On 2022-02-17 17:10:34, user Peter Brodersen wrote:
Really nice work, and a pleasant read. The suppression of the growth defect of strains with unmethylated G2922 by Nog2 T195R and H392R is beautiful. This will certainly be a new addition to the RNA modification lecture in the 2022 version of my RNA Biology course!
On 2022-02-17 15:54:08, user Sankar Chandra Basu wrote:
Peer-reviewed version now published at:<br /> Vaccines 2022, 10(2), 301; https://doi.org/10.3390/vac...
On 2021-12-30 07:12:13, user Sankar Chandra Basu wrote:
Graphical Video abstract:
On 2022-02-17 01:12:41, user Marta wrote:
Thank you for uploading the article. I'm not an specialist in evolution and can't critisize the methods. But I detected some problems in the ontology table. Lgals7 is not a Channel and Plagl1 does not codify for monoamine oxidase. Maybe I´m missing the point. Can you explain the table?
On 2022-02-16 21:23:10, user Alex Cope wrote:
Now published in Bioinformatics: https://doi.org/10.1093/bio...
On 2022-02-16 17:37:06, user michael fleischhacker wrote:
nice paper, for the next step, i.e. testing this approach with blood from "real patients", I would prefer testing patients with a benign breast disease instead of healthy women,
michael
On 2022-02-16 01:03:10, user Michael wrote:
Recording useful metadata in a standard format is definitely a laudable goal. Building on OME, as discussed, is great. But I find that a far larger problem than instrument settings is not having information on the biology (and other reagents). Knowing that an image was taken with a 60x N.A. 1.4 Nikon planapochromat lens at 100 ms with an Andor Zyla 5.5, LED excitation at 555 nm, bandpass emission 580+/- 40 nm, with Micromanager 2 beta with ImageJ 1.53q23 running under Java 1.8.005.93b is irrelevant if you don't know the cell type and molecule labeled. In fact, we could change every detail of the technical minutiae about the microscope to digital file and the only important thing would be the cell type and molecule labeled. Metadata need to prompt the user to include details about the experiment. Proprietary systems do save the technical details pretty well, at least for standard imaging, but none record the biology (or whatever is being imaged). One critical field that’s should be added a phrase stating the goal of the experiment (like a tweet). Every notice that when you go for a clinical diagnostic medical procedure that the technician enters all sorts of data about the patient or pulls them out of an existing record? This is what is missing from microscopy metadata. This is where there really is a crisis.<br /> Sample prep and biological samples are mentioned in the introduction, but are largely absent throughout the text. However, these are the most important data that need to be recorded with the images.
On 2022-02-15 12:18:04, user Chengxin Zhang wrote:
In Fig 2a, each figure has only 6 lines; but the figure legend on the left hand side lists 8 methods. Are two methods missed in this comparison?
On 2022-02-14 13:24:42, user Jessica Polka wrote:
The following feedback has been provided by members of the ASAPbio Preprint Reviewer Recruitment Network
Summary: Romero-Becerra et al. show in this work the consequences of the loss of the p38 kinase activator MKK6 in mice. Their data presented in this manuscript include a reduction in lifespan and a cardiac phenotype that starts in young mice and is characterized by cardiac hypertrophy that ends up in cardiac dilatation and fibrosis. The cardiac phenotype is also present in two cardiac-specific MKK6 KO models, which is consistent with an important role of MKK6 in the heart. Importantly, they present mechanistic data to propose a model in which MKK6 deficiency leads to hyperphosphorylation of MKK3-p38γ/δ and increased mTOR signaling, a well-known cause of cardiac hypertrophy. The paper is well structured, the data is clearly explained and the results are relevant in the sense that they identify a novel pathway in the development of cardiac hypertrophy. After reviewing this preprint, these are our questions and comments to the authors that we think may help to improve this manuscript.
Major points:
The image that is represented in Fig. 2D may not be the best to represent the size effect observed for cardiac fibrosis. Most of the mice seem to show a similar degree of fibrosis. Also these mice are 23-24 month-old, long after the first mice start to die due to cardiac dysfunction, so this specific population of Mkk6 KO mice may show some kind of resistance to develop cardiac dysfunction when compared to others that died before. If cardiac dysfunction is the cause of the death, it should be present earlier in life. Thus, measurement of cardiac fibrosis at an earlier time point would be important to sustain authors’ claims.
The investigation of the two cardiac-specific KO is very relevant for the conclusions of this study. It would be important to know whether those cardiac-specific KO develop similar phenotype compared to that developed by the global KO, besides the cardiac hypertrophy already described.
There is no mention of the n of animals used for the data shown in Fig. 5. For example, Fig. 1A has panels with n=2 and n=1, whereas Fig. 1B and Fig. 1C has n=4. This should be better described, and mentioned in figure legend.
Minor points:
Data shown on Fig.1 re MKK6 KO animals is shown at different ages. It would be beneficial for the message of the paper to clarify the progression of symptoms in these mice. For example, Fig. 1C shows phenotype at 20 months of age, but then kyphosis is shown on Fig.1D at 19 weeks of age. It could be a typo but anyway paper would improve by clarifying this aspect.
Fig. 1D does not have a scale bar but taking into account that MKK6 KO animals are smaller, if images have been enlarged to show better the kyphosis, a scale bar is needed. Authors may discuss this possibility if they do not have the data available.
Authors claim that MKK6 KO mice have decreased BW due to increased browning of the adipose tissue and increased energy expenditure. However, it seems they have not considered the possibility that they have a reduced food intake, which could be the case due to the ataxia present in these animals.
On Fig. 2, the observed size effect on different cardiac parameters may be better visualized if authors include the baseline value 0 for each parameter measured.
Scale bar units in Fig. 2D does not match with what is stated in Fig legends.
After the mention of puromycin, the source of the remaining antibodies is not mentioned.
Why did the authors use a global p38gamma but a MCK-driven p38delta-specific KO to study the role of these 2 kinases in the phenotype of MKK6 KO mice? We found no reasoning behind that explained in the manuscript and doing so would help the readership of this paper.
On 2022-02-14 06:33:30, user Alexey Polilov wrote:
This preprint was published in Nature https://doi.org/10.1038/s41...
On 2022-02-13 15:32:52, user İsmail yıldırım wrote:
Hi
My name is İsmail YILDIRIM, and ı am a PHD student in Turkey, my thesis about cytokine storm. I tried your experiments but I am getting dirrent results. And I want to ask is the infection done once on the first day or is this process continued for 3 or more day? And what do you think about gavage method be effective in case of infection?
Thanks for your help
On 2022-02-13 08:40:12, user Computational Systems Biology wrote:
This article has been published at https://www.mdpi.com/2079-7....
On 2022-02-12 04:05:27, user S. Ecret wrote:
This is a really well written and clear preprint. Given the subject matter, it is also written very fairly with regards to the critique of the labs whose work is being re-assessed. Well done to all those involved. I sincerely hope that the concerned labs will respond in a productive and open-minded manner.
I am not qualified to discuss most of the technical aspects of this preprint but had a suggestion that might be, if nothing else, fun to do with the Infernal/R-scape work:
(Writing from memory so please excuse any mistakes in names)
The authors aptly show that the Hox9a hTL shoes no evolutionary signal for a conserved RNA structure. They also discuss inconsistencies with cryo-EM data with regards to the P4-ES9S interaction.
Infernal/R-scape cannot natively work on trans interactions - but perhaps by stitching the P4 and ES9S sequences from the already studied ~550 genomes together with a synthetic, fixed stem loop sequence, Infernal/R-scape could be used to assess the likelihood of a evolutionary signal for the trans interaction? The idea would be that a P4 and ES9S sequence from a single genome would be stitched together and then subjected to assessment after alignment.
I'm not sure what the Eddy or Rivas groups would say about such a use of their tool..
On 2022-02-11 19:22:10, user Joel McManus wrote:
Tom Dever's group has a paper in press from an independent study with Rachel Green (ref 30 in our preprint) that also shows Hox gene 5' UTRs are much shorter in developing mouse somites and don't include the putative IRESes. They also found the putative Hoxa9 IRES has promoter activity. On a positive note, they show Hox genes (including Hoxa9) have conserved functional uORFs. Exciting stuff!
On 2022-02-11 21:17:19, user Jacob Roberson wrote:
Hello everyone. I'm interested to know: Has this been accepted anywhere or is it moving along anywhere? Are the same results combined anywhere else? Thanks.
On 2022-02-11 19:56:15, user smd555 smd555 wrote:
" especially when we consider individual I4110 from Dereivka I (Ukraine Eneolithic) as one of the earliest representatives of their genomic makeup" - and what about another samples from Dereivka, such as I5882 and I5884 - did you involve them in the analysis?
On 2022-02-09 03:27:24, user Davidski wrote:
Hello authors,
I think the qpAdm models in this preprint can be better.
Some suggestions:
don't use any present-day samples, like Mbuti, in the right pops
ensure that the right pops are clearly older than the left pops, so as to prevent gene flow from the left pops to the right pops from confounding the analysis
try to find a more proximate and accurate source of the farmer-related ancestry in your test samples by using a wide range of ancient farmer populations in the right pops
try to find a more proximate and accurate source of the forager-related ancestry in your test samples by using a wide range of ancient forager populations in the right pops.
On 2022-02-11 14:28:21, user Mikhail Schelkunov wrote:
There is a statement in the manuscript "For each query, we greedily collect only the best alignment for each location (option ‘--range-culling --max-target-seqs 1’)". However, I see no "<br /> --max-target-seqs 1" in the code of Proovframe 0.9.7.
On 2022-02-10 19:10:55, user Alan Zahler wrote:
Revised manuscript is in press at PLoS Genetics<br /> https://journals.plos.org/p...
On 2022-02-10 16:22:54, user Jo Wolfe wrote:
The peer reviewed paper is now published: https://royalsocietypublish...
On 2022-02-10 15:48:21, user RAHUL wrote:
Comments:<br /> Defective localization of proteins is associated with various neurological disorders.Protein quality control employs many factors responsible for the recognition and degradation of faulty proteins. The critical step is to recognize the misfolded proteins and triage them for degradation or refolding. The author demonstrates that SGTA is involved in the deubiquitination of mislocalized proteins quality control. SGTA works in conjunction with USP5 to exert its effect. This work would be of broad readership for the protein quality control field as its attempts to unravel how proteins can escape proteasomal degradation.<br /> Major points:<br /> What would happen to MLP clearance upon ERAD pathway activation by thapsigargin, if the author demonstrates this, would perhaps improve the paper and pinpoint the importance of SGTA and USP5 role in context MLP quality control.<br /> In Fig 2A author claim that OP91 (non-glysosylated-0-CHO) form is specifically downregulated upon USP5 siRNA. But in fig2C and Fig4B OP91 both (glycosylated and non-glycosylated forms) are down-regulated upon USP5 siRNA. How do authors reconcile these opposing observations?<br /> Fig 3 author concluded that USP5 is a DUB that specifically plays a role in the cytosolic quality control of MLPs.Evidence is not sufficient enough to support the claim because USP5 localizes to the nucleus and cytosol more than ER. Should show the same by the biochemical fractionation experiments upon USP5 siRNA treatment.
Minor points
Fig1 A Input % loading does not reflect on blot? Provide explanation.<br /> Why in Fig1b input is more than SGTA pull downs? Should show the % loading on each lane.<br /> Fig 2A should mention 0-CHO vs 1-CHO meaning in legend.<br /> In Fig3A- It would be better if respective protein levels are shown by blotting against which siRNA was used.<br /> Fig5 SGTA-V5 blot needs to be improved.<br /> For statistical analysis, data points should be shown on the bar graphs in all figures. <br /> For some key experiments microscopy data would be better.<br /> Does only USP5 appear in SGTA mass spec.? Should share the mass spec data.<br /> USP5 can localize to the nucleus and cytosol. Does USP5 can regulate the nucleus specific MLPs?
On 2022-02-10 10:38:39, user Vaibhav Srivastava wrote:
Very nice work. Please check our work based on molecular modelling, where we also discussed the increased binding affinity of the Omicron’s spike protein with hACE2.<br /> https://www.biorxiv.org/con...
On 2022-02-10 09:40:35, user Richard Edwards wrote:
This paper has now been published in Molecular Ecology Resources: https://onlinelibrary.wiley.com/doi/abs/10.1111/1755-0998.13574
On 2022-02-09 21:06:42, user Assaf wrote:
reference 56 discusses <br /> fetal abnormalities associated with TMEM94, not TMEM97
On 2022-02-09 19:27:24, user MPMI Lab CZ wrote:
Dear authors, how can I get the access to the dataset 1 and dataset 2? If it is possible to send it on email mjanda04@jcu.cz.<br /> Nice paper!<br /> Thank you very much!<br /> Martin
On 2022-02-08 22:51:11, user Fraser Lab wrote:
The application of new machine learning techniques to the protein structure prediction problem has produced revolutionary results in recent years, culminating in the AlphaFold2 performance at the most recent CASP competition. These approaches often leverage deep multiple sequence alignments, which may limit their applicability to sequences without many (sequenced) homologs and to designed proteins. The most important aspect of this paper is that it is determined to take distinct approaches to AlphaFold to the problem, differentiating on speed and (lack of) reliance on multiple sequence alignments. While the compute efficiency is not likely a major breakthrough in the applications that are the major focus of this paper (protein structure prediction of relatively small proteins), the embedding of this approach within a protein design cycle could make it multiplicatively important (and additional speculation to that end would be warranted in the discussion). Given the sophisticated nature of all of these approaches, RGN2 stands out as something that can help unpack what drives the “black box” to work well and it could have an invigorating effect on the field to see how a wider range of model ablations tease out the contribution of different parts of RGN2. The contributions to geometric representation are likely to have an impact in other approaches as well. Overall this paper nicely synthesizes several innovations (Language Models, Geometric Representations, etc) in a fast moving discipline and includes an important new benchmark set of “orphan” proteins.
Major Points:
The experiments in this manuscript detail proteins with no homologous sequences. It would be interesting to see a more granular tradeoff between single sequence structure predictors and MSA-based structure predictors for varying MSA depths. See https://proceedings.mlr.pre...<br /> Figure 2-Right as an example plot that could be adapted for the task in this study. It is possible that many methods perform well without MSAs for many targets.
Minor Points:
Do RF/AF2 perform better when there is structural (even in absence of sequence) similarity to a known protein or are the orphans/designed proteins topologically distinct enough to make this question silly?<br /> How are disulfides handled, especially in the context of cyclic peptides?
Lines 68-70 allude to single sequence structure prediction being useful for function. I find it unlikely that structure predictor outputs would be useful for functional/fitness prediction. Could the authors find any indication that their models can perform fitness prediction on DMS datasets organized by DeepSequence, Envision, or FLIP [1,2,3]?<br /> [1] https://www.nature.com/arti...<br /> [2] https://www.cell.com/cell-s...<br /> [3] https://www.biorxiv.org/con...
How well calibrated is RGN2? Are there probability outputs that are reliable estimators of the model’s confidence?
Did the authors use the entirety of UniParc as-is or perform some type of downsampling based on sequence identity clusters? In general, rebalancing the train dataset sequences based on 50%/80%/90% sequence identity clusters could enable a more versatile representation useful for structural and functional prediction tasks.
Lines 147-149: The ProtTrans https://www.biorxiv.org/con... model uses a span masking objective derived from T5 in NLP https://arxiv.org/abs/1910.... which is essentially very similar to what the authors use.
Line 153: why didn’t they finetune?
Could authors provide a comparison on how RGN2 would perform with respect to language models that perform contact prediction such as ESM-1b https://www.pnas.org/conten... or MSA-Transformer https://proceedings.mlr.pre...
Given the authors previous record, we assume the code will be available soon: https://github.com/aqlabora... is out of date...
James Fraser (UCSF) and Ali Madani (Salesforce Research)
On 2022-02-08 20:31:53, user smd555 smd555 wrote:
Dear authors! My question is - what is the current understanding of the genetic origin of the Baltic bronze (namely, samples from Kivutkalns)? Can they be unambiguously derived from previous known genetic components, or is some component missing? Thank you.
On 2022-02-08 20:02:28, user Julie Secombe wrote:
This is an excellent example of a KDM5A regulating a transcriptional program critical to cellular differentiation using both its canonical histone demethylase activity in addition to non-demethylase functions.
On 2022-02-08 14:54:31, user Aleksi Husso wrote:
Published now in BMC Microbiology:<br /> Maternal microbiota-derived metabolic profile in fetal murine intestine, brain and placenta<br /> https://bmcmicrobiol.biomed...
On 2022-02-08 06:24:04, user Kadamba wrote:
Why minimum quality score of 10 was enforced? as quality score of 20 considered to be standard. Is there any particular reason?
On 2022-02-08 00:04:42, user Yang Xu wrote:
Nice Job!<br /> I wonder whether you block these flippases causing the death of bacteria due to the abundant undecaprenyl-phosphate inside bacterial cells.
On 2022-02-06 14:39:34, user Patrick O'Keefe wrote:
Is the ancient DNA sequence data from the paper available publicly at ENA or some other site?
On 2022-02-06 09:31:19, user Jae Rodriguez wrote:
Please check Fig 1A for inconsistencies. For example, Pentecost Island is indicated as dark green squares in the legend while appearing as circles on the map. Vanua Lava, while it is included in the legend is not labeled on the map. Aore is on the map but not in the legend. And many others.
On 2022-02-05 21:14:43, user Sophie Astrof wrote:
The peer-reviewed version of this paper can be found here:<br /> https://www.ahajournals.org...
On 2022-02-04 12:11:30, user Tim Nies wrote:
We thank the Team of Alizée Malnoë for their work and appreciate their feedback. Below you can find our answers to the points made in the review
Comments<br /> We suggest moving part of the model validation section of the<br /> results, shown in Figures 8 (and 9), to the start of the manuscript.<br /> This rearrangement would show the reader that the mathematical model<br /> used in the in silico simulations can accurately reproduce<br /> experimental data, before the parameter-dependent changes to NPQ and<br /> ՓPSII are simulated. In the current arrangement, the reader needs to<br /> have prior knowledge that the changes in NPQ and ՓPSII shown in the<br /> simulations are accurate, before the herein updated model has been<br /> validated.
Answer: This is a good point! We changed the order of the section<br /> accordingly.
- Fig8. Regarding the validation of the mathematical model by comparing to<br /> experimental PAM measurements with different SP durations, or<br /> different delays of AL onset from Fm measurement, with the simulated<br /> data: what is the rationale for choosing these, how about testing the<br /> other parameters such as AL intensity and frequency of SP? Please<br /> comment on the impact of the different parameters on e.g. the NPQ<br /> measurement and rank them by stronger to lower effect based on your<br /> simulations and experiments. Also a historical perspective/physiological relevance of delaying the SP from actinic onset would be welcome! How about giving recommendation to researchers in the field to have Fm determination/SP right at onset<br /> of illumination, with no delay, to prevent further confusion (and<br /> similarly have the final SP in AL on, followed by AL off with no<br /> delay).
Answer: We started the project as simulation study and then decided<br /> which experiments to perform for underlining our point. We chose the<br /> SP durations, or different delays of AL onset from Fm measurement<br /> according to two points. 1. We wanted to include a parameter that<br /> showed an effect in our simulations and one that didn’t. 2. We also<br /> talked with our collaborators, which of the parameters would be<br /> easily measured with their equipment. To rank the parameters by<br /> stronger to lower effect based on our simulations and experiments is<br /> a good idea! However, which parameter has the biggest impact is<br /> research question depended. A final answer can thus not be given.<br /> What we can say is that the delay before and after switching on and<br /> off the actinic light has the biggest impact in our simulations. It<br /> is necessary to give recommendation for future use of PAM devices.<br /> However, in this manuscript we decided not to give them, because this<br /> needs a previous discussion with multiple researcher working in many<br /> fields ( a consortium)
- Line 326. Regarding the use of another model of photosynthesis, we found<br /> this very interesting and suggest that a comparison of the<br /> simulations generated by the two mathematical models using the same<br /> set of parameters be included as a main or supplemental figure, and<br /> its description be included in the results section. The GitLab link<br /> (line 330) doesn’t specify which exact file to look at.
Answer: This is a good point! We will create a figure. We will<br /> provide a more specific path to the file in the GitLab.
- Line 127. “We have used 500 µmol s−1 m−2 as the default light<br /> intensity of AL.” For simulations, an intensity of 500 µmol m−2<br /> s−1 was used, but for experiments (line 152) “The intensity of<br /> red AL was set at approx. 457 µmol m−2 s−1”. We understand<br /> that matching the actinic light during the experiment to 500 µmol<br /> m−2s−1 cannot be possible, alternatively we suggest that the<br /> simulations be carried out at 457 µmol m−2 s−1 for sake of<br /> consistency. Importantly, is 457 µmol m−2 s−1 the value given by<br /> the manufacturer for the chosen setting, and did you measure it to<br /> confirm its value? (depending on instrument calibration, usage and<br /> age, the light output can be different than set)
Answer: Thank you for this suggestion we changed the default value in<br /> the simulation from 500 to 457 µmol m−2 s−1. We asked<br /> our collaborators to check the light intensity.
- Line 204, 205. “The calculated steady-state NPQ values are higher for SP<br /> intensities below 3000 µmol s−1 m−2”, according to Fig.5, it<br /> seems that the threshold is rather 2000, than 3000 (or 4000).
Answer: Thank you for your comment we will discuss the exact value again. Our<br /> first impression was that it could be actually a little bit lower than the value given in the manuscript.
- Fig7. To test the “actinic effect” of SP duration, we would suggest to<br /> perform a simulation with AL=100 µmol m−2 s−1 AL and/or AL=0 to<br /> check whether SP themselves can induce NPQ. According to Fig8A<br /> (experimental), it seems that at 0.8s, NPQ is indeed slightly higher<br /> than with shorter SP duration.
Answer: We did the simulation and included it in our simulation files. In fact, the<br /> simulation showed that only a rather small amount of NPQ is generated<br /> with AL=0.
- Line 370, a necessary addition would be to list here, or write a template<br /> of, what you suggest for minimum information is needed as standard<br /> for the community. It could be similar to Table 2, and needs to<br /> include duration of AL on, off and AL quality.
Answer: Thank you for this suggestion. We discussed about giving a<br /> list of standard information. We believe that something like this<br /> will be necessary in the future. However, to assemble a useful and<br /> complete list a previous broad discussion must be held including<br /> experimental and theoretical plant physiologist, working in a broad<br /> range of fields. All those fields using PAM can have different views<br /> on what is important. Because we are just a small group of<br /> scientists, we would not like to create such a list as part of the<br /> manuscript. However, we hope to give further impetus to such<br /> endeavors
Minor comments<br /> -Line 46. “Allow”, should be “allows”<br /> - Line 75. “Groups but also” should be “groups experimentally, but also”<br /> -Line 115. Replace higher by vascular.<br /> - Line 140. 26C is higher than standard temperature for Arabidopsis growth (22C), what’s the rationale for choosing this temperature?<br /> - Line 150. Define Fv and explain if the 5s of far red light is turned on at the very<br /> beginning of the experiment i.e. before time 0.<br /> - Line 153. “default settings (10)”, specify “set at value of” 10. We<br /> suggest writing a small table with these parameters (see major<br /> comment).<br /> - Line 161. Which leaf did you choose, younger or<br /> older? This information is important to state, see differences with<br /> leaf age for example in Bielczynski et al. Plant Phys (2017) doi:<br /> 10.1104/pp.17.00904.
Answer:<br /> Thank you for your comments. We will make the suggested changes. In<br /> the experiments far-red light was used for all experiments. We used<br /> leaves of approximately the same age the plants were approx. 5 weeks old. We<br /> will double check with our experimental collaborators
-Line 173-174. We suggest that the SP time points are moved to the<br /> methods section.<br /> - Line 185-186. “In the upper panel….derived<br /> NPQ and ՓPSII”, this whole sentence can be removed as it should be<br /> clear from the figure legend.
Answer: Thank you for these comments.
Line 211. “Far more” how many did you look at?
Answer: In the current version of the manuscript we included a literature<br /> survey.
- Fig. 6. “6A and 6A”. Should be “6A and 6B”<br /> - Line 234. “Switching on AL with the first SP in light-triggered after 1 s”<br /> suggest rewording as it was unclear what light-triggered means.<br /> - Line 241. The observed effect is likely due to the total conversion<br /> of zeaxanthin to violaxanthin for long periods of dark-adaptation. <br /> - Line 243. Suggest changing “whereas” should be “however” as<br /> it is clearer.<br /> - Line 256. Define PMST.<br /> - Line 264-268. We suggest moving this block of text to the discussion section.<br /> - Line 264. “AL is another important information” should be “AL<br /> is another important piece of information”
Answer:Thank you for these comments we will implement them
-Fig. 8B and 8D. As the simulated curves seem to all overlap, and<br /> often in this study we look for fine nuances between data, we think<br /> it would be beneficial to read the simulated curve superimposed on<br /> top of the experimental data allowing a fair comparison and analysis<br /> between the two types of data. Displaying the same graphs at a larger<br /> scale would help to read them. To help in this, we propose<br /> Figure 8 to be divided in two figures, since Fig. 8A-D is related to<br /> “SP experiment” while Fig. 8E-H is related to the “delay<br /> experiment”. This would allow the size of the panels to be<br /> increased to help the reader interpret the data.
Answer: Thank you for your suggestion. We will try different setups.
