On 2023-11-10 16:44:10, user KJ Benjamin wrote:
Interesting approach, but I'm confused why the authors would model population instead of genetic ancestry? The authors use ADMIXTURE to show a great degree of mixed ancestry, but do not examine the effect of genetic ancestry, but "population grouping". This would be extremely influenced by environmental factors that are differences across and within continental groups.
On 2023-11-10 00:14:27, user Alan Rose wrote:
This is an impressive manuscript reporting a stunning amount of work that reveals an interesting and underappreciated feature of gene regulation in plants, namely that sequences downstream of the transcription start site (TSS) can have major roles in regulating expression. My only complaint is that it does not sufficiently cite previous work that reaches many of the same conclusions. Findings reported here that were previously published for Arabidopsis include the observation that sequences downstream of the TSS, in exons and introns, play a major role in controlling transcription (Rose and Gallegos, 2019, Scientific Reports 9:13777), that these sequences are unlike animal enhancers because they have no effect when moved upstream of the TSS (Rose, 2004, Plant Journal 40:744 and Gallegos and Rose, 2017, Plant Cell 29:843), that a motif containing the sequence GATC boosts expression in a dose-dependent manner and that mutating nucleotides within the GATC motif reduce its effect (Rose et al., 2016 Plant Molecular Biology 92:337). The Rose and Gallegos 2017 paper is cited but only as the reason for using the TRP1 promoter and for identifying the motif similar to GATC. I realize that the number of references is limited in some journals, especially those with a high profile (where I would really like to see this work published), but these seem too pertinent to omit.
On 2023-11-09 17:10:12, user Reade wrote:
The biological concepts of the paper are easy to understand and follow as one experiment leads to another. The toy figures at the beginning of the figures outlining the experimental overview are very useful. There are some grammatical errors in the paper for example “casual relationship” which should be causal. Which statistical test is being used is unclear and at times I believe the wrong statistical analysis is used. For example, figure 1 states that Wilcoxon test or one-way ANOVA is used for comparison, but nothing indicates which analysis is used for which figure. Furthermore, when doing relative expression it is unclear what the expression is compared to, some graphs indicate it is relative to IRPL13a, but it looks like this is not true as in figure 2F the MFN2+/+ HEY1 and ID3 are both set to 1, suggesting that is what is being compared. The labeling of figures also makes it difficult to identify what is being compared, again in figure 2F it is unclear what the p values are indicating as they are over more than two groups. There are a few figures that I would like to see controls to compare against the date, example in figure 3A and 4I.
On 2023-11-09 15:17:53, user Bertram Klinger wrote:
Thank you for the nice explanation of expectation maximisation.
However, in my eyes your algorithm does not get rid of the spillover signal. <br /> In Fig3C the correlated distribution is the result of spillover from channel Yb172 into Yb173, as can be seen nicely in Fig3D where this correlation vanishes with the same antibody labeled to a channel which Yb172 does not spill into (Sm147D). Instead your algorithm seems to only set low signals to NA.
To undermine this point, Fig1a shows that the spill-in signal from Yb172 into the Yb173 channel is on average 2.7. Assuming the the mean of Yb172 bead to be of similar strength as Yb173 (~6.2) then for a cell population without Yb173 we would expect a difference of roughly 3.5 (in log scale) between the two channels if purely driven by spillover. Which is what can be seen in Fig3C for the CD3-low population ( i.e. they do not express CD3).
On 2023-11-09 00:17:42, user Pooja Asthana wrote:
Summary:<br /> Identifying and modeling low occupancy structural changes and binding events is a major goal of protein crystallography. The most sensitive methods used to detect low occupancy changes require crystallographic datasets to be isomorphous, which often limits their applicability. To address this limitation, the authors have developed MatchMaps, an pipeline that performs map subtraction in real space rather than reciprocal space, thereby eliminating the need for isomorphous data. The MatchMaps approach takes measured structure factor amplitudes from two states: the ON state (the interesting/ligand-bound/perturbed state) and the OFF state (the ground/apo/unperturbed state). Next, the algorithm performs rigid body refinement of the OFF state model (e.g. a model built/refined using only the OFF state data) using both the ON and OFF structure factor amplitudes. The electron density maps are then aligned by a rotation-translation matrix derived from alignment of the ON and OFF refined models. The authors apply MatchMaps to four different cases studies and where applicable, compare the results with isomorphous difference maps
We tested the MatchMaps algorithm with some published datasets of the SARS-CoV-2 NSP3 macrodomain with ligand bound at 10-30% occupancy (https://zenodo.org/records/..., ligand-bound datasets UCSF-P0628, UCSF-P2193, UCSF-P2227 and apo dataset UCSF-P0110) along with some unpublished data. The program is well documented and easy to install. With some generous help from the authors, we successfully used MatchMaps to reproduce ligand density observed in isomorphous difference maps calculated using the same datasets. The initial issue we encountered had to do with the default solvent mask, but this was overcome based on their advice. We also successfully ran matchmaps.ncs to calculate a difference map between the two macrodomain protomers in the P43 crystal form (chain A and B of apo dataset UCSF-P0110).
Overall, the preprint is well written and the figures are clear and helpful. The major success of this work is the development of a method for the real-space subtraction of electron density maps to visualize structural changes between non-isomorphous datasets. This provides structural biologists with a powerful tool for visualizing structural differences between X-ray diffraction datasets and therefore will be of broad interest to the community. The major limitation is whether MatchMaps can be used to detect structural differences that are not detected using isomorphous difference maps, or to model structural differences that are not apparent by comparing refined coordinates. Although visualization is helpful, the real power in a tool such as this would be in its ability to detect and model low occupancy states.
Major points<br /> The manuscript could be strengthened by including an example where MatchMaps detects a structural change that was not detected by calculating isomorphous difference maps or by comparing refined coordinates. The authors show how MatchMaps removes artifacts due to misaligned models (Figure 3g), but it is unclear to us whether MatchMaps can detect new structural changes. Put another way, it’s unclear to us whether structural changes that result in non-isomorphous datasets would be better visualized using MatchMaps versus a simple comparison of coordinates.<br /> MatchMaps produces two maps by default, one with a solvent mask applied and one without. We are curious why a map with a solvent mask is calculated. This mask is based on the OFF model, so any features of the difference map corresponding to structural changes outside of the solvent mask will be removed. If the solvent mask is required to remove noise in the MatchMaps generated maps, then it would be helpful to discuss this and give examples where the solvent mask was necessary (because this goes somewhat against the claim made by the authors that MatchMaps maps are less susceptible to “uninteresting signal” - line 215). The solvent masking also was a challenge for us in detecting some fragments, but was resolved by working through different options with the authors. An expanded discussion of the merits and limitations of solvent masking (and when to depart from defaults) is therefore likely to be helpful to many users.<br /> Do the authors envisage that MatchMaps could be used to model structural changes or just to visualize them? Along these lines, a comparison with the PanDDA algorithm might be helpful (Pearce, N. M. et al. 2017, Nature Communications). PanDDA can be used to both detect and model low occupancy states, but is most effective when data sets are isomorphous (so is typically used to detect and model low occupancy ligands obtained by soaking). Can the authors imagine an extension to MatchMaps where multiple datasets are averaged to create the OFF map in a similar way to PanDDA? Improving the signal-to-noise of the OFF map might remove the need for the solvent mask.
Minor points<br /> Figure 3c/d. Can the authors comment on differences between the isomorphous difference map and the MatchMaps map? The density is similar but not identical. This is subjective, but to us the MatchMaps density looks a little noisier.<br /> Line 95. Are the data scaled with SCALEIT and then truncated (line 95)? Wouldn’t the reverse be more appropriate (e.g. truncation followed by scaling)?<br /> We were grateful for the -verbose flag in the command line, however, this only prints the output from SCALEIT/phenix.refine. Would it be possible to modify this flag to print the output from all the programs?<br /> Line 104. How are the maps placed on a common scale?<br /> Figure 2f. Is there positive difference density associated with the terminal ribose (or the unmodeled nicotinamide) in the MatchMaps? It would be helpful if the figure legend indicated what part of the model the maps are contoured around. <br /> Line 350. The text says ±2.5 σ but the figure says ±1.5 σ.<br /> Line 161-162 - figure references do not refer to the correct panels. <br /> Line 185-189 and fig. 4d label and text does not match- open:closed conformation/ H-bond
Review by Pooja Asthana, Galen J. Correy & James S. Fraser (UCSF)
On 2023-11-08 20:29:54, user P. Bryant Chase wrote:
Molecular basis for the "Abbott effect"? Bud Abbott was thrilled to know it was still being investigated in the 1980's, and would surely be thrilled to see this work if he was still living.<br /> Abbott BC & Aubert XM. (1952). The force exerted by active striated muscle during and after change of length. J Physiol 117, 77-86.
On 2023-11-07 13:19:53, user Pedro H. Oliveira wrote:
This is a very interesting manuscript.<br /> It was a pity however to not have seen discussed in this work the recent findings on defense systems' co-localization published here (https://www.biorxiv.org/con.... I believe the latter work will also be useful to update a few of the claims mentioned by Wu et al. in their Introduction.
On 2023-11-06 14:56:20, user jfritscher wrote:
I question the practice to benchmark against a tool from 5+ years ago (MetaPhlAn2) that has massively improved in the meantime to demonstrate the own tool's performance. For the same reason I do not think the results in Fig 3 are in any way telling about the performance of MAGinator in the light of state-of-the-art tools. It is claimed that subspecies-level resolution is gained by using GTDB-tk. This is questionable has GTDB-tk resolves at species level and thus the increase in "resolution" is merely a result of using a different taxonomy and not because actual subspecies resolution (whatever that is anyway) is achieved. Further, I would not use "de novo identificiation" is this context.
On 2023-11-06 12:55:04, user Faraz K. Mardakheh wrote:
Congrats Mathias and the team. It is good to finally see this preprint out.
For any interested readers, I should also mention our preprint describing a very similar method (named TREX) which came out a few months before, since it is not cited in your preprint:
On 2023-11-06 10:32:22, user MoMo wrote:
Hi,I like your work very much and wish you to publish the final version soon. I would like to point out that TP53 mutants may not be "unfunctional". Some missence point mutations (e.g. R175H, R273H) result in gain of function (GOF). You cited the paper by Escobar-Hoyos showing this. I also found that GOF mutant p53 regulate splicing of VEGFA, however the mechanism was different (Pruszko et al., 2017). It would be interesting to use your bioinformativ tools and skils to compare alternative splicing in GOF p53 mutants versus loss of function.
On 2023-11-06 04:33:44, user Raghu Parthasarathy wrote:
The title really needs "in rats" (i.e. "in Female and Male Rats"). Otherwise, it is at best unclear and at worst suggests very general experiments about male and female animals of all sorts.
On 2023-11-06 00:38:13, user Sergio Contreras Liza wrote:
In this research we try to demostrate the effect of microbes (bacteria) on the production of potato seed tubers. Azotobacter sp.and Bacillus sp. were the most important genus in the form of consortia, for tuber number and weight.
On 2023-11-05 08:37:13, user Manuela Giovannetti wrote:
Dear Olga and co-authors,<br /> I have just read your paper and I want to compliment for the high level of your study. Your data are very interesting and worth of depth consideration. I have only a doubt, concerning the retrieval of bacteria other than endobacteria in your spore. As you may know, we have retrieved many bacteria strictly associated with AMF spores (after 15 washings). Actually, you performed a de-contamination of spores, with H2O2 and chloramine T, so you assumed that the retrieved bacteria were endobacteria. As our previous works described the occurrence of bacteria within the different layers of spore walls, I wonder whether they may have been protected from de-contaminating agents in such a peculiar niche. This is why we defined them as "stricty associated". With all my best wishes and regards, Manuela Giovannetti
On 2023-11-03 18:45:39, user Marouen Ben Guebila wrote:
scTranslator bioRxiv public review
Summary: Quackenbush lab journal club review of “A pre-trained large generative model for translating single-cell transcriptome to proteome by Liu et al., 2023.” This work is motivated by the lack of sc-proteomics data sets because they are limited by available sequencing technology. The paper presents a transformer-based model called sc-translator and employed 31 cancer data sets for training and validation. Training is based on a 2-stage process. Stage 1 is training on bulk (pre-training) and Stage 2 is training on sc data sets. The model is based on favor+ which is a transformer attention mechanism
Pre-training: In the isoforms prediction example, it seems that isoforms are predictive of each other e.g. CD49a and CD49b and not through model precision towards each isoforms. Correlation seems to be driven by a small number of data in the plot (Upper right portion of the correlation plot). Also, application on new dataset other than PBMC is warranted here to assess generalizability. Immune response is harder to predict: rare immune cells
The application on new data showed that pre-training is very important, however it is not clear why cosine is used and not correlation as an evaluation metric.
Downstream tasks:<br /> - More benchmarks on the attention interaction network are needed. It would be great to see a few examples of which genes regulate which proteins and their biological interpretations.<br /> - Pseudo-KO experiments also need to be benchmarked. There are many CRISPR knockout datasets which can be used for validation. Biological interpretation is missing for these experiments.<br /> - It would be nice to have a conduct GSEA in KO experiments<br /> - Finally for cell clustering (Figure 5a), it is likely that batch effects in true proteins are subpopulations for CD4 and not driven by batch.
Potential future use:<br /> - It would be nice to predict protein levels in a different setting such as drug response for example.<br /> - Conduct more benchmarks for KO experiments beyond EGFR and TP53
Additional comments:<br /> - Lack of evaluation in a real-life setting without the presence of protein data that can be used to fine tune the model first<br /> - Would it be possible to build a model for each protein or protein class which seems to make more sense because post-translational modifications vary between proteins and therefore fitting a single model can overlook these differences.
On 2023-11-03 15:51:53, user Corresponding Author wrote:
We - the authors of this manuscript - appreciate a Community Review of this manuscript posted here: https://zenodo.org/records/.... We agree with the overall assessment of the reviewers.<br /> 1) For the method description, we have cited previous publications and mentioned ‘as described previously’. Based on the reviewers' suggestion we will further describe the methods in detail to clarify the reviewers' concerns. In addition, we will include the age and sexes of mice in the legends of each figure. We will upload a revised version of this manuscript in a few months. eLife journal will publish the manuscript.<br /> 2) We agree with the reviewers that additional experiments are necessary for in-depth analyses of how elevated glycosuria increases compensatory glucose production. The goal of this project was to provide a foundation for future studies that will be informed by the list of secreted proteins identified using plasma proteomics, some of them may be correlative and others causal. At this time, it is not feasible to test each of the identified protein for its causal role in enhancing a compensatory glucose production. <br /> 3) eLife will publish a revised version of this manuscript in a few weeks.
On 2023-11-03 14:34:03, user Alex wrote:
It is not clear about the background of the used mutants. Some lines have WS background (for example, ahk3-1 and ahk3-2 - Wisconsin University lines WS-2) [Nishimura et al., 2004], while others - Col-0. <br /> Authors, however, always used only Col-0 as a control. <br /> Please, provide the proper background description for every used line.
On 2023-11-02 17:48:33, user Adil Muneer wrote:
Published version of this article titled "Non-canonical function of histone methyltransferase G9a in the translational regulation of chronic inflammation" is now available: https://www.sciencedirect.c...
On 2023-11-01 16:33:32, user Jae Choi wrote:
Published in Research Article, J Mar Biol Oceanogr Vol: 12 Issue: 4 (https://www.scitechnol.com/...
On 2023-10-31 15:03:11, user Scott C Thomas wrote:
For table 1, it looks like citation 17 used an Illumina HiSeq platform. "Libraries Preparation and Sequencing<br /> Libraries were prepared using the Nextera DNA Library Preparation kit (Illumina) and sequenced on an Illumina HiSeq platform (leading to 40,552,111 ±9,650,536 reads/sample)."
Also, Qiagen is a company, not an extraction kit. Qiagen manufactures many of the kits listed in table 1, so it is confusing to have "Qiagen" listed as a DNA-Exk.
On 2023-10-30 08:40:11, user Estel Collado Camps wrote:
Dear authors,<br /> I've learned a lot from reading your pre-print! I'm intrigued to see what language models will mean for deeper understanding of complex biomedical data in the near future. <br /> I have noticed that a few UMAPs (see for example figures 3 and 4) have slightly different shapes in the differently colour-coded versions (a vs b, c vs d). As a non-expert, I can imagine that this can easily be missed while updating figures. I thought it would be beneficial to everyone to give a heads-up.
On 2023-10-29 09:08:48, user BBB Prair wrote:
Fascinating study as expected from the Yanai lab. I work on DTPs as well. I read the whole preprint and watched the Match Onco seminar by Prof. Yanai about this work. Maybe I missed some point in the paper but I wonder why the identified IC50 for drug-naive Kuramochi cell line is ~2 uM? In my own measurements, using both CellTiter-Glo and SRB assays in a 12-concentration range, 72-hr format, I always calculate an IC50 in the range of 150 to 200 uM in Kuramochi cell line for olaparib. These values are also supported by measurements in the GDSC (both versions 1 and 2) project. Did the authors check this? This might be an issue in the context of drug adaptability since the cell line, in bulk, is already poised to adapt by tolerating low uM olaparib concentrations used in the study (<160 uM).
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 2023-10-24 08:15:41, user CDSL JHSPH wrote:
Hello!
I found this paper very compelling. Nice work! It is really exciting to see that receiving both vaccines concurrently enhances the protectiveness of the COVID-19 booster. I have two questions:
Why is it that the group that received the two vaccines on different days were not administered the flu vaccine on the same number of days apart after the COVID vaccine? In the study it was stated that the flu shot just needed to be administered any time within 4 weeks from the time of the COVID booster. Could this have made the results more difficult to interpret? If you were to redo the study, do you think that the data would be more reliable if the second group all had the later flu shot administer on the same day, or does that not seem to matter?
Why was the Ebola vaccine used as the control vaccine in this case?
Thank you! And again, nice job!! :)
On 2023-10-23 04:34:17, user CDSL JHSPH wrote:
Greating Dr. Barouch and colleagues,
First, I want to commend you on investigating this timely research question regarding the immunogenicity of concurrent versus separate COVID-19 and influenza vaccination.
As I was reviewing your work, some aspects caught my attention which might further enhance its clarity and comprehensiveness. I understand that the study participants in MassCPR might have enrolled voluntarily. If this is the case, there could be potential selection bias to consider. It might be beneficial for readers to see a demographic table that provides baseline characteristics for both groups. Additionally, it would be helpful to understand the factors that influenced participants to either receive both vaccines simultaneously or at two separate intervals. Clarifying this could help readers discern if there might be any inherent differences between these two groups.
It also would be enlightening if you could expand on the potential mechanisms of the specific immune interactions that may be driving the increased IgG1 with concurrent vaccination? This could reveal important biology behind your findings.
I believe addressing these points could enhance the comprehensiveness of your paper. I hope these suggestions are helpful as you continue developing this research project.
Thank you for sharing your work and for your consideration.
On 2023-10-20 01:55:50, user CDSL JHSPH wrote:
Hello! I hope this message finds you well. I would like to express my sincere appreciation for your paper. Your research on the immunogenicity of these vaccines, especially in the context of concurrent versus separate administration, is of significant importance in the current landscape of emerging COVID-19 strains. I have a couple of queries that I hope you could kindly address:
Given that the influenza vaccine undergoes changes each year to adapt to evolving strains, I am curious about the potential impact of these changes on the observed results. Do you believe that the higher and more durable SARS-CoV-2 antibody responses associated with concurrent administration would remain consistent across different influenza seasons?
It appears that your study design involves a comparison between concurrent and separate administration, and the results are promising. Could you kindly provide more information on the research methodology? Specifically, was your study a randomized controlled trial (RCT), and if so, was blinding or randomization implemented?
Once again, I would like to express my gratitude for your valuable contribution to the field. I understand the dedication and effort that go into such research endeavors, and your work is commendable. I look forward to any insights you can provide regarding my queries.<br /> Thank you for your time, and I appreciate your consideration of these questions.
On 2023-10-27 19:33:35, user Federico wrote:
The claim that you have generated brown adipocytes is overstated. There is no clear proof morphologically or by significant changes in gene expression (UCP1) that would support brown adipocyte character. I would revisit that experiment.
Showing tissue that has formed (and analyze it histomorphological) would make the in vivo work much more convincing. In line with that, survival for up to 28 weeks seems overstated as IVIS data thresholding doesn't seem to be corrected for background noise.
On 2023-10-27 15:59:31, user Ashraya Ravikumar wrote:
In this manuscript the authors have tested the hypothesis that the MSA constructed by AlphaFold2 (AF2) contains information about the distribution of different conformational states of a protein. Whereas the current way of thinking about AF2’s MSA-predicted Cβ–Cβ distance maps focuses on their power to provide binary classifications of inter-residue contacts, the authors propose that Cβ–Cβ distances should instead be thought of as a set of collective variables that approximate a Boltzmann distribution. This is a novel hypothesis that lends AF2 the ability to decipher the conformational Boltzmann distributions of proteins. The authors test this in the contexts of protein dynamics, mutation impacts, and protein-protein interactions. They start with analyzing the correlation between AF2 contact distance and spin label distance distributions obtained from EPR spectroscopy using T4 lysozyme as a model, finding a general agreement despite broader AF2 distributions. Following this, they explore if AF2 can approximate free energy changes in systems that contain multiple biologically important minima, using EGFR KD studies for this purpose. AF2 accurately identifies altered contact distance distributions corresponding to active or inactive conformations in several mutations, indicating a sensitivity to alterations that stabilize particular conformational states. Next, they assess sensitivity to thermodynamically destabilizing mutations. AF2 was able to predict different contact distance probabilities for disruptive mutations like L198R in UBA1, but was less sensitive for milder mutations like L198A. Lastly, AF2’s sensitivity to protein-protein interactions was explored using the μ-opioid receptor (μOR). Although the helix displacement distances observed in the predicted structure of isolated and complexed μOR do not exactly match with expected values, AF2 did successfully predict differences in select contact distance distributions of active/inactive-state μOR. Demonstrating that Cβ–Cβ distance probabilities from the same AF2-learned distribution reflect distances observed in differentially behaving domains of a protein lends strong support to the hypothesis that AF2 contact distance distributions can approximate conformational distributions.
The manuscript explores the correlations and sensitivities of AF2 predicted Cβ–Cβ distances across a variety of protein contexts, giving a broad view of its capabilities and limitations. Transitions between the various sections flowed well, and overall the writing was well worded and easily comprehensible. In addition, the presentation was balanced. It doesn’t just focus on the success of AF2, but also highlights where its sensitivities might vary or fall short, providing a balanced view of its capabilities. Given limited computational resources, the conformational space explored by MD and MCMC simulations is limited by their initial states. AI methods are instead limited by how informative their system definitions (MSAs and pre-set theoretical or experimental contact distance distributions) are, allowing AI methods, such as the AF2 method outlined by the authors, to more effectively sample conformational space. This is a very fascinating implication of their work which the authors have briefly mentioned in the discussion. This (and the connection to Figure 7 in the paper) warrants a deeper discussion, but the main conclusions the authors come to are within the scope of the manuscript, and are backed up by the evidence presented.
There are a few points we would like to bring to the attention of the authors to strengthen the manuscript further.
Major points:
1.There are some difficulties interpreting Figure 2. <br /> (a) It is important to mark the distances between the two chosen pairs of atoms in the active and inactive state. Without this information, the purpose of Figure 2D is unclear and Figure 2D, F and G are difficult to understand. <br /> (b) What is the threshold distance to classify a state as active or inactive?<br /> (c) Figure 2E seems confusing with different axis and ranges.<br /> 2. In case of DDR1, does the MD simulations reflect the peak distances (between 7.5 and 10.0 Å for DFG-in and between 16.0 and 18.0 Å for DFG-out) observed for AF2 distance distributions? Also, the probability distribution shift towards shorter distances for Y755A does not seem particularly strong at first glance. Is this why the double alanine mutant was included? Are there also MD simulations of the double mutant that show a reduced preference for the DFG-out conformation?<br /> 3. The overall results on EGFR mutants are striking. Many of these mutants (most notably L858R have structures deposited in the PDB (ID:2ITT and many others) that are potentially part of the overall training of AF2/OpenFold. Can you comment on how this might affect the results?
Minor Points:
As mentioned earlier, the connection between the Discussion and Figure 7 is not well established. The authors could expand on their writing and/or make the figure more simplified to match the discussion better.
Jessica Flowers, Angelica Lam, Ashraya Ravikumar, James Fraser
On 2023-10-27 15:13:53, user JO wrote:
Is it possible to show where the ecRNH and ttRNH would fall relative to the clusters in Fig. 9? Their identity to the AncA/B/C sequences? And key residues that vary between ecRNH and the most homologous Anc sequence?
On 2023-10-27 14:47:25, user Joseph H Vogel Beckert wrote:
"To add to this uncertainty, the pilot test coincided with international discussions on the fair and equitable sharing of benefits from the access and use of digital sequence information (i.e., genomic sequences) under the Nagoya Protocol adding increased uncertainty surrounding the legal compliance landscape57."
There should be some mention of "unencumbered access" through the proposed modality of "bounded openness over natural information". The sentence above references a Comment from Nature Communications that trumpets "de-coupling" access from benefit-sharing. "De-coupling" means independence and is probably not what its 41 authors meant. Similarly, any reference to a multilateral mechanism for ABS without recognition of the overarching implications of the economics of information, i.e. the justification of "economic rents", introduces bias and thus undercuts the presumed scientific neutrality of the manuscript..
