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 2023-09-15 06:25:08, user Gaston C wrote:
This preprint has now been published in a journal https://pubs.acs.org/doi/10.1021/acs.biochem.2c00619
On 2023-09-14 19:40:53, user Stacey Deleria Finley wrote:
this paper has now been published:
"Genome-scale modeling predicts metabolic differences between macrophage subtypes in colorectal cancer"
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 13:49:29, user Benoit B. wrote:
We advise readers that our preprint has been published and a link will be forthcoming.
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 2023-09-14 13:05:49, user Xiaotong Yao wrote:
When comparing eRNA expression within ecDNA and outside, did you correct for copy number differences?
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 2023-09-13 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 2023-07-14 16:23:14, user John Smith wrote:
This conclusion doesn’t quite make sense.
No question bat ZC45 & ZXC21 from Zhoushan island, Zhejiang province (the most east part of china) were chimeras between bat SARS-Cov-1 lineage and *ancestral* bat SARS-CoV-2 lineage. Ancestral bat SARS-CoV-2 viruses may once existed in central region
The Germany estimation of ~90 years of split makes sense.
On 2023-09-13 01:11:49, user Stanley Yu wrote:
Now, it is published by Aging-US. https://doi.org/10.18632/ag...
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 08:25:59, user Gregory Ehx wrote:
This article is now published in Genome Biology: https://genomebiology.biome...
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 2023-09-08 10:26:26, user Sebastian Lequime wrote:
Manuscript is now published in Molecular Biology and Evolution https://doi.org/10.1093/mol...
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 12:24:15, user Courtier wrote:
Your article is very interesting. Has it been published? We would like to include your results in our database Gephebase.
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 2023-09-03 02:44:50, user Beiyan Nan wrote:
This manuscript has been published (10.1038/s41467-023-41082-3).
On 2023-09-01 09:16:00, user Qingyao wrote:
Our manuscript is now online, at Molecular & Cellular Proteomics: https://doi.org/10.1016/j.m...
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 21:54:30, user Irina Catrina wrote:
This is now published (August 8th, 2023) in RNA Biology https://doi.org/10.1080/154....
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 2023-08-29 09:12:26, user Benjamin Mullish wrote:
Final published version of this paper is here: 10.1038/s41467-023-40872-z
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 2023-08-28 18:45:31, user David Ashbrook wrote:
This has now been published (under a different name) https://doi.org/10.3390/gen...
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 19:25:01, user Laura Trinkle-Mulcahy wrote:
This work has now been published as:
Mehta V, Decan N, Ooi S, Gaudreau-Lapierre A, Copeland J, Trinkle-Mulcahy L. SPECC1L binds MYPT1/PP1b and can regulate its distribution between microtubules and filamentous actin. J Biol Chem 299(2): 102893-102911. PMID: 36634848.
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 20:07:18, user Reinhard Hinterleitner wrote:
This manuscript has now been published in Immunity<br /> https://www.sciencedirect.c...
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 2023-06-20 05:04:23, user Tobias Dansen wrote:
Thank you for your valuable input! The methods paper on measuring DAAO activity is already available through Biorxiv: https://www.biorxiv.org/con...
On 2023-08-25 16:21:32, user Pawan wrote:
Hi Sir, Congralutalions. I have one suggestions in figure 5a where you have shown MaHSF11 is cytoplasm and nuclear localization. But figure looks like it a endoplasmic reticulum having high master gain in confocal microscopy. You have to look this figure again with low master gain and ER marker gene (it is available in TAIR). It seems that their is an interaction between ER and nucleus for MaHSF11. Good Luck
On 2023-08-25 08:40:01, user MC wrote:
It will be interesting to see further progress and results.
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 2023-08-25 08:31:30, user petrilli wrote:
this pre-print is now published https://doi.org/10.26508/ls...
On 2023-08-24 23:24:41, user Ramon Velazquez wrote:
This article has now been published in Acta Neuropathologica. https://link.springer.com/a...
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 15:43:29, user Theo Sanderson wrote:
This work was published here: https://www.nature.com/arti...
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 11:08:37, user Iqra Jamil wrote:
Hi! I can't access supplementary files.
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 19:51:00, user Marisa Korody wrote:
Published version can be found here:<br /> https://www.liebertpub.com/...
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 2023-08-18 13:47:48, user Aggie Turlo wrote:
This article has been accepted for publication in Stem Cells published by Oxford University Press. DOI: 10.1093/stmcls/sxad060
On 2023-08-17 01:59:02, user Mituru Nishiguchi wrote:
This article has been published on Scientific Reports in July 2023.<br /> https://doi.org/10.1038/s41...
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 2023-08-15 01:56:26, user Luohao Xu wrote:
This preprint is now published at https://www.nature.com/arti...
On 2023-08-14 17:54:14, user Ioannis Tzanetakis wrote:
Now published in Plant Disease as https://doi.org/10.1094/PDI...
On 2023-08-14 13:09:55, user Virginia Savova wrote:
Now published https://www.fortunejournals...
On 2023-08-13 02:56:12, user Takanobu A Katoh wrote:
This preprint is published from DOI: 10.1126/science.abq8148
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 2023-08-12 13:52:44, user Youssef M Eltalkhawy wrote:
Very impressive and promising model
On 2023-08-11 17:51:20, user Jo Wolfe wrote:
This paper is now published in Molecular Biology and Evolution https://academic.oup.com/mb...
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 2023-08-09 11:49:47, user Alex Cope wrote:
Now at Molecular Biology and Evolution: https://doi.org/10.1093/mol...
On 2023-08-09 11:11:29, user Alexis Sperling wrote:
This is now published: DOI:https://doi.org/10.1016/j.c...
On 2023-08-09 11:08:59, user Alexis Sperling wrote:
This is now published:https://doi.org/10.1098/rso...
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 2023-08-05 12:07:19, user Ira Schildkraut wrote:
Now published in PLoS ONE 18(7): e0286435. https://doi.org/10.1371/jou.... pone.0286435
On 2023-08-05 12:00:01, user Ira Schildkraut wrote:
Now published in Genome Res. 2022;32: 162–174. doi:10.1101/gr.275784.121
On 2023-08-04 23:19:12, user michael_in_adelaide wrote:
This paper is now published in Biology Methods and Protocols,
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 15:03:25, user Visitor Comment wrote:
Interesting. My relatives who had life threatening Covid were left with altered serum calcium concentrations with PASC, some high, some low. Could E be enough to alter serum calcium dynamics? Could this protein continue to play a part in long Covid, even years later?
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 18:15:08, user Tim Hoffman wrote:
This work is now published in Science Signaling!
On 2023-08-01 17:26:04, user Jesse Rinehart wrote:
A revised version of this paper was recently published in Molecular Systems Biology
"System-wide optimization of an orthogonal translation system with enhanced biological tolerance"
(2023)0:e10591https://doi.org/10.15...
On 2023-08-01 14:16:34, user Angela Ortiz wrote:
Thia article has been already published in MICROBIOLOGY SPECTRUM since May 15th: https://doi.org/10.1128/spe...
On 2023-08-01 13:01:32, user Jessica Resnick wrote:
This paper has been published: Resnick, J.D.; Beer, M.A.; Pekosz, A. Early Transcriptional Responses of Human Nasal Epithelial Cells to Infection with Influenza A and SARS-CoV-2 Virus Differ and Are Influenced by Physiological Temperature. Pathogens 2023, 12, 480. https://doi.org/10.3390/pat...
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 2023-08-01 07:51:25, user Björn Laabs wrote:
We are happy to announce, that this preprint have been published in Advances in Data Analysis and Classification (https://doi.org/10.1007/s11...
On 2023-07-31 22:22:46, user MarCelula wrote:
Have you looked at the rods in your retinal organoids?
On 2023-07-31 14:12:18, user Scott Jarmusch wrote:
Really nice stuff! Always great seeing examples of siderophore discovery using molecular networking. I think you are missing a few key publications regarding desferrioxamines and MS/MS based elucidation. Mainly
-A. M. Sidebottom, A. R. Johnson, J. A. Karty, D. J. Trader and E. E. Carlson, ACS Chem. Biol., 2013, 8, 2009–2016
-https://pubmed.ncbi.nlm.nih...
-10.1039/D0MO00084A
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 2023-07-30 20:08:21, user Winfried Hinrichs wrote:
published in: <br /> Communications Biology, 5, 805 (2022). <br /> https://doi.org/10.1038/s42...
On 2023-07-28 09:04:13, user arpitmathur wrote:
I would like to point out that in carrying out survival analysis, authors divided patients into two categories top 40% and bottom 40% . There is a literature from medical statsitics that says that there should be no division of patients into groups. Rather regression should be used for survival analysis purpose.
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 2023-07-26 06:49:29, user Jose M. Mejia Oneto wrote:
Please note that this manuscript is now published in ACS Central Science: https://pubs.acs.org/doi/10...
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 18:00:48, user Ryan wrote:
This article is now published in Frontiers in Genetics! Check it out here: https://www.frontiersin.org/articles/10.3389/fgene.2023.1125967/full
On 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...
On 2023-07-20 19:55:47, 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 one of the authors of this preprint.
