On 2020-08-04 07:46:49, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-08-04 07:43:47, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-08-04 00:54:09, user Thomas Munro wrote:

A correction to the crystal structure report has now been published, accepting this revision: https://doi.org/d5jj

On 2020-08-03 23:34:40, user Peter C. wrote:

Great work ! I have 2 questions :<br /> In your group's previous paper (Fletcher et al., 2020), you showed that VCP inhibitors and Arf4 agonists would improve NIS trafficking to the plasma membrane. You were pointing out the existence of FDA approved VCP inhibitors while you mentioned Arf4 agonists did not exist yet. Did you identify any Arf4 agonist effect of the drug you screened in your new study ? Or do they act in a different way on NIS trafficking ?<br /> More fundamentally, it seems you don't expect BRAF/MEK inhibitors alone to produce a sufficient level of NIS expression and trafficking to the PM to induce RAI uptake. A Phase II clinical trial, Meraiode, is underway to check the efficiency of this drug combination. Do you have some indication that NIS trafficking will not be good enough with this strategy ? Or do you think the drug you propose is an alternate solution with potentially less adverse event ?<br /> Again, congrats for this great paper !

On 2020-08-03 20:16:36, user Timmy wrote:

An increased pulsing (transient depolarisation) frequency in the fmt mutant, relative to wild type, was reported by El Zawily et al. (Figure 6). Maybe relevant for your hypothesis?

On 2020-08-03 17:09:06, user Ali K wrote:

People should focus that antibodies are not the ultimate goal. Its the T cell immunity as this paper shows. Good results, need to see primates next or humans?

On 2020-08-03 06:41:55, user Ali K wrote:

I believe this approach helps differentiate the field of vaccines. A lot to unpack here. 1. What type of S did they use? Can they help distill that? 2. Interesting rationale to use N in the vaccine. 3. You can fit both N and S together? Can it be too much at one time? Perhaps you split the dose regimen in human trials. 3. It’s an Ad5 virus, they did something unique to it for delivery?

On 2020-08-01 13:07:47, user Don Bullock wrote:

Think it yields great possibilities considering some of the scary potential S mutations that can occur else where.

On 2020-08-03 11:59:43, user P.M. Gonzalez-De-La-Rosa wrote:

Hello,

Thanks for helping us know more about this enigmatic organisms.

Could you please share your enrichment results? The section in the supplementary material does not contain a link or table.

Also, I recommend to describe the clusters as just having high coverage rather than inferring that they have high copy numbers. This is because the high coverage could be explained by the collapse of multiple copies of a repeat into a single copy (https://www.nature.com/arti.... Hence it is possible that even though these subscaffolds have high coverage they have the same molarity as chromosomes.

Best wishes,<br /> Pablo

On 2020-08-03 11:39:11, user Bartosz Rozycki wrote:

The authors use here the non-Hamiltonian coarse-grained IDP model that they have introduced in Ref. [7]. In our recent joint work [8], however, we have demonstrated that this non-Hamiltonian model introduced in Ref. [7] fails to generate with the Boltzmann distribution, and proposed an improved IDP model that is consistent with the Boltzmann distribution. Why do the authors still use this non-Hamiltonian model of Ref. [7]?

On 2020-08-03 08:54:19, user Alan Handyside wrote:

Response to Ford et al<br /> These observations provide an important contribution to our understanding of the mechanisms causing abnormal mitotic divisions in preimplantation human development in vitro, resulting in aneuploidy of mitotic origin. They are also of interest from a clinical standpoint because of the impact on IVF success rates, mosaicism, etc but their true significance from the standpoint of natural conception is not known.<br /> Although we agree that the first mitotic division is particularly prone to errors, we would argue there is considerable evidence that similar abnormalities, particularly tripolar divisions can occur later in cleavage and at the blastocyst stage. This includes time lapse observations on large series of embryos, correlated with development to blastocyst and clinical outcomes (Zhan et al 2016) and laser confocal observations of tripolar and other spindle abnormalities in both cleavage stages and in trophectoderm cells at the blastocyst stage (Chatzimeletiou et al 2005).<br /> We also recently published genetic evidence (using SNP genotyping and crossover mapping to fingerprint each parental chromosome) linking tripolar divisions to clones of cells with almost identical subdiploid chromosome sets which continued to divide but failed to contribute to the blastocyst (Ottolini et al 2017; McCollin et al 2019). If this occurred in the first mitotic division the resulting embryo arrested at late cleavage stages. If tripolar cleavage occurred in a later division the descendent cells were excluded from the blastocyst.<br /> Christian Ottolini, Michael Summers and Alan Handyside<br /> School of Biosciences, University of Kent, Canterbury, UK

Zhan Q, Ye Z, Clarke R, Rosenwaks Z, Zaninovic N. Direct Unequal Cleavages:<br /> Embryo Developmental Competence, Genetic Constitution and Clinical Outcome. PLoS<br /> One. 2016 Dec 1;11(12):e0166398. doi: 10.1371/journal.pone.0166398. PMID:<br /> 27907016; PMCID: PMC5132229.

Chatzimeletiou K, Morrison EE, Prapas N, Prapas Y, Handyside AH. Spindle<br /> abnormalities in normally developing and arrested human preimplantation embryos<br /> in vitro identified by confocal laser scanning microscopy. Hum Reprod. 2005<br /> Mar;20(3):672-82. doi: 10.1093/humrep/deh652. Epub 2005 Feb 2. PMID: 15689349.

Ottolini CS, Kitchen J, Xanthopoulou L, Gordon T, Summers MC, Handyside AH.<br /> Tripolar mitosis and partitioning of the genome arrests human preimplantation<br /> development in vitro. Sci Rep. 2017 Aug 29;7(1):9744. doi:<br /> 10.1038/s41598-017-09693-1. PMID: 28851957; PMCID: PMC5575028.

McCollin A, Swann RL, Summers MC, Handyside AH, Ottolini CS. Abnormal<br /> cleavage and developmental arrest of human preimplantation embryos in vitro. Eur<br /> J Med Genet. 2020 Feb;63(2):103651. doi: 10.1016/j.ejmg.2019.04.008. Epub 2019<br /> Apr 14. PMID: 30995534.

On 2020-08-03 01:30:54, user Carlos Moura wrote:

Maybe I'm just stunned but I understand that maybe there is a way to trigger the nitrogenase production without the nifA and the cascade that it creates? That's it? If so this was proposed before by anyone?

On 2020-08-02 22:51:23, user AC-B wrote:

Hello. Interesting findings. Where are the supplementary figures? They don't show at the usual location.

On 2020-08-02 22:50:59, user E. Castedo Ellerman wrote:

In terms of lessons learned, it might be worth mentioning in the <br /> paper the pithy easy to remember software guideline of writing "DRY" [1] code. The second bug mentioned in the paper is a classic example <br /> motivating 'DRY'. It is one of the kinds of bugs writing <br /> DRY code is intended to avoid.

[1] https://en.wikipedia.org/wi...

On 2020-08-02 22:18:47, user Cartouche 74 wrote:

Considering that the cranial morphology of all Corded Ware populations was markedly different from that of the Catacomb/Potapovka/Sintašta/Andronovo groups, the dominance of R1a-Z93 in the present sample requires some explanation. I would not bet much money on the assumption that the Fatjanovo people were the ancestors of Andronovo/Sintašta (as colleague Davidski maintains). Rather, it looks like an introgression of R1a-Z93 from the steppe.

On 2020-08-02 20:52:11, user Raghu Parthasarathy wrote:

I think I'm missing something important in this paper; perhaps you can clarify. The paper claims to show "how sleep may affect amyloid β fibrillization." However, what is actually given is (1) a model of how the production rate of soluble amyloid β influences the amounts of fibrillar vs. soluble Aβ and (2) a statement that *if* sleep alters the production rate of soluble amyloid β, it will alter fiber formation. Obviously, this doesn't support the claim made in the title, and so I wonder if I'm missing something -- perhaps there's strong evidence in measurements reported elsewhere that sleep alters soluble Aβ production? Otherwise, one could just as easily rewrite the paper to state "... how bananas may affect amyloid β fibrillization" by positing without evidence a connection between bananas and soluble Aβ production. I assume I'm missing something, since this seems like such a strong issue.

On 2020-08-02 20:00:50, user pb2145 wrote:

In Table S1, the sequence for FtsZ-linkerQrich is missing the C-terminal helix sequence. Is that the actual sequence of the construct or just an omission in the manuscript?

On 2020-08-02 01:35:26, user Razie Amraei wrote:

Great Job!!!! But I am so surprised that the authors did not cite the first paper on CD209L and CD209 in SARS-CoV-2 cell entry, available on PubMed since more than one month ago (https://pubmed.ncbi.nlm.nih...

On 2020-08-01 22:29:22, user IJ wrote:

The Methods section says that the peptides were searched against a "custom SARS-CoV-2 protein database". Please specify what was included in that database. All 6-frame translations? Only UniProt proteins? Did it include all of the various ORFs that have been proposed overlapping ORF3a (and please specify the coordinates because the names for these have been ambiguous)? This information is needed in order to know if some ORFs were not found because there were no peptides or because they weren't included in the search. Thank you.

On 2020-08-01 21:51:29, user Sung-Hou Kim wrote:

Response to Rokas et al.<br /> Organism Tree of Life: Gene phylogeny vs. Whole genome<br /> phylogeny<br /> JaeJin Choi and Sung-Hou Kim*<br /> Department of Chemistry and Center for Computational Biology<br /> University of California, Berkeley, CA, 94720, USA<br /> Abstract<br /> Whole genome/proteome Tree of Life (ToL) may be a more appropriate representation for Organism ToL than the currently-and widely-used Gene/protein ToLs.

