On 2018-04-02 08:01:48, user GuestFive wrote:

Data for Kenes_MBA is missing from the supplements.

On 2018-04-11 03:59:05, user bennedose wrote:

When it comes to IE language in India, Kurgans and India have no connection whatsoever. The two can be compared using the sources linked below

Below is a paper that describes Rakhigarhi graves of the IVC in detail<br /> http://journals.plos.org/pl...

Here is how Gimbutas described Kurgans - which is pretty much the same as those described in this paper by Vagheesh Narasimhan et al <br /> https://indo-european.eu/20...

"According to Gimbutas, the “Kurgan people” are evidenced by single graves in deep shafts, often in wooden chests (coffins) or stone cists marked by low earth or stone barrows; the dead lay on their backs with legs contracted; they were buried with flint points or arrowheads, figurines depicting horses’ heads, boars tusk ornaments and animal tooth pendants. Human sacrifice was allegedly performed during the funeral ceremonies,and sometimes ritual graves of cattle and other animals were added. This is said to contrast with what Gimbutas called the culture of Old Europe (i.e. the earlier Neolithic of the Balkans), who “betray a concern for the deification of the dead and the construction of monumental works of architecture visible in mortuary houses,grave markings, tumuli, stone rings or stone stelae, and in the large quantity of weapons found in the graves”."

On 2020-05-06 23:02:20, user Michael Hendzel wrote:

I'd like to note that this paper arose from independent projects taking place in our lab and the Hansen lab that complemented each other. We had a previous independent version of this manuscript that is also posted on BioRxiv. Given the extensive changes in authorship, communicating authors, and content, we have opted to post this as a separate submission. The original related submission from the Hendzel lab can be found here: https://www.biorxiv.org/con...

On 2021-10-15 04:15:07, user Jung Ho Hyun wrote:

Let me know anyone interested in (either AAV construct or cKI mice)!

On 2025-09-13 03:46:09, user Israel Bamidele wrote:

Nice read

On 2023-11-13 10:43:48, user Gary Mirams wrote:

This is a very nice use of a mathematical model of patch clamp compensations to account for artefacts in fast sodium recordings at 37C.

Just a little note that whilst Lei et al. (2020) introduced the artefact model without supercharging/prediction compensation, we expanded on that to include those effects in the artefact model version published in Chon Lok Lei's thesis: https://chonlei.github.io/t...

On 2020-06-01 10:48:04, user Justin Byrne wrote:

The Network Ecology research group at Newcastle read through this paper today for our journal club. We were pleased to see an ecological paper making use of the full capabilities of dada2 and deseq2 on a fungal dataset.

We thought that good work had been done in the experimental design to control for the effects of environmental gradients, although would have preferred if there was an attempt to verify how successful this was. We generally empathised with the difficulties of selecting sample sizes before knowing the degree of variability in sample diversity, and how the costs of sequencing and sample preparation can limit the size of these kinds of studies. It appears that the study size was appropriate for the simple diversity metrics; but, as the authors highlight themselves, may struggle to identify consistent features of their networks.

Overall, we struggled with the strong and coherent message provided by the initial results, that becomes less clear by the inclusion of these ecological networks. However, we strongly feel that it is important for this work to be conducted, addressing how networks can and cannot be integrated into biomonitoring.<br /> We would have liked more information about how the technical replicates, negatives, and positives were incorporated into the analysis. We also would like to know how samples were normalised and pooled prior to sequencing to ensure that equal concentrations of DNA were combined for sequencing. Furthermore, given that deseq2 allows for normalising sample abundance by read depth, it would be interesting to have more discussion of the benefits and drawbacks of using read abundance as an input to biodiversity metrics.

We were very interested by the results that suggested that differences in network links resulted from rewiring rather than taxa turnover. Given recent work - Co-occurrence is not evidence of ecological interactions, F. Guillaume Blanchet (2020) – has looked at potential pitfalls for inferred networks, we would be interested to see if it becomes revisited in the discussion in the final article.

Thank you for an interesting, informative, and convincing paper.

On 2019-03-06 06:40:11, user Debarun Acharya wrote:

Please correct the spelling of pseudogenization in page 3 line 18.

On 2019-04-05 13:16:47, user EVAHPI Research Group wrote:

A very exciting finding from our group! The protozoa Giardia intestinalis is capable to produce two extracellular vesicles that differ in size and phenotypical effect.

On 2019-12-16 23:12:26, user Felix Wu wrote:

Supplementary Data will be uploaded once the paper has gone through review. If you would like access before, please email us (flw2113@cumc.columbia.edu).

On 2020-04-15 09:12:04, user Mounia Chami wrote:

Great work,

You forgot to cite this paper

Localization and Processing of the Amyloid-? Protein Precursor in Mitochondria-Associated Membranes.

Del Prete D, Suski JM, Oulès B, Debayle D, Gay AS, Lacas-Gervais S, <br /> Bussiere R, Bauer C, Pinton P, Paterlini-Bréchot P, Wieckowski MR, <br /> Checler F, Chami M.

J Alzheimers Dis. 2017;55(4):1549-1570. doi: 10.3233/JAD-160953.

PMID:27911326Free PMC Article

On 2018-03-21 21:07:42, user Miguel Thomson wrote:

I’m not genticist, but I’m interested in linguistics. In the article it is said that “the Gennargentu region also has elevated levels of pre-Neolithic hunter-gatherer ancestry and increased affinity to Basque populations”. It is precisely in the region of Barbagia, along the Gennargentu massif, where the Basque linguist Juan Martin Elexpuru has found the highest density of Basque-like toponyms, which is described in his recent book “Euskararen aztarnak Sardinian?” (“Vestiges of the Basque language in Sardinia?”). Therefore, there is full geographic coincidence between genetics and toponymy. I have a question: is the affinity between Basques and Sardinians of the Gennargentu region found in the Neolithic or in the hunter-gatherer part of the genome?

On 2016-12-07 23:02:14, user Eran Elhaik wrote:

Very nice work on the Sardinians people that followed up on some of our previous (uncited) findings http://www.nature.com/ncomm....

On 2020-04-03 23:35:32, user David E. Shellenberger wrote:

The popular media are misconstruing the study. For an intelligent discussion of the research, see this piece:

"Coronavirus can infect cats — dogs, not so much:<br /> But scientists say it’s unclear whether felines can spread the virus to people, so pet owners need not panic yet."<br /> https://www.nature.com/arti...

"There is no direct evidence that the infected cats secreted enough coronavirus to pass it on to people, she says."

"Saif says that none of the infected cats showed symptoms of illness, and that only one out of the three felines exposed to infected animals caught the virus."

On 2019-07-07 15:07:26, user Robert Gourdie wrote:

This manuscript was accepted for publication pending minor revision in the Journal of the American Heart Association July 3, 2019.

On 2022-02-10 09:40:35, user Richard Edwards wrote:

This paper has now been published in Molecular Ecology Resources: https://onlinelibrary.wiley.com/doi/abs/10.1111/1755-0998.13574

On 2016-05-10 23:23:36, user Debbie Kennett wrote:

I'd be interested to know how your phasing algorithm compares to Underdog, an adaptation of Beagle, developed by AncestryDNA which is designed to cope with a large dataset now approaching two million people. It currently uses a reference panel of 300,000 genotypes. Details are provided in their White Paper: http://dna.ancestry.com/res...

On 2024-09-03 13:55:06, user Vikash P. Chauhan wrote:

We deposited the plasmids from this manuscript (vPE and xPE) at Addgene. Give them a try and send us your feedback: https://www.addgene.org/browse/article/28248655/

On 2018-08-27 05:56:35, user Sulev Reisberg wrote:

Please take a look at our paper of very similar topic: http://journals.plos.org/pl... We used two large GRS-s (both containing thousands of SNPs) and showed that the differences between GRS distributions (and therefore high/low risk estimations) for Europeans and Africans based on GRS can be even larger - the opposite to each other. We show that risk allele frequencies tend to be higher in African than European population which is the cause of getting higher GRS values there. Moreover, the frequencies of the SNPs used for GRS calculation contain strong population component - even without applying any GRS weighing.

On 2023-04-28 23:25:26, user Charles Warden wrote:

Hi,

Thank you very much for posting this preprint.

For the 2nd author affiliation, I believe that there is a minor typo:

Current: Sothern

Corrected: Southern

Thank you again!

Sincerely,<br /> Charles

On 2023-12-01 11:50:10, user Ready Boy wrote:

Zaprionus tuberculatus was already reported from mainland France in 2020 (see Mouttet and al., Phytoma issue 738, november 2020). Z. tuberculatus is known from Hérault (2018) and also in Corsica (2020).

On 2024-11-20 06:54:52, user Olivier Espeli wrote:

Nice work Emily and Kirsten. Congratulations!

On 2025-07-01 20:31:41, user Laura Sanchez wrote:

The preprint by Young et al describes the design and implementation for post ionization for mass spectrometry imaging samples. Specifically, the team utilizes transmission mode MALDI followed by cold plasma ionization source (SICRIT) with analytes then being analyzed in a timsTOF Pro. The work was able to achieve subcellular MSI which they showed across a variety of tissues and cells to highlight their application. Furthermore, they fortuitously discovered that pre-staining with cresyl violet enhanced ionization of nucleotides and helped facilitate subcellular imaging. Overall this paper provided excellent figures with exciting data, was highly rigorous in the reporting of the lipid species and nomenclature used, and with the instrumental design and assessment of the staining impact on the resulting images. We also enjoyed that this could provide staining and MSI on the same tissue rather than having to take serial sections; this is a really exciting aspect of this work. Below please find critiques for the authors considerations.

1. The comparison between pre and post CV staining was striking. Perhaps a little more discussion here could be helpful, it seems like in addition to the sample preparation creating the cavitations, do the authors posit that CV itself might be acting as a matrix as well to help facilitate ionization? Would other stains have a similar effect?

2. Our understanding of this instrument is that this is still fundamentally MALDI-2 like, in that the SICRIT is acting as a secondary ionization source, can the authors perhaps confirm this or make it more clear in the writing, that both neutral and ions are created during the transmission mode and further ions are created by the SICRIT. If this is not what is happening, then perhaps the writing could be adjusted.

3. For all MSI figures, it would be helpful to include the SMART ( https://doi.org/10.1002/jms.4904) "https://doi.org/10.1002/jms.4904)") reporting standards in the figures both main text and SI are encouraged to increase the transparency of the experiments. One thing we discussed was the time these experiments might take for the areas and the spatial resolutions achieved.

4. One thing that was unclear, is the intersection of how the cold plasma vs the more traditional MALDI-2 would drive the specifics for ionization of the nucleotides and lipids. Specifically could the CV be compatible with normal MALDI-2? We appreciate that the same spatial resolutions would not be achieved but it would be interesting to really isolate how the different changes really impact the subsequent detection of analytes to better understand how to optimize the system or pick the system for different biomolecules in the future.