-Fig. 8F and 8H. Plot titles “Delay NPQ/ՓPSII Sim lation”, should<br /> be “Delay NPQ/ՓPSII Simulation”<br /> - Fig. 9 seems to be redundant as the reader should be able to observe the difference<br /> between the two independent experiments by comparing Figure 8A and<br /> 8E. We therefore suggest that Fig. 9 be removed.<br /> - Line 286. “Measurements are” should be “measurements have been”<br /> - Line 289-303. We suggest moving this block of text to the<br /> introduction section<br /> - Line 324. Replace “many” by “all”!<br /> - Line 351. “Agreements” should be “agreement”<br /> - Line 361-372. We feel that the points made in this block of text have<br /> already been made earlier in the manuscript and repeated several<br /> times. Therefore this block of text can probably be omitted as it is<br /> redundant.
Answer: Thank you for the comments we will try to make improvements
-General comments concerning the figures: we suggest adding dark/light<br /> bars to the top of most plots in Figures 3B-C, 4B-C, 6A-D, 7A-B,<br /> 8A-H; as it would improve the readability/interpretation of the<br /> plotted data. Fig. 2-8, figure identifier letters are presented in a<br /> different font style than the rest of the text, throughout the<br /> document. While we recognize them to be hyperlinks, we think font<br /> style should be uniform.
Answer: Thank you for pointing this out. We will add light bar where applicable and<br /> change the font style of identifiers.
blockquote { margin-left: 1cm; margin-right: 1cm; background: transparent }p { margin-bottom: 0.25cm; line-height: 115%; background: transparent }a:link { color: #000080; so-language: zxx; text-decoration: underline }
On 2021-12-21 19:45:19, user Alizée Malnoë wrote:
The manuscript by Nies et al. demonstrates how changing pulse amplitude modulation (PAM) parameters can affect non-photochemical quenching (NPQ) and photosystem II yield (ՓPSII). Using in silico simulations of PAM experiments, the authors illustrate how NPQ and ՓPSII are affected by varying: i) the delay between measurement of maximal fluorescence Fm and the onset of the actinic light (or between turning off AL and measurement of Fm’’ in the dark), ii) the intensity of the actinic light, iii) the frequency of the saturating pulses, iv) and their duration. Nies et al. finish by validating their in silico model, and suggesting that scientists using PAM must provide the details of all of the parameters listed above in the methods section of any publications to allow their experiments to be accurately reproduced and modeled. We enjoyed this manuscript, however, we have some comments and suggestions for improvement, listed below.
Major comments<br /> - We suggest moving part of the model validation section of the results, shown in Figures 8 (and 9), to the start of the manuscript. This rearrangement would show the reader that the mathematical model used in the in silico simulations can accurately reproduce experimental data, before the parameter-dependent changes to NPQ and ՓPSII are simulated. In the current arrangement, the reader needs to have prior knowledge that the changes in NPQ and ՓPSII shown in the simulations are accurate, before the herein updated model has been validated.<br /> - Fig8. Regarding the validation of the mathematical model by comparing to experimental PAM measurements with different SP durations, or different delays of AL onset from Fm measurement, with the simulated data: what is the rationale for choosing these, how about testing the other parameters such as AL intensity and frequency of SP? Please comment on the impact of the different parameters on e.g. the NPQ measurement and rank them by stronger to lower effect based on your simulations and experiments. Also a historical perspective/physiological relevance of delaying the SP from actinic onset would be welcome! How about giving recommendation to researchers in the field to have Fm determination/SP right at onset of illumination, with no delay, to prevent further confusion (and similarly have the final SP in AL on, followed by AL off with no delay).<br /> - Line 326. Regarding the use of another model of photosynthesis, we found this very interesting and suggest that a comparison of the simulations generated by the two mathematical models using the same set of parameters be included as a main or supplemental figure, and its description be included in the results section. The GitLab link (line 330) doesn’t specify which exact file to look at.<br /> - Line 127. “We have used 500 µmol s−1 m−2 as the default light intensity of AL.” For simulations, an intensity of 500 µmol m−2 s−1 was used, but for experiments (line 152) “The intensity of red AL was set at approx. 457 µmol m−2 s−1”. We understand that matching the actinic light during the experiment to 500 µmol m−2s−1 cannot be possible, alternatively we suggest that the simulations be carried out at 457 µmol m−2 s−1 for sake of consistency. Importantly, is 457 µmol m−2 s−1 the value given by the manufacturer for the chosen setting, and did you measure it to confirm its value? (depending on instrument calibration, usage and age, the light output can be different than set)<br /> - Line 204, 205. “The calculated steady-state NPQ values are higher for SP intensities below 3000 µmol s−1 m−2”, according to Fig.5, it seems that the threshold is rather 2000, than 3000 (or 4000).<br /> - Fig7. To test the “actinic effect” of SP duration, we would suggest to perform a simulation with AL=100 µmol m−2 s−1 AL and/or AL=0 to check whether SP themselves can induce NPQ. According to Fig8A (experimental), it seems that at 0.8s, NPQ is indeed slightly higher than with shorter SP duration.<br /> - Line 370, a necessary addition would be to list here, or write a template of, what you suggest for minimum information is needed as standard for the community. It could be similar to Table 2, and needs to include duration of AL on, off and AL quality.
Minor comments<br /> - Line 46. “Allow”, should be “allows”<br /> - Line 75. “Groups but also” should be “groups experimentally, but also”<br /> - Line 115. Replace higher by vascular.<br /> - Line 140. 26C is higher than standard temperature for Arabidopsis growth (22C), what’s the rationale for choosing this temperature?<br /> - Line 150. Define Fv and explain if the 5s of far red light is turned on at the very beginning of the experiment i.e. before time 0.<br /> - Line 153. “default settings (10)”, specify “set at value of” 10. We suggest writing a small table with these parameters (see major comment).<br /> - Line 161. Which leaf did you choose, younger or older? This information is important to state, see differences with leaf age for example in Bielczynski et al. Plant Phys (2017) doi: 10.1104/pp.17.00904.<br /> - Line 173-174. We suggest that the SP time points are moved to the methods section.<br /> - Line 185-186. “In the upper panel….derived NPQ and ՓPSII”, this whole sentence can be removed as it should be clear from the figure legend.<br /> - Line 211. “Far more” how many did you look at?<br /> - Fig. 6. “6A and 6A”. Should be “6A and 6B”<br /> - Line 234. “Switching on AL with the first SP in light-triggered after 1 s” suggest rewording as it was unclear what light-triggered means.<br /> - Line 241. The observed effect is likely due to the total conversion of zeaxanthin to violaxanthin for long periods of dark-adaptation. <br /> - Line 243. Suggest changing “whereas” should be “however” as it is clearer.<br /> - Line 256. Define PMST.<br /> - Line 264-268. We suggest moving this block of text to the discussion section.<br /> - Line 264. “AL is another important information” should be “AL is another important piece of information”<br /> - Fig. 8B and 8D. As the simulated curves seem to all overlap, and often in this study we look for fine nuances between data, we think it would be beneficial to read the simulated curve superimposed on top of the experimental data allowing a fair comparison and analysis between the two types of data. Displaying the same graphs at a larger scale would help to read them.<br /> To help in this, we propose Figure 8 to be divided in two figures, since Fig. 8A-D is related to “SP experiment” while Fig. 8E-H is related to the “delay experiment”. This would allow the size of the panels to be increased to help the reader interpret the data.<br /> - Fig. 8F and 8H. Plot titles “Delay NPQ/ՓPSII Sim lation”, should be “Delay NPQ/ՓPSII Simulation”<br /> - Fig. 9 seems to be redundant as the reader should be able to observe the difference between the two independent experiments by comparing Figure 8A and 8E. We therefore suggest that Fig. 9 be removed.<br /> - Line 286. “Measurements are” should be “measurements have been”<br /> - Line 289-303. We suggest moving this block of text to the introduction section<br /> - Line 324. Replace “many” by “all”!<br /> - Line 351. “Agreements” should be “agreement”<br /> - Line 361-372. We feel that the points made in this block of text have already been made earlier in the manuscript and repeated several times. Therefore this block of text can probably be omitted as it is redundant.<br /> - General comments concerning the figures: we suggest adding dark/light bars to the top of most plots in Figures 3B-C, 4B-C, 6A-D, 7A-B, 8A-H; as it would improve the readability/interpretation of the plotted data. Fig. 2-8, figure identifier letters are presented in a different font style than the rest of the text, throughout the document. While we recognize them to be hyperlinks, we think font style should be uniform.
Jack Forsman and Andre Graca (Umeå University) - not prompted by a journal; this review was written within a preprint journal club with input from group discussion including Alizée Malnoë, Jingfang Hao, Maria Paola Puggioni, Pierrick Bru, Aurélie Crepin, Wolfgang Schröder.
On 2022-02-04 10:19:48, user Kenneth De Baets wrote:
Highly interesting and a great improvement compared to previous studies. However, i remain critical on how you can be sure that the nematodes were in suspended form for thousand of years on end or rule out contamination. It is clear that permafrost can be a dynamic environment so not all material sampled from a particular place needs to derive from the time (e.g., mixing is possible). Also, even carbon dating - how can you be sure that the nematode alive in more recent times (or various instances) did not integrate "old" carbon through food sources or otherwise. I would argue that extraordinary claims need extraordinary evidence. Note that i would find it highly interesting if it could be proven and also have nothing against the hypothesis of long-term presence in suspended form over geological timescales itself.
On 2022-02-03 20:41:57, user Investock Real wrote:
Yes, I am also interested. I guess that pollution will have some effect, it would not be the same in the country side that in a big city such a New York. Radiation is also a powerful mutagenic, places such as Hiroshima or Chernovil would increase the probabilities of mutation, right?
On 2022-01-29 03:15:47, user D wrote:
Can anyone give some sort of ballpark probability of the mutation mentioned in the preprint occurring? Thank you.
On 2022-02-03 18:10:48, user Ankush Sharma wrote:
The data can be explored in detail at our dedicated shiny server at https://cancell.medisin.uio.no
On 2022-02-03 00:35:40, user Ragnhild Eskeland wrote:
All code can be found at https://github.com/Eskeland...
On 2022-02-03 00:32:39, user Ragnhild Eskeland wrote:
For more details on the neuronal differentiation protocol please see: https://www.biorxiv.org/con...
On 2022-02-03 16:50:36, user Anu wrote:
I saw that this paper is in press , but was wondering if we'd be able to access supplementary tables/files before it is published in a peer-reviewed journal? Thanks!
On 2022-02-03 16:00:38, user Xiaofei Bai wrote:
We published a very similar story back in 2020.<br /> https://doi.org/10.1242/dev...
On 2022-02-03 00:30:29, user Ragnhild Eskeland wrote:
This differentiation protocol is applied in our multi-omics study: https://www.biorxiv.org/con...
On 2022-02-02 15:23:28, user Xiaojie Qiu wrote:
After 3.5+ years of perseverance, this work is finally online in Cell today! https://www.sciencedirect.c.... Feel free to read (we made it open access) and please let us know how do you think.
On 2022-02-02 13:30:23, user Alexis Darras wrote:
Dear readers,
Please note that this manuscript is now published at https://doi.org/10.3389/fph...
Best regards,<br /> Hope you'll enjoy it,
A. Darras
On 2022-02-01 22:24:36, user Oliver Lab wrote:
Glad to know that our original discovery by Xiaolei Liu in the Oliver lab (https://www.nature.com/arti... about the novel lymphoangiocrine role of lymphatics in general and Reelin in particular in organ growth, regeneration and repair are further expanded to intestinal stem cells.
On 2022-02-01 14:20:28, user Phazaca_R wrote:
Hello authors, I am in no position to comment on the scientific aspects of this study but it seems really interesting and I am looking forward to it.<br /> I came across a small possible error. According to Zahiri et al., 2012 the genus Eudocima should be in the tribe Ophiderini, but here I see it placed in Phyllodini. Has there been some other revision that places it in Phyllodini?
On 2022-02-01 13:50:39, user Naveen Kumar wrote:
This work is very similar to our paper "Triphasic DeepBRCA-A Deep Learning-Based Framework for Identification of Biomarkers for Breast Cancer Stratification" : https://ieeexplore.ieee.org..., except that it integrates the first two phases. However, the authors have also applied the technique to multi-omic data and other cancer data. <br /> In fairness, our paper should have been cited.
On 2022-01-31 20:35:11, user Tomás Matus wrote:
This preprint has been accepted for publication and the citation is as follows:
Orduña, L., Li, M., Navarro-Payá, D., Zhang, C., Santiago, A., Romero, P., Ramšak, Ž., Magon, G., Höll, J., Merz, P., Gruden, K., Vannozzi, A., Cantu, D., Bogs, J., Wong, D.C.J., Huang, S.-s.C. and Matus, J.T. (2022), Direct regulation of shikimate, early phenylpropanoid and stilbenoid pathways by Subgroup 2 R2R3-MYBs in grapevine. Plant Journal. doi:10.1111/tpj.15686
On 2022-01-31 10:59:31, user James Fellows Yates wrote:
I would like to commend the authors for revisiting the subset of the results from the original paper that were quite dubious - something that many in the field have been quite skeptical of.
In that vein, I would like to make a small recommendation to cite the following paper from already in 2017, where the consumption of moss claim was already argued to be extremely unlikely:
Dickson, J. H., Oeggl, K., & Stanton, D. (2017). “Forest Moss”: no part of the European Neanderthal diet. Antiquity, 91(359). https://doi.org/10.15184/aq...
This could be cited for example around llines 41-45. This would add further support to the results of this preprint.
Please note the following:
On 2022-01-31 10:31:41, user Federica Calevro wrote:
An incredibly interesting paper from Koga et al., who transformed the <br /> free living bacterium Escherichia coli in an insect <br /> mutualist ... which powerful tool to experimentally study the <br /> evolutionary mechanisms underlying microbial symbioses!
On 2022-01-28 20:16:36, user corihuel wrote:
Dear Authors,
Your work was recently reviewed and discussed by the Bacterial Pathogenesis and Physiology Journal Club here at the University of Alabama at Birmingham (UAB). As part of our review of pre-prints, we compile comments from our discussion that we think may better your publication.
Overall, our group found the manuscript to be a very interesting read with detailed information on the structure/function of SteD emerging. We can tell that considerable thought that went into each experiment as well as figure production. Your lab has shown an exceptional amount of rigor in your experimental designs that made it difficult to refute your findings. This study was very well done, and we all enjoyed discussing it.
Below we point out some comments and aspects that we feel could improve on the manuscript.
1) We felt that the text was a little difficult to follow. Though it is probable that this will be alleviated once the paper has been properly formatted, as the figures help a great deal in understanding the text.
2) We very much appreciated the short anecdotes in the manuscript explaining the specific actions of the chemicals used for your experiments. None in our journal club work this closely with transport systems and it made understanding your work much easier.
3) We were curious about your justification for using a melanoma cell line in your studies rather than an APC line like BMDMs? We’ve noticed that it has been used for other Salmonella studies, but we think it necessary that you justify in the text why you use this cell line.
4) The order of your figures is a little confusing, specifically figures 1-3. We think it would really help if you were to either combine Figures 1 and 3 in some manner or reorder them so that Figure 3 comes just after Figure 1, rather than being interrupted by Figure 2. This would streamline the reading and comprehension of your data greatly.
5) On the topic of Figure 3, we were curious as to why you found the specificities you did and yet continued to use the region 13 mutation rather than the S68A G69A mutations in your experiments for Figure 4. Especially given the problems you had with region 13 mutation expression and release from Salmonella.
6) Our group wanted to extend our compliments to your inclusion of the protein diagrams you had throughout your paper. The visualization made it easy to understand the mutations made and really helped with the overall comprehension of the paper and the experiments you were completing. On this note, however, we don’t think it necessary to highlight the F and Y residues in Figure 7. They are discussed in the text but are not tested in the figure. That depiction would be better included in a supplemental figure showing the experimental results from those mutations.
7) Lastly, we believe Figure 5C should be moved to supplementary since it only confirms that your siRNA worked as intended.
Sincerely,<br /> UAB Bacterial Pathogenesis and Physiology JC
On 2022-01-28 16:22:36, user Andrea Thorn wrote:
It is in print! <br /> Acta Cryst. (2022). D78<br /> https://doi.org/10.1107/S20...
On 2022-01-28 16:17:28, user L Caitlin Martin wrote:
Great pre-print! One comment, though, the estimated radiocarbon (?) dates of ancient Y pestis samples on the map in Figure 1A are too small to be legible. Related, it is not clear if these are median age estimates (as I assume?), if they are directly dated or by proxy, or indeed if they have been calibrated radio-carbon dates or not, all of which is crucial information to understand their cross-comparison. It may also aid the analysis of the proposed phylogenetic tree to include the same dates next to the sample names, space-permitting. Best of luck with publishing.
On 2022-01-27 16:41:35, user Tera Levin wrote:
My lab recently read and discussed this preprint during our journal club. Overall, we enjoyed this manuscript discussing the multi-kingdom interactions of bacteria, fungi and amoebae, and their implications for the evolution of pathogenicity and endosymbiosis in natural environments. We thought the impact of the bacterial endosymbiosis on the fungal interactions with other eukaryotes was very cool, the experiments were well controlled, and the phenomenon was well documented.
Major comments and suggestions:<br /> The claims made in the title are considerably broader than what was shown in the data, specifically the idea that the amoebae are "drivers" of both endosymbiosis and pathogenicity. While the impacts of the endosymbiosis on Dicty are very nicely shown, an important part that is missing is: what are the impacts of Dicty on the fungi/bacteria, either alone or during symbiosis? How do amoebae alter their endosymbiosis or pathogenicity? <br /> There is no demonstrated selection by the amoebae on either of these traits, both of which are important pieces to make this "driver" claim. Relatedly, given that the endosymbiosis generally increases fungal spore resistance to multiple environmental stressors (Fig. 6C), it could easily be that abiotic stressors are 'driving' some of these phenotypes in natural microbial populations.
Possible ways to improve this gap include:<br /> - mixing different ratios of symbiotic and aposymbiotic fungi and placing these under an amoeba predation regime. Do the symbiotic fungi end up with a larger population size? In the mixed cultures, are aposymbiotic fungi preferentially eaten?<br /> - An experimental evolution study in which aposymbiotic fungi are incubated with extracellular symbionts +/- amoeba predation. Does amoeba predation select for re-initiation of the symbiosis?<br /> - Adding selective pressures from a couple of different amoeba species in this experiment, as it might vary considerably across amoebae<br /> - Rephrasing the title to remove the evolutionary 'driver' framing.
Minor comments:<br /> - The figure labeling and figure legends could be clarified in Figures 3, 4, and 8,<br /> - In Fig. 4F, why was such a small percentage of the R. pickettii conditioned medium added? You showed earlier in this figure that it takes a relatively large % of conditioned medium to see an effect.<br /> - In Fig. 6E-H, given that the cell wall composition looks different by TEM in D, we recommend performing these staining experiments in parallel in symbiotic and aposymbiotic strains to get a hint of how the cell wall has changed.<br /> - Fig 8 B seems to show similar data (but with less information) than Fig 8A. Can you include 8A only, with an 8A-like graph for the swollen spores as well (currently 8C)?
On 2022-01-26 15:39:59, user aswarren wrote:
Figure 1 caption groups (A,C) (B,D) but the axis labels and values disagree with the caption.
On 2022-01-26 05:18:22, user SKomaki wrote:
A related study is now published in the Journal of Biogeography (Vol. 48:2375-2386) (doi: 10.1111/jbi.14210). There are lots of improvements.
On 2022-01-25 17:02:15, user Dr Ed MoreiraBahnson wrote:
This preprinet has been peer reviewed andpublished in : Biomaterials Science https://pubs.rsc.org/en/Con...
On 2022-01-25 17:01:36, user Dr Ed MoreiraBahnson wrote:
A new version of this was uploaded to bioRxiv with a different name, and resulted in a different entry. The newer preprint is at : https://www.biorxiv.org/con...<br /> Additionally, the manuscript was ultimately published in : Biomaterials Science https://pubs.rsc.org/en/Con...
On 2022-01-24 22:53:20, user Dylan Valencia wrote:
I wanted to point out that the scale bars in figure 5B should be the same lengths to make it more easily interpretable. Thank you.
On 2022-01-24 19:29:14, user WEI YAN wrote:
The article has been published on NPJ Biofilms and Microbiomes.<br /> doi: 10.1038/s41522-021-00260-1<br /> https://www.nature.com/arti...
On 2022-01-24 16:16:01, user Andre Schwarz wrote:
Dear Liang, Julia, and colleagues,<br /> Beautiful and exhaustive work. This will definitely serve as a standard for future work.<br /> I presented this work today in our journal club (it was very well received) and wanted to share some of the comments with you: <br /> 1) In order to address how much of the absent elongation states upon antibiotic treatment are due to reduced particle number, could you repeat the same classification with a subset of the untreated dataset? I.e. take 13,418 (as in SPT) or 21,299 (as in Cm) particles of the untreated dataset, repeat the classification, and see<br /> how many classes you get?<br /> 2) To independently validate your visual polysome classification/assignment, could you run a polysome gradient and see whether the numbers of di-, trisomes, etc. roughly agree?<br /> Best wishes,<br /> Andre
On 2022-01-24 12:54:23, user Michael wrote:
To my knowledge, no other papers have shown phosphorylation of METTL3 by CDK9. More work will be required to confirm whether CDK9 phosphorylates METTL3 as currently, we have only seen it by phosphoproteomics.<br /> There is a recent paper showing phosphorylation of METTL3 by ERK: https://www.sciencedirect.c...
On 2022-01-20 02:25:51, user WEI wrote:
Which paper demonstrates the phosphorylation of CDK9 on METTL3?
On 2022-01-24 11:28:26, user Nikolas Haass wrote:
Now published: Commun Biol 5: 91 https://www.nature.com/arti...
On 2022-01-23 14:42:19, user CGPF wrote:
Quote
The Steppe pastoralist-related gene flow occurred in the context of the spread of CWC<br /> and BBC cultures in Europe around 3,200-2,500 BCE (lines 580-581)
(…)
Starting in ~3,200 BCE, the Yamnaya-derived cultures of Corded Ware Complex and Bell Beaker complex spread westwards, bringing steppe ancestry to Europe (lines 560-562)
Unquote
There is no archeological record for a spread of CWC well before ca. 2900 BCE
On 2022-01-23 11:17:11, user MD wrote:
This paper states on page 8.:
"Proto-Ugric peoples emerged from the admixture of Mezhovskaya and Nganasan populations in the late Bronze"
This contradicts to an older paper:
Y-chromosomal connection between Hungarians and geographically distant populations of the Ural Mountain region and West Siberia, Post et al. (2019), Nature
"Phylogenetic tree of hg N3a4 has two main sub-clades defined by markers B535 and B539 that diverged around 4.9 kya (95% confidence interval [CI] = 3.7–6.3 kya)."
Please peer review accordingly.
On 2022-01-22 11:12:46, user Gaurav Sharma, PhD wrote:
This article is now published in Nature Biotechnology. <br /> Please have a look. <br /> https://www.nature.com/arti...
On 2022-01-21 22:03:41, user Debelouchina Lab wrote:
Hello! This is the Debelouchina Lab at University of California, San Diego. We have begun doing preprint manuscript reviews during our “journal clubs” as a way to enhance our engagement with current literature and to hopefully assist with the manuscript if possible! Our lab also studies the behaviors of biomolecular liquid-solid transitions – with a focus on protein structure. We selected this manuscript out of curiosity for the spatial origins of solidification in liquid-liquid phase separated systems.<br /> Liquid-liquid phase separation (LLPS) is central to the spatiotemporal organization of biomolecules in the cell. Many of the proteins that are thought to mediate LLPS have also been found in pathological aggregates and fibrils that are associated with neurodegenerative disease. It has been demonstrated that liquid-like phase separated bodies can adopt gel-like or solid morphologies over time, which suggests that LLPS droplets may serve as nucleation points for pathological aggregates. This manuscript interrogates this process by characterizing the spatial characteristics of the liquid-to-solid transition within individual alpha-synuclein condensates using a set of fluorescence and infrared microscopy techniques. The authors found that droplets solidify form a central focal point that can be imaged through associated changes in fluorescence lifetime (via fluorescence lifetime imaging, FLIM) and protein secondary structure (via Fourier transform infra-red microscopy, FTIRM). To emphasize this significance in the text, we think it may be helpful if the authors added more background and discussion of previous literature on the spatial origin of solidification.<br /> These findings are exciting as they add new insight into biomolecular liquid-to-solid transitions, and relevant due to the potential role for liquid-to-solid transitions in neurodegenerative disease. We find that the combination of fluorescence microscopy techniques used here presents a strong model for studying spatiotemporal material properties of biomolecular condensates, which are challenging to characterize from a structural perspective due to their inherent heterogeneity and sensitivity to environmental factors. The power of these techniques is shown in their ability to complement the FLIM data into protein mobility (FRAP), structure (FTIRM), and interaction (FRET) components, providing a comprehensive look into the liquid-to-solid transition. We appreciated the use of small fluorophores rather than fluorescent proteins, as well as the confirmation by fluorophore-free techniques (TEM & cryo-SEM). Overall, we find that the data and the resulting model for the spatiotemporal dynamics of the liquid-solid transition are compelling.<br /> One area we are curious about is the sample handling, keeping a sample hydrated for 20 days is difficult. Would you be able to add a few words about the robustness of this moisture chamber in the main text? These aspects of the experimental design might not be obvious to a reader unfamiliar with the practical considerations of experiments like this, so more discussion would be helpful to anyone trying to reproduce the experiments. In a similar vein, a paragraph about the practical aspects of FLIM in the context of LLPS would be helpful. We also wondered about the necessity of the solidification timeline, how would the microscopy procedures described here work for a system that progresses to solid much faster than 20 days? What are the time limitations of these techniques? Would a faster system be expected to have the same center-growth effect as seen here?<br /> We were surprised that droplets appear to solidify from the exact center of the droplet in every case. If the model for solidification is that it begins from a (random) nucleation point, then why would droplet solidification always begin exactly in the center, as opposed to the inner or outer center regions that are mapped in Figure 1. We were left wanting more information about this, especially since FLIM is capable of resolving changes on these scales. It would be interesting to see if there are any cases where solidification does not begin from the exact center of the droplet. <br /> Some minor comments:<br /> -While the figures are clear and well-organized, a more colorblind-friendly palette could be used.<br /> -Infrared is occasionally hyphenated throughout the text.<br /> -The abstract figure may be clarified if the FLIM images were all of a single droplet, matching the cartoon.<br /> -The schematics describing the planes on the droplet are beautifully done and very helpful to understanding the figures.<br /> -Figure 1: formatting error with (e) placement.<br /> -Figure 2: (c) As we are unfamiliar with FTIRM, we thought it may be useful to have the corresponding secondary structure to each wavenumber (like the supplementary table 1 information) in the figure. Similarly, while supplementary figure 7 has a monomer and fibril control, we would have enjoyed that in the main figure.<br /> -Figure 4: (c) We wonder how consistent these recoveries are for several different droplets at the same time point.<br /> -For the TEM data (Fig 5), the results are a little bit different from other attempts to perform TEM on LLPS systems (for example, here: https://pubs.acs.org/doi/10.... A discussion of precedent would be appreciated in the main text. <br /> -Supplementary Fig. 11: We thought these EM images were fascinating and are curious if such images exist elsewhere for biomolecular condensates.
