from the R code in the appendix you have not actually carried out subgroup analysis but have subsetted your data and run rma. Subgroup analysis would involve looking at the differences between subgroups (e.g. male versus female; or USA versus the rest of the world or brassica versus other crop cover types). Am I misunderstanding the code and the approach here?
With the meta-regression you might need to think (or help the user of the shiny/website think) about the theoretical basis to the model - dredging the model (all possible combinations of variables) might lead to some combinations that are not reflective of underlying mechanisms being modelled. I have had this comment before from reviewers in the past using the same/similar approach.
Subgrouping the model will require higher power as will meta-regression. The probability of a type 1 error is going to be quite high, and will get higher as you reduce the number of studies and sample sizes. How will you deal with this and communicate the risk to end users?
From my point-of-view it seems like you are throwing away very useful information (by subsetting the limited data) and risking bad inference. Using a Bayesian model (of some sort - it could be a meta-analysis or decision model) would allow this information to be useful perhaps?
Finally, I think one question that remains (but perhaps some of Alec's work touches on this) how much does local evidence actually make a difference -is there a big-enough return to the manager or farmer for this local subsetting given it's limitations to make a difference to the "local" effect estimate. This is a decision problem - I would be interested in exploring this more.
I hope these comments help. I really like the concept and I would love to see this up and running. I think it could be very useful to embed meta-analysis & ES in conservation decision making.
Cheers<br /> Matt
On 2020-07-02 20:17:58, user Begossi Alpina wrote:
After revision of the data included in this study, the study stands in BioRxiv as it is. The abstract was translated to Portuguese along with some parts of CS and LK and included in the e-book Garoupas e Pescadores (Brasil) (Groupers and Fishers Brazil), in order to provide them available to Brazilian/Portuguese speaking students and researchers. Alpina Begossi, July 2, 2020.
On 2020-07-02 18:43:33, user Joe Ferreira wrote:
This looks great,fantastic short term and long term solution. Think you got a real winner<br /> Yes,best ive read that is not so ''complicated '' to test,and has been tested on mice.<br /> Good luck to all people involved,excited for you.
On 2020-07-02 18:15:08, user Jianzhi Zhang wrote:
The authors claimed that the results in Fig. 2 of Chen and Zhang (Genome Biology and Evolution 2013) are misleading. That figure was a test of the claim made in Table 1 of Paul et al. (Nature 2013), which showed a significantly higher fraction of multihit genes on the lagging strand than on the leading strand. Hence, comparing the observed ratio of the fractions of multihit genes on the two strands with the expected ratio under neutrality is critical to testing Paul et al.’s hypothesis that lagging strand encoding provides benefits not enjoyed by leading strand encoding. Our Fig. 2 showed that the observed ratio is not significantly different from the neutral expectation.
In Chen and Zhang (2013), we considered two neutral models in our simulation. In Fig. 2a and 2b, only observed variant sites in the sequence alignment were considered variable, while in Fig. 2c and 2d, all sites were considered variable. The expected numbers of multihit sites and genes under neutrality depend on the specific neutral model assumed, but the expected ratio is quite robust to the model variation. Because the true model is unknown and can be different from the two considered, comparing between the observed and expected numbers on each strand is dangerous. Therefore, for both theoretical and practical reasons, one should compare between the observed and expected ratios. When these ratios are examined, the findings of this bioRxiv paper, now based on larger data than in 2013, continue to support Chen and Zhang’s conclusion of no evidence for the adaptive hypothesis of lagging strand encoding. Furthermore, even on the basis of the comparison between the observed and expected numbers, this bioRxiv paper offers no support that lagging strand encoding provides benefits not enjoyed by leading strand encoding.
Christopher Merrikh emailed me on May 25, 2020, asking for the raw data used in making Fig. 2 of Chen and Zhang (2013). I emailed him the requested data on May 28, 2020, and copied Houra Merrikh and a co-Editor-in-Chief (coEiC) of Genome Biology and Evolution. The coEiC confirmed the receipt of my email. Writing to Houra Merrikh on May 30, the coEiC even referred to my May 28 email. This bioRxiv paper is factually incorrect in stating that I did not respond to the authors’ request for the raw data in Chen and Zhang (2013).<br /> Jianzhi Zhang
On 2020-07-02 18:01:31, user Kate wrote:
Interesting paper! What reference database did you use for assigning bacterial taxonomy? Minor point: alpha is intra and beta is inter, not vice versa. Were any of the negative controls sequenced?
On 2020-07-02 17:37:56, user James Xinzhi Li wrote:
There is no data on NCBI under GSE152766 as indicated in the preprint.
On 2020-07-02 17:20:22, user Arunava Roy wrote:
I thought CoV-2 ORF3B has a 20 aa truncation compared to CoV and is not expressed (only 22 aa long).
On 2020-07-02 14:08:55, user Alejandro Berrio wrote:
The position you refer in the following section as 11,408 is actually 14,408.
In this context, it is of note that D614G is in linkage disequilibrium with<br /> two other derived mutations (nucleotide positions 3037 and 11,408) that have experienced<br /> highly similar expansions. All three mutations are found in around 72% of the genomes in our<br /> global SARS-CoV-2 alignment, and rarely occurin isolation with 99% (16,538/16,677) of genomes<br /> with the D614G mutation also carrying the derived alleles at the two other sites.
Best regards
On 2020-07-02 13:45:06, user Concerned Biophysicist wrote:
This is very cool work, and the public engagement of folding at home aspect is great to raise awareness/excitement about computational biophysics.
As a scientists working in the field however, I do wonder if having "to Combat Covid 19" in the title might be crossing a threshold that we as a field collectively decided exists for a reason. Much (most?) of the applied word work in computational chemistry and biophysics is on disease related proteins, and many of the methods we all work on have relevance to drug discovery, so we could all be constantly claiming/marketing most of our papers as "fighting X disease" or "towards a cure of Y diseases" while in reality most of what we do is fundamental basic science, with eye towards pharmaceutically relevant discovery in the future. This science is just as important as applied pharmaceutical research in the research ecosystem, and does ultimately lead to tools and insights that are relevant to the pharmaceutical industry, but I think there is something to be said about keeping some of our powder try in terms of the claims we make about what is essential basic science and what is pharmaceutical research, so as not to create an arms race in the field to market all of our methodological work as having a dramatic immediate effect in curing disease, and end up devaluing and lowering the profile of the essential basic science that makes all this research possible.
Bluntly speaking, if we all start slapping "to cure cancer" on the titles of every paper that is about developing molecular simulation or drug discovery tools and every paper that studies proteins related to cancer, we may drum up a little buzz and be able to eek some extra press in the short term, but eventually, there is backlash to overselling a field. Other scientists will start to view all of our claims of the value of and potential pharmaceutical relevance of our work as oversold and less credible. This skepticism could creep into funding priorities and funding decisions (for national funding agencies and VCs), so it can effect more than the just the labs that are pushing the boundaries of how boldly we claim that "computational biophysics research = curing disease".
I get that folding at home is playing a different public facing role in our field than most academic and pharmaceutical/biotech labs, and I think a lot of it is great, but the simplification/boldness of some of the claims does make me worry a bit about an inevitable backlash for the entire field,
On 2020-07-02 12:04:40, user Guillaume Bilodeau wrote:
Hello. I like your paper. It will be a very useful tool to provide. However please recheck your references, the date does not match many time between text and reference and see in table 1 for Bilodeau et al...<br /> Regards
On 2020-07-02 10:31:09, user Bart van de Vossenberg wrote:
The URL to the ToBRFV nextstrain build has been changed to enable a secure and encrypted connection to the data. You can reach the build via: https://nextstrain.nrcnvwa....
On behalf of the authors
On 2020-07-02 09:54:53, user Institut für Immunologie wrote:
Our paper has been accepted for publication in Journal of Extracellular Vesicles on 27th of June 2020.
On 2020-07-02 08:56:10, user °christoph wrote:
Fantastic work. Clearly a study that advances the understanding of FtsZ-dependent divisome formation and therefore deserves rapid peer-review and publication. However, here - and elsewhere - it has become fashionable among authors to refer to previous work on their subject as "...remains poorly understood." This expression tends to devalue previous efforts, which is especially unfair in this case. A phrasing like "...not completely understood" or, maybe, "...not well understood" could express a more modest recognition of their own work as yet another of the customary incremental steps towards an understanding.
On 2020-07-02 05:17:24, user Davide Battini wrote:
This prepress is too "strange".
These SARS-CoV-2 seem more like the official flu virions.<br /> What exactly have you 'purified'?<br /> The purification and filtration of 'pure' virions must be done first, on the BALF material, not after the cell culture amplification of scrambled BALF, especially if the culture is made on immortalized 'Vero' cells.
Why do mini spikes, post fusion, along the crown, have such a clearly different count between TEMs and Cryo-EMs images?<br /> Based on the spikes root positions, of those 'survivors', the frequency is too high.
Much more material is needed.<br /> Maybe it would be useful to publish all the cryoEM and TEMs images taken during the study?
On 2020-07-02 04:45:03, user Andy Flies wrote:
This paper is now published in Science Advances "A novel system to map protein interactions reveals evolutionarily conserved immune evasion pathways on transmissible cancers" https://advances.sciencemag...
On 2020-07-02 02:10:51, user 박지상 wrote:
As a student who is majoring in medicine, it seems to me that Haplotracker would be an essential tool for many biological researches, especially those related to mitochondrial haplogrouping. I anticipate to see how the Haplotracker would contribute to the development of biological sciences and eventually to medicine.
