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

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

On 2017-07-24 13:56:08, user Philipp Berens wrote:

This is a long-overdue, very careful behavioral paper showing that mice can discriminate natural scenes quite well. It will be important for any study on natural mouse vision.

We discussed the paper in our journal club and a few questions came up:

1) The paper uses just one target image, which makes it hard to assess which strategy is used by the mice to discriminate the target from the distractors, e.g. could it be that the mice mainly look at the presence of sky at the top of the image? Fig. 4a could help to answer this question if all images were shown. If e.g. the sky was the feature the mice look at, one would expect that the percent correct is lower for all images with sky at the top that predicted by the psychometric curve. Have you considered training a small group of mice on a second target? Can the mice show that sort of flexibility?

2) We found the behavioral consistency between the different mice surprisingly high, likely even higher than what one would expect from humans in a similar task (suitably adjusted in difficulty). Do you have any thoughts on what caused the remarkable consistency?

3) In the first version of the article, there was a figure with a reaction time analysis. Interestingly, RTs for several mice seemed longer for images with higher performance / lower SSI. This may suggest that the mice quickly judged the high SSI images similar to the target, and therefore did not pay proper attention and made more mistakes, while they carefully investigated the obviously different images. What is your take on that and why was the RT analysis removed?

4) Despite the fact that the rotation of the upper x-axis is noted in the figure legend, it took us a while to grasp Fig. 7b and figure out what is going on, especially since the upper x-axis is light grey.

I hope the comments are useful!

On 2020-02-27 02:37:42, user Michael wrote:

This paper would be much stronger if it explained up front how these reagents are superior to or uniquely complement, add functionality, to existing ones. There are mentions of these aspects later and towards the end of the paper, but the abstract reads more like an introduction than a statement of the strengths of the new fluorescent proteins.

Also, please note the endings added to two sentences pulled from the paper. Was this investigated for each of the new probes?

Ideally, the fluorescent marker combines favorable spectroscopic properties (brightness, photostability) with specific labeling of the structure or compartment of interest **while minimally perturbing the intrinsic physiology.**

Regardless of the application, it is crucial to use markers that show specific, crisp labeling and minimal spurious, non-specific localization **while minimally perturbing the intrinsic physiology.**

Also, quantification of stability and brightness compared to other probes would be a great addition.

On 2020-04-24 14:12:26, user Nicolas wrote:

hi,<br /> I love the multifaciatus, I like a lot your article and i like 3D printing too.<br /> Is there a possibility to get the neothauma 3D model ? (like the stl)

thank you,

Nicolas

On 2016-05-13 11:17:03, user Anna Need wrote:

A really great paper, allowing us to further restrict the pool of likely contributing variants in case/ control studies of neuropsychiatric illnesses, and move a step closer to being able to provide clinical genetic diagnoses for some people with these conditions.

Are you able to find out whether the parents carrying the ANK2 and RGL1 variants have any neuropsychiatric phenotype, and whether it was de novo or inherited in them?

Also, when you calculated the rate of class 2 variants for schizophrenia, did you exclude the variants only seen in the EXaC patients with schizophrenia? Or should we assume that the real rate is a bit lower as some that seem to be class 2 are actually recurrent de novo mutations contributing to schizophrenia and absent in controls?

(N.B. Not sure if this is an appropriate comment for this forum but there seems to be a typo in Supplementary table 3, the rate for congenital heart disease is given as a fraction and as a percentage for schizophrenia).

On 2025-05-17 13:11:07, user Antony Merlin Jose wrote:

This preprint was split into two papers published in microPublication Biology. One describing the development of the assay ( https://www.micropublication.org/journals/biology/micropub-biology-001514 ) and another describing the difficulty reproducing associative learning when butanone is paired with starvation ( https://www.micropublication.org/journals/biology/micropub-biology-001568 ).

On 2017-09-21 14:25:55, user Jean Manco wrote:

Exciting new samples! Could you please clarify the location of the Alexandria sample. There are several Alexandrias in Ukraine. Your map shows the one west of the Dnieper, but your supplementary information describes the one in Kupyansk district, Kharkov region on the left bank of the river Oskol, fairly close to the border with Russia.

On 2018-05-02 09:43:01, user Fredy Barneche wrote:

Very original findings on the molecular links between Polycomb-based gene regulation and the organization of the nuclear lamina in plants, organisms that lack homologs of animal lamins. Waiting for journal publication, and more about other PWO family members!

On 2018-12-17 12:42:35, user Julien Fattebert wrote:

I think you should also look into:

Rosenblatt et al. 'Effects of a protection gradient on carnivore density and survival: an example with leopards in the Luangwa valley, Zambia' that seems relevant to your discussion of contrasting densties across the gradient of human distrubance from settlements to reserve cores;

and possibly Williams et al. 'Population dynamics and threats to an apex predator outside protected areas: implications for carnivore management';

and Ramesh et al. 'Low leopard populations in protected areas of Maputaland: a<br /> consequence of poaching, habitat condition, abundance of prey, and a <br /> top predator'

On 2020-05-18 14:02:51, user OxImmuno Literature Initiative wrote:

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

On 2020-06-18 12:14:55, user ConservationBytes wrote:

Note: This paper is currently in review through the Peerage of Science system (peerageofscience.org)

On 2021-04-14 13:04:34, user Martin R. Smith wrote:

This is a very instructive and encouraging study (even if the quality of dating you have available is somewhat better than is normal for my stomping ground in the Cambrian...).

Your simulated datasets look useful too, and really handy that they're available through Zenodo – I hope you won't mind my using them? One thing I wasn't quite clear on was the relationship between the "trees" and "samp_trees" objects: am I right in thinking that a tree reconstructed from morph\_seqs[[i]] is compared with samp\_trees[[i]]?

One small bugbear: given the limitations and biases of the Robinson–Foulds distance, I wonder whether you might get clearer results with a more discriminating tree distance? I've reviewed some alternatives at https://doi.org/10.1093/bio... , and these are implemented in the R package TreeDist: http://ms609.github.io/Tree...

On 2020-08-12 15:23:12, user Debora Marks wrote:

Seems like a great paper Po-Ssu; Worth mentioning other generative models of proteins? Riesselman (VAE, Nature Methods 2018) and Riesselman (generate antibodies 2019)<br /> https://www.biorxiv.org/con...<br /> Oh – and the first paper on folding from coevolution – try 2011 PLoS One Marks et al <br /> In any case we’d love someone from your lab would do a journal club for us?

On 2014-05-06 09:59:34, user Bastian Fromm wrote:

http://onlinelibrary.wiley....

On 2014-04-11 14:40:07, user Robin Friedman wrote:

The point that presence or absence of miRNAs is not ideal phylogenetic data seems irrefutable. However, I think there's a significant piece missing from the discussion, namely the number of conserved targets of the miRNA. The introduction states that newly acquired miRNAs are under "strong purifying selection" and therefore are not expected to be lost. This is certainly true for the most ancient miRNAs, which also tend to be expressed broadly and highly, and have many conserved targets. However, microRNAs that are very deeply conserved tend to be expressed at very low levels and have very few conserved targets (see http://www.ncbi.nlm.nih.gov... and http://www.ncbi.nlm.nih.gov... "http://www.ncbi.nlm.nih.gov/pubmed/18955434)"). So most miRNAs are probably subject to little purifying selection, excepting the 50-100 most highly expressed mIRNAs, having many conserved targets.

In other words, treating all miRNAs as equal seems to be an obvious oversimplification (that it seems others in the field have made). I wonder how the results would change if the authors limited themselves to, for example, the 87 "highly conserved" miRNA families we used in PMID 18955434.

Thanks for making your preprint public!<br /> -Robin Friedman

On 2018-02-23 09:35:24, user Guillaume Devailly wrote:

Thanks to the authors for sharing this very interesting work.

I was catch up (a bit late) by the provocative title, and I would like to comment a few points regarding the overall interpretation of the presented results. This is not an attempt of pre-publication peer review, but only a discussion.

1) The interpretation presented by the authors goes partially against a huge pile of literature, briefly exposed by the authors in the introduction:

"DNA methylation of gene promoters is frequently inversely correlated with transcriptional activity,(2, 4, 5), and abolition of DNA methyltransferase activity through chemical inhibition or genetic disruption causes global demethylation and activates numerous genes (6, 7)."

While I understand it might not be possible to do a comprehensive literature review, I would like to mention that many more papers than the 5 cited are supporting such observations.

As the authors mentioned in the introduction:

"However, these observations are correlative and challenging to interpret because genome-wide demethylation could have widespread and complex downstream effects upon chromatin structure and gene expression."

Since the effect of the ZF-D3A expression increase the global level of DNA methylation (from 63% to 68.2%) by a substantial amount, it could be mentioned that:

"The author’s observations are challenging to interpret because genome-wide hyper-methylation could have widespread and complex downstream effects."

