On 2022-10-13 16:55:18, user Jacob Bobonis wrote:
This preprint was fused with another preprint (https://www.biorxiv.org/con... "https://www.biorxiv.org/content/10.1101/2020.06.22.160168v1)"), and both were published together in the following peer-reviewed manuscript:
https://www.nature.com/arti...
All the best, and on behalf of the rest of the authors,<br /> Jacob Bobonis.
On 2020-06-17 17:57:50, user D614G wrote:
The D614G mutation in SARS-CoV-2 Spike increases transduction of multiple human cell types
On 2020-05-22 16:51:11, user Reviewer#2 wrote:
Isn't there a bias in the statistic by selection? You pick only ancestral nodes, so the recurrent mutation has to occur afterwards, and if there is no other mutation you cannot time this discrepancy. That introduces a (possibly substantial) bias towards ancestral offspring. So the implicit assumption of the RoHo statistic being 1 under neutrality does not seem to hold? That might be the explanation why so many of your medians are substantially below 1.
On 2020-09-11 14:49:08, user Jake Aronowitz wrote:
Hi Dr. Scharff,
Thanks so much for your kind words on the manuscript. We are very excited about these data. I think your suggestion to include a greater discussion about the postulations from the Tokarev (young active and well connected) paper can only serve to strengthen our manuscript. It will put our data into a fuller context and tell a more complete story about new neurons in HVC
Thanks again!
On 2018-03-15 13:56:48, user Mick Watson wrote:
Hello!
For gene mapping of the reads or to compare the metatranscriptome gene predictions, you may be interested in the 913 novel rumen microbial genomes we assembled and published recently
https://www.nature.com/arti...<br /> Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen
Cheers<br /> Mick
On 2018-11-29 10:47:10, user Chicken Tikka Masala wrote:
Nice work but it misses the Material and Methods section.
On 2021-06-30 10:09:09, user Deepak wrote:
The basic characterization of the mouse model included in this preprint is published separately. Interested readers can look up at <br /> https://doi.org/10.1007/s12... or <br /> https://www.ias.ac.in/artic...
On 2019-11-29 10:50:30, user Matti wrote:
Where is the full list with differentially regulated genes?
On 2022-03-03 19:11:05, user Brett Chrest wrote:
Misleading conclusion: "We showed that mRNA vaccine (Pfizer) changes mitochondria by downregulation of cytochrome c resulting in lower effectiveness of respiration (oxidative phosphorylation) and lower ATP production."
Given the highly dynamic nature of the mitochondria, is is not always clear that a reduction in Cyt c is pathologic. Given the fact that intensity hardly changed (and also not statistically significant) in normal astrocytes when incubated with mRNA, the concern for alternations is weak. As seen in Fig 4., this was seen once again seen in Fig 5A.
*This point above should be highly emphasized in the abstract since this paper is already being circulated online in order to discredit the vaccine and intensify hesitancy.*
The entire justification for the mRNA being in direct contact with brain cells/tissue is unclear and not based on scientific data:<br /> "COVID-19 mRNA has been recovered from the cerebrospinal fluid [11], suggesting it can cross the blood–brain barrier (BBB)"
The manuscript highly contradicts the methodology of the paper:<br /> "Also, mRNAs are short-lived molecules. The vaccine’s messenger does not stay inside the cell indefinitely, but is degraded after a few hours, without leaving a trace."
Highly misleading abstract sentence:<br /> "mRNA vaccine produce statistically significant changes in cell nucleus due to histone alterations."
On 2019-05-27 13:15:29, user Zhihua Han wrote:
Wonderful, I want use this on human intervertebral disc
On 2021-04-08 22:20:07, user Charles Warden wrote:
Hi,
Thank you for putting together this preprint.
I wish Genome Research had a Disqus-style comment system, similar to some other journals. I am currently taking something that I wanted to post as a comment on a different Genome Research article, and I instead will post it on PubPeer. So, in general, that is an option, even though I do not think that it is ideal for something truly intended as a comment.
I think I would have to take a closer look to develop more of an option on this specific matter, but I did have a couple thoughts:
1) My impression from this Tweet was different than my impression from the beginning of this preprint:
https://twitter.com/KVittin...
I can see certain parts of the preprint where I think the tone is more like a comment or complementary study. However, that is different than a "Contradictory Results" preprint or a PubPeer posting (assuming that the intention is to flag something about the study that may need to be corrected or retracted). I am not sure if that can still be changed for this preprint.
My preference would probably to be something closer to my impression of the Tweet (as positive as possible), but it is not immediately clear to me if that is the intention. Essentially, I am noting that I am not entirely clear about the severity of the message.
For example, I think there are usually limitations to simulated data. I can also believe that there are viewpoints that may benefit from being made more clear, beyond what is within the paper. However, I believe that I would agree with the idea of limitations due to extra noise in the RNA-Seq data, and I am not sure why this has to be "contradictory" instead of "alternative" results.
I am not sure if it helps, but I have some notes about the RNA-Seq DEG method limits here. While I could see how someone could call the discordance "contradictory," I would say it is more of an overall limit (where no 1 strategy is best, and you can't lock down everything in advance). In that sense, I would say different freely available methods complement each other, rather than detract from each other.
It is certainly possible that I missed something, since I did not have a strong opinion when I first saw the Varabyou et al. 2020 paper. However, unless I am missing something important, I wonder if a slightly different tone might help?
In terms of material linked from my blog post (and other experiences), I think what I have seen matches some extra noise in the Salmon gene lists (relative to the TopHat2 / STAR htseq-count gene lists), but not with a horrible amount of extra noise. So, in terms of what I see in the abstract, I think my experiences might agree with the last sentence in the abstract (even though I am talking about extra noise in differential gene lists and the authors are talking about detection of expression in that particular sentence). Is there something different that you are concerned about?
2) You say that "All data and scripts are available upon request.". I am not sure how large the intermediate files are in this situation. However, I think code and perhaps some processed data can be made available through means like GitHub, Zenodo, etc. (similar to what you have on FigShare).
In general, I think it is best to avoid providing materials upon request, if at all possible. So, I am not sure what is the limitation (or how much you are not providing already), and I think either making more available and/or making clear what subset you couldn't provide might help.
Best Wishes,<br /> Charles
On 2022-07-10 01:25:17, user Charles Warden wrote:
Hi,
Thank you very much for posting this preprint.
I think this provides a number of useful references and comparisons.
I also have a few comments:
1) While rMATS and MISO are popular programs, I don't think they are necessarily my preferred starting point for splicing analysis.
For example, I might consider the following as starting points for splicing analysis:
a) QoRTS + JunctionSeq (an extension of DEXSeq with a separate dispersion estimate for junctions versus exons)<br /> b) Custom analysis of the tab-delimited splice junction counts (SJ.out.tab) from STAR.
Perhaps b) is too open-ended. However, if you labeled the junctions like genes or transcripts, you could calculate Count-Per-Million (CPM) values and perhaps use traditional differential expression methods (DESeq2, edgeR, limma-voom, etc.).
Unlike a), there would not really be specialized splicing visualization for b) without additional coding.
However, both of these strategies do not fit the description of "alternative splicing events (ASEs) are conceptualized as binary choices". Instead, each junction is treated like a feature.
So, I can see how that can complicate comparisons. Nevertheless, limma-voom and DESeq2 are used as part of this package. If I look at the ASE-methods.R code and the supplemental methods, I think the main difference is the "Treatment:Isoform" interaction variable.
In other words, if you had 2 groups with 3 replicates and 2 junctions as part of splicing event, am I understanding the that the difference between SpliceWiz and what I would have otherwise expected is that you now have 12 count measurements (2 junctions) instead of 6 count measurements (1 junction)?
So, if I am understanding everything, I am not sure if you have comments with respect to why a strategy more like JunctionSeq (or what I might write with custom code for individual junctions) is not preferable and not provided as an option within SpliceWiz?
2) I thought Figure S4 was interesting and useful.
However, the retained intron counts are quite different, as also mentioned in the text: "Of the 15,188 extra retained introns annotated by SpliceWiz, 15,144 (99.7%) of these would have been excluded due to this extra criterion."
Identifying additional differential intron retention events might be helpful, but I also wonder if perhaps this also corresponds to additional false positives. For example, I am also not sure how often there might be pre-mRNA and/or pseudogenes where no introns are spliced (not a single retained intron event). For example, if there are confounding factors not sufficiently modeled in simulated data, then I would expect estimates of accuracy could be considerably different than in actual data.
3) As a minor point, there is a typo in the description for Figure 5: "Heirarachical" instead of "Hierarchical".
Thanks Again,<br /> Charles
On 2017-08-03 18:17:41, user JBrophy wrote:
Great work! Check out Caliando et al, 2014 (DOI: 10.1038/ncomms7989). They created a kill switch that is stable for 90 days (~1,700 generations).
On 2021-05-15 10:20:44, user Alvaro M. Guimerá wrote:
It turned out that the random network generation algorithm was systematically overestimating the instances of 'additive' responses in the presence of constitutive signals. The reason was because inhibition was being modelled as an increase in the rate of the reaction that reversed the activation. Eg. Bactive + NegReg --> Binactive + NegReg. This resulted in a large pool of inactive enzyme that provided a lot of substrate for the mass action reaction to leap forwards in the presence of a stimulus. When inhibition was modelled differently, for example as the molecule being sequestered and then regenerated: Bactive + NegReg --> Binhib + NegReg, and then Binhib --> Binactive, the instances of additive responses greatly diminuished to become a small minority of cases compared to a blunted response.
Overall, it is a conceptually interesting idea that the necessity of biological systems to self-regulate might create an attractor state of lower sensitivity to which constitutive signals push the system into.
What might ageing look like at the molecular level? According to the molecular habituation concept proposed, it would be something like this:
https://github.com/amguimer...
May any of this be of some use to someone, somewhere. Good luck!<br /> Alvaro Martinez Guimera
On 2023-03-22 10:19:38, user Sam Roberts wrote:
In the 50:50 BaTP:UTP competitive transcription experiment you can't tell from figure 2B whether U or B are being incorporated preferentially as stated in the text. You could potentially however tell this from the HPLC data in 2c iii). To do this you need to calculate the relative molar extinction coefficients on the HPLC using a concentration gradient of standard samples and measuring their relative absorbance. Then normalise your HPLC integrals from 2c iii) against those coefficients
On 2021-09-16 22:05:04, user Dyche Mullins wrote:
Vimentin Intermediate Filaments and Filamentous Actin Form Unexpected Interpenetrating Networks That Redefine the Cell Cortex
https://doi.org/10.1101/202...
Members of the Mullins laboratory read this paper together and discussed it during a recent journal club meeting. Following the meeting, we drafted the following set of comment with the aim of helping the authors revise the work for final publication..
Briefly, this manuscript describes in vitro and live-cell experiments aimed at understanding the connection between intermediate filaments and the actin cytoskeleton. As the authors note, this is an understudied topic, with many fundamental unanswered questions. The experiments described in the paper employ a variety of approaches, including rheometry, fluorescence microscopy, and cryo-electron microscopy. The major results claimed by the authors include evidence for extensive interactions between vimentin IF and actin cytoskeletal systems in the cell periphery and effects of IF networks on actin monomer diffusion.
Specific comments/concerns:
In the Introduction the authors state that it is “...generally thought that F-actin and VIFs form two co-existing but separate networks.” This is not an entirely fair description of the field. There is a significant body of work on interactions between intermediate filaments and elements of the actin cytoskeleton [e.g. myosin II/IF interactions described in Svitkina et al. (1996). J Cell Biol 135(4):991-1007], and how the actin cytoskeleton can regulate IF network architecture [e.g. this review by Chang and Goldman (2004). Nat Rev Mol Cell Biol. 5(8):601-13; and numerous research papers, including Schoumacher et al. (2010) J Cell Biol. 189(3):541-56 and Serres et al. (2020) Dev Cell. 52(2):210-222]. A more inclusive discussion of previous work on the interaction of the two cytoskeletal components would improve the presentation.
The title mentions the cell cortex, but there is almost no mention of the cortex per se in the Results or Discussion.
The figure captions should contain more experimental details. For example, captions should contain information on cell types, microscopy methods, and molecular probes (e.g. is the actin in Figure 1 phalloidin stained?). They should also contain other relevant details, such as buffer conditions, temperatures, and how many times an experiment was repeated.
The authors should consider combining Figure 1 and 2. It is a little unclear what is being highlighted by the arrows in Figure 1, as the two networks appear to be quite separate, even in this region of the cell. Figure 2 makes a better case for overlapping network elements.
In Figure 2, it is unclear whether the two networks are actually interpenetrating or simply present in different focal planes. Because the raw data includes z-stacks, it would be useful to show an XZ or YZ or 3d projection of the data.
Figure 2 should contain some (even rough) quantification of the percentage of observed cells that displayed such unambiguously interpenetrating networks.
The electron microscopy in Figure 3 is beautiful and quite convincing. The minimum distance argument, however, does not rule out the possibility that the actin and intermediate filaments are linked by plectins. The crosslinks could be present in areas where the filaments are further apart. Also, to convince the reader that they can distinguish between filaments based on width, the authors should provide a histogram of all the observed filament widths. Does the histogram show a clear bimodal distribution?
For Figure 4, please cite some previous studies using this indenter method and/or provide further details on the method: does it apply uniaxial or isometric stretch? Some raw images before, during, and after stretching would be more useful than the recovery plot shown in B.
As noted above the caption (or description) of Figures 4&5 should contain the number of biological replicates. P values calculated with n=number of cells (rather than the number of replicates) are not appropriate. We recommend simply removing the P value calculations.
Unfortunately, the data presented in Figure 5 are not convincing:<br /> a. Firstly, P values calculated on the number of cells instead of the number of biological replicates are artificially small and should not be reported. The trend is intriguing, but does it hold up over multiple rounds of experiment?<br /> b. Secondly, the FRAP bleaches both monomers and filaments. To look at only monomers, a method like FCS would be better.<br /> c. Instead of the curves in B, raw images would be more informative.<br /> d. When vimentin is knocked out, does the rest of the cytoskeleton change to compensate? Currently the explanation for these results is that there could be direct protein-protein interactions between Vimentin and G-actin which affect G-actin diffusion. The author favored this over the model where G-actin diffusion is limited by physical/steric obstruction of Vimentin networks. However the current data is unable to fully delineate these two models as the Y117L mutant forms a sort of intermediate filament (“intermediate” in length relative to the long filament), which intuitively could physically obstruct G-actin diffusion in an intermediate fashion (which is observed by this experiment). Perhaps using another mutant form of Vimentin that is simply unable to form any filaments but is present in cellular protrusions could help clarify the mechanism of G-actin diffusion limiting.
It is unclear what precisely the reader is to take away from Figure 6. The concentration of actin filaments is so high that one cannot make out individual filaments or their interactions with vimentin. It does not seem very surprising that two proteins can polymerize in the presence of one another. Is the point mainly that these two proteins can mix? Are there examples of the literature of other filament-forming protein phase separating from actin, or other conditions where this mixing doesn’t occur? A negative control would make this data more convincing.
On 2021-06-23 11:58:40, user Tom Jacobs wrote:
Interesting. I like the work. Just as you write, folks generally do not include the Cas- or gRNA/crRNA-only controls anymore. This was done back when CRISPR took off but it has largely been dropped.
I wonder about the sRNA sequencing. Did you observe the intact DR + spacer in the reads, or only the processed forms? It looks like your library prep protocol would have excluded larger fragments. You mention the reads are 21-24 nt in length, but which part of the DR+spacer does that contain? Is it a single species? Or spread out across the 28-nt spacer?
sRNAs should be produced along the entire length of your TRV vectors. Maybe what you are observing with the viral work is just the production of a functional siRNA from the viral RNA (standard VIGS) and not from a crRNA? Unless I missed it, all the results show that the presence of the crRNA vector is sufficient, but is there evidence that expression of the crRNA is required? I'm curious what would happen if you removed the U6 promoter and/or the DR. Or change the initiating nucleotide. That could establish that GIGS requires crRNA expression and not just presence.
On 2017-06-15 11:11:46, user Jacob H Hanna wrote:
The last three papers from Smith group describing human transgene free Reset cells, including this one, have failed to describe ability to generate teratomas. Mouse naive pluripotent cells have the intrinsic "self organizing capacity" to enter a formative state after in vivo SC injection and make teratomas within 4-8 weeks. I find this stunning and wonder whether the human "reset" cells being induced do not qualify to be annotated as pluripotent at all. <br /> I hope the authors can clearly and directly address this in their finalized published paper.
On 2020-06-27 17:28:58, user Daniel wrote:
wonder if conditions such as visual snow syndrome (VSS) and ringing in ears (Tinnitus) could be treated by Neuralink. both conditions seem to be linked to neurological disorders which tend to coincide together and is hypothesised to be a sensory issue even though the disorders happen in different parts of the brain, visual and auditory but are somehow linked. Does anyone know if there is any possibility or potential ways to go about treating such complex conditions with Neuralink?
On 2022-05-18 22:40:11, user Peter Hickey wrote:
Would you please make the GitHub repository publicly available.
On 2017-02-18 21:18:50, user AdamMarblestone wrote:
-"Autoassociative dynamics in the generation of sequences of hippocampal place cells" http://science.sciencemag.o...
-"Spatial Sequence Coding Differs during Slow and Fast Gamma Rhythms in the Hippocampus" http://www.cell.com/neuron/...
On 2017-11-20 20:34:25, user AdamMarblestone wrote:
-"Thalamic projections sustain prefrontal activity during working memory maintenance"<br /> https://www.nature.com/arti...
On 2021-05-04 05:32:49, user Lavinia wrote:
Could you also expand on why you don’t think these “granules” are RNaseL dependent, as they still seem to form independent of PKR. Are these granules you observe as a response to dsRNA CHX sensitive? Emetine sensitive? What about ISRIB? (Key characteristics that differentiate canonical SGs from RLBs)
On 2017-03-30 14:10:20, user Seth Bordenstein wrote:
Hey David,
I've got a comment on the pp below. We've taken careful efforts in several papers to not muddle phylosymbiosis with coevolution, as the former is a measurable pattern and the latter is a process, namely one that is quite restrictive and may/may not drive phylosymbiosis. The two terms are not meant to be associated by a loose definition as the pp below intimates.
"Somewhat looser still, and approached more from a phenomenological standpoint, work on animal microbiomes have shown that microbial communities can co-diverge with their hosts in a process termed phylosymbiosis[47]. A challenge when discussing both of these later situations is that, although both *could* describe a coevolutionary process, other evolutionary dynamics could give rise to similar population patterns."
Here's what we write in PLOS Biology 2016
"As this outcome is not likely due to coevolution, cospeciation, or <br /> cocladogenesis of the entire microbial community from a last common ancestor, "phylosymbiosis" was proposed as a new term that does not necessarily presume that members of the microbial community are constant, stable, or vertically transmitted from generation to generation [1,12]."
I suggest the following change that will make it more accurate:
"Work on animal microbiomes demonstrates that microbial community relationships can parallel the phylogeny of their hosts in a process termed phylosymbiosis[ 47]. Phylosymbiosis is different from strict or flexible definitions of coevolution because it does not assume congruent splitting from an ancestral species, and host-microbiota assembly mechanisms could vary in space and time and be newly assembled each generation.
On 2020-07-11 08:09:59, user acidic_compartments wrote:
Nice story. Its good that you have cited Finnigan et al., 2012 for identifying a sorting signal. But, look into Banerjee and Kane, 2017 MBoC, for how Stv1 is retained at the TGN. It is guided a lipid, Phosphoinositol-4-phosphate (PI4P) and the W83KY sequence in Stv1 is bound by PI4P and retained at Golgi. This is probably distinct from the retention mechanism thought by Bryant and Stevens 1997 which you have cited.
On 2022-01-29 22:46:43, user Werner Hemmert wrote:
Dear Authors,<br /> for magnetic stimulation the current through the coil is relevant, not the voltage. A description of the coil is completely missing (number of turns?) Also very important would be the generated B field and its gradient. Or an estimation of the induced E-field (and its gradient), at least an order of magnitude...
On 2024-01-11 20:52:03, user Aaron Puri wrote:
This has been published at mBio: https://journals.asm.org/do...
On 2016-07-12 00:22:02, user Davidski wrote:
There's a bit of an issue with your TreeMix analysis.
You included northern Russians from Kargopol in your Eastern European set. These Russians come from a former Uralic speaking region, so they have relatively high Siberian admixture. At the same time, your TreeMix run doesn't include Siberians.
As a result, the East European set gets an admixture edge from the Yoruba from Nigeria, which doesn't reflect reality very well.
The Kargopol Russians aren't relevant to Ashkenazi population history, so they can be dropped from the analysis. This should clean up the TreeMix run a bit.
On 2018-01-31 06:16:31, user Wouter De Coster wrote:
Dear authors,
This looks like an interesting study, although completely out of my field. I am a bit surprised by a sentence from the abstract: "two semi-professional cliff divers (both male, mean age 19.3 years)". I don't think the mean age is a good descriptor if you only have N=2. For all we know, one of your bungee jumpers could be 0.5y old and the other 38.1.<br /> With N=2 I don't think there is a good summary statistic. My background knowledge of the subject is too limited to judge, but this struck me as statistically odd.
Cheers,<br /> Wouter
On 2021-01-28 14:32:33, user chaohw3619 TMU wrote:
This paper has been published in Nature Communications.<br /> https://www.nature.com/arti...
On 2018-02-22 14:45:15, user Jon Pierce wrote:
To view our response to this critique by Keays et al, please check out this link below. <br /> Sincerely, Jon Pierce (jonps@austin.utexas.edu) and Andres Vidal-Gadea (avidal@ilstu.edu)
On 2025-04-02 13:02:42, user Gaurav Verma wrote:
Very comprehensive study and highlights the significance of mitochondrial health in the context of TBI and its potential role in the development of neurodegenerative diseases. However, the OCR values in pmol/min is very low and doesn't corroborates with the acceptable range from agilent which is from 20-160 pmol/min (Source: Characterizing Your Cells Using OCR Values to Determine Optimal Seeding Density). Would like to hear the thoughts. Good luck.
On 2022-10-13 12:53:12, user Ryan Kessens wrote:
It's amazing how intolerant the Pikm-2 allele is of changes to the sensor Pik allele. I'm particularly surprised that the RGA5 HMA domain in Pikm-1 was not tolerated by Pikm-2 considering the fact that nanobody domains can be incorporated into Pikm-1 and coexpressed with Pikm-2 with some success. Do these seemingly contradictory results surprise you? Do you think there is something special about the nanobody structure that makes it a good replacement for the HMA domain in Pikm-1? Do you think coexpression of Pikp-2 with Pikm-1 pikobodies would result in less autoactivity?
On 2020-03-21 18:45:53, user S E wrote:
Can I asked you how you got the gene orthology of the DEG could you explain it in details
On 2018-12-19 09:25:59, user sjones54 wrote:
Dietary studies that move beyond correlative factors and address, in an experimental fashion, transgenerational effects are of critical importance and I commend the authors for their undertaking. However, I have some reservations regarding this study. One of which is that, to my knowledge, 'lifetime reproductive fitness' cannot be accurately determined in Drosophila melanogaster via the methods described. Given the highly stochastic nature of a 'fly lifetime', alternative methods are appropriate to avoid generation of what amounts to random noise. An examination of the authors’ data (specifically the bimodal distribution) appears to confer with this point. Perhaps the removal of the low points (0-10 offspring), indicative of vial collapse, could help, post hoc, to obtain a more accurate assessment. Moreover, I am uncertain of any work in D. melanogaster that demonstrates a clear link between body weight and fitness. As it stands I remain skeptical this paper represents a concrete contribution to the on going dietary dialogue.
