On 2022-08-31 11:17:49, user Nándor Lipták wrote:
Dear Authors,
In our previous study, we found mosaicism in founder (F0) rabbits, generated by CRISPR/Cas9 gene editing:<br /> doi: 10.3390/app10238508
It is also a common phenomenon in CRISPR/Cas9 gene edited mice.
Have you also detected mosaicism in your founder rabbits?
On 2020-07-04 09:20:51, user Antonio Cassone wrote:
We detected and reported on D614G mutation of SARS-2-CoV genome in Italian isolates , proposing the same interpretation about its functional value (enhanced virus transmission) based on biostrctural S1 change( ; see Benvenuto et al. Evidence for Mutations in SARS-CoV-2 Italian Isolates Potentially Affecting Virus Transmission J.Med.Virol., 2020, Jun 3:10.1002/jmv.26104. doi: 10.1002/jmv.26104.) We congratulate the Authors for providing direct evidence supporting D614G their and our own interpretation.
On 2022-01-20 16:24:22, user David Curtis wrote:
When you state that rs59185462 is associated with rheumatoid arthritis it might be helpful to point out that this variant is in the HLA region and that it is well established that particular HLA alleles have strong effects on risk of rheumatoid arthritis. The obvious explanation is that the observed association with rs59185462 is a consequence of it being in LD with causative HLA alleles.
On 2024-06-04 17:49:18, user phillip kyriakakis wrote:
Cool paper!
A few thoughts:
1) It would be great to see how this compares to the PhyB-PIF version<br /> 2) Blue light should activate PhyB/PhyA, it would be great to see different blue light doses to see how sensitive it is to blue light, not if it is sensitive to blue light. (See "Multi-chromatic control of mammalian gene expression and signaling" and "Multichromatic Control of Signaling Pathways in Mammalian Cells")<br /> 3) I am not sure what biological replicates means. Where three independent experiments done, or just three biological replicates, one experiment? If a single experiment, this should be made explicit and perhaps written as N = 1.<br /> 4) PhyA could be written as PhyA-NT instead of delta. Delta implies it is a knock out or something. Peter Quail used the "NT" notation and that has been used a lot since, so it would be easy for others to follow. <br /> 5) What are the effects of far-red light, perhaps with and without blue light? (See "Multi-chromatic control of mammalian gene expression and signaling" and "Multichromatic Control of Signaling Pathways in Mammalian Cells")<br /> 6) Would be nice to see blue and red systems multiplexed. Perhaps using DRE as in "Efficient photoactivatable Dre recombinase for cell type-specific spatiotemporal control of genome engineering in the mouse"
I am not suggesting these experiments or changes are needed to be published, but could improve the usefulness.
On 2021-03-09 00:43:51, user Jacob Matiyevsky wrote:
In our recent research journal club, my colleagues and I chose to discuss your paper and we found it to be extremely interesting. I thought that overall your work did an excellent job at elucidating TAK1’s role in retinal neovascularization and showing that it’s inhibition could be a potential therapy for retinopathy. Each of my colleagues focused on a particular aspect of your paper in-depth, and my focus was on the studies done with OIR rats. I really enjoyed how your team looked at a wide range of effects stemming from TAK1 inhibition. That being said, we found ourselves craving a more comprehensive interpretation for the vaso-obliteration data in figure 7C and what you might have hoped to see with oxozeaenol injections in that case. In general, it might also be helpful to provide additional commentary on the importance of the differing results between the low and high oxozeaenol treatments across the effects you tested for readers to better understand which dose might be better. Also, in figure 6A I noticed that the hyperoxic level was illustrated to be 75%, but based on the rest of your paper I believe that was meant to be 80%. Finally, we thought that the use of immunostaining for the microglial adhesion assay was fantastic and the interpretation for it was extremely strong. We thought that perhaps doing something similar in figure 1 to characterize TAK1 expression in the human retina would strengthen your claim regarding high levels of TAK1 expression there.
On 2017-04-22 02:32:50, user AdamMarblestone wrote:
-"A Multiplexed, Heterogeneous, and Adaptive Code for Navigation in Medial Entorhinal Cortex" http://www.cell.com/neuron/...
On 2017-12-10 18:00:21, user AdamMarblestone wrote:
-"Memory Aware Synapses: Learning What (Not) To Forget" https://arxiv.org/abs/1711....
On 2020-04-21 02:25:55, user Sinai Immunol Review Project wrote:
Main Findings:<br /> This study reports the identification of in-silico screened epitopes capable of binding MHCI (CTL), MHCII (HTL), and B cells with high immunogenicity that can be formulated with Ochocerca volvulus activation-associated secreted protein-1 (Ov-ASP-1) adjuvant into two multi-epitope vaccines (MEVs) for SARS-CoV-2. CTL, HTL, and B cell linear epitopes were identified, scored, and percentile-ranked utilizing respective IEDB server tools. SARS-CoV-2 polyprotein, surface (S) glycoprotein, envelope (E) protein, membrane (M) protein, nucleocapsid (N) protein, and several open reading frame proteins were screened in silico for potential CTL, HTL, and B cell epitopes. CTL epitopes were identified by the “MHC-I Binding Predictions” IEDB tool with default parameters of 1st, 2nd, and C-term amino acids; epitopes were ranked by total score combining proteasomal cleavage, TAP transport, and MHC scores combined. HTL epitopes were identified by the “MHC-II Binding Predictions” IEDB tool, which gives a percentile rank by combining 3 methods (viz. combinatorial library, SMM_align & Sturniolo, score comparison with random five million 15-mer peptides within SWISSPROT). B cell linear epitopes were identified by the “B Cell epitope Prediction” IEDB tool, which searches continuous epitopes based on propensity scales for each amino acid.
From these proteins, 38 CTL top percentile ranked epitopes, 42 HTL top scorers, and 12 B cell top scorers were used for further analysis. Candidates were then analyzed for epitope conservation analysis (number of protein sequences containing that particular epitope), toxicity, population coverage, and overlap with one another. 9 epitopes that overlapped among all three types (CTL, HTL, and B cell linear) were then analyzed for interaction with HLA binders, showing stable binding with A*11:01, A*31:01, B3*01:01, and B1*09:01, and TAP, demonstrating ability to pass from cytoplasm into the ER. Two MEVs were formulated using the top CTL and HTL epitopes, which were then analyzed for physicochemical properties, allergenicity, and potential to induce IFN-gamma production. Final 3D modeling, refinement, and discontinuous B cell epitope analysis were completed to optimize the space-occupancy of the MEVs. This rendering was used to assess docking with TLR3, the major domain used by Ov-ASP-1. Codon adaptation optimization yielded cDNA capable of high expression in mammalian host cells. Taken together, this in-silico study produced two MEVs containing CTL, HTL, and B cell epitopes capable of eliciting potent cell-mediated and humoral responses for HLA types representing up to 96% (SD 31.17) of the population. Further in vitro study is warranted to confirm its clinical potential.
Limitations:<br /> In silico approaches are based upon models, however accurate, that make certain assumptions and contain biases inherent to training data. Synthesizing and testing a few candidates alongside their initial findings would make this method far more robust. It remains to be seen the efficacy of screened epitopes and corresponding multi-epitope formulations function in vitro and in vivo models.
Significance:<br /> This study reports an in silico approach to producing multi-epitope vaccines that can produce potent adaptive immune responses. Utilizing protein databases, established protein modeling, folding, and docking algorithms, as well as population analysis, the team identifies 38 MHCI-binding, 42 MHCII-binding, and 12 B cell epitopes that can be linked with Ov-ASP-1 adjuvant to form stable proteins. These proteins are shown to dock well with HLA-alleles, TAP, TLR3, and to induce IFN-gamma responses.
Review by Matthew Lin as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.
On 2015-04-17 09:37:19, user Davide Verzotto wrote:
Proceedings of the Fifth RECOMB Satellite Workshop on Massively Parallel Sequencing,<br /> RECOMB-Seq 2015, Warsaw, Poland.
Regular/paper talk (27% acceptance).
On 2024-01-24 13:15:06, user Aiman Saab wrote:
This study is now published in Nature Neuroscience:<br /> https://www.nature.com/arti...
On 2025-10-22 06:16:15, user KJ Flynn wrote:
Interesting work .. two comments/suggestions for your consideration:
For plankton, one of the major routes to establish whether something is critical is to have it described in a simulation and conduct a sensitivity analysis. The problem is that the sorts of details in this work never get into plankton models. This disparity between especially omics and models has been raised most recently by Flynn et al. (2025) https://doi.org/10.1038/s41559-025-02788-3 . It would be really helpful for you to flag how this work could usefully inform next generation plankton models.
My second point concerns 'mixotroph'. From the tone of the discussion on this point, I assume you refer to photo-phagotrophy, as undertaken by mixoplankton. The problem with 'mixotroph' is that diatoms are strongly mixotrophic, via photo-osmotrophy (a trait well studied back in the 1960's etc. and exploited in algal biotech). I suggest that you consider using 'mixoplankton' to identify those organisms which are photo-phagotrophic. Checking organisms against the contents of the Mixoplankton Data Base of Mitra et al. (2023) https://doi.org/10.1111/jeu.12972 , may help as this catalogues trophic modes and many other features of these organisms.
On 2018-06-22 14:02:35, user rachid bejjani wrote:
very exciting !
On 2025-08-11 14:10:51, user Noah Lemke wrote:
Please find the published version of the manuscript here:
On 2019-08-06 16:03:30, user François Charih wrote:
Very interesting paper indeed. The results are consistent not only with the new paper by Blacher et al., but also with an observed impact of nicotinamide riboside supplementation on ALS progression by de la Rubia et al., 2019.
See Amyotroph Lateral Scler Frontotemporal Degener. 2019 Feb;20(1-2):115-122. doi: 10.1080/21678421.2018.1536152.
On 2021-04-29 16:28:33, user WebbsWonder wrote:
Really nice! A suggestion for a change in title. "UBP12 and UB13 stabilize COP1 to promote CRY2 degredation". Current title suggests a complete revision of Ub model, but your conclusion makes clear that is not what you are suggesting. If I have understood it all properly!
On 2020-03-27 13:48:28, user Varun Sharma wrote:
Need a clarification, the novel variation that was observed by authors in the spike protein can be used for the detection.
On 2024-01-13 06:56:24, user seth musker wrote:
The peaks in late April may be due to the City Nature Challenge?
On 2019-04-18 17:57:11, user Paul Macklin wrote:
accepted!
On 2016-02-10 17:03:00, user Jose Manuel Alonso wrote:
Dear Matteo,
Nice fits to our data. As you know, our original PNAS paper (1) already indicated that differences in luminance response linearity between ON and OFF pathways are likely to originate in the photoreceptor. This is stated in the abstract, significance statement, results and discussion of our paper. We support this interpretation with measures of responses from receptive field centers and surround in thalamus and with modeling (the equation that you use for your fits is identical to our equation 2 provided in the supplementary material).
In our model (Equations in the supplement) adaptation was defined by c50(bg) and n(bg) of the Naka-Rushton function both being affected by the luminance level of the background. It is interesting that you get excellent fits to one of our data examples with changing one parameter of the Naka-Rushton function, but there is no direct evidence that our thalamic recordings reflect only photoreceptor adaptation (see Fig. 3E-H for population data). In fact there is evidence of subtractive adaptation to prolonged lights in ganglion cells (2), which would be reflected in thalamic cells. Just want to make sure that readers of bioRxiv do not get confused.
All the best,
Jose Manuel and Qasim
On 2020-03-03 18:14:59, user Hafiz Muzzammel wrote:
AoA where are results for IFN gama by IFN epitope server and what results it showed to you and how you got that
On 2017-11-20 01:42:44, user JL Speleo wrote:
This sounds really interesting. Will the software be made available on the github repo before peer-review? I am quite interested in trying it out.
Cheers,
J.
On 2019-08-20 20:04:33, user Froylan Calderon de Anda wrote:
Really nice pre-print. Unfortunately, some manuscripts related to one of the gene encoded in the 16p11.2 region are not properly listed in the introduction. TAOK2 has been shown to affect spine formation (Yasuda S, Neuron. 2007;56(3):456-71; Ultanir SK, Neuron. 2014;84(5):968-82; Yadav S, Neuron. 2017;93(2):379-93.), basal dendrites formation (de Anda FC, Nat Neurosci. 2012;15(7):1022-31), animal behaviour, and brain morphology (Richter M Mol. Psychiatry 2018).
On 2023-08-11 09:19:40, user Bart Knols wrote:
The reasoning (abstract) that 'However, sterilization by traditional methods renders males unfit, making the creation of precise genetic sterilization methods imperative.' is not correct. It is a justification for the type of research conducted here but does not do right to classical (radiation-based) SIT. See for instance the article by Bouyer and Vreysen (2020) titled 'Yes, irradiated sterile male mosquitoes can be competitive!' (Trends Parasitol., 36, 877-880). Our own research has shown the same, that doses of irradiation sufficiently high to induce satisfactory sterility in mosquitoes whist safeguarding their competitiveness is possible. Article upon article that focuses on gene drive or other gene engineering approaches uses 'lack of competitiveness' as a justification for moving away from classical SIT. This view stands to be corrected.
On 2024-08-27 12:31:01, user Kate wrote:
If the authors have not read our paper I suggest they do so. https://doi.org/10.1038/s41586-022-05546-8
On 2013-12-12 17:18:38, user AdamMarblestone wrote:
Thanks for these excellent comments! Will be incorporated into the next version.
On 2019-07-03 18:31:06, user Charles Warden wrote:
1) A peer-reviewed version of this article is now available:
https://bmcgenomics.biomedc...
2) Since BMC Genomics doesn't have a Disqus comment system, I apologize for posting this here (although I hope this changes in the future).
However, I noticed "HPV" was defined as an abbreviation in the peer-reviewed version, without being used in that version of the paper.
In the pre-print, I do see some analysis of "72 TCGA HNSC tumor samples with valid Human Papillomavirus (HPV)," but it looks like they changed the comparison to tumor-versus-normal (while accidentally keeping the abbreviation in their draft).
On 2022-01-20 02:18:53, user Corazon Ngelangel wrote:
This has been published online in the Philippine Journal of Health Research and Development. Please see link: https://pjhrd.upm.edu.ph/in...
On 2022-08-14 02:28:16, user C.S. Raman wrote:
This paper is now published: https://doi.org/10.1021/acscatal.2c00316
On 2018-06-27 02:31:06, user bgulko wrote:
If anyone is considering covering this in a journal club or reading group and would like involvement or presentation support from an author, I'd be happy to participate. I'm presently in the San Francisco Bay area, please don't hesitate to contact me! --Brad Gulko
On 2016-03-20 21:24:30, user James Wilson, M.D. wrote:
I would be very cautious about the use of the word "pandemic". While Fauci et al have argued for a liberal definition of the term (i.e. NOT necessarily full penetrance of the infectious agent into the broader human population), it is a politically charged term. It is our perspective that overuse of this term may not be ethical, as it tends to play to political fear (and hence, funding) without providing a valid contextualization of the threat.
You are going down the right path in contextualizing this as a tropical belt issue versus a pole-to-pole issue. Zika thus far is not showing any signs of autochthonous transmission in temperate regions.
The overall backdrop is a decrease in credibility of the entire global public health enterprise, thanks to hyperbole and highly reactive response behavior. The precedence includes the translocation of West Nile to the western hemisphere, SARS, pandemic H1N1, MERS, translocation of CHIK, and of course, Ebola (to name a few). Providing balance to the assessments on Zika is of broader importance than that of a simple academic paper.
On 2021-04-22 13:38:53, user Michael Hoffman wrote:
This preprint seems to be missing a Methods section referred to within. Methods are not described with the minimum level of detail one might expect for a scientific publication.
On 2019-06-03 20:39:45, user Jon Moulton wrote:
When discussing the disagreement between Morpholino and mutant phenotypes, the possibility of genetic compensation concealing the MMP9 loss-of-function phenotype is not raised (Didier Stainier has shown this to be a mechanism causing mutant-morphant phenotype differences).
On 2020-08-10 11:48:43, user Yi-yi wrote:
No evidence for basigin/CD147 as a direct SARS-CoV-2 spike binding receptor
On 2016-06-16 00:47:59, user PornHelps wrote:
"Sharing this manuscript with scientific community through bioRxiv"<br /> This is false. This is a public website, as you are aware.
"Sexual Arousability Inventory (SAI)"<br /> Which is not a measure of sexual desire, so no, you did not control for it.
"For majority of people it seems to be an entertainment."<br /> False, the overwhelming majority of use is for masturbation. People don't watch porn eating popcorn. If you didn't assess orgasm and masturbation separately, then any "effects" you document could actually just be due to masturbation anyway.
How powered were you to detect those "not significant" effects in this little fMRI study?
Why do exclude women, who actually respond just as strongly (neurally) to men to porn? Sexism in science much?
Shall I keep going? Or would you remove this unethical post pre-publication? Should I check with the other authors directly to see if they actually consented to having their manuscript, un-reviewed, posted on a website accessible to the general public?
On 2019-08-26 14:08:17, user taras Pasternak wrote:
Great work! How specific was SA effect? In our hand 20 µM SA have a significant effect on leaf, but higher concentration give rather non-specific effect.
On 2019-01-19 10:33:17, user Constantin Rothkopf wrote:
This is a pre-print of an article published in Scientific Reports. The final authenticated version is available online at: <br /> https://doi.org/10.1038/s41...
On 2019-04-11 12:02:44, user Polarity_elegans wrote:
This manuscript has now been accepted for publication at Current Biology: https://www.cell.com/curren...
On 2021-02-10 14:25:10, user David Carlson wrote:
This looks like really exciting work. Just a comment - some of the figures (e.g., 1c-h) are very difficult to read, even after zooming in heavily. Perhaps increase font size?
On 2020-08-29 20:48:17, user Manfred Wuhrer wrote:
Interesting to see that the recombinantly produced N protein is glycosylated. However, I doubt that the N protein of the intact virus is likewise glycosylated. I assume that the recombinant N protein is steered to the ER and Golgi explaining its glycosylation. For the N protein produced by the SARS-CoV2 infected cell I expect that it will not enter ER and Golgi but rather dwell in the cytosol upon translation and therefore may lack glycosylation.