In responding to the Letter to the Editor by Li et al. (1) regarding our paper (2) we would like to remind the authors of the Letter that: (a) The concept and structure of organism Tree of Life (“organism ToL”) have been evolving since Darwin’s time and expected to continue to evolve as the types and amount of genomic data, as well as the methods of analyzing<br /> the data and constructing the ToL, improve; and (b) As a surrogate for the organism ToL, we use whole proteome sequences (predicted by all protein coding genes) to construct “whole proteome ToL” in contrast to the commonly used “gene ToLs”. The former uses Information<br /> Theory (3)-based “alignment-free” method (4), to compare the whole proteome<br /> sequences, while the latter uses “alignment-based” method (5) on only the<br /> reliably-aligned regions of a set of select gene or protein sequences, which account<br /> for a very small fraction of a whole proteome. <br /> Although the grouping patterns of the organisms agree<br /> well in both types of the ToLs, the branch-lengths and branching orders of the<br /> trees do not. This difference is<br /> expected because of the differences in (a) the contents of input data (a set of<br /> select gene/protein sequences for gene trees vs. all gene/protein sequences for whole proteome tree) and (b) the basic<br /> assumptions of the types of mutations causing the evolution of organisms, from<br /> which branch-lengths of the trees are derived (positional substitution mutation<br /> only for gene trees vs. all types of mutations<br /> for whole proteome trees). These differences are two of the major reasons for the differences in the tree topologies as we pointed out in our paper (2), and also shown in Fig 1A of the Letter (1). However, the authors interpret the figure as showing the “inaccuracy”<br /> of the FFP method (1) (rather than “difference”(2)), an interpretation we do not accept. They argue that our whole proteome ToL has “many<br /> relationships that strongly contradict the current consensus view of the tree<br /> of life”, suggesting that the authors take the current consensus gene ToLs as<br /> the “gold standard” for the organism ToL, which we do not agree with, because the gene trees are not the organism tree. The gene trees may be suitable for constructing one or more narratives on the evolution of those selected genes, but not of the organisms. We need to start a discussion on the issue of whether the current gene ToLs are a proper surrogate for the organism ToL and on whether whole proteome ToL has merit to be considered as a possible alternative to the gene ToLs in providing another independent view for the organism ToL, especially at present when the availability of new whole genome sequences of diverse life forms is expanding rapidly. <br /> References<br /> 1. Li, Y, David, K, Shen, X et al. Feature Frequency<br /> Profile-based phylogenies are inaccurate. bioRxiv, doi.org/10.1101/2020.06.28.....<br /> 2. Choi, JJ, Kim, S.-H. Whole-proteome tree of life suggests a deep burst of organism diversity. Proc of the Natl Acad Sci U S A vol.117, no. 7, 3678–3686 (2020)<br /> 3. Claude S. A Mathematical Theory of Communication. Bell Syst Tech J. 1948; Vol.27: <br /> 379–423.<br /> 4. Zielezinski A, Vinga S, Almeida J, Karlowski WM. Alignment-free sequence comparison: benefits, applications, and tools. Genome Biol. 2017;18:186.<br /> 5. Genetic analysis software. National Center for Biotechnology Information. Retrieved July 26 3, 2020.

On 2020-08-01 16:18:05, user Matt wrote:

Very interesting paper; the insights into within population reproductive structure and population size from these methods would hugely increase the information we get from ancient dna.

It could be a useful supplement to the paper to provide the information in Supp. Data 2 in the form of a spreadsheet / table providing individual sample references. This could have many archaeological uses in association with traditional archaeological information (grave inventories), isotope data, bioarchaeology (skeletal analysis for features associated with RoH patterns), etc, which could yield new insights. So it seems maybe a shame or missed opportunity to to have individual ancient sample IDs effectively redacted from the supplements.

On 2020-07-31 17:28:29, user Davidski wrote:

Hello authors,

Thanks for the interesting preprint. On first inspection, however, there are a couple of issues with the geographic terms in your paper.

Now, I know that you're trying to come up with terms that fit geography, archeology and genetic clusters, but these examples really stick out as being misleading:

• most of the samples that you put into the PC steppe category (figure 2 F) aren't from the Pontic-Caspian steppe, which is located in Eastern Europe. They're actually from the Kazakh steppe, which is located in Central Asia. See here...

https://en.wikipedia.org/wi...

How about if you refer to this grouping as "Western steppe"? This is actually what it is, because the term Western steppe includes both the Pontic-Caspian steppe and the Kazakh steppe.

• most of the samples in your Central Asia grouping aren't really from Central Asia. They're actually from West Asia, because Iran is most certainly located in West Asia, not Central Asia. Alternatively, you can call this the Iran-Turan cluster.

As I said, I do realize that this is a genetic paper, not a geographic paper. But you can't shift the location of a major geographic feature, like the Pontic-Caspian steppe, almost entirely from one continent to another without people noticing that something is way off, and thus possibly extrapolating that the rest of your paper is not reliable. Cheers

On 2020-08-01 15:27:13, user Oliver Pescott wrote:

Note that this preprint contains a number of statements that directly misrepresent what Pescott & Jitlal (2020) wrote, and we would encourage anyone reading this preprint to also consult the original paper: https://peerj.com/articles/...

This preprint is in review at PeerJ, and I (Pescott) have submitted an open review that will also be available when the article by Smart et al is published. Suffice to say, I encourage those interested in the debate to read the final published version of this preprint, rather than this version, which, as I say, mis-characterises several statements made by Pescott & Jitlal.

On 2020-08-01 04:30:38, user MS wrote:

The whole finding depends crucially on the "fact" that RaTG13 was indeed found in nature (bat feces), in 2013. Given that RaTG13 was made public in 2020, jointly with the virus itself, makes this assumption at least doubtful.

On 2020-08-01 01:58:55, user Joseph Wade wrote:

A couple of points about the cited references:

• Reference 24 has been retracted (https://pubmed.ncbi.nlm.nih....

• In the Discussion, the authors describe "the previous report of B. subtilis growth inhibition by D-form of Tyr, Leu, and Trp, and the L-form of those amino acids counteracting this effect". The cited paper (reference 23) shows that this growth inhibitory effect is largely due to a mutation in the dtd gene in the strain that was used.

On 2020-08-01 01:14:14, user Janice Ana Herrera wrote:

is this vaccine where and when this come out?

On 2020-07-31 20:45:10, user Matt Raybould wrote:

Thanks for your work on cross-reactive SARS-CoV/SARS-CoV-2 binding antibodies! Please consider submitting your Fv sequences to CoV-AbDab upon publication, as we are tracking data on all antibodies known to bind coronaviruses: http://opig.stats.ox.ac.uk/...

On 2020-07-31 20:21:47, user Marek Svoboda wrote:

I believe this article has been published in Nature Biotechnology: https://www.nature.com/arti...

On 2020-07-31 15:41:05, user Jan Mellema wrote:

The question also arises, whether individual virus particles are associated together to quasi spherical 'droplets'/aggregates with an amount of water or without water or other cellular liquids/content. This clumping will determine the sizes of the aggregates and therefore the distance in air after leaving the mouth/nose of an infected person. Probably there is a limit in the aggregated virus particles and therefore also in the distance they are travelling in air.

On 2020-07-31 13:12:11, user John Neu wrote:

It is great that someone is looking into this. However my concern with this study is the lack of a positive control experiment for famotidine. Since these results fly in the face of the modelling study (especially figure 3.). An inactive famotidine sample could explain this result.

On 2020-07-19 23:08:46, user Henrique Sommer Vianna wrote:

I think famotidine acts as imunomodulator in the th1/th2 ballance, IL-6 production and cytokines cascade!

On 2020-07-31 07:11:32, user Vera Eory wrote:

And one more thing: would the authors consider publishing the dataset?

On 2020-07-31 06:53:18, user Vera Eory wrote:

A very throughout article with good details to be<br /> reproducible and an insightful presentation of the results. Here are a few<br /> suggestions on areas/issues, which could also be included. 1) The authors could<br /> introduce other papers who did a similar exercise and explain if the methodology<br /> used (e.g. in boundaries, treating missing data) is the same or differs in some<br /> aspects. 2) It would be nice to see (e.g. in the intro) a short note on how<br /> sunflower oil compares with other oil crops in terms of GHG and other key<br /> environmental indicators. 3) Though the focus of the study is GHG, other<br /> environmental effects of sunflower production are also very important, notably<br /> biodiversity effects and reactive nitrogen pollution (other than nitrous oxide)<br /> – could the authors mention that with some data presented from review studies,<br /> even comparing sunflower oil with other common crops (just to put the GHG<br /> problem in context)? 4) Though LUC was not included in the analysis, the<br /> discussion could explain what this might mean if sunflower oil production was<br /> to expand more quickly (e.g. due to being prioritised over palm oil by<br /> consumers).

On 2020-07-31 01:05:31, user Cassie wrote:

Peer-reviewed version here:

https://doi.org/10.1371/jou...

On 2020-07-30 12:10:14, user Shankar Srinivas wrote:

Thank you for starting the discussion here Alfonso, and for the detailed, helpful comments. Our responses (on behalf of all the authors) below:

• You rightly point to several recent single cell transcriptomic characterisation of non-human primate embryogenesis. In addition to the ones you cited, there is also the study from Niu et al. (PMID: 31672917). Comparing the human gastrula data with these would certainly be interesting, although there are a number of caveats (some of which you also point out). The Nakamura data set is valuable but unfortunately there are relatively few cells from the stages comparable to our CS7 gastrula, making a meaningful comparison difficult (36 cells at E16 and 53 cells at E17 and of these, approximately half annotated as epiblast). The in vitro cultured embryos are exciting for the opportunities they open up, however, there are several factors that can confound a meaningful comparison. For example, for the cultured human embryo data, the stage is not comparable (they had to stop at 14 dpf) and again the number of cells is relatively small (70). Similarly, in the Ma et al. dataset, at 17 dpf, there are only 43 cells from embryonic tissue. More importantly, given that these samples are cultured, it would be difficult to determine whether any differences between human and the monkey are due to the species differences or the culture. <br /> For these reasons, although we were tempted to compare the human gastrula data with these data-sets, we decided to prioritise comparisons with the mouse because it would provide clearer insights.

• Regarding neural differentiation and ‘marker’ gene expression/co-expression: as you say, SOX2 and OTX2 are co-expressed in the rostral neuroectoderm, but this doesn’t imply that cells co-expressing these two markers are necessarily neuroectoderm. Epiblast cells also co-express these two markers - eg. see mouse gastrula atlas - https://marionilab.cruk.cam... . Just looking at markers can be a blunt tool that does not lend itself to categorical classification, particularly of related cell types/states. Therefore, wherever possible, we used orthogonal information (location of cells) to help annotate the clusters. As you note, we found expression of SOX2 and OTX2 in the rostral domain, but they weren’t only in the rostral domain – they are also co-expressed caudally (the Epiblast cluster is 45% rostral and 55% caudal). In Sup fig 6, we look quantitatively at several markers.

• Regarding amnion: We mention in the text that the cluster we annotate as Ectoderm likely includes both embryonic and extra-embryonic (=amnion) ectoderm. Regarding your point about POSTN as a marker of the amnion – as you say, a cursory look may indeed lead one to annotate that cluster as amnion, but if one looks deeper, at the data in the paper you cite (Dobreva et al. PMID: 29884675), one can see clear expression of POSTN in the yolk sac mesoderm (Figure 3a) as well as amniotic mesoderm. So though POSTN is undeniably a ‘marker’ of amniotic mesoderm, it is equally a ‘marker’ of the yolk sac mesoderm. Moreover, 69% of the cells from that cluster were collected from the yolk sac, arguing against it representing amnion. This again demonstrates the danger of allowing ‘marker’ genes to take on a life of their own.<br /> An interesting point to consider is the remaining 31% of cells in this cluster that are spatially allocated to the embryonic disk. The simplest explanation for this is the imprecision of the micro-dissection, which might have left behind a little yolk sac around the fringes of the embryonic disc. An alternative explanation however is that this 31% represent amniotic mesoderm (which, along with YSM would be expected to be POSTN +ve) and would imply that at CS7, amniotic mesoderm is transcriptionally very similar to yolk sac mesoderm.

• Regarding hemogenic progenitors - none of the text books on human embryology that we use speak of the blood forming at E13/E14 and the review by Alexander Medvinsky that you cite also indicates that the earliest this occurs is between CS7 and CS8. There is disagreement in the human embryology literature regarding the correspondence between Carnegie Stages and ‘Embryonic Days’ or ‘days post fertilisation’ that can cause confusion. To add to this confusion, as we know from the mouse (eg see the Lawson and Wilson 2016 staging) there is a considerable embryo to embryo variability in the rate of development, so it can be tricky to estimate the precise age post-fertilisation of an embryo on the basis of its carnegie stage categorisation. This is why as far as possible, we used CS throughout the preprint and gave a reasonably broad range of days this might correspond to. <br /> Additionally, in our analysis there is much more detail than the mere indication of the presence of primitive blood islands: we have identified specific cell populations that would be thought to arise much later and have never been described in human at this early stage before, e.g., EMPs.

