On 2018-11-16 23:39:09, user Well Left wrote:

Open source is the right way to do HASCIA. Thanks for this contribution, all.

On 2019-08-24 04:04:20, user sandeep chakraborty wrote:

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

There is another OT in the mtDNA, lesser enriched, but same features - ie has the ONP adaptor, and cuts a few bp from the PAM

On 2019-10-10 15:22:57, user Peter-Bram 't Hoen wrote:

Thank you very much for an impressive effort and a great resource. The GTEx project is a primary example of how rewarding collaborative research efforts are. The leading data analysts have demonstrated rigorousness in their analyses, with many different and complementary approaches. The senior authors have demonstrated great leadership.<br /> A few critical comments from my side to help improve the paper before publication in a peer-reviewed journal.<br /> 1. I miss a pan-tissue analysis of eQTLs, where the tissue-specific expression levels, and possibly even tissue:eQTL interaction effects, are taken into the model. This should have more power than analysis at the level of individual tissues, in particular for sQTLs, which are shown to be less tissue-specific. The interaction effects may reveal more tissue-specific eQTLs than currently identified.<br /> 2. I find the statement that “77% of the trans-eVariants that are also cis-eVariants appear to act through the cis-eQTL” a bit misleading, as around 50% of the trans-eVariants are not a cis-eQTL in the first place. Furthermore, it may be that mediation analysis on the trans-eVariants that are not meeting the cis-eQTL threshold, still show a significant mediation effect.<br /> 3. I do not understand why the correlation between cis-eQTL effect size and gene expression is almost as likely to be negative as positive. This would be rather logical when the authors have calculated this based on the effect size itself (and the allelic effect can be both negative and positive), but from the text and figure 6 I seem to appreciate that they have worked with the absolute effect size (although the paper does not formally state this). Can the authors provide plausible reasons for a negative correlation between the expression level and the cis-eQTL effect?

On 2020-06-18 13:25:44, user Matthew Faulkner wrote:

This work is now published in the RoyalSociety of Chemistry, Energy and Environmental Science Journal - https://doi.org/10.1039/D0E...

On 2020-08-17 18:53:09, user OxImmuno Literature Initiative wrote:

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

On 2024-07-03 16:05:42, user Jeffrey Duncan-Lowey wrote:

Congratulations on this interesting and important work establishing phage defense systems as a widespread and abundant source of gene cassettes of unknown function in functional mobile integrons.

Some work relevant to these findings -- a group has recently studied the type I CBASS system studied here (pic135AB) demonstrating that pic135B homologs, called Cap15 (interpro entries: PF18153/IPR041208), are cyclic di-nucleotide-activated beta-barrels that embed in and disrupt the bacterial membrane to cause cell death, validating the predicted role in membrane translocation (line 148). https://pubmed.ncbi.nlm.nih...

On 2023-07-05 03:48:32, user Dhananjay Huilgol wrote:

This preprint is now published in Neuron: https://www.cell.com/neuron...

On 2018-03-25 19:37:30, user Alan VanArsdale wrote:

I find, based upon morphology, that Yuan and Huang 2017 are correct, neandertal is of African origins. I expect via Spain and Italy mostly, by boat. I think that neandertal ancestry before they left Africa has been classified as modern human, or maybe no longer here.

On 2017-10-02 14:29:41, user Arne Mooers wrote:

A published response (from Sept 12 2017) to this work from Caccone and colleagues can be found here: doi: 10.1111/eva.12551

On 2018-12-05 12:29:05, user Ken Cameron wrote:

The NMR spectra are consistent with GDP loading. The 15N HSQC would be quite different for GMPPnP. Residues for most of switch I and II are not assigned for GMPPnP loaded KRas. The assignments of A18, S39 and I55-D57 all correspond to KRas.GDP literature and are not assigned for KRas.GMPPnP due to the well documented ms dynamics of this loading state. HSQC spectra would be fairly straight forward to fully assign from literature assignments. <br /> This paper should be corrected with full assignments and text and figures labelled as KRas.GDP.

On 2025-10-21 09:12:30, user Fajie Yuan wrote:

The paper has been published in Nature Biotechnology: https://www.nature.com/articles/s41587-025-02836-0

On 2020-07-15 20:50:55, user Jeffrey Ross-Ibarra wrote:

While the connection between repeat content and life history in plants is known, this paper does a nice job of suggesting a connection between telomere length and flowering time in three plant species. I think the main thing that could help, although a big ask, is to connect telomere variation to life history mechanistically. TERT knockouts in thaliana exist, for example (and if my quick read is correct, live longer and fail to flower). But work on a mechanism would go a long way to reassuring that the results aren't simply correlative.

I would like to see the selection analysis done without ascertaining the two haplotypes. Perhaps iHS or something would be good here? I worry ascertainment of the two haplotypes may give spurious signals of selection.

I would like to see genome size used as a covariate in analyses throughout the paper. We know genome size correlates with flowering time, and if I understand the approach to counting repeats correctly, I could imagine a scenario where two plants with similar telomere length nonetheless get different estimates because genome size changes the relative proportion of kmers.

I think given how strong population structure is in thaliana, using more than the first few PCs may be warranted. I'd also like to see some comparison/discussion of these results to the telomere-length mapping in Abdulkina et al. (https://www.nature.com/arti... "https://www.nature.com/articles/s41467-019-13448-z#MOESM1)"), which are not impacted by flowering time and don't find TERT as a candidate gene (maybe both haplotypes aren't present in their parents?). Of course, TERT makes sense as a candidate and their results overlap with a RIL pop, so I don't doubt this finding. Nonetheless, I think more stringent control of pop structure and comparison to the MAGIC pop are probably warranted.

Maybe also worth comparing other repeats -- do we see the same trend if we look at other common repeat types? Long et al. 2013 (https://www.nature.com/arti... "https://www.nature.com/articles/ng.2678)") find massive difference in ribosome repeat in thaliana between populations that also differ in flowering time (and perhaps worth noting the connection between ribosome biology and telomeres in Abdulkina et al.)

Some discussion of the percent variation explained I think is warranted. In each of the three species, telomere abundance explains at most a few percent of the variation in flowering time. Is this expected?

On 2021-04-27 05:43:00, user Min Zhu wrote:

The full version of this manuscript is online in PNAS. https://www.pnas.org/content/117/9/4781.short

On 2017-07-03 05:29:18, user Pavel Prosselkov wrote:

Not good enough. No evidence of SMARCC1 binding to FOXP2, neither "...a different pattern of FOXP2 expression". And why it has to be different?

On 2022-08-03 10:50:51, user Iratxe Puebla wrote:

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Ruchika Bajajand Michael Robichaux. Review synthesized by Michael Robichaux.

The manuscript presents a cryo-electron microscopy focused study of a recombinant type V-K CRISPR-associated Cas12k transposon recruitment complex from Scytonema hofmanni that is DNA-bound and includes a complete R-loop formation. In addition to mapping the assembly and interactions within this transposon complex, the study also details the discovery of ribosomal protein S15 as an essential component for the transposition activity of the complex. The work presented in this manuscript may contribute to the development of new programmable CRISPR-associated genome-engineering tools in eukaryotic cells.

Major comments

• The figures in the manuscript are generally well-organized and clear. In particular, the 2D diagram of the Cas12k-TnsC complex in Figure 1A is a useful figure panel; however, please consider refining the diagram for readability by replacing the current nucleotide sequence rearrangement with simpler shapes or graphics.

• For the structural complex models in Figure 2, please consider adding annotations that highlight both the completed R-loop as well as the 122? angled confirmation of the PAM distal to proximal DNA, which are both features that are highlighted in the Results section text.

• The title for the “TniQ nucleates TnsC filament formation” Results section and the title for Figure 4 are both possibly overstated since these mechanistic conclusions are based solely on transposition assay results.

• In the discussion, please consider revising the language used to describe the mechanism of transposon complex assembly (the model in Figure 7) to better justify a rationale for proposing a “cooperative” assembly mechanism that is based on the data in this manuscript, which is a structural assessment of the whole complex and its sub-complex interactions.

Minor comments

• In the first section of Results section, consider adding a description of the recombinant system used to purify the protein complex used for cryo-EM as done for the Figure 1 legend (“V-K CRISPR-associated transposon system from Scytonema hofmanni (Strecker et al., 2019)”).

• For Figure S1B, the orientation map is not clear, an adjustment to the color contrast may improve the clarity of this panel.

• For the cryo-EM data in Figures S2, please better define the TnsC oligomer organization (i.e., hexameric, variable). Also for Figure S2, please consider improving the image contrast for the angular distribution images in panel B.

• For Figure S3, both the incomplete R-loop and the missing Cas12k-sgRNA + TsnC contacts described in the text for this non-productive complex structure are not evident or identifiable in the models presented in the figure. Please consider annotations or descriptions in the figure legend.

• For Figure S4, please consider defining all rotations and dispositions that make up the conformational rearrangements in the RuvC domain, as described in the Results section text.

• For Figure 2, please consider adding a 2D diagram of the current complex structure in comparison to previously-reported structural models.

• The organization of Figure 3 is too busy, please consider re-formatting for clarity.

• For Figure S8, please consider including a “zoomed-out” image of the Cas12k+S15 structure.

• In the concluding paragraph of the Discussion section, please elaborate more on how the findings from this work may impact the “genome engineering application of CRISPR-associated transposons”.

Comments on reporting

• As outlined in Figure S1, 75K particles were used for the final cryo-EM reconstruction of the Cas12k-TsnC recruitment complex. Please consider discussing the structural elements or discrepancies of the other classified particles.

• Table S2 and S3 appear to be missing.

• In the “TniQ recognizes tracrRNA and R-loop” Results section, please specify which TniQ and tracrRNA mutations reduced transposition activity.

Suggestions for future studies

Please consider future studies that address the relevance of this transposon complex structure to physiological processes via cell-based assays.

On 2017-09-07 21:05:26, user AdamMarblestone wrote:

-"Behavioral time scale synaptic plasticity underlies CA1 place fields" http://science.sciencemag.o...

On 2017-04-16 16:55:26, user AdamMarblestone wrote:

-"Gain control by layer six in cortical circuits of vision" http://www.nature.com/natur...

On 2017-08-05 18:30:58, user AdamMarblestone wrote:

-"Learning to Act by Predicting the Future" https://arxiv.org/abs/1611....

On 2019-12-12 08:02:21, user Ronald Noë wrote:

The introduction of this paper reinforces my suspicion that many people working on underground mutualisms don’t understand ideas like biological market theory, because they don’t understand that natural selection works at an individual level. The present authors use the term ‘cheater’ as a synonym for ‘parasitic species’, while the term is used in evolutionary models (notably those based on game theory) for individuals that (often only temporarily) deviate from a mutualistic or cooperative strategy. The fallacy of understanding evolution as the result of natural selection at the species level is apparent in several of the papers cited in the introduction. It would be disastrous when this would continue putting people active in this field on the wrong foot. I suggest starting with classics such as Williams 1966 ‘Adaptation and natural selection’ and Dawkins 1976 ‘The selfish gene’ and then work it out from there and hopefully realize that when ‘group selection’ hardly ever explains anything, then ‘species selection’ certainly doesn’t. Models and empirical analyses in this field should be made with selection at the individual level in mind, even though ‘individual’ is admittedly sometimes hard to define in plants and notably in fungi (see Noë & Kiers 2018 TREE). One should still try to identify the ‘packages of genes’ that are the units of selection, i.e. the targets of natural selection with traits such as cooperative or parasitic strategies.

On 2020-12-11 16:06:50, user José L Medina-Franco wrote:

Very nice work! Two previous diversity analysis of fungal products and metabolites have been published in Chemoinformatic expedition of the chemical space of fungal products FUTURE MEDICINAL CHEMISTRY, 2016 8, 1399-1412. http://dx.doi.org/10.4155/f... and Scaffold Diversity of Fungal Metabolites FRONTIERS IN PHARMACOLOGY, 2017, 8, 180. http://dx.doi.org/10.3389/f...