On 2020-12-14 15:23:10, user Ben wrote:
@reporters<br /> Please read the abstract (or even just some tweets from scientists) before you write your report. The title is overstated and walked back immediately in the abstract. Please be mindful of your influence.
On 2020-11-09 16:37:20, user anon wrote:
Very surprising to see some of the most basic controls missing here. A simple "(LNF+mRNA) without exosomes" control is nowhere to be found, and all of the results shown can easily be due to treatment with liposomal mRNA on its own with exosomes spiked in. There's no evidence that the exosomes are helpful or do anything at all. The expression of the SARS-CoV-2 mRNA in cultured cells using patient sera is also unusual, as a Western blot from cell lysates with a monoclonal antibody would give more information, including confirmation that the full-length antigen is expressed.
I guess this is a pre-print, but some of the basic experimental design, materials/methods, and controls leave quite a few open questions.
On 2022-01-24 19:29:14, user WEI YAN wrote:
The article has been published on NPJ Biofilms and Microbiomes.<br /> doi: 10.1038/s41522-021-00260-1<br /> https://www.nature.com/arti...
On 2015-07-13 12:08:42, user Celia Rodrigues wrote:
If the author wanted to make a point, the example chosen of the DNA helix by Watson and Crick was the worst one. It just shows how you can easily steal the work of someone else and publish it quickly. It is not even a proper scoop as they didn't have any raw data. Imagine all the reviewers of this world getting a really good manuscript with an original idea that will change the world and they quickly publish their "bold" idea without any data and get all the credit before the group with the raw data and paper almost ready to publish. I agree that some flexibility could exist and that maybe this isn't the perfect system, but your example poorly illustrates your point. It even makes me doubt anything is said in the text after that.
On 2023-10-19 00:34:34, user Michael Polymenis wrote:
The paper has been published in GENETICS (see here https://pubmed.ncbi.nlm.nih... "https://pubmed.ncbi.nlm.nih.gov/34849864/)").
On 2018-04-20 13:24:10, user Neil Kad wrote:
This is a really beautiful piece of work that highlights the importance of the region around residue 50 in forming the protofilament interface. <br /> Interestingly, in 2015 we previously showed that this region was important in amyloid fibril formation by using an in vivo semi-rational peptide selection method. In our paper we created a peptide based on the 45-54 region of a-syn that actually inhibited fibril formation.
The paper is here: https://dx.doi.org/10.1074%...
I think it would be a useful addition to the discussion since it is very pertinent to your conclusions.
On 2017-08-24 09:49:57, user Wouter De Coster wrote:
Dear authors,
This is great work and I'm eager to try this method after my holidays. It is essential that the community explores the possibilities of improving on existing tools and pushes the field forward and this is a nice contribution.
I think the preprint is well written and I have a few minor comments/suggestions:<br /> Abstract:<br /> - "Here, we report the first deep learning model - Chiron - that can directly translate the raw signal to DNA sequence, without the error-prone segmentation step."<br /> If I'm not mistaken Albacore is also moving away from event detection/segmentation, see also https://community.nanoporet... The same argument returns later in your manuscript. Now you are probably correct, but if you consider submitting this to a journal and going through peer review I think Albacore might be using raw data by then. The plans to move away from segmentation have been around for a while, but Chiron is still the first to implement it in practice.
Introduction:<br /> - "The device then uses the signal to determine the nucleotide sequence of the DNA strand"<br /> => It is a minor detail, but basecalling is not performed on the device itself.
Comparison with existing basecallers:<br /> I read on twitter that you are also retraining on human data. It is apparent from Table 1 that all tools perform worse on human data, so I think this is definitely an application in which improvements are very relevant and will likely make a big impact. Perhaps you can comment on why the basecallers perform less on the human data? The accuracy of Chiron is impressive given the fairly limited training dataset you employed for this analysis.
Areas left undiscussed are nucleotide modifications and basecalling of direct RNA, which would be worth exploring I guess and potentially have an important impact.
A typo:<br /> -Albacore is considered the ’gold standard’ in terms of accuracy, but as it is not open source, we cannot comment on **it’s** implementation.<br /> => its implementation
Cheers, <br /> Wouter
On 2023-05-25 19:19:48, user Nicola Asuni wrote:
I am maintaining an updated version of the variantkey library at:
On 2022-10-17 18:21:49, user Julian C wrote:
Citation: Candia J et al, Scientific Reports 12:17147 (2022).
On 2017-12-05 13:38:00, user James Lloyd wrote:
Very interest results from an elegant set of experiments, thank you for posting to a pre-print server.
The only comment to try and improve the paper slightly is that I think some more explicit focus on the overlap of differentially expressed genes and regions with gains/losses in repressive chromatin. For example, does expansion of heterochromatin in XO males lead to repression of any of these newly marked genes, as one might expect from previous work on changes in PEV? I get the impression from the work that this effect is modest or nonexistent, and most of the expression changes are linked to sex determination (a very interesting result), but I think some more explicit focus on this would be really useful for the reader.
On 2017-01-25 21:27:41, user Spencer wrote:
Final version:<br /> http://www.cell.com/structu...<br /> DOI: http://dx.doi.org/10.1016/j...<br /> PMID: 28111021
On 2019-10-21 00:55:10, user Jean-Michel Ané wrote:
A 20% decrease in Hartig net boundary to root circumference when CASTOR/POLLUX or CCaMK are knocked-down is not what I call "a very subtle decrease in ectomycorrhizae". See Figure 10D of Cope et al. (2019) http://www.plantcell.org/co....<br /> I totally agree that CASTOR/POLLUX and CCaMK are obviously dispensable for some ecto-mycorrhizal associations but, at least in the case of Populus, @KevinCope18 has demonstrated that they play a significant role in this association.
On 2018-10-11 14:05:32, user Luigi Antelmi wrote:
Thanks for sharing this idea!<br /> Question 1: Why you use the log-likelihood to compare the models and not the ELBO, that should be a proxy to the data evidence?<br /> Q2: How do you compute the KL term in the non-gaussian prior cases?<br /> Q3: Are you willing to publicly share your code?<br /> Thanks for any answer you can give!
On 2019-06-22 14:11:57, user Mariana Mateos wrote:
Peer-reviewed version now available at https://doi.org/10.1038/s41....<br /> Mateos, M., N. O. Silva, P. Ramirez, V. M. Higareda-Alvear, R. Aramayo, and J. W. Erickson. 2019. Effect of heritable symbionts on maternally-derived embryo transcripts. Scientific Reports 9: 8847
On 2023-04-13 15:40:20, user ENK wrote:
1). There is a typo on page 11, I think. "as clusters associated with cell types and/or organ formed grouped when TF family members were clustered phylogenetically"
2) Fig 2C is missing an explanation/label for the shading gradient variable.
3). For figs 6A and 6B, you do not indicate what cluster 6 is. I would also encourage the authors to put the cluster identities in the figure itself or in the figure description, not just in the body of the text.
Generally, I would encourage the authors to go over the figures again with consideration with ease of audience interpretability in mind.
On 2018-03-21 18:25:41, user Rob Striker wrote:
This is an important contribution to thinking about hiv therapy. I think it should sail through the peer review process
On 2017-08-10 21:13:49, user Anders wrote:
The web-interface for Spot-On can be found at: https://spoton.berkeley.edu/<br /> Please let us (Maxime or Anders; contact details on website) know if you have any suggestions or requests for features or if your tracking algorithm output is not currently supported.
On 2020-03-09 04:09:12, user sufyan ashhad wrote:
Please see the peer reviewed published version of this manuscript here:<br /> DOI:https://doi.org/10.1016/j.n...
On 2020-08-17 07:32:54, user Paniz Farshadyeganeh wrote:
Dear Authors,
I could not find any link to supplementary data. I would like to know if it is possible to have it?
Bests,<br /> Paniz
On 2020-09-21 08:18:32, user Jouke- Jan Hottenga wrote:
See this: Am J Hum Genet. 2000 Jan; 66(1): 279–292. PMID: 10631157<br /> A General Test of Association for Quantitative Traits in Nuclear<br /> Families.
Interested in the comparisons between the TDT, classic linkage sib-pair analyses and these methods, because all are a different take on - but converge to - the same principle of explaining variation in human traits.
On 2018-05-15 07:05:45, user Hannah Gruner wrote:
Fascinating work!
I’m intrigued what signaling pathway Ddx3x is important for given the lack of change in canonical Wnt signaling. I’d be curious if non-canonical Wnt signaling may be involved considering the PCP pathways role in cortical neuron maturation (PMID:26939553; 19332887).
The intermediate progenitor increase in the Ddx3x LOF reminds me of a similar phenotype observed Slit1/2 and Robo1/2 mutant animals (PMID:23083737). As Slit-Robo signaling is associated with intellectual disabilities (PMID:12195014), and Slit2 and Robo1 are highly enriched in the Oh, et al. 2015 iCLIP data, it would be interesting to see if this signaling pathway is affected in Ddx3x knockdown as well.
On 2021-09-05 14:38:51, user Rodrigo Lorenzi wrote:
I just took a look at the article. My question is: what happens when someone vaccinated is infected by a variant. Do they produce new antibodies against this variant or the only antibodies at work are those induced by the vaccine?
On 2025-04-18 06:52:54, user Eric Marechal wrote:
A timely study that sheds light on the evolution of secondary plastids originating from red algal endosymbiosis
On 2024-12-23 05:56:36, user David Lloyd wrote:
Dear Joel and Brokoslaw,
I read with interest your preprint and it seems like a nice piece of work based on well established methods. However, I do encourage you to please cite the original research and papers that led to your current implementation. For example, your equations 1 and 2 and wording for Neural Activation Model seem directly taken from Lloyd and Besier J Biomech 2003 equations 1 and 2, and Buchanan et al J Appl Biomech, 2004, equations 3-7 and 12. The concept to add this Hill Type muscle model muscle force dynamics also stems from this 2003/2004 work, although your work does not include tendon models. This work by myself and long line of PhD students and research fellows across many different laboratories around the world, has also been led to the development of using these model for real-time applications (e.g., Pizzolato et al, IEEE TNSRE, 2017; Durandau et al, <br /> IEEE TBME, 2017) real-time control of exoskeletons (e.g, Durandau et al, IEEE T-RO, 2022; Durandau et al, JNER, 2019), and function electrical stimulation. (e.g., Hambly et al, IEEE ICORR, 2023). Again I encourage you to cite the original research papers and not make claims like "we developed a new EMG-to-activation model" and "Our new EMG-to-activation model begins with activation dynamics..." Nevertheless, I encourage you to continue this line of research, especially the impedance control.
Kind Regards
David Lloyd
On 2019-02-26 18:08:53, user Cory Sheffield wrote:
Did you look for differences between males and females for each species? Not only are males typically smaller, but emerge faster (i.e., from eggs which are laid last in the tunnel), which seemingly would support your trend. But this occurs in both late emerging species (i.e., those wintering as mature larvae) which have more variation in emergence time, and in those early spring emerging species with narrow emergence times. So, is the early emergence of males only because they are smaller, or is there something else involved? What about larger males?
Also, did you look for differences in body size based on the size of nesting tunnel the occupants were in? Tunnel diameter will influence body size, so it would appear that when a species nests in a smaller diameter tunnel, it will emerge faster than a conspecific from a larger tunnel. Was this the case?
Why not look to see if the pattern is supported within a taxon (ie Megachile). Megachile inermis is are largest native Megachile species in Canada that uses trap nests, but you have several smaller species that emerge later in your figure. Thus, how do you know the variation is not due to something other than body size? Perhaps timing of emergence is based on synchrony with floral hosts for species with more dietary restrictions, or for parasitic taxa whose emergence times are typically later than their hosts? Could food quality influence emergence time? Are cleptos larger than their hosts to emerge later?
On 2020-08-13 08:23:35, user Martin R. Smith wrote:
This is an interesting study and a promising approach. <br /> My one question would be whether the Robinson-Foulds distance is the most suitable measure of tree distance on which to base the linkage ratio. The RF distance suffers from a number of shortcomings, many of which are exacerbated when pectinate (i.e. fully unbalanced) trees are involved, as moves of a single leaf can 'knock out' a disproportionately large number of splits – so it might have particular scope to produce misleading results in the examples that you have used.<br /> I've reviewed some possible alternatives in Smith (2020), Bioinformatics, doi:10.1093/bioinformatics/btaa614 , and fast implementations are available in my R package 'TreeDist' – though unfortunately I don't yet have a python front end to the underlying C++ code. The quartet distance might be particularly relevant, as the distance between two entirely random trees takes a constant value (1/3) – would this obviate the need to generate unliked topologies in order to normalize the linkage ratio?
On 2016-05-21 01:38:57, user Jim Hofmann wrote:
Shouldn't this be "unexplored"?:<br /> "Until recently model selection remains an explored topic and the impacts of using different models on inferring biogeographic history are poorly understood."
On 2023-03-02 22:22:51, user Evan Saitta wrote:
Congratulations on the study! It is very interesting and plays an important role in collating this useful data!
I have explored sexual dimorphism, including in body mass, in extinct organisms (https://academic.oup.com/bi... "https://academic.oup.com/biolinnean/article/131/2/231/5897459)"). I am jealous of your extant research subjects!
If you are looking for feedback on your preprint, then I am happy to give my thoughts (for whatever those are worth).
I think your second figure is a more apt portrayal of the data than your first, because it presents the data with a mind towards effect size statistics (i.e., it reports the estimated magnitude of dimorphism and the uncertainty in that estimate without additional interpretation).
Namely, I think that the secondary methodological step of designating each species into a categorization of dimorphic or monomorphic might obscure the excellent data you have amassed.
I certainly understand and appreciate your use of objective criteria to assign a monomorphic label (i.e., when the 95% confidence interval straddles zero in estimated dimorphism magnitude). However, any finite population of males and females is not expected to have an effect size of precisely zero, even if just for stochastic reasons rather than reasons of sexual selection (or lack thereof!).
So, what does the "same size" category actually include then?
Those species that are labelled as "same size" between males and females could be those with relatively modest magnitudes of dimorphism (i.e., near, but not exactly, zero) and/or those with small sample sizes and therefore higher uncertainty (i.e., larger confidence intervals).
For example, if you assume that these 39% of species that fall into the "same size" category are roughly equally likely to sit either just barely above or below zero effect size, then that would mean about 63.5% of species in orders with 10 or more taxa have an estimated effect size that places average male size greater than average female size -- albeit that many of those species have modest dimorphism and/or high uncertainty.
That would seem (to me at least) to differ from the conclusion that males are not larger than females in most mammals, which I assume is derived from the "larger males" category being less than 50%, at 44%.
I applaud your use of effect sizes and confidence intervals! However, I worry that by using these confidence intervals to then make a dichotomous (or trichotomous?) categorization, the method then becomes prone to the same shortcomings as does binary significance testing based on p-values (an approach that is becoming more and more criticized: https://www.nature.com/arti... "https://www.nature.com/articles/d41586-019-00857-9)").
Of course... I could be wrong!
Did I understand your work correctly? Do my comments make sense? Am I totally mistaken about something here?
PS. I was Princeton EEB undergraduate class of 2014 (Advisor: Gould) and will be attending reunions this year. Perhaps we can meet up at some point to discuss your fascinating work, and maybe you can give me some advice about how to deal with these pesky fossils!
Go Tigers!<br /> Evan Saitta
On 2023-03-19 19:02:36, user Clay McCann wrote:
Not quite clear here on what constitutes "poisoning" when the LD-50 for cannabis remains unknown, when cannabis is one of the least toxic substances known to humanity, and especially when humans have no CB receptors in the brain stem (making it literally impossible to overdose on cannabis). This "study" represents more than half of all cannabis research, instrumentalized as drug scare propaganda.
On 2018-06-21 23:23:47, user Andrés Lanzós wrote:
Interesting work!. I'm curious to know whether these lncRNA candidates are also frequently mutated in cancer (https://www.nature.com/arti... "https://www.nature.com/articles/srep41544)"). Maybe authors could perform the intersection with those driver candidates.
On 2020-02-02 07:28:12, user tian maller wrote:
I want to use durgbank's dataset .How can you modify the code to see the prediction of the drug-target
On 2025-04-15 13:14:47, user Donald R. Forsdyke wrote:
THE "ACCENT" OF DNA
You can explain the "de-extinction" problem, be it with mice or dire wolf, historically by considering the four bases in DNA sequences:
Chargaff circa 1950 discovered that DNA base composition (not sequence) was a species characteristic, simply expressed as GC% (as opposed to AT%).
So, there were GC%-rich species and AT%-rich species, with the exact values differing between species.
We biochemists and others discovered circa 1990 that actually the difference was due to short sequences (k-mers).
Thus, for k=3. GC%-rich species would be enriched in GTC, GGA, GGC, CAG, etc. Whereas for an AT-rich species ACT, AAG, AAT, TGA, etc.
Given 4 bases (A, C, G, T), for k=2 there would be 4x4 = 16 possibilities. For k=3 there would be 4x4x4 = 64 possibilities.
In practice the range varies from k=3 to k=8.
Fragments of DNA from, say, a soil sample, will correspond to a variety of species in the sample. But just by assessing the k-mer patterns in the fragments, those corresponding to each species can be identified.
Then you can look at the fragments corresponding to one species and examine long sections to identify gene sequences (viewed as "sentences" or "word strings").
So, k-mers can be seen as the "accent" or "dialect" of DNA that relates to what species it belongs to. Unless you take that into account you cannot make a new species by just inserting a few genes to change appearance.
Just as accent can influence reproductive choices between humans (remember Eliza Doolittle), so it influences the reproductive isolation that is the defining characteristic of a species.
[A paper in the December 2024 issue of the Journal of Theoretical Biology goes into more details. Or see my textbook - Evolutionary Bioinformatics (3rd edition, 2016).]
