On 2018-03-20 18:04:20, user Jeffrey Ross-Ibarra wrote:

Neat paper! Are the N3 and T3 numbers switched in Table S2? Also, why not include demography in the forward sims?

On 2025-12-02 14:42:29, user Deepak Modi wrote:

The manuscript is published and available at https://www.nature.com/articles/s41420-025-02799-w

On 2025-11-14 16:10:53, user Ciara McCabe wrote:

https://doi.org/10.1016/j.ibneur.2025.10.020

On 2016-01-23 13:54:31, user Emanuel Fronhofer wrote:

Some, although admittedly not terribly much, research has been performed previously on the evolution of dispersal kernels. Maybe of interest, especially in the context of experimental evolution and dispersal kernels: Fronhofer et al. (2014) Spatially correlated extinctions select for less emigration but larger dispersal distances in the spider mite Tetranychus urticae, Evolution 68: 1838-1844. http://www.dx.doi.org/10.11...

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

Just checked PDB and found no record for 7BUY.

On 2015-09-23 03:00:53, user Peipei Xiao wrote:

in your paper, you have repeated your process in brainspan and kang RNA-seq datasets. how do you extract the RNA-seq brain region in fMRI data, since you mentioned in those two datasets brain regions were defined by gyral landmarks rather than brodmann areas. Does there have the brain mask of gyral landmarks, or you still used brodmann area by RNA-seq brain regions' corresponding brodmann label.

On 2021-02-18 10:54:32, user Dom San wrote:

this has been published?

On 2021-03-22 04:36:00, user Manvi Gupta wrote:

sir can i get code of this paper if it is in python language.

On 2022-02-06 09:31:19, user Jae Rodriguez wrote:

Please check Fig 1A for inconsistencies. For example, Pentecost Island is indicated as dark green squares in the legend while appearing as circles on the map. Vanua Lava, while it is included in the legend is not labeled on the map. Aore is on the map but not in the legend. And many others.

On 2016-07-30 20:25:44, user Birgit Brüggemeier wrote:

There is a typo in the abstract: "We show that the speech modulation spectrum is highly consistent _cross_ 9 languages" should be "We show that the speech modulation spectrum is highly consistent _across_ 9 languages".

On 2025-09-10 07:44:10, user Thibaud Decaens wrote:

Now published in Oecologia, 207, 151<br /> https://doi.org/10.1007/s00442-025-05788-z

On 2018-08-27 20:05:45, user Mina Bizic wrote:

Dear Jake, we have a lot of "dead cyanobacteria" experiments (some less intentionally than others :-) ). Indeed we missed adding them to the current version. No CH4 production is observed.

On 2019-07-09 10:43:04, user Siri Leknes wrote:

Hi, interesting and important work! <br /> BUT: morphine is not the main active metabolite of heroin, 6AM is! See work by Boix, Kvello and others

Also I miss discussion of how to determine “equipotent” opioid doses

On 2023-04-07 17:28:15, user Seth Taylor wrote:

Please see the final publication of these data in Cell, doi:10.1016/j.cell.2021.06.023 at this link: https://www.sciencedirect.c...

The data can be explored at the CeNGENApp: https://cengen.shinyapps.io.... Full datasets can be found at www.cengen.org.

On 2020-05-22 19:51:11, user Kenneth W Witwer wrote:

This preprint confirms some previous findings that miRNA:EV ratios are quite low, and that in some cell culture supernatants (as also suggested elsewhere for biofluids), most miRNAs are found outside EVs. Also that host EV proteins are much less fusogenic than those of viruses, particularly those like VSV.

I think that the greatest disagreements with this manuscript, which includes rigorous approaches, will be around how strongly the conclusions are presented. In my opinion, the authors certainly have a right to be a little provocative in their language, but perhaps some more caveats could be introduced in revision. It's still possible that longer exposure times, different conditions, etc. could lead to uptake with some functional relevance.

A few random comments:

"These experiments also indicated that, depending on individual reporter plasmids, 20–300 miRNA copies per cell reduced the luciferase activity by half (data not shown)."<br /> -Showing these results would greatly strengthen the paper by showing how little miRNA would be needed.

"A higher ratio of EVs per cell led to a reduction of the Renilla luciferase signal probably because a very high EV concentration was toxic to the cells"<br /> -This was quite interesting to me, as we tend to see a trophic effect of EVs in other systems. I am not sure that we can generalize this result.

Regarding Figure 6C: I would prefer to see, additionally, an experiment where miRNA mimics were introduced to the donor cells, not just miRNA-expressing plasmids, to be sure plasmids were not transferred. Although since no effect was observed, this does not affect the current conclusions.

I may have missed it, but where are the viability data? The methods mention viability tests, but I did not see the results. Dying cells may release large amounts of miRNA, and this could greatly affect EV vs non-EV miRNA ratios.

Figure 7A was interesting and puzzling to me. I would have expected that the mini-UC pellet would be the least pure and most "contaminated" with non-EV miRNA, followed by SEC-separated material and then density gradient. If this were the case, one would expect higher miRNA:particle ratios for the UC pellet. However, the UC pellet seems to yield fewer RNAs per particle than the other I'm not sure how much we can read into this, but the result does not seem entirely consistent with the conclusion that more purified EVs have lower RNA:particle ratios. A nice addition to this figure would be to show results from the input, too. There, one would expect many more RNAs per particle compared with the separated fractions (at least for particles in the size range detected by NTA).

On 2020-01-29 22:19:10, user Vivek Bhardwaj wrote:

Hello. Congrats on this quick TSS profiling method! It would be nice to have at least a qualitative comparison of your protocol with our MAPCap method, published last year.

https://www.nature.com/articles/s41467-019-11115-x

On 2021-03-23 15:03:51, user Joseph Tan wrote:

In line with this journal article:

Viral Integration and Consequences on Host Gene Expression

https://www.ncbi.nlm.nih.go...

On 2020-12-16 14:27:27, user Albert Heim wrote:

As a clinical virologist I am suprised about the introduction and background of the study which resulted in a (form my view) peculiar hypothesis (genomic integration of SARS-CoV-2). I don't want to comment on the way this hypothesis was tested, merely on its background.<br /> Long term detection (several weeks to a few months) of any respiratory virus (e.g. Rhinovirus, Influenzavirus) after an acute infection is "business as usual". However, systematic follow up testing of these patients was not usual, but if a patient was diagnosed with Flu A in January and comes down with another respiratory infection in March, it is not surprising to detect e.g. HMPV and Flu A in March. If the analysis is done with real time PCR, you will find e.g. Ct 18 for HMPV and CT 37 for Flu A, so the diagnosis in March is "HMPV infection" and the detected Flu A is a little bit "left overs" from January. <br /> In general: If you use multiplex PCR diagnostics about 5 to 10% of all diagnostic respiratory samples can be positive for two or three viruses, usually one of these is highly positive (the real culprit) and the other(s) are found close to the LOD (left overs of previous infections). <br /> In COVID-19 patients, we follow up virus loads in respiratory specimens. These decline rapidly with convalescence but remain at levels close to the LOD (and therefore intermittently positive) for many weeks. This is an anticipated result as with other respiratory viruses. The respiratory tract contains hairs, mucus, tonsillary clefts, sinus and many other structures where a little bit of any "dirt" (e.g. a few of the billions of capsids produced in an infection) can persist. Even on "clean" surfaces (e.g. stainless steel) of a laboratory, you can find these viral contaminations by highly sensitive PCR if not meticulous decontamination measures were performed. No one would however build a hypothesis from this finding that SARS-CoV-2 has a specific mechansm to perist on (or: integrate in) stainless steel. Such a PCR result merely shows imperfect decontamination of a surface (but there are no decontamination at all done in the respiratory tract, neither brushing with SDS nor with sodium hypochloride nor flushing with fresh water as the least cleaning measure). Anyway, these results do not show infectious particles. Even if a few of these capsids were (theoretically) infectious, these were too few to cause an infection.

On 2024-01-01 14:00:13, user Robert Arlinghaus wrote:

This is a very interesting paper. I would like to draw attention to previous evolutionary selection experiments in other model species, especially zebrafish, that the authors either do not cite or I think misrepresent. The authors for example refer to Uusi-Heikkilä et al. (2015) saying that the positive size selected zebrafish became shyer. But if you look carefully at the paper, the positive size selection treatment did not differ from the controls (no effect on personality), while it was the negative size selection that became bolder. A number of follow up studies on the behavioural response were completed, several papers (not cited by the authors) first authored by Valerio Sbragaglia and later by Tamal Roy. Imporantly, examining boldness effects was found to be strongly context-dependent for the positive size selection line. In some cases there were strong trends for it to become bolder, consistent with the fast life history, but in the presence of predation threat, either no differences to control or shyer behaviour was found (e.g., Sbragaglia et al. 2022, Am Nat). Very consistently, the negative size selection line was found to be always bolder. So, in short, there was an asymetric selection response and a strong context dependency of the behaviorual effects in the positive size selection line. Importantly, the results did not disagree with the expectation (e.g., fast life history should be bolder) as claimed in this manuscript, but ecological context in which the experiment was completed moderated the response. Very consistent was the finding that the negative size selected line was consistently bolder. Imporantly, theory as shown in Andersen et al. (2018) showed that positive size selection with our without additional behavioural selection may bring about either bolder or shyer behaviour, depending on the size at which selection acts and which traits are under selection. I raise this to perhaps more critically evaluate past research and to compare your outcomes with our experimental evolutionary experiments to provide the full picture.

On 2023-02-02 17:27:12, user JeRui Chang wrote:

We apologized that there is a typo in the name of the github repo. This link should work. https://github.com/jrchang6...

On 2019-06-03 07:47:43, user Ashwin wrote:

How to access supporting information for this article. ?

On 2020-05-13 16:44:21, user Anita Bandrowski wrote:

Hi, we're trying to improve preprints using automated screening tools. Here's some stuff that our tools found, and our team verified. If we're right then you might want to look at your text, but if we're not then we'd love it if you could take a moment to reply and let us know so we can improve the way our tools work. Have a nice day.

Specifically, your paper (DOI:10.1101/2020.03.04.976662); was checked for the presence of transparency criteria such as blinding, which may not be relevant to all papers, as well as research resources such as statistical software tools, cell lines, and open data.

We did not detect information on sex as a biological variable, which is particularly important given known sex differences in COVID-19 (Wenham et al, 2020).

We also screened for some additional NIH & journal rigor guidelines:<br /> IACUC/IRB: not detected ; randomization of experimental groups: not detected ; reduction of experimental bias by blinding: not detected ; analysis of sample size by power calculation: not detected .

We found that you used the following key resources: cell lines (1) software (8) . We recommend using RRIDs to improve so that others can tell exactly what research resources you used. You can look up RRIDs at rrid.site

We did not find a statement about open data. We also did not find a statement about open code. Researchers are encouraged to share open data when possible (see Nature blog).

More specific comments and a list of suggested RRIDs can be found by opening the Hypothes.is window on this manuscript, direct link https://hyp.is/sgFLao-sEeqy...<br /> References cited: https://tinyurl.com/y7fpsvzy"

On 2018-11-13 20:43:58, user Wilfried Guiblet wrote:

Please find the last version of this manuscript at:<br /> https://genome.cshlp.org/co...

On 2016-04-06 15:55:40, user Benjamin Haibe-Kains wrote:

We are looking for feedback from the community. Hope to hear from you!

On 2017-07-25 05:14:38, user Yonatan Cohen wrote:

Hey Ashram, I read your paper and first let me say great work! you mention in your work 7 transcriptome cell states and also that there is 5 genes that are <br /> to distinguish state 1 from other states you mention that the list is in Table S1 but its not shown, can you upload the sup data?

On 2017-11-28 21:31:15, user Brendan O'Fallon wrote:

I was pretty excited about this paper, but after reading it I'm worried that there are some unfounded claims and serious analysis issues. For instance, they claim that:<br /> "This deep learning model has no specialized knowledge about genomics or next-generation sequencing, and yet can learn to call genetic variants more accurately than state-of-the-art methods" <br /> ... but the variant discovery technique is basically just haplotypecaller, which has strong, biologically informed assumptions about diploidy, common forms of variation, and even error modes (for instance, specialized values regarding homopolymer extension). Bit of a letdown.

In addition, when comparing called variants to the truth set, they only match based on chromosome and left (start) position, and ignore the end position and alt bases entirely! Given that different callers may represent variation in many different ways, especially for tricky variants in repetitive or low-complexity regions, I'm worried that this just favors callers that tend to put a variant (any variant) at the start position. Why not use vgraph or vcfeval, which correctly match calls to truth even when variant representations differ widely?

I imagine that in the not-so-distant future deep learning will be an important component of variant discovery... but I'm not so sure it will look like this.

On 2016-07-02 05:26:27, user Tommy Tang wrote:

where can we download the data?

On 2016-07-02 02:45:55, user Yaniv erlich wrote:

what is the link to apply for data access?

On 2019-04-24 20:27:38, user Olavo Amaral wrote:

* 1) How does quality of reporting affect integrity of science? For example,<br /> do we know if adherence to reporting guidelines improves likelihood that the<br /> science is sound and useful to others?

