On 2023-06-19 04:48:51, user Bernie Taylor wrote:

Did Homo naledi make art or were these images of bears, giraffes and elephants engraved by Ice Age Homo sapiens in South Africa? Take a closer look at the evidence. https://beforeorion.com/ind...

On 2023-06-19 04:46:37, user Bernie Taylor wrote:

Homo naledi art at the Rising Star Cave or within the migrations of Ice Age Homo sapiens? Find the evidence at https://beforeorion.com/ind...

On 2023-06-19 01:25:00, user Xilan Dong wrote:

Does this learning model take into account the MHC anchor residues vs TCR contact residues in peptide?

On 2023-06-16 15:39:53, user Liubov wrote:

Dear authors! Couldn't find variation in the LOXHD1 gene mentioned in Table 2. ClinVar reports that variation ID 176561 is invalid. There may be a typo in the table

On 2023-06-16 08:40:00, user yuchen wrote:

Where could i find your supplementary figures?

On 2023-06-15 17:28:22, user Grace Donnelly wrote:

This paper brings to light some interesting new information about RNA G-quadruplexes and their associated proteins. The overall flow of the paper, namely the shift from theoretical predictions to experimentation was a good segway into your web application. However, I did have some critiques, mostly regarding the figures. One general comment is that all schematics with red and green should be recolored for colorblind readers.

Fig. 2a) Perhaps an overlay or neighboring graph showing what the expected CD specter for G4A4 RNA would be helpful to readers that this does in fact resemble what is expected.<br /> Fig. 2b) Indication of significance needed.<br /> Fig. 2c) This hierarchical clustering schematic should be much larger, and possibly contain a legend for interpreting the opacity of the colors.<br /> Fig. 2d) The label at the top is unnecessary and slightly misleading, and the labels at each axis should be larger.<br /> Fig. 3b,c) Pie charts should include percentages/quantities for all portions.<br /> Fig. 4d) Placement of p-value is confusing, perhaps place it over the area where the differences become significant.<br /> Fig. 5a) The x-axis labeling here is confusing. One solution could be to just put “neutral” and “very high,” or assign values to them, like “neutral” as 0 and “very high” as 1. I also noticed that your graph lines are closer towards the middle, however this could have been missed and should be made to appear more obvious to the reader.<br /> Fig. 5b, 6c) Color-coordinating the proteins listed in these figures based on classification/function would provide a richer understanding of the trends observed in G4 binding propensities. While this would interrupt the current color scheme in 5b, perhaps bold font would be sufficient to indicate controls.<br /> Fig. 6a,b) Data points are too clustered in these graphs; differently colored dots would probably work better.<br /> Fig 6c) This graph has some distortion (vertical stretching).

On 2023-06-10 06:51:10, user Emilio wrote:

Hi there!<br /> Here's some constructive criticism after reading this paper, if at all helpful:

1. Aside from the few grammatical errors and occasionally awkward sentence structure, I think that the basis and purpose of this paper was very well-explained. However, I do feel as though the usage of the term “interactome,” while certainly valid in the context of this paper, does occasionally leave much to be desired in terms of clarity. If possible, I think further elaborating on the specific implications this research has with regards to its significance towards the interactome would be helpful for the reader to better understand the importance of the paper. In other words, further fleshing out which particular interactions are implicated in or significant with respect to your research would, in my opinion, help give the reader more clarity in appreciating the importance of your work.

2. It appears as though the paper as a whole lacks much in the way of hard statistics (F-tests, Z-tests, etc). Instead, much of the figures seem to rely more-so on summarizing apparent raw data with little context or clear explanation to accompany it. Figure 3 is a major example of this. While it does say that a two-way ANOVA was used in order to calculate statistical significance, the figures themselves don’t necessarily reflect that all too well. Also, figure 3b won’t be visible to those who are red-green colorblind, so I’d advise changing the colors on that!

3. The web application you have provided is quite impressive! I applaud your work in that regard immensely. However, I think a more in-depth explanation of its mechanics within the paper would help a lot with regards to reader clarity. For example, providing a more clear definition of “propensity” in the context of RNA-G4 binding would go a long way in making the potential applications of your web tool more apparent to the reader.

Overall, this paper was a very interesting and informative read.<br /> Keep it up!

On 2023-06-15 15:10:02, user Brett Chrest wrote:

Respectfully, image quality and consistency needs to be redone. There are typos and formatting errors. To prevent the impression that this paper was rushed, these issues should be corrected and resubmitted to the preprint server.

On 2023-06-14 19:41:30, user Gerry Smith wrote:

This article has been published in Advances in Genetics.

RecBCD enzyme and Chi recombination hotspots as determinants of self vs. non-self: Myths and mechanisms.

Subramaniam S, Smith GR.

Adv Genet. 2022;109:1-37. <br /> doi: 10.1016/bs.adgen.2022.06.001. <br /> Epub 2022 Sep 2.

PMID: 36334915

On 2023-06-14 18:08:47, user Hiroaki YABUUCHI wrote:

Now published in PLOS ONE doi: 10.1371/journal.pone.0285716

On 2023-06-14 16:05:51, user Helene wrote:

Interesting manuscript. At first sight however, the western shown in Figures 2F and 4D do not look like experimental data in the uploaded pdf. I encourage the authors to check the pdf and provide the original data.

On 2023-06-14 13:33:30, user Tom Smith wrote:

The method for ePRINT is missing.. from a manuscript presenting ePRINT. Please can you add the method to the methods section!

On 2023-06-14 10:29:46, user Flint Dibble wrote:

For those interested, I have posted a video peer-review of the preprint, 'Evidence for deliberate burial of the dead by Homo naledi' here on YouTube: https://youtu.be/9iN9t393QQI

Given the wide media attention this preprint has received in the press and an upcoming Netflix show on the find, I chose to do it in video format. This way, a critical review can reach both colleagues and the general public. It is freely accessible to all. And even one of the authors of this preprint commended the video format of a review like this.

My review delves in depth into various details and issues with the claims made in this preprint, and I look forward to seeing any revisions made in future drafts of the paper.

Thanks,<br /> Flint Dibble<br /> Marie Sklowdowska-Curie Research Fellow<br /> School of History, Archaeology, and Religion<br /> Cardiff University

On 2023-06-13 15:10:47, user Ranjan Kumar Sahu wrote:

This article has been published in the journal Drosophila Information Service, Dec-2021, Volume-104, pages 34-41.<br /> link: www.ou.edu/journals/dis/DIS...

On 2023-06-12 20:38:08, user Tychele Turner wrote:

I was recently informed that “on-board methylation calling” is now available on the Revio sequencer through PacBio. In our preprint, we included the kinetics information to enable detection of methylation with primrose because we were unaware it was now available onboard. If you would like to know more about file sizes without kinetics information here is a link, the company gave us: https://www.pacb.com/wp-con....

For our preprint, the file sizes listed in Table 1 include the kinetics information, which is why they are large. I am a major fan of getting all the data possible for our research, out of experiments and big data generally. Hope this is helpful. Tychele N. Turner, Ph.D. email: tychele@wustl.edu

On 2023-06-12 17:52:47, user Kevin McKernan wrote:

The LAMP primers described in this preprint are not functional LAMP primers. The F3 and B3 primers do not flank FIP and BIP and the FIP and BIP primers do not have Inversion sequences. They are just long primers, thus this is not a LAMP reaction. It is difficult to verify any of this work as no enzyme conditions are offered for either the RT-qPCR assay or the LAMP assay presented in the paper.

On 2023-06-11 20:15:33, user Investigate Explore Discover wrote:

Overall, I think that this is a solid paper that elucidates the mechanisms of neutrophil interaction with TGF-β, but there are many points that could be further improved upon.

See my full review of the paper below:<br /> https://www.researchhub.com...

On 2023-06-09 18:05:22, user Stacy Clark wrote:

The authors state that 'disease resistant' trees have been developed through hybridization and genetic modification'. This is inaccurate and misleading. The hybrids are not yet fully blight resistant, according to TACF's own data and other publications. The GMO chestnut has been shown to be resistant in very short time periods and in very restricted environments. They do not exhibit durable resistance. I suggest they change the wording accordingly to better reflect the reality of the current situation or cite papers that show durable resistance.

On 2023-06-09 08:56:29, user Berislav Bosnjak wrote:

Thank you for your comments. Please note that the majority of them have been addressed during the revisions of the manucript, which is now pubslihed in Cell Reports: <br /> https://www.cell.com/cell-r...<br /> Berislav Bosnjak

On 2023-04-05 15:39:35, user UTK Micro Immunology JC wrote:

Summary. <br /> Murine cytomegalovirus (MCMV) is a widely used animal model for understanding the pathogenesis of its’ human counterpart, Human cytomegalovirus (HCMV). To initiate a productive infection the virus must first gain access to a host cell. MCMV has various glycoproteins on its surface that interact with specific host cellular receptors depending on cell type. It was recently shown that Neuropilin-1 (Nrp1) is important for MCMV entry into a variety of cell types. Depending on the cell type MCMV utilizes different viral glycoproteins to attach and enter host cells. In fibroblasts, viral entry favors the utilization of viral glycoproteins gB in conjunction with gH/gL/gO known as the trimer. In endothelial, epithelial or myeloid cells, viral entry occurs through the use of gB, the trimer and another complex made up of gH/gL/gO/Mck2 which is known as the pentamer. Mck2 or mouse chemokine 2, has dual functionality in both viral entry and chemokine function. Currently it has not been elucidated the host cellular receptor that Mck2 utilizes for entry into host cells. Using a CRISPR/Cas9 screen, this study identifies the MHC-I molecule is implicated in MCK2 dependent entry into macrophages.

Positive feedback. <br /> I felt that the way the paper is organized was logical and easy to follow. The color coding of the different viruses helped to follow along in the graphs. In the in vivo experiments, utilizing both plaque assays and fluorescence levels to confirm results made them more convincing. The restoration of the phenotype by complementation of B2m and CD81 made the results more convincing. Utilizing the two different viruses that either have or lack MCK2 definitely strengthens their argument. In examining the B2m relationship with Mck2, performing the experiments both in primary cells and immortalized cells strengthens the argument. Using different virus strains that have different genetic manipulations of MCK2, is beneficial for showing that the phenotype is due to a defective protein and not just that specific mutation of the protein in that virus strain.

Major Concerns<br /> Given that most of the initial experiments are done in cell culture, I would have expected there to be more replicates. Also why there are different numbers of replicates used between the different virus groups? <br /> Characterizing stromal cells as anything not Cd11c positive is a reach.<br /> The lack of substantial infectivity of these viruses, regardless of the presence of MCK2, in most of these cell lines makes the data hard to believe <br /> I wonder if the current in vivo data can truly tell if lack of H-2 molecules impacts dissemination. Alternatively, it could impact the rate of virus growth in the SG or in other tissues. To truly understand whether dissemination is impacted one must use barcoded viruses.

Minor concerns

While infectivity using these reporter viruses has been assessed by flow cytometry previously, I think that performing a plaque assay would further validate results.

List the actual p values instead of using the star annotation

Minor spelling errors (pg. 25 the strain C57BL/6 is spelled incorrectly)

For Figure 4 C-E, it would be helpful to make the scales on each of the graphs the same to be able to compare between all three graphs.

For Figure 5D, it would be beneficial to show the isotype control in the same panel as the MHC-1 to confirm increase/decrease of expression

For those not in the field, I felt that there was not enough emphasis on what type of cellular entry MCK2 functions in, which would help the reader get a more complete understanding of the results.

For figure 7, it would be more convincing that the viruses lacking MCK2 are in stromal cells if there was a specific marker used for stromal cells<br /> For figure 3C, it is a little unclear what the middle column is demonstrating if it is either a locus or reference sequence. This could be easily clarified in the figure legend or materials/methods section. <br /> In page 5 of the results, when talking about the defective MCK2 and how it was repaired, it would be helpful to make it more clear to the reader for how it was defective and how it was repaired. <br /> Why was the viral load in SG measured at day 7? What if a later time point (e.g., day 14) viral load is the same for two types of the viruses? This needs to be checked.<br /> Which specific H-2 molecules (L,D,K) are important for infection? This could be an interesting point of discussion.<br /> b2M-deficient mice may have weird NK cell response that could play a role in control of MCMV. Can the authors confirm that NK cells were not involved in viral control in these mice?<br /> In experiments even with MOI=1 infection rate is very low, <20%. Why? Would waiting for longer time to detect infected cells allow detecting all cells as infected?

On 2023-06-09 08:00:30, user Yusuke Okazaki wrote:

Thank you for sharing the exciting results. I appreciate your valuable work, which significantly expanded the known freshwater phage diversity and their ecological importance. I would like to kindly bring to your attention that our study has also reported the quantitative significance of cysC/cysH genes among freshwater phage genomes (doi:10.1111/1462-2920.14816).

On 2023-06-08 17:09:33, user KA Garrett wrote:

The peer-reviewed and updated version of this paper is available open access here: https://journals.asm.org/do...

On 2023-06-08 17:05:11, user KA Garrett wrote:

The peer-reviewed and updated version of this paper in PhytoFrontiers is available here: https://apsjournals.apsnet.org/doi/abs/10.1094/PHYTOFR-01-23-0004-SC

On 2023-06-08 13:45:21, user Angela Fuentes Pardo wrote:

Please note that this preprint was already peer-reviewed and a link will be forthcoming in biorxiv. Meanwhile, this manuscript is publicly available here: https://doi.org/10.1111/eva...

On 2023-06-08 10:49:55, user Dinesh Kumar wrote:

The name (Trilliumosides A and B) given to compounds are already published names. please check the below link and change the names.<br /> Molecular networking-based strategy for the discovery of polyacetylated 18-norspirostanol saponins from Trillium tschonoskii maxim.<br /> https://doi.org/10.1016/j.p....

On 2023-06-07 13:15:03, user Patrick Schwartz wrote:

You can find the published version of the article out now in PLOS Genetics:

https://doi.org/10.1371/jou...