2) If I understood correctly (notably figure 2B), most of the effects of ZF-D3A was to put some unmethylated regions up to an intermediate methylation state (i.e. from 5% methylation up to 35% methylation). As far as I know, most of the regions of the genome are either almost fully methylated (> 85%) or almost fully unmethylated (< 15%). The authors convincingly showed that induced intermediate methylated regions are unstable and quickly return to there original, unmethylated states in their experimental system. This observation raises two additional, unresolved, questions: i) might a stronger DNA methylation induction be more stable (i.e. a complete induction of methylation, from <15% to more than >85% on a given region). This question is sadly very difficult to answer, as it is technically challenging to induce such a strong targeted hyper-methylation. ii) If one where to induce partial de-methylation of some fully methylated regions, will those newly induced intermediary methylated regions be as unstable, and will they return to a fully methylated state?

As the induced methylation are quickly lost after the ZF-D3A expression is lost, the lack of long term gene repression is in agreement with there promoter methylation level (that was back to an unmethylated state).

3) I found figure 5B quite misleading. I would suggest a more symmetric representation of the up-regulated and down-regulated gene numbers, such as this one (attached with the comment, or alternatively: http://image.noelshack.com/... )

https://uploads.disquscdn.c...

Indeed, if I understood correctly, upon expression of the ZF-D3A construct, of the 2,063 UMR gaining methylation by a delta mCG > 0.3, 63.3% of the corresponding genes where downregulated (FC < 0.9), 13.2% where stable, and 23.5% were upregulated (FC > 1.1).

I can understand that some of those effects are either secondary events, or mediated by the DNMT3A-containing construct, and might not be due to the observed hyper-methylation.

Nonetheless, I find it quite difficult to title the manuscript “Frequent lack of repressive capacity of promoter DNA methylation” while showing that 63.3% of the genes with an induced promoter hyper-methylation (delta-mCG > 0.3) had there expression reduced.

Indeed, one can see the glass half empty. But granted that: 1) the hypermethylation were of limited strength (the delta-mCG is almost never greater that 0.5), 2) the apparent lack of corresponding strong chromatin state modificationd (figure 2, 3 and 4), and 3) the rapidity of the observed effect (48h after the beginning of the induction), I would personalty interpret this result as a beautiful evidence in favour of frequent repressive capacity of promoter DNA methylation in gene expression.

Despite those remarks on result interpretation, I would like to congratulate the authors on the work done, and to thanks them for sharing those results as a pre-print.

Guillaume Devailly

On 2018-05-14 14:29:19, user Jens Staal wrote:

Very cool!

Hopefully this will lead to easy/efficient CRISPR/Cas9-mediated genome editing. It would be very interesting to see how different conserved innate immune signalling components (TLR, RIG-I, TRAF2, TRAF6, CARD-CC/Bcl10/MALT1, A20, CYLD, ...) influence infection/colonization by symbionts on one hand and "bleaching" on the other hand.

On 2021-06-17 21:05:43, user Huanglab wrote:

Final version has been published https://www.mdpi.com/1422-0...

On 2021-10-27 09:05:18, user Dong wrote:

The R package Miso also allows for user-defined isotopologue pattern search. Maybe you could consider citing this package: https://academic.oup.com/bi...

On 2024-12-13 04:23:00, user Florentine Rutaganira wrote:

This preprint has been published at Cryobiology: https://www.sciencedirect.com/science/article/pii/S0011224024003389

On 2022-09-30 22:28:44, user MIT Microbiome Club wrote:

The discussion should mention a limitation of this method is its inability to detect interactions that depend on spatial structure (it is known that higher order interactions can depend on spatial structure DOI: 10.1038/nature14485) to fast growers, and to only single carbon source (DOI: 10.1038/s41559-020-1099-4). It should also be noted that any interactions that affects flourescence rather than growth would be misinterpreted.

On 2016-12-05 07:33:41, user Marc RobinsonRechavi wrote:

The term "higher eukaryotes" is not very informative, and can mean different things to different people. Moreover only 1 species is used, so the most logical would be to title "in Arabidopsis". Alternatively, a more informative description would be something like "perform in a large eukaryotic genome with alternative splicing".

On 2020-12-11 02:24:54, user theasdgamer wrote:

This paper is very much needed to put to rest the asymptomatic covid superspreader myth.

On 2021-09-03 09:15:06, user Ghislaine Dehaene wrote:

Now published in PNAS: Gennari, G., Marti, S., Palu, M., Flo, A., & Dehaene-Lambertz, G. (2021). Orthogonal neural codes for speech in the infant brain. PNAS, 118(31).

On 2021-04-13 20:27:42, user ingokeck wrote:

Hi Jody, my old software is still on sourceforge. I don't know if it still works with the modern SPM, but you can try. https://sourceforge.net/pro... <br /> I am very happy that my old research has been picked up again. Unfortunately I wasn't able to secure funding for experiments back then and had to abandon it.

On 2022-01-12 00:36:11, user Marco Gerdol wrote:

The manuscript has been peer-reviewed and it is now available online on Virus Research: https://www.sciencedirect.c...

On 2015-07-12 04:27:12, user MichaelGlotzer wrote:

This is a very important contribution and the benefits of a widely read and widely cited preprint server will eventually be evident to any active biologist. Concerning citation, a major stumbling block which is hopefully solvable, is the incorporation of BioRxiv submissions in PubMed, which is the de facto site for finding biological literature.

An temporary alternative would be the creation of a site that searches both PubMed and BioRxiv and integrates as well into the average biologists workflow as PubMed does.

Listing preprints in pubmed will greatly accelerate the transition.

On 2015-08-03 15:43:14, user Kathryn Miller wrote:

These are serious problems and this article contains a great deal of information that is helpful in sorting out the many issues. The data presented is useful and important, as are the perspectives about impact on career progression. Quantitating how much is required now vs. in the past is extremely difficult for all the reasons raised in the article and in the comments. However, many of us who have been 'around' for a long time have experienced the issues that are described in this article. It important to continue to the dialog and to work towards solutions, both short term and long term. Thank you, Ron.

On 2022-08-10 23:02:59, user Moritz Oberlander wrote:

I was a little bit disappointed that you did not show the proteolysis of C-terminal domain at TTMV-ly1 homologs as well, at least one or two with 99% identity; for instance:

541 KWGGDLPPMSTITNPTDQPTYVVPNNFNETTSLQNPTTRPEHFLYSFDERRGQLTEKATK TTMV-ly1: French children

541 KWGGDLPPMSTITNPTEQPTYVIPNNFNETTSLQNPTTRPEHFLYSFDERRGQLTEKATK safia 523-10: Tanzania children

541 KWGGDLPPMSTITNPTDQPTYVIPNNFNETTSLQNPTTRPEHFLYSFDERRGQLTEKATK xz029-anello-1: China children

C-terminal domain changes at the TTMV-Ly1 homologs only in positions 557.aa and 563.aa

I know that anelloviruses are “orphans” but they may have some “siblings". I think it’s important for an infectious study, scaling up VLP production, and to avoid a misleading degradation of the C-terminal domain at the TTMV-Ly1.

On 2014-01-14 17:41:12, user Dennis Evangelista wrote:

Submitted to a journal on 10 Jan 2014 but returned without review, resubmitted to another journal on 13 Jan 2014, revised version to be uploaded shortly.

On 2018-04-28 23:53:01, user Ruibang wrote:

As suggested by Jared Simpson, the author of Nanopolish:

Nanopolish is recommended that you run it over small windows (1-5Mbp) to reduce memory usage since it has to load all of the large fast5 files into memory. If you parallelize over small windows you might be able to run over the whole genome.

The benchmark of Nanopolish will be updated in the next revision.

On 2025-05-16 13:31:58, user Greggory Heller wrote:

The article sites multiple supplementary figures (17 in fact) but they do not seem to be published with either the Elife version or the bioarxiv. How can I (and the rest of the public) access them?

On 2020-02-12 19:59:48, user David Marjanovic wrote:

The statement above to the contrary, and unlike its three preprint versions, this paper (v4) is not a preprint and has been certified by peer review by PCI Paleo (Peer Community In Paleontology). Read the editor's recommendation here.

The trick is that the PCI journals are "journals without a journal": they peer-review and layout papers, but don't store them on their own website – instead, they leave them on preprint servers.

On 2018-11-06 11:24:13, user Nibi wrote:

Regarding the wash solution in the "DNA Preparation for NGS in RGEN-D Protocol" section at page 24-25, what composition did you use?<br /> And also, regarding the SRA data "SRP151278" at page 36, I couldn't access using the accession number. Have you deposited the data?

On 2022-02-14 06:33:30, user Alexey Polilov wrote:

This preprint was published in Nature https://doi.org/10.1038/s41...