On 2023-04-12 07:24:22, user Odyssey wrote:

Hi, great article, thaks!<br /> You provide accession numbers but there is nothing in SRA archive from NCBI at those numbers (SAMD00576609-SAMD00576640).<br /> Can you please share the raw data for 16S amplicons?

On 2018-08-28 19:04:03, user Charles Warden wrote:

Thank you for posting this interesting paper.

The discussion mentions a preference towards zFPKM. However, it seems to me that Figure 1 indicates that there may be something that is not ideal about how the precision metric is calculated (since the trends are opposite for precision versus correlation), and I think Figure 2 indicates that FPKM shows the best clustering for samples in different species (particularly if the dataset of origin is considered).

While I also think is is worth emphasizing that determining the "best" strategy can be difficult (and I would recommend some testing with different processing for each project), I mostly thought the results presented in this paper provided validity to using TPM or FPKM values for QC / visualization / analysis. Is there something that I am missing and/or possibly misunderstanding?

On 2020-03-30 11:46:50, user Nikolas K Haass wrote:

This is an amazingly elegant study showing that p53 plays a central role in lymphedema and can be targeted as a therapeutic strategy.

On 2024-08-26 16:09:08, user Matthew Bowler wrote:

now published in Structure as https://doi.org/10.1016/j.str.2024.07.007

On 2018-11-15 02:48:08, user BU_Fall_NE598_Group2 wrote:

Summary: <br /> Anastasiades and colleagues report promising findings regarding interneuronal subtypes which show significant expression of D1 receptors (D1-Rs). The paper identifies a gap in knowledge about which projection neurons primarily express D1-Rs in the PFC, which justifies their endeavor to examine D1-receptor-expressing neurons in the mouse prelimbic PFC. To accomplish this, they employ retrograde tracers, electrophysiology, in-situ hybridization, two photon microscopy, and histology methods to selectively differentiate populations of projection neurons and interneurons. They found that D1-Rs are strongly expressed in the the IT neuron subpopulation found in L5 and L6. Additionally, they reported that D1-Rs are absent from Parvalbumin (PV+) and somatostatin (SOM+) expressing neurons. Furthermore, their results indicated that D1-Rs were selectively enriched in VIP+ interneurons and that the activation of D1-Rs enhances both excitatory and disinhibitory microcircuits in the PFC. While these findings are intriguing, the manuscript could be improved via the following critiques.

Merits: <br /> This manuscript includes a comprehensive introduction. The usage of AAV-CaMKII-EGFP virus was an effective way to label glutamatergic neurons during the investigation. The authors worked diligently to provide convincing evidence through a variety of techniques that support their claims and conclusions. Additionally, the structure of each section ends with a summary statement, which helps the reader understand the takeaway of each figure and reinforces the big picture of their work.

Specific Critique:<br /> The title of the manuscript should be amended. It is worded in a way that suggests that D1 dopamine receptors are the only receptor which modulates the projection of neurons and interneurons in the prefrontal cortex for this subset of cells. However, if these neurons were sequenced, other receptor types will be present. Therefore, it is inaccurate to suggest that they entirely modulate the projection. Further analysis of these cell types with RNA-seq or fac sorting could offer insight to other genes at play.

In Figure 1, the researchers don’t indicate whether the probe used is specific. They should also show that the tissue is healthy to verify that the DAPi nuclei were not damaged. A potential negative control to show that the in situ D1 probe was not damaged could be to produce a D1-R KO animal and use a probe to show that the probe doesn’t bind to other receptor subtypes. To show that the neurons with D1-Rs were in fact excitatory, they could have used the marker CamKII. Overall, Figure 1 is a very thorough analysis showing that the transgenic mouse line that the authors use, D1-tdTomato, in fact labels D1+ cells with tdTomato. Although this data is reassuring to see, it may be more appropriate for the supplement.

While the dendrite reconstructions are clear, we believe Figure 3 would be enhanced by greater quantification and comparison between cell types aside from the provided difference in dendritic length. Reporting data on the number of branches, number of branch points, and number of end tips would all be beneficial to further characterize the morphological structure of these cell types.

When discussing Figure 9, at the end of page 20, the authors claim that the proportion of D1+ VIP+ cells that are CR+ is “very similar” to the proportion of D1+VIP+ cells that exhibit irregular spiking patterns. The authors seem to be implying that because these proportions are similar these two populations could be made up of the same neurons. However, the authors did not present any data to support this claim. Presenting traces with irregular spiking patterns from recordings of D1+ VIP+ CR+ neurons would adequately support such a claim.

Future directions:<br /> Previous studies have shown the correlation between dopamine and working memory in the prefrontal cortex (Surmeier et al 2007). Following identification of these cell-specific D1 receptor cells in the PFC, it would be interesting to further investigate the function of these cells. For example, these cells may have specific behavioral implications for working memory. Cell specific ablation of identified D1 receptor neuron, followed by a variety of working memory tasks (such as delayed non-match to sample or delayed non-match to position), could provide some insight to the function of these cells.

DARPP-32 is a protein involved in the signaling pathway initiated by D1 receptors and is encoded by the Ppp1r1b gene in mice. It would be interesting to see how inhibiting this protein’s function, by blocking translation of the Ppp1r1b gene, would affect the performance of mice during working memory tasks.

Minor Concerns:<br /> Near the end of the introduction (end of pg 4), the authors list many techniques that they use in the paper, this seems somewhat awkward and irrelevant--it is more interesting to show us how these techniques were used (as in the results), rather than just listing them. Also on page 4, it would be clearer if authors referred to their techniques as “in-vitro” electrophysiology as opposed to “ex-vivo”.

Further explanation of what is meant by “voltage sag” (page 11 line 4) is necessary. This concept could be more powerful if explained properly.

There is no in-text reference to Figure 1G. All main text figures (and supplementary information for that matter) should be referenced in the text.

The data described in Figure 3 (such as in the box and whisker plots) should be described in more detail. They should show replicates. For Figure 3A and 3C, it could be advantageous to include data for more morphology characteristics. Additionally, in 3E and 3F statistics should be shown to determine whether the addition of SCH significantly affects the firing rate with respect to the baseline (?AP = 0).

The hotspots for the injection sites in Figure 4 are a bit unclear. The injection coordinates given were helpful but identification of the 3D spread of the injection would be resourceful as well. In other words, quantify the injection sites.

In Figure 5, panels A and B seem to be representing the same data in the exact same way, the only difference being the color scheme in panel B. Only one panel is necessary here (the other can be moved to the supplement). In addition, panel C does not seem to add any new information, especially since there are so many overlapping lines, it is hard to distinguish which data correspond to which brain area. Similarly, in Figure 6C, the left panel does not seem to provide any information that the right panel does not already address. Furthermore, since there are so many cells clustered together, it is difficult to make out the overall trend anyway. The right panel is sufficient to represent the data here.

In Figure 6B, there seems to be a small typo on the y-axis, the graph does not seem to be indicating %overlap, but rather just %D1 or %CTB.

In Figure 7E, statistics should be shown to determine whether the addition of SKF significantly affects the firing rate with respect to the baseline (?AP = 0).

In Figure 9A, there could be a more thorough characterization done by testing different current to make a summative curve of the total inputs and outputs. A control looking at VIP interneurons without D1 receptors could be included as well.

On 2020-04-18 19:46:44, user Oliver Van Oekelen wrote:

Is the data available in a repository anywhere? Would be great to allow cooperation and speed up the impact of this data on drug discovery!

On 2017-12-18 22:28:53, user Mikhail V Matz wrote:

This is a very timely and extensive study of DNA methylation in a basal metazoan organism. Roles of DNA methylation in Metazoa remain unclear (beyond promoter methylation that is repressive but specific to vertebrates) and this study provides very important fundamental information. Fig. 1 is one beautiful example – and surprising, too! I totally did not expect to see prevalence of methylation in introns (just change the title to “DNA methylation landscape”, “epigenetic” is too broad). Overall, the experiment and sequencing effort are extensive and the quantitative results are very solid. My concerns are mostly about presentation and interpretation of the results.

Lines 75-91 and title of Fig 2 contain several sweeping claims that methylation actually causes things, such as suppression of transcriptional noise and suppression of variability of expression. Meanwhile, the evidence is purely correlational – given the data, it is impossible to say what causes what, or all these are caused independently by some unobserved factor. Please make sure, throughout the paper, that causation is never claimed (or otherwise implied by the context) based solely on correlation. Use language “linked to”, “associated with”, “correlates with”.

Lines 86-87: “consistent with the repressive nature of methylation on expression.” This is a very confusing phrase since it directly contradicts the data presented (Fig. 2 a,b) as well as several previous studies. Unlike promotor methylation, gene body methylation (GBM) is not associated with lower expression, instead, it is more prominent in highly expressed genes. GBM and promoter methylation are entirely different in function as well as in evolutionary history (promoter methylation is specific to vertebrates). This distinction is essential to maintain, so the statements “the function of methylation is conserved” is quite confusing (which methylation are we talking about? GBM? We are still not sure what the function is. Promoter? It is not conserved itself)

I have a problem with the notion that high-number exon prevalence over exon 1 in RNAseq data is good evidence of spurious transcription initiation. Please provide references to the literature where this has been experimentally established, because I can easily think of several alternative explanations.

I do like the methylation~noise association! But since you have distinct gene classes, can you plot them as more conspicuously different point colors? Also, I am surprised to see three gene classes – according to Dixon et al and other GBM papers, two classes make the most sense. Do you have a justification for three?

L121-123: “Analyses on laser-microdissected oral and aboral tissues further highlighted that most of the selected genes displayed strong and consistent tissue-specific methylation patterns, similar to findings in vertebrates” – this is an important result, can we have a figure illustrating it? And more details about how the methylation differences were quantified in this case?

One of my major concerns: I always strongly oppose discussions of detailed gene-interaction networks in non-model organism based on model organism data, such as Fig. 3 b, lines 143-146, 164-174, Extended data Fig. 1 and 2. Call it my private peeve, but I do not believe such detailed discussions are justified since (i) annotations of individual genes across great phylogenetic distances are often missing, uncertain or just plain wrong, to an unclear extent, and (ii) the degree of conservation of gene interactions is entirely unclear. Moreover, typically gene-wise discussions are little more than enumerating observations that fit some pre-conceived idea, without a clear null hypothesis (i.e, there is no robust criteria to tell whether the apparent support for the idea is stronger than expected by pure chance). The problem is, genes are many and data are noisy, making possible to find support for practically any idea, if one only looks hard enough. I therefore urge the authors to stay at the level of broad changes that can be associated with clear statistical significance measures, such as whole GO terms and/or pathways (Fig. 3 a, b), and abstain from discussing individual genes or their interactions.

That said, discussion of TRAFs might be interesting, not in context of JNK pathway but in the context of prior literature. TRAFs in corals appear to be unusually diverse and keep surfacing again and again under various environmental treatments, potentially constituting an important coral-specific plasticity mechanism not found in other creatures.

L 197-210 and Fig. 4: Change in cell size and skeletal morphology is a very cool result! The paper is written in a way suggesting (L197) that the hypothesis of larger cell size came *after* seeing specific genes doing something. If the authors can attest that this indeed was the order of events, and not the other way around (noticed larger polyps => found larger cells => picked genes that “made sense” to explain this), I will be completely fine with keeping the connection between gene-wise expression and larger sizes (this would be really awesome, in fact). But if not, not – because then it would be an example of a tendency I lamented about two paragraphs ago.