Dr. Lauren Porter's discussion focused on her research on the evolutionary selection of proteins that can assume different folds, particularly in response to stimuli. The key point of the preprint is the prediction of sequence features for fold-switching proteins, which undergo relatively dramatic secondary and tertiary structure changes. They specifically study homologs that switch from an alpha-helical to a beta-sheet state, a significant change. Their research suggests that fold-switching is not random but is evolutionarily selected, a finding that challenges current understanding in the field.
Dr. Porter’s favorite figure connects different sequence alignments with the helical and beta sheet structures, identifying positions in the alignment that make unique contacts to the beta sheet fold and the alpha sheet fold.
Dr. Porter is particularly interested in feedback on their argument that evolution selects for these proteins and makes them different. She acknowledged that some of the figures might be hard to interpret for different communities (e.g. evolutionary biologists might find some protein structure aspects confusing and some of the evolutionary insights to be obvious, whereas structural biologists might be surprised by some of the evolutionary biology). Looking forward, they plan to use these results to build on existing methodology to reliably predict two folds from one sequence. They hope to achieve this within the next year.
Dr. Porter also shared her thoughts on the preprinting system, discussing the complications around reviewing preprints and benchmarking against methods that have not yet been peer-reviewed. She also expressed how having more accountability in terms of commenting and making changes to preprints could elevate the scientific discourse.
This preprint presents a novel perspective on the evolutionary selection of fold-switching proteins. The community is encouraged to provide feedback, particularly on the argument that evolution selects for these proteins.
On 2023-07-20 14:51:45, user Yencheng Chen wrote:
Please include 2 previous publications with similar ideas in your reference.
https://www.nature.com/arti...
https://doi.org/10.1021/acs...
Thanks,<br /> Yen-Cheng
On 2023-07-20 09:35:16, user Dmitrii Kriukov wrote:
Amazing, clear and precise work! Thank you!
On 2023-07-20 09:13:24, user Dmitrii Kriukov wrote:
This is a great paper I found so long! Thank you for your work! You express many thoughs I had and even more.
Minor comments to your work:
Fig 3: "Black indicates observed variance; grey unobserved". - there is no grey entities, only black circles.
Fig 10: "MRL" in the legend
I would be excited to see also the experiment with KD method versus real data and its comparison to different MLRs.
On 2023-07-19 16:49:38, 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 one of the authors of this preprint.
Dr. Lauren Jackson's discussion focused on her research on the interaction between β’-COP and the ArfGAP, Glo3, and its role in maintaining post-Golgi cargo recycling. The key point of the preprint is the identification of how a key regulatory molecule (a GAP protein) regulates an important membrane trafficking co-complex. They knew these two elements interacted, but pinpointing where the interaction was happening among the seven subunits was challenging. They identified specific amino acids and tested predictions in a yeast model system.
The figure they are most proud of, Figure 4, was improved through peer review, as reviewers requested an additional experiment to be conducted in cells. This figure demonstrated the interaction from both perspectives.
Dr. Jackson was excited to get the preprint out there, as it allowed her to attend an online conference and gain visibility during a challenging time. She also found preprints to be beneficial for grant applications. Most of the feedback they received came from peer review, and the preprint story differs from the final published version as they took out a crystal structure and turned it into its own paper during the review process.
Potential areas of confusion might arise from the fact that they never nailed down the structure of the interaction between β’-COP and Glo3. They faced technical challenges and some methods suggested by reviewers had already been attempted but were unsuccessful and were outlined in the correspondence with the reviewers coordinated by the journal.
Looking forward, they plan to do more structural biology to get the supercomplex (potentially by tomography or by moving in the nanodisc direction, having done some work on tubules).
Dr. Jackson shared her positive experiences with preprints, noting their importance and the benefit of having a pool of reviewers. She is open to using Review Commons in the future and suggested that increasing the interactions with scientific societies or funder mandates could drive further innovation in this area.
On 2023-07-19 16:44:56, 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 one of the authors of this preprint.
Dr. Alex Guseman's discussion centered on his research on inhibiting SARS-CoV2 viral entry by targeting spike glycans. The key point of the preprint is the interaction of lectins with spike proteins, which facilitates viral entry. The research uses dynamic light scattering to demonstrate that treatment with lectins rapidly aggregates spike proteins. The team used fluorescence binding assays to fit an isotherm to a 1:1 ratio, although the exact stoichiometry remains unknown.
The figure they consider most important proposes that multivalent interactions are essential and that these interactions depend on glycans. Interestingly, they found that other Covid variants are also inhibited.
Potential areas of confusion might arise from understanding the concept of multivalent interaction and the importance of stoichiometry in the interaction of spikes with the BOA lectin. The team emphasized that these interactions are not within spike proteins, but rather between BOA and multiple other spikes.
The team is seeking feedback on additional experiments for future studies that could further explore their model. While animal models of infection may be the ultimate test, this is beyond the scope of this study for both effort and cost. The authors are looking for community feedback on how future biophysical experiments can probe the nature of the multivalent interactions.
Since the preprint was posted, some other lectins have been tested and shown to have an inhibitory effect. The team believes it might be valuable to look at other interactions in the future. They also want to explore more about the monovalent version that is not interacting in the same way.
Dr. Guseman's experience with preprint feedback has been positive. This preprint has received a few hundred reads and ~60 retweets, and he hopes for more community engagement. He believes preprints are essential and that they help with efficiency.
On 2023-07-19 14:11:28, user Karel Muller wrote:
Would it be interesting to compare the evolution rate with other CO2 fixing enzymes in plants (PEPC)? Maybe add a word or two in the discussion?
On 2023-07-19 12:56:20, user Pat Schloss wrote:
A reader pointed out a small, but significant typo in the first version of this preprint. The sentence, "They then randomly select that many sequences, with replacement from each sample", should have "without" rather than "with". Thus the sentence would read "They then randomly select that many sequences, without replacement from each sample". This correction will be included in the next version of the preprint
On 2023-07-19 07:13:45, user Yoshimi Kawamura wrote:
This paper was published in The EMBO Journal as “Cellular senescence induction leads to progressive cell death via the INK4a-RB pathway in naked mole-rats”. <br /> doi: 10.15252/embj.2022111133.
On 2023-07-19 03:58:28, user Sishuo Wang wrote:
Data availability.<br /> The scripts to do pRTC and bootstrap-based approach hessian for MCMCTree are available at https://github.com/evolbegi... and https://github.com/evolbegi... respectively. Other scripts and data are available at https://figshare.com/s/1b39....
On 2023-07-18 17:23:40, 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 one of the authors of this preprint.
Dr. Stephanie Wankowicz's discussion centered on her research on the refinement of multiconformer ensemble models from multi-temperature X-ray diffraction data. The key point of the preprint is not the introduction of a new technique, but rather a tutorial for people to collect and process crystallography datasets. One of the steps in this tutorial is the creation of multi-conformer models, which represent different protein states. The work builds on existing methodology, emphasizing that multiconformer models in high-resolution structures provide a better model.
Her favorite figure, Figure 1, guides people through the steps to process, model, and refine multi-temperature X-ray data. However, she also highlighted a figure that shows the ability to capture different conformations of water, demonstrating the power of this technique.
Dr. Wankowicz is seeking feedback on the clarity of the paper, particularly from individuals who have never used this technique before. She is also interested in suggestions on how the algorithm could be improved. She acknowledged that readers might find the paper confusing if they are not willing to test the tool/method directly, emphasizing the need for user experience.
She shared her positive experiences with preprints, noting that they could benefit the scientific community by making the review process of data more transparent. She also suggested that she has become a better reader of papers because she has had to analyze the validity of preprints herself.
This preprint offers a valuable tutorial on the refinement of multiconformer ensemble models from multi-temperature X-ray diffraction data. The community is encouraged to provide feedback, particularly on the clarity of the paper and potential improvements to the algorithm. The author's future plans to continue refining and updating the preprint present exciting avenues for further exploration and discussion.
On 2023-07-18 17:18:27, 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 the authors of this preprint.
Dr. Neel Shah and Anne van Vlimmeren's preprint, titled "The pathogenic T42A mutation in SHP2 rewires interaction specificity and enhances signaling," focuses on the study of a phosphatase with a variety of disease-associated mutations. The mutation they most focused on is deep in the N-SH2 binding pocket, and while it was known to alter binding affinity, the researchers discovered that it also changes specificity. This mutation results in the loss of a hydrogen bond, but surprisingly, this enhances binding affinity. The mutation also confers a specificity change for many peptides.
The researchers' favorite figure in the preprint is the one that tested different peptides and where they mutated lysine 55 to arginine, showing that the effects of this residue were coupled to residue 42. They are most excited to receive feedback on how the binding correlates to cell signaling and whether this correlates 1:1 with binding affinity. Long term, they are interested in how to further explore the broader cellular interactions of this mutant.
They anticipate that some might find the three other mutants that impact binding affinity to a lesser extent confusing. The researchers are also curious to see if people are as compelled by the complementarity of their MD and biochemistry as they are, and what potential blind spots they might have missed.
The researchers are strong advocates for preprints, seeing too many upsides to not use them. They find them helpful when applying for grants as they show progress, and believe that getting preprints out should be equally celebrated as publishing to a journal. They encourage the community to provide further comments and feedback on this work!
On 2023-07-18 17:14:52, 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 the authors of this preprint.