On 2019-07-22 08:50:23, user Alexander Bruce wrote:

Dear Amy,

Thanks for a compelling and well conducted study - just how the Tead4/Yap complex switches from repressor and activator in a pluripotency/ differentiation related gene context is fascinating.

ATB, Alex Bruce (Czech Republic)

On 2022-02-17 17:10:34, user Peter Brodersen wrote:

Really nice work, and a pleasant read. The suppression of the growth defect of strains with unmethylated G2922 by Nog2 T195R and H392R is beautiful. This will certainly be a new addition to the RNA modification lecture in the 2022 version of my RNA Biology course!

On 2019-01-12 18:58:57, user Peter Rogan wrote:

This article has now been published: <br /> Mucaki et al. Signal Transduction and Targeted Therapy (2019) 4:1; https://doi.org/10.1038/s41...

On 2017-12-19 18:53:05, user C. Andrew Frank wrote:

The contents and the author list on this biorxiv page reflect the state of the paper upon initial submission. A completed revision is published at eNeuro. The revision also reflects the final authorship list, arrived at through revision experiments and editing: Catherine J. Yeates, Danielle J. Zwiefelhofer, C. Andrew Frank.

On 2020-05-24 15:17:35, user Reviewer#2 wrote:

RoHo is the ratio of # offspring of a node carrying the derived mutation to the # offspring carrying the ancestral state. So the latter ("NonA progeny"), the white leafs in Fig. 2) are a central ingredient of the statistic. If there is a inherent bias in offspring towards being ancestral because sub-trees branching off before the "red" mutation (that are not resolvable if there is no mutation), then the assumption of RoHO being 1 under neutrality (Fig. 3) does not hold. And I fear that for realistic mutation rate this bias can be very substantial.

On 2022-09-01 04:23:39, user Titos wrote:

Published: https://www.nature.com/arti...

On 2019-10-24 03:48:44, user Victor Higareda Alvear wrote:

hello I am a little confused. OTU counts means pair-end sequences right?

On 2020-05-29 00:16:40, user Kalon Haduian wrote:

Dear Ashot, thank you very much for your work, I'm sorry that I didn't ask in the context of this article, but do you know if Arshakid Mausoleum samples will be examined?

On 2025-01-03 18:39:13, user Ryan wrote:

I believe this manuscript is now published in Nature Methods:<br /> https://www.nature.com/articles/s41592-023-01779-1

On 2015-09-23 09:35:14, user Peipei Xiao wrote:

In fMRI data processing and analysis step, after MNI normalization, you calculating mean signal time course for brodmann ROI. As brodmann atlas in Talairach space. Does there any problem?

On 2017-10-20 07:45:23, user PerSister wrote:

Nathalie Balaban noted the importance of the initial inoculum already at 2004 (with a bi-phasic mathematical model that emphatize the importance of measuring the whole death-curve). <br /> See: http://science.sciencemag.o...

On 2020-02-01 18:29:02, user Donna Carpenter Smythe wrote:

Insightful read..congratulations on your methods for sterile inner core sampling..looking forward to further research on these extremely important glacial novel microbes and their implications.

On 2022-02-23 08:23:27, user Mingyue Wu wrote:

Very interesting work that complements our understanding of the role of the ancient intraspinal sensory neurons in mammals. To be clear, zebrafish CSF-cN are known to send ascending projections up to targets in the hindbrain , but not midbrian according to (Wu et al., 2021). The sensory neurons are found to project to somata of hindbrain motor neuron and axons of reticulospinal neurons.

On 2024-02-12 14:59:12, user Dianne Little wrote:

Nice set of recommendations that will be useful for the field. Some of the citations in text don't match correct citations in the refence list.

On 2017-08-10 06:51:42, user clare turnbull wrote:

Nice paper from James Brenton on on copy-number signatures, with sigs of HRD, BFB, WGD and others. Sigs can also predict survival https://t.co/h91WZqOG0i

On 2018-07-17 15:25:10, user Rob Beynon #FBPE wrote:

OK, I have never done this before, but a straw poll of the twitterverse suggest that this is the point of preprint servers. I have previously reviewed this manuscript for another journal, and found some problems. This is what I have done:

=Run a PDF comparison tool to compare the previous and this version - the changes are modest, at best<br /> =Look to see whether new data were included - none are included<br /> =Check to see if my comments previously had influenced the revision. not to any great extent

I always sign my reviews, so the authors know me, and I have no worries about doing this, other than the fact that I am making this prior review public. If I have missed a situation where my previous comments were acted on, I apologise, because I have not 'reviewed' this manuscript in the same way.

(The second review also had major concerns, but it is not my place to comment on those here)

Here is my review, verbatim. Where the authors have responded to my previous review, I have marked with ***<br /> EDIT: 18/7 - I should have acknowledge that the authors now cite two prior papers on exactly the same topic that I had to point out to them

==========================================<br /> ACCEPTED REVIEW 5TH FEB, 2018<br /> REVIEW SUBMITTED 16th FEB 2018<br /> ==========================================

Comments for the Author (Required):<br /> Please note that from 2016, I will only review papers if I can sign my name at the bottom. I am perturbed by the increasing hostility and pettiness of reviewers, let alone some quite unacceptable delaying practices. Inevitably, this raises the question of poor reaction form authors, but I have NEVER experienced this. Rather, I believe that open reviewing greatly enhances the quality of the dialogue. Hence, my review is signed below.<br /> Rob Beynon

This is a straightforward application of QconCAT technology for quantification of allelic variants of UDP glucuronosyltransferase 2B15. There are only two variants that need quantification, and it could be argued that QconCATs are overkill. I would certainly recommend removal of the term 'novel', it really isn't.

More seriously, the authors do not address this paper: https://www.ncbi.nlm.nih.go... or this paper: https://www.ncbi.nlm.nih.go... that also quantifies, using QconCATs, the same enzymes, and includes activity determination.

The authors must therefore find more novelty that they are currently claiming. It is disappointing that they do not refer to these papers, and it is not really acceptable to fail to search the literature fully. Even the 2017 paper was published last October.<br /> *** The papers are now cited in the biorxiv version

There are number of issues that, to my mind, have not been adequately addressed -

1. QconCAT design<br /> Why such a large QconCAT when this could have been completed with a single peptide containing both variant peptides? This is not well explained, and the properties of the complete QconCAT are not described.

2. Digestion efficiency<br /> The authors rightly included multiple flanking amino acids to increase equalisation of digestion rates. Arguably 15 amino acids is overkill, but that is not critical. What is critical is direct evidence of the digestion rates of the two proteins (analyte/standard) are in harmony - although I stress that the kinetics of the digestions do not have to be the same, for absolute quantification the important factor is that both digestions reach a plateau. I recommend that the authors have a look at this paper: <br /> https://www.liverpool.ac.uk... in which we discuss the issues of differential proteolysis. The goal is not to equalise digestion rates but to ensure that both reactions reach completion. I would ask that this be included - the time dependence of digestion is not a difficult experiment. The rate of digestion is not solely the domain of primary sequence, but also, the intrinsic structural digestibility of the native versus the standard protein.

3. Quantification of the standard<br /> The authors do not describe quantification and handling of the QconCAT. I note there are cysteine residues in the sequence, which can lead to issues of aggregation, making quantification more challenging. How do the authors know they added 75ng of the standard? We would usually use a common peptide, added to the QconCAT, against which we quantify using an accurately determined 'light' common standard peptide.

4. Analysis of data. It is good to see the Skyline pages, and the assays look robust. However, presumably the raw data will be deposited somewhere also?

A further point, the results are really underwhelming, not in the data per se, but in the presentation. Why not produce a detailed analysis of the relationship between RNA (with errors) and protein (with errors) for both homozygotes and heterozygotes? I felt the paper fell flat and lacked insight or detailed interpretation. These areas (ASE) are a domain where QconCATs could really add value - I also believe that the evidence for cis regulation is at present tenuous.

Overall, the relationship (of) this paper to previous work renders it much less strong. I think the pressure on the authors is to produce significant added value. It is hard to be 100% enthusiastic about publication at the moment.

One last request - the figure legends are woefully inadequate. They should be much more informative.<br /> *** To be fair, the legends are now better.

I regret that I cannot be more positive at present.

On 2018-02-01 08:54:01, user Nancy Yu wrote:

This package seems to require R >= 3.5.0 which is not available yet (as of 2018-02-01) ?!