On 2023-06-07 07:58:44, user lei liang wrote:

The description in introduction part: ' Homozygous hcf106-mum1 maize seedlings expressed a non-photosynthetic, pale green mutant phenotype only in the absence of Mu activity (Mu-inactive) '. I don't think that's correct here, it should be 'in the presence of Mu activity'

On 2023-06-06 21:28:01, user CJ San Felipe wrote:

Virtual screening (VS) has emerged as a powerful method for quickly screening vast libraries of compounds and reducing them down to a small pool of candidates that can be investigated in a time and cost efficient manner. While great improvements have been made in both reducing the computational time as well as the biophysical modeling of ligand binding, the success rate of VS can still vary between targets and limitations exist. Traditional virtual screening methods make simplifying assumptions about empirical parameters and consequently VS may not capture the intricacies of molecular recognition. These scoring function simplifications lead to inaccuracies in predicting receptor-ligand poses and relative affinities. In this paper, the authors address whether incorporating experimental electron densities (ED) of ligand-bound structures can correct for these simplifications in the scoring function and therefore improve enrichment and diversity of compounds in VS. Based on a pre-existing crystal structure of a receptor, the authors calculate the intensity of ED at grid points and incorporate those values into a modified scoring scheme which they call ExptGMS. Incorporating ED of the binding site also introduces ED of solvent molecules and alternative side chain conformations which provides information about pocket dynamics and accessibility that can be relevant to ligand binding. Such information might implicitly encode the displacement of water molecules, rearrangement of side chains upon ligand binding or other features. These aspects may be simplified (rigid structures) or omitted (solvent) in other VS programs. <br /> To test the effectiveness of their approach, they benchmarked their method against existing ligand or receptor-based VS software showing a small improvement in balancing positive hit identification and hit diversity. The authors then optimized their algorithm by factoring in a low resolution cutoff to the ED. High resolution ED will result in high intensity grid points; because the authors scoring function favors ligands that occupy ED peaks this could lead to bias against compounds that miss the exact centers of those peaks. Therefore, the authors also included low pass filtered (maps calculated only using low resolution reflections) data which produces a more smoothed distribution of ED intensity which they argue accounts for more conformational variability, however, this must be balanced against using too low resolution data, which would result in information loss about ligand fitting to features in the density volume. <br /> They then tested it against the SARS CoV 2 protease 3CLpro which yielded several compounds that exhibited IC50’s in the low micromolar range. <br /> The major strength of this paper is a protocol for incorporating ED of bound ligands to alter the scoring function used in specific receptor binding sites in VS. Currently, this procedure produces comparable results to existing VS software such as GlideSP, which do not have any ED term. Many existing docking algorithms work iteratively by gradually introducing more complex terms; they are aided by scoring functions which assign greater weight to molecules that can form empirical interactions with the receptor. In some cases, a ligand is missed either because the correct binding pose can’t be found or because the scoring function penalizes a lack of strong interactions. In these cases, the ED based scoring function could be utilized to aid the scoring function. The inclusion of the solvent ED in mapping out the binding site and aiding in ligand placement to be particularly interesting from the perspective of developing VS docking tools that take advantage of apo-structures that don’t have ligands already bound to them, which could expand the use of this method. The major weakness of this paper is, as mentioned by the authors, that this method is limited to proteins that have electron density with ligands bounds which limits its utility for receptors with no known ligand bound ED (although see point above for potential for expanding the domain of applicability). Given the current implementation of ExptGMS, we are curious to know if the authors tried generating electron density grids based on the solvent density in the binding site alone. Augmenting VS scoring functions by incorporating experimental ED may further improve docking scores by aiding the placement of molecules using existing binding data of ligands as well as solvent; however, currently the performance improvements offered by this method are modest.<br /> Major points:

In figure 2, the authors compare how well ExptGMS performs relative to other virtual docking programs by examining the number of top 10, 50, and 100 compounds that are detected and the diversity of compounds. The 2D plot used does not sufficiently describe how differences between datasets used in the DUD-E database could affect these results. For example, did ExptGMS (and the other programs) do much better with some datasets than others? It would be helpful if the authors could show representative graphs from individual datasets to make the differences in performance more clear.

In figure 2 the authors benchmark ExptGMS against several VS software programs that are either ligand or receptor based. They note in figure 3 that their method can improve false negative/positive hit detection. What was the overall false positive/negative rates between the methods used? Do the authors see a relationship between the number of top 10, 50, and 100 compounds and the false negative/positive rate? Does this depend on the choice of software (ex ligand vs receptor based VS)? It would be helpful if the authors could further elaborate on the false positive and negative hit detection rates between their method and the existing VS methods.

We feel the 3CLpro in vitro assay description is unclear as it is written. Is this a peptide displacement assay? Further, what was the construct for the peptide/choices of fluorophores? Further explanation of how the in vitro assay was constructed and performed would be helpful.

In Figure 5, the authors describe one possibility for why ExptGMS with different resolutions complemented each other by using solvent exposure in the pocket. How are t-test results in box plots ? We feel that it is difficult to judge whether the red and blue boxes in each resolution have statistically significant differences.

In Table 1, the authors try to demonstrate the usefulness of multi-resolution analysis using a machine learning model. The table seems to show the advantage of the combination between GlideSP and multi-resolution ExptGSM. However, we are concerned about multiple hypothesis testing. This is also related to Figure 5. The authors tested more and more things about multi-resolution analysis but did not show a principled test. It would be helpful to get a deeper understanding about multi-resolution analysis if the authors could provide their thoughts and tests related to the principle.

Minor points:

Supplementary materials are mentioned but not available under biorxiv posting.

Reviewed by CJ San Felipe, Hiroki Yamamura, and James Fraser (UCSF)

On 2023-06-06 20:45:27, user Ananya wrote:

I really enjoyed reading this paper and applaud that it questions a popular belief regarding the direct role of mitochondrial reactive oxygen species on DNA damage. It was nice to see results supporting the hypothesis and the further extension that proposed a new way of targeting cancer cells. However, I have some suggestions regarding the methods and presentation of data:

1. Since the manuscript explaining the method of measuring DAAO activity from oxygen consumption rate is still in preparation, it becomes hard to verify your experimental results and possibly replicate the experiment. Releasing the manuscript as soon as possible and referencing it in this paper would be helpful.
2. The graphs in figure 4 seem to be made using a one-way ANOVA, but since the text compares between the two different cell populations of RPE1-hTert-DAAOH2B and RPE1-hTert-DAAOTOM20, it may be better to perform a two-way ANOVA.
3. I could not find the supplementary figures.
4. It would be beneficial to use statistical tests to analyze the data in Figure 5C to provide more information regarding the expression of senescence markers in the cells. Since the data looks non-parametric, a suggestion is to use the Kruscal-Wallis test to perform a one-way analysis of variance. Additionally, it needs to be clarified what the horizontal blue lines represent.

On 2023-06-06 19:54:02, user Wilson wrote:

• For the method of quantifying DAAO dependent H2O2 based on oxygen consumption rates, has the manuscript been released yet? It may be beneficial to release the paper to understand oxygen consumption rate results.
• In Figure 3C and 3D, there is a transformation from the raw count in 3C to a percentage of DNA in the tail in 3D. It may be helpful to leave the data in its raw count to show total amount of DNA in the tail. Does the Fiji software have that option?
• For the crystal violet staining in 2E, is it applicable to use spectrophotometry to quantify the number of cells in each plate?

On 2023-06-06 19:55:28, user Min Woo Sung wrote:

Interesting. R domain getting close to ATP binding site of NTD sounds very similar to the role of corresponding region in vascular KATP channel. ED domain in vKATP is likely playing a regulatory role in nucleotide binding and gating.

https://doi.org/10.1073/pna...

On 2023-06-06 19:52:14, user Aayushi wrote:

After reading through this paper thoroughly, the results were very interesting to me, particularly the novel ideas about levels of spike protein production and the potential effects of this evolution of vaccine efficacy in the future. <br /> Some concerns we had that we feel could improve the paper if addressed were:<br /> The use of only male hamsters as the in vivo model did not seem very well justified, if there was some reasoning to this choice it would be useful to explain in the methods or discussion. <br /> Clarifying which lung lobe the sample is taken from would make the data more clear as Covid-19 is often an upper respiratory infection<br /> The omission of data from many figures, such as the S2’ data from the western blot densitometry figures and and the 7 dpi viral titer, can be confusing for readers<br /> Having the figures state non-significance in the comparisons where statistics were done but no significance was detected would clarify the images. <br /> The background was a bit hasty for those unfamiliar with the most current research in SARS-CoV-2, and could be expanded.<br /> Overall, this paper was well written and the findings are clearly meaningful to the research community, and I highly appreciate the contributions of this lab to the field.

On 2023-05-23 10:47:31, user jessica wrote:

I thought this paper was very interesting and has great potential to influence the way we treat COVID-19. I thought it was impressive to see both in vivo and in vitro approaches, as well as comparison between time periods and locations. I wanted to make the following comments and ask the following questions concerning the paper:

In which lobe of the lung were samples extracted from? It may be beneficial to specify this. <br /> Why were only male hamsters used? It is somewhat concerning, considering the differences between males and females in ways such as weight, which was an important comparison point in your paper (Fig. 2H).<br /> Would it be possible to include the omitted trials somewhere in the figures, even in the supplemental figures? For example, I saw that samples from 7 dpi were taken. However, data from 7 dpi was not included in the relevant figures (Fig. 2I and Fig. 2J). I also noticed trials that were stated to have occurred elsewhere in the paper, but later omitted in the figures, in Fig. 1I, Fig. 3B, Fig. 3F and Fig. 3G. <br /> Have you considered any other tests for significance, aside from a student’s t-test? With repeated trials, tests such as a t-series may be a better fit. One example of this comment being potentially relevant is Fig. 2H.

Overall, I enjoyed reading this paper and I look forward to the work that this lab does in the future!

On 2023-06-06 09:22:38, user D P wrote:

I like the experimental design of figure 3 F-N, the results are very instructive for the field！Good luck with your publication!

Best<br /> YD

On 2023-06-05 11:13:47, user Peter Sabol wrote:

Nice paper.<br /> Just a minor technicality - for the final publication, don't forget to indicate the At code for the genes mentioned in the paper.

On 2023-06-03 14:55:56, user Andrew Borchert wrote:

Very interesting and useful work. I am wondering: how do you reconcile your observation of a correlation between ATP and heat output with prior observations that heat shock itself can lead to an increase in ATP concentration for E. coli?

https://microbialcellfactor...

I think you touch on this with some of the mutant analysis, but I wonder if you can describe in more detail how you can distinguish between high ATP being the cause of increased heat output vs. higher ATP being in response to increased heat output?

On 2023-06-02 16:31:58, user Laurent Seroude wrote:

This preprint has been peer-reviewed and accepted for publication by Human Molecular Genetics on June 2nd 2023.

On 2023-06-01 23:23:29, user Joseph Wade wrote:

The following is a review compiled by graduate students participating in the Infectious Disease Journal Club, Department of Biomedical Sciences, University at Albany, SUNY:

This paper aims to (i) develop a model of Enterobacteriaceae infection utilizing the greater wax moth Galleria melonella as a host, and (ii) test the efficacy of antibiotics and a bacteriophage cocktail in decolonization of the larvae. The authors establish an invertebrate model of gut colonization, which potentially has multiple benefits over current mouse models (e.g., lower cost, reduced ethical concern, simpler upkeep, less training needed); with further development, this model could be impactful for studies of infection by Enterobacteriaceae species. The presentation of the data throughout the paper is clear, and the data quality is good. The main conclusions of this paper are supported by the data. However, the choice of strains and antibiotics mean that there are a limited number of datapoints to address clearance of bacteria using antibiotic or bacteriophage treatment. Furthermore, more work is necessary to confirm the utility of this model system in recapitulating human infection and for determining the efficacy of potential treatments.

Major Comment:<br /> In Figure 7, it would be helpful to include additional strain-antibiotic/phage combinations. The combinations already tested mostly involve strains that are inherently resistant to antibiotic or phage (e.g., Kp 419614 is resistant to both antibiotics, and Kp 14520 is resistant to both phages), limiting the information that can be gleaned from these experiments, including the relative efficacy of antibiotic and phage treatments. It would be informative to also test antibiotics that the strains are all susceptible to, and/or to include strains that are not resistant to the chosen antibiotics and bacteriophages.

Minor Comments:<br /> 1. Table 1 does not have units for what we presume are MICs.<br /> 2. Can the authors comment more on the normal microbiome composition of G. melonella, and specifically whether there are any species that could potentially outcompete the K. pneumoniae or E. coli strains used in the study?

Suggestions for Additional Experiments:<br /> 1. It would be informative to compare a range of bacteriophage and antibiotic concentrations in the experiment shown in Figure 7.<br /> 2. The authors use the same infectious doses (10^5 and 10^6) for the additional two Kp strains and three E. coli strains based on the determined effective dose for Kp14520. It would be worthwhile to determine the optimal infectious doses for each of the other strains.<br /> 3. It would be interesting to see the impact of bacteriophage treatment on colonization when the larvae are injected with the bacteria (rather than force-feeding).<br /> 4. It would be interesting to examine the microbiome composition of the larvae and the impact of bacteriophage treatment on microbiome composition.

On 2023-06-01 19:01:23, user Jennifer Leo wrote:

Having ground-truthed several of these areas along the US Gulf Coast, I can say that the map grossly misclassifies marsh area in these locations. If we can assume that it has missed marsh habitats consistently across the globe, the marsh estimates are consistently incorrect. Additionally, you can't equate tidal marsh with the generic moniker, "wetland".

On 2023-06-01 15:29:30, user Sebastian Franken wrote:

An updated version of the manuscript has been publshed in Frontiers In Pharmacology!

https://doi.org/10.3389/fph...

On 2023-06-01 08:23:33, user Tom Belpaire wrote:

Now published in iScience doi: https://doi.org/10.1016/j.i...

