On 2017-01-11 02:55:17, user Nathan Pearson wrote:

Eager to read this! But please indulge us pedants, and retitle as 'Deconvolving...' (Yes, usage evolves, but hopefully it doesn't evolute...;-)

On 2023-12-15 00:42:07, user Cristy Mendoza wrote:

Hi. I found your work to be fascinating and very relevant due to frequent antibiotic resistance occurring with many different bacteria. The importance of the work was very well emphasized. However, there were parts of the paper that were confusing and restricted my ability to understand the conclusion of the work performed. Below are a few suggestions that I hope you take into consideration:

• Besides a few experiments, the statistical analysis tests were not listed in the paper. It is highly significant the test that is being performed is well known to the audience so that they can interpret how the data's significance was gathered.
• In Figure 3, there are significant stars on the graphs, yet no test is listed for the audience to know. Additionally, I am still unaware of what is being compared in these figures and what the significance ultimately represents.
• Presenting Figure 7 earlier in the paper and fully describing the xenophagy pathway that is proposed before LC3 and Bafilomycin, for example, are introduced in experiments will be very helpful. Because there was little to no explanation of LC3 and Bafilomycin in the paper, I initially did not understand why they were tested in the experiments.
• Carry out more experiments that prove the xenophagy pathway to be true. For example, in Figure 7, mTOR is shown to be in the pathway, yet no tests were performed to make sure this aligns with the theory.
• Avoid using similar controls to statistically compare it to many experiments carried out, such as Figure 1. This is because the more statistical tests that are run using the same control for the different treatments, there is a higher chance that one may result significant out of chance. What would be suggested would be carrying out control experiments for each treatment group.
• The sample sizes are never given for experiments. To rely on the data and that there is not too little power, it would be very helpful to know the sample sizes of experiments.
• Labeling is preferred to be consistent. For example, if "Baf" is used to indicate Bafilomycin, that abbreviation should be used to refer to Bafilomycin in all figures.<br /> This paper is very interesting and has the potential to go further as it is very important to understand how and why bacteria can resist antibiotics. I wish you all the best with the paper!

On 2017-08-19 10:24:56, user Hideo Fushimi wrote:

I can't find any "Dataset" (p.43) on denoted GEO archive GSE92742 (https://www.ncbi.nlm.nih.go... "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE92742)"). Do someone happen to know where are they?

On 2017-10-30 20:57:46, user N.M. Shahir wrote:

Fascinating paper! Two questions though; 1) I'm curious why you didn't compare your results to DESeq2? and 2) I'm curious as to why you didn't also look into batch effect in alpha diversity measures?

Best,<br /> N

On 2017-12-20 17:24:43, user Peter Woody wrote:

A link in the paper is dead:<br /> https://data.broadinstitute...

On 2017-11-20 17:39:07, user Craig Kaplan wrote:

This paper is truly excellent- very much appreciate the careful and comprehensive approach. I am wondering if the authors were aware of this recent, relevant work from the Becksei group, supporting the observation of much shorter half-lives, and examining correlations among previous approaches for mRNA decay : http://advances.sciencemag....

On 2020-04-02 21:09:56, user Sinai Immunol Review Project wrote:

Potent human neutralizing antibodies elicited 1 by SARS-CoV-2 infection

Ju et al. 2020

Keywords: monoclonal antibodies, neutralization, antibody cross-reactivity, Receptor Binding Domain

Summary

In this study the authors report the affinity, cross reactivity (with SARS-CoV and MERS-CoV virus) and viral neutralization capacity of 206 monoclonal antibodies engineered from isolated IgG memory B cells of patients suffering from SARS-CoV-2 infection in Wuhan, China. All patients but one recovered from disease. Interestingly, the patient that did not recover had less SARS-CoV-2 specific B cells circulating compared to other patients.

Plasma from all patients reacted to trimeric Spike proteins from SARS-CoV-2, SARS-CoV and MERS-CoV but no HIV BG505 trimer. Furthermore, plasma from patients recognized the receptor binding domain (RBD) from SARS-CoV-2 but had little to no cross-reactivity against the RBD of related viruses SARS-CoV and MERS-CoV, suggesting significant differences between the RBDs of the different viruses. Negligible levels of cross-neutralization using pseudoviruses bearing Spike proteins of SARS-CoV-2, SARS-CoV or MERS-CoV, were observed, corroborating the ELISA cross-reactivity assays on the RBDs.