The discussion with Dr. Joey Davis and Barrett Powell focused on their research on learning structural heterogeneity from cryo-electron sub-tomograms with tomoDRGN. The key point of their preprint is the untapped potential in cryoET datasets for understanding the spatial distribution of different complexes and conformations in the cell. They describe a different approach using machine learning methods, with the goal of putting things in a cellular context.
The researchers faced challenges in exploring how portable their original cryoDRGN framework is to Electron Tomography (ET) data. While some elements worked well, such as the decoder network, encoding the raw data was difficult due to the shift from one particle being one image to having multiple images of a particle.
Their favorite figure, Figure 6, demonstrates the potential of tomography to generate high-resolution, individualized pictures. They feel that this figure in particular highlights the promise of visualizing how a complex, such as the ribosome, may adopt different structures and interact with distinct cofactors depending on its subcellular localization.
The researchers are excited about the possibility of discovering new biology through their paper and are eager to continue hearing feedback on use cases, particularly in cases where tomoDRGN has helped users uncover structural heterogeneity that helps them better understand how their complexes of interest work. They also expressed some concern about potential confusion regarding the generative model and the process of classification.
Looking forward, they plan to explore the relative species of ribosomes and correlations between complexes. They also mentioned how many emerging ideas in the field are indicating paths to computational shortcuts for processing the tilt-image-series data directly without explicitly generating tomograms - success in this area could be a game-changer in the field.
This preprint presents a novel approach to understanding structural heterogeneity using cryo-electron sub-tomograms and machine learning. The community is encouraged to provide feedback!
On 2023-07-17 15:47:38, user Larry Madden wrote:
Hi really interesting paper. Is FLAG available to download and test or is it proprietary software?
On 2023-07-17 00:05:11, user Lladser Research Group wrote:
The published version of the paper can be found at the Journal of Mathematical Biology, "On latent idealized models in symbolic datasets: unveiling signals in noisy sequencing data." <br /> Here is the full citation: J Math Biol. 2023 Jul 10;87(2):26. doi: 10.1007/s00285-023-01961-1.
On 2023-07-15 08:52:31, user Eyal Privman wrote:
Dear Trost et al.
Interesting paper! I looked into the alignment simulation methods because I was particularly intereseted in how you simulated indels. The "mimick" method is very interesting. I haven't seen that before. It sounds like it could produce much more realistic alignments, and if so - I would highlight this method to bring it to the attention of our community.
A problem with simulations based on some indel model is that they unrealistically use a fixed indel rate across the alignment. Natural protein sequences have large variation in indel rates. When I tested the effect of alignment errors on positive selection inference I saw that without indel rate variation there wasn't a significant effect, but when I introduced indel rate variation I saw a strong effect. I did that by simulating alignment blocks with different indel rates, which I inferred from empirical alignments of HIV protein sequences.
Best,<br /> Eyal Privman
Privman, E., Penn, O., & Pupko, T. (2012). Improving the performance of positive selection inference by filtering unreliable alignment regions. Molecular biology and Evolution, 29(1), 1-5. https://academic.oup.com/mb...
On 2023-07-14 23:30:00, user Zach Hensel wrote:
The revised manuscript overlooks the dispositive analysis first suggested to the authors, to my knowledge, in the first week of September 2022. The manuscript’s hypothesis of an endonuclease “fingerprint” of a synthetic origin in the SARS2 genome makes a testable claim: if regions around the sites composing the “fingerprint” are sampled in nature, engineered nucleotides will stick out like a sore thumb.
Authors were told about this test in the first week of September 2022 when people independently noted the recombinant evolutionary history and that almost all elements in the “fingerprint” are sampled in a handful of the most closely related genomes. Others rephrased essentially the same test, with Francois Balloux commenting to Alex Washburne on September 5, 2022:
Assuming we wished to follow up on this, the next step would be to test if high homology can be found to different Sarbecoviruses for (some of) the 6 fragments defined by the restrictions site (ie. there's no reason to expect natural breakpoints to match restriction sites).
This step was not taken. And it was not a difficult step. Shortly after the manuscript’s publication, Crits-Cristoph and colleagues rigorously showed that the hypothesis fails this test: https://github.com/alexcritschristoph/ancestral_reconstruction_endonucleases – the conclusion is noteworthy considering the public record, which demonstrates bias in site selection and post hoc selection of statistical tests. In fact, this manuscript’s hypothesis gained attention only after Justin Kinney, who is acknowledged for his assistance on the manuscripted, prompted the discussion by suggesting a different hypothesis about a different restriction endonuclease, BsaXI.
In the comments section of V1 of this manuscript, Alex Washburne proposed a second test of his hypothesis, claiming that “the rapid loss of this pattern is indicative of its evolutionary instability, suggesting what we observe in the SARS-CoV-2 ancestral state is not a stable pattern resulting from recombination, but a transient, unstable pattern that perhaps went against selection and reverted back once the infectious clone was subjected to selection from considerable onward transmission.” While this statement makes some dubious claims and another test is not needed, this comment shows that Washburne considers fitness changes in mutations at these sites to be another test of his hypothesis. This is a test that Washburne can conduct based upon published analysis of the fitness impacts of mutations: https://github.com/jbloomlab/SARS2-mut-fitness – as Washburne and co-authors have not published the results of this test, I will briefly do so here.
The mean, median, maximum, and minimum fitness change estimated for point mutations in the “fingerprint” of the 5 BsmBI or BsaI sites in SARS2 are -1.7, -1.4, 2.2, and -6.5. The same calculations for 1000 random samples of 30 nucleotides give -1.7, -1.5, 1.8, and -6.4 (see link above on interpreting these numbers, or simply note their similarity). A search on https://cov-spectrum.org/ shows that point mutations or deletions for one or more of these 30 nucleotides have been reported in 0.75% of sequences sampled in the most recent 3 months. Point mutations or deletions for one or more of 30 random nucleotides (a single random sample; results will vary) have been reported in 0.96% of sequences in the same period. All in all, the main point of interest in these 30 nucleotides is the attention given a hypothesis of a “fingerprint” of synthetic origin that was effectively disproven before this manuscript was published.
Finally, considering the countless number of equivalent hypotheses, I suggest that a better effort would be immune to these tests (and I can think of at least one example myself). It is critical that a manuscript of this type demonstrate that there is an unbiased rationale behind the hypotheses tested and that is plainly not the case here. One simply needs to observe that “longest fragment” is referred to 20 times in the manuscript, while “shortest fragment” goes unmentioned.
On 2023-07-14 16:17:43, user Tanai Cardona Londoño wrote:
Fascinating. I wonder if you've had a chance to come across my paper comparing some of the evolution of ATP synthases with Photosystem II, the water splitting enzyme, of oxygenic photosynthesis. There are some remarkable similarities in their pattern of evolution, like the emergence of a catalytic and non-catalytic subunits, the phylogenetic distances between these subunits, and the overall rates of evolution of the subnits through their diversification... doi:10.1016/j.bbabio.2021.148400
On 2023-07-14 14:43:11, user C.S. Raman wrote:
My Rebuttal to Wolfgang Buckel's Correspondence about our paper is now published. Please see: https://doi.org/10.1021/acscatal.3c02319
On 2023-07-12 01:53:11, user Sankar Chandra Basu wrote:
Now published in Journal of Molecular Modeling: <br /> https://link.springer.com/a...
On 2023-06-24 16:29:14, user Sankar Chandra Basu wrote:
Accepted, 20th June 2023, In-press, Journal of Molecular Modeling, Publishers: Springer Nature.
On 2023-07-11 18:40:20, user argonaut wrote:
"This suggests that the people at both sites
genetically related individuals varied in the places where they resided over their lifetimes" ... "some evidence that families sourced food<br /> from different landscape contexts, either through variation in direct consumption or<br /> through variation in consumption of animals eating these plants."
Have you taken into account the slash-and-burn farming strategy of LBK and the constant displacement that it takes?
On 2023-07-10 09:22:24, user Ramon Crehuet wrote:
Now published in J. Chem. Theory Comput.: https://pubs.acs.org/doi/10...
On 2023-07-09 21:48:22, user Stephanie Wankowicz wrote:
Summary: In this study, researchers used 3D variability analysis (3DVA) combined with atomistic molecular dynamics (MD) simulations to investigate the dynamic motions of human asparagine synthetase (ASNS). By solving the structure of ASNS and performing 3DVA, they suggest that a single side chain's dynamic motion (Arg142) regulates the interconversion between open and closed forms of an intramolecular tunnel. The opening of this tunnel allows for the translocation of ammonia, which is necessary for ASNS’s catalytic function. MD followed up on this initial finding to determine exactly how
The study highlights the power of cryo-EM in identifying localized conformational changes and demonstrates how conformational dynamics can regulate the function of metabolic enzymes with multiple active sites. However, the lack of experimental electron density shown in the figures (or available publicly) makes it difficult to assess the claims in this study. Additional forward tests of the importance of this blockage via mutagenesis may also uncover why it must be regulated. If this is out of the scope of the current paper, it should be hypothesized and speculated upon in the discussion.
Major Points:
Minor Points:
Stephanie Wankowicz and James Fraser
On 2023-07-09 15:21:21, user Charles Feigin wrote:
You might consider an alternative name for velvet to avoid confusion with the genome assembler Velvet, which is extremely well known and has >10,000 citations.
On 2023-07-09 14:02:36, user Bianca Migliori wrote:
The repository associated with this work can be found here: https://github.com/NYSCF/NY...