On 2021-08-16 17:36:20, user Frank William Soveg wrote:
Figure 2 and the supplementary materials are missing from the manuscript. Can the authors please make this available? Thank you.
On 2018-12-03 12:03:22, user Seamus Holden wrote:
As of 03/12/2018 there is a bioRxiv technical issue preventing viewing of the latest version of the supplementary material of this manuscript. As a temporary solution, the latest SI may be viewed here: https://t.co/a9LPu8oAFB.
On 2018-02-28 23:56:14, user KWANYOUNG KO wrote:
According to the paper, SELENOW translocation to the nucleus require intreaction with 14-3-3??
On 2016-05-19 13:48:30, user Javier Quílez wrote:
In Supp. Figure 7, shouldn't it read "The fraction of human genes with no annotations (i.e. dark matter) has decreased consistently over time"?
On 2022-05-23 17:50:48, user Michael G LaMontagne wrote:
This manuscript has been accepted for publication in Frontiers in Microbiology.
On 2018-02-21 16:48:31, user Helder Maiato wrote:
New preprint by the Sullivan Lab that is relevant to this discussion:
On 2019-10-09 20:54:16, user Yibing Shan wrote:
The stated 120 Å receptor-receptor separation by the crystal FERM dimer model was a mistake. The concern about that model may have to do with the position of K279 of EpoR, which is only 5-residue away from the transmembrane helix but some 20 Å away from the membrane.
On 2017-03-04 21:40:09, user BrainsOnWaves wrote:
Hello, folks & congrats for a new paper!
I wonder whether you could reconsider the title as it now seems to state a mereological fallacy (cf. Bennett & Hacker)
On 2025-03-31 14:46:25, user Neelesh Patra wrote:
Published in Plant Physiology and Biochemistry<br /> Volume 222, May 2025, 109679 with DOI: https://doi.org/10.1016/j.plaphy.2025.109679
On 2022-08-13 17:57:32, user Rajender Singh wrote:
Dear Authors, <br /> Lopez et al. is not the right reference as you have stated in your manuscript in the line 'The sequences in the nuclear genome with mitochondrial origins are called numts and their integration process itself is called numtogenesis (Lopez et al., 1994).'
You should replace this with other suitable references, which I am mentioning here;
Migration of mitochondrial DNA in the nuclear genome of colorectal adenocarcinoma. PMID: 28356157
Single molecule mtDNA fiber FISH for analyzing numtogenesis. PMID: 28322800
Numtogenesis as a mechanism for development of cancer. PMID: 28511886
I hope you will take a note of my comment.
Thanks.
Dr. Rajender Singh<br /> Senior Principal Scientist and Professor
On 2016-09-23 09:20:17, user Javier Forment wrote:
Nice and useful work! Thanks! Maybe I'm wrong, but I didn't find in the manuscript how can I start my own Jupyter notebook inside Galaxy, other than making a copy of yours one and editing it.
On 2020-06-08 18:33:25, user Alpesh Pansheriya wrote:
Herd immunity is baseless in country like India.
On 2016-10-10 23:47:19, user Anshul Kundaje wrote:
Very nice paper. A few questions and clarifications.
On 2022-11-15 15:46:13, user Leonid Sazanov wrote:
From Prof. Leonid Sazanov, IST Austria.
This preprint describes the first structures of mitochondrial complex I from Drosophila melanogaster (Dm). The work is done carefully technically and is a valuable addition to the current set of complex I structures from various species, previously lacking representatives from insects or Protostomia clade in general. Complex I from Protostomia, in contrast to that Deuterostomia including mammals, appears to lack so-called “deactive” state, which is important for mechanistic discussion. In this study authors find that apo (i.e. in the absence of any substrates or turnover) Dm complex I (DmCI) can adopt two main conformations, resembling so-called “open” and “closed” states seen previously with other species. Uniquely, one of DmCI states is characterized by the ordered N-terminal helix of the accessory NDUFS4 (18 kDa) subunit, which wedges between the peripheral (PA) and membrane (MA) arms. This was not seen in other structures and appears to be a specific feature of Dm and closely related species. Authors suggest that the helix may temporarily “lock” this DmCI conformation. However, it may instead just reflect the ordering of a particular structural element in one of complex I states, as seen for different parts of complex I in other species.
Overall, the new DmCI structures are consistent with our recent mechanistic proposals [1, 2] and complement the emerging picture. However, the discussion of the two states in this work is very confusing in my opinion, which is why I wrote this comment.
It is surprising that the DmCI states were labelled as they were (locked open and closed) while it is clear that it should be other way round (locked closed and open). DmCI states were assigned by authors on the basis of PA-MA angle if complex I is viewed sideways, as we did for ovine complex I originally [1, 3]. In what authors called here the closed state this angle is very slightly smaller than in the other state, thus the assignment. However, it is clear from Fig. 4- suppl. 2A and movies that the main difference between the DmCI states is the rotation of PA, not the closing/opening of PA-MA angle.
As we noted in our latest paper [2], the PA-MA angle is not a good indication of open or closed state – PA tilts in Ovine but mainly twists/rotates in E. coli. In E. coli the states are related by PA clockwise rotation (when looked from PA tip) when going from closed to open state. In the recent paper on Chaetonium complex I [4] – form 1 is clearly open state, form 2 is clearly closed (in our updated nomenclature as below). They are related by the PA clockwise rotation (when looked from PA tip) going closed-to-open (2-to-1). I.e. it is the same overall change as in E. coli. ??
Therefore open and closed states should be attributed not by PA-MA angle, as we noted [2], but on the basis of:<br /> ?Open state - OPEN Q cavity (mostly disordered key loops, especially ND3) and pi-bulge in ND6 (as well as flipped out into lipid ND1 Y156 in E. coli / Y142 ovine).<br /> ?Closed state – CLOSED Q cavity (mostly ordered key loops, especially ND3), no pi-bulge in ND6 (as well as flipped in into E-channel ND1 Y156 in E. coli / Y142 ovine).
?So what was called locked open state in DmCI in fact clearly corresponds to closed state in our nomenclature (ND3 loop ordered, no pi-bulge). What was called closed state in DmCI is in fact open state (ND3 loop disordered, pi-bulge present). The only difference with E. coli open state is that NuoC beta1-2 loop is retracted in DmCI but is inserted into Q cavity in E. coli (incidentally, some of labels describing E. coli features are wrong in Fig.5-suppl1BC). However, in Ovine this loop is disordered in open state, so its conformation is not absolutely defined by the state (unlike ND3 loop and pi-bulge). Another difference is that in DmCI open state PSST loop is not flipped as in Ovine. However, in E. coli this loop does not flip either, so again its conformation is not absolutely defined by the state. Considering the re-assignment of states as we suggest then the PA rotation going closed-to-open is in the same direction in DmCI as in E. coli. A similar rotation was also noted in another recent manuscript on DmCI [5].
In summary, after re-assignment it is clear that main features defining closed and open states (in our nomenclature) in DmCI are the same as in Ovine, E. coli and Chaetonium. It is possible that under turnover conditions in Drosophila even more of the features will become consistent (such as NuoC beta1-2 loop insertion in open state), however the assignment is already unambiguous. ??
So to avoid confusing readers about what is open and what is closed state it would be great if authors renamed the classes according to our latest nomenclature as above.
?One potential question is that in parallel paper [5] (otherwise mostly consistent with this study) in Dm2 state (open state in our nomenclature) ND3 loop apparently remains ordered. However, Agip et al. did only global 3D classification on the entire complex I molecule which, according to our experience, is unlikely to fully separate classes - then any Dm1 (closed) state particles still present in Dm2 class would easily show ND3 loop density – we have seen this a lot when classification in not converged. Additionally, the resolution of Dm2 class is quite low.??
Considering authors comment here on the poor density of some regions in our Ovine deactive structures, I need to note that these data were post-processed with high B-factor suitable for the main bulk density. ND5-HL, TMH16ND5, NDUFA11 are indeed not well defined but are still present as we can re-activate this prep. However, if one applies blurring B-factor of about 100 in COOT (or filter maps to about 4A) to the deposited densities of deactive states, then except for open1, all other states (especially open3 and open4) show very clearly relocated ND6 TM4 density together with loop blocking PA/MA movements. It is clear that after full deactivation ND5-HL, TMH16ND5, NDUFA11 become flexible but still associated with complex, while ND6 TM4 together with its loop relocates. <br /> ?<br /> Authors also mention in discussion that in Yarrowia both open and closed states were observed. However, as we discussed in the SI of our paper [2], only one conformational state was observed under turnover conditions in Yarrowia. It resembles the open state of Ovine CI – pi-bulge present, ND3 loop disordered, etc. The reported conformational changes in Yarrowia CI [6] may in fact reflect the deactive to open state transition, and the closed state remains to be properly classified out.??
It is also a bit strange for authors to criticize our E. coli paper [2] on the basis on Kolata/Efremov paper [7] – we have clearly shown that the resting E. coli state is promoted by DDM detergent (which was used in [7]) and this is why we took a lot of care to fully purify enzyme in milder LMNG detergent, with clear data showing it is stable in LMNG. Further, air-to-water interface argument from authors is not applicable to our data – grids were made with continuous carbon layer support, so protein is never exposed to air during blotting/freezing. <br /> ?<br /> Authors also state that “thermophilic yeast Chaetonium thermophilum CI, which is found in multiple resting states, none of which corresponding to the open state seen in other species” [4] However, Chaetonium two states correspond very closely to Ovine open and closed states, as authors themselves state in [4]. So the point of the statement above is not clear.
It seems like in the discussion the authors try hard to suggest alternatives to our mechanism, even though there are no real factual arguments here. One particular argument is that open states of complex I could be all deactive (as still suggested for mammals [5]) and do not participate in the catalytic cycle, with only closed state being part of catalytic cycle. However, all the new emerging data from species which do not have deactive state, i.e. E. coli [2], Chaetonium [4] and even including current Drosophila structures point out that closed-to-open transitions as part of catalytic cycle are universal.
Overall, I hope that the discrepancies above will be corrected in the final paper.
References
On 2020-07-13 09:06:35, user OxImmuno Literature Initiative wrote:
On 2024-03-29 20:49:08, user Scott Saunders wrote:
Nice preprint! You may want to cite this paper. "Sean A. Higgins, Sorel V. Y. Ouonkap, and David F. Savage. Rapid and Programmable Protein Mutagenesis Using Plasmid Recombineering."
On 2021-09-12 02:24:50, user Raghu Parthasarathy wrote:
Interesting paper! If you're going to claim a power law (such as an inverse square), however, it would be good to see the data plotted on a log-log scale, so that the scaling exponent is obvious, and also to see a robust fitting of the exponent value. Also, I don't see that the datapoints are available to the reader -- is there a supplemental data link missing? Thanks!
On 2021-03-07 18:12:39, user Marek Bury wrote:
That's really promising! What is current review status? Do you have any information on blood glycyrrhizin levels after ingestion? Any research pending in vivo/clinical?
On 2024-01-19 04:01:11, user Pamela Bjorkman wrote:
This paper was published as: Barnes, CO, Jette, CA, Abernathy, ME, Dam, K-M A, Esswein, SR, Gristick, HB, Malyutin, AG, Sharaf, NG, Huey-Tubman, KE, Lee, YE, Robbiani, DF, Nussenzweig, MC, West, AP, Bjorkman, PJ (2020) SARS-CoV-2 neutralizing antibody structures inform therapeutic strategies. Nature 588: 682-687. PMCID PMC8092461 doi:10.1038/s41586-020-2852-1
On 2018-01-17 18:32:23, user Aaron Liston wrote:
How would this change if you used the most up to date angiosperm phylogeny? e.g. https://phytozome.jgi.doe.g...<br /> The important difference is the Malpighiales (poplar, flax, manihot) share common ancestry with the Malvidae and not the Fabidae. Earlier angiosperm trees were biased by over-reliance on the plastid genome.
On 2021-03-25 11:44:39, user Radagast1980 wrote:
Fascinating. It is very interesting how cases of mimicry in structural color are being documented more often. This is another example of mimicry in a beetle with structural color : https://doi.org/10.1098/rsb...
On 2016-05-27 20:40:01, user Marcey Kliparchuk wrote:
Chris Portier former Head of the National Toxicology Program stated, “ this is the best designed animal study every conducted on this topic.” Ron Melnick, who led the NTP study design team, confirmed these leaked results to Microwave News stating, “The experiment has been done and, after extensive reviews, the consensus is that there was a carcinogenic effect.”
In 2011, the International Agency for Research on Cancer (IARC), a committee of the WHO, classified RF radiation as a Group 2B carcinogen in the same category as lead and DDT. Alarmingly, several scientists who were members of the IARC working group involved with this classification now conclude the risks are much greater than originally thought. For example, Dr. Dariusz Leszczynski warns that RF-EMF should be classified as a Group 2A carcinogen, and Dr. Lennart Hardell reports that several studies indicate a Group 1 classification is justified, placing RF-EMF in the same category as tobacco, asbestos, and benzene.
For example, Dr. Dariusz Leszczynski MSc, DSc, PhD states “In conclusion, I consider that currently the scientific evidence is sufficient to classify cell phone radiation as a probable human carcinogen – 2A category in IARC scale. Time will show whether ‘the probable’ will change into ‘the certain’. However, it will take tens of years before issue is really resolved. In the mean time we should implement the Precautionary Principle. There is a serious reason for doing so.”
Dr. Lennart Hardell “Based on the Hill criteria, glioma and acoustic neuroma should be considered to be caused by RF-EMF emissions from wireless phones and regarded as carcinogenic to humans, classifying it as group 1 according to the IARC classification. Current guidelines for exposure need to be urgently revised.”
On 2019-08-08 09:06:22, user Rosalind Arden wrote:
Keen to read this interesting study. It would make easier reading if the acronyms were cut out. They impose a cognitive load on everyone but the Authors (who are 'cursed with knowledge'!)
On 2016-08-04 12:07:26, user D Samuel Schwarzkopf wrote:
This manuscript has now been published in PLoS One: dx.doi.org/10.1371/journal....
On 2019-10-02 11:47:37, user Danielle Kurtin wrote:
Hello,<br /> Thank you for preprinting this paper; it's been useful in the literature review I'm conducting for the start of my PhD. <br /> I noticed a few types in the manuscript. For example, the first sentence of the introduction begins as "Despite most neuroimaging studies still tend to treat human brain features as stable and homogeneous characteristics within a group, it is important to highlight that, in contrast, individual variability may play a relevant role in this context [1] [2]." Perhaps the following may be more correct: "Most neuroimaging studies tend to treat human brain features as stable and homogeneous group characteristics; however, it is important to highlight that individual variability may play a relevant role in this context [1] [2]."<br /> Let me know what you think, and thank you again!<br /> Cheers,<br /> Danielle
On 2020-05-01 17:36:54, user Peter W. Mullen wrote:
It appears above that Dr. Lazic's first name is misspelled (the "n" is missing).
On 2021-03-18 18:45:07, user John Carlson wrote:
Great work, great to know there is some protection from reinfection from Covid. I assume that there is still an unknown with variant Covid strains. Thanks
On 2023-08-30 08:41:17, user Jose E Perez-Ortin wrote:
This new model for explaining mRNA<br /> buffering is a very interesting piece of work. We would like to suggest some<br /> possible improvements to be considered by the authors in this preprint stage before<br /> it becomes published in a journal.
In some parts of the manuscript it is said<br /> that mRNA buffering is perfect as total mRNA concentration and even individual<br /> mRNA concentrations are invariant. We think that this is overblown. For<br /> instance, graphs in Sun et al 2013 (ref. #9; Figure 1),<br /> the variability in total mRNA may be as high as 50%. In fact, in García-Martínez et al 2004 (ref. #15;<br /> Figure 2) we published that during the carbon source change mRNA concentration<br /> changes also by a factor of 2. We wonder if this could be important for the modeling<br /> because it seems that on the advantages of the RS model is that it predicts<br /> robust buffering, contrarily to previous feedback models.
The manuscript misses citation of some<br /> papers that we consider important for the field of mRNA buffering, such as Mena et al 2017 (doi:<br /> 10.1093/nar/gkx974). This paper is especially relevant because the current<br /> preprint describes in the Introduction section that total mRNA concentration is<br /> constant as the cell volume increases (refs. 19-22) but forgets to mention this<br /> piece of work, which was the first one to show that degradation rate perfectly<br /> balances production rate during cell volume change. Instead of our paper, the<br /> preprint cites ref. #27, which is 4 years older than Mena et al 2017.
Garcia-Martinez et al<br /> 2023 (doi: 10.1016/j.bbagrm.2023.194910) is also highly relevant. We described in that<br /> article a mathematical model that explains mRNA buffering using a simpler<br /> mechanism consisting only one mRNA binding factor that co-transcriptionally imprints<br /> mRNAs. That model also predicts that synergistic changes in synthesis and<br /> degradation rates will provoke faster and stronger responses, as described in<br /> some experiments. We also previously published a multiagent model in Begley et al 2019 (10.1093/nar/gkz660),<br /> which combines mRNA imprinting and feedback mechanisms. That paper also<br /> demonstrates that Ccr4 and Xrn1 act in parallel with different sets of targets<br /> genes. We also have demonstrated in that paper and in other two (Begley et al 2021 doi:<br /> 10.1080/15476286.2020.1845504; and Medina et al 2014 doi:<br /> 10.3389/fgene.2014.00001) that protein factors, such as Ccr4 and Xrn1 act not<br /> only in transcription initiation level but also in elongation . We think it<br /> would be nice this manuscript to discuss the differences of these models with<br /> the proposed RS model.
Finally, as for the model in Figure 4c, we do not understand why the<br /> activation of a degron used by Chappleboim et al 2022 (ref. #16) only<br /> degrades cytoplasmic Xrn1 molecules (Xc) and leaves Xp molecules intact. All<br /> Xrn1-degron molecules (Xc, Xp, Xn) will be proteolyzed after Auxin addition.<br /> This can affect the predictions made by the RS model.