L225-241: I feel like this part of discussion/conclusions, talking about possible functional link between methylation and phenotype, its specific molecular mechanisms, and adaptive value, goes way beyond what is warranted by the data. The data do not establish the functional connection between methylation and phenotypic plasticity, and they do not show that observed changes really led to better fitness under new conditions.

The authors also take it as a given that plasticity would facilitate evolution by allowing “more time for genetic adaptation to occur” (L236-237). However, it more common to assume that plasticity would reduce the strength of selection and therefore slow down genetic adaptation. Please provide references from theoretical evolutionary biology supporting your view.

Lastly: one specific hypothesis I would really like the authors to consider: that observed methylation changes could be due to change in cell type proportions (which are differentially methylated), rather than being a result of methylation adjustments within each cell type. Perhaps data on methylation differences among microdissected tissues (mentioned briefly on L 121-123) could be used to explore the validity of this hypothesis?

cheers - and please review my bioRxiv preprints!

Misha

On 2020-01-20 23:04:52, user Peter Sorger wrote:

This paper has now appeared in Elife as follows:<br /> 2019 Nov 19;8. pii: e50036. doi: 10.7554/eLife.50036.

On 2016-02-24 21:32:08, user Fabien Campagne wrote:

My lab developed the Goby framework, which you included in the benchmark.

Could you clarify which command line options you used when running each tool for these comparisons?

For Goby, you need to know that default options are equivalent to GZIP compression. They are not the state of the art approaches that we published in Campagne et al PLOS 2013. If you want these, you need to activate them (see command line flags described in our paper).

On page 4, you write " Goby were run with Java v1.7. All were run with default parameters", so I am think you may have benchmarked against the GZIP codec.

The data you present seem to suggest this as well, since our prior evaluations comparing CRAM and Goby found a large compression efficiency difference for Goby on RNA-Seq reads (of course, it is possible CRAM has made major progress since we conducted our benchmark).

On 2017-01-04 13:30:26, user JoelK wrote:

Very intriguing work. Great to have the video. If I understand the method you implemented for RNA expression profiling, you are making 20 libraries of probes, with each library consisting of the same 23 genes. Each library has different concentrations of the probes (random linear combs). (Is it correct that some probes are limiting in each library?.) Each library is used in a single qPCR reaction for each sample. So, 20 SYBR qPCR reactions per sample. 10 ng RNA per sample. From a practical standpoint, did you consider or try using RASL-seq (basically the same method, but using sequencing as an output). With RASL-seq, you could multiplex at much higher levels than the qPCR based method. Interesting to think how one might design a method to apply to single-cell RNA profiling...

On 2017-01-15 01:04:34, user Christopher Mason wrote:

Very interesting, and thanks for posting.<br /> Since most scRNA-seq, GTEx, and TCGA data is polyA-primed and not enriched for ncRNAs, it seems the model would benefit even further from leveraging some ribo-depleted data sets to find more composite measurements Like here: http://www.nature.com/nbt/j..., and others coming soon. There are, after all, more ncRNAs than protein-coding RNAs, and this likely means your model could get >2x as good with more data, especially since ncRNAs (not to mention circRNAs and other RNAs) are very tissue- and stage-specific.

On 2017-05-02 15:59:54, user Elana Fertig wrote:

Great work -- such an important area https://www.usnews.com/news...

On 2017-11-10 01:22:47, user ztech wrote:

This study is full of flaws. Firstly, it uses the very Western extreme of Antolia, and labels it as "Anatolia". There is 100's of miles East,that went un-sampled. A newer study, with samples slightly further to the East, found populations to be significantly more related to Iranian_N, proving there was westward movement off off the Iranian plateau. Second, the PCA suffers from some serious skew. Notice that some pakistanis/Indians are actually on top of the Caucaus groups. It's absurd to suggest Pakistani/Indians were even closer to Europeans than Iranian Neolithics were. When Indian/Pakistanis are included with the exclusion of ancient ASI skeletons, native to India, the PCA gives the impression they were fairly close to Iranians. The study was missing ancient DNA from India. Finally, on the PCA, Iranians ARE about equally close to Caucaus groups as they are Indians.

On 2022-09-14 22:22:18, user Ohainle Lab wrote:

How is hexameric capsid binding to host factors facilitating viral infection? What is the model and at what stage of viral replication would this be important? Assembly? Post-assembly? Would there be a way to test this?

On 2020-10-20 01:39:59, user Joshua Corbin wrote:

Congratulations on the beautiful and informative study! I look forward to seeing it published

On 2018-03-21 19:13:17, user James Fellows Yates wrote:

We find this novel application of ancient dental calculus metagenomes an appealing example of how historical samples can be used to demonstrate host-associated microbial evolution. However we would like to make a few suggestions regarding the use of ancient microbial data in this study.

The following are three major recommendations that we feel will strengthen the results of this aspect of the study:

1) One major challenge in analysing ancient DNA is false positive identification of taxa due to the presence of modern contaminating environmental organisms (Warinner et al. 2017, Ann. Rev. Hu. Gen). We suggest to run your TM7-identified reads through mapDamage2 (https://ginolhac.github.io/..., Jónsson et al. 2013, Bioinformatics) to help authenticate that the TM7 DNA that you have detected in the ancient samples is indeed ‘ancient’. The program will generate plots that should show elevated cytosine to thymine deamination patterns at the termini of the fragmented DNA if the reads are truly ancient (Sawyer et al. 2012, PLoS One). This is particularly important for the data from Weyrich et al., who reported substantial soil contamination in their Neanderthal calculus samples (2017, Nature, Supplemental tables S5, S7). This would then be clearer evidence of age-related DNA damage than Supplementary Table 4, as referred to in Figure 2.

2) Related to this, it would be useful to the reader to justify why a 1% or 15% genome coverage is enough for proof of the presence of this species (assuming that ‘1% mapped’ in the section ‘Reduced genomes from Environment…’ refers to 1% genome coverage, rather than 1% of reads in the library). The high risk of false positives resulting from mismapped and/or modern contamination may play a role here, and TM7-identified reads may come from environmental DNA not originating from the individual’s oral cavity. Additionally, while there is a paucity of data for the TM7 strain from the Neanderthal sample (‘1%’), further evidence to show this strain is indeed from the oral cavity (such the presence of a marker gene) would be useful confirmation here that this strain is authentic and not derived from a soil TM7 ; such as with the tree for the medieval sample. Furthermore, is the low percentage of TM7-identified reads also the reason why genome assembly was performed only on the Warinner et al. 2014 (Nat. Genet.) data, despite the El Sidron1 sample being described as ‘well preserved’? Clarification on how the preservation of B61 and El Sidron1 was assessed, as well as justification for using one or the other, rather than both, throughout the manuscript would be welcome.

3) Finally, the suggestion that human acquisition of TM7 during animal domestication is highly speculative. This is demonstrated by an ‘increase’ of mapped reads between a single Neanderthal and a single Medieval sample; however, factors such as sequencing effort, relative abundance, and individual microbiome variation are not taken into account. Far more samples from relevant time periods would be necessary to substantiate such a claim. Thus, we feel the paper would benefit by having this statement removed entirely.

We would finally like to make a few minor comments regarding the structure of the paper:

* Make an individual section for the renaming of the TM7 groups, as this does not stand out despite being a major aim of the paper, according to the last paragraph of the introduction. Make Supplemental Table S2, which lays out the new naming convention, a main text table for this new section.<br /> * Clarify in both the results and the methods that the Warinner et al. 2014 data was used in both assembly AND mapping. In the methods section this data is only described in the mapping section, suggesting a genome bin was generated from mapping, and this may confuse the reader.<br /> * Provide more detailed information in the methods section regarding the parameters used for all software. Currently it is not possible for the reader to reproduce or assess the reliability of the analysis performed.<br /> * Roary cannot be reliably used to cluster genomes across species (Page, et al. 2015, Bioinf.), only within a single species, yet the authors have used it to cluster genomes across a phylum. The title states that the members of the Saccharibacteria phylum are highly diverse, which is countered by the fact that they clustered well by Roary. Can the authors comment on this discrepancy? These results imply that the individual organisms are actually all the same species, which means the naming convention suggested in Supplemental Table S2 will need to be adjusted.<br /> * Several of the Supplemental tables have very small text and are difficult to read and parse. Consider putting them in a standalone spreadsheet file and making the spreadsheet file available as a supplemental file. Additionally, the tree currently displayed to the side of Supplementary Table 1 is offset from the labels in the table itself, and is difficult to visualise.<br /> * In the text, it is stated that B61 has mild to severe periodontal disease; however, the original publication (Warinner et al. 2014) provides detailed oral pathology records and characterizes the individual as having moderate to severe periodontal disease with specific clinical features in the Supplementary Information.<br /> * Please correct the idiosyncratic use of capitalization and italicization when referring to species throughout the text. For example, “Nanosynbacter Lyticus”, “ecoli-like”, “streptococcus thermophilus”, “in vibrio genomes,” etc.<br /> * Citation 12 has been badly formatted

James A. Fellows Yates (fellows[at]shh.mpg.de)<br /> Christina Warinner (warinner[at]shh.mpg.de)<br /> Department of Archaeogenetics, Max Planck Institute for the Science of Human History, Germany

Irina Velsko (ivelsko[at]clemson.edu)<br /> Department of Biological Sciences, Clemson University, USA

References:<br /> Jónsson, H., Ginolhac, A., Schubert, M., Johnson, P. L. F., & Orlando, L. (2013). mapDamage2.0: fast approximate Bayesian estimates of ancient DNA damage parameters. Bioinformatics , 29(13), 1682–1684. https://doi.org/10.1093/bio...

Page A.J., Cummins C.A., Hunt M., Wong V.K., Reuter S., Holden M.T.G., Fookes M., Falush D., Keane J.A., Parkhill, J. (2015) Roary: Rapid large-scale prokaryote pan genome analysis. Bioinformatics,31(22):3691-3693. https://doi.org/10.1093/bio...

Sawyer, S., Krause, J., Guschanski, K., Savolainen, V., & Pääbo, S. (2012). Temporal patterns of nucleotide misincorporations and DNA fragmentation in ancient DNA. PloS One, 7(3), e34131. https://doi.org/10.1371/jou...

Warinner, C., Rodrigues, J. F. M., Vyas, R., Trachsel, C., Shved, N., Grossmann, J., … Cappellini, E. (2014). Pathogens and host immunity in the ancient human oral cavity. Nature Genetics, 46(4), 336–344. https://doi.org/10.1038/ng....

Warinner, C., Herbig, A., Mann, A., Fellows Yates, J. A., Weiß, C. L., Burbano, H. A., … Krause, J. (2017). A Robust Framework for Microbial Archaeology. Annual Review of Genomics and Human Genetics, 18, 321–356. https://doi.org/10.1146/ann...

Weyrich, L. S., Duchene, S., Soubrier, J., Arriola, L., Llamas, B., Breen, J., … Cooper, A. (2017). Neanderthal behaviour, diet, and disease inferred from ancient DNA in dental calculus. Nature, 544(7650), 357–361. https://doi.org/10.1038/nat...

On 2021-01-20 08:25:11, user kostas wrote:

Hello and thank you for work and the interesting approach. I hope the authors understand that the first "element" behind their title, is to provide some evidence that the primers do work, and this is not totally clear to me at least in this version of the manuscript. Also as a reviewer this would be the first thing i would like to see documented.

On 2023-02-20 09:49:57, user Jheronimus wrote:

1st paragraph: the possessive form of [it] doesn’t have an apostrophe.

On 2023-11-28 15:50:39, user JustGuesting wrote:

https://www.pnas.org/doi/fu...

Would suggest that Ano6 does not belong in the subheading "Chloride Channels".

On 2019-11-23 05:08:40, user Awais wrote:

You have written Malus domestica throughout your manuscript which is wrong. You need to correct it to Malus x domestica.