On 2020-07-01 15:39:36, user Chulsung Park wrote:
This research paper portrays a well-designed tool to navigate through mitochondrial haplogrouping. I am excited to see such research as this will be very useful in future genomic research!
On 2020-07-01 13:23:48, user 김범준 wrote:
I think that Haplotracker, a newly introduced web based tool, could be sucessfully applied into diverse medical or forensic fields as an alternative of next sequencing protocols for accurate mitochondrial haplogrouping
On 2020-07-01 07:55:22, user 고재홍 wrote:
Mitochondrial function and genetic research is currently one of the most popular areas. This research should be very well-received in that it has pioneered new fields in the current trend of modern research, which continues to increase the interest of all human beings, such as prevention/treatment of metabolic diseases and life extension. As the author claims, the results highlight the potential for the use of their web application as an alternative to full genome sequencing for easy haplogrouping, which may be useful in related fields.
On 2020-06-23 00:47:17, user 김경용 wrote:
Haplotracker is a great and wonderful tools for tracking mitochondrial haplogroups in ancient, forensic, anthropological, and medical fields.
On 2020-07-01 19:10:35, user Jordan Berg wrote:
Metaboverse v0.1.3b released:<br /> - Allows for spaces in file names and folders<br /> - Allows to be run in Windows in non-admin mode
https://github.com/Metabove...
Let us know if you have any feedback! https://forms.gle/4z51DMnag...
On 2020-07-01 15:50:30, user Robert P Christen wrote:
which is the immune response if the first infection is with Dengue virus and subsequently is Zika virus? It generate same response from inmmune system and cell T
On 2020-07-01 13:06:21, user Jan Nagel wrote:
Very nice paper! Ancestral state reconstruction is quite sensitive to missing data and taxa. Do you think the conclusion that homothalism is the ancestral state in Phyllostictaceae will remain unchanged if more species' thallism is determined and added to the reconstruction?
On 2020-07-01 12:37:09, user D Chon wrote:
Giuseppe Pezzotti is part of the Sintx team per Sintx as a lead investigator and consultant for the company. Sintx CEO Sonny Bal said this was an independent study, yet someone getting paid by Sintx was in on this. What’s up with that?
On 2020-07-01 11:10:42, user Andrea Luchetti wrote:
Very interesting paper, thank you for sharing as pre-print! However, a citation error in the branchiopods section: in fact, Anostraca DO NOT exhibit lower mitochondrial substitution rate than Notostraca and Diplostraca, anostracans actually showed a higher substitution rate (please, check on Luchetti et al., 2019 - Zool Letter 5:15).
On 2020-07-01 09:15:02, user Jeroen van Vugt wrote:
The conference paper resulting from this version (version 4) of the preprint paper is now available via the link: https://dl.acm.org/doi/abs/... . This conference paper is peer-reviewed.
On 2020-07-01 08:45:51, user David Curtis wrote:
I looked at frequencies of TMPRSS2 variants in severely affected cases against a large number of controls in the UK Biobank sample:<br /> https://www.medrxiv.org/con...
There were no variants with big differences in frequency in the exome sequenced subjects.
Because it is common, this variant was chip genotyped in all subjects. I'm afraid the allele frequencies are about the same in the subjects who were tested and were positive (who had severe disease) as in the controls:
CHR SNP A1 A2 MAF_A MAF_U NCHROBS_A NCHROBS_U<br /> 21 rs12329760 T C 0.2131 0.2274 1272 973644
On 2020-07-01 03:15:22, user Zhuang Wei wrote:
The author information of [https://doi.org/10.1101/870253] is corrected here:<br /> Zhuang Wei1, 6, *, Ching-Wen Chang2, 6, Van Luo3, Beilei Bian4, Xuewei Ding5
1Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China.
2Institute of Oral Biology, National Yang-Ming University, Taipei, Taiwan, China.
3SINOPEC Yangzi Petrochemical Co., Ltd., Nanjing, China.
4Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia.
5School of Communication and Information Engineering, Shanghai University, Shanghai, China.
6These authors contributed to this work equally.<br /> *Corresponding author
On 2020-06-30 23:58:38, user Chen-Song Zhang wrote:
(Dear Jose and Dr. Sabatini, I do not know what happen to my earlier comment, but I just posted it again here, and no need to reply)
Dear Jose,
Happy to see your work that just occurred online.
I know that mTORC1 has a lot of “context-dependent” glucose sensors responsible for its regulation (although I am not an expert on mTORC1), but based on your data, and the experience I have had on the ALDO-v-ATPase-AXIN axis for the regulation of AMPK in response to low glucose, I strongly suggest you to re-consider the possibility that ALDO is a also sensor for DHAP.
As you know, we have delineated the pathway that senses low glucose, but I am excited to see a possible role of ALDO also in sensing the presence of sufficient glucose from your very work. We first must be reminded that ALDO is an integral component and is structurally required for maintaining an active v-ATPase complex, which was initially characterized by the Gluck’s group (PMID: 14672945, 11399750, 17576770); and certainly the activity of v-ATPase is also necessary to the activation of mTORC1 as shown by Dr. Sabatini’s lab. We also observed a strong inhibition of v-ATPase activity in ALDO-KD MEFs, as monitored by acidity of lysosomes (see our TRPV paper in 2019), and in your own data in Fig. 2D (no mTORC1 was activated in response to glucose stimulation without ALDO’s presence). Therefore, knocking out ALDO is not a legitimate way to deduce its function in glucose sensing. I would suggest you to use the catalysis-defective D34S ALDO mutant, which still binds its substrates like FBP, DHAP and G3P, to see what happens to mTORC1 in the presence or absence of glucose.
Another critical data you used to rule out the involvement of ALDO is shown in Fig. S5E. It carries a lot of information and I believe it was labor-consuming. However, in my opinion, it instead confirms that compared with GAPDH, ALDO plays a major role in glucose sensing and mTORC1 regulation in HEK293T cells, as you observed that re-addition of DHA completely restored the p-S6K signal in low glucose in TPI-KO cells while it only partially restored the p-S6K signal in ALDO-TKO cells.
I would point out that effects of AXIN1 KO on AMPK activation are certainly cell type specific. In fact, we have shown/discussed the presence of AXIN2 in 293T cells, which shows its redundancy with AXIN1 in regulating AMPK (you can refer to our Cell Research paper published on 2019). I would also like to take this chance to thank you and Dr. Sabatini: I had the pleasure to have discussions with Dr. Sabatini in 2017 during an EMBO Workshop in Naples, where he showed the AXIN1-KO data on 293T cells to me, and I suggested the possible existence of such AXIN redundancy in certain cell types including 293T cells prior to our actual characterization on AXIN2 in these cells.
Wish you all the best and good luck to your work!
Chensong
On 2020-06-23 05:11:54, user Hawks T wrote:
Very interesting discovery, in absence of glucose DHAP is sufficient to activate mTORC1
On 2020-06-23 04:52:22, user Hawks T wrote:
In HEK293T cells, AXIN2 was compensating for AXIN1 when AXIN1 was depleted. https://www.nature.com/arti...
On 2020-06-22 11:11:52, user Katharina Leithner wrote:
Very elegant work. Did you consider GNPAT, the enzyme catalyzing the first step in plasmalogen biosynthesis as the DHAP sensor?
On 2020-06-30 20:00:46, user El_Sid wrote:
Line 503 is back to front, no? The strains *with* the mutation had higher fusels/esters?
On 2020-06-30 20:00:26, user Manish Kumar wrote:
Fig 1: Please correct the color order of those dots. Red and yellow dots should be flipped on one side of the objective. An object farther from MO1 will get imaged closer to MO2.
On 2020-06-30 16:50:46, user Bruno Lopes Abbadi wrote:
Dear, I would like to congratulate you on the manuscript and say that I have some observations to make. In the methodologies section on cell culture, limiting dilution and isolation, the final volume in each well after adding the medium, virus, and cells is not very clear; would it be 150 uL? What was the final FBS concentration in each well? In addition, it was not very clear why you performed serial dilutions of the virus along lines 2-12. What is the purpose of this? Another question: from which well was the collection of 50 uL to infect the cells in the 24-well plate, since several wells may have a cytopathic effect? Finally, what is the purpose of infecting cells in 24-well wells? Did you use them only to count plaque-forming units, or to increase the virus titer? I hope that these considerations can further improve the quality of your manuscript. All the best.
On 2020-06-30 08:32:05, user Benoit B. wrote:
Thanks for using and citing the ReMap catalogue ! It is always great to see ReMap being heavily used https://doi.org/10.1093/nar...
On 2020-06-30 08:11:53, user OxImmuno Literature Initiative wrote:
On 2020-06-29 23:30:29, user Leyla Mahrokh wrote:
Another cool paper from Arash's team, but there is no supplementary file has been mentioned in the article.
On 2020-06-29 20:02:07, user Jing Peng wrote:
Dear authors,<br /> My name is Jing Peng, a scientist from UC Davis. I am happy to take this opportunity to congratulate you on the publication of the paper “FoodMine: Exploring Food Contents in Scientific Literature” in the bioRxiv. The idea of using computational methods to analyze published studies to enlarge and annotate food composition databases from the scientific literature is fascinating.