On a related note it is of concern that the phrase “To make sure that female reproduction was not limited by male quality, a new male was transferred into each vial every second week, or immediately if escaped during handling or found dead.” was borrowed, albeit modified with respect to the timing, from work by Pekkala et al 2011 without reference to this work. As the protocol also appears to have been taken from this work (although used there for Drosophila littoralis), convention dictates referencing would be appropriate.
On 2025-07-10 11:57:39, user Benoit Lefebvre wrote:
Now published in Nature Communications doi: 10.1038/s41467-025-60717-1
On 2020-02-13 02:43:05, user naysayer wrote:
Dear Dr. Abdel Hamad, <br /> Thank you for your comments. And more importantly, I wanted to personally thank you again for your prompt and detailed previous explanations of your findings that you send us by email that you have reiterated in your comments here. I think it is important that these discussions will now be public. We had submitted a paper to Cell for consideration as a direct commentary to your article, but they did not think it was worth publishing, and considered our alternative interpretation of the nature of DE cells as ‘too speculative’. We were about to let it go, but we have been persistently queried by people working on single-cell sorting data that have run into similar issues like the ones in our original eLife paper on cell-cell complexes (PMID 31237234), namely the challenge of distinguishing complexes from singlets in standard flow cytometry and single-cell RNA sequencing. The current revised manuscript draft in biorxiv has been submitted to Cytometry Part A, where we can hopefully have a technical and public discussion on the challenges encountered and compare datasets and opinions. As you pointed out, we did not use diabetic samples. And as we confirmed here, we did find 'real' dual-expressors in our imaging data. Our manuscript was not written (and doesn't have the data) to put the conclusions from your Cell paper into doubt. But I hope that you will agree that it is worthwhile pointing out that it is much more challenging to avoid cell-cell complexes in flow cytometry than the use of 'singlet gates' seem to imply.
Sincerely,
Bjoern Peters
On 2018-11-16 21:46:09, user Alex Naka wrote:
There's one unfortunate omission in this preprint - we forgot to acknowledge Dr. Connie Cepko, who very graciously shared CreDOG AAVs to help us pilot those experiments.
On 2020-05-12 09:59:00, user Teun Klein Gunnewiek wrote:
For the published article (m.3243A > G-Induced Mitochondrial Dysfunction Impairs Human Neuronal Development and Reduces Neuronal Network Activity and Synchronicity - 2020; Cell Reports..), see:
https://www.sciencedirect.c...
or
On 2016-09-01 12:57:07, user Steven Ludtke wrote:
First, let me say that this development is highly laudable and will, indeed be of great value to the community. However, there are major technical issues with the specific speedup numbers cited here. 4 of the latest generation GPU cards, which are so new they are still difficult to get are being compared to 5 year old Intel CPUs, which are considered "end of life", and are massively slower than new generation chips with more cores and new SIMD instructions. Additionally, it seems that a number of optimizations were made to the code itself, such as use of single-precision and deeper changes which could also impact the CPU, but have (apparently) not been ported there.
Don't get me wrong, I'm not disputing that the GPU has value, and that it is more cost effective than the CPU in the present study, I am simply saying that the very large factors in absolute speedup and cost-effectiveness cited in this manuscript are massively biased towards the GPU.
On 2020-03-15 16:39:44, user joncloke wrote:
My knowledge of epidemiology is very slight indeed, however I read a fair bit about the 1918 flu epidemic in which the second wave was deadlier than the first because the virus had evolved.
I know Covid-19 is very different from flu, but I wanted to ask (since we already know it is evolving) if it isn't possible for the same thing to happen with Coronavirus?
Which might mean trying to infect monkeys with the same original strain of virus would be pretty meaningless?
On 2023-03-19 11:28:06, user Ben Long wrote:
The statement "Successful expression of the functional ?-carboxysome in<br /> tobacco chloroplasts led to an increase in biomass production" is incorrect. This could never happen unless there were also bicarbonate transport systems in the chloroplast membrane. We make this clear in the cited paper (Long et al., 2018) and others on the subject.
On 2022-05-20 15:59:28, user Tosha Kelly wrote:
This manuscript is now published in PLOS ONE! Take a peek here: https://doi.org/10.1371/jou...
On 2019-04-19 21:10:00, user Jared Ellefson wrote:
Might also want to consider citing this paper! :) https://www.ncbi.nlm.nih.go...
On 2019-12-07 00:55:07, user Charles Warden wrote:
Thank you for posting this paper. The supplemental materials were especially nice and detailed!
For others wishing to cite the low-coverage sequencing method, is it fair to say that you defined "low-coverage sequencing" as ~5x (with 95% as "high accuracy")?
I am asking because I think some people might think 0.5x is "low" coverage.
In that case, I thought my Nebula lcWGS was OK for some things, but not others (like making decisions based upon specific variants).
At 0.5x, these would be my concerns:
http://cdwscience.blogspot....
and (again, at 0.5x) these would be what I thought was OK (ancestry and relatedness):
http://cdwscience.blogspot....
I was also concerned there would be issues in species whose variation was less well-characterized than human (such as a cat, or I assume the coral in this study). Do you think that would be fair (for 0.5x low-coverage Whole Genome Sequencing)?
On 2020-08-06 06:33:34, user Yu-Chen Ling wrote:
This article is published in the journal Soil Biology and Biochemistry entitled "Bacterial predation limits microbial sulfate-reduction in a coastal acid sulfate soil (CASS) ecosystem" (https://doi.org/10.1016/j.s... "https://doi.org/10.1016/j.soilbio.2020.107930)"). You are welcome to read or download it through this link before September 22, 2020: https://authors.elsevier.co...
Another two publications in the same project are:<br /> Ling, Y.-C., Berwick, L., Tulipani, S., Grice, K., Bush, R., Moreau, J.W., 2015. Distribution of iron- and sulfate-reducing bacteria across a coastal acid sulfate soil (CASS) environment: implications for passive bioremediation by tidal inundation. Frontiers in Microbiology 6, 624. https://doi.org/10.3389/fmi....<br /> Ling, Y.-C., Gan, H.M., Bush, M., Bush, R., Moreau, J.W., 2018. Time-resolved microbial guild responses to tidal cycling in a coastal acid-sulfate system. Environmental Chemistry 15, 2–17. https://doi.org/10.1071/EN1...
On 2021-12-02 20:18:22, user Stefano D. Vianello wrote:
Some of the statements and assumptions in this paper are very odd to read as a non-US reader.
"the fact that the U.S. has the best science education system in the world" is a very strong statement that surely would need references, or at least contextualisation vs the criteria used for this assessment. How did the authors reach the conclusion that the US science education system is better than that of every single other country in the planet? Have these analyses been performed in other countries? Which studies are the authors sourcing from?
"By almost any measure, the U.S. remains the world leader in basic and applied research. Individuals affiliated with U.S. institutions or companies have received 47% of all Nobel Prizes in physics, chemistry, and physiology or medicine and 51% of all patents awarded by the U.S. Patent and Trademark Office. U.S. scholars were the largest share of top cited authors published in the 2020 H5 citation index of the top five life science journals".
In the same way that the majority of Nobel Prizes have been won by men and this does not mean that men are leaders in the life sciences, the majority of US Nobel winners does not necessary imply that the US is a leader in the life science. Rather, it likely tells more about structural biases in the evaluation of science and in scientific participation and output in the life sciences, and bias in the Nobel attribution process. Similarly, papers in the life science are already heavily skewed towards US representation. Even with equal citation numbers, the majority of papers within any citation tier would thus also be from the US. The definition of what "top life science journals" are is also clearly built on anglophony and US-centric axiologies. The authors seem to see meritocracy in academic aspects that are clearly not acritically so, and that are in fact rife with Matthew effect and US-favourable biases. In the absence of more comprehensive considerations on these topics, these passages of the paper read very odd. Because the main conclusions and recommendations in this paper do not in fact even seem to need such forceful prescriptions of US supremacy, I feel these passages could even be removed.<br /> .
On 2024-11-05 13:49:01, user Damien Garcia wrote:
The article has been published could you link it to the publication:<br /> https://doi.org/10.1093/plcell/koae175
On 2020-07-12 18:04:14, user Paul Gordon wrote:
Hi, thanks for posting this work. The text mentions supplemental material, but I do not see any supplemental material attached to this manuscript on bioxriv. Will you be posting it? Thank you!
On 2020-06-18 12:03:44, user Amin Zi wrote:
Quantitatively minor myelin protein is not functionaly minor! Ermin KO mice, when the quality of the myelin sheath is really matter; it is time to revisit minor CNS myelin sheath’s proteins. ERMN could be a novel MS susceptibility gene, inside-out or outside-in that's the question !
On 2020-06-05 15:18:06, user David Melville wrote:
Final version here:<br /> doi: 10.1074/jbc.RA120.012964<br /> "Small sequence variations between two mammalian paralogs of the small GTPase SAR1 underlie functional differences in coat protein complex II assembly"
On 2017-07-20 08:48:39, user Colm Ryan wrote:
this is now published in Cell Systems (http://www.cell.com/cell-sy... "http://www.cell.com/cell-systems/fulltext/S2405-4712(17)30230-2)").
The major changes from the preprint are <br /> 1) we now include CRISPR screens <br /> 2) we cover more driver genes <br /> 3) we provide functionality to identify genetic dependencies observed in multiple distinct screens
We have also provided a short (~20 minute) tutorial in the supplement to introduce the site's main functionality.
On 2024-11-12 22:52:53, user Francesco Iorio wrote:
now published in Nature Communications<br /> https://www.nature.com/articles/s41467-024-53163-y
On 2018-04-05 11:57:09, user Andrew Millar wrote:
Thank you for this exciting work on lipid pathways in O. tauri, which I am comparing with our results in yesterday's preprint https://doi.org/10.1101/287862.
Could you specify which version of the predicted O. tauri proteome you used in the proteomics analysis, please? For example, the protein that you highlight as XP_003080099.1 seems to be an inactive RefSeq entry (corresponding to Q016B4 that was deleted from UniProt in November 2014). The corresponding gene model is shorter in the current genome annotation for ostta06g04530, e.g. http://bioinformatics.psb.u...
On 2018-12-11 17:59:41, user Emily Neubert wrote:
This paper focuses on proteins involved in initiating COPII-coated vesicle transport of collagen from the ER. Previously, the authors showed cTAGE5 recruits Sec12 to ER exit sites (ERES) for efficient Sar1 activation, and cTAGE5 interacts with Sec23. However, the cTAGE5 and Sec23 interaction had not been fully elucidated. This paper investigates the significance of this interaction in general COPII vesicle formation, and in more specifically, collagen export. Collagen export is a primary focus of this paper because given its large size, it is only recently becoming evident how COPII vesicles are able to accommodate such a large protein. The authors first proved cTAGE5 enhances Sec23’s GAP activity toward Sar1 by measuring Pi release when Sar1 and Sec23/24 are in the presence of cTAGE5. It is important to note that cTAGE5 without Sec23/Sec24 did not result in Pi release, suggesting cTAGE5 doesn’t act as a GAP alone, but enhances the GAP activity of Sec23. The authors then concluded the cTAGE5 and Sec23 interaction was necessary for collagen secretion by measuring ER collagen levels in cTAGE5 mutants with reduced Sec23-binding affinity. Overall, this paper was able to reveal cTAGE5 enhances the GAP activity of Sec23 towards Sar1, and this interaction is necessary for collagen export from the ER.
This paper logically organizes their experiments and provides straightforward reasoning, so the reader can easily follow the thought process of the authors. The conclusions made from experimental results are explained in terms that tie it back to the main scope of the paper: investigating cTAGE5 as a Sar1 regulator for collagen export. In addition, by using many different in vitro techniques, the authors were able to further support each conclusion with proficient results. However, I have some critiques regarding a conclusion made of their 4PA mutant, immunofluorescent experimental designs, and the organization of some figures.
The authors were able to identify two cTAGE5 mutants that lack Sec23 binding activity (RG and 4PA), both of which have mutations in the PRD domain. When analyzing the GAP activity of Sec23, it was only enhanced with the RG mutant. This suggests the positions mutated in the 4PA mutant are responsible for enhancing Sec23 GAP activity. However, the authors do not state this conclusion and focus instead how the GAP-enhancing activity and Sec23 binding can be separable within the PRD region. Although this is an accurate conclusion from these results, I believe this conclusion doesn’t add to the specific aims of the paper. The authors may not have focused on the 4PA mutated regions being responsible for enhancing GAP activity of Sec23 because there are four regions mutated and identifying which is responsible is outside the scope of the paper, although it would be an interesting future direction. The authors could perform similar binding assays to those in this paper to determine if the aforementioned region for Sec23 enhanced GAP activity is necessary and/or sufficient.
The immunofluorescence experiment investigating reduced cTAGE5 Sec23 binding and collagen VII secretion was not well designed nor efficiently presented in Figure 4. However, I did find how the authors tested the effect of RG and 4PA mutants on collagen secretion very easy to understand and well designed (the mutants were transfected into cells with a complete cTAGE5 knockdown to see if collagen secretion was rescued). The measurement of accumulated collagen VII was through immunofluorescence in arbitrary units, but the authors do not state if they normalized the number of cells transfected and those that were not per each experimental group before measuring the intensity of fluorescent signal. This would drastically affect the results made in Figure 4 as if more cells were transfected, for example, then there is a higher probability of increased fluorescent signal and thus an increase in collagen accumulation in the ER. Also, the authors did not address how they were able to differentiate the immunofluorescence from FLAG-tagged collagen and that from collagen’s autofluorescence (Croce & Bottiroli, 2014). Furthermore, statistical tests done on the data from Figure 4 were only comparing the non-transfected and transfected cells within each experimental group (i.e. WT, RG, 4PA), and were not comparing the collagen immunofluorescence of transfected cells to the control knockdown to see if the phenotype was rescued for each mutant. Since the overall conclusion made from this experiment was neither mutant was able to rescue the block of collagen by the cTAGE5 knockdown, the statistical tests should have been comparing if the levels of accumulated collagen of transfected cells were significantly different than the knockdown control. Finally, it is unclear if known ER and cytoplasm immunofluorescent antibodies were used to confirm the accumulated collagen was actually in the ER and collagen secreted was actually in the cytoplasm when measured.
Lastly, some of the figures in this paper could have been more appropriately presented to correlate to the conclusions made in the results section of the paper. Figure 1 should have had parts C and D combined in order to directly compare how the enhancement of Sec23 GAP activity between cTAGE5 and Sec13/31 differs. In the text, the authors conclude cTAGE5 “more efficiently” does this than Sec13/31, although since Figure 1C and D are separated, it is difficult to see how the difference between the two lines is significant to support this claim. If their definition of “more efficiently” isn’t statistically significant, then combining the two graphs is not required, although it would still be helpful to see how the two experimental groups compare side-by-side. Next, Figure 2B is a wonderful way to see protein interactions as yeast hybrid assays are very black and white, however a description of the assay and/or the reporter gene being used would have been helpful for someone unfamiliar with yeast two-hybrid assays. Looking at Figure S1C and D, there are many distinct bands seen on the gels attempting to resolve the proteins used in the GTPase hydrolysis assay that are not the target proteins being pulled down. The samples, therefore, are not very pure and yet were used for subsequent experiments. It may be the case the proteins were gel purified prior to use in the GTPase hydrolysis assay, but then the authors should have mentioned this in the methods.
On 2022-06-24 19:05:28, user Larissa Dougherty wrote:
We want to thank the participating reviewers in ASAPbio’s Crowd Review for taking the time to provide thoughtful feedback for our preprint. We have responded to some comments below and in the next version, will revise the manuscript accordingly.
“The majority of the conclusions about MAPK signaling are drawn based on the treatment with the BCI compound whose selectivity is unclear. It is possible that BCI could directly inhibit other phosphatases involved in ciliogenesis such as CDC14, PPP1R35. A reference pointing to the selectivity of BCI towards MKPs or alternatively rescue experiments with the inhibitor U0126 could address this issue.”
We have cited Molina et al. 2009 who showed specificity for BCI hydrochloride in zebrafish. BCI targets primarily DUSP6, but also exhibited some activity towards DUSP1. In this study, the authors had also used zebrafish embryos to check expression of 2 other FGF inhibitors, spry 4 and XFD, in the presence of BCI but found that their effects were not reversed. In addition, they checked the ability for BCI to suppress activity of other phosphatases including Cdc25B, PTP1B, or DUSP3/VHR and found that BCI could not suppress these phosphatases. Though this is not to say that BCI is not inhibiting these proteins mentioned, but BCI inhibition has previously been found to be more specific to MAPK phosphatases.
In addition, we have previously confirmed that U0126 has a slight lengthening effect on Chlamydomonas which further implicates this pathway in cilium length tuning (Avasthi et al. 2012).
“It is shown that BCI leads to transient activation of the ERK activity which peaks within 30 minutes and starts fading away after around 60 minutes. However, most of the effects are studied at 2 hours, when the changes in the cilia length are most apparent. But the ERK activity at this time point is unclear. Simultaneous measurements of ERK activity and cilia length would strengthen the correlation between the two processes.”
While ERK activity spikes early after BCI treatment, what we are assaying here are downstream effects following ERK activation. Our experiments primarily address these eventual outcomes rather than the immediate molecules participating in signaling. Here we show that ciliary shortening is a downstream effect, though we also show that ciliogenesis is immediately inhibited as well (30 minute and 60 minute timepoints included) to show that these processes are stopped in their tracks, but it takes 2 hours to see the measurable large-scale changes to the cell. We agree that MAPK is unlikely to still be active at the 2 hour time point given that ERK activation is decreased within 60 minutes.
“Specific comments<br /> Introduction:”
Thanks for the suggestions on wording. We will make minor edits to the wording per the helpful suggestions for clarity.<br /> “Results: <br /> Figure 1 <br /> Figure 1D – It is unclear in the figure whether the P-value is calculated between concentrations 0 µM and 45 µM, or between 0 and all three other concentrations. A similar comment applies to Figure 1H and Figure 1J.”
We will revise the figures to indicate individual P-values from multiple comparisons. In Figure 1C, both 15 µM and 30 µM are significantly different from 0 µM. In Figure 1H and J, the differences between the control and 1.56 µM as well as the control and 3.13 µM are significant for ciliary length. For percent ciliation, they are not significantly different.
“Figure 1F – Was any axonemal marker other than acetylated tubulin used to control for tubulin acetylation defects?”
We have also measured Arl13B as a marker with and without acetylated tubulin staining and found consistent results regarding ciliary shortening in hTERT-RPE1 cells. In addition, we have measured acetylated tubulin in Chlamydomonas cells and have found consistent results with ciliary length changes compared to other markers such as non-acetylated B-tubulin and FAP138-GFP.
“Figure 2<br /> Figure 2C – It is unclear if there is a difference in the fluorescence intensity distribution. A line profile along the cilia would indicate if there is any change in the spatial distribution of KAP.”
While there may be additional effects on intra-ciliary KAP-GFP distribution that impact ciliary phenotype, we expect the decreased ciliary KAP-GFP to largely explain the profound effect on ciliary growth.
“Figure SF 2C – Is it possible to elaborate more on what specific conclusion this data suggests.”
Figure SF 2C acts as a control for Figure 3H. After a single regeneration event, cilia cannot initially regrow in BCI, but ultimately, at this lower concentration of BCI used, cilia can slowly begin to regrow possibly after overcoming the acute ERK activation with BCI. Additionally, after a single regeneration, there is enough ciliary protein present to normally regenerate cilia to half length (Rosenbaum et al., 1969). In Figure 3H, we show that upon completely depleting the protein pool through 2 regenerations (the first in the protein synthesis inhibitor cyclohexamide), cilia cannot begin to regrow after several hours until it is washed out. What we see here is that with existing ciliary protein present, though this protein cannot participate in immediate ciliogenesis until the cell overcomes BCI, the cilia can ultimately regrow. Following complete ciliary protein depletion and washout of BCI, cilia cannot regrow for several hours, which indicates a defect in ciliary protein synthesis during the BCI treatment period.
“Figure 3 <br /> Figure 3B – Is there any reason why the BCI-induced regulation of MAPK signaling affects ciliary protein synthesis in particular? There seems to be no reduction in total protein synthesis.”
In Figure 3B, we are quantifying the amount of KAP-GFP in the cell body versus in cilia. Consistent with our data that there is reduced entry of KAP-GFP into the cilia, we see this occur when we quantify this protein. These data are a fractionation of the cell body and cilia protein rather than a readout of protein synthesis. BCI prevents entry of KAP-GFP into cilia. These data suggest that although the quantities of protein are similar in BCI vs. control cells, the distribution of KAP-GFP is increased in the cell body and decreased in the cilia. It is not that there is a ciliary protein synthesis defect that we are seeing in Figure 3B, but the localization of ciliary proteins are altered in BCI.
“Figure 4A – A clearer description of how BCI “partially” disrupts the transition zone would be beneficial. Cross-sectional imaging of the transition zone with higher concentration of BCI might make changes in the structure more apparent.”
By “partially” disrupts the transition zone, we are referring to BCI altering some protein composition without altering the complete transition zone structure. This suggests that BCI is not directly impacting or disrupting the entire transition zone, just parts of it. We see a change in NPHP4, but the lack of structural changes by EM suggests that the proteins giving rise to the EM-visible structures are relatively unperturbed.
We agree that it might be easier to see visible changes with higher concentrations of BCI. Interestingly though, we do not see a dose dependent change in NPHP4 fluorescence at the transition zone. The addition of BCI decreases the signal uniformly at all concentrations. It remains, however, a possibility that other transition zone proteins may be affected more drastically with BCI than NPHP4.
“Figure 5<br /> Figure 5A – 36 µM BFA affects cell morphology and may affect the viability of the cells, can some further clarification be added about this and the concentration used.”
In reference to impacting cell viability, for these experiments, we could not wash out 30 µM BCI paired with 36 µM BFA. Either this was too toxic or had very potent effects on the cell that prevented them from reassembling cilia. However, with the slightly lower concentration of 20 µM BCI paired with 36 µM BFA, we were able to wash out the drugs successfully and rescue ciliary regrowth. At this lower concentration, we noted that cilia shorten faster and more drastically than in either drug alone which is represented in the graph. We did not graph the higher concentration of 30 µM BCI paired with 36 µM BFA due to inability for cilia to regrow post washout. Given that the lower concentrations allowed us to draw conclusions about the membrane source, we plan to remove the sentence about toxicity at 30 µM BCI.
In reference to morphology, we cite Dentler 2013 who went into detail about how 36 µM BFA collapses the Golgi using EM. Dentler also shows that the Golgi is an important source of membrane for cilia which is ultimately why cilia shorten in BFA. In our study, we wanted to see if BCI impacted Golgi-derived membrane traffic. We looked at the Golgi with EM and did not see collapse despite the faster ciliary resorption seen with coupling 20 µM BCI and 36 µM BFA, though we did not look at EM with the paired drugs.