On 2023-07-09 13:54:52, user Yue Li wrote:
Here is a 15-min talk discussing the high level idea of this paper at BIRS Single-cell Plus: https://www.birs.ca/events/...
On 2023-07-07 16:38:37, user Zach Bennett wrote:
ADO does not have a 2-his 1-carboxylate facial triad, like that of HIF prolyl hydroxylases, they contain a 3-his facial triad that binds the Fe II. See https://doi.org/10.1016/j.j... https://doi.org/10.1021/acs...
On 2023-07-07 11:51:33, user Ivan Scotti wrote:
Dear colleagues,
nice paper!<br /> I have a comment: the PCA in Figure 1 seems to suffer from the "horseshoe (or ark) effect", which is typically caused by the unimodal distribution of variables and their consequent non-linear correlation.
In this situation, I believe that the Axis 2 should not be interpreted, as it is only a distorted representation of Axis 1. Basically, I think this plot should be interpreted as showing an East-to-West gradient only (which is already a very nice result!)
Ivan S.
On 2023-07-06 22:14:16, user Charles Murtaugh wrote:
This is a very nice paper, the data are convincing and the analyses careful. There's one typo, I think: referring to Fig. 3C, the text states, "In the context of a CTX injury, sustained and temporal Hh activation significantly blocked IMAT formation, albeit to a lesser extent than when activated at 4 dpi". "Than" in the last clause seems to imply that 4 dpi activation was more effective than other strategies, which is not what is shown. I think simply removing "than" would correct the sentence to indicate that 4 dpi activation is less efficacious than sustained, 0 dpi or 2 dpi, which is what the figure shows.
Looking down the road, it might be interesting to try and mess around with the myofibroblast differentiation program (e.g. interfering with MRTF?), to see if the FAP-to-myofibroblast transition is essential for the anti-myogenic effects of high HH signaling. It would also be interesting to compare the secretome of these cells to that of preadipocytes developing in the HH-off condition, to see if there are separate or shared factors mediating myogenic inhibition under these different conditions.
On 2023-07-06 13:37:54, user Liudmyla Kondratova wrote:
In the article, the authors present a tool for investigating the co-occurrence of transcription factors (TFs) and binding grammar within regulatory regions. This algorithm exemplifies the application of NLP methods in the context of biological data. The tool utilizes a Market Basket analysis to identify known co-occurring TFs based on the locations of their transcription factor binding sites, regardless of the data source. The authors have made modifications to the Market Basket approach to accommodate the specificities of the transcription factor regulation process. The enhanced model takes into account the multiple occurrences of the same TFBS within a defined window. Moreover, the algorithm considers the order and orientation of co-occurring TFs, which represents a significant improvement over previously published tools. Lastly, the tool performs a sophisticated analysis of identified TF pairs, which is visualized using various analytical tools, including differential and orientation analysis, calculation of TFBS distance to a transcription start site, and network reconstruction. The tool's performance was compared to existing tools and validated using open-source data.<br /> The authors made several modifications to the Market Basket approach; however, the descriptive connection between different parts of the algorithm is somewhat unclear at times. For instance, they mention overcoming the threshold issue of the cosine association score, which is a limitation of this method, by calculating the Z-score of significance. Initially, it seems like they calculate the Z-scores of cosine values. However, upon examining the script available on GitHub, it becomes evident that they actually calculate the Z-score of the distance between two TFs. The assessment of the normality of the distance distribution is also performed. Unfortunately, the publication does not emphasize this important aspect of the algorithm.<br /> Another noteworthy aspect is related to Figure 2.A. It is observed that the number of co-occurring pairs differs across cell types, which raises intriguing questions. Is this variation primarily influenced by biological factors or technical factors? Is this variation an expected outcome? Or does it correlate with the number of peaks present in the CHIP-seq data utilized for validation?
On 2023-07-06 11:17:24, user Nick Leigh wrote:
This is a well written and clear manuscript comparing successful and defective heart regeneration in zebrafish versus medaka, respectively. The experiments are well designed and the interpretation is careful and thorough. These kinds of studies are essential and, now powered by single cell sequencing, can cast wide nets that enable unbiased description and investigation of this process. As clearly stated by the authors, the description provided here undoubtedly provides numerous follow-ups, questions, and hypotheses about regenerative success and failure. The authors should also be commended on creating a webtool to allow others’ to query their dataset.
“Cross-species data integration was effective as both zebrafish and medaka cells were represented in each major cluster”. Agreed that across the major clusters there is good agreement. I’m more curious about if this is potentially overfitting–are you losing a different cluster only present in one species? From published data, could we expect any different clusters between these two species? (addressed a bit later on with zEP cells). In general, it may be worth exploring a couple other strategies for cross species integration to try and prove this further (point 6 also addresses this). <br /> The scale of the interferon-deficiency in the medaka is striking. It’s mentioned that DAMPs from necrotic cells could be a driver of interferon responses, but building on some of your prior work (Balla et al. 2020 PMID: 32413307), are the zebrafish all harboring some virus at this point and the medaka not? Could a viral/microbiome-related reason result in lack of IFN signaling. Relatedly, it would be interesting to see if medaka have type IV interferons (https://www.nature.com/arti... (and if these are included in this one-to-one comparisons/ if they are even annotated in the current version of the zebrafish genome). Finally, is there evidence of any DAMP response? For example, are there still other chemokines and cytokines (potenitlaly NFkB nuclear translocation) being produced in medaka and just specifically not an IFN signature? This is getting at the question of whether this is specifically lack of IFN signaling or if medaka are hyporesponse to, for example, DAMPs. <br /> Is recruitment responsible for increased macrophages in zebrafish or is it expansion of tissue resident cells? This could affect the conclusion drawn in medaka that they are not recruiting macrophages. <br /> Figure 3H, the proportion of TNFa positive cells is reported, but what about the absolute number? Given the relatively higher numbers of macrophages in the zebrafish it would be interesting to see how these compare. The ratio of pro versus anti-inflammatory macrophages could be an interesting metric to report. Do the zebrafish ever mount a substantial pro-inflammatory response? It’s suggested that highly regenerative animals undergo a quick switch from pro- to anti-inflammatory and this is important for regeneration, but data demonstrating that is sparse at best and the question remains if there is ever robust a pro-inflammatory response in regenerative animals. <br /> Paragraph starting with “We know relatively little about the makeup..” is a bit unclear. What type of cells are you referring to? Are these the fibroblast-like cells or fibroblasts? The concluding sentence leads one to believe fibroblasts are benign studied, but earlier on it’s discussed that “epicardial cells cells expressing collagens”. Do you find collagen expression by macrophages? (https://pubmed.ncbi.nlm.nih.... Are mmp15/16 implicated in regeneration? <br /> Regarding the zEP cells and their potential uniqueness to zebrafish, it would be interesting to explore a samap or other tools and see if they still remain separate (https://github.com/atarasha..., https://www.biorxiv.org/con..., note: this paper integrates zebrafish heart single cell data with 4 other species and could be worth looking at). As noted by the authors, more work is needed here. Whole mount FISH of hearts from both zebrafish and medaka would be quite interesting to see if zEPs can be detected anywhere. <br /> The mammalian studies are interesting and could be worth expanding. It would be insightful to tie back into the first few figures and the major findings there. Can you learn anything new from the mouse dataset with the perspectives gleaned from the fish comparison? For example, what is happening with the ISGs in the mouse? It could also be interesting to compare to salamander heart regeneration to provide another evolutionary intermediate (https://www.nature.com/arti... <br /> Do primordial cardiomyocytes wane with age? Do larval/developing medaka contain these cells and do these young medaka regenerate their hearts? (perhaps not experimentally feasible). <br /> What is the role for the compact myocardium when not in regeneration? Why is there so much diversity in its size across species? <br /> Do you think there is a unifying reason for lack of regeneration in medaka? You uncover quite a few differences.
Minor stuff: <br /> This is a biased comment, but it would be really interesting to know if there is divergence between replicates. You could pull out each sample with some genotype-based demuxing. Check out: https://www.life-science-al.... This might also aid with DE analysis (https://www.nature.com/arti... <br /> “To investigate the contributions of epicardial-derived cells to the fibrotic response, we re-clustered all cells expressing epicardial-specific markers tcf21 and tbx18, and re-clustered them into four…” a bit confusing with double re-clustering here. <br /> Do medaka lack cortical cardiomyocytes or are they just less abundant? The last line of the figure 6 results section suggests an absence with the use of “lack”. <br /> One could consider side-by-side violins might better illustrate between time point comparisons. <br /> Figure 6E and G with numbers for cluster labels is not super clear. Perhaps these could be labeled with the top markers they express or more info added to figure legend to explain. Including on the figure the species for E-F and G-H could also help orient readers more quickly.
On 2023-07-06 08:56:03, user Deepak Nair wrote:
The authors state that "when re-analyzing previously<br /> published structures we find that the geometry of B-family polymerase active sites does not convincingly support the “polar filter” model". However, this study provides more evidence for the existence of the polar filter. When there is reduced interaction between N828 and the phosphate moiety of the incoming nucleotide, the rNTP can move slightly such that the 2'-OH will no longer clash with the steric filter. If there is proper interaction between N828 and the phosphate moiety, then it is not possible for the rNTP to move to neutralize the steric filter. The Asn residue is conserved in other B-family DNA polymerases and all the available crystal structures show the presence of the interaction between the conserved Asn and the phosphate moiety. The M644 residue is present below the triphosphate moiety. The observation that the M644G mutation leads to reduction in the interaction of N828 with the phosphate moiety of the incoming nucleotide is very interesting because the C-beta atoms of the two residues M644 and N828 are located about 9 Angstroms from each other.