SARS-CoV-2 RBD specific B cells constituted 0.005-0.065% of the total B cell population and 0.023-0.329% of the memory subpopulation. SARS-CoV specific IgG memory B cells were single cell sorted to sequence the antibody genes that were subsequently expressed as recombinant IgG1 antibodies. From this library, 206 antibodies with different binding capacities were obtained. No discernible patterns of VH usage were found in the 206 antibodies suggesting immunologically distinct responses to the infection. Nevertheless, most high-binding antibodies were derived by clonal expansion. Further analyses in one of the patient derived clones, showed that the antibodies from three different timepoints did not group together in phylogenetic analysis, suggesting selection during early infection.

Using surface plasmon resonance (SPR) 13 antibodies were found to have 10-8 tp 10-9 dissociation constants (Kd). Of the 13 antibodies, two showed 98-99% blocking of SARS-CoV-2 RBD-ACE2 receptor binding in competition assays. Thus, low Kd values alone did not predict ACE2 competing capacities. Consistent with competition assays the two antibodies that show high ACE2 blocking (P2C-2F6 and P2C-1F11) were the most capable of neutralizing pseudoviruses bearing SARS-CoV-2 spike protein (IC50 of 0.06 and 0.03 µg/mL, respectively). Finally, using SPR the neutralizing antibodies were found to recognize both overlapping and distinct epitopes of the RBD of SARS-CoV-2.

Caveats

Relatively low number of patients

No significant conclusion can be drawn about the possible correlation between humoral response and disease severity

In vitro Cytopathic Effect Assay (CPE) for neutralization activity

Huh7 cells were used in neutralization assays with pseudoviruses, and they may not entirely mimic what happens in the upper respiratory tract

CPE assay is not quantitative

Duplicated panel in Figure 4C reported (has been fixed in version 2)

Importance of findings

This paper offers an explanation as to why previously isolated antibodies against SARS-CoV do not effectively block SARS-CoV-2. Also, it offers important insight into the development of humoral responses at various time points during the first weeks of the disease in small but clinically diverse group of patients. Furthermore, it provides valuable information and well characterized antibody candidates for the development of a recombinant antibody treatment for SARS-CoV-2. Nevertheless, it also shows that plasmapheresis might have variability in its effectiveness, depending on the donor’s antibody repertoire at the time of donation.

Review by Jovani Catalan-Dibene as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

On 2020-08-17 18:22:11, user OxImmuno Literature Initiative wrote:

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

On 2020-08-20 19:12:48, user Kausik Bhattacharyya wrote:

https://bmcmicrobiol.biomed...<br /> link to the article

On 2020-06-08 13:55:57, user Simon Drescher wrote:

SDPC should be 1-stearoyl-2-decanoyl-sn-glycero-3-phosphatidylcholine instead of "2-decyl" - since two ester bonds are in SDPC.

Futher, I think the relevant literature is not mentioned in this article, for example:

Lewis et al. 1994 Biophys. J. 66, 207-216; "EnigmaticThermotropic Phase Behavior of Highly Asymmetric Mixed-Chain Phosphatidylcholines That Form Mixed-lnterdigitated Gel Phases" describing the the phase behavior of SDPC, the lipid used in this study.<br /> and

Lewis et al. 1994 Biophys. J. 67, 197-207; "Studies of Highly Asymmetric Mixed-Chain Diacyl Phosphatidylcholines that Form Mixed-Interdigitated Gel Phases: Fourier Transform Infrared and 2H NMR Spectroscopic Studies of Hydrocarbon Chain Conformation and Orientational Order in the Liquid-Crystalline State"<br /> ...to name only two.

Finally, statements such as "It is not currently known what biophysical properties membranes composed of asymmetric lipids will display" are not true, since these properties are already known. Also, the fact that asymmetrical phospholipids show chain interdigitation is not new.

On 2014-08-26 14:55:10, user Guest wrote:

Yes, an extremely interesting paper.

And I find it fascinating that an almost complete mtDNA NumtS has been found in the Algerian sequence HDGP01275.

But what is the Haplogroup ? <br /> And, I think it a missed opportunity that the authors have not determined this.

Unfortunately, the GenBank sequences KM281512-34 have not, as yet, been made available; but once the details of the NumtS are published it should be fairly easy to find the Haplogroup and make a suggestion as to the age of the NumtS directly from this.

On another point, I would like to point out that the authors have not directly referred to:<br /> Herrnstadt C, Clevenger W, Ghosh SS, Anderson C, Fahy E, Miller S, Howell N, Davis RE.<br /> "A Novel Mitochondrial DNA-like Sequence in the Human Nuclear Genome."<br /> Genomics. 1999 Aug 15;60(1):67-77.<br /> and it is unclear as to what their studies have revealed about this prominent NumtS of fairly recent origin. Is it one of the ones they are going to submit to GenBank ?