• Regarding PGC: Existing studies of PGCs are either from NHP or from cultured embryos, while ours is the first unequivocal demonstration of the presence of PGC in a in utero developed human embryo as early as CS7.

• Regarding the node: trying to identify it is certainly on our to-do list.

As you mention, we think there are still lots of insights that will emerge from this dataset. While we focused on some discoveries we found particularly interesting, we are aware of the extreme richness and complexity of these data and look forward to insights emerging from the analyses of others with expertise and interests different to ours.

Shankar and Antonio

On 2020-07-26 07:29:09, user Alfonso Martinez Arias wrote:

Very impressive handling of a unique specimen that should and will<br /> be a reference for this work in the future where the most challenging feature is and will remain, to obtain specimens. The handling and bench marking of the embryo are excellent and it is good to see how the authors managed not to lose much tissue and extract some information.

Leaving behind the not negligible mystic rightly associated with such<br /> young human embryos I have some suggestions for the analysis. While there is little developmental biology associated with gastrulating human embryos a lot is known about their embryology and histology that should be a reference for this type of work. The images of the embryo are very informative and allow a comparison with the existing collections. The embryo is rightly defined as CS7/8 by the position of the streak, perhaps already regressing. The authors are right in<br /> suggesting a relatedness to mouse E7.5+, something supported by the incipient prechordal plate; perhaps related to a HH5 chicken embryo with which the human embryo is often compared because of the disc organization of the epiblast.

The choice of single cell RNAseq for the analysis of the sample<br /> is, most likely, the correct one. In terms of the landscape of gene expression, the authors indicate that this is a resource and it will be good to see what analysis by other groups yields though, in the meantime, it is important tobear in mind that over the last few years a couple of manuscripts provide single cell information about the early stages of gastrulation in primate embryos which the authors might want to use in their analysis.

In particular, Nakamura et al. Nature 2016 PMID: 27556940 contains<br /> single cell data up to E16, E17 from cynomolgus monkey embryos which should lead to an interesting comparison (and notice that these are also embryos developed in utero). Ma et al Science 2019 PMID: 31672918 provide information, also from monkey embryos but this time cultured until E22 (neural plate stages) In terms of human, `Xiang et al 2019 provide single cell data for E14/15 PMID: 31830756, though these are cultured embryos. There is no reference to this work and it would be very interesting if the authors included this data on their analysis.

The data reported has much to discuss. The authors claim that there is no neural component and yet report the expression of OTX2 and SOX2 in the anterior domain of their embryo and ascribe this expression to epiblast, At the equivalent stage in the mouse, the overlapping expression of these genes in the similar region heralds the emergence<br /> of the brain primordium in a proneural ectoderm and this is likely to be the case here.

The lack of amnion cells appears surprising considering the age of the specimen but a cursory look at the data reveals the presence of genes that in mouse are associated with amnion e.g POSTN (see e.g Dobreva et al. Development 2018 PMID: 29884675 and Stem Cells Int 2012 PMID 22966238) which is allocated to yolk sac; the authors might want to revise their annotation.

The presence of mature hemogenic progenitors amongst cells of the<br /> yolk sac is interesting but perhaps not that surprising as it is known that primitive blood islands emerge associated with the yolk sac as early as E13/14 (see Medical embryology text books and the review of human hematopoiesis Ivanovs et al. Development 2017 PMID: 28676567<br /> and refs therein) so it is not that surprising to find them in this embryo. The expression of hemoglobin is certainly intriguing in particular the fact that a small number are embryonic, though Bloom, B. and Bartelmez, G.W. (1940) Am. J. Anat. 67, 21-53 doi:10.1002/aja.1000670103 suggest the presence of hemoglobin expressing cells associated with 19 days embryos, the approximate stage of this one.

It is also not that surprising to see PGCs as their presence in<br /> early gastrulating primate embryos has been described before in embryos (Nakamura et al, above) and also in hESC derived embryonic structures Xue et al. Nature 2018 PMID: 29784997). On the other hand it is surprising that having mentioned the presence of a morphological node and some elements of a notochord signature, they don’t make a reference to the node in the single cell analysis.

Much more will come to light in future analyses of this valuable resource.

At the start of the molecular analysis of the human embryo it is<br /> important that we are aware not only of existing related analysis but, more importantly, of the rich and extensive analysis of human embryology

On 2020-07-30 05:07:24, user ASM wrote:

Summary of this study:<br /> 1. We have sequenced 137 and analyzed 184 whole-genomes sequences of SARS-CoV-2 strains from different divisions of Bangladesh.<br /> 2. A total of 634 mutation sites across the SARS-CoV-2 genome and 274 non-synonymous amino acid substitutions were detected.<br /> 3. The mutation rate estimated to be 23.715 nucleotide substitutions per year.<br /> 4. Nine unique variants based on non-anonymous amino acid substitutions in spike protein were detected relative to the global SARS-CoV-2 strains.

On 2020-07-29 21:25:01, user Charles Warden wrote:

Thank you for posting this pre-print.

I am sorry, but I think I am a little confused.

Your competing interest statement says "The authors have declared no competing interest." However, you also say "Disclosure: A patent application has been filed (ref#202011018132) on subject matter described in the publication."

I thought filing or obtaining a patent creates a conflict of interest. Am I misunderstanding something?

On 2020-07-29 20:15:50, user Ekaterina Dadachova wrote:

The major flaw of this article is that the researchers did not use non-melanized form of C. sphaerospermum as a control or did not use any fungal or biological material on the control side of their plates at all! So they cannot claim that shielding which is really low in their case and is probably much lower than the experimental error is due to the melanin in fungus. The manuscript has a lot of assumptions such as 40% melanin contents of a fungus - ??? Making melanin is an energy consuming process for fungal cells and making that much melanin would lead to energy exhaustion of the cells. The manuscript completely ignores previosly published studies on radioprotection with fungal melanin. Experimentally measured linear attenuation coefficient for fungal melanin was reported in Dadachova E et al. Pigment Cell Melanoma Research 2008, and the linear attenuation coefficients for synthetic melanins were calculated by Monte Carlo algorithm in Schweitzer AD et al PLOS ONE 2009. The idea of using melanized fungi for radioprotection in space was expressed in Pacelli C. et al. Environ. Microbiol 2017 and extended upon in the comment by Cordero R. in the same issue. With very little experimental evidence the paper greatly overstates the significance of the results.

Sincerely,

Ekaterina Dadachova, PhD

On 2020-07-29 14:07:25, user Gabriel Moagar-Poladian wrote:

Maybe other materials have better gamma attenuation properties but what I consider amazing are two facts: 1) the resilience of fungi to gamma radiation (sometimes gamma irradiation is used to disinfect/kill bio-pathogens); 2) the ability to use the harmful radiation for the benefit of life.

On 2020-07-28 15:28:23, user Ciprian Popa wrote:

The best way to prove this straight away is to take x-ray images with increasing exposure time.

On 2020-07-28 14:01:26, user Giuseppe Guidarelli wrote:

I propose the following comments:<br /> 1) it does not seem to me that there is a demonstration that the difference in absorption between control and fungi is statistical confirmed.<br /> 2) do you think that using GM is the best way to determined the absorbed dose when the energy spectrum of radiation is unknown or non monocromatic?<br /> 3) It seems to me useless using fungi to shield radiation instead of any other material with asufficient absorption coefficient.<br /> Best regards.<br /> Giuseppe Guidarelli

On 2020-07-29 18:00:44, user Jordan Berg wrote:

Metaboverse v0.1.4b released:<br /> - Fixes minor/non-critical dependency issues<br /> - Adds error log output file for error messages that do not fit in alert pop-up box for easy troubleshooting

https://github.com/Metabove...

On 2020-07-29 16:35:08, user Priscilla Turelli wrote:

Happy to announce the future publication in Science Advances!

On 2020-07-29 15:36:22, user Matt Olm wrote:

In this manuscript Murray et al. argue that bacterial diversity does not cluster into distinct species groups, but instead exists as a continuum. The article is a rebuttal against two recent manuscripts, Jain et al. 2019 (Jain et al., 2018) and Olm et al. 2020 (Olm et al., 2020), which both compared large numbers of bacterial genomes and found the existence of sequence-discrete microbial species. Jain et al. compared 90,000 genomes from a public database and found that a “gap” exists at around 95% average nucleotide identity (ANI), providing support for the 95% ANI threshold that is commonly used for species-level delineation. Murray et al. argue that the 95% ANI gap is a result of cultivation biases in the genome set used, and present compelling evidence that comparing biased genome sets can lead to the spurious detection of species clusters. Olm et al. compared a large number of genomes that were assembled from metagenomic DNA extracted directly from the environment (and thus without cultivation bias) and also found the existence of a “gap” at around 95% average nucleotide identity (ANI). Further, Olm et al. used multiple metrics designed to measure recombination frequency to show that recombination could be the cohesive force driving the formation of species clusters at 95% ANI. In the final three paragraphs Murray et al. present theoretical arguments against these findings, however these arguments have no data to support them and misinterpret the cited literature.

Argument 1- Biased genome sets can spuriously detect species clusters. The authors present compelling evidence that biased genome sets can spuriously detect species clusters. However, we would not expect a phylogenetically normalized dataset to show a sequence-discrete trend even if bacterial species did exist in nature, as the normalization process itself would remove them. Any genome set generated in silico will have or not have clusters depending on how the genome set was made. To test whether sequence-discrete populations exist in nature, we need to evenly sample across all microbial cells in nature. Metagenomic approaches, which extract and sequence DNA directly from the environment, are the most fair way we have of doing this today.

Argument 2 - Metagenomics creates biased genome sets. On line 94 it is stated: “Although having no cultivation bias, metagenome assemblies do favor abundant strains over rare ones. This bias can lead to the appearance of genetic clusters when diverse but rare strains in the community are excluded from the assembled genomes”. With deep metagenomic sequencing it is possible to assign over 95% of the DNA in the community to individual genomes, as is the case with the infant gut dataset analyzed in Olm et al. Further, if certain strains are truly more abundant in natural ecosystems than others, then they should be counted as such when testing the sequence-discrete species hypothesis. If strains in the “gap” are consistently at significantly lower abundance than other strains, that would still signify the presence of sequence discrete populations.

Argument 3 - Metagenomic studies identify genetic continuums in nature. On line 104 it’s stated: “Interestingly, several metagenomic studies have revealed 104 genetic continuum in nature”. This quote cites two papers, Hallam et al. (Hallam et al., 2006) and Caro-Quintero et al. (Caro‐Quintero, 2012). Hallam et al. presents a single example from a marine Crenarchaeota, which belongs to the domain Archaea, not bacteria. Contrary to the authors’ statement, Caro-Quintero et al. cites several examples confirming the existence of sequence discrete populations with a different metagenomic approach (read mapping), that also will detect low abundance strains that can not be assembled. Read mapping studies published since Caro-Quintero et al. have also identified sequence-discrete populations in nature (Bendall et al., 2016).