The existing food composition database is unbelievably lacking in critical information of most of the actual composition of food. The current food databases are asymmetrical. For essential nutrients such as mineral and vitamin, food scientists have identified each specific type such as iron, zinc, vitamin C, and vitamin D. Each compound has its unique name and related compound-specific research. But for most of the non-essential nutrients, there is only a vague “class” name for them, such as carbohydrates. There are lots of unique and independent compounds in the "class" carbohydrate, and they each have a specific name and feature. However, current food databases contain neither their names nor their functions. We need to understand each chemical compound and its effects. If food databases are lacking in such basic and important information, how do nutritionists provide the most effective advice to the population? Right now, most people, including some scientists, acquiesce to the vague definitions of those nutrients and the shortage of annotations in the food database. It is easy for people to lose the vision of measuring all compositions in food. But it is the food compositions that help us understand diet and the relationship between diet and food. Without such basic information, talking about diet is insubstantial.
The central idea of using scientific literature as a database and extracting information from those data is engaging. This approach demonstrates the successful extraction of novel compounds that were not included in existing food databases. If taken to its logical conclusion, it is indeed imaginable as the authors suggest to recommend diets based on the chemical composition of the food. However, this logic and its lack of imagination of food and health more broadly is a problem I have with the paper. Food exists in multiple dimensions. Compounds that are beneficial to people’s health are one important reason for people to choose food, but not the only one. When people think or talk about the food, they will not only talk about the chemical compounds of food, but also describe the appearance, taste, smell, and texture of food. Appearance and smell would contribute to the first impression of food. If food does not exhibit an attractive appearance and flavor, people will hesitate to taste it. Even with appearance and odor that are themselves attractive to people, without delicious taste and texture, people will still give up on the experience. So only measuring chemical compounds of interest to health and ignoring the other aspects of food is limiting. Food is joy. A strategy based on chemical compounds solely to give food recommendations is emotionless.
Food is multi-dimensional and so are people and they are different. Since each individual has his/her own sensory preference, they choose foods and diets based on their preferences. So, the brilliant idea of constructing a chemical compound network in food, even considering taste may not be sufficiently precise to provide useful food advice for the whole population. In order to individualize diet and give more focused food advice, each individual's diet preference is key. How do the authors imagine that their methods could measure the responses of people to foods with sufficient accuracy to capture their diet preferences? In place, such databases would create a more complete food network combined with food composition network annotated for personal preference. As food databases become more thorough and acquire the dimensions of individual dietary preferences, we could imagine using technologies and computational methods to provide more precise, sustainable, and enjoyable food for people.
In the end, I would like to congratulate the authors for such inspiring ideas, using computational methods to extract information about chemical compounds in food to expand existing food databases. I look forward to more multidimensional research to define future food database structures and contents. As a person who is going to work in food systems, my future in food depends on usable information and enlarged food composition databases.
Best,<br /> Jing Peng
On 2020-06-29 14:43:04, user Angélica Resende wrote:
Dear readers,
This preprint differs considerably from the published version since the preprint has been revised since it last posted on bioRxiv so you should view the final published version link<br /> https://nph.onlinelibrary.w...
Sincerely,
Angelica
On 2020-06-29 11:48:04, user Grigorios Koulouras wrote:
We recently posted a highly-related article: https://doi.org/10.1101/2020.06.25.170431<br /> It seems we neglected to cite this article. We apologize for this omission, which we will fix at our next revision stage.
On 2020-06-29 03:36:51, user aarontay wrote:
Very nice. I was doing similar analysis for my institution and just wondering about how much the results vary when moving from WOS, Scopus, Microsoft academic as well as the range of years involved. Analysing with diff Unpaywall dumps at different time is also novel. However I wonder if the most important factor the use of unpaywall itself might be worth studying using other services like CORE discovery or Open access button APIs.
On 2020-06-29 00:39:21, user Charles Warden wrote:
Thank you very much for posting this response.
This pre-print was very helpful in terms of helping me understand the discussion.
In the most respectful way possible, my impression was that the tone escalated the situation. I can understand how the previous pre-print could upset the authors, and I have some questions that I think could be beneficial to the broader community in the second part of this comment.
However, to first try and better explain my opinion:
1) The earlier Liu and Zhang et al. 2020 pre-print mentioned “[Merrikh and Merrikh 2018] assumed that a negative GC skew means that the gene has been recently inverted…To the best of our knowledge, no study has used the GC skew alone to infer gene inversion in bacteria” and that they used BLAST hits and parsimony to define inversions. However, the abstract for this pre-print says “Our reanalysis of the Liu and Zhang data reveals that the main problem is that the authors simply do not understand the meaning of the term “inversion””. So, my opinion is that i) this communicates a negative rather than neutral tone and ii) I do not completely agree with this conclusion.<br /> -------->I think a neutral response could be “different methods were used to identify inversions”, but that may or may not be the best alternative wording. For example, I would say the scenario described with the helpful illustration in Figure 2 of this pre-print would be an inversion, but there can be advantages to studying subsets of inversions.<br /> -------->I would currently agree that GC skew alone should not be used to define inversions, so I believe that is a valid point to communicate in some way.<br /> -------->Similarly, I would say the “reversion” in Figure 1 could be an inversion, especially if there was enough support from multiple species (without any evidence of an additional, more distant 2nd inversion event). I don’t believe more than one outgroup was used for any genomic analysis, but I also don’t believe that it is fair to say the term “inversion” was “simply” not understood in the broader context.
2) While not strictly within this pre-print, responses like “This paper is written in a deceptive and unprofessional manner” (on Twitter or the earlier comment) communicated a more personal tone that I didn’t notice in the Liu and Zhang et al. 2020 pre-print (although specific examples might help me understand your perspective).<br /> -------->For example, I would consider a claim that something is deceptive is more severe than saying something is inaccurate.
To be fair, there were other examples where I thought tone was reciprocal.
I also appreciate that you provided supplemental tables outlining the difference in interpretation for individual genes (for 3 species).
In terms of the communication style, if a reader has an opinion similar to mine, then perhaps answers to the following questions might help others in the future?
a) What public commentary medium would you consider to be the most professional response?
b) Are there specific requests that you would like to see, if other readers would like to express concerns in a respectful way (but also in a transparent, public medium)?
As a possible answer for a), I probably would have added a comment instead of a pre-print. However, I am interested in hearing other ideas.
It might help to provide something like the earlier specific examples for b), but I think non-specific, general guidelines could also be helpful.
I don’t currently have as strong an opinion about the other scientific points (beyond the earlier GC skew and inversion, and possibly some http://disq.us/p/2a8c04a about some other points where I think there was some confusion/misunderstanding). So, as one specific example for b), my individual opinion is that the title could be re-worded, but I don't think I am placing the same emphasis on specific points that the other authors did.
As another specific example for b), given the sensitive nature of the topic, I can imagine how it might make the original authors more upset than necessary to add the term “error-prone” in “even the inversion rates computed from their error-prone inferences supports the mutation-selection balance hypothesis rather than the adaptive hypothesis”, when the concern was expressed in the previous sentence. I also need to work on improving my own word choice and communication (and recognizing unconscious bias), since de-escalation is important for having discussions to improve understanding.
Thank you very much for posting this detailed response.
I think this is an interesting topic of research, and I wish you the best in your future discoveries.
Sincerely,<br /> Charles
On 2020-06-28 20:29:30, user Trans Mogrifier wrote:
This is a timely manuscript during the ongoing quest to render CAR-T therapy efficacious in the context of solid tumors. It is interesting to see that the authors improved on previous ODD-reliant oxygen-dependent CAR expression in effector T-cells (Juillerat Sci Rep 2017). They added the well-established and previously reported hypoxia responsive elements to drive the expression of their CAR on top of the previously published ODD modification. The novelty lies in this combination; while clearly not ground-breaking, it is at least a logical incremental step to get closer to the goal of avoiding CAR-T side effects.
***I was quite disappointed reading this pre-print manuscript - there is substantial overselling going on here! ***
There are four major areas of concerns and many more experiments and in-depth analyses are needed to evaluate and validate this 'HypoxiCAR' and perhaps making it worthwhile in the future (and not just another buzzword).
In summary this manuscript is very very preliminary and so it is concerning this is currently under consideration at Nature Communications ( https://nature-research-und... ), which, IMHO is clearly not deserved.
Their construct is preliminary in nature (not optimized and only partly fit for purpose; see points (1-2) below) and the in vivo validation is hugely lacking (see points (3-4) below).
Areas of major concern:<br /> (1)<br /> The situation describing normoxia/hypoxia is not so clear-cut as presented in this manuscript. <br /> It is widely known that oxygen concentrations differ between tissues (summarized here: PMID 24588669) whereby HIF1alpha stabilization has been reported to be about half-maximal at up to 2% oxygen depending on cell/tissue types. The authors do not report a careful analysis of HypoxiCAR expression or cytokine release in the relevant oxygen concentration range (they present it for 0.1/1/5/20% oxygen but not for the critical oxygen concentration range between 1 and 5% where physiologic hypoxia occurs, e.g. 2-7% oxygen). While initially appearing promising, the utility of this CAR can not be assessed properly without data covering this intermediate/physiologic hypoxia range.<br /> It is therefore highly recommended the authors perform more experiments and report data in vitro for the range 1-7% oxygen.