On 2023-10-26 22:50:15, user ELSA COUVILLON wrote:
Dear authors,<br /> Overall, I felt your paper was interesting to read and highly relevant to the SARS-CoV-2 pandemic. These findings could be extremely valuable for identifying preventive measures for mitigating disease transmission, and I thought the experimental question addressed by your paper and the experimental design –incorporating a PSV entry assay and a syncytia formation assay– was cohesive. However, when reading and presenting at a journal club, some questions and comments came up that I would like to share with you.<br /> First off, I would like to point out a few simple fixes that I found. For one, Figure 4 is titled “Effect of turmeric extract and curcumin on PSV entry in 293/ACE2 cells,” however, the experimental results exhibited in Figure 4 only deal with curcumin, so I feel renaming that figure would be more accurate. <br /> Additionally, I’m curious as to why in Figure 4b, the PSV entry assay treatment conditions included the following:<br /> non-treated 30 mins/SARS-CoV-2 16-18h<br /> curcumin 30 mins/SARS-CoV-2 16-18h<br /> curcumin 30 mins/SARS-CoV-2+curcumin 16-18h<br /> but did not account for the effect of non-treated 30 mins/SARS-CoV-2+curcumin 16-18h? I was curious as to the logic for excluding that particular treatment combination, which I feel could’ve been a good control for comparing the infection rate of SARS-CoV-2 compared to SARS-CoV-2/curcumin without the additional variable/impact of the initial 30 min treatment. As a side note, it might be beneficial to readers to make more clear, what is meant by “curcumin pre-treatment”; does that refer to the 30 min “black arrow” segment, or the 16-18h “gray arrow” segment in which the virus has a “+ curcumin” label? <br /> One final thing I want to point out is the use of statistics in this paper. You state that a t-test is used throughout, however, I believe that an ANOVA might be more effective here, not only to reduce the number of tests you have to run on the data (and therefore reduce the risk of making a Type I error), but also so that you can show the readers comparisons between each and every group, and not just between each individual group and the control. Additionally, it would be helpful if there were asterisks or “n.s.” consistently shown for every statistic (for example, this is done on Figure 5b, but not Figure 4d), along with a key on every graph indicating the significance levels indicated by each asterisk, to help clear up some confusion about interpreting significance and statistics. Going back to the Figure 4 example, in the paper, it is stated that, “The results indicated that curcumin reduced PSV entry, especially for curcumin pretreatment before the addition of PSV (P = 0.035)” in reference to Figure 4d; however, in the actual graph, there isn’t sufficient statistical representation to confirm this conclusion (no statistics are shown for comparing Cur 1uM and the control) and additionally, I had a hard time determining what defined the pre-treatment when flipping between Figures 4b and 4d.<br /> Ultimately, I feel the paper is a great start and could mainly benefit from a few changes to encourage more clarity surrounding the ways in which different treatments were defined, the labeling and annotation of figures, and display/application of statistical tests. I look forward to following it through pre-print.
On 2023-10-26 22:41:47, user Srijit Seal wrote:
For more information about BioMorph and download the datasets used, please visit https://broad.io/BioMorph
On 2023-10-26 06:27:29, user Tom Hitch wrote:
You may wish to use the up-to-date taxonomy for these taxa. Prevotella copri was reclassified as Segatella copri last year; https://lpsn.dsmz.de/specie...
On 2023-10-26 06:22:08, user Pablo Rivera-Fuentes wrote:
This manuscript is now published in a peer-reviewed journal. I suspect that no link was made automatically because the last name of the first author changed during the peer review process. Here is the link to the published article: https://www.nature.com/arti...
On 2023-10-25 00:53:17, user CDSL JHSPH wrote:
Great work! I think this is very important for future research regarding T. cruzi and it's genome's contribution to Chagas disease. Sequencing this strain, whose whole genome has never been sequenced, is a significant contribution. Despite some limitations, this was done successfully and I think it will push others to work on sequencing genomes from other strains, particular those from field samples. I think this is a great step for further research in looking at the relation between transposable elements and multigene families. The methodology in this study was very convincing. I especially like the extra steps done to address the limitations of the Busco Score. I think comparing ORF length of assemblies from other strain with similar characteristics really helped improve your methodology. The paper had a logical flow, but I do believe limitations should have been further explained in the discussion. I think this would have really improved this paper. Overall, this was great work that opens up to more questions regarding this field which I hope this team or other researchers would look at in the future. Not only was a whole genome of this strain sequenced, but it was done using ONT nanopore sequence alone which can provide a less expensive method for sequencing T.cruzi genomes. A few questions that came to mind was how different do you think your results would have been if you used ONT nanopore sequencing with supplementation of other technologies. Also how different would your results have been if you used a field isolated samples instead of a lab strain? Do you think you would have found a greater correlation between transposable elements and multigene families?
On 2023-10-25 00:37:30, user Jessica Garvin wrote:
Hi! I found your research incredibly engaging to read about. The variety of test results that you shared was especially notable. Since publishing this paper, have you worked on any other projects similar to it? I think it would be an interesting find to see whether or not you would have similar findings with strains comparable to the Tulahuen strain. Wonderful job on your work!
On 2023-10-24 01:24:43, user Anshule Takyar wrote:
Hello! This is a good piece of work, and the value to the field is very evident. It is great to see novel sequencing methods like Oxford Nanopore sequencing being validated more and more, and by employing a Nanopore-only approach, you have probably helped to assuage some of the anxieties of others in the field regarding this technique. I had a few questions and recommendations regarding this technique. Have you sequenced T. cruzi, or the Tulahuen strain specifically, with short-read sequencing? Are there any hurdles involved with that? Also, do you think that by assembling this genome using the help of short-read sequencing, you would have gotten a better result? Additionally, I think that it would be helpful to show in a figure which coding regions are not impacted by transposable elements, as that would increase the significance of your work. Other than that, I really liked this work, and congratulations!
On 2023-10-24 17:32:53, user Jianhua Xing wrote:
It is nice to see more efforts on learning the governing equations of gene regulatory networks from single cell data, and thanks for mentioning our dynamo work. Congratulations on the work. I notice that some discussions on dynamo are not accurate --unfortunately it has happened repetitively in the literature such as stating dynamo requires data with metabolic labeling only and the vector field gives only lear relation between a regulator and its target gene. Related to what discussed here, with the dynamo vector field one can predict cell states NOT covered by the data. That is, dynamo is a generative model. So the criticism on using embedding is not justified. One uses low-dimensional manifold embedding (e.g. in Dynamo) to simplify the model (with reduced number of parameters to specify), and it is well-established that a dynamical system typically falls to a low-dimensional manifold after a transient period of time. A famous example is the 3-variable Lorenz model. Starting from any initial state, it falls to a strange attractor with dimensionality 2.06
On 2023-10-24 15:20:08, user kamounlab wrote:
We’ve discussed this note today and we have a question regarding Figure 1G. It's essential to ensure that RBA1 doesn't negatively affect the agroinfiltration process itself, which could potentially lead to reduced accumulation of the virus and reduced fluorescence, thereby impacting the interpretation of the results.
To address this issue, the experimental design should include appropriate controls to rule out any interference of RBA1 with agroinfiltration.
On 2023-10-24 12:41:18, user Ying Cao wrote:
Apparently, EMT means change of cellular states/properties but not of gene/protein symbols. In the case of EMT, what are epithelial and especially mesenchymal states/properties are not known. What it the scientific meaning of the so-called epithelial-mesenchymal transition?
On 2023-10-23 12:27:01, user Senthil-Kumar Muthappa wrote:
This preprint article is now published, please see: Priya P, Patil M, Pandey P, Singh A, Babu V, Senthil-Kumar M. (2023). Stress Combinations and their Interactions in Plants Database: A one-stop resource on combined stress responses in plants. The Plant Journal, https://doi.org/10.1111/tpj...
On 2023-10-23 08:13:46, user Richard Steeds wrote:
This is a really interesting study in an ultra-rare syndrome that kills a substantial number of patients through cardiovascular complications.<br /> 1. As the authors have acknowledged, we have never seen evidence in any of our studies of sexual dimorphism in adult presentation with disease or on cardiovascular imaging, either by echocardiography or cardiac MRI. We have seen young men and women suffer cardiovascular complications at similar age of onset in their 20s and 30s.<br /> 2. Changes in left atrial area, isovolumic relaxation time and ejection fraction without similar changes in myocardial performance index or global longitudinal strain worry me, as in humans I would expect both to be early indicators of restrictive cardiomyopathy. All of these values are affected by both acute changes in blood pressure (and especially during anaesthesia) and by the longer-term effects of hypertension, so this is an important confounder but again acknowledged.<br /> 3. One feature that is expected in human studies of restrictive cardiomyopathy would be corroborative evidence of pulmonary hypertension, occurring as a result of elevated LV end-diastolic pressure, high LA pressure and thereby pulmonary venous hypertension. Was there any TR in the mice model and any measure of TR maximal velocity?<br /> 4. I am an adult cardiovascular imaging specialist who practices both echo and CMR. I am only too aware of the variability of echo measures of cardiac function on an intra- and inter-observer basis. At heart rates of 350-450 BPM, I remain very concerned by the reproducibility in small numbers of animals - although I recognise these numbers are considered adequate in the animal physiology world. When I look at the box plots, there is often a wide spread of results, and no idea is given in the manuscript of the intra-observer measurement for example of ejection fraction - I understand that the person was measuring blind and was a single experienced sonographer...but in our practice, we recognise that in experienced hands, EF may vary in humans at up to 10% between scans at heart rates of 70BPM.
On 2023-10-19 12:51:38, user Russell Cox wrote:
It would be good to know the actual titres of polyketides for the different systems in mg/L - then comparisons could be made with other related systems such as DEBS1-TE in various hosts including filamentous fungi (https://www.mdpi.com/1410198)
On 2023-10-19 01:02:59, user Jonathan Eisen wrote:
Minor comment - in many parts of the manuscript you refer to "16S rRNA sequencing data". It would be more accurate to refer to this as "16S rRNA gene sequencing data".
On 2023-10-19 00:34:34, user Michael Polymenis wrote:
The paper has been published in GENETICS (see here https://pubmed.ncbi.nlm.nih....
On 2023-10-18 18:45:43, user Vanessa Staggemeier wrote:
Moura et al. evaluate the loss of suitability areas for non-flying mammals in the Caatinga in two future periods (2060-2100) under climate change effects and what would be the expected changes in the biotic composition of communities.
The authors employed an interesting approach with restrictions on species dispersal in the models and the results contribute to predicting the effects of climate change in this biome.
We see the importance of focusing on biotic changes and % of range loss, but it is our belief that adding the final predictions for each species in the supplementary material, in terms of maps and range shifts (direction of shift), it would be worth and informative because this information is important for managers and decision makers (those who manage conservation units but also to the researchers working on specific taxa).
We also think that including a more detailed discussion about some species that have been modelled in other previous studies could enrich the work and make some of the results obtained here clearer. For example, why species with a wide distribution such as Callicebus barbarabrownae would lose their entire area of suitability in 2060? Other studies, such as Barreto et al. 2021 and Gouveia et al. 2016 found different results, could you attribute this to the methodological choices?
We think the words used in the bibliographic review were not wide enough to include studies with mammals in the Caatinga because some important references are out of the included papers. The chosen words are mainly related to the biome or region. Maybe another approach would be to review occurrence records in a systematic way looking for articles with species names (as keyword) based on a preliminary list of mammals.
Including latitude and longitude in the maps it would be more informative and including political division of states could help to subside discussion for specific regions of Caatinga.
We wrote this comment during a meeting to discuss preprint papers that occurred by September, but I was able to post it just now.
I saw that the paper was accepted yesterday, so I am not sure if our suggestions/questions will have some worth to the authors (feel free to reply or not), but we decided to contribute with them anyway.
Many congratulations for your article! Although we think that some points could be different, we are sure that article is a nice contribution to understand potential effects of climate change in Caatinga :)
Comment written at the Laboratório de Ecologia Vegetal, Evolução e Síntese (LEVES) at the Universidade Federal do Rio Grande do Norte, RN, Brasil. Joined this meeting: Vanessa Staggemeier, Hercília Freitas, Víctor de Paiva, Yan Gabriel, Alexander Chasin, Rhuama Martins, Vitoria Alves, Jose Nilson dos Santos, Rafael Rocha dos Santos, Maria Luiza and João Paulo Câmara.
References<br /> 1) Barreto, H. F., Jerusalinsky, L., Eduardo, A. A., Alonso, A. C., Júnior, E. M. S., Beltrão-Mendes, R., ... & Gouveia, S. F. (2021). Viability meets suitability: distribution of the extinction risk of an imperiled titi monkey (Callicebus barbarabrownae) under multiple threats. International Journal of Primatology, 1-19.
2) Gouveia, S. F., Souza‐Alves, J. P., Rattis, L., Dobrovolski, R., Jerusalinsky, L., Beltrão‐Mendes, R., & Ferrari, S. F. (2016). Climate and land use changes will degrade the configuration of the landscape for titi monkeys in eastern Brazil. Global Change Biology, 22(6), 2003-2012.
On 2023-10-17 08:52:43, user DL wrote:
Very interesting paper and deep insight into the mechanism. However, no functional data regarding the detergent or DTT conditions are shown. I'd really like to see electrophysiological recordings of HCN1_wt, HCN1_CC mutation and HCN1_CCA mutation under a) DTT application and b) CHS/LMNG application/incubation to show the physiological/functional relevance of the resolved putative Intermediate and Open states.
On 2023-10-17 00:19:42, user Abram Magner wrote:
We, the authors of ``A Deep Learning Architecture for Metabolic Pathway Prediction'', thank the authors for pointing out the existence of duplicate entries in our datasets and for pointing out that we did not upload all of our code for data download from the KEGG database.
We have addressed the latter issue by uploading our data download script, keggpuller.py, to the project Github. This code was used to download molecule records from the KEGG database and store them in a commma-separated value format. This resulted in 6669 records. The dataset was then further processed to a simpler form to reduce each record to a SMILES string followed by a comma-separated list of letters indicating pathway class membership (this is smiles_property.txt). We refer to this as the multi-class dataset. We also considered the problem of classification of a compound as either being a member of a single, given pathway class or not. We refer to the resulting dataset as the single-class dataset.
The authors are correct that the resulting datasets contain duplicate entries. The single-class dataset contains six duplicates out of 4545, while the multi-class dataset contains 1740 out of 6669.
We have re-run our experiments on the datasets with duplicates removed. The results for single-class classification did not change. The table of results for multi-class classification can be found at this location.
We note that the accuracies of most methods dropped, including ours. The accuracy statistics for ensemble logistic regression increased.
However, we also note that the central results of our paper remain intact -- the relative ordering of accuracy of different machine learning methods (other than ensemble logistic regression) on the data remains the same, and the superiority of our method over the others that we evaluated remains. Indeed, this is expected because we ran all methods on the same datasets, using the same training/test split methodology.
We have uploaded the de-duplicated datasets to the Github page. The authors are correct to encourage the use of the de-duplicated datasets. We will also post a correction to our paper.
On 2023-10-15 07:39:00, user Ben Dickie wrote:
Hi,
Really nice work. Please consider reporting your DCE/DSC methods using the new OSIPI lexicon to improve standardization.
https://onlinelibrary.wiley...
I’m very happy to help integrate the correct terminology (ben.dickie@manchester.ac.uk).
Best wishes,
Ben
On 2023-10-15 07:36:09, user Ben Dickie wrote:
Hi,
Really nice work. Please consider reporting your DCE methods using the new OSIPI lexicon to improve standardization. https://onlinelibrary.wiley.... I’m very happy to help integrate the correct terminology (ben.dickie@manchester.ac.uk).
Best wishes,
Ben
On 2023-10-14 20:57:10, user Christophe Leterrier wrote:
Figure 3 is repeated 2 times in the pdf file, Figure 2 being absent. Is it possible to upload a corrected manuscript as revision? Thank you.
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 2023-10-12 15:56:16, user Michael McLaren wrote:
It would be useful to see a comparison to another method that also uses a Poisson / Multinomial distribution to handle issues associated with low + zero counts. In particular I would be very interested to see a comparison to Justin Silverman's fido package (https://jsilve24.github.io/..., though since fido is a Bayesian framework I imagine the comparison may not be as straightforward.
On 2023-10-11 14:01:06, user Gilles wrote:
Is there any positive control for FoxP3/CD25 stainings ? the cytometry stainings are so poor it is difficult to conclude anything.
On 2023-10-11 10:42:55, user Vladimir Chubanov wrote:
The paper presents an interesting model. It would be great if the author would attempt to connect the model to available experimental evidence. For instance, clinical assessment of the patients with loss-of-function mutations in TRPM6 and conditional mutagenesis of Trpm6 in the kidney vs intestine of mice demonstrated the prime role of intestinal magnesium absorption in the systemic balance of magnesium.
On 2023-10-11 08:17:50, user XiaoFei Zhao wrote:
Just out of curiousity, did the authors compare performance (F-score, etc.) with the tool at https://github.com/genetron... ?
On 2023-10-11 03:38:39, user Pietro Roversi wrote:
The paper has appeared in https://doi.org/10.1016/j.i...
On 2023-10-10 23:01:20, user Alexander Chase wrote:
excited to see the incorporation of GH/CBM into BGCs - likely linking some of them to primary metabolism. should also check out our latest work on Salinispora here:<br /> https://doi.org/10.1128/mBi...
On 2023-10-10 13:37:06, user Tom Langton wrote:
There is a distinct lack of transparency becasue data are not linked in supplementary material and neither is the stata code. Even if the code was there it would be difficult to reproduce – stata is not very widely used.
Does the analysis decisively link badger culling to decline in bovine TB in cattle? Culling is confounded with complex changes to gamma and other testing and other measures. There are places in the ms where a claim is made for a link ‘The effect of badger culling…’ but then later: ‘However, this data analysis cannot explicitly distinguish…….’ APHA have stated more than once that the breakdown data alone are insufficient to show an effect of culling.
However, the DID approach is inappropriate because the 52 cull and pre-cull study areas are surrounded by and in close contact with each other and so are not adequately discrete in space and time, breaking a DID requirement. Notably in the four-year period 2016/17 -2019/20 pre-cull and cull areas closely juxta position. Pre-cull and cull interventions may influence change in adjacent pre-cull and cull areas in an irregular and unpredictable manner, via the constant movement of cattle with different intervention histories.
Some of the very important bTB testing controls used and the timing of their introduction are not correctly described and some not even mentioned.
The approach mixes data from 46 High Risk Area study areas with 6 from the Edge Area. These have very different epidemiological and disease control history profiles and the reason for mixing them is not explained. Pre-cull gamma testing was intense in the Edge Area. While the manuscript mentions additional gamma testing from 2017, gamma testing was erratic between areas and over time. There were considerable numbers of gamma reactors in many of the cull areas in cull years 1 and 2 with similar disclosures to years 3 and 4 but pre-cull use in the HRA was generally low.<br /> In both HRA and Edge areas OTFW incidence was declining when many of the additional disease control rules were intensified, and badger culling introduced. The government chief scientist view was that distinguishing and determining with any precision any contribution of badger culling to disease control is not possible, is therefore maintained. It is impossible to distinguish between the effects of badger culling and disease control measures using the DID approach in any meaningful way.
The comment made on being unable to match cull and control is unevidenced and a 2022 published and peer reviewed study of this has not been cited, nor alternative approaches such as matching a series of individual farms in cull and control which would have been simpler and easier to monitor on a quarterly basis since 2018, as was the expectation of a High Court ruling.
The suggestion that incidence is the better indicator of true burden goes against APHA’s own epidemiology and the greatly clarified understanding of SICCT test sensitivity and specificity. The specificity change resulting from SICCT severe interpretation can be estimated and applied to OTFS, providing, with OTFW, a safe index of known infection. Gamma testing has shown numbers of unidentified reactors in both identified bTB infected and unidentified herds, and this ‘hidden’ reservoir remains undocumented although its size can be deduced.
OTFW relates by its nature to older infections with lesions, yet it is newer infections (OTFS) that indicate disease control success (elimination) more accurately, over time. There is no reason to believe that OTFS+OTFW (as above) is not the superior approach and that OTFW is half of the full picture. During the RBCT, prevalence was growing considerably and OTFS rising so the analogy made in this manuscript is inappropriate and trying unconvincingly to defend the historic reference source.
By reducing the pre-cull analysis to one data point the significant pre-cull decline is hidden and this also masks true trends.<br /> Because the methods are inappropriate, the results are flawed and the conclusions are flawed. There is no way to distinguish between different interventions and change in herd breakdowns since 2013 with the approach taken.
There is also need to see clarification of the analysis that is presented in the Appendix – this does not seem to be the output from the constrained DID analysis, but something else not fully described in methods which ‘matches’ it – which stata process was used ? It is the first analysis referred to in the main text. As well as showing that the BCP effect accounts for less than 2% of breakdowns it doesn’t look quite right that the BCP effect is based on error dof – pseudoreplication?
On 2023-10-09 12:16:03, user Taise Gonçalves wrote:
First, I would like to congratulate the authors, because this article is very well done. The diagram presented at the end of the introduction (Figure 1), exemplifying the expected results, adds a lot to understanding the text and it is an enriching differentiator for the manuscript. The data analyzed were obtained from environmental monitoring for 3 decades, which the authors were able to synthesize in very concise and accurate results. It certainly represents the best assessment to the hypotheses of pre-adaptation and limiting similarity to date.
Taíse Gonçalves - Master's student - Fungi, Algae & Plant Biology Program - UFSC - Brazil (On behalf of the PLENTBio Journal Club; plentbio.wixsite.com/alcant...:gXjqaj9KUVrPkIjzaJ8r_UkXvwc "plentbio.wixsite.com/alcantaralab)")
On 2023-10-09 12:13:10, user Taise Gonçalves wrote:
The main conclusion of this research states that oceanic islands showed higher phylogenetic endemism, based on both metrics used by the authors, i.e. denoting palaeoendemics and neoendemics. This pattern, however, have been already stated in a recent study (Veron et al. 2019). In this sense, the discussion about the processes involved in the origin of such floras could be improved by the ideas presented by Vasconcelos et al. (2021). In this study, the authors propose that the terms neoendemism and paleoendemism should be replaced by assessment in relation to the actual biogeographic and macroevolutionary processes that had occurred.
Moreover, although the authors provide a methodological discussion regarding the scale of the areas analyzed, a stronger emphasis should be put on it. Several large continental areas, for instance, represent large vegetation domains (i.e., Mata Atlântica and Cerrado), which include high number of endemism however unevenly scattered in island-like habitats throughout these areas.
Vasconcelos, Thais; O’Meara, Brian C.; Beaulieu, Jeremy M. Retiring “cradles” and “museums” of biodiversity. The American Naturalist, v. 199, n. 2, p. 194-205, 2022. DOI: https://doi.org/10.1086/717412
Veron, S., Haevermans, T., Govaerts, R. et al. Distribution and relative age of endemism across islands worldwide. Sci Rep 9, 11693 (2019). https://doi.org/10.1038/s41...
Taíse Gonçalves - Master's student - Fungi, Algae & Plant Biology Program - UFSC - Brazil (On behalf of the PLENTBio Journal Club; plentbio.wixsite.com/alcant...:gXjqaj9KUVrPkIjzaJ8r_UkXvwc "plentbio.wixsite.com/alcantaralab)")
On 2023-10-09 12:05:37, user Taise Gonçalves wrote:
I congratulate the authors for the study and would like to add a perspective that could improve the reach of the manuscript, as well as to clarify a specific point.
The three hypotheses proposed (lines 171 - 185) has been described as the predicted associations, reflecting correlation patterns instead of the causal mechanisms leading to those patterns. As an example of correlation, in discussion (line 437), the authors report that the higher taxonomic, functional, and spectral diversity found at middle elevations has already been tested all over the world. Could the focus of the hypotheses be stated as the process(es) behind these patterns instead of the correlations per se?
The species Phacelia secunda was selected to analyze intraspecific trends between traits and environment, based on the fact that it is present across the elevation gradient (line 224). It would be interesting to know if Phacelia secunda is the only species occurring along the all elevation gradient. If not, why the authors selected it specifically?
Taíse Gonçalves - Master's student - Fungi, Algae & Plant Biology Program - UFSC - Brazil (On behalf of the PLENTBio Journal Club; plentbio.wixsite.com/alcant...:gXjqaj9KUVrPkIjzaJ8r_UkXvwc "plentbio.wixsite.com/alcantaralab)")
On 2023-10-09 11:26:07, user Arda Sevkar wrote:
It appears that an incorrect strain was used for the alignment of Mycobacterium leprae. As indicated in the supplementary materials, the MRHRU-235-G strain was utilized for this purpose. Notably, the NCBI genome page designates this strain as the "reference strain". Unfortunately, that's not true. In the literature, alignment and genotyping are consistently carried out using the strain labeled as "TN" (NCBI Genbank accession number: AL450380.1)."<br /> Additional information regarding this subject is available in the following sources: Pfrengle2021, Krause-Kyora2018, Schuenemann2018/2013, Benjak2018, Monot2009
On 2023-10-09 08:48:33, user ALFONSO DARMAWAN wrote:
Hello,
First of all, I enjoyed reading through the whole paper and it was really interesting to see the single suspension cell specifically in the progress of FRT. I would say the paper has showed very thorough and clear figures overall; nonetheless, I would love to follow-up with some suggestions regarding some of the figures. In figure one, I have seen the visual profiles between the Diesterus and Estrus cycles; however, it would be great to see the whole visuals for all 4 cycles to have more thorough comparisons how the cells are phenotypically changed over each cycle. In figure 2, I have seen cell-expressions for BEpC, MAIT, and F were not showed in the b figure because I saw some significant changes in between phase for those expressions and it would be nice to see them within the plot as well. In addition to figure 3, might as well of showing the scRNA-seq data in other cell-type expressions of Tgfb2 and correlate them with their most corresponding expressions within each of the organ. Lastly in figure 4, it would be great to also show us the other expressions besides Alpl because it's been slightly confusing to change every expression from one figure to another without vivid and brief explanations alongside with it. Thank you!