On 2023-07-05 20:58:03, user Francisco Cleilson Lope Costa wrote:
We would like to inform the paper is now published at DOI: 10.1590/S1678-3921.pab2023.v58.03042.
Best,<br /> The authors.
On 2023-07-05 10:35:26, user Jukka Kallijarvi wrote:
Now published in Cell Reports Methods, https://doi.org/10.1016/j.c...
On 2023-07-04 11:41:03, user Zbyszek Boratyński wrote:
It is very interesting paper. There was recent developments following McNab seminal work on relation among metabolism, body mass and home range size; on both inter- and intra-specific levels. These recent development in the experimental and comparative studies could help to resolve some ideas. Especially in the context of individual costs of mobility that seems to define daily activity and home range sizes.
E.g.:
Enriquez-Urzelai U, Boratyński Z. 2022. Energetic dissociation of individual and species ranges. Biol Lett 18:20210374. 10.1098/rsbl.2021.0374
Boratyński Z. 2020. Energetic constraints on mammalian home range size. Func Ecol, 34: 468-474. doi: 10.1111/1365-2435.13480
On 2023-07-04 06:56:22, user Eduardo Oliveira wrote:
Would it be possible to share the sequences of the 228 generated proteins?
On 2023-07-03 14:55:21, user Elías Herrero Galán wrote:
This original preprint was finally published as two separate papers:
On 2023-07-01 18:19:52, user Rod Page wrote:
Notes to self, other relevant papers include The big four of plant taxonomy – a comparison of global checklists of vascular plant names and Fungal names: a comprehensive nomenclatural repository and knowledge base for fungal taxonomy
On 2023-06-30 09:31:40, user Chanel Thomas wrote:
Dear authors<br /> We read your paper as part of our Genomes Journal Club at the Forestry and Agricultural Biotechnology Institute (FABI) at the University of Pretoria. We’d like to share a few of our thoughts and comments with you.
We thought this was a really wonderful paper. One of the things that we found really exciting and novel was that you were not able to identify one characteristic that confers pathogenicity to banana (in contrast to e.g. tomato pathogenicity that can be traced back to chromosome 14 of Fol4287). This suggests either that a unifying characteristic does not exist for banana pathogens or that it is perhaps broken up into different components of the disease - e.g. a specific thing that causes browning, another that causes softening, etc which may be controlled by different genes, potentially harboured within different ARs. We thought that this was a message that you could state more clearly in the paper and in your abstract.
Given the title of your manuscript, we interpreted that the message you chose as your selling point for the paper was the issue of segmental duplications and their role in evolution. We felt that we lacked the necessary background information to understand the significance of this. It would be really helpful to have some information on gene duplications in the introduction. From Fig. 6e, we could tell that segmental duplications were something unique to Fol4827, but it was unclear on how significant this is as opposed to having another type of duplication. For example: why are segmental duplications particularly important in evolution and/or why was it a surprise that they were involved. Fig. 6a gave a good explanation of the basic differences between the duplication types but (1) we felt that information would be useful earlier in the paper and (2) a written explanation would complement the figure nicely.
Another query that came up was related to the RNA data from 8 days post inoculation. It is not clear in the methods which strains were used to infect the Cavendish bananas or which strains were inoculated onto the PDA medium prior to RNA extraction. Given that different races are distinguished by their pathogenicity on different subsets of banana varieties, we wondered if there are any implications for the RNA-seq results? I.e. if strains were used that are not usually found infecting the type of Cavendish used for inoculation in this study, would the RNA expression results possibly be impacted? <br /> The figures in the paper were informative and conveyed the data well. However some of the multi-panel figures were not always intuitive to read. For example, Figure 6 c,d and e are intended to be read together, which you can figure out by reading the legend, but a visual cue such as some background shading or a box would improve the readability. Similarly, the background shading in Fig. 1 that links panel d to the earlier panels wasn’t noticed by most of us (only 1 member picked it up) so it may be worth making that clearer.<br /> Overall we found this to be a really well executed study and we all thoroughly enjoyed reading and discussing it.
On 2023-06-30 07:49:21, user Arnauld Sergé wrote:
Interesting work, but with one annoying concern to me: how can you say right from the abstract that “Analysis of SPT data can be challenging due to the lack of comprehensive user-friendly software tools” while many algorithms have already been published? In fact, you even cite several of them in your references. All these works have been published upon peer review, each with its own field of application, performances and limits of course, based on classical approaches or, more recently, on artificial intelligence. It's certainly worth discussing, but I wouldn't mention it, certainly not in that way, in the abstract.
On 2023-06-26 21:44:16, user Charles Warden wrote:
Hi,
First, thank you very much for posting this preprint!
I thought that the video from this Tweet was very helpful in helping me read through the preprint more carefully:
https://twitter.com/Gencove...
This preprint covers some important topics, and I hope that this comment system can help myself as well as others.
When you are able to do so, I would very much appreciate if you could provide feedback on the following questions/comments:
General Points:
1) I am sure that there are additional ways to make ancestry assignments, but I am not sure if it might help to have some way to indicate a possibility of "unclear" ancestry?
As one possible option, running unsupervised ADMIXTURE with k greater than 6 would match what I thought helped when trying to estimate ancestry for my pet cat:
PDF for Re-Analysis of Raw Domestic Cat Data
If I understand correctly, then I am not sure if that might help reduce small fractions of unexpected ancestry in the tigers?
Also, as a minor point, I created that report a while ago. I understand that "regular" coverage for Whole Genome Sequencing costs much less now, even if ordered from the same company.
2) Am I correctly understanding that a custom strategy with 106 tiger reference genomes is being used for the imputations? In the Supplemental Excel file, I see an “Unimputed” category. There are more than 106 “Unimputed” rows, but there are 32 rows for “Liu et al. 2018” and 64 samples listed as either “Armstrong et al. 2020” or “Armstrong et al. 2022”. That 96 instead of 106, but does that mean that GEN1, SRR836354, and SRR7651465 are “Generic” tigers that were not used for imputation training? Even though the coverage depth was >20x, SRR836354 and SRR7651465 are listed in the “Imputed” category (in the Supplemental Excel File).
Given some drop in raw accuracy for my cat data versus human data (in the link from my second comment on Twitter), I thought it might be worth raising a question (especially if tiger variation is less well understood than domestic cat variation). As long as some samples are independent of training, I think that the conclusion can be OK. For example, even if the individual variant error rate was 15% or higher (to be less accurate than my cat's Gencove imputations), then maybe that is OK across a long enough segment (or the whole genome). However, if I understand correctly, then I think the results for 5594-DP-0001_S3 (as well as the single-ancestry “Unimputed” samples) could potentially be over-fit? If that is correct, then perhaps adding a designation for “training” and “validation” samples within the plot (or the description) might be OK?
Also, strictly speaking, the reference in the main text was “Armstrong et al. 2021” (versus “Armstrong et al. 2020” or “Armstrong et al. 2022”). However, if I am understanding correctly, then I think that could be a minor update.
3) I am sure that there are better ways to estimate local ancestry, relative to what I did with my cat and some public SNP chip data. However, with a somewhat limited number of SNPs, I encountered some issues in getting accurate RFMix local ancestry estimates for "Western" versus "Eastern" ancestry.
If I understand correctly, then I believe something different being used for global ancestry. For example, Figure 1A describes providing supervised ADMIXTURE results, and I see a citation for the Alexander et al. 2009 ADMIXTURE publication (in the Supplemental Methods). So, as far as I can tell, I think this is OK.
In other words, I apologize if I am missing something important. However, can you please help explain the parts of the results that are influenced by RFMix estimates (and if there are results in this publication to show better performance than I saw for my pet cat)?
Specific (Probably Minor) Points:
a) "variant calling was subsequently performed by Gencove using the Genome Analysis Toolkit (GATK) v4.1.4.1"
I uploaded FASTQ files for running Gencove on samples for genomics data myself and my cat. Is there something different that was done for the tiger samples? In general, I would not expect that GATK was used for variant calling (by the lab or collaborator), if Gencove was used for imputation.
Did Gencove also assist with “regular” coverage variant calling, and/or is there might be anything else that I am misunderstanding?
b) I was not able to access https://github.com/jaam92/T...
Perhaps this repository needs to be made public? I could access https://github.com/jaam92.
c) Minor Typo:
Current: Data associated with this stud has been deposited into bioproject number<br /> Corrected: Data associated with this study has been deposited into bioproject number
Again, thank you very much for posting this preprint! This has helped me become more familiar with big/wild cat genomics!
Sincerely,<br /> Charles
On 2023-06-26 16:22:07, user Lili Blumenberg wrote:
FYI the reference links in the paper are pointing to the methods reference when I hover over or click on them.