We appreciated the chance to read and review this manuscript,<br /> The Debelouchina Lab
On 2022-01-21 16:11:10, user Julia Drube wrote:
We were able to publish this great piece of work in International Journal of Molecular Sciences: <br /> Please check out that revised version here: https://www.mdpi.com/1422-0...<br /> https://doi.org/10.3390/ijm...
On 2022-01-21 15:22:14, user Ritesh Aggarwal wrote:
This manuscript is now online at Biophysical Journal - Cell Press. Read the peer-reviewed version at https://doi.org/10.1016/j.b...
On 2022-01-21 15:21:05, user Ritesh Aggarwal wrote:
This manuscript is now online at Biophysical Journal - Cell Press. Read the peer-reviewed version at https://doi.org/10.1016/j.b...
On 2022-01-21 14:41:53, user Keith Robison wrote:
This is a very useful manuscript on the accuracy of the two long read platforms. However, since both are evolving quickly and ONT in particular has frequent basecaller updates and two different chemistrys (R9 and R10) with different error properties, it is truly critical in a publication such as this to specify the read chemistry used and what version of basecaller even if that information can be found by digging into the original data-generating reference.
On 2022-01-21 14:01:15, user Dr. Daniel Brugger wrote:
Dear colleagues,<br /> please note that a revised version of this preprint has now been peer-reviewed and accepted by Poultry Science under a new title. The pre-proof version went online today and is accessible under the following link:
On 2022-01-21 11:16:24, user Leon N Schulte wrote:
Very nice manuscript! The lincRNA KB-1507C5.4 is MaIL1 by the way, which we published as an interferon-regulator (PMID: 32241891). It is also up-regulated in human monocytes during COVID-19 in our scRNA-seq datasets (https://doi.org/10.1101/202....
On 2022-01-20 16:38:23, user Mathurin Dorel wrote:
Just a few remarks that would improve an overall rather good paper:
On 2022-01-20 16:24:22, user David Curtis wrote:
When you state that rs59185462 is associated with rheumatoid arthritis it might be helpful to point out that this variant is in the HLA region and that it is well established that particular HLA alleles have strong effects on risk of rheumatoid arthritis. The obvious explanation is that the observed association with rs59185462 is a consequence of it being in LD with causative HLA alleles.
On 2022-01-20 14:12:44, user Luis R. Pertierra wrote:
Just a small suggestion, maybe replace precipitation to relative humidity to examine the effect of moisture/aridity, perhaps the low precipitation but high water availability in polar regions is a confounding factor. Cheers
On 2022-01-15 03:53:20, user pierre wrote:
global mapping with 2000 points is nonsense, but the geospatial modelling of this article is also nonsensical.
On 2022-01-20 09:38:57, user Martin R. Smith wrote:
Good to see the caution in using the RF distance here. You might be interested in assessing accuracy using alternative methods that can be used where a "true" topology contains polytomies, such as the information-theoretic generalised RF distances of Smith (2020, Bioinformatics; https://doi.org/10.1093/bio... ) or the uncertainty-adjusted "Similarity to Reference" quartet distance of Asher & Smith (2022, https://doi.org/10.1093/sys... ; implemented at https://ms609.github.io/Qua... ).
On 2022-01-20 09:02:19, user Martin R. Smith wrote:
An interesting study, and the higher accuracy of the most likely tree from the posterior Bayesian distribution is (perhaps?) what we might hope to expect. Is the RF score the best tool to measure topological accuracy here though? As it has a number of known shortcomings, it is possible that systematic error is behind some of the difference in performance between methods. I've reviewed some alternative distance measures, which might be more robust, in Smith 2020, Bioinformatics, https://doi.org/10.1093/bio... .
On 2022-01-20 02:18:53, user Corazon Ngelangel wrote:
This has been published online in the Philippine Journal of Health Research and Development. Please see link: https://pjhrd.upm.edu.ph/in...
On 2022-01-19 20:39:00, user Kyungtae Lim wrote:
Impressed with this study about alveoli formation in mice, which looks likely sharing similar alveolar patterning mechanism in human alveolar development.
On 2022-01-19 18:00:44, user Lindsey Block wrote:
Hi, very interesting work. I'm curious as to whether the EDEG can be used on EV samples isolated from EDTA plasma. I see it helped with serum, heparin, CPD, and citrate but don't see it mixed with EDTA EV samples (figure 1). Thanks!
On 2022-01-19 09:46:43, user Partha Mondal wrote:
This work is now peer-reviewed and published. See this:
On 2022-01-19 02:13:47, user Jinlong Zhang wrote:
If you use the rdacca.hp package, please cite:
Lai, J., Zou, Y., Zhang, J., & Peres‐Neto, P. R. (2022). Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca. hp R package. Methods in Ecology and Evolution. doi:10.1111/2041-210X.13800
For more information, please refer to: https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13800
Many thanks!
On 2022-01-18 17:40:10, user Titus Brown wrote:
Here's a blog post for y'all: http://ivory.idyll.org/blog...
On 2022-01-18 16:42:28, user Thierry Tran wrote:
This study is very rich in data and the approach is truely nice. I can only agree since I used a similar methodology on kombucha, of course with some variations. I am very happy to see that the conclusions of my paper (10.3390/foods9070963) are similar. I hope that this manuscript will be properly reviewed and published in case of acceptance.
On 2022-01-18 05:22:24, user Zhiming Alexander Yu wrote:
Fantastic!!! It's really too simple and practical, but why can't we think of it.
On 2022-01-17 23:34:21, user Nora Franceschini wrote:
This paper has been published. https://pubmed.ncbi.nlm.nih...
On 2022-01-17 19:43:52, user innabiryukova wrote:
This study investigates mRNA transfer at the transcriptome-wide level (the transferome) between heterogeneous human-mouse cell populations in cell culture using RNA-sequencing. Unlike the intensively studied transfer of small regulatory RNA and fragmented RNA by extracellular vesicles, relatively little is known about full-length mRNA transfer between adjacent cells via direct cell-to-cell tunneling nanotubes (TNTs). Dasgupta et al provide a comprehensive set of the high depth bulk RNA-sequencing experiments and downstream bioinformatics analysis to determine overall levels of the most abundantly transferred RNA. They show that RNA transfer to the acceptor mouse cells is non-selective and the amount of transferred RNA strongly correlates with the endogenous level of gene expression in the donor human cells. They validate these results using a variety of orthogonal approaches (e.g. qRT-PCR, smFISH together with advanced imaging techniques, spatially separated cell culture) that overall support: 1) The relatively low level of endogenous mRNAs and lncRNAs transfer via TNTs. 2) TNTs as the predominant route for transcriptome-wide mRNA transfer. <br /> In addition, both cis-RNA motif-enrichment analysis and synthetic RNA reporter assays in cell culture back-up the authors’ conclusions on non-selective RNA transfer via TNTs. Lastly, the authors observed stark changes in the acceptor mouse cell transcriptome in response to co-culture with the donor human cancer cells, including the upregulation of cancer-associated genes as result of oncogenic-like transformation. This is a carefully performed study based on a simple and quantitative method. Overall the conclusions are well justified and supported, limitations of the study are clearly stated.<br /> The only minor comments and suggestions that I have are:<br /> 1. The authors performed polyA(+) RNA-sequencing. How abundant could be the 3’ end partially decayed mRNA transferred to the acceptor cells?<br /> 2. The direct correlation between RNA and protein levels in MCF7 is shown previously in (Edfors et al 2016). Is it known that the TNT-transferred MCF7 mRNAs undergo translation in the acceptor mouse cells?<br /> 3. TNTs are also shown to transfer proteins. Is there any overlap between the TNT-transferred mRNA and proteins? <br /> The points 2-3 are very challenging to address but could be discussed.<br /> 4. Fig 3C. Is mRNA half-life in K562 consistent with the mRNA half-life rates in MCF7?<br /> 5. I find the ability of the transferred RNA to down-regulate cell immunity-related genes interesting. Could this be an indirect effect? <br /> The authors used unique mapped reads in downstream bioinformatics analysis. Is there any estimation of the multi-mapped reads in the TNT-transferred RNA fraction?
On 2022-01-17 17:30:09, user Sebastien Leclercq wrote:
Dear authors, this study 's objectives are good, we indeed need a benchmark tool to evaluate the various AMR gene detections programs in a metagenomic context.
But I am more doubtful about the methodology. I have 3 main comments.
First, it would be useful to provide the AMR genes detected in the assembled genomes of the selected isolates. If only the reads are available, assemble them with SPADES. Do the search with the differents tools and combine detections which match at the same locus.<br /> This will helps the reader to know what exactly is expected from the artificial mock metagenome. For instance, I can't believe that fARGene finds 713 distinct genes after condensation, and even the 33 genes for RGI is suspicious. Comparing detections from the mock with the expected number from assembled genomes will provide some new useful metrics.
This will help to provide more details on what is detected/not detected (which genes) and why. Related to the reference database/models ? To the defaults matching parameters ? Other ?
Second, the way the mock community is constructed is inadequate. Metagenomic data reflect complex microbial communities (of hundreds of taxa) for which a small minority are AMR pathogens. Putting 9 highly multi-resistant pathogens together at equal proportion is not consistent with real metagenomes. In this regard, reducing the coverage from 100x to 50x and 5x doesn't help because AMR genes represent the exact same relative abundance in terms of reads in the 3 datasets. I would suggest to get a bunch of other isolate's sequences, from sensitive bacteria and include them in excess to simulate the whole bacterial community. The proportion of pathogens could then be modulated (e.g. 1/50, 1/100, 1/1000) to simulate the various conditions (healthy / infected patients).
Last and more scientifically speaking, I don't really get for which kind of purpose the AMR gene detection will be used, and how the connection with phenotypic resistance will be treated (inference ?). The manuscript refers several times to clinical diagnosis use of metagenomic data, but I don't understand how.
For instance, several E. coli strains concurrently exist in the gut, so detecting a bla-TEM gene in the metagenome will not help to infer which E. coli is resistant. And it may actually not be a E. coli at all since most AMR genes can be found in different species.
I hope these few comments will help to improve this work, I don't have time to proceed to a full review.
Sebastien.
On 2022-01-17 17:28:37, user Samyuktha Suresh wrote:
This article is now published in the Cancers journal and can be accessed here: https://doi.org/10.3390/can...
On 2022-01-17 17:11:18, user Martin R. Smith wrote:
If you wanted a more systematic way to identify which specific fossil (or indeed extant) taxa might be acting as rogues, you might be interested in the approach I've recently proposed in Systematic Biology: https://doi.org/10.1093/sys...
On 2022-01-17 16:49:55, user Martin R. Smith wrote:
Nice to see the multifaceted exploration of the tree distribution in Fig. 6. I wonder whether the RF distance is giving the complete picture here, given its potential for bias (See Smith 2020, Bioinformatics, 10.1093/bioinformatics/btaa614 )? <br /> In particular, I wonder whether the apparent clustering in its tree space and the dissimilarity to the geodesic space are genuine, or artefacts – I've found that RF tree spaces are often problematic (Smith 2022, Syst Biol, https://doi.org/10.1093/sys... ).
On 2022-01-17 16:44:58, user Martin R. Smith wrote:
Nice to see these tree space analyses. I've found that the Kendall-Colijn distance emphasizes shape over topology -- see<br /> https://doi.org/10.1093/sys...<br /> I wonder whether you'd see the same patterns if you constructed tree spaces using metrics that focussed more directly on phylogenetic relationships, and in more than two dimensions?
On 2022-01-17 16:41:26, user Martin R. Smith wrote:
Great to see an analysis of tree space here. You might be interested in my recent review of tree space methods in Systematic Biology, https://doi.org/10.1093/sys... . In particular, RF distances often introduce significant bias in tree spaces, and two dimensions can misrepresent the true structure. There might be value in checking whether other tree distances corroborate your results, or depict the structure with less distortion in two dimensions.
On 2022-01-17 13:22:07, user Kerstin Menck wrote:
The mauscript has been officially published in JECCR: https://jeccr.biomedcentral...
On 2022-01-17 12:56:11, user Nicolas Rivron wrote:
Dear Cheng Zhao et al.,
Thank you for this first attempt at unbiasedly comparing the transcriptomic state of the cells of early human embryo models. Merging datasets to create reference maps, and applying multiple methods to identify cell fates is really important and it is crucial to ensure an appropriate understanding of the predictivity of the model. Your analysis matches some of the original analyses by the articles you study, despite using a different reference map. We were happy that you come to the same conclusion as us that about 3% of our cells are not matching EPI, trophoblast, or extraembryonic endoderm lineages, which is encouraging (note that we did not use the dataset from Zheng et al. in our merged reference map). Please find below a couple of remarks that we hope will help you with the revision process.
Our first main concern is that you are focusing the interpretation of your analysis on one study (Liu et al. 2021). Instead, we think that it would be better to propose tools and data that everyone can work with and use to interpret data and form conclusions (your website is a good start in that direction). The scientific process is fallible by nature and we think it is important to openly give the results without dismissing the original proposals, in order to gradually improve our understanding of the cell states produced within these models. We understand the specificities of the publication format but we think that this first concern can be easily addressed by adapting the title and the text.
The second main concern is that you are not taking into account the embryonic stages. A primary goal of an embryo model is to form cells reflecting a specific stage, in that case, the human blastocyst stage (days 5 to 7). After, from day 8, the embryo starts implanting into the uterus and forms the stages following the blastocyst one. During these transitions, cell states change rapidly to fulfil a sequence of different functions (e.g., the TE becomes polar TE to mediate the attachment to the endometrium). As such, ensuring the predictive power of the model requires that the model forms blastocyst-like cells. The lack of assessment of the stages of the Trophoblast-like cells (TLCs), Epiblast-like cells (ELC), and Hypoblast-like cells (HLC, a.k.a. PrE) lead to ambiguous interpretations.
These stages might be evaluated by increasing the number of Seurat clusters and affiliating a stage to each cluster based on the percentage of staged embryo cells within each cluster. This affiliation can be controlled by looking at the expression of stage-specific markers: the zones of the blastocyst-stage are well defined on your map by some markers of the EPI (e.g., SUSD2, KLF17 [PMID: 29361568]) or of the trophoblasts (e.g., CDX2).
Also, the annotation of the TE in Figure 1A is misleading. The term TE is generally used to describe the trophoblastic tissue that forms the wall of the blastocyst (E5-7). After implantation, the TE generates other trophoblast cell types including syncytiotrophoblasts, or cytotrophoblasts, this later state being captured by the hTSCs derived by Okae et al., which were also included in your UMAPs. In this figure 1A, you used the term TE for trophoblasts harvested from blastocysts cultured in vitro for up to E14. We think it prevents clarifying the limit of the blastocyst trophoblasts. Regarding the EPI, your Seurat cluster C0 is partly representative of the blastocyst EPI but also clearly captures early PrE cells and post-implantation EPI (E10). Increasing the cluster resolution would be essential to obtain clusters that better capture the blastocyst EPI. Although it is not clear if the cells of in vitro cultured blastocyst truly progress as expected, we believe that including the C7 and C9 space to define ELCs, when you are evaluating a model of the blastocyst, is misleading.
Instead of focusing on the stages, you have rather focused on the lineages. However, the amniotic and mesoderm cells originate from the EPI. As such, treating the amnion and mesoderm (although it includes some extraembryonic mesoderm that might not originate from the EPI) as cell types outside of the 3 lineages makes your analysis ambiguous. This is especially true for Extended Data Fig.2b, in which you classify the cells into the following families: ELC, HLC, TLC, MeLC, AMLC, and Undefined. This classification mixes both the stages and the lineages. For example, MeLCs is the descendant of the EPI lineage at a post-implantation stage and thus represent the same lineage, but they are represented in an independent bar. On the contrary, the bar with the TLCs includes both pre- and post-implantation trophoblasts from the same lineage but different stages. It would be better to use only one parameter, either lineage or stage, for this graph. If you use lineages, the MeLC bar, and arguably the AMLCs, bar should be added on top of the EPI bar.
Overall, we think that identifying the stages and the tissues, including the polar TE, the mural TE, the blastocyst EPI, the post-implantation/pre-gastrulation EPI would be a great achievement to benchmark the models. We think it might need a feasible refinement of your analysis, similar to the analysis that led you to uncover X dampening and the polar/mural states in human blastocyst (Petropoulos et al. 2016).
A third concern is that you are making statements about the quality of the models based on the ratios between the different cell types (e.g., EPI, TE, PrE) present in the scRNAseq datasets. It is known that blastocysts contain more TE than EPI, and more EPI than PrE (Niakan and Eggan 2013). This was also measured and recapitulated in our model (see Figure 1H, ~60%/35%/5%) using immunofluorescence, and thus based on the expression of selected marker proteins. However, these ratios are not similar in the scRNAseq datasets, with a prominence of EPI as compared to TE. This unbalance cannot be due to a delay in trophoblast specification because the number of trophoblasts was measured within blastoids at the protein level and the RNA precedes the protein. A most likely explanation is that the numbers and ratios in the scRNAseq data do not reflect the ones in the blastoids because there are biases due to the dissociation and sorting processes that precede the sequencing. We believe you have also observed in the blastocysts that, upon dissociation, they release a majority of TE and only a few EPI. As such, evaluating the quality of blastoids based on the numbers in the scRNAseq dataset is probably not justified, unless you have another explanation that we did not grasp from your text.
In figure 2C, it would make it more clear if the title would explain that this pseudo-bulk is based on 187 TE, TSC and Amnion genes.
Finally, the neural network (NN) approach was confusing for us. We definitely commend you for using new tools, however, we think it should be better explained in order to clearly understand the results that are presented. We understand that space constraints might limit the expansion of this part of your manuscript. As it is presently presented, we understand that your NN analysis yields somehow contradictory results than the UMAPs. The data points at the possibility that we have formed cells transcriptionally resembling the blastocyst EPI. For example, on your UMAP, our ELCs are shifting from the PXGL hPSCs space to the blastocyst EPI space, which suggests that the blastoid environment might normalize their transcriptome. These ELC are also within the KLF17/SUSD2/IFITM1/PRDM14-high space, which are blastocyst-stage EPI markers [PMID: 29361568]. We are surprised that, using your NN, some of those cells were attributed a primitive streak identity. It would be highly informative to know which cells have changed fates, and the gene signatures associated with this. Doing so systematically for all cells “re-annotated” seems an important improvement to guide the biologist through your analysis (just like you did to explain how you re-annotated some of Tyser et al or Xiang et al samples).<br /> We are convinced you have thought about it, but it might be suited to add figures showing that the NN did not make use of other parameters to attribute cell fates, such as the read depth, which varies a lot between datasets. For example, Yanagida et al., comprises cells with >6000 genes/cell whereas the Kagawa et al. dataset includes cells >2000 genes/cells. The datasets used for the training are typically using cells with > 3000 genes per cell. The percentage of mitochondrial genes could also have had an important influence. Overall, this might lead to non-comparable datasets.<br /> Also, it is difficult to have a clear idea of “off-target” samples. It would help if you would provide a number next to the predicted off-target cells reflecting the percentage as compared to the total number of cells.<br /> Finally, how did you control that your reference samples were large enough? Specifically, using ~25 cells from the Tyser et al. dataset as a reference for the amnion is probably not sufficient. Also, the depth of identification of amnion cells or extraembryonic mesoderm cells is really sparse in the peri-implantation dataset, which might be a better reference, as they are closer to the fate transitions that are being modeled.<br /> In summary, although the NN is an important tool for the future, we are wondering if the current quality of both the embryo and the models datasets are sufficient.
Thank you for considering those points.
Best wishes,<br /> Nicolas Rivron, Laurent David, Alok Javali, Harunobu Kagawa, Heidar Heidari.
On 2022-01-17 10:24:56, user Martin R. Smith wrote:
I notice that several nRF distances in Fig. 7 are very close to 1. The shortcomings of the RF distance – in particular its proclivity to saturation – become more pertinent when trees are dissimilar. I wonder whether a more robust tree distance metric would give a more complete picture of how tree similarity varies across groups and matrices? These issues and possible solutions are reviewed in Smith 2020, Bioinformatics, https://doi.org/10.1093/bio...
On 2022-01-17 01:47:11, user AA G wrote:
"the 0-domain of CCoV-HuPn-2018 has an increased rate of molecular evolution",sorry,but i can hardly find results which can prove this assumption,this research take the GARD 7th part to beast2 but not 0 domine，i just wondered,is that by less positive-sites?
On 2022-01-17 01:23:00, user Thidathip Wongsurawat wrote:
Great work! Would you release the primer set in the peer-reviewed publication? Thank you.
On 2022-01-17 01:01:56, user Angela wrote:
I read with great interest this report of biallelic LOF MBD4 variants’ predisposing to colorectal cancer and polyposis. The combination of CRC and MDS/AML predisposition you describe is reminiscent of the ribosomopathy Diamond-Blackfan anemia. (I am unaware of any associations between DBA and uveal melanoma.) CRC in DBA patients was most recently described in Lipton et al. Genes. 2022; 13(1):56. https://doi.org/10.3390/gen...<br /> Have you looked into an association at all? A mechanistic connection between the two conditions is not intuitive. I am unaware of any studies investigating the mutational signature of DBA CRC tumors, although I would certainly be interested in reading such a study. I look forward to hearing your thoughts on this.
Angela K. Snow
Comments are my own and do not necessarily reflect the views of my institution or my PI.
On 2022-01-15 18:50:31, user Tunefish wrote:
Q:Why syrian hamster are choosen for these kinds of experiments?
On 2022-01-14 21:02:57, user Andor Kiss wrote:
Any way to see the Supplemental Data file?
On 2022-01-14 19:43:50, user Ken Prehoda wrote:
I spent about 90 minutes reading this preprint and am providing my thoughts on it in case they might be of use to the authors or anyone else. I am not an expert in this field but am very interested in the topic. Overall I enjoyed reading the paper and feel like I learned a lot from it.
My summary of the paper (to see if I “got it”): the authors raise the question of how PIP2 is involved in so many cellular processes and propose testing the hypothesis that PIP2 is organized into separate spatial domains that carry out different functions. The authors note the correlation that PIP2 is enriched at different functional sites but also that it is not clear whether PIP2 is upstream or downstream from any particular function. I began to get confused at around line 64 (version 1 of the paper) where the authors make some distinction about lipid rafts vs other types of structures that PIP2 might regulate (for example, on line 65 where they say “could this regulate”, I was unsure of what “this” referred to). Ultimately, I understand the question of the paper to be about whether or not PIP2 movement in the plasma membrane is slowed when it is in functional membrane domains as one might expect it to be. They note that PIP2 has been found to move rapidly but based on measurements of bulk PIP2 movement. They also note that, in extracellular lipids, the cytoskeleton has been shown to slow diffusion and this is a key justification of their hypothesis but without looking up the referenced papers I am very unsure about what this means. Finally they describe their approach to the problem - the use of sptPALM to monitor single particles - and briefly describe their results.
The authors begin the description of their results by noting that they are monitoring “free” PIP2 that is bound to sensor molecules, since binding to the sensor prevents binding to effector proteins - an important point that is nicely reinforced by Figure 1. The authors note that biosensor-bound PIP2 has the same diffusion coefficient as free PIP2 which is very surprising. The authors spend some time establishing the rigor of their technique which I found, as someone who knows very little about it, to be convincing and comforting. A key assay is whether or not the diffusion is Brownian in nature, or if it is not (i.e. affected by something). I got a little lost in the description of the results in the paragraph starting at line 140 thinking that the conclusion is that the dynamics are mostly Brownian but there are some differences. At line 183 the authors note a deficiency in the mean diffusion coefficient– that it will not detect trajectories with different classes. I’m intrigued but a little confused - in other words, different collections of trajectories could yield the same “D” - interesting! The author’s solution is to calculate D for different classes of trajectories. Overall the main result so far is that PIP2-biosensory movement is mostly unhindered, but a small amount is potentially hindered.
The next section is about how specific cellular membrane structures influence PIP2-biosensor movement. The first result is the PIP2-biosensor complex is not hindered at ER-PM contact sites. The authors introduce a control involving chemically induced dimerization of the biosensor but I was somewhat confused as to what this experiment as actually controlling for. They show that they can detect hindered movement but my confusion may arise because that wasn’t something that I considered to be an issue. Ultimately they find that PIP2-biosensor dynamics seem to be the same in most cortical structures but not in spectrin and septin containing structures- a very interesting result!