“Figure 6<br /> Figure 6C – The three categories mentioned in the text are not mentioned in the figure.”
We have included measurements for full microtubule cages only for clarity in the main data; however, in the supplement we have included distinctions in the measured data between full vs. partial cages to provide a more complete story where the full-cage-only measurement may not tell the whole story.
On 2019-02-25 21:50:26, user Jason Kwan wrote:
UPDATE: A revised version of this paper has been accepted for publication in Nucleic Acids Research. In response to peer review, that version includes validation with extra sequencing datasets derived from synthetic metagenomes. Stand by for DOI and other citation info when it becomes available.
On 2018-03-26 07:30:26, user Tal Shay wrote:
Note, this is a complementary analysis to our PNAS paper - http://www.pnas.org/content...<br /> Tal Shay
On 2015-05-31 18:39:48, user Devon Ryan wrote:
It's nice and all that error correction leads to fewer mismatches, but how does it affect mapping accuracy? The most common use of RNAseq is to get counts for differential expression. How is that affected? Adding some simulated datasets would make that sort of comparison feasible.
On 2024-07-13 07:51:37, user alexander_zlobin wrote:
Hi, I commented on the same issue before, but there is still one figure in the SI that retain the confusion between HID and HIE states of the catalytic His in serine triad proteases. This is figure S49, and it should be corrected.
On the unrelated topic, are you planning to provide your datasets later? I am particularly interested in all PDB entries you found and classified into GSA/TSA. As you of coarse are familiar, PDB searches are quite tiresome, and having this data already available would help tremendously.
Sincerely yours,<br /> Alexander Zlobin<br /> MeilerLab Leipzig, Germany
On 2021-04-23 22:57:23, user Aartee Potnis wrote:
Do we have any information on susceptibility to blood clots for the CureVac vaccine ?
On 2025-11-13 09:21:40, user Rik Henson wrote:
Nice work, but you might want to check out https://pubmed.ncbi.nlm.nih.gov/14741668/ and https://pubmed.ncbi.nlm.nih.gov/9798751/
On 2025-08-26 09:19:43, user Constant VINATIER wrote:
Feedbacks about your preprint : <br /> https://doi.org/10.1101/2025.07.21.665525
About registration: <br /> We could not find any information about the pre-registration of your study in the pre-print. Pre-registration involves documenting the hypotheses, methods, and/or analyses of a scientific study prior to its conduct (10.1073/pnas.1708274114; 10.1038/s41562-021-01269-4). If your study was pre-registered, we strongly encourage you to include the registration number in the pre-print, ideally in the abstract make this important information easy to retrieve, as this practice enhances transparency and reproducibility. If the study was not pre-registered, this should be acknowledged as a limitation. For future studies, we recommend pre-registering on an appropriate repository.<br /> About Protocol Sharing: <br /> you have used good research practice<br /> About the Statistical Analysis Plan Sharing: <br /> We did not find the Statistical Analysis Plan (SAP) for your study. If you have one, we encourage you to share it as supplementary material or deposit it in a repository such as the Open Science Framework ( https://osf.io ). You can then include a statement in the Methods section indicating that your protocol is openly available (e.g., 'The SAP for this study is available at (link)/ in the supplementary'). Sharing your SAP will help readers better understand your study and enable them to reproduce it if they wish to test it.<br /> About Deviations and/or changes<br /> We could not find any information about potential deviations or changes to the protocol in your pre-print. Since such deviations are common, if this applies to your study, we strongly encourage you to include a subsection titled Changes to the Initial Protocol in the Methods' section and discuss these changes as a potential limitation of your results. If any deviations occurred during your study, please specify them in this new subsection.<br /> About Data sharing / FAIR Data<br /> We found insufficient information about your data sharing approach. Data should be findable, i.e. data are to assigned a globally unique and persistent identifier (for instance there is a DOI assigned to the dataset, or data are registered or indexed in a searchable resource). Data should also be accessible, i.e. data are retrievable by their identifier and can be accessed following an open, free, and universally implementable protocol. As your data id not sensitive data, we encourage you to share it openly on a data sharing repository (Dryad, etc.) and include the Digital Object Identifier (DOI) in the Methods section. If you want more information about good practices of data sharing, visit https://www.go-fair.org/ <br /> About Code sharing<br /> We could not find any information about your (statistical) code. Sharing code is important for enhancing transparency and reproducibility, especially since it does not contain sensitive information. We encourage you to openly share it on a code sharing platform (Github, Codepen, CodShare, etc.) and include the Digital Object Identifier (DOI) in the Methods section. If you want more information about Code sharing https://fair-software.nl/ .
On 2021-07-30 10:11:56, user David Ron wrote:
This study takes on the interesting question of mouse Ern2's role in goblet cell development and function, mucous layer integrity and the intestinal host-microbe interface.<br /> The emphasis, understandably, is on the mechanistic basis of the fitness-benefit arising from the duplication of the ancestral gene that gave rise to the two ERN paralogues: the broadly expressed ERN1 and mucous producing secretory epithelium-selective ERN2 of vertebrates.<br /> The finding that epithelial-selective depletion of mouse Xbp1, phenocopies the consequences of germline deletion of Ern2 is taken as evidence that Ern2 exerts at least some of its effects in goblet cells, via XBP1 - an effector common to the products of both ERN genes (IRE1a and IRE1b). This conclusion is plausible, but the data presented in figure 1A and 3A also suggests that the consequences of XBP1 depletion in terms of Alcian Blue positive cell number are 2X more severe than those of Ern2 deletion (~0.17 cells per crypt in wildtype, versus ~0.12 in the Ern2? and ~0.06 in the XBP1?). This may arise from XBP1 having a broad role in tissue development, with secondary consequences on goblet cells, but may also arise from a contribution of IRE1a (the product of the Ern1 gene) to XBP1 splicing in goblet cells - in other words reflecting functional redundancy between the two ERN isoforms in mucous producing cells. The latter possibility seems at odds with the emphasis in the abstracts on non-redundancy of the two isoforms, which suggests important qualitative differences between the two, whereas the two genes may in fact be pullin.g in the same direction, in so far as their effector functions are concerned.
On 2020-07-23 18:52:23, user Abigail Solitro wrote:
First author Abigail R. Solitro here - I would simply like to correct a mistake in the reporting of the figure legends above. The legend currently linked with Figure 4 actually describes the data presented in Supplementary Figure 4. The legend for the data presented in Figure 4 should read: Figure 4. Tissue levels of acridine-based compounds. Athymic nude mice with subcutaneous A549 xenograft tumors were treated daily with 30 mg/kg HCQ, QN, or VATG-027 by oral gavage. After 28 d, whole blood samples were collected by retro-orbital bleeds and tumors, lungs, livers, and kidneys were removed by necropsy. All samples were taken 3 h after the last dose. Sample size was 9-11 animals per treatment group (HCQ = 10, QN = 11, VATG-027 = 8-9). (A) Whole blood, (B) tumor, (C) lung, (D) liver, and (E) kidney samples were analyzed for parent compound concentrations by LC/MS/MS against an internal standard.
On 2025-04-02 08:59:43, user Lee Berger wrote:
On 2021-07-05 14:11:13, user zaishuiyifang wrote:
Why does the R.Affinis ACE2 not bind to the RATG13, which was discovered from R.Affinis? Why does the RATG13 bind even better to human ACE2?
On 2020-04-30 08:22:53, user Stephanie Kath-Schorr wrote:
This preprint is lacking crucial citation of previous works (Kath-Schorr group)! Even the title is similar to our recent publication in Angewandte Chemie (C. Domnick, F. Eggert, C. Wuebben, L. Bornewasser, G. Hagelueken, O. Schiemann*, S. Kath-Schorr* "EPR distance measurements on long non-coding RNAs empowered by genetic alphabet expansion transcription" doi:10.1002/anie.201916447 !
On 2015-01-16 09:35:17, user Jacob H Hanna wrote:
we did not check this, but I assume since basically the route is similar to iPSCs, then likely yes. Has to be tested directly though!!
On 2016-06-15 21:03:12, user Devon Brewer wrote:
Thank you, Professor Read, for your comment and important questions.
The rapidity and extent to which the isotope range changes as one moves away from the community vary from site to site depending on the particular geochemical and geological features of the area. If a large area that encompasses the ranges of multiple distinct human communities has little variation in the isotopic signature, it would be difficult to distinguish individuals from different communities living in this area. Other areas may exhibit sharp variation in isotopic signatures on relatively small geographic scales, making individuals from separate communities relatively easy to identify.
In many archaeologic settings, it is not possible to know with confidence the distance between concurrent, neighboring communities, as an unknown proportion of such communities (sites) have not yet been discovered.
Kusaka and colleagues studied one pair of roughly concurrent, neighboring sites, Inariyama and Yoshigo. Although located roughly 15-20 kilometers apart, these two sites have almost non-overlapping local isotopic ranges (0.7091-.0.7100 for Inariyama, 0.7086-.07092 for Yoshigo). Kusaka and colleagues constructed these ranges by sampling flora in varying locations in a 10 kilometer radius of each site. The overlap in the two communities' isotope ratio ranges is between 0.7091 and 0.7092. Four of the 12 Inariyama locals (all men) fell in this overlap and thus could have grown up in Yoshigo. Five of the 25 Yoshigo locals (4 women, 1 man) fell in this overlap and thus could have grown up in Inariyama.
Kusaka and colleagues also studied another pair of neighboring (but not conccurent) sites, Ota and Tsukumo, that are approximately 35 kilometers apart. The local isotope ratios for these sites overlap considerably (0.70847–0.71071 for Ota and 0.70780–0.71014 for Tsukumo). Other reports I summarized include ranges of isotope ratios for more widely separated locations.
I appreciate your mentioning this limitation, which I will also highlight in a revision to this article. As I noted in the Discussion, to overcome many of the limitations, the ideal study design in future research would include analysis of strontium isotopes and mitochondrial DNA (and possibly Y chomosomes) for individuals from concurrent, neighboring communities.
On 2020-08-06 09:00:32, user NimwegenLab wrote:
Please see our response to this comment at: https://www.biorxiv.org/con...
On 2023-12-19 21:15:00, user Amanda Janesick wrote:
This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this article is published in Nature Communications, and is available online at https://doi.org/10.1038/s41...
On 2017-03-13 18:38:28, user Peter Ellis wrote:
Unless I'm missing something, all your pediatric sarcoma data comes from one source, and all your adult sarcoma data from another. This means there is perfect confounding between the biological effects you're looking for and non-biological batch effects (different sequencing technology, different operators, different read depths, etc).
How can you possibly tell whether the fact that they cluster apart is due to genuine biology or a meaningless batch effect?
On 2025-07-10 17:37:31, user Jink wrote:
I was wondering why Indian genomes lack X and Y chromosomes altogether for 50,000 years in figure 4c..!! Yet, Indians managed to top the table of population explosion..!! As this is published in "Cell" now, I guess the authors, editors and reviewers will have to give an explanation what happened at figure 4c, how the editor and reviewers missed the so obvious thing, and whether or not X and Y chromosome data affects other figures..!!
On 2021-12-09 21:49:17, user Raghu Parthasarathy wrote:
You might find it interesting that the first light sheet microscopes, over 100 years ago, used scattered light. See e.g. https://www.semanticscholar... , and some papers noted in my post: https://eighteenthelephant....
By the way: "novel" is mis-spelled.
On 2021-02-15 08:14:56, user Blaz Stres wrote:
The much needed link between Biobakery tools and GTDB - MAGs (metagenome assembled genomes (https://doi.org/10.1093/mol...) "https://doi.org/10.1093/molbev/msz237))") information and NCBI genome taxonomy is finally here. We were really looking for more streamlined and reproducible updates to HUMAnN3 database.
On 2017-03-04 00:59:01, user Davidski wrote:
Hello authors,
Your PCA is affected by some pretty awesome projection bias. So much so that your Baltic foragers cluster with modern Europeans, which contradicts your own formal statistics.
Try using a subset of the modern samples as references, and then project both the ancient samples and another subset of modern samples using lsqproject.
It doesn't matter if your second subset of modern samples is missing markers, because lsqproject will fix that.
Then plot only the ancient samples and the second (projected) subset of modern samples. I think you might be amazed by the accuracy of the new results.
Cheers
On 2020-04-07 02:13:51, user Diamond Lab Members wrote:
In the manuscript "FRET-based Tau seeding assay does not represent prion-like templated assembly of Tau fibers," Kaniyappan et al. use a combination of light scattering, electron microscopy, atomic force microscopy, and STEM to show that GFP fusion tags prevent tau RD from forming paired helical filaments (PHFs). They extrapolate these findings to conclude that cell-based tau aggregation FRET assays, such as those developed by the Diamond lab, do not measure "the transfer of a pathological conformation." Unfortunately, the authors do not provide experimental evidence to support their main claim and thus their conclusions are not sound.
The key limitation of this paper is that while the authors’ central argument concerns the performance of cell-based FRET assays, their conclusions are drawn entirely from in vitro assays with no cell-based data presented. It is assumed, for instance, that in vitro fibril formation from purified tau-GFP species is representative of the process of aggregate formation in live cells. Based on this assumption, the authors predict FRET-based models cannot function by detecting disease-relevant tau conformations; however, this prediction is not tested. In the absence of cellular data, no conclusions regarding cell-based assays can be established. Of note, our tau FRET cell lines are available on American Type Culture Collection (https://www.atcc.org/produc... "https://www.atcc.org/products/all/CRL-3275.aspx)").
Another critical drawback of the current study includes the use of short linker sequences in their fusion proteins. Kaniyappan et al. propose that GFP fusions inhibit Tau RD from forming PHFs because “a tightly packed cross-B-structure[…] is not compatible with the size of an attached GFP molecule.” In testing this hypothesis, the authors use a Tau-GFP fusion protein with a linker length of 13 residues (STVPRARDPPVA); in our cell-based FRET assay, we use a fusion protein with a linker length of 21 residues (THKEFCSRRYRGPGIHRSPTA). Given that the linker length between the core of the fibrils and the attached GFP molecule will critically affect steric hindrance, we find this to be a significant confound when extrapolating the in vitro data to cell-based assays. Further, there are additional differences in the constructs used in the Kaniyappan manuscript when compared to our studies Kaniyappan: Tau RD ?K-GFP-6xHis; Holmes: Tau RD P301S-CFP and Tau RD P301S-YFP. We are happy to provide our Tau-GFP plasmids to any laboratory upon request, and have done so on multiple occasions.
Another shortcoming of this paper is the emphasis on PHFs as the sole criterion on which to measure disease relevance. Kaniyappan et al., conclude that Tau RD ?K-GFP form elongated particles "distinct from PHFs," however, the authors provide no criteria on which to base this distinction. As an example, the electron microscopy data shows that Tau Fl-wt fibrils have an apparent diameter of 24 nm while Tau RD ?K-GFP fibrils have an apparent diameter of 37 nm (a 1.5 fold difference). However, PHFs are known to be highly polymorphic in their structure with ranges spanning 2.6 fold in diameter (Barghorn and Mandelkow 2002; Wegmann et al. 2010). Given that tau fibrils formed in vitro are inherently polymorphic, the authors must make explicit the basis on which Tau RD ?K-GFP fibrils do not constitute "bona-fide PHF-like filaments" and provide a statistical analysis of these differences.
Most critically, Kaniyappan et al. misrepresent the purpose of our cell-based FRET assay. The assay was engineered to detect exogenous pathological tau conformations, not to create endogenous PHFs. Within this framework, we and others have used this biosensor assay to detect pathologic tau aggregates in immunoprecipiated brain lysates from mice (Holmes et al. 2014) and humans (Furman et al. 2016), to track tau pathology through neuroanatomical circuits (Kaufman et al. 2018), to identify subcellular compartments that harbor tau aggregates (DeVos et al. 2018), to evaluate the efficacy of anti-tau therapeutics (Yanamandra et al. 2013; DeVos et al. 2017), and to characterize distinct seed-competent tau monomer (Mirbaha et al. 2018), including forms of monomer that encode distinct strains (Sharma et al. 2018). Additionally, aggregates that form in the FRET biosensor cells fluoresce in the presence of the amyloid-binding dye X-34, indicating a beta-sheet structure (Sanders et al. 2014). Further, tau immunodepletion of mouse and human brain lysates eliminates tau seeding in the FRET assay (Holmes et al. 2014), consistent with a model in which exogenous tau aggregates directly serve as templates for aggregation. Perhaps most importantly, similar cell-based assays detect and propagate unique tau conformational strains that have subsequently been propagated serially through a rodent model based on full-length (1N4R) human tau, and back into biosensors (Sanders et al. 2014). The simplest interpretation of this work is that tau-GFP fusions are faithfully propagating unique tau aggregate conformations.
Given that the FRET cell-based assay has been used productively by multiple labs and in multiple publications to make powerful predictions about tau pathology, we ask that the authors use comparable experimental conditions when testing their hypotheses and to restrict their conclusions to those directly tested by their experimental paradigm.
Brandon Holmes, MD PhD<br /> Jaime Vaquer-Alicea, BS<br /> Sarah Kaufman, MD PhD<br /> Marc Diamond, MD
REFERENCES:<br /> Barghorn S, Mandelkow E. 2002. Toward a unified scheme for the aggregation of tau into Alzheimer paired helical filaments. Biochemistry 41: 14885-14896.<br /> DeVos SL, Corjuc BT, Oakley DH, Nobuhara CK, Bannon RN, Chase A, Commins C, Gonzalez JA, Dooley PM, Frosch MP et al. 2018. Synaptic Tau Seeding Precedes Tau Pathology in Human Alzheimer's Disease Brain. Frontiers in neuroscience 12: 267.<br /> DeVos SL, Miller RL, Schoch KM, Holmes BB, Kebodeaux CS, Wegener AJ, Chen G, Shen T, Tran H, Nichols B et al. 2017. Tau reduction prevents neuronal loss and reverses pathological tau deposition and seeding in mice with tauopathy. Science translational medicine 9.<br /> Furman JL, Vaquer-Alicea J, White CL, 3rd, Cairns NJ, Nelson PT, Diamond MI. 2016. Widespread tau seeding activity at early Braak stages. Acta Neuropathol.<br /> Holmes BB, Furman JL, Mahan TE, Yamasaki TR, Mirbaha H, Eades WC, Belaygorod L, Cairns NJ, Holtzman DM, Diamond MI. 2014. Proteopathic tau seeding predicts tauopathy in vivo. Proceedings of the National Academy of Sciences of the United States of America.<br /> Kaufman SK, Del Tredici K, Thomas TL, Braak H, Diamond MI. 2018. Tau seeding activity begins in the transentorhinal/entorhinal regions and anticipates phospho-tau pathology in Alzheimer's disease and PART. Acta Neuropathol.<br /> Mirbaha H, Chen D, Morazova OA, Ruff KM, Sharma AM, Liu X, Goodarzi M, Pappu RV, Colby DW, Mirzaei H et al. 2018. Inert and seed-competent tau monomers suggest structural origins of aggregation. Elife 7.<br /> Sanders DW, Kaufman SK, DeVos SL, Sharma AM, Mirbaha H, Li A, Barker SJ, Foley AC, Thorpe JR, Serpell LC et al. 2014. Distinct Tau Prion Strains Propagate in Cells and Mice and Define Different Tauopathies. Neuron.<br /> Sharma AM, Thomas TL, Woodard DR, Kashmer OM, Diamond MI. 2018. Tau monomer encodes strains. Elife 7.<br /> Wegmann S, Jung YJ, Chinnathambi S, Mandelkow EM, Mandelkow E, Muller DJ. 2010. Human Tau isoforms assemble into ribbon-like fibrils that display polymorphic structure and stability. The Journal of biological chemistry 285: 27302-27313.<br /> Yanamandra K, Kfoury N, Jiang H, Mahan TE, Ma S, Maloney SE, Wozniak DF, Diamond MI, Holtzman DM. 2013. Anti-Tau Antibodies that Block Tau Aggregate Seeding In Vitro Markedly Decrease Pathology and Improve Cognition In Vivo. Neuron 80: 402-414.
On 2019-01-28 13:22:54, user Jorge Almeida wrote:
On 2024-01-28 16:58:08, user William Foley wrote:
Interested to see your manipualtive experiment with PEG blocks and herbivore diet. I think that the use of PEG as an adjunct to herbivore diets has outstripped any evidence of what it really does. Which tannins are bound by PEG? All tannin groups or only some?. Why is the emphasis on condensed tannins and not on elagitannins? Were elagitannins absent from your savvanah site? I think its important to acknowledge that we don't really understand what PEG does! Windley et al (2016) made some useful comments on this point but data is sparse. The interaction between tannins and herbivore nutrition is not simple with both positive and negative effects and I think your article would be stronger if this was acknowledged.! Finally the studies by Foley and Hume and Marsh did not take place in penned domestic ruminants as you state nor did they focus on diet selection as claimed.
On 2019-09-30 23:46:43, user Alan Rose wrote:
An updated version of this manuscript has now been published in Scientific Reports and is available at https://rdcu.be/bRMnP
On 2023-01-11 10:17:29, user emr wrote:
Hello,
I'm very interested in your analysis pipeline to study MHC genes in my cohorts, but I don't see any package, repository or website to perform this imputation?
Would you share your models ? Or a pipeline analysis for other immunogenomics researchers? That would be great !
otherwise, any help to set up this pipeline in my lab would be welcome !<br /> I hope we can discuss it by email or via twetter!
best regards,<br /> Laura LOMBARDI
On 2020-02-21 04:39:01, user Jackie wrote:
Congratulations to your great finding in your recent paper<br /> Unfortunately we reported this mutation and the furin cleavage site on 21th,Jan on researchgate https://www.researchgate.ne...
Although our paper was written in Chinese, the figure 1 and the English abstract clearly tell readers what we found.<br /> This virus killed many Chinese. So this finding has political meaning to our country and people.<br /> I hope you can cite our paper in your published version.
Xin Li, Guangyou Duan, Wei Zhang, Jinsong Shi, Jiayuan Chen, Shunmei Chen, Shan Gao, Jishou Ruan.<br /> A furin cleavage site was discovered in the S protein of the 2019 novel coronavirus (In Chinese). <br /> chinaXiv:202002.00004, doi: https://doi.org/10.12074/20...
On 2020-09-14 04:55:42, user Sisi Chen wrote:
Hi @Azim Dehghani Amirab, the code is now public at https://github.com/thomsonl... ! Check it out!
On 2019-09-02 21:05:28, user Patrick Sexton wrote:
This is a potentially useful tool. However, the methods do not describe how authors, during manual curation of the data, assessed whether a reported biased agonism profile was sufficiently robust to include in the database. The literature is full of examples where lack of mechanistic understanding of quantitative pharmacology (including partial agonism, system reserve, log versus linear response measures etc) leads to misinterpretation of data in the biased agonism field. Without this, there is a risk that the database may add to misinformation rather than moving the field forward.<br /> Just my 2 cents worth....
On 2022-10-03 09:44:44, user Iratxe Puebla wrote:
Review coordinated via ASAPbio’s crowd preprint review
This review reflects comments and contributions by Vasihvani Ananthanarayanan, Sam Lord, Rinalda Proko, Luciana Gallo, Sónia Gomes Pereira, Asli Sadli, Mugda Sathe, Parijat Sil. Review synthesized by Iratxe Puebla.