On 2021-02-28 11:04:58, user Andrew Teschendorff wrote:

In this 2021 commentary, Rahmani et al effectively acknowledge most of the criticism we raised in our earlier preprint Jing, H., Zheng, S.C., Breeze, C.E., Beck, S. & Teschendorff, A.E. Calling differential DNA methylation at cell-type resolution: an objective status-quo. bioRxiv 822940 (2019), following our recommendations, to add the PPV metric in their evaluations, and including smoking EWAS, a scenario for which a more reasonably gold-standard set of loci exists. Most importantly however, they also alter their method, presenting a new TCA (X|Y) model. Yet, in the abstract of their commentary, they state that we “misused” their TCA method, falsely claiming that the TCA (X|Y) model was “part of their original TCA paper”. However, in our critique of the TCA paper, we implemented TCA using the recommended settings as specified in their TCA R-package. Specifically, the TCA method implemented in their TCA R-package (1.1.0) was TCA (Y|X), with the recommended marginal model as the default, which is the one we therefore used, since this is the version of TCA that was also used in their original TCA paper, and for which comparative results to CellDMC were presented in the main figures of their TCA paper. Thus, there was no misconception on our side. We were bound to using the version of TCA as presented and compared to CellDMC.<br /> Moreover, the fundamental misconception was made by Rahmani et al since they compared the TCA (Y|X) joint and marginal models to our CellDMC algorithm, which by construction implements a marginal conditional test, using only the sensitivity metric for evaluation, ignoring the all-important PPV metric. Had Rahmani et al included the PPV metric in their evaluations, this would have alerted them to the very low precision of the TCA (Y|X) model. Indeed, it is precisely because we pointed out to them the need to consider the PPV metric, that Rahmani et al have now altered their recommended TCA method from “TCA (Y|X) marginal” to “TCA (X|Y) marginal conditional”, which we note is now a very similar model to CellDMC. Indeed, we note that CellDMC models the data (X) in terms of the phenotype or exposure (Y), and implements a marginal conditional test. This explains why according to say Fig.1 in their new commentary, CellDMC and TCA (X|Y) perform almost identically.

On 2020-03-12 10:25:45, user Matt Hodgkinson wrote:

I was asked to comment on this by Richard Van Noorden for this Nature piece: Hundreds of scientists have peer-reviewed for predatory journals doi: 10.1038/d41586-020-00709-x. It’s useful to study the peer review of potentially predatory journals, especially as this is one of the factors that people are most concerned about with these journals. The recent consensus definition that the authors cite (and which I co-authored with some of them) deliberately left out peer review quality because measuring this is presently too subjective, and this kind of research helps fill that gap. Taking advantage of Publons data is innovative.

I'm not surprised that reviewers for potentially predatory journals are more likely to be from lower-income countries. These journals are often essentially local journals masquerading as international journals. Also, this matches the demographics of their authorship and they will likely ask past authors to review for them (as do reputable journals), though we cannot check this because prior publication by the reviewers in potentially predatory journals was not assessed and those data are not available.

The search found only 5.2 reviews per potentially predatory journal versus 27.7 reviews per reputable journal, showing a clear under-representation of potential predators - however, this might be confounded by the publication volumes of these journals. Even when deliberately enriching for potentially predatory journals (half their sample), the authors still found that 90% of reviewers had never posted a review to Publons for a potential predator, which is somewhat comforting. That number cannot be generalized as "10% of reviewers have reviewed for a predatory journal": a representative sample of journals or researchers would likely show a much lower proportion reviewing for potentially predatory journals. Cabell's may have required confidentiality to allow use of their list, but without the data being available it's not clear whether there is a skew in the number of reviews for potentially predatory journals, i.e. were some much more represented than others?

Finding some potential predators had claimed reviews does not indicate that they necessarily conduct peer review as most researchers would recognise it - the authors point to several previous examples of evidence of superficial review and this is what Richard found too when speaking to such reviewers. They are likely going through the motions and using these reviewers as a fig leaf, what I've previously referred to as a "cargo cult". Those who are happy with the process may not know any better, especially if they have no experience of high-quality peer review in a reputable journal, either as an author or as a peer reviewer.

Table 1 gives some interesting insights, though I'd have liked to see a formal statistical analysis of the predictors of frequency of reviewing for potentially predatory journals. The most senior reviewers either did no reviews for potential predators or only did a few - those who did none were less prolific in both their reviewing and publishing than those who did a few; that may be a numbers game (fewer reviews means fewer chances to be caught out) or not reviewing for potential predators may be an indication of better judgment. At the other end of the scale, those with many potentially predatory reviews had a substantially higher rate of reviewing (4.5 times higher than than with no reviews for potentially predatory journals), but they were much more junior and had a much lower publication rate - these researchers appear to be inexperienced and reviewing indiscriminately. However, junior researchers can and do provide high-quality reviews (Evans et al. The characteristics of peer reviewers who produce good-quality reviews. J Gen Intern Med 8, 422–428 (1993). DOI:10.1007/BF02599618), which makes it difficult in practice to use such demographics as a proxy for peer review quality. That said, reviewers appearing to be over-committed could be a useful proxy for low quality. Sites such as Publons that have reviewer leaderboards may be unwittingly gamifying peer review, rewarding volume over high-quality scholarly engagement - so kudos to them for shining a light on the problem (Publons responded to that point in Richard’s piece by noting that they only include reviews for journals in Web of Science in their leaderboards).

The geographical information was only available for 56% of reviewers, but the results are unsurprising as I noted above. A comparison to the demographics of the 6,611 reviewers who never posted a review for a potentially predatory journal to Publons should have been included as a control analysis. The authors estimate that the reviewers of potentially predatory journals collectively took 30,000 hours to complete the reviews, but that assumes that the time taken to review in reputable journals is generalizable to reviewers who do dozens of reviews for potential predators. I would bet these reviewers are not so diligent.

On 2020-08-16 15:26:56, user SW wrote:

I keep coming back to this wonderful paper, and I hope that these methods can be used to analyze more COVID-19 patients! One question: for donor M, if you look at the "pre-existing SARS-CoV-2-reactive CD4+ clones in the CM subpopulation 1 year before the infection," how big of a role do they play in the response to SARS-CoV-2? What fraction of responding CD4+ cells are derived from this (presumably cross-reactive) memory population? I am trying to get a sense for whether these preexisting memory cells were very important in fighting off the infection or whether they were just some small fraction of the overall response. Thank you.

On 2018-04-13 17:21:34, user patty wrote:

Can the labs provide the raw fastq or BAM files? Other bioinformaticians can test their pipelines and determine how well they do. There is no point in sequencing this data again and again.

On 2018-01-02 15:48:11, user Guillaume Rousselet wrote:

Looks very interesting. Are the data available online? I would be interested in the data used to generate the histograms in figure 3 in particular.

On 2024-07-04 13:19:42, user Gyawali, Rajan wrote:

Hi,

Could you please link this preprint to the published journal in Briefings in Bioinformatics titled "CryoSegNet: accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and attention-gated U-Net". The link to the published version is https://academic.oup.com/bi...

Thank you!

On 2020-06-04 07:44:19, user Billy Bostickson wrote:

Any Updates from the authors? <br /> Perhaps they need to refocus the paper on RaTG13 to enhance the validity of the findings?

On 2020-10-26 11:57:48, user Oscar Conchillo-Solé wrote:

In the abstract it is mentioned:<br /> "It changes an arginine (R) residue to histidine (H) at position 364"<br /> and in the body text:<br /> "The impact of this spike protein R364H variant (Figure 1)"<br /> However in the Figure1: <br /> "The enlarged inset shows the location of R634H mutation (blue)"<br /> Apart from that, there is no mention to the mutation R364H in the Table1.<br /> I believe there is some kind of confusion here.<br /> In the uniprot sequence for this protein "P0DTC2" the position 364 is an Aspartic (D) while position 634 is an Arginine (R). <br /> Is it possible that the mentions to the residue 364 are errors while the correct one is 634?<br /> thank you.

On 2021-05-01 14:25:56, user Mackenzie Mathis wrote:

Hello! Nice paper, but I would like to point out a (simple) error. You state in the methods you used "DeepLabCut’s pre-trained human pose model (v2.1.7)" and cite us, thanks! This is great, as you use the model within our framework, but This is NOT a DeepLabCut trained model.

As we have no preprint/paper yet about the model zoo, we have descriptions on the website (modelzoo.deeplabcut.org), which states that this model is *only* DeeperCut, embedded in our software, and we please ask you cite that paper if you use the model: https://link.springer.com/c....

As such, you are comparing OpenPose, AlphaPose, and DeeperCut performance. I hope this is helpful, as it's important to get this right. I would also add that you might want to report the benchmark performance of these algorithms, for example, the rankings/performance for these three algorithms has been systematically tested here: https://paperswithcode.com/... (OpenPose is ranked 25, DeeperCut is ranked 27 as of April 2021)

On 2020-10-11 08:11:10, user Marta Nabais wrote:

This is an interesting and relevant paper by Battram et al, exploring the correlation structure between DNA methylation probes from Illumina 450K array, to estimate proportion of phenotypic variance explained across all sites. It is a useful study to provide evidence for which traits EWAS will likely yield successful identification of associated DNA methylation sites, albeit conclusions are possibly still very limited by sample size.

As pointed out by the authors, since DNA methylation is a reversible process and this was done using a prospective study design, the inference of association cannot be through causality (as it is in the case of GWAS data, due to SNPs surveyed being in LD with causal variants). Thus, would it perhaps be best to re-name the EWAS-heritability estimates, to avoid driving the reader into thinking this term is comparable with the SNP-heritability term? For example, Futao et al, who recently developed a REML method for 'omics data (including DNA methylation), used rho2 to refer to proportion of phenotypic variance explained by DNA methylation sites. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1718-z

On 2021-12-02 15:34:05, user Ester Eckert wrote:

Great article! Super interesting data. I would not say that it disagrees<br /> with our results, it just further zooms in and shows how amazingly complex zooplankton<br /> microbe interactions are and how much we still have to discover.

On 2025-03-25 14:23:39, user JANNE MARKUS TOIVONEN wrote:

I would like the authours to double check the statement that "RPL3L ribosomes associate with mitochondria". As far as I have understood it is the opposite, ribosomes with RPL3 associate with mitochondria in RPL3L knockouts.

On 2024-10-10 07:25:37, user Flo Débarre wrote:

The points raised were dealt with in https://doi.org/10.1093/ve/vead077 . The paper was published after the preprint, but is cited and discussed in the peer-reviewed version of the work ( https://doi.org/10.1016/j.cell.2024.08.010 ).

As we do not believe that the comments made in this comment section have been in good faith, we will no longer reply.

On 2025-07-10 15:28:16, user Mina Bagheri wrote:

Hi, I have a few questions<br /> For counterfactual generation you brute-force over all the potential perturbations in the cell. How do you define this search space? How many iterations is this?<br /> Also did you consider using gradient-based cf generation methods? My understanding of counterfactual generation was to use the model weights directly, otherwise this is just insilico testing

On 2022-07-16 17:09:16, user carrental wrote:

Very cool and fresh story. How comes that the C-terminal fragment of the receptor enters the mitochondria?

On 2017-09-09 00:40:08, user Abdulbaki Agbas wrote:

We have published this manuscript in a peer-reviewed journal, Future Science OA. The editor read our bioRxiv version and contacted us to consider their journal. It worked very well.