5. Figure 4D doesn’t seem to help aid in the excitement of this methodology, specifically the microscopy doesn’t match the ion images. Or did the authors expect that only one cell line would be different compared to the other three? Could they better comment on some of the biology maybe if this what they expected?Figure 4C. Was the point to demonstrate colocalization of the dyes with the lipids? It could be helpful to highlight why the data might be meaningful and address the outstanding biological challenges. Figure4 B, C, D weren’t in order for increasing spatial resolution.

6. Can the authors comment on why only positive mode was done for the lipid analysis? Can this set up do negative mode? It might be helpful to comment on to better understand the rationale.

7. Why was 6 ppm vs 5 ppm for the lipid annotations? Typically for Organic Chemistry or Natural Products we utilize 5 ppm for HRMS.

8. Figure 3 this is very cool - is this known that Adenosine has a punctate pattern? The significance of the findings was not discussed, if this is a new biological observation that would be helpful to highlight this more so that the importance of subcellular metabolomics is really needed. Highlighting the significance of the findings would help bolster the future applications.

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

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

On 2020-01-29 15:17:36, user David Minde wrote:

fully published now: <br /> Biotin proximity tagging favours unfolded proteins and enables the study of intrinsically disordered regions https://t.co/oNY0QtIRjq https://t.co/6cLLtRu60j

On 2017-01-10 04:50:22, user Matan Shelomi wrote:

You used one flow cell for one milk sample: what if you wanted the microbial community of several samples? With barcoding, how many samples could be analyzed at once on a single flowcell?

On 2017-11-24 13:26:50, user Kirti Prakash wrote:

Dear Readers, <br /> Please note that #132571 is the original version of the paper and #121061 is the revised version. <br /> https://www.biorxiv.org/con...<br /> Feel free to get in touch if you would have any questions. <br /> Sincerely, <br /> Kirti Prakash

On 2018-05-15 21:49:16, user Dr. Bernard Straile wrote:

I'd like to know the genes you did identify ! Just to see if there is any correlation with the genes which I am clinically working with. (Bioresonance)

On 2018-11-12 07:54:41, user Michel Thiebaut De Schotten wrote:

Awesome work! Bravo

On 2017-03-21 17:57:11, user Liberate Science wrote:

Two questions:

1) is this a pre-print (preliminary version of this paper)?<br /> 2) why do three of the authors, at least two of them "ethical heavyweights" not have an ORCID?

On 2018-10-05 20:12:10, user ofoxofox wrote:

A simplified explanation of this paper, along with additional resources, is published at the link below: https://goo.gl/8VsGMt

On 2018-08-31 13:07:33, user Deborah Talmi wrote:

The files needed to run the experiments reported here are available from:<br /> https://www.research.manche.... Turns out meta-data cannot be added to the ms in any other way once it is accepted.

On 2025-04-16 00:39:10, user Ji-Woo Park wrote:

This preprint has now been published in Nature Communications: https://doi.org/10.1038/s41467-025-58856-6 . A link to the published version will be added soon.

On 2021-03-30 21:09:34, user yumi imai wrote:

Now accept and in press @JCI Insight. https://doi.org/10.1172/jci...

On 2021-09-29 17:05:51, user Marcus Oliveira wrote:

The work performed by Song and colleagues investigates the metabolic roles of LETMD1 protein in brown adipocyte energy metabolism and the systemic physiological consequences to<br /> mice. The authors identified that whole-body genetic deletion of LETMD1 promotes consistent reductions in mitochondrial structural and functional markers including reduced expression of mitochondrial proteins, low respiratory rates, and calcium ion levels. Such effects were paralleled to brown adipocyte whitening and accumulation of lipid droplets as well as systemic metabolic defects including impaired thermogenesis, cold intolerance, hyperglycemia, and insulin resistance, particularly under high fat diet. Although the results are very interesting and indicate that LETMD1 plays a role in regulating mitochondrial processes, the mechanistic bases underlying the systemic metabolic consequences caused by LETMD1 are not properly supported. Particularly, the relationship between mitochondrial Ca2+ and LETMD1 should be pursued by the authors to better substantiate the claim that systemic metabolic effects of LETMD1 KO results from altered mitochondrial Ca2+ and dynamics. Thus, I have observed some caveats and limitations of the present study as pointed out below, which authors might find useful to strength their conclusions.

Major comments:<br /> 1) The authors did not provide proper background literature to support the working hypothesis. For example, on the second paragraph of introduction, page 5, the authors state that “Interestingly, by analysis the expression profiles of LETMD1 in human and mice, we found that LETMD1 was highly expressed in the metabolism relative tissues, especially brown adipose tissue (BAT), and the expression of LETMD1 was significantly reduced in adipose tissues of the obese people and the high fat diet (HFD) induced mice.” I think this is a critical aspect to substantiate the studies on LETMD1 in adipose tissues. Alternatively, the authors should revise this paragraph clearly stating that background evidence is unpublished/preliminary.

2) I have observed four important issues on the animal model used. First is which sub-strain of black 6 mice LETMD1KO were generated? Given that several sub-strains of black 6 are available, with distinct mutations that affect energy and redox metabolism<br /> (https://www.nature.com/arti... "https://www.nature.com/articles/s42255-018-0018-3?WT.feed_name=subjects_genetics)"),<br /> this is a crucial point that authors should clearly define. Second, the authors did not specify which sex and age were used throughout the work. Third, since the authors used a whole-body KO in their study, one might argue that factors released by tissues other than BAT generated by LETMD1 depletion might have<br /> caused the observed metabolic effects. This seems particularly important considering that LETMD1 is highly expressed in liver as well. Although this specific issue was not fully addressed, the authors should add a cautionary note at the discussion section, as well as balance their statements about the specific role of LETMD1 in BAT. Finally, since mice were maintained at 22oC which activates a thermogenic program (https://pubmed.ncbi.nlm.nih... "https://pubmed.ncbi.nlm.nih.gov/29697140/)") to maintain core body temperature, the authors should balance their findings to consider how LETMD1 deletion would affect metabolism at thermoneutral conditions.

3) The authors should balance whether the observed whitening of BAT in LETMD1KO mice is a consequence of impaired brown adipocyte differentiation program or, a loss of mature brown adipocyte biomarkers. Specifically, given the strong and a consistent reduction in mitochondrial markers, one might think that either mitochondrial biogenesis is impaired or mitophagy is activated in LETMD1KO mice. Does LETMD1 deletion affect mitochondrial mass/content in other tissues/models?

4) There is a clear trend towards increase in lipid droplets size in LETMD1KO even in chow diet, which might involve increased expansion of these structures due to improved association of mitochondria to lipid droplets. Indeed, recent evidence demonstrated that a population of mitochondria associates to lipid droplets in BAT to support lipid droplet expansion (https://www.ncbi.nlm.nih.go... "https://www.ncbi.nlm.nih.gov/pubmed/29617645)"). It would be very interesting to see whether LETMD1 is differently located among mitochondrial populations in BAT and, if that is the case, then how LETMD1 contributes to lipogenesis and lipid droplet expansion.

5) Considering that mitochondrial Ca2+ levels do not significantly rise upon adrenergic stimuli in LETMD1KO adipocytes, which mechanism mediates this effect? Since mitochondrial Ca2+ homeostasis is controlled by the calcium uniporter and NCLX activities (https://pubmed.ncbi.nlm.nih... "https://pubmed.ncbi.nlm.nih.gov/32620768/)"), how LETMD1 regulate mitochondrial Ca2+ homeostasis through these targets? Since the metabolic effects of LETMD1KO in mitochondria involve an imbalance of Ca2+, and adrenergic stimuli cause a rise in Ca2+ levels, I think it would be very informative how LETMD1 deletion would affect respirometry of norepinephrine activated adipocytes. If LETMD1 negatively regulates NCLX expression/activity, then LETMD1 deletion would increase NCLX levels facilitating Ca2+ extrusion from mitochondria ultimately increasing respiratory rates. Also, as NCLX deletion promote adipocyte apoptosis (https://pubmed.ncbi.nlm.nih... "https://pubmed.ncbi.nlm.nih.gov/32620768)"), it is possible that LETMD1 deletion would render adipocytes more resistant to apoptosis by preventing mitochondrial permeability transition and limiting Ca2+ accumulation.

Minor comments:

1) The authors should carefully revise the writing to improve some<br /> sentences as the following example at page 4 “LETM1 was identified as a key component of ions transporter in mitochondria, including Ca2+/H+ transportation, K+/H+ exchange, Na+/Ca2+ exchange, and Mg2+ transportation to maintain the mitochondrial biology and cation homeostasis”. Additional corrections should be made at: <br /> a) the last paragraph of page 5 “Therefore, we hypothesize…”.<br /> b) The first paragraph of page 6 “Mechanistically, our findings…”.

2) The authors should better explain the different groups in graph legends. For example, in Figure 1C it is not known what the meanings of red and green bars are (which one is BAT and WAT?).

On 2025-05-06 21:54:54, user Young Cho wrote:

Key findings

The first primary discovery discussed in this paper is that age affects DNA methylation (DNAm) in different ways depending on the location in the genome. For example, the researchers found that promoter regions experienced hypermethylation with age, while transposable elements experienced hypomethylation. The second discovery was that female dogs had lower methylation in X chromosome-linked long interspersed nuclear element-1s (LINE1s) than male dogs. This was unexpected because X chromosome inactivation would normally lead to much higher methylation, and thus a possible explanation is that females are more susceptible to age-related decline in methylation than males. The third discovery is that size is largely associated with the rate of methylation decline, where larger dogs lose LINE1 methylation with age at a significantly higher rate than smaller dogs. This may be a large part of the reason why larger dogs tend to have shorter lifespans.

Results

Figure 1 shows how the researchers prepared, processed, and annotated the blood samples from 864 dogs in order to measure the DNAm at various CpG sites throughout their genome.

Figure 2 shows how age affects DNAm at different locations in the dogs’ genomes.

Figure 3 shows that X-linked and age-associated X-linked LINE1s are more methylated in male dogs compared to female dogs.

Figure 4 plots LINE1 methylation against age, showing that large dogs lose LINE1 methylation faster and have shorter lifespans than small dogs.

These figures effectively show each significant conclusion the researchers made. I liked that only the major figures were included in the main part of the paper, while other less important, supporting data was labeled as supplementary figures.

Supplemental figure 1 shows that there was no significant difference in the sizes of the female and male dogs used in this study, and also that each dog was appropriately categorized by size so that there was minimal overlap.

Supplemental figure 2 gives an overview of the CpG sites that were analyzed in this study, showing CpG densities, region lengths, mean methylation, and high sample coverage.

Supplemental figure 3 shows that promoter regions that are affected by methylation as one ages are often associated with important biological functions, such as integrated stress response signaling, immune response, etc.