On 2021-11-06 20:07:56, user Binks Wattenberg wrote:
We find this to be a very exciting and compelling study that establishes that the turnover of the ORMDL proteins is regulated by sphingosine-1-phosphate signaling in vascular endothelial cells. We do, however, have a different model as to the role of this system as a homeostatic mechanism controlling sphingolipid biosynthesis.
We consider the ORMDLs to be regulatory subunits of SPT which, like many regulatory subunits, are not intrinsically inhibitory until they are triggered by a ligand. Our evidence strongly indicates that the ligand for the SPT/ORMDL complex is ceramide. With this in mind, we envision that the S1P regulation of ORMDL stability overlays an acute and direct ORMDL-dependent regulation of SPT by ceramide. In our view, the S1P-dependent stabilization of the ORMDLs maintains them as ceramide-sensitive regulators of SPT. In the absence of S1P signaling, degradation of the ORMDLs renders the SPT complex insensitive to ceramide and therefore strongly increases SPT activity.
Below we outline evidence which brings us to this view. But before doing so, we would like to emphasize one of the exciting and important aspects of the work outlined in this pre-print. Considering that S1P signaling is mediated by the G-protein linked S1P receptors (in this case S1PR1), it is an interesting possibility that other cell types with different requirements for control of sphingolipid biosynthesis will utilize the same downstream signaling, perhaps mediated by other G-protein receptors, to control ORMDL levels. A hint of this is found in the regulation of ORMDL turnover by cholesterol loading reported by Gulshan and colleagues (Autophagy. 2015;11(7):1207-8).
The experiments that underlie our view of that the enzymatic activity of the SPT/ORMDL complex is directly responsive to ceramide levels is as follows. It is important to emphasize that the bulk of these studies were performed in Hela cells. The biochemistry of the SPT/ORMDL complex itself is likely independent of cell type, but additional regulatory mechanisms, such as those presented in this pre-print, are doubtless cell-type dependent:
Sphingoid bases do not mediate an acute ORMDL-dependent regulation of SPT. We tested the identity of the sphingolipid that triggers ORMDL inhibition of SPT by feeding Hela cells sphingosine. This results in an almost complete inhibition of SPT. This is consistent with the sphingosine inhibition of SPT activity in intact cells originally reported by Kondrad Sandhoff’s group (Mandon EC, van Echten G, Birk R, Schmidt RR, Sandhoff K.Eur J Biochem. 1991 Jun 15;198(3):667-74). This inhibition is entirely ORMDL-dependent. Importantly, we demonstrated that sphingosine inhibition of SPT was completely blocked by preventing ceramide generation with the ceramide synthase inhibitor Fumonisin B1 (Figure 2 of J Biol Chem. 2012 Nov 23;287(48):40198-204 and Figure 2 of J Biol Chem. 2019 Mar 29;294(13):5146-5156. ). Thus, inhibition by sphingosine requires its conversion to ceramide. S1P generation, enhanced by sphingosine feeding, is not blocked by Fumonisin B1, yet SPT inhibition is ablated. Therefore, in this system, S1P does not appear to have a role in regulating SPT activity in Hela cells under the short-term conditions that we used. We concluded from these experiments that the triggering sphingolipid is ceramide or a complex sphingolipid such as sphingomyelin or a glycosphingolipid, but not a sphingoid base.
Elevation of ORMDL levels alone does not lead to SPT inhibition. We have shown that inhibition of SPT by ceramide In Hela cells and human bronchial epithelial cells (HBEC) cannot be explained by increased ORMDL protein expression. We do not observe an increase in ORMDL protein expression in response to C6-ceramide treatment of Hela Cells (Figure 5, Siow et al 2015), under conditions in which SPT activity is strongly inhibited. And we demonstrate that ectopically increasing ORMDL protein expression in either Hela cells or HBEC does not result in inhibition of SPT activity (J Lipid Res. 2015 Apr;56(4):898-908). In both of these cells there is sufficient ORMDL at steady state to serve the needs of SPT regulation, yet ceramide is strongly inhibitory. We make the point in this paper that the stoichiometry of ORMDL to SPT expression is important and make clear that it is likely that in some cell types changes in ORMDL expression will impact on SPT regulation. This is consistent with the response reported in this pre-print in response to S1P signaling.
Biochemical reconstitution demonstrates a direct inhibitory effect of ceramide. We have reconstituted ceramide-triggered, ORMDL-dependent inhibition of SPT in isolated membranes in a biochemical assay in which neither protein synthesis, turnover, nor post-translational modifications can occur. We are confident that this biochemical reconstitution reflects a physiological response of the SPT/ORMDL complex to ceramide. We demonstrated that the response to ceramide is strictly stereospecific with respect to ceramide stereoisomers. Only the native, D-erythro ceramide stereoisomer triggers ORMDL-dependent inhibition of SPT. Moreover, we addressed the possibility that the short-chain ceramides that we routinely use (for their solubility properties) might not reflect physiological inhibition. We generated native chain-length ceramide in the isolated membranes using the endogenous ceramide synthases. This ceramide was strongly inhibitory (Figure 2 of J Biol Chem. 2019 Mar 29;294(13):5146-5156).
The recently published structures of SPT/ORMDL complexes reinforces the view of the ORMDLs as regulatory subunits. The ORMDLs are firmly embedded in the structure. Moreover, comparison of the structures in the substrate-free and substrate-bound state indicates that the ORMDLs inhibit SPT via an amino-terminal sequence that reversibly inhabits the substrate binding site of SPT. These structures suggest that the inhibitory sequence must be stabilized in the active site of the protein to achieve inhibition. We propose that ceramide binding to the complex accomplishes this stabilization.
Taken together, our data and that presented in this pre-print form the picture that the ORMDLs are involved in multiple levels of regulation of SPT. A direct and rapid inhibition by ceramide, and the loss of that regulation when the ORMDLs are degraded as a result of interrupted S1P signaling. There is doubtless more to come and we look forward to further discoveries illuminating regulation of this essential system and the physiological impact of that regulation.
On 2020-03-21 18:46:54, user S E wrote:
Hi <br /> Can I asked you after I got a list of DEG with gene ID how can I get the gene names or the orthology human genes?
On 2018-07-27 00:19:01, user markyz wrote:
Very interesting paper. I was wondering whether you tested the accuracy and speed of the gene counts that are provided by STAR aligner, which eases i/o demands during data processing. Here is an example of how it can be run.
STAR --runThreadN $THREADS --quantMode GeneCounts --genomeLoad LoadAndKeep \
--outSAMtype None --genomeDir $STAR_DIR --readFilesIn=$FQ1 $FQ2<br />
Mark Ziemann, PhD
On 2025-10-19 13:28:31, user Graeme Nicol wrote:
This looks like an interesting and valuable study. There are a couple of additional recent AOA virus publications that might be relevent for your analyses ( 10.1038/s41396-022-01341-5; 10.1093/ismejo/wrae205) "http://doi.org/10.1093/ismejo/wrae205)") . Also just wanted to mention it is ammonia-oxidizing archaea.
On 2020-02-17 13:53:52, user SummerBreeze wrote:
On 2021-09-13 22:31:04, user tetech2 wrote:
The stem cell tumor problem is having to be addressed:<br /> Identifying alterna-<br /> tive reprogramming strategies to restore youthful gene<br /> expression with lower neoplastic risk is therefore desirable.<br /> Toward this aim, we have shown that transient reprogram-<br /> ming with multiple subsets of the Yamanaka Factors in-<br /> duces highly similar transcriptional effects to the full set,<br /> and that a distinct multipotent reprogramming system can<br /> confer youthful expression. These results suggest the fea-<br /> sibility of disentangling the rejuvenative and pluripotency<br /> inducing effects of transient reprogramming and serve as a<br /> resource for further interrogation of transient reprogram-<br /> ming effects in aged cells.
On 2020-03-27 05:12:57, user Steve Lilac wrote:
EMT as a mechanism of trastuzumab and lapatinib resistance. In my opinion the question why in breast cancer epithelial cells are mostly HER2-high and mesenchymal cells are HER2-low is deliberated and perfectly investigated. Answer: because of different chromatin architecture. The paper is suggesting that HER2 gene stands with epithelial phenotype and can be silenced during EMT similar to other epithelial marks. When an epithelial HER2+ cell undergoes EMT, the cell looses HER2 expression due to chromatin closure . Quite sensible. The most parts of study is done on genomics and epigenomics data from depositories which is impressive. I think this is a great example for how different raw data are to be analyzed in a correct way by other researchers to compile meaningful results. The in silico data contains those from patients and cell culture that is confirmed by experimental results.
On 2019-10-10 13:50:21, user Sebastian Pfeilmeier wrote:
Endophytes are getting into the focus of microbiota research, as they are in close contact with the plant and both organisms are likely to influence each other. I was wondering whether it would be even more informative to compare not only "diversity" of different plant species and tissues, but also look for high abundant taxa that are commonly found as endophytes in plant species/tissue. After doing the effort of collecting all the rawdata from various studies, would it be possible to do this analysis?
On 2023-06-20 06:12:32, user jean-philippe hugnot wrote:
This article is now published:
Ripoll, C., Poulen, G., Chevreau, R. et al. Persistence of FoxJ1+ Pax6+ Sox2+ ependymal cells throughout life in the human spinal cord. Cell. Mol. Life Sci. 80, 181 (2023) doi: 10.1007/s00018-023-04811-x.
The article has been upgraded with new figures and data and some errors present in figures and supplementary figures in the Biorxiv version have been also corrected.
JP Hugnot
On 2024-01-30 14:18:50, user Jason Bubier wrote:
Now published at
On 2025-04-10 12:53:55, user Huiwang Ai wrote:
I think the authors just don't understand computational protein design. RFdiffusion is used to generate the shape of the binder, not sequences. You then need other follow-up computational tools.
On 2023-03-29 19:29:44, user Krzysztof Kotowski wrote:
Check out also this latest work as a part of the literature review: https://doi.org/10.1002/pro...
On 2020-08-12 14:09:47, user JRLatham wrote:
This will potentially have a significant impact on SARS-CoV-2 origin theories, including our own: https://www.independentscie...
On 2020-11-05 10:58:56, user Luca Venturini wrote:
Interesing article, thank you.
How does SICILIAN measure against our previously published Portcullis? <br /> At a first reading the main difference seems to be the building of the model across multiple samples (portcullis performs this operation per-sample).
On 2023-09-19 18:56:14, user Lloyd Fricker wrote:
Confirmation of results is an essential part of science. Our original finding that the peptide PEN is an agonist of GPR83 was already verified by two different laboratories (see Foster et al, 2019, “Discovery of Human Signaling Systems: Pairing Peptides to G Protein-Coupled Receptors”, Cell, PMID: 31675498; and Parobchak et al, 2020,<br /> “Uterine Gpr83 mRNA is highly expressed during early pregnancy and GPR83 mediates the actions of PEN in endometrial and non-endometrial cells”, FS Science, PMID: 35559741). However, when another laboratory can’t confirm what was published, it is important to consider differences between the studies. With this in mind, there are several issues with the study presented here in the BioRxiv report by Giesecke et al. Additional experiments described below would be very helpful.
The HEK293 cell line has been reported to express GPR83 mRNA in many studies listed in GEO Profile, and this is confirmed in the current study (Figure 1A). In the figure showing overexpression of HA-tagged GPR83 in HEK293 cells, the distribution is largely intracellular, maybe ER or Golgi (Figure 1B). Thus, it’s not relevant to consider ‘100-fold’ overexpression based on mRNA, as the amount that is correctly folded and expressed at the cell surface may not be much different than in the native HEK293 cell line. Furthermore, the authors show that PEN peptide does produce a robust increase in phosphoERK in Figure 4A (compare the lanes labeled ‘control’ and ‘PEN’). Furthermore, it looks like the PEN-mediated increase in phosphoERK is several fold higher in GPR83-transfected cells than in control cells (Figure 4A). Although the quantitation panel in Figure 4B doesn’t show an increase, there are very large ‘error bars’ reported to be SD, but for N=2 doesn’t SD really mean the range of duplicates? Also, please show all data points in the bar graph! In any case, it would be nice to see a larger N for these studies. But best of all would be a knock-down of GPR83 in HEK293 cells using siRNA, or a related approach.
Another point is that in the PEN-GPR83 peptide-receptor system, signaling assays that are distal to the receptor activity (cAMP, PLC) tend to give variable results – this has been previously noted in our original study (Gomes et al, PMID: 27117253). This also appears to be the case with the TANGO assay where we have recently found that long-term treatment with PEN (16 hrs) causes a desensitization of the receptor leading to a complete loss of signal (our unpublished observations). There are also issues with the concentration dependence, and ‘u-shaped’ curves where high concentrations of PEN fail to produce the effects seen with lower concentrations (PMID: 27117253). The authors should repeat the studies with shorter times and lower concentrations of PEN.
The lack of binding with Tyr-PEN seen by Giesecke et al. could be due to the presence of an internal His residue in human PEN (YAADHDVGSELPPEGVLGALLRV). Tyr-PEN used in our previous studies for iodination was the rat sequence, which does not have a His. Because His residues can be iodinated using the chloramine T procedure used by Giesecke et al, this can potentially affect binding. It would be good to test binding with iodinated rat Tyr-PEN, to avoid the His residue. Also, why the C-term amide group? That’s not part of PEN.
For the Ca++ assays, Giesecke et al. used G?16, but our previous studies used G?16/i3 (PMID: 27117253). This is not a minor difference. Ideally, Giesecke et al could repeat the experiments with G?16/i3.
Finally, protease inhibitors were used in the binding studies by Giesecke et al, which is good. It is not clear if such inhibitors were used in all other studies with PEN, such as those described in Figure 2 and 3. In the absence of protease inhibitors, PEN could be degraded during the assays and this could have accounted for the negative results.
Sincerely,<br /> Ivone Gomes,<br /> Lloyd Fricker,<br /> Lakshmi Devi
On 2025-11-05 08:42:19, user Anastas wrote:
May be cite this one too?<br /> https://pubmed.ncbi.nlm.nih.gov/38301362/
On 2020-07-10 17:24:25, user Robert Carnahan wrote:
See final version now online at Nature Medicine https://www.nature.com/arti...
On 2023-05-01 09:27:13, user Julian King wrote:
Just found a similar study already published in Nature Communications.
For a more detailed structural and functional analysis:
https://www.nature.com/arti...
The authors show the cryoEM structures of C3 and C3b with ISG65 and also show that ISG65 inactivates the C5 convertase of the alternative pathway.
On 2023-09-26 12:35:11, user Caio wrote:
This article is now published at IMA Fungus.<br /> https://imafungus.biomedcen...
On 2020-05-02 01:51:05, user Paul Wolf wrote:
The big question right now is what was the intermediate animal that tranferred covid-2 from bats to humans. This study was about ferrets, which is one possible vector, since this is how the scandalous gain of function research works. Once a virus spreads on its own throughout a ferret population, it has adapted to the ferret which is apparently similar to the human system.
I wonder why pangolins weren't included in this study? Is there some reason to believe covid-2 was transmitted by cats and ducks? The pangolins receptors are apparently a close match to covid-2's, which is where this theory came from, that it came from a seafood market that sold them. But it's not known whether pangolins can actually be infected with covid-2.
On 2022-10-06 13:59:43, user Afshin Beheshti wrote:
This pre-print has been accepted in Cell Reports in October 19, 2021 and can be found here: https://www.cell.com/cell-r...
On 2020-08-04 00:54:09, user Thomas Munro wrote:
A correction to the crystal structure report has now been published, accepting this revision: https://doi.org/d5jj
On 2019-05-26 22:16:50, user DeboraMarks wrote:
Dear Authors
You might want to consider comparing your approach for variant prediction to results from following two paper: plausibly the state-of-art for variant prediction from sequence:
2.( Unsupervised) Variational autoencoder, Riesselman, Ingraham, Marks "Deep generative models of genetic variation capture the effects of mutations" . Nature Methods 2018 <br /> https://www.nature.com/articles/s41592-018-0138-4<br /> Compared to 40 deep mutational scans
On 2018-04-26 06:36:20, user pierre wrote:
Hi there, <br /> Thanks for this nice work. Would it be easy to test several of your experiments comparing with Simka [1] instead of Mash? Both tools are based on kmers comparisons, but Simka does not subsample input data, and thus reaches a higher precision.
Have a nice day, <br /> Pierre Peterlongo
[1] Benoit, G., Peterlongo, P., Mariadassou, M., Drezen, E., Schbath, S., Lavenier, D., & Lemaitre, C. (2016). Multiple comparative metagenomics using multiset k-mer counting. PeerJ Computer Science, 2, e94.
On 2023-03-14 13:25:16, user Paraskevi Athanasouli wrote:
This preprint has been published and a link will follow.
On 2016-07-30 13:34:21, user Paul Janssen wrote:
A darn pitty that this new platform is only available for researchers <br /> and research groups in the UK - one may wonder whether in response the <br /> UK should be excluded from access to the renowned EMBL resources (www.embl.de) paid by European money .... and whether the EBI (http://www.ebi.ac.uk/) "http://www.ebi.ac.uk/)") should be relocated to Belgium or elsewhere on the main continent !!!