Also, the authors have not discussed the very old NumtS that are common to the Human, Chimpanzee and Rhesus Monkey.<br /> (see perhaps my paper at: http://www.jogg.info/51/ind... )<br /> and shown that their computer program is capable of identifying them.

On 2014-08-24 12:47:21, user Douda Bensasson wrote:

Thank you for a very interesting and carefully controlled study!

One thought I had when reading it, is that you could compare the observed number of polymorphic numts to the number that we predict under neutrality in Bensasson, Feldman and Petrov 2003, J. Mol Evol (http://tinyurl.com/n4y48t5) "http://tinyurl.com/n4y48t5)"). In this paper, we used the ages of fixed numts in the human genome to estimate the number of polymorphic numts under neutrality. Using our estimate of pi[numt] = 2.07, we would predict that in a sample of 989 individuals you would see only 15 polymorphic numts. Why do you see almost 10 times more? Maybe most of the low frequency numts you see are slightly deleterious? Or maybe the estimate of nucleotide diversity (pi[pointsubs]) that we used does not reflect your sample well and so does not control for demography? If you could estimate nucleotide diversity (pi[pointsubs]) for your sample you could make a prediction about how many polymorphic numts you expect under neutrality and so estimate what proportion are slightly deleterious. We would also expect that the number of high frequency numts (e.g. freq>0.1) would be close to the neutral prediction. If most of the rare numts you have seen are not neutral then this would have implications for the study of human disease.

On 2019-05-24 15:13:57, user Peter Ellis wrote:

Transcription and translation are ongoing throughout spermatogenesis. This model has no basis in known cell biology.

On 2016-08-25 02:19:55, user Russ Poldrack wrote:

This is a really interesting and thoughtful piece that will be important reading for anyone in the field of cognitive neuroscience. I have a few comments that I hope will help make it clearer and more accurate.

• "BOLD response may spillover 3 to 5 millimetres away from neural activity because the brain supplies blood to adjacent areas — it “water[s] the entire garden for the sake of one thirsty flower”" - This is mixing together a couple of issues. It's true that the hemodynamic response is broader than the neuronal activation, but not by 3-5 mm. The “flower-watering” effect is probably on the order of hundreds of microns. The substantial spread in standard (i.e. 3T gradient-echo BOLD) fMRI is due primarily to the fact that this imaging technique has substantial contributions from venous signals that can spread fairly far from the neuronal activation.

• "Extraneous to the actual imaging itself, most statical models require some spatial smoothing in addition to the smoothing that is intrinsic to fMRI data acquisition.” - misspelling of statistical. also, I would disagree with this claim - it is increasingly common to analyze data without any smoothing, especially when one is not relying upon Gaussian random field theory.

• "Neural similarity is not recoverable by fMRI under a burstiness coding scheme.” - this seems to rely on the strong assumption that burstiness is just like regular firing, only with a different temporal organization. this is far outside my knowledge base, but I can imagine that differences in the synaptic physiology of bursting vs. constant firing might be evident from BOLD. Also, see this regarding synchrony: http://www.mitpressjournals...

• The general conclusions seem to rest heavily on the specific deep networks used in this analysis, which are trained on the categorization problem. Thus, it’s not surprising that the high-level representations show less overlap between categories - the training has worked! However, it’s not clear to me how well categorization training approximates what mammals learn as they come to perceive the world. It would be useful to have additional discussion regarding the impact of this particuclar topic on the generality of the conclusions.

On 2021-11-19 13:27:22, user UAB BPJC wrote:

We (the Bacterial Pathogenesis and Physiology Journal Club at the University of Alabama at Birmingham) read this manuscript this week with great interest. Our compiled comments are listed below. We hope the authors will find them helpful.

Introduction<br /> 1) The authors make the claim that “While several hundred ISGs with various known functions have been identified, IFN has primarily been studied in its role in orchestrating anti-viral immunity. The role of IFN signaling in response to bacterial products, and how this may influence immune homeostasis in particular, is poorly understood.” There is a great deal of literature about the role of IFN signaling in non-viral responses, including bacteria (some of which the authors then go on to discuss). Several reviews in the recent years have collected this data in a way that gives a more complete understanding, including Gutierrez et al who seem to have published data in 2020 describing a mechanistic pathway by which beneficial bacteria activate Type I IFN signalling [1-4]. Perhaps the authors had a specific instance in mind (such as a mechanism, a specific type of response, or specific T-reg response), but in saying the role of IFN signaling in response to bacterial products is poorly understood in general dismisses a great body of work in the field.<br /> 2) The use of “tonic signaling” “tonic IFN expression”, and “basal IFN expression” is a little confusing. Consider clarifying what is meant by “tonic” and whether it is different from “basal” expression of IFN. <br /> 3) The final sentence in the authors’ introduction seems to be reversed, in that their data suggests that commensal microbiota promotes intestinal homeostasis via type I IFN signaling, whereas they say that type I IFN signaling promotes homeostasis via commensal microbiota.