On 2023-06-26 14:51:59, user Chris Sell wrote:
Beautifil work!<br /> It is worth noting that the high levels of Ku 70 and Ku80 in human cells has been reprted previously as well as an increase in DNA end binding, the first step in NHEJ which is dependent upon Ku, in longer lived species. See Lorenzini et al 2009, Mech Aeging Devel 2009;130<br /> https://doi.org/10.1016/j.m...
On 2023-06-26 08:44:48, user Jonathan wrote:
Hi,<br /> Thank you for this very interesting paper.<br /> The current manuscript refers to supplementary figures but they don't seem to have been uploaded. Could you please share them ?<br /> Best regards<br /> Jonathan
On 2023-06-23 20:06:53, user Sangmin Lee wrote:
Check our companion paper by Dowling et al:<br /> https://www.biorxiv.org/con...
On 2023-06-23 12:31:46, user Douglas Yu wrote:
Hi, Figure 4B is missing from the PDF. thanks
doug
On 2023-06-23 04:35:02, user Stephanie Wankowicz wrote:
The mineralocorticoid receptor forms higher-order oligomers upon DNA binding
Summary:
This paper aims to answer the question of the oligomeric states of the mineralocorticoid (MR) in the nucleus and when bound to DNA hormone response elements (HREs) in vivo. Using Number & Brightness (N&B) analysis, they investigate the oligomeric state of MR in the presence of different ligands and mutations/truncations to identify what controls different oligomeric states. While they comparisons they performed between different ligands and constructs of MR show qualitative differences, this paper is missing some key controls, particularly in the localization of the nucleus and MMTV array, which prevent us from thoroughly assessing the paper.
Major Comments:
1) Provide details and controls on identifying the nucleus versus cytoplasmic versus DNA binding/MMTV array. Only labeling these with the molecule of interest (MR) is inappropriate. To assess if MR is congregating at the MMTV or some other location in the nucleus, the MMTV array must be labeled with something other than GFP, allowing simultaneous visualization of the array and the MR oligomerization state.
2) Explain the varying oligomerization states you observe across your dataset. Can you provide ranges of oligomerization states across your results? How should we interpret mixed populations? What were your criteria to decide whether a construct dimerizes, oligomerizes etc.
3) The manuscript has varying points for each condition (for example, in Figure 1B, there are 490 single cells for one condition, with 36 single cells for another condition). Please explain why there is so much variety in the number of data points.
Minor Comments:
1) The introduction could be improved by expanding on details on the transcriptional crosstalk of MR/GR and the observations of GR at MMTV (and clarify this is the data the rest of this paper is compared to).
2) Please clarify the construct of the cell line and if endogenous MR is knocked out.
3) In Fig. 4 C, the MR-N579/GC-470C mutant array displays only 11 data points, while the figure legend says it contains 22.
4) The figures with agonist or antagonist would be clearer if the agonist or antagonists were labeled.
5) In the section ‘MR and GR do not share the same dimerization interfaces’. Please provide some context for the D-loop and P-loop. Figure 3A could be improved by showing where these are structurally or among the entire sequence.
6) Please specify how many independent experiments were run for each condition.
8) The authors describe that imaging happened 30min - 2h after ligand adding. Please specify what experiment was incubated with ligand and for how long. Is it possible that the signal is increasing proportionally with longer incubation times? A comparison in the Supplementary would be helpful.
Reviewed by Stephanie Wankowicz, Lena Bergmann, and James Fraser (UCSF) <br /> 10.5281/zenodo.8072766
On 2023-06-22 23:59:14, user Matthew Klein wrote:
Do the authors recommend the OMNImet®•GUTME-200 kit for metabolomics? I'm looking to use for my own research.
Thank you,<br /> Matthew Klein, PhD Candidate, UC Davis
On 2023-06-22 13:45:50, user Daniel Woldring wrote:
Are the software files for AbFold available for public use?
On 2023-06-22 09:04:55, user Gatta KRS NARESH wrote:
This article publish and Peer reviewed in Journal of Biomolecular Structure and Dynamics <br /> https://doi.org/10.1080/073....
On 2023-06-21 01:01:50, user Tyson Swetnam wrote:
I noticed the preprint does not have a link to the Supplemental Materials. We have hosted the Appendix text and Supplemental Tables and Figures (with code) here: https://datacommons.cyverse.org/browse/iplant/home/shared/publications/swetnam_et_al_2023_bioRxiv
On 2023-06-21 00:46:58, user Elliot Swartz wrote:
In conclusion, my recommendation would be for major revisions to both manuscripts prior to publication. In particular, the major areas for focus in the full LCA are as follows:
There is likely no market for cultivated meat produced with pharma-grade ingredients, and the value of modeling production in this way is dubious. Accordingly, the “PF” scenarios in the LCA should be excluded as they are highly misleading. These scenarios do not reflect “near-term” cultivated meat production, there is no requirement for pharma-grade ingredients or specifications to successfully grow animal cells, the justification based on concern for endotoxin is not sound, and it is highly questionable whether the proxy study used to estimate the environmental impact of pharma-grade purification is representative of cell culture media production.
The GCR scenario should be excluded because even lab-scale data using non-optimized cell lines are far more (about 3x) efficient. The AAR and HGM scenarios are still useful and aligned with estimates from other published studies.
Because of these issues, the overall conclusions should be reconsidered. The analyses should be redone with scenarios that model near-term cultivated meat production based on current practices and the best available information.
On 2023-06-21 00:46:35, user Elliot Swartz wrote:
a) The paper has an entire section of its introduction called “The limitations of existing ACBM LCAs,” but this section does not actually mention any of the more recent LCA studies, which are briefly described below and also referred to throughout this document. In fact, none of these studies are cited at all in either of the papers. Notably, the conclusions (but not necessarily all underlying findings) in this paper — particularly the “PF” scenarios based on flawed assumptions — deviate significantly from every peer-reviewed study published to date.
Tuomisto, 2022: This study uses bench-scale data and non-optimized cell lines and media to examine environmental impacts when cells are grown in hollow fiber bioreactors. This study is informative for understanding how some aspects of cultivated meat production may look today in early-stage startups.
Kim, 2022: This study uses primary lab-scale and pilot-scale data from cultivated meat manufacturer SciFi Foods to examine the environmental impact of their hybrid beef burger.
Sinke, 2023: This study uses data from over 15 different companies involved in the manufacturing and supply chain of cultivated meat to examine environmental impacts when cells are grown at a scale of 10,000 metric tons annually, set in the year 2030. This study is informative for understanding what cultivated meat production is anticipated to look like when it reaches commercial scale at the end of this decade.
a) The study claims to model “near-term” cultivated meat production, and this is used as a major justification for the inclusion of pharmaceutical-grade media scenarios. No definition of “near-term” is provided. Does “near-term” mean 3 years? 5 years? 10 years? 20 years? The production model used in the study is based on Humbird’s techno-economic analysis (Humbird, 2021), which models cell growth in 20,000L bioreactors in a facility that outputs nearly 7,000 metric tons of meat per year. Additionally, the Humbird analysis assumes a market size of 100,000 tons of annual cultivated meat production. The cultivated meat industry is not operating at these scales today or in the next several years and thus it is difficult to reconcile how the study models “near-term” cultivated meat production. This seems to be a case of trying to have your cake and eat it too.
b) The Humbird analysis has higher energy use compared to other studies (i.e., Sinke, 2023) due to differing assumptions surrounding cleanroom infrastructure, and a discussion of these differences in the context of actual or anticipated practices in the cultivated meat industry is warranted.
a) How does cultivated meat compare to conventional beef on other environmental indicators? Table 2 contains 10 different environmental metrics for cultivated meat, yet only two are discussed in the text of the paper, and none of the other corresponding metrics are listed for conventional beef. Why is this information and discussion omitted?
b) “Environmental impact” is discussed throughout the text of the paper, but in reality, only carbon footprints or fossil fuel depletion is discussed. Environmental impact is much more than emissions. The text of the paper should be changed to reflect the actual comparisons being made.
On 2023-06-21 00:44:27, user Elliot Swartz wrote:
There are several issues with this pre-print study, described below:
a) As discussed in comments on your other manuscript (https://www.biorxiv.org/con..., the amount/concentration of ingredients in Essential 8 is not optimized for cultivated meat production, and no companies would go to market using this off-the-shelf formulation. The consequences of assuming that Essential 8 would be used in the GCR and AAR scenarios in this study result in extremely poor yields of 0.87 to 3.43 grams of biomass per liter of media, corresponding to 292 liters to 1,148 liters of media per kilogram of meat (GCR to AAR scenarios, respectively). As discussed below, these scenarios represent very inefficient baselines from which to model.
Despite the AAR scenario being inefficient, the carbon footprint of 19.2 kg CO2eq/kg of meat is still 68% lower than the median carbon footprint of conventional retail beef (listed as 60 kg CO2eq/kg in this study). Indeed, the entire results section is misleading as it frames the findings as comparing worst-case and unrealistic scenarios for cultivated meat to best-case scenarios for conventional beef.
The last sentence of the abstract states, “The results indicate that the environmental impact of near-term ACBM production is likely to be orders of magnitude higher than median beef production if a highly refined growth medium is utilized for ACBM production.”
As I’ve described above in comments on your other manuscript, the highly refined growth medium scenario is reflective of an academic exercise, not near-term cultivated meat production. The results actually indicate that models of inefficient cultivated meat production still have significantly lower carbon footprints than median conventional beef production.
b) To further illustrate this inefficiency, we can compare the scenarios in this study to other published studies, as shown in Table D.9 from Sinke, 2023, which also contains the enhanced catabolism scenario from Humbird, 2021 — similar to the HGM scenario in this study (the difference being the amino acids sourced from hydrolysates as opposed to fermentation).