On 2023-06-01 06:58:01, user Raos wrote:

Very nice work!

Could you please elaborate what exactly is shown in Figure S2C?

The caption is :

C) Example of how a specific protein isoform (UniProt ID Q7Z460-5) is tiled. Top panel: ESM1b effect scores over the left window (1≤i≤1022; orange), the right window (458≤i≤1479; green), and the final weighted average throughout the entire protein’s length (blue). Middle: ESM1b effect scores over the left window. Bottom: ESM1b effect scores over the right window.

It is not clear to me WHICH effect scores these are? Did I understand correctly that you do the following:

• you mask every position individually in each window.

• you compute the LRR scores per position per window.

• you combine LRR scores for all positions that are in multiple windows with the weights you show in the plots.

Under this assumption, which values are plotted in C then? Is it one specific LRR to some random mutation or is it something different?

Thank you!

On 2023-05-31 03:17:43, user Yusheng Liang wrote:

Interesting work! I have a couple of questions about the results. When did you start to see the difference in body mass between WT and KO mice? If you think the fiber type shifting to more oxidative fiber why the KO mice show reduced oxygen consumption in muscle? What do you think might be the reasons for reduced muscle mass? Are the muscle fiber size just smaller or they also have reduced cell number due to cell death? Also, did you see some pathological changes in KO mice with age? How does NFE2L1 loss disrupt the protein synthesis/degradation balance? Did you check the changes of ERAD, autopahgy and UPR?

On 2023-05-30 10:04:39, user Dave Hayman wrote:

Thanks, this is an interesting study. I don't usually suggest self citation, but our work that used the previously published CoV RdRp gene fragments from NZ bats to calibrate the molecular clocks for CoVs might interest you, because you comment on the age, but our analyses here puts that into context and helps us understand CoVs in bats generally. https://www.sciencedirect.c.... Dave

On 2023-05-30 03:25:59, user Francois GRANER wrote:

• I could not find the "Bibliography" section. References are called throughout the text but are not found at the end of the manuscript.

• It is repeteadly written that surfaces with a constant mean curvature are portions of a sphere. This is a basic mistake, see <br /> https://en.wikipedia.org/wi...<br /> https://en.wikipedia.org/wi...

On 2023-05-29 00:51:12, user Investigate Explore Discover wrote:

I really enjoyed reading through this paper and thought it was very well done. Here is a peer review done on this paper. Hopefully the authors can find it helpful

https://www.researchhub.com...

On 2023-05-28 22:02:06, user Andrew J. Crawford wrote:

Sample quality is a key challenge in all genomics project, so I really appreciate this preprint Most of biodiversity is not conveniently located next to an ultracold freezer, of course. Many thanks for sharing! Just wanted to point out that the EBP paper cited at the end of paragraph 1 is not shooting for reference genomes for all eukaryotes by 2025 but by the end of 2030, since 2021 is referred to as the "end of the first full year of the project", and Lewin &l (2022) propose a 10-y timeframe." [The EPB paper also proposes a Phase I achieving "An annotated reference genome for one representative of each taxonomic family of eukaryotes (∼9,400 species) in 3 y".]

On 2023-05-26 19:11:30, user bill wrote:

Very nice! fyi, BLI is bio-layer interferometry.

On 2023-05-26 15:31:52, user Jose Eduardo Soto wrote:

In Figure 2C, OrgB and OrgC labels appear to be swapped. Similarly, in scheme Figure 2D, OrgA is mislabeled as OrgG.

On 2023-05-26 09:25:11, user Willem H. Koppenol wrote:

The source of H2O2 is the weak point of this paper. Only in distilled water, or with some phosphate present, as here, can two hydroxyl radicals combine to form H2O2. Under any other conditions, hydroxyl radicals are scavenged.

On 2023-05-25 19:19:48, user Nicola Asuni wrote:

I am maintaining an updated version of the variantkey library at:

https://github.com/tecnickc...

On 2023-05-24 09:23:26, user Dr. Christos Chinopoulos wrote:

Lesch-Nyhan disease (LND) patients suffering from HRPT1 deficiency have never been reported to exhibit any cancers.

On 2023-05-23 21:35:50, user Sarah wrote:

Minor Notes: <br /> • In figure 1C there is a minor formatting issue with its Y-axis.<br /> • In the first paragraph of the introduction you misspelled patterns “shaping developmental expression patters”.<br /> Questions:<br /> • While I do appreciate you sequenced ~100 embryos per group, could you add more technical replicates to show how robust this signal is? I would be more convinced your study is reproducible if these trends hold up with different clutches of embryos.<br /> • For figure 3A, I understand how you decided to bin each of the genes. The cut-off (<5%) for maternal only genes seems reasonable; however, the cut-off (>65%) for zygote only genes feels somewhat arbitrary, especially considering your modeling results in figure 4D. Could you comment on how your characterization of genes in figure 3A compares with your modeling results in figure 4D? <br /> • For figure 3C, numerous of us had a hard time distinguishing between zygotic and maternal-zygote genes. Could you recolor it to be more discernible?<br /> • While I appreciate what you are showing us in figure 5B, your results fall a little flat. You did this impressive scRNA-Seq experiment; however, you are showing us aggregated data which you could have been able to generate from bulk RNA sequencing. For the genes which were expressed in a cell-type specific manner, is there anything novel you can highlight? <br /> • For figure 7, can you see if there is a depletion of those motifs in the zygotic sequences which are expressed at the same time?<br /> • I think you did a good job validating your findings with examples from the literature. Could you speculate in the discussion on how this could impact the field moving forward?<br /> Appreciation: <br /> • Thank you for making your data available via Shiny App.<br /> • Your discussion of technical limitations was appreciated from someone with basic knowledge of scRNA-Seq.

On 2023-05-23 11:48:26, user Fanny Cavigliasso wrote:

A peer reviewed version of this manuscript is available in Evolution Letters: https://doi.org/10.1093/evl...

On 2023-05-23 03:53:12, user Louise wrote:

For the SLE dataset, did you use GSE137029 or GSE174188? The code seems to say Perez et al GSE174188, but the manuscript says Mandric et al GSE137029?

On 2023-05-22 22:45:16, user Fraser Lab wrote:

Summary:

In protein engineering projects, it is always desirable to screen as efficiently as possible. Screening a relatively small number of variants becomes especially important when enzyme activity cannot be coupled to a high throughput sequencing readout. The major goal of the paper is to provide a proof of concept scoring and filtering system for selecting among proteins generated using computational methods to meet this challenge of efficient screening. They consider proteins generated using 2 machine learning methods and one phylogenetic method (ancestral reconstruction).

The end result is a scoring filter combining the language model ESM-1v (which uses only sequence information) and the deep learning method ProteinMPNN (which is trained directly to find the most probable amino acid for a protein backbone predicted by AlphaFold2). After accounting for some simple idiosyncrasies of merging generative models with reality (ensuring starts with Met, removing repetitive sequences, accounting for localization signals) with heuristics, their filtering steps results in an enrichment of active sequences.

The major success of the paper is a pipeline that actually works for selecting active sequences both in the experiments they conduct and (to some extent) literature examples. The table of potential protein failure modes is particularly useful as a baseline approach and reference for people designing sequences with computational methods. It is especially insightful to see how few deliberate filtering steps in the training process can have a big change in the outcome.

We expect that a combination of sequence and structure-based filters will be used for prioritizing screening resources in the future. This paper lights the way of how to do that. The next steps will be to take into account structural features beyond stability (which is presumably covered by the AF2/ProteinMPNN), such as catalytic residue positioning, pocket size complementarity to substrate, etc. These are presumably implicitly captured by ESM-1. The next logical step (beyond this paper) is to go beyond statistical combination of these two scoring features to account for such features explicitly or with a new integrated deep learning approach.

Major points:

We are a bit confused about the exact value and sequencing of each part of the selection/filtering pipeline. We interpret experiment 3 as:<br /> Apply ESM-1v and Quality Filters and then apply a ProteinMPNN filter on top of that. <br /> Select Negative Controls by selecting sequences that fail the first filter (ESM-1v and Quality Filters) but are within 1% sequence identity to the closest natural sequence for some positive.<br /> The quality checks discussed in the supplementary information seem to have substantial impact. If the selected control sequences failed this quality check, it’s not clear whether the success of the pipeline is due to these heuristic quality checks or due to the computational filtering. These filters are biologically simple such as starting with a methionine, removing long repeats and not having a transmembrane domain - and it is kind of amusing to one of us (JF) that generative models have these pathologies so commonly. More discussion on why these filters were applied and what the distribution of effects were for the quality filters vs the insilico filters would help clarify the impact of each stage.

This confusion then extends to determining how each of the two computational methods affect the selection. The authors contend that “no single metric would be sufficiently generalizable to screen against multiple sequence failure modes” and hypothesize that ProteinMPNN and ESM-1v “may capture distinct features.” However, because negative controls were selected only after failing the initial ESM + Quality Filters, its impossible to know what effect adding ProteinMPNN on top of ESM had. This is even more relevant given that the structures used to obtain proteinMPNN scores are first generated with Alphafold. Alphafold can be computationally intensive (expensive to run) and therefore it is imperative that we understand how much this part of the pipeline contributes to the overall success of the selection process. The authors themselves contend that “Structure-supported metrics, including Rosetta-based scores, AlphaFold residue-confidence scores, and likelihoods computed by neural network inverse folding models, take into account protein atom coordinates potentially directly capturing protein functionality, however, they can be impractical to compute, especially when evaluating thousands of novel sequences.” This is something that can potentially be teased out. In the case of the paper only 200 proteins were selected using ProteinMPNN, however, if many sequences end up passing the ESM filter and budget allows it would be within reason to expand this random ESM selection.

In summary, it is a bit hard to tell (without some ablation studies) which different pipeline components and filters drive the results. Additionally, it would have helped if these same quality filters were applied in Round 2 but that doesn’t seem to be the case? A deeper discussion on the selection of quality filters would also point the way forward with combining more “functional” structural features as outlined above.

Minor points:

1) The author’s generalize the results with a few literature examples: “similar results were obtained by independently validating COMPSS on previously published datasets of six enzyme families generated by models not considered in the present study.” Looking at the results in more detail reveals that some of these (including one that we generated!) are very small samples and this caveat should be discussed. In 3 out of the 6 studies, only 1 sequence was selected by their pipeline. In another of the 6, 2 were selected. In all 4 of these studies, a number of actives were missed. The limited number of selected sequences makes it hard to know how effective the pipeline really is in these 4 studies. Further, with such a stringent filter is not practical especially when we consider the fact that the authors don’t discuss the level of activity across positive and negative active compounds. It’s entirely possible that you could miss very active sequences and select only moderately active sequences. In one retrospective, the results were truly similar, however in the last other study, the filters worked far from intended.

Even more, my team has observed in its own work that the sensitivity of machine learning models for scoring can be heavily dependent on the sequences the models have seen before. It would have been useful for the authors to consider how the tested enzymes overlap with the model training data to understand whether these scorers generalize outside the models training distribution.

2) The authors largely discount natural sequence identity as a metric:<br /> “Surprisingly, neither sequence identity to natural sequences nor AlphaFold2 residue-confidence scores were predictive of enzyme activity.”

I think it’s important to qualify this with the fact that we are looking at sequences in the 70 to 90% range with very little dynamic range here. In their first experiment they looked at sequences in the 70 to 80 range. in their second they look at sequences in the 80 to 90 range. In their third experiment they looked at sequences in the 50 to 80 range but their filters end up selecting for sequences in the 70-80 range anyways. So it’s possible that locally, identity might not select for select for activity but globally, it could be a first filtering step on its own (which maybe is obvious and hence why it’s not more qualified?). Also to note is that sequence identity seemed to fare as well as or better than other metrics in identifying functional GAN-generated sequences and could be its own generative method:

More problematic I think is figure 3f and figure 3g:

It seems like the inactive controls are largely in a separate part of the tree compared to the active sequences passing and control. Does this have anything to do with the fact that these features failed the sequence based quality filters. Second,it suggests an approach where if you have some idea of where to focus on in the tree you could use sequence identity to those natural sequences as a metric for selection . Of course this information may not be readily available but the authors should discuss whether we could have hypothesized that the failing controls would have failed beforehand by considering their phylogenetic origins.

Technical points:

1) There is some problem with this sentence:

“CuSOD training sequences had only a single Sod_Cu domain, while MDH had an Ldh_1_N followed by an Ldh_1_C domain and no other Pfam domains that generally only rarely occur in 6.3% and 1.7% of sequences in both families, respectively.”

It’s much better captured in the supplementary material:

“For CuSOD, 1,632 out of 25,701 proteins (6.3%) had aberrant architectures. For MDH, 1,127 out of 65,639 (1.7%) had aberrant architectures.”

2) It’s not clear where/how they selected the natural test sequences for rounds 1 and 2. We assume it’s from the curated set of data but that’s not necessarily a given, further it seems that in round2, sequences were selected to span the range of esm scores. Was this done for the test natural sequences as well?<br /> “Only 13 test natural sequences were selected, as we had already screened five similar natural sequences in the remediation for Round 1.”

“Besides the identity range, the experimentally tested sequences were selected to span the entire range of scores on each metric (Supplementary Table 4)”

3) The authors should be more explicit on the natural sequence identities in each round. If you check the supplement you can find this information if you pay attention to the figures or check the supplement but I think that it should be explicitly stated in the section “Round 2: Calibration data for COMPSS” that sequences are selected in the 80-90 range and in Round3 that the filters resulted in sequences with >69% identity.

4) The following section is confusing:

“To further test the hypothesis that poor truncation selection was responsible for the lack of observed activity in the Round 1 CuSOD natural test sequences, we assayed an additional 16 natural SOD proteins (pre-test group)…”

It should be stated at the beginning that 14 of the 16 test sequences are CuSOD sequences and 2 of the sequences are FeSOD sequences vs letting the reader figure that out later in the paragraph. Additionally, it would help the audience to say explicitly that 3/7 bacterial sequences with clipping also passed or include the table from the supplement up front. 3/7 doesn’t seem clearly distinct from 4/5.