The preprint studies the molecular function of Arl15, a member of the Arf-like GTPases (Arls) group, which has been linked to magnesium homeostasis. The manuscript reports that Arl15 localizes in the Golgi and plasma membrane, including filopodia. The dissociation of Golgi or the expression of Arf1 dominant-negative mutant leads to a mislocalization of Arl15 to the cytosol. Knocking down Arl15 results in reduced filopodial number, altered focal adhesion kinase organization, and enhanced cargo uptake. Arl15 knockdown decreases cell migration and enhances cell spreading and adhesion strength. The findings point to a functional role for Arl15 in the Golgi.
General comments
Figures 1,2, 3 - The images display one representative example, recommend providing quantification (e.g. PCC/Manders) across several biological replicates, as well as information on the type of images reported, single slice, max Z projection etc.
For the bar plots, the paper reports the number of cells as well as the number of times the experiment was repeated, which is excellent. However, it is unclear whether the SEM error bars and p-values were calculated based on the number of repeats (correct) or based on the number of cells (incorrect). Can clarification be provided for this point. See https://doi.org/10.1083/jcb... and https://doi.org/10.1371/jou....
Throughout the paper there are several references to ‘data not shown’ - please report the data for those items.
Specific comments
Introduction, first paragraph - Suggest shortening the paragraph, particularly regarding the description of the different Arls and their relationship/correlation with all diseases.
‘These results show that similar to HeLa cells, Arl15-GFP localizes to PM along with filopodia and Golgi in all mammalian cell types’ - Suggest revising the fragment to ‘all the mammalian cell types tested in the study’, to avoid generalizing to every mammalian cell type.
‘the localization of Arl15-GFP to PM however remained unchanged as compared to DMSO treated cells (Fig. 2A).’ - Fig 2A only compares mCherry-UtrCH against Arl15-GFP. To support this claim, Arl15-GFP would need to be compared to WGA-AF, as in Figure 1, and their colocalization quantified to confirm that it remained unchanged.
‘We treated mCherry-UtrCH expressing HeLa:Arl15-GFP stable cells with a small molecular inhibitor of Rac1 (CAS 1177865-17-6) or Cdc42 (ML141)’ - Please report the concentration of both inhibitors.
‘Overall, these studies indicate that neither actin depolymerization nor the key regulatory molecules of filopodia/lamellipodia affect the localization of Arl15 to PM/Golgi.’ - The visualization reports Arl15-GFP v mCherry-UtrCH, to support the claim please check against WGA/GM130 as in Figure 1. Also, Figure 2c Arl15 for FAK inhibitor looks different from the DMSO control, recommend confirmation with WGA staining. Can also some explanation be provided for the fact that the Arl-15 in Figure 2A and 2C DMSO looks quite different from 2B and 2D despite the stable cell line with uniform expression?
‘which mislocalized Golgi pool of Arl15 without affecting its PM localization (Fig. 2D)’ - There does not seem to be a marked difference in Arl15-GFP's intracelluar localisation in cells with and without microtubules, and the PM signal appears slightly reduced in the Nocodazole-treated cells. Is it possible to please quantify the localisation?
Figure 2 -The quality of the images from panels B and D looks very different from those of A and C. Can some clarification be provided, were the same microscope, camera, and settings used?
Figure 3 - It would be good to mention the role of brefeldin A as an ATPase inhibitor to provide context for why it is being used.
‘Surprisingly, Arl15-GFP localized to the cytosol as similar to Arf1-GFP in GM130 dispersed cells that are indicative of brefeldin A treatment in HeLa cells (Fig. 3A).’ - It may be worth clarifying the reference to a surprising result, considering the nocadozol results would this result not be expected? It may also be worth providing some comments about the possible PM localisation difference when Golgi is disrupted with nocadozol vs BrefeldinA/golgicide A. It seems that the PM localisation is also affected in the BrefeldinA/golgicideA treatments.
Figure 3A ‘Cells were treated with DMSO (as control), brefeldin A or golgicide A for 24 h followed fixation’ - Please comment on the 24-hour period, BFA would be expected to work in minutes timescale: https://rupress.org/jcb/art...
Supplementary Fig 2A - The blots for Arl15 endogenous are very different between S2A and S2B. Also a 40% knockdown of Arf1 decreases the level of Arl15 by 17%. Can some comments be provided on the significance of this decrease.
Figure 4 - Is the SEM over 3 independent experiments or total number of cells from the three experiments? What was the criteria used to define a structure as filopodia?
‘However, we continued with Arl15V80A,A86L,E122K cytosolic mutant to study the functionality of Arl15 in HeLa cells’ - It may be worth specifying the reason to use the V80A,A86L,E122K form instead of the more simple V80A alone?
‘To test whether the mislocalized Cav-2 and STX6 are targeted to lysosomes in siArl15 cells’ - Please comment on why colocalisation with lysotracker or lamp1 positive structures was not examined instead of treating the cells with bafilomycin A1? Note that bafilomycin A1 also inhibits retrograde membrane traffic at the ER–Golgi boundary: https://www.molbiolcell.org...
Figure 5 - Please clarify whether the quantification of images was done on images taken from the same microscope? Also, suggest arranging the figures in a way that the quantification and images are not so far apart from each other.
Figure 5D - It is unclear how the western blot of EGFR showing total EGFR is indicative of what happened to its trafficking, this appears to be in contrast to the increase in transferrin uptake data. Recommend normalizing the transferrin uptake to surface transferrin levels as one can have higher uptake simply because there is more transferrin receptor instead of actual changes in trafficking rates.
‘Nevertheless, the reason for the partial loss of STX6 and caveolin-2 localization from Golgi in the ASAP1/2 knockdown cells requires investigation’ - Text earlier mentioned "However, we have not observed any significant change in Arl15 and its dependent cargo (caveolin-2 and STX6) localization to Golgi in siASAP1/2 cells " and there does not appear to be any difference in the siASAP1 or siASAP2 on Fig 6. However, in Figure S3 there is a slight reduction in the intensity. Can this be clarified?
Methods ‘Post chase, cells were washed with 1X PBS, fixed with 3% formaldehyde…’ - Please report for how long and at which temperature the fixation step was completed.
On 2016-05-18 20:40:10, user Anonymous wrote:
Dr. Wilson, et al--The paper contains numerous factual errors and misrepresentations of fact, making your conclusions untenable. Reference to Meryl Nass's unsupported theories makes your own bias evident. Although the subject of the Rhodesian anthrax event merits serious academic treatment, your paper only obscures the subject.
On 2021-02-19 11:09:42, user Jibby N Frantz wrote:
The lead measles-vectored COVID-19 vaccine candidate (MV-ATU2-SARS-CoV-2) described in this paper was not introduced into clinical trial. Our data emphasize that a strong and stable expression of the spike antigen from an early promoter of measles virus genome is crucial for high immunogenicity and protection.
On 2019-05-28 15:21:10, user Darren Thomson wrote:
We published this Spk phenomenon in Candida albicans a few years ago, if you want to take a look.
On 2023-12-13 11:50:32, user Vasilis Promponas wrote:
An enhanced, peer-reviewed version of this work has now been published (open access) in the Journal of Medical Virology: https://doi.org/10.1002/jmv...
On 2018-05-14 13:53:46, user Laurent Gatto wrote:
Access to the software (https://bitbucket.org/mschn... "https://bitbucket.org/mschnau/ms-pycloud/downloads/)") is unfortunately restricted.
On 2020-04-24 08:09:01, user Maciej Smialek wrote:
https://doi.org/10.3390/cel...
You can find the peer-revied article published in Cells within this link
On 2020-12-14 00:11:14, user Corinne Vacher wrote:
Justin Byrne Thank you very much for your constructive comments. Our article is now published in Molecular Ecology Resources: Barroso-Bergada et al. 2020. Microbial networks inferred from environmental DNA data for biomonitoring ecosystem change: strengths and<br /> pitfalls. Molecular Ecology Resources,online https://doi.org/10.1111/1755-0998.13302
On 2017-10-21 12:33:56, user Larkspur wrote:
ABSOLUTELY FALSE!! - Beekeepers lost nowhere near 33% of their colonies last year or any other year for that matter. Since 2006 when the misnomer CCD was trotted out - by an environmental "journalist" who didn't do their fact checking, the number of colonies in the US has soared from 2.1 million to 3.1 million in 2016. A 50% increase. For several years running, the 4 major metrics for the beekeeping / honey production / pollination industry have been on the rise. 1-number of colonies, 2-total pounds produced, 3-pounds per hive, 4-price per pound. The average losses for commercial beekeepers who operate 98% of the colonies in the US has averaged 8% - 12% every year for many decades. Steven Lechner, Busy Bee Farm, Larkspur, Colorado. #fakebeenews #beenews #fakenews
On 2018-01-05 22:51:12, user Jake Choby wrote:
I wonder if the antibodies are cross-reactive to S. aureus Cas9 not because of S. aureus colonization, but because of colonization by common coagulase-negative staphylococci for two reasons:
1) the vast majority of S. aureus isolates, based on a quick BLAST of annotated genomes, do not encode Cas9. In fact, the success of S. aureus as a pathogen is in part reliant on genes encoding toxins, metabolic enzymes, and antibiotic resistance that are parts of mobile genetic elements moved around the S. aureus pan-genome by phages. The loss of CRISPR in S. aureus may have set its evolution apart from other commensal staphylococci, see brief discussion in Holt et al (2011) PMID: 21813488. I think that it is unlikely that the humans surveyed here are or ever were colonized by a strain of S. aureus that encodes Cas9.
2) the S. aureus Cas9 used here has high sequence homology to Cas9 of coagulase-negative Staphylococci that are common human commensals, including S. hominins, S. epidermidis, etc. and cross-reactivity of human antibodies to SaCas9 here could be explained by reactivity to the Cas9 of these commensals.
Regardless, antibodies reactive to this protein may be a major hurdle for biotech applications of SaCas9.
On 2020-01-31 13:22:11, user Silas Kieser wrote:
Thank you for this preprint. I was looking up your parameters for the gene clustering. For your information, the parameter you used wouldn't prevent partial genes to become the cluster representatives according to the docs (https://github.com/soedingl... "https://github.com/soedinglab/mmseqs2/wiki#target-coverage)").
On 2020-04-13 14:59:39, user Sinai Immunol Review Project wrote:
Main Findings (Immunological) <br /> - Analyzed nasal airway epithelial transcriptome data from 695 asthmatic and healthy children (GALA II study, no COVID-19) to determine WGCNA networks. TMPRSS2<br /> gene was contained in a set of three highly correlated networks <br /> exhibiting strong enrichments for IL13-induced mucus secretory cell <br /> genes and canonically Type-2 inflammation pathways. ACE2 gene was correlated with interferon response and cytotoxic immune signaling eigengene enrichment. <br /> -TMPRSS2 upregulated and ACE2 downregulated upon rIL13 treatment in muco-ciliary air-liquid interface (ALI) cell culture; supporting transcriptome analyses. Also, scRNAseq data from tracheal airway epithelial cultures chronically stimulated with IL-13 also showed congruent changes in TMPRSS2 and ACE2. <br /> - Used metagenomic analysis from dataset to find 18 asymptomatic children with four different coronaviral sequences (CoV; OC43, JKU1, 229E, NL63). Compared 11 subjects with highest CoV infection to 571 CoV-naïve subjects, with 37 rhinovirus (HRV)-infected subjects serving as ‘basal/normal respiratory virus’ comparators. <br /> - Induction of cytotoxic immune response was considerably higher in CoV-infected subjects, compared to HRV-infected individuals. IL10, IL1B, IFNG, IFNA2, STAT1, and ACE2 upregulation shared for CoV or HRV infections. IL6 upregulated and genes associated with CD8 T cells, DCs and NK cells specifically enriched in CoV infections. <br /> - Identified eQTLs for ACE2 and TMPRSS2 using GALA II dataset and used multi-variable modeling to estimate the relative contribution of these factors to population variation in these genes (not reviewed here).
Limitations <br /> - Dataset is large, but having asthmatic young subjects as part of GALA II study might skew WGCNA analyses to enrich for T2 biomarkers and secretory phenotype genes. Will be important to replicate analyses in another independent non-COPD/allergy/asthma dataset and reconcile ACE2+TMPRSS2 correlation with T2 emergence. <br /> - Provides rationale for increased cytotoxic responses in CoVID-19 cases, but no explanation for ACE2 and TMPRSS2 anti-correlative trends in epithelia upon T2 inflammation. <br /> - Will need ALI experiments with IL13 addition in CoV-2 infection models to ascertain changes in ACE2/TMPRSS2 to be consistent with existing patient data for COVID-19.
Significance <br /> - Study strongly suggests airway epithelial TMPRSS2 expression is highly upregulated upon Type 2 inflammation, specifically by IL13 stimulation of epithelia. Also, ACE2 expression (and therefore viral infectivity) is inextricably linked to interferon response and could sabotage initial inflammation. <br /> - Changes in immune contextures observed in asymptomatic CoV+/HRV+ subjects suggest remodeling of airway epithelia even after viral resolution, which can impact future infections with COVID-19 and outcomes. <br /> - Results showing IL10, IL1B, IL6 enrichment and cytotoxic programs (CD8, NK, DC) in CoV+ subjects supports other concurrent studies showing their importance in cytokine storm and points to broader conserved inflammatory pathways worth targeting in COVID-19.
Reviewed by Samarth Hegde as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.
On 2020-06-15 23:44:08, user Nabina Paudyal wrote:
Very nice work highlighting the importance of Vanderwaals contact energy at specific sites to function based. Really liked how the experiments were designed based on MD simulations and in turn experiments validated the model. Wasn't sure what you meant by charge transfer in the abstract. Isn't it just Vanderwaals energy?
On 2018-04-16 16:01:49, user David Howard wrote:
The published version of this manuscript is now available from Nature Communications:<br /> https://www.nature.com/arti...<br /> doi:10.1038/s41467-018-03819-3
On 2024-02-01 20:02:04, user Jeff Petruska wrote:
Greetings from the author. I submitted the manuscript to BioRXiv mostly because I recognize that there may be prior descriptions of the muscle in other languages or in sources I did not review and I look for the community to offer comment. Please leave a comment or contact me directly if you have information about this muscle being described elsewhere, or if you are aware of analogs or homologs in other species. Thank you.
On 2020-07-21 14:49:46, user Paula de la Barra wrote:
Hi,
I found your paper very useful and hope to see it published soon. Just a heads up for some typos in the supp material: in table 12 it reads thicklip grey mullet and I think it should be thinlip. Besides, some of the years in the literature referenced in the supplementary tables should be checked (e.g. I think Joyeux 1017 should be Joyeux 2017).
Thanks for sharing the pre print!
Paula
On 2024-12-05 15:09:17, user Paul Macklin wrote:
Please note that this manuscript is now published (with slightly different title, and updates) at npj Systems Biology and Applications:
On 2016-05-29 02:42:58, user Chris Harbinger wrote:
What medical equipment was used to show the impact on the body? Was it a MRI to show the brain lights up after prolonged Cell usage?
On 2017-08-03 13:20:05, user Bourguet Denis wrote:
This preprint by Wolf Blanckenhorn has been peer-reviewed by two reviewers and recommended by Rodrigo Medel for Peer Community in Evolutionary Biology. Peer-reviews and the recommendation can be found here: https://evolbiol.peercommun...
On 2021-01-11 14:55:00, user Ariane wrote:
Hello,<br /> We tried the online tool and the one downloaded from GitHub. We didn't get the same results. Are they 2 different versions (and which one would be better)? Or do you use specific parameters on the online tool?
On 2020-04-26 20:46:01, user Keith Robison wrote:
Interesting concept and execution. But the background on coronavirus has a glaring error: coronaviruses are positive sense RNA viruses and never use reverse transcriptase in their lifecycle.
On 2021-04-09 10:22:20, user Kirk Overmyer wrote:
This paper has now been published in the journal IMA Fungus and can be accessed via the following link: https://imafungus.biomedcen...
On 2019-06-07 20:30:44, user J Wallace wrote:
Is anyone else concerned that Equation 2 puts the PCAs _after_ the SNP? (I checked the R code; it does it too, then appears to take simple Type I SS for p-values.) That seems like a basic methodological error. SNP effects should always be fit _after_ population structure. The github repository was missing the PCA file so I couldn't check it on the supplied code, but I bet fixing that would resolve most/all of the "problems" mentioned in this paper. (If the issue persists, I'd like to see if/how it relates to using kinship matrices, which are just as common as PCs.)
On 2017-11-14 14:36:09, user Philipp Koellinger wrote:
Here is an online seminar presentation of this article:<br /> https://youtu.be/t8RPK-HTX2E
On 2020-04-20 17:35:56, user Heeyoun Hwang wrote:
Impressive. But, I am checking the proteme data of S protein. I got very similar result of human proteome using IP2 Search engine, but SARS_CoV_2_nsp12 and SARS_CoV_2_nsp5 were also identified with high score and high PSMs in SARS_CoV_2_S raw files (3). Did you search the raw files against all database? In your Sup table 1, I think the contamination between other COVID proteins is missed. I will check other files, too.
On 2020-04-14 09:09:14, user eseporras wrote:
Interaction data now available at the IntAct database: https://www.ebi.ac.uk/intac...
On 2017-03-29 06:37:29, user Benjamin Haibe-Kains wrote:
I checked the code for Jacob and Speed and it appears that the random signatures have been selected from the whole microarray chip (or the large set of common probes between microarray chips used in the study) and not the set of 'age' genes as claimed by 'Jamie'.
Although I have found Jamie's response of high interest, I am not in a position to check the validity of his claims. I strongly suggest the response to Jacob and Speed's critics to be published as a separate biorXiv manuscript or at the very least, a link to the data and code should be provided for further scrutiny by the scientific community.
On 2020-05-06 09:43:04, user Jan Konvalinka wrote:
Very good and important report. There are more and more papers coming in on DDI2 which is encouraging for those of us who started to look at that strange "would -be protease" six years ago.<br /> It might be relevant to point out that very similar data have been recently published by Fassmanova et al. in Cancers: https://www.mdpi.com/2072-6...
On 2020-11-05 01:24:50, user Gyeongdeok kim wrote:
I want to know whether the spiked mutations into normal sample(HCC1395BL) is from real tumor sample(HCC1395) or just synthetic thigs....
On 2018-11-27 15:58:08, user Gilles Marodon wrote:
Very nice work. I'm just wondering about figure 3C. Is it correct to perform statistical testing of Treg vs total CD4+ T cells knowing that Treg are included in the latter and probably at various proportions? Anyway, the difference is so marked with Tconv (no need for statistics here!) that adding total CD4+ T cells does not add much to me.
On 2015-11-26 20:10:57, user Egle wrote:
Lithuanians are Balts<br /> Mordovia is a Finno-Ugric<br /> Please correct figure 2
On 2023-10-14 09:42:51, user Daisuke Kitamura wrote:
We did not provided you the cell line, "40L-MEF", whatever it is. By the way, we generated "40LB" based on Balb/c 3T3 cell line, not on MEF, as described in the Ref. 26.
On 2024-01-03 01:25:21, user Claudiu Bandea wrote:
New evidence supports the hypothesis that Borgs are incipient viral lineages <br /> (Claudiu Bandea, Dec 28, 2023)
The discovery of Borgs as giant extrachromosomal elements, presumably inhabiting Methanoperedens archaea, was first published in 2021, in bioRxiv [1]. More than a year later, the study was also published in Nature under a slightly different title and content [2]. The study, which reported the sequencing and analysis of more than a dozen Borg genomes (661,708 to 918,293 kb in length), including four genomes that were fully curated and analyzed, found no evidence of viral characteristics.
On the basis of these results, the authors asserted the following: “We can neither prove that they are archaeal viruses or plasmids or minichromosomes, nor prove that they are not. Although they may ultimately be classified as megaplasmids, they are clearly different from anything that has been previously reported” (all quotes in Italics) [2]. This statement raises a critical question: what kind of evidence would warrant the classification of Borgs as viruses, megaplasmids, or minichromosomes? Surprisingly, the authors did not address this essential issue.
Despite the Borgs’ apparent lack of viral characteristics, in a commentary entitled “Will Borgs Illuminate the Evolutionary Origin of Ancestral Viral Lineages?” [3], I suggested that Borgs are incipient viral lineages and, thus, illuminate one of the biggest mysteries in biology – the origin of viruses.
Remarkably, in a new article published in bioRxiv by the same group [4], we learn that, after all, Borgs do encode numerous putative viral proteins, including several capsid proteins, as well as proteins implicated in the replication, recombination, and spread of Borgs to new host cells. The new study presents additional evidence, including a high ratio between the number of Borgs and their presumed Methanoperedens hosts and a distinct methylation pattern of their genomes, which point to an extracellular stage in the Borgs’ life cycle and to their viral nature.
As I outlined in my previous commentary [3], the rationale for proposing that Borgs might be incipient viral lineages, even in the absence of the conventional physical, biochemical, and biological features historically used to define viruses (see below), was rooted in the Fusion Hypothesis regarding the evolutionary origin of viruses [5-7].
According to this hypothesis, the ancestral or incipient viral lineages originated from ecto- or endo-symbiotic or parasitic cellular lineages that fused with their host cells. By fusing with their host cells and discarding their cellular membrane, these lineages transitioned to new type of biological organization and structure (see below), which gave them full access to the host cell resources, including the host’s ribosomes and other components of the translation machinery. After synthesizing their specific molecules and replicating their genome using the resources found in their special environmental niche (i.e., the host cell), this new type of organisms induced the assembly and morphogenesis of reproductive, transmissible forms, which started a new life cycle by fusing with other host cells.
The absence of a cellular membrane within the host cell presented the incipient viral lineages with unique reductive evolutionary opportunities, not readily available for parasitic or symbiotic cellular lineages, which led to a myriad of new viruses with diverse lifestyles and biochemical composition. As outlined below, the fusion model completely changes the conventional views regarding the nature of viruses, their evolutionary origin, and their role in shaping the evolution of cellular lineages.
The nature of viruses
Ever since viruses were identified more than a century ago as infectious agents that passed through filters thought at that time to retain all microorganisms, they have been conceptually identified with the virus particles, or virions - the transmissible infectious forms in the viral life cycle. Accordingly, viruses have been defined based on the physical, biochemical, and biological properties of these particles, as illustrated in virtually all scientific literature and textbooks to date.
For example, in his seminal book, The Molecular Biology of the Gene, James Watson, who was highly familiar with nucleic acids, as well as with viruses [8], wrote: “All viruses differ fundamentally from cells, which have both DNA and RNA, in that viruses contain only one type of nucleic acid, which may be either DNA or RNA” [9]. A decade later, in A Dictionary of Virology, viruses were defined as “Infectious units consisting of either RNA or DNA enclosed in a protective coat” [10], and in the 1990s, a classic microbiology textbook, Zinsser Microbiology, stated that viruses “consist of a genome, either RNA or DNA, that is surrounded by a protective protein shell” [11].