On 2024-09-12 07:42:51, user maud de dieuleveult wrote:

Published here https://www.tandfonline.com/doi/full/10.1080/15592294.2021.1917152

On 2022-12-15 16:08:05, user Scott B Hansen wrote:

So what about statins? I get this question a lot. Cholesterol in the brain and blood are regulated separately, but influence each other. A hydrophobic statin with a high fat diet may be beneficial early on. The hydrophobic statin would cross into the brain and reduce astrocyte cholesterol and the high fat diet would compensate for some loss of cholesterol in the blood. However, in later stages when the blood brain barrier is open, cholesterol likely comes from blood. In that case, a hydrophilic statin may be better.

Statins are also complicated by alterations to prenylation substrates e.g, geranylgeranyl. Prenylation works opposite cholesterol to move protein out of GM1 domains. Raising their concentration could affect the proposed system independent of cholesterol.

On 2024-04-18 19:38:40, user Rishav Mitra wrote:

Summary:<br /> Transglutaminase 2 (TG2) is a GTP binding/ protein-crosslinking enzyme with therapeutic potential in various conditions such as cancers, Celiac disease, and neurological disorders. TG2 is thought to have two major conformational states, an inactive GTP-bound closed state and a crosslinking-active Ca2+-bound open state. Other groups have previously reported X-ray structures of TG2 that reveal the structural basis for the regulation of transamidation activity by GTP/GDP and Ca2+. Although these studies have suggested that guanine nucleotides and Ca2+ allosterically regulate TG2 activity by inducing global conformational changes, direct evidence for conformational transitions has been lacking so far. The authors of this paper have previously shown that a small-molecule inhibitor, TTGM 5826, inhibits the protein crosslinking activity of TG2 by stabilizing the open conformation. Interestingly, TTGM 5826 prevented the growth of cancer cells which led the authors to conclude that the open conformation is cytotoxic. Therefore, locking TG2 in the open state by small molecules could lead to new therapeutic strategies. <br /> In this study, the authors have investigated how the binding of guanine nucleotides, calcium, and small-molecule inhibitors affects the open and closed conformational states of TG2 using small- angle X-ray scattering (SAXS) and single-particle cryoelectron microscopy (cryo-EM). Additionally, they focused on the discovery of improved small molecule inhibitors compared to TTGM 5826. The major success of this paper is the finding that TG2 can undergo a reversible conformational transition in solution between closed and open states under physiological GTP and Ca2+ concentrations. In addition, the authors have found a new conformational state inhibitor, LM11, that is more potent than TTGM 5826, although the evidence to support the connection between drug potency and TG2 conformational specificity in cells is weak. The authors show that the LM11-bound state has a different conformation from the Ca2+-bound open state. The major weakness of this paper is the lack of mechanistic information to explain the potency of LM11. Hopefully further structural studies will provide further details on the conformational changes induced by LM11 and other inhibitors. <br /> Major points:

In Figure 3D, the authors explained the different conformations between WT and the R580K mutant under GTP conditions by Kratky plots and fitting using CRYSOL. A Kratky plot normalized by Rg may be a better way to discuss the conformations since normalized Kratky plots emphasize conformational differences. In such a plot the weak shoulder in around q = 0.15A-1 of “open dimer”, which likely comes from the dimer conformation's symmetry, can then be emphasized and discussed.<br /> In the text regarding Figure 4, the authors mentioned 3DFSC but it is not provided in the figures. 3DFSC is one of most important plots in cryo-EM analysis to verify directional resolution and density isotropy. <br /> In Figure 4A, the authors said that the homology model generated from TG3 was an excellent fit for the map under Ca2+ conditions. Considering Figure S2, the fit looks excellent certainly. But it was just a visual evaluation, and quantitative scores to validate the degree of fitness like the Q score should be provided.<br /> In the text mentioning Figure 4B, the authors said “a homology model of TG2 bound to Ca2+ at the three conserved binding sites and found that it was in good agreement with the cryo-EM map”. However, this sentence does not seem to match the figure because Figure 4B shows the calcium-binding sites and the related residues in the model, not including the cryo-EM map. Therefore, we suggest that Figure S2 which shows how the calcium-binding sites fit the map is included in Figure 4 instead of Figure 4B.<br /> In Figure 4, SAXS and Cryo-EM under Ca2+ conditions showed different conformations based on each protein concentration. Do you have information about the concentration of TG2 in human cells and how this relates to regulation? <br /> The authors explained that TG2 R580K mutant forms higher-order oligomers at lower Ca2+ concentrations compared to WT TG2 from Figure 4. However, at this stage, proof that WT TG2 forms higher-order oligomers seems to be only the I(0) value of the red SAXS profile in Figure 4C. In addition, since the profile is well-fitted to the calculated open-dimer profile, readers might not notice the increased I(0) value. Figure S7B looks like a more direct proof of WT TG2 higher-order oligomers under Ca2+ conditions. Therefore, we suggest that Figure S7B is included in Figure 4 or is mentioned in the text related to Figure 4.<br /> In general, SAXS has technical limitations in confirming the presence of oligomeric species due to the possibility of non-specific aggregates, precipitates, and buffer components scattering at low q values. Addressing these sources of low q scattering either through explicit mention in the text or furnishing more direct evidence of the TG2 oligomers may enhance the strength of the claims.<br /> The authors mention that using 3 uM TG2 in cryo-EM made it possible to capture monomeric TG2. The SAXS experiments required higher concentrations (25 uM) for sufficient signal. Given the importance of TG2 dimers in this study, the authors might consider measuring the affinity constant for self-association to confirm that the stoichiometry (homodimer/monomer) in the different experiments is indeed what they expect based on the solution behavior of TG2.<br /> Can the authors explain the significance of the differences in fluorescence emission at each arrow point for no drug vs. LM11 treatment in the BODIPY-GTP binding assays in Figure 5A?<br /> Some discussion regarding the limitations in using two cell lines that possibly differ in expression levels of genes other than TG2, membrane permeability, metabolic activities etc. to assess LM11 potency, can align the conclusions more closely with the data.

Minor points:

Table S2 is not mentioned in the text. <br /> In Figure 3D, it is better to describe what concentration of GTP the experimental curves have clearly. Certainly, we can read those based on the values of Rg in Figures 2 and 3B but that’s a bit unfriendly.<br /> There seems to be a typo in the text of Figure 4C. “yellow, see Figure S3C” looks like the correct text because Figure 3C does not include SAXS profiles.<br /> Figures 2 and 3 can be combined to make it easier for the reader to compare between TG2 WT and R580K.<br /> The term “saturated conformational state” in the legend for Figure 3B is not meaningful.<br /> Are the % cell viability data for LM11 and TTGM 5826 normalized to vehicle control?

Reviewed by Hiroki Yamamura, Rishav Mitra, and James Fraser (UCSF)

On 2019-07-18 05:30:52, user Olivier Gandrillon wrote:

Dear authors

This is definitely a nice and important piece of work. I nevertheless find distressing that you seem to ignore a very closely related work using the very same idea of coupling 2 state models of gene expression and inferring conncetion parameters values, published in the two following papers:

Herbach, U., Bonnaffoux, A., Espinasse, T., and Gandrillon, O. (2017). Inferring gene regulatory networks from single-cell data: a mechanistic approach. BMC Systems Biology 11:105 .

Bonnafoux, A, Herbach, U, Richard, A, Guillemin, A, Gonin-Giraud, S, Gros, P.A. and Gandrillon, O (2019). WASABI: a dynamic iterative framework for gene regulatory network inference. BMC Bioinformatics 20:220.

I definitely would think a comparison of the two modeling approaches might be informative to the readers.

Best

Olivier

On 2018-06-27 15:02:36, user John Smith wrote:

Really great work, this dataset will be a fantastic resource for benchmark/comparisons against single cell data. Any chance of a full download of the summary data being made available before publication? Really looking forward to having a look at the data. Thanks!

On 2025-03-03 16:11:50, user hansdb wrote:

One Corded Ware (CGG107476) and two Bell Beaker individuals (CGG106805 and CGG106737) from your interesting study belong to Y-DNA haplogroup I2-L38 (aka I2a1b2a).<br /> As decribed in my citizen scientist paper ( https://www.academia.edu/115363574/RECONSTRUCTING_THE_JOURNEY_OF_Y_DNA_HAPLOGROUP_I2_S2555_TO_I2_L38_Tracing_Genetic_Footprints_Across_Time_and_Space ) this haplogroup was also related to:<br /> * an EBA Unetice sample from Germany (I0114)<br /> * an EBA Unetice sample from Bohemia (I7959)<br /> * an IA Hallstatt sample from Germany (MBG008)<br /> * an IA La Tène sample from Bohemia (I17327)<br /> * an IA La Tène sample from France: GLN32

Most interestly this haplogroup also occured among the Urnfield epoch Lichtenstein clan from Osterode-am-Harz. Since it is mentioned (paragrapghs 194 to 196) that very few genomes from the Urnfield Culture exist (as a result of the cremation practice) it would be interesting if the well-preserved samples of the Lichtenstein cave could be integrated in your study.

On 2019-05-14 13:06:09, user John Wilson wrote:

Dear Readers,

In the spirit of this preprint server, we respectfully solicit any questions, comments or thoughts that would assist this line of research. Thank you all, sincerest regards,

John

On 2022-01-26 19:57:55, user Andras Lakatos wrote:

A really groundbreaking concept and elegant delivery. Congrats to the team!

On 2021-09-01 19:27:42, user Chen Sun wrote:

This paper developed a universal cryo-EM fiducial for the study of protein-nanobody complexes and validated with two membrane proteins. The only concern is, the process of producing nanobody for membrane protein is long and expensive. A minor point is that the mask used for SFig.3f FSC is obviously too tight.

On 2018-07-11 19:37:30, user Andrew Fleet wrote:

Nice structure and interesting paper, especially in light of the other recently published structures for Ptch1. It’s also nice to see that you validate our previously published results showing that Ptch1 can repress Smo and respond to ligand in the absence of parts of the C-terminus and the large cytoplasmic loop.

On 2025-10-29 15:47:04, user Marco wrote:

I was unable to find Table S1.

On 2017-09-21 23:34:25, user R. Ahmad wrote:

Yikes! Already this has been sitting idle for a month? Doesn't make sense. Maybe a fatal flaw with this pre-print?

On 2020-07-03 12:43:47, user Dieter Egli wrote:

Linking the two studies:<br /> Reading frame restoration at the EYS locus, and allele-specific chromosome removal after Cas9 cleavage in human embryos<br /> https://www.biorxiv.org/con...

On 2017-08-21 16:14:39, user Friedemann Zenke wrote:

Having thought about putative rapid compensatory processes for quite some time now, I greatly appreciate modeling efforts in this direction and I read this preprint with great interest. The model’s simplicity is appealing and its behavior intuitively makes sense. I thought of sharing some of my thoughts that I jotted down while skimming through the manuscript.

First, I was wondering about the notion and importance of a single shared receptor pool in the model. If I understand the model correctly, a strong LTP event at one synapse creates some kind of “vacuum” which then causes depression at other synapses by depleting the shared pool. However, from what I recall from most mechanistic models for the induction of early phase LTP, the assumption generally seem to be that the receptors which are inserted into the postsynaptic density are already present at the synapse or its immediate vicinity. This seems to slightly deviate from the notion of a single shared pool. However, on the slower timescales of receptor trafficking, a shared pool seems a pretty reasonable assumption. I guess this could in principle be modeled as a combination of localized synapse specific pools that are coupled on a slower timescale. However, such a slower coupling may introduce additional lag and have other side unwanted side effects. I was wondering whether the authors have thought about this and if they think it would affect their main findings in any way.<br /> Second, there is at least some experimental evidence (Oh et al., 2015) suggesting that synaptic competition for a shared resource is *not* the cause of synaptic normalization, but that rather third messengers are involved. I was wondering what the authors think about this and whether the model could be “re-interpreted” or “re-invented” for such a third messenger scenario. <br /> Third, while reading I was reminded of the study by Delattre et al. (2015) which has pitched a similar idea of competition for a shared resource. This might or might not be relevant to the study. If I remember correctly, the study was based on a direct comparison to experimental data.