Discussion

The discussion clearly outlines the main findings of this study. The first of which, correlates age with methylation in the genome. Additionally the methylation of LINE1s, a class of autonomous transposable elements, had a lower frequency in females than males. Lastly, the rate of DNA methylation in LINE1 was also associated with dog size. These conclusions were also supported by previous studies in other model species. For instance, the loss of methylation of LINE1s in other mammals was also associated with age, an observation also made in dogs. Furthermore, the authors conveyed the importance of this study (i.e. the higher frequency of LINE1 methylation in males) through linking the loss of silencing in Y-linked LINE1s with shorter life span, as this was observed in human studies.

Methods

In the methods section, describing blood sample collection, mention of transporting blood samples between universities was included. However the temperature at which these samples were at during this time period was not recorded. Additionally the amount of sample taken was not mentioned. Rather, this section conveyed what cohort of dogs their samples were taken from, who isolated specific cells from the samples, and why these peripheral blood mononuclear cells were isolated. Additionally, the method to extract the peripheral blood mononuclear cells may be present in reference 115. At the time of writing this review, this reference could not be accessed.

Sample annotation, describes how information about the dogs’ backgrounds were obtained, first through survey. Which was utilized to categorize the dogs by size. Following this, a machine learning model calculated adult dog heights from 0-4. Where 0 equated to ankle height and 4 was waist high. For this, it may be preferable to designate height values or ranges rather than descriptions, as these heights are subjective. However, utilizing calculated or predicted heights allowed several advantages compared to survey or self-reported data, which was also included.

RRBS was used to identify CpG sites by converting unmethylated cytosines to uracils, which is highly appropriate for this study, because it allowed the researchers to find and analyze any age-related changes in methylation across their samples. These sites were grouped into 47,393 regions based on their distances from each other, such that CpG sites from the same region were no further than 250 bp apart. This was an important preprocessing step because CpG sites that are close to each other are likely to experience similar levels of methylation, and thus any sites located further could have negatively skewed their data. Furthermore, a large majority of CpG regions (80%) had greater than 5x coverage in more than 95% of samples, ensuring that the data they collected was consistent and therefore reliable.

The researchers annotated their CpG regions by using previously made data as a reference. For instance, they used NIH CanFam4, which is the current reference genome for Canis lupus familiaris, to align their sequenced data with. They were also able to map chromatin states (e.g. active promoters, enhancers, heterochromatin, and inactive regions) using Son et al.’s epigenetic map.

PQLSeq was used for region-based differential methylation analysis because, as RRBS provides methylation and total read counts for CpG sites, PQLSeq uses binomial proportions to model the methylation data as an age-related factor and account for fixed and random effects of sex, predicted height, and genetic relatedness.

With a generally large sample size of 864 dogs, each with their own larger set of methylated regions, it was necessary to use the Sol Supercomputer in order to compile and process all of the data efficiently and accurately.

Strengths and limitations

This paper was enlightening. There was a clear reason as to why this research should continue as these results can shed light on epigenetic processes that occur as mammals age. The goal of this paper was also achieved, as the authors recorded that DNA methylation plays a role as dogs age, where specific DNA regions experience changes in methylation frequency. The methods utilized to obtain these results and the following data supported their conclusions. Wherein their discussion section, ample prior results in various model species supported their results. Furthermore, the authors stated some limitations in their study but were able to come to conclusions utilizing papers focusing on other species. Thus, analysis of their results were sufficient.

However, there are areas for improvement. There are minor adjustments that may need to be considered before publishing, which were detailed in the editorial decision. Furthermore, in terms of the cohort utilized, the number of female to male subjects and breed sizes varied between these categories and was not addressed. Which may inspire the question of how many subjects would be considered sufficient for the conclusions made in this paper. Additionally, figure clarity can be improved, specifically for Figure 4. As in print, the color differentiating breed size are very similar, to increase visibility it may be preferable to utilize other colors or dotted/dashed lines. However, these are minor changes and inquiries that do not diminish the importance of this paper.

Editorial decision

There are some formatting issues. For instance, in the third paragraph of the discussion section in the fourth sentence, stating, “A genetic mechanism underlying this breed’s increased cancer risk. . .” between, “breed’s,” and, “increased,” is a double space. Additionally, in the third paragraph of the introduction section, DNA methylation’s acronym (DNAm) is defined for the second time, which can be omitted, as the acronym was fully spelled out in a previous paragraph. The references are not formatted using current APA format. Furthermore there are some inconsistencies with the addition of DOIs, as only a couple references have them. In references that name species, they are not italicized. Additionally reference 115, at the time of writing this review, was not accessible. More clarity would also be preferable regarding how 864 subjects were chosen out of the approximate 1,000 dogs enrolled in the cohort used. Furthermore, during sample annotation, height values categorized by a machine learning model were described rather than providing a range of measurements, as the descriptions were subjective (e.g. “ankle high”). Otherwise, the value of this study is clear and this project is certainly valuable to understanding the impact of methylation and aging in mammals. Overall, the paper was a great read.

This paper was organized into sections in a way that made it very easy to follow. We especially liked that major conclusion statements were bolded and made as subheadings in the discussion section, and that only figures that directly supported those conclusions were included in the main part of the paper. One other minor thing that we would say could be improved upon is the golden retriever case study — in order to show that age-related methylation patterns affect dogs on a breed-specific level, we feel like it would have been interesting to see the comparison of methylation in golden retrievers to distinct breeds rather than just non-golden retrievers. Overall, I would accept this paper with very minor revisions.

On 2023-11-10 23:23:41, user Hui Wang wrote:

The study suffers from two serious flaws that undermine its credibility:

1. The authors overlook the fact that the aromatic ring of tyrosine 90 is essential for the SH3 domain hydrophobic pocket structure. They incorrectly present the Src90E mutation as mimicking phosphorylated tyrosine, ignoring the expected disruptive effect of almost any mutation at this site. This flaw raises doubts about the validity of their interpretation, as the observed data may be a result of the grossly disrupted SH3 domain binding site rather than of simulating Tyr90 phosphorylation.

2. The study neglects the tightly regulated nature of Src kinase activity through phosphorylation by Csk. Previous research by Erpel et al. 1995 has demonstrated that the mutation of Tyr90 to alanine impairs the interaction with Csk and the negative regulation of Src kinase. As the mutations Src90E and Src90A are supposed to disrupt the SH3 domain binding pocket in essentially the same way, the authors fail to acknowledge this crucial aspect. This oversight undermines the study's reliability and suggests that the similarities observed between Src90E and Src527F may be solely due to the impaired interaction with Csk.

These flaws significantly impact the study's findings and raise concerns about the thoroughness and accuracy of the authors' interpretation. Caution should be exercised when considering the conclusions presented in the study.

On 2017-12-27 16:59:23, user Luke Hoekstra wrote:

pg 22, line 4 -- I think you are missing a negative sign for the MQRankSum threshold.

On 2020-01-14 16:35:39, user Anne-Catherine Prats wrote:

This paper is now published in eLife 2019 Dec 9;8. pii: e50094. doi: 10.7554/eLife.50094.

On 2021-06-15 11:04:54, user Iain Cheeseman wrote:

My co-authors and I welcome additional public comments on this work. Thanks! #FeedbackASAP

On 2016-07-19 09:17:59, user Nelson wrote:

What is the definition of Sub-Saharan Africa in that study? Based on the archaeological evidence, the Africans most likely related to the Natufians are Nile Valley Sudanese and other North Africans of the Eastern Sahara. Were their modern representatives included in this study?

On 2017-08-02 08:19:23, user David Howard wrote:

A revised version of this manuscript incorporating only the HRC imputation panel data (as other panels were subsequently found to contain errors) is available from: http://www.biorxiv.org/cont...

On 2015-09-19 15:23:31, user Brad Chapman wrote:

Thank you for this work. It's incredibly useful to have additional public datasets for assessing low frequency variations. Are the sequenced reads and validation truth sets you used currently available? Thank you again.

On 2021-02-04 05:30:17, user Megumi Iizuka wrote:

I noticed, though that the data in their presentation material is from Day 0 to Day 8.<br /> https://investors.vaxart.co...<br /> Neutralizing antibodies generally don't appear until Day 7. I just don't know why their data only shows the response for day 0 and 8. I wonder why when they started in october 2020, they would only have prelim data for 8 days. Is this a normal timeframe for a small company with 35 subjects?

On 2025-10-03 15:37:19, user Clemence Fraslin wrote:

This preprint has now been published in Genetics Selection Evolution in August 2023. Would it be possible to add the link to the published version? Thanks

Fraslin, C., Robledo, D., Kause, A. et al. Potential of low-density genotype imputation for cost-efficient genomic selection for resistance to Flavobacterium columnare in rainbow trout (Oncorhynchus mykiss). Genet Sel Evol 55, 59 (2023). https://doi.org/10.1186/s12711-023-00832-z

On 2017-08-04 22:08:43, user Ailong Ke wrote:

Page 9, line 11: "Ku and Csn2 bind the same type of substrate - linear double stranded DNA [ref] - and might thus interfere antagonistically". What is the rationale here to only cite the 2013 Arslan study in NAR, but omit the earlier 2011JBC study on the structure-function of Csn2? The title of the 2011 study states"Crystal Structure of Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-associated Csn2 Protein Revealed Ca2+-dependent Double-stranded DNA Binding Activity". Ku is not the only other protein that binds to linear dsDNA.

On 2020-08-18 11:40:23, user Ryan Y. Wong wrote:

This manuscript is now published.

https://rdcu.be/b6k0X

https://doi.org/10.1038/s41...

On 2025-10-14 15:48:16, user Asya Makhro wrote:

Thank you very much for your comment, we greatly appreciate your interest in our preprint.<br /> Our publication focuses on the nonimmune incompatibility between two species, which is why we concentrated on compromised oxygen delivery between mother and fetus. I have read the paper you mentioned and completely agree that multiple mechanisms were most likely involved in the decline of archaic populations. While Piezo1 also defines a blood group, the amino acids responsible for blood group type are located in the extracellular domain and do not influence Piezo1 channel properties (PMID: 36122374, Figure 4) or, consequently, RBC metabolism.<br /> The choice of the Ser307Gly variant was inspired by Svante Pääbo’s lecture, in which he compared archaic protein variants with modern ones based on 1000 Genomes data. A list of the proteins can be found in the supplementary materials of PMID: 24679537. At that time, protein alleles with low frequencies (<1%) were not detectable in such a small sample of modern genomes.<br /> The Ser307Gly variant appears to have undergone negative selection. For example, the archaic allele of a Piezo1-linked gene, MC1R, reached up to 70% in some East Asian populations (PMID: 24916031), but the archaic Piezo1 variant is absent in these individuals, as genomic data were obtained from the 1000 Genomes Project. Another archaic allele of Piezo1, E1839K, is preserved at higher frequencies. I did not verify this variant in archaic genomes, but its designation as archaic is based on comparative analysis. Approximately 5% of Asians (gnomAD) and all other modern hominid species carry K instead of E, reflecting a prevalence rate more consistent with neutral expectations for an archaic allele.<br /> Thank you for mentioning Denisovans. I did not examine their sequence, but I would expect that most archaic humans carried this variant, particularly if our hypothesis regarding the reproductive barrier is correct.