We can then add the GCR and AAR scenarios from this study in this same format (based on the DMEM/F12 formulation containing glutamine), along with another study by your colleagues at UC Davis, O’Neill, 2023, which models an off-the-shelf mouse myoblast cell line called C2C12, the same cell line used in the CMB scenario above from Tuomisto, 2022.
Sinke.low<br /> Amino acids (g/kg CM) = 200<br /> Sugars (g/kg CM) = 320
Sinke.med<br /> Amino acids (g/kg CM) = 283<br /> Sugars (g/kg CM) = 400
Sinke.high<br /> Amino acids (g/kg CM) = 400<br /> Sugars (g/kg CM) = 500
Tuomisto_CMB<br /> Amino acids (g/kg CM) = 448<br /> Sugars (g/kg CM) = 1270
Tuomisto_CMB128<br /> Amino acids (g/kg CM) = 197<br /> Sugars (g/kg CM) = 557
Tuomisto_CMC<br /> Amino acids (g/kg CM) = 196<br /> Sugars (g/kg CM) = 557
Humbird_WT.hydro<br /> Amino acids (g/kg CM) = 453<br /> Sugars (g/kg CM) = 816
Humbird_Enhanced.hydro<br /> Amino acids (g/kg CM) = 388<br /> Sugars (g/kg CM) = 360
GCR (Risner)<br /> Amino acids (g/kg CM) = 1263<br /> Sugars (g/kg CM) = 3616
AAR (Risner)<br /> Amino acids (g/kg CM) = 321<br /> Sugars (g/kg CM) = 920
HGM (Risner)<br /> Amino acids (g/kg CM) = 260<br /> Sugars (g/kg CM) = 350
O’Neill (2023)<br /> Amino acids (g/kg CM) = 250-275<br /> Sugars (g/kg CM) = 1100-1500
As illustrated by data from other studies, including those from your colleagues at UC Davis, the GCR scenario is an outlier that does not represent current or future cultivated meat production. It is highly questionable if such a scenario is even warranted for inclusion in the study, given that it requires 3 times as many amino acids and sugars to create 1 kg of meat compared to estimates from non-optimized, off-the-shelf cell lines. Despite this, at a carbon footprint of 75.4 kg CO2eq/kg of meat, it is still only 25% higher than median conventional beef production and far lower than the worst forms of beef production.
On 2023-06-21 00:35:48, user Elliot Swartz wrote:
Other comments:
Phenol red is not food safe. It should be removed from the analysis as it will not be included in cell culture media for cultivated meat production. Furthermore, bioreactors are equipped with pH sensors, negating the need for a pH indicator in the media.
Sinke, 2023 uses naphthalene sulfonic acid as a proxy for HEPES, which may also be considered for use in your model.
Modeling the incorporation of antibiotics into the cell culture media is illustrative of how much of an environmental burden such a choice would carry. In this regard, its inclusion in this study is useful. But it is misleading to present the use of antibiotics in Beefy-9 as the default, as this is not relevant to cultivated meat media that will actually be used in production. Beefy-9 contains antibiotics because the experiments in the corresponding paper (Stout, 2022) are performed in plastic dishes, which do not have the same degree of sterility control as a bioreactor. The manuscript fails to mention that antibiotics are not anticipated to be used for the production of cultivated meat, which is a major benefit of this way of meat production (McNamara & Bomkamp, 2022). There are two products from two companies in the United States that have received FDA clearance as well as multiple products from a single company that have been approved for sale in Singapore. None of these products are produced using antibiotics in the manufacturing process (only small amounts of antibiotics may be used during biopsy and initial cell isolation).
On 2023-06-21 00:34:40, user Elliot Swartz wrote:
a) The study by Wernet et al is the basis for a 20x multiplication factor applied in various scenarios “to account for additional processing associated with active pharmaceutical ingredient production.” This study looks at a 12-step chemical synthesis process to develop an active pharmaceutical ingredient (API). The relevance of this synthesis process to the majority of ingredients in Essential 8 is unclear, and the justification for using this study as a proxy for a single media ingredient (let alone all of them in the worst-case scenario in the full LCA) is not adequately explained. Furthermore, the energy mix used in this study as well as the actual purification processes used are not clear, making it even more difficult to assess its relevance to cell culture media and cultivated meat production.
Overall, the environmental metrics for pharmaceutical products are very scarce, and information about processing steps is difficult to obtain. A similar problem currently exists in the cultivated meat industry, hence the impetus for this research. A discussion of these limitations, especially in the context of selecting this single study as a proxy for the refinement of cell culture media ingredients, is warranted.
b) According to data we collected based on pharmaceutical-grade production of recombinant proteins produced in microbes (Sinke, 2023), downstream processing and purification make up about 33-50% of total facility energy use. Following your approach, this would lead to a factor of added energy use of 2x. A single data point can thus lead to a completely different model. This introduces great uncertainty and ignores the variety in upstream and downstream processing options leading to different environmental results.
c) In the Wernet 2010 study, it is stated that 65-85% of the impacts are energy-related (Fig 3). Given that this study was published 13 years ago and energy mixes have changed, this study is likely an overestimate of the actual impact of the production of the same API today or in the future, as the grid continues to reduce its reliance on fossil fuels. What would the impact be if predominantly renewable energy was used?
a) In an article published in Thin Ink, it is stated, “He [Derrick Risner] also said they used E8 growth medium because that was identified by GFI as a growth medium which could be scaled.” It is important to clarify that the media cost analysis published by GFI uses Essential 8 because it is a serum-free media formulation with a publicly-available composition. It is not stated in the analysis and should not be assumed that Essential 8 would be used for cultivated meat production. Rather, as described in Sinke et al 2023, the cell culture media composition will be based on the needs of the cells and is expected to deviate from commercially-available formulations designed for other purposes. Indeed, even the DMEM/F12 basal media formulation used in Essential 8 has been shown to contain ingredients that are nonessential or at suboptimal concentrations for pluripotent stem cell culture (Lyra-Leite, 2023). No cultivated meat companies are going to market using off-the-shelf Essential 8 or other common formulations. The decision to model Essential 8 with an off-the-shelf composition leads to inaccuracies in the downstream environmental impact model for cultivated meat production (discussed further in (B) below).
a. Assumptions for energy mixes used in the study are not stated. More information is needed to validate the calculations presented in the study.
On 2023-06-21 00:32:40, user Elliot Swartz wrote:
a) Several cell culture media suppliers I spoke with were puzzled by the focus on endotoxin removal as a challenge and why this concern over endotoxin was being used as a justification for including the [pharmaceutical-grade] purification factor (PF) scenarios in the full LCA.
First, it is important to clarify to the reader that endotoxin is not primarily a food safety concern, as the end product is ingested rather than injected into the bloodstream. Concern for endotoxin in the context of cultivated meat is therefore related to cell culture performance rather than the safety of the end product.
b) Different cell lines and cell types are affected by endotoxin differently. As stated in the research papers cited in the study, “endotoxins do not act directly against cells or organs but through activation of the immune system, especially the monocytes and macrophages, thereby enhancing immune responses (Magalhães, 2007).” Accordingly, cell lines that are not derived from the immune system have been shown to tolerate much higher levels of endotoxin. For example, multiple cell lines, including widely used cells such as 3T3 and CHO, displayed no detectable effects on cell growth with endotoxin levels as high as 20 ng/mL (Epstein, 1990). These concentrations are far higher than the specifications for pharmaceuticals, which are measured on picogram scales — hence, using pharmaceutical-grade purification processes to meet pharmaceutical specifications for endotoxin removal is not a requirement for successful animal cell culture. This is especially true in cultivated meat, where the cells are not derived from the immune system.
c) There are many ways to reduce endotoxin in the raw materials used for media production, but the simplest way is by seeking out raw materials that are low in endotoxin to begin with. This is standard practice for many key basal media ingredients such as glucose, salts, and trace elements. Furthermore, each raw material coming from a supplier is routinely tested for endotoxins as part of established quality management systems and the final milled dry powder media production batch can be tested upon request before it is released to the customer.
d) Endotoxin removal is a byproduct of many purification processes, as the pre-print mentions. Ultrafiltration is often used in the production of materials such as hydrolysates, amino acids, and proteins, but ultrafiltration is not a requirement for cell performance. Nonultrafiltered hydrolysates have been shown to perform just as well as ultrafiltered ones. As stated in the study, “low endotoxin levels were detected in all hydrolysate samples that were used for testing, suggesting that ultrafiltration is not necessary as an endotoxin risk-mitigating activity.”