That's a very good question, and I'm not sure we have this kind of data. People tend to look at reporting as a measure of minimal things to allow for the reproducibility of studies, but I'm not aware of empirical data linking the two concepts. Systematic<br /> replication initiatives seem to offer an interesting opportunity to do that (see https://www.nature.com/articles/s41562-018-0398-0), and we do mean to analyze quality of reporting as a predictor of reproducibility in the Brazilian Reproducibility Initiative (https://doi.org/10.7554/eLife.41602), but we won't have results for that until 2021.

* 2) Did the evaluators perceive lower statistical reporting in published<br /> versions as helpful to the science (e.g. removing over-reporting to make more<br /> concise and clear) or unhelpful (e.g. constraining full results to simpler less<br /> detailed statements)?

We never asked this, but we could. That said, the difference favoring preprints in statistical reporting is small, exploratory and far from achieving any kind of statistical significance, so I’m not sure we should take it for a fact based on our data alone.

* 3) I note it's hard to tell at what stage work has been preprinted: the v1 here may not be truly the first version submitted to any journal. How likely is it that the preprints with embedded figures had already gone through a round of journal-led peer review?

We haven’t really looked, actually. It’s certainly a possibility – maybe we could compare date of publication vs. posting to have an idea of whether peer review is likely to have happened in the journal of publication, but the only way to assess if they<br /> might have undergone peer review in other journals would probably be to ask authors. I would guess that most v1s have not gone through peer review beforehand, and that this shouldn’t be different between articles with embedded vs. non-embedded figures (if anything, most automatically generated PDFs in journal submission systems will lump figures at the end). Nevertheless, this might be something to check more closely in the paired comparison stage of the project, in which we will actually have a published version to compare to.

4) Does this survey pick up any change in conclusion between preprint and publication? (Not that I can see from your Qs or scope.) Do you plan to address this in the next batch of preprint-published pairs mentioned at the end of this preprint?

No they don't (and they probably won't in the second stage either). The questionnaire is focused on really objective reporting items. Moreover, we are having preprints and published versions analyzed by different evaluators to minimize the possibility that one version might help them find information that was hard to extract in the other one.<br /> That said, it's a great question, and it doesn't seem that hard to answer. That said, this one might benefit from having evaluators with expertise in the specific area of the<br /> preprints - and might be more easily done by a group of researchers in a specific field evaluating preprints in their area of expertise. It also inevitably requires researchers to look at both versions and give more subjective opinions, which increases the chances of bias, but I definitely think it's worth doing.

* 5) Would it help to also be able to refer to any published review reports for any of these preprint-published pairs, where available?

That is indeed an interesting question; that said, I would guess that the percentage of journals publishing peer review reports is still too low to allow a meaningful sample to be obtained. Nevertheless, if we manage to obtain a handful of those, it’s probably worth checking to see whether items that change between preprints and peer-reviewed versions were indeed pointed out for reviewers – although it might be hard to extrapolate general conclusions from these examples.

* 6) Is the methodology for this next phase available in advance?

We have an addendum of our OSF project at https://osf.io/g3ehr/ describing the second step of the project (which is already underway).

* And finally, separate to the results presented here and purely out of interest: what did the team learn by experiencing this survey process? For example: how difficult was information to find in preprints versus publications [your subjective Qs at the<br /> end of the survey]? What was it like managing a collaborative evaluation project?

As I mentioned in our post at the ASAPbio blog (https://asapbio.org/amaral-quality-first-results), I felt that the process ran surprisingly smoothly - although that's in large part thanks to the managing abilities of our first author, Clarissa Carneiro. One thing we did learn was how hard it is to have a set of questions that applies to any kind of article – we tried to be as broad as possible in our inclusion criteria to maximize representativeness, but that inevitably reduces the adequacy of the measures for individual articles. Another thing to take into account is to try to find the right balance between objectivity and subjectivity: from the rather homogeneous agreement among evaluators, I think we were successful in making them objective. That said, one could argue that this choice led us to miss important, more subjective issues concerning article quality – but asking those would likely require reviewers with expertise in the area of the papers.

On 2021-08-09 13:14:21, user Shuaibing Ren wrote:

Equation 5: P(G) should be P(Gl)

On 2016-12-19 07:23:22, user Boris V. Schmid wrote:

"The first scenario assumes that plague was introduced multiple times to Europe from a<br /> common reservoir between 5,000 to 3,000 BP. Here, the bacterium would have been<br /> spread independently from a source, most likely located in Central Eurasia, to Europe at<br /> least four times during a period of over 1,000 years (Figure 1), travelling once to<br /> Lithuania, once to Estonia, and two times to Southern Germany. "

The authors could refer to my work in http://www.pnas.org/content... , where I show the climatic and epidemiological evidence of a similar process of multiple reintroductions of plague from Central Asia during the 400 years of the second plague pandemic. Would seem highly relevant for readers.

On 2018-12-05 09:02:56, user Ken Cameron wrote:

Would be great to see some more biophysical data on these compounds to show direct binding to KRas. MST can be quite fickle. NMR would show clearly if they are binding and if they are sub micromolar (expect slow exchange). The crystallography shudl also be straight forward for compounds with this affinity. Lots of other compounds out there that claim direct Ras binding that turn out not to bind to Ras!!

On 2020-06-28 01:47:50, user Logan wrote:

When do you expect this to be published in a journal? I'm a little picky about formatting, so I'd love to see this work in its final form.

On 2020-10-26 19:11:32, user Critical Dissection wrote:

Great work with the development of anuran. The application of the sponge microbiome was fascinating and exciting, but how do the advantages conferred by the anuran software translate into information relevant for microbiologists without in-depth network construction experience? I strongly recommend including more definition of the parameters used in your experiments and greater explanation of conclusions so that an even wider audience may be reached.

On 2019-07-20 14:35:06, user Georg wrote:

The sample VK 542 from Ukraine, Chernigov is completely out of place. It is obvious that it has nothing to do with Vikings, or, to be precise, with the members of Rurik dinasty. Namely, even if we neglect the man's Y-DNA haplogroup, it should be noticed that he has absolutely no Scandinavian (or even Baltic) component in his autosomal DNA.

On 2016-04-10 07:03:27, user Warrick Nelson wrote:

This is an interesting technique that can be extended to<br /> identify smaller populations of an insect than for example the psyllid biotypes<br /> (Jackson et al 2009)(Lopez et al 2013 http://dx.doi.org/10.1603/E... "http://dx.doi.org/10.1603/EC12258)"), or haplotypes<br /> (Swisher et al 2012 http://dx.doi.org/10.1603/E... (Swisher et al 2014 http://dx.doi.org/10.1093/j... "http://dx.doi.org/10.1093/jisesa/ieu023)").<br /> With two of these haplotypes in the Washington region, this could explain the<br /> anomaly reported in the Othello region. Both<br /> the bio/haplotype methods and the one reported here have potential for<br /> determining sources and movements of psyllids from overwintering sites/hosts to<br /> annual crops like potato. These results also appear to substantiate the<br /> refutation of the popular belief that these psyllids migrate annually from<br /> Mexico to Washington (Nelson et al 2014 http://dx.doi.org/10.3958/0... "http://dx.doi.org/10.3958/059.039.0121)"),<br /> including the reported modest turnover in genetic makeup suggesting overlapping<br /> ranges of distinct subpopulations.

Speculation of a third host plant – there is a long list of<br /> Solanaceous plants, introduced and native, annual and perennial, present in<br /> Washington. Broadening the range of<br /> overwintering hosts beyond a single species would be useful. Early reports also<br /> suggested collection of this psyllid on conifers in mountainous regions, for<br /> example Crawford, 1911. A number of other triozid psyllids are known to<br /> overwinter on conifers, for example the carrot psyllid in Scandinavia that also<br /> vectors Liberibacter solanacearum. Further, movement of psyllids from<br /> geographically distant regions could occur on commercial plants, e.g. tomato<br /> seedlings from California, moved to the region for seasonal cropping or<br /> ornamental purposes.

On 2014-10-06 08:42:59, user Santiago (INIA-CIFOR, Madrid) wrote:

Great paper, thanks for posting it here

On 2023-11-07 13:19:53, user Pedro H. Oliveira wrote:

This is a very interesting manuscript.<br /> It was a pity however to not have seen discussed in this work the recent findings on defense systems' co-localization published here (https://www.biorxiv.org/con... "https://www.biorxiv.org/content/10.1101/2023.08.12.553040v2.full.pdf)"). I believe the latter work will also be useful to update a few of the claims mentioned by Wu et al. in their Introduction.

On 2019-05-14 06:26:40, user Ashwani Kumar wrote:

The full article related to above article : <br /> Structural basis of hypoxic gene regulation by the Rv0081 transcription factor of Mycobacterium tuberculosis <br /> has been published with some modification in <br /> FEBS Letters<br /> Volume 593, Issue 9<br /> Pages: 982-995<br /> May 2019<br /> https://doi.org/10.1002/187...<br /> https://febs.onlinelibrary....<br /> https://febs.onlinelibrary....

On 2020-06-27 07:02:38, user OxImmuno Literature Initiative wrote:

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

On 2022-08-28 23:09:38, user Peter Hickey wrote:

Where can this software be downloaded? I could not find a link in the paper or via Google.

On 2021-04-26 22:37:49, user Esmeralda R. wrote:

I was looking for Histone H4 variants with a role in different types of cancer, then I bumped into this publication, now at Nucleic Acid Research. <br /> Thanks for posting the link to the now published article too. <br /> It will make my paper on histone modifications, much better. <br /> Esmeralda from Real Gramas

On 2017-03-08 15:54:47, user Jean Manco wrote:

I see that there was a typo in my last post (about a typo!) :)

Discrepancy between tables re sample BOVO1b. MtDNA H in Extended Data Table 1 and Supp. table 2. But supp. table 1 has H27.

On 2020-02-04 09:03:12, user Sebastian wrote:

Great paper! Regarding Fig. 6b: IL-11 tends to activate stat in vitro only at rather high concentrations and when the cells are cell lines and become transformed through extended culturing. I would blot for non-canonical pathways such as ERK and you should see the activation in organoids and primary cells. Keep up the good work, Sebastian Schafer

On 2021-09-10 08:09:18, user Robert Briddon wrote:

I have some concerns, both major and minor, about this manuscript. I will deal with the minor concerns first. <br /> The manuscript is not easy to read – it is not reader friendly. The text deals with a large number of sequences, derived from the authors own work and obtained from the databases. These are all referred to by their database accession numbers. So, for example, when the text refers to the virus phylogenetic tree saying “MYVMV tree (Figure 1b) produced two major branches. All the MYVMV isolates from Pakistan were clustered into clade I under branch A while the Indian isolates were grouped into both the branches.” the figure does not show this – it is just a collection of anonymous database accession numbers. To confirm what the authors claim the tree shows, the reader would need to refer to a supplementary table or the database. Things could be so much better. The manuscript quotes Brown et al. (2012) which first suggested the use of “isolate descriptors” with up-dates in

Brown, J.K., Zerbini, F.M., Navas-Castillo, J., Moriones, E., Ramos-Sobrinho, R., Silva, J.F., Fiallo-Olivé, E., Briddon, R.W., Hernández-Zepeda, C., Idris, A., Malathi, V.G., Martin, D.P., Rivera-Bustamante, R., Ueda, S., Varsani, A., 2015. Revision of Begomovirus taxonomy based on pairwise sequence comparisons. Arch. Virol. 160(6), 1593-1619.

So at each mention of sequence EF373060, for example, you would put “MeYVMV-BenIN:Bar:06”. This gives the reader all the information needed to know what the sequence is – so, in this case, species Mesta yellow vein mosaic virus; strain Bengal; country of origin India; place of origin Barackpore; year of isolation 2006 and the accession number. This makes it much easier for the reader. Note that similar proposals, betasatellte species and isolate descriptors, have been made for betasatellites - see here.<br /> https://talk.ictvonline .org/taxonomy/p/taxon omy-history?taxnode_id=20165 479

Note that

Briddon, R.W.; Brown, J.K.; Moriones, E.; Stanley, J.; Zerbini, M.; Zhou, X.; Fauquet, C.M. Recommendations for the classification and nomenclature of the DNA-beta satellites of begomoviruses. Arch Virol. 2008, 153(4), 763-81. doi: 10.1007/s00705-007-0013-6.

is somewhat out-of-date.

In the list of sequences used for the analysis there is a lot of duplication. All the reference sequences (sequences denoted as NC_XXXXXX) should be deleted. These duplicate the actual sequences – so for example NC_009088 is the same as EF373060.