On 2023-10-06 11:03:43, user Anacleto Silva de Souza wrote:
This manuscript was modified and published in the Journal https://doi.org/10.1007/s10...
On 2023-10-05 22:14:42, user VK Ulaganathan PhD????????, M.Tech ( wrote:
It is published and open access.<br /> Here is the full text: https://doi.org/10.1038/s41...
On 2023-10-05 22:12:40, user VK Ulaganathan PhD????????, M.Tech ( wrote:
It is published and open access.<br /> Here is the full text https://doi.org/10.1002/cbi...
On 2023-10-05 12:53:24, user Matteo Brilli wrote:
I was checking the multialignment provided for download on the website. I am not an expert of supermatrix approach, but I have 20 years experience with phylogenetics, even if it is not my main occupation. Now, it turns out the multialignment has NO positions where all sequences have a non-missing character. The median number of Ns per sequence is around 32k and the alignment has 36327 sites. Across those sites, the minimum number of Ns is over 700, with a median number of Ns per site around 4000. Now, my question is, is ML able to reconstruct a satisfactory phylogenetic tree in these conditions? I understand missing data can be accounted for during reconstruction, but I suspect that if there are only (or almost only) missing data, the approximation will be far from reality.
On 2023-10-05 10:36:59, user Oliver Wright wrote:
Very interesting work! We have attempted a similar approach to understand the evolution of conserved viral protein domains, and found that the alignments generated by Foldseek for our particular dataset were of insufficient quality to generate a reliable phylogenetic tree. We also tried to generate alignments using 3Di sequences, but again struggled to generate reliable alignments due to divergence. We're curious whether you have found a way around this with your approach. Does your analysis workflow include a quality control for the alignments, or are you using the unaltered Foldseek output? Would you be willing to share your alignments?
On 2023-10-05 06:23:48, user John McBride wrote:
Thanks for the comment. This is a very good question. I will refer to the energy-minimized structures as "relaxed" and otherwise "unrelaxed". In this work I only analyzed the relaxed structures.
There were so many different things to check that I never got round to checking the effect of energy minimization originally, but I did make sure to save all of the unrelaxed structures. So I re-ran some of the analyses (originally done on the relaxed structures) with the unrelaxed structures.
(1: Structure correlations) When comparing AF-PDB correlations for pairs of structures where sequences differ by 1-3 mutations, relaxed and unrelaxed structures give almost identical results. For the case of no mutations, the correlations are actually higher for unrelaxed structures (r=0.38) compared to relaxed structures (r=0.33). This would reduce the residual correlations in Figure 1H by about 0.05. For reference, in Figure 1H the residual AF-PDB correlations range from about 0.15 to 0.35.
(2: Blue fluorescence correlation) I checked the strongest AF-phenotype correlation, from Figure 2C. For unrelaxed I get r = -0.92, for relaxed I get r = -0.93.
It seems that the energy minimization does help, but it is certainly a minor part of the overall prediction.
Bear in mind that this analysis is nowhere near as extensive as what went into the paper, so I might have missed something. The paper has now been accepted, so I don't think it is possible to add this in a new version.
Thanks again for asking this important question. I'm personally glad to know the answer (even if it is a bit preliminary).
On 2023-09-21 08:42:03, user Diego del Alamo wrote:
This is a comment about version 5 of the manuscript.
These results are thorough, compelling and persuasive. They also stand in contrast with other papers, published in the aftermath of alphafold's release, that argue the opposite.
The main concern for me is the absence of any testing or discussion surrounding the relax step of the pipeline - the manuscript never uses the words "relax", "minimization", and "openmm" (the package used by alphafold for all-atom minimization), and I did not find details in the accompanying github repo. It is therefore unclear how much of the results should be attributed to the neural network itself and how much should be attributed to the minimization step following structure prediction. We can't rule out the possibility that the strain being measured results from this second step. Were that the case, it would cast doubt on the role of the alignment and templates as the authors suggest in the discussion.
On 2023-10-04 01:20:14, user Laboratório de Interação Veget wrote:
Dear Authors, I am Elaine Cotrim Costa, a researcher at the Federal University of Minas Gerais (Brazil), affiliated with the Plant Interaction Laboratory (LIVe) coordinated by Professor Juliane Ishida. LIVe hosts an activity called the "Preprint Club," where we select preprints for reading and critical review. I have chosen your preprint: "The Penetration of Sunflower Root Tissues by the Parasitic Plant Orobanche cumana Wallr. is Intracellular." This is an interesting, well-written, and well-illustrated paper describing a robust and scientifically sound study. The study demonstrates that intrusive cells invade living host cells intracellularly, leading to the degradation of the cell wall. These results can contribute to the selection of candidate genes for resistance to the parasitic plant Orobanche cumana. In our discussion of the paper, we have some suggestions for you to consider. In general, the mechanism of intracellular and extracellular penetration of haustorium has been discussed in parasitic plants. Therefore, we suggest that the main contribution of the article to the state of the art of parasitic plants should be made clear in the introduction, discussion, and conclusion.
Title <br /> Line 2: I suggest including the name of the sunflower species and the plant families in the title, and removing the full stop. <br /> Line 5: What is the authors' affiliation? I suggest including this information.
Key message <br /> Line 12: Please check the species name Helianthus annus L. It looks like a letter "u" (Helianthus annuus L.) is missing. <br /> Line 17: I suggest removing Orobanche cumana from the keywords, as it is already in the title, and adding another keyword to broaden the scope of article search.
Introduction <br /> Line 42: I suggest including the author of Orobanche cumana. <br /> Line 43: Remove the comma. <br /> Line 44: Which component of the root exudates are you referring to? Could you specify? <br /> Line 54: Could you please provide the full name of the gene? <br /> Line 57: I believe it's important to specify how the phloem connection is established, as it has received less coverage in the literature compared to xylem connection. <br /> Line 72: I suggest leaving only 'Orobanchaceae' in the parentheses. <br /> Line 74: For standardization, include the family name 'Pelargonium zonale' and remove 'family' from the parentheses for 'Convolvulaceae.' The ending 'aceae' already indicates the family."
Materials and methods <br /> What is the sample number? I suggest including light microscopy and Toluidine Blue O staining analyzes in the text.
Results and discussion <br /> Line 170: Insert a comma after “By contrast” <br /> Lines 181-183: I was intrigued by how the nutrient transfer process occurs in the early stages. Do you have any hypotheses? Figures: It would be interesting to insert an acronym in the figures indicating parasite plant (P) and host plant (H), as well as the host and parasitic plant tissues.
Figures: I suggest positioning the figures vertically, as the sections are longitudinal.
Concluding remarks <br /> Could you provide answers to questions 2 and 3 and consider including at least one sentence addressing these questions posed in the introduction?
References <br /> Lines 243, 286, 292, 294: Please check the reference pages. <br /> Line 254: Please verify whether the journal name should be abbreviated. <br /> Lines 255 and 258: Please check if the author's last name is Dos Santos or just Santos.
Sincerely yours, <br /> Elaine Cotrim Costa
On 2023-10-03 12:21:32, user Ashok Palaniappan wrote:
A much enhanced version has been peer-reviewed and published in PeerJ doi: 10.7717/peerj.14146<br /> The implementation of miR2Trait is supported with a GitHub wiki.
On 2023-10-03 07:47:00, user Matthias Hoetzinger wrote:
Nice work!
A question regarding interpretation of ????:
In the discussion it says:<br /> "For the extreme case C. burnetii, the idea of ???? = 0.29 means that the most similar genome in a different community within a network of over 200 thousand genomes shares only 29% genetic identity with the representative genome..."
Here it should probably say:<br /> "...shares only 71% genetic identity with (or shows 29% genetic dissimilarity to) the reference genome...", right?
On 2023-10-02 17:42:18, user Neil Greenspan wrote:
The manuscript by Killian et al. is a valuable contribution to the investigation of both the biological and biophysical aspects of the humoral immune response elicited in the context of allogeneic organ transplantation. I do, however, have some reservations regarding the interpretations of the authors.
1)<br /> The authors suggest that individual amino acid residues shared between an<br /> allogeneic HLA antigen and a self-HLA antigen should be viewed as “self.” I<br /> view this act of classification as problematic. When a donor HLA antigen<br /> differs by one or more amino acids from a host HLA antigen encoded at the same locus, the entire protein is classified, at least from some perspectives<br /> routinely adopted in transplantation immunology, as non-self.
One way to rationalize this view, which may conflict with the perspective expressed by the authors in this manuscript, is to suggest that what matters in<br /> antibody-antigen interaction are the thermodynamic roles of the amino acids that constitute HLA antigens, not their identities. The claim is that the relevant biochemical/biophysical properties of a given shared amino acid at a particular position in the primary structures of the self and allogeneic HLA molecules can be altered meaningfully as a consequence of the one to several amino acid differences between these proteins. For example, a lysine or tryptophan that is oriented slightly differently in the self vs. the allogeneic molecules or that is more or less likely to fluctuate in certain directions is not necessarily thermodynamically equivalent in the two proteins.
2)<br /> If the above assertion is accepted, then the claim that breaches of tolerance<br /> are critical for damage to the allograft is not demonstrated. While it is of<br /> interest to know that self-reactive B cells are generated it is not clear from<br /> this study that the antibodies produced by these B cells cause graft damage in vivo. While I acknowledge the evidence that autoreactive anti-A*24:02 antibodies can bind to allogeneic A*01:01 with potentially meaningful intrinsic affinities, that is a necessary but not sufficient condition for contributing meaningfully to clinical allograft tissue damage, especially in the context of a single patient with an autoimmune disease. Experiments designed to test the hypothesis, in a broader range of transplant patients, that such antibodies do contribute to allograft rejection episodes would be of interest.
In the context of the potential role of autoantibody responses in allotransplantation, it has been accepted for some years that generation of autoantibodies to a variety of proteins can accompany alloimmune responses to an allograft. Some investigators have offered evidence that the presence of such antibodies is associated with damage to allografts. At present, I do not think we know with certainty the extent to which, if at all, such autoantibodies contribute to allograft damage or whether they can do so in the absence of pathogenic alloantibodies.
On 2023-10-02 03:15:47, user Serge Smirnov wrote:
Hi! What is "D" on the left side of Eq. 1? Thanks.
On 2023-10-02 00:06:33, user LEVIn - Unicamp wrote:
We discussed this preprint in our journal club and enjoyed reading it. We collectively agreed it's an exciting contribution to understanding how biological invasions shape invasive species' trophic niche and functional morphology in new environmental contexts. We all think the manuscript is well written and the figures are excellent! During our discussion, a major point that came up deals with how the hypothesis (lines 88-90) is motivated and then connected with the results. A more conceptual contextualization of the hypothesis in the introduction (e.g., explaining the ecological release hypothesis in the 3rd paragraph) could help readers to generalize the results beyond the study system and attract a more diverse readership interested in niche variation and biological invasions. Also, as the results combine a substantial body of statistical analyses aiming to understand variation in functional morphology and trophic niches across species, ontogenetic stages, sexes, and invaded vs. native ranges, presenting predictions after the hypotheses could help readers to navigate the results. For example, in light of the ecological release hypothesis, what is expected regarding morphological and body size variation across native and invaded areas? Our final point of discussion is related to the interpretation of the observed niche contraction in the invaded range. As replicates representing invaded vs. native ranges are sampling sites in space (Fig. 1), clarifying whether observed niche contraction emerges via lower variability in resource use across sites and/or within sites would be interesting. This is a key point to connect the results with the ecological release hypothesis. I hope you find these comments constructive; discussing this manuscript in our journal club was great. Congrats on your work, and good luck with the following steps!
Laboratório de Ecologia das Variações Individuais (LEVIn), State University of Campinas (Unicamp), Brazil
On 2023-10-02 00:05:18, user LEVIn - Unicamp wrote:
We discussed this preprint in our journal club and enjoyed reading it. We collectively agreed it's an exciting contribution towards a more efficient and comprehensive use of camera traps as a powerful tool to monitor biodiversity. We appreciated the concerns the authors had to create a user-friendly and flexible approach, which will certainly appeal to a diverse public of ecologists and wildlife biologists. As tropical ecologists, most of the questions that came up during our discussion were related to applying this approach to tropical, species-rich ecosystems. For instance, we wondered whether models would perform similarly (e.g., classification accuracy) in megadiverse communities where the local pool of species is larger and morphological variation (both across and within species) may also be amplified. Would this imply having a more robust training set? We understand this is not something trivial to predict based on the current set of species, but a deeper exploration of how species' traits influence prediction accuracies would be very welcome in this direction. For example, does the model have lower performance for species with smaller body sizes and/or coloration more similar to the background? Or perhaps within groups of species that are more similar in size? We also wondered whether detection and classification accuracy varies across diurnal and nocturnal records, which could bias predictions. Congrats on developing such a powerful tool for ecologists and wildlife biologists, and good luck with the next steps of this work!
Laboratório de Ecologia das Variações Individuais (LEVIn), State University of Campinas (Unicamp), Brazil
On 2023-10-01 16:32:44, user Dave Bridges wrote:
Check out the final version of this paper at https://doi.org/10.1155/202...
On 2023-09-30 14:04:49, user Erik Choueri wrote:
The study seeks to address the significant question of whether rivers act as barriers to gene flow in Neotropical primates—a topic that carries substantial weight in understanding primate evolution and speciation processes in these diverse and ecologically important regions. While the overarching theme is commendable, there are certain aspects that warrant attention and further refinement: <br /> Although the purpose of the study is to test the hypothesis of rivers acting as barriers to gene flow in Neotropical primates, the introduction excessively focuses on the Amazonia. We suggest to bring the impact of rivers on primates in other Neotropical or global biomes. Additionally, the role of other physiographic barriers in primate structuring has been underexplored. Many statements lack references, some of which are fundamental to the work, such as a source showing primate distributions associated with rivers (lines 84:87) or proposing river width as a proxy for molecular dissimilarity in undersampled areas (lines 116:117). The justification for using mitochondrial DNA markers is weak and exposes limitations that are not exclusive to mtDNA. The line 140 brings confusion about the geographical scope of the study, stating that it would cover only South America and not the Neotropics. The objective 3 proposes to model geographical regions that lack additional taxonomic explorations, but no methodology is proposed for this, and the results and discussion briefly touch on such topic.<br /> Overall, there is a lack of clarity in the methodology description, and we suggest that the authors assume that the analytical details of a scientific paper should prioritize its replicability. Regarding spatial analyses, how accurate are the IUCN Red List distributions for all Platyrrhini species or to what extent were physiographic barriers used as estimates to define the boundaries of these polygons? Also, IUCN specialist maps would probably use rivers as the limit of the distribution for species species and subspecies, thus, testing rivers as barriers using this dataset could potentially be a bit circular. What criteria were used to define "mountains" (a threshold for altitude? Topographic roughness? Any reference?). In the case of Andean Mountains for example, instead of separating sister species-pairs, the separation could be older and at the genus level. It is also inappropriate to consider the width of the river at the midpoint of the species boundary, as organisms could potentially cross it at any point, especially where the river is narrower. It is also unclear how (or if) the effects of geographical distance on genetic divergence (isolation by distance) were controlled.<br /> Regarding molecular analyses, does the quantity and spatial extent of sampling adequately represent the distribution and haplotypes of species? Could taxonomic uncertainties be affecting these data? Is the locality of these sequences described as geographic coordinates or the name of the sampling site? How were the data linked to species: were DNA sequences obtained from taxonomic studies confirming identification or the authors verified the respective collected specimen? Were phylogenetic topologies and genetic distances inferred considering which nucleotide substitution models? Was any analysis of model selection per partition performed (e.g., PartitionFinder)? If not, some justification is needed for the methodology chosen.<br /> Maps of sampling points and phylogenetic trees would greatly facilitate the overall interpretation of results, mainly Figure 3. Furthermore, it is important to explicitly assume the expected topology for a scenario in which the river promoted speciation. Would it be reciprocal monophyly? Additionally, we suggest that the results of genetic dissimilarity and statistical analyses be placed in a table (lines 289 to 308). The lack of phylogenetic support listed in "Suspect taxonomy" should be interpreted with caution, as it may present analytical biases related to the use of few mitochondrial sequences considering inappropriate nucleotide substitution models.<br /> We miss the integration between the results and the literature. Much of the early discussion refers to a redundant description of results (lines 374:404), followed by information about the dispersion of Platyrrhini across rivers. We suggest that these two sections could be worked cohesively and in a complementary manner. The link between the low phylogenetic support in areas without geographical barriers and the suggestion of incorporating geographical barriers into taxonomic descriptions is not clear. The low supports "when no geographical barriers was evident" may reflect deficient haplotype sampling in these areas, since molecular sampling is generally denser near rivers than interfluves in Amazonia. Finally, a point brought up in the Abstract but missing from the discussion is that the findings of the study suggest that the formation of riverine barriers coincides with speciation events, but nowhere are the dates of river formation or species diversification mentioned. Such an interpretation should be avoided since the genetic structuring can be promoted by rivers in vicariant or secondary contact contexts.
On 2023-09-30 14:02:05, user Murali Gopalakrishnan Meena wrote:
Our preprint has been accepted for publication in PNAS Nexus (https://doi.org/10.1093/pna..., which is openly accessible. The published article will be linked to this preprint soon. We have significantly revised the contents of the preprint based on feedback from the peer-review process. Please use and refer to the published journal article for the latest information.
On 2023-09-30 10:46:37, user Dilawer Khan wrote:
The authors fell into the same trap as most other authors in using admixture proportions to infer relatedness of contemporary populations to various ancients in figure 5.
For example, a sanity check using IBS or even something like qpWave in cladality mode would have shown that WHG is NOT more related to Southeast Asians and Indians than to Near-Eastern populations. The same applies to other maps in figure 5.
Most scientists tend to forget the basic principles of ancestry proportions, to wit, ancestry proportions will change based on how distal or proximal the sources are to the target individuals. The accuracy of the calculations depends on how closely the actual sources align with the test sources.
For example, in their admixture calx the authors use sources such as ANF and CHG which are more proximal to Near-Easterners than to SE Asians and Indians to infer WHG proportions. Although arguably ANF and CHG are important sources for Near-Easterners, they’re not for SE Asians. That’s why figure 5 incorrectly shows WHG higher in SE Asians than in Near-Easterners.
Had the authors used more proximal sources such as South Asian or SE Asian Hunter gatherers, then the result would have been lower WHG in SE Asians than Near-Easterners which would have been correct.
In conclusion, authors should not use a admixture proportions to infer relatedness between two populations. One-to-one comparison methods such as IBS, IBD, or qpWave with a proper set of outgroups will yield much more accurate results.
On 2023-09-29 22:35:52, user Brooke Morriswood wrote:
Note that this preprint (v2) was updated as a result of peer review of the first version (v1). It is however non-identical to the final published version in Journal of Cell Science. For that, FigS2 was deleted, and Figure2 was moved into the supplementals; in addition, around 1000 words were cut (mostly from the Discussion) in order to comply with JCS' figure/word limits.
As such, this v2 version of the paper is a kind of "director's cut" ;-p<br /> For the succinct version, visit the JCS version; for aficionados of this particular topic, you can enjoy the longer version here. :-)
On 2023-09-29 21:10:20, user disqus_mtg7x7eXMb wrote:
The pre-print Kim et al. [1] shows promising pharmacodynamic data for an LRA (Galunisertib, given as 20 mg/kg for 14 days, in multiple cycles) to purge the SIV reservoir in HAART treated SIV infected monkeys. They corroborate the finding with the use of a radiolabeled anti-env imaging probe, which the authors claim can be used to detect tissue areas of increased viral replication in the body when the probe uptake in those areas increases.
This radiolabeled probe was already used in a 2022 publication [2] from the same team (Samer et al. JCI-Insight, reference number 35 of the Kim et al. pre-print [1]), to show the increase in SIV viral production in monkeys treated with HAART following the LRA administration given for a shorter period of time at lower doses (5-10 mg/kg for 7 days).
In the 2023 pre-print, however, the first cycle of the LRA did not induce those very high increases in probe uptake in lymph nodes or the gut of animals as reported in the 2022 [2] paper in which the animals received the LRA at lower doses for a shorter period compared to the 2023 pre-print [1].
Why higher doses and longer duration of the LRA in 2023 are not revealing those high increases in probe uptake seen in the 2022 paper?
One possible explanation is that those high increases in SUV uptakes seen in the 2022 paper are the result of non-specific uptake of the probe, based on certain details of the 2022 published images (more in note-*1).
The latter seems a reasonable explanation for the Samer et al. paper [2] given that the LRA showed a systemic effect (see Suppl Fig S6 in Samer et al., in which the effect of the LRA is measured in the peripheral blood mononuclear cells), hence it is unlikely that the red spots in the PET images show up only in the Axillary cluster of lymph nodes ipsilateral to the injection sites and not in any other lymph nodes clusters in the body. See for instance video 3 and video 4 of the [2] paper in the links repasted below for quick access (under note-1*), the two animals with the highest increase in Lymph nodes uptakes show red spots only in the Axillary nodes ipsilateral to the injection sites, but not on the opposite site of the Axillary nodes nor in other lymph nodes clusters of the body, such as the inguinal lymph nodes.
The authors however showed that the LRA did induce an increase in viral replication from PCR of inguinal nodes tissues shown in Table 2 and Fig 4A of [2]. For instance, the A14X064 showed ~100 fold increase in CAVL-RNA in inguinal nodes, yet from video 4 https://insight.jci.org/art... the only nodes that light up in red in the PET images are the Left Axillary nodes ipsilateral to the extravasation of the injection site on the Left arm of the monkey. <br /> Lack of increase in specific binding of the probe in the lymph nodes, despite the evidence of increase in viral RNA in some of those tissues based on PCR, points to a lack of reproducibility of the anti-env imaging system, or, at most, to a poor sensitivity of the imaging system.
On the other hand, this lack of reproducibility that comes across by comparing the 2022 and 2023 imaging data, which are based on previous two publications from the same imaging team in 2015 [3] and 2018 [4], questions indeed those earlier two nonhuman primates papers [3, 4] too, in which the authors showed evidence that this imaging system is capable of detecting residual levels of viral replication in HAART treated animals, the latter being an attribute of an imaging systems very sensitive to detect changes in target (gp120) molarity. <br /> The latter consideration assumes that the binding affinities of the radiolabeled F(ab’)2 fragment of the 7d3 used in [1, 2] and the radiolabeled 7d3 used in [3, 4] are similar, which seems a fair assumption based on what is implied in the Methods sections of the [1, 2] articles, although in vitro binding data have not been reported in the [1, 2] articles (more in note-*2).
Similarly, there is no evidence of increase in gut SUV levels after the first two weeks of Galunisterib administration in the 2023 pre-print Kim et al. [1]. This contrasts with the observation reported in the 2022 [2] of a substantial increase in gut uptake seen for instance in animals (A14X027 (video 1) or A14X013 (video 7) or A14X064 (video 4)) after only one week of the LRA administered at lower doses for shorter periods compared to the [1] 2023 study in pre-print.
An alternative explanation for the increase in gut radiotracer uptake seen in the 2022 paper is that the increase in bowel uptake reflects a well-known phenomenon of non-specific intraluminal uptake of the radiolabeled antibody\F(ab’)2 fragments [5, 6] (see more under note-*3.1)).
In other words, the hypothesis that the increases in probe uptake in lymph nodes and the gut seen in the 2022 paper after the first LRA-cycle are fully explained by non-specific uptake of the probe appears consistent with the lack of increase in probe uptake in the same anatomic compartments in the 2023 study in which animals received the same LRA at higher doses for longer periods of time.
Finally, another important difference between the 2022 [2] and the 2023 [1] imaging data is that in the latter [1], the increase in probe uptake (given as SUV, standardized uptake value) is now seen in the lymph nodes, gut (and many other regions of the body, the latter a feature that points to a non-specific nature of the biodistribution) at later cycles of the LRA administration, when also the heart (blood pool) probe uptake is increasing, which is happening in all animals at the later cycles of [1]. As described by mathematicians in the 80’s, when changes in input function take place, the use of the SUV to quantitate changes in probe uptake could be misleading and requires mathematical modeling of the time activity curves generated in serial imaging along with an input function [7], or at least a normalization on the blood pool (heart).
This phenomenon of the increase in heart uptake (which indicates blood pool) following Galunisertib administration was not noted in the animals of the [2] publication. Only in one animal of the previous 2022 publication, the authors claimed increase in the radiotracer uptake of the heart. However, the figures and videos associated to this particular animal (A14X004) reveal a potentially important inconsistency, as described under note-*3.2.
It would be helpful, in general, to standardize measurements of covariates generated in different labs, especially in studies that are closely related to each other like the [1] and [2] studies. Note under note-*4 lack of standardization in measurements of CAVL viral RNA and CAVL viral DNA between [2] and [1] studies.