On 2018-06-12 22:07:46, user J7614 wrote:

This is interesting study to demonstrate in vivo mammalian UPRmt in heart physiology. Authors pointed out not only the good points of the study but also the limitations which is to be appreciated. But I have several questions regarding the study:

1. Although UPRmt is generally defined as a transcriptional program, one can think the effector is protein since the essence of UPRmt is 'proteostasis'. Thus I wonder if the authors checked the increase of protein level of known to be the players of UPRmt--HSPD1, HSPA9--in the time frame of the protective effect (6h).

2. ATF5 targets not only UPRmt genes but also others related to many different cellular pathways (PMID: 29137451). Moreover, a recent study reported that doxycycline induce ATF4, rather than ATF5 in mammalian cell culture (PMID: 28566324). In any case, this means there are other possibility that doxycycline and oligomycin exerted its protective effect through pathways other than UPRmt, which could also be regulated by ATF5. How can authors nail down the conclusion to such from the observation in the study?

3. Mammalian UPRmt players in the mitochondria, are yet to be determined fully comparing to those of c.elegans. But still there are other known UPRmt players, especially mitochondrial proteases--CLPP, LON at least in c.elegans. If the authors can provide the expression level of the comprehensive players of UPRmt, it'd bolster the conclusion of the study.

4. In the 1st paragraph of the discussion section, ref. 34 noted that protein synthesis is the top priority of consuming ATP pool. However, the authors cited the reference to argue that the very process is low priority, thereby oligomycin didn't elicit mortality. Moreover, ref. 34 didn't mention anything about protein import. I wonder if I understood correctly.

Overall, this is interesting study and I look forward to the future findings.

On 2021-12-09 11:54:09, user Jonathan D G Jones wrote:

This is a really interesting paper - just did it for our journal club. Here's a suggestion. The recent Jijie Chai paper on 2'3' cAMP formation from TIR proteins implicated a key catalytic cysteine 3 aa before the catalytic glutamate. That is present in all the examples in Fig 1C except in the TNP proteins. That's likely why the TMP TIR domains don't activate defence. Authors should highlight that Cysteine as well as the glutamates, and should comment on that possibility

On 2018-12-19 23:23:30, user whiskyxy wrote:

Nice finding! I guess that may happen in many other draft genomes

On 2021-05-20 17:14:04, user Tanya Whitfield wrote:

Congratulations on this beautiful work.

The ‘unknown structures' that you have imaged in the olfactory epithelium and skin in Suppl. Fig. 32 are rodlet cells, which have been described in several previous papers, including in the zebrafish. See: <br /> Hansen and Zeiske (1998) The Peripheral Olfactory Organ of the Zebrafish, Danio rerio: an Ultrastructural Study. Chem. Senses 23: 39-48<br /> DePasquale, J. A. (2020). Tropomyosin and alpha-actinin in teleost rodlet cells. Acta Zool. 00, 1–10. doi: 10.1111/azo.12344

We included some discussion on rodlet cells in our recent paper on a different cell type, olfactory rod cells, which have a single actin-rich projection (and are also visible in your Suppl. Video 14). See Cheung et al. (2021). Olfactory Rod Cells: A Rare Cell Type in the Larval Zebrafish Olfactory Epithelium With a Large Actin-Rich Apical Projection. Front Physiol. 12:626080. doi: 10.3389/fphys.2021.626080<br /> PMID: 33716772

On 2019-03-03 15:21:30, user Stef Garasto wrote:

This is a pre-print of a paper with the same title accepted by the IEEE conference on Neural Engineering 2019 (NER2019).<br /> Copyright 199x IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

• Stef Garasto (first author)

On 2020-12-13 21:18:43, user Patrick Sexton wrote:

I have some questions where additional information would help me interpret the presented data:

The in vitro experiment is poorly described and does not appear to be robustly performed...<br /> What is the scale (axis labels)? Is this an accumulation assay (+IBMX)? <br /> Were the data converted to actual cAMP levels?<br /> How long is the antagonist pre-incubation? How long is the total stimulation with GIP?<br /> This looks like a single experiment with duplicate repeats - what is the experimental "n"?

Is there anything known of potential activities of the antagonists for other signaling/regulatory processes for the mGIPR (e.g. pERK, receptor internalisation)?

Caveat on the next comments - I am not a physiologist, so it might just be my ignorance: <br /> Fig. 1H. Why does the GIP2-A antagonist "increase" blood glucose in the IPGTT in fasted, lean mice? Isn't this experimental design supposed to bypass the endogenous GIP response?<br /> Fig. 1J. Why does exogenous mGIP stimulate insulin secretion in 4 h-fasted lean mice, prior to IP glucose bolus? Why is there no rise in insulin secretion with mGIP with high glucose from the IP bolus? Is the administered GIP below effective concentration by this time?

It would also be good to understand the metabolism and clearance of the antagonists used in the chronic studies.<br /> What is the mouse PK on the GIP1-A? On what basis is the claim made that the osmotic mini-pumps will maintain an effective antagonist dose? Where is the evidence that this occurs?

What is the mouse PK for the GIP2-A? How does this compare to the PK for liraglutide?

On 2023-10-23 04:54:51, user David Davies-Payne wrote:

In Box 1 - on Haptoglobin

The gut permeability marker zonulin, which is reduced in the post-acuteCOVID condition multisystem inflammatory syndrome in children (MIS-C) (Yonker et al. 2021) is the precursor for haptoglobin-2.

Should that read "elevated"?

On 2021-06-27 19:41:46, user Ionut Ce wrote:

That is interesting. I will try to try something similar! It's been two years since I've read the study and I've learned a lot. This would be a good direction for my pHD.

On 2018-12-14 13:28:47, user David Curtis wrote:

Especially interesting to see NRXN3 and KMT2C in list of genes with recurrent de novo protein truncating variants in schizophrenia. Both very similar to genes previously implicated (NRXN1 and SETD1A).

On 2017-11-20 16:42:28, user Evelien Adriaenssens wrote:

First of all, thank you for creating this package! <br /> In the R vignette, I saw that the negative control libraries of the example had a lower read count than the true samples. In my experience, this is not always the case. When working with low biomass samples, the presence of inhibitors in the actual samples can give lower read count than in negative controls. Do the statistics still hold up in these cases? Or should other settings be used? <br /> (You might have addressed this in the paper and I might have just missed it)

On 2022-04-06 07:33:18, user David Smith wrote:

X- ray and gamma-ray radiation interacts with either the bond between the innermost electrons in an atom (photoelectric absorption), with the electrons individually without regard to their presence in an atom (Compton scattering) or, if the energy is high enough, with the high electric field in the immediate vicinity of the nucleus to convert to an electron positron pair. None of these mechanisms cares about the chemical bonds between the atoms, so there can be no meaningful difference between 1 g/cm2 of melanin and 1 g/cm2 of pretty much any other organic compound. (This is not necessarily the case for charged particle radiation, like the deuterons in reference [33]). The chemical formula of melanin is C18 H10 N2 O4, which has no high-Z elements in it that could enhance x- and gamma-ray attenuation, as noted (with no reply) by VGR Subramanian. I'm elaborating here for future readers. In the current version of the article the authors back off slightly from their claims, but they still ignore the basic physical mechanisms that imply that organic material is organic material as far as x- and gamma- radiation shielding is concerned. Whether it is living material, melanin, etc. cannot make it perform better than any other random slab of CHNO compounds. To suggest otherwise would require a standard of proof high enough to justify overturning 125 years of settled physics on the interaction of radiation with matter.<br /> -David Smith<br /> University of California, Santa Cruz

On 2024-08-08 13:15:41, user F. Laclé wrote:

Also, if you can publish your model code in a repository would be great for reproducibility (the model itself is not necessary I reckon). As you know, there are much more system configuration elements to consider, which makes reproducibility efforts complicated. Publishing your model code would allow others to attempt and improve the reproducibility challenges.

On 2016-07-02 18:06:31, user Serghei Mangul wrote:

How can we apply for data access?

On 2022-02-10 19:10:55, user Alan Zahler wrote:

Revised manuscript is in press at PLoS Genetics<br /> https://journals.plos.org/p...

On 2019-10-17 21:21:13, user Trevor Hastie wrote:

No, correct as it stands. Intercept is not penalized, so it always comes in prior to any penalized variables.

On 2019-03-16 10:58:46, user David Peters wrote:

The study is missing several basal crocodylomorphs: Junggarsuchus, Pseundhesperosuchus, Trialestes, Lewisuchus, Gracilisuchus, Saltopus, Scleromochlus and Terrestrisuchus.

On 2020-12-25 23:03:38, user Björn Brembs wrote:

This is a very interesting piece of work on a behavior that couldn't be more iconic, insect flight. Congratulations!<br /> I only have a short remark about a citation of two of our publications:

In line 415-418 you write: "PKC-d, a member of the Protein Kinase C <br /> family and is known to modulate flies ability to learn from their <br /> environment, especially during flight."<br /> WRT our two publications, the Gorostiza et al. paper does not treat PKC at all, and Colomb and Brembs is work on not learning from the environment, but the opposite of what you write, the fly learning about its own behavior without any other environmental cues.<br /> Best regards,<br /> Björn

On 2020-05-31 21:10:36, user Simon Anders wrote:

Dear Authors

may I ask a question about your Supplementary Figure 5?

I have tried to understand your estimate of sensitivity and specificity, as shown in your Figure 1e. You state a sensitivity of 95.6%, i.e. you detected 66 of your 69 positive samples, and a specificity of 99.2%, i.e., you correctly called negative for 131 of your 132 negative samples (69*0.992=66 and 132*0.992=131), and you state that these values were found when using a decision threshold of 11,140 quantiflour units.

However, if I draw a line at 1,1*10^5 in your Supplementary Figure 5, I see a larg part of the dark red dots (i.e., of the positive samples) below the threshold (i.e., as false negatives). And if I lower the threshold to get most positive samples above it, I would get very many false positives (gray dots above the threshold). So, this Supplementary Figure 5 cannot be what gives rise to your ROC curve in FIgure 1e. Then, what is it? And do you also have the plot of quantiflour results versus qPCR result that gives rise to the ROC curve?

I'm probably just reading your plot wrongly, but I can't figure out how these two figures don't contradict each other.

Thanks in advance for an explanation.

Simon

On 2023-02-21 10:48:37, user Ivan Scotti wrote:

Very cool analysis!<br /> In a different way (focusing on allele frequency variance rather then on clines) we came to similar conclusions here:

https://onlinelibrary.wiley...