(2a) <br /> The authors claim to achieve a 'dynamic' on/off switch. This is misleading as the on-switch is dependent on CAR protein expression (which takes time!), while the off-switch depends on CAR protein degradation; consequently both processes are relatively slow. Moreover, the precise consequences of adding the ODD domain of HIF1alpha to the integral membrane CAR protein are not well understood (HIF1alpha is proteasomally degraded while many plasma membrane proteins are lysosomally degraded). Also, the data the authors base their claim of 'dynamics' on is not convincing: there is 40% CAR retention 24h post re-oxygenation to normoxia and 20% after 36h (longer time points are missing) pointing, not unexpectedly, towards a rather slow switch off. <br /> In terms of mobile CAR-T cells this is potentially problematic as the cells could get 'activated' through CAR expression at one place, migrate elsewhere and cause unintended off-site effects due to CARs still being present. Therefore, the selling point of reduced off-site effects is rather a red herring until proven otherwise widely in different solid tumor contexts.<br /> Hence, it is highly recommended that the authors carefully study the kinetics of degradation of their CAR.<br /> (2b)<br /> The authors only show CAR expression to support their claims about the dynamics of the on/off-switch but no IL-2 or IFNgamma secretion experiments. What about granzyme or perforin? What is the lag time in all the latter molecules’ production/release after the end of CAR signalling? That must be studied systematically, too.<br /> (2c)<br /> Moreover, they also provide some data with a variant construct including HRE but lacking the ODD, and it is apparent from this data that the HRE element leaks quite dramatically (>60% expression under normoxia; FigExt2). <br /> Together this demonstrates the rather preliminary nature of this construct and shows that it is neither properly validated nor optimized.
(3)<br /> The tumor models presented are concerning as the CAR-T administration always happened at rather small tumor volumes; the authors should have waited until the tumors reached sizes of 75-100mm3 so they are properly established.<br /> Furthermore, the tumor models used are presented in a quite inconsistent manner:<br /> - in FigExt.4 they compare HypoxiCAR with untransduced T-cells but not the T4-CAR (ovarian cancer model SKOV-3).<br /> - in Fig.3 they perform some experiments in a tongue squamous cell carcinoma (HN3).<br /> - in FigExt.3 they show some data in the LL2 murine model (Lewis lung carcinoma), another model; syngeneic but without immuno-analysis etc; oxygen concentrations likely different in this lung tissue context compared to the other tissues.<br /> - none are properly orthotopic.<br /> - in vitro data are from SKOV-3 ovarian cancer cells only.<br /> - in Fig.4 they show human HNSCC tissue data to demonstrate that T-cells can reach hypoxic areas (as defined by HIF1-alpha expression as the sole marker) but this is rather unlinked to the rest of the manuscript. And this figure does not really have any relevance to the rest of the manuscript because one cannot connect the level of HIF1alpha expression as per immunostaining/microscopy to the levels needed/desired for HypoxiCAR activation in vivo; really, an in vivo tumor model is required to link the two domains - including pimonidazol vs HIF1alpha staining and investigating potential other origins of HIFalpha stabilization such as, for example, NF-kB (PMID: 18393939).<br /> Together, this shows again that the manuscript provided is rather preliminary and lacking consistency also in the tumor models/context.
(4a) Quite disappointingly there is no data at all addressing rather important aspects of cell-based immunotherapy, namely that under hypoxic conditions the upregulation of inhibitory molecules of the B7 class (e.g. PD-L1) has been reported (PMID: 29910728), which consequently could significantly interfere with HypoxiCAR infiltration and survival (the therapeutic cells could well be killed/senesce before relevant amounts of the HRE-driven CAR become expressed in them due to low oxygen levels, if they even manage to infiltrate the tumors).<br /> (4b) Lastly, there are no relevant tumor models present that are syngeneic in nature (LL2 is not relevant in the context of the rest of the paper as it is lung cancer; it is just another tumor model used without appropriate analysis), hence what is shown here is solely relevant for the presented xenograft systems and cannot easily be translated to humans.
Moreover, the authors did not provide on donor variability of produced CAR-T.<br /> (There is a Supplementary Figure 8 references, which is not only wrongly numbered but simply not provided).
On 2020-06-28 16:44:54, user Rhyothemis wrote:
Could dexamethasone augment the effects of calcitriol?
Dexamethasone Enhances 1α,25-Dihydroxyvitamin D3 Effects by Increasing Vitamin D Receptor Transcription*<br /> https://www.ncbi.nlm.nih.go...
On 2020-06-28 12:40:30, user OxImmuno Literature Initiative wrote:
On 2020-06-28 12:30:44, user OxImmuno Literature Initiative wrote:
On 2020-06-28 10:50:48, user Waseem El-Huneidi wrote:
The reported findings are interesting, the findings suggest that transient hyperglycemia associated with COVID 19 is related to low insulin secretion due to affected exocytosis, so i am wondering if there is any data about C-peptide levels in COVID 19 patients which reflect low insulin level (and can eliminate other potential extra-pancreatic sources of hyperglycemia, e.g. insulin resistance). another concern, what if the observed hyperglycemia is related to Glucagon/ insulin ratio, i.e. what if the glucagon concentration was affected, taking into consideration that the findings are based on pancreatic Islets (which include alpha and beta cells), i mean is there a possibility that the effect was on glucagon secretion, peering in mind that alpha cells exhibit similar exocytotic mechanism as of beta cells.<br /> thank you for the interesting findings
On 2020-06-28 00:05:02, user Fraser Lab wrote:
I reviewed this for a journal:
On 2020-06-27 20:38:23, user Alexis Rohou wrote:
These are stunning results - congratulations to all involved.<br /> I'm obviously very interested in understanding the impact of the monochromator and aberration corrector. Figure 1b seems key here, but I am not sure it presents a fair comparison: The oscillations in the Krios Mono data suggest this was taken at very low defocus compared to the "regular" Krios data. This will definitely affect detectability of Thon rings, and might even affect any optical damping envelope present in that system.<br /> I would encourage the authors to reproduce this figure with comparable defocus values to remove any doubt.
On 2020-06-27 15:19:05, user jie huang wrote:
Hi, guys:
This guide is really useful!
it would be nice to add "guide" to use LDpred-funct, and SbayesR (implemented in GCTB), which claims to perform better than LDpred.
Also, a lot of people are using millions of SNPs to generate PRS these days, and do that on the UK Biobank data (N ~ 500,000). I don't know if Ldpred actually works for that. There is a BioRxiv paper from Stanford University titled "A Fast and Scalable Framework for Large-scale and Ultrahigh-dimensional Sparse Regression with Application to the UK Biobank". It would be good to "guide" how to use tools like this one.
Thanks!<br /> Jie
On 2020-06-27 07:02:38, user OxImmuno Literature Initiative wrote:
On 2020-06-27 06:59:05, user OxImmuno Literature Initiative wrote:
On 2020-06-27 06:55:31, user OxImmuno Literature Initiative wrote:
On 2020-06-27 06:48:33, user OxImmuno Literature Initiative wrote:
On 2020-06-27 02:05:24, user Alexander Novokhodko wrote:
Hello,
I believe that there's a need to make a correction here: In the text you say "When focusing on the sole RBD, from amino acids 319–541, 13 variants arise, all with a relative frequency less than 0.1% and 10–20 absolute occurrences."
When I looked at Supplementary Table 1, I found four mutations in that range of the spike protein with a frequency > 0.001. If I understand correctly, Percentage = 100*frequency. Thus, Asn438Lys, Ser477Asn, Thr478Ile, Val483Ala have frequencies > 0.1%. Also, why is Ser477Asn absent from Figure 2A?
Thank You,<br /> Sincerely,<br /> Alexander Novokhodko
On 2020-06-26 17:30:26, user Paul Robustelli wrote:
Additionally, to be consistent with the wide literature on chemical shift prediction from IDP ensembles, one should probably just report the RMSD of each shift-type prediction, since that value has a clear meaning and is commonly what is reported.
It would also be great to see some free energy landscapes for the lowest temperature replicas (Ie. Rg vs. Helical Contact or AlphaRMSD (https://www.plumed.org/doc-.... I'm guilty of not including them in a99SB-disp paper, but going forward I'm hoping this becomes more standard
On 2020-06-26 17:16:30, user Paul Robustelli wrote:
I enjoyed your paper and the results of sampling obtained by HREX are very encouraging.
One quick comment: Plotting Experiment vs. Computed chemical shifts (Figure 2) is generally uninformative, if not a bit misleading, since it visually suggests that all these ensembles have the same level of agreement. You should always subtract off the random coil shift (http://www-vendruscolo.ch.c... is one server of many for calculating them), so that we can see deviations betweensecondary chemical shifts. The differences between the predictions are going to be on the order of 0.10-~2.00pm, so plotting them on axis that spans 30 or80.0ppm (mostly to capture variation in random coil chemical shifts) doesn't make a lot of sense. Also, the Shiftx2 algorithm has a sequence based prediction component that one shouldn't use when trying to compare structural features of ensembles, since it just averages all the structural based predictions with a database, structure independent, sequence based prediction, so you should use only the Shiftx+ module to calculate shifts from your simulations. If there is not homologous sequence in the Shiftx2 database, then these two predictions will be the same.
Paul Robustelli<br /> Dartmouth College
On 2020-06-26 16:00:55, user ChrisdeZilcho wrote:
The sensitivity of SARS-CoV-2 to Interferons is a very interesting observation with regard to its viral evolution. <br /> Type I - IFNs are normally produced by lymphocytes (NK cells, B cells and T cells), macrophages, fibroblasts and endothelial cells from all mammals as an important component of the immune response against viruses. Homologous IFN molecules have also been found in birds, reptiles, amphibians and fish species. IFN is therefore an essential part of an effective antiviral immune response. It activates surrounding virus-infected and non-infected cells, which consequently form proteins (RIG-I, MDA5, TLRs), which inhibit further (virus) protein synthesis in those cells and on the other hand cause the degradation of viral RNA. IFN-α has previously been used therapeutically in the treatment of chronic viral hepatitis for several years.