Notes and Bibliography
note-*1. Figure 3H of [2] shows that the highest increases in lymph node (LN) uptake are in monkeys A14X060, A14X064, and A14X013. In these animals, PET images show clear evidence of significant extravasation of the injected probe into the subcutaneous tissues ipsilateral to the high LN uptake. A fourth animal (A14X027) has a slight increase in LN uptake with smaller extravasation. The four animals above are 4 of the 5 animals that the authors claim in the Abstract of [2] show increase in probe uptakes in lymph nodes caused by the administration of the LRA. It is well known, however, that when radiolabeled antibodies\fragments are intentionally or unintentionally (infiltration) administered by subcutaneous or intradermal route, they find their way into the lymphatics and then non-specifically concentrate in regional draining nodes [8], as it appears to be the case for the animals listed above since the radiotracer uptake in LNs was much higher on the side of infiltration than the contralateral side or than in distant nodes. For this reason, the SUV analysis should not have included those LNs in Figure 3H. Without those LNs, however, it looks like from the published images that there is no increase in probe uptake in the LNs, as claimed in the abstract of [2] in which a causal link between Galunisertib administration and increase in LNs in probe uptake is inferred from the data analysis.<br /> Video1…8 from the JCI-I link<br /> Video 1 (A14x027, suv-scale=1.5) : https://insight.jci.org/art...<br /> Video 2 (A14x037, suv-scale=1.5): https://insight.jci.org/art...<br /> Video 3 (A14x060, suv-scale=1.5): https://insight.jci.org/art...<br /> Video 4 (A14x064, suv-scale=1.5): https://insight.jci.org/art...<br /> Video 5 (A14x004, suv-0.3, kidney and liver removed) : https://insight.jci.org/art...<br /> Video 6 (A14xX005, suv-scale=1.5): https://insight.jci.org/art...<br /> Video 7 (A14x013, suv-scale=1.5): https://insight.jci.org/art...<br /> Video 8 (A14x004, suv-scale=1.5): https://insight.jci.org/art...
note-*2. In Samer et al. [2] we read “The 64Cu-DOTA-F(ab′)2 p7D3 was previously validated in SIV-uninfected macaques (62)”, with ref 62 being ref [3] of this document. In the pre-print Kim et al. for the characterization of the probe the Samer et al. [2] reference is provided. <br /> However, in ref [3] only the intact 64Cu-DOTA-7D3 and not the F(ab’)2 was tested in vitro in data presented in Suppl Figure S1 of [3]. In particular, Suppl Fig S1C of [3] shows approximately 2-fold only difference in radiotracer uptake in a competition assay using gp120 expressing cell lines, that are known to express gp120 at much higher levels than primary cells (e.g. pbmc, spleen or lymph node cells). Of note, the competition assay of S1C shows rapid loss in binding when the radiotracer is incubated with only 25% more of the non-radiolabeled (cold) ligand, which is unexpected for high affinity binding ligands. <br /> All these four imaging NHP studies [1-4], produced by the same imaging team, lack autoradiography analyses. The latter ex-vivo technique generates powerful data for the in vivo inference, as typically done in oncological pre-clinical research, because, as mathematicians have shown when they first attempted to extract quantitative information from the PET images [7], what the in vivo imaging is revealing is not a signal proportional to the absolute concentration of the target, but rather a signal that is proportional to the binding capacity of the probe, which is the product of the probe affinity times the concentration of the target. The implication is that if the latter is very low, we can have in our hands a very high affinity ligand, yet we will not be able to generate an SUV level that is predominantly explained by specific binding of the probe. In absence of autoradiography data, some evidence of binding capacity can be generated by implementing in vitro cell binding assays using primary cells (e.g. PBMC or splenocytes or lymph node cells from infected animals and compare the binding to same cells from uninfected animals). This was done only in the first publication [3] in Nature Methods, in which a two-fold (only) difference in SUV uptake was observed by comparing uninfected and infected spleen and lymph node cells (S1B) ex vivo incubated with the radiotracer. <br /> However, the binding data were generated using cryopreserved cells without cold-competition assays; the latter would be useful to rule out, for instance, putative higher non-specific uptake due to higher cell death in the infected cells following their thawing. In general, it would be helpful to increase the sample size of S1B of the 2015 publication [3] (for instance only one well for uninfected lymph node cells were used for that piece of data, which precludes any robust conclusion from the data), as well as to produce autoradiography studies, as mentioned above, in which tissue sections are incubated with close to kd concentration of the probe and after washing, the tissue sections uptakes are compared to the non-specific uptake generated by pre-incubating the adjacent tissue sections with large amount of the cold (i.e, non-radiolabeled) probe to block all gp120 receptors in the tissue sections.
Additional validations of the observed increased SUV uptakes in SIV infected animals, or following the administration of an LRA, as claimed in the 4 NHP publications, is particularly warranted given the state-of-art research in this area and given that, because of the high costs and demanding resources associated to these studies, few laboratories in the world have the capacity of reproducing these experimental data.
note-*3.1 The study [2] did not appear to exclude in the analysis of the gut areas that are consistent with the intraluminal antibody excretion in bowel segments, because details of how the gut SUV uptake was obtained were missing from the Methods section of the [2] publication. The new [1] pre-print states “To quantify the signal in the gut tissue, the body segment below the stomach to above the cervix was initially isolated. The Gut’s SUV was then calculated by extracting the spleen, both kidneys, liver, and bones within the designated body region using Boolean operations.” The latter approach , if adopted also in the [2] publication with those high levels of gut SUV probe uptake seen soon after cycle 1 in some of the animals, again proves that those areas are consistent with the intraluminal antibody excretion (e.g. stools) in bowel segments were not excluded in the analysis, however, this is not what is commonly done in antibody imaging studies [5], because it is known that this phenomenon of non specific uptake in the gut due to the excretion of these types of radiotracers can occur.
note-*3.2 Figure 3F in [2] is a figure obtained from Supplemental Video 5 (https://insight.jci.org/art... ), with baseline and post-LRA images displayed with SUV-rainbow-scale = 0.3 for animal A14X004. Based on the legends, all other animals are displayed at SUV-scale =1.5. (baseline is before LRA (i.e first panel to the left) and middle and right panels are images at week 1 post LRA for one week and week 2 post-LRA for another week, respectively). <br /> Based on the legends, and consistent with the CT anatomy of the Video 5, the Video 8 https://insight.jci.org/art... shows the same images, before subtracting liver and kidney, displayed at 1.5 SUV. If we try to picture how the Video 8 (baseline, left panel) would look like by putting our hand on top of the liver and kidney to mask these two organs, it appears that what is left is an image that is the same image displayed in Video 5 (first panel to the left) or Figure 3F (first panel to the left). However, the legends state that the scales are different for Video5\Fig3F (0-0.3) and Video 8 (0-1.5), hence also the colors of the baseline images of Video 5 and 8 should be the different.<br /> In other words, Video 8 and Figure3F\Video 5 are incompatible. The evidence that Video 5 and Video 8 of reference [2] require to be harmonized for the validity of the whole dataset, can be also deduced by looking at the rainbow scale of the images. The rainbow scale goes from black to blue to green to yellow to red. So if we fix it to max SUV=1.5, it means that if an SUV uptake is 1.5 or higher, it will show up as red, and all the other colors would indicate levels below SUV=1.5 …for instance green is around 0.7. Now, if the rainbow is fixed to max Suv=0.3, it means that whatever is 0.3 or higher will be red, and green is in the middle, around 0.15. If Figure 3F\Video 5 is correct but the mistake was done in Video 8 (left panel was set at SUV scale 0.3 instead of 1.5 like written in the legends), then once displayed on a scale 1.5, the left panel of Video 8 should show a liver and kidney in color bluish...(which is not seen in the liver and kidney of any other animals, hence the latter scenario would point to a very fast biodistribution of the probe, which is consistent with a damage of the probe). <br /> Two different versions of the Samer et al. paper can be found online, the PMC version and the JCI-Insight version, which differ primarily on the biodistribution of the A14X004 (video 5 and video 8).
Some of the differences between JCI-Insight link (https://insight.jci.org/art... current version online modified in June 2023) and the PMC-link (dated November 2022 https://www.ncbi.nlm.nih.go... ) are here outlined:
A)<br /> JCI-I: However, a probe generated using a rhesus IgG1 Fab against an irrelevant antigen 64Cu-DOTA-F(ab′)2 pIgG1 in an SIV-infected macaque was used as further control (Supplemental Figure 8 and Supplemental Video 9). <br /> PMC: However, a probe generated using a rhesus IgG1 Fab against an irrelevant antigen 64Cu-DOTA-F(ab′)2 pIgG1 in an SIV-infected macaque was used as further control (Supplemental Figure 8 and Supplemental Video 2). <br /> Note, Suppl video 2 in PMC link shows images of A14X037, hence unrelated to the sentence above in the PMC link.<br /> B)<br /> JCI-I: A smaller increase in the gut was also present in A14X004 and A14X060 (Figure 3G, Supplemental Video 5, and Supplemental Video 8).<br /> PMC: A smaller increase in the gut was also present in A14X004 and A14X060 (Figure 3G, Supplemental Video 5, and Supplemental Figure 1). <br /> In this case too, Supplemental Figure 1 in PMC link shows figure title TGF-β inhibits HIV-1 latency reactivation by PMA in ACH-2 cells, hence unrelated to sentence in the PMC link.<br /> Consistent with the changes above, the supplemental materials in PMC do not show Supplemental Video 8 and Video 9. The videos legend, however, is the same on both links, i.e. points to the existence of additional two videos (called movie S1 and movie S2) in both the PMC and JCI-I links. None of the two links however call movie S1 or movie S2 in the main text, so this is an editing mistake probably propagated for 3 corrections made on the JCI-Insight publication, the last one dated June 14th 2023 based on the Version History section linked to the publication https://insight.jci.org/art... .
C) Video 1 link of the PMC link does not contain the animal ID listed in the legend, but animal A14x004 displayed at 0-1.5 SUV scale (what became Video 8 in the JCI-I link)<br /> D) the PMC link date is Nov 2022, the JCI-I link shows that changes were made in June 2023 based on the third upload of the supplementary material file.
note-*4. Figure S3 from the [1] pre-print shows CAVL-RNA in the gut with unit measurement [copies/ml] and ranges 0-30; in the 2022 [2] paper Fig 4A shows the same covariate but with unit measurement [copies/10to6] cell-eq and ranges (0.1-1,000) log-scale; <br /> Figure S4A from the [1] pre-print show CAVL-DNA in different organs with unit measurement [log copies/10to4 cell-eq, range 0-5]…; in the 2022 [2] paper Fig 4C shows the same covariate but with unit measurement [copies/10to 6] y-axis transformation unclear;
Kim, J., TGF-β blockade drives a transitional effector phenotype in T cells reversing SIV latency and decreasing SIV reservoirs in vivo. 2023.<br /> https://www.biorxiv.org/con...
Samer, S., et al., Blockade of TGF-beta signaling reactivates HIV-1/SIV reservoirs and immune responses in vivo. JCI Insight, 2022. 7(21).<br /> https://insight.jci.org/art...
Santangelo, P.J., et al., Whole-body immunoPET reveals active SIV dynamics in viremic and antiretroviral therapy-treated macaques. Nat Methods, 2015. 12(5): p. 427-32.<br /> https://pubmed.ncbi.nlm.nih...
Santangelo, P.J., et al., Early treatment of SIV+ macaques with an alpha(4)beta(7) mAb alters virus distribution and preserves CD4(+) T cells in later stages of infection. Mucosal Immunol, 2018. 11(3): p. 932-946.<br /> https://www.ncbi.nlm.nih.go...
Beckford-Vera, D.R., et al., First-in-human immunoPET imaging of HIV-1 infection using (89)Zr-labeled VRC01 broadly neutralizing antibody. Nat Commun, 2022. 13(1): p. 1219.<br /> https://pubmed.ncbi.nlm.nih...
Hnatowich, D.J., et al., Pharmacokinetics of the FO23C5 anti-CEA antibody fragment labelled with 99Tcm and 111In: a comparison in patients. Nucl Med Commun, 1993. 14(1): p. 52-63.
Mintun, M.A., et al., A quantitative model for the in vivo assessment of drug binding sites with positron emission tomography. Ann Neurol, 1984. 15(3): p. 217-27.
Keenan, A.M., et al., Immunolymphoscintigraphy and the dose dependence of 111In-labeled T101 monoclonal antibody in patients with cutaneous T-cell lymphoma. Cancer Res, 1987. 47(22): p. 6093-9.<br /> https://pubmed.ncbi.nlm.nih...
On 2023-09-29 02:49:04, user Nadine Keller wrote:
Please find the published version in the Journal of Hydrology here: https://doi.org/10.1016/j.j...
On 2023-09-29 01:11:11, user Kevin Corbett wrote:
Xibing, Thanks for the reminder and my apologies for not citing this important paper! We'll be sure to cite it in the revised version and the final published version. - Kevin
On 2023-09-28 21:27:59, user LabTerra wrote:
Dear authors,
First and foremost, we would like to congratulate you on your work. The text is relevant given the context of climate change and despite the inherent complexity of this subject and the extensive analysis conducted, you effectively guided the discussion in a clear and compelling manner. We also found the research idea and the results obtained to be quite intriguing, showing how different climate variables influence beta diversity and its components. In particular, the taxonomic diversity being more aligned with phylogenetic and functional diversity in the tropics as opposed to temperate and polar regions, is a very interesting finding.
That being said, we believe that some of the results could have been further explored in the discussion section and that the introduction could use additional information to highlight the work's importance and clarify some of the choices made, such as more details on why the LGM was chosen for comparison.
Below, we provide a list of specific suggestions that we hope could contribute to your work, especially for the clarification of some of the results and methods used and for a more comprehensive discussion section.
List of specific comments:
The importance of your work and how it relates to current climate change could be further emphasized in the introduction section.<br /> It is mentioned that precipitation seasonality was the main variable explaining total beta-diversity. This result could be better explored in the discussion, as it was only briefly mentioned in the results section.<br /> Consider integrating some of the limitations identified in the methodology section in the discussion as well. For example, the explanation on how the gaps in the functional traits dataset could affect your results.<br /> As it is mentioned in the discussion section, the climate changes that are happening now are different from the changes that happened in the last glaciation. We believe the comparisons made between them and the conclusions reached could be expressed with less certainty.<br /> While the importance of conserving a network of protected areas in regions with rare species is indicated in the discussion section, this subject could have received more attention. It's also worth emphasizing in the text that such actions will likely not be enough to stop climate change-driven extinctions on their own.<br /> The high beta-diversity in regions such the Sahara transition and the USA is an intriguing result that could be investigated.<br /> In the models, the r² values are high for the combined variables, but relatively low for individual variables. The study focuses on the effects of the LGM anomaly, but we believe that a more in-depth exploration of the interactions among the studied variables would be beneficial.<br /> We found it unclear what corresponds to a “species” (used in taxonomic diversity) in the context of phylogenetic and functional diversity. Do these correspond to functional and phylogenetic groups?<br /> Figure 1a caption is somewhat confusing, as it mentions beta diversity but only shows LGM anomaly.<br /> In figures 2 and 4, we recommend that all sub-figures share a consistent scale to facilitate comparisons between them.<br /> Figures 2 (j, k and l) and 4 could use a different color scale. Light yellow is especially difficult to discern from the background white.<br /> The inclusion of a table with the description of all environmental variables used to compare with beta-diversity might be beneficial to the understanding of the reader.
We hope that our suggestions will help in further improving the quality of your work.
Kind regards,
The LabTerra team
On 2023-09-28 15:29:05, user Lennart Wirthmueller wrote:
The proteomics data described in this preprint are now available in the PRIDE database with the following identifiers.
Dataset S1 - PXD045780
Dataset S2 - PXD045511, PXD045544, PXD045545
Dataset S3 - PXD045638, PXD045558, PXD045548
Dataset S4 - PXD045726
Dataset S5 - PXD045549
Dataset S6 - PXD045550
On 2023-09-28 02:36:38, user Arpita Goswami wrote:
Hi, the preprint is now accepted in Biology methods and Protocols (OUP). https://doi.org/10.1093/bio...
On 2023-09-27 16:50:48, user Vanessa Staggemeier wrote:
This study is very interesting, the authors provide a perspective on the conservation status of all angiosperms. The topic is timely, and the manuscript brings a useful tool to build a fast evaluation of the extinction risk for species that have not yet been assessed by the IUCN.
The high number of species without conservation status assessment (~82% of all vascular plants) was a big surprise to us, taking this number into account we believe the proposed tool can help to advance in the classification of conservation status. Predictions of extinction risk could help to prioritise assessment efforts for species predicted with high certainty to be threatened and also for species with uncertain predictions, which could be triaged as Data Deficient and prioritised for further fieldwork.
We enjoyed reading this manuscript very much, but we have some questions that were raised during a lab meeting planned to discuss this preprint, which we leave below.
1) We know that many new species described in recent years are listed as threatened in the description, however this information is not included in the IUCN Red List. So, our question is, why is this information not reaching the IUCN, what alternatives could be applied to overcome this issue?
2) We saw that it is available a more recent data on the Human Footprint, which is Sanderson et al. but it is a preprint article (https://doi.org/10.32942/os..., why was this reference not used?
3) In 2017 was published an update on ecoregions, we saw that the changes from Olson et al. 2001 are few, but why did you choose to work with the oldest instead of Dinerstein et al. (2017)?<br /> Dinerstein, E., Olson, D., Joshi, A., Vynne, C., Burgess, N. D., Wikramanayake, E., ... & Saleem, M. (2017). An ecoregion-based approach to protecting half the terrestrial realm. BioScience, 67(6), 534-545.
We also leave some suggestions to the authors:<br /> 1) It would be interesting to map the results by biome (the results were shown by botanical country, Fig. 2). It is our belief that the species in the botanical country of Northeastern Brazil are from the Atlantic Forest (AF) and not from the Caatinga because the level of degradation is higher in the AF and there are also many more studies in the AF than in the Caatinga. This map would be of more interest to researchers working on specific biomes.
2) We think it would be useful to provide a list with the classification (conservation status predictions) and degree of uncertainty for each species.
3) Some tables in the Supplementary Information that are .csv files (e.g. Table S3) are not accessible … or we do not know how to access them.
Congratulations to all the authors, this paper is an excellent contribution, providing predictions of conservation status for each species, which can provide a basis from which full Red List assessments can progress more quickly.
Comment written at a meeting of the Laboratório de Ecologia Vegetal, Evolução e Síntese (LEVES) at the Universidade Federal do Rio Grande do Norte, RN, Brasil. Joined this meeting: Vanessa Staggemeier, Hercília Freitas, Víctor de Paiva, Cassia Oliveira, Rhuama Martins and João Paulo Câmara.
On 2023-09-25 16:32:21, user Leonardo Couto wrote:
Dear Authors,
I am Leonardo Couto, a master's student at the Federal University of Minas Gerais (UFMG), located in Belo Horizonte, Minas Gerais, Brazil. I am currently being supervised by Professor Juliane Karine Ishida. Throughout my academic career, I have been studying the interaction between plants and microorganisms, and your preprint titled "Disease Resistance correlates with Core Microbiome Diversity in Cotton" caught my attention. Recently, our research group received an invitation to evaluate preprints in order to gain experience and contribute to the advancement of science. Therefore, I have chosen your work for discussion in one of our meetings. In collaboration with other colleagues, we have brought forth some suggestions to assist in improving your article, and we hope they may prove useful to you.<br /> We noticed a lack of introductory content in his introduction, which would be essential to better understand his study.<br /> (Lines 95-100) - The explanation of cotton leaf roll disease (CLCuD) and its economic implications for the country is provided only at the conclusion of the preprint. We believe these topics could be explored in more depth in your introduction.<br /> We also note that the text is not structured into categories such as introduction, methodology, results, and conclusion. We understand that this may be in line with the journal's guidelines.<br /> In your methodology section (lines 62 - 68), we missed a more detailed description of your samples. For example, we are curious about the distribution of samples among susceptible, partially tolerant, and fully tolerant varieties. Furthermore, it would be useful to know how many samples are attributed to epiphytic leaves, endophytic leaves, rhizosphere, and endophytic roots. We suggest that compiling this data into a supplementary table could be beneficial.<br /> These are some small contributions we offer to help you further improve and refine your work. We hope these suggestions are valuable to you in advancing your research.
Yours sincerely,
Leonardo Couto<br /> Master's Student, Federal University of Minas Gerais (UFMG)
On 2023-09-25 13:25:38, user Lander De Coninck wrote:
Hi,
Great manuscript! I completely agree that we need to sequence more individual mosquitoes, if we ever want to understand the complex relationships between insect-specific viruses, arboviruses and their hosts.
However, I have one big remark that I hope you consider to change in the published version of this manuscript. On line 175, you mention 393 mosquito-associated viruses and you define them as the 'core mosquito virome' for the rest of the manuscript. I feel that this term is too broad to describe just all your mosquito-associated viruses (some of them might be found in only one or very few individual mosquitoes). In general, a core microbiome can be defined as a set of taxa that consistently occur within a given habitat type or host (see https://journals.asm.org/doi/10.1128/msystems.01066-22 and https://www.pnas.org/doi/full/10.1073/pnas.2104429118). Also, Shi et al. (Microbiome, 2019), one of the first papers to describe a core virome in mosquitoes, describes the core mosquito virome as "a set of viruses found in the majority of individuals in a particular mosquito population".
Could you change the use of this term in your manuscript to avoid confusion for readers and have a consistent use of the term "core virome" in future research?
Kind regards,
Lander
On 2023-09-25 08:31:40, user Fernando Perez wrote:
The peer-reviewed and final version is available here: https://www.nature.com/arti...
On 2023-09-24 20:09:58, user Prof. T. K. Wood wrote:
This work fails to cite our work, which has been used by 40 labs since 2013, to create an uniform population of persister cells through chemical pretreatment (doi:10.1128/AAC.02135-12) and instead indicates incorrectly, “one challenge in mechanistic research on persisters is the enrichment of pure persisters.” Also, this work fails to cite the only mechanistic model for persistence: ribosome dimerization (doi.org/10.1016/j.bbrc.2020...:vvLQGhdcuqrN__QUPD2ivE3-d5g "doi.org/10.1016/j.bbrc.2020.01.102)") and fails to cite the first single cell works on persister resuscitation (doi:10.1111/1462-2920.14093 & doi.org/10.1016/j.isci.2019...:N5cNvOkLn_Ksn053qV3G6Ofwii0 "doi.org/10.1016/j.isci.2019.100792)"). It also fails to recognize the perils of PI staining (not all non-red cells are viable, doi:10.1111/1462-2920.14075).
On 2023-09-23 22:43:21, user masfique@gmail.com wrote:
I missed responding to your comment. It is a nuclear localization signal (NLS). The experimental evidence confers the existance of a functional NLS at the predicted protease site.
On 2023-09-23 18:11:19, user David Bacsik wrote:
The final version of this manuscript is available in eLife at: https://elifesciences.org/a...
On 2023-09-22 23:48:41, user Duncan Muir wrote:
Summary:<br /> Arrhenius/Eyring behavior for enzymes (i.e., a linear increase in kcat with temperature) is predicted by transition state theory. Nevertheless, deviations from this minimal model have been observed over the years[4]. Work from groups including Klinmman[27,28] and Daniel and Danson[21] have exemplified this unexpected, non-Arrhenius behavior of enzymes and have sought to apply models to these observations, including an equilibrium between active and inactive states of the substrate-bound enzyme[21]. Åqvist and colleagues [5] have sought to distinguish between competing models, including the activation heat capacity model from this manuscript, and the equilibrium model. In the literature, there are contradictory reports regarding the role of activation heat capacity [22,8], leaving the debate on the origin of non-linear temperature dependence of activity unresolved.
This paper aims to characterize conformational changes during catalysis through the lens of activation heat capacity. The authors used MalL as a model system and conducted kinetics experiments over a range of temperatures to calculate changes in the heat capacity of activation. Using molecular dynamics, the authors simulate a narrowed conformational landscape of a transition state-like MalL in comparison to wild-type. Based on the observed kinetic behavior of MalL across a wide range of temperatures, the authors presented MMRT-2S, a modified version of Macromolecular Rate Theory (MMRT) that accounts for a cooperative transition between the enzyme-substrate conformation and the transition state-like conformation. The major strength of this paper is high quality temperature-dependent kinetic data collected over a range of temperatures typically not explored, which allows us to evaluate complex models to describe unexpected enzymatic behavior. The major confusion we have with this paper is the lack of details needed to aid the reader in interpreting the data: namely not presenting the hydrogen bond measurements and statistics to demonstrate significance, and a lack of controls or citations for kinetic assay assumptions. Overall, this paper presents an intriguing explanation for the role of conformational changes in catalysis to reconcile diverse observations noted in enzyme literature.
Major points:<br /> Substrate Saturation, Denaturation Controls, and Assay Set-UpAccording to the methods section, saturating concentrations of p-nitrophenyl-α-D-glucopyranoside were assayed. It would be helpful if exact concentrations were reported for reproducibility of the work. Furthermore, it is important to mention if Km has been reported at the range of temperatures assayed for this substrate, to confirm saturation.
The text mentions that nonlinearity has been reported in this absence of denaturation; this statement could be strengthened if controls or references were included to corroborate this.
From the description in the methods section, we believe the enzyme is not incubated at the assay temperature prior to the reaction, so over the course of the limited reaction time (45 seconds) we are unsure if the enzyme has reached a folded/conformational equilibrium. Furthermore, we think it would be informative for readers if the authors include discussion about foldedness being unlikely to be a contributor to changes in kcat.
Mutant Structure RationaleThe rationale for creating the S536R mutant is unclear to us. Although we agree that “introducing new hydrogen bond networks at the surface of the protein” is one way to perturb conformational dynamics and rates, we are unsure why arginine was selected as the residue for this. We are also unsure why the X-ray structures were determined in the presence of urea.
Mutant / TLC ActivityThe mutant structure shows a distinct ensemble via the PCA projection. It is important to discuss if activity data collected for this mutant should be expectedly different from WT based on the MMRT-2S model.