Ivan Scotti

On 2019-05-07 14:12:56, user Zaved Hazarika wrote:

Dear Marziyeh<br /> I have performed a similar work. However in our MD of ZnO NP in water (4nm,2838 atoms, Amber99sb ff), all same parameters considered, the ZnO NP is not getting stabilized over 100ns time. Can you kindly provide your raw data to cross check with ours?<br /> Regards<br /> Zaved Hazarika<br /> Research Scholar<br /> Dept.of MBBT,<br /> Tezpur University<br /> India<br /> email:zaved@tezu.ernet.in

On 2022-01-11 18:03:29, user Krishna C. Aluri wrote:

The authors presented an interesting work to understand the role of mitochondria in multiple sclerosis (MS) progression. The current therapeutic strategy largely focuses on the management of immune response there is an unmet need for better understanding progression and developing novel strategies to slow down disease progression. The authors took a methodical approach and demonstrated that by reviving mitochondrial activity by overexpressing Pgc-1? in an experimental autoimmune encephalomyelitis mice model (EAE) they were able to rescue the EAE mice better than the WT mice.<br /> For their EAE mouse model, the authors used C57Bl mice immunized with myelin oligodendrocyte glycoprotein 35–55 peptide. Even though this model was widely used to understand multiple sclerosis, it is understood that the model is monophasic i.e. there is no clinical progression of disease after onset as presented in humans. It would be interesting to see how other EAE models such as Biozzi ABH mice respond to Pgc-1? overexpression especially on the relapsing episodes. The cuprizone model is another interesting model where mitochondrial density increase was similar to post mortem MS pathophysiology.<br /> The authors did show induction of Pgc-1? increased calcium clearance, but they did not study the effect on ATP production. It will be informative if they can compare ATP levels in Pgc-1? induced and wild-type cells.

On 2024-05-31 09:07:32, user Klaus Harms wrote:

Article now published in Mol Microbiol: https://doi.org/10.1111/mmi...

On 2023-01-07 22:12:55, user Francisco W. G. Paula-Silva wrote:

This manuscript has been published. Please find the reference as follows: Lorencetti-Silva F, Arnez MFM, Thomé JPQ, Carvalho MS, Carvalho FK, Queiroz AM, Faccioli LH, Paula-Silva FWG. Leukotriene B4 Loaded in Microspheres Inhibits Osteoclast Differentiation and Activation. Braz Dent J. 2022 Sep-Oct;33(5):35-45. doi: 10.1590/0103-6440202204827. PMID: 36287497; PMCID: PMC9645171.

On 2018-08-07 14:04:59, user Colin Davenport wrote:

Hi,

I might have missed it, but couldn't find the repository in the document? Any idea why ?

https://github.com/luoyunan...

Best,<br /> Colin

On 2014-06-06 03:01:37, user Darya Vanichkina PhD wrote:

Thank you for the very timely and interesting paper!

Could you please clarify, in the methods, when you say you used htseq in "intersection" mode, was that "intersection-strict" or "intersection-nonemtpy"?

On 2020-05-07 23:28:08, user clorene wrote:

WHO is collecting information about the coronavirus and spearheading efforts to find a vaccine. This preliminary report should be shared with the World Health Organization for a "peer review."

On 2015-03-17 04:21:53, user Mathieu Lajoie wrote:

Extremely useful. Thanks!

On 2016-05-28 10:08:11, user Mustafa Abeer wrote:

Why I feel that study design should have included SD rats not exposed in utero? It seems that, study was more appropriate to warn pregnant females of not excessively using cell phones.

RFRs loose energy as they go away from antenna. Is there any specific distance from the living organism/tissue considered to cause this low incidence of brain and heart tumors? It may be important to verify that the phone at a distance should be safe while it isn't being used.

On 2018-01-13 11:46:29, user Grimm wrote:

Nice data. Like the result.

Two ideas regarding enhancing the presentation to appeal to a more general audience. The current figures are all quite complex. Fig. 2 being a good example: A and B show essentially the same (A being the more important representation for the text, I think, so B could be moved to a Fig Sx; which gives more place for A). Legend misses the meaning of lowercase a,b,c,d,e and their colour codes. If you want to keep A and B, the scale should be the same.

Missing is also a simple representation of the main result (level of inter-species admixture). Would it be possible to make a pie chart for each of the four species showing the proportion of shared genetic traits (genes)?

With respect to the candidate genes incongruent to the phylogeny, there should be a map in the main text indicating the geographic spread of the analysed samples in south-western France, maybe using the geological map as background and/or the new high-resolution Köppen map (maybe too coarse in your case?) of the Vienna group (http://koeppen-geiger.vu-wi... "http://koeppen-geiger.vu-wien.ac.at/present.htm)"). Something relating to the association you mention: robur - northern/Cf/moist, petraea - northern/Cf/dry(er), pyrenaica - southern/submediterran/acidic, pubescent - southern/mediterran(Cs?)/limy .

Finally, regarding species identification

I take that the sequenced individuals were morphologically straightforward to identify. Is it possible to produce any morphological/morphometric data to illustrate this for the sampled individuals, e.g. to do a PCoA showing the individuals grouping in four seperate clusters/clouds? It would give the preferred scenario (secondary contact) an easy-to-grasp backup. The species isolated and evolved characteristic morphologies, which remain stable in the local context (near ? sympatry, a map would really be nice) despite the secondary gene flow. Hence, they are good species and not subspecies or ecomorphotypes (see the papers by Mallet on species concepts)

Cheers, Guido

On 2023-04-01 23:15:29, user Vitaly V. Ganusov wrote:

Review of the paper by Shin et al. “Lung injury induces a polarized immune response by self antigen-specific FoxP3+ regulatory T cells “ (MICR 603 Immunology JC)

Summary.

We know that central tolerance – removal of T cells specific to self antigens – is not 100% efficient and some self-reactive T cells do accumulate in the periphery. This leaky process is likely responsible for some autoimmune reaction observed in humans. However, how such self-reactive T cells are activated remains poorly defined. The authors developed an interesting system where they have T cells recognizing a specific antigen that was engineered to be expressed in lung epithelial cells (OVA + 2W + gp66). By using the antigen with several epitopes this allows to investigate how T cell response to one of these epitopes impacts endogenous immune response to other epitopes. Interestingly, authors found that transfer of T cells specific to gp66 epitope into mice does not result in inflammatory response to 2W epitope by endogenous, 2W-specific CD4 T cells. Instead, the authors observed expansion of 2W-specific Tregs. Response was different in the lymph vs. lung. Interestingly, after primary response, immunization with 2W peptide with an adjuvant did not result in expansion of conventional, 2W-specific T cells indicating induction of tolerance. Expansion of 2W-specific Tregs was also observed by intranasal inoculation of LPS into mice. Overall, this study provides an interesting view on how ongoing immune response may influence response of self-specific CD4 T cells.

Positive feedback.

There are a lot of interesting things about this paper. First, the system to have lung-restricted antigen that has several well defined epitopes is highly innovative. The methodology to accurately count the number of naive T cells in the whole mouse (we talk about 10-100 cells per mouse!) is impressive. Looking at endogenous response, without transfer of monoclonal TCR-Tg T cells is really fundamental. The way how authors look at two tissues - lymphoid (lymph nodes) and lung - is important. The use of LPS injection as a model for lung injury is interesting as it also allows to look at actual pathology (mouse weight) as a medically relevant read-out. The text is short (perhaps in some places too short, see below for comments) and figures are relatively clear (see comments). Having an experimental layout for how the mice were treated, along with what was harvested for each experiment was very useful. Finally, having many different lines of mice is very impressive!

Major Concerns

I do not understand how transfer of naive T cells results in pathology in the lung (Fig 1 results). Per basics of immunology, 3 signals are needed to activate T cells - i.e., there is a need of inflammation to induce immune response and trafficking to the lung. Perhaps activated T cells were transferred but that was not clear from experimental design in Fig 1. Authors must provide better rationale of how transfer of naive T cells causes IgM in BAL to increase. Tracking immune response of transferred cells (e.g., activation markers, division history by CFSE, cell numbers in LNs/spleen over time) would be needed. Also, it would be very important to perform titration experiments to show how the number of transferred T cells impacts pathology. Similarly, why day 7 was chosen as the point to measure the endogenous response was not clear.

While measurements of T cells in lymph nodes and spleen are typically efficient (most cells are recovered), isolation of activated T cells from nonlymphoid tissues, especially the lung is highly inefficient and may be biased (some subsets could be better isolated than others, PMID: 25957682). Confirming the results of Treg bias in lung samples must be done with using microscopy. Furthermore, when T cells are isolated from tissues due to contamination with the blood, cells in the circulation may be detected as in the parenchyma (24385150). Experiments must be repeated to include intravascular staining to separate cells in the blood vs. parenchyma to indicate that Tregs in the lung are in fact in the lung.

I found it weird that the authors claim that 2W-specific Tregs are responsible for suppression of endogenous responses to 2W upon antigen+adjuvant injection and yet, depletion of Tregs did not result in a new response. A simpler interpretation is induction of anergy in endogenous T cells upon exposure to Ag in the absence of strong inflammation. Text must be carefully curated to avoid bias towards one favorite explanation.

Focus on SLOs and lung is clear but I wonder if using another control peripheral tissue that did not express the antigen could be useful. For example, measuring T cell accumulation in the liver may be a useful control.

It was not clear if expression of OVA is actually restricted to the lung. Perhaps some more thorough analysis of other tissues would be helpful to verify the absence of leakiness of the gene expression.

Minor concerns

Having numbers for lines in the paper could allow for better referencing to specific statements made in the paper.

While for most immunologists Tregs are FoxP3, some younger researchers may not know this. Mentioning that this is how you define Tregs would be useful. Also, assessing the function of these T cells would be useful.

Please do not use “ns” or “**” to denote statistical significance. Use actual p values, e.g., p=0.34 or p=0.012. Additionally, indicating fold difference between groups (effect size) could be also useful.

In introduction: Whether autoimmune responses are driven by naive T cells or by cross-reactive memory T cells is unclear. Cross-reactivity may be a simpler explanation given that memory T cells may require lower thresholds for activation.

Authors should describe better different epitopes used in the construct, e.g., gp66 is from LCMV.

Why did authors use gp66-specific CD4 T cells and not OVA-specific OTII cells? Are the results the same is using T cells of a different specificity?

Are the detected Tregs derived from the thymus or are these “converted” naive T cells to the Treg phenotype? I don’t think that the current data allow to discriminate between these alternatives.

When indicating difference in expansion in the Results section, please indicate how much (how many fold) is that expansion.

How is the lung injury by LPS dependent on the LPS dose? Perhaps this needs to be discussed.

I wonder that measuring kinetics of response, e.g., before day 7 and after, may be useful. We know that exposure to self antigens typically results in deletion of naive CD8 T cells (10843383)

Which specific LNs were isolated? This probably should be listed in materials and methods section.

I wonder if plotting some data as paired (e.g., Fig 1 - 2W vs. SMARTA) could reveal some additional information.