5) What’s the reason for changing the esm-msa sampling method in round 2? Did they observe some benefit or was this purely a computational choice?

6) I think the text for a and b are switched in the figure 2 description. a is the AUC figure and b is the correlation figure. Further for figure a If the test sequences are natural sequences, is the identity score meaningless here?

7) From the supplement: “We skipped the 'starts with M' filter because very few of the sequences in these sets start with M, and did not subset by identity to closest training sequence.” This modification to the pipeline should be mentioned in the discussion of the external validation tests. Or they should speculate what would happen if they just added a M at the beginning of every sequence?

8) Looking at the figures in the supplement e.g. Fig 30 it seems like they had quantitative activity values. It would have been nice to discuss if there was any correlation between scores and activity for ranking purposes. Was this not included because of variance in the assay?

Joel Beazer (Profluent) and James Fraser (UCSF)

On 2023-05-22 07:34:22, user Valerie Wood wrote:

Hi, please note that drp1 is a synonym for many human genes. https://www.genenames.org/t... you should use the standard name DNM1L to refer to the human protein. Also S. cerevisiae has many genes with the synonym DRP1, the standard name is DNM1 for this reason.<br /> If there is a problem, please contact HGNC or SGD.

On 2023-05-21 14:34:15, user Yudi Lozano wrote:

This paper has been published as Lozano et al., 2020 (Root trait responses to drought are more heterogeneous than leaf trait responses) in Functional Ecology

On 2023-05-19 00:23:53, user Zhou Chen wrote:

Thrilled to share that our first known client-loaded EMC, a real multiple TM segment channel:chaperone structure is out now @Nature. Check the link below:<br /> https://doi.org/10.1038/s41...

On 2023-05-17 05:59:59, user Mayra Calderon wrote:

Great paper! I really liked the clarity of the figures and the step-by-step explanation of each method used, as well as the confirmation of background information through the different methodologies. Figure 1, specifically, was very well put together, setting the foundation for the rest of the experiments and the preliminary data used in subsequent experiments and figures.

It was also fascinating to see the different drug assay experiments conducted using both in vivo and in vitro methods, as well as the use of different model organisms such as mice and humans. The advancement of combination drug therapy in targeting cancer cells and affecting cell proliferation is very promising, not just for LUAD but for different types of cancers, as mentioned in the paper.

Although the research conducted was clear and thoroughly explained, the figures were a bit confusing to understand and could have benefitted from supplementary figures or further clarification of their intended meaning. Some figures did not mention the significance of the data, while others did. It was challenging to differentiate which sets of data were significant and which ones were not, as some contained "ns" (not significant) and others were left blank. Additionally, figures like Figure 4a included significance markers without an explanation of why the data was considered significant in the first place. Figures such as Figure 7a and Figure 7c were also confusing in terms of what determined their level of significance. However, the results were explained clearly, and the interpretation of the data sets, despite the challenges, was possible based on the written results of each figure.

On 2023-05-16 06:13:21, user Zibai Lyu wrote:

I appreciated how you applied CRISPR screening to identify tumor-intrinsic genes that inhibit anti-tumor immunity and combined it with traditional ICB treatments. I also enjoyed your thorough experiment designs and well-organized figures. However, I have a few comments regarding your figures and the statistical methods used in this paper.

1. Figure 3 C-D and F-J: I noticed an inconsistency in the statistical method used. In the Methods section, you mentioned that an unpaired t-test was used, but in the figure legends, you indicated that statistics were calculated using a paired t-test. Since you were comparing two independent groups, an unpaired t-test would be the correct test to employ.

2. Figure 3 D, H, and J: You indicated that a t-test was used. However, given that a t-test is a parametric test that requires data to be normally distributed, and there were a significant number of outliers in your figures, it could be better if you provided the Shapiro-Wilks normality test results to prove that your data were indeed normally distributed, or considered using non-parametric tests if your data were not normally distributed.

3. Figure 3B: In the Results section, you concluded that the innate immune response was more responsible for impaired growth of COX-2 deficient tumors, but you only compared the NK population and CD8+ T population given the information from Figure 3B. Therefore, it could be better if you refined your conclusion.

4. Figure 4F: The data were clearly right-skewed, so it could be better if you considered using non-parametric tests or provided the Shapiro-Wilks normality test results if the data were indeed normally distributed.

5. Figure 4H: It could be better if you provided the global p-value for the Cox-proportional hazard model.

6. Kaplan-Meier survival curves: Although you did not compare multiple groups at once, you graphed them in the same figures. Therefore, it could be more accurate if you calculated the Bonferroni-corrected α value and used the adjusted significance level.

On 2023-05-16 05:13:32, user Rachel Jiang wrote:

I really enjoyed reading your paper which included well-designed experiments and explored interesting novel therapeutic treatments targeting the KRAS-induced COX-2 immunosuppressive pathway in LUAD. Some general stats-related and stylist comments I had were:

1) In Figure 5h, the significance level of the difference between the Gzmb mRNA quantity of anti-PD-1 and celecoxib was shown as "0.05", which should probably have been "n.s." since the figure legend only defined significance levels for p-values less than 0.05. In addition, the significance level was shown for anti-PD-1 and the combination treatment for Ifng and Gzmb, but not for Prf1 and Cd40 (instead the significance level for celecoxib and the combination treatment was shown). I was a little confused by this inconsistency since the main conclusion of Figure 5 was that celecoxib increases efficacy of anti-PD-1 treatment, for which I thought it would be better to show the significance levels of the differences between anti-PD-1 and the combination treatment for all four anti-tumor genes (as you did in Figure 6f).

2) I was also a little confused by the visualization of Figure 5g, specifically what exact comparisons correspond to the significance levels shown. The figure legend mentioned that a two-way ANOVA was performed but I thought perhaps a one-way ANOVA would be more appropriate since it seems that only a single type of T cells for different treatments were compared with one another. The results section could also explain a bit more regarding this figure that could clear up any confusion.

3) For all the ANOVAs performed, it would be helpful to specify which post-hoc you performed.

4) Just stylistically, it would be helpful if each figure could be followed by its own figure legend to make it easier to interpret the figures.

But overall, the paper is great and your findings are really exciting!

On 2023-05-17 05:15:51, user Michael Forrest wrote:

I've reported this relationship form before. <br /> See Figure 11 (on Page 37) of my bioRxiv preprint: https://www.biorxiv.org/con...

On 2023-05-16 23:37:19, user MICR 603 wrote:

Summary.

Since the emergence of SARS-CoV-2, the causative agent of the disease COVID 19, various vaccines and treatments have been developed that have been shown to be effective in reducing risk and severity of clinical outcome. Some limitations of current COVID19 treatment options is the inconsistency of their therapeutic efficacy across strains as well as possible unknown off-targets. To address these concerns, the use of llama-derived nanobodies (Nbs) has become of interest. Nbs have been shown to have a high affinity and specificity for targets, therefore decreasing risk of effects coming from possible unknown off-targets. In this study, llama-derived Nbs capable of neutralizing several SARS-CoV-2 strains were identified. In the In vivo model, Nbs were indicated to effectively induce protection in several lung tissues including the brain. These findings suggest Nbs as possible therapeutic agents for protection and treatment of SARS-CoV-2.

Positive feedback.

This paper looked at the exciting research area of advancement upon monoclonal antibodies through Nbs. The researchers did a thorough job of explaining the genomic structure of SARS-CoV-2 as well as the interaction occurring during the initial attachment phase (lines 58-66). An exciting advancement of this study was the identification and characterization of SARS-CoV-2 neutralizing Nbs. These findings were made more impressive with their consistency found among varying strains as well as in both in vivo and in vitro design models. The use of several strains is important as individual strains can have very different effects and outcomes. The figures were nice with the appropriate size (not too small) being used for readability and the layout was not overwhelming with not too many figures being placed together (example Figure 3). I also liked the color scheme used as even though similar ones were used, varying shades were used to clearly contrast such as magenta as opposed to a lighter pink (example Figure 3 D). I especially liked Figure 10 for its ability to encapsulate a lot of information. The authors did a good job of explaining their research to those outside the field. <br /> The methods used to derive antibodies was well explained and will be helpful for those who would like to expand upon the research using this methodology.

Major Concerns

Different sources of S protein were used for different immunization points. Example the first two immunizations used were from one source and then third immunization is obtained using S protein from a different source. Moreover, they mention that the S protein parts encoded on expressed vectors are slightly different, how? (page 5 line 110) Authors should provide more detail into plasmids used, a table of recombinant proteins being used would be good. This is important as it probably represents the variability observed in Figure 2 when showing S protein binding of nanobodies.<br /> For Figure 6, an interpretation was not given in the discussion. It was mentioned that the Delta variant was not able to be neutralized by any of the non-RBD binders (pg. 11 line 279), but the researchers did not revisit this to share any hypothesis as to why this might be happening.

In Figure 2, it might be good to see raw data and a correlation coefficient (R^2) to see how good the fit is. It might be helpful to include Km in the figure instead of just in a table.

Proper positive controls in protection experiments are missing. For example, protection against various SARS-CoV2 variants with new nAbs could be compared with existing tools, e.g., mAbs used in clinic and/or current antivirals. Otherwise, it is hard to know whether the new tools are better than existing ones.

Minor concerns

The researchers briefly discussed ways in which their current work could be expanded upon (line 443-446), but did not mention the current limitations of their study. Discussing limitations is helpful for the reader in better understanding the results from the paper. <br /> What are some other routes that nanobodies can be introduced into the mouse besides intranasally? Will this route translate to humans?<br /> Full organs were used for calculating SARS-CoV2 titers. Was different localization of the virus seen in different areas of the brain? It might be interesting to discuss if they saw any differences and suggest this as a future direction. https://www.nature.com/arti...

Figure 2 A-data points for NB-45 are difficult to see. Perhaps the data and the fits could be divided into classes instead of all in one figure.

Figure 4 B, it is not made clear why the concentrations of nanobodies being used was chosen. Example NB-39 and Nb-43 is at 10 ug while the other nanobodies are administered at 20ug.

It would be good to be more clear about the criteria for mice to be euthanized (such as the amount of weight needed to be lost). Some of the weight loss seems to be small (according to our standards) for euthanization.

Figure 4 legend could include the amount of mice (assume 5), but this is not clear.

In Figure 2, the variability of Nbs binding to Spike protein is very different (2A.) This is in contrast to the little variability noted between Nbs binding to RBD (2B.) This finding is not discussed in the paper.

This may not need to be expanded upon, but I was curious why 4 days post-challenge was chosen to harvest tissues to evaluate the effect of nanobodies on virus titers (line 716 pg.28). It would be good to compare this time point to other time points. Do you have samples from surviving mice that you could look back at?

In Figure 5, are the statistical values noted in Figure 4 also supposed to apply to Figure 5? Also, the significant difference between treatment groups in the same tissues with different letters is noted in figure legend 5, but it is somewhat confusing what is meant. More clarification in the figure legend would be helpful.

The discussion portion did not reference all figures that were discussed. For example, the Biliverdin competition assay was mentioned, but not properly referenced (page 15, line 378).

Animals only have clinical signs and cannot have symptoms as is described in (line 716). A clear distinction should be made between symptoms and clinical signs. https://jamanetwork.com/jou....

Figure 5, you could add a figure for each tissue and connect data points by mouse for each nanobody. This will help to see if specific mice have a consistency in nanobodies across tissue types.

Figure 9, orientation of protein is flipped frequently (Figure 9A). Some papers may require the structure be kept the same throughout the figure.

S1D, a caption stating what genes or products are being amplified (like a schematic) would be helpful.

S3, states that length of antibodies ~133 nucleotides. Why did they amplify 700 nucleotides? It should be 400 nucleotides only.

In SF. 5, maybe determine a better way to present Western blot data and revisit to describe why there are dots and not strong bands. Could WB be quantified using something like densitometry?

On 2023-05-16 09:26:53, user Yasunari Fujita wrote:

Dear Scott

We greatly appreciate your valuable comments. We have responded to<br /> each comment below.

COMMENT 1<br /> Firstly, I would like to congratulate the authors on their study. I really love <br /> the inventiveness of the experimental set up! The results are really <br /> unexpected. The finding that phosphate starvation proceeds ABA mediated <br /> drought responses is interesting on a mechanistic basis and will likely <br /> have direct implications for crop management practices. The field-to-lab<br /> experimental pipeline looks really effective and I look forward to more<br /> people taking up this approach.

RESPONSE 1<br /> Firstly, we would like to apologize for the very late reply. Thank you so much <br /> for your interest in our research and your positive evaluation. Our data<br /> presented here showed that PSR response occurs earlier than ABA <br /> response under mild drought. We used to study drought stress in <br /> artificially controlled environments, but we have always been interested<br /> in what happens in the actual field, at the molecular level. We had <br /> heard a lot about the lab-to-field concept, but we would like to take <br /> our research from the field to the lab. The first and corresponding <br /> authors have actually interviewed farmers about drought conditions <br /> around the world, collaborated with drought researchers around the <br /> world, and traveled to drought research sites to see the realities. In <br /> listening to such voices in the field, we have been warming up our <br /> thoughts about what we should do as plant molecular biologists for a <br /> long time. Each year we conduct field trials of mild drought induction <br /> by ridges in soybean in the same way as in the paper, and we hope to <br /> find many new and important mechanisms in many more years of data. We <br /> would appreciate your continued advice, comments, and guidance.