Results<br /> 1) The authors discuss the expression of IfnB and Mx1 in their germ-free/monocolonized/specific pathogen-free experiment. Why is Mx1 important? What is it? Later on the authors identify it as an IFN-induced gene, but best practice would be to do so as part of the rational behind the experiment, rather then waiting until later to explain its significance. <br /> 2) Sample size irregularity and lack of error bars makes Figure 1A difficult to believe. <br /> 3) The “specific pathogen-free” mouse description is questionable for several reasons. Firstly, what pathogen is lacking? This is not addressed in the paper. Secondly, how were these mice developed? Were they developed by colonizing germ-free mice with a cocktail of microbes minus a specific one? Germ-free mice have many issues with immunological development that may skew or complicate the data, including issues in innate and adaptive immune cell development. In the methods the authors say they were ordered from Jacksons Laboratory, but there is no stock number given for these mice and a basic search does not return results that are “specific pathogen-free”.<br /> 4) In Figure 1A, there is no unmanipulated positive control (ideally a non-germ free WT mouse) for comparison. While the Specific Pathogen Free samples are a good indicator, they are still a manipulated strain. The authors would benefit from having a non-germ free WT mouse line as a control, especially considering the immunological developmental issues in a germfree mouse.<br /> 5) For Figure 1B, the Y axis is labeled mIFNb (pg/ml). For consistency with Figure 2B, the axis should be labeled “IFN? (pg/mL)”.<br /> 6) In Figure 1C there is no description of how the authors ensured only CD11c+ cells were being screened from the lamina propria tissue isolation. There is no described enrichment step in the paper, nor an isolation method. Additionally, the methods do not list a CD11c antibody in the flow cytometry list, which makes it difficult to interpret if the flow cytometry results of the pSTAT1 expression is gated off a CD11c+ population, a different population (such as CD4+ T cells), or total cells.<br /> 7) For Figure 2, the authors title the figure “B. fragilis induces IFN? expression in dendritic cells to coordinate Treg response”, but nothing in the figure discusses CD4+ cells, let alone Tregs. This figure specifically demonstrates that culturing with B. fragilis induces IFN? expression and downstream STAT1 phosphorylation in dendritic cells. While this may be involved in Treg development/activation, the data presented in no way demonstrates a direct connection between B. fragilis and Treg responses. Later on, the authors demonstrate this result, but in this figure the data does not support the claim made by the title.<br /> 8) From Figure 3 on, the authors no longer use the GF cells in their experiments, which is a disappointment, as they had such a district deficit in type I IFN signaling. Using the IFNAR-/- mice accomplish the effect of preventing type I IFN signaling but doing the same experiments using BMDCs from GF mice with and without a B. fragilis pulse would be interesting and would perhaps strengthen the argument that the commensal bacteria are important in both priming and driving type I IFN signaling. <br /> 9) For figure 3A, the axis is confusing, as the total population is not clear – is it 10% of all cells in the well are CD4+, Foxp3, and IL10 positive? Is it 10% of all CD4+ Foxp3+ T cells are IL-10+? If the axis was renamed to be more in-line with the text, that would help clear up the message of the figure. The text indicates that you are discussing the % of Tregs that are producing IL-10, which seems most reasonable, but the current axis could suggest that it’s 10% of all cells in the culture, and the figure legend suggests that the y axis represents the % of CD4 T cells in the culture that are Foxp3+ IL-10+. <br /> 10) For Figure 3C, it seems as though this should be a figure by itself that comes before Figure 3, or at the very least be Figure 3B, because the claim of Figure 3 is demonstrated in the current Figure 3B, which shows that IFNAR signaling in BMDCs is required for IL-10 production in Tregs. The current figure 3C shows the effect of losing IFNAR signaling in DCs alone, and should therefore go before the effect of this loss in DCs on Tregs. By changing the arrangement of figures the story flows more cleanly: B. fragilis treatment of DCs drastically increases their ability to trigger IL-10 production in Treg cultures > Loss of IFNAR signaling in BMDCs drastically affects the expression of many IFN-regulated genes and eliminates the effect of B. fragilis treatment > The loss of IFNAR signaling also impairs the ability of DCs to trigger IL10 production in Tregs, regardless of B. fragilis treatment. Conclusion: B. fragilis primes DCs to trigger IL-10 production in Tregs in an IFNAR-dependent manner.<br /> 11) Also for Figure 3 in its entirety, the DCs used in this experiment (according to the methods) are IFNAR1-/-, while in the text and in the figure they are listed as “IFNAR-/-“. They still have IFNAR2 and this should be noted. <br /> 12) For Figure 4B, a more detailed explanation of how the authors developed this analysis and what it is intending to show needs to be provided. There is next to no explanation of this particular result, only what the authors take it to mean. There’s no explanation of what the parameters of tSNE1 and tSNE2 are, or why the MLN seems to have a cluster of BF cells in the lower left region while the cLP has them disbursed. Indeed, the text suggests that both the MSN and cLP have BF cells clustered, but the cLP plot shows a disbursement of these cells in all the regions… In all this is a confusing figure that doesn’t really add anything to the paper without clarification as to what it is supposed to be showing.<br /> 13) For Figure 4C, the genes need to be listed in the same order for effective comparison between the two tissues. At a glance, it appears that MLN and cLP cells have a highly similar expression pattern… however, the genes are not the same in these two datasets. BP treated MLN cells have Oas2 as the most upregulated gene while BP treated cLP cells have Ifit3 as the most upregulated gene. Looking at the figure as is now would suggest that the two populations are fairly similar, but in reality there are several genes that are differentially expressed between.<br /> 14) Also in Figure 4C, the gene set offered is a very small subset of the type I interferon responding gene family. While a small set of this subset are differentially expressed, what is the significance overall? This dataset needs more gene expression data to show a more complete picture and justify the claim that bacterial colonization induces type I IFN signatures in intestinal Tregs.