Bats were shown to elicit a particularly strong immune response against viruses through activation of IFN-pathways. (Cara E. Brook et al., eLife 2020;9:e48401): <br /> “The experiments and model helped reveal that the bats’ defenses may have a potential downside for other animals, including humans. In both bat species, the strongest antiviral responses were countered by the virus spreading more quickly from cell to cell. This suggests that bat immune defenses may drive the evolution of faster transmitting viruses, and while bats are well protected from the harmful effects of their own prolific viruses, other creatures like humans are not.” https://elifesciences.org/a...
So if the virus multiplied in a natural environment in mammals, bats in particular, it would be expected that it would have developed counter-mechanisms to IFN in its viral evolution. This is clearly the case with SARS-CoV. The "old" SARS-virus, which originates in bats and allegedly jumped to later intermediate hosts (civets/raccoon dogs), does not appear to be as sensitive to recombinant IFN as its "new" relative. SARS-CoV-2 is much more sensitive to recombinant Type 1 IFN in cell culture. This was similarly shown by Emily Mantlo et al. "Antiviral activities of type I interferons to SARS-CoV-2 infection", Antiviral Res. 2020 Jul; 179: 104811. https://www.ncbi.nlm.nih.go...
Interestingly, these studies, as well as in numerous publications before the outbreak in 2019, used the IFN-α/β -defective Vero E6 cells to cultivate SARS CoV. The kidney cells from African Green Monkeys lack the ability to produce Type I Interferon (IFN) (Naoki Osada et al., DNA Res. 2014 Dec 21(6): 673–683.). The cell line is popular not only due to its IFN-deficiency, but because of its ACE2 expression on the cell surface and similarity to human epithelial cells, many research laboratories worldwide have used them for years in the cultivation of natural and artificially generated SARS viruses in the laboratory.
It has been shown previously that the Vero E6 cell line proved to be particularly permissive towards SARS-CoV-2 - more than any other cell line tested with a standard CPE assay. The Vero E6 cell line is used not only in virus research, but also routinely in the production of vaccines for rotaviruses, inactivated polio vaccines, and for Japanese encephalitis vaccine.
The results above suggest that SARS-CoV-2, unlike its relative SARS-CoV-1, developed in an environment where IFN did not seem to play a role. However, since virtually all mammals use IFN in their immune response (bats in particular), why is CoV-2 so sensitive in contrast to CoV-1? What does it mean in terms of its evolution in mammals? Would that explain the lower virulence of CoV-2 compared to CoV-1 in most patients that actually develop mild symptoms? Would antiviral IFN-drugs prove to be effective against CoV-2 such as Avonex®, Rebif®, Plegridy®, Betaferon®, Extavia®, Intron-A®, Roferon®-A and is IFN-α more effective than IFN-β?
I would be interested in the opinion of scientists in the field, since my only conclusion would be, that SARS-CoV-2 may not have developed in an environment, where Interferons play a major role in the hosts immune defence. Of course this is purely speculative.
On 2020-06-26 10:15:50, user Ersa Flavinkins wrote:
Major issue with the article: the vector, the pcDNA3.1-N-myc/C-C9 vector, is not found nor availible from catalogue in anywhere. All the ACE2 proteins are stained with anti-C9 antibodies--indicating that the cloned part is not the entire mRNA.
The original specification of the c-myc/c9 vector was stained by the anti-c-myc antibodies on the cell surface--so there is an additiona signal peptide in fromt of the c-myc tag in the vector.
no pcDNA3.1 vector have an AgeI site and XM_017650263.1 is not cut by either AgeI or Acc65I. As the human, civet and rat ACE2 gene is specified to have their signal peptide removed before cloning into their vector, the vector must carry it's own signal peptide--which is before the c-myc tag as the original thesis at ref.55https://www.ncbi.nlm.nih.gov/pmc/ar... and ref.34 https://www.ncbi.nlm.nih.go...
specified the staining of the cells via antibodies targeting the c-myc tag on the N terminii of the ACE2 receptors.
This leave all the receptors--the Human,Civet and the Rat--with an N-terminal C-myc tag. and the Ferret badger, Rhesus, Raccoon dog, Hog badger, Free-tailed bat, Rabbit, cat and dog ACE2 receptors may potentially contain parts of the signal peptides themselves or even the entire signal peptide. The Rs bat and pangolin ACE2 receptors were cloned into an unknown vector and there is no way of telling whether the Signal peptide, c-myc tag or other AAs were retained or not. However, as these were all marked as C9 tagged on the C-terminus, the exact cloned part must not include the C-terminal stop codon or other parts of the mRNA since the natural Stop codon will prevent C9 tag expression.
There is no indication of the N-terminal clone site for the 2 ACE2 proteins, but the Human, Civet and Rat ACE2 is specified to have the signal peptide sequence removed. and therefore an additional signal sequence must be included before the C-myc tag in the vector to enable cell surface display.
As the article specifies that the ACE2 proteins expressed from such vectors have a "N-terminal c-myc tag and a c-terminal C9 tag", the tage expressed as specified have serious issue with steric clashing with the other S1 RBD monomer and therefore downplaying the Human, Rat and Civet ACE2--this may be even more severe with the other ACE2 and the exact N-terminal status of the Rs and pangolin ACE2 receptor is impossible to tell. Over all, this experiment is heavily contaminated and there is no way to actually deduce the results by just their method section alone. As no published vector available offers simultaneousy the N-myc and C-C9 tagging capability in the protein product, it may or may not be the same vector as specified before.
At best, it may downplay the ability of hACE2 to mediate entry with the PP assay by steric clash with the Tag and potential AAs in front of them--indicating an intentional overplay of Rs bat and pangolin ACE2 receptor by handicapping the rest with a bulky protein tag and a potential antibody binding to the tag, all of which clashes with the rest of the S glycoprotein and significantly decreases the entry efficiency, at worst--if the specified N-myc/C-c9 vector is the same as the vector described before, it mean that none of the PP assays are trustable as actual, unbiased data.
Notably, the PP assay result described here is in conflict with another paper https://www.biorxiv.org/con... using the exact same protocol but specified a different N-terminal tag--the HA tag, again on the N terminus of their ACE2 proteins. Notably, the Rs bat and Rat receptor affinities, as well as the Feline and pangolin receptor affinities, as by PP assay, were inverted in the 2 publications. As well as the Feline and Rabbit receptor affinities--despite the feline and rabbit are specified as being tagged using the same protocol in both publications--c-myc in this and HA in the other.
Unless the exact cloning sequences of the vectors and the inserts are published, neither publications can be used as an exact indicator of the true affinities of the ACE2 to the S glycoprotein, and none of the publications may be used as a true indicator, in isolation or in tandem, of the true affinities of animal ACE2 to the SARS-CoV-2 Spike glycoprotein.
On 2020-06-26 06:43:54, user Nafeesa Yaqoob wrote:
A good paper but upon comparing with https://www.biorxiv.org/con... we are having a contradictory statement between Taiwan and Nepal strains as in this paper it is said that Taiwan strain is identical with Wuhan while in other paper it is stated that Nepal is identical with Wuhan. And as my research is going on i don't know with which to proceed. If anyone can assist please.
On 2020-06-26 02:28:45, user dalia mohmd wrote:
This is really a great effort to analyse and annotate all cell types in lung using different techniques. I was trying to learn more and about the data but I ran into problem. There are some clusters that appear as NA and does not have annotation. What does it mean.
On 2020-06-26 00:41:45, user Chris Mungall wrote:
This looks interesting and useful. I think the manuscript should mention the relationship to the GSC MIxS standards (https://gensc.org/). It seems potentially complementary, STORMS is for reporting on results, and MIxS is for describing the sample and processing. But there are surely overlaps, and I think it would be useful for the reader to be oriented where STORMS sits in relationship to established standards. Thanks!
On 2020-06-25 16:59:55, user Jitin Singla wrote:
Please refer latest version
On 2020-06-25 15:07:00, user Chun-Hao Pan wrote:
Dear readers: We added additional information to the paper published in Molecular Oncology that was not included in the BioRx draft. Thank you!
On 2020-06-25 14:57:10, user Aliun Ngom wrote:
How do you compute the distance between two pairs of samples when there are missing values between the two samples? Many thanks.
On 2020-06-25 09:44:32, user Joerg Gromoll wrote:
Important piece of work. The authors are not detailing the isolation procedure for SCs and on the obtained purity. To me the cell culture fotos are not typical for mitotically quiescent SC and look more like fibroblast like cell type, which is highly proliferative ?
On 2020-06-25 08:18:30, user Andrew Calcino wrote:
Hello,<br /> It appears that the link provided for the genome assembly and annotation is not functioning. Is it possible to fix this or to provide another link where we can download these files? Thanks very much in advance.
On 2020-06-25 06:14:52, user Pradeep Anand Ravindranath wrote:
Is there a reason why PointSite was not compared with AutoSite?
On 2020-06-25 01:12:02, user Zhengguang zhang wrote:
Good job! During the interaction between pathogen and its host, the switch of<br /> cellular pH plays critical roles in the transition from biotrophic growth to<br /> necrotrophic growth. Here, You-Liang Peng uncovered the nuclear localized PacC222<br /> and PacC559 function as the key switch that sense and manipulate the host<br /> cellular pH to regulate the temporal hemibiotrophy of M. oryzae.
On 2020-06-24 19:45:30, user Charles Warden wrote:
Thank you for posting this pre-print:
1) I think it is a minor formatting error, but I think there is supposed to be a line between the center and "Base Accuracy" at the top in the legend for Figure 5B.