Conclusion: I found this to be a very interesting study with many interesting results. I was somewhat confused by the presentation. For example, the abstract states, “how a single class of lipid molecules independently regulate so many parallel processes remains an open question” but I’m not sure that this question is being addressed in the paper. My takeaway is that the paper asks if the dynamics of free PIP2 (PIP2-biosensor complexes) is the same in different cortical structures as it is outside of them. The answer seems to be that for the most part it is and that the data and arguments presented in the paper make a convincing case for this. Making the paper more clear about what it is really about (if my takeaway is correct), and also discussing what factors might influence whether or not free PIP2 would move through cortical structures freely, could be helpful improvements to readers.
Minor comment
On 2022-01-14 18:46:27, user Ruben L Gonzalez Jr wrote:
Note that this preprint has now been published in Methods in Enzymology https://doi.org/10.1016/bs....
On 2022-01-14 18:34:17, user Peter Chuckran wrote:
Link to published article
On 2021-12-09 17:18:42, user Peter Chuckran wrote:
Accepted in FEMS Microbes under the title: Variation in genomic traits of microbial communities among ecosystems
Article link forthcoming
On 2022-01-14 16:29:55, user Jean-François Ponge wrote:
Just I wonder whether 10 cm below litter is enough to sample macrofauna. As explained in the paper, 25 x 25 x25 cm monoliths are currentl used to sample earthworms (TSBF method). Why using the same surface but not the same sampling depth. I fear several soil-dwelling macroinvertebrtates will be underestimated by this method, certainly good for meso- and micro-fauna, but not, to my opinion, for macroinvertebrates. Do you have dat that allow you to sustain that 10 cm is enough?
On 2022-01-14 16:09:05, user Iratxe Puebla wrote:
The manuscript reports the result of a survey of authors of preprints posted to bioRxiv in the period November 2013 to December 2018. The survey asked respondents about their motivations for posting preprints -or not- for the papers they published in scientific journals the previous five years. The main additions compared to earlier surveys on the topic relate to the fact that the survey asked for answers according to whether the respondent was a corresponding author or a co-author on the paper(s), and that for the group of respondents who had posted preprints for some papers but not others, the survey asked about any differences between the papers (e.g. on self-reported novelty or quality of the work), to establish whether a selection bias by authors may take place when making decisions on what papers to post as preprints.
The results around motivations for posting preprints correlate with the results of earlier surveys: main motivations are quick dissemination of research and increasing awareness of the research. The respondents also reported an expectation that posting the preprint would bring a benefit in terms of online dissemination of the work e.g. via social media.
The survey responses according to whether the author acted as corresponding author or not are interesting as they suggest that preprinting decisions are mostly driven by researcher choice: a higher proportion of respondents posted preprints when they acted as corresponding author, compared to a lower proportion when they were a co-author, as this involved less autonomy in decision making.
The preprint landscape in the life sciences has changed hugely in the last two years. The survey was carried out in early 2020 for authors who had published a preprint in the 2013-2018 period, and it is likely that many researchers previously not familiar with preprints are now at least aware of this publication model. It would thus not be surprising if trends have evolved since the survey was done, a follow-up survey (as a follow-up study) would be informative and allow for an interesting exploration of trends around motivations and decision making for preprint use in biological sciences.
I am not sure I agree with the interpretation that the responses displayed in Figure 8 are at odds with each other. Even if an author considers the papers with and without preprint of similar novelty/quality, I do not think it is surprising for them to have an expectation that the preprint may boost attention/citations, for several reasons: prior research has noted an association between having a preprint and levels of attention/citations, the fact that the preprint disseminates the work earlier and thus may start accruing citations earlier, there is a preprint community on Twitter so the author may expect wider dissemination via those community channels.
Survey design - It may be interesting to learn more about how the survey was developed? Were the questions chosen according to the authors’ knowledge or were question options from earlier surveys considered? Was the questionnaire pilot tested prior to deploying the survey, and if so, were any changes made to the wording of specific questions?
Analysis of free-text comments - Is there some context/justification for having a single coder for the comments? Was the option of additional coders considered to check for any differences between coders/reduce potential bias?
I realize the sample size for certain disciplines may be too small but I wondered about some discipline-level analysis for the responses. Different disciplines are at different stages of use and experimentation with preprints so there may be discipline-level differences on whether authors are making choices based on the perceived quality or novelty of their paper. The qualitative comments note that some authors choose to preprint as protection from scooping while others choose not to preprint due to fear of being scooped, I wondered if views here may be driven by discipline-level variation in use of preprints, i.e. if in my discipline preprinting is common, I may be more likely to preprint as a scooping-protection approach than the opposite. This goes beyond the scope of this paper, but may be a follow-up exploration.
There is a thorough discussion of the limitations of the survey, a few additional items for consideration:<br /> - This is a survey based on self-reported responses, it may be worth noting that there is no stipulated benchmark for quality or novelty, and thus this may introduce nuance or subjectivity in how the question around novelty or quality is interpreted.<br /> - The survey was run in March-April 2020, when there were lockdowns in place in different countries, may lockdown measures have affected response rates by certain groups (certain locations, gender) particularly impacted by lockdowns?<br /> - It is noted that the survey is focused on a single preprint server, there are different preprint servers for biological sciences, it may also be relevant in the future to explore comparisons across servers for biology. Even within biological sciences there may be differences between authors who post to preprint servers directly, and those who post preprints via deposition offered by publishers at the stage of journal submission.
On 2022-01-13 22:43:32, user Anne Peters wrote:
Now published here: DOI: 10.1111/ele.13950<br /> Ecology Letters. 2022
On 2022-01-13 19:09:15, user Angelo D'Alessandro wrote:
Congratulations! Great study! In case you are interested - we did study the RBC interactome, with a focus on the N-term cytosolic domain of band 3 with Xlinking proteomics approaches (and other structural methods) here - https://pubmed.ncbi.nlm.nih...
On 2022-01-13 02:08:19, user Seth Winfree wrote:
Dataset for this manuscript is now available:<br /> https://doi.org/10.5281/zen...<br /> Demonstrations coming soon at:<br /> https://vtea.wiki
On 2022-01-12 21:43:15, user Clara B. Jones wrote:
1 ... this article relies heavily on the "ecological constraints model" first proposed by S.T. Emlen in 1982 ... it seems appropriate to suggest that this classic Am Nat should be cited ...<br /> 2 ... throughout the publications on social mole rats derived from Clutton-Brock's lab, the presence of Castes is employed as a necessary diagnostic criterion for Eusocial classification ... as evident from even a cursory reading of EO Wilson's [1971] Insect Societies, as well as, Holldobler & Wilson's [1990] Ants, many social insect species are characterized by "totipotent" workers [like social mole rat "helpers"] who are not [more or less] sterile ... this observation is not disputed in the social insect literature ... indeed, numerous researchers divide the Eusocial category into "primitively Eusocial" [totipotent workers] & "advanced Eusocial" [more or less sterile Castes] ... this is an important distinction relative to the preprint by Thorley et al. because, as Gene Robinson [1992] & many others have pointed out, social insects exhibit a high degree of "phenotypic plasticity," a diagnostic criterion that might describe certain findings in the paper under discussion ...<br /> 3 ... as a related aside, these authors do not mention (a) "division of labor," a character trait universally employed as a diagnostic criterion for eusocial classification, in addition to the characters, (b) overlap of generations, (c) "cooperative breeding" and the presence of "helpers," & (d) "specialization" ... unless i am mistaken, the two eusocial mole rats generally classified, Eusocial--Damaraland & naked mole rats--exhibit "specialization" in the form of "temporal division-of-labor" ["age polyethism"], thus, meeting all of the generally-accepted criteria for Eusocial classification ...<br /> 4 ... this paper is not "tight" though, as indicated by its title, the authors' paper has a clear goal in mind ... instead, they veer off in numerous directions throughout their text, & it is not clear to me that the extrapolated, deviating commentary is useful rather than obfuscating ... perhaps the paper should have notes if the authors wish to add possibly relevant material for thought aside from allusions to material not related directly to "fitness" and "group size" ...<br /> 5 ... though one might question or request clarification about numerous definitions & other decisions made by Thorley & his colleagues, in the service of brevity, it is necessary to, finally, point out that Wilson '71 and Holldobler & Wilson '90 document the very wide range of architectures that are, diagnostically, "Eusocial" ... in his 2018? book, Genesis, EO Wilson suggests that numerous mammal species deserve Eusocial classification, &, in my 2014 Springer Brief on mammalian social evolution, i suggest that "cooperatively breeding" mammals should be classified with the Eusocial mole rats ...<br /> 6 ... all scientists welcome new ideas & clarifying, rather than obfuscating, empirical, as well as, experimental & quantitative, tests of the literature in their fields ... this preprint by Thorley et al. does not, in final analysis, modify the conclusions advanced in the work by Bennett, Faulkes, & others, going back to Jarvis' classic research in the 1980s, who have studied social mole rats and classified them, "Eusocial" ...
On 2022-01-12 16:41:58, user Saeed Sh wrote:
This is an interesting subject of research. Indeed the authors are assessing spawning activity in aquatic species based on environmental DNA which is a non-invasive methodological approach of research for monitoring spawning activity on aquatic species. They studied on two medaka species (Oryzias latipes and O. sakaizumii, in cross experiments and under laboratory conditions. They proposed an environmental DNA approach for monitoring and understanding spawning activity in fish. Their results are suggestive that an eDNA spike occurred in only male species after spawning activity AND THAT the magnitude of the eDNA spike was dependent on the number of spawning activities. They also demonstrated that spikes in the eDNA concentration are mainly caused by the release of sperm during spawning activity, and it can be used as evidence of spawning in field survey. Although the whole experimental design seems clear and straightforward, I would suggest that care should be taken intro account when interpreting laboratory -based finding into filed results. It would be also interesting to extend the discussion part about whether stress related behavioural responses or eDNA concentrations could be related to daily activities and also does personality may matter at all? In terms of data reproducibility it would be also helpful if the author provide the data accessible on online repository services for further post research examination by other researchers.
On 2022-01-12 14:21:34, user Rafael Najmanovich wrote:
Review of Improving AlphaFold modeling using implicit information from experimental density maps. https://doi.org/10.5281/zen...
On 2022-01-12 00:36:11, user Marco Gerdol wrote:
The manuscript has been peer-reviewed and it is now available online on Virus Research: https://www.sciencedirect.c...
On 2022-01-11 20:42:36, user Mina Bizic wrote:
I would like to congratulate Rachel Szabo and colleagues on their great work and effort put into this manuscript. The goal of analyzing such a high number of particles has been something I have been calling for ever-since my work cited in the comment by Dr. Jacob Cram (Bizic-Ionescu et al., 2018). It’s exciting to see the efforts you have made in this direction.
It’s equally exciting to see that my conclusion from 2018 that the initial colonization of particles is stochastic, is strongly featured in your paper title and well supported by your results.
As Dr. Cram has mentioned in his comment, we discussed your study and have come up with several aspects that we feel deserve some attention and most likely to be better addressed in the manuscript. Some of these aspects were raised by Dr. Cram in his comment. However, we felt that our opinions on this manuscript were dissimilar enough to warrant separate comments, with some observations that overlap and some that differ.
My general query goes to the applicability of the results to the natural environment, given several biases introduced by the chosen experimental system. I will list here my opinion on the source of these biases.
1) The concentration of seawater is likely to have generated an unrealistic microbial community. This is for three reasons (A) concentration of particle-attached microbes, (B) concentration of large bacteria, and (C) non-concentration of DOM: <br /> (A) Filtering the water through a 63 µm mesh should leave all particles smaller than this size in the water The subsequent step of gentle centrifugation most likely further concentrated these microparticles increasing their abundance above natural concentrations. <br /> (B) The gentle centrifugation likely selected for larger bacteria, as smaller cells may not be concentrated by a 5 min 4000 g run. <br /> (C) Finally, the seawater DOM on which bacteria can feed was not concentrated in this process. <br /> Therefore, the resulting inoculum used for the experiment contains a size-selected microbial community and a microparticle enrichment which in the absence of ambient DOM will rapidly drive the experiments towards consumption of the particulate organic matter at rates not representing the natural environment.
2) The incubation time and small volumes: While samples have been collected already after 12 h the experiment ran for 166 h in a closed microwell. It has been shown by many as well as by my colleagues and I that after 24 h at the latest, the community in the experiment does not represent the environmental one (for example: Baltar et al., 2012; Ionescu et al., 2015; Herlemann et al., 2019). Therefore, seeing such long experiments conducted in fully closed systems, as in this paper, makes me wonder to what degree the rates of events observed in the lab are similar to rates in nature.
3) One possible problem with the incubation system used, is the effect of the microwell surface on microbial activity. Ploug and Jorgensen (1999), for example, came up with the net-jet system for measuring microprofiles on organic matter aggregates. However, aside of the effect of direct contact of particles with surfaces on particle properties and the microbial activity on it, a second issue is the formation of biofilms may form on the surfaces of the incubation system. Heterotrophic activity is known to increase in closed incubation systems (e.g. Fogg and Calvario-Martinez, 1989; Ionescu et al., 2015). Though it was shown that these biasing effects will occur regardless of bottle size (Hammes et al., 2010), these will likely have a stronger effect in very small incubation volumes (Herlemann et al., 2019), consuming oxygen and nutrients. I don’t recall reading whether the O2 concentration was monitored? My guess is that the system became anoxic relatively fast, unlike it would be in a natural environment. How does this affect the nature of associated (and active) bacteria?
Having said that, I support the authors’ overall conclusion and applaud the effort that went into the data collection and analyses I am aware from my own work on the difficulties to obtain and maintain such a large number of particles in open systems, such as the one my colleagues and I designed. However, I think that the biases introduced by an experimental system should be openly discussed in the manuscript and if possible, explain how your results remain valid despite them. This is even more important when you often discuss late-stage particles, that are the most to be affected by aspects mentioned above.
Sincerely,
Mina Bizc
References
Baltar, F. et al. (2012) Prokaryotic community structure and respiration during long-term incubations. Microbiology open, 1, 214–224.
Bizic-Ionescu, M. et al. (2018) Organic Particles: heterogeneous hubs for microbial interactions in aquatic ecosystems. Front. Microbiol., 9.
Fogg, G. E. and Calvario-Martinez, O. (1989) Effects of bottle size in determinations of primary productivity by phytoplankton. Hydrobiologia, 173, 89–94.
Hammes, F. et al. (2010) Critical evaluation of the volumetric “bottle effect” on microbial batch growth. Appl. Environ. Microbiol., 76, 1278–1281.
Herlemann, D. P. R. et al. (2019) Individual physiological adaptations enable selected bacterial taxa to prevail during long-term incubations. Appl. Environ. Microbiol., 85.
Ionescu, D. et al. (2015) A new tool for long-term studies of POM-bacteria interactions: Overcoming the century-old Bottle Effect. Sci. Rep., 5.
Ploug, H. and Jørgensen, B. B. (1999) A net-jet flow system for mass transfer and microsensor studies of sinking aggregates. Mar. Ecol. Prog. Ser., 176, 279–290.
On 2021-12-30 15:35:00, user Jacob Cram wrote:
This is a public comment on Szabo et al. “Ecological stochasticity and phage induction diversify bacterioplankton communities at the microscale”, submitted to BioArxiv on Sep 21, 2021.
Understanding the dynamics by which microorganisms attach to and grow on particles is an important and contemporary field in microbial ecology, and in the understanding of the factors that influence the role of particle flux in the global carbon cycle. Szabo et al focus on the randomness of this process. By taking ~1000 identical chiton beads and incubating them in the sea-water from the same sample, and looking at the community structure 100 beads at a time, over the course of seven days, the authors aim to quantify how much variability there is in the microbial take-over of these particles.
The authors applied shotgun metagenomics to each and every particle, focusing on assembling genomes into metagenome assembled genomes (MAGs).
Several key findings stand out to me:
1) There is substantial variability over time in the microbial community structure, and on the number of microorganisms present per particle. <br /> 1a) The authors suggest that random variation in which bacteria attach to the particles and when they attach drives much of this variability.
2) There do not appear to be statistical associations between which microorganisms are on a given particle. That is if a given species “A” is common on particle A and not particle B, that has no bearing whatsoever on the abundance of any other microbe on either particle.<br /> 2a) Such a finding suggests that there are essentially no meaningful interactions between the microbes on the particles. Cross feeding, predation, symbiosis, chemical warfare, all believed to be important for microbial communities (Fuhrman and Steele 2008; Steele et al. 2011) would each be expected to lead to some sort of statistical association between organisms, but in this scenario at least such patterns are essentially absent.
3) The authors looked for contigs (partial phage genomes) and identified which appeared to “bin into the MAGs of their bacterial hosts”, suggesting that they were lysogenic with and therefore part of the genome of at least some members of that host. The more copies of this contig were present, the more active this phage was said to be. They found associations between the activity of these phages and the apparent growth of their hosts and negative associations between bacterial abundance and the presence of these phages.<br /> 3a) The authors suggest that stochastic absence of particular phages can lead to the situations where their hosts can rapidly take over a particle.
I found this to be a very thought provoking manuscript and it raises a number of interesting and testable questions for future research. The sequencing and assembly of so many metagenomes, especially on very low biomass samples is an impressive technological feat (and clearly required diligent work on the part of the authors) which will be of value to the community at large. While some of my comments below are critical, I want to be clear that I was quite impressed with this paper and share these comments because I think the research is important and merits reflection.
I have comments about each of the three main points listed above that I would like to share. I have not, as of yet, been asked to review this manuscript for any journal, but would be happy for any editor to use my comments. After preparing this review, I discussed it with Dr. Mina Bizic and she indicated that she shares my opinions. Dr. Bizic had several additional comments which she plans to make separately.
Comment 1: On Stochasticity
The authors make the case that there is randomness in the attachment and growth dynamics of microbial communities on particles. The authors suggest that because the variability between the communities on the particles is much higher than that of the surrounding water samples. However, I suspect that random variability in which rare taxa end up in each incubation could drive many of the patterns that they see.
As context, in this experiment, chitinous beads (~80 micron diameter) are enclosed, one per well, in 96 well plates and incubated in, 175 ul of sea water. The microbes and particles have been concentrated in this small volume up to ten times by centrifugation. That is, volumes of whole sea-water were filtered, and then centrifuged and the bottom 1/10, presumably containing intact cells and small particles from the environment was retained. This means that each bead is incubated with essentially 1.75 ml of sea-water worth of microbes and microaggregates.
I suspect that microbes that are adapted to degrading chitinous beads are scarce in the water, perhaps near or slightly below a concentration of 1 per 1.75ml. In this case, there could be random variability in whether chitin degrading microbes end up in any given well. Furthermore, a big driver in the randomness between which bacteria are in a well could be the presence of chitinous particles (smaller than the 60 micron filtration cutoff) in the background water. Ambient chitinous particles likely contain communities that would be adapted to break down chitinous beads. If one well happens to have one of these particles that particle is likely to come in contact with the bead near the beginning of the experiment in which case the microbes on the microaggregate can take over the bead. If such particles are absent, then perhaps the takeover of the bead doesn’t happen, or happens more slowly. Thus the stochastic process that drives the variability that the authors see may be in the starting community of the water in which the particle is incubated. If these organisms are rare, they would be likely to be missed by the sequencing, which can only sample the most abundant organisms. As they sequenced the seawater samples to a depth of ~500,000 reads per sample, and maintained about 25% of the samples (Table S5), this means that they essentially considered ~125,000 sequences per sample. Assuming the water had on the order of 1 million bacteria per ml, we might expect that any organism present at lower than ~10 copies per ml would likely be missed by their process. As there is an amplification step in their sequencing (supplementary methods) their method may even be less sensitive to rare organisms.
Indeed, it is clear that the sequencing of the seawater didn’t catch every organism that could colonize the particles because per Table S7, some of the jackpot taxa (taxa that take over some particles) are either never seen or rarely seen in the seawater samples. Since they must have come from the seawater, it is clear that some species are missed by sequencing.
Thus I contend that some of the particle to particle variability is likely from well to well variability in which microbes were stochastically placed in wells with each particle.
On the other hand, it is possible that this stochasticity is environmentally relevant. For instance, an 80 micron bead that sinks through 100 m of the water column only clears a total volume of ~500 μl {π(80 μm / 2) ^2 * 100 m = 503 μl} and so it is possible that microbes beyond this abundance in water would actually be unlikely to encounter a particle as it sinks out of the photic zone, for instance.
Comment 2: On interactions
I’m surprised that there don’t seem to be interactions between organisms, but their graphical lasso based statistics seem reasonable to me.
I’m furthermore surprised the authors did not seem to consider Bižić-Ionescu et al. (2018)’s paper, which has a very complementary design to this paper, but seemed to find the opposite pattern with respect to microbial interactions.
Bižić-Ionescu et al. (2018) presented a very similar project, in which the authors also had replicate particles, though fewer than in the paper by Szabo et al. Key differences were that the authors used a flow-through rolling tank which exposed the particles to more water, and that those authors used (larger) aggregates of algae rather than chitinous beads as their particles. Bižić-Ionescu et al. did not quantify the variability in microbial abundance and so would not have seen the abundance dynamics that Szabo et al. saw, if they had occurred. Like Szabo et al. (this manuscript), they suggested that differences in the timing of microbial colonization of particles drive a lot of the particle-to-particle variability. Bižić-Ionescu et al. also saw statistical patterns that suggested interactions, as well as expression of genes for microbial interactions, including antagonistic processes. I hope the authors will consider the possible differences between the two systems and why those might lead to different dynamics, and what that says about the robustness and environmental realism of the patterns seen in both experiments.
Comment 3: On viral contigs and non-assembled microbes
The authors consider viruses that bin into MAGs which I presume means that they are often or always part of the microbial genome of a particular organism. I am not an expert on this process, but it seems to me a reasonable way of assigning viruses to hosts. I note that other validated tools for metagenomic host assignment are also available (Zielezinski et al. 2021). I presume there are many viral contigs that did not bin to a specific MAG. Why did the authors choose to ignore these?
Similarly the authors focus only on those species that assemble into MAGs, I presume there is a bunch of microbial diversity that doesn’t assemble (since my impression is that in most communities not all sequenced contigs end up as part of a MAG). Could the authors expand on why they chose to ignore this diversity, and what impacts on their analysis only looking at assembled bacteria and not the rest of the microbial diversity might have on the analysis.
I thank the authors for sharing this pre-print in a public forum and encourage them to consider these comments.
Sincerely,<br /> Jacob Cram
References
Bižić-Ionescu M, Ionescu D, Grossart H-P. Organic Particles: Heterogeneous Hubs for Microbial Interactions in Aquatic Ecosystems. Front Microbiol [Internet]. 2018 [cited 2019 Dec 18];9. Available from: https://www.frontiersin.org...
Fuhrman J, Steele J. Community structure of marine bacterioplankton: patterns, networks, and relationships to function. Aquat Microb Ecol. 2008 Sep 18;53:69–81.
Steele JA, Countway PD, Xia L, Vigil PD, Beman JM, Kim DY, et al. Marine bacterial, archaeal and protistan association networks reveal ecological linkages. ISME J. 2011;5(9):1414–25.
Zielezinski A, Deorowicz S, Gudyś A. PHIST: fast and accurate prediction of prokaryotic hosts from metagenomic viral sequences. Bioinformatics. 2021 Dec 14;btab837.
On 2022-01-11 18:10:29, user Saeed Sh wrote:
On 2022-01-11 18:09:04, user Saeed Sh wrote:
Sounds like an interesting manuscript. I have some questions for you to understand better the content of your work.
On 2022-01-11 17:37:40, user Saeed Sh wrote:
Sounds like an interesting manuscript. Would the density of zebrafish larvae be also an issue for their locomotor activity inside circular confinements? Do they habituate differently?
On 2022-01-11 10:23:10, user Frank Kirchhoff wrote:
We appreciate the interest of Nader and colleagues in<br /> our study. However, we feel that they do not reflect our results entirely<br /> correctly. As described in our paper, we ran molecular dynamics analyses for the theoretical description of the interaction. These resulted in a distance between 3.6 and 4.2 angstroms between R403 in Spike and E37 in ACE2 and a strong interaction. Both the distance range and the strength agree with recent studies (Lim et al., Scientific Reports 2020; Williams and Zhan, J. Phys. Chem. B. 2021; Laurini et al., ACS Nano, 2021). The importance of this interaction was confirmed by mutating E37 (Fig. 3). Bix and colleagues propose that the various cell types used in our study may not express integrins. This might be<br /> the case. However, it implies that the up to 50-fold enhancing effect of the T403R change on Spike-mediated infection is not dependent on integrins. Altogether, our experimental data clearly support an important role of T403R in ACE2 interaction. Our results do not exclude the possibility that this alteration may also affect the interaction with integrins. Currently, however, this is just speculative. We would be happy to provide the authors with Spike constructs allowing experimental challenging of their hypothesis.
Best regards
Fabian Zech, Christoph Jung, Konstantin M.J. Sparrer and Frank Kirchhoff
On 2022-01-11 08:36:31, user Dr-Asif Ali wrote:
This article has been accepted in Journal of advanced Research. <br /> Please see the latest Published version for reading and citation.<br /> 10.1016/j.jare.2022.01.003<br /> A putative SUBTILISIN-LIKE SERINE PROTEASE 1 (SUBSrP1) regulates anther cuticle biosynthesis and panicle development in rice
On 2022-01-10 23:28:13, user Daniel Himmelstein wrote:
Thanks to the authors for continuing to develop this important resource, which I first became familiar with when incorporating it into Hetionet.
I recently reviewed the manuscript for a journal and am sharing my review here. Note that this review is for version 1 of the preprint posted on 2021-12-09.