Surely, the authors of these scientific publications were fully aware that, during the intracellular stage of their life cycle, many viruses, such as the “DNA viruses” and retroviruses, have both type of nuclei acids, DNA as well as RNA, and that many viruses are much more complex than a nucleic acid wrapped in a protein coat. Yet, all these renowned scientists fell victim to the concept of viruses as virus particles and used the physical, biochemical, and biological properties of these particles to define viruses. This is a strong example of the power of concepts in science. A concept that clearly misrepresents the experimental findings and observations can persist for decades, or, as in the case of viruses, for more than a century.
Forty years ago, in 1983, I proposed that, like many parasitic cellular lineages, viruses pass in their life cycle through two phenotypically distinct stages: the extracellular, reproductive forms represented by the virus particles, and the intracellular forms in which the viral molecules and components are “free” or dispersed within their host cell [5].
The viral particles are highly specialized structures that are used by some viruses for their transmission to new host cells. This role of viral particles in the viral life cycle explains their properties, including their apparent inert status and the presence of only one type of nucleic acid - DNA or RNA. Many viruses, however, do not produce viral particles, using instead alternative modes of transmission [12]. This fact alone indicates that identifying viruses with the virus particles misrepresents their nature. Nevertheless, the fundamental biological properties of viruses, whether they do or do not produce virions, are expressed during the intracellular stage of the viral life cycle, when viruses replicate their genome and synthesize their specific molecules, many of which are not components of the viral particles.
To identify viruses phenotypically during the intracellular stage of their life cycle with the integrative sum of all their molecules, and to differentiate them conceptually from the parasitic lineages that maintain a cellular membrane within the host cell, I proposed the concept of molecular structure and labeled viruses as molecular organisms [5, 6].
Although the concepts of molecular organisms and molecular structure (which, by analogy with the host cell’s cytoplasm, can be called viroplasm) are more suggestively envisioned within the framework of the fusion hypothesis, these concepts are also applicable in context of the other hypotheses regarding the origin and evolution of viruses (see below). Significantly, these concepts set the foundation for including other biological entities, such as plasmids, endogenous viruses, and viroids, within the same domain of biological organization - the viral domain.
In a commentary entitled “What makes a virus a virus?” [13], Roland Wolkowicz and Moselio Schaechter wrote that the identity of viruses as historically conceptualized and defined (i.e., as virus particles) is missing “the most fundamental aspect of what makes a virus a virus: it breaks up and loses its bodily integrity, with its progeny becoming reconstituted after replication from newly synthesized parts” and that “We are surprised from our own experience that the world of virology has not fully embraced this outlook” .
After the discovery of giant viruses, Jean-Michel Claverie asked, “What if we have totally missed the true nature of (at least some) viruses?” [14], and in a series of publications Patrick Forterre and his colleagues have discussed extensively the limitations of the concept of viruses as virus particles and suggested alternative ways to define viruses and to identify them during the intracellular stage of their life cycle [15-18].
As I discussed in the original publication [5], referring to the intracellular stage of viruses as an “eclipse phase,” denoting the “disappearance” of viruses, was confusing. Likewise, identifying viruses with their genome, thereby ignoring the other viral molecules and components, misrepresents their nature. An alternative approach was to no longer refer to a virus as an individual biological entity, but as an integrated virus-host cell system (i.e., the infected cell0. Recently, Patrick Forterre labeled this integrated system with the term “virocell” [15, 17, 18].
This approach was sharply criticized by Purificación López-García and David Moreira on both scientific and epistemological grounds [19, 20], and recently the virocell term was redefined by DeLong et al., [21], but Forterre rebutted the criticism [18].
Nevertheless, these highly relevant discussions bring forward the acute problems with the dogma of viruses as virus particles and stress the need for a new scientific and academic perspective on viruses, which can productively integrate the extraordinary amount of knowledge about viruses and their role in shaping the life and evolution of their hosts and of the ecosystem in which their live [15, 22-30].
The scientific limitations and academic confusion associated with the concept of viruses as virus particles in virology and related biomedical fields [31-33] remain to be fully addressed. However, questioning the validity of this dogma, which has guided several generations of researchers to extraordinary discoveries and progress in virology, is challenging.
The origin and evolution of viruses
As it would be expected, in the context of the dogma of viruses as virus particles, the hypotheses regarding their evolutionary origin focused on the virions and their structure: (i) thePre-cellular or Virus-first Theory suggested that viruses originated from precellular, self-replicating nucleic acids, or replicons, encoding for capsid proteins; (ii) the Endogenous or Escape Hypothesis suggested that viruses originated from cellular genomic sequences, or replicons, encoding for capsid proteins; (iii) and the historical Regressive or Reductive Hypothesis proposed a reductive transition of parasitic cellular lineages, such as bacteria, into nucleocapsid-like structures.
Within the concept of viruses as virus particles, the validity of the regressive hypothesis was questionable as Salvador Luria and James Darnell pointed out more than half a century ago: “The strongest argument against the regressive origin of viruses from cellular parasites is the non-cellular organization of viruses. The viral capsids are morphogenetically analogous to cellular organelles made up of protein subunits, such as bacterial flagella, actin filaments, and the like, and not to cellular membranes.” [34].
Indeed, many parasitic and symbiotic bacteria have a fraction of the genomic and proteomic repertoire of some viruses. For example, several endosymbionts, such as Carsonella, Hodgkinia, and Tremblaya, have a genome that is less than 200 kb and encode less than 200 proteins [35]. Yet, no symbiotic or parasitic bacteria with highly reduced genomes and metabolic capability resemble virus particles.
As predicted by the fusion hypothesis, only symbiotic or parasitic lineages that have a genetic and metabolic system compatible with that of their host cells would be able to fuse with them and transition to a viral type of biological organization. Accordingly, only bacterial, archaeal, and eukaryotic lineages, hosted by bacterial, archaeal, and eukaryotic host cells, respectively, could evolve into viral lineages [6, 7, 36].
Interestingly, numerous symbiotic and parasitic lineages that inhabit their kin and have reduced genomes and metabolic capabilities have been recently discovered, including highly diverse groups of DPANN archaea and CPR bacteria [37-42]. Hypothetically, some of these archaeal and bacterial lineages are in the process of transitioning into incipient viral lineages [6, 36], similar to the putative cellular ancestors of Borgs [3]. Nevertheless, one of the major appeals of the fusion hypothesis is that, unlike the other hypotheses, it can be addressed experimentally, as some members of these groups archaea and bacteria could be developed as fusion model organisms.
Surprisingly, though, the strongest evidence for the fusion hypothesis is found among more complex organisms - the eukaryotes. According to the fusion model, the nucleomorphs, some of which have a very small genome (<1 Mb) [43], originated from algal endosymbionts that fused with their host cells. Although, currently conceptualized as organelle-like entities, the nucleomorphs are genuine molecular organisms that have maintained their nucleus.
Even more surprising is the fact that numerous parasitic algal and fungal lineages have a life cycle and biological organization that, as I previously pointed out [6], represent overwhelming evidence for the fusion hypothesis. Indeed, several obligate parasitic species of red algae fuse with their host cells and use the host resources, including, in my assessment, the host ribosomes and other components of the translational machinery, to synthesize their molecules, replicate their genome, and induce the morphogenesis of spore-like progenies [44-50].
I cannot overemphasize the significance of these discoveries which support the fusion hypothesis and should be considered breakthrough discoveries not only in the field of parasitology, but also in evolutionary science, and biology.
Many viruses have been discovered serendipitously, including the recent finding in Chaetognaths, a small phylum of marine invertebrates, of two complex viruses, which have yet to be characterized at the molecular level [51, 52]. As more investigators become familiar with the fusion hypothesis and its predictions, it is likely that new types of viruses, as well as of new cellular lineages that are transitioning into incipient viral lineage, will be discovered.
Although, similar to tens of thousands of symbiotic and parasitic cellular lineages, the viral lineages have evolved towards reduced genomes and proteomes, there is clear evidence of frequent exchanges of genetic material with their hosts and other coinfecting organisms [6, 7, 53]. Considering also their high mutational rates, the deep phylogenetic analysis of viruses is inherently difficult [54-58]. Therefore, trying to establish deep phylogenetic relationships among viruses, reaching the origin and early evolution of life, is likely to be a futile effort.
The origin of incipient viral lineages from symbiotic or parasitic cellular lineages by a fusion mechanism is consistent with the current sequence-based phylogenetic analysis indicating orthologous relationships between the genes of some complex viruses and those of their hosts. The fusion hypothesis is also consistent with the complex biology and the life cycle of many viruses [59-62]. Also, unlike the virus-first, and the escape hypotheses, which dominate the current scientific literature [57, 63-65], the fusion hypothesis is consistent with the reductive evolution of thousands of endosymbiotic/parasitic microorganisms, which prompts the critical question: Why would viruses evolve in the opposite way?
Unlike the other two hypotheses on the evolutionary origin of viral lineages, the fusion hypothesis also unambiguously addresses one of the most intriguing scientific and philosophical questions: Are viruses alive? If the viral lineages originated from cellular microorganisms as proposed in the fusion model, then, there are few remaining arguments, if any, against their living status and their rightful place on the Tree of Life [5-7, 66-68].
Finally, it is relevant to mention that the fusion model on the origin of viral lineages is an integral part of a broader perspective - the fusion/anti-fusion theory - regarding the origin and evolution of pre-cellular and cellular lineages, including the archaeal, bacterial, and eukaryotic cellular domains and some of their defining characteristics [7]. Many aspects of this unifying theory, which addresses the major transitions in the history of life, including its origin, can be found as discrete published ideas and hypotheses [69-74].
Luria’s Credo: There is an intrinsic simplicity of nature and the ultimate contribution of science resides in the discovery of unifying and simplifying generalization, rather than in the description of isolated situation - in the visualization of simple, overall patterns rather than in the analysis of patchworks [75].
References
Al-Shayeb, B., et al., Borgs are giant extrachromosomal elements with the potential to augment methane oxidation. bioRxiv, 2021: p. 2021.07.10.451761.
Al-Shayeb, B., et al., Borgs are giant genetic elements with potential to expand metabolic capacity. Nature, 2022. 610(7933): p. 731-736.
Bandea, C., Will Borgs Illuminate the Evolutionary Origin of Ancestral Viral Lineages? bioRxiv, 2021: p. https://www.biorxiv.org/con....
Schoelmerich, M.C., et al., Borg extrachromosomal elements of methane-oxidizing archaea have conserved and expressed genetic repertoires. bioRxiv, 2023: p. 2023.08.01.549754.
Bandea, C.I., A new theory on the origin and the nature of viruses. J Theor Biol, 1983. 105(4): p. 591-602.
Bandea, C.I., The origin and evolution of viruses as molecular organisms. Nature Precedings, 2009; https://doi.org/10.1038/npr....
Bandea, C.I., A unifying scenario on the origin and evolution of cellular and viral domains. Nature Precedings, 2009; https://doi.org/10.1038/npr....
Crick, F.H. and J.D. Watson, Structure of small viruses. Nature, 1956. 177(4506): p. 473-5.
Watson, J.D., Molecular Biology of the Gene. 1976, Menlo Park: Benjamin-Cummings.
Rowson, K.E.K., Rees, TAL and Mahy, BWJ, A Dictionary of Virology. 1981, Oxford Blackwell Scientific.
Joklik, W.K., Willett, HP, Amos, BD and Wifert CM, Zinsser Microbiology. 1992, Norwalk: Appleton and Lange.
Hough, B., et al., Fungal Viruses Unveiled: A Comprehensive Review of Mycoviruses. Viruses, 2023. 15(5).
Wolkowicz, R. and M. Schaechter, What makes a virus a virus? Nat Rev Microbiol, 2008. 6(8): p. 643; author reply 643.
Claverie, J.M., Viruses take center stage in cellular evolution. Genome Biol, 2006. 7(6): p. 110.
Forterre, P., Giant viruses: conflicts in revisiting the virus concept. Intervirology, 2010. 53(5): p. 362-78.
Raoult, D. and P. Forterre, Redefining viruses: lessons from Mimivirus. Nat Rev Microbiol, 2008. 6(4): p. 315-9.
Forterre, P., Manipulation of cellular syntheses and the nature of viruses: The virocell concept. Comptes rendus. Chimie, 2011. 14(4): p. 392-399.
Forterre, P., The Virocell Concept. Encyclopedia of Virology. 2021.
López-García, P., The place of viruses in biology in light of the metabolism- versus-replication-first debate. Hist Philos Life Sci, 2012. 34(3): p. 391-406.
Lopez-Garcia, P., Moreira, D., Viruses in biology. . Evolution: Education & Outreach, 2012. 5: p. 389–398.
DeLong, J.P., et al., Towards an integrative view of virus phenotypes. Nat Rev Microbiol, 2022. 20(2): p. 83-94.
Claverie, J.M. and C. Abergel, Giant viruses: The difficult breaking of multiple epistemological barriers. Stud Hist Philos Biol Biomed Sci, 2016. 59: p. 89-99.
Aylward, F.O. and M. Moniruzzaman, Viral Complexity. Biomolecules, 2022. 12(8).
Correa, A.M.S., et al., Revisiting the rules of life for viruses of microorganisms. Nat Rev Microbiol, 2021. 19(8): p. 501-513.
Moniruzzaman, M., et al., Virologs, viral mimicry, and virocell metabolism: the expanding scale of cellular functions encoded in the complex genomes of giant viruses. FEMS Microbiol Rev, 2023. 47(5).
Moniruzzaman, M., et al., Dynamic genome evolution and complex virocell metabolism of globally-distributed giant viruses. Nat Commun, 2020. 11(1): p. 1710.
Rosenwasser, S., et al., Virocell Metabolism: Metabolic Innovations During Host-Virus Interactions in the Ocean. Trends Microbiol, 2016. 24(10): p. 821-832.
Howard-Varona, C., et al., Phage-specific metabolic reprogramming of virocells. Isme j, 2020. 14(4): p. 881-895.
Braga, L.P.P., et al., Novel virocell metabolic potential revealed in agricultural soils by virus-enriched soil metagenome analysis. Environ Microbiol Rep, 2021. 13(3): p. 348-354.
Alizon, S., et al., Towards a multi-level and a multi-disciplinary approach to DNA oncovirus virulence. Philos Trans R Soc Lond B Biol Sci, 2019. 374(1773): p. 20190041.
Depuydt, C.E., et al., Human Papillomavirus (HPV) virion induced cancer and subfertility, two sides of the same coin. Facts Views Vis Obgyn, 2016. 8(4): p. 211-222.
Bandea, C.I., The Prion Hypothesis at Forty: Enlightening or Deceptive? J Alzheimers Dis, 2022; https://www.j-alz.com/edito...
Enquist, L.W. and V. Racaniello, Virology: From Contagium Fluidum to Virome, in Fields Virology, Vol. 4. Fundamentals, 7th Edition P.M. Howley and D.M. Knipe, Editors. 2024, Wolters Kluwer.
Luria, S. and J. Darnell, General Virology 1965, New-York: Wiley.
McCutcheon, J.P. and N.A. Moran, Extreme genome reduction in symbiotic bacteria. Nat Rev Microbiol, 2011. 10(1): p. 13-26.
Bandea, C.I., Are Antarctic Nanohaloarchaeota emerging viral lineages? Preprints 2019; https://doi.org/10.20944/pr...
Moreira, D., et al., Reductive evolution and unique predatory mode in the CPR bacterium Vampirococcus lugosii. Nat Commun, 2021. 12(1): p. 2454.
Dombrowski, N., et al., Genomic diversity, lifestyles and evolutionary origins of DPANN archaea. FEMS Microbiol Lett, 2019. 366(2).
Naor, A. and U. Gophna, Cell fusion and hybrids in Archaea: prospects for genome shuffling and accelerated strain development for biotechnology. Bioengineered, 2013. 4(3): p. 126-9.
Naor, A., et al., Low species barriers in halophilic archaea and the formation of recombinant hybrids. Curr Biol, 2012. 22(15): p. 1444-8.
López-García, P. and D. Moreira, Physical connections: prokaryotes parasitizing their kin. Environ Microbiol Rep, 2021. 13(1): p. 54-61.
Bokhari, R.H., et al., Bacterial Origin and Reductive Evolution of the CPR Group. Genome Biol Evol, 2020. 12(3): p. 103-121.
Archibald, J.M. and C.E. Lane, Going, going, not quite gone: nucleomorphs as a case study in nuclear genome reduction. J Hered, 2009. 100(5): p. 582-90.
Goff, L.J. and A.W. Coleman, Transfer of nuclei from a parasite to its host. Proc Natl Acad Sci U S A, 1984. 81(17): p. 5420-4.
Goff, L.J., J. Ashen, and D. Moon, THE EVOLUTION OF PARASITES FROM THEIR HOSTS: A CASE STUDY IN THE PARASITIC RED ALGAE. Evolution, 1997. 51(4): p. 1068-1078.
Freese, J.M. and C.E. Lane, Parasitism finds many solutions to the same problems in red algae (Florideophyceae, Rhodophyta). Mol Biochem Parasitol, 2017. 214: p. 105-111.
Blouin, N.A. and C.E. Lane, Red algal parasites: models for a life history evolution that leaves photosynthesis behind again and again. Bioessays, 2012. 34(3): p. 226-35.
Kellner, M., et al., Transfer of genetic information from the mycoparasite Parasitella parasitica to its host Absidia glauca. Curr Genet, 1993. 23(4): p. 334-7.
Schultze, K., et al., Sexuality and parasitism share common regulatory pathways in the fungus Parasitella parasitica. Gene, 2005. 348: p. 33-44.
Ellenberger, S., A. Burmester, and J. Wöstemeyer, The fate of mitochondria after infection of the Mucoralean fungus Absidia glauca by the fusion parasite Parasitella parasitica: comparison of mitochondrial genomes in zygomycetes. Mitochondrial DNA A DNA Mapp Seq Anal, 2018. 29(1): p. 113-120.
Shinn, G.L. and B.L. Bullard, Ultrastructure of Meelsvirus: A nuclear virus of arrow worms (phylum Chaetognatha) producing giant "tailed" virions. PLoS One, 2018. 13(9): p. e0203282.
Barthelemy, R.-M., T. Goto, and E. Faure, Serendipitous discovery in a marine invertebrate (Phylum Chaetognatha) of the longest giant viruses reported till date. Virology: Current Research, 2019. 3(1).
Filée, J., Genomic comparison of closely related Giant Viruses supports an accordion-like model of evolution. Front Microbiol, 2015. 6: p. 593.
Holmes, E.C. and S. Duchêne, Can Sequence Phylogenies Safely Infer the Origin of the Global Virome? mBio, 2019. 10(2).
Claverie, J.M., Fundamental Difficulties Prevent the Reconstruction of the Deep Phylogeny of Viruses. Viruses, 2020. 12(10).
Caetano-Anollés, G., J.M. Claverie, and A. Nasir, A critical analysis of the current state of virus taxonomy. Front Microbiol, 2023. 14: p. 1240993.
Koonin, E.V., et al., Viruses Defined by the Position of the Virosphere within the Replicator Space. Microbiol Mol Biol Rev, 2021. 85(4): p. e0019320.
Barreat, J.G.N. and A. Katzourakis, A billion years arms-race between viruses, virophages, and eukaryotes. Elife, 2023. 12.
Claverie, J.M. and C. Abergel, Open questions about giant viruses. Adv Virus Res, 2013. 85: p. 25-56.
Nasir, A., et al., Untangling the origin of viruses and their impact on cellular evolution. Ann N Y Acad Sci, 2015. 1341: p. 61-74.
Nasir, A., E. Romero-Severson, and J.M. Claverie, Investigating the Concept and Origin of Viruses. Trends Microbiol, 2020. 28(12): p. 959-967.
Seligmann, H., Syntenies Between Cohosted Mitochondrial, Chloroplast, and Phycodnavirus Genomes: Functional Mimicry and/or Common Ancestry? DNA Cell Biol, 2019. 38(11): p. 1257-1268.
Krupovic, M., V.V. Dolja, and E.V. Koonin, Origin of viruses: primordial replicators recruiting capsids from hosts. Nat Rev Microbiol, 2019. 17(7): p. 449-458.
Krupovic, M. and E.V. Koonin, Cellular origin of the viral capsid-like bacterial microcompartments. Biol Direct, 2017. 12(1): p. 25.
Koonin, E.V., T.G. Senkevich, and V.V. Dolja, The ancient Virus World and evolution of cells. Biol Direct, 2006. 1: p. 29.
Forterre, P., Defining life: the virus viewpoint. Orig Life Evol Biosph, 2010. 40(2): p. 151-60.
Kostyrka, G., La place des virus dans le monde vivant. PhD Thesis, Université Panthéon-Sorbonne-Paris I; https://tel.archives-ouvert..., 2018.
Mindell, D.P., The tree of life: metaphor, model, and heuristic device. Syst Biol, 2013. 62(3): p. 479-89.
Tang, S., The Origin(s) of Cell(s): Pre-Darwinian Evolution from FUCAs to LUCA : To Carl Woese (1928-2012), for his Conceptual Breakthrough of Cellular Evolution. J Mol Evol, 2021. 89(7): p. 427-447.
Sinai, S., et al., Primordial sex facilitates the emergence of evolution. J R Soc Interface, 2018. 15(139).
Vig-Milkovics, Z., et al., Moderate sex between protocells can balance between a decrease in assortment load and an increase in parasite spread. J Theor Biol, 2019. 462: p. 304-310.
Babajanyan, S.G., et al., Coevolution of reproducers and replicators at the origin of life and the conditions for the origin of genomes. Proc Natl Acad Sci U S A, 2023. 120(14): p. e2301522120.
Garg, S.G. and W.F. Martin, Mitochondria, the Cell Cycle, and the Origin of Sex via a Syncytial Eukaryote Common Ancestor. Genome Biol Evol, 2016. 8(6): p. 1950-70.
Joyce, G.F. and J.W. Szostak, Protocells and RNA Self-Replication. Cold Spring Harb Perspect Biol, 2018. 10(9).
Luria, S., General Virology. 1953, Hoboken, NJ: J. Wiley and Sons.
On 2021-09-15 12:44:06, user Mohamed Hameed Aslam A wrote:
please provide supplementary table , its missing in this pre print
On 2020-07-11 19:00:49, user RJ wrote:
According to new clade classification by nextstrain A3i falls under 19A clade which is kind of ancestral type and as the pandemic progresses other clades are dominating 19a/A3i clade.
On 2019-07-27 22:06:26, user didier Fesquet wrote:
a new role for PA28g, the poorly understood activator of the 20S proteasome...we wellcome any feedback
On 2016-07-12 18:53:06, user Sarah de Rijcke wrote:
Let's move beyond too simplistic notions of 'misuse' and 'unintended effects' in debates on the JIF: https://www.cwts.nl/blog?ar...
On 2023-12-21 23:58:19, user Rosalyn Fey wrote:
Now published!<br /> "Discovery and Visualization of Age-Dependent Patterns in the Diurnal Transcriptome of Drosophila"<br /> Sebastian B, Fey RM, Morar P, Lasher B, Giebultowicz JM, Hendrix DA. Discovery and Visualization of Age-Dependent Patterns in the Diurnal Transcriptome of Drosophila. J Circadian Rhythms. 2022 Dec 8;20:1. doi: 10.5334/jcr.218. <br /> PMID: 36561348; PMCID: PMC9733130.
https://doi.org/10.5334/jcr... doi.org="" 10.5334="" jcr.218="">
On 2017-01-31 16:21:31, user David Curtis wrote:
Please ignore this comment - it refers to a completely different paper. I don't know how it ended up here. Sorry.