* Oh, W.C., Parajuli, L.K., and Zito, K. (2015). Heterosynaptic Structural Plasticity on Local Dendritic Segments of Hippocampal CA1 Neurons. Cell Reports 10, 162–169.<br /> * Delattre, V., Keller, D., Perich, M., Markram, H., and Muller, E.B. (2015). Network-timing-dependent plasticity. Front. Cell. Neurosci. 220.

On 2024-07-25 20:28:20, user disqus_FNcjH0JLKv wrote:

The paper has been published in Scientific Reports: https://www.nature.com/articles/s41598-024-60991-x

On 2014-02-24 15:06:51, user Johannes E wrote:

I have started a small FAQ (frequently asked questions) for this article: http://genomicsoftraceeleme...

On 2019-11-18 08:14:45, user Tanai Cardona Londoño wrote:

"Our mutation rate estimates for baboons raise a further puzzle in suggesting a divergence time between apes and Old World Monkeys of 67 My, too old to be consistent with the fossil record; reconciling them now requires not only a slowdown of the mutation rate per generation in humans but also in baboons."

If the divergence time of OWM and apes based on the fossil record is actually 35 My, to reconcile rates and the fossil record wouldn't you need an acceleration of the mutation rates rather than a slowdown?

Faster rates leads to younger divergence time estimations because at a faster rate you need less time to accumulate the same amount of mutations... no? :)

On 2023-11-13 12:20:14, user paulanuneshasler wrote:

A peer-reviewed version of this article was published in Communications Biology on Oct 4th 2023 https://www.nature.com/arti...

On 2024-01-20 20:52:12, user Morris, Andrew wrote:

https://doi.org/10.1089/neu...

On 2025-01-20 10:53:31, user Sudarshan GC wrote:

Very interesting paper. I think several tests are missing like calcium influx assay. Would like to see if increase in calcium concentration in media can help to fold increase the EV production. I would also expect to see the elaboration of precise pathway downstream of Piezo1.

On 2021-04-27 16:54:08, user Leighton Pritchard wrote:

This is very interesting work. Thank you for sharing it.

I have a point of confusion. I may be missing something, but I understood that bidirectional/divergent promoters have been recognised for some time in prokaryotes, if not catalogued in depth. For example Beck and Warren (1988, https://mmbr.asm.org/conten... "https://mmbr.asm.org/content/52/3/318.long)") note that "Over 20 [divergent promoters] have been found on the chromosome of E. coli alone," and Rhee et al (1999, https://www.ncbi.nlm.nih.go... "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC24430/)") describe the divergent ilvYC system. Does this contradict the introduction's statement that there is a consensus view that prokaryotic promoters are unidirectional, and the abstract's note that in all prokaryotes promoters are believed to drive transcription in a single direction?

On 2018-07-10 22:38:02, user Mareen Engel wrote:

Hi Nick,<br /> I'm sorry for never answering your comment (thanks a lot though & thanks for the note about the Figures). Unfortunately, we don't see the same negative relationship between m6A and mRNA with the final, higher confidence peak set (see also my comment on the updated bioRxiv & the final manuscript version online/linked soon). It seems also very dependent on how the m6A is quantified, thus I am suspecting that (at least here) it may be to a large part an artifact of the normalization of m6A to RNA input.<br /> Sorry for this & please excuse the long delay. Please feel free to contact me for any more discussions about this!

Best, Mareen

On 2020-04-28 22:48:17, user Perseus Smith wrote:

Define Prakriti?

On 2016-02-02 11:08:13, user Robert Sade wrote:

The revised manuscript has a clarification on pp 25/26 with regard to barrierless folding (Type 0 scenario according to the Energy Landscape Theory), including an additional figure (Fig. 13) on the last page. In addition to these changes there is an additional corollary on page 37 that stems directly from Eq. (A29).

The framework developed in Papers I and II has been used to analyze the non-Arrhenius behavior of FBP28 WW (Article III): http://biorxiv.org/content/....

On 2021-05-08 01:33:43, user Bitty Roy wrote:

Hi I just read this a couple days ago with great interest. Our paper on Mycena in grass roots just came out today. <br /> Roy BA, Thomas DC, Soukup HC, Peterson IAB. 2021. Mycena citrinomarginata is associated with roots of the perennial grass Festuca roemeri in Pacific Northwest Prairies. Mycologia. https://doi.org/10.1080/002...

On 2023-10-16 16:21:21, user Kent wrote:

Now published https://doi.org/10.1186/s40...

On 2020-07-07 11:35:42, user Ludwika Zofia Fortuna wrote:

Basing on what I read so far I must thank you for no weird text layouts. You can read the paper smoothly and find paragraphs easily. Thank you for your research and I will be waiting for further insights about the significance of Rab46 in endothelial cells functioning ????

On 2019-10-21 21:59:24, user art vandelay wrote:

Could you please post the sequences of the adapters, library amplification primers and the sequencing primers?

On 2025-03-13 13:09:46, user marodon wrote:

Related to "Somewhat paradoxically, a recent pre-print study demonstrated a small but statistically significant decrease in basal heat sensitivity in both female and male mice conditionally depleted for Penk expression in systemic Tregs. Mechanical thresholds and other sensory modalities were however not examined ". This sentence is not accurate since the preprint had been significantly improved and was published in eLife as a Version of Record (VOR) in August 2024. Various mechanical tests were performed and are shown in a supplementary figure in the VOR. Moreover, mice depleted of Penk in Treg do not show a decrease in basal heat sensitivity but an increase (they are more sensitive @55° than the controls). These results are confirmed by Mendoza et al using the same mouse model. Best regards

On 2020-11-07 09:41:54, user Sebastian Quilo wrote:

Nice paper in the Biomarker field!<br /> I have a suggestion, it would be better if you add references for the tools used in your work like scikit-learn and PyCM :

scikit-learn :

@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011} }

PyCM :

@article{Haghighi2018, doi = {10.21105/joss.00729}, url = {https://doi.org/10.21105/joss.00729}, year = {2018}, month = {may}, publisher = {The Open Journal}, volume = {3}, number = {25}, pages = {729}, author = {Sepand Haghighi and Masoomeh Jasemi and Shaahin Hessabi and Alireza Zolanvari}, title = {{PyCM}: Multiclass confusion matrix library in Python}, journal = {Journal of Open Source Software} }

On 2019-07-01 19:48:25, user Julius Adler wrote:

July 2, 2019: some changes to April 18, 2019

Drosophila Mutants that Are Motile but Respond Poorly to All Stimuli Tested Mutants in RNA splicing and RNA Helices, Mutants in The Boss

Lar L. Vang and Julius Adler

The following idea was presented in 2011 in “My Life with Nature” by Julius Adler, p. 60:

“Recently I conceived a new idea. “The Boss is the thing inside every organism – humans, other animals, plants, microorganisms – that is in charge of the organism. I don’t mean this in any mystical or spiritual or religious sense, but rather I mean it in terms of chemistry and physics. You may think that The Boss is a wild idea, and certainly the evidence for it is poor, but I think it’s true, and at least it’s a hypothesis to be tested.”

Now we have tested this idea:

Adler and Vang (2016) and Vang and Adler p. 13, 2018) reported Drosophila mutants that lack all responses to external and internal stimuli at 34 degrees but at room temperature these mutants are not deficient. This means that activity by the Boss can be eliminated at 34 degrees but the activity is still present at room temperature.

And they (Vang and Adler, 2016) reported a Drosophila mutant that lacks responses to all stimuli tested at both 34 degrees and room temperature. That indicates that this mutant lacks behavioral action by The Boss.

(It must be admitted that the defects in these mutants were caused by defects in The Boss.)

What is The Boss? It is a mechanism that acts as described in Figure 10 of Adler, 2016:

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

Fig. 10 of Adler, 2016

The idea that each organism has something in control of the organism is novel. Before this, it was believed that each organism has properties that are largely independent of each other. Now it is suggested that all the properties are controlled by a single factor, The Boss, which directs both the interior and the outside of the organism. The Boss is to be found in humans, other animals, plants, and microorganisms. The evidence for this idea is incomplete.

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

Adler J (2016). A search for The Boss: The thing inside each organism that is in charge. Anat Physiol Biochem Int J Vol.1, 2016.

Adler J, Vang LL (2016) Decision making by Drosophila flies. bioRxiv March 24, 2016.

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

On 2016-07-10 18:27:20, user Ganesh Shelke wrote:

hi, One quick question regarding the CD63-Luciferase localisation in cells . Do you think the CD63 +ve EVs isolated from affinity purification comes exclusively from MVBs or can be from membrane budding (Micro vesicles) as they are located both in endo-membranes and cell membrane ??

On 2019-10-18 16:56:56, user Stuart Hameroff MD wrote:

It's probably the other way around. The latest work on anesthetic action (see Wiki page 'Theories of general anesthetic action' suggests anesthetics dampen high frequency oscillations in microtubules which then slows EEG.

On 2022-07-26 16:54:39, user DrDiagnosis wrote:

The authors should take a look at https://doi.org/10.1002/mp....

On 2025-02-23 17:33:44, user Sean Rands wrote:

This work was published last year in Royal Society Open Science - see https://doi.org/10.1098/rsos.231882

On 2020-08-14 13:16:31, user GraemeTLloyd wrote:

Hi Jorge. FYI, Claddis now performs AIC tests with the (renamed) Claddis::test_rates() function and these are likely preferable to an LRT. (Try installing version 0.6.0 from GitHub if you want to play with this.)

On 2021-01-20 02:07:12, user Filip Fratev wrote:

It is not so collegial to not cite previous studies on the topic and to present the results in a way that make the impression that this is the first in silico study about these mutations. Furthermore, it is not clear on the basis of which method these conclusions were drawn, just higher flexability? I can't see any numbers. There are also elementary questions such as why SB14 FF, which creates much larger fluctuations, and not SB19 FF was used? <br /> There are many ambiguities and for me this is a routine study.

On 2023-03-24 23:10:53, user Akshaya Jayakarunakaran wrote:

Hi! Thank you so much for your submission of your paper. The language was very clear and extremely easy to understand for individuals that are not familiar with the field. The employment of xenografts was great. Personally, I was not familiar with the technique and learnt a lot about it through reading your paper. Furthermore, I thought your employment of controls was great and very appropriate. It served well to verify the reliability of experiments. It also serves well to help analyze the experimental data in relation to the control.

The some of the things that could be improved are:

Personally, I felt Figure 3 and 4 could be combined because they seem to be conveying the same thing. <br /> The plot in Figure 2 is a bit unclear and I would prefer a box and whisker plot to convey the data better.

Again, this was a great paper and we really enjoyed reading it. Thank you for your scientific contribution.

On 2019-01-11 13:00:00, user David Howard wrote:

Please note the supplementary figures and tables that accompany this version of the manuscript are the same as the previous version and they can be accessed here: https://www.biorxiv.org/con...

On 2017-11-15 06:03:48, user Andreas Hejnol wrote:

Title "Placozoans are eumetazoans related to Cnidaria"... Placozoans can be replaced with ANY clade or species in the Animal Tree of Life... So this title does not contain any information... I suggest to change it. I will comment on the EvoDevo part in a separate email to you Chris (and Gonzalo).

On 2017-11-01 15:16:34, user dochaynes wrote:

We have uploaded a corrected version of this manuscript. Panel C was missing from Figure 3 in the first version. It is now included. ~~~ Karmella Haynes

On 2018-07-09 19:11:23, user Harry Langdon wrote:

Where can I sign up for the test? I was going to get a new wardrobe but maybe I don't have to.

On 2019-02-26 02:25:25, user Jondice wrote:

It would be great to know which implementation (and version) of SteadyCom was used!