On 2022-01-10 22:09:11, user Katerina Gurova wrote:
Great work! Couple questions: (i) do these data suggest that NIPBL and MAU2 are not cohesin loader, since their binding to chromatin depends on cohesin presence? (ii) if cohesin loading is random, why NIPBL/MAU2 form peaks? should it be expected that they are more or less evenly distributed through chromatin (or transcribed chromatin)? (iii) what is the state of chromatin at NIPBL/MAU2 peaks?
On 2022-01-10 09:22:42, user Kresten Lindorff-Larsen wrote:
We, the authors of this paper, have noted an error in how we set the parameter r0 as input to Pepsi-SAXS. This does not affect the conclusions of the paper. A more detailed description of the error and how it affects the results is described in an erratum which has been submitted to the Biophysical Journal on Dec 5th. This can also be found here:<br /> https://github.com/KULL-Cen...
On 2022-01-10 04:40:46, user jasonchouinard wrote:
There is a very informative video evaluation covering this paper in layman's terms at https://www.youtube.com/wat...
On 2022-01-09 14:26:27, user Zuzana Holubcová wrote:
revised version published https://www.frontiersin.org...
On 2022-01-07 12:42:15, user Pascal Malkemper wrote:
Fascinating study! Would you be able to provide some further information on how exactly you identified the regions of interest after cryosectioning the rehydrated cleared brains? Were the sections fluorescently imaged again at the PALM MicroBeam System or was the identification purely based on coordinates?
On 2022-01-07 09:57:20, user David Bhella wrote:
To help readers understand the process of peer-review, I am adding the peer-reviewer comments and article submission history for all of my preprints. For this article, although I was senior author - I was not corresponding author as the work was largely led by Dr Swetha Vijayakrishnan.
The article was rejected without review at two journals prior to being sent for review at the journal of record. It underwent two rounds of review before acceptance.
Reviewer Comments (Round 1):
Reviewer 1
In their manuscript Vijayakrishnan use Tokuyashi sections for electron microscopic imaging in the frozen hydrated state (‘cryo’). Tokuyashi sections are commonly used for immuno EM imaging in cell biology and then combined with dehydration. Direct imaging in the frozen-hydrated state results in higher molecular preservation compared to dehydration and resin embedding. The method is broadly applicable and relatively straightforward compared to cryo-FIB milling but does not allow comparable resolution levels.
It was interesting to see that this manuscript again highlights the possible usefulness of cryo imaging of Tokoyashu sections. However, on the experimental side the reviewer does not see the novelty. In particular, Bos et al (ref. 13) seems to cover all novelty claims of the manuscript (application to cell culture, correlation with light microscopy). The remaining possibly novel aspect is the analysis of viruses by subtomogram averaging, which may shed some light on the quality of sample preparation. Nevertheless, the description of methods and analysis is somewhat superficial at this point. The conclusions on the association of pUL36 remain somewhat vague and do not appear statistically significant. Given the low resolution (~6 nm) indeed not too much can be concluded. Overall, the manuscript appears to touch on many things, but there is little novelty and conclusive results.
Major points:
Page 5: “To our knowledge however, use of this method has thus far been confined to 3D imaging of tissue specimens 10-13.” This claim appears to be incorrect as Bos et al (ref. 13) applied the approach to cell culture – just as in this manuscript. Thus, it should be specifically stated which new contribution this manuscript makes to the field.
Page 5: “Here we present a modified strategy that combines correlative light microscopy and cryo-ET to locate regions of interest (ROI) in re-vitrified cell sections”. Again: what specifically is the novelty compared to ref. 13?
Page 14: “We successfully implemented this method …”. How do the authors validate their success? There are no quantifications provided. Is the method available?
Page 14: “To our knowledge this is the first attempt to implement this method on sub tomograms.” Previous implementations have already been reported in Schmid et al, PLOS Pathogens, possibly also later.
Probably the major problem in cryo-sectioning is the resulting compression. Thus, the reviewer would have expected an analysis of the effect of compression on subtomogram averages. Such analysis should be relatively straightforward given the available high-resolution structures of capsids.
The resolution of subtomogram averages appears overly low. Have the authors focused alignment and/or resolution measurement on specific parts of the capsids to compensate for compression and/or variable density in the core?
In the discussion the authors only compare cryo imaging of Tokuyashi sections to cryo-FIB milling / cryo-ET. A comparison to high-pressure freezing with freeze substitution and resin embedding should also be included.
Reviewer 2
In this manuscript, Vijayakrishnan et al. present an approach that allows the visualization of cells that have previously been fixed (cross-linking) prior to imaging using electron cryo-microsocpy. In this case the sample is subsequently vitrified in a state where the macromolecules have been chemically altered but in a way that allows direct imaging as opposed to imaging a counterstain, such as osmium or uranyl compounds. The fixation of material is normally avoided due to the significant chemical alteration of macromolecules within sample, and makes the analysis of additional densities associated with any such macromolecule a potential minefield to study. The reviewer appreciates the need to make the analysis of cellular material using cryoEM easier, but is unconvinced that performing structural biology in a background of chemical fixation is an appropriate route to go and will inevitably lead to structural information that is wrong.
The attempt to visualize viral capsids is an interesting application and is one that is sensible. The capsids in the nucleus look to have retained much of their native architecture. The argument put forward that the C-capsids in the nucleus have extra densities present seen in mature capsids is strong, but is beset by a lack of control experiments, a lack of analysis in terms of other material found in their preparations, and a lack of appropriate interpretation of secondary analyses.
The use of fixation using this approach results in the material not being in a “native” state as is the case with regular cryoEM methods. This is significant as this alteration to the macromolecular structure means that any subsequent structural analysis will be potentially affected by artefacts of this approach. This reviewer believes therefore that one must be very careful when analyzing the results of any potential structural analysis in this manuscript.
The authors have not presented the proper controls for some of the interpretation of their results.
A control is needed here to structurally analyse the herpesvirus capsid with the CVSC (positive control) after fixation – this should be relatively easy if you fix the mature virions and do sub-volume averaging on these virions to assess whether deformities in the CVSC structure are introduced. It is a gross misrepresentation to compare this structure in a fixed/unnatural/dead state to one from the EMDB determined in a frozen-hydrated state as has been done in Figure 5.
These controls also apply to the subsequent analysis to determine the architecture of the capsid pore vertex.
The capsids found in the cytosol exhibit significant breakage and are distorted when compared to those in found inside the nucleus (see figure 3) and is not commented in the manuscript. This is a significant concern as it would suggest that there is some damage to the structural integrity of potential targets. It also a shame, as the cytosolic capsids would appear to me to be a great target to compare structurally with the nuclear capsids.
This would also be a concern were anyone wanting to use this approach to target processes occurring in the cytosol, as it seems there is a greater effect on macromolecules in this subcellular compartment.
The cells are initially grown to confluency as a monolayer and then infected with the virus for 12hr. At this time point the cells are fixed which completely kills the cells. The cells are then scraped and pelleted. One assumes that after fixation of cells there is significant disruption to the structural integrity of the cells – a picture or demonstration of the state of the cells after this treatment would help to understand what exactly goes into the subsequent steps. Figure S1 shows widespread DAPI staining illustrating the point that there is significant mixing of compartments making knowing exactly what is being imaged difficult. My concern is that an additional step is needed to ascertain where is being imaged as the DAPI is almost everywhere.
The attempt to classify the capsid 5-fold vertices makes the analysis of the CVSC confusing and brings up further questions about what is really going on as the analysis done here restores the CVSC to the B-capsids.
The techniques outlined aim to address a curious debate in the herpesvirus field – namely whether the capsid vertex specific component (CVSC) is present on C-capsids on the nucleus, and it is important to frame the conclusions of the paper in this context. The protein component has multiple names that reflects the belief among different members in the herpesvirus community as to its true role or when/where/how it functions in relation to the capsid. The CVSC is made up primarily of pUL17 and pUL25 with a significant contribution of two helices from the C-terminal tail of pUL36. pUL36 is a very large protein, and its presence in the nucleus is unlikely in is full-length state. Debate continues in the field as to whether splice isoforms of pUL36 contribute to binding at the CVSC in the nucleus.
In the present study, extra density is visible on C-capsids that is not visible on other capsids types (A and B), though in the case of B-capsids this density is visible after classification. These discrepancies need to be cleared up as the resolution limitation on the capsids makes it impossible to say what components are visible on the CVSC at this point – UL17 and UL25? UL17, Ul25 and extra density (UL36?)?
Washing the sections a few times in PBS after infiltration would seem to this reviewer not wholly effective at removing73 the sucrose. Fig3 – halo around the multimembrane.
The sentence “The pentaskelion density in B-capsids is more prominent than C-capsids; likely owing to far greater numbers of B-capsids (526) used during processing than of C-capsids (125). These data support our suggestion that low occupancy of CATC on B-capsids led to weaker density in icosahedrally averaged density maps. They are clearly visible upon asymmetric reconstruction (Figure 6), but not during symmetric reconstruction (Figure 4).” The significance of this analysis is not clearly explained.
Introduction is far too long – I suggest the authors rewrite in order to make it more concise and streamlined i.e. significance of SPA, the play-off between cryoET and classical methods and the need to find more approachable methods. This introduction could be written with the same effect in half the space.
It would really help the reader to have a correlative figure in the supplement (for example in S1) that goes from light microscopy
Figure 1.<br /> The DAPI stain is present in the field of view of both cytosolic and nuclear regions – why is this?
It is very hard to discern in this figure how the determination of what is nucleus and what is cytoplasm is made.
Figure 2.<br /> Why have the authors excluded fluorescence data from this figure? One would assume this would be the most effective use of their correlative approach as it is possible to actually discern cellular features directly through EM here.
The segmentation in Figure 2b is something of an eyesore. I would redraw or redesign a mean of highlighting the membranes.
It is not easy to see the different types of capsid with this annotation- an A-capsid is not highlighted (left of field of view) for example, and the box is not immediately obvious. Why box and arrow? Why not all box or all arrow?
Panel d is completely unannotated. Why is there a halo around the multilamellar vesicle (that is not a CTF effect)?
Figure 3.<br /> The authors should comment on why the capsids in panels A and B look undamaged, while those in C and D exhibit significant damage/deformation.
Figure 4.<br /> Why do the densities interface between the capsid coat and the inner regions blur as you move from A to B and C? A myriad of cryoEM structures of viral capsids have been determined and do not exhibit such an artefact.
Figure 5.<br /> The comparison in this figure is not appropriate at all for a number of reasons: the structures are determined via different means (fixed vs non-fixed), the structures are at completely different resolutions which I consider to be a cynical attempt to improve how the authors’ own data appear - the figure on the right should at least be presented at the same resolution as the authors. The colour scheme is inadequate to show the CVSV, which should be the only thing visible here to help the reader to see what the authors are referring to in its entirety.
Figure 6. <br /> The data in this figure are in conflict with those shown in Figure 5, and leads to some confusion. Symmetrically-determined 5-fold vertices are classified in an asymmetric manner. Therefore, the number of icosahedrally-related positions for the C-capsids remains the same. The data suggest that if you relax the symmetry then the CVSC density on the C-capsids smears due to low numbers – but this seems completely illogical to me. Why would an ordered density smear? Remember that this structure can be refined to ~3.5Å in cryoEM. If the occupancy in B-capsids is too low to get an effective CVSC in an icohedral reconstruction why would it be better in an asymmetric classification unless the structure of the CVSC is different to that of A-capsids? What happens if you reduce the number of particles from each each virus type to be the same number? Does the B-capsid density also smear?
Once again, using the EMDB structure as shown in C is inappropriate.
Figure S1.
It is almost impossible to know how the authors came to the determination that these are different regions of the cell from this figure. This figure makes it clear that it is hard to determine what parts of the cell belong to where.
Reviewer Comments (Round 2):
Reviewer 2
It is a shame that the current pandemic has resulted in the shutdown of the Authors’ Institute, and the reviewer would like to express their sympathy for this situation. Hopefully, things will change in the coming months.
The lack of experiments validating either of the method or the major results (putative CVSC density on the capsid surface in the nucleus) is still a major concern, and without such experiments it is not possible for the reviewer to recommend publication.
Publishing a single structural result at low resolution and without further validation either from comparison to other subcellular structures (e.g. ribosome or cytosolic capsids) or using biochemical means (e.g. immunolabelling with nanogold of CVSC components) adds to the confusion in the literature as to whether the CVSC is present, in part, whole or not at all, in the nucleus or not and such a publication would not be beneficial. Should analyses on other components also lead to structures that exhibit no difference to results previously published one can be more confident in novel results – though still not 100%.
It is still unclear to this reviewer how exactly capsids are determined to be nuclear from the analysis of Figures 1, 2, and S1. While it is possible to see regions of membranes, the fact that the cells are disrupted using their methodology combined with the presence of DAPI in multiple regions adds to the confusion as to whether the nuclear capsids are indeed nuclear capsids. In Figure 1, it is possible to make out blue dots and red dots separate from one another and together. Capsids also containing DNA and would likely be stained by DAPI. This is not followed on in Figure 2, which is annotated manually. Is it assumed all capsids away from membranous regions are nuclear?
In Figure S1, the caption says nuclei are stained with DAPI. Everything seems to be stained with DAPI.
Figure 4 separates A-, B- and C-capsids. C-capsids would be more prevalent in the cytosol as this is a sign of maturity. Through following Figure 1 and Figure 2 it is not clear how the isolation of populations from subcellular components is achieved. The authors should think about how to make this process clearer.
In terms of the method itself, the Authors propose this as a relatively easy method for routine examination of macromolecules in situ. This should mean that subcellular structures should not be difficult to determine and well know samples should be examined.
This reviewer would like to re-assert the point that chemical fixation in a background macromolecular milieu is prone to artifacts. As such, fixation in cellulo vs in vitro is different. This is reflected in a statement that remains in the text of the manuscript:
“The use of chemical fixative may cause some structural artefacts, possibly contributing to the low resolution of capsid structures in our study (5-6 nm), in comparison to resolutions obtained from subtomogram averaging of proteins from unfixed cryo-ET of for example purified virions (0.8-2 nm).”
In the Authors’ rebuttal, they make the point that gradient fixation methods have been previously employed to determine structures of macromolecular complexes. However, the objective of these methods is to stabilise complexes of recombinantly expressed and isolated macromolecules that are prone to falling apart under buffer conditions. Furthermore, the complexes are known as they are biochemically characterised. The original grafix paper (Kastner et al., 2008) argues that the potential for the technique to improve structure determination is due to homogeneity, and this is borne out by the citations of that article.
There is also a contradiction in logic; if chemical fixation is one stated factor potentially limiting the resolution of the capsids in this manuscript, why then are grafix methods elsewhere able to be used to determine high-resolution structures. Is It due to the presence of cross-linked entities or due to lack of particles? Such questions are why I feel the need for more work is required to validate the major finding.
Finally, a 40-60Å structure is not equivalent to a 3-4Å structure and should not be presented as such.
Reviewer 3
In this manuscript, Vijayakrishnan et al describe the in-situ structures of HSV-1 capsids within the nuclei of host cells determined by subtomogram averaging, coupled with correlative light and electron microscopy (CLEM) and cryo-electron tomography (cryo-ET) of re-vitrified cell sections. Although at low resolutions, the reconstructions of the three types of capsids show the major components of penton, hexon and triplex. In addition, the C-capsids within the nucleus have extra densities, contributed by the capsid-vertex specific component (CVSC), are readily observed. The structural work is interesting in that the authors demonstrates an economic, easier and high-throughput approach to determine the in-situ structures of viruses using re-vitrified sections. However, a number of overstatements or concerns have to be corrected or be addressed before the publication.
In the abstract on page 2: “Our reconstructions reveal that the capsid associated tegument complex is present on capsids prior to nuclear egress.” This is an overstatement. Previous single particle cryo-EM works have demonstrated that the CVSC binds to capsid prior to nuclear egress. (Conway at al., JMB 2010; Homa et al., JMB 2013; Dai et al., Science 2018 and Ref 29)
In the introduction on page 6: “Our data reveal the presence of the CVSC pentaskelion on HSV nucleocapsids in the nucleus, suggesting that capsids may bind the tegument protein pUL36 (VP1/2) prior to nuclear egress.” This is again an overstatement. Previous single particle cryo-EM works have already revealed the presence of the CVSC on HSV C capsids purified from the nucleus.
On page 10: “There has been uncertainty in the HSV field of how pUL36 and pUL37 are recruited to the capsid, if this happens within the nucleus or after nuclear egress. To shed light on this question, we carried out cryo-ET on the mutant lacking pUL37 (FRΔUL37).” Given the low resolution of the HSV capsid reconstruction determined by the authors, this work has no help to solve the uncertainty of how pUL36 is recruited to the capsid.
Page 14: “Moreover, our analysis revealed pronounced star-like CVSC density at the penton vertex in the C-capsids, comparable to previously reported high-resolution structures of capsids within purified HSV-1 virions.” The CVSC density of nucleocapsid from virion are obviously better than the counterpart from the C-capsid. While the nucleocapsid shows strong CVSC densities extending from the penton to the triplex Ta, the C-capsid shows a much weaker and smaller tegument densities that only bind to the penton of C-capsid (Fig. 5).
Page 15: “Our method opens the possibility of determining and characterising specific complexes and their interactions at high-resolution within the functional context of the cell or tissue, providing snapshots of important and dynamic events in biology.” Given the poor resolution of the HSV capsid determined in this work, this statement is hard to be justified.
Page 20: “The subvolumes were subjected to 3D classification with a T value of 5, to reconstruct a single 5-fold vertex, without refining orientations and origins. A total of 10 classes were calculated with one of them identified to have apparent pentaskelion density over the 5-fold axis, corresponding to CVSC, in both B-capsids and C-capsids. " It is well established that all the vertices of HSV C-capsid and virion nuelcocapsid are fully occupied by CVSC, why only one of the ten classes has apparent pentaskelion density over the 5-fold axis in the C-capsid?
Legend for Figure 5 on page 22: “high-resolution structure of purified capsids from within the nucleus at an equivalent resolution. " This sentence should be corrected. At first, the structure is from virion nucleocapsid not from nuclear capsid; Second, the structure has already been filtered to low resolution and could not be stated as high-resolution.
On 2022-01-07 07:07:34, user Hamim Zafar wrote:
Congrats on this article. Given the importance of these concepts, it will be a very helpful article for the community. Please consider citing MARGARET (see https://www.biorxiv.org/con..., which is our new method for inferring cellular trajectory. MARGARET provides an end-to-end framework for trajectory inference and downstream tasks. It can automatically detect terminal states. Furthermore, it can compute cell fate plasticity (aka differentiation potential) along the inferred trajectory and it generalizes the computation of differentiation potential for disconnected trajectories (improvement over Palantir, only other published method that computes differentiation potential).
On 2022-01-06 21:41:01, user Andrew Read wrote:
Really interesting study! I'm curious to see if the mC changes are heritable and how they change over time if the transgene is crossed out.<br /> I'd really like to see the specifics of the Target Region in Fig 3 - where is the EBE, which C's look methylated etc. -- partially because I'm curious about the ampBS-seq result in Fig2 -- is this just reporting methylation on one strand?
On 2022-01-06 16:15:09, user Dr. Martin Stoermer wrote:
Haven't taken it all in yet but I do have a quick Q. Why do you refer to the catalytic dyad as non-canonical when His41/Cys145 is pretty standard for most (all?) betacoronaviruses
On 2022-01-06 15:51:07, user Mattia Deluigi wrote:
Interesting study with a challenging protein. Nice to read about the conformational dynamics of this GPCR.
A few constructive suggestions:
The introduction states that the structure of the NTS1-Gq complex is available (..."ternary complexes with both the heterotrimeric Gq protein..."). However, it is Gi, not Gq. Figure 3E should also be corrected.
Have you considered discussing your findings in the context of these two other publications?<br /> "Probing the conformational states of neurotensin receptor 1 variants by NMR site-directed methyl labeling" (https://doi.org/10.1002/cbi... and "Activation dynamics of the neurotensin G protein-coupled receptor 1" (https://doi.org/10.1021/acs....
You could explain that SR142948 has inverse agonist properties. According to the definition, a true neutral antagonist would be expected to have no impact on the receptor conformational equilibrium.
The CWxP motif could also be mentioned among the conserved microswitches in the introduction.
In the introduction, it could be specified that the ~50% decrease in the volume of the orthosteric ligand-binding site upon agonist binding is compared to the volume of inverse agonist-binding sites. In addition, I believe that the term "extracellular vestibule" is not entirely correct here because the contraction of the ligand-binding site reaches relatively deep in the TM bundle. Orthosteric ligand-binding site or cavity or pocket is possibly the more suitable term.
In Figure 1A, the colors of the bars are different from what is written in the corresponding caption.
All the best,<br /> Mattia
On 2022-01-06 08:28:03, user David Bhella wrote:
To help readers understand the process of peer-review, I am adding the peer-reviewer comments and article submission history for all of my preprints. This article presents the work of a number of students and post-docs that passed through my lab over many years, we attacked the problem from a number of different directions before we achieved an interpretable structure, through the application of Cryo-electron tomography and sub-tomogram averaging.
The paper was rejected without review by two journals. We made it out to review at the next journal we submitted to, but unfortunately the article was rejected following one negative review. I found the quality of that review rather disappointing, but the journal refused our appeal (see below).
Fortunately we had a far better experience at the journal of record where the paper was handled by a very supportive editor and peer-reviewers were positive about our work. The review process there is transparent, the critique is available on the publisher site.
Here is the peer-review report that led to the paper being rejected.<br /> Thanks to reviewer 2 for their constructive report. Reviewer 1 - not so much.
Reviewer: 1
This is a paper that might have been submitted 10 (or even 20) years ago, but is so far from current standards in cryo-EM that I have no enthusiasm for seeing it published, even in a more specialized journal. The authors talk about how the problems frustrated attempts at a Fourier-Bessel 3D reconstruction, but it has been many years since people used such approaches. Modern software, such as Relion or cryoSPARC, all use iterative realspace methods for helical reconstruction. The analysis of the lattice is based upon one horribly noisy power spectrum from one tube. Many other large diameter tubes have been studied at high resolution, and almost all of these involve variability in diameters. The authors should look at Kalia et al., Nature, 2018 on Drp1 tubes, or Junglas et al., Cell, 2021 on PspA tubes to see how such problems are routinely treated. The paper is filled with statements such as how the features they see are "morphologically very similar to previously described decameric and undecameric rings produced by recombinant expression of RSV N" or how "making accurate measurements of the lattice was challenging" or "leading to these densities appearing to be more closely packed in the sub-tomogram average than they actually are". Given all of this, I found all of the modeling highly questionable.
Reviewer: 2
General comments
RSV is an important human pathogen and the main cause of bronchiolitis in newborn children. There is no vaccine nor efficient antiviral compounds against this virus and the exact architecture of virions remains to be deciphered. In this work, the authors have used cryogenic electron microscopy (cryoEM) and cryogenic electron tomography (cryoET) to study the architecture of real RSV particles. They also used a particular technique to obtain these impressive data, the growing of RSV particles directly on transmission electron microscopy grids before flash freezing. This important detail was critical to obtain original filamentous viral particles instead of heterogenous and anarchic shaped virions as seen in previous publications. The use of a 300 keV electron microscope allowed images of unprecedented high quality, revealing a couple of quite unexpected results: (1) viral particles are much more organized than expected; (2) the matrix layer is formed by M-dimers geometrically organized as a curved lattice; (3) the presence of ring-shaped assemblies, likely formed of the nucleocapsid protein N and RNA and packaged within RSV particles in addition to the helical, long and filamentous viral genome encapsidated by the N protein; (4) there is a helical ordering of the glycoproteins on the virus surface (5) … that tend to cluster in pairs.
The structural data presented in this manuscript are novel, convincing and make a significant contribution to the field. The data show for the first time that RSV particles exhibit helical symmetry at two levels, the matrix protein and the surface glycoproteins.
Using the previously resolved atomic structure of M dimers, they modeled the lattice of M dimers that coordinate virions assembly and helical ordering of the glycoproteins at the surface of virions.
The viral genomic RNA, 15 kb in length, is encapsidated by the viral nucleocapsid protein (N) to form a left-handed helical ribonucleoprotein complex. However, when N was previously expressed as a recombinant protein, N-RNA rings were obtained in bacteria or using the baculovirus system; but their presence, their role in infected cells and their possible presence in viral particles was totally unknown. The presence of RNA-N rings in viral particles was unexpected and intriguing result, raising new questions, in particular do these N-RNA rings packaged in virions play a role in the viral cycle or are they packaged incidentally? Do they contain some specific RNAs? The images indicate that they are located around the central nucleocapsid containing the viral genome.
Specific comments
Although the paper is well written, there are a lot of references which are not the right ones, missing or misplaced:
Introduction
“The viral RNA is encapsidated by multiple copies of the viral encoded nucleocapsid protein (N) to form a left-handed helical ribonucleoprotein complex (or nucleocapsid - NC).» Reference 6 (Bakker et al., 2013) should be placed at the end of this sentence as well as ref 14 (Liljeroos et al., 2013).
« This serves as the template for RNA synthesis by the RNA dependent RNA polymerase (RdRp)6,7”: the demonstration that Nucleocapsid serves as a template for the polymerase was not shown in references 6 & 7. This assumption was for a long time inferred from data obtained with paramyxoviruses and rhabdoviruses. In Garcia et al., 1993 (doi: 10.1006/viro.1993.1366), transient coexpression of RSV N and P proteins in eukaryotic cells resulted in the formation of cytoplasmic inclusions that resembled the inclusion bodies found in infected cells. In Garcia-Barreno et al., 1996 (doi: 10.1128/JVI.70.2.801-808.1996), the interaction domains between P and N were identified, then further in Slack and Easton, 1998 (doi: 10.1016/s0168-1702(98)00042-2), Khattar et al., 2001 (doi: 10.1099/0022-1317-82-4-775), Castagne et al., 2004 (doi:10.1099/vir.0.79830-0.), Tran et al. 2007 (ref 33), Asenjo et al., 2008 (doi: 10.1016/j.virusres.2007.11.013). Sourimant et al. in 2015 (doi: 10.1128/JVI.03619-14) showed that P binds L through its C-terminal region, which was confirmed by Gilman et al. (ref 10).