Argument 4 - There is no evidence that recombination is a cohesive force for bacterial species. On line 109 it is stated “Although recombination rate can be influenced by sequence similarity, there is no correlation between the recombination rate and ANI in bacteria, as recombination can also be affected by physical and ecological barriers”. Oddly, the work cited here (Bobay and Ochman, 2017) does show a correlation between recombination rate and sequence divergence (Fig S6), although the authors do make a point that it is not a perfect relationship. More convincingly, in a larger metagenomic dataset, Olm et al. 2020 found a striking correlation between two different pairwise measures of recent recombination frequency and the 95% species boundary.

Works Cited<br /> Bendall, M.L., Stevens, S.L.R., Chan, L.-K., Malfatti, S., Schwientek, P., Tremblay, J., Schackwitz, W., Martin, J., Pati, A., Bushnell, B., Froula, J., Kang, D., Tringe, S.G., Bertilsson, S., Moran, M.A., Shade, A., Newton, R.J., McMahon, K.D., Malmstrom, R.R., 2016. Genome-wide selective sweeps and gene-specific sweeps in natural bacterial populations. ISME J. 10, 1589–1601.<br /> Bobay, L.-M., Ochman, H., 2017. Biological species are universal across Life’s domains. Genome Biol. Evol.<br /> Caro‐Quintero, A., 2012. Bacterial species may exist, metagenomics reveal. Environmentalist.<br /> Hallam, S.J., Konstantinidis, K.T., Putnam, N., Schleper, C., Watanabe, Y.-I., Sugahara, J., Preston, C., de la Torre, J., Richardson, P.M., DeLong, E.F., 2006. Genomic analysis of the uncultivated marine crenarchaeote Cenarchaeum symbiosum. Proc. Natl. Acad. Sci. U. S. A. 103, 18296–18301.<br /> Jain, C., Rodriguez-R, L.M., Phillippy, A.M., Konstantinidis, K.T., Aluru, S., 2018. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 9, 5114.<br /> Olm, M.R., Crits-Christoph, A., Diamond, S., Lavy, A., Matheus Carnevali, P.B., Banfield, J.F., 2020. Consistent Metagenome-Derived Metrics Verify and Delineate Bacterial Species Boundaries. mSystems 5.

On 2020-07-29 15:19:52, user Jonathan Perreault wrote:

In the text, it is mentioned that:

The LIGS method is designed to identify aptamers against complex receptor proteins in their native conformation(14-17). Using LIGS, we reported the identification of a panel of aptamers that can specifically recognize the TCR-CD3 complex expressed in human cultured cells and T-cells obtained from healthy individuals(16). So far, the only known ligands against CD3-TCR are mAbs, and to the best of our knowledge, no alternative synthetic ligands are available.

actually, one of the reference in the same paragraph (ref #15) actually was on the selection of TCR aptamers:<br /> Zumrut et al. Ligand-guided selection of aptamers against T-cell Receptor-cluster of differentiation 3 (TCR-CD3) expressed on Jurkat.E6 cells

was there evidence that this aptamer was not appropriate for the author's objectives?

On 2020-07-29 13:26:10, user Janusz Jurand Petkowski wrote:

Very nice paper! And this is just a tip of an ice-berg. Albumins transport hundreds (at least!) of different substances, both endogenous (e.g. hormones) and exogenous (various drugs, or even scavenge harmful pesticides etc.) the whole field of competition between drugs for binding sites on albumin is a complete black box, very much worth exploring. Congratulations to the authors.

On 2020-07-29 13:23:01, user Jamie Carpenter wrote:

This ensemble ML/AI method sounds really interesting as it appears to handle large multi-dimensional data by compressive-sensing-like stochastic sampling of subsets of manageable size. The reported convergence is very encouraging and hopefully this can be backed up with a rigorous mathematical derivation (e.g., upper bounds on bias, prevision, variances, information, etc.) I wonder if the code is available for community testing and independent validation.

On 2020-07-29 05:04:04, user Felix Breden wrote:

couldn't tell where methods are?

On 2020-07-29 04:02:47, user Fraser Lab wrote:

There is a clinical demand for M. tuberculosis DHFR (MtDHFR) inhibitors, but none currently exist due to low binding affinity or to M. tuberculosis cell wall impermeability. To address this, the authors use a fragment-based drug discovery approach to leverage small, high-affinity compounds against a difficult drug target. The authors briefly describe the importance of being able to selectively target bacterial DHFR while leaving human DHFR (hDHFR) unaffected. The identification of a “glycerol pocket” may help towards this goal. This manuscript utilizes a variety of biochemical and structural methods to identify and characterize promising candidates for further optimization.

The results section begins with a discussion of how the authors screened the library for possible hits using DSF which resulted in 37 hits. However, there is no mention of how these 37 hits were narrowed down to 30. Furthermore, the authors should speculate as to what factors might influence the detection by DSF and the non-detection by STD-NMR for 9 of the 30 compounds.

The optimization of Frag. 1 resulted in improved affinity, but affected glycerol pocket binding, which was the major unique feature. This discussion provides an opportunity to explain how different chemical moieties interact with specific sites of the protein and which sites deserve further exploration, based on desired interaction properties. The surprising shift of the derivatives out of the glycerol pockets would be easier to follow with a modification to Figure 4 that included the parent compound overlaid (perhaps in a very light color and partially transparent?). The discussion of the structural basis for this shift could also be expanded - is it, for example, due to changes in internal ligand conformational preferences? Was there a minor state of the “out of glycerol pocket” pose that was significantly populated, yet undetected in the ensemble? etc.

Minor Points

The electrostatics color bar in Figure 2 lacks units

Table 2 can be improved by aligning the different molecules and bolding the scaffold of Fragment 1 across the derivatives.

It is difficult to evaluate the data in Supplementary Figure 4 due to the image quality.

It would be easier to interpret Supplementary Figure 5 if differences across structures were depicted as changes in B-factor or RMSD on an apo structure. The current caption does not adequately explain the figure.

We review non-anonymously, James Fraser, Roberto Efrain Diaz, and Hector A. Chaires (UCSF) and have posted this review on BioRxiv as a comment.

On 2020-07-28 21:00:43, user SunshineSet wrote:

3% to 4% is quite directly in line with the percent of false positive results for SARS-CoV-2 antibodies. https://www.aafp.org/afp/20...

So--what does this study actually prove?

On 2020-07-28 18:42:45, user Paul Sekhri wrote:

What about transmission of infected dogs and cats to their human companions?

On 2020-07-22 19:15:35, user Dude Dujmovic wrote:

Seropositive: 13 dogs, 6 cats. Seronegative: 20 researchers. Table 1: 8 dogs and 2 cats. Independence test performed to see whether infection in 2 cats depends on the infected household. 1 cat was in infected and 1 cat in not-infected household.

On 2020-07-28 20:06:15, user Tom Terwilliger wrote:

Note: Eq. (4) has a typo. The square root should apply only to the denominator, not the entire right hand side of the equation:

<cc(f1a,f1b)> = <(F + γa + σa)(F + γb + σb)>/[(<(F + γa + σa)2><(F + γb + σb)2> ]½ (4)

On 2020-07-28 15:35:57, user Axel Theorell wrote:

Thank you for this elucidating work! I see that a lot of work went into it and it is impressive how you got the different “worlds” to work together in this pioneering paper.

A few thoughts:

1) As you point out on line 682, 13C Metabolic Flux Analysis is commonly not applied to compartmentalized metabolism (it is understandably not very informative in this case). Then in the section starting from line 697, it is stated that 13C MFA adds little information on top of the thermodynamic-stoichiometric information. Given that 13C MFA is already known to give little information in the case that you investigate, this conclusion is rather expected. To me, it remains an open question whether 13C MFA contributes significant additional information in its primary application field, prokaryotes. Somehow, I’d like to see this mentioned in the discussion.

2) I find the sentence (line 662),

"Furthermore, providing an alternative to a Bayesian approach to estimate flux uncertainties (Theorell et al, 2017), with our approach, we obtain not just one flux solution, but also statistical estimates of the uncertainty of each reaction flux."

rather confusing, since it sounds like the Bayesian approach yields no uncertainty estimates. On a philosophical level, I believe that the approach developed here could be formalized in a language of Bayesian statistics as well (maybe given a few changes) and would then rather be an extension than an alternative.

On 2020-07-28 13:46:14, user Dallan McMahon wrote:

Terrific work! This is a very important area of investigation that deserves more focus and I found the results to be of great interest. Perhaps you may be able to comment on a couple of points: (1) were there any clear effects of the magnitude of BBB permeability enhancement or harmonic/wideband emissions on the transcription of specific genes or activation/suppression of pathways (perhaps sample size is too small to make any conclusions for expression of individual genes)?; (2) Would you expect the estrus cycle to play any role in the differential transcription of any of the genes found to be influenced by anaesthetic or FUS+MB exposure? Thank you.

On 2020-07-28 05:39:46, user Michael Hendzel wrote:

I hadn't looked at these S9.6 staining patterns very closely until recently. It's obvious that they don't pick up DNA-RNA hybrids in cells because the transcription sites in the nucleolus don't stand out and the granular compartment of the nucleolus, which is posttranscriptional, is the most intensely stained region of the cell. Pulse labeling with 5-Fluoruridine for a very brief period (2-5 minutes depending on how fast the cell line takes up the nucleoside) to label the nucleolar transcription sites would highlight this discrepancy very nicely.

On 2020-07-27 23:01:44, user Jubin Rodriguez wrote:

I'm a bit confused when you refer to the term 'maximal growth-rates' in your paper. Shouldn't maximal growth rates be expressed in day-1 and be essentially derived from ln(2)/minimal doubling time*24 or have I grossly misunderstood something here?

On 2020-07-27 20:40:32, user Jon Moulton wrote:

While prior use of a Morpholino oligo for an ezrin knockdown is cited, I found no statement of the kind of antisense molecule used for the knockdowns in this paper. Did this work also use Morpholinos?

On 2020-07-27 06:34:28, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-07-27 06:24:32, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-07-27 04:50:32, user Jasun wrote:

Co-mutations are interesting finding! They are the potential important drivers of the evolution of SARS-CoV-2 and contribute to the transitions of the viral infectivity and pathogenicity

On 2020-07-27 04:33:37, user Jingsong Zhang wrote:

Important and new finding! The co-mutaitons are the potential important drivers of the evolution of SARS-CoV-2 and contribute to the transitions of the viral infectivity and pathogenicity. 'Multi-site co-mutations and 5'UTR CpG immunity escape drive the evolution of SARS-CoV-2" https://www.biorxiv.org/con...

On 2020-07-26 01:46:19, user Greatsun wrote:

Interesting. Important and new finding! The results may provide valuable clues for designing effective treatments.

On 2020-07-26 20:22:28, user Bartholdi C wrote:

Hi Aparajita, thanks for the paper. I am a little bit confused on how you encode the sequence. In the paper, "Input sequences are represented in the form of a 4 × N matrix comprising the flanking ‘N’ nucleotides in the upstream and downstream of both acceptor and donor splice junctions." However, I think the dimension should be 5*L after encoding (L is the length of my sequence), since there are "ATCGN".