In the introduction you say “For theses analyses, we considered all the available MYVMV and betasatellite isolates so far (isolated from mesta plants or associated with mesta plants) in GenBank including the new isolates from Bangladesh (from this study).”. This is said despite the fact that earlier you noted that a second species, Mesta yellow vein mosaic Bahraich virus, is associated with MYVMD and you then appear to include this under the name MYVMV. Mesta yellow vein mosaic Bahraich virus is far more closely related to Cotton leaf curl Multan virus and Bhendi yellow vein mosaic virus, two other malvaceous begomoviruses, than it is to MYVMV. Also you include isolates of MYVMV not “isolated from or associated with mesta plants” – MH628534 was isolated from sunflower. So, it seems that for inclusion in your analysis it is enough for the species to have been isolated from mesta, not necessarily the isolate. Unfortunately then, in selecting betasatellites for analysis, you go against this apparent rule. You ONLY include betasatelittes actually isolated from mesta, which happens to include at least five species - Kenaf leaf curl betasatellite, Cotton leaf curl Multan betasatellite, Mesta yellow vein mosaic betasatellite (previously known as “Ludwigia leaf distortion betasatellite”) Tomato leaf curl Joydebpur betasatellite, and Croton yellow vein mosaic betasatellite. However, unlike for the virus, you do not include isolates the NOT from mesta.

My question is – does this haphazard selection of sequences to analyse mean that the final results tell us anything useful?

Although there is no mention of it, the phylogenetic trees amount to species trees. The microsatellite analysis may be useful, once all the duplicate sequences are removed but can you take sequences from 5 betasatellite species and say anything useful about what is happening in mesta? Similarly, with 5 species and a small sample set, is there anything useful to be gained from a comparison of genetic diversity in Pakistan India and Bangladesh.<br /> One interesting finding overlooked by the authors is that all the betasatellites they sequenced are isolates of Cotton leaf curl Multan betasatellte. This gives strong support to the idea that, at least in Bangladesh, mesta yellow vein mosaic disease is caused by the complex Mesta yellow vein mosaic virus and Cotton leaf curl Multan betasatellte.

On 2022-08-30 11:26:14, user Nándor Lipták wrote:

Dear Authors,

Unfortunately, knockout lethal phenotypes are quite common in mice. We collected the most promising rescuing methods in a review last year, you may find it useful for your present preprint or future experiments:

10.33549/physiolres.934543<br /> https://www.ncbi.nlm.nih.go...

On 2022-11-03 15:59:25, user Donald R. Forsdyke wrote:

MEANING OF “POLYGENES”

The study of Xiong et al. “highlights that, in addition to incompatibility factors with large effects, genomically dispersed polygenes are also abundant in creating butterfly reproductive isolation” (1). Regarding polygenes, they cite a 1992 paper of Naveira (2), rather than his 1998 summary of work carried out with Maside (3). They appreciate my drawing this to their attention and have suggested that readers of their preprint paper would appreciate a formal comment. At issue is whether, when genomically dispersed, the term “polygenes” should be interpreted as “many genes,” or something else.

Pondering why incompatibility factors are so dispersed they write (1):

“One of our key findings is that many factors of perhaps individually small effects are widely dispersed across autosomes or on the Z chromosome. Consequently, average chromosomal ancestry is often more informative of phenotypes than any particular locus. This pattern is similar to the polygenic threshold model of hybrid incompatibility in Drosophila, where abnormal phenotypes depend more on the total quantity of introgression than where introgression occurs in the genome [41, 42, 43].”

Their first reference here “[41]” is to Naveira’s 1992 report on polygenic effects causing sterility in male fruit fly hybrids. However, they do not mention his subsequent detailed studies with Maside that were summarized in 1998 (3). These concluded that, rather than genes per se, “it might be only a question of foreign DNA amount.” Thus, “experiments on the nature of these polygenes suggest that the coding potential of their DNA may be irrelevant.”

The Naviera-Maside hypothesis was cited in a 2000 paper on Haldane's rule (4), but it has been largely overlooked in the literature. There appears to be no discussion of the hypothesis in the paper of Xiong et al. (1), even though the case for it has grown appreciably (5). It is also important in view of their paper's opening remarks on "the sex with the greatest fitness costs." Fitness usually implies genes. However, sterility and fitness do not necessarily go together. A mule is sterile but very fit. So being "fit" is subtle and it may be unwise to imply that a sterile organism is necessarily unfit, especially when that sterility is considered "largely phenomenological." Thus, it is good that Xiong et al. write of "incompatability factors" rather than of "incompatability genes" (1), which implies phenotypes.

(1) Xiong T, Tarikere S, Rosser N, Li X, Yago M, Mallet J (2022) Diverse genetic architectures on the Z chromosome underlie the two rules of speciation in Papilio butterfly hybrids. BioRxiv: doi.org/10.1101/2022.10.28.... (Oct 30)<br /> (2) Naveira HF (1992) Location of X-linked polygenic effects causing sterility in male hybrids of Drosophila simulans and D. mauritiana. Heredity 68, 211–217.<br /> (3) Naveira HF & Maside XR (1998) The genetics of hybrid male sterility in Drosophila. In: Endless Forms: Species and Speciation. (Howard, DJ & Berlocher, SH, eds.), pp. 330-338. Oxford: Oxford University Press.<br /> (4) Forsdyke DR (2000) Haldane's rule: hybrid sterility affects the heterogametic sex first because sexual differentiation is on the path to species differentiation. J. Theor. Biol. 204, 443-452.<br /> (5) Forsdyke DR (2022) Centenary of Haldane's "rule": why male sterility may be normal, not "idiopathic". J. Genetics 101 (1), 26.

On 2020-02-21 21:16:12, user Sharlene Flesher wrote:

I think these references would be great additions, especially with an eye towards looking at human cortex: <br /> https://www.frontiersin.org...<br /> https://www.nature.com/arti...

On 2016-06-20 09:38:50, user mark.steele@blueyonder.co.uk wrote:

If we go back in History the same flat earth resistance applied to Ionizing radiation, Asbestos, Tobacco, This is a step in the right direction of understanding the potential biological effects of Lower frequency parts of the EMF spectrum. The next time you see the neighbor's WIFI picked up by the antenna on you device, just remember its coming through the Brick Work.

On 2016-05-27 17:23:53, user steelnpearls wrote:

Please do not allow those sceptics to stop your studies on this radiation monster to the general public I beg you to ocntinue in spite of negativity from those who are not knowing how they can kill them with the radiation that is put into their brain. But also close to their bodies while still on to produce cancer in bones, breasts, throats, stomachs, gut, rectal, pancreus, colon, and most other for there is a 17 yr old teen with pelvic bone cancer for where he put the cell phone while at school and now is fighting the cancer in her pelvic bone on her right side.

On 2017-07-14 13:23:44, user Imre Gaspar wrote:

Btw, this ms has now appeared in RNA Journal a few days ago:<br /> http://rnajournal.cshlp.org...

On 2021-03-05 04:41:35, user Bonnie Aitken wrote:

Are there trials on the East Coast?

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

Great tool. Thx

On 2021-09-10 06:33:15, user Maju wrote:

Very interesting. Are we finally finding the Magyar genetic legacy here? Or is it rather a mix of Magyar and Gothic (and maybe other elements)?

The Kuline twins would seem, from dates and Catalan-like origin, to have (maybe) arrived there in the Peasant's Crusade (the Occitan Knight's Crusade went via Italy, not via Hungary). A curiosity ultimately but still worth mentioning.

On 2021-04-02 14:33:20, user Heran Darwin wrote:

This paper makes several overstatements about Mtb physiology. For example, narGH has not been shown to be required in vivo (the references they use have nothing to do with their statements), and the mechanisms of Cu toxicity to Mtb have not been determined. I hope the authors correct these statements rather than spread disinformation.

On 2019-06-26 21:33:46, user Charles Warden wrote:

It looks like there is a peer-reviewed version of this paper now available: https://journals.plos.org/p...

On 2024-03-22 13:47:09, user Georg wrote:

As for the identification of the so-called East Scandinavian cluster associated with the I1 Y haplogroup, conclusions about the Baltic source are premature - the isolated autosomal complex is characteristic of groups of hunter-gatherers found from the Mesolithic Iron Gates, Neolithic Northern Germany ostorf003 and possibly EastBaltic

On 2023-04-07 20:24:55, user Patrick Schwartz wrote:

Although not yet linked, the published version of this manuscript can be found at: PMID: 36912021 DOI: 10.1080/07420528.2023.2186122

On 2017-01-11 16:53:25, user yuping wrote:

I wonder is the enrichment score calculated the same way as in broad gsea?

On 2020-10-02 19:41:31, user David Ross wrote:

We received some feedback that this preprint contains too many ideas for one paper. So, we split the story into two parts. The first part, which includes a description of the measurement and a discussion of what the results can tell us about LacI allostery, is posted as a new preprint at https://www.biorxiv.org/con.... A subsequent manuscript will focus on the use of the results for precision engineering of genetic sensors.

On 2020-12-11 21:55:24, user Estela Area wrote:

Great work and excellent conclusions. The data presented in this article supports the role of APP in the regulation of cholesterol transport proposed by these authors. Highly relevant for Alzheimer's disease.

On 2024-10-01 20:16:33, user Beiyan Nan wrote:

This paper has been published in eLife https://elifesciences.org/articles/99273

On 2019-12-27 12:13:13, user Alexander Seifalian wrote:

This all very interesting, also one of the major problems are device cells interface, recently we have work on the interface with functionalised graphene oxide fibre and cells, it demonstrated that it is biocompatible and non-toxic, as well as it is conductive, our work based on conductive electrode for stimulation of brain to reduce tremor for patients who suffer from Parkinson disease.

On 2023-09-25 13:25:38, user Lander De Coninck wrote:

Hi,

Great manuscript! I completely agree that we need to sequence more individual mosquitoes, if we ever want to understand the complex relationships between insect-specific viruses, arboviruses and their hosts.

However, I have one big remark that I hope you consider to change in the published version of this manuscript. On line 175, you mention 393 mosquito-associated viruses and you define them as the 'core mosquito virome' for the rest of the manuscript. I feel that this term is too broad to describe just all your mosquito-associated viruses (some of them might be found in only one or very few individual mosquitoes). In general, a core microbiome can be defined as a set of taxa that consistently occur within a given habitat type or host (see https://journals.asm.org/doi/10.1128/msystems.01066-22 and https://www.pnas.org/doi/full/10.1073/pnas.2104429118). Also, Shi et al. (Microbiome, 2019), one of the first papers to describe a core virome in mosquitoes, describes the core mosquito virome as "a set of viruses found in the majority of individuals in a particular mosquito population".

Could you change the use of this term in your manuscript to avoid confusion for readers and have a consistent use of the term "core virome" in future research?

Kind regards,

Lander

On 2019-09-05 16:30:46, user Arlene wrote:

The paper mentions an assumed likelihood of the a^yt variant giving a fawn phenotype.

Those of us with tested a^yt/a^t dogs can attest to the phenotype of a^yt/a^t dogs being black and tan. I have alerted UCDavis, Paw Print Genetics, Vetgen, and Animal Genetics this is the case, and have been attempting to get the word out on this for a few years now.

These are all a^yt/a^t dogs by test. The owners have provided permission for these photos to be used in the public domain. These are all Tibetan Spaniels where the allele appears to have come in through a tight foundation, by pedigree assessment.

I have a collection of info on different dogs within different breeds where this allele has been identified. Please feel free to contact me for more info.

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

On 2020-03-31 02:52:06, user Sinai Immunol Review Project wrote:

Potent binding of 2019 novel coronavirus spike protein by a SARS coronavirus-specific human monoclonal antibody

Keywords<br /> Monoclonal antibody; Cross-reactivity; receptor binding domain

Summary<br /> Considering the relatively high identity of the receptor binding domain (RBD) of the spike proteins from 2019-nCoV and SARS-CoV (73%), this study aims to assess the cross-reactivity of several anti-SARS-CoV monoclonal antibodies with 2019-nCoV. The results showed that the SARS-CoV-specific antibody CR3022 can potently bind 2019-nCoV RBD.

Major findings<br /> The structure of the 2019-nCoV spike RBD and its conformation in complex with the receptor angiotensin-converting enzyme (ACE2) was modeled in silico and compared with the SARS-CoV RBD structure. The models predicted very similar RBD-ACE2 interactions for both viruses. The binding capacity of representative SARS-CoV-RBD specific monoclonal antibodies (m396, CR3014, and CR3022) to recombinant 2019-nCoV RBD was then investigated by ELISA and their binding kinetics studied using biolayer interferometry. The analysis showed that only CR3022 was able to bind 2019-nCoV RBD with high affinity (KD of 6.3 nM), however it did not interfere with ACE2 binding. Antibodies m396 and CR3014, which target the ACE2 binding site of SARS-CoV failed to bind 2019-nCoV spike protein.

Limitations<br /> The 2019-nCoV RBD largely differ from the SARS-CoV at the C-terminus residues, which drastically impact the cross-reactivity of antibodies described for other B beta-coronaviruses, including SARS-CoV. This study claims that CR3022 antibody could be a potential candidate for therapy. However, none of the antibodies assayed in this work showed cross-reactivity with the ACE2 binding site of 2019-nCoV, essential for the replication of this virus. Furthermore, neutralization assays with 2019-nCoV virus or pseudovirus were not performed. Although the use of neutralizing antibodies is an interesting approach, these results suggest that it is critical the development of novel monoclonal antibodies able to specifically bind 2019-nCoV spike protein.