On 2024-04-29 13:34:46, user Oyinoluwa wrote:

Abstract & Introduction

The abstract is good overall and structured well. It gives a clear insight into the concern at hand and summarizes the key findings to show its significance. However, the research question is not clearly stated. This is something that can be elaborated on in the introductions, but it should also be stated briefly in the abstract. I understand that there is not a lot of research done on the effects of pregnancy to the female microbiota, but it is unclear as to what the research question is. This introduction gives a good background on the issue of pregnancy altering the microbiome. Below are my comments on what could be added or further explained.<br /> 1. Are you trying to determine which organisms are lost/recovered?<br /> 2. Is this study for a general understanding of the effects of pregnancy on women (pre/post birth)?<br /> 3. Are you looking at a specific family of bacteria, fungi or archea that has a meaningful change during both phases (pre- and post-maturation) to see their effects? <br /> 4. Are the women from diverse backgrounds (race)?<br /> 5. Was this study conducted in the same location?<br /> 6. Include more previous studies, if any, stating what others have claimed to be the reasons behind “side effects” of pregnancy <br /> 7. Include your own claims on what you would expect and not expect to see.

Figures and Tables

The table heading and description are good and easy to read. However, make sure to explain all abbreviations.<br /> Table 1. <br /> 1. What is the difference between an t-test and a Fishers exact t-test?<br /> 2. What does SS-days mean? <br /> 3. Also explain why each test was used for the p-value result.<br /> Figure 3. <br /> 1. I am not sure what these diagrams represent.<br /> 2. 3B- What is a vaginal community state type? How were they classified? It is stated that software clusters them, but what are the criteria?<br /> 3. 3A- What do the colors represent? Explain the unknown portion? <br /> General comments for figures<br /> Figures captions need to be clear, like in 1C ii) the title is “Rchress.” Is that observed ASVs? Include clear titles for your figures as well as axis labels. References to the figures from the results section show that the figures support the findings. You can include the reasoning behind choosing these graphical analyses. <br /> Methods, Materials & Analysis

1. In your sample and collection section, it is mentioned that the participants collected their own samples. Is there a reason for this? Why did a professional, specifically a doctor, not collect the samples? Was there a way to determine that the samples were collected and stored properly?
2. Does the study look at women across different races? Consider this please!
3. How was Lctobacillus classified to species level using only the V4 region? That is not possible, the full 16S sequence is required. https://www.nature.com/arti...
4. How did your account for chimeras and PCR errors before going onto further filtering steps?
5. Further explain your controls. It was mentioned that positive and negative controls were removed, but there is no mention of their identity.
6. What analysis was used to determine dominant and rare taxa? This was mentioned in the paper but was not included in the methods section.
7. This study used 16S rRNA analysis to determine species present as different body sites, when the sequencing step is conducted to determine the species what the sequencing coverage is. Is it complete? In addition, how many reads per sample? <br /> a. This was not stated at all in the paper, and it would be good to include it in the analysis portion.

Results, Discussion, Conclusion

In the limitations section, it is mentioned that the samples were not collected from the same women before and after pregnancy. Wouldn’t this affect the results you have presented? It is hard to say whether your results and conclusion support the claims which were made due to this aspect of sample collection. Although your results and conclusions are significant to the study, the validity is questionable.

On 2020-11-18 06:44:18, user kdrl nakle wrote:

Very slim results. 3 types of produce only and nothing to do with SARS-CoV-2. Even the formatting was done in a lazy way. Looks like a paper my students would do.

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

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

Taekjip Ha

Reviewer 1.

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

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

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

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

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

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

Reviewer 2.

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

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

Reviewer 3.

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

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

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

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

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

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

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

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

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

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

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

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

On 2020-11-02 14:34:03, user Think4OnceNurLife wrote:

A later version of this article is published in the Journal of General Virology

On 2019-12-04 10:14:33, user Laurent Guyon wrote:

I have just created a quick video tutorial for first steps in miRViz. You can access in YouTube or in the Download file section (top right on the website http://mirviz.prabi.fr/):: "http://mirviz.prabi.fr/):")<br /> https://www.youtube.com/wat...

A comprehensive pdf tutorial is also available on the miRViz website.

On 2023-05-04 15:56:59, user Jordi Torres-Rosell wrote:

This manuscript has been published and is available at:

https://doi.org/10.1016/j.c...

On 2025-06-28 20:15:21, user Prof. T. K. Wood wrote:

Line 222: the first antitoxin shown to regulate a loci other than the one that encodes it was MqsA, so there is precedence for antitoxin HicB regulating more than its loci (Nat Chem Biol 2011, doi: 10.1038/NChemBio.560).

On 2016-03-05 03:58:49, user disqus_hlLLDdz9K1 wrote:

similar idea in http://www.nature.com/ejhg/... could be interesting if can be compared with ARTP2

On 2017-05-11 22:07:52, user asademiloye wrote:

Please cite as:<br /> A.S. Ademiloye, L.W. Zhang, K.M. Liew, Element-free Multiscale Modeling of Large Deformation Behavior of Red Blood Cell Membrane with Malaria Infection, Proceedings of the 5th International Conference on Computational and Mathematical Biomedical Engineering (CMBE2017), Pittsburgh, PA, United States, 2017: pp. 316-319. doi:10.1101/136648.

On 2021-07-14 14:04:52, user Sandrine CHARLES wrote:

This paper has just been published on-line:

Title

Taking full advantage of modelling to better assess environmental risk due to xenobiotics—the all-in-one facility MOSAIC

Journal

Environmental Science and Pollution Research, (), <br /> 1-14

DOI

10.1007/s11356-021-15042-7

Available as 'Online First':

http://link.springer.com/ar...

On 2023-12-13 01:25:27, user MBIO 600 class wrote:

We reviewed this paper as part of the SDSU graduate course MBIO 600: Seminar in Molecular Biology. Our expertise ranges from ecology, cancer biology, and molecular biology.

In this preprint, Sanhueza et al investigated the effect of different thermal conditions on stress response and welfare of captive fish (Atlantic Salmon). We all agreed that the paper was very interesting and was onto something, but feel that it may not have provided enough evidence to support all of their conclusions. Additional genes or stress pathway components would strengthen their work. To reach a broader general audience (that combines ecology and molecular fields for example), a more general description of some of the methods used would be helpful. More insight on how the natural and farm-raised populations behave would enable readers to put the authors’ results into context. It remained unclear what the final outcome was for the fish. We feel additional metrics to fish health and “wellness” would improve the manuscript. How do farmers “grade” their fish (for example weight; length over width; presence or absence of lesions? Overall, the manuscript offers potentially interesting findings that would be improved by additional clarifications and controls, as detailed below in further comments to follow.

On 2022-08-27 06:56:43, user Iratxe Puebla wrote:

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Claudia Molina Pelayo, Demetris Arvanitis, Pablo Raneo-Robles, Sónia Gomes Pereira. The comments were synthesized by Vasanthanarayan Murugesan.

In this preprint, Hughes et al. describe the interaction between the ER protein PERK and the mitochondrial protein ATAD3A. During ER stress, PERK phosphorylates elF2a leading to reduced global protein synthesis. The authors show that increased interaction between PERK and ATAD3A during such stress attenuates elF2a phosphorylation locally around mitochondria, resulting in continued translation of mitochondrial protein despite a reduction in global protein translation. The authors present multiple lines of evidence to support this claim and the experiments were well performed. The findings may have important implications for the understanding of mitochondrial protein synthesis and the interactions between mitochondria and the ER.

The following suggestions were raised:

Experiments

The manuscript would benefit greatly by measuring protein translation explicitly showing that mitochondrial protein translation is retained despite a reduction in global protein synthesis under certain conditions. That would help determine whether mitochondrial protein translation is protected under certain conditions driven by ATAD3 expression.

The specificity of ATAD3A towards PERK activation requires further experimental validation. Some specific suggestions are:

• Changes in activation of other pEIF2a kinases, such as GCN2 or PKR, could be measured to discard their involvement.
• In Figure S2, protein levels of ATF6 should accompany changes in spliced XBP1.
• ATF4 levels, a downstream marker of the signaling pathway, could be measured.

Manuscript

Recommend providing more details about the experimental protocol when treating cells with ER stressors. Different treatment durations are found throughout the manuscript (30min, 1h, 8h…). More information would be helpful in understanding the election of those time points for different experiments.

In Figure 2, recommend including the blots for the downstream targets ATF4, GADD34 and CHOP at the 30 minutes time point, where the upstream activation starts.

In Figure 2, the differences shown in the representative images for p-eIF2a and ATF4 appear milder than what is shown in the graph. In particular when compared with the interpretation of blots in Fig. S2. It is suggested to include all the blots used for quantification in Figure 2 in a supplemental figure so it can be clear how overexpressing/downregulating ATAD3A has a meaningful effect on this signaling pathway.

Figure 2B shows 5 different (phospho)proteins using the same loading control blot. This approach would require stripping of the membrane after each blotting, can this be specified in figure legends and in the Materials & Methods. Was the membrane stripped after each blot or were different membranes used? If different membranes were used, please indicate so and present the individual beta-actin blots corresponding to each protein as a supplemental figure.