“… thought to occur in virus induced cytoplasmic organelles called inclusion bodies8,9. »: should refer to Rincheval et al., Nat Commun. 2017 too (doi: 10.1038/s41467-017-00655-9), which was the first paper showing that viral RNA synthesis occur in inclusion bodies for RSV.
“The RdRp comprises two proteins: the catalytic large (L) protein and the phosphoprotein (P) that mediates the interaction with the NC 10. »: again, the reference 10 only describe the structure of the PL complex.
“the matrix protein (M), which coordinates virion assembly together with M2-1 ». The role of M2-1 in the architecture of RSV is still debated. Although the location of M2-1 between M and the nucleocapsid was suggested by Kiss et al., 2014 and Liljeroos et al., 2013, Meshram and Oomens in 2019 (https://doi.org/10.1016/j.v... have shown that P, M and F are sufficient for the formation of viral pseudoparticles, which wasconfirmed by Bajorek et al., 2021 (doi: 10.1128/JVI.02217-20). Furthermore, incorporation of N in VLP did not need M2-1 (Forster et al., 2015 ref 15; Fig.6A). Although in Li et al. 2008 (doi:10.1128/JVI.00343-08) some experiments suggested that M2-1 is needed to recruit M to inclusion bodies, this was denied in Bajorek et al., 2021 who also showed that M directly interacts with P.
“M2-1 forms a second layer at the virion interior, under the M-layer, and associates with NCs 13,14. »: again, I think the authors have transformed a hypothesis into an assertion considered as definitively accepted.
“High resolution structures for some of the envelope associated proteins of both RSV and HMPV have been determined by X-ray crystallography, including the matrix proteins 15-17 the F glycoprotein 18,19 and M2-1 20,21. »: again, the first structure of RSV M2-1 was published by Tanner et al., 2014 (doi:10.1073/pnas.1317262111).
Results<br /> Legend of Fig.2: the authors highlighted with colors the presence of glycoproteins, M protein and M2-1 protein on the tomogram (“The lipid bilayer is highlighted in pale blue, the matrix layer in orange and the M2-1 layer in dark blue. »). Although there can be no ambiguity for surface glycoproteins, concerning M and mostly M2-1 the situation is more uncertain. A formal demonstration of the presence of these proteins would require additional experiments such as immunogold labeling (not compatible with cryo-EM) or corelative microscopy. Could it be for example the phosphoprotein? Although highly disordered, this protein could be compacted and folded in the viral particles. The authors should be more prudent and talk of probable or putative localization for these last two proteins like they do in the text where they say “Underlying the lipid bilayer is a contiguous density that we attribute to the matrix protein (M). ».
“The virion interior is densely packed with viral nucleocapsids, mainly having the characteristic herringbonemorphology 31 and suggesting that in common with several other mononegavirales, RSV virions are polyploid 32 (fig 3A, movie S1 timepoint 1m 08s). »: on the picture and in the movies we only see one continuous helical nucleocapsid. Were several nucleocapsids in the same axis along the filamentous viral particles? Were several parallel nucleocaspids observed in some portions? In Fig.3A the herringbone structure is placed at the centre of the viral filament; was it always the case? Was the length of nucleocaspids as expected or were there some truncated genomes?
The presence of N-RNA rings in the viral particles was unexpected and very surprising; the authors say : “….strongly suggest that many of these objects may indeed be N-RNA rings, perhaps being products of aborted genome replication. ». Can the authors exclude that these objects could contain cellular RNAs? Recombinant expression of RSV N protein has shown that there is no apparent sequence specificity for RNA encapsidation. Cellular short RNAs such as tRNA could also be encapsidated in rings.
On 2022-01-06 08:17:02, user David Bhella wrote:
To help readers understand the process of peer-review, I am adding the peer-reviewer comments and article submission history for all of my preprints.
This article was rejected without review at one other journal prior to acceptance after peer review in the journal of record;
Reviewer #1 (Comments for the Author):<br /> In this manuscript, the authors describe the structure of virus-like particle (VLP) of vesivius 2117 using high-resolution cryo-EM. By comparing the structure of the major capsid protein of VP1 of 2117 to the VP1 of other known calicivirus structures including other Vesiviruses such as San Miguel Sealion virus (Chen et al., PNAS 2006), and feline calicivirus (Ossiboff et al. JVI, 2010, not cited in this paper; and Conley et al., Nature 2015), they show that 2117 VP1 exhibit significant differences in the P2 subdomain. They further contend that VP1 of 2117 is more similar to the VP1 of rabbit hemorrhagic diseases virus belonging to Lagovirus genus in the Caliciviridae.
The manuscript is short and succinctly written. The structure determination by single-particle cryo-EM is technically sound. The results are interesting showing divergence of the structural features, although to be expected, in the P2 (sub)domain that is responsible for receptor interactions and cell entry processes. However, firstly, because the entire focus of the manuscript is to show that 2117 exhibits major structural differences in the P2 subdomain compared to other vesivirus structures, and secondly that the authors state that the density at the distal tips of the P2 domain was noisy and difficult to interpret, the following major comments must be addressed.
Major Comments:
1) The authors should include a figure comparing the P2 subdomain sequences of 2117 with that of SMSV and FCV to make a better sense of the observed structural differences, perhaps indicating 1) which regions in the P2 subdomain in 2117 the density is poor, 2) which regions are difficult were difficult to interpret and 3) where the model fitting was poor.
2) Fig S2 with a dark background is difficult to assess the quality of the cryo-EM map and the fitting of the model to density. Consider a white background, it would be helpful to indicate the residue number at some residues. Authors should also consider including representative regions in the density map along with the model in the S and P1 (which I suppose show better definition and fitting).
3) Although the overall model fitting statistics are summarized in the Methods section, authors should provide such statistics for the P2 subdomain region.
4) It is not clear as to what software was used for initial data processing, 2D classification, and 3D reconstruction prior to post-refinement using Relion.
Reviewer #2 (Comments for the Author):
In this short report, the authors describe the cryo-EM structure of the vesivirus 2117 virus-like particle formed by baculovirus-expression of the VP1 gene in insect cells at 3.6 Angstroms and compare it to the structure of FCV strain F9 virion (PDB 6GSH) that the authors have previously published. The authors note that the S domain and N-terminal arm structures are conserved and similar between both structures, but not differences in the protruding domain. Specifically, they note a 22 amino acid insertion between beta strands 2 and 3 of the P2 beta barrel (residues 409-468 in VP1) of FCV. Previously, the authors have shown that this insertion forms a 'cantilevered' arm that upon binding of the FCV receptor JAM-A lifts towards the receptor exposing a cleft in the side of the P2 domain that can accommodate a helix of the VP2 protein. The authors show that the structures of vesivirus 2117 VLPs differs from that of the FCV-F9 virion. Based on this difference the authors note that the absence of the cantilevered arm in vesivirus 2117 leads to its more rounded capsomere; they further suggest that these differences indicate major functional differences in receptor engagement and VP2 portal assembly between the FCV clade of the vesiviruses and other caliciviridae. The paper is well written and easy to follow. My main critique is that the ideas regarding major differences in receptor engagement and VP2 portal assembly between the FCV clade and the vesivirus 2117 are supported by the structural differences between a virion (FCV-F9) and a VLP (2117 without any confirmatory data. Clarification of wording within the text and more conservative language in conclusions would strengthen the report.
Points:
Please make clear in the text that the structural comparison is between a VLP and an intact virion; perhaps add a caveat about possible structural difference imposed by the presence of the genome and VP2.
Is it possible that major functional difference in receptor engagement do not occur and that the conformational changes upon engagement with the functional receptor of Vesivirus 2117 lead to structural changes similar (albeit not identical) to that of FCV with insertion of VP2 helices into a groove in P2? If so please add a sentence stating this.
Is it possible that the cantilevered arm hides a neutralizing epitope that is present in vesivirus 2117 and that the acquisition of the loop insertion allowed the FCV clade to better evade immune detection? If this is possible, please add a sentence to address this possibility.
Minor edits
Results and Discussion, paragraph 2 states that the VP1 subunits are labelled as A, B, and C in Fig 1D. This labelling is absent from Fig 1D. Please add in labelling to this figure panel and clarify each subunit based on color in figure 1 legend as well.
Figure 1 legend wording implies that both panels E and F are shown with rainbow coloring, but only panel F has this feature. Add rainbow coloring to panel E (preferable) or adjust wording in Fig 1 legend.
Please incorporate Figs S1 and S2 into the manuscript to meet JVI's policy. The two movies are fine - nice movies!
Reviewer #2 (Supplemental Material Comments):
The supplemental material consists of two movies which do meet the criteria and two figures, which don't. The author's should incorporate the two supplemental figures into the report.
On 2022-01-06 01:37:06, user Jacob Roberson wrote:
Hi everyone. I see you've gotten accepted. Two last minute suggestions for the acc ver: First "While researchers have long sought..." sounds like a contrast but it's really two agreeing ideas. I suggest "While researchers have long sought to understand short-term adaptation, decreasing sequencing costs in recent years have made it increasingly practical." instead. --- And secondly: "As a further check for contamination, we checked IBD..." seems to need a "whether" between "checked IBD".
On 2022-01-05 23:53:36, user tdlieberman wrote:
The following review of “Rapid and parallel adaptive mutations in spike S1 drive clade success in SARS-CoV-2” by Kistler, Huddelston, and Bedford was completed by Tami Lieberman and students in the 2021 HST.S56 class This review was sent to the authors directly as well; we are posting this review publicly in an effort to normalize scientific discourse in science.
Adaptive evolution is difficult to study on short time scales, particularly in asexual organisms. This manuscript analyzes the evolution of SARS-CoV-2 since its introduction, and is timely and impressive in its ambitions. Never before has so much data existed on a single virus or organism, particularly in the earliest stages of a pandemic. SARS-CoV-2 provides a unique well-powered opportunity to study the genomic loci driving adaptation and its tempo, as multiple lineages have evolved convergently and therefore provide the opportunity for meaningful statistical inferences. As such, this manuscript is of great importance to fundamental biology and applied understanding of the ongoing pandemic. The manuscript convincingly demonstrates that strong adaptive evolution drove genomic changes in SARS-CoV-2, with a large portion of that adaptive evolution concentrated in spike S1. The finding of signatures of adaptation with single nucleotide resolution is of strong clinical relevance and compelling. We have some significant concerns about inference of changing selective pressures and the new method of detecting selection via logistic growth.
Major points<br /> 1) Changes in dN/dS over time might be expected even under constant selection pressure:<br /> Figure 2 makes a visual argument for increasing adaptive pressure on S1 over time. This visual argument is so impactful that it has been largely interpreted on social media and elsewhere as evidence that adaptive selection in S1 is driven primarily by immune evasion. This connection makes sense, as pressure from the human immune system has almost certainly increased as the pandemic has progressed. <br /> However, this analysis might be confounded by the fact that dN/dS measured in asexual populations strongly depends upon the age of the variants included in the analysis -- with more recent mutations showing a more elevated N/S ratio (DOI: 10.1016/j.jtbi.2005.08.037). While the mechanisms driving this often-observed pattern are not completely resolved, the most common explanation is that weakly deleterious mutations are observable on short time scales, but purged from the population on long time scales-- purifying selection takes time to act.<br /> Therefore, even if there was constant adaptive pressure to change the amino acid sequence in S1 since the start of the pandemic, it might be possible to observe an increase in N/S over time from the analysis conducted in Figure 2. Some amino acid changes in S1 are likely adaptive, while others are mildly deleterious. Mildly deleterious mutations can be observed/measured at the tips of the tree, but are highly unlikely to be observed on internal nodes leading to long-lived lineages. Unless we are misunderstanding something, the analysis is conducted in a manner such that mutations dated to earlier in the pandemic are more biased towards long-lived lineages. While ‘early’ mutations include some that do not reach high frequency nor persist for long periods of time (because of inclusion of sequences from each timepoint), rare lineages that were only detected later in the pandemic also contribute to ‘early’ mutations -- while rare lineages that died out (perhaps because they possessed some deleterious mutations) are detected less frequently. This leads to a bias towards successful lineages for ‘early’ mutations but not for ‘late’ mutations. Therefore, an increase of dN/dS over time could be observed in this analysis without any change in selective pressures. To determine that a change in the strength of adaptive pressures occured, an analysis could be performed that removes the impact of purifying selection. For example, an analysis that only considers external leaves on the tree, with a separate tree built for each timepoint considered, might remove this confounder. <br /> It would be helpful to see Orf8 and other genes with high rates of nonsynonymous mutation in Figure 2 for comparison to S1.
2) As metrics of N/S are dependent upon time since the most recent common ancestor, it is expected that N/S should be lower in influenza than a newly emerged pandemic virus. In addition, since this topic is of interest to a wide audience, care should be taken to ensure that comparisons of N/S ratios are not confused with rates of adaptive change.
3) Other questions about increasing selection pressure:<br /> Figure 2 and 3 appear to have a common link-- in that S1 mutations are favored beginning sometime in mid to late 2020. However, all the specific lineage trajectories shown in Figure 3 and the corresponding supplement have an accumulation burst that plateaus before the start of 2021 -- while the dN/dS curve for S1 in Figure 2 continues to increase during this time. Do the authors have an explanation for this? <br /> The clustering of S1 mutations over time is an interesting and important finding. If we are understanding correctly, S1 mutations are not just clustered in time, but are also found on the same exact branches of the phylogeny. If this is the case, it is possible that such mutations reflect compensatory or epistatistic mutations -- for example, a potentially adaptive mutation at one site might require another mutation to compensate for a tradeoff in protein folding. If epistasis at adaptive S1 loci is strong, the finding of clustering of mutations in time would not necessarily reflect a time-dependent selective pressure. To address this the authors might repeat the simulations in Figure 3 while counting mutations that occur on the same exact branch as a single mutational event. If S1 mutations are not on the same exact branch of the phylogeny, the authors might directly explore or reject the possibility of compensatory mutation by showing the ordering of mutations in the S1 protein (consistent ordering would suggest epistasis as in DOI:10.1038/ng.1038 and DOI:10.1038/ng.3148).
4) Calculating growth rate is tricky from biased observational data, and technical choices and confounding variables are capable of creating noise and/or bias that might limit the utility of an approach based on logistic growth rates.<br /> --Growth rate estimates are based on changes in relative frequencies derived from data that was normalized to have equal global distributions at each timepoint. However, the global incidences of disease were not equal at every timepoint. As such, the frequencies of clades in regions with low incidence will be overestimated in this approach, while also being subject to more stochastic noise from superspreading events. This may lead to spuriously high increases or decreases of clade abundances over time. Some analysis with a normalization based on global incidence (perhaps restricted to a geographic area with stable approaches to measuring incidence) or simulation of how sampling impacts downstream results would be helpful for understanding the strengths and limitations of this new approach.<br /> --The values on the y-axis in Figure 1A and Figure 1B -- number of mutations per clade -- are not independent. Different clades within the same lineage (e.g. variants of Delta) will all have both high growth rate and and high # of S mutations because of their shared ancestry. If a lineage happened to contain both a high number of S mutations and an unrelated adaptive mutation, any variants within this lineage would have both properties. As such, it would be important to see these analyses calculated within lineages, or with completely unrelated lineages.<br /> --The comparisons between genes made in terms of correlation of logistic growth rate and number of mutations are not supported by statistics on the uncertainty of the correlation coefficient. It is unclear if the differences between genes and between N vs S mutations are impacted by the number of mutations in each group or how many mutations are needed to get significant signals.<br /> --It is not clear exactly what is meant by ‘the last 6 weeks’ in regards to the calculation of logistic growth rate presented in Figure 1 and elsewhere. Is this the last 6 weeks in the timecourse studied or the 6 weeks after the lineage emerged? In Figure 4C, it appears that logistic growth rate is calculated at different time intervals.<br /> --Logistic growth rates are strongly impacted by competition -- a lineage with an adaptive mutation might decrease because there is another more fit lineage competing with it. Therefore, there is expected to be high variation in growth rate among lineages with the same mutations, and Figure 4A-B suggest this is the case. This might limit the power of this approach. Have the authors considered using the max growth rate instead? <br /> --It would be helpful to see more discussion on the limitations of the method proposed here. What conditions are needed for this method to be accurate and informative? What conditions would break the method?
Minor: <br /> How exactly the number of mutations is calculated in Figure 4A and B should be clarified in the text and methods. Figure 4C makes it seem that convergence is calculated within a particular time interval (as some intervals show convergence while others don’t), but this isn’t indicated anywhere. If convergence is calculated in a given time interval, it would be interesting to understand how these analyses change when convergence is calculated on the whole tree.
The legend in Figure 5B “Mean increase in mutations after specified event” is unclear and at first read was interpreted as a rate. A rate or other normalized value would be more informative here than the absolute number of mutations, as the specified events occur at different times. Numbers of mutations might be normalized by time, compared to the total number of mutations genome-wide after the event, or compared to the number of S mutations in each gene after the event.
The introduction addresses important challenges in identifying evolution on short time scales. The authors should also note the challenges for organisms that do not recombine frequently. In particular, genome-wide hitchhiking (draft) complicates scans for adaptation based on allele frequencies-- because neutral or even deleterious alleles can reach high frequency if on the background of an adaptive mutation. These drafted mutations increase noise, thereby decreasing the signal/noise ratio.
The authors should discuss the minimal correlation found between the number of independent occurrences of a mutation and the mean logistic growth rate for those mutations at more length. We expect convergence to be a strong indicator of positive selection, so the lack of correlation here requires discussion in the context of logistic growth rate method. One possibility is that these mutations are measuring different things. For example, convergent mutations occurring during a particular time window might reflect mutations that are only adaptive on particular genomic backgrounds, or they might reflect mutations that are adaptive for immune evasion with some hosts but incur a pleiotropic cost for spreading efficiency.
It would be helpful to standardize how mutations in Orf1 or its subunits are presented between all figure panels and the text.
On 2022-01-05 15:20:37, user Maxine Holder wrote:
Hi there, thanks for sharing this interesting work! Do you know whether depletion of Rif1 reduces the association of Yap with chromatin? Or the other way round?
On 2022-01-04 16:21:32, user Black Wang wrote:
It's very surprising that you still see LC3 lipidation in ATG5 or ATG3 KO. It does not make sense at all.
On 2022-01-04 11:24:28, user Andy wrote:
for updated version (WETA), please see:<br /> http://wyanglab.org:3838/weta<br /> ( http://wyanglab.org:3838/weta/weta-tutorial.pdf )
On 2022-01-04 09:20:01, user Abraham De silva wrote:
FRAP recovery curves provided in the manuscript and fitting done to estimate t ½ (s) are meaningless. Especially Figure 2b, 3b, and Figure 5f. By definition, t ½ is the time necessary for 50% recovery. It is surprising that in all exponential recoveries after the photo-bleach (0th time), the next time point corresponds to more than 60% of total recovery (Figure 5f). It is the same for all of them; how t ½ can be estimated and compared in this situation. Authors should work on the basics of FRAP data analysis and fitting before making any novel claim. Classical FRAP (at this current FRAME acquisition rate) is too slow to detect the differences. It is the same for some of the recoveries shown in Figures 3b, 2b as well. Authors should re-measure those systems with higher FRAME rates (faster data acquisition, will give more data points before 50% recovery happens) to correctly calculate the real t ½ or use a quicker FRAP method.
On 2022-01-03 19:32:32, user Bin wrote:
Latest version can be found in https://elifesciences.org/articles/73585
On 2022-01-03 19:28:11, user Maria Ana Duhagon wrote:
We noticed a mistake in the abstract that will be amended in a new version of the manuscript soon. <br /> "Our results suggest that the 7mer-m8 seed could be more repressive than the 7mer-A1,..." should have been Our results suggest that the 7mer-A1 seed could be more repressive than the 7mer-m8,...". As is shown in Figure 2B. We apologize for the mistake.
On 2021-12-28 14:05:41, user Maria Ana Duhagon wrote:
This in silico study of cancer RNA-seq data surprises us with its power to identify the regulatory action of the silky tiny microRNAs.
On 2022-01-03 15:33:29, user Jorge Fonseca Miguel wrote:
Light is essential for plant life. The effect of light conditions on<br /> in vitro regeneration of cucumber was studied. This species is<br /> one of the most economically important vegetable crops. Optimized<br /> regeneration systems in selected genotypes are required to achieve<br /> workable efficiency for biotechnological approaches, such as<br /> large-scale multiplication and crop improvement through genetic<br /> transformation techniques.
p { margin-bottom: 0.1in; line-height: 115%; background: transparent }
On 2022-01-01 15:07:58, user hualan_liu wrote:
Very neat technology for arraying mutant libraries!
On 2022-01-01 13:22:23, user 苏韶枫 wrote:
Please check out this paper also: https://www.osapublishing.o...
On 2021-12-31 13:49:44, user Asim Debnath wrote:
The article has now been published in the journal "Viruses 2022, 14(1), 69"; https://doi.org/10.3390/v14... (registering DOI). The title of the article changed to "Discovery of Highly Potent Fusion Inhibitors with Potential Pan-Coronavirus Activity That Effectively Inhibit Major COVID-19 Variants of Concern (VOCs) in Pseudovirus-Based Assays"<br /> A link to the publication is forthcoming from bioRxiv.
On 2021-12-30 16:35:07, user Heidi Arjes wrote:
This preprint does not have a link to the peer-reviewed published article in Current Biology: https://www.cell.com/curren...
On 2021-12-29 11:29:57, user David Quain wrote:
I see this has now been published. My comments below were totally ignored. Peer review totally failed to flag previous work in this area. Disappointed with the process and the authors.
On 2021-12-28 03:10:16, user PRASENJIT MITRA wrote:
The existing dogma is <br /> A+B=AB where A= GLP-1, B= GIP ; AB= GLP1R GIPR dual agonist. The new concept we proposed A'+ B'=C'.<br /> A'=Ex-4, B' =Oxm c terminus (considered not useful)<br /> C'= GLP1R GIPR dual agonist.<br /> Like Oxyntomodulin and GIP, I-M-150847 has scope at C terminus to evolve.
On 2021-12-23 08:11:40, user Gabriel Netsari wrote:
Hi, <br /> Both rs2032640 and rs546062461 are absent from IAM samples, therefore it is impossible to know if they are E-L19 or E-M81. Furthermore, IAM.05 is B-L1388 according to YSEQ Cladefinder.
Cordially.
On 2021-12-22 18:44:38, user Robert Matthews wrote:
Hi guys - interesting paper.; great to see progress on this approach to reducing the reliance on animal models.
I do have some concerns about the sensitivity/specificity values stated in the paper, though - and their implications for the evidential weight provided by the models you test.
The true-positive, false-negative etc numbers making up the 2x2 contingency tables are pretty small, making normality assumptions unreliable. Best practice in such cases is to use Wilson's method (see eg Altman et al 2000 "Statistics with confidence" Ch 10). This leads to point estimates and upper/lower 95% intervals for the sensitivity somewhat different than those in Table 5a/b.
My principal concern, though, is about the specificity, which is stated to be 100%. Given that your study is based on a (small) sample, one really should reflect the uncertainty surrounding the values. Applying Wilson's method leads to typical point estimates for the specificity of around 72%. The upper bound is 100%, but the lower bound is typically around 44%.
Combined with the sensitivity data, this suggest the positive Likelihood Ratio for the various models to be around 2 or so - somewhat less than the infinite value implied by taking the specificity to be 100%, as stated in the table!
Given the importance of the sensitivity/specificity calculations for this type of work, perhaps you could have another look at the values in Table 5a/b.
Looking forward to seeing how the paper progresses!<br /> All the best<br /> Robert
On 2021-12-22 17:42:31, user Astenb wrote:
Typo in Line 395 - hitherto undescribed subclade of **L01a1.**
On 2021-12-22 14:08:08, user reinator wrote:
We have published a peer-reviewed version of this paper on http://doi.org/10.1007/978-...
On 2021-12-21 11:08:56, user Yoshitaka Moriwaki wrote:
Your statement-1: <br /> "The interaction analysis of spike-omicron has revealed that this variant has strong interactions with the ACE2 receptor compared to WHU strain and delta variant. The spike-ACE2-delta complex has almost similar binding affinity with spike-ACE2-WHU complex".
Comments:<br /> For the affinity, Did you used the gbsa (ΔG bind) technique or is it just docking energy? Because the binding strength or affinity(Ki) is determined by the binding free energy, ΔG bind such as gbsa, pbsa or more ensemble TI, FEP extracted via molecular dynamics simulations. Also docking which may be efficient but not particularly accurate; they can be used to predict binding modes not affinity.
statement-2<br /> The strong binding affinity of spike-omicron with ACE2 may result in increased transmissibility and infectivity of the variant. This can be explained by looking at the spread of virus...
Comments:<br /> Again what does "stronger binding affinity" means here because I can't see any validation of your statements or results based on dynamics. Better to validate via dynamics.