I'm surprised that such a small sample could show such a significant effect. With the effect of PRS on risk of schizophrenia, what effect size could we really expect to be present for cognitive functioning? Do we have an idea of the power to detect a plausible effect size given the sample sizes used? It is claimed that the effect is there even in only 180 healthy controls - I really wouldn't expect PRS to perform so well. So much has been done to the data that it's hard to make a judgement. After all the covariates have been included, the correlation coefficient measured may be very different from what the raw data would show. It's striking that in several scenarios the correlation coefficient is almost exactly zero. One wouldn't expect this by chance, it must be a feature of the methods used. It would be nice just to see a scatter plot of PRS against raw BACS scores. I suppose for these results to be real we're saying that the PRS contributes nearly 4% of the variance of the BACS. That does sound like a lot to me.
On 2023-05-11 16:36:06, user sm wrote:
This is an additional example of pathogen effectors hijacking host phosphatase. The similar model has been studied in bacterial, in which bacterial AvrE-family Type-III effector proteins (T3Es) hijacking host PP2A
(Jin, 2016, Plos pathogen), and phytophthora infenstans effector Pi04314 hijacking host protein pp1c (Bovink Nat communication ,2016)
On 2020-12-14 15:49:23, user Dan Harkness wrote:
It's an intriguing observation. It would have been nice to see some longitudinal data. Is this a persistent or transient event? It seems like LINE-1 up-regulation is associated with "disease" via general genome instability perhaps mediated by its RT activity. This would make sense if these datasets came from productively infected individuals. What's missing is the follow up data e.g. 1-week, 4-week, 16-week post-infection, to demonstrate and quantify the persistence. The counter-hypothesis is that of the 100s of millions of cells that contributed to these single-snapshot sequencing libraries, there are some reads from a group of sick cells who are being actively cleared from the body via standard immune response. Is this particular group of cells the ones with the integrated COVID-19 sequences? Utilizing single-cell RNA and DNA approaches - in addition to follow-up time-points - would make a very elegant dataset. Cheers.
On 2021-02-28 16:45:13, user CTGA wrote:
As of February 2021, additional peer-reviewed results (https://www.sciencedirect.c...<br /> show that cellular expression of the S antigen alone is sufficient for <br /> progression into cell-cell fusion, thus leading to syncytia formation; as <br /> they also show that this process is largely unaffected by antibodies <br /> from convalescent patients, there is extra reason for concern, <br /> especially since syncytia formation is now considered a hallmark of <br /> COVID pathology (https://www.thelancet.com/j....
On this background, the corresponding author’s follow-up public comments on his own results (Prof. Jaenisch on January 30th, 2021, see https://finance.yahoo.com/n...,
"One could speculate that such an integration, if indeed happening, might result in more long-term expression of the antigen and thus be <br /> beneficial”,
are becoming increasingly questionable.
On 2021-09-13 17:28:14, user AKANKSHA SINGH wrote:
The manuscript is accepted for publication in the journal- Experimental Biology and medicine
On 2021-03-09 23:04:16, user Francis Arellano wrote:
Overall, the paper was very interesting and informative. The background information was clear and the graphical abstract was useful for visualizing the different pathways. Additionally, the consistent use of p-values across the paper was great! For Figure 1B, it may be beneficial to specify the purpose of the white arrows on the immunostain. Also, it would be a lot easier to see the localization of TAK1 if the immunostain was visualized through separate channels (TAK1, CD31, and DAPI each individually) like in figure 8A. In general, it would be nice to see you state the specific mutation lines (M or S) that are used for the experiments, as it was a bit ambiguous in the text. This also goes for the cell lines used, because as a reader I would like that to be more explicit as well. For figure 2E, it would be good to have a quantification to support the difference in the expression of NFKB in the knockout vs wild-type. Also, figure 2F was never mentioned in the figure legend so more information about that would be very helpful. For figure 3A, it would be great to elaborate more on the volcano plot. How exactly was it used and how did it lead to other experiments? Additionally, did it lead you to further explore any specific genes? For figure 3D, what is the baseline for fold change? Is it the experimental over a control value? The gene labels were on the y-axis, but were not clear about what they meant in relation to a control that was used. Lastly, it may also be worthwhile to look at if TAB1-3 can serve as potential therapeutic targets for retinal neovascularization and to explore more on how they relate to TAK1. E.g. is the therapeutic effect greater when multiple of these genes are knocked out? Looking forward to reading the next version of the manuscript!
On 2019-03-15 12:15:38, user nelzo ereful wrote:
Just curious -- you mentioned you used GATK ASEReadCounter at its default parameters. This filters out duplicated reads. Why is this so? What if the duplicate read(s) is/are authentic expression copies of a gene? thanks...
On 2023-06-09 08:00:30, user Yusuke Okazaki wrote:
Thank you for sharing the exciting results. I appreciate your valuable work, which significantly expanded the known freshwater phage diversity and their ecological importance. I would like to kindly bring to your attention that our study has also reported the quantitative significance of cysC/cysH genes among freshwater phage genomes (doi:10.1111/1462-2920.14816).
On 2020-11-06 17:54:19, user Colin Kretz wrote:
Very nice work. A useful library for identifying novel protease substrates, which is currently a very challenging problem!
On 2025-09-11 14:56:10, user Christoph Jüschke wrote:
Congratulations on your very interesting study! It represents a valuable contribution to the field of therapeutic splicing correction.
We would, however, like to kindly suggest that the manuscript could benefit from the inclusion of additional references to previous work in the field. Specifically, in vivo studies involving intravitreal injection of AAVs to deliver U1 snRNA to the mouse retina have already been conducted and evaluated for efficacy, safety, and potential off-target effects. We believe it would strengthen your manuscript to reference and discuss this study alongside citations of your own previous work (e.g., Balestra et al):<br /> • Swirski S, May O, Ahlers M, Wissinger B, Greschner M, Jüschke C, Neidhardt J. In Vivo Efficacy and Safety Evaluations of Therapeutic Splicing Correction Using U1 snRNA in the Mouse Retina. Cells. 2023 Mar 21;12(6):955.
In addition, the following statements from your manuscript:<br /> “By targeting the 5´ splice site downstream of specific exons, ExSpeU1s can correct aberrant splicing in various cellular and mouse models” and<br /> “To restore defective splicing, ExSpeU1s have been created with sequence changes that permit targeted binding to intronic sequences downstream of the mutant 5’ ss”<br /> could benefit from referencing a related U1 snRNA splice-correction study in optic atrophy—a condition that, like familial dysautonomia, is characterized by retinal ganglion cell degeneration:<br /> • Jüschke C, Klopstock T, Catarino CB, Owczarek-Lipska M, Wissinger B, Neidhardt J. Autosomal dominant optic atrophy: A novel treatment for OPA1 splice defects using U1 snRNA adaption. Mol Ther Nucleic Acids. 2021 Oct 21;26:1186-1197.
Furthermore, to provide a more comprehensive overview of foundational work in the field, including patient-derived cell lines, and acknowledge earlier contributions toward the development of in vivo U1 snRNA-based therapies, the following publications may also be relevant:<br /> • Tanner G, Glaus E, Barthelmes D, Ader M, Fleischhauer J, Pagani F, Berger W, Neidhardt J. Therapeutic strategy to rescue mutation-induced exon skipping in rhodopsin by adaptation of U1 snRNA. Hum Mutat. 2009;30:255–263.<br /> • Glaus E, Schmid F, Da Costa R, Berger W, Neidhardt J. Gene therapeutic approach using mutation-adapted U1 snRNA to correct a RPGR splice defect in patient-derived cells. Mol Ther. 2011;19:936–941.<br /> • Schmid F, Glaus E, Barthelmes D, Fliegauf M, Gaspar H, Nürnberg G, Nürnberg P, Omran H, Berger W, Neidhardt J. U1 snRNA-mediated gene therapeutic correction of splice defects caused by an exceptionally mild BBS mutation. Hum Mutat. 2011;32:815–824.
Finally, we would like to highlight two recent publications that might have implications toward potential risks of U1 therapy. Kim et al. showed that U1 snRNP may affect promoter activity by inhibiting premature polyadenylation, and Nadeu et al. identified recurrent tumour-specific U1 snRNA mutations in mature B-cell neoplasms:<br /> • Kim G, Carroll CL, Wakefield ZP, Tuncay M, Fiszbein A. U1 snRNP regulates alternative promoter activity by inhibiting premature polyadenylation. Mol Cell. 2025;85(10):1968-1981.<br /> • Nadeu F, Shuai S, Clot G, Hilton LK, Diaz-Navarro A, Martín S, Royo R, Baumann T, Kulis M, López-Oreja I, Cossio M, Lu J, Ljungström V, Young E, Plevova K, Knisbacher BA, Lin Z, Hahn CK, Bousquets P, Alcoceba M, González M, Colado E, Payer ÁR, Aymerich M, Terol MJ, Rivas-Delgado A, Enjuanes A, Ruiz-Gaspà S, Chatzikonstantinou T, Hägerstrand D, Jylhä C, Skaftason A, Mansouri L, Stranska K, Doubek M, van Gastel-Mol EJ, Davis Z, Walewska R, Scarfò L, Trentin L, Visentin A, Parikh SA, Rabe KG, Moia R, Armand M, Rossi D, Davi F, Gaidano G, Kay NE, Shanafelt TD, Ghia P, Oscier D, Langerak AW, Beà S, López-Guillermo A, Neuberg D, Wu CJ, Getz G, Pospisilova S, Stamatopoulos K, Rosenquist R, Huber W, Zenz T, Colomer D, Martín-Subero JI, Delgado J, Morin RD, Stein LD, Puente XS, Campo E. Disease-specific U1 spliceosomal RNA mutations in mature B-cell neoplasms. Leukemia. 2025;39(9):2076-2086.
Of course, we understand that not every study in the field can be cited. Our suggestions are intended to help ensure a balanced and comprehensive presentation of the field’s progress, particularly as it clearly relates to U1 snRNA-based splicing correction strategies.
Thank you for your thoughtful work, and we look forward to seeing this research further advance the field.
On 2014-11-05 00:33:50, user Daniel Johnston (Publons) wrote:
Reviews of this article are available on Publons here:<br /> https://publons.com/publon/...
On 2021-01-28 02:50:03, user Paul Wolf wrote:
One bright side may be that if there is convergent evolution, maybe the different strains will be similar enough for the same antibodies and vaccines to work on them. It seems like the problem is when the strains become too different, and reinfection can occur. I assume that's what happened in Manaus.
By the way, in the news today I saw that 1,400 patients are being transferred from Manaus to hospitals across the country. It seems like the most dangerous thing you could do, and the fastest possible way to spread the P.1 variant.
On 2018-10-25 06:37:58, user Patrick wrote:
This has now been peer reviewed and published in the American Journal of Tropical Medicine and Hygiene at http://www.ajtmh.org/conten...
On 2019-07-15 09:35:15, user Jon Tobias wrote:
Barely no signal for SOST in our recent sclerostin GWAS https://www.ncbi.nlm.nih.go..., so this MR is not really looking at effect of lifelong sclerostin levels, presumably some other mechanism responsible; perhaps the SOST SNPs represent a signal for BMD (which is known to be correlated genetically with T2DM)
On 2019-05-07 09:32:09, user Daniel Blaese wrote:
I'm not trying to be pedantic, but shouldn't the title be corrected to "Carbon monoxide dehydrogenases enhance bacterial survival by oxidizing atmospheric CO"? The organism of interest doesn't respire the CO either, it respires oxygen. It is obvious that the authors understand this difference (2nd sentence of the abstract) but I find it very strange to say the organism respires CO when it does not respire CO. Just like E. coli does not respire glucose during aerobic growth. Or am I missing something here?
On 2022-11-23 19:46:10, user Ashley Albright wrote:
Review coordinated as part of an assignment for San Francisco State University undergraduate and master’s students in BIOL/CHEM 667 - Optical Engineering for the Biological Sciences taught by Dr. Ashley Albright, Dr. Mark Chan, and Dr. Ray Esquerra in Fall 2022.
This review reflects comments provided by the following students (in alphabetical order by last name): Michelle Chong, Eleazar DeAlmeida, J. Carlos Gomez, Deannakayte Marucut, Liz Mathiasen, Raquel Reyes, Karina Rodriguez, Abdellah Shraim, Matt Suntay, Yaqoub Yusuf
Comments and review compiled by Dr. Ashley Albright
To understand the role of kinases in the origins of multicellularity and animal development, the authors conducted a small molecule screen of kinase inhibitors in choanoflagellates, the closest living relatives of animals. Genetic tools in choanoflagellates, as well as other emerging model systems are limited. This approach using small molecule inhibitors rather than genetic tools to screen for phenotypic changes will not only be invaluable for choanoflagellate researchers, but researchers using other organisms as well. The authors found that sorafenib, a p38 kinase inhibitor, inhibits choanoflagellate cell proliferation. Furthermore, S. rosetta p38 is activated by environmental stressors (heat and oxidative stress), suggesting a conserved role for p38 kinases. Ultimately, the results of this study show that small molecule screens are a valuable approach to understand biology in emerging model systems, especially when available tools are limited.
Students in our course come from a variety of scientific backgrounds, and many are new to reading scientific papers. Therefore, our comments focus on readability and significance more so than methodology and strength of results.
Comments:
While we appreciate the conciseness of the introduction, we felt that the authors could provide additional information on the background of choanoflagellate research and why they are used as a model organism. The authors do mention, “Choanoflagellates possess homologs of diverse animal kinases (Figure S1) (9–11) and due to their phylogenetic placement are relevant for reconstructing the ancestral functions of animal cell signaling proteins (12, 13).” However, we believe the connection between choanoflagellate research and the origins of multicellularity could be more explicit.
Similar to the comment above, we felt that the authors could provide more background information on small molecule phenotypic screens and limited genetic tools. We understand the benefit that they provide in cases where genetic tools are limited, but how are the tools limited in choanoflagellates exactly? We are also curious about other small molecule screens. Why were kinases targeted in this case?
The results were described in great detail, making interpretations of the data easier for people outside of the field; however, we feel that the connection between results and conclusion in some cases were less clear. As an example, the authors state: “These findings showed that glesatinib disrupts both cell proliferation and tyrosine kinase signaling. Together these observations provide independent support for the hypothesis that kinase signaling regulates cell proliferation in choanoflagellates.” How exactly does tyrosine kinase signaling relate to cell proliferation compared to other kinase signaling?
The authors report that effects of sorafenib on cell proliferation are dose-dependent. We would like to know if this response is density-dependent as well.
Other Thoughts and Questions:
Would this approach yield similar results in other species of choanoflagellates? Other protists? Basal animals? Plants?
We would like to see these experiments repeated for other hit compounds.
The authors mention that this approach is normally used in drug discovery, how does what was discovered through this screen relate to human disease?
On 2017-05-17 14:48:25, user Jean Manco wrote:
Thank you, Hugo Sanders. Beer-making does get a mention in Ancestral Journeys, p. 173. Gordon Childe thought that beer was the great attraction of Bell Beaker, but in fact it is a lot older. It was made by the first farmers in West Asia. It arrived in Britain with the Neolithic farmers. The residues of alcoholic beverages have been found in Neolithic Scotland. See <br /> M. Dineley 2004. Barley, Malt and Ale in the Neolithic, BAR S1213.
On 2020-04-16 22:29:08, user Maximus wrote:
Hi, I would like to use short read sequencing to count the abundance of the various SARS2 transcripts. I need to build a transcriptome file containing the sequence of the most abundant SARS2 transcripts. In addition to the full-length genome, I would like to include transcript sequences of the sgRNAs. Would I be able to use table S2 or S3 for this purpose? From my understanding, these tables should contain the information required to construct an accurate transcriptome for the virus.
On 2017-10-24 21:08:13, user Filipe Maia wrote:
I think all the mandatory recommendations are very reasonable (the only likely point of contention might be deciding if there's a hierarchy in the data which triggers the requirement for granularity), and it's useful to have a set of standard procedures for the recommended and optional guidelines. I hope it becomes widely adopted.
On 2019-05-25 00:06:05, user Anders wrote:
I just noticed that the supplementary materials are not available, even though I thought we uploaded them. Sorry about that. If anyone is interested, they can be downloaded here: https://www.dropbox.com/s/p...
On 2019-09-13 18:09:41, user Timothée wrote:
As much as I see the need to quantify biases and trends in the hiring process, I have a number of concerns with data collection and data release associated to this paper.
As far as I can tell, the inference of gender has been done based on names and pictures and pronouns, which is biased, and is actively erasing colleagues that express gender non-normatively, or are read as a different gender.This is not a mere methodological point; it is a practice that is actively harmful to the overall effort on Equity, Diversity and Inclusion, by specifically applying bias to the more marginalized. I think this should be commented in a lot more detail in the manuscript, but I do not think that the methodology is at all reliable.
Second, this dataset contains nominative information on EU citizens (which is in likely violation of the GDPR), and seems to contains information that was divulged by third parties. As much as I understand that people may have been given their consent to communicate data for the purpose of the analysis, I wonder whether explicit consent for un-masked data publication was given, and what the data retention policy is.
Finally, I was surprised to see no mention of the IRB approval process. This is likely an oversight on the side of the author, but I wish that the preprint could be amended with the IRB approval, or the clear statement that the approval was not needed.
We cannot afford a cavalier attitude towards data publication when it involves people, and I do not think that this preprint does a particularly good job at this (which is not a comment on the quality of the underlying scholarship).
On 2023-02-14 12:50:25, user Daisuke Komura wrote:
Our preprint has been published in Patterns and a link (https://doi.org/10.1016/j.p... "https://doi.org/10.1016/j.patter.2023.100688)") will be forthcoming.
On 2020-05-18 17:00:11, user Mikhail Pogorelyy wrote:
Now published at eLife <br /> https://elifesciences.org/a...
On 2022-01-23 14:42:19, user CGPF wrote:
Quote
The Steppe pastoralist-related gene flow occurred in the context of the spread of CWC<br /> and BBC cultures in Europe around 3,200-2,500 BCE (lines 580-581)
(…)
Starting in ~3,200 BCE, the Yamnaya-derived cultures of Corded Ware Complex and Bell Beaker complex spread westwards, bringing steppe ancestry to Europe (lines 560-562)
Unquote
There is no archeological record for a spread of CWC well before ca. 2900 BCE
On 2016-04-28 14:19:27, user Lionel Christiaen wrote:
Reviewed by third journal submitted to.<br /> Journal decision, after reviews: REJECT, <br /> reviews below (simple copy-paste; we'll respond and comment on modifications with next version).
Reviewer 1 Advance Summary and Potential Significance to Field:
Reviewer 1 Comments for the Author:
This study is well done and very convincing, and that revisions are not
necessary for resubmission to a more appropriate journal.
*****
Reviewer 2 Advance Summary and Potential Significance to Field:
Reviewer 2 Comments for the Author:
I have a number of comments/questions/concerns about this manuscript.
Electroporation of sgRNA drivers. The authors report that pools of
electroporated embryos were used for further analysis. Does electroporation
efficiency vary significantly? How many replicates per sgRNA were performed – it
seems like just a single electroporation was reported for each sgRNA. What
proportion of the resulting embryos were transgenic (aren’t DNA constructs
mosaic ally expressed in Ciona)? Were all of these electroporations from a
single batch of embryos? If not, is there variation from batch to batch? If
there is variation, how does this effect your downstream analysis?
Mutagenesis frequency (pg. 6). This is really the mutagenesis frequency per
haploid allele. The authors should describe how this correlates to a mutagenesis
frequency per embryo, given that not all embryos express the constructs
(electroporation efficiency) and not all cells within a given transgenic embryo
express the transgenes (mosaic transgene expression). Presumably, there is a
distribution of mutations in either allele as well as mutations in both copies
of an allele within any given cell. Is there any information on what this
distribution looks like?
Off-target effects (pg 7). This seems more like a specificity assay rather than
an off-target assay. Mismatches outside of the PAM-proximal sequence can be
tolerated and have been shown to produce DSBs – do any of your predicted sgRNAs
share similar or identical PAM-proximal sequences? Have you tested DSBs on any
genomic regions that have well-conserved PAM-proximal sequences, but have
mismatches outside of this region? Did you consider single nt mismatches to the
PAM proximal sequence, rather than just 2 or 4 nt differences? (2 or 4 nt
mismatches are not likely to be effective in any case). This would be a more
accurate assessment of off target cleavage. One of the papers you reference, Hsu
et al., assay substantially more potential off-target genomic locations; it
would be prudent to include a much larger number of genomic sites to assay
off-target effects. It would be useful to include a supplementary table that
describes these potential off-target sites and the resulting analysis of such sites.
On page 9 you mention that CRISPRScan is a tool for rational sgRNA based on
zebrafish data. You developed your own algorithm (TuniCUT) because you
hypothesized that there would be differences between Ciona and zebrafish.
However, you never compare Ciona sgRNAs designed with CRISPRScan to those
designed with TuniCUT. Do they differ substantially? If not, then what is the
significance of your algorithm? If they are similar, what does that say about
the mechanism of Cas9 activity in Ciona vs. other species? If there is not a
significant difference, then the AT-rich nature of the Ciona genome has little
to no effect on the CRISPR/Cas9 process, only on the ability to locate a
suitable CRISPR target sequence.
Is the training set large enough to provide sufficient discrimination of “good”
and “bad” sites for your algorithm? CRISPRScan used >1200 sgRNAs; you
essentially analyzed < 50 sgRNAs (~20 good and ~20 bad – 25% of the 83 total
sgRNAs). It would seem that this small number of analyzed sites could
significantly skew your results. Again, with no comparisons to CRISPRScan, it is
unclear if your algorithm provides any advantages over existing sgRNA design
algorithms.
On page 9 you mention that you added an arbitrary error of 10% based on plasmid
DNA uptake. What exactly does this mean, and how did you settle on a value of 10%?
It is not clear that you have experimentally demonstrated that your algorithm
can accurately design functional sgRNAs as it seems most of the sgRNAs you
report in this study were the same ones used as inputs to your algorithm. If
this is not the case, then this was not clear from the text. I would have
expected that you would test your algorithm by comparing say one or two dozen
novel sgRNAs predicted to work well vs. one or two dozen sgRNAs that have much
lower expected function. This comparison would at least provide experimental
evidence to support your scoring scheme. Ideally, this should also be compared
with sgRNAs identified by CRISPRScan to assess whether your algorithm is a
better predictor of functional sgRNAs in Ciona.
On pages 10-11, you report on producing large deletions by using multiplexed
sgRNAs. However, your assay only detects the presence of a deletion product. Do
you know what percentage of alleles are deleted within an embryo? Is this a rare
event? Have you analyzed/quantitated the spatial distribution of these events?
Can you compare the relative amounts of “wild-type” to deleted regions as a more
accurate measure of efficiency? In other words, compare short PCR products from
your specific deleted region to similarly-sized PCR products produced from
non-deleted regions. This should provide a more accurate, quantitative
description of your mutation efficiencies.