2) I think there are typos in the caption for Figure 5 (for both A and B):
"radard" --> "radar"
I think those might also be "plots" rather than "charts", but that might be a matter of personal preference.
On 2020-06-24 16:33:22, user Ian Durie wrote:
you should probably reference this article https://pubs.acs.org/doi/ab... in account of 1c was previously done and Figure 4 came to the same conclusion
On 2020-06-24 13:44:35, user Rita de Almeida wrote:
Dear Authors, <br /> Your manuscript is very interesting. Thanks.
We applied our Transcriptogram method to analyze your RNA data, and have some preliminary results. We have applied the same methord for SARS-CoV-1 time series data by Sims ey al (2013) in https://biorxiv.org/cgi/con....<br /> If you are interested, we would be happy to share some preliminary results.
On 2020-06-24 08:36:46, user Sohan Sengupta wrote:
This is a wonderful piece of work. <br /> 1. Is there any HPLC fraction that showed antimicrobial activity against common strains like E. coli, B subtilis or M smagmatis? <br /> 2.Did you try to heterologously express the entire nrps1 BGC.<br /> 3.There is a SARP regulator in the nrps1 BGC. What is the expression level of this regulator during the coculture system. <br /> 4.Does change in pH or salinity of the media has any effect on anti-Phytoplankton activity of crude extract from this strain. <br /> 5. Have you used any commercially availble pure photosynthate as elicitor.
On 2020-06-24 08:11:07, user Gennadi Glinsky wrote:
It makes a lot of sense, because MECHANISTICALLY: Vitamin D + Quercetin + Estradiol alter expression of 244 of 332 (73%) SARS-CoV-2 targets in human cells, thus interfering with functions of 96% (26 of 27) SARS-CoV-2 proteins. <br /> https://doi.org/10.3390/bio... <br /> TRIPARTITE COMBINATION OF PANDEMIC MITIGATION AGENTS
On 2020-06-24 07:24:06, user AH wrote:
Very interesting and elegant study. I have few comments/quires:
1-The rationale and benefit of the RNA-seq analysis of human bronchial organoids is not clear, and the sentence "hBO have higher bronchial functions than A549 cells or hBEpC" does not mean anything.
2-There is no quantification for the pyknotic cells shown, and it seemed that the virus did not cause much damage to the organoids. How do you explain that?
I would expect marked apoptosis, and then the surviving basal stem cells will start to repair/regenerate the organoid, so we need to see staining and quantification of an apoptotic marker, and a proliferation marker, co-stained with a basal cell marker.
3-It is not very clear when camostat was added the medium, probably immediately after the viral infection!? Being an inhibitor of TMPRSS2, it is expected to be more efficient as a protective agent, or early after the infection to prevent the spread of the virus to neighboring cells after its initial replication. We would want to see a comparison to adding it at days 1, 2, and 3, regarding the effect on apoptosis, viral replication, PFU, LDH etc.
Best regards,<br /> Dr. Ahmed E Hegab, MD, PhD<br /> Assistant Professor (research)<br /> Division of Pulmonary Medicine,<br /> Keio University,<br /> Tokyo, Japan
On 2020-06-24 06:09:57, user Jink wrote:
Why the authors have stayed away from experimenting in male mice? Experiments in male mice will give different results in viral load and pathology as the physiology is strikingly different in males. Please think about it.
On 2020-06-21 13:26:07, user OxImmuno Literature Initiative wrote:
On 2020-06-24 03:46:44, user Paul Gordon wrote:
Thanks for posting this interesting work. Can you please clarify a discrepancy between the text which states that both the male and female recovered without severe symptoms, and the GISAID annotation for the male (hCoV-19/India/GBRC4/2020 from what I can tell) which states "deceased"? Thank you for any clarification you can provide!
On 2020-06-23 18:42:11, user Ana Páez wrote:
Hi Johannes, I am trying to understand the protocol and it would be very useful to have access to the vector sequences, both the shuttle and the recipient ones. I see that the information should be in the Appendix 2, but I can't find it. Could you please help me here?<br /> Thanks so much!!<br /> By the way, we love the manuscript, we are trying to implement it in the lab. Congrats!<br /> Ana
On 2020-06-23 16:05:55, user ???? Dr. Jennifer Glass ???? wrote:
What a fascinating study! Wonder if you had a chance to look at our recent study on a similar topic: https://www.biorxiv.org/con...
On 2020-06-23 15:56:38, user SeaDNA Project wrote:
This study was partially funded by the UK Natural Environment Research Council grant NE/N005759/1, 'SeaDNA'.
On 2020-06-23 14:37:12, user lorena porte wrote:
For transparency reasons and in response to Beijing Savant Biotechnology Co. Ltd., the group of authors would like to clarify that:<br /> 1) As explained and discussed in the manuscript, our study used an experimental (off-label) methodology to evaluate four rapid SARS- CoV-2 antigen tests.<br /> 2) This experimental methodology used previously collected UTM samples, which allowed a rapid and direct comparison of all tests with the gold-standard, RT-PCR. <br /> 3) Our experimental study deviated from the manufacturers´ “instructions for use” (IFU). Therefore, the results only reflect on the performance of the tests under the chosen experimental conditions. <br /> 4) Our study was performed in an independent manner, strictly following STARD criteria.
On 2020-06-23 10:29:08, user OxImmuno Literature Initiative wrote:
On 2020-06-23 10:26:07, user OxImmuno Literature Initiative wrote:
On 2020-06-23 10:23:36, user OxImmuno Literature Initiative wrote:
On 2020-06-23 10:19:56, user OxImmuno Literature Initiative wrote:
On 2020-06-23 10:16:40, user OxImmuno Literature Initiative wrote:
On 2020-06-23 09:31:37, user Jan Nagel wrote:
Hi
You state that Botrytis cinerea belongs to the Botryosphaeriaceae. This is incorrect. B. cinerea is in the family Sclerotiniaceae in the class Leotiomycetes. The Botryosphaeriaceae are in the class Dothideomycetes.
On 2020-06-23 08:51:34, user Daphne Bitalo wrote:
This is very pertinent research for the East African region and I cannot wait to see the validation of these markers with food and human samples. I do have one concern though, I never saw the use or description of a negative control in the PCR protocol and neither did I see one on the gel imaging. Do you not feel this would deter abit from the rigour of the PCR protocols used within the study?
On 2020-06-23 08:20:49, user Fabian wrote:
Interesting body of work which is timely for the understanding of potential intermediate host. It is great that the mRNA levels for ACE2 and TMPRSS2 has been evaluated based on organs and age. However, what was left disappointing was that there was no IHC or IF images of the host proteins despite the abstract describing "Histological expression showed that ACE2 and TMPRSS2 are co-expressed with viral RNA." Histology imply anatomical evaluation under the light microscope. Co-expression under microscopy would also require concurrent labeling of multiple protein of interest.
On 2020-06-23 07:26:55, user A scientist wrote:
This study has glaring methodological issues which should be raised during peer-review. It is unsurprising that the EPIC array detects more differential methylation since these results were not corrected for multiple comparisons. It is also unclear why an array-based method is still being used for comparison rather than reduced representation bisulfite sequencing (RRBS) or other sequencing techniques when these are widely available.
On 2020-06-22 23:24:17, user Matt Olm wrote:
This is a great paper that I really enjoyed reading! There are a couple of typos that I found that I figured I would point out.
1) In the legend of Figure 3c, I believe the orange and purple descriptions are incorrect. They should read A and P, rather than Kp + A and Kp + P.
2) In Figure 4d, I believe "Stenotrophomonas" is spelled incorrectly. I'm not sure how relevant it is, but it's also worth pointing out that both Delftia and Stenotrophomonas are known laboratory reagent contaminants (https://bmcbiol.biomedcentr...
Congratulations again on the great paper!<br /> -Matt
On 2020-06-22 20:00:11, user Charles Warden wrote:
Thank you for posting this pre-print.
While I believe you are emphasizing the type of splicing event analysis from programs like rMATS, I noticed that you did reference DEXSeq.
So, if readers where interested in the DEXSeq exon quantifications, then I thought it might be worth mentioning that I have found QoRTS + JunctionSeq to be useful for comparing exons+junctions as separate features:
QoRTs: https://hartleys.github.io/...
JunctionSeq: http://hartleys.github.io/J...
On 2020-06-22 19:44:23, user Giulia Raimondi wrote:
An outstanding work introducing new perspective to improve oncolytic virotherapies thruough codon optimization. Extremely interesting!
On 2020-06-22 18:59:35, user Donald R. Forsdyke wrote:
FUNCTIONAL BASIS FOR PERVASIVE RNA SECONDARY STRUCTURE
.<br /> The author and his colleagues have studied the genomes of both hepatitis C viruses (1) and coronaviruses (2). They note for single strands of RNA genomes that “structural configuration is dependent on both the order of bases and the G+C content of the sequence” (1). They estimate the “sequence order component of RNA structure” by “comparison of minimum folding energies (MFEs) of native sequences with those of the same sequence scrambled in base order” (1). Thus, “subtraction of the mean shuffled sequence MFE from the native MFE yielded an MFE difference (MFED) that represent the primary metric for quantifying RNA structure” (2). In the 1990s this was referred to as “folding of randomized sequence difference” (FORS-D) analysis and was applied both to eukaryotic genes (3, 4) and to viral genomes (5). Here it is referred to as “genome-scale ordered RNA structure” (GORS) analysis. <br /> .