Hydrogen Bond MeasurementGiven the difference in crystal packing between the wild-type and mutant structures, it is important to identify if any of the hydrogen bonds of interest are proximal to a gained or lost crystal contact between the two structures.<br /> Additionally, while truncating the data to a similar resolution will remove high resolution reflections from the mutant structure, the reflections around 2.3Å will likely have higher signal:noise, which complicates analysis. <br /> It would be helpful for readers to have more details on the hydrogen bonds mentioned in Figure 5A – for example, by creating a table of the residues involved in each bond and the amount that the bond was shortened. Further interpretation of the shortened hydrogen bonds would also be helpful; for example, we are curious if the authors consider all the shortened hydrogen bonds as equal contributors to the restricted conformational landscape of the TLC, or if there are certain ones that the authors believe are more impactful.
Additionally, for those unfamiliar with MD, more commentary on the interplay between structural data and MD simulations would be instructive. For example, are residue motions observed in MD simulations consistent with the hydrogen bonding differences observed in the static structure?
ADH ComparisonWe are unsure if the authors’ claims contrasting MalL and ADH at different temperatures are supported by the data, given that there was no MD performed on ADH. For MalL, we were able to visualize the restricted conformational landscape of the TLC via a combination of MD simulations and structural data. For ADH, only Cp is calculated from kinetics data, and there is no structure or MD data presented. Arguing that there is a more restricted conformational landscape based on Cp alone seems insufficient compared to the amount of work done on MalL. Perhaps citing more previous literature on ADH in detail would be helpful for strengthening the contrast between MalL and ADH.
Minor points:<br /> Language / Word Choice:Use of word “significant” should be reserved for demonstrated statistical significance<br /> Qualifiers like “very” were used frequently and distract from the data and claims in themselves, particularly in section headers i.e., “Very High Resolution Structure...”<br /> “Accurate” should be replaced with “precise” when describing collected data, as the data themselves are observations of ground truth, and the narrow distribution of replicates implies high precision
Scope<br /> The claim “The importance of activation heat capacity for enzyme kinetics has been the subject of debate recently” is supported by 3 self-references, out of four total.
In previous work, the authors identify that MMRT applies to chemically-limited catalysis, which should be mentioned in the discussion regarding the applicability of the model, and cite Kern and Hilser’s works on adenylate kinase as an edge case as in the author’s previous reviews.
Figures <br /> In general, the addition of more labels/legends on the plots would help with interpretation.<br /> Specific examples:<br /> Figure 2: labeling the color that corresponds to each mutant <br /> Figure 3: labeling deuterated vs protiated <br /> Figure 3D: labeling which curves correspond to ∆H and ∆S<br /> Figures 4A & 4B: For clarity, the different regions of MalL (lid, active site, loop, etc.) could be labeled here. Furthermore, arrows depicting the motions observed in PCA1 and PCA2 would be informative.<br /> Figure 5A: For clarity, the S536R mutation could be labeled here.
On 2023-09-22 10:19:46, user Adrian Newman-Tancredi wrote:
Dear authors, <br /> Congratulations on this very interesting study with a large amount of data. We indeed agree that targeting 5-HT1A receptors is a promising strategy for addressing pain conditions, among others. Concerning your comment in the Introduction that "adverse side effects led to termination of clinical trials" for befiradol, please note that trials were, in fact, terminated for lack of efficacy in the chosen pain indication, not because of AEs (see https://www.clinicaltrialsr.... Please note that befiradol has successfully undergone clinical testing for treatment of Parkinson's disease with an excellent safety and tolerability profile (see https://www.einnews.com/pr_.... I would be grateful if you can send me a pdf of the accepted publication in due course. <br /> Best regards,<br /> Adrian Newman-Tancredi
On 2023-09-21 10:57:45, user Diego di Bernardo wrote:
Very interesting result. We just published in Hepatology a study of the drug sensitivity of the C2 subtype (i.e. Embryonal in this manuscript) and one of our top computational prediction was Brivanib an unspecific FGFR inhibitor, well in agreement with the findings of this manuscript. Also we found CDK9 inhibitors as effective. To know more here is the link: https://journals.lww.com/he...
On 2023-09-21 10:07:13, user John Meadows wrote:
This is the February 2023 manuscript, with the addition of a couple of references to case studies published subsequently.
On 2023-09-21 08:56:51, user John Meadows wrote:
This is a detailed version of a paper presented at the Radiocarbon conference in Zurich in September 2022. The manuscript was submitted to a prominent multidisciplinary journal in February 2023, and sent for peer review. One review was returned quickly, but the journal failed to obtain a second review, despite replacing the handling editor. After waiting over 6 months, we withdrew the manuscript from this journal and submitted a slightly revised version to another journal, while simultaneously posting this pre-print.
On 2023-09-20 19:29:22, user Colin Reardon wrote:
Interesting paper. The dataset reference for this study does not seem to be correct. "GSE227331" is an accession number for "METTL3-mediated m6A modification controls splicing factor abundance and contributes to CLL progression"
On 2023-09-20 14:35:36, user Damien Gregoire wrote:
Happy to make our results available to the scientific community. We are welcoming feedbacks on the manuscript, comments and questions. DG
On 2023-09-20 08:03:53, user Ramon Crehuet wrote:
I think this paper provides a new perspective on biomolecular LLPS. I have a very specific issue about figure 5. The (e) case is a limiting case of (c) with X_HO tending to 0. But (d) and (f) are completely different. In one case the slope is negative and in the other it is positive? Could it be that the components have been exchanged?
On 2023-09-19 19:13:31, user S. An wrote:
This article is now published:<br /> Miji Jeon, Danielle L. Schmitt, Minjoung Kyoung, and Songon An* "Size-specific modulation of a multienzyme glucosome assembly during the cell cycle” ACS Bio & Med Chem Au (2023) ASAP (https://pubs.acs.org/doi/10...
On 2023-09-19 18:56:14, user Lloyd Fricker wrote:
Confirmation of results is an essential part of science. Our original finding that the peptide PEN is an agonist of GPR83 was already verified by two different laboratories (see Foster et al, 2019, “Discovery of Human Signaling Systems: Pairing Peptides to G Protein-Coupled Receptors”, Cell, PMID: 31675498; and Parobchak et al, 2020,<br /> “Uterine Gpr83 mRNA is highly expressed during early pregnancy and GPR83 mediates the actions of PEN in endometrial and non-endometrial cells”, FS Science, PMID: 35559741). However, when another laboratory can’t confirm what was published, it is important to consider differences between the studies. With this in mind, there are several issues with the study presented here in the BioRxiv report by Giesecke et al. Additional experiments described below would be very helpful.
The HEK293 cell line has been reported to express GPR83 mRNA in many studies listed in GEO Profile, and this is confirmed in the current study (Figure 1A). In the figure showing overexpression of HA-tagged GPR83 in HEK293 cells, the distribution is largely intracellular, maybe ER or Golgi (Figure 1B). Thus, it’s not relevant to consider ‘100-fold’ overexpression based on mRNA, as the amount that is correctly folded and expressed at the cell surface may not be much different than in the native HEK293 cell line. Furthermore, the authors show that PEN peptide does produce a robust increase in phosphoERK in Figure 4A (compare the lanes labeled ‘control’ and ‘PEN’). Furthermore, it looks like the PEN-mediated increase in phosphoERK is several fold higher in GPR83-transfected cells than in control cells (Figure 4A). Although the quantitation panel in Figure 4B doesn’t show an increase, there are very large ‘error bars’ reported to be SD, but for N=2 doesn’t SD really mean the range of duplicates? Also, please show all data points in the bar graph! In any case, it would be nice to see a larger N for these studies. But best of all would be a knock-down of GPR83 in HEK293 cells using siRNA, or a related approach.
Another point is that in the PEN-GPR83 peptide-receptor system, signaling assays that are distal to the receptor activity (cAMP, PLC) tend to give variable results – this has been previously noted in our original study (Gomes et al, PMID: 27117253). This also appears to be the case with the TANGO assay where we have recently found that long-term treatment with PEN (16 hrs) causes a desensitization of the receptor leading to a complete loss of signal (our unpublished observations). There are also issues with the concentration dependence, and ‘u-shaped’ curves where high concentrations of PEN fail to produce the effects seen with lower concentrations (PMID: 27117253). The authors should repeat the studies with shorter times and lower concentrations of PEN.
The lack of binding with Tyr-PEN seen by Giesecke et al. could be due to the presence of an internal His residue in human PEN (YAADHDVGSELPPEGVLGALLRV). Tyr-PEN used in our previous studies for iodination was the rat sequence, which does not have a His. Because His residues can be iodinated using the chloramine T procedure used by Giesecke et al, this can potentially affect binding. It would be good to test binding with iodinated rat Tyr-PEN, to avoid the His residue. Also, why the C-term amide group? That’s not part of PEN.
For the Ca++ assays, Giesecke et al. used Gα16, but our previous studies used Gα16/i3 (PMID: 27117253). This is not a minor difference. Ideally, Giesecke et al could repeat the experiments with Gα16/i3.
Finally, protease inhibitors were used in the binding studies by Giesecke et al, which is good. It is not clear if such inhibitors were used in all other studies with PEN, such as those described in Figure 2 and 3. In the absence of protease inhibitors, PEN could be degraded during the assays and this could have accounted for the negative results.
Sincerely,<br /> Ivone Gomes,<br /> Lloyd Fricker,<br /> Lakshmi Devi
On 2023-09-19 14:25:20, user Courtier wrote:
The part entitled "The recombinant common ancestors became more similar to SARS-CoV-1 and SARS-CoV-2 with increased sampling" should be done on sampling dates rather than publication dates. If the authors want to analyse publication dates, that is fine, but they should also show the same analysis with sampling dates, which to me is more relevant than publication dates.
For the phylogenetic analysis, it seems that the authors did not take into account the sampling date of each virus. It would be nice to check whether the branch lengths estimated by their phylogenetic models are compatible with the sampling dates.
Fig. 2: it would be nice to show in a supplementary table the names of the viruses shown as blue diamonds in Fig. 2.
On 2023-09-13 01:13:46, user Prof. Edward C. Holmes wrote:
The previous comment was not submitted by me and I disagree with its contents.<br /> Prof. Edward C. Holmes, University of Sydney<br /> https://orcid.org/0000-0001...
On 2023-08-04 16:27:45, user Edward Holmes wrote:
The algorithm used to infer recombination break points - GARD - is prone to false positives such that we can all but guarantee the 27-31 recombination breakpoints vastly overestimate recombination in this lineage. The algorithm's greedy methodology for finding incongruence in phylogenetic trees under a gamma site heterogeneity model means the algorithm will misclassify punctuated equilibria and variable rates of evolution as recombination events.
To illustrate this, the authors need only run this algorithm on mammalian mitochondrial DNA or SARS-CoV-2 sequences collected after 2021. Using their methodology, it wouldn't surprise me if they estimate humans & chimps diverged 100,000 years ago or SARS-CoV-2 arose in late 2020. If you reconstructed a recombinant common ancestor for mammalian mitochondrial DNA that clearly do not recombine, you would greedily construct a common ancestor that appears more like humans than the actual common ancestor by allowing the human genome to define its closest relatives at every small segment of the mitochondrial genome, thereby reducing the genetic distance between humans and its "RecCA".
Like Pekar et al.'s use of an HIV model of superspreading and unbiased case ascertainment to claim two basal polytomies implies two spillover events, this paper is an unstable stack of methods poorly understood by the authors applied to achieve the desired conclusions, when a modicum of attention to detail can quickly reveal the fatal limitations of their analysis.
There are ~1,100 substitutions separating RaTG13 - collected in 2013 - from SARS-CoV-2 in late 2019. SARS-CoV-2 acquired ~25 mutations per year when it was spreading in the far larger global human population and there is little to no evidence that bats suffer chronic infections that would accelerate this rate. Consequently, there are ~44 years of evolution separating SARS-CoV-2 and RaTG13, slightly fewer for BANAL-52. The authors' complex stack of models, each with clear limitations and biases known to those who make such models, hides this obvious arithmetic fact that contradicts their conclusions.
On 2023-09-19 13:19:44, user Ema Nymton wrote:
A few comments:<br /> Figure 1 essentially estimates the tMRCA of lineage A and B from 3xB and 1xA sequences. The resulting confidence interval is of course, massive. It is not surprising that it overlaps with other estimates that essentially also estimate the tMRCA of A and B. I fail to see how this adds anything new or supports any conclusion.
Figure 2's use of p-values does not properly account for the extreme sampling bias: sampling intensity around the wildlife stalls was much higher than elsewhere. More sampling leads to better p-values. Tom Wenseleers' analysis here is more proper: https://twitter.com/TWensel...<br /> Figure 2 B is particularly vulnerable to this bias, as the sequencing attempts were extremely clustered.<br /> Figure 2C seems to have omitted some data points: compare with this from Tom Wenseleers again: https://twitter.com/TWensel...<br /> Overall, the data actually points to an area near the market entrance/the toilets/the mah jong rooms, and the "hot areas" are not over the wildlife stalls.
The sampling bias is also particularly visible in figure 3B, which would seem to show an association of human reads with wildlife stalls, when of course, the presence of humans was fairly uniform throughout the market (likely higher concentrations around the entrance/toilets/bathrooms)
Figure 4 is notable because it shows CoVs definitively associated with wildlife had a significantly different distribution than SARS-CoV-2. Either SARS-CoV-2 did not emerge from those wilflife stalls, or too much time had passed to make any conclusions. It also serves as evidence against their argument that SARS-CoV-2 RNA from infected animals would have decayed substantially (as the other CoV RNA apparently had not).
Figure 5 is interesting, but it does not establish where any of the animals came from. It simply rules out Vietnam and a Chinese provine north of Hubei. The authors then proceed to engage in pure speculation about the possibility of the Raccoon dogs coming from an area known to have SARS-CoV-2 like viruses.
Overall, I see nothing new here, just more analysis of very biased and hopelessly impoverished data
On 2023-09-17 03:50:07, user David Bahry wrote:
Prior studies calculated correlations of SARS-CoV-2 detection and animal sequence read abundances in market samples, concluding that SARS-CoV-2 was negatively correlated with mammalian wildlife species (Liu et al. 2023; Bloom 2021).
The intended citation is presumably to (Bloom 2023), not to (Bloom 2021).
After the first-detected market-linked cluster was noticed, this led the search for more cases to focus heavily on the market: i.e., that is simply where the local Chinese authorities looked, as they have made clear in repeated statements (Bahry 2023, Table 1). For instance, the WHO-China joint study emphasizes the "epidemiology surveillance at several hospitals (close to Huanan market), Huanan market and the neighbourhood of Huanan market" (WHO-China 2021, Annex p. 125).
Strangely, the authors do not cite these repeated warnings about sampling bias in the early search, by those who did the search. Nor do they cite my critique (Bahry 2023) of their earlier attempt to control for such bias (Worobey et al. 2022).
Thus, rather than showing the spatial distribution of elevated positivity, their heat map more reflects the spatial distribution of sampling effort (heavily concentrated in the southwest wildlife corner).
An alternative approach would have been to map relative risk itself, estimated with smoothing by pseudocounts (cf. Bahry 2023, Fig. 1). Estimating positivity as s+0.077/n+1, for s positives out of n samples, takes into account both the 7.7% positivity base rate, and noise due to uncertainty in less-sampled stalls.
On 2023-09-18 17:43:46, user J Wallace wrote:
I'm sorry, what new does this actually add? These "laws" appear to just be fundamental properties of DNA as known for literally decades. Please explain how this actually adds new knowledge to the field.
On 2023-09-17 06:30:29, user Diego del Alamo wrote:
This is a comment on version 1 of this manuscript.
The authors present compelling evidence that fine-tuning sequence-based machine learning models (protein language models) on in-house experimental data can accelerate the discovery of high-affinity binders, in this case against CD40. However, the entire manuscript is focused on single-chain nanobodies, not antibodies as the text suggests, and the authors only mention this in second and third paragraphs of Results as well as the caption of Figure 2.
This is an extremely important distinction and I think the authors need to revise their language throughout the document to make this clear; i.e., use the term nanobody, not antibody. Nanobodies differ from antibodies in several key respects, such as loop lengths, which are discussed here: doi.org/10.3389/fimmu.2023..... Relevant to this manuscript is the fact that they comprise a single chain, and are thus amenable to out-of-the-box masked and/or autoregressive protein language models. Standard antibodies consist of two chains; to my knowledge, only one method, which has not been peer reviewed, has been trained on paired antibody sequences: arxiv.org/abs/2308.14300. Thus, several obstacles still exist that prevent the methods described here from being directly translated to standard monoclonal antibodies. The manuscript does not discuss or acknowledge these obstacles.
On 2023-09-15 19:53:34, user Katerina Gurova wrote:
And what is known about the development of the phenotype of Weaver syndrome, are all tissues overgrown? At what stage of development overgrowth start (prenatal is too broad). With your model you can answer these question using mice.<br /> I think it is also important to look on the same histone modifications in differentiated cells.
On 2023-09-15 15:26:35, user Katerina Gurova wrote:
Great study! One of the questions: why on a figure 2B in case of a variant Ezh2 het +WT level of H2K27ac are so much higher than in Ezh2WT?
On 2023-09-15 06:50:22, user Roberto wrote:
The problem relies on in the fact the COVID-19 donors haven't been stratified based on the number of shots they received. Which I think it is of fundamental importance to understand that also mRNA therapy can cause this as they provide high level of spike protein.
On 2023-09-14 18:03:48, user Ziegfeld wrote:
W14 has been shown not to be an M. esculenta (https://doi.org/10.1038/nbt... ) therefore it should not be referred to as a cassava wild ancestor, nor used as such in analyses regarding cassava domestication.
Also, the term "gypsy" is a racial slur and should not be used (see for example https://doi.org/10.31219/os... ). Ty3 could be used instead.
Lastly, the name of Hernan Ceballos is misspelled.
On 2023-09-14 13:46:17, user Andres Marcoleta wrote:
Dear all, this preprint was published as a peer-reviewed enriched version in Science of the Total Environment journal:
On 2023-09-13 10:24:24, user Rohit Satyam wrote:
The reference associated with Mantis tool is wrong. This paper should be cited: https://www.biorxiv.org/con...
On 2023-09-12 18:52:14, user Josh Vermaas wrote:
Any idea how long the free path length would actually be? Ref. 19 puts the number in the single digits of nanometers when lots of lignin is present, but 4F seems to put the number at 10-20nm at minimum.
On 2023-09-12 12:46:54, user LEVIn - Unicamp wrote:
We discussed this preprint in our journal club and enjoyed reading it. We collectively agreed it's a stimulating contribution to understanding how individual specialization emerges and is maintained in natural populations. Most of the literature on the causes of individual specialization focuses on the ecological (i.e., extrinsic) causes of this phenomenon (sensu Araújo et al. 2011 Ecology Letters), while the proximate causes (e.g., functional trade-offs, social status) have surprisingly been little studied. Part of this discrepancy is due to the challenges of testing whether and how individuals' intrinsic traits influence their trophic preferences. This preprint adds a novel level of complexity to the field by quantifying (i) the relative contribution of largely overlooked proximate causes (social learning, maternal effects, genetic factors) and (ii) the simultaneous effects of ecological (i.e., environment context) and proximate causes. We were positively impressed by the quality of the data and statistical analyses during our discussion. The resolution and temporal extent of the data used are unprecedented in the literature, and the Bayesian framework implemented is thorough. As we appreciated the quantitative approaches, our discussion focused mainly on how the question is motivated in the introduction and the major implications of the results. We agreed that the introduction outlines well how the measured factors are expected to drive individual heterogeneity. Still, a more general framing of the research questions could make the manuscript more appealing to a broader and more diverse readership. For instance, the introduction begins by explaining that environmental factors are key drivers of trophic niche variation. However, unraveling how individuality emerges in natural systems, by nature and/or nurture, is a general question that is still widely open in different areas of science - and we believe this manuscript provides exciting results in this regard. In the discussion, the fact that maternal learning, maternal effect, and environment combined explain most of the variance in trophic position (lines 297-299) could be further explored to emphasize the importance of simultaneously studying proximate and ecological causes of individual specialization. Also, the sizable residual observed in the "Maternal learning" model suggests that understanding what generates trophic diversity within populations is far more complex than initially thought, particularly in species with complex social structures, creating a stimulating challenge for future studies. Congrats on this excellent manuscript, and good luck with the next steps of this work!
Laboratório de Ecologia das Variações Individuais (LEVIn), State University of Campinas (Unicamp), Brazil
On 2023-09-12 12:02:55, user Phillip Gordon-Weeks wrote:
Your very interesting experiments on HTT and drebrin in growth cones provide insights into the biology of the T-zone but I think your interpretation of the results could be developed further. HTT depletion clearly induces a re-location (not a mis-location-since drebrin can locate to filopodia) from the T-zone to filopodia. Unsurprisingly, given that the drebrin/EB3/Cdk5 pathway enables the capture of dynamic microtubules by filopodia, this is associated with a striking increase in microtubules in filopodia-actually the most significant change you measured in HTT-depleted growth cones. Drebrin in the T-zone is largely unphosphorylated at S142 and therefore in the folded conformation, which can bind anti-parallel F-actin through one of its two F-actin binding sites. In contrast, drebrin in filopodia is phosphorylated at S142 and therefore in the open conformation, enabling it to bind to parallel F-actin bundles using both F-actin binding sites. Drebrin can cross-link F-actin to dynamic microtubules by binding to EB3 in filopodia. Another manipulation that re-locates drebrin (our unpublished observations) and myosin IIB from the T-zone to filopodia is the inhibition of myosin II by blebbistatin, which essentially disassembles the F-actin in the T-zone removing an impediment to microtubule advance into the P-domain (Hur et al., 2011, P.N.A.S. 108, 5057-5062; Shin et al., 2014, PLoS ONE 9(4): e95212. doi:10.1371; Dupraz et al., 2019, Current Biology 29, 3874–3886). I wonder, therefore, whether HTT depletion also disrupts the T-zone thereby disabling drebrin binding and unhampering microtubule advance.
On 2023-09-10 21:54:32, user Laboratório de Interação Veget wrote:
I am a current graduate student pursuing a Master's degree at a university and in our recent lab meeting discussions, we introduced a new format called the Preprint Club, wherein each student presents and reviews a preprint paper. I have selected a particular preprint for my presentation, and, with input from my lab peers, we have compiled a set of suggestions aimed at enhancing the research.
I want to commend the collective efforts of the authors involved in this study. The study's focus on plant parasitic interactions, given their intricate nature, is truly captivating. Investigating the processes and transmission of ROS signaling is vital for understanding a plant's stress response.
Throughout our discussions, we identified several areas for potential improvement:
(1) Regarding the references, although the bibliography is comprehensive, some sources appear to be secondary. (Please note here the specific references you are referring to, for example, reference A could potentially be replaced with B, etc.).
(2) The absence of a methodology section is notable. While the innovative approach is intriguing, more detailed information about experimental conditions and materials is required for a comprehensive understanding of the results.
(3) Consider incorporating additional graphical representations at intermediary time points, such as 10 and 20 minutes, in addition to the existing representation at 30 minutes, as seen in Fig 1(d) and Fig 2b, d, f.
(4) In Fig 2(g), it is evident that the expression levels of certain genes in the donor plant are comparatively reduced in comparison to the recipient ones. It would be beneficial to include the authors' interpretation of this data to enhance the discussion.
(5) We suggest including an extra supplementary figure that displays the expression levels of homologous genes in Cuscuta corresponding to AtAPX1, AtZAT10, AtZAT12, AtMYB30, and AtZHD5, along with the expression levels of Arabidopsis homologs for CcCSD1, CcNDPK2, and CcGLR2.7.
We hope that our feedback proves valuable in refining your study.
Sincerely.
On 2023-09-10 19:39:50, user Wenderson Rodrigues wrote:
Dear Authors,
I am Wenderson Rodrigues, a Ph.D. student at the Federal University of Minas Gerais (Brazil), affiliated with the Plant Interaction Laboratory (LIVe). My research project focuses on the study of ncRNA in the interaction between parasitic plants and host plants. Our laboratory has initiated an activity called the "Preprint Club" where we train and learn to review preprints relevant to our research areas. I have selected your preprint titled "Long noncoding RNAs emerge from transposon-derived antisense sequences and may contribute to infection stage-specific transposon regulation in a fungal phytopathogen" for reading and critical review.
In this manuscript, Qian and colleagues conduct an extensive study on the identification, classification, and investigation of transposable elements (TEs) and ncRNAs in the genome of Blumeria hordei, a powdery mildew fungal pathogen of Hordeum vulgare (Barley). This is a highly interesting manuscript; the methods are well-documented in the literature, and the results are significant. In my opinion, the authors could provide more information in the Introduction about the infection cycle of B. hordei, as understanding this pathogenic process is crucial for interpreting the presented results. Additionally, here are some specific comments regarding questions and corrections that seemed pertinent to me during my reading.
Specific comments:<br /> Lines 135-136: The presentation of PC and NMDS analyses is confusing in terms of result interpretation because they do not complement each other, as mentioned in the text. How do the NMDS results influence the interpretation of the PCA results?
Lines 172-173: For the 102 TEs, where is the expression data?
Lines 181-183: How did you identify if they are peptide-coding transcripts, and what criteria were used to evaluate their significance?
Figures 3B and C are not mentioned in the text. Figure 3D should be reversed in terms of read mappings to follow the order of citation in the text (RNA-Seq and ONT), or the text could be modified to maintain the order of appearance in the figure.
Lines 203-204: There seems to be a missing punctuation mark in the text.
Line 208: Although Figure 4 is mentioned to display information about the lncRNAs identified in the study (such as exon numbers and size), it might be better to specify in which section of Figure 4 this information can be found, e.g., Figure 4B-C.