On 2020-07-26 17:57:58, user Dude Dujmovic wrote:

This is animal trial, why don't you state this in abstract?

On 2020-07-26 16:56:23, user Yao Qing wrote:

Dear Dr. Rohou,

Thanks for pointing out our mistakes. We agree with your comments. We will fix the problems and describe it more accurately in the peer-viewed version.

Sincerely,

Qing Yao

On 2020-07-25 01:15:30, user Alexis Rohou wrote:

The Fourier Shell Correlation (FSC) in Fig 4C, while indeed crossing the 0.143 threshold for the first time at 4.34 Å, strongly suggests that the corresponding reconstruction is likely uninterpretable beyond ~7 or 7.5 Å. At that resolution, the FSC falls off steeply, from ~0.9 to ~0.35, after which it oscillates and slowly decays towards 0.143. For reference, this suggests that the signal-to-noise ratio at resolutions finer than 7 Å is worse than ~0.5. I would encourage the authors to call this reconstruction's resolution at ~ 7 Å (or whatever the exact resolution at which the FSC falls off sharply). If they wish to call this a 4.34 Å map, they should show features of the map consistent with this claimed resolution, including separation between beta strands, and resolved bulky side chains (Phe, Trp, etc). As it is the figure is consistent with this being a ~ 7-8Å map

Similarly, the map presented in Fig 4B does not look like a 4.3 Å map - at that resolution, the pitch of the alpha helices should be well resolved and some side chains should also be visible. The accompanying FSC is also consistent with a ~6-8 Å map, even though (here also) the authors seem to have followed the "letter of the law" and used the 0.143 threshold correctly (I am assuming that independent half datasets were refined, or that high frequencies were not used during refinement).

The same cannot be said of Figure S7A, where the FSC curve actually crosses the 0.143 line at ~ 5.5 Å, after it fell off sharply around 8Å. Based on this curve, and on the appearance of the map as illustrated, I see no valid reason for the authors to claim a resolution of 4.4 Å.

In the first two examples, Fig 4B and C, the authors may be assumed to have used the standard tools the field offers to validate resolution, and yet have arrived at seemingly erroneous estimates. Assuming the authors followed best practice, this is a failure of the field to provide standardized resolution estimation methods which deal better with quasi-pathological FSC curves.

In Figure S7A, it appears the authors did not follow the normal course in estimating the resolution, which should have given them an estimate of ~5.5Å.

Either way, this reinforces the point that visual inspection of the final map remains an essential part of validating any resolution estimate in single-particle cryoEM reconstructions.

On 2020-07-26 13:33:26, user UAB BPJC wrote:

Review of Zhang et al., “Crash Landing of Vibrio cholerae by MSHA pili-assisted braking and anchoring in a viscous environment” by the University of Alabama at Birmingham Bacterial Physiology Journal Club

Summary<br /> Mannose-sensitive hemagglutinin pili and flagellum are important for the attachment of V. cholerae to surfaces. V. cholerae frequently live in viscous environments such as the human intestines, where the pathogen causes cholera. This paper characterizes the landing dynamics of V. cholerae through cysteine substitution labelling for single-cell visualization and through resistive-force-theory based hydrodynamic modelling. Overall, this paper demonstrated that V. cholerae cell landing includes three phases- running, lingering, and attaching. MSHA pili are proposed to repeatedly attach and detach from the surface, acting as a braking and anchoring mechanism to facilitate cell landing in viscous solutions.<br /> Overall, we found this to be an interesting paper with insightful conclusions. The use of cysteine substitution-based labelling provided valuable information about V. cholerae landing dynamics. With that said, we have some comments that may be beneficial to address and some additional questions for the authors.

General Comments<br /> • There is no explanation for why 2% LB is the media of choice or why methylcellulose is chosen to increase viscosity. Is methylcellulose commonly found in viscous environments in which V. cholerae live?<br /> • Who is the target audience for this paper? Will this be going to a biology-focused journal or a physics/mathematics journal? If it is the former, the language of this paper will need to be tailored for biologists to understand more easily, and more insight should be given as to why the conditions used to study V. cholerae landing are biologically relevant.

Figure-Specific Comments<br /> • It is difficult to distinguish what is happening in some of the microscopy images; increasing the resolution for these images would be helpful.<br /> • Figure 4D: Would be nice to see the angular velocity of more than one cell. <br /> • Include the r value for correlation data.<br /> • Placing Figure 5 earlier in the paper would be a great way to catch the reader’s attention. <br /> • Figure 5D would look better visually as two separate graphs rather than an inset.

Future-Specific Comments/Questions<br /> • It would be interesting to contextualize this paper by providing some of the rheometer data for their two groups (2% LB and 2% LB + 1% MC) and compare them to ocean water, mucin, etc.<br /> • It would be also nice to see coverslip binding experiments with different molecules coating them. <br /> • How many pili are needed for irreversible attachment? How would overexpression of MSHA pili change cell trajectory/attachment?<br /> May be beneficial to use multiple methylcellulose concentrations to observe how different levels of viscosity affect landing dynamics.

On 2020-07-26 12:38:42, user Intawat Nookaew wrote:

Now published in peer review. https://academic.oup.com/na...

On 2020-07-26 06:46:44, user Sahan Laboratory wrote:

Secondary Metabolites will be the future for Drug Design and Development. This paper provide the best explanation for top metabolites from natural compounds.

On 2020-07-24 14:57:21, user salvatore santamaria wrote:

We thank the editors for their comments. While we appreciate their concerns regarding <br /> potency and lack of pharmacological data from in vivo models, this was not the immediate aim of the presented study which already provides multiple novel findings. Compound 4b is the first ever example of a cross-domain exosite small molecule inhibitor for any member of the ADAMTS family of metalloproteinases. It is also the first example in the metzincin superfamily of a glycoconjugate, exerting its inhibitory effects<br /> through the sugar moiety. In addition, through the characterization of compound <br /> 4b, we were able to identify a novel exosite in the Disintegrin-like domain of <br /> ADAMTS-5. This is only the second example of an exosite described in ADAMTS-5, <br /> after that described in our previous publication (Santamaria et al., Sci Rep. <br /> 2019;9(1):10914). Few other examples of exosites have been described in the <br /> metzincin family of metalloproteinases (reviewed recently in Santamaria S, de <br /> Groot R. Br J Pharmacol. 2019;176(1):52-66). For these reasons, we believe that <br /> the results here presented are of broad significance. However, we do agree with<br /> the editors that the compound should be further optimized before being tested <br /> in relevant mouse models, but we also felt that this was outside the scope of <br /> the present paper.

On 2020-07-24 12:24:31, user Paulo wrote:

My hope is that this common respiratory viral antigens microarray will be used to compare asymptomatic, mild, and severe Covid-19 infections, especially early on in their course. This could help determine whether ADE/original antigenic sin associated with previous HCoV infections are responsible in part for the level of severity of infections.

On 2020-07-24 07:58:11, user Alessio Collalti wrote:

The manuscript has been formally accepted for publication at Plant, Cell & Environment journal (doi:10.1111/pce.13858) and is downloadable upon request to the authors at: https://www.researchgate.ne...

On 2020-07-24 03:26:16, user marc wrote:

If you read on the differences between mice and human thymuses and their contribution to the lymph nodes, you will see what I am talking about. "Together, our findings point to a scenario where abrogation of thymic activities affects preferentially the regulatory over the ridding arm of the immune activities elicited by tumors, and argues that higher incidence of tumors with age cannot be solely attributed to thymic output decline." this sentence is very sketchy and I would not submit this to the journal. instead, you should consider what impact your work might have on covid-19, think about it.

On 2020-07-21 08:40:23, user marc wrote:

This result is fantastic and instead you should focus on the positive aspect. You have essentially discovered a way to make T-cell therapy more effective. I had also similarly discovered this theoretically. Anyhow, in essence, even if it is a small contribution in old age (there is a sharp decline in the 9th decade) the thymic output is significant and should be considered as a tool against cancer. It is likely that it is the pathology of the exhausted cell lines that prove to be useless in humans. But I don't think that your experiment can conclude what you say in the last sentence. Unless you amend it to be specific to mice.

On 2020-07-23 18:52:23, user Abigail Solitro wrote:

First author Abigail R. Solitro here - I would simply like to correct a mistake in the reporting of the figure legends above. The legend currently linked with Figure 4 actually describes the data presented in Supplementary Figure 4. The legend for the data presented in Figure 4 should read: Figure 4. Tissue levels of acridine-based compounds. Athymic nude mice with subcutaneous A549 xenograft tumors were treated daily with 30 mg/kg HCQ, QN, or VATG-027 by oral gavage. After 28 d, whole blood samples were collected by retro-orbital bleeds and tumors, lungs, livers, and kidneys were removed by necropsy. All samples were taken 3 h after the last dose. Sample size was 9-11 animals per treatment group (HCQ = 10, QN = 11, VATG-027 = 8-9). (A) Whole blood, (B) tumor, (C) lung, (D) liver, and (E) kidney samples were analyzed for parent compound concentrations by LC/MS/MS against an internal standard.

On 2020-07-23 13:48:47, user Hermine Mohr wrote:

Great tool. Thx

On 2020-07-23 11:41:25, user Lindsay Cole wrote:

Binding affinity of the spike protein decreases by 5 fold, but the infectivity in cell models increased 3-9 fold. If the experimental viral load was saturating ACE2 in the cell culture what is seen is the increase in fusion rate with the mutation. In a sub ACE2 saturation concentration of virus, like in an intial infection of a person with a small number of virus particles, the 5 fold decrease in affinity with the increase in cell fusion rate come out to be about the same.

But ACE2 depletion in the body is one possible mechanism of severe covid-19 disease. Reducing ACE2 binding and so possibly ACE2 depletion which may reduce the percentage of infected people experiencing severe symptoms, while retaining the same overall infectivity would have a well understood evolutionary advantage. A higher number of assymptomatic and mildly symptomatic carriers who are very effective at spreading the virus. If this is the case the infection fatality rate should be lower for the G614 compared to D614.

On 2020-07-23 11:02:57, user Renard Henri-François wrote:

Very interesting manuscript! It challenges concepts considered as accepted.

However, I have a few concerns:

1°) How is endophilin-A1 organized around your neutral tubules when the recruitment is forced by TIL? Does it make a proper scaffold? Maybe I didn't check carefully, but I didn't find any data regarding this aspect in the manuscript.

If endoA1 is just randomly recruited to the membrane without any specific organization, then my second question arises:

2°) How could you exclude that your tubulation effect is not just due to protein crowding? Have you tried to fuse the SH3 domain to something else than endoA1 BAR domain and look if you observe the same effect? You could use a globular protein unrelated to BAR<br /> domains, but also mutants of endophilin BAR domains (lacking H0 helix, etc). Maybe I missed them in the manuscript, but these controls would be necessary.

3°) In these in vitro experiments, how can you be sure that the density and the orientation of TIL are physiologically relevant?

On 2020-07-22 19:33:46, user Richard Sanchez wrote:

interesting work from Ferdosi et al. It beautifully illustrates PTEN as a<br /> novel marker for neddylation inhibition as well as further exemplifying<br /> the integration of multi-omic data.