On 2019-06-18 15:44:59, user Robert Flight wrote:

The presence of multiplicative error is a large part of the reason -omics data are often log or some other transform applied before doing *almost* anything with it, if the person working with the data is aware of these issues.

Given the difficulties in working with the multiplicative error data (as evidenced by some of the very hairy derivations and equations by the authors), I would be very curious to know how the additive correction applied to log-transformed data behaves compared to the full additive + multiplicative correction.

On 2024-08-08 08:28:33, user Ashleigh Shannon wrote:

AUTHORS COMMENT / UPDATE <br /> 21 June 2024

Part of this work, describing the inhibition of both the NiRAN and RdRp domains by the guanosine analogue 5’-triphosphate AT-9010 has now been published in Nature Communications ( http://doi.org/10.1038/s41467-022-28113-1)11 "http://doi.org/10.1038/s41467-022-28113-1)1") . This includes the structural characterization of the mechanism of inhibition at both active-sites, which importantly revealed that the SARS-CoV-2 NiRAN domain contains a guanine-specific pocket. This has now been confirmed in other studies 2,3, and represents a promising avenue for future drug development studies.

Following several contradictory reports on the ability of the NiRAN to NMPylate different cofactor proteins (4–6), we have now carried out additional analysis on the protein-priming activity and NMPylation specificity. This has revealed that our nsp8 product is labeled at the primary amine of a non-native glycine residue, present at the N-terminus of the expression construct (following cleavage with TEV protease), and negating the biological relevance of these findings. Surprisingly, removal of this single residue, exposing the true N-terminal alanine, eliminates all labeling and protein-primed activity, stressing the importance for using proteins with native N- and C- termini. Of note, the current publication does not reflect these new findings, but instead has been left as the initially submitted manuscript.

Later studies have now revealed nsp9 to be the primary target for both NMPylation5,7, and RNAylation8 – a conclusion that we fully agree with.

With the exception of our finding that nsp8 can be used to prime-synthesis, albeit by a non-native residue, there was no other evidence for protein-priming until recently. In October, 2023, Schmidt et al., published the finding that nsp9 is covalently linked to the 5' end of positive- and negative-sense RNA produced during SARS-CoV-2 infection9. This linkage was found to be regulated by its interaction with the host protein, staphylococcal nuclease domain-containing protein 1 (SND1), which was found to specifically recognize the 5’ end of negative-sense RNA and be important for viral RNA synthesis. Although it is highly frustrating to have spent several years focused on the wrong protein, this finding supports the notion that protein-priming is occurring in CoVs, and opens up a plethora of options for further mechanistic and structural studies.

Of note, Schmidt et al. also showed that nsp9-linkage on the (-)sense strand mapped roughly between the genome and poly(A) tail. Intriguingly, we also found a similar specificity for this region. Here we show that the SARS-CoV mRTC (nsp12-nsp7-nsp8) can initiate synthesis through a de novo, NiRAN independent pathway, through the synthesis of a pppGpU primer. This dinucleotide is complementary to the last two nucleotides of the SARS-CoV-2 genome, located precisely at the genome-poly(A) junction. De novo initiation was found to directly compete with the (artificial) nsp8-poly(U) protein-primed synthesis. This shows that the poly(A) tail and 3’ genomic RNA sequence elements guide the positioning of the mRTC to the true 3’-end of the genome.

The specific details behind initiation of RNA synthesis, including the role of SND1 and potential coordination between protein-priming and/or de novo induction remains to be studied. Synthesis initiation, and the precise role of the NiRAN therefore appears to be a complicated story, which remains to be fully elucidated.

Ashleigh Shannon and Bruno Canard

On 2019-03-01 19:20:58, user Nathan Good wrote:

This is a pre-print of an article to be published in Scientific Reports. The final authenticated version will be available online at: https://doi.org/10.1038/s41...

publication date pending

On 2025-10-14 20:45:37, user Yu Lee wrote:

This paper, as well as the previous papers of M. P. Nikitin, is based on the concept of DNA commutation, where affinities for interactions are calculated using custom NUPACK scripts developed by the authors. Could the authors deposit the developed code along with the new paper, since it is central to this manuscript as well as to their previous publications?

The previous papers, which also relied on the same NUPACK scripts, did not include the code despite Nature’s code availability guidelines.<br /> https://doi.org/10.1038/s41557-022-01111-y

On 2018-03-14 20:23:07, user Mick Watson wrote:

Looks great!

Be wonderful to see a comparison of the genomes within to the 913 we recently released: https://www.nature.com/arti... - see https://datashare.is.ed.ac.... for the data

Also 99 MAGs from the moose rumen (https://www.ncbi.nlm.nih.go... "https://www.ncbi.nlm.nih.gov/pubmed/28731473)") and the 8000 MAGs from Parks et al included a good number of rumen MAGs (https://www.nature.com/arti... "https://www.nature.com/articles/s41564-017-0012-7)")

Looking forward to working to reading this properly!

On 2024-08-06 23:47:01, user Jorge Ramirez wrote:

Please add a link to the published version of the paper. The link of the journal publication is: https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1375958/full . https://doi.org/10.3389/fpls.2024.1375958 . Thanks!

On 2018-12-04 19:06:52, user R. Rocca wrote:

Is the supplementary material missing?

On 2017-02-14 18:57:11, user Casey Bergman wrote:

After posting this preprint, we were contacted by Lyudmila Zakharenko who pointed out a possible alternative for the P' phenotype in Ukrainian populations being due to the effects of elevated temperature in the GD assay. The first author's lab is now in the process of replicating GD assays to verify the GD phenotypes of the Ukrainian populations reported here. As a consequence, we have rewritten this manuscript to report only the comparison of genomic P-element predictions with GD phenotypes in the three worldwide populations from North America, Africa and Europe. A preprint of the revised manuscript can be found here: http://biorxiv.org/content/...

On 2020-01-16 02:12:22, user Adeline Boettcher, PhD wrote:

A finalized version of this manuscript can be found at: https://www.frontiersin.org...

On 2020-10-30 20:55:30, user Angel Ni wrote:

Beautiful paper! I thoroughly enjoyed reading this because of how well thought out the methods were. For example, the cell lines were very good fits for the specific experiments they were involved in. HEK293A was a model for kidney cells, Huh7 for liver cells, and Caco-2 for intestinal cells.

I would have appreciated a figure in the introduction that outlines the role of Mpro in virus replication. After a quick search on Google, I was unable to find any descriptions that covered the mechanisms relevant to this paper in good detail. In addition, it would be nice to have a figure with the structures of all the inhibitors that were studied. This would allow easy and direct comparison.

This paper makes a strong case for the therapeutic potential of SARSCoV2 Mpro inhibitors with ketoamide warheads and cyclic structures. I look forward to seeing further research in this area.

On 2017-11-27 16:24:33, user Emily Stephen wrote:

Also, a nitty-gritty question: your permutation-style shuffling procedure to test the significance of a traveling wave randomly permutes the electrode locations within each cluster 1000 times. But e.g. clusters with 4 electrodes will only have 24 possible permutations (4p4), correct? If so, how would this affect your estimated sampling distribution and/or the performance of the hypothesis test?

On 2023-07-28 09:04:13, user arpitmathur wrote:

I would like to point out that in carrying out survival analysis, authors divided patients into two categories top 40% and bottom 40% . There is a literature from medical statsitics that says that there should be no division of patients into groups. Rather regression should be used for survival analysis purpose.

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

On 2024-05-29 06:50:45, user theNiessingLabs wrote:

The authors show in Figures 3 - 4 and in Table 2 in silico-docking studies with an alphafold2-model of PURA as template. In this model, PUR-repeat III is shown as a monomer with an awkward-looking fold. The authors use this docking to suggest a direct interaction between PURA and GLUT1. <br /> Unfortunately, the authors seem to ignore that PUR repeats do not exist as single, monomeric repeats but require dimerization. For repeat III of Drosophila PURA, a high-resolution structure of its homodimeric domain has been reported already in 2016:<br /> https://elifesciences.org/a...<br /> PDB-ID: 5FGO<br /> For repeat III of the human PURA (as used in this study), more recently the homodimeric high-resolution domain structure has also been published:<br /> https://elifesciences.org/a...<br /> PDB-ID: 8CHW<br /> Considering these experimental structures, Figures 3-4 and Table 2 refer to unphysiological folds. As a result, conclusions drawn from these figures have to be considered as entirely wrong.

On 2019-03-21 15:15:48, user Howard Salis wrote:

Overall, this is a well-written & intriguing manuscript with a substantial & diverse collection of supporting data that will add to the ongoing debate over the translational ramp hypothesis. However, I have some simple questions, and even though no one really uses the comments section in bioRxiv, I'll take the dive.

1. In the abstract, the authors state that "The observed difference [in protein abundance] is not dependent on tRNA abundance, efficiency of translation initiation, or overall mRNA structure." but in the main text, they write "In summary, we show that the efficiency of protein synthesis in addition to overall mRNA structure and codon content is strongly dependent on the nucleotide sequence positions 7-15 and the resulting protein amino acid positions 3-5." These two sentences are quite dissimilar, and only the conclusion in the main text is supported by the authors' data.

2. The stability of mRNA structures located solely within the N-terminal CDS sequence are known to alter the rate of translation initiation and protein synthesis rate [https://academic.oup.com/na...]. Further, if the first 15 nucleotides of a CDS are altered, it's very possible that those nucleotides will base pair with the 5' UTR sequence to form additional mRNA structures that will inhibit translation initiation [https://www.nature.com/arti...]. The effect sizes are very large; the presence of one stable mRNA structure (dG = -15 kcal/mol) within 15 nucleotides of the start codon will repress translation initiation by about 850-fold.

3. The authors mention mRNA structure several times, but the manuscript's main text does not include any mRNA structure calculations that test whether their presence & stability could explain the differences in protein abundance or expression level data.

4. Whenever a physical phenomenon has multiple overlapping mechanisms [as is very likely the case here], it's always difficult to design experiments that clearly distinguish the mechanisms and quantify their effects separately. The authors' in vitro data using FRET and mis-charged tRNAs are a step in the right direction towards sussing out these mechanisms. But it's important to place these additional measurements in a systems-wide perspective. For example, if the FRET measurements report an increase in the abundance of ribosome initiation complex, then the cause of that increase could be either an increase in the rate of ribosome recruitment/initiation OR a slow down in the transition to the processive elongation phase. Either cause would have the same effect. This can be clearly shown by deriving a simple kinetic model of the process.

5. The measurements using mischarged tRNAs are great, though error bars and an F-test are absolutely needed to determine whether the differences are significant. Protein abundances are varying by >100-fold, but the measurements of translation processivity are changing by 1.4-fold. Again, multiple mechanisms could be responsible for the overall large change in protein expression levels, and amino acid content in the ramp has the potential to contribute, but it's important to quantify its magnitude.

6. If there are special mRNA and amino acid motifs that specifically alter translation elongation rates, why would they only have an effect when present within the first 5 codons? The ribosome very quickly transitions to a processive state after initiation. Is the argument that a still-bound initiation factor is responsible for mediating these effects, which are lost after the initiation factor dissociates? If so, that would require some proof!

On 2025-05-07 00:43:31, user Young Cho wrote:

General comments

The study provides a great baseline using a novel perspective that connects carcinogenesis to observed epigenetic changes and can benefit future understanding of the effects of formaldehyde exposure. References are effective and well-executed. The title is appropriate. The Abstract appropriately summarizes the study, but could benefit from mentioning the ambiguous response to dose concentrations. Organization and language are appropriate and effective. While the study has great potential to benefit the field, statistical analysis between groups and figures are of particular concern and prevent publication of the study in a prestigious journal.