On page 12 you describe the use of linear PCR products to express sgRNAs. It is
a common practice when generating stable cell lines to linearize a plasmid
before transfection to increase the probability of genomic integration.
Supercoiled plasmids are much less efficiently integrated. Do you know if linear
DNA/PCR products integrate into the Ciona genome? What about supercoiled
plasmids? Is this a cause of concern – the potential introduction of additional
mutations due to the random integration of linear DNAs?
Secondly, you only report a single sgRNA introduced as a PCR product (Ebf.3). Do
you know if this works for a wide variety of sgRNAs? Do the linear products work
better than the corresponding plasmid product? Do you know how much sgRNA
product is produced from the PCR product vs. the plasmid form? Do pools of PCR
products work? It seems that there is far less certainty about the usefulness
of PCR products than plasmid products.
Supplemental protocol (page 50). In your supplementary protocol, you explain
that your pre-designed sgRNAs must first be checked with CRISPRdirect to
identify off-targets and then you must check for polymorphisms with the Ciona
genome browser. Shouldn’t your design algorithm already include this information
for the end user?
*****
Reviewer 3 Advance Summary and Potential Significance to Field:
This manuscript describes an attempt to design high efficient guide RNAs for
Crispr/Cas9 based mutagenesis in the ascidian Ciona. First, authors designed 83
guide RNAs that target genes expressed in the heart precursor cells in order to
measure their mutation efficiencies. Based on this dataset, sequence
preferences of good and not good guide RNAs were extracted. The information
were then used to establish a program designing guide RNAs that are potent to
have good mutation efficiencies in Ciona. Using the program authors made a list
of recommended guide RNAs that covers almost entire region of Ciona genome.
Crispr/Cas9 system provides us an easy method to knockout genes, and the
simplicity of the method is good at genome wide analyses. Ciona is an excellent
model for the genome wide analyses due to its small number of genes encoded in
the genome. The program and the guide RNA sequences described here will be good
resources to support future knockout analyses of this organism.
The experiments were done relatively thoroughly and extensively. Honestly
speaking, I felt that the results are not so much novel, because the sequence
preference of good sgRNA in Ciona generally confirms the previous data in other
metazoans, and the gene functions described here are already known ones.
However, the presented algorism and Ci2KO dataset will greatly facilitate gene
knockouts in Ciona and therefore this manuscript will be quite valuable for
Ciona community. For non-Ciona researchers the methods presented here will be
very helpful for constructing similar dataset and extracting the tendencies of
good guide RNAs for the organisms. For these reasons I favor this manuscript
for the techniques and resource section of Development. The text was written
quite plainly enough for easy understanding.
Reviewer 3 Comments for the Author:
I found several relatively minor but essential flaws that need to be corrected
or addressed before considering acceptance. I hope authors find my comments
useful for improving the manuscript.
How were these 83 sites chosen? I understand that most of the targeted genes
are related to cardiovascular system. I would like to know whether there was
any bias, or they were randomly selected, otherwise all were experimented?,
when picking up some from many potential target sites of a gene. If there is a
bias, I would worry about the possibility that the bias might have influenced
the following experiments such as extracting sequence preferences of guide RNAs.
deletions of FoxF gene by simultaneously introducing two sgRNAs targeting FoxF.
To show the bi-allelic deletions of the gene, in situ hybridization was carried
out. As the control of the experiment, a sgRNA that is not related to FoxF was
used. This is not a good control. Controls should be single FoxF sgRNAs-
introduced embryos. The numbers of examined animals and FoxF-reduced animals
were not shown (or I failed to find them). Adding them is necessary. In Figure
3, colonies 03, 04, 06 are indicated but I could not fully understand what does
the information mean. If authors attempted to show the frequencies of large
deletions, it is an important data and please explain more clearly.
of good one is 25%, or 37% if the underestimation is considered) can or cannot
satisfy researchers, because I felt that mutation rates (or rates of phenotype
appearance) in this study and related manuscripts would not always be so high
enough to find novel phenotypes. Focusing on a specific phenomenon (like
cardiovascular system in this manuscript), meaning that analyzing with expected
phenotyeps, and using more efficient knockout/knockdown methods in parallel
with Crispr/Cas9 would be a practical use of Ci2KO sgRNAs, and discussion like
this kind may help readers' understanding. This comment was done because the
manuscript focuses on the genome wide analyses: an important focus of this kind
of studies is finding unexpected functions of genes. How about the mutation
rate in other organisms in which F0-based analyses have been adopted? Some
comparisons with the data of other animals would be useful too.
rich like that of Ciona. References are necessary for the GC contents of Ciona
and human.
the amount of DNA is quite low. Is the linearization the cause? What does
happen if plasmid DNAs are linearized before electroporation? The comparison
may reveal why low amount PCRed DNA can express genes in high efficiency, and
will be useful to improve electroporation, one of the greatest techniques in
Ciona.
On 2019-12-17 00:31:43, user Chahat Upreti wrote:
Hi! I wanted to ask if you recommend the use of Stringtie2 over Stringtie1 for short reads (50-75 bp). The paper does mention that Stringtie2 is better in terms of memory usage and speed, but for accuracy purposes, would you recommend switching to Stringtie2?
On 2022-01-25 17:01:36, user Dr Ed MoreiraBahnson wrote:
A new version of this was uploaded to bioRxiv with a different name, and resulted in a different entry. The newer preprint is at : https://www.biorxiv.org/con...<br /> Additionally, the manuscript was ultimately published in : Biomaterials Science https://pubs.rsc.org/en/Con...
On 2020-04-16 08:54:22, user Ilario De Toma wrote:
why for the SPIKE protein you do not find ACE2 for example?
On 2020-06-24 19:45:30, user Charles Warden wrote:
Thank you for posting this pre-print:
1) I think it is a minor formatting error, but I think there is supposed to be a line between the center and "Base Accuracy" at the top in the legend for Figure 5B.
2) I think there are typos in the caption for Figure 5 (for both A and B):
"radard" --> "radar"
I think those might also be "plots" rather than "charts", but that might be a matter of personal preference.
On 2022-03-23 14:02:25, user Marcus wrote:
The first comment I have pertains to the antibiotic treatment that the ABX mice received. In the study the mice were given water that contained four different antibiotics. Is this combination of antibiotics truly representative of what is given to human patients. Do human patients receive this strong antibiotic treatment for the same two week duration? The next comments I have apply to the 16S rDNA sequencing methodology. For stool collection, I would provide more details about if the mice were sacrificed in order collect stool samples or if stool was simply collected from the cage. As mice are known to be coprophagic. Adding an internal standard step for the DNA extraction step would help confirm that there are no errors with the DNA extraction method. I would also include the forward and reverse primers and primer sequences that were used for your study. The number of sequences per sample and total number of sequences is a detail that should also be found within this section. If and how PCR errors and chimeras were addressed is another addition to consider. With this, I would include the specific program for this step (i.e. ChimeraSlayer). I would include whether or not there was a step conducted to address the sequencing coverage of your samples. Inclusion of PCR details such as the number cycles, temperature, and time is a detail that should be included. Clarification of how taxa diversity and abundance were decided would be beneficial for the reader. Were Operational Taxonomical Units, OTUs, created for the calculation of alpha and beta diversity. If so, what was the similarity threshold set at for forming OTUs. There should also be inclusion if OTUs abundance was corrected for 16S copy number and genome size.
On 2021-06-26 22:40:59, user Sarah Elgin wrote:
It has been suggested by Jo Handelsman that it would be good to have 5-7 such centers for Research Engagement, distributed across the country, and we concur.
On 2023-02-06 12:17:52, user Iain Wilson wrote:
Interesting paper. One comment on the abstract: the sentence "B3GALT6 covalently attaches glycosaminoglycans (GAGs) to proteins to generate proteoglycans and its germline loss-of-function causes skeletal dysplasias." is not quite correct. B3GALT6 is involved in the biosynthesis of the tetrasaccharide linker carrying glycosaminoglycans (GAGs) on proteoglycan core proteins; the sentence sounds as if B3GALT6 enzymatically transfers the whole GAG chain to the core protein.
On 2017-08-30 04:44:23, user Dr. Sebastian Boegel wrote:
This reads very interesting. Thank you for preprinting your tool. However, i can not access http://gvc.0cancer.me/.
On 2025-02-20 20:30:48, user Jesse Conklin wrote:
This should be linked to the final publication:
Conklin, J. R., Verkuil, Y. I., Lefebvre, M. J. M., Battley, P. F., Bom, R. A., Gill, R. E. Jr, Hassell, C. J., ten Horn, J., Ruthrauff, D. R., Tibbitts, T. L., Tomkovich, P. S., Warnock, N., Piersma, T., & Fontaine, M. C. (2024). High dispersal ability versus migratory traditions: Fine-scale population structure and post-glacial colonisation in bar-tailed godwits. Molecular Ecology, 33, e17452. https://doi.org/10.1111/mec.17452
On 2020-09-02 03:07:10, user Heidi Dunst wrote:
Though the study seems interesting. I am curious to know how authors' came to know <br /> S glycoprotein is a candidate target for this study. what about other proteins of Cov2
On 2021-01-12 17:25:55, user Fraser Lab wrote:
Summary:<br /> This manuscript by Huss, P., et al, is major technological step forward for high throughput phage research and is a deep dive into the deep mutational landscape of a portion of the T7 Phage receptor binding protein (RBP). The author develop a new phage genome engineering method, ORACLE, that can generate a library of in any region of the phage genome. They apply ORACLE to do a deep mutational scan of the tip domain of T7 RBP and screen for enrichment in several bacterial. The authors find that different hosts give rise to distinct mutational profiles. Exterior loops involved in specialization towards a host appear to have the highest differential mutational sensitivity. The authors follow up these general scans in the background of phage resistant hosts. They find mutations that rescue phage infection. To demonstrate the utility of the approach on a clinically relevant task, the authors apply the library to a urinary tract associated clinical isolate and produce a phage with much higher specificity, creating a potentially powerful narrow scope antibiotic.
Overall, the ORACLE method will be of tremendous use for the phage field solving a technical challenge associated with phage engineering and will illuminate new aspects of the bacterial host-phage interactions. It was also quite nice to see host-specialization validated and further explored with the screens done in the background of phage resistance mutations. The authors do a tremendous job digging into potential mechanisms when possible by which mutations could be altering fitness. We especially appreciate how well identity of amino acids tracks host specialization within exterior loops.
We have no major concerns about the manuscript but have some minor comments to aid interpretation. There are also some minor technical issues. We think this manuscript will be of broad interest, especially for those in the genotype-phenotype, phage biology, and host-pathogen fields.
Minor comments:
P5L20: In the introduction to the ORACLE section the authors mention homologous recombination then they mention using 'optimized recombination' that is done with recombinases. This contrast should be mentioned somewhere perhaps to highlight the benefit of having specific recombinases.
P6L16: Using Cas9 to cut unrecombined variants is clever... Cool! This is a real 21st Century Dpn1 idea.
P6L27 The authors state that there is a mild skew towards more abundant members after ORACLE. Why might this be? In iterations more abundant members simply become even more abundant? To be clear this isn't a substantial limitation and it's common to see these sorts of changes during library generation. Just curious. Overall looks like a fantastic method.
P7L6: Authors mention ORACLE increases the throughput of screens by 3-4 orders of magnitude. How many variants can one screen? Is this screen of a little over 1k variants at about the threshold of the assay?
P8L7: The authors assign functional scores based on enrichment and normalize to wild type. Is a FN=1 equivalent to wild type?
P9L5: Awesome!
P10L7: Authors mention R542 forms a hook with a receptor. There should be a citation here.
P10L21: For N501, R542, G479, D540 there are wonderful mechanistic explanations. However, for D520 there is not. Any hypothesis for why this is distinct from the others? Are there other residues that behave similarly? I feel it would be really helpful to have a color scale that discriminates between FN 1 (assuming wild type) and enriched/depleted w/in figure 3A.
P12L4: Authors note residues that are surface exposed yet intolerant to mutations in the previous paragraph. Authors also calculate free energy changes with Rosetta and state free energy maps pretty well with tolerant. What is the 93% based on? Perhaps a truth/contingency table would be useful here to discriminate compare groupings. What residues are in the 7% others. Can the energy scores help understand the mechanisms behind the mutations better?
P12L7: Authors state substitutions predicted to stable and classified intolerant could indicate residues necessary for all hosts. What about those that fall outside of the groupings? Unstable residues can also be necessary.
P14L22L Authors mention comparing systematic truncations, however they do not present any figure. This should be in a figure to aid in looking at the data and would surely be helpful to people in the phage field. A figure should be included here especially because this is one of the main discussion topics at the end of the manuscript.
P16L2: The authors did the selection in the background of a clinically isolated strained and discuss 3 variants that were clonal characterized. Was this library sequenced similar to before?
Figures:<br /> Barplots needs significance tests.
Figure 2C-E ; Fig 3A. All figures are colored white to red. With this color scale it's hard to appreciate which variants are neutral vs those that are enriched. A two or more color scale would be more appropriate. Log-scaling might be wise to get a better sense of the dynamic range that is clearly present in fig2F.
FIg 4F: Needs a statistical test between bar plots.
Fig6A-C: These figures have tiny symbols that represent the architecture at an insertion position. It's probably easier to look at if the same annotations from Fig 4B or C for architecture were used.
Fig6D: needs tests for significance
Supp fig 4E: This figure is the first evidence that the physics chemistry of amino acids w/in surface exposed loops determine host specificity. This is followed up by Figure 4D and E. I would consider moving this to one of the main figures.
Supp fig 5: A truth table could be useful here to test for ability to classify based on rosetta compared to FD. It looks like here that the tolerant residues have a distinct pattern
Why are these colored white to red? Perhaps
Minor typo:<br /> P7L11: relationships not 'relationship'
Reviewed by James Fraser and Willow Coyote-Maestas (UCSF)
On 2023-10-27 23:34:09, user CDSL JHSPH wrote:
Hello! I had a great time reading your paper as it is very important to the field of public health and very informative!
I was wondering, however, if there was enough data that was collected to show the immune response differences in those who had the vaccines separately. I assume that the closer you get the vaccines together, the better your IgG responses will be in the future, but I'd be interested to see if there is a weird window of time that the second vaccine becomes a catalyst for a more powerful IgG response (something random like 9 days after the second vaccine perhaps?) .
Again thank you so much for your effort that you put into this research as it is very important and helpful to so many!
On 2019-05-05 09:31:16, user David Curtis wrote:
I looked at this for schizophrenia with negative results:<br /> https://journals.lww.com/ps...
I don't know whether this was one of the traits you tested? It might be worth discussing why your results differ from mine?
On 2023-07-20 14:51:45, user Yencheng Chen wrote:
Please include 2 previous publications with similar ideas in your reference.
https://www.nature.com/arti...
https://doi.org/10.1021/acs...
Thanks,<br /> Yen-Cheng
On 2020-11-06 13:35:56, user Sebastian Quilo wrote:
Nice work!<br /> I suggest to add references for the tools used in your work like scikit-learn and PyCM :
scikit-learn : <br />
@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 2021-03-23 11:31:32, user POSE delenda est wrote:
Way too much fantasy. Way too much juggling of figures. Not a single independent corroboration. Not a single critical contrast. ¿Who owns this publication?¿Who finances these "researches"?
On 2021-05-29 02:46:35, user Joshua Mylne wrote:
This has been peer reviewed and published in The Plant Cell "Structural and biochemical analyses of concanavalin A circular permutation by jack bean asparaginyl endopeptidase"<br /> https://doi.org/10.1101/202...
On 2021-02-16 17:44:13, user Vincent Prevosto wrote:
Very nice data. Just a minor comment regarding the statement that "Rabies virus has been shown to infect primary sensory neurons in the peripheral nervous system". This is a bit misleading. As a general case, RV does not infect PNS primary sensory neurons. The first review cited in support of the above statement (Ugolini 2010) makes that point very clear, and gives details on the most notable exception: mice. The second citation uses mice (and only two mice, one being a control ...), and the third citation is a study using cultured rat embryo DRG neurons, with interesting results regarding anterograde transport, but not informative as to the infectivity of RV in vivo. I think the statement quoted above would benefit from some clarification along those lines.
On 2022-08-15 09:25:19, user Iratxe Puebla wrote:
Review coordinated via ASAPbio’s crowd preprint review
This review reflects comments and contributions by Ashley Albright, Samuel Lord, Arthur Molines and Sónia Gomes Pereira. Review synthesized by Iratxe Puebla.
The paper studies the involvement of aneuploidy in promoting chromosomal instability and suggests the aneuploid state of cancer cells as a point-mutation independent source of genome instability. The paper reports a considerable amount of data. We outline below some suggestions regarding presentation and the analyses reported:
‘mis-segregation in otherwise pseudo-diploid human cells’ - Please provide some explanation for the term ‘pseudo-diploid’.
‘suggesting that dormant replication origins’ - Please provide a sentence clarifying the meaning of ‘dormant replication’.
‘Cells activate dormant origins in response to reduced fork rate and stalled forks to ensure that the genome gets fully replicated in time’ - Please provide a reference to support this statement.
Figure 3
Recommend re-arranging the order and position of the panels for greater clarity.
‘Interestingly, we found a positive correlation between S phase length and frequency of abnormal mitoses (mean S phase length in control: 603,3 ± 55,4; aneuploid: 728,7 ± 46,2) (Fig. 3c).’ - Figure 3C shows that the cells that have an abnormal mitosis had a slightly longer S phase on average, however there is no correlation analysis done or an analysis around "frequency of abnormal mitosis", recommend revising the sentence.
Figure 3C - Cells with a longer S phase (or cell cycle in general) will receive more light before reaching mitosis. Is it possible that the correlation mentioned is due to photo-toxicity? Longer S phase -> more photo-toxicity -> abnormal mitosis. Recommend adding a control to account for the potential phototoxicity of the imaging.
Figure 4 - Panels C and D show that, among the cells that have foci, the number of foci is increased, either by aneuploidy or by the drugs. However, it is unclear from the data if the number of cells with foci also increases. Would it be possible to plot the % of cells with more than 1 foci for each condition? (as in Figure 4G). Also, C and D are aggregates of multiple experiments, it would be good to show the data per replicates.
‘there was a sub-population of senescent cells in the aneuploid sample (Fig. 5a)’ - Was senescence tested in the normal (euploid) population too (at the same passage)? Is that the sample named as "control" in the figure legend?
‘in aneuploid cycling cells was comparable to that of the controls for at least 3 generations by live-cell imaging (Fig. 6a-c)’ - Suggest clarifying here what the control is, in addition to naming it in the figure legend.
Comments on analyses/reporting
In various figures (including Figures 1H,J,L,N,O; 2C,G,E; 3I; 4C,D; 5H,I; 6H,I,J), there is a concern about the statistical approach to calculate p-values based on multiple measurements or cells within each sample. The t-test assumes that each measurement is independent, and multiple cells within the same sample are not independent. Recommend either not reporting p-values or averaging together the values from each sample and calculating the p-value using those sample-level means. For more information, see https://doi.org/10.1371/jou... and https://doi.org/10.1083/jcb...
For each bar graph throughout the paper, recommend reporting the value of n, in the figure itself, the figure legend, or in the text. Using Figure 1C as an example, this reports a doubling in the number of cells with greater than 10 errors, but the significance of that would vary depending on the number of cells analyzed. Some plots in panels c and f have no error bars, and it would be useful to report the number of experiments.
Almost every figure features representative images. The manuscript includes a massive amount of data already, but it may be relevant to show additional images in the supplement in cases where representative images are used in figures.
Data analysis for RNAseq ‘results were filtered only based on p-value’ - Please clarify why the False Discovery Rate was not taken into the filtering step.
On 2020-08-04 14:29:18, user John L wrote:
This article has been presented in a 15-minute talk at https://jrnlclub.org/resear...
On 2017-10-21 19:47:41, user Marcelo Official wrote:
great paper. congratulations!
On 2020-04-10 04:03:52, user Patrick Sexton wrote:
Just as a minor point of note, our manuscript (your reference 20; full reference https://doi.org/10.1021/acs... "https://doi.org/10.1021/acsptsci.9b00080)") was the first to apply this analysis to GPCRs. In our study, the method allowed us to understand the differences in conformational dynamics between adrenomedullin 1 (AM1) and adrenomedullin 2 (AM2) receptors that is critical for the distinct observed receptor phenotypes. It also revealed co-ordinated motions of the ECD and G protein. We are currently using this method to extensively to study many GPCR complexes.<br /> It works best on high quality, high resolution data.
On 2018-06-16 00:49:27, user S C Lakhotia wrote:
A very interesting study showing apparently random movements of nuclear speckles becoming directional. However, such increase in size of speckles resulting from directional movements and fusion, following inhibition of transcription, may not be true for all the different kinds of nuclear speckles that exist in nucleus. For example, our earlier live cell imaging of the omega speckles in Drosophila (Anand K. Singh & S. C. Lakhotia (2015) Dynamics of hnRNPs and omega speckles in normal and heat shocked live cell nuclei of Drosophila melanogaster. CHROMOSOMA, 124: 367-383, DOI 10.1007/s00412-015-0506-0) revealed a very different dynamics. Heat shock (or other conditions that inhibit nuclear transcription) cause the omega speckles to rapidly disappear from the nucleoplasm while the associated proteins accumulate at a single gene locus (the hsromega locus) to form a big blob of the omega speckle associated proteins whose localisation at this gene site depends upon the presence there of newly transcribed hsromega lncRNAs. The omega speckle associated proteins in this case move back to the destined chromosomal location, in a speckle-free form, mostly by diffusion with nuclear matrix having a role. The difference between the dynamics of omega speckles and the IGC associated nuclear speckles may lie in the fact that while the IGC associated nuclear speckles are present in a limited nucleoplasmic domain, the omega speckles in Drosophila cells are distributed through the nuclear volume. The associated proteins in these two speckles types are also very different.
On 2019-09-28 12:59:28, user Tamir Chandra wrote:
Now published at https://doi.org/10.1016/j.c...
On 2020-10-17 06:59:13, user Subhajit Biswas wrote:
My dear friends and peers,
I would love to receive comments from all of you on our observations.<br /> Does anybody know whether thymidine or uridine analogues have been used in clinical trials and outcome, if any?
Please let us know.....
For our other observations on the current SARS CoV-2 epidemic, read the following papers and related "Comments" section for further discussion and details.
"Dengue antibodies can cross-react with SARS-CoV-2 and vice versa-Antibody detection kits can give false-positive results for both viruses in regions where both COVID-19 and Dengue co-exist"
https://www.medrxiv.org/con...
Best regards.<br /> Subhajit Biswas (Corresponding author)
On 2025-09-19 09:29:01, user Alice Risely wrote:
Thank you for submitting this preprint. I used this paper to practice peer review with my MSc Wildlife Conservation students. I thought the peer review process could be helpful, therefore I have copied the review we created below:
This study aimed to quantify the temporal dynamics of chytrid infection in Yosemite toads, with a particular focus on changes in infection dynamics before and after the first hibernation period in metamorphs. The authors hypothesized that changes in physiology and/or exposure associated with hibernation could alter pathogen infection rates and growth. To test this, they sampled first-year metamorphs across six alpine meadows before and after their first hibernation. They also sampled tadpoles, juveniles, and adults during the same pre-hibernation period to explore infection patterns across age classes, although sample sizes for tadpoles and adults were relatively small, and these cohorts were not sampled after hibernation.