On 2020-07-22 18:35:18, user Guest wrote:

"We first performed 20 simulations (680 µs total simulation time) of two GTP-bound K-Ras proteins (PDB 4DSN) in aqueous solvent (Figure S2A, left). In one simulation, the two K-Ras proteins formed stable interactions mediated in part by a bound GTP (Movie S2). This model is compelling because it provides a direct explanation for the GTP-dependence of K-Ras dimerization. Hereafter we will refer to this model as the GTP-mediated asymmetric (GMA) dimer model. "

"Because K-Ras dimerization occurs at the membrane, we then performed 23 simulations (363 µs total simulation time) of two GTP-bound K-Ras proteins anchored to the membrane by their farnesylated Cys185 (fCys185) residues31 (Figure S2A, right). In one of these membrane simulations (Figure 2A and Movie S3), the K-Ras proteins also formed the GMA dimer; the structure is virtually identical to that obtained from the solvent simulations (Figure 2B, upper panel). "

I'm curious what happens in the 19+22=41 simulations (~990us out of 1040us simulations) not discussed in the manuscript, and if any quantitative analyses/measurees were used to decide on the dimer model that you proposed. Was this structure the only structure that was found in both solvent and membrane simulations? Were any of the other dimers that formed reproduced in multiple simulations? Is there a quantitative metric that could be applied that points to the dimer model you accepted? Did you use mutational data to select the final model? Did you run 23 simulations of membrane association because the first 22 didn't reproduce the solvent model?

I'd also be curious to hear a comment on the computational efficiency/inefficiency of this approach. It seems you've run 1.04 milliseconds of simulations and thrown out 0.990ms to build a dimer model. What happens if you try to use the existing data you used to validate your model (mutation data, NMR line broadening) as a restraint in a docking method such as HADDOCK (https://haddock.science.uu.... Given the key role of salt-bridges, it seems you may have been able to simply search for complimentary electrostatic surfaces to build the dimer model, and then run short MD refinements.

Essentially what am I asking is, do you think this is a good use of long time-scale MD? The amount of simulation required to model a dimer interface is simply astonishing.

On 2020-07-22 16:45:09, user Robin Whittle wrote:

Do you have data on the severity of the COVID-19 patients' symptoms and of the patients' and control subjects' the 25OHD levels?

This research provides a detailed account of a highly significant mechanism in which COVID-19 severe symptoms are caused by hyper-inflammatory immune responses which are caused, at least in part, by inadequate vitamin D levels. A recent article from Newcastle upon Tyne: https://www.medrxiv.org/con... shows that COVID-19 hospital patients have low vitamin D levels and that those in the ICU have lower levels than those in the wards.

Dr Liji Thomas https://www.news-medical.ne... summarised the main points of this article and I have attempted to do so at http://aminotheory.com/cv19... . For links to research on vitamin D and COVID-19 severe symptoms, please see this page, http://agingbiotech.info/vi... , https://github.com/GShotwel... and http://www.drdavidgrimes.com .

Supplementation with vitamin D3 cholecalciferol would take days or a week or so to raise 25OHD levels. There may be problems with liver function where this conversion takes place, and with absorption of orally administered D3. Wouldn't it be better to use oral or IV 25OHD calcifideol (Rayaldee) instead, since this will directly supply the 25OHD the immune cells need?

Better still, if people in general supplement with D3 in sufficient quantities to raise their 25OHD levels at least to the 40ng/ml minimum recommended by many researchers and clinicians (see Charoenngam & Holick for a recent review https://doi.org/10.3390/nu1... then there's a good chance that SARS-CoV-2 throat infection will not progress to the lungs, since the initial immune response will be stronger. If it does reach the lungs, the adequate 25OHD levels will reduce the chance of the ineffective and hyper-inflammatory immune response which drives the destruction of endothelial cells, which leads to the hypercoagulative state which causes most of the harm throughout the body.

On 2020-07-22 16:38:23, user Ian Willis wrote:

If you are reading our paper on bioRxiv, please note that there are minor textual changes in the Discussion compared to the final version available at Scientific Reports. These differences do not affect the conclusions or the interpretations contained in the article on bioRxiv.

On 2020-07-22 11:42:37, user İnci Çetin wrote:

The article addresses a very current topic. The authors underlined that EKC and transportation will be more important because production cost will be higher especially in the pandemic period.<br /> The authors supported their work with six statistical methods and the article discusses the issue in detail. The data range used by the authors is quite sufficient.

On 2020-07-17 09:47:55, user Mestan Şahin Pir wrote:

The article is very well-written and it is easy to follow. In addition, the article well discusses the topic of interest, and deals with a topic with many applications in practice.

In my opinion, the article has its merit and is of interest for the PLOS ONE readership. The length of the paper is appropriate.

It does not include unnecessary extra information. The authors used a data set, which is current and complete.

The data spans 34 years, from 1980 to 2013. The authors indicate the STATISTICS PROGRAM used for the analysis in the paper.

On 2020-07-21 20:56:45, user John Seymour wrote:

Published in Microsystems & Nanoengineering<br /> https://www.nature.com/arti...

On 2020-07-21 17:22:55, user Huanglab wrote:

It has been published on JMC. See link: https://pubs.acs.org/doi/10...

On 2020-07-21 17:01:33, user David E Levy wrote:

Cool result that helps resolve a previous conundrum in the IFN/CoV2 field. Great work!

On 2020-07-21 16:16:03, user LAIDOUDI Younes wrote:

The reviewed version of this article is now published in Parasites and Vectors journal.<br /> DOI: 10.1186/s13071-020-04185-0.

On 2020-07-21 16:05:48, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-07-21 15:58:21, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-07-13 17:59:53, user Roosevelt Silva wrote:

Excellent work showing the importance of NSP1 protein. The authors could cite our work that was recently published, where we determined potential compounds to bind in this region of NSP1 described in the article. Paper: <br /> "Identification of potential drugs against SARS-CoV-2 non-structural protein 1 (nsp1)"<br /> Journal of biomolecular Structure & Dynamics<br /> DOI: 10.1080/07391102.2020.1792992<br /> July 2020

https://www.tandfonline.com...

On 2020-07-21 15:44:09, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-07-21 15:35:38, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-07-21 15:35:13, user Calogero Rinzivillo wrote:

Very intersting, many congratulations and please can I ask to Authors: Host-cell proteonomics (SLAM, TRAM, wall and cytosolic receptors, inflammasomes, sirtuins-caspases, chemokins, etc.) are pivotal for response to CoViD-19: hence such new discovered assembly proteins may play a relevant role to study and to treat this and other hyperinflammatory-related diseases (comprising neurodegenerative and autoimmunitary ones, etc.)!? We have to consider also proteolipodomics and glycoproteonomics related to these very interesting new class of assembly-proteins* reported in this paper ?(Because *these last can be a key to better knowledge of these diseases, partially pathopsysiologically unknown, I suppose). <br /> C. Rinzivillo <br /> https://www.iris.unict.it/b...

On 2020-07-21 14:49:46, user Paula de la Barra wrote:

Hi,

I found your paper very useful and hope to see it published soon. Just a heads up for some typos in the supp material: in table 12 it reads thicklip grey mullet and I think it should be thinlip. Besides, some of the years in the literature referenced in the supplementary tables should be checked (e.g. I think Joyeux 1017 should be Joyeux 2017).

Thanks for sharing the pre print!

Paula

On 2020-07-21 09:38:22, user Alberto Nájera - ????‍???? en ???? wrote:

Please read: https://osf.io/chd2k/ previous to Changeaux first preprint. Our work is already published in https://www.frontiersin.org... and see also: https://www.preprints.org/m...

On 2020-07-21 07:40:53, user MCMF wrote:

Thanks for quoting Fischer et al. (2019). It should be easy to integrate our method into your workflow for evaluation. It is open source and also written in MATLAB: https://github.com/RWTHmediTEC/PelvicLandmarkIdentification. You can cite the program with the DOI 10.5281/zenodo.3384110.

On 2020-07-20 22:40:14, user Lindsay Clark wrote:

I would interpret some of the results differently in the context of our previous work: https://doi.org/10.1093/aob... https://doi.org/10.1093/jxb... https://doi.org/10.1093/aob...

In particular, 'Robustus', 'Hercules', 'Silver Banner Grass', and 'Golf Course' are all one clone according to our SNP studies. Using plastid microsatellites, they had a haplotype (we called S) that was fairly uncommon in M. sacchariflorus but found in the northern part of its range in mainland Asia. A lot of M. sacchariflorus from Japan had haplotypes (U, V, W) that were very similar to the M. lutarioriparius haplotype (R), explaining why Mxg 'Illinois' looks more closely related to M. lutarioriparius in your study than to M. sacchariflorus. We also concluded that M. lutarioriparius was a bottlenecked subspecies of M. sacchariflorus.

Similar to your results, we did find two major haplotype groups in M. sinensis, and we think that M. floridulus and M. transmorrisonensis should be lumped with M. sinensis. I think that M. oligostachyus and M. purpurascens have been mixed up in a lot of gardens and nurseries, so I would use caution there. In our results, purpurascens was pretty clearly a hybrid of M. sinensis and M. sacchariflorus.

On 2020-07-20 17:56:39, user Martin Peverelli wrote:

It would be interesting to include WT SARS-CoV-2 S stability under stressed conditions (Fig. 3).

On 2020-07-20 12:21:12, user jake wrote:

Cool story, nice microscopy! If it hasn't already been pointed out, reference 2 is a conference abstract, but there will likely be other suitable references that you can use

On 2020-07-20 11:00:26, user Majda Bohutínská wrote:

I found the preprint officially published in the Plant Cell now: http://www.plantcell.org/co...

On 2020-07-20 02:25:32, user Danya Prigozhin wrote:

With help and encouragement from @KamounLab, we have made data for this project available via zenodo at https://doi.org/10.5281/zen.... This includes clade membership tables, clade alignments, excel tables and everything needed to run the R scripts deposited at https://github.com/krasilev....

On 2020-07-16 17:44:48, user David Guttman wrote:

Really impressive phylogenetic analysis. One question - why did you use entropy as opposed to sum-of-pairs column scoring? You lose the physicochemical relationships among amino acids with a straightforward entropy score.

On 2020-07-19 23:11:50, user Henrique Sommer Vianna wrote:

I think famotidine acts as imunomodulator in the th1/th2 ballance, IL-6 production and cytokines cascade... i don’t think it have antiviral actions

On 2020-07-19 22:36:17, user sara jane wrote:

I have several concerns with this paper. While it paves ways to explore the deconvolution methods to identify new QTL associations, the use of xCell method doesn’t really seem to make sense.

As far as I understand, XCell is primarily for immune cell types. Deconvoluting all GTEX tissues, irrespective of if they are composed of immune cell-types using xCell and showing some results (numbers) doesn’t seem right. It could be garbage in garbage out. If one estimates the presence of adipocytes in kidney or lungs, xCell still gives some enrichments because, there will be some shared ubiquitous genes commonly expressed in Kidneys and adipocytes. So how does this makes sense ?