On 2021-12-21 07:57:24, user Jose E Perez-Ortin wrote:
The manuscript by Swaffer et al. is a very interesting paper that demonstrates for the first time that the RNA pol II complex is the limiting factor for transcription rate of protein-coding genes. This is an important new finding that, although suspected by previous researchers, was not studied in any organism (to our knowledge). Another important result is the finding that about 50% of the RNA pol II molecules are bound to chromatin. This confirms previous studies made by different laboratories that found a significant part of the RNA pol II not to be actively transcribing (Struhl 2007; Borggrefe et al. 2001; discussed in Pelechano et al., 2010). The fact that not all (or most) RNA pol II molecules are not bound to chromatin, however, does not affect the models that assume that the strong affinity constant of this RNA pol for their targets is what causes an increase in the number of elongating RNA pol II. This increase will be reflected in nascent transcription rate (nTR, see Pérez-Ortín et al 2013 for a detailed discussion) with cell volume increase (Lin and Amir, 2018; Marguerat and Bahler, 2012; Padovan-Merhar et al., 2015; Sun et al., 2020; Zhurinsky et al., 2010; Mena et al., 2017). This is because if the binding depends on the general mass action equilibrium, as Swaffer et al. propose, the increase in nTR will still occur even with 50% of the RNA pol II molecules unbound to chromatin. The key reason is that the number of RNA pol II molecules per cell does not increase in proportion to cell volume, but less than that. This is another important result described in Swaffer et al.: RNA pol II concentration decreases with cell volume. This result, however, was already demonstrated four years ago by our group (Mena et al. 2017) although is not cited by Swaffer et al. The difference between our results and those of Swaffer et al. is that we found no increase at all in RNA pol II molecules per cell within the range of cell volumes studied, whereas Swaffer et al. found that there is an increase but less than required for scaling.
That difference is striking because we demonstrated that nTR does not scales at all with cell size (Mena et al., 2017) in 5 different circumstances: two meta-analysis of published data from P. Cramer group; after α-factor release in synchronized<br /> cultures; by western blot of Ser2-phosphorylated Rpb1; and using a set of cell size mutants that include whi5 and cln3 similarly to those used by Swaffer et al. For our analyses we utilized data obtained with different techniques (cDTA, GRO, western blot) and different physiological situations of cell size change.<br /> The data from Swaffer et al. also come from various different experimental setups. We have no explanation for this discrepancy. However, the results from Swaffer et al. lack explanation for two important questions: the reason for the<br /> decrease in RNA pol II concentration and the ultimate reason for this apparently abnormal behavior of S. cerevisiae cells.
In our publication (Mena et al. 2017) we answered those two questions. The decrease in RNA pol II concentration is due to a particular behavior of the mRNAs that encode for their main subunits (Rpb1,2,3). On the contrary to the rest of the transcriptome (see below) these mRNAs that maintain their nTR (as most of the others) do not suffer a compensatory stabilization: they are outside of the feedback mechanism argued by Swaffer et al. The global mRNA stabilization feedback mechanism was also demonstrated by us in our 2017 paper (Mena et al., 2017). This is again a satisfactory coincidence with Swaffer et al. results but was, again, not cited in their manuscript.
Finally, but very importantly, the reason for the abnormal behavior of S. cerevisiae that seems to behave differently from other eukaryotes studied (Marguerat and Bahler, 2012; Padovan-Merhar et al., 2015; Sun et al., 2020; Zhurinsky et al., 2010) is that it is a budding yeast. That is, it divides asymmetrically. As we shown in our study (Mena et al, 2017) the models based in an increase in nTR with cell volume to keep the production of mRNAs (synthesis rate) constant predict a never ending increasing mRNA synthesis rate in smaller daughter cells along the successive generations. This is a key conceptual problem. We developed a model (scenario #3 in Mena et al., 2017) that seems to be partially similar to the<br /> dynamic equilibrium model of Swaffer et al. in the sense that both assume that RNA pol II concentration decreases with cell volume. However, there is a very important difference between the two models. If the decrease in RNA pol II concentration were not strictly inverse to the cell size increase (i.e. constant nTR for RNA pol II-encoding genes) the conceptual problem would not be solved. Only a perfect maintenance of nascent transcription rates for RNA pol II-encoding genes would solve the asymmetric cell division problem.
A corollary of this discussion is that perhaps the results of Swaffer et al. presented as a general model for eukaryote RNA polymerase II dynamics with cell size are only valid for budding yeasts given that the previous publications on the limiting factor model referred to symmetrically dividing cells.
Additional References not cited in the manuscript:
-Struhl K (2007) Transcriptional noise and the fidelity of initiation by RNA polymerase II. Nat Struct Mol Biol 14: 103–105.<br /> -Borggrefe T, Davis R, Bareket-Samish A, Kornberg RD (2001) Quantitation of the RNA polymerase II transcription machinery in yeast. J Biol Chem 276: 47150–47153.<br /> -Pelechano V, Chávez S, Pérez-Ortín JE. (2010). A complete set of nascent transcription rates for yeast genes. PLoS One. 5(11):e15442.<br /> -Mena A, Medina DA, García-Martínez J, Begley V, Singh A,<br /> Chávez S, Muñoz-Centeno MC, Pérez-Ortín JE. (2017). Asymmetric cell division requires specific mechanisms for<br /> adjusting global transcription. Nucleic Acids Res. 45(21):12401-12412.<br /> -Pérez-Ortín JE, Medina DA, Chávez S, Moreno J. (2013. What do you mean by transcription rate?: the conceptual difference between nascent transcription rate and mRNA<br /> synthesis rate is essential for the proper understanding of transcriptomic analyses. Bioessays. 35(12):1056-62.
On 2021-12-20 23:15:33, user Tianyu Liu wrote:
Hi, I wonder if your team participate in the competition: https://openproblems.bio/#a..., task1. If yes, could you please give me some hints about how to run your scJoint from .h5ad file? Thanks a lot.
On 2021-12-20 19:58:34, user Carles Fèlix Calatayud Aristoy wrote:
Brilliant idea! We are currently giving it a try in the lab!
On 2021-12-20 05:59:52, user Joe wrote:
This paper would be a lot more compelling if the authors could demonstrate phosphorylated Rap actually binds Raf in vivo, otherwise its just simulated nonsense.
On 2021-12-17 21:57:24, user Sam Lord wrote:
This manuscript explores the fascinating interactions between actin networks and clathrin pits. The key findings are that the amount of actin recruited to pits does not seem to correspond to the maturity of the pits, that the actin network likely grows both laterally and inward from the base of pits, and that actin seems to counteract membrane tension. The imaging is interesting and the super-resolution view of clathrin coated pits are nice.
The manuscript also presents evidence that Arp2/3 activity assists pit completion, as CK666 causes longer clathrin lifetimes. The later data about clathrin coat height and actin height are less convincing, because it is unclear whether these results are from multiple rounds of treatment or from one experiment. The authors could strengthen their manuscript by bolstering those later results (in Figs 3-4). The authors claim that Arp2/3 inhibition has the opposite effect when under membrane tension than it does under isotonic conditions. This is very intriguing, but a reader cannot tell if the results are replicated sufficiently to support the claim. How many biological replicates are reported in Fig 3H? In other words, how many times was a sample exposed to the treatment (e.g. CK666)? The p-values should be calculated using the number of biological replicates, not the number of pits measured.
On 2021-12-17 19:21:28, user HY wrote:
This article has just been accepted. You can check the final version here. <br /> https://www.sciencedirect.c...
On 2021-12-17 17:33:28, user Eddie James wrote:
The T cell epitope part of this story seems underdeveloped. Do you need some assistance?
On 2021-12-17 16:24:18, user °christoph wrote:
very interesting study! I didn't see it mentioned in the text, so I'd like to ask whether your linear plasmids carry phage N15-type protelomerase genes (or the host chromosomes?) or are more of the Streptomyces-type with end-bound protein(s)?
On 2021-12-17 09:24:12, user Jocelyn Étienne wrote:
Note that the movies are included, click "Data/Code".
On 2021-12-17 02:57:44, user Rachel Brunetti wrote:
Dear bioRxiv community,
Our initial observation that WASP puncta form in the absence of Cdc42 was due to an overexpression artifact. Upon endogenous tagging of WASP in Cdc42-null cells, we find that the formation of WASP puncta is greatly compromised. This observation is followed up in the revised manuscript, which was recently accepted for publication. A link to the publication will be forthcoming.
Thank you,<br /> Rachel Brunetti
On 2021-12-15 14:53:46, user Albaniza L R do Nascimento wrote:
General comments:<br /> The work developed by Brito and collaborators studies the central viroma of triatomines, as this is little known. The works published in this sense discuss the presence of only one virus already known to infect triatomines of the genus Triatoma infestans, called Triatoma virus. Therefore, this research aimed to investigate the composition of the Rhodnius prolixus viroma, which is totally unknown, in order to elucidate the insect-virus relationship and understand possible impacts of viral infections on the life cycle of these triatomines. Furthermore, the authors discussed the antiviral system of these insects and proposed the use of RNA interference (RNAi) as a tool in vector control strategies. <br /> As a result, the authors identified 7 new (+)ssRNA viruses in R. prolixus, labeled Rhodnius prolixus Virus 1-7 (RpV1-7), and observed that these RpVs are vertically transmitted from females to offspring. Furthermore, a system, active during the oogenesis of R. prolixus, that uses RNAi to protect the germline and embryos of this insect from viral infections has been reported. In this way, RpVs are able to maintain persistent and systemic infections without causing damage to the host, due to a reduction in the viral load.<br /> Overall, the work has an interesting study proposal, as it can contribute to a better understanding of the microbiota present in triatomines, how its transmission occurs, and raises questions about its importance and possible vector control strategies for Chagas disease. However, I would like to highlight some items that can be seen as limitations and propose suggestions for the authors to analyze.
Major comments:<br /> 1) At the end of the Results, it was not clear to me whether the RNAi machinery of the adult females was transferred to the eggs or if they, through this machinery, produced dsRNA against the viral sequences and deposited them in the mature eggs (page 16).<br /> 2) In the discussion, the possibility of influences on the insect's vectorial capacity and possible implications for its reproduction in the presence of these viruses could be addressed. Are these viruses essential for oogenesis to occur correctly? How does the absence or presence of these viruses impact the biology and physiology of the insect? These questions were raised in order to understand the real importance of the presence of these viruses in R. prolixus.<br /> 3) When analyzing other works on virus detection in triatomines, I noticed that all of them conducted their experiments based on the purification of viral particles from insect samples (stool and intestinal contents, for example) (https://doi.org/10.1590/S00..., https://doi.org/10.1016/j.j..., http://dx.doi.org/10.1016/j...:oQPXM9viVd57j4SxrTTuYQ_PpNU "http://dx.doi.org/10.1016/j.ram.2017.04.008)"). Furthermore, in some of them, detection of the viral particle was performed using transmission electron microscopy (TEM) (https://doi.org/10.1099/002.... As in this work the analyzes were performed based only on RNA transcriptomes, I believe it would be interesting to confirm the presence of these viruses in insects by identifying their viral particles, and not just their RNA sequences.
Minor comments:<br /> 1) I would suggest the authors to reorganize the ideas in the introduction to ensure continuity of the subject to be covered. As it's written, the ideas seem to be out of order. I suggest the following construction: introduce the triatomines and medical relevance, R. prolixus and its oogenesis, host/virus interaction, antiviral defense by RNAi, objectives and previous results.<br /> 2) Orthography should be corrected in the first paragraph of Materials and Methods, page 20: “Previtelloegenic stages and...”<br /> 3) I consider that Figure 1A is unrelated to Figures 1B-E and its title “Phylogenetic analysis of the R. prolixus viruses”. I suggest that this figure be placed separately from the others, keeping as title its explanatory sentence “Schematic of an ovariole in R. prolixus”.
On 2021-12-15 07:13:28, user Ujváry István wrote:
I am wondering about the enantiomeric composition of synthetic LFT used in the study. Was it a racemic mixture or the most potent (-)-cis enantiomer. The configuration as depicted throughout the figures of the paper is shown as (3R,4S) and the discussion of binding interactions relates to this enantimomer. To the best of my knowledge, however, the only publication on the actual X-ray structure based stereochemistry indicates (3S,4R) configuration for this substances and it is this the isomer usually called 'lofentanil' [Tollenaere, JP, Moereels, H, & Van Loon, M (1986) On conformation analysis, molecular graphics, fentanyl and its derivatives. In Progr. Drug Res. (Jucker, E., Ed.) pp 91-126, Birkhauser Verlag, Basel]. I am aware of several SECONDARY sources indicating the configuration as in this paper and there is confusion even in SciFinder!<br /> In case racemic mixture was used in thiis otherwise elegant study, could it be that during crystallization only one of the enantiomer was bound to MOP leaving the poorly binding unbound enantiomer in solution? <br /> Stereochemistry matters!
On 2021-12-13 15:01:53, user Dr Josh Berryman wrote:
Please note that we do not refer to spike (S) protein in this paper.<br /> Our findings mainly discuss ORF6 and ORF10 and do not have relevance to any current vaccine.
On 2021-12-13 14:31:41, user Hellen Marianne wrote:
The work developed by Serra and collaborators investigated the role of flavonoid quercetin supplementation in experimentally induced hypertensive animals, evaluating molecular and homeostatic parameters of metabolically active organs, such as: pancreas, liver and adipose tissue. The results suggest that supplementation with quercetin, via gavage, exerts beneficial effects on lipid and glucose metabolism, modulating antioxidant enzymes, insulin signaling and secretion in hypertensive animals. Furthermore, the use of quercetin positively interfered with systemic blood pressure, reducing it and reversing the renal hypertrophic effect caused by the induced stenosis of the left renal artery in 2K1C rats. The antioxidant, anti-inflammatory and enzymatic modulating roles played by quercetin have already been described in experimental models of obesity and diabetes, and the investigation of insulin signaling in a model of renovascular hypertension is the gap that the article aims to fill. Although the results are promising and based on the scientific literature, the experiments carried out were not able to delimit which pathway quercetin interferes with the homeostasis of pancreatic islets. I believe the cause-and-consequence relationships were not well designed and therefore the results do not tell a linear story. Therefore, some methodological and literature-related limitations will be pointed out.<br /> 1) The relationship between insulin secretion, action and signaling in a model of renovascular hypertension was not adequately correlated at the beginning of the introduction (page 3). Perhaps bringing data from experiments with humans, located at the beginning of the discussion (page 9), and, if applicable, showing the modulated signaling pathway(s), would better corroborate the correlation proposed by the authors. Furthermore, in the introduction, angiotensin II is frequently mentioned and generates expectations that it will be investigated during the study, which is not the case. As hypertension, in the renovascular case, the focus of the work, is a secondary disease resulting from a primary metabolic disorder, the association with insulin metabolism and signaling should have been better developed, citing, for example, the role of the kidneys in homeostasis of glucose;<br /> 2) The chemical structure of flavonoids determines their antioxidant action, therefore, the choice of quercetin specifically, and not any other flavonoid, as the object of study was not argued. Physicochemical parameters intrinsic to the molecule should be evaluated, such as the formation and identification of metabolites and solubilization. Furthermore, as this is an in vivo animal model, pharmacodynamic parameters – absorption, distribution, metabolization and excretion – must be considered in order to determine the causal effect of the administration of the molecule in question; <br /> 3) The absence of the Sham group (false operated) exposed to quercetin impairs the analysis of the results of the other groups and the effect of quercetin in the proposed model. Furthermore, as this is a metabolic investigation in animals, the type of food provided (type, brand, etc.) must be detailed;<br /> 4) Regarding the administration of quercetin, it is necessary to present the acceptable daily consumption rate of this flavonoid, so that the meaningfulness of the results takes into account the level (low, intermediate, acceptable or supraphysiological) of intake presented and the reason for choosing such dosage (50 mg/kg/day). Thus, it would be fundamental to present a concentration-response curve (using lower and higher concentrations than the one tested, at least in Figure 1, items “b”, “d” and “g”); <br /> 5) Regarding the preprint formatting, the fact that the figures and the text clash makes the reading less dynamic; <br /> 6) It would be interesting to illustrate, with photographs, the kidneys (left and right) of each animal group, in order to verify their appearance and not just the mass (figure 1, item “d”); <br /> 7) Considering that the effect on energy metabolism is the focus of the work, the investigation of lipolytic (PPARα, AGTL) and lipogenic (LXR, SREBP1c) pathways could have been considered to better conclude the work. Likewise, the activity of isolated enzymes could have been evaluated in vitro in order to better consolidate the enzymatic modulation of quercetin in the experimental model in question; <br /> 8) Many of the Western Blottings (figures 4 and 5) present smear and low quality, thus clashing with the graphic results due to the variability, mainly in figure 4, items “a”, “f” and “h”, which hinders the final conclusion of the article. The inclusion of the uncropped gels in a supplementary material would help with this problem. <br /> 9) Part of figure 5 seems to be loose in the article, for example, in the text there is scarce contextualization and scientific validation for the investigation of muscarinic receptors M1 and M3; <br /> 10)The relationship between the antioxidant effect promoted by quercetin in adipose tissue cannot be directly related to the expression of SOD-2 and catalase enzymes; <br /> 11)It would be interesting to evaluate the modulation of quercetin in apoptosis and inflammation pathways, for example, to relate the possible protective effect to pancreatic islets;
On 2021-12-13 12:45:28, user Thais Lopes Pinheiro wrote:
In this article, Matteo Bocci et al. first evaluated the expression of ACE2 in brains and observed that it is mostly expressed in pericytes. After the analysis of post-mortem brain tissues of COVID-19 patients and controls, they observed a correlation between neurological symptoms and increased ACE2 expression. They also correlated viral infection with injury to the blood-brain barrier and inflammation. Finally, they evaluated the levels of soluble PDGFRβ in the cerebrospinal fluid of COVID-19 patients. <br /> The data to support their conclusions were: through multiplexed immunostaining, the expression of ACE2 receptor was shown to be restricted to a subset of neurovascular pericytes. The authors hypothesized that viral infection to brain pericytes could lead to the disruption of blood-brain barrier, and indeed leakage of vascular fibrinogen into the central nervous system was observed associated with infected samples. They indicated the possible disruption of brain pericytes homeostasis because they observed significantly low levels of soluble PDGFRβ in the cerebrospinal fluid of infected patients. <br /> Overall, this is an interesting study that brings insights into the topic COVID-19 in an elaborate way. In order to improve the manuscript, I´ve made some comments/suggestions bellow as following:<br /> 1) In the introduction page 4, it would be interesting to bring a more detailed approach to brain pericytes: what they are, their location in the neurovascular network, their importance in many neurovascular functions such as angiogenesis, formation and protection of the blood-brain barrier, vascular stability and regulation of blood flow.<br /> 2) In the immunohistochemical methodology, it would be interesting to add how the images were quantified and which parameters were used to classify ACE2 immunoreactivity into low, moderate and high levels.<br /> 3) In the first part of the study of ACE2 expression with post-mortem tissues, it would be interesting to define more clearly its study groups: COVID-19 without neurological symptoms, COVID-19 neurological and controls. Indicating which neurological symptoms were taken into account for such classification and what type of control is being used<br /> 4) Still in the study of ACE2 expression, I felt a lack of images from the other COVID-19 group (e.g., without neurological symptoms) that would represent its non-expression. Would it be possible to include these new data as supplementary figures?<br /> 5) In Figure 1(D): Please include in the legend the abbreviations used, indicating the meaning of Non-ICU and ICU.<br /> 6) In Figure 2(A), SARS-CoV-2 detection step in tissues: identify which controls are being shown, as was done for the COVID-19 images.<br /> 7) In the part of the study where they are measuring the pericytes marker PDGFRβ in cerebrospinal fluid samples, evaluating their homeostatic status and taking into account that a homeostatic imbalance of brain pericytes could lead to a series of disturbances (inflammation, disruption of the blood-brain barrier). I would suggest to include analysis of other inflammatory markers such as IL-6, TNF-β, IFN-ɣ, GM-CSF. Extra inflammatory markers, would give greater robustness to the findings<br /> 8) In general, the authors satisfactorily structure the final discussion, bringing other studies to compose their arguments and results obtained.
On 2021-12-13 12:42:07, user Christophe Grosset wrote:
Paper now available on Communications Biology website: https://www.nature.com/arti...
On 2021-12-13 09:11:24, user Dimitris Petroutsos wrote:
Dear Colleagues at Umeå University, thank very much for your positive feedback on our work, for the time you spent reviewing this this preprint and for all your detailed <br /> and helpful comments. We appreciate a lot this nice initiative! Best regards, Dimitris Petroutsos (for the authors)
On 2021-12-10 16:52:55, user Alizée Malnoë wrote:
The manuscript by Ruiz-Sola et al. investigates the relationship between photoprotection responses, carbon concentrating mechanisms (CCM) and CO2 availability in Chlamydomonas reinhardtii. While photoprotection responses, mediated by LHCSR3, LHCSR1 and PSBS, are traditionally described as triggered by excess of light, this manuscript highlights the role of intracellular CO2 levels (both deriving from the environment and from mitochondria metabolism) in regulating these responses. Indeed, it demonstrated that photoprotection, and especially LHCSR3-mediated responses, are from one side inhibited in conditions in which inorganic carbon is largely available and abundant (acetate and external CO2 supply) and on the other side induced in conditions of reduced CO2 availability. Furthermore, CCM are also induced under high light (HL), in response to a drop in intracellular CO2 levels due to increased photosynthetic carbon fixation.
While changes in the expression levels of both LHCSR3 and CCM genes at different CO2 concentration and under HL respectively, were previously reported, this manuscript has the novelty to connect these observations in an elegant experimental set up with several genetic backgrounds to confirm and prove their hypothesis through the use of mutants affected in mitochondrial respiration and of metabolic modeling. The proposed model for light-independent regulation of photoprotection is convincing and solidly backed-up by data. In addition a role for CIA5 in positively regulating LHCSR3 (and to a lesser extent PSBS) mRNA expression and in negatively regulating LHCSR1 at the post-transcriptional level is shown.
However, we have some comments and suggestions to improve the manuscript, listed below.
Major comments <br /> Figure 3, and corresponding result paragraph pages 6 to 8:<br /> - A large part of the results (1.5 pages) focuses on modelling the interaction between acetate metabolism and intracellular CO2 levels. Although we are not experts in mathematical modeling and thus we are unable to give proper feedback regarding this part of the paper, we think it adds small value to the main results of the paper. This is especially true as the modelling relies on a number of assumptions (listed at the bottom of page 7) which are not supported by literature nor experimental data, weakening the solidity of its conclusions. As it is, only assumption iv (page 7, “the acetate uptake is low (...) for the mutants (as indicated in Fig 2C and F)” is backed up by data. <br /> We suggest moving figure 3 to Supplementary material and shorten its description in the results and discussion. Please also provide better support to justify the assumptions i to iii, as well as the assumption that photon uptake is not altered in the mutants (e.g. do they have similar chlorophyll content?) and make the conclusions more solid.<br /> - Page 6, “In line with the experimentally observed values, we found that the predicted generation times for the icl and dum11 strains (...) did not differ from those of LL grown WT cells”. Please, provide the experimental values for the mutant strains, or rephrase the sentence.
In Figure S1F to K: <br /> - During exposure to L2, the basal fluorescence Fo’ in the presence of acetate (and to a lesser extent CO2) is rising together with the maximal fluorescence Fm’. Please provide explanation or hypotheses for this fact, and if it might or not affect ETR and NPQ calculations. <br /> Also consider replacing “qE” with “fast-induced fluorescence quenching” or simply “NPQ”, as other regulation mechanisms might affect these fluorescence measurements.<br /> - Please precise the time points you used for assessment of Fo, Fm, and calculation of qE.<br /> To make this figure more understandable please provide clearer fluorescence traces in Figure S1 (C-K), showing only Fo, Fm and Fm' (ideally one plot for each genotype to be consistent with Y(II) and NPQ plots, L-N and O-Q) and a separate panel with Fo and Fo'.
Figure 6B and corresponding text page 11:<br /> - Please provide an explanation for the cia5 mutant line accumulating high LHCSR1 protein and not fully reverting to wild type level in the complementation line under VLCO2 (and dark/ air). This aspect needs to be taken into account and clarified, especially in light of CIA5 proposed role as LHCSR1 regulator at the post transcriptional level. Rephrase this sentence “However, LHCSR1 protein over-accumulated in the cia5 mutant under all conditions tested, although the WT phenotype was only partially restored in cia5-C (Fig. 6B)” as this the case only for HL/air.
Minor comments <br /> Title: Please add “algal” to the title, or a similar clarification.<br /> Introduction:<br /> - Page 3, when mentioning carbonic anhydrases (CAH) as part of the CCM please list the ones involved in CCM. Not all CAH are part of CCM (also it is useful to see their names, since the expression levels of some of them are measured in the results part). <br /> - Page 4, in the sentence "Here, using genetic, transcriptomic and mathematical modelling approaches, we demonstrate that the inhibition of LHCSR3 accumulation and CCM activity by acetate is at the level of transcription and a consequence of metabolically produced CO2" please replace "transcriptomic" with "expression analysis on selected genes", since no transcriptomics work has been shown in this manuscript. <br /> - Page 4, please reformulate the sentence "This work emphasizes the critical importance of intracellular CO2 levels in regulating LHCSR3 expression and how light mediated responses may be indirect and reflect changes in internal CO2 levels resulting from light intensity dependent, photosynthetic fixation of intracellular CO2". Based on the previous reports and from this work, we can say that internal CO2 levels are important in regulating activation and inhibition of LHCSR3-photoprotection mechanisms, BUT it does not mean that the light effect is indirect, this has not been proved yet. Furthermore, photoprotection by NPQ could lead to diminished CO2 fixation rate (especially sustained “photoinhibitory” quenching types), thereby increasing internal CO2 concentration which would according to your model repress photoprotective genes. This could be the case for genes involved in qE but may not be a general rule for “photoprotection”. The title could also reflect that aspect by specifying NPQ, qE in lieu of photoprotection.
qRT-PCR results:<br /> - qRT-PCR results are described here as "mRNA accumulation". Please replace this nomenclature with "relative expression levels" or "relative gene expression".<br /> - It is stated in the methods, page 17, that the results presented are normalized on a reference standard gene, GBLP. However, the results presented seem to be (also?) normalized on the WT LL air. Is this correct? If so, please precise or clarify it. Instead of normalizing the data to the WT LL air, we suggest normalizing the transcript abundance of the target genes in each sample to your internal reference standard gene (GBLP) only. <br /> - Please provide a description on how the relative gene expression levels were calculated. We suggest calculating by determining the ΔCt levels of the sample compared to the standard and the 2^(-∆Ct) as final value.