On 2017-04-29 14:50:47, user Vladimir Panchev wrote:
This article contains some very important approaches that could help to overcome two tremendous fallacies relating the stomata with the two essential and vital for the plant mechanisms, the supply of water and CO2 for photosynthesis and phloem sap formation. Yet the title implies that the early stomatal closure is vital for surviving in drought which means that their openness is not vital for the plant. This is in accord with the auhors' Ref. 10 which they cited without pointing out that it reports that in very low stomatal conductance (which shell mean also in complete closure), CO2 is not limiting factor for photosynthesis, not mentioning also the water for such one. Does it not mean that both photosynthesis stuffs come from below, without any vital dependence on stomatal opening? In support of this conclusion, having regard that in nature drought can last months and years with stomatal full closure, does the common sense allows to assume that plants can survive such periods without photosynthesis and sap circulation, if water and CO2 supply depended on stomatal opening? Does this only fact (apart from the huge number of others that we can present) is not sufficient to convince the reader that the key role of open stomata for water an CO2 supply, which the two dogmas ascribe to them is one of the most deleterious fallacies in the entire science, not only in the plant physiology?<br /> Very recently appeared two articles which support Martin-StPaul, et al.’s important conclusion for the vital importance of early stomatal closure during drought. In the first one, Scoffoni et al. (2017) inform that “Outside-xylem vulnerability, not xylem embolism, controls leaf hydraulic decline during dehydration”. The authors established that the decline of leaf hydraulic conductance during dehydration arose first and foremost due to the vulnerability of outside-xylem tissues. We think that for everyone, who is sufficiently familiar with physics, should become clear that, if the outside xylem first dehydrates, the stem xylem embolism during severe drought cannot in any kind be caused by the increased tension produced by the increased “water potential difference between leaves and soil”, as the modern Cohesion-Tension theory (C-TT) (which, on our mind, Martin-StPaul et al. rightly not cited) implies. Is it not quite clear that, if the plant dead occurs by stem embolization, according the C-TT, leaves must dehydrate and die the last, after consuming the entire water above the hypothetical place of water thread disruption? (If C-TT leaves something for leaves' consumption, because it postulates that the drawn up water is entirely evaporated?) Evidently, not realizing that their finding undermines C-TT, Scoffoni et al. (2017) try to present their finding as safety mechanism to prevent xylem embolization. Could the leaves die to prevent xylem sap interruption, or how wilted leaves before dying could resume water transport after watering, if for resuming evaporation and sap pulling they mus firstly regain turgidity?<br /> We think that the fallacious implication that the increased tension during drought produces xylem embolism which is the cause of plant dead is entirely based on the fallacious assumption which Scholander et al. (1965) introduced after diametrically changing their minds from Scholander et al. (1955 and 1962). Using metal (instead of Dixon’s glass chamber) they misled the entire community that the measured in the pressure bomb pressure is the negative pressure in the xylem conduits, thus, reviving the more than half a century almost dead theory. Their opaque (instead of Dixon’s transparent chamber) hides from the users’ eyes the extremely important “striking fact” that Joly reported during the first discussion on C-TT (Darwin, 1896) (entirely neglected). It was that during increasing and relieving the pressure in the glass chamber, the leaf behaves like a Bourdon tube rolling inwards from the edges and simultaneously dropping upon the petiole. This means that plant leaves cannot sustain xylem-to-atmosphere pressure differences, because in such conditions their veins behave as real Bourdon tubes.<br /> Other arguments against the pressure-chamber-technique, which Scholander et al. (1965) introduced, stem from Scholander et al.’s reports themselves. In Scholander et al. (1955) there are at least three very important anti-C-TT reports: The first is that, based on direct measurements, they excluded significant negative pressures, establishing that spring refilling is caused by strong positive pressures; The second is that from the cut grapevine stem all water till the next upper bordered pit goes freely out which points on the absence of sufficient wettability, but, we assert, also that this proves that bordered pit membranes serve as one-way valves, preventing sap back flow; The third is that grapevine’s xylem sap is fully saturated with atmospheric nitrogen in the roots, without releasing it in the conduits (why, if xylem sap is tensile?).<br /> The reader is also invited to look at the conclusion made with the last sentence of Scholander et al.’s (1962): “The root system of mangroves is ventilated by air, and it seems more likely that the separation involves a case of active transport”. Is this consistent with the modern C-TT which teaches that cohesion-tension continues drawing water in the roots? It is interesting that even with their above cited three-years-later publication (Scholander et al., 1965) with which they mislead the world with the revival of this technique for indirect measuring “negative pressures”, disregarding their above cited 1955 and 1962 anti-C-TT results, they gave serious ground to disbelieve their assertion. In Scolander et al. (1965) abstract they write: “In tall conifers there is a hydrostatic pressure gradient that closely corresponds to the height and seems surprisingly little influenced by the intensity of transpiration”. Is it not quite similar to the below cited Dixon (1898, 1914, 1938) conclusion rejecting evaporation? This “surprise” is ignored yet half a century. Moreover, we assert that the last two sentences of that abstract prove that the method does not show any tension, but the ultrafiltration via the cell membrane against the osmotic pressure suggested by Askenasy (1895) cited below. We stress that in the presently mechanistically repeated C-TT variant the also mechanistically cited its original authors are represented only by the tension because:<br /> 1. One of the authors of the original C-TT, H. Dixon, (that Scoffoni et al., 2017 cited), cannot be linked with the modern C-TT, because very soon he realized that Strasbuger’s experiment with the dead tree was misleading with the following statement first made 120 years ago: “With regard to the elevation of water, when the leaves are surrounded by an unsaturated atmosphere, we cannot as yet be dogmatic. But the fact that, when the leaves of plants are killed, they dry up and are unable to furnish themselves with sufficient water from unlimited supply at the base of their stem, argues that surface tension and evaporation forces at their surfaces are in themselves inadequate” (Dixon, 1898, p. 634, 1914, p.24, 1938)?<br /> 2. The present C-TT is also inconsistent with the opinion of the other, also widely believed as the original C-TT author, E. Askenasy, who yet in his first paper (Askenasy, 1895) stated that in the “living, not wounded” plants water enters the roots by osmosis, not by tension? Yet in this first paper, he opposed the leaf surface tension in the non-existing water menisci three years earlier than the above mentioned Dixon’s change of mind. In the same paper, Askenasy suggested evaporation through the cell wall with subsequent imbibition by osmosis, instead of the original Dixon and Joly’s (1895) C-TT which nowadays continues to be described as the sole and absolute truth based on non-existing structures. This was confirmed by the second above mentioned recent report, Buckley et al. (2017) who proposed a mixed-phase water transport outside the xylem, evidently not suspecting that they definitively undermine modern C-TT which relies on tension created on hypothetical water menisci formed between the spongy mesophyll cells. Apart that spongy mesophyll cannot sustain tension, could mixed gas-water phase transmit tension?<br /> References:<br /> Askenasy E (1895) Über das saftsteigen. Ferh. Naturh-med. Ver. Heidelberg N. F. 5: 325-345<br /> Buckley TN, John GP, Scoffoni C, Sack L (2017) The Sites of Evaporation within Leaves. Plant Physiol; 173: 1763-1782<br /> Christine Scoffoni et al. (2017) Outside-Xylem Vulnerability, Not Xylem Embolism, Controls Leaf Hydraulic Decline during Dehydration. Plant Physiol. 173: 1197-1210<br /> Darwin F (1896) Report of a discussion on the ascent of water in trees. Ann Bot os-10: 630-661<br /> Dixon HH, Joly J (1894) On the ascent of sap. Proc Roy Soc London 57: 3-5<br /> Dixon HH, Joly J (1895) On the ascent of sap. Phil Trans Roy Soc London 186: 563- 576<br /> Dixon HH (1898) Transpiration into a Saturated Atmosphere. Proc Roy Irish Acad 4: 627-635<br /> Dixon HH (1914) Transpiration and the ascent of sap in plants. Proc Roy Soc London<br /> Dixon HH (1938) The Croonian Lecture: Transport of Substances in Plants. Proc. Roy Soc London 125: 1-25<br /> Scholander PF, Love WE, Kanwisher JW (1955) The rise of sap in tall grapevines. Plant Physiol 30 (2): 93-104<br /> Scholander PF, Hammel HT, Hemmingsen E, Garey W (1962) Salt Balance in Mangroves. Plant Physiol 37: 722 -729<br /> Scholander PF Bradstreet ED, Hemmingsen EA, Hammel HT 1965 Sap Pressure in Vascular Plants. Negative hydrostatic pressure can be measured in plants. Science 148: 339-346<br /> Vladimir S. Panchev<br /> Adelina V. Pancheva<br /> Marieta V. Pancheva
On 2025-04-11 21:36:35, user Omer Faruk Gulban wrote:
This preprint is now published with a different title "Short-term gradient imperfections in high-resolution EPI lead to Fuzzy Ripple artifacts" at: https://doi.org/10.1002/mrm.30489
On 2018-09-27 20:54:37, user Nils Homer wrote:
Definitely looks promising!
Needs references to the current standards (ex. the Li et al SAM paper). Needs references to the current reference implementations (ex. htslib, htsjdk); "currently-needed functionalities" already supported by these; why isn't the reference implementation contributed to those existing projects with already used APIs? Hopefully the current spec maintainers are asked to be reviewers.
On 2025-05-02 12:25:59, user Matt Agler wrote:
The authors note that it has been brought to our attention that we used the wrong form of the glycoside in Fig 6. The figure uses the L- and not the D-form. We will update the figure in the next round of revisions when we update the manuscript.
On 2019-08-09 18:56:43, user Casey Greene wrote:
The COI statement in this manuscript is painfully flawed. The sole author of this manuscript appears to be the founder of a company that claims this as its first preprint.
On 2019-08-07 23:22:57, user Laura Sanchez wrote:
The manuscript by Marchione et al. describes a novel and exciting method to perform proteomics on archived FFPE tissue. The manuscript is thorough and did not over or understate the value of the findings. The method appears to have broad implications for the use with archival FFPE samples and importantly, does not require great measures or lengthy extra steps in order to achieve proteomic quality achieved with fresh frozen tissues. It was noted that a broad extension could be to incorporate this workflow with FFPE tissue blocks that are being used for imaging mass spectrometry workflows. Coupling the spatial information with the IDs from the HYPERsol workflow would be incredibly powerful for clinical applications. We appreciated the use of color coding and acronyms to attempt to simplify the readability of the manuscript, however we do have suggestions which may improve this further. The supplemental figures were well done and the overall consensus was that some of them may be better suited as main figures although we realize this may be a journal specific limitation on the number of figures that could be included. A full list of major and minor critiques is listed below with hopes that this may help the authors improve readability and strengthen the findings.
Major:<br /> The reference to “flash-frozen results” in the title is not abundantly clear to refer to the FFPE results as having comparable quality to flash-frozen tissue results. Rewording the title would help with clarity.
Figure 1e is missing a figure legend.
Assumed level of knowledge with how tissue samples are usually handled for proteomic experiments is very high, but we are unsure of who the target audience is. Additional references or explanation could help broaden the audience.
In paragraph “in order to compare” on page 2, it does not comment on limitations of HYPER-Sol. It would be helpful to know what protein categories (if any) are missing in HYPER-sol as represented in figure 2f, because a large list of Protein ID’s is difficult to dig through and it is not abundantly convincing that the missing proteins are simply noise.<br /> Acronyms are not consistently used throughout the paper. Mentions of XPM could also be widely replaced as “standard”, and DAS with HYPER-sol. Figure 1b could also benefit from having some sort of legend for the conditions or having them appear directly in the table similarly to how they are depicted in the text on page 1.
We were very interested in the claim that TLE1 expression was only 3-fold more expressed than MPNST. Supplementing the mass spectrometry experiments with immunohistochemistry would be a strong orthogonal validation that the MS method is indeed robust.
The limitations of extending HYPER-sol to historical samples should be further articulated. For instance, even though all of these historical tissues exist, researchers may run into issues finding the metadata due to a lack of historical medical records for archived tissue, thereby limiting the usefulness without the biological context. Perhaps, this can also be framed as a push to digitize existing historical records?
Minor:<br /> Being able to process a 17 year old sample with HYPER-sol is a highlight of the applications of HYPER-sol. Mentioning the historical samples in the abstract would increase interest in the manuscript.
Mixed feelings on use of informal terms such as “gold-mine” and “treasure trove” (pg 1): these could be replaced with “resource”. This terminology is not scientific.
Imaging mass spectrometry specialists routinely use solvents to access FFPE tissues and to call them toxic chemicals may be an overstatement.
Please clarify how much dry mass was used from the historical sample, and if that amount consumed the whole sample. A reference is made to 5 mg in Figure 1, but it is unclear whether or not that amount was also used in the historical sample. Also, were the historical tissues from tissue blocks or from stored tissue? This was not clear in the experimental details.
Figure 1b uses “X”s in the table, even though X is a common abbreviation in the paper. Consider using check marks or another indicator instead.
Figure 1b should include all conditions with acronyms that are referenced in the paper. For example FAS and XAS are not listed here but are mentioned in future parts of the manuscript, this would greatly increase readability.
The spectral libraries referred to in “Mass Spectrometry data analysis” seem to be produced from flash-frozen tissue, but it would be beneficial to specify that this is the case given the variety of possible sources for a spectral library in the figure legend in which it is referenced.<br /> In supplemental figures 1a and 1c, what is the non-soluble material? It is also not clear what the normalization process is for relative residual pellet masses.
In supplemental Fig 2, consider pairing the X-axis so that tissue sources line up between the two graphs. Another option would be to overlap the graphs, where yield and percent solubilized data points have different shapes. We would suggest keeping figure 2b’s X axis in the original order and layering the data from figure 1a on top.
On 2020-07-31 17:28:29, user Davidski wrote:
Hello authors,
Thanks for the interesting preprint. On first inspection, however, there are a couple of issues with the geographic terms in your paper.
Now, I know that you're trying to come up with terms that fit geography, archeology and genetic clusters, but these examples really stick out as being misleading:
https://en.wikipedia.org/wi...
How about if you refer to this grouping as "Western steppe"? This is actually what it is, because the term Western steppe includes both the Pontic-Caspian steppe and the Kazakh steppe.
As I said, I do realize that this is a genetic paper, not a geographic paper. But you can't shift the location of a major geographic feature, like the Pontic-Caspian steppe, almost entirely from one continent to another without people noticing that something is way off, and thus possibly extrapolating that the rest of your paper is not reliable. Cheers
On 2019-06-20 09:17:16, user John Aplin wrote:
How confident are you that Epi9 is a ciliated cell? It is distant from other ciliated groups in the SNE. It is hard to see in your colouring scheme in Fig 2 how it maps in the pseudo time analysis -- is it at the most distant (left hand) end of the secretory cell repertoire? Do you think that ciliated cells are also secretory?
On 2023-09-10 19:39:50, user Wenderson Rodrigues wrote:
Dear Authors,
I am Wenderson Rodrigues, a Ph.D. student at the Federal University of Minas Gerais (Brazil), affiliated with the Plant Interaction Laboratory (LIVe). My research project focuses on the study of ncRNA in the interaction between parasitic plants and host plants. Our laboratory has initiated an activity called the "Preprint Club" where we train and learn to review preprints relevant to our research areas. I have selected your preprint titled "Long noncoding RNAs emerge from transposon-derived antisense sequences and may contribute to infection stage-specific transposon regulation in a fungal phytopathogen" for reading and critical review.
In this manuscript, Qian and colleagues conduct an extensive study on the identification, classification, and investigation of transposable elements (TEs) and ncRNAs in the genome of Blumeria hordei, a powdery mildew fungal pathogen of Hordeum vulgare (Barley). This is a highly interesting manuscript; the methods are well-documented in the literature, and the results are significant. In my opinion, the authors could provide more information in the Introduction about the infection cycle of B. hordei, as understanding this pathogenic process is crucial for interpreting the presented results. Additionally, here are some specific comments regarding questions and corrections that seemed pertinent to me during my reading.
Specific comments:<br /> Lines 135-136: The presentation of PC and NMDS analyses is confusing in terms of result interpretation because they do not complement each other, as mentioned in the text. How do the NMDS results influence the interpretation of the PCA results?
Lines 172-173: For the 102 TEs, where is the expression data?
Lines 181-183: How did you identify if they are peptide-coding transcripts, and what criteria were used to evaluate their significance?
Figures 3B and C are not mentioned in the text. Figure 3D should be reversed in terms of read mappings to follow the order of citation in the text (RNA-Seq and ONT), or the text could be modified to maintain the order of appearance in the figure.
Lines 203-204: There seems to be a missing punctuation mark in the text.
Line 208: Although Figure 4 is mentioned to display information about the lncRNAs identified in the study (such as exon numbers and size), it might be better to specify in which section of Figure 4 this information can be found, e.g., Figure 4B-C.
Lines 233-234: How were the analyses for the identification of putative secreted proteins conducted? Was there a pipeline used for identifying such proteins?
Lines 293-294: The text appears incomplete, possibly due to a typing error.
These are some points that I found relevant to convey to the authors. The research in this preprint is impressive, and it was a pleasure to read and learn from the authors.
All the best,<br /> Wenderson Rodrigues.
On 2016-08-24 20:55:25, user Tal Yarkoni wrote:
This is an innovative and very thought-provoking paper that will hopefully be widely read by researchers working with fMRI. I have two general comments with respect to the authors' main thesis:
As far as I can tell, the authors don't motivate the decision to focus exclusively on sub-voxel representations. They point out that non-smooth sub-voxel representations would be impossible to detect with fMRI, which is an important observation. But surely non-smooth *supra-voxel* representations would still be easily detectable with fMRI. A priori, there doesn't seem to be a good reason to rule out this kind of representation in the brain. As far as I can tell, representational similarity analyses would still work successfully if the brain were composed of hundreds of functionally discrete tiles that were non-smooth at both the sub-voxel and supra-voxel levels. This doesn't seem like a far-fetched possibility; for example, suppose that when people think about penguins, they're somewhat more likely to think about the unusual climate in which penguins live. Representations of climate may be non-smooth, yet reside in fundamentally different brain circuits from representations of physical shape, size, etc. One consequence would be that neural representations of robins would almost certainly more closely resemble those of sparrows than those of penguins even if there were no spatially graded sub-voxel representations at all in the human brain--simply in virtue of sharing a larger number of salient properties with the former than the latter. Of course, I'm not suggesting that there _aren't_ smooth sub-voxel representations in the brain, but simply that the authors conclusion that "the neural code must be smooth, both at the subvoxel and functional levels" doesn't necessarily follow.