1. The enrichments with GWAS, it seems like the authors struggled to find nice examples, as there is almost no exciting loci revealed, and showing a Asthma loci in heart ieQTLs is not convincing. This could be again linked to point 1, using matched reference in terms of cell-type composition.

2. Did authors try to do deconvolution analysis on referencence cell-types that are most relevant to the tissues (matched tissues, precisely), as there are many cell-type scRNA-Seq studies published (e.g. human cell atlas). For e.g taking a reference cell-type expression for kidneys to deconvolute kidney bulk-rna seq etc.

3. So It still boggles me why to use immune cell types to deconvolute all bulk tissues.

4. Yeah, again, I still can’t imagine why deconvoluting using immune cell-types while there are reference cell-type expression for all most all tissues, and many methods that exists to deconvolute bulk-rna.

5. Nowhere in the paper, the limitations of the study discussed, while there are plenty.

I really admire the work done by senior authors of the paper in the genomics space, so please forgive me if my comments sounds rude. I have no intentions to be offensive.

On 2020-07-19 17:44:29, user rituparno chowdhury wrote:

Very Nice work! It is heartening to see that the RBD-ACE2 interaction free energy value obtained here is very similar to what this paper in ChemRxiv(here) got using well-tempered metadynamics simulations.

On 2020-07-19 16:44:56, user Casey Burridge wrote:

"Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) emerged in December 20191,2 and is responsible for the COVID-19 pandemic3". You have referenced this yet I cannot find evidence of this anywhere in that document. In fact, has any study conclusively proved that Sars-Cov-2 causes COVID-19?

On 2020-07-19 07:03:14, user Jen Skuban wrote:

Rotavirus needs trypsin to enter intestinal cells. It's why rotavirus vaccinated kids are immune to Covid 19

On 2020-07-19 02:13:30, user Mauricio Ramírez wrote:

Very interesting preprint. I suggest to merge Figure 1 and 2, where the Figure 1A and Figure 2A should be combined. The liofilization method is not suitable for many yeast species/strains because the reduced viability after dehydratation and freezing. Maybe is an interesting field to add on the discussion about low number of dry yeasts. Good luck with the paper!

On 2020-07-18 22:36:48, user Martin Alberer wrote:

Dear Sirs,

many thanks for this very interesting article. I have two questions: Although differences in glycosylation of the antibodies as a cause for differing effect on the induction of inflammation is a very interesting idea, couldn't it be that the enhanced internalisation of the spike protein bound to antibodies via FcγRΙΙ is causing this effect? For SARS it is known that the spike protein can induce IL-6 and TNF-α via NF-κB pathway (Wang et al. Virus research 2007). On the other hand differences in glycosylation can improve the affinity to Fc receptors and thereby enhancing the internalisation.

When examining the effect of IgG COVA1-18 you used a higher concentration. What was the stoichiometry concerning this antibody and the spike protein compared to the experiments with the patient antibodies. If unbound antibodies in surplus would have blocked the Fc receptors this could explain the reduced production of inflammatory cytokines as less bound spike protein would be internalised.

In my opinion, these results affirm the demand that every vaccine against SARS-CoV-2 has to prove that is not able to induce possible harmful immunologic effects before going into larger trials and broad usage.

On 2020-07-13 23:34:15, user Vanessa McHale wrote:

So does this mean a vaccine is likely to be harmful?

On 2020-07-18 18:40:52, user Randy Barclay wrote:

The present Uyghurs were formed by admixture between Tocharians from the west and Orkhon Uyghurs (Wugusi-Huihu, according to present Chinese pronunciation) from the east in the 8th century AD. The Uyghur Empire was originally located in Mongolia and conquered the Tocharian tribes in Xinjiang. Tocharians such as Kroran have been shown by archaeological findings to appear phenotypically similar to northern and central Europeans, whereas the Orkhon Uyghur people were clearly Mongoloid. The two groups of people subsequently mixed in Xinjiang to become one population, the present Uyghurs.

The takeover by the Karakhanids did not change the essentially Iranian character of Central Asia, though it set into motion a demographic and ethnolinguistic shift. During the Karakhanid era, the local population began using Turkic in speech – initially the shift was linguistic with the local people adopting the Turkic language. While Central Asia became Turkicized over the centuries, culturally the Turks came close to being Persianized or, in certain respects, Arabicized.

The Turkic Qarakhanid and Uyghur Qocho Kingdoms were both states founded by invaders while the native populations of the region were Iranic and Tocharian peoples along with some Chinese in Qocho and Indians, who married and mixed with the Turkic invaders.

Yunusbayev et al. (2015) suggest a clearly mongoloid origin for the proto-Turkic people. During and after the migration into Central-Asia, these tribes mixed partially with Indo-European nomads. Yunusbayev et al. note that "genetic studies have not identified a clear-cut unifying genetic signal for the Turkic peoples, which lends support for language replacement rather than demic diffusion as the model for the Turkic language’s expansion." Yunusbayev et al. found that "most of the Turkic peoples studied genetically resemble their geographic neighbors," which agrees with the elite dominance model of language expansion. Yet, western Turkic people share "an excess of long chromosomal tracts" which are identical with Turkic people from "present-day South Siberia and Mongolia (SSM), an area where historians center a series of early Turkic and non-Turkic steppe polities," lending support to "a previously hypothesized area of Mongolia and southern Siberia. According to Robbeets, the proto-Turkic people descend from the proto-Transeurasian language community, which lived the West Liao River Basin (modern Manchuria) around 6000 BCE and may be identified with the Xinglongwa culture. They lived as agriculturalists, and later adopted a nomadic lifestyle and started a migration to the west.

On 2020-07-18 08:25:51, user Önder Kartal wrote:

Our paper has now been published by Genome Biology, <br /> see https://genomebiology.biome...

On 2020-07-18 00:50:12, user Alok Sharma wrote:

A comprehensive tool to do single-cell RNA-seq data clustering. It can also perform, outlier detection, estimating number of clusters, and batch correction. This is a python package

On 2020-07-17 20:29:28, user Yige Luo wrote:

@benshahary<br /> Very cool paper! I'm intrigued to see a bona-fide chemoreceptor gene affects both the production and perception of inhibitory mating signals in drastically different cell types. I like the relatively simple yet elegant behavioral experiments designed to test hypotheses. I also like your proposed auto-receptor explanation for the function of GR8a in oenocytes. Overall, I think it is well-written, plus being hypothesis driven, and make the reading process very enjoyable!

I recently present your paper to my lab mates in a journal club. I have read the manuscripts a couple times and put some thoughts into it. Here are my two-cents if you find it useful.

From my modest understanding of the paper, the first half of the results (Fig 1 and 2) are trying to establish facts that

1) Gr8a affects the production and perception of male-borne inhibitory mating signals and <br /> 2) those male-borne inhibitory mating signals are transferable,

as written in [line 162-163], [line 168-169] and [line 183-184]. The other half the the results are trying to demonstrate that the nature of inhibitory mating signals are pheromones (CHCs), and to nail down to a few candidate alkenes (9-C25,7-C25 and 7-C27).

My biggest concern is that from the existing data, some links connecting the dots are not fully justified. For example, the evidence from Figure 3a-c and Figure 4 are strong enough to support that Gr8a mutant males produce less inhibitory pheromones, including 9-C25,7-C25 and 7-C27. However, the evidence that trying to support that Gr8a mutant males transferred less inhibitory pheromones to female after mating are questionable.

Figure 3e shows no clear separation of pheromone profiles of females mated either with wt or mutant males. The paper writes in line 208-210 that there is no

qualitative

difference, and goes on exploring

quantitative

differences by pair-wise comparison. Although Figure 3f shows that the level of nC29 differs between treatment and control, it does not surprise me that the vast majority pheromones, including 9-C25,7-C25 and 7-C27, do not pass the significant level at 5%. Based on my modest statistical training, permutation MANOVA is a distribution-free MANOVA, which is a multivariate version of ANOVA. Therefore, the rule that pairwise comparison is warranted only if one rejects the null hypothesis of global test (PerMANOVA in this case) still applies.

That being said, it is still probably true that Gr8a mutant affects the production of some transferable inhibitory mating signals, inferred from Figure 2f. This makes me very curious about what actually get transferred to female to make her unattractive. Nevertheless, my interpretation on the relations among a) male-borne inhibitory mating signals, b) transferable inhibitory mating signals, c) pheromones and d) candidate alkenes would be that:<br /> 1) d) belongs to c), which is a subset of a)<br /> 2) b) is also a subset of a)<br /> 3) b) and d) do not overlap, because Gr8a mutant do not affect the transfer of d)<br /> 4) b) and c) may overlap, but the overlapping part is not detected in Gas-Chromatography.

Besides this biggest concern, there are some small miscellaneous comments/ good-intended curiosities:

I. Do you plan on investigating Gr66a?<br /> Gr66a transcripts are found in abdominal tissues from both sexes (Table 1), despite the paper reports negative results from line 118-123. Similar to Gr8a in this study, Gr66a is also involved in L-Canavanine avoidance behavior.

II. What do you think of the different chemical properties between L-Canavanine and candidate alkenes?<br /> It seems to me that these chemicals are quite different, at least in terms of charge and water-solubility. To me it is somewhat challenging to conceive a single GR can respond to both.

III. C25 and C25& 9-C25 entries in Table 2 and Figure 3b<br /> It seems to me that absence of C25 in wild-type males in Figure 3b is due to the technical difficulty of resolving the peak co-elution between C25 and 9-C25. I think it's safer to combine them and do a single test. Or considering re-run the same samples on non-polar GC-columns?

IV. What do you think that cause substantial increase in copulation latency from Figure 2e to Figure 4 and 5?<br /> From Figure 2e, it seems that it takes on average 3~5 min before copulation occurs. However, the median copulation latency boosts to 15+ min in the control panel of Figure 4 and 5. I guess the vortexing process during perfuming experiment may cause disturbances to the target fly and affect its copulation latency? Or could it be a person-to-person variation, since Figure 4 and 5 are added between Jan-2019 and current manuscript?

V. What do you think of the control in Figure 4?<br /> In Figure 5 many independent controls are used for female perfuming experiment with candidate alkenes, but there is only one control for male perfuming experiment. It seems that genotypes for males are not the same (Table 8, also line 389-391).

VI. Do you plan to do perfuming experiment on Gr8a mutant?<br /> It seems to me as a natural follow up to conduct perfuming experiment on Gr8a mutant to consolidate the link between Gr8a perception and candidate alkenes.

VII. How do you think of testing the auto-receptor model?<br /> From structural prediction, Gr8a has 7 trans-membrane domain, do you have data to show the sub-cellular localization of GR8a on membrane?

On 2020-07-13 06:11:01, user Yige Luo wrote:

Very cool paper! I'm wondering if there is an mistake on supplemental materials. Supplemental Table 2 (Table S2) and Table S3 look the same to me...

On 2020-07-17 19:12:42, user Kherim Willems wrote:

Published article available now available at Nanoscale under the title "Accurate modeling of a biological nanopore with an extended continuum framework" with doi: 10.1039/D0NR03114C.