When comparing different viral isolates, the authors note that usage of synonymous codons often allows a protein sequence to remain functionally constant while affording flexibility to<br /> the structure of the encoding RNA. Yet GORS analysis “reveals many differences from the better characterised discrete elements of folded RNA found in RNA virus genomes” (1). Such structures tend to serve local functions (e.g. replication initiation, ribosomal interactions, translation initiation, RNAseL susceptibility) and are conserved between different isolates. <br /> .
Since “MFED values greater than zero were observed throughout the genomes of each genotype analysed,” GORS is considered “pervasive throughout the genome” (1). This is held to “challenge the prevailing paradigm of viral structures being discrete elements”<br /> (1). Thus, it is regretted that “the functional basis for the adoption of pervasive RNA secondary structure is unknown” (2), and “the broader underlying reasons for virus genomes becoming structured in this way require considerable further investigation” (1). However, the “endeavour to understand its biological purpose” (2) began in the 1990s (3-5).<br /> .
Explanations in terms of genome repair mechanisms and speciation have a considerable literature that is listed elsewhere (6). For the evolution of hepatitis C viruses, where “structural conservation was evident at subtype level only” (1), the globally “substantial<br /> degree of RNA structure re-invention” found in each subtype should signify the emergence of reproductive isolation barriers that could facilitate its persistence in a common host species, while enhancing possibilities of evolution into distinct viral species (6).<br /> .
Coronaviruses having globally even greater MFEDs than hepatitis C viruses (2), then pressures for persistence would seem much greater. An impairment of “kissing” loop interactions between co-infecting subtypes would favor subtype persistence (by excluding recombinational blending). Greater variation in loops than in stems (2) would be consistent<br /> with this. Since the base order-dependent folding components (FORS-D, MFED) are<br /> derived by subtraction, values of base composition-dependent folding components FORS-M) can be routinely factored into analyses (3-5). <br /> .
1.Simmonds P, Cuypers L, Irving WL, McLauchlan J, Cooke GS, Barnes E, STOP-HCV Consortium, Ansari MA. (2020) Impact of virus subtype and host IFNL4 genotype on large-scale RNA structure formation in the genome of hepatitis C virus. bioRxiv: https://doi.org/10.1101/202....<br /> .<br /> 2.Simmonds P (2020) Pervasive RNA secondary structure in the genomes of SARS-CoV-2 and other coronaviruses – an endeavour to understand its biological purpose. bioRxiv: https://doi.org/10.1101/202....<br /> .<br /> 3. Forsdyke DR (1995) A stem-loop "kissing" model for the initiation of recombination and the origin of introns. Mol Biol Evol 12, 949-958.<br /> .<br /> 4. Forsdyke DR (1995) Conservation of stem-loop potential in introns of snake venom phospholipase A2 genes: an application of FORS-D analysis. Mol Biol Evol 12, 1157-1165.<br /> .<br /> 5. Forsdyke DR (1995) Reciprocal relationship between stem-loop potential and substitution density in retroviral quasispecies under positive Darwinian selection. J Mol Evol 41, 1022-1037.<br /> 6.Forsdyke DR (2019) Hybrid sterility can only be primary when acting as a reproductive barrier for sympatric speciation. Biol J Linn Soc 128, 779-788.
On 2020-06-22 18:43:24, user Donald R. Forsdyke wrote:
FUNCTIONAL BASIS FOR PERVASIVE RNA SECONDARY STRUCTURE<br /> .<br /> The authors have studied the genomes of both hepatitis C viruses (1) and coronaviruses (2). They note for single strands of RNA genomes that "structural configuration is dependent on both the order of bases and the G+C content of the sequence" (1). They estimate the "sequence order component of RNA structure" by "comparison of minimum folding energies (MFEs) of native sequences with those of the same sequence scrambled in base order" (1). Thus, "subtraction of the mean shuffled sequence MFE from the native MFE yielded an MFE difference (MFED) that represent the primary metric for quantifying RNA structure" (2). In the 1990s this was referred to as "folding of randomized sequence difference" (FORS-D) analysis and was applied both to eukaryotic genes (3, 4) and to viral genomes (5). Here it is referred to as "genome-scale ordered RNA structure" (GORS) analysis.<br /> . <br /> When comparing different viral isolates, the authors note that usage of synonymous codons often allows a protein sequence to remain functionally constant while affording flexibility to the structure of the encoding RNA. Yet GORS analysis "reveals many differences from the better characterised discrete elements of folded RNA found in RNA virus genomes" (1). Such structures tend to serve local functions (e.g. replication initiation, ribosomal interactions, translation initiation, RNAseL susceptibility) and are conserved between different isolates.<br /> . <br /> Since "MFED values greater than zero were observed throughout the genomes of each genotype analysed," GORS is considered "pervasive throughout the genome" (1). This is held to "challenge the prevailing paradigm of viral structures being discrete elements" (1). Thus, it is regretted that "the functional basis for the adoption of pervasive RNA secondary structure is unknown" (2), and "the broader underlying reasons for virus genomes becoming structured in this way require considerable further investigation" (1). However, the "endeavour to understand its biological purpose" (2) began in the 1990s (3-5).<br /> . <br /> Explanations in terms of genome repair mechanisms and speciation have a considerable literature that is listed elsewhere (6). For the evolution of hepatitis C viruses, where "structural conservation was evident at subtype level only" (1), the globally "substantial degree of RNA structure re-invention" found in each subtype should signify the emergence of reproductive isolation barriers that could facilitate its persistence in a common host species, while enhancing possibilities of evolution into distinct viral species (6). Since the base order-dependent folding components (FORS-D, MFED) are derived by subtraction, values of base composition-dependent folding components (FORS-M) can routinely be factored into analyses (3-5). <br /> .<br /> 1.Simmonds P, Cuypers L, Irving WL, McLauchlan J, Cooke GS, Barnes E, STOP-HCV Consortium, Ansari MA. (2020) Impact of virus subtype and host IFNL4 genotype on large-scale RNA structure formation in the genome of hepatitis C virus. bioRxiv: https://doi.org/10.1101/202....<br /> .<br /> 2. Simmonds P (2020) Pervasive RNA secondary structure in the genomes of SARS-CoV-2 and other coronaviruses – an endeavour to understand its biological purpose. bioRxiv: https://doi.org/10.1101/202....<br /> .<br /> 3. Forsdyke DR (1995) A stem-loop "kissing" model for the initiation of recombination and the origin of introns. Mol Biol Evol 12, 949-958.<br /> .<br /> 4. Forsdyke DR (1995) Conservation of stem-loop potential in introns of snake venom phospholipase A2 genes: an application of FORS-D analysis. Mol Biol Evol 12, 1157-1165.<br /> .<br /> 5. Forsdyke DR (1995) Reciprocal relationship between stem-loop potential and substitution density in retroviral quasispecies under positive Darwinian selection. J Mol Evol 41, 1022-1037.<br /> .<br /> 6. Forsdyke DR (2019) Hybrid sterility can only be primary when acting as a reproductive barrier for sympatric speciation. Biol J Linn Soc 128, 779-788.
On 2020-06-22 13:45:38, user David Curtis wrote:
I just have some comments on the second paragraph. Maybe the cited references would clear this up but the paragraph reads like an explanation and there are a few claims which are not obvious.
I understand why an allele under positive selection will be surrounded by a region of LD. As selection drives the increase in frequency there is less time for recombination to occur and the haplotype as a whole becomes more frequent.
What happens under negative selection is less clear. I understand that an allele which is disadvantageous will become less frequent. But where does it appear from in the first place? You've written it as if a mutation arises which may well have a negative effect. But at that point its frequency is close to zero. It is also in 100% LD with nearby variants. The way it is written it's as if some negative mutation magically arises at a certain frequency and then its frequency diminishes. So I don't understand this bit.
I'm fine with recent variants being likely to be deleterious. Are you saying that a weakly deleterious variant may become less rare through genetic drift? It's not obvious how a strongly deleterious variant can survive at low frequency. Why doesn't it just disappear? Or are all strongly deleterious variants very recent (and hence not shared through populations, just within kinships)?
And then you have "older variants ... over time will acquire LD with recent variants". Again, this is not very obvious. Old variants might be expected to lose all LD through recombination. I suppose it's true that the only variants which could be in LD with them will be new variants. But isnt this more saying something about the new variants than old variants? That new variants show LD (with both old and new variants) whereas old ones do not (at least with each other).
"So when negative selection is operating, we should expect to find more and more effects with larger and larger effect size at lower LD score (or total LD) and lower heterozygosity." I'm afraid I don't understand this. I don't really have a clear idea of your model for a situation where negative selection is operating. Is it a recent mutation which is on its way out again? Or is it the ancestral allele which is now being replaced by one which is advantageous? I understand why a recent variant with a deleterious effect might be rare (i.e. low heterozygosity). But then I would expect its LD to be high, not low, because it is recent.
As I say, maybe all of this is explained in the cited literature but I feel the paper would be easier to understand if these issues were clarified.
On 2020-06-22 13:39:12, user Giuseppe Rotondo wrote:
Very intresting because natural selection selcts by more affinity and not by less affinity..so it should be an evolutionary advantage to select mutations making Spike with less Ace2 affinity binding
On 2020-06-22 12:51:00, user Phil Schiffer wrote:
Boasting that this genome complements D. melanogaster and C. elegans is overdoing it quite a bit. There are many other good protostome genomes out there these days.
On 2020-06-22 00:27:02, user Jun Wang wrote:
This pre-print is now published in Cell Research. Check the published version here:<br /> https://www.nature.com/arti...