Lines 233-234: How were the analyses for the identification of putative secreted proteins conducted? Was there a pipeline used for identifying such proteins?
Lines 293-294: The text appears incomplete, possibly due to a typing error.
These are some points that I found relevant to convey to the authors. The research in this preprint is impressive, and it was a pleasure to read and learn from the authors.
All the best,<br /> Wenderson Rodrigues.
On 2023-09-10 12:27:33, user Klaus Eckelt wrote:
Accepted at the IEEE VIS 2022 Workshop on Visualization in Biomedical AI.
Workshop: https://vis-biomed-ai.githu...
On 2023-09-10 02:32:25, user Tushar R. wrote:
Summary of the work:
Antibiotic resistant bacteria remains a global threat to human health and disease. A main target of antibiotics is the bacterial ribosome. To address the problem of antibiotic resistance in the clinic, Mesa et. al. isolated different Pseudomonas aeruginosa’s strains from cystic fibrosis patients over 8 years that displayed resistance to aminoglycoside antibiotics. They conducted genomic sequencing and discovered these isolates contain a 12 nucleotide deletion in the rpflF gene that encodes the universal large subunit ribosomal protein 6 (uL6). This mutation has previously been reported in the paper by Halfon et al. (2019) which includes the structural comparison between the mutant and wild type ribosome.
In this manuscript the authors determined “~86 ribosomal structures” structures of mutant and wild-type ribosomes with different antibiotics using single particle cryoEM to understand how resistance develops in these mutants. Their main results indicated the uL6 mutation rewires the ribosome conformational landscape and displays resistance to aminoglycosides. Their study confirmed canonical binding sites for aminoglycosides, but new binding sites were discovered for tobramycin in the uL6 mutants. Most importantly, the uL6 mutants displayed a tradeoff between aminoglycoside resistance and chloramphenicol sensitivity, dubbed as collateral sensitivity. In summary, this article provided an in depth structural analysis of antibiotics complexed with the ribosome from clinically relevant antibiotic resistant bacteria, however, there are a few major and minor concerns that we would like the authors to address.
Major points.
Would it be possible to structurally rationalize why uL6 is more ordered in closed conformation of ribosome and not in the open conformation in case of the mutant? and the same for with and without antibiotics? Would it be possible to make a 1:1 comparison between the uL6 densities in mutant and wildtype datasets for different classes?
In the current version of the manuscript we couldn’t see a figure showing the uL6 density in wild-type datasets except in Ro1 class. Could the authors observe any density for uL6 in reconstructions from RC, RT and RK datasets and how does it differ (or not) from the respective mutant classes? (Note: it is difficult to interpret whether uL6 is well ordered or not from supplementary figure S4)
How is H69 affected by the uL6 mutation? What are the structural features that connect these two which can prove that the retracted and stretched conformations of H69 are a cause of uL6 apart from the fact that they only exist in the L6M dataset? Could you explain this for example, by looking at the interactions that connect H69 and uL6 mutation region in WT structure?
In Fig3B-I, where uL6 is shown to act like a wrist band, it is unclear how the allostery actually manifests structurally. We don’t really have a specific interaction map for R vs L ribosomes such that we can get to know how the interactions are altered in L which then leads to AGA inhibition/CHL sensitivity. Having something like a chord plot showing key interactions that regulate allostery and then highlighting specific interactions that are affected in L ribosome datasets will help.
The authors termed a specific part of the 23S rRNA as “50S vulnerable region” because they could see that there was an increase in the flexibility of this region. It is unclear how this flexibility was quantified and what the reference was in quantifying it (i.e. it is flexible in comparison to what?). It is also a bit confusing where exactly this region is located since figure 2F is quite cluttered. There can be two figures, one showing the r proteins and the other just showing the vulnerable region.
It would be useful if the authors could highlight the PTC in figure 2F since the proximity of “50S vulnerable region” to PTC is not very obvious. For example H95 which is part of SRL, lies ~60-65Å away from the PTC.
The authors use Ro1 structure as a reference even for analysis of L6M datasets. The reason stated for this selection is that it represents the most abundant class of apo R ribosome and that it is the extreme opposite in terms of both antibiotics bound and presence of uL6 mutation. Although this is true, we feel Lo1 is still probably the “true reference” for the L6M datasets because all the structures bound to the antibiotics have the uL6 mutation. And so it would still be useful to look at the differences between Ro1 and Lo1 followed by re-running the analysis by using Lo1 as a reference instead of Ro1.
To improve the overall analysis, the authors should consider combining all data sets and process it as a single dataset so as to obtain a more coherent understanding of the structural changes. This would provide a clearer representation of the latent space and the population differences. CryoDRGN generated maps should then be compared to assess if the conformational distribution is still similar to what has been obtained in the current version of the manuscript. This would be helpful in addressing the potential for generating maps based just on the latent space, focusing on the characteristics that differentiate different states and potentially revealing previously unseen conformational states.
We feel that the musical analogy in figure 7E forms a cryptic model because A) It requires an inherent understanding of musical terminologies which is not ideal, and B) We are not sure what the authors exactly mean by retuning and detuning of the conformational landscape as R, L, LK, LT have different conformational landscapes, but the authors assign these to notes that are in ‘harmony’ as explained in the discussion (section titled “the well-temepred ribosome”) which is confusing.
Another point regarding the musical analogy is that the terms ‘in-tune’ and ‘out of tune’ should be with reference to a specific property. In principle, it should be based on the conformational landscape but that doesn’t hold true as mentioned above. If this is in reference only to the growth curves, then it means that the structural results haven’t been addressed in the final model. We therefore feel that the authors should come up with a simpler model that is easier to understand and yet includes the results from structures.<br /> The authors include the paper by Halfon et al. (2019) in their list of references (23), but perhaps should also include a bit more on the discoveries of that paper and what the unresolved questions were motivating this paper in the introduction.
Minor points.
There may be a typo about universally conserved nucleotides in the decoding center between E. coli vs P. aeruginosa. (on page 9, line “...the universally E.coli conserved nucleotides A1486 and A1487…” these are supposed to be A1492 and A1493 for E.coli and A1486 and A1487 for P.aeruginosa.)
When the term “allosteric activator” is used (in the abstract), it should refer to a series of correlated structural changes due to binding of Tobramycin. This is not explained in the paper. How can we say that the existence of 5 binding sites is a result of “allosteric activation” when there is no explanation about how the different binding sites structurally affect each other (even 2 out of 5)?
The color coding for the two antibiotics (T/K - red and CHL - gray) can be seen in the legend but not in figure 7b, . Here, we see the distributions for WT and L6M dataset but not for the datasets with the two antibiotics. Do they superimpose?
In figure 4a, please define the color coding in the legend for uL6.
Figure citation missing on page 9 (“..the sharp kink observed in the retracted H69 helix eliminates the interactions with h44.”).
We are not sure why the LT conformational space is larger than L in Figure 7D because the L dataset clearly had more heterogeneity. Could the authors explain this?
The distribution figures presented in the paper could be streamlined to convey the main points more effectively. The authors should clarify the meaning of the left and right distributions, as well as their percentages based on total particles. Considering the potential differences in the data set, displaying the particle distribution on a logarithmic scale could provide a more accurate representation.
On 2023-09-09 08:58:15, user Fabian Westhaeusser wrote:
Great work! But really wondering why you did not mention the work of Dietrich et al. ("Towards Explainable End-to-End Prostate Cancer Relapse Prediction from H&E Images Combining Self-Attention Multiple Instance Learning with a Recurrent Neural Network") or Walhagen et al. ("AI-based prostate analysis system trained without human supervision to predict patient outcome from tissue samples"), which are super relevant to this topic?
On 2023-09-09 08:50:02, user Fabian Westhaeusser wrote:
Great work! But really wondering why you did not mention the work of Dietrich (https://arxiv.org/abs/2111.... or Walhagen (https://www.sciencedirect.c..., which are super relevant to this topic?
On 2023-09-08 14:10:43, user Kaveh Bazargan wrote:
It should be noted that the errors were brought to light on PubPeer: https://pubpeer.com/publica...
On 2023-09-07 13:33:58, user DeBroski R Herbert wrote:
There were several errors made with this manuscript due to the hastiness of posting this version. We the authors are aware of errors made with flow cytometry based calculation of respiratory tract tuft cell numbers (several orders of magnitude higher than actual values). Also, there are some images that need to be replaced ti ensure that no unwitting duplication occurred. These errors will be corrected before this work goes out for peer review. DRH
On 2023-09-07 17:31:29, user AL wrote:
Thank you for presenting the importance of information leakage and being intentional with how to split the data.
There are a few typos that I wanted to bring to your attention: <br /> - top of pg 4, awkward phrase with 2 already's<br /> - first sentence of pg 6, compile instead of complie<br /> - pg 7, splitting was spelled as spitting<br /> - bottom of pg 7, "to evaluation"
On 2023-09-07 11:03:58, user Brother Nemiel wrote:
The article has been published >3 months ago here https://onlinelibrary.wiley.com/doi/10.1111/tpj.16327
On 2023-09-06 14:50:01, user Jeferson Leal Silva wrote:
This review reflects comments and contributions by Gabriela Albuquerque Lúcio da Silva and Jeferson Leal Silva. This review resulted from the graduate-level course "How to Read and Evaluate Scientific Papers and Preprints" from the University of São Paulo, which aimed to provide students with the opportunity to review scientific articles, develop critical and constructive discussions on the endless frontiers of knowledge, and understand the peer review process.
In this preprint Veryard et al. points out that for the past 25 years, ecological research has shown that biodiversity positively impacts ecosystem functions like biomass production when other variables are constant. Earlier experiments focused on grassland plant communities, but newer ones indicate similar benefits in plantations and forests. There's been limited research in tropical environments. This study presents preliminary results from a field-scale experiment in southeast Asia, examining the role of tree species diversity in restoring lowland tropical rainforests. Findings from Sabah, Malaysian Borneo suggest that active restoration, such as tree planting, accelerates forest recovery. This recovery is even more pronounced when using a diverse mix of tree species.<br /> The Sabah Biodiversity Experiment, conducted in the Malua forest reserve, involves different restoration methods on 500 ha of logged tropical forest. Observations reveal that the restoration is most effective when a diverse set of tree species is used for replanting. The study supports the idea that the positive relationship between biodiversity and ecosystem functioning seen in other ecosystems also exists in SE Asia's lowland tropical rainforests.<br /> The data are reported in a clear way and the manuscript is well written. Data are consistent with the currently existing literature.
Comments
● About the soil preparation or pre-planting methods, it was highlighted that in one of the treatments the removal of lianas was used. Therefore, it would be interesting to highlight whether controls such as leaf-cutting ants, termites and weeds, fertilization methods and soil correction methods were used or not, even as standard for all treatments.<br /> ● Perhaps the objective of the experiment was not to identify the diversity of species more than 10 years after planting, but considering that there was a survey of data in the field to compare the information from the satellite analysis, it would be interesting to obtain such information to also show that species diversity still exists in the places, since this is also a relevant indicator to consider areas as restored. In addition, this indicator can help to identify potentially monodominant species or even to identify the optimal number of species to be planted to maintain a desired final diversity.<br /> ● It would also be important to highlight in the text the density of individuals planted per area, to reinforce that the increase in the indexes analyzed originated to the diversity of species and not just to a large density of individuals planted per area.
On 2023-09-05 15:39:35, user Joshua Goldford wrote:
Dear Alan, <br /> Absolutely! Thank you for bringing this to our attention. All subsequent versions of the manuscript will include this citation.<br /> All the best,<br /> Josh
On 2023-09-05 11:45:27, user Alan Bridge wrote:
Dear authors,
would you consider citing this work as the source of ChEBI annotations for UniProt records?
Coudert E, Gehant S, de Castro E, Pozzato M, Baratin D, Neto T, Sigrist CJA, Redaschi N, Bridge A; UniProt Consortium. Annotation of biologically relevant ligands in UniProtKB using ChEBI. Bioinformatics. 2023 Jan 1;39(1):btac793. doi: 10.1093/bioinformatics/btac793. PMID: 36484697; PMCID: PMC9825770.
Many thanks and good luck with the submission!
On 2023-09-04 13:15:03, user Stephen White wrote:
This paper is now fully published and can be found in two parts - the cell biology was published in this paper<br /> · A Nrf2-OSGIN1&2-HSP70 axis mediates cigarette smoke-induced endothelial detachment - implications for plaque erosion<br /> Cardiovascular Research, Volume 119, Issue 9, July 2023, Pages 1869–1882, https://doi.org/10.1093/cvr...
The CFD analysis of coronary artery flow in plaque erosion patients is found in this paper:<br /> · Identification of the haemodynamic environment permissive for plaque erosion<br /> Scientific Reports volume 11, Article number: 7253 (2021) <br /> https://rdcu.be/dlbE4
On 2023-09-04 10:54:36, user Monika Čikeš wrote:
It is an interesting article, especially since it combines in vitro and in vivo research. However, I could not find the number of mice in the study, which would add more value to the research. Moreover, it would be interesting to investigate the role of β and γ subunits of AMPK on other cervical cancer cell lines.
On 2023-09-04 07:36:10, user Helena Storchova wrote:
Please, look at the recent paper by Abeyawardana et al. 2023, PSB: The FLOWERING LOCUS T LIKE 2-1 gene of Chenopodium triggers precocious flowering in<br /> Arabidopsis seedlings.<br /> The FTl2-1 gene of C. ficifolium and C. quinoa (which is CqFT1A in your nomenclature) functioned as a strong activator of flowering in Arabidopsis. Although it is a homolog of sugar beet BvFT1, it lacks the amino acid changes necessary for the repressor function. It cannot be concluded that it is repressor of flowering, based on its downregulation durinh eraly flowering.
On 2023-09-03 19:42:31, user David Grant wrote:
A very interesting and timely paper.
One of the powers of electronic publishing is the ability to do simultaneous searches across multiple sites and papers. However for this to work a common vocabulary must be used. The authors use a non-standard form of the gene model names in the Wm82 genomic sequence. The correct form is<br /> Glyma.09g053700<br /> not<br /> GLYMA_09G053700
See <br /> https://www.soybase.org/cor...<br /> for details.
On 2023-08-31 13:39:20, user Gregory Voth wrote:
Dear Authors,
We congratulate you for your work on simulating lipid droplet biogenesis at the MARTINI coarse-grained resolution. We also thank you for citing three papers from our group. However, I am leaving this comment because our papers were not adequately nor accurately cited in your manuscript.
First, we have already shown that asymmetric tension decides a budding direction in J Phys Chem B 126 (2022): 453-462 using our simulations. This is consistent with your findings, and none of your text mentioned this.
Second, we have already carried out a large-scale coarse-grained simulation of lipid droplet biogenesis with seipin, published in Elife 11 (2022): e75808. This includes not only nucleation but also maturation and budding. We have further found and discussed the critical role of seipin transmembrane segments in maintaining a neck structure. In particular, based on our simulations, we proposed a mutant construct, which was further validated by experiment in our paper. The final structure of our CG molecular dynamics simulations is consistent with the experimental structure. In that regard, our work has been cited in your paper only for nucleation but did not receive proper credit for budding and maturation. In particular, we disagree with the following two sentences in your manuscript:
"The function of seipin is also not completely clear: simulations and experiments suggested that it may trap triglycerides (13-15), therefore affecting LD nucleation and growth by ripening, but its localization at the LD-ER contact site raises questions on a possible role also in the budding process.”
"LD nucleation and phase separation were observed in simulations before (7,13,22,23,38), and occur on fast time scales (below the microsecond); in contrast, the budding process has never been observed so far, neither in simulations nor experimentally."
I hope our concerns are properly addressed during revision so that we do not have to write a comment to the journal in which your paper will be published. Thank you.
Gregory Voth
On 2023-08-30 18:43:21, user LEVIn - Unicamp wrote:
We discussed this preprint in our journal club and enjoyed reading it. We collectively agreed it’s an exciting contribution to our understanding of how urbanization reshapes bird communities and their traits. During our discussion, a major point of debate was the ability of the Urban Association Index (UAI) to capture fundamental aspects of urban tolerance. The supplementary material explains important details on how this metric was obtained, but a few questions remained after our discussion. In particular, as the number of records has the potential to influence the mean UAI, widespread, very common species – even if they are equally common in cities and rural areas – may exhibit high UAI mean because most of the records from eBird come from urban areas (i.e., sampling bias). In this scenario, UAI may be amplified for abundant and/or eye-catching species. Perhaps the authors could check the correlation between mean UAI and the number of records to see the nature of this relationship. In this same line of thought, it might be opportune to explore how UAI varies across native vs. invasive species. We understand the reasoning for not differentiating native vs. exotic species (lines 114-116), but for species that were recently introduced through pet trade (e.g., Yellow-crested Cockatoo, Monk Parakeet - which are among those with the highest UAI values), not necessarily UAI is measuring tolerance to urbanization, particularly when their native habitats are not represented in the dataset.
We also discussed that a possible approach to illuminate the biological meaning of UAI would be looking at the relationship between mean UAI and variance in UAI. Figure S5 shows how variance varies across species, apparently independent from the mean UAI. In this perspective, for instance, species with high UAI and high variance in UAI tend to be highly associated with cities but also occur in non-urban areas. In turn, species with high UAI and low variance in UAI are strict to cities, and so on. Congrats on the manuscript, and good luck with the next steps of this work!
Laboratório de Ecologia das Variações Individuais (LEVIn), State University of Campinas (Unicamp), Brazil
On 2023-08-30 08:41:17, user Jose E Perez-Ortin wrote:
This new model for explaining mRNA<br /> buffering is a very interesting piece of work. We would like to suggest some<br /> possible improvements to be considered by the authors in this preprint stage before<br /> it becomes published in a journal.
In some parts of the manuscript it is said<br /> that mRNA buffering is perfect as total mRNA concentration and even individual<br /> mRNA concentrations are invariant. We think that this is overblown. For<br /> instance, graphs in Sun et al 2013 (ref. #9; Figure 1),<br /> the variability in total mRNA may be as high as 50%. In fact, in García-Martínez et al 2004 (ref. #15;<br /> Figure 2) we published that during the carbon source change mRNA concentration<br /> changes also by a factor of 2. We wonder if this could be important for the modeling<br /> because it seems that on the advantages of the RS model is that it predicts<br /> robust buffering, contrarily to previous feedback models.
The manuscript misses citation of some<br /> papers that we consider important for the field of mRNA buffering, such as Mena et al 2017 (doi:<br /> 10.1093/nar/gkx974). This paper is especially relevant because the current<br /> preprint describes in the Introduction section that total mRNA concentration is<br /> constant as the cell volume increases (refs. 19-22) but forgets to mention this<br /> piece of work, which was the first one to show that degradation rate perfectly<br /> balances production rate during cell volume change. Instead of our paper, the<br /> preprint cites ref. #27, which is 4 years older than Mena et al 2017.
Garcia-Martinez et al<br /> 2023 (doi: 10.1016/j.bbagrm.2023.194910) is also highly relevant. We described in that<br /> article a mathematical model that explains mRNA buffering using a simpler<br /> mechanism consisting only one mRNA binding factor that co-transcriptionally imprints<br /> mRNAs. That model also predicts that synergistic changes in synthesis and<br /> degradation rates will provoke faster and stronger responses, as described in<br /> some experiments. We also previously published a multiagent model in Begley et al 2019 (10.1093/nar/gkz660),<br /> which combines mRNA imprinting and feedback mechanisms. That paper also<br /> demonstrates that Ccr4 and Xrn1 act in parallel with different sets of targets<br /> genes. We also have demonstrated in that paper and in other two (Begley et al 2021 doi:<br /> 10.1080/15476286.2020.1845504; and Medina et al 2014 doi:<br /> 10.3389/fgene.2014.00001) that protein factors, such as Ccr4 and Xrn1 act not<br /> only in transcription initiation level but also in elongation . We think it<br /> would be nice this manuscript to discuss the differences of these models with<br /> the proposed RS model.
Finally, as for the model in Figure 4c, we do not understand why the<br /> activation of a degron used by Chappleboim et al 2022 (ref. #16) only<br /> degrades cytoplasmic Xrn1 molecules (Xc) and leaves Xp molecules intact. All<br /> Xrn1-degron molecules (Xc, Xp, Xn) will be proteolyzed after Auxin addition.<br /> This can affect the predictions made by the RS model.
On 2023-08-29 17:35:00, user Seese wrote:
But this shows that there ARE bacteria within these breast tumor tissues .... just not in the cancer cells in these images?
On 2023-08-28 22:08:55, user Debika Datta wrote:
Article has been published in Nature Communications.<br /> Please find the link below:
https://www.nature.com/arti...<br /> https://doi.org/10.1038/s41...
On 2023-08-27 13:26:07, user Prof. T. K. Wood wrote:
By and large miss the most prevalent defense system, toxin/antitoxin systems, since these are poorly represented in DefenseFinder.
On 2023-08-26 22:44:48, user Prof. T. K. Wood wrote:
Just like it when it was shown first with E. coli in 2012 with a 12,000-fold increase in persistence with H2O2 (doi:10.1111/j.1751-7915.2011.00327.x), oxidative stress increases persistence with B. cenocepacia. Please cite this. Also, you should cite the only mechanistic persister model: ribosome dimerization (https://doi.org/10.1016/j.b..., which is far more likely than referring to toxin/antitoxin systems.
On 2023-08-26 12:34:13, user Gabriel Smedley wrote:
I have considerable concern about a conflict of interest by two of this paper's authors. Those being Erick J. Lundgren and Rhys T. Lemoine respectively.
Professor Lundgren has published rather questionable work attempting to justify the presence of invasive species by claiming may have replaced certain extinct Late Pleistocene megafauna in a 2020 paper. And Professor Lemoine published a paper in 2022 where he and a colleague gave certain invasive species terms on "states of nativeness".
In short, both professors advocate using Pleistocene Rewilding to try making the case for invasive species around the globe, and the supposed findings of this paper are simply a way of trying to get people on board with the idea. After all, if the mass megafauna die off thousands of years ago was entirely human caused, it would strengthen the case for Pleistocene Rewilding using current invasive species.
On 2023-08-25 18:07:39, user Felippe Truglio Machado wrote:
This review resulted from the graduate-level course "How to Read and Evaluate Scientific Papers and Preprints" from the University of São Paulo, which aimed to provide students with the opportunity to review scientific articles, develop critical and constructive discussions on the endless frontiers of knowledge, and understand the peer review process. We are Felippe Truglio Machado and Rebeca Bueno-Alves, PhD students at the Department of Biochemistry at University of São Paulo.<br /> This article does a really good job demonstrating how mitochondrial hydrogen peroxide affects genomic DNA, with a clear and concise methodology. It puts a perspective on how we should address our concepts when we say how mitochondria oxidant production contributes toward nuclear damage, specifying if it’s a direct or indirect one, something that hasn’t been demonstrated yet. The article aimed to analyze whether hydrogen peroxide produced in mitochondria could potentially contribute directly toward nuclear DNA damage, considering the distance this species would have to travel within the cell. A highly interesting methodology for control induced hydrogen peroxide was developed based on the expression of the enzyme D-Amino Acid Oxidase (DAAO) at different sites within human cells. DAAO was anchored to the outer membrane of the mitochondria or to the nucleosomes. This method proved to be quite promising, particularly regarding its use in studies requiring more continuous or compartmentalized exposure to H2O2, as this could better mimic the physiological cell hydrogen peroxide production, instead of an exogenous burst treatment. This approach is useful for a handful of studies, especially for those focused on mitochondrial DNA integrity. <br /> Based on the presented results, the researchers were able to conclude that peroxide formed in mitochondria cannot significantly affect nuclear DNA directly, but they do not rule out the possibility of some indirect impact, warranting further studies in this regard. Although this work really is a great contribution to our current knowledge into mechanisms of oxidative nuclear damage, especially regarding the possibility of its use in future studies, it could really benefit from a different approach in some of its statements.