COMMENT 2<br /> I do have a couple of points that I believe the authors could discuss in <br /> more depth. In their ridge trials, the geometry of the soil may play a <br /> role. Phosphate starvation induces a lot of lateral roots close to the <br /> soil surface, to maximise phosphate capture. In the ridge set-up, <br /> lateral roots are restricted to only a single plane. Is it possible that<br /> this contributes to the PSR in ridge-grown plants? It is also possible <br /> that increased rooting depth under mild-drought treatment also reduces <br /> phosphate uptake. If the authors see a reduction in PSR gene expression <br /> after re-watering ridge plants, this may help to rule out geometric <br /> explanations. It should be noted that the pot experiments do already <br /> indicate this to some extent.

RESPONSE 2<br /> Thank you for your interesting comments. Since the beginning of the ridge <br /> system, we have always carefully examined the possibility of unintended <br /> effects, such as changes in root morphology, caused by mild drought in <br /> the ridge system. In this light, we showed that irrigation compensates <br /> for the growth reduction caused by ridges (Fig. 2) , suggesting that <br /> various factors, such as slight changes in root shape due to ridges, are<br /> not substantially affected. As you also mentioned, we also used pot <br /> tests to substantiate our conclusions, as it is sometimes difficult to <br /> perform detailed molecular physiological analyses using only ridges. We <br /> therefore have analyzed Pi and ABA levels and PSR gene expression after <br /> re-hydration from pot tests with different plant species and soil types <br /> and have obtained results that can rule out geometric explanations, <br /> which we will update soon.

COMMENT3<br /> I am also not so sure about the authors’ conclusion on why PSR is induced at <br /> mild drought but suppressed under severe drought “Under severe drought <br /> conditions, given the circumstantial evidence, our observations would <br /> support the notion that PSR induction is suppressed by a relative <br /> increase in Pi concentration due to a decrease in leaf water content <br /> (Fig. 4).” I would argue an alternative, more straight forward <br /> hypothesis, that plants simply prioritise drought stress over this level<br /> of phosphate starvation when drought is severe. Would the authors <br /> expect to see a recovery of the PSR if Pi levels dropped even lower?

RESPONSE 3<br /> Thank you for pointing this out. We have also analyzed Pi and ABA levels and <br /> PSR and ABA-responsive gene expressions in response to changes in soil <br /> moisture content to demonstrate whether this hypothesis is true and will<br /> update the data soon. Research on the link between PSR and mild drought<br /> is just getting started, so future in-depth studies may provide data to<br /> support your hypothesis.

COMMENT 4<br /> Once again, thanks to the authors for their very through-provoking study!<br /> I look forward to hearing from you,

Scott

RESPONSE 4<br /> We greatly appreciate your great interest and appreciation of our work.

Yasu

On 2023-05-13 07:30:59, user Yasunari Fujita wrote:

Dear Scott

We greatly appreciate your valuable comments. We have responded to<br /> each comment below.

COMMENT 1<br /> Firstly, I would like to congratulate the authors on their study. I really love <br /> the inventiveness of the experimental set up! The results are really <br /> unexpected. The finding that phosphate starvation proceeds ABA mediated <br /> drought responses is interesting on a mechanistic basis and will likely <br /> have direct implications for crop management practices. The field-to-lab<br /> experimental pipeline looks really effective and I look forward to more<br /> people taking up this approach.

RESPONSE 1<br /> Firstly, we would like to apologize for the very late reply. Thank you so much <br /> for your interest in our research and your positive evaluation. Our data<br /> presented here showed that PSR response occurs earlier than ABA <br /> response under mild drought. We used to study drought stress in <br /> artificially controlled environments, but we have always been interested<br /> in what happens in the actual field, at the molecular level. We had <br /> heard a lot about the lab-to-field concept, but we would like to take <br /> our research from the field to the lab. The first and corresponding <br /> authors have actually interviewed farmers about drought conditions <br /> around the world, collaborated with drought researchers around the <br /> world, and traveled to drought research sites to see the realities. In <br /> listening to such voices in the field, we have been warming up our <br /> thoughts about what we should do as plant molecular biologists for a <br /> long time. Each year we conduct field trials of mild drought induction <br /> by ridges in soybean in the same way as in the paper, and we hope to <br /> find many new and important mechanisms in many more years of data. We <br /> would appreciate your continued advice, comments, and guidance.

COMMENT 2<br /> I do have a couple of points that I believe the authors could discuss in <br /> more depth. In their ridge trials, the geometry of the soil may play a <br /> role. Phosphate starvation induces a lot of lateral roots close to the <br /> soil surface, to maximise phosphate capture. In the ridge set-up, <br /> lateral roots are restricted to only a single plane. Is it possible that<br /> this contributes to the PSR in ridge-grown plants? It is also possible <br /> that increased rooting depth under mild-drought treatment also reduces <br /> phosphate uptake. If the authors see a reduction in PSR gene expression <br /> after re-watering ridge plants, this may help to rule out geometric <br /> explanations. It should be noted that the pot experiments do already <br /> indicate this to some extent.

RESPONSE 2<br /> Thank you for your interesting comments. Since the beginning of the ridge <br /> system, we have always carefully examined the possibility of unintended <br /> effects, such as changes in root morphology, caused by mild drought in <br /> the ridge system. In this light, we showed that irrigation compensates <br /> for the growth reduction caused by ridges (Fig. 2) , suggesting that <br /> various factors, such as slight changes in root shape due to ridges, are<br /> not substantially affected. As you also mentioned, we also used pot <br /> tests to substantiate our conclusions, as it is sometimes difficult to <br /> perform detailed molecular physiological analyses using only ridges. We <br /> therefore have analyzed Pi and ABA levels and PSR gene expression after <br /> re-hydration from pot tests with different plant species and soil types <br /> and have obtained results that can rule out geometric explanations, <br /> which we will update soon.

COMMENT3<br /> I am also not so sure about the authors’ conclusion on why PSR is induced at <br /> mild drought but suppressed under severe drought “Under severe drought <br /> conditions, given the circumstantial evidence, our observations would <br /> support the notion that PSR induction is suppressed by a relative <br /> increase in Pi concentration due to a decrease in leaf water content <br /> (Fig. 4).” I would argue an alternative, more straight forward <br /> hypothesis, that plants simply prioritise drought stress over this level<br /> of phosphate starvation when drought is severe. Would the authors <br /> expect to see a recovery of the PSR if Pi levels dropped even lower?

RESPONSE 3<br /> Thank you for pointing this out. We have also analyzed Pi and ABA levels and <br /> PSR and ABA-responsive gene expressions in response to changes in soil <br /> moisture content to demonstrate whether this hypothesis is true and will<br /> update the data soon. Research on the link between PSR and mild drought<br /> is just getting started, so future in-depth studies may provide data to<br /> support your hypothesis.

COMMENT 4<br /> Once again, thanks to the authors for their very through-provoking study!<br /> I look forward to hearing from you,

Scott

RESPONSE 4<br /> We greatly appreciate your great interest and appreciation of our work.

Yasu

On 2023-05-16 09:25:26, user pierre wrote:

As the MRNA cannot enter the nucleus, there is absolutely no chance than it can interact with the DNA. Furthermore, even if MRNA was by some miracle be in presence of DNA, interaction would require a specific reverse transcriptase, as you know that there is not one RT, but one for any MRN, which come associated with the RNA of the retroviruses.

On 2023-05-15 17:11:44, user Karma Bertelsmann-Mohn, PsyD wrote:

I hope someone may still be following the progression of this research. If so, please reach out!

On 2023-05-15 12:23:58, user Rubayet Elahi wrote:

Now accepted in eLife. https://doi.org/10.7554/eLi...

On 2023-05-15 10:09:37, user Matthew Daniels wrote:

This pre-print gave rise to two publications which are both open access

JMCC 2023<br /> https://doi.org/10.1016/j.y...

and

Circ Res. 2019 Apr 12;124(8):1228-1239 https://doi.org/10.1161/CIR...

On 2023-05-15 06:30:01, user Andrew Francis wrote:

Published version (open access) is available here: https://link.springer.com/a...

On 2023-04-03 23:24:56, user Andrew Francis wrote:

This paper has been accepted by the Bulletin of Mathematical Biology (3rd April 2023), and a link to the published version will appear here once it is available. In the meantime, please contact the authors for a copy of the final PDF.

On 2023-05-14 10:54:09, user Euan Brown wrote:

Interesting single observation though the title is misleading. What does ‘abnormal’ mean here? And the ‘nightmare' scenario is perhaps overly dramatic.

We would also appreciate if you revise this publication that you properly cite our 2006 paper (Brown et al 2006). In the section 'Cephalopods including Octopus insularis, O. vulgaris, and the common cuttlefish (Sepia officinalis) are known to display rest-activity cycles with ultradian periodicities, similar to the sleep states of amniotes (Cephalopods including Octopus insularis, O. vulgaris, and the common cuttlefish (Sepia officinalis) are known to display rest-activity cycles with ultradian periodicities, similar to the sleep states of amniotes (Meisel et al., 2003; Meisel et al., 2011; Frank et al., 2012; Iglesias et al., 2017; Medeiros et al., 2021).). Should cite our paper after Meisel et al., 2003. The section , 'These sleep states have been also associated with brain activity in octopuses, supporting differences in neural activity in wake and sleep states akin to vertebrate sleep should cite (Brown et al 2006: as well as Gutnick et al., 2023).<br /> thank you!

On 2023-05-13 15:27:12, user Mario Inchiosa wrote:

This preprint has been accepted for publication. A link will be provided when it is published.

On 2023-05-12 19:14:21, user Joseph Wade wrote:

The following is a review compiled by graduate students participating in the Infectious Disease Journal Club, Department of Biomedical Sciences, University at Albany, SUNY, USA:

This paper uses computational analyses of bacterial genomes to determine the distribution of anti-phage defense systems in actinobacterial species. The authors look at the diversity of defense systems, their enrichment in actinobacteria relative to other bacterial clades, and their genomic location. The authors suggest that some defense systems are enriched in the genomes of actinobacteria relative to other bacterial clades, while other systems are de-enriched. The authors also show that different defense systems are encoded in different regions of Streptomyces chromosomes, suggesting differential importance in phage defense. The fact that many defense systems are found more or less frequently in actinobacteria versus other bacteria, including some defense systems being completely absent from actinobacteria, highlights the fact that previous searches for defense systems have likely been biased in terms of the species used.

The paper is well written, and with a few exceptions, the data are clearly presented. The analyses seem appropriate for the questions being asked. However, there is a concern that the genomes used are in some cases heavily biased towards individual species, most notably Mycobacterium tuberculosis. This bias could lead to erroneous conclusions about the relative abundance of some classes of defense systems in actinobacteria.

Major comment<br /> The species used for Actinobacteria and Proteobacteria are not representative of the phylum in each case. For Actinobacteria, ~9% of genomes are from Mycobacterium tuberculosis. For Proteobacteria, ~19% of genomes are from Escherichia coli and ~15% are from Klebsiella pneumoniae. This is especially problematic for M. tuberculosis, a species for which there is very little variation across strains. Some analyses may be influenced by these biases in the genomes used. For example, Figure 1B shows data for mycobacteria, but ~55% of the mycobacterial genomes analyzed are from M. tuberculosis. In Figure 1E, enrichment of gp29_gp30 is likely to be strongly influenced by M. tuberculosis genomes; this is a rare defense system that happens to be found in most M. tuberculosis genomes, so it likely appears a lot more enriched in actinobacteria than it really is. The same problem likely applies to other systems that appear enriched in actinobacteria. The analysis in Figure 1D will be heavily skewed by E. coli and K. pneumoniae. We recommend that the authors include only one genome per species. This will reduce potential biases. The authors should also include some text to highlight that biases in which genomes are sequenced may lead to biases in their data, since biases are inevitable based on the availability of genome sequences.

Minor comments<br /> 1. In the Introduction, it would be helpful if the authors could include a few sentences to describe what is known about the defense systems that are discussed most frequently in the paper, i.e., RM, Wadjet, CRISPR-Cas and Lamassu.<br /> 2. In Figure 2, could the authors show the same species in panels A and B? This would be less confusing.<br /> 3. Can the author’s speculate a bit more on why some BGC’s cluster at the ends of chromosomes?<br /> 4. Figure 1 legends for 1C and 1E are flipped.<br /> 5. In the Conclusions section, the authors discuss the possibility of undiscovered defense systems in Actinobacteria, given that the DefenseFinder program can only detect systems that are already known. Can the authors describe how novel defense systems could be identified based on their data for actinobacteria?<br /> 6. Figure 1D is confusing and does not indicate which systems are more/less abundant. The data would be better represented in a form similar to Figure 1E.<br /> 7. Figure 2B shows some examples of defense islands. Can the authors comment on the frequency with which defense systems in actinobacteria cluster in defense islands, and how this compares with other bacterial clades?<br /> 8. In Figures 3A and 3B, it is difficult to tell which systems are being shown in each concentric ring. Perhaps the authors can point out the few defense systems that are widely conserved.<br /> 9. The colors of bars in Figure 1E should be described in the legend.<br /> 10. For Figure 3C, it would be informative to show data only for Streptomyces, and to indicate in that plot which defense systems were inferred using Pagel’s lambda to be horizontally acquired.<br /> 11. Figure 3C considers only plasmids and prophages as mobile genetic elements. Can the authors look for other types of element, e.g., transposons?<br /> 12. It would be helpful to include a list of gene families for each class of defense systems analyzed. If DarTG includes toxin-antitoxin systems that are abundant in M. tuberculosis, the authors should mention that.

Suggestion for an additional analysis<br /> The authors have generated a rich dataset of defense systems in actinobacteria. They could use this dataset to look for candidates for novel defense systems using a “guilt by association” approach, i.e., look for gene families that are enriched in their proximity to known defense systems.

On 2023-05-12 18:22:48, user Roy Faiman wrote:

Hi Ben, nice work. Not sure if this has been published yet but you may want to take a look at Faiman et al. 2022 on the use of hydrogen stable isotopes for marking a village population of Anopheles coluzzii. We showed 20% survive the 7 month long dry season

https://www.nature.com/arti...