On 2020-07-17 15:40:17, user Martin R. Smith wrote:

Congratulations on this detailed re-analysis. I'd be interested to see the size of the distance between optimal trees in some form of non-arbitrary units: RF distances are difficult to interpret (not to mention the biases in the metric itself). I've recently proposed improvements to the RF metric that allow distances to be measured in terms of the total information content of the tree topology (Smith, 2020, doi:10.1093/bioinformatics/btaa614), which might provide a clearer quantification of the importance of model selection.

On 2020-07-17 15:29:06, user Martin R. Smith wrote:

This is an interesting review of some tree distance metrics, but I wonder whether concordance with the Robinson–Foulds metric is really a desirable property for a metric to exhibit: as you mention, the RF distance has a number of biases, and often exhibits undesirable behaviour.

Indeed, most of the methods you address are simply functions of the number of nodes that two trees have in common, and the total number of nodes in the trees – doesn't the close mathematical relationship between the calculation of the metrics all but guarantee a close correlation in their values?

I have found (Smith 2020, Bioinformatics, doi:10.1093/bioinformatics/btaa614) that the best-performing measures of topological similarity are in fact poorly correlated with the RF distance, and often with each other.

I'd also encourage you to explore the quartet distance further: this metric provides a useful way to separate the contributions of agreement among trees (i.e. accuracy) and their information content (i.e. resolution) – would the two-dimensional ternary plots in Smith, 2019, Biology Letters, doi:10.1098/rsbl.2018.0632 satisfy Swofford's criteria?

On 2020-07-17 15:02:31, user Paul Gordon wrote:

Very interesting, thanks for posting. In the text, 305 genomes are described, but in Table S1 there are 222 Austrian genomes. Is this due to duplicate sampling, not listing genomes outside the superclusters, or something else? Thanks for any clarification you can provide!

On 2020-07-17 14:56:21, user Martin R. Smith wrote:

This looks like a very useful tool! <br /> Can I suggest that the Robinson-Foulds distance is not necessarily a robust measure of similarity between inferred trees and true trees, as it is subject to a number of biases and its lacks a natural unit. Some more suitable alternatives are reviewed in Smith (2020), Bioinformatics, doi:10.1093/bioinformatics/btaa614 – these might give a clearer picture of the performance difference between RaxML and Treerecs.

On 2020-07-17 11:33:12, user Tanai Cardona Londoño wrote:

Hi, thank you for this interesting study.

I wonder if there is anything you can say, at this stage, regarding the ancestral placement of photosynthesis genes. One could imagine that the origin of photosynthesis, both anoxygenic and oxygenic, would have had a tremendous impact on the evolution and diversification of Bacteria, so I wonder if there is any way that you could say something about it... not based on the topology of the species tree of Bacteria, but on the placement of key photosynthesis genes.

I had a look at your table of 3723 gene families that you used for the ancestral proteome inference and could not find any of the key unambiguous photosynthesis genes (some chlorophyll synthesis enzymes + photosystems). Is this because they are not well “suited for functional annotation”?

For example, I failed to notice the subunits of protochlorophyllide and chlorophyllide reductases, which are also linked to nitrogenases and another enzyme in methanogenesis, some of these are quite highly conserved. Or the type II photosystem core subunits: PufL/PufM (Proteobacteria/Chloroflexota/Others) and PsbA/PsbD (Cyanobacteria)… as they evolved through a series of interesting duplications and HGT events; as well as those of type I photosystems (PscA, PshA).

I guess that these were not optimally annotated (?). However, they are very highly conserved so they must have made it through the E-value threshold of your initial ALE dataset.

I have argued in my papers on the evolution of photosynthesis that the evolutionary relationship of these photosynthesis gene families, in particular because of the deepest duplication events, would indicate an early origin of photosynthesis occurring before the radiation leading to the emergence of the known groups of phototrophs: that would place both photosystem origins before the LCBA, according to your Root 1 and 2 topologies (that would be my prediction).

I would also predict that the ancestral nodes of the gene family of type II photosystems, which should include PufL/PufM/PsbA/PsbD trace to the LCBA node (Root 1 and 2) or the node representing the LCA of Terrabacteria. And the same would be the case for the gene family of type I photosystems.

Note that type I and type II photosystems core subunits are also homologous to each other but share no longer sequence identity... due to age and structural rearrangements at their origin.

Equally, I would predict that the duplication leading to protochlorophyllide reductase subunit L (BchL/ChlL) and chlorophyllide reductase subunit X (BchX), homologous to nitrogenase NifH, would be traced to the LCBA node.

What I really like about your study is that it could potentially validate or reject these predictions without inputting the biased perceptions and rationales of the researchers (like myself haha :D or others), giving that this is historically a very contentious subject.

Is there any chance that you could help me look these gene families up in your ALE inference analysis? Or would they not be accessible?

Thanks again!<br /> Tanai

On 2020-07-17 10:05:27, user msp wrote:

Also, on reading your manuscript carefully there are a few issues with how our CHiCAGO method is presented. They may well be ironed out at peer review, but I'm happy to chat in the meanwhile if you find it helpful. I can simply post my comments here if you prefer.<br /> Mikhail

On 2020-07-17 08:53:16, user Tartaglia Lab wrote:

Fantastic work! There is very good agreement between our predictions released last April at https://www.biorxiv.org/con... and the interactions (Supplementary Table 1) identified in the human liver cell line HuH7. Comparing the highest and lowest fold changes of experimental interactions, the Area Under the ROC curve reaches values > 0.90

On 2020-07-16 15:54:09, user Cindy Darnell wrote:

This is a really neat dive into morphology!<br /> I have a question about the medium. If you are adding EDTA to your trace metals solution and then filtering out the precipitate (likely iron), doesn't that suggest the metals are no longer at the concentration indicated? I'm confused about adding a chelator to a metals solution.

On 2020-07-16 15:43:10, user Rebecca Goldstein Zitnay wrote:

Have the methods here described by the reference "Tatler, 2016" been published? Here it is cited as a personal communication yet forms the primary motivation for this article. This technique would be widely applicable for mechanical studies in the lung. A detailed understanding of how these experimental studies are conducted and their outcomes as compared to the modeling results is necessary to understand the impact of this work and should be described here or as a separate publication.

On 2020-07-16 11:34:28, user Gavin Williams wrote:

Very nice work! It might be useful to compare the results of your comprehensive study to an earlier one that we carried out with AcpP:FabF via photocrosslinking. We interrogated more than 20 mutations of FabF in that study but did not include biochemical assays. Ye & Williams, Biochem, 2014, 53, 7494.

On 2020-07-16 09:17:03, user BenjaminSchwessinger wrote:

This is some lovely work. Thanks for sharing. I have a question around your final paragraph of the results that reads the following.

"This was a surprise, because the majority of methylated sites are at CGs. Our findings suggest the presence of a Ztdim2-dependent mechanism that specifically targets and mutates CA/TG but not CGs in sequence repeats during mitosis. Such a mutator resembles the hitherto undescribed but much looked for mitotic version of RIP."

I was wondering if this is actually not the mutator but different efficiency of DNA mismatch repair in different (di)nucleodite contexts. Have you looked at the DNA repair machinery related to G-T mismatch repair? Have you looked at the broader nucleotide context of mutations? Might be related to the fact that A-T bonds are weaker then C-G bonds? Just some thoughts.

On 2020-07-16 06:58:54, user Naito Motohiko wrote:

From Author M. Naito

Thank you for being interested in my manuscript.<br /> The program codes for drawing the graphs are also available on dx.doi.org/10.17504/protoco...

On 2020-07-16 03:30:04, user Performance Genetics wrote:

Did the you have access to any further accelerometry data. Given the over-riding principle of 1:1 coupling between stride frequency and breath, I would have thought there may be further consideration as to when Vo2 is met and for how long it is maintained?

On 2020-07-15 22:09:42, user Fabiano Menegidio wrote:

Genome available in a public database?

On 2020-07-15 20:50:55, user Jeffrey Ross-Ibarra wrote:

While the connection between repeat content and life history in plants is known, this paper does a nice job of suggesting a connection between telomere length and flowering time in three plant species. I think the main thing that could help, although a big ask, is to connect telomere variation to life history mechanistically. TERT knockouts in thaliana exist, for example (and if my quick read is correct, live longer and fail to flower). But work on a mechanism would go a long way to reassuring that the results aren't simply correlative.

I would like to see the selection analysis done without ascertaining the two haplotypes. Perhaps iHS or something would be good here? I worry ascertainment of the two haplotypes may give spurious signals of selection.

I would like to see genome size used as a covariate in analyses throughout the paper. We know genome size correlates with flowering time, and if I understand the approach to counting repeats correctly, I could imagine a scenario where two plants with similar telomere length nonetheless get different estimates because genome size changes the relative proportion of kmers.

I think given how strong population structure is in thaliana, using more than the first few PCs may be warranted. I'd also like to see some comparison/discussion of these results to the telomere-length mapping in Abdulkina et al. (https://www.nature.com/arti..., which are not impacted by flowering time and don't find TERT as a candidate gene (maybe both haplotypes aren't present in their parents?). Of course, TERT makes sense as a candidate and their results overlap with a RIL pop, so I don't doubt this finding. Nonetheless, I think more stringent control of pop structure and comparison to the MAGIC pop are probably warranted.

Maybe also worth comparing other repeats -- do we see the same trend if we look at other common repeat types? Long et al. 2013 (https://www.nature.com/arti... find massive difference in ribosome repeat in thaliana between populations that also differ in flowering time (and perhaps worth noting the connection between ribosome biology and telomeres in Abdulkina et al.)

Some discussion of the percent variation explained I think is warranted. In each of the three species, telomere abundance explains at most a few percent of the variation in flowering time. Is this expected?

On 2020-07-15 20:18:55, user Peter McPherson wrote:

Its clear that clathrin-mediated endocytosis is a crucial first step in infection. A biological process to target. Appreciate any feedback.

On 2020-07-15 20:02:08, user Nick Miller wrote:

Nice work!

One minor technical question. Might the "lag" seen in DvvGr43-mediated effects (lines 444 - 454) simply be due to a lag in receptor knockdown by RNAi? Even if RNAi gets rid of the DvvGr43a mRNA quickly, it could still take a while for protein turnover to clear out pre-existing receptor molecules.

On 2020-07-15 15:31:20, user Robert Carnahan wrote:

This paper was accepted at Nature and is now available online with plenty of new data! https://www.nature.com/arti...

On 2020-07-15 10:37:57, user Ji wrote:

Hi,

Have you 'ACTUALLY' monitored the infectivity of CoV2 in your Calu3 cell system? <br /> (for example, cell staining with an anti-viral protein antibody and checking by immunofluorescence etc..) <br /> Because, the CoV2 infectivity to Calu3 cell line is not that great. <br /> (You will know if you check by immunofluorescence)

Although you used moi 2 for infection, I think you must be 100% sure that all the cells were indeed infected by virus.<br /> Otherwise, you cannot make a conclusion that CoV2 is unable to block innate immune responses by exogenous stimulation (e.g. poly I:C or IFNb stimulation).

I am quite sure that poly I:C transfection or IFNb stimulation will stimulate almost all the cells in the dish, but I am not sure CoV2 will infect almost all the cells in your dish.

On 2020-07-15 02:35:33, user Shinya Fushinobu wrote:

We are happy to distribute the expression plasmid of StSOR. Contact me.