How were Tr1 cells gated? Some flow cytometry graphs may be useful here (Suppl Fig S2)

Suppl Fig 3 would benefit from experimental design panel.

On 2017-05-06 00:07:15, user Ruslan Soldatov wrote:

Dear Authors,

nice job with aligning trajectories! I have a technical concern about terminal branch F1. If it emerges predominantly because of the cell cycle signature, it can reflect cell cycle-induced heterogeneity, but not differentiation outcome. Did you try to reproduce this branch after removing cell cycle effect?

On 2023-09-04 10:54:36, user Monika Cikeš wrote:

It is an interesting article, especially since it combines in vitro and in vivo research. However, I could not find the number of mice in the study, which would add more value to the research. Moreover, it would be interesting to investigate the role of ? and ? subunits of AMPK on other cervical cancer cell lines.

On 2020-10-26 22:10:00, user David Ianova wrote:

The authors write "we did not recombine any species name in the LCVP" but I can see hundreds of "comb. ined.", e.g. Silene saxosa (A.P.Khokhr.) comb.ined.<br /> Also the automated matching seems to create many errors. e.g. Melandrium gracile Tolm. (from Siberia) is listed as a synonym of Silene gracilis DC. (from the Medit.), clearly a lot more work is needed to make this scientifically defensible.

On 2023-08-03 11:42:55, user Anthony Baptista wrote:

The following Github contains the R code developed for this research: https://github.com/anthbapt...

On 2020-04-29 12:34:20, user Ural Yunusbaev wrote:

Is the 225Mb honeybee genome small enough for the Tapestry?

On 2025-08-26 09:21:59, user Constant VINATIER wrote:

Feedbacks about your preprint : https://doi.org/10.1101/2025.07.23.666382

About registration: <br /> We could not find any information about the pre-registration of your study in the pre-print. Pre-registration involves documenting the hypotheses, methods, and/or analyses of a scientific study prior to its conduct (10.1073/pnas.1708274114; 10.1038/s41562-021-01269-4). If your study was pre-registered, we strongly encourage you to include the registration number in the pre-print, ideally in the abstract make this important information easy to retrieve, as this practice enhances transparency and reproducibility. If the study was not pre-registered, this should be acknowledged as a limitation. For future studies, we recommend pre-registering on an appropriate repository.<br /> About Protocol Sharing: <br /> We did not find the protocol for your study. If you have one, we encourage you to share it as supplementary material or deposit it in a publicly available repository such as the Open Science Framework ( https://osf.io ) or Zenodo ( https://zenodo.org/) "https://zenodo.org/)") . You can then include a statement in the Methods section indicating that your protocol is openly available (e.g., 'The protocol for this study is available at (link)/ in the supplementary'). Sharing your protocol will help readers better understand your study and enable them to reproduce it if they wish to test it.<br /> About the Statistical Analysis Plan Sharing: <br /> We did not find the Statistical Analysis Plan (SAP) for your study. If you have one, we encourage you to share it as supplementary material or deposit it in a repository such as the Open Science Framework ( https://osf.io ). You can then include a statement in the Methods section indicating that your protocol is openly available (e.g., 'The SAP for this study is available at (link)/ in the supplementary'). Sharing your SAP will help readers better understand your study and enable them to reproduce it if they wish to test it.<br /> About Deviations and/or changes<br /> We could not find any information about potential deviations or changes to the protocol in your pre-print. Since such deviations are common, if this applies to your study, we strongly encourage you to include a subsection titled Changes to the Initial Protocol in the Methods' section and discuss these changes as a potential limitation of your results. If any deviations occurred during your study, please specify them in this new subsection.<br /> About Data sharing / FAIR Data<br /> We found insufficient information about your data sharing approach. Data should be findable, i.e. data are to assigned a globally unique and persistent identifier (for instance there is a DOI assigned to the dataset, or data are registered or indexed in a searchable resource). Data should also be accessible, i.e. data are retrievable by their identifier and can be accessed following an open, free, and universally implementable protocol. As your data id not sensitive data, we encourage you to share it openly on a data sharing repository (Dryad, etc.) and include the Digital Object Identifier (DOI) in the Methods section. If you want more information about good practices of data sharing, visit https://www.go-fair.org/ <br /> About Code sharing<br /> We could not find any information about your (statistical) code. Sharing code is important for enhancing transparency and reproducibility, especially since it does not contain sensitive information. We encourage you to openly share it on a code sharing platform (Github, Codepen, CodShare, etc.) and include the Digital Object Identifier (DOI) in the Methods section. If you want more information about Code sharing https://fair-software.nl/ .

On 2024-01-18 16:00:45, user Richard H. Ebright wrote:

ACE2 humanized mice are the standard experimental model, and best-available experimental model, for assessing pandemic potential of SARS coronaviruses in humans.

ACE2 humanized mice possess human SARS-virus receptors and are studied, expressly, because they can predict infectivity, pathoigenicity, and pandemic potential of SARS viruses in humans. "Normal" mice do not possess human SARS-virus receptors and therefore cannot, and do not, predict infectivity, pathogenicity, and pandemic potential of SARS viruses in humans.

If the authors actually believed the claim that "outcomes cannot be applicable to humans" they would not have performed the research, and they would not have written "This underscores a spillover risk of GX_P2V into humans."

On 2021-10-29 18:46:31, user Kevin Tyler wrote:

This is lovely work and credibly resolves a lot of discussion about what might be going on in a robust and well evidenced piece of work.

On 2020-06-06 01:35:56, user azhao wrote:

The title of this report gives an impression that this report has discovered the origin of the novel Croronavirus that causes COVID-19 disease. But it does not: "For these reasons, we cannot rule out an origin for the clade of viruses that are progenitors of SARS-CoV-2 that is outside China, and within Myanmar, Lao PDR, Vietnam or another Southeast Asian country". The title could be more specific to the work. which is using "a phylogeographic Bayesian statistical framework to reconstruct virus transmission history between different bat host species and virus spatial spread over evolutionary time".

Under "Ancestral hosts and cross-species transmission" section: "The phylogenetic reconstructions for ?-CoVs in China suggest an evolutionary origin within rhinolophid and vespertilionid bats (Fig. 2A)". And later, "Chinese ?-CoVs likely originated from vespertilionid and rhinolophid bats (Fig. 2B)". So both ?-CoVs and ?-CoVs are likely originated from the same two kinds of bats?

Near the end of Discussion section, "Importantly, the closest known relative of SARS-CoV-2, a SARS-related virus, was found in a Rhinolophus sp. bat in this region(20)", What is "this region"? There is no context to show the name of "this region".

A question about Figure 1.B Map of China provinces, why the CoVs sequences for Shanghai and Jiangsu are not available?

On 2025-05-21 11:40:21, user Stefano Pagliara wrote:

This manuscript has now been published in PLoS Biology https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3003155

On 2020-03-27 10:01:00, user Amos Bairoch wrote:

Very nice paper. You can add in your final version that your new A549 DHODH-/- cell line will be assigned the RRID: CVCL_YZ46 in the Cellosaurus.

On 2020-11-23 03:57:31, user ygc_smu wrote:

The ggtree 2017 paper was selected as a feature article to celebrate the 10th Anniversary of the launch of Methods in Ecology and Evolution. https://methodsblog.com/202...

On 2020-07-03 16:41:10, user Laura Sanchez wrote:

Dear Eldjárn et al, this preprint was discussed in a lab meeting and we would like to offer the following for review. Thank you for posting this very interesting manuscript. Best, The Sanchez Lab

The manuscript by Eldjárn et al. describes the development of a computational method to enable large-scale linking of gene cluster families (GCFs) and molecular families (MFs). This method uses multiple complementary scoring functions, combining both feature-based and correlation-based approaches, which, the authors state, allows for more effective prioritization of valid links between GCFs and MFs than using the individual scoring functions. The manuscript provided a very nice summary which integrated information from many different fields to set up the problem they were trying to address in the introduction. However, the utility of this method could not be tested as the documentation was absent from the repository links and the manuscript could benefit from a concrete example (actual metabolite linked to a specific BGC) to more effectively show its advantages over current techniques. Below is a list of major and minor critiques for this preprint.

Major:

Figure 1 could be re-done to better visually demonstrate the problem the authors are trying to address. For instance, a box around the higher scoring population would be helpful for the reader to understand the problem as the numbers in the figure legend are difficult to correlate to the visual. The purpose/conclusion for Figure 1B is unclear. Moreover, Figure 1B is unrelated to 1A and out of order in terms of where IOKR was discussed in the manuscript.

Figure 2 shows a problem with the range of the expected value and variance (which varies with GCF and MF size) before standardizing the correlation score, yet, there is no chart to show how this changes after the correlation score is standardized. The authors should consider adding charts to show how this changes after the score is standardized, as well as an interpretation to help the reader understand why this change was necessary. For example, it is unclear whether a yellow (high) or blue (low) score is more desirable and it is unclear what the ideal distributions would look like. Additionally, the scales on the charts are inconsistent and make them difficult to interpret since they utilize the same color gradient. We suggest labeling the scale high-low rather than numbered if the values are not comparable. The authors should consider inverting the Y axis so it starts with zero at the bottom and increases as you move up the chart.

It is unclear how Figure 1 and Figure 2 are related. Is Figure 2 explaining how the problem in Figure 1 was fixed? If so, the authors should consider combining the two figures such that Figure 1a and Figure 2 are combined and Figure 1b is presented later in the text along with the section discussing IOKR framework.

The authors should consider using more concise language to help communicate the utility and limitations of this method more effectively. For example, the authors should use the term “bacteria” instead of “microbe” because the databases feeding into the program are heavily biased toward bacterial metabolites, and fungal metabolites are not well represented nor are they in the three datasets tested in the manuscript. It would also be worth considering giving the new score introduced in this manuscript a name, and referring to it by that name throughout the paper to avoid confusion.

This manuscript could benefit from a concrete example (actual metabolites linked to specific BGCs). A firm example with compound names linked to a specific gene cluster would help the reader evaluate how well the method performs compared to traditional methods. The manuscript does evaluate the performance of this technique using “verified hits”, but the identity of those hits and how they were verified remains unclear (unless the verification was the original report, which was also somewhat unclear). For example, the BGC listed at the top of Figure 6 (BGC0000137) encodes rifamycin, a commonly known bacterial metabolite. The authors should consider revealing the identity of at least one verified metabolite and providing a list of “hits” for the BGC encoding that metabolite with associated scores. This would allow readers to more effectively evaluate the new scoring method and determine what a “good score” looks like. A good choice for an example would be a metabolite/ BGC pair where a link was observed using this method and not other methods..

We appreciated that the authors discussed the data dependent limitations. They were apparent when reading the manuscript, and although they were briefly addressed in the discussion section, they might be more thoroughly discussed. The authors should consider drawing attention to biases toward specific organisms or metabolite classes in the databases feeding into the program, and discuss how those biases might affect the results and limit the usefulness of this scoring method to specific applications.