On 2023-05-12 17:40:58, user Maurice Franssen wrote:

Fascinating research and results. One comment: the moth shown in Supplementary Figure 1i is not Noctua pronuba but its sister species Noctua fimbriata. I do not think that species will behave differently but the authors better show a specimen of N. pronuba.<br /> Maurice Franssen, amateur entomologist, Wageningen, the Netherlands

On 2023-04-24 18:15:31, user Juanjo wrote:

That's not exactly.<br /> Yellow lights didn't affect the insects.<br /> If all the lights in the night were yellow, insects would not die.

On 2023-05-12 16:25:18, user Stefano Campanaro wrote:

Dear Joao Pedro Saraiva,<br /> I checked your paper and I suggest you to fix the citations of the different resources.<br /> The pipelines you mentioned in the analysis in my opinion are not properly cited:<br /> 1-The pipeline "8k" you mentioned is not a pipeline, but Donovan used Metabat with different settings. You should mention Kang for the metabat software.<br /> 2-The Multi-Metagenome pipeline (MM) you mentioned is again not a pipeline but a very old approach that (in my opinion) is outdated.<br /> 3-The third pipeline you mentioned "Karst and colleagues (DT)" is obviously the more accurate because he implemented many different binning tools and then he dereplicated the results. Here you should mention the binning software used.

In my opinion you should report more precisely the binning software used in order for the readers to understand better the procedure used.<br /> I hope my suggestions will be useful to clarify these sections in the manuscript.<br /> Sincerely<br /> --<br /> Stefano

On 2023-05-12 10:16:13, user Neyret-Kahn wrote:

Now published in Oncogene: https://doi.org/10.1038/s41...

On 2023-05-11 19:02:58, user Jingyi Jessica Li wrote:

Here is the published version: https://www.nature.com/arti...

On 2023-05-11 16:36:06, user sm wrote:

This is an additional example of pathogen effectors hijacking host phosphatase. The similar model has been studied in bacterial, in which bacterial AvrE-family Type-III effector proteins (T3Es) hijacking host PP2A

(Jin, 2016, Plos pathogen), and phytophthora infenstans effector Pi04314 hijacking host protein pp1c (Bovink Nat communication ,2016)

On 2023-05-11 14:02:47, user Amelia wrote:

PREreview of “Single-cell profiling of complex plant responses to Pseudomonas syringae infection.”

Amelia H. Lovelace1, Sarah Pottinger1, George Seddon-Roberts2, Emma Turley2

1 The Sainsbury Laboratory, Norwich Research Park, Norwich, UK<br /> 2 The John Innes Centre, Norwich Research Park, Norwich, UK<br /> * All authors contributed equally to the review and are listed in alphabetically based on last name

Overview<br /> At the time this paper was posted to Biorxiv, it was the first to report single-cell RNA sequencing (scRNA-seq) analysis of plants during infection and therefore is of great significance and interest to the microbe-plant interactions community. The authors use scRNA-seq to compare cell populations from Arabidopsis thaliana during mock or pathogen infection by the Pseusomonas syringae. Through this comparison, they identified a distinct subset of cells that are responsive to this bacterial pathogen. Through pseudotime trajectory analysis they demonstrate that cells transition from an immune state to a susceptible state. From this analysis they identified known and unknown marker genes for these two states and characterized them through promoter report lines. Using confocal imagery, they evaluated these reporter lines during infection over both time and space. Together the authors uncover the herterogenity during pathogen infection and this dataset is a great resource to the community.

We all enjoyed reading and discussing the manuscript. Overall, the manuscript is well written, and the findings are novel and exciting. The bioinformatics analysis is robust and consistent with previous scRNA-seq methods. However, the methods used to obtain these cells for scRNA-seq provide limitations to their interpretations and should be addressed. Below, we include some general comments as well as some specific comments/questions for some figures that came up during our discussion. We hope the authors find these useful.

General Comments<br /> The authors use the tape sandwich method to enrich mesophyll cells from mock and infected tissue; however, by removing the epidermal layers this overlooks the first line of defence in a natural P. syringae infection. This biases the scRNA-Seq results making it difficult to draw correlations with their seedling infection assays which aim to characterize immune and susceptible maker genes identified through these results.

The authors misinterpret the trajectory analysis where they correlate time with pseudotime. They state that their susceptibility gene expression analysis at later infection is consistent with their pseudotime trajectory analysis; however, their scRNA-Seq analysis was only performed on one time point. As such pseudotime may be more analogous to spatial distance from the infection site. The authors performed limited spatial characterization experiments of their marker immune and susceptibility genes to support their pseudotime analysis.

Lastly, a major concern of ours is the low number of cells and seemingly one biological replicate sample used to produce this data. Their data would be more robust if more biological replicate samples were used and more cells were sequenced.

Specific Comments by Figure<br /> Overall maintaining consistent use of colors across figures will help especially when red is associated with immune and blue for susceptible in figure S3 but the opposite for Fig 2

Figure 2<br /> • Immune and susceptible labels could be added to panel A, so as to make the graphic easier to understand. <br /> • Additionally, the clusters M1-M5 are difficult to distinguish from one another in panel A. It is not clear what the colored labels in panel B are for.<br /> • The authors used an unsupervised trajectory analysis in which the starting point they used was other. Does the same trajectory play out if they changed the starting point to a different group of cells?<br /> Figure 3<br /> • The authors state “The expression of all immune markers was significantly reduced at 72 hpi when plants exhibited chlorosis and water-soaked symptoms (Figure 3C and 3D)” but there are no pictures of these symptoms in this figure.<br /> • The authors state that LipoP1 is expressed in guard cells in the absence of infection yet no stomatal markers were used to demonstrate this in panel C.<br /> Figure 5<br /> • Focus on substomatal cavities, yet no use of stomatal marker genes, which might make localisation more convincing.<br /> Figure 6<br /> • While the top section of this figure is very helpful, we felt that the lower panel including the line graph might be confusing to a reader.<br /> Figure S1<br /> • Suggested that ΔhopQ was no different to the WT Pst DC3000 in terms of infectivity, but could infect both N. benthamiana and A. thaliana. Why was ΔhopQ used in downstream analysis if no N. benthamiana infection models were used?<br /> Figure S2<br /> • Scaling of panels A and B might be misleading, as the scale of panel A is ten-fold larger than that of panel B.<br /> • Panel C doesn’t contain a statistic on the figure. There is a figure (without a p value) in the text, but this makes the figure harder to evaluate.<br /> • Panels G-N contain a lot of darkness, making correlation difficult to discern. Perhaps removal of cells with a value of 0.00 would make the panels easier to read, as well as making patterns easier to identify. The authors state “that the vast majority of cells profiled in this study are similar in cell type as those profiled by others”. Based off the methods used by the authors, the majority of the cell types are mesophyll and in other methods used this would also be the same given that mesophyll cells have a larger representation in leaf tissue. However, many cell types are missing from the authors data which is difficult to see using the visualization methods provided.<br /> • Panel D has no y-axis label.<br /> • What function does panel E serve? With the amount going on in this figure, perhaps it could be excluded if it is unnecessary.<br /> Figure S3<br /> • Hard to see assigned clusters on panels A-C. Perhaps an overlay similar to Figure 2A would be advantageous.<br /> • What does each cell in panel B represent? There seems to be too few to represent each individual cell from RNA-seq dataset.<br /> • In panel D, perhaps GO terms should be clustered grouped based on expression in each cluster. A dot plot might also illustrate data better.<br /> Figure S4<br /> • This figure is quite difficult to decipher and might be more easily interpreted as a Venn diagram.<br /> Figure S5<br /> • Pseudotime trajectory should be labelled more clearly, with clear labelled transitions, so that cluster states can be more easily assigned.<br /> Figure S6<br /> • There is no second rep for FRK1. Why is this?<br /> • The authors state that LipoP1 is expressed in guard cells in the absence of infection yet no stomatal markers were used to demonstrate this.

On 2023-05-11 13:43:40, user ADRIAN TREVES wrote:

Pre-publication review of "Forecasting dynamics of a recolonizing wolf population under different management strategies" by Petracca et al. https://doi.org/10.1101/202...

Reviewed by<br /> Adrian Treves, PhD<br /> Professor of Environmental Studies, Founder and Director of the Carnivore Coexistence Lab, University of Wisconsin-Madison<br /> +1-608-890-1450<br /> http://faculty.nelson.wisc.... (which includes full disclosures of potentially competing interests in the CCC.php page)<br /> Direct inquiries to atreves@wisc.edu

11 May 2023

I appreciate that Dr. Petracca and colleagues posted their manuscript to a preprint server to facilitate independent review and scientific debate. Such preprints are a healthy step in our field to improve the reliability of science.

Also I acknowledge the risk posed by preprints, such as policy-makers or the public running with results or inferences before they have been approved by qualified peer scientists. I think two aspects of the preprint process guard against such undesirable outcomes: (a) peer reviews attached to the preprint as a comment should serve to caution against such precipitous use of preprints, and (b) the authors can reinforce the need for caution in subsequent revisions to the preprint, even citing their pre-reviewers. The science-policy interface in which this work lies is fraught with difficulties.

Also I acknowledge these sorts of models are complex and difficult to parameterize realistically with confidence. None of my comments or criticisms below are meant to undermine the hard work put in, but rather they are meant to improve the final product, improve outcomes for wolves, and improve the policy that may result from applied research. Thanks in advance for reading my comments in that spirit.

I have chosen not to cite much research below, instead calling the authors’ attentions to our website (above) where peer-reviewed substantiation of all my assertions can be found. I welcome peers’ emails to atreves@wisc.edu if anyone has trouble finding the evidence.

Most of my comments relate to Tables 1 and 2 and the associated scenarios.<br /> A question about Table 1: the caption includes "Lethal removal rate was calculated directly from state agency records." Please provide those with annual numbers and locations (East or West) to help the reader understand the geographic and spatial context of that assertion.

The annual lethal removal rate was a single point estimate of 0.04. I don’t understand why this was treated as a constant not bracketed by annual variability? Later, the authors wrote "In scenario 1 (“Baseline”) we simulated all relevant factors, as described below, at levels observed in the data collection period (2009-2020)." All factors include those affecting the human-caused mortality, right? There are numerous studies documenting a variable annual rate of lethal removal. There seem to me to be other issues with assuming a constant annual lethal removal rate in baseline and the scenario for increased removals below.

The assumptions that seem to be made about constant annual lethal removal in the baseline or the increased removal scenarios might be summed up as "livestock losses will never get better or worse so long as the current rate of removal is applied randomly to wolf packs and entire packs are removed." I don’t mean to caricature the assumption, I mean to make it plainer so it can be scrutinized.

1. If lethal removal is assumed to be effective in preventing livestock loss as WDFW has implied in the past, then it seems surprising that the model would treat it as ineffective or needing constant renewal. Can this be justified scientifically and by reference to articles that have not themselves been undermined by subsequent work? I call your attention to recent reviews of the literature on lethal removal which indicates unpredictable effects of lethal removal of wolves, resulting in increases, decreases and no change in livestock losses depending on study and site and years (the latter of no effect in the majority of cases, see studies of wolf removal by Grente, Krofel, and Santiago-Ávila.

2. Is predation on livestock random? If not, how does the imposition of a random scheme affect the model (a sensitivity analysis would be useful); many studies reveal that predation on livestock is not spatially random or uniform. Rather livestock losses are sometimes highly predictable from spatial features and wolf pack demographics. Therefore, I also call your attention to risk models that are analogous to resource selection functions, which have been used to model livestock loss in our region among others (see my lab website and search for "risk" and "forecast" please).

3 . Has WDFW lethal removal eliminated entire packs and in what percentage of cases? This baseline information might be helpful in interpreting the scenarios. I discuss partial or entire pack removal further below.

I was confused by the increased removals scenario and the harvest scenario. Given they are differentiated I have to assume increased removals is NOT public hunting, trapping, hounding, etc. It is unclear what conditions might lead to such an increase in lethal removals. The authors wrote "In scenarios 4 and 5 (“Increased removals”), we simulated an increased number of lethal management removals such that 30% of the wolf population[*] would be removed every four years, corresponding to an annual removal rate of 8.5%." Does this replace the baseline removal rate or supplement it? I didn’t see a scientific justification for the value of 30% and I don’t understand where 8.5% came from (30 /4 = 7.5%). Even if I add the baseline it does not reach 8.5%. I’m sure I’m missing something but the calculation could be clarified.

Another concern about this scenario is that it uses a flat mortality rate (% of population) regardless of conditions. That seems to simulate population reduction (sometimes called culling) but applied randomly to entire packs. Given that is a highly unusual pattern of management, it would help to understand the rationale behind it. See below where other more common scenarios are NOT considered. Therefore, I do not understand the criteria applied when selecting scenarios that deserve modeling and scenarios that do not deserve modeling.

"Harvest"<br /> See issues with terminology in the section on Minor comments below.<br /> Every 6 months: This is an unusual off-take pattern. Readers may be tempted to assume that the policy-makers among the authors or their superiors in state agencies are planning two seasons of wolf-killing per year. The authors might wish to address why such an unusual wolf-killing system was included in this paper. Also, the method that allows only adults or juveniles yet simulates twice-a-year 'harvest’ assumes the public can avoid killing pups. Is there evidence for that assumption? The assumption seems dubious on its face but regardless it requires some consideration of methods of 'harvest’ and accidental non-target killing.

Additive: While this is more conservative than any compensatory scenarios, it still does not acknowledge the many sources of evidence for super-additive mortality when the public begins killing wolves: Creel, Vucetich, Chapron, or when wolf-killing is liberalized in general: Santiago-Avila, Louchouarn, Suutarinen, Liberg, Treves. There are now more than ten studies quantifying the super-additive effects on population dynamics or the undocumented losses of wolves when killing is liberalized (I.e., undocumented deaths that can be attributed to policies of liberalized killing).

The OMISSION of any alternative scenario with super-additive mortality and the OMISSIOn of alternative scenarios with increases in illegal killing triggered by the harvest and increased removals scenarios are problematic. I capitalized the word OMISSION to emphasize that they are not scientific decisions but value-based decisions about which scenarios to publish and which not to publish.