Reviewed by D.L.O as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

On 2021-10-02 13:52:53, user Filippo Ferrario wrote:

The peer reviewed paper is now published. https://online.ucpress.edu/...

On 2014-07-16 17:15:25, user Jenna Morgan Lang wrote:

Has any effort been made to take a bioinformatic approach to deal with the taxa that are found in the negative controls?

On 2018-04-04 18:15:13, user Arlin Stoltzfus wrote:

@JasonDeKoning, you might want to read McCandlish and Stoltzfus (2014). This is (I'd like to think) the definitive review on the origin, development and uses of what we call origin-fixation models and what you are calling the weak mutation rate formulation. Citing this could save you some pages in your introduction (it also might make you reconsider calling origin-fixation models "classical", given that they only emerged in 1969 and are antithetical to the thinking of Fisher, Haldane and Wright). This paper, like yours, also tries to problematize origin-fixation models, arguing that they are used without regard to their problematic assumptions, and that we ought to be treating them as hypotheses rather than just tools. https://www.journals.uchica...

On 2021-02-18 01:58:33, user Simon Castillo wrote:

Might be good to see this https://onlinelibrary.wiley...

On 2021-12-10 16:52:55, user Alizée Malnoë wrote:

The manuscript by Ruiz-Sola et al. investigates the relationship between photoprotection responses, carbon concentrating mechanisms (CCM) and CO2 availability in Chlamydomonas reinhardtii. While photoprotection responses, mediated by LHCSR3, LHCSR1 and PSBS, are traditionally described as triggered by excess of light, this manuscript highlights the role of intracellular CO2 levels (both deriving from the environment and from mitochondria metabolism) in regulating these responses. Indeed, it demonstrated that photoprotection, and especially LHCSR3-mediated responses, are from one side inhibited in conditions in which inorganic carbon is largely available and abundant (acetate and external CO2 supply) and on the other side induced in conditions of reduced CO2 availability. Furthermore, CCM are also induced under high light (HL), in response to a drop in intracellular CO2 levels due to increased photosynthetic carbon fixation.

While changes in the expression levels of both LHCSR3 and CCM genes at different CO2 concentration and under HL respectively, were previously reported, this manuscript has the novelty to connect these observations in an elegant experimental set up with several genetic backgrounds to confirm and prove their hypothesis through the use of mutants affected in mitochondrial respiration and of metabolic modeling. The proposed model for light-independent regulation of photoprotection is convincing and solidly backed-up by data. In addition a role for CIA5 in positively regulating LHCSR3 (and to a lesser extent PSBS) mRNA expression and in negatively regulating LHCSR1 at the post-transcriptional level is shown.

However, we have some comments and suggestions to improve the manuscript, listed below.

Major comments <br /> Figure 3, and corresponding result paragraph pages 6 to 8:<br /> - A large part of the results (1.5 pages) focuses on modelling the interaction between acetate metabolism and intracellular CO2 levels. Although we are not experts in mathematical modeling and thus we are unable to give proper feedback regarding this part of the paper, we think it adds small value to the main results of the paper. This is especially true as the modelling relies on a number of assumptions (listed at the bottom of page 7) which are not supported by literature nor experimental data, weakening the solidity of its conclusions. As it is, only assumption iv (page 7, “the acetate uptake is low (...) for the mutants (as indicated in Fig 2C and F)” is backed up by data. <br /> We suggest moving figure 3 to Supplementary material and shorten its description in the results and discussion. Please also provide better support to justify the assumptions i to iii, as well as the assumption that photon uptake is not altered in the mutants (e.g. do they have similar chlorophyll content?) and make the conclusions more solid.<br /> - Page 6, “In line with the experimentally observed values, we found that the predicted generation times for the icl and dum11 strains (...) did not differ from those of LL grown WT cells”. Please, provide the experimental values for the mutant strains, or rephrase the sentence.

In Figure S1F to K: <br /> - During exposure to L2, the basal fluorescence Fo’ in the presence of acetate (and to a lesser extent CO2) is rising together with the maximal fluorescence Fm’. Please provide explanation or hypotheses for this fact, and if it might or not affect ETR and NPQ calculations. <br /> Also consider replacing “qE” with “fast-induced fluorescence quenching” or simply “NPQ”, as other regulation mechanisms might affect these fluorescence measurements.<br /> - Please precise the time points you used for assessment of Fo, Fm, and calculation of qE.<br /> To make this figure more understandable please provide clearer fluorescence traces in Figure S1 (C-K), showing only Fo, Fm and Fm' (ideally one plot for each genotype to be consistent with Y(II) and NPQ plots, L-N and O-Q) and a separate panel with Fo and Fo'.

Figure 6B and corresponding text page 11:<br /> - Please provide an explanation for the cia5 mutant line accumulating high LHCSR1 protein and not fully reverting to wild type level in the complementation line under VLCO2 (and dark/ air). This aspect needs to be taken into account and clarified, especially in light of CIA5 proposed role as LHCSR1 regulator at the post transcriptional level. Rephrase this sentence “However, LHCSR1 protein over-accumulated in the cia5 mutant under all conditions tested, although the WT phenotype was only partially restored in cia5-C (Fig. 6B)” as this the case only for HL/air.

Minor comments <br /> Title: Please add “algal” to the title, or a similar clarification.<br /> Introduction:<br /> - Page 3, when mentioning carbonic anhydrases (CAH) as part of the CCM please list the ones involved in CCM. Not all CAH are part of CCM (also it is useful to see their names, since the expression levels of some of them are measured in the results part). <br /> - Page 4, in the sentence "Here, using genetic, transcriptomic and mathematical modelling approaches, we demonstrate that the inhibition of LHCSR3 accumulation and CCM activity by acetate is at the level of transcription and a consequence of metabolically produced CO2" please replace "transcriptomic" with "expression analysis on selected genes", since no transcriptomics work has been shown in this manuscript. <br /> - Page 4, please reformulate the sentence "This work emphasizes the critical importance of intracellular CO2 levels in regulating LHCSR3 expression and how light mediated responses may be indirect and reflect changes in internal CO2 levels resulting from light intensity dependent, photosynthetic fixation of intracellular CO2". Based on the previous reports and from this work, we can say that internal CO2 levels are important in regulating activation and inhibition of LHCSR3-photoprotection mechanisms, BUT it does not mean that the light effect is indirect, this has not been proved yet. Furthermore, photoprotection by NPQ could lead to diminished CO2 fixation rate (especially sustained “photoinhibitory” quenching types), thereby increasing internal CO2 concentration which would according to your model repress photoprotective genes. This could be the case for genes involved in qE but may not be a general rule for “photoprotection”. The title could also reflect that aspect by specifying NPQ, qE in lieu of photoprotection.

qRT-PCR results:<br /> - qRT-PCR results are described here as "mRNA accumulation". Please replace this nomenclature with "relative expression levels" or "relative gene expression".<br /> - It is stated in the methods, page 17, that the results presented are normalized on a reference standard gene, GBLP. However, the results presented seem to be (also?) normalized on the WT LL air. Is this correct? If so, please precise or clarify it. Instead of normalizing the data to the WT LL air, we suggest normalizing the transcript abundance of the target genes in each sample to your internal reference standard gene (GBLP) only. <br /> - Please provide a description on how the relative gene expression levels were calculated. We suggest calculating by determining the ?Ct levels of the sample compared to the standard and the 2^(-?Ct) as final value.

Paragraph "LHCSR3 transcript accumulation is impacted by acetate metabolism": <br /> - page 4, it is not clear in here the transition between TAP and HSM media.<br /> - page 4, rest of the text and figures legends, please indicate CO2 concentration in ppm (according also to figure 6D) instead of 5% CO2.<br /> - icl-C line not behaving the same.

Paragraph "CO2 generated from acetate metabolism inhibits accumulation of LHCSR3 transcript and protein": <br /> - Page 5, “RHP1 (...) encodes a CO2 channel shown to be CO2 responsive and to accumulate in cells growing in a high CO2 atmosphere”. It is unclear here if RHP1 is sensitive to intracellular, extracellular, or both levels of CO2. Please better describe how the protein levels reflect the intracellular CO2 concentration.<br /> - Since Figure 1 includes results both described in this and in the previous paragraph, we suggest grouping the results described in Fig1 in a single paragraph and make a shorter but clearer description of the results.<br /> - Fig 1: you could merge Fig 1A and C in a single plot with WT icl, icl-C and dum 11 in LL and HL to make the comparison between the mutants clearer. Also, the same can be done for the panels B and D.

Paragraph “Impact of carbon availability in other qE effectors”<br /> - Page 8, "We took HL acclimated cells that typically accumulate both LHCSR3 and LHCSR1 proteins (Fig. S2A) and performed photosynthetic measurements in the absence or presence of 20 mM sodium bicarbonate; the bicarbonate addition was just before performing the photosynthetic measurements. As expected, bicarbonate enhanced rETR (Fig. S2B) and….almost completely suppressed qE despite the fact both LHCSR3 and LHCSR1 had accumulated in the cells (Fig. S2)". The accumulation of these proteins was not checked in presence of bicarbonate in this particular experiment (the bicarbonate was added shortly before measuring photosynthetic parameters). Please, rephrase the sentence.<br /> - Page 9 and Figure 4B and Figure 5C " PSBS protein accumulation could not be evaluated because it was not detectable under the experimental conditions used. " It is surprising you could not detect PSBS in these conditions (600 uE), while it was possible in the conditions described in Fig 6B. At least the HL conditions (600 uE) were the same in these two experiments. Please provide an explanation for this, or if it is not possible, rephrase without mentioning PSBS expression and accumulation in the text and for clarity reasons remove Fig4A. <br /> Paragraph “CCM1/CIA5 links HL and low CO2 responses”<br /> - Page 9, "To elucidate the molecular connection between photoprotection and CCM, we analyzed mRNA accumulation from the CCM genes encoding LCIB and LCIE (involved in CO2 uptake), HLA3, LCI1, CCP1,CCP2, LCIA, BST1 (Ci transporters), CAH1, CAH3, CAH4 (carbonic anhydrases) and the nuclear regulator LCR1, all previously shown to be strongly expressed under low CO2 conditions (see (49)for a review on the roles of each of these proteins and (45)for the more recently discovered BST1)." Please provide the whole name for the reported abbreviation of the proteins that were not mentioned earlier in the text.

Paragraph “Intracellular CO2 levels regulate photoprotective and CCM gene expression in the absence of light”<br /> - Page 11 and Figure 6C: the figure is unclear, making the quantification hard to pick up and understand. Please consider replacing the “LHCSR3 (r.u.)” line above the panel by a histogram clearly displaying the LHCSR3/ATPB ratio; add error bars. If no repeats/error are available, please refrain from using these quantification data and rephrase the paragraph page 11 to replace quantitative statements ("...which was reflected by a 3-fold change in the accumulation of the protein…", "and 21 fold (protein) compared to air dark conditions (Fig. 6A-C)...", "...and protein level (by a factor of~9)...") by qualitative ones.<br /> - Page 11, "This CIA5-independent regulation of mRNA in the presence of light could account for the contribution of light signaling in LHCSR3 gene expression, possibly via phototropin (10)" This should be discussed properly in the discussion section.<br /> - Page 11, “the cia5 mutant did not accumulate significant amounts of LHCSR3 protein under any of the conditions tested (Fig. 6B)” The lack of LHCSR3 in HL in the cia5 mutant is quite striking considering that its transcript level is quite high and similar to wild type. Please provide a possible explanation for this observation.<br /> - Page 12, please replace " in accord" with "in line" or "it fits the hypothesis" <br /> - Page 12, Fig 6E, for clarity, please develop the statement "In contrast to LHCSR3, sparging with VLCO2 only partly relieved the suppression of transcript accumulation for the CCM genes in the presence of DCMU (Fig. 6E)". For instance, consider adding “..., bringing it back to LL levels instead of the accumulation observed in HL in the control (see dotted line in Fig. 6E)”.