In Figure 3A, arrows indicating the contact sites between ER and the mitochondria would be helpful in highlighting the colocalization of the two proteins. Please also provide scale bars for the images.

In Figure 3D, the #contacts per mitochondria, it is important to specify the area of images analyzed. It is unclear that n=45 images from 3 separate experiments refers to 45 images per experiment or a total of 45 images pooled from 3 experiments. Please clarify.

Recommend discussing the limitation of experiments using a single siRNA for loss-of-functions studies and experiments using cell culture.

On 2018-11-22 11:39:25, user Martin R. Smith wrote:

If you used a random tree instead of a molecular tree to generate hypothetical ancestors, would your approach improve the congruence of morphological results with the random tree?

On 2017-05-03 15:33:36, user Lynn Amon wrote:

Has the raw sequencing data been deposited to any public databases?

On 2017-02-02 21:20:17, user Miriam Goodman wrote:

Cool beans! Excited to dive into the data. Thanks for posting the preprint.

On 2020-12-02 12:49:37, user Alan Herbert wrote:

The P193 residue is in the Z? domain of human ADAR . Along with N173, it is directly involved in binding the left-handed Z-conformation. Mutations to these human residues directly map to Mendelian disease confirming a biological function for the left-handed alternative Z-DNA conformation and its role in regulation of interferon responses. Citing the earlier work would help readers and strengthen the case made by the authors.<br /> "Herbert, A. Mendelian disease caused by variants affecting recognition of Z-DNA and Z-RNA by the Z? domain of the double-stranded RNA editing enzyme ADAR. Eur J Hum Genet 28, 114–117 (2020). https://doi.org/10.1038/s41..."

On 2024-07-02 23:49:22, user Brian wrote:

It has been reasonably well-established that if there is sufficient water, transpiration rate must not be restricted for the purpose of conserving water early season to gain benefits late-season. Even the current study shows "Early-season water use was positively correlated with above-ground biomass, challenging the assumption that early-season water conservation can be leveraged for late-season benefits". This study explores three treatments, all fully or partially irrigated. As authors' concluded that "We question the efficacy of LT traits, highlighting the physiological link between water use and carbon gain, and the potential opportunity costs of reduced early-season growth", I am unsure whether such treatments were the best choice. LT trait has been proved beneficial when soil moisture is scarce, and/or soil profile is deep enough to store sufficient water to be used late-season.

On 2018-08-12 06:34:58, user Rod Whiteley wrote:

Love the idea and approach, but I'm having issues actually running the analyses. <br /> I'm getting a login requester and an error on the webpage (http://www.estimationstats.com)<br /> https://uploads.disquscdn.c... https://uploads.disquscdn.c... <br /> So I tried R and reticulate isn't compatible with R 3.3.2.

Do you have a standalone R package for dabest or is there a way to get the webpage working I don't know about?

Thanks again for your work,<br /> Rod

On 2019-04-10 15:54:17, user Fraser Lab wrote:

The idea is that by examining the structural context of these mutations you might arrive at a simple biophysical explanation for the results.

On 2020-02-13 10:47:13, user Priyanka wrote:

Am I only one who can't find the tool or package they used for their simulation and modelling

On 2020-02-01 12:00:59, user jvkohl wrote:

That would be true only if everyone continues to ignore these 2 facts

1) Koel et al. (p. 976) show that major antigenic change can be caused by single amino acid http://science.sciencemag.o...

2) A two-amino acid change in the hemagglutinin of the 1918 influenza virus abolishes transmission https://science.sciencemag....

On 2020-02-01 14:53:18, user ????????? ??????? wrote:

No. This "bat coronovirus" https://www.ncbi.nlm.nih.go... was submitted after this article was written. "Submitted (27-JAN-2020) CAS Key Laboratory of Special Pathogens"

On 2025-06-26 05:37:17, user DS wrote:

You might be interested in our previous work identifying the role of SerpinB2 in ECM remodelling in PDAC<br /> https://www.nature.com/articles/onc201763

On 2017-03-02 08:39:31, user Sujai Kumar wrote:

Please add comments/criticisms here. We're also happy to schedule a Skype QA if you have a journal club that wants to discuss this paper. There are a few new analysis types that we would especially like feedback on.

On 2018-06-02 07:41:27, user jinghuazhao Zhao wrote:

I made an attempt with depict, which shows impressively tissue enrichment at the nervous system reported earlier. See https://github.com/jinghuaz....

On 2016-07-06 08:46:09, user Alejandro Fernandez Woodbridge wrote:

What about "non identifing" data: Can we get a nice bigwig with the mutation rate that highlights regions with high functional (low snp count) or a dbSNP type of file with all 150M variants (I work on a proteogenomics lab, we need to consider all possible coding mutations)

On 2016-06-16 09:11:09, user Jerven Bolleman wrote:

The paper has been published in the Journal of Biomedical Semantics.<br /> http://jbiomedsem.biomedcen...

On 2024-10-01 15:34:23, user Thierry Alquier wrote:

A revised version of this article is now published in Brain, Behavior and Immunity at the following link (Open Access): https://pubmed.ncbi.nlm.nih.gov/39326768/

On 2025-05-05 07:05:57, user Roberto wrote:

Good morning, and thank you for your comment. Copying and pasting should work by using Ctrl + C and Ctrl + V.

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

Great paper.

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

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

Can you comment on this?

On 2017-06-23 20:23:57, user Gennadi Glinsky wrote:

Interesting concept linking the evolution of human-specific regulatory loci associated with control of pluripotency to development and function of human brain.

On 2019-03-11 20:52:31, user Julius Adler wrote:

March 11, 2019

Add the following to the end of the Discussion of October 1, 2018

Drosophila Mutants that Are Motile but Respond Poorly to All Stimuli Tested:<br /> Mutants in RNA Splicing and RNA Helicase, Mutants in The Boss<br /> Lar L. Vang and Julius Adler

Relation of this paper (October 1, 2018) to the idea of generalized arousal:

Specific responses have now been recognized as different from a generalized response, also known as generalized arousal.

The idea of a generalized arousal was described by Donald Pfaff, Lars Westberg, and Lee-Ming Kow (2005): “Many of the major advances in neurobiology during past decades dealt with specific central nervous system responses to particular environmental or internal stimuli…Fundamental to all emotional and motivational states is the concept of ‘arousal’ of the central nervous system, the 'activation of behavior.' Going under different names - elementary , fundamental, global, or generalized arousal - all of these terms refer to the same primitive capacity of the vertebrate central nervous system. Arousal underlies all mammalian behaviors…Gene/behavior thinking, even in Drosophila, is moving beyond single gene effects (Robert Anholt, 2004).”

As to arousal in Drosophila, Bruno van Swinderen and Rozi Andretic (2003) have written: “Changes in levels of arousal, such as occur during sleep or attention, most likely accomplish adaptive functions common to most animals. Recent evidence demonstrating changing arousal states in Drosophila melanogaster complements other behavioral research in this model organism. Herein we review the methodology related to the study of circadian rhythms, sleep and anesthesia where arousal, or lack of it, plays an essential role…Wakefulness is a state of increased arousal compared to sleep.”

The Boss (Adler, p. 60 of 2011) is related to generalized arousal but The Boss goes further, see October 1, 2018, above.

Adler J ( 2011) My life with nature. Annu Rev Biochem. 80:42-70.

Anholt RRH (2004) Genetic nodules and networks for behavior: lessons from Drosophila. BioEssays 26:1299-1306.

Pfaff D, Westberg L, Kow L-M (2005) Generalized arousal of mammalian central nervous system. J Comp Neurol 493:1-22.

van Swinderen B, Andretic R (2003) Arousal in Drosophila. Behav Proc 64:133-144.

Vang LL, Adler J (October 1, 2018) Drosophila mutants that are motile but respond poorly to all stimuli tested: Mutants in RNA splicing and RNA helicase, mutants in The Boss. bioRxiv

On 2018-02-14 07:47:10, user Jason Kroll wrote:

Note the title change from version 1 to version 2. <br /> The older title was "Multiple sources of variability drive a stochastic cell fate decision in C. elegans"

On 2020-06-06 13:03:25, user OxImmuno Literature Initiative wrote:

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

On 2015-08-12 16:15:22, user Jeramiah Smith wrote:

This article has been published

The sea lamprey meiotic map improves resolution of ancient vertebrate genome duplications

Jeramiah J. Smith and<br /> Melissa C. Keinath Genome Res. August 2015 25: 1081-1090; Published in Advance June 5, 2015, doi:10.1101/gr.184135.114

On 2019-03-28 15:49:53, user bioRxiv wrote:

Dear Charles Warden, blog posts that refer to bioRxiv preprints are linked on the preprint page automatically. The author does not need to do anything to make this happen. Thank you for your question. If you have any further questions, please feel free to email us at biorxiv@cshl.edu. Warm regards, The bioRxiv Team

On 2023-10-20 14:53:27, user Avolyn Fisher wrote:

This work is really fascinating and might help explain why women have higher rates of depression, instances of headache and are more susceptible to various forms of dementia. I hope that's being considered in this area of research since the disparity between men/women rates of mental health and brain ailments is somewhat inexplicable. Perhaps this is a breakthrough in explaining the gender disparity.