Value-based decisions are akin to unstated assumptions derived from personal or organizational preferences / beliefs / policies. Assumptions about parameter values or interactions between variables should be transparently stated and usually justified scientifically. Unstated assumptions in a modeling paper seem to me to be scientific missteps because the range of possible parameter values was circumscribed for reasons that are not transparent or justified by peer-reviewed research.

Also, please note that an attempt to scientifically justify circumscribed parameter values might require an even-handed summary of evidence for and against the assumed constraints on parameter values. For example, the increased removal scenarios (currently unjustified) might be paired with a lowered removal scenario or a scenario that curbs ongoing mortality sources such as poaching or vehicle collisions, hypothetically. To me it seems easier to evaluate alternative scenarios even-handedly than to justify the current ones.

Furthermore, my concern is that the decisions about which scenarios to publish in the current manuscript leave unanswered 'why these scenarios and not others?' And the authors do not touch upon alternative scenarios for how wolf-human coexistence might play out differently. Instead, the scenarios presented in this paper are a subset of wolf-human coexistence and that subset is slanted towards negative views of wolves (more killing). For example, there is nothing scientific telling us to simulate lethal removal at level x or y. We explored this problem in sustainable use models in Frontiers in Conservation Science in 2021.

My criticism is meant to be constructive as it is not too late to adapt your models to positive wolf-human coexistence scenarios, such as those involving provisioning to improve wolf reproduction or survival, increasing wild prey bases in regions with low prey, better enforcement against unregulated, human-caused mortality, use of non-lethal methods to protect livestock etc. I understand WDFW might never undertake such actions but that does not constrain scientists seeking approximations of reality. Also, administrations change, private actors / organizations sometimes step in, and background conditions change especially for a simulation run for 50 years. <br /> I hope you see how a subset of scenarios was presented for non-scientific reasons.

Please remind readers that the selection of scenarios is value-based not science-based. Moreover, the selection of parameters within scenarios may also be value-based. For example, partial pack removals — simulated in your methods when "excess" removals are randomly assigned to another pack short of full pack removal — is NOT suggested to be effective in any study, even Bradley et al. 2015. Moreover, can the latter study even be used to justify the effectiveness of removal of entire wolf packs? I don’t think so. Consider that Santiago-Ávila et al. 2018 showed Bradley et al. 2015 was not reproducible until and unless the methods are clarified. Also, the 2018 article identified a possible statistical bias favoring lethal removal. If the data were to be shared (another hallmark of reproducibility), the bias minimized, and the methods clarified, one might argue that full pack removal has a scientific basis. But we’re not there yet.

Because I noticed omissions of scenarios and circumscribed parameter values without explicit statement of assumptions and missing literature, I offer a comment on potentially competing interests. T

The scientific community has changed position on this in recent years and is increasingly recognizing the potentially distorting effects of values and ideology on scientific research. Nothing is necessarily disqualifying but all should be disclosed fully and transparently. Ideological commitments expressed through memberships in civil society and professional societies (e.g., TWS or AFWA), institutional policy positions (e.g., WDFW’s current policies), and personal affiliations or rivalries, might all place pressures on individuals that reflect competing interests. These can affect the unstated assumptions, literature reviews (what is cited and summarized versus omitted) and the methods chosen and analyses used, in addition to the traditional issues relating to financial interests. I am not referring to one or two articles being missed but a pattern of omitting peer-reviewed research in highly ranked international journals as I noted here. I emphasize the issue of potentially competing interests as a way to inspire greater public confidence in the scientific endeavor. Thanks for your kind attention.

Again I admire your decision to publish preprints so that pre-publication review has an opportunity to influence the future manuscript and perhaps public policy.

Minor concerns<br /> Terminology: <br /> The term "recovery" has a meaning in US federal and state endangered species law as you all no doubt are aware. Recovery in its legal sense may lead policy-makers to shift regulatory schemes to down- or delist wolves. Therefore it is not a value-neutral scientific term and could be viewed as prejudicial. I see passages in your text where recover(y) is appropriate but others where it was used to refer to recolonization or population growth. There I recommend instead using recolonizing or geographic spread or numerical rebound which do not imply a legal status. This seems especially relevant when scenario outcomes suggest a low likelihood of achieving legal recovery.

Relatedly, I recommend careful consideration of certain jargon words that may be mainstream in wildlife management but are not commonplace in ecological sciences or policy among all publics – and may have value-based or moral connotations, e.g., harvest and depredation. In place of harvest I suggest "permitted, regulated wolf-killing by the public", because harvest is a euphemism that holds implicit assumptions about the values of wolves and motivations of humans who participate. To see why not to use 'depredation’, look at the first definition in the Oxford English Dictionary. I used it for years but now see the error.

Finally, the discussion of non-lethal methods might benefit from updating to include studies since 2010 on livestock-guarding dogs, and systematic reviews of effectiveness 2016-2021.

On 2023-05-10 16:34:31, user Jukka Kallijarvi wrote:

Now published in Nature Communications.

Purhonen, J., Banerjee, R., Wanne, V. <br /> Mitochondrial <br /> complex III deficiency drives c-MYC overexpression and illicit cell <br /> cycle entry leading to senescence and segmental progeria.

, 2356 (2023). https://doi.org/10.1038/s41...

On 2023-05-09 22:54:47, user Daphne wrote:

Summary

CryoET allows researchers to view biomolecular complexes in their native state via in situ imaging of cells, as opposed to purifying such complexes and viewing them in isolation via single particle cryoEM. However, traditional sample preparation of thin cell sections is laborious and can introduce artifacts. In this paper, the authors quantify the sample damage introduced by cryo-FIB milling, a newer technique proposed as an alternative to traditional sample preparation. Although much has been done to understand how radiation damage affects particles in single particle cryoEM, the same cannot be said for understanding how cryo-FIB milling damage affects samples in cryoET. As cryo-FIB milling becomes more widely used, downstream workflows and data interpretation will be helped greatly by understanding the mechanism and characteristics of damage caused by cryo-FIB milling itself.

This paper aims to characterize the damage caused by cryo-FIB milling by using a model system, the ribosome. The authors utilize a previous template matching method they developed, 2DTM, as a broad metric for comparing and quantifying damage. The major success of this paper is highlighting practical considerations for sample preparation based on quantification of damage from cryo-FIB milling. The major confusion we have with this paper is in interpreting the data for the mechanism of cryo-FIB milling damage, and whether alternate explanations for the data have been explored. This paper provides helpful insight into what one can expect from cryo-FIB milling their samples, and lays the groundwork for optimizing sample preparation.

Major points

We are unsure what new information the plots in Figures 1G and 1H provide relative to Figures 1E and 1F. It makes sense that the 2DTM SNR values plateau in the thicker lamella, as there is a larger region of undisturbed particles. However, because the 2DTM SNR is also included in Figures 1E and 1F in an untransformed and more intuitive coordinate frame, we are unsure what new interpretations come out of Figures 1G and 1H, and why this coordinate frame is necessary.

Although Figure 3 shows that damage from FIB milling is distinct from typical radiation damage in cryoEM, the statement “This is consistent with a model in which the FIB-damaged targets have effectively lost a fraction of their structure, compared to undamaged targets, possibly due to displacement of a subset of atoms by colliding ions” currently relies solely on the use of 2DTM SNR as a measure. This could be more strongly supported by solving structures or 2D classes of LSUs from varying depths in the lamella to observe the damage. Solving these structures from the datasets and low pass filtering them to different spatial frequencies (as done in Figure 3) would be helpful to see if there are consistent changes at the molecular level that could explain the propagation of radiation damage as a result of displacement of atoms due to colliding Ga+3 ions.

Figure 2A illustrates how variable 2DTM SNR values can be. Outlining potential workflows for experimental validation of this ribosome dataset and other datasets would be helpful for those wishing to benchmark cryo-FIB milling damage on their own particular system of interest.

In the discussion section, the practical recommendation that particles more than 30 nm deep can be used for subtomogram averaging seems at odds with the earlier observation that damage can be seen up to 60 nm deep from each lamella surface.

The 2DTM approach and the estimation of radiation damage relies on the reduction in correlation between the ideal, undamaged template and SNR. Can there by any other reasons for a reduction in this correlation other than FIB milling and/or electron exposure? e.g. stacking of multiple particles in Z-direction in case of higher ice thicknesses leading to reduced detectability.

The fact that the damage makes it impossible to pick entire particles suggests that the scale of radiation damage due to Gallium ions makes the entire particle explode. Are there any instances in which you see differential damage meaning that only a part of the particle is damaged and the rest is intact? I would expect this since different parts of particle are exposed at different points along Y axis. Otherwise, it can be concluded that the damage is always of “whole” particles but there is no conclusion clearly stating this. This could be worth exploring.

Figure 3F: it is well established that at low scattering angles, the electron scattering factors of negatively charged atoms can be negative. Was this considered when calculating the SNR from phosphorous atoms in the phosphodiester bond?

Minor points

In Figures 1G and 1H, we suggest using nanometers instead of Angstroms as the x-axis label. This will make it easier to interpret the blue dotted lines as the edges of the lamella.

In Figure 1, it is unclear if “relative 2DTM SNR” of Figures 1E and 1F are exactly the same as “2DTM SNR” of Figures 1G and 1H.

How does the 2DTM method take into account the radiation damage in template structure? Would it be possible to recapitulate the template model based on the known mechanisms of radiation damage due to exposure to electron beam during imaging so as to use a close to ideal model for template matching?

It would be helpful if the rationale for selection of undamaged, ideal templates in 2DTM is explained in more depth. For example, are these from single particle cryoEM data with the minimum possible radiation damage or are they from other experimental techniques?

What does the dotted line in Fig 2D represent?

Why are there more outliers (outside the lamella thickness) in 120 nm sample (fig 1G) than in the case of 200 nm lamella? What does the existence of these outliers indicate?

Are particles averaged over Y-axis in fig 1G and H?

What is the error in measurement of electron exposure and how is it propagated in measuring the radiation damage?

-Tushar Raskar, Daphne Chen, and James Fraser

On 2023-05-09 17:44:29, user Valerie Wood wrote:

This recent GO update https://academic.oup.com/ge... describes the work in progress to improve annotation accuracy and ontology structure. An upcoming paper will including improvements to the transfer of annotation by orthology. By adhering to current annotation practice, it should not be the case that annotation quality is eroded by automated database annotation over time. However, this does highlight the importance of manual curation to remove out of data annotations and automated mappings.

On 2023-05-09 16:10:16, user Valerie Wood wrote:

Nice work, however the members of the Gene Ontology consortium works actively to improve annotation (both manual and automated), and are currently removing more annotations than adding by prioritising the identification/ removal and propagation of outdated annotations see http://geneontology.org/sta...

On 2023-05-09 07:22:54, user Cedric Maurange wrote:

Beautiful work really, congrats! Would be nice to acknowledge the previous work on Chinmo and Broad in the neuroepthelium:<br /> - Dillard C, Narbonne-Reveau K, Foppolo S, Lanet E, Maurange C. Two distinct mechanisms silence chinmo in Drosophila neuroblasts and neuroepithelial cells to limit their self-renewal. Development. 2018 Jan 25;145(2):dev154534. doi: 10.1242/dev.154534. PMID: 29361557.<br /> - Zhou Y, Yang Y, Huang Y, Wang H, Wang S, Luo H. Broad Promotes Neuroepithelial Stem Cell Differentiation in the Drosophila Optic Lobe.<br /> Genetics. 2019 Nov;213(3):941-951. doi: 10.1534/genetics.119.302421. Epub 2019 Sep 17. PMID: 31530575; PMCID: PMC6827381.

On 2023-05-07 23:36:25, user Zach Hensel wrote:

There is a negative correlation between the abundance of SARS-CoV-2 and mitochondrial material from raccoon dogs and hoary bamboo rats.

This sentence was recently cited in an article in the NY Times with an unfortunate and wrong headline ("Why Does Bad Science on Covid’s Origin Get Hyped?" David Wallace-Wells, 3/May/2023):

Overall, across the full database of genetic material found in the market, the presence of raccoon-dog DNA was negatively correlated with the presence of SARS-CoV-2 material: When samples had more raccoon-dog genetic material, there was actually less SARS-CoV-2 than was found in other samples.

An article in Nature (Dyani Lewis 04/May/2023) reports that "there was no such association that made sense" and "In fact, the strongest associations were with species, such as fish, cows and goats."

I argue that most positive and negative correlations reported in this manuscript and in the Liu et al preprint (2022) do, in fact, make sense. Liu et al reported sampling rationale (Nature 2023. Extended data tables 2 and 3). Sampling on 1/Jan was largely premised on proximity to human COVID-19 cases with disease onset in mid-to-late December 2019. Sampling on 12/Jan was for "environmental samples from stalls that sold livestock, poultry, farmed wildlife" an examination of maps sampled stalls shows that sampling had little relation to reported COVID-19 cases.

I reproduced the visualizations in Fig 5 and colored spots based on species category in the market context (e.g. human, meat, fish, wildlife). I additionally examined correlations for data from 1/Jan only.

Examining 1/Jan and 12/Jan separately shows little correlation. What is left for 1/Jan is largely expected positive correlation from correlated error on 1/Jan for species with the high abundance on a day when there are no samples negative for viral RNA.

It is only when combining 1/Jan and 12/Jan data, or when combining all sampling dates, that the reported correlations are observed with all parameter combinations: positive correlations for meat and fish, and negative correlation for some wildlife species. This is an artifact of combining sampling focused on COVID-19 cases on one day, with sampling focused to a large extent on wildlife sales on subsequent days. The expectation is that later samples will be lower in viral RNA because of lack of proximity to a COVID-19 case and also sample degradation.