In Figure 6, for BGC0001228, it appears as though IOKR alone provided a higher score for the verified link than the combined score. Can the authors comment on the features of the data that led to this anomaly, and provide suggestions on how to determine the best scoring method?

We are excited at the prospect of using this tool. At the time we accessed the paper for discussion on 6/23, NPLinker did not have any documentation in the github repository so we were not able to evaluate how well it functions. The authors should provide comprehensive documentation. Additionally, a figure outlining how to use NPLinker to analyze a real dataset would be helpful, either in the manuscript or documentation.

Minor:

The metabolite used as an example in the introduction (C35H56O13) has a very unique molecular formula which is easy to link to a BGC (if the metabolite product is known), for example, NP Atlas only returns one hit for this molecular formula. The authors should consider picking an example that would have multiple hits.

The bottom of section 2.2 states “In the case of Figure 1, the standardised scores<br /> are 0.0 and 2.65, favoring the right-hand pair”, but in Figure 1 the two scenarios are positioned vertically, not horizontally.

The caption for Figure 5 says verified links are colored green, but in the figure they are red.

On 2019-01-23 10:38:42, user Nour Alhanafi wrote:

Thank you for the nice explanation of KPG-miner tool and its application. Pathway analysis is very important for gaining insight into the biology of deferential expressions in different conditions and increasing the explanatory power. However, there is so far no consensus definition of a pathway. Furthermore, different pathway databases have different representation of the same pathway, sometimes lack the contextual information and may even have some contradictions. I believe this tool may help the researchers, with no programming knowledge, analyze their high-throughput experiment results. However, in my opinion, it is still not sufficient to have one database as a resource to interpret the results. I think it is better to integrate the knowledge deposited in the major pathway databases. Therefore, I suggest you extend KPG-miner tool to aggregate functional annotations from many pathway databases.

On 2020-01-26 23:06:15, user rikster wrote:

R naught has almost no predictive value. Post mortem value only. For example, it never takes seasons into account, Norovirus for example. Its not only seasonal but the British call it Winter Vomiting Disease and they euphamize most everything!

Influenza is highly seasonal and its not one bug either. In fact its practically a variant or multiple variants for every infected person. What's its R naught? It depends not only on the bug but events in the season which we at welloinc.com have the data organized to show. We saw Wuhan on December 30th go through a low friction few days for big spread. Twice again in January. With a 10-14 day incubation period (spitballing), the events combined were resonant and hence the beginning of the epidemic. We see more to come. We can only see 3-5 days ahead. The problem with knowing this is the problem of the invisibility of infection. Countdown 10-14 days to knowing how things went. Right now we expect events from last week to see large upticks of cases in early February.

Not pretending to know much about this new coronovirus but the events that drove Wuhan are different than SARS2003. That SARS2003 blew up in and around South China gave it friction for spreading. I have all the indoor humidity data from 2003, most cities in the world. You'll see Toronto with very little friction, Hong Kong in March with three periods of low friction (high spread). You'll see the ease of spread at Amoy area (sewerage leak) and the days where the ease of spread at Prince of Wales led to two big waves in the hospital to visitors and health care workers.

We automated the marvelous work that Singapore did with SARS in 2003. They shut it down faster than any other country. They did it at borders, schools and hospitals. Turned out that except for one case, all the cases came from non-patients at hospitals.

This is a standard of care in some hospitals in the US. Also childcare and jails. www.welloinc.com for more informaiton rik.heller@welloinc.com . Put an exclamation mark or mark it important so I'll notice it over my daily deluge. Rik

On 2020-02-05 19:13:31, user Llevar wrote:

This manuscript is now published Nature Biotechnology - https://www.nature.com/arti...

On 2022-06-25 19:02:04, user Tom Kerppola wrote:

A paper that examines the specificity and mechanisms for Keap1 moderation of the transcription of virus induced genes is now available on the Immunology web site with the doi 10.1111/imm13527. The URL is https://onlinelibrary.wiley.com/doi/10.1111/imm.13527

On 2023-01-30 14:31:40, user Leduc cécile wrote:

"In vitro, they used a fixed vimentin-Y117L mutant, which stopspolymerizing at ULF stage." No, we used this mutant in cells, not in vitro. Please read more carefully the paper from Vicente et al and correct your statements which are not accurate.

On 2025-10-31 06:25:41, user xiaojun_ wrote:

I’m curious, how do you control for sampling bias in geography or collection time, given the uneven GISAID data? And do you think this SHAP-based framework would still hold up for viruses with strong recombination signals like SARS-CoV-2?

On 2020-12-15 04:51:49, user Jaelyn Rae wrote:

Does this suggest how Covid-19 might impact those with rare metabolic conditions, such as acute hepatic Porphyrias, or more specifically, Hereditary Coproporphyria? Would the metabolic disruption caused by Covid-19 cancel the overproduction of porphyrins in those with these rare genetic mutations?

On 2017-03-07 11:34:27, user Micheal H wrote:

I enjoyed reading this paper, I like it. My rudeness isn't coming from a dislike of your paper it's coming from confusion and anger. What's the point of sequencing 127 genome from Neolithic peoples who we already have plenty of DNA from? Why not expand passed Eastern Germany, Hungary, and Northern Spain? Why are you guys so stuck on those locations?

What was the point of this paper? What new discoveries did it make? To be honest it was a complete waste. Your skills would have been put to better use getting Neolithic DNA from regions other than Eastern Germany, Hungary, and Northern Spain.

Maybe try the Ukraine or Syria or Iraq or Italy next time.

On 2019-08-31 05:38:48, user AbdulB1 wrote:

Author should do further research about bacterial Flora in the gut if they insist something is different in the gut. They should also make probiotics and perform clinical trials

On 2020-02-01 09:42:54, user cyclin cyclin wrote:

Both coronavirus and HIV are not DNA genomes, but RNA...

On 2020-01-31 21:32:38, user Jason Weir wrote:

I blasted each of the four 2019 - nCoV inserts shown in Table 1 and received 100% identity with a number of other hits other than HIV-1. For example, BLAST-P results for insert RSYLTPGDSSSG received 100% identity with Spike glycoprotein from a bat coronovirus with Genbank accession number GenBank: QHR63300.1. It is thus much closer to a known bat coronovirus than it is to HIV-1. Likewise each of the other three inserts have 100% amino acid identity to other non-HIV sources. The paper is thus highly misleading in that it does not discuss the other blast hits to non HIV-1 related sources, some of which have higher similarity than those from HIV-1. The implications of the paper that 2019 - nCoV coronovirus has elements of HIV-1 virus inserted into it should be treated with skepticism.

-Jason Weir (Department of Ecology and Evolutionary Biology, University of Toronto)

On 2022-01-20 09:38:57, user Martin R. Smith wrote:

Good to see the caution in using the RF distance here. You might be interested in assessing accuracy using alternative methods that can be used where a "true" topology contains polytomies, such as the information-theoretic generalised RF distances of Smith (2020, Bioinformatics; https://doi.org/10.1093/bio... ) or the uncertainty-adjusted "Similarity to Reference" quartet distance of Asher & Smith (2022, https://doi.org/10.1093/sys... ; implemented at https://ms609.github.io/Qua... ).

On 2024-03-24 15:18:31, user smd555 smd555 wrote:

When is the data planned to be published and available?

On 2020-02-24 23:33:21, user Fraser Lab wrote:

This manuscript by Jones and colleagues probes the structural and functional consequences of beta-2 adrenergic receptor (ß2AR) mutations through a deep mutational scan (DMS). The authors develop a transcriptional reporter-based assay to test the effects of amino acid positional mutations on ß2AR signal transduction, and further inform their findings with unsupervised learning algorithms to predict functionally critical and structurally conserved GPCR features.

Studying GPCRs at both a structural and signaling level has the main challenge of GPCRs existing and working as a complex. This limits traditional biochemical and biophysical approaches toward unveiling nuanced structure-function relationships. Individual mutations might affect trafficking/folding, internalization, ligand binding, allostery, and coupling to binding partners (and likely some combination of any of these). The assay here lumps these together by measuring the transcriptional output (induces the transcription of a cAMP-responsive luciferase reporter) upon binding an agonist (isoproterenol).

The authors use this data to dig into generalizable GPCR mutational tolerance, population genetics to identify the presence of variants of clinical significance, and structurally score positions where mutations alter activity. Most of the paper is written from the perspective that the major outputs are the result of a functioning receptor at the membrane being able to bind to iso and transmit the signal (and we agree that this is the most parsimonious interpretation). However, there is a bit of a missed opportunity to identify outliers that may alter other areas (trafficking, endocytosis, endosome based signalling, which may be relevant for Iso in this system: https://www.ncbi.nlm.nih.go... "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5267521/)"). They also implement unsupervised learning to cluster positions based on mutational tolerance and chemical properties. The results here are not particularly surprising, but nicely distinguishes clusters based on the presumed physical chemical environment and functional constraints. The authors might want to comment on the potential for UMAP-style approaches to work across larger datasets of perturbations (many diverse chemicals rather than dose response of ISO) and what can be learned by changing the problem to clustering the inputs rather than the residues (as we did here by a simpler PCA approach: https://www.ncbi.nlm.nih.go... "https://www.ncbi.nlm.nih.gov/pubmed/30037883)") once more datasets are collected.

Overall, this provides an amazing benchmark dataset and a few novel findings. The major new finding of the study is an uncharacterized WxxGxxxC motif that is conserved across several species and GPCR subfamilies, in addition to carrying highly mutational intolerant residues. The authors ultimately propose that this "structural-latch" may play a role in stabilizing the extracellular transmembrane region, and potentially ligand binding.

The major weakness of this work is an experimental follow-up, even if small, to the main hypotheses provided from computational analysis. If mutations to the WxxGxxxC motif show a significant functional impact given the DMS data, and could impact the transmembrane interface for ligand binding based on structural information, why not test those mutations in an assay designed more to isolate that feature? Similarly, there is some discussion and testing of proline mutations on the C-term. These seem primed to alter some aspects of internalization and the interaction with that machinery. Pointing the way forward for how the multifaceted aspects of GPCRs can be dissected with high throughput assays would be enlightening (or pointing out why those dissections remain the purview of low through assays!).

Minor points/questions:

ADRB2 variants were synthesized in oligonucleotide microarrays split into 8 segments and integrated into the cell line. Additional details on the scheme and numbers/statistics on coverage, library wt representation, and evenness would be important to discuss and show – especially for reproducibility. (Rubin et al...Fowler, Genome Biology, 2017)

The authors conduct the DMS experiment under four different isoproterenol conditions and normalize measurements to forskolin treatments. Experimental details on the forsoklin activation in their assay or reference for this treatment would aid in interpreting the normalization approach.