On 2020-06-21 13:18:56, user OxImmuno Literature Initiative wrote:
On 2020-06-21 13:14:38, user OxImmuno Literature Initiative wrote:
On 2020-06-21 13:02:59, user OxImmuno Literature Initiative wrote:
On 2020-06-21 12:59:53, user OxImmuno Literature Initiative wrote:
On 2020-06-21 12:57:02, user OxImmuno Literature Initiative wrote:
On 2020-06-21 12:52:55, user OxImmuno Literature Initiative wrote:
On 2020-06-21 12:39:31, user OxImmuno Literature Initiative wrote:
On 2020-06-21 10:09:31, user Richard Anney wrote:
https://www.biologicalpsych...
For ASD Twas it would be worth referring to findings of Pain et al 2019.
On 2020-06-21 07:46:00, user Andre wrote:
CRYM (mu-crystallin) is an important enzyme in amino acid metabolism. Otherwise known as ketimine reductase. Any manuscript mentioning it should cite the original papers as regards this.
On 2020-06-21 07:40:24, user aminah alqahtani wrote:
That is really great work. Could you please clarify which reference genomes that you use ?
On 2020-06-21 06:48:36, user Dominik Schienstock wrote:
Looks great! Is the software freely available for testing?
On 2020-06-21 00:19:30, user Aleona Swegen wrote:
Hi, very interesting paper! Do think it is possible that the 0.98 Da delta mass could be a citrullination (also characterised by 0.984 Da change - https://immunology.sciencem... )?
On 2020-06-20 19:07:30, user Charles Warden wrote:
For those interested in the discussion, I thought it might help to point out that there has been a response:
Best Wishes,<br /> Charles
On 2020-06-20 17:32:01, user stoyan denev wrote:
"The steppe groups from Yamnaya and subsequent pastoralist cultures show evidence for previously undetected farmer-related ancestry from different contact zones"
Detected: https://genetiker.wordpress...
On 2020-06-20 17:18:45, user Igor Cesarino wrote:
Excellent work, guys! I have a few questions:<br /> - the co-localization between the monolignol oxidases involved in CS formation with CASP means that they physically interact? So, is this physical interaction needed for a function of a monolignol oxidase in CS formation? Did you guys ever screened CASP partners (protein-protein interaction screening) to find more specific targets?<br /> - Thinking about a compensatory mechanism in the 9x lac mutant, in which the up regulation of other LACs might be enough to produce a normal CS, why did you exclude the major 3 lignin-related laccases from the analysis (LAC4, 11 and 17)? For instance, the work of Chou et al (2018; doi:10.1093/jxb/ery067) showed that PER64 is also localized in cell wall domains of aereal parts of the plant; I'm not sure about their localization in the endodermis in WT plants, but maybe upon disruption of other LACs, those major lignin-related LACs are upregulated<br /> - Did you guys evaluate the stem lignification of such higher order mutants? Do the plants grow normally? This could further help elucidating the function of such monolignol oxidases
On 2020-06-20 15:13:20, user Niraj Dhar wrote:
Sir, I read your full paper. It has a lot of promise and it can help other researchers to study about the Sars Cov-2 virus. I hope your paper can help them to find the cure. <br /> And congratulations on completing the paper.
On 2020-06-20 10:23:42, user Sam Clamons wrote:
From the methods: "Final versions of the pan-tissue clock, liver<br /> clock, blood clock, brain clock, and "human-rat" clock can be found in Supplementary Material."
I'm not actually seeing such a model in the supplementary material. Am I missing something obvious here? Is the model actually shown somewhere?
On 2020-06-19 20:22:55, user Alicia Kraay wrote:
This article has been accepted for publication in the International Journal of Epidemiology, published by Oxford University Press. A link will be forthcoming.
On 2020-06-19 18:28:54, user Michael Gilchrist wrote:
Accepted and published in BMC<br /> Genomics on May 20,2020.
On 2020-06-19 14:35:55, user IJ wrote:
In listing the mutations that accompany D614G, the paper incorrectly refers to "C-to-T mutation at position 14,409". It should be 14,408.
On 2020-06-19 14:24:38, user Rebecca D wrote:
It would be interesting to see the changes in the fecal microbiota between the first and second FMT for those patients that received a second FMT.
On 2020-06-19 13:24:22, user Muthukumar B wrote:
There are 20 duplicate entries are present in your supplementary table 1. One accession number has 2 country location.
On 2020-06-19 12:41:46, user Tim Coulson wrote:
This paper has been reviewed and recommended on the PCI Ecology platform. The recommendation and reviews can be found here: https://ecology.peercommuni.... I am not intending to submit this to a journal as I am interested in contributing to the Open Science, open peer-review, and open publishing model. Tim (the author)
On 2020-06-19 08:49:48, user Dr. Sebastian Boegel wrote:
Thank you very much for your interesting. <br /> In the abstract you wrote: "We see the role of Fluoxetine in<br /> the early treatment of SARS-CoV-2 infected patients of risk groups."<br /> However the text misses a discussion in which this statement is further described. How can your findings be translated into translation? <br /> Thank you
On 2020-06-19 08:09:06, user Lynn Dicks wrote:
A small comment: There is an error in the Figure 1 legend, which labels the lower panels as c) and d) when they should be b) and c).
On 2020-06-19 03:44:43, user Martin Sadowski wrote:
The peer-reviewed version of this manuscript is now available online in Cancer & Metabolism: <br /> https://rdcu.be/b43g7
On 2020-06-18 22:32:17, user Heng Chooi wrote:
Really interesting work! Look forward to seeing how this develop. Also check out our dCas12a version of CRISPRa activating metabolite production @biorxivpreprint https://doi.org/10.1101/202... just accepted in ACS Syn Biol. Cheers
On 2020-06-18 20:28:33, user Lee Kerkhof wrote:
Alfonso Benitez-Paez and Yolanda Sanz were the first to test rRNA operon sequencing on the ZymoBiomics Mock Community with MinION (doi: 10.1093/gigascience/gix043) while Kerkhof et al. demonstrated consensus building for error correction using rRNA operon profiling from soil/bioreactor DNA with the MinION (doi: 10.1186/s40168-017-0336-9). It is unclear why this prior work is not cited in your submission.
On 2020-06-18 20:07:54, user itellu3times wrote:
Terrific work. Would there be some other diagnostic, perhaps based on T-cells, that could measure and predict this?
On 2020-06-18 01:47:08, user kathydopp wrote:
QUESTION: What does this research imply regarding the possible development of herd immunity against the SARS-COV2 virus? Could it possibly imply that persons having mild COVID19 cases could be infected more than once and, possibly, infect persons susceptible to developing serious cases who have not yet been infected?
I hope not but would like to know what you think.
On 2020-06-18 16:34:55, user Laurent Seroude wrote:
Accepted manuscript available at: https://doi.org/10.1139/gen...
On 2020-06-18 13:47:46, user Julian Shillcock wrote:
Our article has just appeared online at Soft Matter:
On 2020-06-18 13:25:44, user Matthew Faulkner wrote:
This work is now published in the RoyalSociety of Chemistry, Energy and Environmental Science Journal - https://doi.org/10.1039/D0E...
On 2020-06-18 12:17:21, user Marco Gerdol wrote:
Supplementary Materials available here: https://mega.nz/file/angRkJ...
On 2020-06-18 12:14:55, user ConservationBytes wrote:
Note: This paper is currently in review through the Peerage of Science system (peerageofscience.org)
On 2020-06-18 10:22:48, user rdrighetto wrote:
Dear Alexis,
Thank you very much for this contribution to more robust assessment of the resolution and quality of cryo-EM maps.
I have a suggestion for Table 1 (and related examples given throughout the manuscript): I believe it would be helpful to include a column showing the fraction of Nyquist, in addition to the real-world example with typical numbers.
Best wishes,<br /> Ricardo
On 2020-06-18 10:16:38, user rdrighetto wrote:
Dear Philip and Dmitry,
Thanks for this important contribution to the cryo-EM methods, it is very useful.
In lines 2-3 of page 15 you state that "particle orientations for apoferritin (EMPIAR-10216) are only 3 assigned to one asymmetric unit, out of its eight symmetry-related copies.".<br /> However, octahedral symmetry implies 24 symmetry-related copies instead of eight. Similarly, tetrahedral symmetry implies 12 symmetry-related copies instead of four. This is how these symmetry groups are implemented in RELION and FREALIGN.
My question is therefore: is the above statement correct (and correctly implemented in the software)?
Best wishes,<br /> Ricardo
On 2020-06-18 09:57:33, user Marisa Martin-Fernandez wrote:
An interesting article. Readers might be interested in seeing what the architecture of the ligand-independent oligomers looks like, in Zanetti-Domingues et al, Nat Comm 2018 (https://doi.org/10.1038/s41.... In this paper we showed that the asymmetric kinase dimer is only involved in the formation of ligand-independent dimers but is not compatible with ligand-independent oligomer formation.
On 2020-06-18 07:36:53, user Ole Herman Ambur wrote:
Great work in describing a likely selective pressure for eukaryotic sex I have been wondering about.
On 2020-06-18 03:12:46, user Aaron Wilk wrote:
Some details concerning the PBMC dataset published by Wilk, Rustagi, Zhao, et al. as reported in this preprint are incorrect. That dataset profiles 7 patients, not 8. Additionally, this preprint reports that 4 of the patients from the original study are "mild." While this preprint does not report which patients are labeled "mild", all 7 patients in the original study were hospitalized and need to be classified as severe. In fact, all but one patient were in the ICU. Therefore, this dataset cannot be used to support this preprint's conclusions regarding phenotypic differences across the spectrum of disease severity.