Discussion<br /> - Page 13, "Increased CO2 levels were found to dramatically repress LHCSR3 mRNA accumulation, in agreement with previously published works (34, 35), but had little impact on accumulation of LHCSR1and PSBS transcripts". It is hard to say if it has a little or no impact on PSBS gene expression. We suggest not putting emphasis on the PSBS expression levels difference.<br /> - Page 14, beginning of last paragraph, “Our data demonstrate that most of the light impact on LHCSR3 expression is indirect”. Please tone down these sentences and discuss them with regards to the recent study by Redekop et al. (ref. 46). We suggest replacing this sentence with "Our data demonstrate that besides LHCSR3 gene expression variation together with changes in the light environment, it is also tightly linked to CO2 intracellular changes”. <br /> - Page 14 "It is tempting to propose that CO2 could be considered as a retrograde signal for remote control of nuclear gene expression, integrating both mitochondrial and chloroplastic metabolic activities". This sentence is very speculative, although clearly marked as such. To further soften the point, please consider adding “Further studies will have to be carried on to confirm or infirm this possibility”. <br /> - Page 15 "The CIA5-independent light-dependent induction of photoprotective genes possibly involves phototropin, as previous shown (10), but may also involve retrograde signals such as reactive species (46, 77). Our findings also highlight the need to develop an integrated approach that examines the role of CO2 and light, with respect to CO2 fixation, photoreceptors, and redox conditions on the regulation of photoprotection and to consider photoprotection in a broader context that includes various processes involved in managing the use and consequences of absorbing excess excitation". If you want to discuss photoprotection relationships with photoperception etc you should give more context, otherwise it is not easy to catch for people who are not familiar with this possible connection. The data of this manuscript do not show any experiments related to photoperception, yet and it has been mentioned in four times in the paper. In our opinion this does not fit in the discussion of this manuscript.<br /> - Data S2A, please replace “reaction names” by “enzyme names”.<br /> - Figures S1C to K, Figure S2C, Figure S4A to C, it is stated that the fluorescence is normalized to Fm, when it seems to be normalized to the maximum fluorescence reached during the experiment (highest Fm’ point). Please correct either the figures or the legend.<br /> - Figure S2B, it is stated that the statistical analyses are shown in the graph, though they appear to be missing.

Maria Paola Puggioni and Aurélie Crepin  (Umeå University) - not prompted by a journal; this review was written within a preprint  journal club with input from group discussion including  Alizée Malnoë, Jingfang Hao, André Graça, Pierrick Bru, Jack Forsman.

On 2022-06-11 01:40:19, user KeninSydney wrote:

I may have misread the paper but shouldn’t one step have been to have two groups of mice without tumours and attempt to train the ants to select one of the groups?

Maybe ants can distinguish individual mice by urine?

On 2020-11-09 12:37:00, user michela riba wrote:

Hi, what about connecting to the QIIME2 community and propose a Kraken2 plug in to approach the part of feature classification?

I have had this suggestion in the forum<br /> https://forum.qiime2.org/t/...

I am using qiime1, qiime2 and Kraken in my experience

On 2023-05-30 03:25:59, user Francois GRANER wrote:

• I could not find the "Bibliography" section. References are called throughout the text but are not found at the end of the manuscript.

• It is repeteadly written that surfaces with a constant mean curvature are portions of a sphere. This is a basic mistake, see <br /> https://en.wikipedia.org/wi...<br /> https://en.wikipedia.org/wi...

On 2020-05-05 18:59:09, user Taekjip Ha wrote:

Replication stress causes replication forks to stall, which results in single end double strand breaks (DSBs). Double strand breaks can be repaired through homologous recombination (HR) or non-homologous end joining (NEHJ), however it is currently unclear how the final repair pathway is decided, what order the repair proteins are recruited to the DSB, and where along the DNA relative to the DSB the repair proteins bind. In vivo evaluation of these processes, particularly the spatial and temporal recruitment of repair proteins, has been limited due to the resolution constraints of fluorescence microscopy. In their manuscript titled “Super-resolution visualization of distinct stalled and broken replication fork structures”, Whelan et al. utilize multicolor single molecule super resolution microscopy, dSTORM, to gain understanding of seDSB repair pathways and recruitment of repair proteins. Using this technique, the authors are able to resolve individual stalled replication forks, induced DSBs, and recruited repair proteins from HR and NEHJ pathways. They demonstrate specific labeling of DSBs, and are able to characterize protein recruitment to stalled RF versus DSBs.

Topoisomerase I, Ku, and MRE11 all colocalize with DSBs induced by CPT treatment of cells. Interestingly, the authors find that Ku and MRE11 are both recruited to DSBs at approximately the same time however Ku dissociates within 1 hour whereas MRE11 remains associated to upwards of 4 hours. Evaluation of the positioning of Ku and MRE11 on the DNA relative to the DSB indicates that these two proteins are not interacting with each other, rather Ku is localized close to the DSB while MRE11 is further away from the DSB on the nascent DNA. In contrast, RAD51 and RAD52 colocalize to stalled replication forks but not DSBs. Upon conversion of stalled replication forks to DSBs (through Veliparib PARP inhibition), Ku and MRE11 colocalization to the DNA increases. These results identify two distinct groups of first responder proteins that are mutually exclusive, with one group recruited to stalled replication forks (RAD52, RAD51, and RECQ1) and the other group recruited to seDSBs (MRE11, Ku).

Major Issues<br /> 1. It would be helpful, particularly for readers not in this field, if a cartoon schematic comparing HR and NEHJ and the proteins from each pathway that are discussed in this paper was included in the introduction.<br /> 2. In multiple results figures (1A, 1E, 3A, 3B, 4A), control samples have non-random (greater than 1) colocalization values. What explains this occurrence? Is there an alternate method of analyzing or portraying the data to clarify this (or text explanation)? <br /> 3. Is it possible that the regressed strand of DNA can bind TUNEL? Recognizing that TUNEL labeling is relatively broad, is there another detection method to more specifically identify seDSBs? <br /> 4. Figure 4: The authors conclude that RAD51 and RAD52 both are first responders to stalled replication forks, however they only include images demonstrating colocalization of RAD51 with DSB, Ku, MRE11, and RECQ1.

Minor Issues<br /> 1. In the introduction, the authors abbreviate single-end double strand breaks as “seDSBs” however throughout the rest of the paper “DSBs” is used, which is a bit confusing. If the authors are indeed talking about the same type of double strand break, it would be beneficial to have consistent abbreviations throughout the paper.<br /> 2. Figure 3 A,B and Figure S4: Treatment time for cells differ depending on drug used (NCS for 1hr, HU for 4 hours). However, the control is exposed to the drug solvent for one time point. Does exposure of control cells to the drug solvent induce different results based on time of exposure?<br /> 3. Page 19, second paragraph: colocalization percentages of Ku and MRE11 in the text do not match the corresponding figure (Fig. 3 D, E) <br /> 4. This work utilizes U-2 OS cells, which is a cancer cell line. a cancer cell line for this work. How do the results compare to RF stalling, DSB formation and repair in a non-cancerous cell line?<br /> 5. Figure 5B demonstrates increased colocalization of DSB, Ku, and MRE11 with naDNA upon Veliparib treatment after CPT. It would be interesting to evaluate how colocalization of RAD51 and RAD52 with naDNA changes with these treatments. The model proposed in panel C indicates that these proteins dissociate upon double strand break formation, which would be strengthened by the inclusion of a similar experiment in B but for RAD51/RAD52.

On 2024-11-23 00:09:48, user Lee Bardwell wrote:

Nice work! Are the supplementary tables available?

On 2017-04-25 09:45:12, user Christoph Nowak wrote:

Title... Alzhiemer's Model...

On 2021-03-18 12:37:22, user C. Gilbert wrote:

I can't find the supplemental tables? Are they available?

On 2018-11-06 09:33:43, user dalloliogm wrote:

The methods presented in the paper are very interesting. However there is still something I don't fully understand yet. The authors suggest to use independent covariates when adjusting for FDR. For example, in a eQTL study you may want to give more importance to the SNPs closer to the target gene, as these are more likely to be real associations. However, what is the advantage of including these weights at the FDR level, instead of covariates during the previous analysis? Shouldn't a FDR correction be agnostic to any hypothesis?

On 2019-04-07 13:55:08, user Ankita Jha wrote:

It is interesting. Under regulation of Tc-fog and Tc-mist you mention like Drosophila,ventral tissue specification is under Toll signaling. Is it really known? Ventral specification in Drosophila is under Twist Snail regulation but Toll signaling controls Ectoderm specification under Eve-Runt control. Role of fog in blastoderm formation is widely conserved, Even in Drosophila early dsRNA (high conc) injections lead to severe defects.

On 2018-12-06 22:50:20, user Haloom wrote:

Thanks for posting this! I am wondering if the supplementary information is available any where?

On 2021-09-09 18:15:11, user Tania Gonzalez wrote:

The final peer-reviewed version was published in Epigenomics volume 13, no. 13: https://www.futuremedicine.... with https://doi.org/10.2217/epi.... Request it for free on ResearchGate if you don't have access to the journal.

On 2022-11-11 10:20:50, user Giorgio Bergamini wrote:

Great paper, and very interesting tool! The Github link does not look working on my side. Do you know when it might become available? Thanks

On 2017-10-13 19:24:41, user David Lowry wrote:

An error was detected by a reviewer of the manuscript for Figures 2A and 2B. The green dots should have been labelled as trans. We are currently redoing the analyses and will post a new preprint once those are completed.

On 2020-03-06 13:28:08, user Krzysztof Pyrc wrote:

check out our paper on APOBEC and coronaviruses: https://www.ncbi.nlm.nih.go...

On 2025-09-24 13:20:02, user Andries Peeters wrote:

Could it be that the reference given for the example of coral reef restoration (van Elsas et al., 2012) is not correct?

On 2017-12-21 03:03:51, user BenjaminSchwessinger wrote:

I love this work. It is such a fundamental biological<br /> question of how an obligate biotrophic fungus infects two highly distinct plant<br /> species. Some real fascinating biology.

Here are some thoughts and comments on this<br /> manuscript:

· Some active voice in the<br /> abstract would make it more accessible.

· Line numbers would have been<br /> great.

Intro:

· ‘is qualified of macrocyclic’<br /> should read ‘as’ not ‘of’

· the citation for Germain et<br /> al., 2017 needs to be corrected

MM:

· This also refers to the<br /> results. It would be great to see how many of the RNAseq reads did not map to<br /> the reference gene models. Could you identify novel genes that were previously<br /> missed from the annotation as the adequate expression data for both hosts were<br /> missing? Is this novel RNAseq data being incorporated to new rounds of<br /> annotations? If you had poplar RNAseq data if would be great to compare the RNAseq<br /> mapping rate overlapping with gene models poplar vs. larch.

· PLEASE deposit all analysis scripts<br /> on github and NOT on demand. This would be great for people that want to<br /> compare RNAseq and microarray data. I really liked your quantile comparison<br /> approach. Scripts need not be perfect. Every little helps!

· do I understand correctly that<br /> you only included genes in your diff analysis that were expressed both in<br /> RNAseq and microarray analysis?

Results/Discussion:

· for the KOG enrichment analysis,<br /> it would be nice to show how many genes miss any annotation. I guess this will<br /> be around 50%. This reverse to the KOG analysis in ‘Secreted proteins is the<br /> only overrepresented category among DEGs detected on larch’. I see that these<br /> are mentioned later on.

· I was wondering if the increase<br /> in specifically expressed SP genes on larch vs. poplar (Figure 6 B) could be an<br /> artefact of the micro-array vs. RNAseq analysis. Were all these larch specific<br /> genes expressed in the microarray at all? It would be much more convincing and<br /> reassuring to see some qRT-PCR analysis of the differentially expressed SP in<br /> poplar vs. larch. Using the identical technique would very much strengthen the<br /> argument made.

· in addition to the SSP gene<br /> family expression analysis (which could be really alleles of each other as<br /> well) did you observe any allele specific expression using SNPs as markers.<br /> E.g. only one of the SNPs is expressed in one host vs. the other.

· One important consideration for<br /> comparing acieal vs telial phases of rusts is that in the acieal phase rusts<br /> are mono-karyotic haploytes. Hence all genes required for this life-phase need<br /> to be allelic aka have two copies in the diploid phase. In future, when fully<br /> phase Mlp genomes are available, it would be interesting to see if some of the<br /> poplar specific SSP are singletons with a corresponding allele in one haploid<br /> genome.

On 2025-06-23 14:59:42, user Jiayi Liu wrote:

Hi. This paper is published: https://academic.oup.com/nar/article/52/4/1613/7595399

On 2018-01-03 20:24:55, user ScottTriGuy wrote:

Interesting, but perhaps not surprising.

Curious: do research applications that focus on diseases that predominantly affect women (eg autoimmune disorders) receiving fewer approvals?

On 2018-09-19 12:49:12, user Xian Liu wrote:

I tried Cancer3D, but it did not work. It returned '500 - Internal Server Error Internal Server Error'.

On 2020-02-11 19:13:42, user Humberto Debat wrote:

Following WHO Director-General briefing on 2019-nCoV on 11 February 2020 available at https://www.who.int/dg/spee... authors may update figure 5B lower right panel to include: Corona Virus Disease (COVID-19)

On 2020-04-22 23:39:51, user Michael wrote:

The paper says it has supplemental videos. I don't see how to access them. Am I overlooking something obvious? Would greatly appreciate being pointed to a URL for these.<br /> Thank you!

On 2020-11-22 12:07:23, user Alejandro Bermejo wrote:

In this article (https://www.gavinpublishers... "https://www.gavinpublishers.com/articles/case-report/Annals-of-Case-Reports/lighting-up-dark-areas-of-covid-19)"), CD147 was proposed as a receptor but using protein N and cyclophilin A for COVID-19.

On 2020-05-11 13:24:30, user Liz Miller wrote:

This preprint was the subject of the weekly Miller lab lockdown journal club. Although some of the approaches are not within our expertise we enjoyed reading this manuscript and it highlighted nicely the importance of analysing the system of interest in a physiological setting.