Overarching Comments<br /> 1) The general conclusion of this paper is that type I IFN signaling in DCs, induced by commensal bacteria, is essential for Treg activation:<br /> a. From the summary: “Bacteroides fragilis induced type I IFN response in dendritic cells (DCs) and this pathway is necessary for the induction of IL-10-producing Foxp3+ regulatory T cells (Tregs).” <br /> b. From the Introduction: “Notably, B. fragilis induced IFN? and type I IFN signaling in dendritic cells (DCs) are required for commensal induced Foxp3+ Treg responses”<br /> c. From the Results: “Thus, type I IFN signaling in DCs is critical for commensal bacteria to direct Treg responses, even when IFN signaling is intact in T cells.”<br /> However, Figure 3B shows that while it is important for activation, these T cells are still activated and IL-10 production is still triggered at substantially higher levels over the untreated controls. Essential would suggest that the inability to perform this signaling would completely inhibit the DCs’ ability to trigger IL-10 production, or at the very least bring the expression level of IL-10 down closer to untreated controls.

2) This paper represents a good first pass of the data, but the authors need to reevaluate the extent of their claims. There are several experiments that need to be either repeated with higher sample sizes (Figure 1A for example) or re-evaluated for a less broad interpretation (Figure 3B).

3) Figure colors should be reworked to make the differences more distinct. In Figure 1, for example, it is difficult to tell that the Bf samples are blue while the SPF samples are black. The RNA-seq data colors make it difficult to compare differences in expression in the middle of the spectrum (Yellow is different from blue, but blue-green is difficult to detect from green-blue).

4) In general, the authors show convincing data for commensal bacteria playing a role in this type I IFN – DC – Treg process. However, there are two major issues with the authors’ interpretations. The first is that they only show priming with commensal bacteria is necessary for these effects – maintenance is not discussed. Secondly, the use of words like “essential”, “necessary”, “inhibit”, and “required” are not appropriate in many conclusions, such as the title of Figure 3. While the lack of IFNAR1 signaling impairs IL-10 production, it does not inhibit it. There is a reduction but not a loss of function.