Even if one assumes that the signal detected by fMRI is in fact driven entirely by smooth sub-voxel representations, it still wouldn't follow that the neural code must be smooth at the sub-voxel level. All we would be able to conclude is that there is at least *some* component of the signal that is smooth. This would not preclude other neural codes from existing, and in fact, we already have abundant evidence of non-smooth sub-voxel representations. For example, ocular dominance columns clearly exist, and if fMRI is unable to detect them, that reflects a limitation of fMRI, not a generalizable claim about the way the brain represents information. While I'm not a systems neuroscientist, I would imagine that there are any number of examples in the systems neuroscience literature of non-smooth, but highly structured sub-voxel representations that would probably be completely undetectable with fMRI. So I think the authors may want to be more circumspect about the conclusions they draw. Their results don't really show that only a subset of neural coding schemes are plausible; rather they suggest that whatever neural representations fMRI is capable of detecting are likely to stem from either (a) smooth representations (either sub- or supra-voxel) or (b) non-smooth supra-voxel representations. This leaves open the possibility (and it seems like a very real one) that the vast majority of information represented in the brain is not represented in a way that is amenable to detection with fMRI.
Setting these concerns aside, I think this is still a paper that should be of great interest to most cognitive neuroscientists. One point that is made very elegantly here is that the nature of neural representations does not have to be (and probably isn't) uniform across the brain. In particular, the authors put forward a compelling argument for the possibility that brain regions higher in the processing stream--and that are more likely to represent very abstract, multidimensional information--may not be amenable to imaging at all. This point should give many fMRI researchers pause when considering studying, e.g., the representational structure of prefrontal cortex. At the very least, the manuscript raises a number of important questions that should spur further discussion within the neuroimaging community.
On 2020-07-16 06:58:54, user Naito Motohiko wrote:
From Author M. Naito
Thank you for being interested in my manuscript.<br /> The program codes for drawing the graphs are also available on dx.doi.org/10.17504/protoco...
On 2020-12-07 07:45:11, user Alex wrote:
Could you expand on the two samples they tested? What were they looking for with Yaroslav the Wise? Thanks!
On 2022-01-24 16:16:01, user Andre Schwarz wrote:
Dear Liang, Julia, and colleagues,<br /> Beautiful and exhaustive work. This will definitely serve as a standard for future work.<br /> I presented this work today in our journal club (it was very well received) and wanted to share some of the comments with you: <br /> 1) In order to address how much of the absent elongation states upon antibiotic treatment are due to reduced particle number, could you repeat the same classification with a subset of the untreated dataset? I.e. take 13,418 (as in SPT) or 21,299 (as in Cm) particles of the untreated dataset, repeat the classification, and see<br /> how many classes you get?<br /> 2) To independently validate your visual polysome classification/assignment, could you run a polysome gradient and see whether the numbers of di-, trisomes, etc. roughly agree?<br /> Best wishes,<br /> Andre
On 2018-05-12 13:20:33, user Atul Butte wrote:
You guys might be interested in our paper on 3 phenotypic subtypes on getting type 2 diabetes
https://scholar.google.com/...
Ethnic differences in the relationship between insulin sensitivity and insulin response: a systematic review and meta-analysis
Keiichi Kodama, Damon Tojjar, Satoru Yamada, Kyoko Toda, Chirag J Patel, Atul J Butte<br /> Diabetes care 36 (6), 1789-1796, 2013.
On 2019-07-03 08:01:16, user i came here from twitter wrote:
great paper! Just wanted to let you know that you have left a TODO note in the Star Methods: "(add picture in supplementals of courtship arenas build by Daniel)"
On 2020-01-16 03:00:34, user David Ron wrote:
The theme of co-evolution, as it relates to the recognition of JDP and Hsp70 is nicely developed in this scholarly paper. It might be interesting to consider the suppression of the DnaJ D35N mutation by DnaK R167H, alluded to in the discussion, as a case of co-evolution affecting the mechanistic step. It might be helpful were the authors to digress further on the basis of the functionality of the R167H mutation in DnaK? Does it maintain the interaction with D517-SBD and D429-linker? How does it suppress the HPD to HPN mutation? Could this pair serve as fodder for an MD simulation such as the one presented for the wildtype DnaK/DnaJ pair in figure 2b?
On 2025-04-04 14:34:39, user Victor Corces wrote:
It's possible that H3K9me3 and H3K27me3 are acting through changes in DNA methylation. It would be interesting to check
On 2018-02-02 23:08:09, user Gustavo wrote:
Could you share the codes?
On 2020-10-07 23:47:22, user UAB BPJC wrote:
Review comments on “Staphylococcus aureus secretes immunomodulatory RNA and DNA via membrane vesicles” by the University of Alabama at Birmingham Bacterial Pathogenesis and Physiology Journal Club
Summary: This paper discusses how S. aureus is able to secrete extracellular membrane vesicles (MV) that contains immunomodulatory RNA and DNA and their delivery to intracellular host receptors.
This paper has very easy to read language and little use of jargon which is nice to find in scientific papers. The methods are described in much detail which is very nice. However, it is a bit long, especially the results section. Some of the details in the results may be better suited to be added to the either the introduction or discussion. For example, line 116-117 “it is conceivable that nucleic acids could be released through spontaneous phage-dependent or independent cell lysis” would be better suited in the discussion than in the results. The conclusion that RNA packaged in MVs from S. aureus has immunomodulatory effects is very interesting! It is also neat that there is currently a paradigm shift in that endosomal TLRs are not just signaling for IFNb in viral infections and this paper is playing a role in that shift. This paper could be more impactful if some of the current figures were added to the supplement and addition of experiments with more controls and addition of sequencing RNA/DNA as well as looking at which proteins are packaged in these MVs and whether of not they are playing any immunomodulatory roles. Additionally, the immunofluorescent images in this paper are beautiful.
Major points:
* Biological vs technical replicates are not clear which is used in each figure.
* What are these RNAs that are in the MVs? Are they random or purposely packaged specifically? Sequencing these RNAs as well as DNAs would add very valuable information.
* A readout of mRNA levels of IFNb is informational but there would be more value/impact if ELISAs to look at the effect on protein levels would be add value
Minor points:
Figure 1: S. aureus release MVs enriched in proteins and nucleic acids. <br /> * Panel A<br /> o Bradford assay is not the best at detecting levels of proteins in lipid rich conditions<br /> o This graph should be separated by each of the components protein, lipid, DNA, and RNA because relative fluorescent levels would not be the same for each of these components and comparisons between macromolecules cannot be done. This maybe better to show separated by macromolecule rather than by fraction.<br /> o Additionally, the label relative fluorescence levels is misleading because the Bradford readout is colorimetric and not fluorescent. <br /> o It is not clear what the normalization is. What is 100% in the peak fraction for each method?
* Panel B<br /> o Negative stained TEM images of purified MVs are not very clear. Are there better ways to show this? Perhaps just using membrane staining dyes and fluorescent images? <br /> o The scale bars are appreciated
* Panel C<br /> o This may be better suited as supplemental data
* Panel E<br /> o This is a common issue throughout the paper, it is not clear whether representative of “3 independent experiments” means that we are seeing all the data from all 3 experiments or just from 1. It would be nice to know whether these are biological replicates or technical replicates. <br /> o Another issue throughout the paper is that only mRNA is shown for the changes in IFN-b. It would be nice to show protein changes as well via ELISA. <br /> o This data are very interesting!
Figure 2: Purified S. aureus MVs induce significant IFN-b mRNA expression in cultured murine macrophages. <br /> * RAW264.7 cells are a macrophage-like cell line but are not actually macrophages. <br /> * A and B are nice to see how you decided on using 5ug and 3h timepoints but this data could be added to the supplemental <br /> * Again, not clear if technical or biological replicates and in C it would be good to add an ELISA to further validate your results.
Figure 3: Detergent, Benzonase, and Proteinase K sensitivity of MV-associated Nucleic Acids and Figure 6: Benzonase-treatment reduces MV-mediated IFN-b mRNA expression in macrophages<br /> * Combining of this figure and Figure 6 would make more sense, especially since your diagram of how Benzonase treatment of MVs affects the RNA isn’t until figure 6 but you use the Benzonase in Figure 3. <br /> * Additionally, in Figure 6b there is no untreated control which would be valuable. <br /> * How do you explain the decrease in nucleic acid levels in Benzonase treated MVs if you are claiming that MVs protect the RNA from this kind of degradation later on in the paper?
Figure 4: The RNA content of S. aureus MVs consists of Benzonase-sensitive and Benzonase-resistant subpopulations and Figure 5: the DNA content of S. aureus MVs is resistant to Benzonase treatment. <br /> * These figures could be combined into one figure because they address the same point. <br /> * Technical vs biological replicates?<br /> * Can you sequence the RNA/DNA isolated from these analyses? If C is averages of triplicate experiments wouldn’t it be more valuable to show all the data points and not just the averages?
Figure 7: Dynamin-dependent endocytosis is likely involved in MV-mediated induction of IFN-b in RAW264.7 cells<br /> * Figure 7a lacks an untreated control for dynasore and bMVs treatment<br /> * Dynamin is involved in other processes besides endocytosis, how can you be sure this is not affecting your results?<br /> * B would be nicer if you could show bafilomycin and chloroquine with the inhibitory arrow/line directly on the endosomal acidification instead of the arrow<br /> * C lacks bMVs only control <br /> * Bafilomycin has off target effects on IFNb so you would need to also do bafilomycin and chloroquine only controls to verify the effect you have is directly because of acidification.
Figure 8: MV-associated RNA induces IFNb largely through endosomal TLR signaling in murine macrophages<br /> * Again, it would be nice to see each data point represented and not just the means of the experiments. <br /> * 8A is very nice, especially the usage of the cGAMP control. <br /> * This figure has some really interesting data! <br /> * In B, why did you do only an IRF3 and IRF7 KO but did not include a TLR 9 as well?<br /> * The breaks in the axes in C-E can make the changes look more meaningful than they actually are especially since the scales are different in all 3 graphs
Figure 9: Treating MVs with benzonase reduces the IFNb mRNA expression in both WT & TLR3-/- macrophages compared to macrophages stimulated with untreated MVs<br /> * The difference in treatment of TLR-/- cells with MV vs bMVs was not explained<br /> * Neat results!
Figure 10: S. aureus bMVs and their associated RNA cargo is delivered into wildtype macrophages <br /> * Beautiful images!<br /> * It would be nice if you could include quantifications of the colocalization of MVs and RNA for both A and B<br /> * Are the scale bars for A and B accurate? Looking at the macrophages in the bottom right corner of both A and B, they look like vastly different sizes even though the scale shows that they are in the same scale.
Figure 11 <br /> * Nice schematic! <br /> * Are you showing Bafilomycin and chloroquine disrupting the TLR signaling separately of endosomal acidification? <br /> * Maybe show that the endosomal acidification impacts the levels of IFN
On 2022-08-03 21:21:03, user smartalec wrote:
page 8: "The identity of potential drivers of SCLC metastasis on chromosome 16p, the top gain (Supplementary Fig. S7B), remains unknown, but genomic gain of 16p13.3 has been associated with poor outcome in prostate cancer (48) and this region contains the PDK1 gene, coding for a component of the PI3K/AKT pathway." Its not PDK1 which lives on Chr2. The correct gene is PDPK1.
On 2025-05-07 00:48:26, user Young Cho wrote:
The paper focuses on the discovery and synthesis of small molecules that target the p300 histone acetyltransferase (HAT), a key enzyme involved in epigenetic regulation. The researchers identify a series of N-phenylbenzamide analogs, including activators (YF2, RA010900, RA010160, RA010168) and inhibitors (JF1, JF10, JF16), exploring their effects on lysine acetylation of histone 3 at residues K18 and K27. Through structure-activity relationship (SAR) analysis, the study found that the alkyl side chains and specific substitutions on the N-phenylbenzamide scaffold critically influence whether a compound activates or inhibits p300. Despite its poor metabolic stability and rapid degradation in human and murine liver microsomes, YF2 emerged as the lead molecule for its strong activation profile.
The paper effectively supports its conclusions through clear data presentation, including detailed chemical structures, metabolic stability tables, and molecular docking simulation. Figures illustrate the structural differences between activators and inhibitors, while enzyme activity data (EC50 and IC50 values) validate the authors’ hypotheses. However, the study lacks a direct comparison of docking scores, making it challenging to contextualize binding efficiency across compounds. Nonetheless, YF2's successful docking into the p300 bromodomain binding site, along with its SAR insights, provides a solid foundation for future optimization of HAT modulators, offering promising therapeutic avenues for treating neurodegenerative diseases like Alzheimer’s and certain cancers.
Introduction:
The introduction effectively sets the stage for the study by clearly outlining the importance of histone acetyltransferases (HATs), particularly p300, in epigenetic regulation and its relevance to diseases such as Alzheimer’s and cancer. It provides a well-structured explanation of how histone acetylation affects gene expression and protein synthesis, emphasizing the therapeutic potential of targeting p300. The authors also successfully highlight the gap in current research, noting the limitations of histone deacetylase (HDAC) inhibitors and the need for more selective HAT modulators. This thoughtful framing makes a compelling case for why their work on designing novel small molecules to modulate p300 activity is both innovative and necessary. The inclusion of the background information on the structural domains of p300 and its functional overlap with CBP adds further depth, helping readers grasp the enzyme’s complexity and prodrug potential.
There are a few areas for improvement though. While the introduction presents a strong scientific rationale, it could benefit from a more streamlined discussion of the p300/CBP structural features, as certain sections verge on being overly technical without immediate relevance to the study’s aims. Although the authors mention previous HAT activator scaffolds like CTPB, they do not sufficiently explain their limitations beyond solubility and permeability, missing an opportunity to underscore how their new compounds address these shortcomings. A clearer statement of the study’s specific hypotheses, beyond the general goal of identifying p300 modulators, would strengthen the narrative and better guide the reader into the results section. Overall, the introduction is solid and informative but could benefit from slight refinement for focus and impact.
Results:
The results section presents a clear and methodical exploration of newly synthesized N-phenylbenzamide analogs designed to modulate p300 activity. The study effectively categorizes these compounds into activators (YF2, RA010160, RA010168, RA010900) and inhibitors (JF1, JF10, JF16), providing enzyme activity data (EC50 and IC50 values) for histone 3 acetylation at lysine 18 and 27. Notably, YF2 emerged as a strong p300 activator, showing EC50 values of 155.01 nM (K18) and 72.54 nM (K27). The figures, such as Figure 1 and Table 1, are clear and easy to interpret, directly supporting the authors’ claims. Also, the metabolic stability tables highlight YF2’s limitations, revealing a poor half-life of 10 minutes in murine and 4.35 minutes in human liver microsomes, pointing to the need for further optimization.
Despite these strengths, the results section has some limitations. While the structure-activity relationship (SAR) analysis effectively links molecular modifications to biological activity, like how smaller alkyl groups promote activation and longer, branched chains drive inhibition, there is a lack of direct docking score comparisons. This omission makes it challenging to fully contextualize YF2’s binding efficiency relative to other compounds. Furthermore, while YF2’s molecular docking into the p300 bromodomain is visualized and described, a more quantitative comparison of binding affinities would strengthen the conclusions. Overall, the results are well-supported by data, but additional docking metrics and a clearer link between metabolic findings and compound design strategies would enhance the section’s impact.
Discussion:
The discussion section effectively describes the findings within the broader context of histone acetyltransferase (HAT) research, emphasizing the therapeutic significance of p300 modulators. The authors highlight how their study builds upon previous work involving small-molecule HAT activators like CTPB and CTB, which were hindered by low potency and poor pharmacokinetics. By designing N-phenylbenzamide analogs, they address these limitations and identify both p300 activators (YF2, RA010168, RA010900) and inhibitors (JF1, JF10, JF16), advancing the field by offering a new chemical framework with improved activity. The paper stresses the relevance of HAT activation as a promising strategy for enhancing histone acetylation, particularly for neurodegenerative diseases like Alzheimer’s and contrasting it with the more commonly studied HDAC inhibition. This shift from HDAC to HAT targeting reflects a nuanced approach to epigenetic drug discovery.
The discussion could benefit from more direct comparisons to the potency and pharmacokinetics of prior compounds like CTPB and CTB to better highlight the progress made. While the paper acknowledges YF2's strong p300 activation profile, it downplays its poor metabolic stability, mentioning it only as a future optimization target without exploring strategies for improvement. Although the structure-activity relationship (SAR) insights are valuable for linking side chain and alkyl group modifications to compound behavior, the discussion stops short of offering predictive models or design principles for future analogs. A more critical reflection on the challenges of balancing activation potency with metabolic stability would strengthen the discussion’s impact. The section solidly contextualizes the research but could benefit from deeper analysis of the study’s limitations and clearer comparisons to previous findings.
Suggestions:
This paper presents a strong foundation in the development of p300 histone (HAT) modulators, with the key discovery of N-phenylbenzamide analogs that act as either activators or inhibitors; however, a clearer hypothesis regarding the mechanistic underpinnings of how specific structural changes drive either activation or inhibition would sharpen the study’s impact.
The results section effectively supports the study's claims through clear data presentation, including well-labeled figures and tables, but one notable gap is the lack of docking score comparisons via graphing tools, which limits the ability to fully contextualize YF2’s binding efficiency relative to other analogs. Including this data would provide a more robust evaluation of each compound’s molecular interaction with p300, further reinforcing the SAR analysis. These visual comparisons, such as graphs mapping SAR trends or docking results, would enhance the clarity and impact of the presented data.
The discussion successfully contextualizes the study within the broader scope of HAT research, contrasting the new analogs with previous compounds like CTPB and CTB; however, the discussion does not fully address the poor metabolic stability of YF2, mentioning it briefly without proposing solutions. Suggesting strategies, like pro-drug approaches, targeted structural modifications, or lipid nanoparticle (LNP) delivery, would strengthen the discussion’s practical relevance. While the SAR insights are well-documented, the paper stops short of exploring why certain side-chain modifications shift compounds from activators to inhibitors, beyond size considerations. A speculative explanation based on molecular modeling or enzyme dynamics would add depth to the analysis.