The study applies a combination of in vitro and in vivo approaches to address the differential contribution of clathrin light chain (CLC) neuronal splice variants to vesicular trafficking and their impact on functionality in vivo. The authors assessed the biophysical behaviour and budding abilities of clathrin cages formed with different light chain isoforms. Then these observations were compared to electrophysiological measurements in CLCa and CLCb KO mice. The in vivo analysis is consistent with the biophysical measurements but also reveals an unexpected difference between the two isoforms. Beautiful electron microscopy supported both the in vitro and in vivo observations.

Following our group discussion we offer some brief comments:

· We suggest that for the benefit of a broader audience, addition of a diagram showing the domain structure of clathrin light chain a and b isoforms similar to that from Brodsky (2012). This would also complement the model in Figure 6 with regard to the discussion of the potential contribution of the neuronal CLCa/b to the interface with clathrin heavy chain.

· The model testing shown in Figures 3e-i seemed like a bit of a diversion. On further discussion, we wondered if the important point here was that although the different isoforms seem to form morphologically different lattices (Figs. 1 and 2), they behaved similarly to each other with respect to membrane angles at buds. This has implications for the mode of clathrin coat assembly and membrane deformation, but as non-experts in this area, some of the nuance was lost on us.

· Not being mouse researchers, we were (perhaps naively) surprised by the large differences in readings for the WT littermates of the two groups (Figure 4). However, the clear difference between the CLCa and CLCb knockouts in the size of readily releasable vesicle pools (as indicated both by the electrophysiology and EM experiments) was striking. A more expansive description of the authors’ opinion on the potential mechanism responsible for the difference in the readily releasable vesicular pool with regard to the preferential interaction of CLCa with actin would have been helpful to us.

· The data in Figure 5c suggested a difference in fusion, whereas in Figure 4 no difference in fusion was detected. Again, we were not particularly qualified to interpret the electrophysiology, so a more detailed explanation of the difference between these two assays and the significance of their outcomes would be helpful.

We appreciate you sharing your work on BioRXiv.

Reference: Brodsky, F. M. (2012). Diversity of clathrin function: new tricks for an old protein. Annual review of cell and developmental biology, 28, 309-336.

On 2013-11-21 09:49:57, user Kristian wrote:

Figure 4 is difficult to visually interpret because it uses a 3D projection. I would just use a simple 2D plot a la Figure 2.

On 2023-10-31 11:39:45, user Graham wrote:

Published version available here:<br /> https://www.nature.com/arti...

On 2023-01-30 04:41:07, user Daniel Ferris wrote:

Nice paper, good work. There are some spelling errors in the draft. Also, you might want to take a look at: Kuo AD (2002) The relative roles of feedforward and feedback in the control of rhythmic movements. Motor Control 6, 129–145. There is some overlap in the discussion points from your study and the Kuo, 2002, work.

On 2022-09-14 14:47:30, user Grimm wrote:

It's not the topic of the paper but I'd love to see the STRUCTURE results for k < 6. Your reference data are another mosaic stone in revising the species concept of western Eurasian beeches.

Also, I wonder, given the fit of your reference data (Fig. 1) with studies that focussed on western Eurasian beeches and the underestimating current species concept (cited Denk 1999a,b; Gömöry & Paule 2010; Cardoni et al. 2022), whether one should still use "Oriental beech" in the singular. I'd write "Oriental beeches" to stress the fact it's more than one biological entity. Especially given that you can identify introduced hybrids between Greater Caucasus Oriental beeches (i.e. F. orientalis s.str.) and European beech (F. sylvatica s.str.) and not just between Oriental (F. sylvatica subsp. orientalis) and European beech (F. sylvatica subsp. sylvatica); and in the light of of this being indeed a showcase for the genetic diversity in the Oriental beeches, which, indeed, surpass that in the European beech, in all data assembled so far and highlighted in this study.

Regarding the aspect of level of diversity (especially with regard to F. sylvatica and detection-capacity for [introduced] hybrids), adding the STRUCTURE profiles with k < 6 (it says, you run with k = [1,10]) as supplementary information could be an additional selling point for highlighting the different Oriental beeches as not a single but several, possibly also climatically non-identical, and differentially related to the European beech, genetic resources for the European beech forests and with respect to climate change and AGF potential (which may differ between the Oriental beech spp.)

On 2025-07-09 15:55:47, user Michael Ailion wrote:

This paper examines the evolution of X chromosome dosage compensation in nematodes. There are several interesting findings. The condensin-mediated dosage compensation complex is shown to have evolved recently in the Caenorhabditis lineage. In addition, a different duplication of SMC-4 occurred in two Pristionchus species, suggesting that Pristionchus independently evolved a similar condensin-mediated mechanism of dosage compensation (“parallel evolution”). Caenorhabditis and Pristionchus share other signatures of condensin-mediated dosage compensation, namely X-specific topologically-associating domains (TADs) and enrichment of the H4K20me1 chromatin mark on the X chromosome. The data supporting these conclusions are strong and the underlying experiments are rigorous. Additional interesting observations are made regarding dosage compensation mechanisms in two other nematode lineages, Oscheius and Steinernema. Oscheius is found to lack X-specific TADs, but has enrichment of H4K20me1, while Steinernema lacks both X-specific TADs and H4K20me1 enrichment. These results suggest that condensin-mediated dosage compensation may have evolved in the presence of an existing mechanism that involves H4K20me1. Additionally, Oscheius is shown to have X chromosome dosage compensation, but in Steinernema, dosage compensation is found to be incomplete. Unlike the results for Caenorhabditis and Pristionchus, some of the data for Oscheius and Steinernema are not as clear-cut and thus, the support for some of the conclusions is not as strong. There are perhaps a few places where the caveats could be pointed out and the conclusions toned down, but for the most part, I think the authors are appropriately cautious in interpreting their data. Overall, this is an interesting study that raises further interesting questions about how dosage compensation mechanisms evolve and are constrained. I have only relatively minor comments.

1. TADs. Hi-C data shown in Fig 2B are used to infer whether X-chromosome TADs are present. The data are quite clear for P. pacificus (TADs), C. elegans (TADs), and O. tipulae (no TADs). C. remanei is also scored as having X-specific TADs, though the TAD features are less obvious. It is argued that this is because the C. remanei data come from mixed-stage and mixed-sex worms (published elsewhere), so features specific to female X chromosomes in somatic cells would be diluted out. This is a reasonable argument. Also, in Fig 2B, Steinernema hermaphroditum is inferred not to have X-specific TADs. But to my eye (admittedly untrained for Hi-C data), the S. hermaphroditum pattern looks very similar to C. remanei. And the S. hermaphroditum data (also published elsewhere) has the same caveat as C. remanei of coming from mixed-stage animals that makes the TAD features less obvious. Additionally, the S. hermaphroditum data were obtained using a different Hi-C method that is restriction enzyme-independent. So it is unclear how it is conclusively determined that C. remanei has TADs and S. hermaphroditum doesn’t, especially given that the data come from different labs using different methods. Further explanation of the criteria used to score these data and make these conclusions would be useful.

2. Dosage compensation. Data show that all species analyzed except Steinernema carpocapsae have clear dosage compensation. S. carcocapsae data are somewhat less clear. Fig 4E shows that for two X-linked scaffolds, expression in females is higher than males, indicating a lack of complete dosage compensation. However, the difference is less than two-fold, suggesting that there is some dosage compensation. Also, of the two-scaffolds, the difference is only statistically significant for one of the two (Fig S11), even though both are X-linked, weakening the conclusion that this species lacks dosage compensation. Is it possible that the lack of full dosage compensation could be due to contamination of the data with more germline expressed genes?

3. The authors suggest the interesting possibility that the lack of complete dosage-compensation in Steinernema may be due to a couple of autosome to sex chromosome fusions in this species. In this case, one might expect full dosage compensation in some parts of the chromosome, and no dosage compensation in others. Can the data be analyzed to determine if dosage compensation varies in different regions of the Steinernema X chromosome, especially corresponding to different nigon elements? Or is there partial dosage compensation all along the X?

4. H4K20me1. Based on the data in Fig 5, the authors conclude that like C. elegans, P. pacificus and O. tipulae have H4K20me1 enrichment on the X chromosome, but S. hermaphroditum does not. The data for C. elegans and S. hermaphroditum are quite clear. However, there appears to be less H4K20me1 enrichment in P. pacificus and O. tipulae in the chromosome-wide data shown in Fig 5A. In both these species, there appears to be a higher level of H4K20me1 on the representative autosome, and to my eye, O. tipulae does not even show enrichment on the X. Is it possible to quantify levels of enrichment between species on autosomes and the X? It would be nice to see the ChIP-Seq tracks for all autosomes in a supplemental figure, not just the single representative autosomes shown in Fig 5A. To their credit, the authors recognize that X enrichment of H4K20me1 is weaker in O. tipulae and do statistics to back up their claim, but the statistics seem to be done only on H4K20me1 enrichment in gene bodies. Is there a difference between enrichment on the whole chromosome versus gene bodies in O. tipulae?

5. In the discussion, the authors suggest based on parsimony that dosage compensation was present in the common ancestor of the different nematode groups, but lost in the lineage leading to Steinernema. Though I agree this is the most likely scenario, isn’t it equally parsimonious to say that the common ancestor lacked dosage compensation, but that it evolved separately in Brugia (i.e. two gains of dosage compensation in nematodes instead of one gain and one loss)? So should it be stated that the authors’ model is the most parsimonious?

Reviewed (and signed) by Michael Ailion

On 2021-05-04 16:32:32, user Lucas Graybuck wrote:

A peer reviewed and updated version of this article is available with Open Access in eLife: https://elifesciences.org/a...

On 2020-06-04 00:10:15, user klim wrote:

This is amazing and really impressive work! Why is the cold FEG energy spread asymmetric?

On 2021-04-01 17:11:39, user Alexander Kozik wrote:

This is very interesting and important discovery of new types of repeats in mitochondrial genomes. There are at least two parallel observations of similar tandems in other species:

Lin et al. Comparative analysis reveals the expansion of mitochondrial DNA control region containing unusually high G-C tandem repeat arrays in Nasonia vitripennis. Int J Biol Macromol. 2021 Jan 1;166:1246-1257.<br /> https://pubmed.ncbi.nlm.nih...

Kinkar et al. Nanopore Sequencing Resolves Elusive Long Tandem-Repeat Regions in Mitochondrial Genomes. Int J Mol Sci. 2021 Feb 11;22(4):1811.<br /> https://pubmed.ncbi.nlm.nih...

In my opinion these tandem repeats are very different from those found in plants. Discussion about plant-like features of the quagga mussel mitogenome and recombination might be misleading.

On 2019-08-11 21:07:59, user KellerAsaf wrote:

Very exciting findings. I hope you find these comments useful:

• It would be more appropriate to treat sex as a variable, and to frame the (statistical) hypothesis such that it compares treatment AND sex. Analyzing males and females separately does not test your hypothesis about sex differences.
• Ditto for analyses of different concentrations of morphine.
• Did you test for signs of somatic withdrawal?
• Would be useful to report effect sizes.

On 2021-05-28 18:28:26, user morgandjackson wrote:

Hi, I was keyed onto your recent in-press paper (Molecular Biology & Evolution) by a colleague and figured it would be prudent to leave a comment here regarding the status of your new species "Bombus incognitus". I'm not sure whether the intent was to describe "B. incognitus" as a new species or whether you're just using "Bombus incognitus" as a placeholder name for communication purposes as you continue to unravel the evolutionary patterns within the populations, but as it currently stands the name "Bombus incognitus" does not meet the requirements of the International Commission of Zoological Nomenclature (ICZN) and is thus not considered a valid species or available name yet. If the intent is to formally describe your cryptic population as a putative new species, I would recommend you become familiar with the requirements for doing so as outlined by the ICZN in The Code, particularly chapters 3 & 4 which detail the steps and requirements necessary to publish a new name that can be recognized officially. It may seem inane and inconsequential, but there remains the possibility that the organism you're referring to could be misconstrued by others or the name you've proposed replaced with something else entirely by someone else, creating a web of complications for future studies involving the taxa in question. Good luck.

On 2018-03-27 19:31:48, user Lukas Fiederer wrote:

Thanks for updating!<br /> On top of the conference paper we now have a preprint https://doi.org/10.1101/282848 describing our online deep conv net BCI.<br /> I'd love to get feedback and a citation.

On 2020-01-16 19:55:30, user Tom Radivoyevitch wrote:

In Figure S4, patient 15, did the TKI start at month 0 or did it start at month ~40?

On 2019-08-20 03:17:56, user Roger wrote:

http://genome.grid.wayne.edu:9091<br /> or <br /> http://piquelab.grid.wayne.edu:9091<br /> should work, but the shinyApp may take sometime to load, unless there is a problem with multiple people trying to connect with the same port. I will try to deploy it differently if it still gives problems.