Major comments:<br /> ● The discussion but not in the introduction discuss oxidative DNA lesions and repair by the BER pathway. In the topic “mitochondrial H2O2 release does not induce genomic DNA damage” represented by figure 3, it would be great to assess speciafically BER proteins, which are essential for oxidized base repair. Two additional scenarios must be considered in this situation: one in which the BER pathway is synchronized and single strand breaks are being repaired by POLB as soon as they are generated by glycosylases/ape1, therefore, without much of an increase in single strand breaks. The other one in which the repair proteins are oxidized and repair is not initiated, therefore mutagenic lesions such as 8-oxoG would not impair DNA replication, not inducing cell cycle arrest, which would lead to infidelity of DNA replication instead. So, experiments focused on the BER pathway would help to substantiate the results. Suggestions include measuring OGG1, APE1, or PolB, adding them to the western blot data, or even “BER signaling proteins” such as PARP1. With the present data, we know that peroxide production from the nucleus and mitochondria can cause or not cause nuclear DNA damage (DNA strand breaks), but the mutation rate caused by these types of damage and/or by the mutagenic lesions is not determined. In sync with this line of thought, it would also be interesting to detect damage and survival in the cells after a few days to see how the possible mutations could be established and impact on cell physiology. <br /> ● It would be important to have results that can characterize the experimental model established by the group. In the first figure, they show through the measurement of oxygen consumption that the model was effective in generating H2O2 after the addition of D-Alanine, both in the lineage expressing DAAO in the nucleosomes and in the outer mitochondrial membrane. In order to ascertain whether the addition of the DAAO enzyme with or without D-Ala, by itself, could generate an impact on mitochondrial function or non-mitochondrial oxygen consumption, a comprehensive bioenergetic characterization of the model as a whole would be really beneficial This would ensure that the observed changes are attributable to the specific impact of DAAO and not something indirect through changes in oxidative phosphorylation.<br /> ● The paper could be really improved by adding an assessment of mitochondrial DNA. First, it’s important to differentiate the two genomes. Only nuclear DNA damage was measured, so when DNA damage is mentioned it is important to address this limitation. Second, despite having far less coding genes compared to nuclear DNA, mtDNA oxidative damage and mutation should not be excluded from the discussion. Although mt H2O2 could not contribute directly toward tumorigenesis in the nucleus as stated, it could cause mutations in mtDNA, and this could also have detrimental consequences that should be worth mentioning in the discussion. Also, since the model is already available, future studies analyzing controlled mtDNA damage and mutations caused by peroxide would be a great contribution, since mitochondrial dysfunction caused by impaired mitochondrially-encoded protein synthesis has a big impact in a plethora of disorders. The nuclear genome is of course the main character in cancer, but mtDNA should not be excluded regarding its importance in cell metabolism. Experiments such as detecting mtDNA copy number by PCR and measuring oxygen consumption using different mt complex inhibitors would add a lot to this work, and would provide an overview of H2O2 production by D-Amino Acid Oxidase DAAO impact on mitochondria. In the absence of this data, it's challenging to determine whether any alterations are due to impacts on nuclear or mitochondrial DNA.<br /> ● Regarding the cell survival data, a clonogenic assay and a MTT assay could yield interesting insights and complement the crystal violet data presented in Figure 2. The methods used in the article assessed the effect of H2O2 after 24 hours of treatment, but it would be worthwhile to observe their impact over longer periods. Therefore, a clonogenic assay would be quite valuable to reinforce the data and allow for more comprehensive conclusions, and it also would enable to assess cell survival in a quantitative way. The MTT assay could complement this data since it can be used to analyze cell viability in a mitochondrial metabolism dependent way.<br /> ● Regarding cell death induced by ferroptosis, it would be good if mitochondria-mediated cell death (citC/Caspase9) could be measured, and also mitophagy. Since a lot of damage is being generated in mitochondria it would be interesting to see its impact on other cell death mechanisms and mitochondrial degradation.
● Some minor statements: <br /> ○ Figure 3A could be quantified and presented in graphs for better data visualization <br /> ○ In figure 4, the treatment time scale could be aligned to the left like the other figures instead of in the middle.
On 2023-08-25 08:32:28, user Giovanna Barba Spaeth wrote:
The paper has been now published in mBio. Here the link to its final version:<br /> DOI: https://doi.org/10.1128/mbi...
On 2023-08-24 17:38:10, user Charles Warden wrote:
Thank you very much for voluntarily withdrawing an article after noticing a problem that could affect the conclusions.
I hope that a revised preprint or publication with the other data can be provided in the near future.
On 2023-08-24 08:03:44, user Ewan MacDonald wrote:
Great paper, really interesting approach. It's an exciting tool to further investigate how the organisation of GPI anchored proteins is coupled to endocytosis.
In the discussion you don't comment on the increases of Kd will increase the probability that a undefined requisite number of binding events is achieved in order to rearrange the membrane such to induce tubulation.
On 2023-08-23 16:47:42, user L.W. Wang wrote:
interesting! But I have some questions about this work.
On 2023-08-23 00:42:42, user Saurabh Mahajan wrote:
The hyperlinks to Crossref and Pubmed for some references (e.g. 1, 3) are not pointing to the correct articles.
On 2023-08-22 18:45:00, user Camille Augusto wrote:
This review resulted from the graduate-level course "How to Read and Evaluate Scientific Papers and Preprints" from the University of São Paulo, which aimed to provide students with the opportunity to review scientific articles, develop critical and constructive discussions on the endless frontiers of knowledge, and understand the peer review process.
The manuscript discusses changes in body temperatures of two distinct species of nesting sea turtles (Dermochelys coriacea and Eretmochelys imbricata) over a period of seven years. The experimental efforts proved to be sufficient to fulfill the research objective, with emphasis on the use of a non-invasive method, considering that these are threatened species. The manuscript is very well written with a cohesive introduction that offer adequate information allowing the reader to easily follow the discussion of the obtained results. However, two points could be better addressed: i. geographic contextualization could be described with more precision and details, and a map could be helpful for the reader; ii. a more complete discussion about the threat status of both species would be useful to contextualize the relevance of the work. Below are some comments.
On 2023-08-22 17:17:49, user Rory O'Keeffe wrote:
The DOI of the published version in IEEE Transactions on Neural Systems and Rehabilitation Engineering:<br /> 10.1109/TNSRE.2023.3291748
On 2023-08-22 17:11:14, user Stephanie Sibinelli wrote:
Reviewed by: Julia Takuno Hespanhol and Stephanie Sibinelli de Sousa
This review resulted from the graduate-level course "How to Read and Evaluate Scientific Papers and Preprints" from the University of São Paulo, which aimed to provide students with the opportunity to review scientific articles, develop critical and constructive discussions on the endless frontiers of knowledge, and understand the peer review process.
In the study titled “Toxinome - The Bacterial Protein Toxin Database”, Danov et al. developed a publicly accessible website compiling the information on bacterial toxins and antitoxins from all available species from five distinct databases (SecRet6, BactiBase, TADB, BAGEL, UniProt). This system offers user-friendly browsing options, enabling users to explore the compiled data by organism or by Pfam code. Additionally, the website incorporates advanced search tools allowing searches based on amino acid sequences or specific keywords like product name, organism name, Pfam ID, and Pfam name. A particularly intriguing observation of the dataset was the identification of ‘Toxin Islands’, genomic regions that encode multiple toxins and/or antitoxins. These Toxin Islands not only provide information about known toxins but also serve as indicators of potential unidentified toxins within these genomic regions. The paper demonstrates the utility of this concept by providing an example where a hypothetical protein encoded within a Toxin Island exhibits structural similarities to a known toxin. Although the creation of the database and website is quite impressive, the compiled information could have been further explored.
Major comments
In line 150, "The resulting dataset was then manually curated for quality assurance, and toxin or antitoxin genes erroneously included were removed”. Could you please elaborate on the procedure of this manual analysis? What specific criteria were employed to identify and eliminate false positives? This information is crucial to ensure the reliability of the identified toxins and antitoxins.
A more comprehensive discussion of the existing databases would greatly enhance the work. It would be beneficial to elaborate on the total number of proteins present in each database and highlight any intersection between them. This additional information will provide a clearer picture of the data's scope and contribute overall credibility of the analysis.
Although the analysis of toxin presence in bacteria living across varying temperature ranges does yield intriguing insights into the evolution of extremophiles, this aspect appears distant from the central topic of the work and offers a relatively superficial analysis. It would also be interesting to include other information: 1) what is the toxin distribution by habitat type and hosts; despite being a well-studied area, this association would lend support to the dataset's intrinsic conclusion; 2) target specificity and protein domains, what is the frequency of toxins targeting specific cellular components (lipids, proteins, nucleic acids, metabolites) based on protein domains (Pfam); 3) what is the toxin diversity among different phylogenetic groups; figure 6 explores this aspect to some extent, however, the toxin types (domains) are not mentioned.
The concept of Toxin Islands is crucial to the study and could be explored more deeply. While Figure 6 focuses on the topic, only the toxin and antitoxin counts are provided. Details about toxin types within each island could be valuable. An observation is highlighted regarding the Bacilli class, whether a higher toxin count than the antitoxin count is observed. The hypothesis in the discussion part suggests that Bacilli may have an abundance of toxins targeting eukaryotes, potentially explaining the lower need for antitoxins. This hypothesis could be further investigated using the Toxinome data itself.
The work uses protein structural prediction tools to examine the specific case of unannotated toxin and antitoxin found in the Toxin Island of Thauera phenylacetica B4P (Figure 7). It would be beneficial to the work to highlight other examples.
Minor comments
Figure 2 appears to be very complex, leading to difficulties in following the numerical sequence. This figure could be separated into Panels A and B.
In Figure 3, consider providing annotations for Archaea species. Additionally, indicate on the phylogenetic tree the specific bacterial clades that have a depletion of toxins and antitoxins.
There is no reference to a figure or table in line 285: "As we expect, there is a high correlation between toxin and anti-toxin content (R = 0.6581, pvalue = 7.59x10-13)".
There is no reference to a figure/table/supplementary material to line 143: "To increase the number of toxins and immunity proteins into our database we used protein domain information. We added 219 and 94 toxin and antitoxin domains, respectively, to the resulting toxin gene set that we downloaded from the Pfam database.".
The data present in Tables 1-4 could be effectively visualized using graphs, which would enhance the clarity and comprehension of the information.
In the discussion (line 440): "For example, certain hyperthermophilic and halophilic Archaea were described to produce bacteriocins called sulfolobicins and halocins, respectively". It would be interesting to know if the authors could find these toxins in the compiled Toxinome dataset
On 2023-08-22 12:27:24, user Bruno Pelozin wrote:
This assessment of a preprint is part of a research evaluation course within my Ph.D. program, in which I was tasked with selecting a preprint manuscript to formulate questions and suggestions for improvement. I was delighted to come across your article, and the experience of reading it has been truly remarkable. It has been a rewarding endeavor to contemplate potential alternatives and novel experiments that could further enhance the significance of your work. I hope that you receive this message and find value in these collaborative insights.
The lncRNA Sweetheart regulates compensatory cardiac hypertrophy after myocardial injury.
In the elegant study by Rogala et al., they demonstrated that the expression of the long non-coding RNA "sweetheart RNA" (Swhtr), which is expressed in the same region as the crucial transcription factor Nkx2-5, exerts favorable effects on both cardiac function and morphology in animals subjected to left anterior descending artery ligation (LAD). The Swhtr lncRNA is chromatin-localized, and although its deletion does not induce cardiac structural changes during embryonic phases or fetal lethality, nor yield structural or functional cardiac modifications in adulthood, its recovery in transgenic animals induced an enhancement in cardiac morphology and function following myocardial infarction induced by LAD surgery. Although its precise function remains undefined, this manuscript suggests its influence on genes associated with the NKX2-5 transcription factor.
Major
1- I found the introduction to be quite engaging. However, enhancing the overall coherence of the work could involve establishing a more explicit connection between collagen deposition, scar formation, and fibrosis within the context of a myocardial infarction model. Addressing this interrelationship in the introduction would be particularly advantageous, given its enduring significance over time, as opposed to the relatively less prevalent occurrence of compensatory hypertrophy.
2- In the study conducted by Werber et al. (2014), the group generated a comprehensive dataset elucidating the transcriptional landscape of cardiac tissue. Notably, they identified a specific RNA molecule that exhibits exclusive expression within the heart tissue context. Conversely, in the context of the present research, the rationale behind investigating the lncRNA Swhtr appears to lack clarity Establishing a logical connection with the Nkx2-5 factor could potentially serve to justify the selection of the lncRNA under investigation. This strategic link could bolster the rationale for studying lncRNA Swhtr and its potential relevance within the broader context of cardiac biology.
3- I appreciate the researchers' elegant approach in illustrating the spatial distribution of lncRNA across diverse embryonic stages. Nevertheless, I am left wondering whether the researchers considered expanding their analysis to encompass additional developmental stages, such as the newborn (NB) phase. This query stems from the findings highlighted in the current study, where the NB phase emerges as the highest lncRNA window expression and, conceivably, a pivotal period of heightened lncRNA activity (Bridges et al., 2021; Gomes et al., 2017). Remarkably, this pattern aligns with the outcomes derived from Fluorescence in Situ Hybridization (FISH) analyses conducted on cultured cardiac cells (Fig. 1H). Thus, contemplating lncRNA dynamics during the newborn phase could offer valuable insights into its plausible functional roles and accentuate its significance within the intricate framework of cardiac biology.
4- In figures 3A-F, the inclusion of a Sham WT control group would be highly valuable. While I recognize the challenges associated with conducting additional experiments for a new group, the incorporation of Sham animals could substantially enhance the interpretability of the results. Specifically, the comparison between disease-induced (LAD) effects on both WT and KO (Swhtr3x/pA3xpA) groups and their corresponding Sham WT counterparts would offer insightful insights into survival rates and cardiac function data.
For instance, the introduction of a Sham WT group could illuminate noteworthy differences in ejection fraction. It is conceivable that the KO animals subjected to LAD would exhibit a potentially significant reduction in ejection fraction when contrasted with the healthy Sham WT group. In contrast, both the WT and Transgenic animals may not display statistically significant deviations from the Sham WT group, thereby accentuating the therapeutic benefits conferred by the treatment. Incorporating a Sham WT control group would contribute significantly to the robustness of the findings, allowing for a more comprehensive assessment of the treatment's efficacy and its impact on cardiac function under both normal and disease conditions.
5- I believe that a more comprehensive characterization of the animals subjected to LAD would greatly enrich the study. Showing fibrosis and hypertrophy could lead to more holistic understanding of the animals' phenotype. Also, presenting the expressions of specific collagen types, particularly collagen III, which typically manifests in the initial weeks following an infarction, would help. This additional analysis would provide a deeper insight into the fibrotic response triggered by the myocardial infarction and contribute to a comprehensive appreciation of the tissue remodeling process. Furthermore, the inclusion of molecular markers associated with cardiac hypertrophy would be a significant asset to the study.
6- Extending the observation period for animals subjected to LAD and tracking them until the development of heart failure would offer a comprehensive perspective on morphological and functional cardiac changes over time. This longitudinal approach holds the promise of uncovering the dynamic role of the identified lncRNA in disease progression, providing valuable insights into its potential as a modulator or indicator of pathological processes associated with heart failure.
7- An elegant dimension could be added to the study if the researchers were to explore the effects of lncRNA Swthr deletion and gain of function in alternate models of hypertrophy. Considering the potential role of this lncRNA in governing cardiac hypertrophy, examining its impact on both pathological and physiological hypertrophic scenarios could yield valuable insights. Employing the transverse aortic constriction (TAC) model for inducing pathological hypertrophy would provide a robust hypertrophic stimulus, while unveiling the lncRNA's effects in physiological hypertrophy through aerobic exercise sessions would establish a nuanced connection between hypertrophy modulation and lncRNA activity. By encompassing these distinct scenarios, the study could present a comprehensive perspective on lncRNA's role in different hypertrophic contexts, thereby enhancing our understanding of its regulatory influence on cardiac adaptation.
8- The utilization of cardiac tissue culture is indeed an intriguing aspect of the study. Expanding this approach, perhaps in future studies, to encompass diverse infarction zones, including border and remote areas, would not only enhance the study's sophistication but also provide invaluable insights into the nuanced RNA profile characteristics of distinct zones adapting to the pathological stimulus. Moreover, performing an RNA expression profile analysis within the hearts of transgenic animals subjected to LAD holds promise in uncovering potential gene alterations and regulatory effects mediated by the lncRNA. This multifaceted exploration could unravel a comprehensive landscape of gene expression changes triggered by the lncRNA, thereby contributing to a deeper understanding of its intricate role in the cardiac response to myocardial infarction.
Minor
I believe it would be important to use Medical Subject Headings for the keywords, I suggest: Cardiac hypertrophy, long non-coding RNA, myocardial infarction.
In figures 1C-E the researchers interestingly use the lacZ reporter technique. It would be interesting and necessary to present the technique used in the methods.
The sentence to describe Swhtr labeling in heart and other tissue was a bit confusing and could be improved in clarity (line 199-122).
In Figure 1F, I believe there was a significant difference; if possible, provide the statistical symbol. Also in figure F, did the authors use any housekeeping gene to correct the expression? I think it would be more interesting to show the relative expression versus the absolute.
During the manuscript, Swhtr and Swhtr lncRNA are used, I particularly think the name followed by lncRNA is more elegant.
I believe that if the 1G figure were presented with separate bars, it will make the result much easier to understand.
I believe it would make the text easier to understand if 3G figure were moved to position 3B
It would be very interesting to demonstrate in figure 3H, in addition to the knock-out and Tg, also the LAD surgery and the time course.
Line 218, I think you addressed the wrong image (is it 3G?).
In the methods it would be important to define the term ES cells and mESCs (Embryonic stem cells) (line 342); Line 415, 465, 466, 467 use qPCR as in the rest of manuscript. It would be important to state in the manuscript how much tissue or cells were used for RNA extraction. It would also be nice to tell in the body of the text the time it took to extract the tissues, they are described in the methods, but I believe that it would enhance the work by being close to the results.
Werber M, Wittler L, Timmermann B, Grote P, Herrmann BG. The tissue-specific transcriptomic landscape of the mid-gestational mouse embryo. Development. 2014 Jun;141(11):2325-30. doi: 10.1242/dev.105858. Epub 2014 May 6. PMID: 24803591.
Singh, S. R., Hoffman, R. M., & Singh, A. (Eds.). (2021). Mouse Genetics. Methods in Molecular Biology. doi:10.1007/978-1-0716-1008-4
Bridges MC, Daulagala AC, Kourtidis A. LNCcation: lncRNA localization and function. J Cell Biol. 2021 Feb 1;220(2):e202009045. doi: 10.1083/jcb.202009045. PMID: 33464299; PMCID: PMC7816648.
Gomes CPC, Spencer H, Ford KL, Michel LYM, Baker AH, Emanueli C, Balligand JL, Devaux Y; Cardiolinc network. The Function and Therapeutic Potential of Long Non-coding RNAs in Cardiovascular Development and Disease. Mol Ther Nucleic Acids. 2017 Sep 15;8:494-507. doi: 10.1016/j.omtn.2017.07.014. Epub 2017 Jul 28. PMID: 28918050; PMCID: PMC5565632.
On 2023-08-22 02:11:44, user abangr wrote:
The published version of this preprint can be found here:
On 2023-08-21 20:04:04, user Kevim Bordignon Guterres wrote:
This review resulted from the graduate-level course "How to Read and Evaluate Scientific Papers and Preprints" from the University of São Paulo, which aimed to provide students with the opportunity to review scientific articles, develop critical and constructive discussions on the endless frontiers of knowledge, and understand the peer review process.
The manuscript "Adaptation of the Mycobacterium tuberculosis Transcriptome to Biofilm Growth" delves into the intricate variations in Mycobacterium tuberculosis (Mtb) transcription patterns. It uncovers disparities between the ancestral L4 strains and those exposed to selective pressure for biofilm formation. This previously unexplored phenomenon holds potential insights into host interactions, making it a compelling avenue for comprehending disease pathogenesis.
MAJOR POINTS:
** This preprint represents a continuation of the research group's prior study published in 2022. Within this work, the researchers skillfully leverage transcriptomics techniques to illuminate the intricate irregularities that govern biofilm formation in mycobacteria. The employed methodologies not only establish a robust foundation but also showcase a meticulously outlined methodology. However, it's noteworthy that the text's structure occasionally reintroduces specific passages in both the results and recommendations sections. A potential benefit to the text could be achieved by addressing and eliminating these redundancies.
** The introduction and discussion lack information describing mycobacteria and biofilms in general. Nontuberculous mycobacteria (NTM), for example, are well described in terms of their ability to form biofilm. These environmental and opportunistic organisms represent a problem for water treatment systems and in the hospital environment.
** The discussion about phenotypic variation is superficially described. A more thorough discussion of this phenomenon can contribute to the findings.
** As described, MMMC duplication has already been observed by other authors. Under which growth conditions has this been seen? Does this corroborate or contrast from the point of view of biofilm formation?
** The origin of the strains is not exactly clear. The strains chosen deserve a brief description of the patients from whom they were collected and their pathogenesis. In addition, was any characterization performed on the resistance profile of the strains? The relationship between biofilms and susceptibility should be further explored.
MINOR POINTS:
* Acronyms are used before their description, which makes it difficult to read.
On 2023-08-21 17:16:09, user Cristiane Paula Gomes Calixto wrote:
Revision comments from: <br /> Cristiane Paula Gomes Calixto <br /> Flaviane Lopes Ferreira<br /> João Francisco Canal <br /> João Henrique Servilha<br /> Lucca de Filipe Rebocho Monteiro<br /> Victória de Carvalho
The manuscript titled “Epigenetic and transcriptional landscape of heat-stress memory in woodland strawberry (Fragaria vesca)” aims to investigate the inheritance of heat-induced epigenetic and transcriptional changes in Fragaria vesca through asexual reproduction. The study analyses genome-wide DNA methylation and differential gene expression in the initial generation (heat-stressed and control) and their three subsequent non-stressed asexual generations. The authors observed a decreasing transfer of the stress-induced molecular memory across the generations. Their work has originality/novelty, and we believe the biological question they seek to answer can be interesting for the plant sciences community.<br /> We would like to provide some suggestions which we believe might enhance the quality of the manuscript. Please be aware that these suggestions are not exhaustive.<br /> Major comments<br /> • Please include additional information so as to allow the research to be replicable and reproducible. For example, saying “9:00-11:00 a.m.” might not be precise enough. Using the specific light zeitgeber would better inform when samples were harvested in the diel cycle (lines 140 and 161). Another example, the description “Illumina paired end read sequencing (150 bp)” appears to omit crucial details concerning the specific options utilised in the NGS experiment. Important information, such as mRNA selection method, library construction kit, sequencing platform, and the strand-specificity of reads, among other factors, should be included. Line 192: Please state which transcriptome was used with Salmon. Line 282: Which clustering method was used to build the heatmaps?<br /> • The claim that “… genes linked to gibberellin pathways may contribute to a short transcriptional memory.” should be discussed with the literature.<br /> • Line 642-644: Kindly review the claim in relation to what is depicted in the figure.
Minor comments<br /> • We recommend English editing to enhance grammar and clarity. <br /> • Scientific names must always be italicised. In the first appearance of the species, it is also required to list the person (or team) who first made the scientific name of that taxon available. <br /> • Lines 131, 134 and 144: could you please add the light intensity in µmol m-2 s-1?<br /> • Line 135: Is there a specific scientific or practical rationale for maintaining consistent temperatures in stress assays throughout both day and night, while implementing varying diel thermos-cycles for control and recovery conditions?<br /> • Line: 158: We found it a bit difficult to understand what was actually collected.<br /> • Line 166: please, add the reference where we can find more details on the bisulfide method used.<br /> • Line 193: It would make it easier for the reader to understand what the authors mean by DEG if the DESeq2 default parameters were described here. Is it log-fold change, p-value cut-off, etc?<br /> • Lines 205-207: Could you provide information on the duration of the heat-stress treatments?<br /> • Lines 264-267: Do terms like "low," "hypermethylation," and "hypomethylation" refer to a comparison with data from control samples? The comparison between different samples was not really clear to us. The same applies to “significantly different” (line 281).<br /> • Figure 1A: We think this figure could be improved to help the reader understand the temperatures used for CM. Additionally, could you confirm whether the application of 24°C on recovery days precisely occurred for 48 hours? It seems that the temperature might not be exactly 24°C, and we think the figure could provide more precise details.<br /> • Figure 1B: Why are scissors, “2w” and “sampling” shown only on the right-hand side of the figure?<br /> • Figure 1C: Detecting differences among samples based on the y-axis is proving to be challenging for us. The authors might want to contemplate plotting by C contexts on the x-axis, or alternatively, segmenting the y-axis into three distinct regions where resolution could be enhanced around 1-5, 13-17, and 38-42.<br /> • Figure 3B: Is it possible to apply colour shading similar to that seen in a heatmap for this figure?<br /> • Figure 3D: Kindly review the genes mentioned in the figure legend in relation to what is depicted in the figure.<br /> • Line 280-281: The phrase between the brackets seems a bit confusing. We recommend rephrasing it for clarity.<br /> • It might be advisable for the authors to verify whether they are employing a colour-blind-friendly palette.<br /> • Some of the finer details in the figures are quite challenging to discern, making it difficult to interpret the results.<br /> • The expression patterns of several FvHSFs were described previously (López et al., 2022), some also undergoing promoter demethylation. How does the expression patterns of these HSFs change in response to a temperature gradient challenge? We believe the paper would considerably improve if heat-shock proteins and chaperones are also investigated.
On 2023-08-01 12:18:50, user Cristiane Paula Gomes Calixto wrote:
My lab is really interested in this paper! I'm considering having my group engage in the peer review process by providing constructive feedback to improve your manuscript. Are you open to our comments? If not, we completely understand.
On 2023-08-21 16:16:34, user Fredy Barneche wrote:
Comment from the authors: a curated and modified version of this manuscript has now been published in Cell Reports (https://doi.org/10.1016/j.c....
On 2023-08-20 12:29:58, user David Ron wrote:
Evidence that phosphorylated eIF2 underlies the S-phase arrest imposed by the novel culture conditions hinges largely on the reversal of this process brought about by application of the compound ISRIB. This is a logical inference, however the authors' description of ISRIB's mechanism of action is factually incorrect: ISRIB acts downstream of phosphorylated eIF2 to interfere with downstream signalling (this critical event requires binding of ISRIB to eIFB); ISRIB does not impair eIF2 phosphorylation, as stated in the article. This point was established in the very first description of ISRIB (Sidrauski et al. 2013, PMID: 23741617) and elaborated on further by the 2015 publication cited as a reference here.<br /> David Ron, University of Cambridge
On 2023-08-16 19:37:19, user David Buckley wrote:
Did the Bulk sequencing of the aorta include the PVAT or was it cleaned off prior?
On 2023-08-16 14:53:12, user Michael Prummer wrote:
In the meantime, this article has been published in NAR Genomics & Bioinformatics, doi: 10.1093/nargab/lqad058
On 2023-08-16 13:06:21, user Pierre-Luc Germain wrote:
Very interesting contribution, I'd just like to make two comments.
First, it's wrong to write that scDblFinder is "formerly known as doubletCells". They're two methods developed independently, and it's simply that doubletCells was moved to the same package, but still as an independent method.