Paragraph "LHCSR3 transcript accumulation is impacted by acetate metabolism": <br /> - page 4, it is not clear in here the transition between TAP and HSM media.<br /> - page 4, rest of the text and figures legends, please indicate CO2 concentration in ppm (according also to figure 6D) instead of 5% CO2.<br /> - icl-C line not behaving the same.
Paragraph "CO2 generated from acetate metabolism inhibits accumulation of LHCSR3 transcript and protein": <br /> - Page 5, “RHP1 (...) encodes a CO2 channel shown to be CO2 responsive and to accumulate in cells growing in a high CO2 atmosphere”. It is unclear here if RHP1 is sensitive to intracellular, extracellular, or both levels of CO2. Please better describe how the protein levels reflect the intracellular CO2 concentration.<br /> - Since Figure 1 includes results both described in this and in the previous paragraph, we suggest grouping the results described in Fig1 in a single paragraph and make a shorter but clearer description of the results.<br /> - Fig 1: you could merge Fig 1A and C in a single plot with WT icl, icl-C and dum 11 in LL and HL to make the comparison between the mutants clearer. Also, the same can be done for the panels B and D.
Paragraph “Impact of carbon availability in other qE effectors”<br /> - Page 8, "We took HL acclimated cells that typically accumulate both LHCSR3 and LHCSR1 proteins (Fig. S2A) and performed photosynthetic measurements in the absence or presence of 20 mM sodium bicarbonate; the bicarbonate addition was just before performing the photosynthetic measurements. As expected, bicarbonate enhanced rETR (Fig. S2B) and….almost completely suppressed qE despite the fact both LHCSR3 and LHCSR1 had accumulated in the cells (Fig. S2)". The accumulation of these proteins was not checked in presence of bicarbonate in this particular experiment (the bicarbonate was added shortly before measuring photosynthetic parameters). Please, rephrase the sentence.<br /> - Page 9 and Figure 4B and Figure 5C " PSBS protein accumulation could not be evaluated because it was not detectable under the experimental conditions used. " It is surprising you could not detect PSBS in these conditions (600 uE), while it was possible in the conditions described in Fig 6B. At least the HL conditions (600 uE) were the same in these two experiments. Please provide an explanation for this, or if it is not possible, rephrase without mentioning PSBS expression and accumulation in the text and for clarity reasons remove Fig4A. <br /> Paragraph “CCM1/CIA5 links HL and low CO2 responses”<br /> - Page 9, "To elucidate the molecular connection between photoprotection and CCM, we analyzed mRNA accumulation from the CCM genes encoding LCIB and LCIE (involved in CO2 uptake), HLA3, LCI1, CCP1,CCP2, LCIA, BST1 (Ci transporters), CAH1, CAH3, CAH4 (carbonic anhydrases) and the nuclear regulator LCR1, all previously shown to be strongly expressed under low CO2 conditions (see (49)for a review on the roles of each of these proteins and (45)for the more recently discovered BST1)." Please provide the whole name for the reported abbreviation of the proteins that were not mentioned earlier in the text.
Paragraph “Intracellular CO2 levels regulate photoprotective and CCM gene expression in the absence of light”<br /> - Page 11 and Figure 6C: the figure is unclear, making the quantification hard to pick up and understand. Please consider replacing the “LHCSR3 (r.u.)” line above the panel by a histogram clearly displaying the LHCSR3/ATPB ratio; add error bars. If no repeats/error are available, please refrain from using these quantification data and rephrase the paragraph page 11 to replace quantitative statements ("...which was reflected by a 3-fold change in the accumulation of the protein…", "and 21 fold (protein) compared to air dark conditions (Fig. 6A-C)...", "...and protein level (by a factor of~9)...") by qualitative ones.<br /> - Page 11, "This CIA5-independent regulation of mRNA in the presence of light could account for the contribution of light signaling in LHCSR3 gene expression, possibly via phototropin (10)" This should be discussed properly in the discussion section.<br /> - Page 11, “the cia5 mutant did not accumulate significant amounts of LHCSR3 protein under any of the conditions tested (Fig. 6B)” The lack of LHCSR3 in HL in the cia5 mutant is quite striking considering that its transcript level is quite high and similar to wild type. Please provide a possible explanation for this observation.<br /> - Page 12, please replace " in accord" with "in line" or "it fits the hypothesis" <br /> - Page 12, Fig 6E, for clarity, please develop the statement "In contrast to LHCSR3, sparging with VLCO2 only partly relieved the suppression of transcript accumulation for the CCM genes in the presence of DCMU (Fig. 6E)". For instance, consider adding “..., bringing it back to LL levels instead of the accumulation observed in HL in the control (see dotted line in Fig. 6E)”.
Discussion<br /> - Page 13, "Increased CO2 levels were found to dramatically repress LHCSR3 mRNA accumulation, in agreement with previously published works (34, 35), but had little impact on accumulation of LHCSR1and PSBS transcripts". It is hard to say if it has a little or no impact on PSBS gene expression. We suggest not putting emphasis on the PSBS expression levels difference.<br /> - Page 14, beginning of last paragraph, “Our data demonstrate that most of the light impact on LHCSR3 expression is indirect”. Please tone down these sentences and discuss them with regards to the recent study by Redekop et al. (ref. 46). We suggest replacing this sentence with "Our data demonstrate that besides LHCSR3 gene expression variation together with changes in the light environment, it is also tightly linked to CO2 intracellular changes”. <br /> - Page 14 "It is tempting to propose that CO2 could be considered as a retrograde signal for remote control of nuclear gene expression, integrating both mitochondrial and chloroplastic metabolic activities". This sentence is very speculative, although clearly marked as such. To further soften the point, please consider adding “Further studies will have to be carried on to confirm or infirm this possibility”. <br /> - Page 15 "The CIA5-independent light-dependent induction of photoprotective genes possibly involves phototropin, as previous shown (10), but may also involve retrograde signals such as reactive species (46, 77). Our findings also highlight the need to develop an integrated approach that examines the role of CO2 and light, with respect to CO2 fixation, photoreceptors, and redox conditions on the regulation of photoprotection and to consider photoprotection in a broader context that includes various processes involved in managing the use and consequences of absorbing excess excitation". If you want to discuss photoprotection relationships with photoperception etc you should give more context, otherwise it is not easy to catch for people who are not familiar with this possible connection. The data of this manuscript do not show any experiments related to photoperception, yet and it has been mentioned in four times in the paper. In our opinion this does not fit in the discussion of this manuscript.<br /> - Data S2A, please replace “reaction names” by “enzyme names”.<br /> - Figures S1C to K, Figure S2C, Figure S4A to C, it is stated that the fluorescence is normalized to Fm, when it seems to be normalized to the maximum fluorescence reached during the experiment (highest Fm’ point). Please correct either the figures or the legend.<br /> - Figure S2B, it is stated that the statistical analyses are shown in the graph, though they appear to be missing.
Maria Paola Puggioni and Aurélie Crepin (Umeå University) - not prompted by a journal; this review was written within a preprint journal club with input from group discussion including Alizée Malnoë, Jingfang Hao, André Graça, Pierrick Bru, Jack Forsman.
On 2021-12-13 03:40:59, user kellen westra wrote:
Pre-print Review on “Copper(II) Gluconate Boosts the Anti-SARS-CoV-2 Effect of Disulfiram In Vitro”
Summary:<br /> The research done here in this paper does a great job at studying how we can better fight COVID-19. Here they look at two different medications, Disulfiram, an anti-alcoholism drug, and copper gluconate which is a common food additive or copper supplement. The study looks at how the mixture of these two drugs effects the anti-SARS-CoV-2 activity at the cellular level. They compare this to how well the two drugs effect the activity of the anti-SARS-CoV-2 activity on their own. They found that a 1:1 ratio of these drugs does a very good job, going from around 67% for each drug on their own to over 90% against -SARS-CoV-2 when joined together. They also found that the EC50 for the 1:1 combination was even lower than that of the two on their own.<br /> This research is very applicable to todays day because of the pandemic we are going through, although we are obviously most interested with what happens in vivo. I thought the research was very interesting though, and if it could be replicated in vivo could be very useful in fighting COVID-19. I thought the paper itself could use some work, but the information was there and it is important to get it out there.<br /> Areas for improvement:<br /> Major: I thought some of the major things that needed some improvement was the lack of an introduction and conclusion. The paper had great information and did a great job explaining the research, but an introduction is necessary to give background knowledge of research done in the past, why the research is important, etc. A conclusion was also missing and that would have helped the paper flow more and wrap up the research in a concise and understanding manner.<br /> Minor: There were a few grammar issues, but nothing too terrible. The author also went off on a tangent that was hard to understand, and didn’t really fit in the last paragraph of the results and discussion (starting at line 37).
On 2021-12-12 23:19:28, user Jorge Eduardo Chang Estrada wrote:
General comments:
The work presented by Harris; Nekaris and Fry investigates a possible event of coevolution between primates and venomous snakes, focusing on the resistance of the α-1 nicotinic acetylcholine receptor against α-neurotoxins. This hypothesis is based on the finding that members of the Cercopothecidae and Ponginae families of primates possess α-1 nicotinic acetylcholine receptor alleles that are more resistant against neurotoxins of Naja sp venoms, snakes from the Elapidae family that have a similar evolutionary history: evolving initially in Africa and later spreading throughout Asia. I believe that the hypothesis is very interesting and the authors provide an interesting overview of it in the manuscript. However, it is not clear to me whether the authors can conclude an event of coevolution based on the data presented: a deeper analysis of it may be needed. I also think there are some limitations in the analysis presented (as pointed below), and I hope my observations help the authors to improve this work.
Major comments:<br /> 1. The authors provide a series of dendrograms without time divergence data. It would be useful to provide a time divergence data showing a temporal coincidence between primates and snakes evolutionary events to support the hypothesis. <br /> 2. A sequence alignment of the α-1 nicotinic acetylcholine receptors of different primates is presented showing different mutations between the receptors from different species. It would be interesting if the authors could discuss how (at least some of) these mutations could help in the resistance mechanism, based on what is known about the binding of the neurotoxins to the α-1 nicotinic acetylcholine receptor. <br /> 3. As your work presented, the clades of Platyrrhini and Lemuriformes were more susceptible to the venom of Naja then the other species that live in common areas with them. Both clades are separated from Africa and Asia and from the pressure from the Naja sp venom. Is there a possibility that these clades coevolved with neurotoxins found in venoms from Elapide members sympatric to these clades ?<br /> 4. Venomics studies showed that snake venoms may vary depending on the environment even within the same species. It was not very clear to me whether the venom source was from individuals collected from the same or different locations, and how this variable may affect the coevolution with primates? See the work of Calvete 2008 Snake venomics and antivenomics of the arboreal neotropical pitvipers Bothriechis lateralis and Bothriechis schlegelii. J Proteome Res. 2008 Jun;7(6):2445-57. doi: 10.1021/pr8000139.<br /> 5. The uses of the techniques may be more described to clarify the importance of the essays. I am not sure if this essay may demonstrate coevolution. I consider that, more experiments maybe apply in a more specific way. The limitations of the assay may generate a not clear conclusion. <br /> 6. Is there any evidence that other toxins from the venoms showed a similar pattern of sensitivity/resistance?<br /> 7. In order to propose a coevolution event, I would expect to observe a reciprocal effect between the two groups. What would be the characteristics evolved by the snakes that could have been impacted by the primates? Is there any evidence of such event? If not, that may be an adaptation of primates to the selection pressure of the snakes, and not necessarily a coevolution event.
Minor comments:<br /> 1. The authors report to have statistically analyzed the data, but that analysis is not presented in the figures. <br /> 2. I could not find the supplementary material. Access to this material may help readers to better understand the results.<br /> 3. The figure 4 have the indication of figure 2<br /> 4. The clade Lorisiformes present a high susceptibility to almost all venoms, except that from the venom of Naja kaouthia. How do you interpret this result? The clade was very susceptible to the venom of Naja siamensis, which is also an Asia cobra.
On 2021-12-12 22:00:12, user Abc wrote:
Nice paper, but what does "ca." mean in reductions? I can guess from inference, but would prefer an explicit definition. Google and Bing are useless for this term.
On 2021-12-11 20:57:58, user Kayene Micheli wrote:
General considerarions of preprint “Infection and transmission of SARS-CoV-2 depends on heparan sulfate proteoglycans” by Bermejo-Jambrina, M. et al
General considerations:<br /> The work of Bermejo-Jambrina, M. et al aims to study the importance of heparan sulfate in the transmission and infection by the SARS-CoV-2 virus. In the article, the authors demonstrated that heparan sulfate proteoglycans present in the extracellular matrix of cells function as binding receptors for SARS-CoV-2. Neutralizing antibodies against SARS-CoV-2 isolated from patients with COVID-19 interfered with the binding of SARS-CoV-2 to heparan sulfate proteoglycans, which may be an additional antibody mechanism to neutralize the infection. It was also seen that SARS-CoV-2 binding and epithelial cell infection were blocked by low molecular weight heparins, as well as human nasal cell infection, and that dendritic cells and mucosal Langerhans cells are able to capture SARS-CoV-2 via heparan sulfate proteoglycans and transmitting the virus to ACE2-positive cells. The data presented in the article are very important to understand the mechanisms of infection and transmission of the SARS-CoV-2 virus, which is responsible for the COVID-19 pandemic that still devastates the world; however, I noted some points and limitations about the article that will be punctuated below, which the authors might find useful to strengthen their conclusions.
Comments:
Some points were commented during the article results, but they but they were not covered in the introduction, which makes it difficult to understand the article. For example, it would be interesting to include in the introduction a brief explanation about dendritic cells and their importance, what syndecans are and what their function and talk about exostosin-1.
In the result “SARS-CoV-2 targets dendritic cells for dissemination” is said about the dendritic cell and its importance in that context, but they do not explain why this specific cell was used and from which reference they took this knowledge.
I understood that liver cells express ACE2, but why didn't you use cells from the gastrointestinal tract or the respiratory tract from the beginning? They are more correlated with the virus, and there are references demonstrating that the expression of ACE-2 is higher in these cells than in the liver cells?
In the discussion of the results presented in Figure 3, letters E and F, it is not clear why you used only Calu-3 cells and stopped using Caco-2.
In the results shown in Figure 4, letter B and Figure 6, letters H and I, why didn't the experiments also continue using heparin? Given that it is widely used in the clinic as well, these results would be interesting.
Why syndecan 4 stopped being used after figure 4? In the results shown both syndecan 1 and syndecan 4 have good results, I think it would be interesting to continue the experiments with both, or justify the choice to focus only on syndecan 1.
In Figure 7, the authors state that nasal cells have ACE-2 and that SARS-CoV-2 can infect the cell directly, but the result is very low, close to 0, is the result really significant?
Another important point would be to comment on which unfractionated heparin was used. Although it is known that porcine intestinal mucosa heparin is the most used heparin in the world, but there are countries that use only bovine heparin and its use has grown recently, so I think it would be important to identify which animal source is.
It would be interesting to improve the writing of the text of the methodology “pseudovirus infection assays”, as it stands the text is vague and confuse. Also, I think ng is not a good cell unit.
The way of showing the results is not clear, as the normalized value bars for 1.0 and 0.0 do not have an error bar, and graph 7B has no error. Therefore, the statistics need to be revised.
The axis of the graphs in figure 7A are very small and must be improved to facilitate visualization.
On 2021-12-11 10:15:27, user Sandrine CHARLES wrote:
Dear readers, I am pleased to inform you that our preprint is now published online et fully accessible fro free from:<br /> https://www.theoj.org/joss-papers/joss.03200/10.21105.joss.03200.pdf
On 2021-12-11 02:25:01, user Nicholas McGregor wrote:
A very useful model! I have long been bothered by Michaelis-Menten parameter error analysis. I am going to be doing all of my fitting with y = a*x/(a/b + x) from now on.
On 2021-12-10 19:37:06, user Tiago Lubiana wrote:
Hello, I am one of the authors of the preprint. If you have any questions/comments/critics just let me know!<br /> Best,<br /> Tiago
On 2021-12-10 15:56:23, user Leo G. wrote:
Molnupiravir is also carcinogenic, cytotoxic, & creates coronavirus mutations that are unlikely to happen naturally!
On 2021-12-10 10:55:27, user Wiep Klaas Smits wrote:
Hi Katie, very nice work! I saw that one hit is referred to as CD630_02364; but I think such locus does not exist. Do you mean CD630_23640? Unfortunately, I dont see the Table S1 in the preprint, so maybe it was correctly annotated there?
On 2021-12-09 21:49:17, user Raghu Parthasarathy wrote:
You might find it interesting that the first light sheet microscopes, over 100 years ago, used scattered light. See e.g. https://www.semanticscholar... , and some papers noted in my post: https://eighteenthelephant....
By the way: "novel" is mis-spelled.
On 2021-12-09 13:30:13, user R. Rocca wrote:
You have misstated that haplogroup I2a2 is most common in Serbia. In fact, it is most common in places like Germany. See the SECOND frequency map on this link for reference: https://www.eupedia.com/eur...
On 2021-12-09 06:47:40, user Robert George wrote:
Great paper. One aspect which might be worth considering is the apparent settlement hiatus between Gravettian (~ 29 kbp) and Epigravettian (~ 26 kbp) in Italy (grosso modo) (see C14 curves https://www.sciencedirect.c... . Striking also is the prevalence of Y-hg I2a2 in pre-Neolithic Italian pops, suggesting a founder effect
On 2021-12-09 11:54:09, user Jonathan D G Jones wrote:
This is a really interesting paper - just did it for our journal club. Here's a suggestion. The recent Jijie Chai paper on 2'3' cAMP formation from TIR proteins implicated a key catalytic cysteine 3 aa before the catalytic glutamate. That is present in all the examples in Fig 1C except in the TNP proteins. That's likely why the TMP TIR domains don't activate defence. Authors should highlight that Cysteine as well as the glutamates, and should comment on that possibility
On 2021-12-08 22:57:38, user Nicholas Gladkov wrote:
Hello,
Thank you very much for the fascinating paper about the sex differences in the metabolism of glutamine. The paper was very much an interesting read, and I have learned much about the subject area. Below are some comments and suggestions for the paper:
Figure 1A/B: It may be beneficial to include the original data to the supplemental figures. If there are limitations by converting the data down to z-scores, possible data may not be lost.
Figure 1D: Clarification as to what “number of metabolites” means.
Figure 2A: A comment as to if the male and female brain tumors being on different hemispheres should be ignored or is of importance to the paper.
Figure 3B: The paper mentions that male cells incorporate 20% more nitrogen from [13C5 15N2]Gln into nucleotides than female cells (Fig. 3B). It may be helpful to add an explanation as to how the graph displays this increase in 20% (explaining how it is quantified). An overlay of the two graphs may make it easier to compare the two. The graph is also missing a y-axis.
Figure 5E: In the paper, it is mentioned that NAC significantly restored cell number in male and female BSO treated cells (Fig. 5E). But in figure 5E, the females do not show significant differences between the control and the MAC treated cells.
Overall, the experiments were well conducted, and I enjoyed reading about additional evidence supporting that anti-cancer therapies may have sex specific differences.
Thank you very much for taking the time to read my suggestions for your paper, and I hope they may be of help.
All the best in your future research.
On 2021-12-08 16:38:14, user Victor Corces wrote:
It looks like the total intrachromosomal contacts >20 kb is around 160 million for the control. You can't say anything about changes with such a small number of reads
On 2021-12-08 14:17:56, user Nikolas Haass wrote:
Now published in eLife:
Quantitative analysis of tumour spheroid structure.<br /> Browning AP, Sharp JA, Murphy RJ, Gunasingh G, Lawson B, Burrage K, Haass NK, Simpson M.<br /> Elife. 2021 Nov 29;10:e73020. doi: 10.7554/eLife.73020. Online ahead of print.<br /> PMID: 34842141
On 2021-12-07 15:23:48, user Sankar Chandra Basu wrote:
Readers are requested to kindly redirect themselves to a peer-reviewed version of the preprint published in Frontiers in Bioinformatics:
On 2021-12-06 20:58:47, user disqus_8AVEuorTBu wrote:
It is worth noting that the LKGG sequence matching MYH6/7 discussed here is the exact site where SARS-CoV-2 PLpro cleaves its polyprotein. Reynolds et al. used an equivalent method of searching homologous sequences and then experimentally verified that this MYH6/7 sequence is cleaved by SARS-CoV-2 PLpro and more slowly by MERS PLpro (doi: 10.1021/acsinfecdis.0c00866). This cleavage prediction was, however, not replicated by any neural networks trained on more diverse PLpro cleavages (doi: 10.1101/2021.10.04.462902). Without any evidence of autoantibodies against this site, I think it’s much more likely that COVID-associated myocarditis is caused by cleavage and not mimicry causing rare genetic variant-dependent autoimmunity.
On 2021-12-06 19:46:28, user Alizée Malnoë wrote:
Schumacher et al. discuss the metabolic pathways evolved by green plants to degrade chlorophyll molecules. The authors combined a large-scale comparative phylogenomic approach with biochemical characterization of putative novel phyllobilins to shed light on how the degradation pathway evolved. This manuscript points to the evolution of chlorophyll degradation, in particular the later detoxification steps, having accompanied the green lineage’s transition to land. An extensive list of orthologous genes from a diverse number of species was identified.
The manuscript stands out for the wide evolutionary view on the chlorophyll degradation pathway, which is neither an easy nor a common research subject. The in silico approach for phyllobilins identification is quite innovative and will surely give great hints for biomolecules discovery. A comprehensive bioinformatics work has been done to carefully identify and select the genes analyzed in the manuscript and the related phylogenetics figures are outstanding.
Although this work is of great interest, we have some comments that could be addressed in the next version.
Major comments<br /> - We would suggest to tone down the title as it may be that less or no chlorophyll catabolites were detected in mosses, charophytes and chlorophytes due to the smaller number of species analyzed (Fig.5), and some of these clades may have evolved other phyllobilins exporting and modifying proteins. Please discuss these possibilities. <br /> - Page 9, line 16: regarding the identification of 15 putatively novel phyllobilins, mass spectrometry data together with in silico produced list of diagnostic ions are presented to support these structures. Would it be possible to provide additional confirmation via a standard (an internal or a synthesized one) or alternatively to state which other molecules these m/z plus profiles could be corresponding to? How likely is it that they correspond to other unrelated compounds?<br /> - Page 5, line 11, comment on two species lacking CAO. Are they lacking chlorophyll b?
Minor comments<br /> - Intro, page 3, line 19, explain why it is unlikely that nitrogen remobilization be a conserved evolutionary trigger.<br /> - Page 8, line 15, what is the carbon source used for heterotrophic growth? Line 19, instead of “etc” list all conditions tested.<br /> - Page 9, line 24, could you discuss or introduce whether oxidations are spontaneous or enzymatic?<br /> - Could you comment in the discussion about the significance of Chl breakdown catabolites in cellular signaling from an evolutionary point of view? <br /> - Methods, in the paragraph “Plant material growth and chlorophyll degradation induction”, page 15, lines 1-12: several of the growth conditions are missing e.g. at which temperature S. moellendorfii was growing? At which temperature and humidity was the dark incubation of leaves performed?<br /> - In the Sup. Figure 6 B, the structure of phyllobilin 3b is not shown. It could be nice to have that too or to explain why it is not shown. Please, also include the relevant spectra from the remaining identified compounds as supplemental data (assuming that the 4 presented spectra/structures are also of novel compounds).<br /> - Maybe we missed it, but where can we find the identifiers of the genes that were used in the gene trees shown Figures 2, 3 and 4?<br /> - The Sup. Table 1 is first cited in the Methods. Maybe, cite it in the introduction (page 4, line 12) in order to cite it chronologically with respect to Sup. Table 2.<br /> - Page 9, lines 3 and 23, “SFig. 6” should read SFig. 4.<br /> - Page 5, line 34, please indicate the gene names corresponding to the abbreviation for SGR2 and SGL.<br /> - Minor grammatical errors and typos are present in the text, e.g. page 24:<br /> line 24, “loss” should be “lost”<br /> line 29, “one orthogroups” should be “one orthogroup”<br /> line 33, “do belongs” should be “does belong”<br /> line 35, “expect” should be “except”<br /> line 36, “have derived a pre-existing enzyme” should be “have derived from…”
Domenica Farci, Sam Cook (Umeå University) - not prompted by a journal; this review was written within a preprint journal club with input from group discussion including Alizée Malnoë, Maria Paola Puggioni, Aurélie Crépin, André Graça, Jack Forsman, Jingfang Hao, Pierrick Bru, Jianli Duan.
On 2021-12-06 17:57:34, user jonipsaro wrote:
Interesting findings. We noticed that the reaction buffer for the new EfPiwi+Gtsf1 assay includes tRNA. Is this a connection to our work from Cell Reports earlier this year (https://doi.org/10.1016/j.c... Any idea what happens without tRNA?
On 2021-12-06 16:21:53, user Domenico Maiorano wrote:
This preprint has been now accepted for publication in Nucleic Acids Research and a link will be forthcoming. Of note, the implication of translesion DNA synthesis in replication of pericentromeric heterochromatin is laso now reported in human cells by Ben Yamin et al., EMBO J 2021, PMID: 34533226. Further, Twayana et al., PNAS 2021, PMID: 34815340, also now report an implication of TLS pol eta in promoting genetic variation at common fragile sites, suggesting conservation of function during evolution.
Domenico Maiorano