On 2023-07-18 17:14:52, user Madeleine Rostad wrote:

July 13th-15th at the 37th Annual Symposium of The Protein Society, ASAPbio conducted a series of 20 minute “Live Preprint Q&A” sessions. The following is a summary of our conversation with the authors of this preprint.

The discussion with Dr. Joey Davis and Barrett Powell focused on their research on learning structural heterogeneity from cryo-electron sub-tomograms with tomoDRGN. The key point of their preprint is the untapped potential in cryoET datasets for understanding the spatial distribution of different complexes and conformations in the cell. They describe a different approach using machine learning methods, with the goal of putting things in a cellular context.

The researchers faced challenges in exploring how portable their original cryoDRGN framework is to Electron Tomography (ET) data. While some elements worked well, such as the decoder network, encoding the raw data was difficult due to the shift from one particle being one image to having multiple images of a particle.

Their favorite figure, Figure 6, demonstrates the potential of tomography to generate high-resolution, individualized pictures. They feel that this figure in particular highlights the promise of visualizing how a complex, such as the ribosome, may adopt different structures and interact with distinct cofactors depending on its subcellular localization.

The researchers are excited about the possibility of discovering new biology through their paper and are eager to continue hearing feedback on use cases, particularly in cases where tomoDRGN has helped users uncover structural heterogeneity that helps them better understand how their complexes of interest work. They also expressed some concern about potential confusion regarding the generative model and the process of classification.

Looking forward, they plan to explore the relative species of ribosomes and correlations between complexes. They also mentioned how many emerging ideas in the field are indicating paths to computational shortcuts for processing the tilt-image-series data directly without explicitly generating tomograms - success in this area could be a game-changer in the field.

This preprint presents a novel approach to understanding structural heterogeneity using cryo-electron sub-tomograms and machine learning. The community is encouraged to provide feedback!

On 2021-07-05 11:44:19, user Anthony Mathelier wrote:

The manuscript has now been peer-reviewed and is up at https://bmcgenomics.biomedc...

On 2019-05-15 12:38:52, user Lionel Barnett wrote:

Well, that's embarrassing! Seems I'd misread eq. 8, and assumed that minimising the generalised variance for the disconnected model was equivalent to minimising the trace -- and therefore delivering the same "constrained Yule-Walker equations" as the latter operation. Well, at least my analysis was correct - given the false premise :-/

Thanks to Masafumi Oizumi (private communication) for clearing that up for me - and congratulations to the authors on an ambitious and cohesive piece of work.

On 2023-10-27 15:59:31, user Ashraya Ravikumar wrote:

In this manuscript the authors have tested the hypothesis that the MSA constructed by AlphaFold2 (AF2) contains information about the distribution of different conformational states of a protein. Whereas the current way of thinking about AF2’s MSA-predicted C?–C? distance maps focuses on their power to provide binary classifications of inter-residue contacts, the authors propose that C?–C? distances should instead be thought of as a set of collective variables that approximate a Boltzmann distribution. This is a novel hypothesis that lends AF2 the ability to decipher the conformational Boltzmann distributions of proteins. The authors test this in the contexts of protein dynamics, mutation impacts, and protein-protein interactions. They start with analyzing the correlation between AF2 contact distance and spin label distance distributions obtained from EPR spectroscopy using T4 lysozyme as a model, finding a general agreement despite broader AF2 distributions. Following this, they explore if AF2 can approximate free energy changes in systems that contain multiple biologically important minima, using EGFR KD studies for this purpose. AF2 accurately identifies altered contact distance distributions corresponding to active or inactive conformations in several mutations, indicating a sensitivity to alterations that stabilize particular conformational states. Next, they assess sensitivity to thermodynamically destabilizing mutations. AF2 was able to predict different contact distance probabilities for disruptive mutations like L198R in UBA1, but was less sensitive for milder mutations like L198A. Lastly, AF2’s sensitivity to protein-protein interactions was explored using the u-opioid receptor (uOR). Although the helix displacement distances observed in the predicted structure of isolated and complexed uOR do not exactly match with expected values, AF2 did successfully predict differences in select contact distance distributions of active/inactive-state uOR. Demonstrating that C?–C? distance probabilities from the same AF2-learned distribution reflect distances observed in differentially behaving domains of a protein lends strong support to the hypothesis that AF2 contact distance distributions can approximate conformational distributions.

The manuscript explores the correlations and sensitivities of AF2 predicted C?–C? distances across a variety of protein contexts, giving a broad view of its capabilities and limitations. Transitions between the various sections flowed well, and overall the writing was well worded and easily comprehensible. In addition, the presentation was balanced. It doesn’t just focus on the success of AF2, but also highlights where its sensitivities might vary or fall short, providing a balanced view of its capabilities. Given limited computational resources, the conformational space explored by MD and MCMC simulations is limited by their initial states. AI methods are instead limited by how informative their system definitions (MSAs and pre-set theoretical or experimental contact distance distributions) are, allowing AI methods, such as the AF2 method outlined by the authors, to more effectively sample conformational space. This is a very fascinating implication of their work which the authors have briefly mentioned in the discussion. This (and the connection to Figure 7 in the paper) warrants a deeper discussion, but the main conclusions the authors come to are within the scope of the manuscript, and are backed up by the evidence presented.

There are a few points we would like to bring to the attention of the authors to strengthen the manuscript further.

Major points:

1.There are some difficulties interpreting Figure 2. <br /> (a) It is important to mark the distances between the two chosen pairs of atoms in the active and inactive state. Without this information, the purpose of Figure 2D is unclear and Figure 2D, F and G are difficult to understand. <br /> (b) What is the threshold distance to classify a state as active or inactive?<br /> (c) Figure 2E seems confusing with different axis and ranges.<br /> 2. In case of DDR1, does the MD simulations reflect the peak distances (between 7.5 and 10.0 A for DFG-in and between 16.0 and 18.0 A for DFG-out) observed for AF2 distance distributions? Also, the probability distribution shift towards shorter distances for Y755A does not seem particularly strong at first glance. Is this why the double alanine mutant was included? Are there also MD simulations of the double mutant that show a reduced preference for the DFG-out conformation?<br /> 3. The overall results on EGFR mutants are striking. Many of these mutants (most notably L858R have structures deposited in the PDB (ID:2ITT and many others) that are potentially part of the overall training of AF2/OpenFold. Can you comment on how this might affect the results?

Minor Points:

1. There is some ambiguity in the statement, “The central hypothesis of this manuscript is that the collective contact distance distributions predicted by AF2 contain relevant information that can approximate Boltzmann distributions provided the relevant conformational states can be adequately described by these contact distances.” We suggest adding to this such that a stronger connection is formed between the theory section and the remainder of the paper. For example, the authors could explain that the contact distances specified in each section are the set of CVs you describe earlier, “we identify a set of CVs, ? = (?1, ?2, …, ?m)...”. It would also be helpful to clarify that the distributions predicted by AF2 represent the ensemble averaged observable, as described by equation 4. Lastly, the authors mention that these distributions can approximate Boltzmann distributions, but this is somewhat vague. This could be reworded to say that AF2 distributions can approximate experimentally derived Boltzmann distributions of the same distance.
2. The authors are comparing C?–C? distances determined by AF2 to spin label distances from EPR. This is explained in the methods section, but the procedure for adjusting the spin label distances to facilitate a meaningful comparison between them and the AF2 distances is somewhat unclear. To make a stronger justification for why these are comparable, the authors could clarify the procedure. For example, some context from the authors’ previous paper, De Novo High-Resolution Protein Structure Determination from Sparse Spin labeling EPR Data: “[distance from spin label] dSL is a starting point for the upper estimate of dC?, and subtracting the effective distance of 6Å twice from dSL gives a starting point for the lower estimate of dC?” could be beneficial. Including a rank correlation coefficient, as hinted above, could also help emphasize that the results demonstrate “similar relative probabilities among the contact distances for AF2 and EPR”
3. In the comparison of distance distributions between AF2 predictions and EPR measures, the major peaks of the two distributions are similar but in certain cases (127CB - 154CB, 120CB - 131CB), some additional peaks are found beyond 10A. A statistical comparison of the distributions, perhaps using a KS test, will help in evaluating the significance of the similarities.
4. Typo in Hamiltonian Equation 1 (should be momentum squared)
5. In the T4 Lysozyme example, how were the six contacts between the 12 unique residues found?
6. In Figure 5, the fourth row could have more discussion/explanation. What does the colorbar represent? There is no label.

7. As mentioned earlier, the connection between the Discussion and Figure 7 is not well established. The authors could expand on their writing and/or make the figure more simplified to match the discussion better.

8. Jessica Flowers, Angelica Lam, Ashraya Ravikumar, James Fraser

On 2023-12-07 15:11:52, user Baudino wrote:

Dear authors,<br /> We published in Science in 2015 a non canonical cytoplasmic pathway for geraniol biosynthesis in rose. In 2023 we showed that it was supported by a special G/FPPS and that IPP/DMAPP was provided by the MEP pathway. So you cannot say that "MEP pathway usually takes central stage for monoterpene biosynthesis under most scenarios" and it would relevant to cite our work :<br /> Magnard et al Science 2015<br /> Conart et al PNAS 2023

Sincerely yours

On 2024-03-10 08:35:38, user Dmitrii Kriukov wrote:

Thank you for the interesting reading! I have following comments/questions:

Major:<br /> - Definitely the current state of the research suffers from insufficient validation. Please, reproduce your analysis on (Thompson, 2018); (Meer, 2018) datasets as well as other single-cell hepatocytes datasets like (Gravina, 2016.)<br /> - It is not theoretically clear how the exponent in PC-1 component is related to the one in Gompertz law. Provide more theoretical explanation as one, for example, was proposed in (Vural, 2014, Phys. Rev.)<br /> - The exponential fit to PC1 scores seems to be unreliable because I expect a large confidence interval for this parameter due to the small number of data points. Please, add confidence interval for the parameter. <br /> - I also recommend to compare exponential fit with other model families like parabolic or sin or others. AIC criterion could be used here for model comparison.<br /> - "Such a pattern of exponential growth in both mean and variance is indicative of stochastic instability of the organism state..." - this is the key phrase I saw in multiple papers from your group. I assume using this statement you implicitly refer readers to the Wiener process property of increase variance linearly with time. But I do not know which well-known process has exponential increase in variance. Could you please elaborate this explanation more in the text by adding the necessary literature references?<br /> - In your previous paper (Aging clocks, entropy and limits of age reversal) you obtained linear relation for human blood PC1 scores, no relation for PC2 scores and hyperbolic relation for PC3. My question is why PC2 in humans shows no relation with respect to some function?<br /> - I also interested why you changed methodology of CpG-sites pre-selection by comparing with the previous work in humans?<br /> - "The distribution of the loading vector components for the exponential feature, DNAm-PC1, displays heavy tails, indicating the presence of sites significantly associated with this process" - is the order of PCA loadings stable? Did you test the CpG sites with boostrap procedure, by subsampling the dataset and checking the stability of PC-loadings? <br /> - In figure 4b you demonstrate that CR mice demonstrate higher PC2/tBA values than Control. But what if this observations is due to the covariate shift between two datasets which was caught by PC2 and not caught by PC1 axis? This could explain the differences by a pure data distortions without attracting more complex theory.<br /> - No code<br /> - No supplementary info

Minor:<br /> - "...as heavy regularization tends to select a number of features approximately equal to the sample size, based on their correlation to the target phenotype." - could you please add a reference to this theoretical result. My experiences with complexity penalization says other.<br /> - In the regards of problems with clocks, adding remarks on biomarkers paradox, multicollinearity and uncertainty problem would be beneficial.

On 2020-08-19 18:15:33, user Alan J. Fridlund wrote:

Enjoyed the article. Appreciate the (dated) cites of my work. Here are my thoughts:<br /> 1. People brought together inevitably fall into social roles that will inevitably affect their performance, and remember, all participants in an experiment are actors on the experimental stage - they know they're being montioried in some way, and most participants aim to please once they figure out what's wanted. Also, two strangers brought together form a "blind date."

1. Two problems with mimicry accounts: (a) What prevents runaway mimicry? Why aren't people just mimicking everyone everywhere? It can't be automatic the way "chameleon" theorists have it. (b) What about reciprocal expressions? One person can glower and the other cringes (dominant/submissive threat, or as Basic Emotions people put it, anger provoking fear). One person can blush and look coy and the other gives a big smile. One person can sneer and the other can look quizzical. Lots of problems here. You can check out my recent articles on ResearchGate or read Ruth Leys's Ascent of Affect

On 2019-08-01 20:56:15, user Diego Jimenez wrote:

Excellent approach to construct a minimal effective consortia.... Apparently, stochastic events plays important role in the consortium structural composition !!!

On 2020-05-07 07:53:00, user Wiep Klaas Smits wrote:

This looks quite interesting. However, the paper does not seem to have any reference to code in a repo, or web implementation. This severely limits the uptake as a "tool" (that is suggested by giving the algorithm a name). I hope the authors will fix this.

On 2020-02-17 03:07:12, user Joo-Seop Park wrote:

A revised version of this manuscript has been published. <br /> https://www.nature.com/arti...