While the study employs appropriate methods of molecular docking, cell-free enzymatic assays, and metabolic profiling, there are clear areas for improvement. The lack of in vivo validation leaves a critical gap, as the compounds' efficacy and toxicity remain untested in a physiological context, which is necessary for drug development. Addressing YF2’s instability is also crucial, as the current data raise concerns about its drug viability. Overall, the paper presents innovative findings and expands the field of p300 modulation, but revisions should focus on providing strategies for improving YF2’s stability, including more comparative docking data, and offering deeper mechanistic insights into the activator and inhibitor behavior of N-phenylbenzamide analogs. With these enhancements, the study would be a strong candidate for publication in high-impact journals like the Journal of Medicinal Chemistry or ACS Chemical Biology.
On 2022-01-24 22:53:20, user Dylan Valencia wrote:
I wanted to point out that the scale bars in figure 5B should be the same lengths to make it more easily interpretable. Thank you.
On 2020-02-28 16:01:49, user Xukai Li wrote:
The new version is at https://github.com/xukaili/...<br /> https://twitter.com/xukai_l...
On 2025-03-27 18:10:43, user Luciano Marcon wrote:
I would like to respectfully point out a misrepresentation regarding our work cited as reference [16] (Raspopovic et al., 2014).
In your manuscript, you write:<br /> “In contrast, many studies with realistic biological geometries either neglect growth or fail to explore how growth rate influences the emergence of patterns [16, 20, 21, 22].”
However, this characterization does not accurately reflect our study. In Raspopovic et al. (2014), we explicitly analyzed the influence of growth on pattern formation, as shown in Figure 3A and Figure 23 of the supplementary data. This analysis was based on a limb growth model derived from experimental clonal data (see also Marcon et al., 2011), and understanding how growth modulates pattern emergence was one of the central aims of that work.
We kindly ask you to consider correcting this point to more accurately reflect the content and focus of our study.
On 2023-07-21 18:00:48, user Ryan wrote:
This article is now published in Frontiers in Genetics! Check it out here: https://www.frontiersin.org/articles/10.3389/fgene.2023.1125967/full
On 2021-05-21 14:14:59, user R Greg Thorn wrote:
Nice work and potentially an important concept in invasion biology, but please clarify the identification step of your bioinformatics pipeline. An approximate match in QIIME/UNITE is not an identification. Talaromyces marneffei is (we hope!) unlikely to be a common fungus in Illinois soils. It is a serious human pathogen that is, so far as we know, restricted to southeast Asia, centered on Laos, Cambodia and Vietnam. Some other IDs are equally suspect. Please don't let this get into print.
On 2016-01-07 17:12:02, user Charles C. Mann wrote:
Interesting paper. I suspect the Luckey estimate (1972:1292) is actually taken from an earlier Luckey paper (Luckey, T.D. 1970. Gnotobiology is Ecology. American Journal of Clinical Nutrition 23:1533-40), see table 1.
On 2020-05-04 21:38:47, user Daniel Himmelstein wrote:
My review of version 2 of this preprint is available online. The TLDR summary is that the approach and utility of SciScore are important advances, however the lack of data and code availability for the study limits its impact.
On 2021-06-15 14:22:08, user NITIN NARWADE wrote:
Dear Authors,
Really wonderful study.
Is it possible to access the raw data OR processed scRNA-Seq count matrices for this study?
Thanks in advance!!
Regards,<br /> Nitin Narwade
On 2025-08-31 18:31:56, user Jens-Christian Svenning wrote:
Very interesting work! Please note this related study for Greenland: https://royalsocietypublishing.org/doi/10.1098/rstb.. Best wishes, Jens
On 2017-06-07 11:37:39, user Casey Hanson wrote:
The website server is down right now. It will be up again ASAP.
On 2019-09-18 06:54:36, user Jeremiah Stanley wrote:
Hello authors. It was quite a brave attempt to explore the role of 5HT in macrophages. I have a logical question. There is an interplay of 5HT2B and 5HT7 in modulating the macrophage. So when an antagonist is used against a particular receptor, the 5HT in the medium will be acting more on the other receptor. For example here, the antagonist to 5HT2B was used. without the antagonist, 5HT would be acting on both 2B and 7. But after antagonist addition, 5HT will be acting on only 7. Can this be a reason for the antagonist to not nullify the action of the agonist? Interplay of receptors?
On 2020-06-21 07:40:24, user aminah alqahtani wrote:
That is really great work. Could you please clarify which reference genomes that you use ?
On 2023-06-24 16:29:14, user Sankar Chandra Basu wrote:
Accepted, 20th June 2023, In-press, Journal of Molecular Modeling, Publishers: Springer Nature.
On 2016-05-16 14:14:19, user gerton.lunter wrote:
Tx. Yes, that question has been bugging me too. FW/BW is not possible as far as I can see; but some version of conditional sampling may be possible, although it's like to be approximate (the only algorithm I know for exact conditional sampling uses FW/BW...)
On 2015-04-07 23:09:59, user aaron wrote:
Creationists have been saying this for years. Man originated in northern Africa. After the flood, the sons of Japeth migrated north into Europe. Their skin, while darker than now, simply adapted to the environment over years. I thought this was common sense. Guess not!
On 2019-08-28 13:04:17, user Filipe wrote:
Dear Nathan C. Medd and colaborators, congratulations for your study it's really interesting. I'd like to pointing only one little mistake in figure of pg 34 about Mogami Viruses structure. According with the image, the glycoprotein signature it's present on ORF 3 (with 685 AA), but, according with a fast BLAST analysis, this ORF represents an hypothetical nucleoprotein and the glycoprotein signature it's present on ORF 1 (with 1157 AA) which make sense in orientation when we compare the Mogami virus structure with Shayang Fly Virus 1 structure (Glyco-VP2-Nucleo-RdRp).
Again, congratulations for this study.
All the best.
On 2020-01-09 12:59:35, user Sabyasachi Rakshit wrote:
This is now published in Biochem J.<br /> https://doi.org/10.1042/BCJ...
On 2020-08-28 14:59:42, user Xuemei Cao wrote:
So many paper about the clear mechanism of antisense of FRQ in Neurospora is clear. It is so bad, the author did not mention that.....
On 2020-09-30 18:25:51, user Holger Claus wrote:
I am almost sure that the calculated UVC dose for the moving regimens of 2.13 and 0.66mJ/cm2 are wrong (much too low). The actual dose must be measured. Also deactivating 6log with 0.66mJ/cm2 is magnitudes outside of other research
On 2020-12-02 21:30:36, user Alexis Rohou wrote:
I was asked by a journal to review this manuscript. Below is my review
***
This manuscript explores the observation that Thon rings visible in amplitude spectra of micrographs decrease in amplitude as a function of spatial frequency (distance from the origin in F space) and that this decrease is more pronounced in micrographs collected with larger objective lens defocus.
Since the height of Thon rings from image of test specimens can be taken as an estimator of recoverable signal-to-noise ratio in experimental data recorded under identical conditions, this has led many practitioners to prefer to collect data as close to focus as possible. The dominant assumption in the field has been that the observed defocus-dependent contrast attenuation is due to imperfect spatial coherence of the electron source, but this manuscript provides compelling evidence that another phenomenon is responsible.
The authors note that a significant amount of signal is delocalized beyond the edges of the field of view and so cannot be recovered. Further, the authors point out that single-sideband (SSB) signal in the collected image (be it from features in the field of view but near its edges, or delocalized from features not present in the field of view), while it contributes power to the image, does not contribute to Thon rings because its amplitude is not modulated by the CTF.
I find the authors' evidence in support of this compelling:<br /> - experimentally, the nodes (local minima) between Thon rings to not reach the "noise floor" as would be predicted if all contrast in the image arose from phase contrast attenuated by a spatial-coherence envelope. Computationally, the authors show that this "Thon ring floor" is raised under conditions where more of the recorded image power consists of SSB signal (increased defocus or small field of view)<br /> - theory predicts that, at the fluencies normally used in cryoEM, the spatial coherence of the illumination supplied by modern eletron sources is such that one would not expect significant defocus-dependent attenuation effects<br /> - most compelling, the relative intensity of Thon rings in actual images is well predicted by the fraction of image features for which signal for both side bands is recorded (Fig 4)
My only significant reservation with this manuscript is about the "messaging", and specifically this sentence of the abstract: "The principal conclusion is that much higher values of defocus can be used than is currently thought to be possible". <br /> While the authors have convinced me that the negative effects of defocus were misunderstood and overstated, their claim that higher defocus could be used with no ill effect should be qualified (preferably in the abstract, and in the main text) to make it clear that they are only referring to the imaging part of the experiment, and not the image processing part of experiments, where high defocus values would force users of most packages to use very large box sizes at various parts of the process creating unusually large computational burdens, and/or other problems may occur. If the authors want to keep the claim as is, they should add experimental results that support it, e.g. high-resolution apoferritin reconstructions obtained from both low and high defocus datasets, along with characterization of the mean SSNR, ResLog plot, or similar, in each case. Probably better to keep the paper more or less as is and just qualify this claim, in my opinion.
Beyond that, I have more minor suggestions / questions.
(1) Abstract: I'd encourage the authors to consider removing the sentence remove about correcting mag distortion ("We also show (...) many orientation") - if I understood correctly, this becomes very significantly only at very large defocus, and only if averaging spectra to 1D curve before fitting. For these reasons, I think this is a rather minor point of the paper. In the context of the abstract, I think this aside distracts from the main message
(2) Abstract: "and Ewald sphere correction". Perhaps I missed it, but I don't recall reading in the main text an explanation of why defocus should allow for better Ewald sphere correction, or a demonstration that this is the case. I suggest removing this from the abstract, or adding text explaining this, or a citation to a reference that does (on that note, after a quick re-read of Russo & Henderson 2018, I also don't see an obvious demonstration there that higher defocus yields better Ewald sphere curvature correction, but I'd happily stand corrected).
(3) Page 3: "This is because compensating information, which unfortunately is of no use, may enter the image from features that are outside the field of view." On first read, this sentence confused me - I think because the phrase "compensating information" threw me off. How about something like "This is because unrelated single-side-band signal delocalized from features outside the field of view may enter the image."?
(4) Page 4: "Since delocalized (...) high defocus values to record images (Russo and Henderson 2018b)". I think readers who like me are not well versed in the optics and maths of SSB imaging, this statement is difficult to understand. Could it be explained a little further / clarified? To spell out my confusion: why does the feasibility of recovering SSB information even the absence of the Friedel mate mean that it should be advantageous to operate at higher defocus?
(5) Same paragraph ("We note that information in (...) become greatly reduced"). This whole paragraph argues (I think) that collecting highly-defocus images is OK, yet wasn't one of the points of Downing & Glaeser (2008), cited in this paragraph, that the larger the defocus the lower the more CTF correction schemes or Wiener filters fail at retrieving all of the information (due to the "twin image" problem). My apologies If I'm mis-understanding - if that's the case perhaps other readers will also need a bit more hand-holding through this paragraph.
I loved all the detail poured into M&M, so I suggest specifying further:<br /> (6) Page 5: "annular zones of 1 reciprocal-space pixel" - how was interpolation done here? Nearest neighbor?<br /> (7) Page 5: "floated" - I assume this means adding a constant so that the average value is zero?<br /> (8) Page 6: "Smooth curve" - fix capitalization. Also, what kind of smooth curve?
Results:<br /> (9) Page 6: "The integrated power at 2.35 Å" - measured how? In real space in the white box?<br /> (10) Page 6: "(67% of intensity)" - 67% of which intensity?<br /> (11) Page 6: "~0.23 nm" - to guide the eye, please add a second x axis in figure 2, or replace the existing one, so that we can look for the 0.23 nm feature.
(12) Page 7: "The mean value of this noise spectrum can be regarded as the "zero baseline" for the power spectra of images recorded with a specimen". This noise floor will rise as a function of the number of electrons incident upon the detector. The choice of illumination condition when collecting "no-object"/"beam-only" images for these experiments is therefore important. I assume that the authors used the same illumination conditions as had been used in the actual experiment with a specimen. Is this correct? Either way, could the authors briefly mention somewhere what illumination conditions were used for this? <br /> -- I expect that using the same illumination condition would lead to an overestimate of the height of the noise floor. Indeed, during experiments with specimens, some fraction of electrons will be lost to apertures, leading to an overall decrease in the average number of eletrons reaching the detector. One may thus expect the actual noise floor in "with-specimen" experiments to be even lower, perhaps making the authors' point even more striking.
Discussion:<br /> (13) Page 7: "did not prevent images at 8 um defocus from being recoded at a resolution of 1.44 Å". Is this shown somewhere? Fig 1C shows 1.3 um defocus, not 8 um.
(14) Figure 2a: could the X axis be re-labelled, or also labeled with spatial frequency in nm-1 or Å-1 - this would help locate the 3.5 Å bump mentioned in the discussion
(15) Suppl Figs 4 and 5: here also, having a second X axis, or a second set of labels with spatial frequencies would be helpful.
(16) Figures S4 and S5: The lower bound of the Thon rings is "raised" with increased defocus, as predicted by the increase in SSB signal, but why is this lower bound so much higher at around 0.5 Nyquist, while remaining low at the origin and edges of F space? Is this predicted by the model? Does it correspond to the FT of the shape of the circular mask used in generating the simulated images?
(17) Page 9: "to interference between the contributions (...) which is 2a". This sentence reads as though the two SSB beams are interfering constructively or destructively with each other. Unless I'm mistaken the interference is between the scattered beams and the unscattered beam, is it not? That's certainly what the next sentence seems to say.
(18) Page 9: "The persistence of lattice images within (...) displaced from the particle". Likely because of my lack of expertise, and specifically because I do not know what the "coherence diameter" is, this sentence was lost on me.
(19) Page 10: "We note that this behavior is different (...) envelope function". For completeness, how about adding a supplementary plot overlaying the observed behavior (as in Fig 4) and the prediction from the spatial coherence (at whatever beam characteristics best fit the data, to point out perhaps that an unrealistic illumination semi-angle would be needed to fit the data)? This would help readers like myself who are not quite certain what one would expect such plots to look like if spatial coherence were really at play here.
(20) On the subject of Figure 4, I am curious about why the last few points of the 2.3 Å series seem so far off the prediction. The authors made a point of saying that the power spectra were so oversampled that even at that frequency, they had 3 pixels sampling each ring. So why the discrepancy, if not undersampling/aliasing? This made me curious: what would an equivalent plot from the simulation data look like? Would the Thon ring amplitudes from this synthetic experiment be a closer match to the predictions (dashed lines in Figure 4)? If not, perhaps this mismatch is due to poor sampling of these very fine rings at high defocus after all?
Summary and conclusions<br /> (21) Here might be a good place to formulate some caveat about the practicalities of processing data collected at very large defocus.
Figures & supplements<br /> (22) Figure S5: this would seem to argue strongly against evaluating the power spectrum using patches - would the authors agree? if so, how about mentioning it in passing somewhere? The optimal way to compute power spectra for the purpose of CTF parameter fitting is still a topic being discussed in the literature of late, and this observation would seem to be relevant.
On 2016-09-12 13:33:31, user Margaret Kosmala wrote:
This paper has been accepted in the peer-reviewed journal Frontiers in Ecology and the Environment. It is expected to be published in early 2017.
On 2019-11-03 10:40:59, user Jubin Rodriguez wrote:
Very interesting study! I was curious to know from the personal experience of the authors if GRiD exhibits better accuracy than iRep with regards to estimation of replication rates (of course I understand from reading your paper that both methods apparently do not fare that great with slowly growing bacteria!)? The reason I ask is cos' I'm getting a lot inconsistent replication rate values between iRep and GRiD for the same MAGs. For example I have a MAG (75.1% completeness and zero contamination & strain heterogeneity; 12X coverage) for which iRep estimates the replication index value to be 1.95 while GRiD outputs a value of 1.24. My gut feeling is that the GRiD estimated value is more closer to reality here but there's no way for me to be absolutely sure here since I've not made any real-lab measurements like you've done for your study. I was hoping to know if you would have any sort of inputs here to share from your own experiences?
On 2021-03-26 09:06:21, user ??? wrote:
Whether I can use my own data(protein and peptide fasta file) to generate features for make predictions?
On 2019-10-29 08:38:39, user Fridtjof lund-johansen wrote:
The next thing is to use this for antibody-based interactomics!
On 2025-01-23 18:08:17, user Laleh Cote, Ph.D. wrote:
I am the corresponding author for this paper ( https://doi.org/10.1101/2023.05.08.539924 ), which has now been published at PLOS One, here: https://doi.org/10.1371/journal.pone.0317403
Thanks, bioRxiv team!
On 2024-10-19 18:49:33, user Alessio Ciulli wrote:
This comment is to advise readers that our preprint has been published in Nat Commun 15, 8885 (2024). https://doi.org/10.1038/s41467-024-52871-9
On 2021-05-27 17:09:25, user Allan Konopka wrote:
I found this work via Antonia Fernandez-Garcia’s blog post from summer 2020, and thought it very intriguing. As I have a deep interest in physiological microbial ecology, I have wondered for some time now “whither metagenomics?” and this approach that categorizes GC’s by their “knownness” is helpful. Muren asked me to make further comment on a tweet (https://twitter.com/Hamatsa... "https://twitter.com/Hamatsa50/status/1397586142178865154?s=20)") <br /> here, to hopefully start a conversation.
So first, what is the objective of applying metagenomics? Sometimes stated (at least in grant proposals) is to “develop a predictive understanding of microbial communities.” But this implies knowing the function of the relevant gene products in adequate specificity (i.e., what specific biochemical function they carry out). We could all come up with lists of important functions, but let me identify 3 which I think are particularly problematic re: the databases of information.