On 2020-09-13 07:34:50, user OxImmuno Literature Initiative wrote:

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

On 2021-01-29 09:05:33, user Meghamsh Teja wrote:

In fig.3A, I need some clarification. First, size exclusion chromatography was performed, fractions were collected and then subjected for SDS-PAGE. Color codes represent the type of the sample and color coded box represents their respective gel analysis result. In gel3, according to color code, only nucleosomes were added, so how did Mer2 appear on gel. Can someone explain it ?

On 2018-10-19 14:14:24, user Tim Fessenden wrote:

Where are the video files?

On 2021-05-12 12:11:06, user George Perez wrote:

If I'm not mistaken the other biopharmaceuticals never had their vaccine data Peer-reviewed by scientific community either.<br /> Great news for Inovio Pharmaceuticals, AGAIN!

On 2022-02-26 22:52:48, user David Borhani wrote:

The one presumed deer-to-human transmission case is described without epidemiologically relevant details. Could the Authors please comment on the following?<br /> 1. Was the proband a deer hunter? If so, details of contact (e.g., casual contact at a farm or in the wild; dressing a killed deer; masked or not; any cuts and or mucous membrane exposure; aerosolization; etc)?<br /> 2. Was the proband instead related to a deer hunter (e.g., family member who was exposed once the dressed deer was brought home)?<br /> 3. Was the proband instead somehow else in close contact with a deer?<br /> 4. Any relevant food (venison) contact?<br /> 5. Timing of the known close contact relative to the proband obtaining a PCR test?<br /> 6. Prior health status of the proband?<br /> 7. Clinical course of the infected proband?<br /> 8. Regarding lack of further spread from the proband: Is it obvious why not (e.g., strict isolation measures put in place)? Or, conversely, was there opportunity for further spread, but nonetheless it did not occur?

Separately, could the Authors please post Ct values for the deer and human proband samples?

On 2024-05-09 15:38:48, user Yi-HsuanPan wrote:

This paper had been accepted by Nature Communications volume 15, Article number: 3746 (2024) <br /> https://doi.org/10.1038/s41...

On 2016-06-15 10:24:58, user Adam Kampff wrote:

Some of the supplementary videos are rather large. We have uploaded them to YouTube for easier viewing: http://www.kampff-lab.org/s...

All feedback much appreciated!

On 2022-10-31 15:53:29, user Daniel Lüdke wrote:

Figure 3e: It is not indicated what the different groups represent (assume control and DC300)

On 2016-07-15 09:44:17, user Gianluca Corno wrote:

The methodology suggested is simply impressive, and I see the hundreds of potential applications! I wonder if you need to specifically design a model for every enviornment (i.e. a small pond, coastal waters, streams, wwtps) and for every condition (punctual samplings, time series), or the model can apply directly to the different environments somehow implementing the diverse ecological conditions...

On 2022-06-30 18:01:00, user QuiPrimusAbOris wrote:

Interesting piece of work substantiating the role of CAFs in tumorigenesis with some specific mechanism. The authors emphasize here the obvious TRANS effect (Fibroblast --> Epithelium). But the key question alas remains not answered: What makes the BRCA1 mutated epithelial cell convert the normal fibroblasts into (pre)CAFs? Can wildtype fibroblasts also become, with same ease, tumor promoting CAFs in this model?<br /> it shold be reminded that with germline BRCA1 mutation we have the rater unusual but interesting setting of having an oncogenic mutation in both the epithelium and stroma. <br /> The more usual setting is to have the mutated epithelium be surrounded by genetically wildtype fibroblasts - which still are converted into CAFs.<br /> This aspect is not addressed, not even discussed. It is also not clear what the authors mean by "control" when they say it since they do not specify it (there are many questions in this regard): BRCA1 wildtype cells from cancer-free healthy individuals or from precancerous, non-BRCA1 tissues.. Would be nice to have both types f controls. etc. Also the genotype of the fibroblasts with respect to BRCA shold be specified in every experiment.

On 2019-07-28 21:51:35, user Donald R. Forsdyke wrote:

POSITIVE SELECTION OF THE IMMUNE REPERTOIRE<br /> This interesting new contribution to bioRxiv (1) may inspire physicists to address immunological problems – a splendid goal! However, it does not accurately describe positive selection of the immune repertoire.<br /> .<br /> Positive selection means precisely that – positive selection – i.e. cells are actively selected for some characteristic that is likely to contribute positively to immune function. This contrasts with negative selection which actively removes cells because they have a characteristic that does not contribute positively, and may actively impair, immune function. <br /> .<br /> This active selection for what is becoming recognized as “near-self” reactivity, has a long history that is recently outlined (2). There are three fundamental thymic processes, death (or inactivation) by neglect, death (or inactivation) by negative selection, and positive selection. The following statement (1) wrongly indicates that positive selection should be equated with death by neglect:

"If the receptor fails to bind any self-peptide, even weakly, it will probably fail to bind any protein and the cell carrying these receptors is discarded {a process called positive selection which removes 80% of immature cells}.”

.<br /> 1. Altan-Bonnet G, Mora T, Walczak AM. (2019) Quantitative immunology for physicists. Biorxiv Here.<br /> 2. Forsdyke D. R. (2019) Two signal half century: from negative selection of self-reactivity to positive selection of near-self <br /> reactivity. Scand. J. Immunol. 89: e12746 Here.

On 2021-06-09 02:14:15, user Daniel Cameron wrote:

Very catchy title and some quite interesting results. Xiaotong Yao suggested I raise my terminology concerns as a biorxiv comment:<br /> ‘Loose ends’ is a catchy title but I am concerned that the use of new terminology for existing concepts has the potential to confuse readers. The definition you are using for ‘loose ends’ is equivalent to concept of a single breakend variant defined in the VCFv4.1 specifications about a decade ago.

Whilst it is only recently that variant callers have actually explicitly reported VCF single breakends (GRIDSS2 from SR/OEA/breakend assemblies, PURPLE from unexplained CN transitions), the loose end/single breakend concept has a long history of implicit usage. For example, NovelSeq (Sahinalp, 2010) uses an orientation-constrained assembly approach (OEA+/OEA-) very similar to that of jabba to produce what this paper calls loose end assemblies (although what NovelSeq does afterwards is different). The assembly similarity can also be seen in GRIDSS1 which uses the terms ‘breakend assembly’ and ‘anchoring reads’ when discussing it’s separate assemblies of forward/- and backward/+ orientation reads/breaks. Viral integration detection software has a similar history of implicit usage of loose ends/single breakends without explicitly using either terminology (only the recently published VIRUSBreakend uses the term).

In summary, this paper would benefit from briefing mentioning the long history of the loose end concept, and how it has been incorporated into existing tools.

Some additional thoughts to consider:

One interesting benchmarking comparison you could incorporate is against WEAVER. It also generates junction-balanced breakpoint graphs using integer programming, but its conceptual model does not appear to include single breakends. In my benchmarking (https://doi.org/10.1101/781013, Figure 3), I found that weaver failed badly on some samples and I suspect this was due to missing high copy SVs causing all junction balancing solutions to be terrible (thus reporting unbelievable ASCNs). Comparing to WEAVER (or comparing jabba MIP results with/without single breakend support) would be a good demonstration of how essential single breakend support is to the junction balancing process itself.

we found that almost half (48%, 12,068 of 25,271) of loose ends arose from Type 0 junctions that were missed during genome-wide analysis”

Is this a consequence of waiting till after SV and CN calling is done before looking for single breakends or due to the choice of caller? Would another SV caller (e.g. manta) reduce this? How many of the single breakends found by jabba can be found by stand-alone single breakend SV calling (e.g. GRIDSS2)? I notice jabba allows a BND style VCF input. Does it support single breakend BND calls?

Single breakend from SV calling and single breakends unbalanced CN junctions seems very much like they are complementary approaches so combining them should improve the final jabba results. I suspect this will be especially true for complex events as they frequently containing many clustered SVs and accurate copy number determination becomes more difficult the shorter the CN segments become.

On 2018-01-14 18:03:54, user Esteban Vargas wrote:

Too little done by Neuraminidase inhibitors

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

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

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

Following our group discussion, we have some brief comments:

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

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

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

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

On 2020-03-18 18:38:34, user piper m treuting wrote:

What were the sexes of the Rhesus?

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

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

Peer-reviewed full article visible withing this link

On 2025-09-24 07:06:09, user Fabrizio Vacca wrote:

Comments, suggestions and constructive criticism are welcome!