On 2021-11-03 13:53:58, user Prof. T. K. Wood wrote:

L 59: Both “there is little evidence for a clear genetic basis or the molecular mechanisms involved for the persistence trait” and “…the genetics and mechanisms of persistence are yet unknown” are patently false statements. Persistence is an elegant response to myriad stresses, as shown already through single-cell studies, though not reported here. See doi 10.1016/j.bioflm.2019.100018.

Persistence non-heritable; that is the whole point. There are no genetic changes in persister cells and they don’t require 19 years to form. Resistance arises from mutation and often the mutations are cumulative without noticeable changes in MIC. So authors must use genome sequencing and show no genetic change before calling cells “persisters” as they do in this manuscript.

L 190: how long were the cells treated with Abs prior to plating to measure CFU? Should be at least 3 hr and kill curves should be added to demonstrate these are persister cells; i.e., that prolonged exposure does not lead to a reduction in CFU.

Persistence is no more “a stepping stone to resistance” than any other cell type: persisters wake and when they wake, these non-persister cells mutate like all microorganisms. But dormant cells don’t mutate.

On 2019-01-11 01:18:26, user Jun Xia wrote:

this paper has been published in Cell<br /> https://doi.org/10.1016/j.c...

On 2023-07-17 00:05:11, user Lladser Research Group wrote:

The published version of the paper can be found at the Journal of Mathematical Biology, "On latent idealized models in symbolic datasets: unveiling signals in noisy sequencing data." <br /> Here is the full citation: J Math Biol. 2023 Jul 10;87(2):26. doi: 10.1007/s00285-023-01961-1.

On 2020-07-09 03:02:59, user Eric C Holmes wrote:

We would like to acknowledge related work describing the activity of UGT76B1 that has also been shared on BioRxiv:

The glycosyltransferase UGT76B1 is critical for plant immunity as it governs the homeostasis of N-hydroxy-pipecolic acid. Lennart Mohnike, Dmitrij Rekhter, Weijie Huang, Kirstin Feussner, Hainan Tian, Cornelia Herrfurth, Yuelin Zhang, Ivo Feussner.<br /> bioRxiv 2020.06.30.179960; doi: https://doi.org/10.1101/202...

On 2017-09-30 22:07:59, user Alan Rose wrote:

This paper has been published under a revised title: "Intron DNA sequences can be more important than the proximal promoter in determining the site of transcript initiation. (2017) The Plant Cell 29:843-853.

On 2014-04-30 15:49:24, user Ian Dworkin wrote:

As Turner et al. posted their data, I have done some analyses of their data. In parallel Marla Sokolowski has provided me phenotypic data on larval path lengths for the same set of lines. I have put all of scripts and data for the analysis up on github, as well as my interpretations of their findings, and some of my concerns about the experimental design how it influenced their estimates, and what this can support for the conclusions they draw.

The whole repository (data, scripts and figures) can be found here

https://github.com/idworkin...

I have also created a markdown file that contains both the analysis and output (and my interpretation) which is available in the repository here:

https://github.com/idworkin...

While I urge interested parties to go through the analysis I have performed (or perform their own using the available data), these are my general conclusions:

1) The nature of the experimental design used in Turner et al. (incomplete blocking with a severely unbalanced design) has lead to confounding sources of genetic variation with environmental (day-to-day) variation. Indeed, despite the experiments being done across 18 days spread across 3 months, there were seven lines measured on only a single day, based on the reported data.

2) In addition the pathlengths reported in Turner et al. are ~50% the length of those generally reported in the literature, or from my re-analysis of the pathlengths for the same set of lines as measured in the Sokolowski lab.

3)The correlation between larval pathlengths reported here in Turner et al. and for the same set of lines from the Sokolowski lab is at best, moderate (0.47). This lack of correlation may be due in part to issues I raised in point 1 above, as well as other differences in experimental protocol. Based on all of this, It is unclear if Turner et. al. is measuring the same larval pathlength phenotype as the Sokolowski lab. Turner et al. also point out that the phenotype they are measuring may not be directly comparable to that published in the literature.

On 2018-10-02 14:05:51, user squad 4 lobes neuro wrote:

BU_FALL_BI598_Group4

Subcortical structures exhibit hard-wired circuitry that motivates many innate behaviors, such as survival instinct. In the visual system, the degree to which subcortical structures follow these same rules is uncertain. Reinhard et al. sought to understand the circuitry that enabled the superior colliculus to reliably and specifically process visual information received from the retina and distributed to downstream brain nuclei. Specifically, the authors used monosynaptic viral tracing and molecular markers to determine if the wiring pattern of the colliculo-parabigmeinal circuit and the colliculo-pulvinar circuit is hard-wired or flexible. Two strains of mice were used in these experiments: PvalbCre and Ntsr1-GN209Cre. Stereotactic injections of herpes simplex virus (HSV, hEF1a-TVA950-T2A-rabiesG-IRES-mCherry, MIT viral core, RN716) were made into the parabigeminal nucleus of PvalbCre mice and lateral pulvinar of Nstr1-GN209Cre mice. Stereotactic injections of rabies virus (EnvA-coated SAD-?G-GCaMP6s RV) were then made into superior colliculus of both mice strains (Figure 1). Antibodies against GFP and ChAT were used to create dendritic density profiles of chosen retinal ganglion cells (Figure 2). SMI32 antibodies and anti-CART antibodies were used to identify and characterize alpha cells and ON-OFF direction selective retinal ganglion cells, respectively (Figure 3-4). Morphological and molecular marker data was then used to cluster cells, and t-distributed Stochastic Neighbor Embedding, tSNE, was employed to visualize the 12 clusters (Figure 5-6). The authors concluded there are 3 distinct groups of cells: 6 clusters projecting preferentially to the parabigeminal nucleus, 3 clusters projecting favorably to the pulvinar, and 3 clusters projecting to both collicular targets (Table 1). The presence of a dedicated set of connections in these circuits suggests that visual information routed through the superior colliculus partly follows hard-wired rules.

Before addressing some major and minor criticisms, we wanted to point out some strengths of this paper. The manuscript helped to characterize the circuitry underlying the visual system and provide a basis for further investigation. In general, the results were laid out in an easy to follow manner and supported the paper’s conclusions. In Figure 1A and 1E, the schematics representing the injection strategy were helpful to visualize how the transsynaptic tracing was performed. Figure 2 clearly and effectively depicted a difference between the size and stratification of retinal ganglion cells that project to parabigeminal nucleus and pulvinar, while also demonstrating that sampling bias was not the cause for these results. In Figure 3 and Figure 4, the authors successfully showed the projection patterns of alpha retinal ganglion cells types and ON-OFF direction selective cells. Furthermore, Figure 6 and Table 1 clearly enabled the reader to visualize and compare the 12 clusters identified in this experiment, as well as provide an inclusive summary of the findings of the papers, what is known, and what needs further investigation. This allowed the reader a comprehensive view of the paper in context of the larger scientific community. It was also refreshing that the authors acknowledged the papers shortcomings in the discussion section. However, after performing a thorough review, we would like to suggest possible changes to improve the overall argument.

First, it was stated that the EnvA-coated rabies virus injected into TVA-receptor expressing cells decreased the bias of infection found in normal rabies virus (lines 288-290, page 15). Quantification of this statement is suggested. Moreover, viral toxicity is a concern; we would like confirmation that both HSV and EnvA-coated rabies virus do not harm the neurons in any way that would disrupt the integrity of the experiment. One suggestion is to show high magnification images of healthy neurons within the time scale of the experiment. In Figure 1B and 1F, histological section after parabigeminal nucleus and pulvinar injections are shown. However, the magnification and resolution is not high enough for readers to adequately view the neurons post-injection.

Second, the retinotopic location of the herpes simplex virus injection into the parabigeminal nucleus and the pulvinar is unknown (lines 293-294, page 15), and rabies virus is injected into the superficial layers of the superior colliculus (lines 61-61, page 3). The size of these structures and the retinotopic nature of the superior colliculus justify the need for evidence of correct site of injection in the superficial layers to show that the same retinal ganglion cells are being labelled.

Third, a positive control could have been performed to clarify if projections from the parabigeminal nucleus and the pulvinar overlap on superior colliculi cells or remain distinct from one another. One possible way to do this would be injecting green retrograde CTB into the parabigeminal nucleus and red retrograde CTB into the pulvinar. If there is overlap between superior colliculi cells, yellow fluorescence would be observed. If the projections synapse onto separate superior colliculi cells, green and red fluorescence would be observed.

Fourth, a negative control should have been performed to ensure the specificity of the HSV injection into the parabigeminal nucleus and pulvinar and the EnvA-coated rabies virus injection into the superior colliculus. A possible negative control could be an HSV transfection without TVA into parabigeminal nucleus or pulvinar, while EnvA-coated rabies virus is still injected into superior colliculus. In this case, no green fluorescence should be observed in the presynaptic, retinal cells.

Fifth, while the introduction sufficiently sets up the argument and emphasizes currents gaps in our understanding of the visual processing system, it references hard-wired circuits and flexible networks in relation to behavioral responses (lines 24-25). We would suggest focusing these explanations on the molecular and cellular aspects of the circuits, as this pertains more immediately to the overall argument of the paper and would provide the reader with a concrete understanding of just what the authors are looking to distinguish in the colliculo-parabigmeinal and colliculo-pulvinar circuits.

Finally, there are some minor criticisms that we would like to bring to light. In Figure 6, individual cluster profiles are depicted in dark grey for CART- or SMI32-positive cells and light grey for cells of unknown molecular identity (page 10). These colors are difficult to differentiate; we suggest utilizing two distinct colors to adequately distinguish the cells. Throughout the paper there were several typos: “weraree” (line 125, page 6), “Cruz-Martin et al.” (line 305-306, page 150, and “understanding” (line 306, page 15). Furthermore, to improve the flow of the paper the authors should consider using abbreviations for the brain regions that are repeatedly mentioned, such as “PBg” for parabigeminal nucleus.

Despite these criticisms, we believe that with the changes mentioned above this paper adequately justifies it conclusions and creates a good foundation for further exploration of the identified cell types and the hardwired circuits of the visual system.

On 2021-10-26 23:22:30, user Xin Chen wrote:

We appreciate that the authors tested our previous results using new reagents and methods. However, we have to point out that there is a big misunderstanding of our published work. First of all, asymmetric histones do NOT imply the existence of “immortal histones” as the authors hypothesized and used to make predictions in their experimental design. In fact, distinguishing old versus new canonical histone must be in the context of cell cycle progression: Old refers to the pre-existing histones before S phase and new refers to newly incorporated ones during S phase. These two populations can be distinguished by the tag-switch or photoconversion methods only after the switched or converted cell goes through one complete S phase and enters the subsequent M phase. Moreover, the new histones with switched or converted labels will mature over time during cell cycle and gain old histone features, and thus there are no “immortal” histones. However, we are not seeing any labels in this work that indicate active cell cycle progression, which is very concerning given these tissues are ex vivo for more than 40 hours.<br /> Second, it would be highly appreciated if the authors include germline versus somatic cell markers in their figures. As of now, it is impossible to tell whether the weak H3 signals in Figure 1C and 1E come from germ cells or somatic gonadal cells. The bright spot in Figure 3E was interpreted as hub cells, which are quiescent somatic cells. If this is the case, it would be very strange that such a quick old to new H3 turn-over occurs in these cells, as indicated in Figure 3E legend.<br /> Finally, we have to point out that our previous results were entirely misinterpreted in the “Alternative Hypothesis 2” in Figure 2, because we are not assigning random stem cells (GSC) and progenitor cells (SG) together as pairs — all GSC-GB pairs we analyzed are still connected by the spectrosome structure (Tran et al., 2012; Xie et al., 2015; Wooten et al., 2019), indicating that they are daughter cells derived from one GSC division. Furthermore, our previous conclusions were not solely based on the post-mitotic GSC-GB pairs, but also on stem cells undergoing asymmetric cell divisions, based on fixed and live cell imaging.<br /> In summary, this work is based on both misunderstanding and misinterpretation of our work, leading to an incorrect hypothesis. Additionally, there is no single dividing stem cell or a pair of daughter cells derived from stem cell division shown in this work that can lead to the conclusion of “Symmetric Inheritance of Histones H3 in Drosophila Male Germline Stem Cell Divisions”. We hope these comments clarify several critical points for both the authors and the readers of this preprint. Thank you for your attention!<br /> Xin Chen<br /> Johns Hopkins University

On 2022-09-16 21:43:47, user Simon Zhongyuan Tian wrote:

Simon Zhongyuan Tian, Guoliang Li, Duo Ning, Kai Jing, Yewen Xu, Yang Yang, Melissa J Fullwood, Pengfei Yin, Guangyu Huang, Dariusz Plewczynski, Jixian Zhai, Ziwei Dai, Wei Chen, Meizhen Zheng, MCIBox: a toolkit for single-molecule multi-way chromatin interaction visualization and micro-domains identification, Briefings in Bioinformatics, 2022;, bbac380, https://doi.org/10.1093/bib...