Summary Response:<br /> The authors make a good effort in unraveling a complicated mechanism of commensal microbes’ effects on host immunity. The authors present a good deal of convincing data that show that commensal bacteria effect the ability of dendritic cells to trigger Treg IL10 expression. A more rigorous investigation into the mechanism of this phenotype is warranted, however, as the data does not show this activity to be essential the Treg activation. <br /> From the data presented, the authors are safe in arguing that commensal bacteria like B. fragilis prime dendritic cells, making them more sensitive to type I interferon signaling and more capable of inducing type I interferon signaling in a manner that more effectively drives Treg activation (as measured by IL10 production). Additional experiments to measure other factors of Treg activity would bolster the authors’ claims. <br /> 1. Ma, Y. et al. (2020) The Roles of Type I Interferon in Co-infections With Parasites and Viruses, Bacteria, or Other Parasites. Front Immunol 11, 1805.<br /> 2. Kim, B.H. et al. (2011) A family of IFN-gamma-inducible 65-kD GTPases protects against bacterial infection. Science 332 (6030), 717-21.<br /> 3. Gottschalk, R.A. et al. (2019) IFN-mediated negative feedback supports bacteria class-specific macrophage inflammatory responses. Elife 8.<br /> 4. Gutierrez-Merino, J. et al. (2020) Beneficial bacteria activate type-I interferon production via the intracellular cytosolic sensors STING and MAVS. Gut Microbes 11 (4), 771-788.

On 2021-03-03 22:48:22, user Tesla wrote:

At least part of the claims are refuted here it seems: https://www.biorxiv.org/con...

On 2019-07-02 16:52:42, user Yeh Lab wrote:

Beautiful work, unexpected mechanism, and important finding! Thanks so much for sharing on preprint with the community!

On 2020-08-18 06:20:49, user Yishai wrote:

Unfortunately, this paper's claim seems to be an artifact. We have used the protocol described and indeed get similar-looking cells. However, RNA-seq revealed that they were, in no way, neuronal cells - rather than remained monocytes.

On 2018-06-14 16:29:05, user Barbara Koenig wrote:

Very exciting and innovative method - it will allow to open plenty of new approaches to study complex traits that are based on social interactions in rather "natural" groups of mice. The method goes much beyond what is currently available for unsupervised tracking of not only social interactions but also body postures and movements - all that in incredible details. Will be great to see the method being applied to various questions and topics where individual phenotyping is a must.<br /> Barbara Koenig, University of Zurich

On 2020-06-10 03:41:44, user Paul Gordon wrote:

Hi, thanks for posting this. I'm wondering if you will be depositing the P1 sequence in GISAID? I did not see this information anywhere in the manuscript.

On 2021-09-20 04:52:35, user Cyrille Delley wrote:

Hi Vincent and Simone,<br /> Thanks for sharing this interesting preprint. I was wondering, since you are using the TSO and pT primer during the PCR, wouldn't that potentially cause your TSO primers to introduce new UMIs during each PCR cycle and thereby inflate your read counts? Having a constant spacer would presumably increase this effect. Am I missing something here?

Best,<br /> Cyrille

On 2025-07-07 13:33:52, user Christoph Guschlbauer wrote:

None of our work cited in various places in this preprint (i.e., Zakotnik et al. 2006, Guschlbauer et al. 2007, Page et al. 2008, Hooper et al. 2009, Hooper 2012, Ache and Matheson 2012, Blümel et al. 2012, Ache and Matheson 2013, von Twickel et al. 2019, and Guschlbauer et al. 2022) claims or implies that passive forces could be sufficient to support the weight of an insect or any animal. To claim or suggest otherwise (as done in lines 33-35) is incorrect and sets up a misleading straw man that misrepresents our work. All statements in the preprint regarding our work related to this specific matter need to be removed or edited accordingly. For instance, the investigations, calculations, and interpretations in Hooper et al. 2009 are solely about limbs that are not being used in stance or other loaded tasks (indeed, the article's title specifically refers to "unloaded" leg posture and movements). Trying to use this work to predict whether passive muscle forces alone can support a stick insect against gravity requires considering much more than the oversimplified calculation given in lines 290-292. Other “back of the envelope calculations” (lines 299-300) are likely also insufficient and erroneous. The discussion in lines 289-304 needs to be edited accordingly.

We contacted the corresponding author first on May 12, 2025, and once more on June 09, 2025, to explain our concerns in detail and to ask for the preprint to be revised accordingly so that our work is not misrepresented in the public domain.

Scott L. Hooper, Christoph Guschlbauer, Ansgar Büschges, and Tom Matheson

On 2020-05-27 20:08:58, user Andrés Morales wrote:

Nice work and very useful protocol.