There is discussion of the mutational tolerance of the intracellular loop ICL3, but what do you hypothesize is the reason ICL1 is generally intolerant of mutations?

What exactly distinguishes the globally intolerant clusters (clusters 1 and 2) in Fig 4? It seems there is a tighter range of activity to isoproterenol in cluster 2 than in 1 for all mutations and chemical properties, but does this get ranked differently than cluster 1?

A different color scheme for the logo plot in Fig 6A would improve clarity – it's difficult to distinguish F from L in the WxxGxxxC motif, making it look almost like an E.

Were all computational analyses done on Class A GPCRs or did you branch out at any point to investigate even more ancestral commonalities shared amongst classes? While ß2AR is a Class A GPCR, a short statement identifying it as such early in the text would be useful.

Labeling the first structure presented of the receptor in Fig 3E would be useful in orienting the reader.

We review non-anonymously and have posted this comment on the preprint at BioRxiv, James Fraser and Gabby Estevam

On 2017-07-22 08:07:27, user huthaifa wrote:

What do we mean by allele count and allele number for particular variant in the ExAc browser ?

On 2015-12-15 13:51:34, user Leslie Vosshall wrote:

Nice work!-very cool to expand the role of IRs in biology.

Here are my comments on the paper, ordered by appearance of line number<br /> in the manuscript not by urgency/priority

L139 what is the evidence that IR21a is endogenously expressed in<br /> DOCCs? Anything beyond the Gal4 line? Antibody/in situ? My former PhD student Kenta<br /> Asahina was the king of larval in situs, but that was done before the IRs were<br /> cloned!

L166 did you do a rescue of Ir25a just in the Ir21a DOCCs? Ir25a is<br /> *everywhere* which is a problem for claiming specificity

L192 I don’t understand how brv1 is showing such a clear phenotype, and<br /> not necessary for DOCC responses. Must be some other cells elsewhere that<br /> require this?

L195 I thought the conclusion here was overly strong given the strength<br /> of the evidence offered

L226 This is a great experiment—very elegant

L216 wondered why you did pan-neuronal Ir21a expression rather than<br /> just go with the much more selective HC>Ir21a. You could consider showing<br /> the HC result in the main figure, and putting the pan-neuronal as data not<br /> shown. Always makes me nervous to put a protein like that into *all neurons*

L246-253 could equally be a cell biological problem with trafficking<br /> unrelated to any functionally relevant co-factor. I would not be so forthright<br /> here (unless you have the answer in the form of the co-factor in hand already)

L257-259 I agree with this conclusion. I think Ir25a is receptor for<br /> heat just as much as orco is a sensor for ethyl acetate. It’s the wrong way to<br /> look at this. Of course a ‘co-receptor’ will have a selective phenotype, but<br /> it’s wrong to conclude that it is the subunit responsible for the specific<br /> sensory cue.

Figure 1 I don’t understand why you are doing huge temperature swings<br /> of 14oc vs 20oc. You say these neurons are extremely sensitive to small changes<br /> in temperature, why not image under those conditions. Also you have the chance<br /> to analyze the kinetics of the response to extract party of the answer. Since<br /> your temperature ramps are slow, you could calculate the onset of calcium<br /> signal and the rise time, etc.

Figure 2b I found the cartoon very busy and confusing. All I cared<br /> about was what the temperatures at the extremities were and that was not<br /> labeled

Figure 2c what is navigational bias, not defined?

Figure 2c are the Ir21 mutants actually PREFERRING the cold?

Figure 2 general: it looks like you are not doing single animal<br /> tracking here. Can you revise the data presentation to extract additional<br /> information on speed, turning, path tortuosity, etc etc rather than just a<br /> single index number.

Figure 3 very pretty! But could integrate into another figure because<br /> it’s making a single elegant point, while the other figures are pretty crowded.

Figures 4-5 my lab had the hardest time with this experiment. We<br /> understand what you are trying to do here, but it’s not easy to explain or<br /> understand. Isn’t IR21a already expressed in these cells? What happens if you<br /> overexpress Ir25a? or Ir8a? Or some other random protein? I worry that the<br /> small bumps in Figure 5d are some nonspecific problem with the neuron. Can’t<br /> exclude that with the current data? It looks like there is still some low<br /> amplitude cycling in Ir21a mutants? (Figure 4d, f). Do you think that’s real?

On 2018-08-13 13:06:38, user Alan S Bias wrote:

Final published version of article can be found at: http://www.pr.bioflux.com.r...

On 2021-01-16 10:15:47, user Phil Carl wrote:

This article has now been peer-reviewed and published in the Western Ocean <br /> Journal of Marine Science in 2018. The correct citation of this paper <br /> is:<br /> Laboute P, Borsa P (2018) A feeding aggregation of Omura‘s whale <br /> off Nosy Be, Mozambique Channel. Western Indian Ocean Journal of Marine Science 17, 93-97. doi : 10.1101/311043

On 2021-02-18 19:33:07, user Asten Bryant wrote:

I would like to see an analysis that shows how much North African specific ancestry these people had.

On 2023-05-22 07:34:22, user Valerie Wood wrote:

Hi, please note that drp1 is a synonym for many human genes. https://www.genenames.org/t... you should use the standard name DNM1L to refer to the human protein. Also S. cerevisiae has many genes with the synonym DRP1, the standard name is DNM1 for this reason.<br /> If there is a problem, please contact HGNC or SGD.

On 2019-03-06 21:05:53, user Victor Cueto wrote:

This article was published in the journal Ornitologia Neotropical. The complete cite is:

Cueto, V.R. & C.A. Gorosito. 2018. Seasonal changes in bird assemblages of a forest-steppe ecotone in North Patagonia. Ornitología Neotropical 29: 349-358

The article can be downland in:

http://journals.sfu.ca/ornn...

On 2017-03-05 13:20:27, user Judith Yanowitz wrote:

Extends the work we recently published in Current Biology (Machovina et al, 2016) showing crossover specific stabilization of the SC.

On 2017-11-28 20:30:54, user Philip Adeyemi Adeniyi wrote:

Nice work.

On 2020-11-18 06:34:32, user Chaitanya wrote:

Im curious to see how the authors could distinguish between crowding effects and viscosity. Certainly a fascinating question, since if crowding doesnt reduce dynamics is it drag? <br /> - chaitanya athale

On 2021-08-09 08:24:42, user Jiangming Sun wrote:

All values in a row (black) are 0 in Fig. 1A. Shouldn't remove such row first before any matrix operations?

On 2021-10-26 02:22:47, user Lisa Pieterse wrote:

Very impressive and comprehensive work published on the changing immune landscape as a factor of ageing. Inclusion of immune cell composition and activation states provided an interesting gauge into the diminished nasal mucosal T cell reservoir, and perhaps overdependence on granulocyte function, in older adults. Has your group considered looking into Th17 subset composition amongst colonized and uncolonized children, young adults, and older adults? It would be immensely interesting to see if there would be a difference in Th17 populations within the nasal mucosa of older adults with or without S. pneumoniae colonization especially. As you point out in your paper, your group observed diminished T cell density and activation within older adults (Figures 1C, 1E, 1I), but did not include data on Th17 composition or Th17-producing cytokines such as IL-17, IL-21, or IL-22, although I may have missed this. Thanks in advance!

On 2021-11-05 11:28:22, user Ben Dubin-Thaler wrote:

Great work NYC virus hunters!

On 2022-08-24 20:22:59, user Paul Carini wrote:

Nice paper! I wonder if the extreme mis-estimation of growth rates by DNA or protein SIP could be explained by exuded substances used to form biofilms in soil. DNA and protein are both used to construct extracellular matrices in biofilms. Biofilms are also thought to be an important component of soil microbial communities. DNA or protein that makes it into a biofilm would presumably be labeled by stable isotopes. This could be an example of non-growth related activity that would incorporate a label. Just a thought on an otherwise cool paper.

On 2018-06-23 20:26:42, user Maria Constantinou wrote:

I just read this preprint and I think the findings that subicular bursts have a special role in spatial coding are very interesting. The idea that subicular bursting has distinct information encoding capacity also agrees with some of our own observations that bursting neurons can encode features of the local field potentials, although our experimental setup did not allow us to investigate spatial encoding. These are the links to our relevant work if you would like to have a look: <br /> https://doi.org/10.3389/fnc...<br /> http://dx.doi.org/10.1016/j...<br /> I think the results presented in this preprint are an important piece in the puzzle of spatial navigation, for which we know mostly about the encoding mechanisms of the entorhinal cortex and hippocampus and much less of the subiculum. Looking forward to see the final version.

On 2020-03-26 20:03:35, user William J Warman wrote:

New preprint. A dynamic 6,000-year genetic history of Eurasia's Eastern Steppe.

On 2017-03-16 11:57:37, user Eugene Katrukha wrote:

Great work! It is a pity that Supplementary Movies and Figures are not available. <br /> There is typo, KIF560 is (+) plus end directed kinesin (KIF5(1-560)).<br /> In addition, your measurements of kinesin run length/speed is in good agreement with our data (1.32 um/s and 1 um) using QD+GFP-nanobody in live cell, check http://biorxiv.org/content/... (soon to be published)

On 2016-08-19 17:14:35, user Robert Bruggner wrote:

Very cool comparison guys! Nice to see this sort of rigor and systematic approach to this problem.

A couple of thoughts that probably don't change your conclusions substantially:

FWIW, the "Wards" linkage method as implemented in Rclusterpp runs much faster and produces better clustering results (than the average / euclidian combo) by my previous evaluations. I just clustered all events in the 2015 Levine Marrow 32 dataset on my 2013 Macbook (4 cores) in about 25 minutes.

The "standard" hierarchical clustering methods that Rclusterpp implements are not tuned or designed specifically for flow cytometry data + the population identification problem and I believe many of the methods in your comparison are. In some sense, this sort of presents an "apples to oranges" comparison situation. At the very minimum though, it is a really nice demonstration of how much better domain-tuned algorithms perform relative to generalized clustering algorithms. In that respect, I think there are some useful adaptations of hierarchical clustering that make it better suited to flow cytometry work, but obviously I'm biased :)

On 2016-08-09 14:48:31, user Jeremy Leipzig wrote:

Interesting approach although mtDNA is not targeted by exome sequencing and variant callers are not thinking about haploid "chromosomes". Still, if the reads are there then they should not be returning that many FNs at such depths. Paper did not address the potential for NUMTs to be playing a role.

On 2025-05-05 19:39:43, user ??? wrote:

Manuscript lacks mechanistic explanation of how SSNA1 regulates centriolar architecture for ciliogenesis and a clear theoretical framework to contextualize its role in centriolar biology. Findings are disconnected, needing a cohesive model to unify SSNA1’s function in ciliogensis.

On 2021-12-13 15:01:53, user Dr Josh Berryman wrote:

Please note that we do not refer to spike (S) protein in this paper.<br /> Our findings mainly discuss ORF6 and ORF10 and do not have relevance to any current vaccine.