Second, your results are in contrast with other benchmarks, which you explain by more "realistic scRNA-seq datasets". I'm obviously not entirely disinterested here, but I think this is very misleading: you don't show any evidence that the traditional benchmark datasets do have unrealistic patient or batch effects, and omit to mention the critical fact that, as far as I know, the fatemap samples are homogeneous cell lines, which is far from being more realistic (people do scRNAseq on complex tissues much more often than on cell lines). I think a fairer description would be to abandon the "realistic/unrealistic" labels, describe your data as it is, and hence that your observations are basically about homotypic doublets, which the tested methods are very bad at detecting (but also don't claim to do). The lack of real difference between adjacent/distant seems to indicate pretty clearly that you're essentially dealing with homotypic doublets.
On 2023-08-16 08:41:23, user L Scott Blankenship wrote:
You've cited my paper - thanks! https://doi.org/10.1039/C7E...<br /> But incorrectly<br /> 1) You've cited it for the clause "With their high surface area,..." my paper makes no mention of the surface area of cigarette butts themselves. You need a better citation for this.<br /> 2) You've got my name wrong.<br /> I have no comment on the science though.
On 2023-08-16 01:52:25, user 李坤 wrote:
I have a small question, why is it possible to compute r2, which doesn't seem to fit the data distribution requirements
On 2023-08-15 21:00:41, user Elijah Taeckens wrote:
This has now been published as part of the conference proceedings for the IEEE Conference on Neural Engineering.
DOI: 10.1109/NER52421.2023.10123745 <br /> Link: https://ieeexplore.ieee.org...
Please update accordingly.
On 2023-08-12 20:42:26, user Steve wrote:
Has any thought been given that the etchings or engravings may have been a map of the Rising Star Cave system? When some of the etchings are overlaid, they seem to closely align with the cave's system.
On 2023-08-12 19:35:33, user Charles Warden wrote:
Hi,
Thank you very much for posting this preprint.
This comment relates to the methodology and my personal experience.
Essentially, I have a number of data types (SNP Chip, Exome, Illumina Whole Genome Sequencing, and PacBio HiFi Whole Genome Sequencing for myself). You can see part of those results if you scroll down to "Raw Re-Analysis for HLA Typing" on this page.
My HLA-A, HLA-B, and HLA-C (which I believe are the "class I" HLA genes) had consistent results that I believe can be reliable.
However, at least for myself, I had concerns about the SNP chip imputations for HLA-DRB1, HLA-DQA1, and HLA-DQB1 (which I believe are the "class II" HLA genes). The Introduction and Supplemental Tables have HLA-DRB1 results, and that is part of why I wanted to post this comment.
If I am correctly understanding that this paper makes noticeable use of SNP chip imputations, then I have the following questions:
1) If I do a quick literature search, I think my own results may be consistent with Pappas et al. 2018 (but perhaps less so with Karnes et al. 2017). While I had to learn more about HLA/MHC genes, I think it may make sense that it should be easier to make assignments for some genes over others. Do you agree or disagree with that conclusion?
2) I thought SNP2HLA and HIBAG were relatively common methods for HLA imputations from SNP chip data. However, is there another method available where I can test generating HLA imputations for my sample and see if they are more consistent with the sequencing results (for the "class II" HLA genes)?
If I understand correctly, the GitHub page for this publication describes using SNP2HLA (which I don't think gave reliable imputations for HLA-DRB1, HLA-DQA1, and HLA-DQB1 with my SNP chip data, either for 23andMe or Genes for Good). However, even if that is true, I don't know if the precise settings can have a noticeable effect on the HLA imputation results?
Thank you very much!
Sincerely,<br /> Charles
On 2023-08-11 09:19:40, user Bart Knols wrote:
The reasoning (abstract) that 'However, sterilization by traditional methods renders males unfit, making the creation of precise genetic sterilization methods imperative.' is not correct. It is a justification for the type of research conducted here but does not do right to classical (radiation-based) SIT. See for instance the article by Bouyer and Vreysen (2020) titled 'Yes, irradiated sterile male mosquitoes can be competitive!' (Trends Parasitol., 36, 877-880). Our own research has shown the same, that doses of irradiation sufficiently high to induce satisfactory sterility in mosquitoes whist safeguarding their competitiveness is possible. Article upon article that focuses on gene drive or other gene engineering approaches uses 'lack of competitiveness' as a justification for moving away from classical SIT. This view stands to be corrected.
On 2023-08-09 23:47:50, user Ashraya Ravikumar wrote:
Review of "Predicting Relative Populations of Protein Conformations without a Physics Engine Using AlphaFold2"
The development of machine learning algorithms, most notably Alpha Fold 2 (AF2), have improved the speed, quality and accuracy of protein structure prediction. A next challenge is to use these approaches to predict alternate conformations and the effects of sequence variants on structure. Considering the ubiquity of functionally significant fold-switching and order-disorder transitions, developing the ability to predict these alternate conformations has the potential to inform the discovery of new drug targets. Similarly, the conformational equilibrium of drug receptors relates to their affinities for drugs, highlighting the importance of predicting the relative population of different conformations.
Previous research has found that subsampling the input multiple sequence alignments in AF2 and increasing the number of predictions was able to sample alternative structures of the same target protein, even capturing different fold-switching states of known metamorphic proteins. Prior work has also generated conformational ensembles through reducing the max_seq:extra_seq parameter values and used these ensembles as starting points for molecular dynamics simulations to sample more conformations of interest such as cryptic ligand binding pockets.
Here, the authors use a similar approach of MSA subsampling to discover alternate conformations and their relative populations of certain proteins purely using the AF2 pipeline without the need for extensive MD simulations. They demonstrate how subsampling MSA by modulating the max_seq:extra_seq parameters can generate ensembles of protein conformations whose relative populations correlate with experimental knowledge. They test AF2’s capacity to predict differences in conformer populations with two example proteins–Abl1 tyrosine kinase core and granulocyte-macrophage colony-stimulating factor (GMCSF). With Abl1, they found that AF2 can qualitatively predict the effects of mutations on active state populations of kinase cores with up to eighty percent accuracy. They also found that their method predicted most of the activation loop intermediate states in the active-to-inactive transition of the kinase core, performing comparably to predictions obtained from multi-microsecond MD simulations. Despite the paucity of sequence data for GMCSF compared with Abl1, they were able to predict the extent of variation in backbone dynamics among GMCSF variants, which allowed them to conclude that AF’s prediction engine could decode population signals from relatively scarce data. Overall, the results are very interesting and encouraging and the manuscript is well written. We have the following points which we feel, if addressed, could make this manuscript stronger.
Major points:
Minor points:
Review written by Ashraya Ravikumar and Sonya Lee with input from other Fraser Lab members at UCSF
On 2023-08-09 18:40:04, user Jiahua Tan wrote:
The perspective of tackling the ratio compression caused by the isolation interference in this paper is interesting. It seems that the tool is designed for single plex experiment if I am correct. Are there some ways to run the tool for multiplex experiments simutaneously so that we can make use of all available cores in parallel processing to reduce the run time?
On 2023-08-08 23:50:19, user Alberto J. Martin wrote:
Hi, just wondering how the presence/absence of organisms compares using Poore's and this approach. Quantitatively approaches could disagree but agree on the qualitative analysis
On 2023-08-07 19:03:29, user Thomas Munro wrote:
This is ingenious, and I hope something like this becomes a standard tool for non-native English speakers. The web-based tool https://www.deepl.com/write provides similar suggestions. One excellent feature of that is interactivity: if a suggestion is good overall, but one word is wrong or out of place, clicking on that word or section lists alternatives. Clicking on an option rewrites the whole suggestion accordingly. Would that be possible with this approach, i.e. asking for multiple suggestions for the same section, or incorporating a manual edit into another run of the model?
On 2023-08-07 16:04:53, user Sofia Pauleta wrote:
The manuscript has now be published on Biomolecules<br /> https://doi.org/10.3390/bio...
On 2023-08-07 14:09:23, user LUCIANO RODRIGO LOPES wrote:
Dear Professor Porter and colleagues,
I have read your scientific article with great attention and interest. The initiative to include new species of deer as an experimental model to verify susceptibility to SARS-CoV-2 is of remarkable importance. The susceptibility and virological surveillance analyses involving the white-tailed deer (WTD; Odocoileus virginianus) have demonstrated the potential spillover of SARS-CoV-2 into wildlife. However, this appears to be just the tip of the iceberg. By adopting new species of deer as a susceptibility model, similar to your approach, we can better predict new scenarios involving SARS-CoV-2.
According to your results, it is concerning to learn about the potential susceptibility of mule deer and their ability to transmit SARS-CoV-2 with real infection capacity, whereas we observed that elk have lower susceptibility to this virus.
In an analysis involving deer ACE2 protein sequences (https://doi.org/10.1007/s10..., I compared the binding sites that SARS-CoV-2 uses to enter the host cell. The mule deer shares the same binding sites with the WTD, while the elk has a different site, in addition to the evolutionary distance with the deer of the genus Odocoileus. I argued that this variation in an ACE2 binding site could decrease elk susceptibility to SARS-CoV-2. It appears that our results corroborate each other.
I conclude my comment here by congratulating you on the interesting work and publication by your group.
Sincere regards
Luciano
On 2023-08-07 02:51:04, user Marco Podobnik wrote:
The article has been accepted for publication in Development: https://doi.org/10.1242/dev...
On 2023-08-05 15:10:27, user Flo Débarre wrote:
In case someone else is confused about what happened to the data in the email shown in Figure 2 of this version of the preprint ("EXAMPLE SRA DELETION FROM ANOTHER STUDY" in the previous comment):
SRR11119760 and SRR11119761 were made public again on June 16, 2021; on that day, they were also synchronised on the other INSDC repositories, ENA and DDJB. June 18 was the date at which the data were pushed to the cloud. <br /> The data were therefore public before the preprint was even sent to bioRxiv, and not, like the previous comment could indicate, as a response to the preprint being shared.
On 2023-08-04 16:32:57, user Alex wrote:
The final version of this paper is available here in Scientific Reports under the title "A universal tool for predicting differentially active features in single-cell and spatial genomics data"
On 2023-08-04 13:13:33, user Paschalis Athanasiadis wrote:
Please find the published version here:<br /> https://doi.org/10.1016/j.i...
On 2023-08-04 01:38:44, user Yun H. Jang wrote:
Can you double check if the light intensity of the DLP printer is 0.08W/cm2 (80mW/cm2)? As far as I know, the maximum light intensity of the Lumen X printer is much less than that. Thanks.
On 2023-08-03 11:42:55, user Anthony Baptista wrote:
The following Github contains the R code developed for this research: https://github.com/anthbapt...
On 2023-08-03 10:20:48, user David Curtis wrote:
The recently published description of the Coding-Variant Allelic-Series Test (COAST) is presented as if the method were novel, whereas in fact the approach is in principle identical to the method of weighted burden analysis implemented in GENEVARASSOC and SCOREASSOC, which was first published in 2016 (1,2). The authors of the new paper write: "We define an allelic series as a collection of variants in which increasingly deleterious mutations lead to increasingly large phenotypic effects, and we have developed a gene-based rare-variant association test specifically targeted to identifying genes containing allelic series." (1) The paper first describing the application of the weighted burden test stated that "weights were also allocated according to the effect of the variant" and provided a table documenting how increasingly deleterious mutations were assigned increasingly large weights (2). Since then, the weighted burden test has been used in several published analyses of exome sequence data, including of the same UK Biobank dataset which was used for the COAST analyses (3). It is striking that it is reported in the abstract that "COAST detects associations, such as that between ANGPTL4 and triglycerides" while there is no citation to my own paper using the same dataset which explicitly reported that the weighed burden test detected association between ANGPLT4 and hyperlipidaemia, although admittedly this did not reach exome-wide significance (4). Application of the weighted burden test has however yielded other novel gene discoveries which are exome-wide significant (5,6). There is also a published exploration of the performance of different weighting schemes (7).
When one method implements an approach that is at least similar to one already published, it is customary to refer to the pre-existing approach and discuss differences. Sometimes a formal comparison of performance may be carried out. Describing the earlier work allows the readership to put the new work in context. However the paper describing COAST fails to cite any of the papers describing or utilising weighted burden analysis.
References
On 2023-08-02 20:27:41, user Vishal wrote:
rbcS-T1 is a bit confusing choice for the trichome version, since there is already a rbcS-T1 for the mesophyll version (the gene that comes from tomentosiformis parent - along with T2, T3a, T4 and T5). may be rbcS-Tri is more appropriate?
On 2023-08-01 19:44:42, user Madeleine Rostad wrote:
July 13th-15th at the 37th Annual Symposium of The Protein Society, ASAPbio conducted a series of 20 minute “Live Preprint Q&A” sessions. The following is a summary of our conversation with two of the authors of this preprint.
The discussion on this preprint by Dr. Daniel Keedy and Virgil Woods revolved around the unexpected changes observed in protein dynamics upon ligand binding, as revealed through Hydrogen-Deuterium Exchange Mass Spectrometry (HDX-MS). The researchers used this technique to compare two different types of ligands, and their key figure, a rainbow map, illustrates the differences in HDX reaction rates over time. Red residues indicate increased HDX exchange, suggesting higher solvent accessibility and conformational changes, while blue areas represent either distinct conformational changes or the binding interface.
The researchers expressed excitement about receiving feedback on how their HDX approach reveals additional information over methods such as crystallography. They also expressed some concern about potential confusion regarding the interpretation of exchange rates and the benefits of HDX over crystallography. They are particularly interested in feedback from scientists who are familiar with analyzing HDX data with alternative software that can incorporate EX1 kinetics.
Looking forward, the team plans to collect and analyze more data from HDX and crystallography of small molecule allosteric modulators, focusing on the L-16 site, a less conserved part of the PTP1B structure. In future work, they want to explore other identified binders, but only a few have shown an effect. HDX will be important because they have also struggled to obtain crystal structures and aim to determine where binding is occurring and identify inhibitors. They also plan to study new mutations.
This preprint presents a fascinating exploration of protein dynamics upon ligand binding, and the researchers' approach of using HDX-MS offers a unique perspective. The community is encouraged to provide feedback, particularly on the interpretation of HDX data and the potential benefits of this technique over crystallography.
On 2023-08-01 12:27:32, user Maciej Migdał wrote:
Hi, the paper is now out in BMC Bioinformatics: https://bmcbioinformatics.b...
On 2023-08-01 12:04:07, user Matt Jones wrote:
This work has now been published in the Journal of Experimental Botany- https://doi.org/10.1093/jxb...
On 2023-07-31 22:22:46, user MarCelula wrote:
Have you looked at the rods in your retinal organoids?
On 2023-07-31 11:02:41, user Dr. KIF1A wrote:
The authors descirbe that " Here we employed this assay to perform a comprehensive characterization of 16 KIF1A neurodegenerative disease mutations, including 12 heterozygous (V144F, S58L, A202P, R216H, R216P, L249Q, R316W, T99M, G102D, S215R, E253K and T312M), 2 homozygous (A255V and R350G) and 3 polymorphic (T46M, V220I and E233D)."
(1) Polymorphic mutations are not disease-associated. They have been found in healthy people.
(2) As far as I know, T312M is not associated with any disease. Hamdan et al. (2011) used KIF1A(T312M) as a positive control in their experiments.
(3) "12 + 2 + 3 is 17, not 16. Is this a simple mistake?"
(4) Please add symptoms and citations to table 1.
On 2023-07-28 08:26:09, user Hitesh Mistry wrote:
The data presented n the article prompted the funding of ACTOv (https://classic.clinicaltri... by UCL. The preclinical data presented doesn't appear to support the clinical study fro the following reasons.
Clinically the study is stated as comparing 6 cycles given every 3 weeks of fixed dose of carboplatin versus adaptive therapy arm which is also 6 cycles but where the choice of dose is based on CA-125 dynamics, with N of 40 per arm. ( Note, as an aside it must be noted that on clinicaltrials.gov there is no mention of how the dose will be adpated based on CA-125 dynamics - there is also no protocol and so no understanding of the size of effect that the study is powered to capture.) Preclinically the authors gave 60mg/kg once every 4 days for 3 doses in the standard of care arm but allowed weekly dosing for up to 20 weeks. This is not a like for like comparison! The standard of care arm should have had fxed dose weekly for the 20 weeks. This would have best mimicked the clinical trial they propose as the schedule is fixed only teh dose is changed. Thus the data does not support the clinical trial being proposed. Furthermore there are other issues with the preclinical study, discussed below.
The authors chose to randomise 2 mice to the vehicle and 2 mice to the continuous therapy but 3-5 to the adaptive therapy. (Note, two tumours were grown on each mice.) This is a really odd design fro a preclinical study, the uncertainty around the size of effect between the arms will be large and add to that the continuous and adaptive doses are not comparable with regards to schedule. Next, it appears the tumour take was not all that successful in general. In Figure 4 B top-panel we see that the tumour were not growing conosistently from one measurement to teh next at tiem of randomisation. This is evident by looking at the vehicle arm where post randomisation 3 of the mice are not growing at all for a few weeks post time 0 with one never growing at all and simply remaining constant. Continue down to the middle panel the vehicle looks really odd, we have two tumours out of the 4 not growing properly at all with one spontaneously shrinking after about 4 weeks. These issues in the vehicle group imply the cell-line chosen has not taken propoerly in the mice. It suggests little work was done to understand the optimal conditions for the cell-line to grow well on the back of teh mice. With such erratic control are much larger N would be needed to ascertain differences between different dosing regimens.
It is somewhat worrying that such preclinical studies are being used to support Phase 2 RCTs. It does not appear the preclinical study informed the clinical study design and that the preclinical study itself was not designed appropriately to assess if adaptive dosing is superior to continuous.
On 2023-07-27 18:16:14, user Gavin Douglas wrote:
Congrats to the authors on this manuscript -- this seems like a great method for assessing selection efficacy across species, and I'm excited to try it out.
One question I had was whether the CAIS would change much if dataset independent values of Fa (the frequency of amino acid 'a' across the entire dataset of 118 vertebrate genomes in your analysis) were used. For instance, would the resulting CAIS change much if each Fa was set to 0.05? I believe this is the only variable that could make CAIS hard to compare across species in different datasets (e.g., if different Fa values were used when computing CAIS for those species).
Also, I was a bit confused at times about whether 'frequency' referred to the counts of instances vs. the proportions of instances. I believe it is the proportion in all cases, but could be good to specify if you are revising another draft prior to publication.
All the best,
Gavin Douglas
On 2023-07-25 22:33:45, user sergeimirkin wrote:
This preprint combined with the accompanying preprint https://www.biorxiv.org/<br /> content/10.1101/2022.07.04.498737v1 was published in Nucleic Acids Research: https://<br /> doi.org/10.1093/nar/gkad441.
On 2023-07-25 17:24:20, user Luis Lopez wrote:
We previously tested CoQ2 and idebenone in skin fibroblasts from patients with primary CoQ deficiency. None of them worked in terms of restoring ATP production; and CoQ2 was not toxic for the cells. See: https://doi.org/10.1371/jou...
On 2023-07-25 11:52:08, user Bjarke Jensen wrote:
This is potentially a highly interesting study! It is also my opinion, however, that it is crucial to describe in more detail the evidence that links HCM to I467V and the evidence that links LVNC to I467T. Otherwise it is hardly substantiated how point mutations at the same residue in beta-myosin heavy chain lead to distinct cardiomyopathies.
My apologies if the salient information is already in the manuscript and I missed it.
Best wishes
Bjarke
On 2023-07-24 14:19:58, user Madeleine Rostad wrote:
July 13th-15th at the 37th Annual Symposium of The Protein Society, ASAPbio conducted a series of 20 minute “Live Preprint Q&A” sessions. The following is a summary of our conversation with two of the authors of this preprint.
The conversation with Dr. Kevin Gardner and Danielle Swingle revolved around their research on the diversity of function and higher-order structure within HWE sensor histidine kinases. The key point of their preprint is the exploration of the variability in this family of histidine kinases, challenging the conventional understanding that they need to be membrane-bound or always exist as dimers. Their research has identified monomers, constitutive dimers, and proteins that fluctuate between these states. Interestingly, they found a light-sensitive histidine kinase that is active in the dark, contrary to expectations of the rest of the family.
The figure they are most proud of presents different clusters of homologs of the light-activated monomer that their lab discovered in 2014, clustering into three different families. It also includes two models: one of a monomer sensing kinase and another of a dimer sensing kinase.
The authors appreciated the feedback they received on their initial preprint, which was submitted through eLife. The feedback was constructive and inspired them to revise their work post-Covid, resulting in the current Version 2 of the preprint.
Potential areas of confusion might arise from their efforts to engineer a monomeric HK based on their discoveries, specifically in distinguishing the different regions between the monomer/dimer swap. They also highlighted the value of preprints in facilitating dialogue about emerging science, offering a balance between the immediacy of social media and the lengthy review process of traditional publishing.
Looking forward, they are interested in exploring reverse signaling proteins and their intriguing dynamics and structural components. This research offers a fresh perspective on histidine kinases, challenging conventional understanding and opening up new avenues for exploration. The community is encouraged to provide feedback and engage in dialogue to further enrich this research.
On 2023-07-21 15:41:06, user Chenyang Li wrote:
This preprint has been published (https://doi.org/10.1016/j.x... and a link provided by the bioRxiv system will be forthcoming.
On 2023-07-21 14:08:39, user Ian Sudbery wrote:
Forgive me if this comment is a duplicate, I tried to post already, but it was marked as spam.
I believe that this manuscript is largely based on a missunderstanding of the nature of ATAC-seq data. It is important that people processing ATAC-seq data with MACS use single end mode, together with the --no-model, --extsize and --shift options.
In correct usage of tools can easily lead to sub-optimal results. The MACS tool was original built to analyse ChIP-seq data when most sequencing data was single-ended, and the use of it in the default single-end mode when the data is pair-ended, can indeed lead to results that could be better if the full data set was considered.
In ChIP-seq DNA is fragmented, and then fragments that are bound by a protein factor are isolated by immunoprecipitation. The protein factor could be bound at any place in the recovered fragment, but we only sequence the ends. Because fragmentation positions are random, we can look at the genome positions that are covered by the most fragments, and identify them as the most likely binding positions for the protein factor of interest.
Because a protein factor could be bound anywhere in a fragement, it is imporant to know the extent of the fragment. In the days of single-end sequencing, this could only be achieved by guess work, and clever statistics. An average fragment size would be estimated from the data, and single end reads extended to cover this. But this was always only a guess. With pair-ended data this guess work is unneccessary, as we know where the ends are. So if you have paired-end ChIP-seq data, and use single end mode in MACS2, you are a) discarding half the data, and b) making a guess at fragment length when you don't need to guess. However, ny guess is that in practice the differences are fairly minor.
However, this manuscript is based not on ChIP-seq data, but on ATAC-seq data and ATAC-seq data is fundementally different in nature. In ATAC-seq we use a transposase to create DNA fragments by "transposing" a sequencing adaptor into the genome. Since the transposase can only attack nucleosome free DNA, we can use the locations of transposition to identify regions of open chromatin.
Whereas in ChIP-seq we are trying to identify a single location, which, for a single-fragment, could be anywhere in that fragment, in ATAC-seq we know exactly where the location of interest is for any fragment. Actaully there are two locations of interest, and they are located exactly at either end of the fragment. The area inbetween these two ends is of no interest. In ATAC-seq, rather than read pairs being connected to come up with a candidate region for protein binding (as in ChIP-seq), they are two seperate, independent, and unconnected samples from all open-chromatin locations in the genome, and should be treated thus.
Consider the following situation with two nucleosome free regions and some ATAC reads (* marks closed chormatin, - open)
```
|>>> <<<|
|>>> <<<|
|>>> <<<|
|>>> <<<|
*|---------|*|---------|***
```
Using MACS in paired end mode would lead to the following depth profile:
```
##############
##################
#######################
###########################
*|---------|*|---------|***
```
That is, the two nucleosome free regions are merged into a single extended peak. However, if we use the standard ATAC-seq approach of treating each read as independent (either map both ends as single ended, or map paired-end and then alter flags to mark all reads as read1), shift the read 3' by X bases and then extend the read by 2X bases to get this:
```
|>>>>>| |<<<<<|
|>>>>>| |<<<<<|
|>>>>>| |<<<<<|
|>>>>>| |<<<<<|
*|---------|*|---------|***
```
And then with MACS2 in --no-model mode, you would get this depth profile:
```
##
# ######
########### ########
############### ############
*|---------|*|---------|***
```
That is there are two peaks, that more or less match the two open chromatin regions.
On 2023-07-21 08:26:14, user julien chiquet wrote:
Hello, thank you for your work. <br /> I was curious to know which version of PLNmodels you used for your simulations: I recently lowered the tolerance of the optimization algorithms, and corrected a typo in the objective function of one of the models that could have an impact on the results. <br /> On my side, the AUC and AUPR with my simulation parameters give a clear advantage to PLNnetwork, SpiecEasi and SparseCC over GLasso/NeighborhoodSelection, although they are not specifically designed to help compositional approaches win... A simple example of simus with AUC and AUPR results is available here, for your information. Would be happy to give PLNnetwork <br /> I'd be happy to help give PLNnetwork its best shot!
scripts simus<br /> AUPR<br /> AUC
On 2023-07-21 08:17:14, user Charlie Cairns wrote:
Dear all, congratulations on this very nice work! I think an <br /> important citation is missing from the Aspergillus niger literature, <br /> which used a similar approach four years ago:
Nucleic Acids Research, Volume 47, Issue 2, 25 January 2019, Pages 559–569, https://doi.org/10.1093/nar...