Premise: the instantaneous activity rates of microbes are limited by the fluxes of an essential resource (for chemoheterotrophic bacteria, this is most often the diversity and concentrations of organic energy substrates)<br /> Inference: the breadth, levels of expression, and biochemical affinities of specific transport proteins are critical to understand interspecific competition in natural habitats.<br /> Problem: inadequate specificity – if “known” as (for example) an ABC transporter, this isn’t helpful in predicting in which cases a microbe has a selective advantage. [please correct me if there is recent work that improves this issue]
Premise: microbes/microbial communities rarely (if at all) exist in steady-state conditions. Rather, there are both regular and stochastic environmental perturbations to which organisms may evolve different strategies in response. [Side note: my fav paper on this is Nature’s Pulsing Paradigm, Estuaries 18: 547-555 (1995) by the three Odum brothers. Although about estuaries, easy to think how it applies to other systems and down to microscale.]<br /> Inference: Genes for regulation will be key here. <br /> Problem: I haven’t found much metagenomics work that addresses these regulatory proteins [please correct if necessary, as I have not done an exhaustive search of literature]. Likely (?) similar problem to transporters – motifs identifiable, but specificity of binding site unknown.<br /> Although most genome-scale simulation models (generally of one organism) generate a steady-state solution (and hence less useful ecologically), one can apply heuristics to simulate what you think you know re regulation (but this is outside metagenomics itself)
Premise: The extreme end of the “Pulsing Paradigm” are microbes in highly spatially structured habitats (soils, deep sediments, etc) in which the resource pulses are temporally rare<br /> Inference: evolutionary strategies that favor low/very low rates of metabolism (dormancy) better than the “optimistic” one high macromolecular content in terms of maintaining viability until the next pulse<br /> Problem: relatively weak understanding by microbial physiologists of dormancy (going beyond endospores)
On 2019-03-14 01:10:12, user Charles Warden wrote:
I noticed that the earlier EDGE paper wasn't cited in this pre-print (or listed in the "software" section of the Storey website):
https://academic.oup.com/bi...
Perhaps this citation should be added?
I thought the "ODP in EDGE (Storey et al. 2005)" plot in Figure 2 of the previous EDGE publication reminded me of the mODP plots in Figure 3 this pre-print. It looks like the "UW Biostatistics Working Paper Series" citation in the previous EDGE paper later became the peer-reviewed citation [3] in this pre-print (and I'm sure you understand everything better than I do). However, I thought the main result that caught my eye was Figure 2 in the earlier EDGE paper, so I thought I should say something.
I hope everything is going great with you!
On 2021-01-11 12:03:54, user YC Foo wrote:
Hello! Many thanks for making this available here! May I know if it would be possible to gain access to the Supplemental Tables mentioned here in the article, specifically Sup. Table 2? I'm afraid I've not been able to locate them from the text. Many thanks!
On 2024-11-30 19:30:43, user avtrader wrote:
BioRxiv Violation
The author claims no competing interests, yet he and others have said he is the Science Director for Mission Ivorybill. Mission Ivorybill's stated goal--is to save the Ivory-billed Woodpecker. Mission Ivorybill or the related, The Louisiana Wilds do not seem to be federal non-profits via a 501-c3 data base search.
Mission Ivorybill raises money via various channels and seems to be a commercial entity; this violates BioRxiv's publication rules for authors in addition to being a non-disclosed conflict by the author. Mission Ivorybill also has Go Fund Me efforts, publications, etc. and the organization is IBWO-centric. The author participates vigorously in marketing Mission Ivorybill. He controls and/or participates in Mission Ivorybill's media and marketing efforts such as their Facebook page and Zoom public presentations. The author reviews evidence gathered by Mission Ivorybill yet fails to disclose the relationship tainting this prepaper's Abstract and Conclusions.
Mission Ivorybill is mentioned in the subject paper. The author is well known to exaggerate claims of Ivory-bills recently proselytizing a video of a Tufted Titmouse was an Ivory-billed Woodpecker is association with a Mission Ivorybill presentation and on social media. The founder of Mission Ivorybill quickly distanced himself from the false Ivory-billed claims by the author.
The author receives organizational support, professional and informal introductions, recognition, publicity and public face-time from Mission Ivorybill/The Louisiana Wilds perhaps in return for his often aggressive marketing efforts for Mission Ivorybill. His efforts have even included researching and contacting employers, to disparge and econimically damage people who disagree with him on the Ivory-bill's status.
The author may have purposely not disclosed this unambiguous conflict and his association with a possible commercial entity whose only product is the Ivory-billed Woodpecker. Related the subject BioRxiv prepaper has some hyperbolic, marketing-like claims that are not based in science.
On 2018-02-23 14:06:51, user Mikkel C. Vinding wrote:
Interesting finding. Surprising how lousy scientist is at keeping time. I will be sending links to this next time I have to arrange a conference. With this amount of people going over time, it is hard to blame the individual, conference organizers have to step up to their job.
To improve the paper, I would suggest that you report how you constructed the regression model that you use. For example, what are the fixed effects and what are the random effects (are there any random effects? There ought to be in this design), and what precise are the predictors in the model? It would also be nice to know what software you used for statistical tests. Perhaps due to not knowing how the regression was done, I have a hard time interpreting the p-values: Are they the result of an ANOVA or are they the p-values of the regression coefficients (beta)? In the latter case, it might be considered controversial to base conclusions upon the p-values (see https://stats.stackexchange... "https://stats.stackexchange.com/questions/185360/t-value-associated-with-nlme-lme4)"). Finally, it would be interesting to see whether there is an interaction between gender and career step - to see if the stereotype that old male professors just don't give a damn about anything holds up.
Keep up the good work.
On 2019-02-19 08:48:44, user Mike Fainzilber wrote:
The authors state in the introduction that "Successful generation of a long 3´ UTR knockout mouse using CRISPRCas9 in mice has not been yet been reported". I beg to differ. Apart from the Xu et al BDNF long UTR model cited in the manuscript, the following models have been generated and published with in vivo phenotypes and mechanistic explanations
Miller et al, Neuron, 2002: Targeted mutagenesis of CaMKIIalpha 3'UTR shows LTP and memory phenotypes due to loss of the protein in dendrites - https://www.ncbi.nlm.nih.go...
Perry et al, Neuron, 2012 and Cell Reports, 2016: A floxed allele of the long 3'UTR of importin beta1 was generated. Cre mediated excision of this allele removes the protein from sensory axons, with effects on regeneration (the 2012 paper - https://www.ncbi.nlm.nih.go... ) and on axon growth rates (Perry et al., Cell Reports, 2016, https://www.ncbi.nlm.nih.go... ).
Terenzio et al., Science, 2018: Crispr/Cas9 mediated deletion of mTOR 3'UTR has in vivo consequences for general local translation in axons, and for neuronal survival after nerve injury - https://www.ncbi.nlm.nih.go...
On 2020-09-04 12:42:15, user Sadegh Nabavi wrote:
The findings in this paper go well beyond learned fear responses. I wouldn't be surprised if similar changes in responses are observed in other defensive behaviors. It would be interesting to test Nlgn3-/y line in innate defensive behaviors. It is known that dPAG controls escape response to a looming stimulus.
On 2025-06-16 05:52:19, user Jack Durant wrote:
Potential bias for gram-negative bacteria introduced in the DNA isolation or amplification methods? There are several reports of the isolation of Gram-positive bacteria from the symbiotic cavities.
On 2021-12-17 19:21:28, user HY wrote:
This article has just been accepted. You can check the final version here. <br /> https://www.sciencedirect.c...
On 2016-07-27 10:33:29, user Vladimir Seplyarskiy wrote:
Dear Iain and David,
I am really like your very well-timed article.<br /> I would like to point out that different numbers of mutation on transcribed and non-transcribed strands may be interpreted in a slightly different manner.
Methylated cytosines in CpG context after deamination converted to thymine and thus do not cause polymerase stalling and cannot be detected by TC-NER. Moreover, nucleotide and trinucleotide composition vary between strands, therefore even the same mutation rate on transcribed and non-transcribed strands causing the different number of mutations.
Also, transcription-dependent asymmetry may be a consequence of different mutation forces on different strands, but not solely repair.
Good luck in journal.
With best regards,<br /> Vladimir Seplyarskiy
On 2014-10-20 20:23:41, user TRS wrote:
In supplementary matérial Saudi Arabia is with Morocco and other North African countries! An error?
On 2023-07-19 12:56:20, user Pat Schloss wrote:
A reader pointed out a small, but significant typo in the first version of this preprint. The sentence, "They then randomly select that many sequences, with replacement from each sample", should have "without" rather than "with". Thus the sentence would read "They then randomly select that many sequences, without replacement from each sample". This correction will be included in the next version of the preprint
On 2017-01-21 22:01:05, user German Dziebel wrote:
Suppl Fig S9 is interesting. The authors state that "Using the Y chr sequence of a ~49,000-year-old Neanderthal from El Sidron Spain (Mendez et al., 2016), we found indeed closer genetic distance to this Neanderthal for haplotype A0, A1a, B, E, D and C, in the order of low to high distance which happens to correlate with degree of African ancestry, relative to G and HIJK (Supplementary Figure S9). These results indicate that admixture of F AMH with Neanderthals may have resulted in African-like descendants with ABCDE megahaplotype who largely preferred to live in the Southern hemisphere." What stands out to me on this figure is the fact that A0, A1a and B are closer to the Neanderthal sequence than C,D, G and HIJK. Could the most divergent human Y-DNA sequences have introgressed from Neanderthals? At the same time, hg E, which is most frequent in Africa doesn't seem to be closer to the Neanderthal sequence than non-African C and D.
On 2024-12-31 19:17:23, user Kate Keller wrote:
This paper has now been accepted for publication:<br /> https://pubmed.ncbi.nlm.nih.gov/38809543/
On 2025-05-27 19:12:26, user Mehtab Singh wrote:
This paper is now published in Plant Biotechnology Journal. <br /> Link: https://onlinelibrary.wiley.com/doi/10.1111/pbi.70146
On 2019-01-08 05:55:31, user Isaul wrote:
Ok, now can you explain the China sounds?
On 2024-03-17 07:37:14, user Hiroshi Mori wrote:
In order to properly use this tool, users must purchase a KEGG FTP license. In the config.yml file of this tool, the following description exist. "KEGG_FTP_DATA_DIR: '/scratch/shared_data_new/KEGG_FTP/2023-10-11/kegg' # Replace with your KEGG FTP data directory".<br /> Authors should mention this restriction (i.e. KEGG FTP license required) in the manuscript.
On 2019-07-24 15:23:44, user Miguel Valvano wrote:
Very interesting manuscript. I would like to make the suggestion that the clusters called "cps" be separated between O antigen (genes located between GalF and gnd) and cps [proper] genes located opposite from GalF. I am not sure for Klebsiella, but I know the is valid for Escherichia, Salmonella, Shilgella, Enterobacter and many others. You cannot properly call cps (as defined in this paper) as group 1 and group 4 capsules.
On 2017-04-07 21:26:19, user Eran Halperin wrote:
In more details, we found several major fundamental statistical flaws in the analysis in this manuscript. This manuscript posits six main criticisms of the claims made in our earlier response letter that discusses advantages of the ReFACTor method (Rahmani et al., Nature Methods, 2017). This manuscript misses several crucial parts of our letter, most of which are in our paper’s supplementary file (http://www.nature.com/nmeth... "http://www.nature.com/nmeth/journal/v14/n3/extref/nmeth.4190-S1.pdf)"). The above is true both for their recent correspondence as well as this manuscript. We outline a few examples of these flaws below:
The first section of this manuscript incorrectly states that ReFACTor breaks down when applied to cancer tissue in an EWAS setting. They argue that the permutation test we performed in our analysis (Rahmani et al., Nature Methods, 2017) is not valid. However in our permutation analysis, we constructed the list of “true positives” based on the exact same criterion used by Zheng et al. in their correspondence (Zheng et al., Nature Methods, 2017). In particular, we required differences in methylation between the two “controls” and the remaining methylation levels to have over 50% difference in mean methylation. Most importantly, we instructed users in the original ReFACTor paper, in our response letter to Zheng et al., and in the software associated with ReFACTor, to apply the feature selection step of the ReFACTor algorithm on the controls when the number of true positives is expected to be large. However, this manuscript ignores these instructions as well as many of the other details we provided about the breast cancer analysis (Supplementary Note 1 and Supplementary Note 2 in Rahmani et al, Nature Methods, 2017).
The second section of this manuscript tries to respond to our claim about the inapplicability of the method RMT (Teschendorff et al., 2011) for determining the dimension of ReFACTor. However, the section does not acknowledge our key point; RMT can not determine how many signal components capture the cell composition variation in data. Moreover, as we demonstrated in our recent correspondence (Rahmani et al., Nature Methods, 2017), comparing the performance of the two methods using small datasets cannot provide significant evidence for the improvement of one method over the other. The remainder of issues raised in this section were already addressed in Supplementary Note 3 of our paper.
In the third section, Zheng et al.’s manuscript does not accurately reflect our experiment’s protocol. In our experiment, we divided the samples into two groups on the basis of their cell-composition. Our experiment did not change the methylation levels to any degree. Therefore, we did not consider a scenario for which ReFACTor was designed, as claimed by Zheng et al. We merely assigned a "case" or a "control" label to each sample. Assignment of a label was based on the individual's estimated cell composition. Since the methylation data were not changed, the ReFACTor components calculated for each dataset are precisely the same ReFACTor components one would use in a real study. The batch effects noted by Zheng et al. would only have effects on EWAS results when they are correlated with the phenotype. In our case, the phenotype (case/control status) was generated only on the basis of estimating the cell type composition. Therefore, batch effects could be introduced only if the reference-based method captures batch effects. For more details, please see Supplementary Note 3 of our response letter (Rahmani et al, Nature Methods, 2017).
The relatively low R^2 values in the GALA data are due to a time lag (four months) between procurement of blood samples for measuring methylation and procurement of samples for measuring cell counts. While not ideal, this time lag will not affect the comparison between methods, as the noise added due to the time lag affects all methods equally. Moreover, at the time of publication, there was no other public dataset that had both cell counts and methylation data of this scale.
In the fifth section, Zheng et al do not accurately reflect the assessment of performance we presented in our paper. For example, the Rheumatoid Arthritis EWAS in the original ReFACTor paper (Rahmani et al. 2016) was not designed to show that ReFACTor outperforms the reference-based approach. In contrast, in our recent manuscript (Rahmani et al., Nature Methods, 2017), we provide strong evidence that ReFACTor has better performance over the reference-based methods (see Supplementary Note 3).
The sixth section of this manuscript contains several problematic assertions and unjustified claims. In the original ReFACTor paper (Rahmani et al. 2016), we compared ReFACTor with all the reference-free methods that we were aware of by the time of writing the paper. We chose reference-free methods that correct for cell type composition in methylation; we also considered a few methods from the gene expression literature (e.g., PEER). We find surprising the accusation that we did not compare ReFACTor with RUV. The authors of this manuscript (Zheng et al.) have published other papers in which they did not compare their suggested methods with RUV (e.g., Houseman et al. 2014, Houseman et al. 2016, Koestler et a. 2016, Teschendorff et al. 2017). We do not argue that comparison to RUV or to other methods is not useful, however the claims made by the authors of Zheng et al. are double standard. Finally, the McGregor et al. analysis did find SVA to be more stable in comparison to the alternatives in some scenarios, however, this study did not consider ReFACTor in their benchmarking, as their study was conducted while the ReFACTor paper was under review.
On 2020-09-18 02:09:42, user Maria Ingaramo wrote:
Summary: for now, we recommend using the S11 tag at the N-terminus of target proteins.
Details:<br /> We'd like to thank Dr. Abby Dernburg for pointing out that our S11 fragment, which ends in two glycines, might act as a C-terminal degron signal (doi.org/10.1016/j.cell.2018... "doi.org/10.1016/j.cell.2018.04.028)"). We've successfully tagged proteins at both the N-terminus and the C-terminus, but we have not established that these yield similar expression levels. We take this concern very seriously, and we're checking this now. Results will be posted here and at andrewgyork.github.io/split_wrmscarlet. In the meantime, we recommend avoiding the potential issue by attaching the S11 fragment at the N-terminus. If C-terminus tagging is required, we suggest the alternative S11 sequence YTVVEQYEKSVARHCTGGMDELYK.
-Maria Ingaramo
On 2017-09-25 19:32:35, user Ignazio Maria Viola wrote:
The published version includes a significant enhanced analysis of the flow field including gamma_2 vortex detection criteron and q criterion.
On 2020-06-02 09:21:09, user Marcos wrote:
Published in Nucleic Acids Research <br /> https://academic.oup.com/na...
On 2019-01-27 11:18:19, user Interested_party wrote:
Excellent paper; very useful. Could you provide information as to how the FnCpf1 PS were cloned into the vectors? Looks like you using a TGAG overhang, did you modify pCRISPomyces 2 to remove the gRNA scaffold? Also, will you be making these vectors available?
On 2022-10-23 03:07:17, user Wenwu Wu wrote:
An interesting study. Genes associated with cuticular wax and flavonoid biosynthetic pathways are highly expressed in leafy bracts, likely shedding lights. I wonder whether these genes are among the convergent natural selected genes in the species.
On 2024-09-13 21:17:20, user Bennett Fox wrote:
out in PNAS: https://www.pnas.org/doi/full/10.1073/pnas.2221150120
On 2020-02-14 19:29:28, user Greg Gomes wrote:
Hi Guest,
One of the authors here. This is precisely the type of feedback/discussion that BioRxiv is great for! Thanks!
The smFRET distances in Piana et al., are inferred from smFRET transfer efficiencies using an assumed homopolymer model. In our manuscript (and others) we show that deciding which, if any, homopolymer model is appropriate for a particular IDP is difficult, and allows considerable flexibility in the inferred distances. Our results compare smFRET effeciencies directly rather than using derived values from the data via polymer theory assumptions.
That being said, I take the point that phrases such as "novel", "new", and "for the first time" could be (or are) claims of priority, which are difficult or impossible to assess - and should therefore be avoided. The updated version will cite the referenced paper, as it is work that should be acknowledged.
On 2016-01-13 16:31:58, user Natalie Davidson wrote:
In Figure 2a the description says SE-duplicates are orange and PE-duplicates are blue, shouldn't the labels be switched?
On 2021-04-05 06:49:52, user Ajay K Pandey wrote:
Nice work! congratulations! <br /> but am not sure if authors have discussed in light of these two originals!<br /> https://www.frontiersin.org... <br /> and <br /> https://pubmed.ncbi.nlm.nih...