Fabrizio

On 2022-01-10 23:28:13, user Daniel Himmelstein wrote:

Thanks to the authors for continuing to develop this important resource, which I first became familiar with when incorporating it into Hetionet.

I recently reviewed the manuscript for a journal and am sharing my review here. Note that this review is for version 1 of the preprint posted on 2021-12-09.

On 2020-03-15 00:06:24, user Irene Kung wrote:

Have you immobilized it onto a substrate yet? If so, how much does that reduce the affinity of 47D11 for its target? Any flow cytometry data?

On 2024-11-05 13:53:35, user Damien Garcia wrote:

The article has been published please link the archive to the published manuscript.<br /> https://doi.org/10.1093/plphys/kiab529

On 2019-03-16 02:10:00, user Kim Park wrote:

The results will be more convincing in brain tissues combined with single cell RNA-seq and single cell ATAC-seq, Dr. Li-Huei Tsai's recent work on AD using single cell techniques reveal interesting findings.

On 2021-02-28 21:22:27, user Ana Christoff wrote:

Article now published<br /> Frontiers in Microbiology<br /> https://doi.org/10.3389/fmi...

On 2023-09-12 12:02:55, user Phillip Gordon-Weeks wrote:

Your very interesting experiments on HTT and drebrin in growth cones provide insights into the biology of the T-zone but I think your interpretation of the results could be developed further. HTT depletion clearly induces a re-location (not a mis-location-since drebrin can locate to filopodia) from the T-zone to filopodia. Unsurprisingly, given that the drebrin/EB3/Cdk5 pathway enables the capture of dynamic microtubules by filopodia, this is associated with a striking increase in microtubules in filopodia-actually the most significant change you measured in HTT-depleted growth cones. Drebrin in the T-zone is largely unphosphorylated at S142 and therefore in the folded conformation, which can bind anti-parallel F-actin through one of its two F-actin binding sites. In contrast, drebrin in filopodia is phosphorylated at S142 and therefore in the open conformation, enabling it to bind to parallel F-actin bundles using both F-actin binding sites. Drebrin can cross-link F-actin to dynamic microtubules by binding to EB3 in filopodia. Another manipulation that re-locates drebrin (our unpublished observations) and myosin IIB from the T-zone to filopodia is the inhibition of myosin II by blebbistatin, which essentially disassembles the F-actin in the T-zone removing an impediment to microtubule advance into the P-domain (Hur et al., 2011, P.N.A.S. 108, 5057-5062; Shin et al., 2014, PLoS ONE 9(4): e95212. doi:10.1371; Dupraz et al., 2019, Current Biology 29, 3874–3886). I wonder, therefore, whether HTT depletion also disrupts the T-zone thereby disabling drebrin binding and unhampering microtubule advance.

On 2022-03-30 10:17:29, user GP wrote:

don't miss on relevant literature:<br /> https://www.frontiersin.org...

On 2018-10-22 20:18:15, user Pavan wrote:

Please see Nature Communications (https://rdcu.be/9Nr8) "https://rdcu.be/9Nr8)") for the final manuscript. Note that there is an inversion in the bioRxiv pre-print whereby the relationship between A1 neural activity and turning behavior is flipped.

On 2021-09-30 10:32:19, user Tim Weil wrote:

This preprint has been accepted for publication in Developmental Cell. The published title will be: "Adaptable P body physical states differentially regulate mRNA storage during early Drosophila development". A link will be forthcoming shortly.

On 2021-01-05 19:12:54, user Johanna N. wrote:

Hi

I have two minor questions/comments regarding the method section:<br /> 1) For how long did you incubate the cells with EdU? I assume it's rather a short time.<br /> 2) You state that you normalize cell=level data by using median and MAD from "empty wells". Do you maybe rather mean from "untreated" wells?

On 2021-11-18 11:28:27, user Kresten Lindorff-Larsen wrote:

The manuscript by Bock & Grubmüller describes a detailed, multi-pronged computational study of the complex and important effects of cooling during sample preparation for cryo-EM. The paper is generally easy to read, appears technically sound and provides relatively clear results that will be of interest both to theoreticians and practitioners of cryo-EM.

Over the last 10 years cryo-EM has delivered increasingly high-resolution structures that in some cases now rival those of e.g. X-ray crystallography. In addition to examining the structures of macromolecules, cryo-EM may also provide more detailed insights into their energy landscapes because it in principle is a single-molecule technique that enables the visualization of the conformational distribution of molecules.

These advances leave two questions that have been difficult to answer. First, to what extent does the “average” structure under cryogenic conditions reflect the ambient temperature “average” structure [realizing that the term average here is somewhat misleading, the authors will understand what is meant]. Second, to what extent does the distribution of conformations (conformational ensemble) present in the cryo-EM sample reflect the distribution at ambient temperatures [leaving aside the technical difficulties of determining structural models of these ensembles from experiments]. While the first question can to a certain extent be answered by comparing structures solved at cryo-conditions with those at ambient temperature (by crystallography), the second question lies at the heart of the utility (and large potential) for cryo-EM to study conformational ensembles.

This study provides welcomed data in an area that has been lacking detailed and quantitative modelling, and where experiments are difficult. The results are promising in the sense that they support the idea that cryo-EM can to a large extent capture conformational ensembles at ambient temperatures. Importantly, the study provides a framework to think about these problems in a more quantitative manner that will hopefully spur additional experiments and analyses.

Specific comments:

Major<br /> p. 4:<br /> I must admit that I found the RMSF-based analysis somewhat difficult to follow in places. First, just to be sure could the authors confirm that in each case the RMSF is calculated “locally” that is using an average over the specific simulation as reference. Second, when I look at Fig. S1 it appears that there are still some changes in the RMSF curves even towards the end of the simulations that are of the same magnitude (but in the opposite direction) as those observed during cooling. Is that correct or am I looking at the figure in the wrong way?

Also, while I realize that it is difficult to boil down a complex ensemble to one or a few numbers that can be tracked, it would be useful with alternative ways of looking at the ensembles. Are there local differences that are not captured by RMSF? What about rotamer distributions. I will leave it up to the authors whether to explore these issues further in this paper.

p. 11:<br /> In terms of future experimental studies, what kinds of tests of the models could the authors envision? For example, the authors discuss work by Chen et al (Ref 24) on differences depending on the starting conditions. Do the authors’ analytical model capture such effects? Do the authors’ results lead to specific criteria for selecting good model systems to test the effects of cooling on conformational ensembles?

p. 11/12:<br /> Maybe the authors could also briefly discuss the relationship to other techniques that rely on (rapid) cooling including ssNMR and EPR. I realize that the cooling process is different, but it might still be worth speculating on how the approaches and models the authors present could be extended to other situations. In this context I’d also like to point out relevant work from Rob Tycko studying protein folding by ssNMR with rapid injection into a cold isopentane bath (https://dx.doi.org/10.1021%... "https://dx.doi.org/10.1021%2Fja908471n)").

Minor<br /> p. 1/2: In the discussion of molecules settling into the lowest free energy minima at slow cooling rates, it might be worth making it clear that these minima may well be different from the minima at ambient temperatures.

p. 4: In the T-quenching MD simulations I couldn’t easily find whether the simulations were performed using pressure control and if so how.

p. 6: “the atoms are subjected to harmonic potentials with a force constant c which are uniformly distributed in an interval from –d to d” makes it sound like it is the force constants that are between -d and d. Consider rephrasing.

p. 6 “Model3 is a combination of model2 and model3,” should be, I guess, “Model3 is a combination of model1 and model2,”

p. 6: It is not clear what value of the pre-exponential factor that the authors use. I did not go through the maths, but I would assume that the choice would affect the “effective” barrier heights e.g. in Fig. 4. It would be useful if the authors would clarify this, given that there has/is some discussion about what pre-exponential factors are relevant for conformational changes in biomolecules.

p. 11: The authors write “Biomolecules can thermodynamically access more conformations at room temperature than at the cryogenic temperature”. While that is probably mostly true, examples such as cold-denaturation suggest it isn’t universally true.

Kresten Lindorff-Larsen, University of Copenhagen

On 2020-07-05 16:20:10, user David Curtis wrote:

If you want to evaluate the performance of this method you should compare it with SCOREASSOC, which was pretty successful at identifying genes for Alzheimer's disease.

https://www.biorxiv.org/con...<br /> (It's published now in Annals of Human Genetics.)

On 2017-04-06 16:18:55, user Katherine DeLong wrote:

We have a number of concerns regarding Nieuwland et al.'s (2017) <br /> multi-lab effort. Here, we highlight features of their project that <br /> undermine confidence in their purported non-replication of the DeLong et<br /> al. (2005) findings: http://kutaslab.ucsd.edu/Fi...