The authors found that tadpoles were free of infection, whereas infection prevalence increased to about 20% in first-year metamorphs before hibernation and then rose sharply to over 90% after hibernation. Infection prevalence and intensity remained high the following summer. They suggest this striking increase is linked to hibernation itself, though the underlying mechanisms remain unclear. Two hypotheses are brought up in the discussion: (1) metamorphs may already be exposed prior to hibernation, but immune suppression during hibernation allows pathogen proliferation to detectable levels, or (2) metamorphs enter hibernation chytrid-free and become infected during hibernation. Notably, no effects of infection on morbidity were detected, suggesting Yosemite toads may act as asymptomatic carriers.
Overall, I found the study nicely written, interesting and relatively well designed, with some exceptions. The methods are largely appropriate for the central question of whether hibernation in first-year metamorphs contributes to chytrid persistence in this population. The inclusion of a conceptual figure of the study design was particularly helpful. I have no major concerns regarding the methodology.
That said, I did find parts of the manuscript confusing and have several suggestions for improvement. My primary conceptual criticism is that the results apply only to first-year metamorphs. To test more robustly whether hibernation itself drives changes in infection dynamics, it would have been valuable to examine adults or even other nearby species that do not hibernate (although I understand that these would be subject to different climates). Including these comparisons would strengthen the case that hibernation per se influences infection prevalence. As it stands, the study convincingly demonstrates a link between dormancy and infection dynamics in metamorphs, but not necessarily beyond this cohort. I encourage the authors to specify this in the abstract and to emphasize that the mechanisms remain unresolved.
I also found the introdiction quite confusingly structured and did not fully outline what is known from previous studies or provdie enough information on the ecology of the study species and why it makes a good model system to test these questions. I have more detailed comments on the introduction below.
Section-Specific Comments:
Introduction<br /> The introduction clearly states the study’s aims but would benefit from more detailed context on the current state of knowledge. Summarizing prior studies in greater depth - for example, Kasler et al. (2023), which is mentioned in the discussion but not the introduction - would help the reader understand the state of the knowledge and where the gap or conflicting evidence arises. At present, the introduction focuses too heavily on highlighting the results, while the rationale, state of the field, and knowledge gaps are underdeveloped.
A bit more information on the ecology of Yosemite toads would also be helpful. For instance: Can chytrid transmission occur during hibernation? Do toads hibernate individually or communally? Why is this species particularly interesting for examining infection dynamics beyond the fact that it hibernates? The authors also assume that everyone knows about chytrid – the fact that is causes huge population declines and extinctions, and any known factors on its spread and persistence, are not even mentioned.
The structure of the introduction is somewhat disjointed, with results and knowledge gaps raised in multiple places rather than in a logical sequence. I suggest beginning the introduction with chytrid fungus (or emerging fungal pathogens more generally), followed by discussion of seasonal and life-history drivers of infection. Dormancy could then be introduced as a potentially important factor early on – but starting with dormancy when this paper is fundamentally about understanding drivers of chytrid infection and maintenance is possibly confusing to some. Notably, the authors argue that their results “challenge current thinking about seasons,” yet the introduction contains little background on known seasonal effects on chytrid dynamics. Providing this context would make their justifications and interpretation more compelling.
Finally, sample sizes are difficult to find in the text. Including these in Figure 1 and briefly outlining the study design at the end of the introduction would improve clarity on methods.
Overall, a thorough restructuring and expansion of the introduction would make it more informative and logically ordered.
Methods<br /> I had questions about the modelling framework. Why were pre- and post-hibernation cohorts analyzed separately? Would a zero-inflated model have been more appropriate for intensity data, and were these approaches considered? Presenting model outputs and explanatory power in a supplementary table would improve transparency.
Additionally, it would help to explain why juveniles and adults were only sampled at a single meadow and time point, rather than following the same design as metamorphs. Clarifying these decisions would improve understanding of the study’s scope and limitations.
Results<br /> It is unclear why only two figures are presented in the main text, with the remainder in the Supplementary Materials. Some of the supplementary data would be valuable in the main body, ideally integrated into a single figure that allows direct comparison across life stages and survey periods.
Figure 3 should include sample sizes, as in Figure 2. It is also unclear why tadpoles are not represented in this figure, given their relevance to the infection trajectory.
Discussion<br /> Much of the contextual information in the discussion would be better placed in the introduction (e.g., references such as Kasler et al. that address similar questions). This shift would allow the discussion to focus more sharply on how this study advances understanding and to highlight its limitations (e.g., small adult sample sizes, lack of non-hibernating species for comparison).
Supp Materials<br /> Note that zeros are missing in one of the intensity figures, and that the order of the meadow sites is not always matching across figures.
On 2025-10-07 12:59:05, user Evolutionary Health Group wrote:
We at the Evolutionary Health Group ( https://evoheal.github.io/ ) really enjoyed this paper.
Here are our highlights:
This study investigates bacterial adaptation to cold shock tracking the fitness time dynamics of thousands of mutants.
The authors demonstrate that adaptation unfolds in distinct phases: early survival depends on membrane fluidity and cell wall remodeling, while later recovery relies on RNA-level regulation and ribosomal function. They highlight a synergistic role of ribosomal methyltransferases in restoring protein synthesis after translational arrest.
We admire the time-resolved design + comparative analysis of two model species, which helps distinguish conserved from species-specific elements of the cold shock response.
On 2020-10-08 19:24:19, user gletreut wrote:
Please see our response to this comment at: www.biorxiv.org/content/10.1101/2020.10.06.315820v1.
On 2018-07-02 14:31:00, user Huw A. Ogilvie wrote:
This looks like a really well designed study! One thing I would be interested in seeing is how the choice of model affects credible intervals for diversification rates. I would expect that when data is simulated under a more complex model (e.g. BD with a UCLN clock) but parameters are estimated under a simpler model, the credible intervals will be too narrow. This could be true even for conditions where the simpler model accurately estimates lambda etc.
For example in Ogilvie et al. 2017 I found that when data is simulated with a UCLN clock, the supposed 95% HPDs for branch lengths inferred using a strict clock included the true value only 75% of the time. I wonder if the same holds true for diversification rates, and vice versa when the inference model is more complex than the simulation model if the credible intervals will be too wide.
On 2020-02-03 09:18:44, user Dottor Leopardo wrote:
identity /similarity to amino acid residues in key structural proteins of HIV-1 is QUITE COMMON in nature (starting from the human Red Blood Cells surface).
On 2020-05-19 22:16:09, user Sophie Gryseels wrote:
A corrected and improved version of this study is out now in PNAS: https://www.pnas.org/conten...
On 2023-07-06 08:56:03, user Deepak Nair wrote:
The authors state that "when re-analyzing previously<br /> published structures we find that the geometry of B-family polymerase active sites does not convincingly support the “polar filter” model". However, this study provides more evidence for the existence of the polar filter. When there is reduced interaction between N828 and the phosphate moiety of the incoming nucleotide, the rNTP can move slightly such that the 2'-OH will no longer clash with the steric filter. If there is proper interaction between N828 and the phosphate moiety, then it is not possible for the rNTP to move to neutralize the steric filter. The Asn residue is conserved in other B-family DNA polymerases and all the available crystal structures show the presence of the interaction between the conserved Asn and the phosphate moiety. The M644 residue is present below the triphosphate moiety. The observation that the M644G mutation leads to reduction in the interaction of N828 with the phosphate moiety of the incoming nucleotide is very interesting because the C-beta atoms of the two residues M644 and N828 are located about 9 Angstroms from each other.
On 2019-10-21 01:04:38, user Jean-Michel Ané wrote:
And a detail to fix in your introduction... orchids also associate with ascomycetes, see for instance this recent paper: https://link.springer.com/a... . but there are other examples too.
On 2020-05-09 18:27:32, user Alex Crits-Christoph wrote:
Thanks to the authors for sharing this paper and software! I can confirm that CheckV is easy to install and use, and very fast to run on a large number of viral genomes.
Previously, many viral genomes from metagenomes could only be assumed complete if there was sequencing evidence that the contig that they were on is circular. While this isn't perfect (sometimes internal repeats could cause an incomplete contig to appear circular), it was a reasonably reliable indication of completeness. Of course, many viral genomes are actually linear, so it was difficult to publish a linear viral genome and be sure that the sequence was 'complete'. One enticing aspect of checkV is its ability to possibly assign a completeness estimate to these linear sequences. This completeness estimate ("AAI Completeness") is based on gene comparisons to a large set of reference sequences.
I wanted to test the completeness metrics in some fair and unbiased way. In order to do this, my logic was to take novel circular viral contigs (determined by VirSorter and my own scripts), with the assumption that these genomes are almost certainly complete, and see if the AAI completeness of these genomes was in fact near 100%. The important aspect of this benchmark is that these genomes were not in (and likely quite distantly related to) genomes in the checkV database.
The first set of genomes was from this baby gut microbiome publication:
https://advances.sciencemag...
Where the viral genomes were deposited in FigShare and not a centralized database. Running checkV on these ~2500 phage genomes only took a few minutes. The mean completeness for all viral contigs was considered about 50%.
However, passing checkV only circular genomes from this dataset, the mean AAI completeness for *high confidence* guesses just about at 100% with little variation. This is remarkable - the program was able to guess that these genomes were complete just from their gene content. In this dataset, about 81% of the circular viral contigs could have their completeness estimated with high confidence. It is really nice how checkV both tells you the estimated confidence of the completeness estimation, as well as the hit in its database with the highest gene similarity. This is key because it helps researchers understand how this completeness estimate was derived (by comparison to a related phage) and lets researchers dive into the details of this synteny comparison.
I then passed it a larger set of highly divergent viral genomes from soil. The AAI completeness was remarkably accurate for estimating that circular viral genomes were in fact 100% complete for about 17% of the genomes ("high confidence"). All of these soil viruses are extremely divergent and novel, so it was not surprising that the percentage of high confidence estimations was far lower than for the viral contigs from the human gut. The completeness estimations at low and medium confidence had a large range, but that is evident in their labels.
In conclusion, it seems as though checkV is very good at three key things. (1) The first is estimating how accurate its AAI completeness estimate will be - labeled as "high confidence", "medium confidence", and "low confidence". These labels seem to correlate very well with the accuracy of the final prediction. (2) Secondly, for high confidence estimations, checkV seems to report very accurate completeness metrics. This will be very useful for researchers who want to estimate the completeness of many of their viral contigs. Depending on how novel their viruses are, they could be able to accurately estimate completeness for ~20%-80% of their viral genomes. (3) Finally, checkV is transparent about how it arrives at these completeness estimates, reporting the closest hit in its database and the degree of similarity.
Beyond these tests, I am still a bit hesitant about assigning a completeness estimate for viral sequences that, at the end of the day, is based on similarity to reference sequences. This could unfairly penalize or misrepresent highly divergent viruses, of which there are untold thousands to still be discovered. But this is why it is so important that checkV reports its degree of confidence - and that researchers probably mostly stick to the "high confidence" estimations, and take the medium/low confidence observations with a healthy grain of salt.
On 2020-08-02 22:18:47, user Cartouche 74 wrote:
Considering that the cranial morphology of all Corded Ware populations was markedly different from that of the Catacomb/Potapovka/Sintašta/Andronovo groups, the dominance of R1a-Z93 in the present sample requires some explanation. I would not bet much money on the assumption that the Fatjanovo people were the ancestors of Andronovo/Sintašta (as colleague Davidski maintains). Rather, it looks like an introgression of R1a-Z93 from the steppe.
On 2019-02-10 01:43:54, user Ana Lúcia Tourinho wrote:
Our new paper discussing the changes in harvestmen assemblages driven by Mega dam in the Amazon is preprinted, check it out! https://doi.org/10.1101/542969
On 2020-01-16 16:36:35, user Binxu Wang wrote:
A few typos in the preprint....<br /> 1) Figure 5 caption, panel b is typoed as a "marker size. a, Neurometric curves for"<br /> 2) In the Methods part, Neurometric fitting section , \theta_k in equation should be \theta_0 seems like typo ......
On 2020-12-10 22:44:51, user Isabel wrote:
I enjoyed reading this paper and learning about a very promising combination treatment to reduce pancreatic cancer in mice. I was very impressed with the evidence presented in this paper and I thought it was great that it is understandable for people with many different scientific backgrounds. After looking at this paper, I noticed that a slightly different version was published later. I think that it was a good idea to not include the PD-L1 data in the published version of this paper because it might be confusing for some readers and does not contribute to the hypothesis of this paper. However, I am interested in this data and I wonder if this is being looked into further, and if it could possibly be the subject of a new paper following this one. I thought the way you presented the data in figure 1 was very clear, and it allowed the readers to rule out any bias or statistical errors by showing them the raw data on the graphs. I also really enjoyed looking at the tumor cross-sections in figure 3 and being able to see the tumors at different magnifications made the evidence even more convincing. Lastly, I thought the potential mechanism of NAM+GEM figure was accurate and made sense, but the way it was presented was a little confusing. A simpler flowchart with more descriptions and less arrows might be helpful. Overall, I thought this paper was very clear, had an excellent introduction, and was very convincing. I am excited to see the therapeutic application of these results.
On 2024-10-31 14:51:48, user YC Han wrote:
This study holds considerable promise, but its findings are significantly undermined by the notable delay in developmental stages observed in embryos injected with CRISPRi/a + sgRNAs, as evident in Figures 1-3. For instance, the embryo injected with CRISPRi + tyr sgRNAs marked with 48 hpf in Figure 1B, appears to be developmentally equivalent to a 30 hpf wild-type embryo, which exhibits substantially fewer pigment cells than embryos at 48 hpf. This discrepancy raises concerns about the direct impact of the CRISPRi/a + sgRNAs injection, whether or not the delay development was caused by an unintended consequence.<br /> I strongly recommend that the authors reexamine their results and conduct a comparative analysis of CRISPRi/a + sgRNAs-injected and control embryos at equivalent conventional developmental stages, as described by Kimmel et al (1995, PMID: 8589427). This crucial consideration will not only enhance the study's rigor but also provide valuable insights to the zebrafish research community.
On 2024-08-01 10:06:03, user Tim Dreßler wrote:
Also (maybe) another one (second to last page from the discussion): Although we noted overall stronger responses to shifted feedback during vocal production, on average vocal responses did not do a better job distinguishing different feedback manipulations than would be expecte (--> expected) based on passive coding
Also if some of the authors is reading this: Would you mind explaining this sentence in a bit more detail to me (tim.dressler@uni-oldenburg.de). Would be awesome! Thanks!
On 2023-02-28 13:34:03, user Mehdi Ghram wrote:
Hi, <br /> This project research has been published in The EMBO journal:<br /> https://doi.org/10.15252/em...<br /> Thanks
On 2023-11-02 21:38:56, user Marisol wrote:
Here are a couple of points for readers to consider:
Soula et al., agree that by increasing the group size severalfold, the statistical results often change. It is worth emphasizing, that the group sizes in our study were either on par with or notably larger than those reported in the study by Iaccarino et al.
In the spirit of full disclosure, it's important to note that the authors of this manuscript have a vested interest as they own a company that manufactures and sells 40-Hz stimulation devices ( https://optoceutics.com)
On 2016-08-04 08:54:18, user Wolfgang Huber wrote:
The work is concerned with clustering of gene expression profiles from RNA-seq data, using data transformation and Gaussian mixture models. The manuscript is well-written and of high technical quality. I have a few suggestions for improving it further.
Main points:
1.) The transformation to p_ij and then to g_arcsin, g_logit is interesting, and worthwhile considering. However, as the authors note on p.4, there are also other, obvious, and well-established candidates for transformations, such as log(n+c), VST, rlog, moderated CPM (all should be followed by mean centering). Since the title of the ms is "Transformation and model choice..." I would consider it important to include these in the study. One possible result could be that "it doesn't matter very much", or that one or another of these candidates really does poorly; in any case, this would be interesting for readers, as the choice of transformation often creates a good deal of anxiety.
2.) The arguments for Gaussian mixture models (GMM, e.g. p.16) are well taken, but are a bit old-fashioned, "20th century". Nowadays, there also very good resampling based methods for assessing cluster stability, cluster membership, etc. See, e.g., the clue package on CRAN, or the "Cluster Stability Analysis" Section in the vignette of my Hiiragi2013 Bioconductor package. Adding these methods would be interesting, although I could understand if the authors decide it is out of scope. In that case, I suggest that at least the claims on exclusive utility of GMMs for doing such stability assessments be toned down.
Smaller points:
3.) I very much appreciate the provision of an Rmarkdown vignette reproducing all plots in the paper. This is exactly how it should be done. Here two more suggestions, which go beyond with what is required for journal publication, but would greatly increase the impact and quality of this research: To allow execution by readers, I recommend also providing the .Rmd file, not only the rendered PDF. Moreover, to avoid 'code rot' and other reproducibility issues, I recommend submitting the Rmarkdown document (e.g. in the form of a package vignette) to a repository with a build system, such as CRAN or Bioconductor, which will make sure the code actually runs on any regular computer (no dependencies on private files), with current versions of R, etc.
4.) p.4 "As previously noted, each of these transformations seeks to render the data homoskedastic" -- I do not think this is correct. Homoskedasticity is the stated goal of the variance-stablising transformation, but not of the others.
5.) p.4 "... but does not facilitate clustering together features with similar patterns of expression across experiments." -- where is the evidence for this claim? (cf. Point 1 above)
6.) p.4 Notation overload in the equation for p_ij: the symbol j on the right hand side is used for two different things
7.) p.5 "becomes even more apparent when considering the normalized expression profiles p_ij (Figure 1C)." -- is this not a circular argument if Cluster 1 was itself obtained from the p_ij?
8.) p.5 "This means that the vector of values p_i are linearly dependent ... For this reason, we consider two separate transformations of the profiles pij to break the sum constraint ..." -- Even though the sum constraint is replaced by a more complicated constraint, the dependency is not broken. Is this argument really tenable (or needed)?
9.) p.5 What are the values (used by the software) for g_logit for p_ij = 0 or 1? Both can and will happen in practice.
10.) p.7 It would be helpful if plots showing the graphs of these transformations could be provided.
11.) p.10 "filtering genes with mean normalized count less than 50" -- This seems like it could be a too stringent threshold, especially in, say, developmental studies, where certain genes are completely switched off in some of the conditions. Indeed these tend to be the most important genes.
Very small point:
12.) Introduction: "Increasingly complex studies of transcriptome dynamics are now routinely carried out using high-throughput sequencing of RNA molecules, ..." <br /> Indeed what is being sequenced in RNA-Seq are cDNA molecules. Direct sequencing of RNA is also possible but (currently) usually not called RNA-Seq.
Typos:
p.8 compatability
p.15 hierarhical clustering
This review was prepared by Wolfgang Huber.
On 2024-05-09 19:30:15, user Sophie Astrof wrote:
This paper has been published:
https://doi.org/10.1161/CIR... Circulation Research. 2024;134:e112–e132
On 2025-01-08 09:30:44, user Robert King wrote:
github repos are not accessible?
On 2023-09-26 04:52:14, user Emma McHugh wrote:
Now published: https://doi.org/10.1128/msp...
On 2024-11-20 15:06:42, user Nicolas Locatelli wrote:
This manuscript has been published in BMC Genomics w/ open access on 11/20/2024 with the title: "Chromosome-level genome assemblies and genetic maps reveal heterochiasmy and macrosynteny in endangered Atlantic Acropora". The "now published in..." link at the top of this preprint will be coming soon.
On 2022-10-14 15:53:54, user Pedro Herrero-Vidal wrote:
Work published a NeurIPS proceedings 2021
https://proceedings.neurips...
Cite as: <br /> Pedro Herrero-Vidal, Dmitry Rinberg, Cristina Savin, Across-animal odor decoding by probabilistic manifold alignment. Thirty-fifth Conference on Neural Information Processing Systems, 2021
On 2018-02-13 13:31:13, user Silas Kieser wrote:
Ok, uses less memory for 10x smaler assembly
On 2020-01-22 08:08:13, user Shillah Simiyu Mangwa wrote:
Yesterday, the Chinese Health authorities reported that human-to-human infection is possible, and the virus has killed slightly more than 6 people. How then do your findings suggest that the virus " does not readily transmit between humans and should theoretically not cause very serious infection"?
On 2018-09-18 12:51:15, user Martin W. Hahn wrote:
Interesting paper. I am surprised about the very high abundance of Polynucleobacter bacteria. This is really unexpected. It would really be great if you could convincingly show that Polynucleobacter bacteria are so abundant in a multicellular organism.
Some comments:
(i) You created a new family, i.e. Oxylobacteraceae. I think you meant Oxalobacteraceae, however, the genus Polynucleobacter belongs to the family Burkholderiaceae. I recommend the NCBI taxbrowser for checking taxonomy of organisms.
(ii) I am not completely convinced that you really detected Polynucleobacter bacteria in your samples. The long 16S rRNA sequence generated by you is really low quality because of the many, many 'N' in the sequence. It is not possible to perform sound phylogenetic analyses (or taxonomic classification) with such low quality sequences. I could imagine that the detected organisms represent a new genus of Burkholderiaceae bacteria closely related to the genus Polynucleobacter. I recommend cloning of PCR products prior to sequencing. Furthermore, a huge amount of sequences including many genome sequences is available in Genbank. You even could design some PCR primers more specific for Polynucleobacter strains, which should increase the sequence quality.
(iii) Are you sure that the detected Polynucleobacter bacteria were really associated with the tapeworm and do not represent a contamination from the water in which the sticklebacks were maintained? Polynucleobacter bacteria are ubiquitous in natural freshwater systems and where also detected in tapwater.
(iv) Please pay some more attention to Polynucleobacter taxonomy (see your tree in supplementary materials). The subspecies P. necessarius subsp. necessarius and subsp. asymbioticus do not exist any more. A taxonomic revision was published two years ago (http://ijs.microbiologyrese... "http://ijs.microbiologyresearch.org/content/journal/ijsem/10.1099/ijsem.0.001073)"). Unfortunately, Genbank is frequently not up-to-date.
(v) It is well known that Polynucleobacter strains (species) with an obligately endosymbiotic lifestyle evolved, however, currently these endosymbionts are exclusively known from ciliates. Do you have any idea if the Polynucleobacter detected by you dwell inside our outside of cells? <br /> Interestingly, endosymbiotic Polynucleobacter strains have some signatures in their ribosomal genes caused by increased mutation rates and decreased or lack of purifying selection (https://onlinelibrary.wiley... "https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1462-2920.2006.01144.x)"). High quality 16S rRNA sequences may provide a hint on the lifestyle of the detected strains
Good luck for publication of this paper in a peer-reviewed journal.
On 2020-03-16 07:42:23, user Ahmed Sayed Abdel-Moneim wrote:
https://www.biorxiv.org/con...
The authors built their research on the finding of Chen et al., 2005<br /> [https://academic.oup.com/ji...] and the similarity between SARS-CoV-2 SARS-CoV spike protein [S not SP as mentioned by the authors]. <br /> However, Chen et al., 2005, confirmed that SARS-CoV spike protein could not bind to CD147 but they found that the N protein could only bind and speculated that N protein is relocated to the surface from the core during maturation of the virus.