There is one point that you might want to revise. In your manuscript, you mention that "other reports from in situ analysis that reported 27.5% binucleation". However, in our studies of liver tissue, we found that around 75% of hepatocytes are binucleated - in total agreement with your study. You might be interested in having a look at our manuscripts to have some quantitative comparison of the morphological parameters of different cellular and sub-cellular components of (mouse and human) liver tissue. We reconstructed liver tissue sections ~100 um thick:

https://elifesciences.org/a... (Morales-Navarrete H., el at..A versatile pipeline for<br /> the multi-scale digital reconstruction and quantitative analysis of 3D tissue architecture.Elife, 2015)

https://elifesciences.org/a... (Morales-Navarrete H. el at. Liquid-crystal organization of liver tissue. eLIFE. 8:e44860, 2019)

https://www.nature.com/arti... (Segovia-Miranda F., et al. Three-dimensional spatially resolved geometrical and functional models of human liver tissue reveal new aspects of NAFLD progression. Nature Medicine. 25 (12), 1885- 1893, 2019)

On 2020-06-05 19:42:24, user Alexandra Beliavskaia wrote:

Dear authors, the preprint text mentions supplementary data, but there is no supplementary data linked to the preprint itself. Would you be so kind to point me to where one can find it? Thank you!

On 2020-07-02 20:30:49, user Paul Gordon wrote:

Thanks for posting this. I tried to find CNP0001111 in the sequence database pointed to in the manuscript, but there are no matching records. Is the data under embargo, or was there a typo? Thanks for any clarification you can provide.

On 2020-12-04 04:56:07, user Greg wrote:

Beautiful electron microscopy of those mitochondria.

On 2021-03-25 21:43:35, user Phil Hugenholtz wrote:

Very interesting paper. Please see https://www.nature.com/arti... for naming conflicts, e.g. UBP9

On 2021-12-17 17:33:28, user Eddie James wrote:

The T cell epitope part of this story seems underdeveloped. Do you need some assistance?

On 2025-12-01 15:42:06, user Sherif wrote:

Great job

On 2017-01-26 00:12:21, user German Dziebel wrote:

My coverage of this paper is at http://anthropogenesis.kins.... The review contains a couple of clarifying questions for the authors. First, what's your interpretation of the worldwide distribution of EDAR gene that codes for a number of traits including dental shoveling which you believe diffused from Asia to Africa. Second, how do you explain the fact that Amerindians have a special connection to West Eurasians (as seen in whole-genome analyses, Mal'ta and Kostenki aDNA and in the sharing of Y-DNA P clade that's not attested in Ust-Ishim aDNA) that's not shared by East Asians

On 2017-10-18 13:02:13, user Camilo Libedinsky wrote:

Hi, very nice piece of work. I have a question about the 10 clusters in Fig. 3. Would you also see 10 clusters if you trained on less than 20 tasks (say, 15)? In other words, does the number of clusters plateau at some point? or keeps on increasing with number of tasks trained?

On 2019-05-17 09:40:35, user Wouter De Coster wrote:

Dear authors,

Thank you for your interesting article. I would like to point you to an article which is worth discussing in this context: https://www.sciencedirect.c... <br /> Admittedly, I don't know much about machine learning but I am very enthusiastic about its application in large datasets in the context of genetics. However it seems that a better AUC is obtained using a more straightforward regression approach.

Regards,<br /> Wouter De Coster

On 2022-11-01 08:44:37, user Crawdaunt wrote:

This article desperately needs a more descriptive title involving polyadenylation somehow

On 2018-11-15 15:13:42, user BU_FALL_BI598_G5 wrote:

Critical review #2 Cell-type specific D1 dopamine receptor modulation of projection neurons and interneurons in the prefrontal cortex<br /> Paul G. Anastasiades, Christina Boada & Adam G. Carter*<br /> Group 5- Amber Shang, Joseph Sisto, Simran Shah

Overview

Dopamine (DA) modulation in the prefrontal frontal cortex (PFC) has profound implications in its functionality and physiology, and can therefore influence cognitive and reward-related behaviors. This DA input has been correlated with functions such as working memory and attention, and its dysregulation could lead to neuropsychiatric disorders such as schizophrenia. Despite its importance, DA receptor expressions at pyramidal neurons and interneurons remain unknown. Out of all the subtypes of DA receptors in the PFC, D1 receptors (D1-Rs or DRD1s) are the most abundant and their functions are most highlighted for PFC-dependent behaviors. However, there is currently little understanding, even conflicting reports on which projection neuron population, interneuron and pyramidal subtypes express D1-Rs to collectively modulate PFC outputs.

In hopes to characterize the D1-R expression and its DA circuitry in the mouse PFC, more specifically the prelimbic PFC, Anastasiades and coauthors combined ex vivo electrophysiology, in situ hybridization and viral tracers in multiple transgenic mouse lines to selectively target different populations of neurons in the PFC. The authors were able to determine that D1-Rs are found in subsets of intra-telencephalic (cortico-cortical) neurons and VIP+ interneurons, and can further selectively enhance their AP firing.