On 2019-11-18 08:30:14, user Wolfgang Graier wrote:

Congratulation to the team, this a very nice and excellent work. May I ask to kindly consider our recent findings when discussing your excellent findings:

1. Klec, C; Madreiter-Sokolowski, CT; Stryeck, S; Sachdev, V; Duta-Mare, M; Gottschalk, B; Depaoli, MR; Rost, R; Hay, J; Waldeck-Weiermair, M; Kratky, D; Madl, T; Malli, R; Graier, WF. Glycogen Synthase Kinase 3 Beta Controls Presenilin-1-Mediated Endoplasmic Reticulum Ca²+ Leak Directed to Mitochondria in Pancreatic Islets and ?-Cells.<br /> Cell Physiol Biochem. 2019; 52(1): 57-75.

2. Klec, C; Madreiter-Sokolowski, CT; Ziomek, G; Stryeck, S; Sachdev, V; Duta-Mare, M; Gottschalk, B; Depaoli, MR; Rost, R; Hay, J; Waldeck-Weiermair, M; Kratky, D; Madl, T; Malli, R; Graier, WF. Presenilin-1 Established ER-Ca2+ Leak: a Follow Up on Its Importance for the Initial Insulin Secretion in Pancreatic Islets and ?-Cells upon Elevated Glucose.<br /> Cell Physiol Biochem. 2019; 53(3): 573-586.

I think your data truly are very important and, in my humbling opinion, together with our findings mentioned above add to the current correction on the principles of the mechanisms of insulin secretion.

Thank you very much and good luck in publishing.<br /> Best,<br /> Wolfgang Graier

On 2025-06-30 15:27:28, user John McBride wrote:

Cool work! Any plans to release data / code?<br /> Personally I'd have interpreted these results differently at times, but I understand the need to produce compelling narratives...

For example, if you put the reaction time difference (20 ms) in context, you could have a different interpretation of relevance. One such context is the limits of human perception, which is about 5 ms for very sort stimuli, and scales with stimuli over about 250 ms.

Another point up for discussion is effect sizes (rather than 'significance'). Normally if I do an analysis with >1000 samples/participants, and I get a p value of 0.048, that means the effect size is so small that even if it's not random, it's an extremely small effect size (I'm not quite as experienced with GLMMs, so I lack a bit of intuition here). Personally I would scramble the participant results across stimuli and see what the p-value turns out to be, just to be sure that it's not just overfitting (as far I can see, there's quite a few parameters in the model). I've definitely seen cases where this level of 'significance' can be generated by better than 1 in 20 odds, and I assume that this is related to overfitting and hidden non-randomness in any randomly-generated data. Whether it's still significant or not, I'd prefer to see effect sizes discussed in some sort of meaningful way. Like, how many times would I have the same preferences as another animal when selecting stimuli? Perhaps that's the horizontal bar with whiskers in Fig. 1A? It'd be nice to know the number (e.g. 55%?).

I'd also be interested to see a graph where the same (or equivalent) measure of agreement was plotted for human-human agreement, animal-animal agreement, and human-animal agreement.

And I don't think this statement is properly qualified, "Our global survey discovered that humans share acoustic preferences with other animals, spanning insects, frogs, birds, and non-human mammals." Technically you showed a group-level effect, which says nothing about the individual species. So one might wrongly take away from your statement that humans share acoustic preferences with each of these animals. When in actuality, it could be that a few species have a strong enough effect to bring up the average, and other species may share no acoustic preferences.

Still, cool paper. But I would be open to the possibility that the results are actually random, and perhaps not try to make such a strong claim with effects that are borderline random.

On 2023-07-09 15:21:21, user Charles Feigin wrote:

You might consider an alternative name for velvet to avoid confusion with the genome assembler Velvet, which is extremely well known and has >10,000 citations.

On 2019-07-15 15:14:39, user Artur Wlodarczyk wrote:

Correction: concentration of Na2CO3 in the 5xBG medium should be 0.472 mM. Both 5xBG and 5xBGM were sterile-filtered, not autoclaved.

On 2019-12-30 18:36:11, user Jonathan Weissman wrote:

We have critical concerns regarding this preprint by Dr. Reddy and co-workers. In particular we have previously communicated data that we feel directly refute the claims in this preprint to Dr. Reddy, yet they are ignored here. We therefore feel it is important to provide some context. A more complete description of our findings will be presented within 30 days following external review required by the terms by which we obtained some materials.

1. Although Dr. Reddy and co-workers suggest that the microtubule-destabilizing activity of rigosertib is mediated by a degradation product present in formulations obtained from commercial vendors, they are aware of our results demonstrating that pharmaceutical-grade rigosertib (>99.9% purity) and commercially obtained rigosertib elicit qualitatively indistinguishable phenotypes across multiple assays. The two compounds have indistinguishable chemical-genetic interactions with genes involved in modulating the microtubule network, both destabilize microtubules in cells and in vitro, and both show substantially reduced toxicity in cell lines expressing the L240F tubulin mutant, a rationally designed mutant in which the rigosertib binding site in tubulin is mutated. Our results demonstrate that pharmaceutical-grade rigosertib kills cancer cells by destabilizing microtubules, in agreement with our original findings and refuting the claims in this preprint.

2. Dr. Reddy and co-workers claim that the resistance conferred by our L240F tubulin mutant is non-specific without acknowledging that these claims are in direct conflict with a previously published report by an independent third party (Patterson et al. PMID: 31302152). In particular, Dr. Reddy and co-workers suggest a lack of specificity of the L240F tubulin mutant because in their hands expression appeared to confer mild resistance to the PLK1 inhibitor BI2536, but Patterson et al. conducted an essentially identical experiment and found that expression of the L240F tubulin mutant did not confer resistance to BI2536 (see Figure 7 in the manuscript by Patterson et al.). Their failure to cite or otherwise acknowledge Patterson et al. is all the more surprising, as we have previously brought this paper to their attention. Regardless, the combination of our previously published data and those presented by Patterson et al. firmly establish the specificity of the L240F tubulin mutant.

3. Dr. Reddy and co-workers claim that cells expressing the L240F tubulin mutant fail to proliferate in the presence of rigosertib, but we had demonstrated in our original manuscript that rigosertib-treated cells expressing the L240F tubulin mutant proliferated at the same rate as DMSO-treated cells over the course of multiple days (Fig. S6F in our original manuscript), and observe the same behavior with pharmaceutical-grade rigosertib. Thus, rigosertib-treated cells expressing L240F tubulin do not undergo senescence, as suggested by Reddy and co-workers, but actively proliferate.

Altogether, we conclude that there is no merit to the claims in this preprint and feel that any evaluation of the potential of Rigosertib as a clinical agent should be made in the context of these findings.

On 2019-08-07 13:15:48, user Masa Tsuchiya wrote:

This paper reveals that

1. Whole genome expression is dynamically self-organized through the emergence of a critical point (CP): co-existence of distinct expression response domains (critical states). this happens at both the cell-population and single-cell levels through the physical principle of Self-Organized Criticality (SOC).

2. Coherent-Stochastic Behavior (CSB): Coherent behavior emerges in stochastic expression in both critical states and whole-expression:<br /> i) In whole expression, the dynamics of the CM of stochastic expression converges to that of the whole expression (Genome-Attractor).<br /> ii) In the critical states, the dynamics of the CM of stochastic expression converges to that of the corresponding critical state (Critical-State Attractors).

3. The Genome-Attractor guides global coherent expression, whereas critical-state attractors guides local coherent expression emerged in critical states through heteroclinic critical transition.

4. Characteristics of the CP are given by<br /> i) A fixed point during a specific biological regulation such as reprogramming and cell differentiation,<br /> ii) The center of mass (CM) of whole expression according to temporal expression variance, which is the order parameter of the self-organization of the whole genome expression,<br /> iii) ON-OFF state: A specific transition of the higher-order structure of genomic DNA corresponding to the CP, which suggests that the CP competes between the active (swelled or coil: ON) and inactive (compact or globule: OFF) states,<br /> iv) The Genome-Attractor: A change in the CP such as ON-OFF switch induces a global impact on genome expression - the origin of the genome-wide coherent expression waves.

5. A potential Universal Mechanism of Self-Organization can be interpreted in terms of the Genome-Engine: An autonomous critical-control genomic system is developed by a highly coherent behavior of low-variance genes (sub-critical state) generating a dominant cyclic expression flux with high-variance genes (super-critical state) through the cell nuclear environment: the sub-critical state acts as the generator (source) of SOC control of the whole expression, whereas the super-critical state acts as the sink.

6. Cell-fate change occurs<br /> i) When: The timing of the erasure of the genome-attractor of the initial state,<br /> ii) How: Coherent perturbation on the genome engine through the activation of the CP.

On 2018-10-30 13:36:28, user Samantha Smith wrote:

I am getting 404s on some of the github links https://github.com/smangul1...