It is true that in today’s cultivated meat industry, many amino acids are supplied via individual microbial fermentation processes that could carry endotoxins. However, these amino acids are commonly being sourced at food-grade specifications today where they have not displayed issues with cell viability (see #1.a.iv above), and there is a strong push to develop media supply chains where the primary source of amino acids (and other vitamins and trace elements) are derived from hydrolysates that inherently contain lower amounts of endotoxin than amino acids produced in bacteria.
e) Overall, it is unclear if the concerns regarding endotoxin relate to the raw materials themselves or to bottlenecks in the preparation of complete media, which may include various processing, filtration, and finishing steps. Could this be clarified? For example, for the former concern, a single raw material may be high in endotoxin, but by the time it is processed and combined with many other ingredients, endotoxin levels can be substantially diluted. Therefore, concern over endotoxin in raw materials has to be taken in the context of that specific raw material’s concentration in the finished media. For the latter concern, many methods of filtration are available for use, and similarly to other industries (e.g., beer, wine), fit-for-purpose filtration technology and SOPs that balance the needs of the cultivated meat industry (e.g., cost, safety, performance) will be established with time.
f) The justification for modeling TGFb production in CHO cells to avoid endotoxin is also questionable. There is a large negative incentive to manufacture proteins in animal cells, which is far more costly than microbial systems. While it is true that much of today’s TGFb supply is produced in animal cells, it is also true that microbially-produced TGFb is sold today with low enough endotoxin levels to support cultivated meat companies and others performing animal cell cultures. Companies working on producing recombinant proteins in plants with no endotoxins are also planning to manufacture TGFb. Thus, researchers and companies can source microbially-produced TGFb with low endotoxin today, and the supply of TGFb manufactured in non-animal cells will only increase in the future.
On 2023-06-21 00:30:40, user Elliot Swartz wrote:
There are several issues with this pre-print manuscript, listed below:
1) Assumptions about pharmaceutical-grade media do not align with reality.
The manuscripts as well as quotes in the press come off as authoritative on the use of pharmaceutical-grade media in the cultivated meat industry, but it’s unclear where this authoritativeness is derived from. For example, in New Scientist:
“This “pharmaceutical-grade” level of purification is required so that there are no contaminants such as bacteria or their associated toxins in the broth, says Risner”
a) The DMEM/F12 carbon footprint calculated in this study (0.062 kg CO2eq per liter) is only 8% higher than the carbon footprint of DMEM/F12 when back-calculating from the environmental impact of basal medium ingredients used in another model (personal communication with the co-authors of Sinke, 2023). Given this small difference in modeling of the complex basal media, almost all of the difference in the overall results of the study compared to Sinke, 2023 is due to applying an unnecessary 20x factor to these ingredients (discussed in #3 below), which is claimed to represent the impact of additional purification required for more refined pharma-grade media ingredients.
b) The overall model for Essential 8 is still useful for the field, as LCA database inventories for cell culture media ingredients are currently incomplete.
c) Although the focus of the study is not on costs, costs and feasibility must also be considered when modeling different scenarios that are portrayed as being representative. Previous techno-economic models (Vergeer, 2021), including your own (Risner, 2020), demonstrate that using pharmaceutical-grade media results in costs that are several orders of magnitude higher than conventional meat costs. It is simply not possible to bring cultivated meat to market using pharmaceutical-grade inputs. This is known by everyone in the industry, so attempting to portray this as a realistic scenario is neither accurate nor beneficial to the analysis.
d) The UC Davis Cultivated Meat Consortium’s external advisory board contains 10 individuals including myself and several cultivated meat startups and input suppliers that would have been happy to discuss this topic with you. It is unclear why you did not reach out to advisory board members to assess the current practices of the industry and to ensure the accuracy of your assumptions. As a result, you’ve come to a conclusion — which is critical to the key findings — that does not represent the most recent science.
e) Media input suppliers are sourcing and selling food-grade ingredients to cultivated meat manufacturers today. Food-grade materials are highly regulated and go through extensive testing by the raw material manufacturer/supplier with further validation at production/finishing facilities. They are often at the same or similar levels of purity as their pharma-grade counterparts (e.g., as in Kanayama, 2022) and thus are suitable for use in cell culture often with minimal differences in performance compared to pharma-grade counterparts. Food-grade amino acids are produced at scale by many large suppliers that are already plugged into the cultivated meat supply chain.
As stated by Cellular Agriculture Europe, some companies are already using basal media that consists of 99% food-grade ingredients. Meatable has stated they are using media that is 70% food-grade. While some media components are still sourced pharma-grade, the statement, “At the moment, all cultivated meat is grown in pharmaceutical-grade nutrient broths,” which appeared in the original New Scientist article, is incorrect.
f) In the paper, it is stated, “Utilization of commodity grade growth medium components such as glucose for animal cell growth is unlikely unless the components undergo an endotoxin separation process.” In fact, Nutreco has shown that feed-grade glucose can perform just as well in animal cell culture applications as pharma-grade glucose.
g) If pharmaceutical-grade purification is required, how can this be reconciled with the numerous papers and other data points that show sufficient cell viability and growth in media that contain food-, fragrance-, or feed-grade ingredients, many of which are purified using simple protocols by students in a lab? Certainly, these studies weren’t meeting pharmaceutical specifications for all media ingredients. Non-exhaustive examples include:<br /> - Plant protein isolates used to replace animal albumins <br /> - Food-grade methylcellulose can enhance the performance of serum-free media <br /> - Media derived from algae and fermented okara<br /> - Fragrance-grade oleic acid used in cultivated chicken production<br /> - Integriculture’s food-grade basal media <br /> - Nutreco’s use of food-grade amino acids and feed-grade glucose<br /> - The ShojinMeat project growing cells in ingredients acquired from a grocery store
On 2023-06-20 09:58:53, user Laboratory for Cell, Tissue & wrote:
Thanks a lot for sharing this very interesting piece of work! I have a question on the recovery of cells with/without fixation; after digestion do you obtain the same cell number? This is especially interesting when it comes to tissue digestion. <br /> best,
On 2023-06-19 11:25:43, user Adrien Jolly wrote:
These views are my own only (I did not involve any of my past or present colleagues and collaborators)
Thank you for this ambitious endeavour, I have some comments.
Comment on the discussion:
While I agree with your point on exponential distributions (we make exactly this point ref. 36), the claim that one generally cannot fine-tune the variability of the cycle phase length with ODEs (p 15-16) is misleading. we do exactly that with our sub-steps approach (that you mention) which in fact permits the modulation of the cycle phase length variability from quasi invariable to exponential. <br /> It has actually been shown that mammalian cycle phase durations follow Erlang distributions (as they arise from our sub-steps). In our work, we estimate the coefficients of variations of the cycle phase length when identifiable from the data and discuss the information generally contained in this regard in our thymidine analogue incorporation experiments.
Comments on the agent-based model:
Minor comment<br /> (1) EdU/BrdU incorporation. In my hands EdU is not immediately detectable in the thymus following injection (as is often assumed) and it takes more than 30 minutes to label all the cells in S phase (admittedly it was IP injection, and as you perform intravenous injection, labeling should be significantly faster). <br /> Here, if I understand correctly, you assume 0.5% of DNA being labeled is sufficient for detection which would label most cells after a couple of minutes in S phase when the analogue is present. Did you check this was the case (thymus collection 10 min after injection for instance)?<br /> You further assume that labeling stops abruptly after 45 minutes. However it does not seem plausible for incorporation to cease suddenly and a gradual decay of the analogue availability seems more realistic.
Since you have time course data which can reduce the dependence of the result to the initial labeling phase, the effect of these assumptions might be very limited but it would still be useful to check to what extent these assumptions affect your result.
(2) I found difficult to identify exactly what data you use for fitting (which is of course essential to judge the results) and how the data is assigned to your model, I think some clarifications would really be beneficial to the readers.<br /> Do you use exclusively EdU/BrdU information or do you combine this information with total DNA content (distribution of cells across the cell cycle)?
Here is a point of particular concern:
(3) I understand that you do not allow transition from cycling to "long G1"/”quiescent”, sometimes finding these “long G1” cells represent >90% of a given population. Given the duration of each stage of thymic development (60 h for total DP for instance according to your 2017 review) it seems very unlikely that labeled cells do not contribute significantly to the quiescent subpopulations within 20 hours and in any case, this should certainly not be assumed a priori.
From my own experience ignoring the cells transiting to quiescence/long G1 after the initial label incorporation might greatly distort your result by affecting the rate of entry in S phase. <br /> I expect the introduction of transition rate should improve your fit when a quiescent population is present (for later time points in particular) although I cannot conclude based on the data you currently present. <br /> In general, I find the exclusion of cells from the dynamics (which sometimes turn out to represent the overwhelming majority of the population) to be an extreme decision and I don’t think this should be made without strong evidence (simulations?) that this does not invalidate your result.
(4) Along the same line, differentiation and transmission of labeled or unlabeled cells between compartments should be considered carefully. Differentiation can certainly affect percentages of labeled cells in a downstream compartment over time.
While in some cases, the influx compared to local proliferation can be negligible (given the difference in size between compartments and respective cycling properties), it is a point which should be addressed for each cell compartment.
If I understand correctly, your model poses that cells leaving a compartment are replaced exclusively by non-labeled cells. This is not neutral and, in some cases, may cast significant doubts on your predictions. For instance DN4 are directly downstream of the highly proliferative DN3b, and DN4 cells will be progressively replaced mostly by labeled cells as time goes on.
At the very least, it should be discussed compartment by compartment why you think the assumption of exclusive influx of non-labeled cells holds given what is known of T cell development dynamics.
While you have certainly built an important dataset, the manuscript at its current stage gives the impression that some essential features of the EdU/BrdU dynamics have been overlooked in the agent-based model. hope my comments will prove helpful.