Introduction

The introduction clearly reports the gaps in knowledge of FA exposure carcinogenesis which the study hopes to bridge. Current studies relating to the topic are referenced. Objectives are clearly stated. Lacking some justification for the methodology.

Methods

The methods are well explained, reproducible and appropriate as they provided a representative cell line to study the effects of FA. Furthermore, the use of LC and tandem MS/MS allowed for precise peptide separation and identification. The simplicity of this experimental design allows for the responses to the treatments to be clearly identified, and creates the baseline for further examination into FA exposure carcinogenesis. Statistical analysis methods need revision.

Results:

Thorough explanation of figures and results. For comparison between groups, and particularly for PTM-combined peptide fragments, additional statistical tests may be required for proper analysis since PTM-combined groups may not be independent.

Figures clearly present the data and support for the researcher’s conclusions. They are effective in showing comparisons between different sites and effects of formaldehyde exposure. Because most figures are box-and-whisker plots, color selection of each figure could be more intentional. The number and sizes of the figures is excessive, particularly since some data described in the figures are not included in the discussion.

Discussion

The discussion extensively interprets the results using epigenetic cancer markers described by previous studies, but is overly focused on the application of observed epigenetic changes directly to cancer, potentially overlooking confounding effects between the two. Strengths of the study are briefly discussed. Despite mention of exogenous vs endogenous FA exposure in both the abstract and introduction, there is little discussion of how the results of the current study elucidate the DNA adduct hypothesis. Generally, the discussion also lacks possible future directions and assertion of the valuable baseline that the study provides for future research. Weaknesses of the study are also not thoroughly discussed. Increased suppression in response to the lower concentration groups should also be further discussed, possibly with the suggestion that further studies should focus on a wider range of concentrations and exposure durations. The study could also benefit from discussion of possible differences caused by cell line origins when comparing epigenetic markers from other studies.

Detailed Comments:

Introduction

Given that lung cancers were not mentioned to be associated with FA exposure, what is the justification for using the BEAS-2B cell line?<br /> Methods

Section 2.6: The student’s t test assumes a normal distribution of data while non-parametric one-way ANOVA does not. In addition to the non-parametric one-way ANOVA, consider a non-parametric comparison such as Dunn’s test with correction instead of the student’s t-test.<br /> Results

Section 2.1: Table 1 and Table 2 take up a large amount of space, and fragments with large p values serve little purpose. Additionally, in the following fragment, “KSAPSTGGVKKme1PHRme140” in Table 1, the methylation notation is not subscripted for K37.<br /> Section 3.5: The X and Y axis of Figure 6 are inverted compared to all other figures in the study.<br /> Section 3.7: Figure 8 is too large and has too many included datapoints. In groups with smaller variance, the color is barely visible of each treatment is barely visible. Consider limiting the scope of the graph since there is no comparison between groups.

On 2016-11-08 02:31:25, user Nir wrote:

Awesome :)

On 2019-09-19 08:55:20, user H. Etchevers wrote:

This is exciting work and I congratulate the authors on the technical and also presentational prowess. In addition, I played around with their online tool which was wonderful. A few days later, there was an update of my Kaspersky Antivirus definitions and reputation files, and suddenly(since week 38 2019), and ironically (given the name similarity) the website is flagged as a "risk". This is reproduced on their online check: by entering http://kasperlab.org/mouseskin into the tool here: https://virusdesk.kaspersky... . I've submitted it for further examination as a likely false positive, since EVERY other site declares it clean on https://www.virustotal.com and others, but it is blacklisted with Kaspersky FYI.

On 2020-11-21 05:12:35, user Areli Lopez wrote:

I thoroughly enjoyed reading this paper and learning more about sleep regulation and particularly sleep homeostasis. I thought that the presentation of data and results progressed nicely by first establishing normal calcium activity in LPO neurons and then transitioning into how the deletion of NMDA receptors affects these normal conditions. I felt that the inclusion of both sleep deprivation and sleeping medication provided a nice holistic approach to analyzing all aspects of the potential effects of the GluN1 deletion on sleep. However, I would have liked to see a bit more organization and clarity in the figures. For example, in figure 2E, the two plots showing Mean GCaMP6s signal were not labelled, even though they represented distinct conditions (EMG and delta power). Additionally, the choice of colors on these plots are less than ideal due to the similarity of the lime and cyan used for NREM and REM, respectively. I would suggest using a color with greater contrast for REM points, such as orange. Figure 3D could use better organization as well, as the use of shading to distinguish light period versus dark period plots is not very effective and clear labels would better fit the data. An additional recommendation would be to make the legend in figure 3C larger, as both the line icons and text itself are very small and difficult to see. Overall it is a well-written paper, but could use a few adjustments particularly with the presentation of figures.

On 2018-06-18 07:04:19, user Amala Mol wrote:

Article seems very informative and the author disclosed common parameters in virus self assembly in detail which is useful for virus Nanotechnological applications.

On 2016-02-18 18:48:43, user John Smith wrote:

The full version of this paper was published in Frontiers in Neuroscience on 27th Jan 2016 as "Identification of Vulnerable Cell Types in Major Brain Disorders Using Single Cell Transcriptomes and Expression Weighted Cell Type Enrichment"

On 2020-05-20 22:03:18, user Jim wrote:

Name of x-axis should be "Fold change (log2)" instead of "Fold change".

On 2018-12-07 05:53:11, user Michael Inouye wrote:

Thank you all for a really engaging paper! It's generated a lot of interest and was widely read in my lab so we decided to do a review together. Hopefully our comments are useful, so I've pasted them below:

Overall comment: Population stratification is a form of confounding, i.e. we cannot easily disentangle whether the apparent genetic association with phenotype is (i) due to true aetiology (ii) due to simply tagging different sub-populations/ancestry with different 'environment'/lifestyle factors that are unrelated to aetiology or (iii) a mix of both. Further, if we see an association of polygenic scores with principal components or ancestry, this does not *in itself* (without further analysis/evidence) mean that it is only confounding by ancestry and not true aetiological signal.

MAJOR COMMENTS:

1. It's known that polygenic scores exhibit geographical variation, which can be due to genotype frequencies and/or the per-variant polygenic score weights being correlated with geography/ancestry. If the polygenic score is confounded, the target dataset has population structure correlated with the phenotype, and there is sample overlap between training and target dataset, it becomes very difficult to disentangle. Simulations under various scenarios could provide some more concrete insight here.

2. Overlapping samples are a major and well-known biasing factor (overfitting). Please clarify if the first part of the Results (pg 5 - 14; Tables 1, 2; Figures 1-4) pertaining to the GIANT-based polygenic score(s) include overlapping FINRISK samples in the derivation and target datasets (N~2,300). The removal of all overlapping samples is necessary for eliminating this bias, and it is common to do this before performing subsequent analysis. Pg 15, paragraph two appears to try to address this point (or estimate how much overlapping samples are biasing the analysis) but is not really persuasive: increasing the overlap doesn't tell one how much the (unquantified) existing overlap is biasing the results. Our experience is that small overlaps can lead to (sometimes substantially) inflated effect size estimates. Without more in-depth investigation of the effects of the overlapping samples it is not possible to properly assess whether the inflation in prediction is primarily due to population stratification or sample overlap.

3. It is common for polygenic score prediction analyses to adjust for genetic PCs (typically the top 3-10 or more PCs). From pg. 27 "Phenotypic differences predicted by PS", it seems that the when the scores are used to predict the phenotype, there is an adjustment for sex, age, and age^2 (and BMI for WHR). It's not clear whether also adjusting for PCs *in the target dataset* (FINRISK) eliminates/reduces the apparent stratification exhibited by GIANT-height (i.e., the 3.5cm west-east difference). This is important because if we can adjust the score with PCs and fix the issue, that would reduce the severity of this problem and we could use existing scores. If even adjusting for PCs doesn't remove stratification, this would potentially require deriving new scores which is more difficult (especially if only summary statistics are available).

4. Besides population stratification and sample overlap, another potential reason for the apparent large estimates for height by the GIANT height score is that GIANT is a meta-analysis of different cohorts, each of which could possibly have had slightly different QC or imputation, and/or different adjustment for potential structure. Since the contributing cohorts are typically of different ancestry, these small differences could accumulate and indirectly contribute to further confounding with ancestry. In comparison, UKB is more ethnically homogeneous and genotyped+imputed all in essentially the same way. This may help explain why the GIANT height score shows such extreme differences between east and west Finland (in absolute terms) and why even selecting SNPs with P>0.5 still has some association with height (but not for UKB).

MINOR COMMENTS:

1. Are all the scores approximately normally distributed in FINRISK or could there be outliers that could potentially exacerbate the apparent east-west difference?

2. On a practical level, some things that could be done to assess score for stratification:

3. As the preprint suggests, generating scores from SNPs unlikely to be associated with the phenotype (e.g. P>0.5) is a useful negative control (assuming no sample overlaps). This seems to work well for the UKB-height summary stats. This could also be performed multiple times to form a null distribution to which the ‘real’ score would be compared.
4. As the preprint suggests, checking the correlation of the scores with PCs. If the correlations are high, this should warrant further investigation.

5. Adjusting the score PCs in the target dataset and assessing whether the association with the phenotype is substantially changed (usually attenuated) as a result. Another alternative is using (generalised) linear mixed models in the target dataset to adjust for relatedness/structure, though this can become difficult for a large dataset (e.g. UKB).

6. It has been pointed out by others that Finland is actually genetically heterogeneous relative to other even European populations (e.g. PC1 vs PC2 in 1000 Genomes). In addition to the above, it would be useful to know how the relative heterogeneity of the Finnish population affects the conclusions.

7. The manuscript doesn't say that *all* scores are biased/confounded, however it's important to acknowledge this and make clear that polygenic score analyses need to be acutely cognizant of these issues. The preprint ultimately shows the importance of evaluating each score for population structure, as different scores can show substantially different levels of stratification on the same dataset.

On 2020-03-28 08:34:15, user Robert George wrote:

Thanks for all the samples. Wishing for more data from Neolithic China

On 2015-04-17 10:39:49, user Helena wrote:

now published in eLIFE http://elifesciences.org/co...

On 2020-03-24 15:57:40, user Brice Curtin wrote:

Can you comment on why the lactam appears as a hemiaminal in the crystal structure? This is shown in Fig. 2 where the geometry is not that of a lactam, and the 3D structure wouldn't match the covalently-modified structure in Extended Data Figure 1c. This change in structure was not discussed in the text.

On 2025-04-21 07:14:30, user Rainer Spanagel wrote:

Congratulations on your outstanding multi-site study. This work exemplifies exactly what preclinical research requires to advance knowledge in a replicable and more translational manner.

I would like to bring to your attention our previous research, which reported similar findings. In Meinhard et al. (NPP, 2000 doi: 10.1038/s41386-020-0694-z.), we investigated the effects of psilocybin on alcohol relapse behavior and found that while there were some acute effects, there were no long-lasting effects beyond 24 hours, regardless of dose, treatment schedule, or sex. This study began as a multi-institutional investigation into the effects of psilocybin on rat behavior, funded by the EU in 2019-2022 ( https://www.neuron-eranet.eu/projects/Psi-Alc/) "https://www.neuron-eranet.eu/projects/Psi-Alc/)") . Following the lack of long-term effects observed with psilocybin, we transitioned our focus to R-ketamine. We recently published a multi-institutional preclinical randomized controlled trial (referred to as a pre-RCT) in Meinhardt et al. (NPP, 2025 doi: 10.1038/s41386-025-02071-w.) with R-ketamine.

On 2022-04-08 06:37:30, user Nikolas Haass wrote:

now published: J R Soc Interface 19: 20210903<br /> PMID: 35382573; doi: 10.1098/rsif.2021.0903

On 2019-08-01 04:19:30, user David Sinclair wrote:

We will be testing effects on hair.