Overall the authors did an excellent job explaining the motives behind their study, using concise language throughout the paper and logically retracing each step of the experiment. They utilized various viral tracers such as AAV-synaptoTag and AAVretro-Cre-mCherry to map neuronal projections. Moreover, they used either heterozygous D1-tdTomato mice, or heterozygous D1-tdTomato mice crossed with either homozygous GAD-Cre, PV-Cre, SOM-Cre, VIP-Cre or heterozygous 5HT3a-Cre mice to produce interneuron specific mice and then injected AAV-FLEX-EGFP into the PFC of each mouse line to label the inhibitory neurons. (results section). The authors further categorized neuron subtypes based on their electrophysiological properties and finally looked at the roles of D1-Rs using D1-R antagonist/agonists. However, the methods and results presented here merit some comments and unresolved questions/concerns.

Major Criticisms

First, the title of the article Cell-type specific D1 dopamine receptor modulation of projection neurons and interneurons in the prefrontal cortex could be misleading because it implies D1 is the only gene that defines the cells. Although the authors continues to explain the different dopamine receptor subtypes in the introduction section and why they focused on D1-Rs, it would be appreciated if the title could better reflect the content.

Second, the group utilizes in-situ hybridization (figure 1), which uses proteases to break down the tissue. There is a possibility that this could be not only too harsh on the tissues but also the probes can bind to the nuclei. Therefore, we suggest the authors show a confirmation of the healthy tissue and potentially use DAPI staining to verify that proteases bind to mRNA, not to the nucleus. Furthermore, it would be appreciated if the group showed a negative control group to verify that the Td-tomato mRNA probes correctly obtained overlap with the D1+ neurons in the mice. One way to achieve this is to probe D1-R KO mice with the Td-tomato probes in order to observe no overlap.

Third, in figure 3, it would be appreciated if the group showed additional parameters regarding the D1+ and D1- neurons’ morphology and physiology in order to verify a difference in structure between the two populations. For example, total dendritic length, maximum branch order, total number of branches, total number of branch points and/or sholl analysis can be included to further signify a difference between D1+ and D1- neuron populations. Additionally, current clamp was performed in both figure 3 and 9. However the output was not specifically quantified. We suggest instead of using one pulse, the authors could use increasing pulses to draw an input-output curve for better quantification in order to control for spikes caused by artifacts.

Fourth, in figure 4, it is not clear which region of the PFC received the injection. This could be problematic because depending on which part of the PFC has the most injections, for example prelimbic PFC, infralimbic PFC etc., the observed projections will be different. Therefore we suggest the group quantify their injection sites with coordinates and hot spots and observe to what extent the injection has spread. It will then be possible to determine which section of the PFC is more selectively targeted. In addition, the injection site may have been biased.

Minor Criticisms

We suggest the authors reformat the article for the convenience of reading.

On 2019-11-26 14:48:46, user Rob Moran wrote:

Interesting study! It's particularly interesting that a ColV plasmid is contributing to virulence and antibiotic resistance in ST101. It sounds like (lines 255-256) the resistance gene region in pEC121.B might be derived from RR1, which we've described previously (<br /> doi: 10.1089/mdr.2017.0177). If so, this represents further dissemination of this plasmid lineage.

On 2023-11-09 15:17:53, user Bertram Klinger wrote:

Thank you for the nice explanation of expectation maximisation.

However, in my eyes your algorithm does not get rid of the spillover signal. <br /> In Fig3C the correlated distribution is the result of spillover from channel Yb172 into Yb173, as can be seen nicely in Fig3D where this correlation vanishes with the same antibody labeled to a channel which Yb172 does not spill into (Sm147D). Instead your algorithm seems to only set low signals to NA.

To undermine this point, Fig1a shows that the spill-in signal from Yb172 into the Yb173 channel is on average 2.7. Assuming the the mean of Yb172 bead to be of similar strength as Yb173 (~6.2) then for a cell population without Yb173 we would expect a difference of roughly 3.5 (in log scale) between the two channels if purely driven by spillover. Which is what can be seen in Fig3C for the CD3-low population ( i.e. they do not express CD3).

On 2020-02-10 16:01:25, user Marius Pachitariu wrote:

For support, please open an issue on http://github.com/MouseLand.... However, note that cellpose has been designed for segmenting cell somas, not neurites, and so it is unlikely to help you in this case.

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 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.