Lastly, for 12/Jan there is correlation for species disproportionately found in stall 6-29/31/33, with no known link to a human COVID-19 case, and later sampling shows that this was a reason for concern in February/March 2020. This is the stall with samples analyzed by Crits-Cristoph et al (2023). The 182 animal samples reported linked to this stall include rabbit (85), hedgehog (65), snake (24), bamboo rat (5), other (3). Unfortunately, there are no reported samples of animals linked to this stall from other species: raccoon dog, malayan porcupine, palm civet, and human.

On 2023-05-02 16:06:52, user Zach Hensel wrote:

Exploration of these interactive plots shows that it takes a great deal of cherry picking of parameter combinations to put any plausibly infectable hosts at the top of the list of species whose mitochondrial material is most correlated with the abundance of SARS-CoV-2.

I am curious what parameter combinations were attempted and how reasonable it is to call them "cherry picking." Sampling on 1/Jan was biased to locations known to be linked to humans recently infected with SARS-CoV-2 and is expected to show correlation between SARS-CoV-2 RNA and nucleic acids from both humans and species sold at these locations.

Sampling on 12/Jan was biased towards locations known to be linked to wildlife sales without, as far as I know, reason to expect a positive SARS-CoV-2 result by PCR or sequencing.

In my hands, the only "cherry picking" that is required is including samples negative for SARS-CoV-2 and not including sampling from 1/Jan when it was focused on locations of human COVID-19 cases. The result is that samples with reads for "Amur hedgehog" and "Malayan porcupine" consistently rank highly with any choice of parameters otherwise. This is also true if examining correlation between species reads and positivity by PCR.

It is difficult to say it's "cherry picking" to focus on the one day with sampling not biased to locations expected to have reads mapping to SARS-CoV-2. However, I agree with comments by Débarre and Crits-Cristoph that this type of correlation analysis is problematic. In the end, in pointing to multiple species it points to one location. That location and locations and animal carcasses linked to it were subsequently sampled hundreds of times, so clearly investigators saw correlation in this data and acted on it.

On 2023-04-28 19:46:22, user Flo Débarre wrote:

In this preprint, Bloom re-analyses a dataset of metagenomic data recently shared on open repositories by Liu et al. (2023). A subset of these data, previously made available on GISAID, was also previously analyzed by Crits-Christoph et al. (2023) – of which we are co-authors.

1) Bloom’s analysis largely confirms the genetic identification of wildlife species by Crits-Christoph et al. (2023).

The identification of animal species differed between Liu et al. and Crits-Christoph et al., in particular regarding the abundance of raccoon dog genetic material in sample Q61. Bloom’s analysis, done with similar but independent methods from Crits-Christoph et al., largely replicates their findings.

A comprehensive Github repository allows any interested reader to conveniently check Bloom’s results by themselves.

2) The correlation analysis presented by Bloom contains numerous flaws.

Bloom reproduced the correlation analysis initially presented in Liu et al. (2022), but Bloom’s analysis, as conducted, is inappropriate to answer the question of which animal hosts shed the viral material detected. Crits-Christoph et al. (2023) were missing data to carry out such an analysis, but warned against its use. Here we repeat and expand the arguments presented in Crits-Christoph et al.. These points would need to be taken into account for a correlation analysis to be valid, but were not by Bloom (and often just cannot be):

a) The outbreak had been ongoing for weeks in the market when the samples were collected, and multiple humans had been infected. By then, most virus in the market will come from human cases, especially as stalls containing human cases were targeted first, when sampling was performed on January 1. The large number of samples from these stalls with sick humans will highly influence correlational analyses. As shown in Worobey et al. (2022) on similar data obtained via qPCR, both the distance from known human cases and the distance from wildlife stalls independently contributed to the spatial variation in viral positive samples. The number of human cases at the time of sampling means that even if infected animals were present at the market, an underpowered correlation analysis of the sequencing data would be unlikely to have revealed it.

b) The correlation analysis treats all animals of a given species as an homogeneous group. There were however multiple stalls selling the same animal species, but likely not from the same supply sources. The presence of uninfected animals in one stall would affect the result of a correlation analysis, but would not invalidate the potential presence of infected animals in another stall.

c) The samples themselves are not homogeneous: for instance, within-sample animal diversity is different when the swab was taken directly from fish packaging than from the ground.

In addition, wildlife stall samples tend to be composed of a diverse menagerie of animal species RNA/DNA (a consequence of animals housed directly on top of each other and equipment shared between them), while samples related to human cases elsewhere in the market tend to be fairly simple. Therefore, by correlating abundances within just the positive samples, human-shed samples tend to have very high relative abundances for human DNA (or, e.g., human and fish), while animal shed samples tend to have lower abundances for any given species. This is a natural consequence of sequencing abundances being relative, not absolute.

d) SARS-CoV-2 read counts can differ for multiple reasons: there can be different quantities of virus shed in the first place, and different times since virus deposition will influence detection, as RNA degrades over time. While it is not possible to precisely date the time of viral deposition, there are two important considerations. First, if wildlife introduced the virus first to the market, animal-shed virus would (on average) occur before human sheddings. Second, wildlife stalls were sampled multiple days after stalls with reported cases (January 12 vs January 1). Put together, this means there would have been substantially more time for virus shed by animals to decay than virus shed by humans.

e) Virus presence is detected through RNA, host presence through mtDNA, which degrade differently over time. Relative abundances therefore depend on the time difference between when the different genetic materials were deposited and when they were collected.

f) Stalls were sampled multiple times. A correlation analysis would need to take into account spatial proximity and in particular model the per-stall effect.

g) The correlations reported in Bloom’s figure 5 are calculated solely on samples that contained any positive reads for SARS-CoV-2, and were therefore sequencing-positive for the virus. Therefore, this is not a test of which species were more likely to be in samples that tested positive for the virus, but how abundant each species was in the samples that did test positive. This is an important distinction.

For these reasons, both positive and negative correlations are largely uninterpretable:

• A positive correlation is not indicative of infection. As an enlightening example (also highlighted in Crits-Christoph et al.), the most correlated species in Bloom’s analysis is a fish species that is not susceptible to SARS-CoV-2.

• A negative correlation is not indicative of lack of infection (see above).

3) Some samples appear to be missing in the analysis, which contained high viral read numbers: A20, F13, F54.

4) Bloom chose to align reads to chordates’ mitochondrial genomes, but did not justify this choice. Only mammals are known to be infected by SARS-CoV-2, so an analysis of just mammalian RNA/DNA, as presented in Crits-Christoph et al., is most relevant to the question of which species may have shed the virus in a particular sample. If the aim was to provide a better picture of the diversity of animals sold at the Huanan market, all metazoa should have been included, to be able to identify seafood for instance (a key early case was a shrimp seller).

However, the end result is that Bloom’s 20% chordate inclusion threshold for this table results in most of the samples with both wildlife RNA/DNA (e.g. raccoon dogs) and viral RNA being excluded. The claim that only one sample of all samples with raccoon dog DNA that contains SARS-CoV-2 is therefore inaccurate: please see the supplementary data of either Crits-Christoph et al. or Bloom et al. for comparative results on the true number of such samples (which is at least 5, depending on the given analysis).

5) Bloom’s comments on the definition of positive samples overlook the difference in sensitivity between qPCR and sequencing in environmental sampling: qPCR is highly sensitive, with near single molecule detection resolution, and correlates with the absolute load of the virus in the sample. Sequencing, however, is both less sensitive (even when deeply sequencing), and highly dependent on the quantity of what else is in the sample. Thus, it is very challenging to identify SARS-CoV-2 in samples sequenced from the environment without any kind of enrichment performed: this is because the virus is comparatively rare compared to abundant environmental microbes (bacteria, fungi) as well as DNA/RNA shed by animals and humans. This is why most environmental viral sequencing is done after performing a viral enrichment (either physical or amplicon). For a similar comparison of unenriched SARS-CoV-2 sequencing, see the supplementary table S1 of Crits-Christoph et al. 2021 (“Genome Sequencing of Sewage Detects Regionally Prevalent SARS-CoV-2 Variants”), where 5 unenriched samples with viral Ct values in the range of 30-32 were deeply sequenced, and only 1-17 SARS-CoV-2 reads were obtained. For this reason, we do not encourage using only sequencing data quantitative values and ignoring qPCR positive samples. There is no indication in the Liu et al. dataset that we have seen to indicate the qPCR results were impacted by contamination. Indeed, 112/116 samples that were reported negative for qPCR were also negative by NGS, indicating a high degree of agreement between the two approaches, and that qPCR is merely more sensitive.

In addition, the definition of a positive sample cannot be done independently of the provenance of the sample: even at very low read count, the presence of other positive samples in the same stall at the same date is indicative of sample positivity.

6) Citations to news articles are vague or misleading. It would be preferable to stick to arguments presented in scientific documents, rather than rely on newspaper articles.

a) As rationale for his analysis, in the Introduction of his preprint, Bloom claims that “Other scientists pointed out that if the raw data were shared, it would be possible to expand upon the analysis in the 2022 Chinese CDC preprint to determine if the abundance of SARS-CoV-2 genetic material correlated with material from other species (Cohen 2022a,b)”, where the two Cohen citations are news articles. The authors quoted in these articles (Robertson, Rambaut, Holmes) did not mention Figure 4a because they thought the analysis was useful enough to be reproduced, but because it was possible that other animals’ genetic material was present in the environmental samples, and these data were not shown.

b) By conflating their views with misinterpretations of news reports written about their work, Bloom misrepresents the conclusions of Crits-Christoph (2023):<br /> Crits-Christoph et al. (2023) did not conclude they had found the intermediate host and it was raccoon dogs: their report clearly stated that they could not “identify the intermediate animal host species from these data”, and that the presence of infected animals was a “plausible explanation” for their results. And they explicitly stated that “the most abundant animal in the sequencing data of a particular sample is not necessarily the source of the virus in that sample”.

[signed]<br /> Florence Débarre & Alex Crits-Christoph

On 2023-04-27 15:46:20, user zukunft2go wrote:

Dear Prof. Bloom,

thanks a lot for your very detailed, objective analysis.<br /> I’d still like to add a few comments:

a) In my opinion, your statement that “raccoon dogs are susceptible to<br /> SARS-CoV-2” (SC2) requires further limitations.<br /> It was never shown that SC2 strains that circulated at the Huanan seafood<br /> market can infect raccoon dogs. Barua et al (1) conclude that minks are only<br /> susceptible for SC2 variants that have the D614G or other enhancing mutations.<br /> The only study that ever showed that at least some raccoon dogs can be infected<br /> if 2ml of viral supernatant are inocculated intranasally (Freuling et al (2))<br /> used a D614G SC variant. According to a personal conversation with Dr.<br /> Freuling, they were unaware of the relevance of D614G and no other virus was<br /> available at the time. As opposed to thousands of other animals, no infected<br /> wild raccoon dogs were ever found.

b) Freuling et al detected approximately 10,000 fold lower maximal viral loads<br /> in raccoon dog nasal swaps as compared to viral loads in human nasal swaps (3).<br /> Thus, even in samples containing much less human than raccoon dog nucleic acids,<br /> the SC2 RNA is more likely to come from a human.

c) As you mention, Liu et al state that human DNA was removed. As far as I know, commercially available<br /> kits do not differentiate between human and animal DNA. However, it is obvious<br /> that animal DNA in most cases could have come from blood (raccoon dogs were<br /> also butchered at some markets), while human DNA would likely have come from<br /> saliva (skin/hair particles are usually filtered out). DNA from blood is known<br /> to be much more stable as compared to DNA from other fluids (4), likely as it<br /> can be stabilized in coagulated particles. This could lead to a selective<br /> removal of human nucleic acids.

d) Steve Massey found that sample Q61 contains significantly more reads from<br /> human common cold viruses than from SC2 (5). It is not plausible that those<br /> came from a raccoon dog. Maybe you would like to contact him and ask if he would<br /> like to publish this.

e) Your Figure 4 nicely shows that for every species except for human there are<br /> some “high SC2/no resp. species” nucleic acid samples. This could be even be<br /> more informative than a correlation, as there were so many more humans that<br /> animals at the market, of which most were likely non-infected, and as human<br /> nucleic most certainly did not come from blood (comment c).

f) It is could be interesting to figure out how old respective animal reads were by determining RNA/DNA ratios. I am no expert here, but this could be done by comparing coding strand/non-coding strand reads or exon/intron reads.

Best regards, Valentin Bruttel

1) https://link.springer.com/a...<br /> 2) https://www.ncbi.nlm.nih.go...<br /> 3) https://journals.asm.org/do...<br /> 4) https://www.ncbi.nlm.nih.go...<br /> 5) https://twitter.com/stevene...

On 2023-05-06 17:16:15, user Peter Rogan wrote:

This article has been accepted for publication, but has not yet appeared in print. See https://doi.org/10.1093/rpd...

On 2023-05-06 15:50:59, user Marcelo Kauffman wrote:

Do you have a github repository with the scripts and data used for training and validating?

On 2023-05-05 18:39:19, user V wrote:

Interesting work. The link to OpenPath dataset is missing. Is that available?

On 2023-05-05 16:32:26, user Katie Lotterhos wrote:

This article is published in PNAS https://www.pnas.org/doi/10...

On 2023-05-05 13:08:32, user Ling Chin Hwang wrote:

The link to the published article is now available at: https://doi.org/10.1093/nar...

On 2023-05-05 08:06:00, user Kasparas Petkevicius wrote:

Very interesting manuscript with lots of great work. Well done to all the authors involved.

The authors clearly demonstrate that increased mitochondrial ceramide causes CoQ depletion and leads to insulin resistance. However, I believe some claims in the paper are somewhat over-interpreted, e.g. 'One possibility is that palmitate induces insulin resistance in L6 myotubes via a ceramide-independent pathway. However, this is unlikely as palmitate-induced insulin resistance was prevented by the ceramide biosynthesis inhibitor myriocin (Fig. 1A) and we observed a specific increase in C16-ceramide levels in L6 cells following incubation with palmitate, which was also prevented by myriocin'.

Ceramide potently reduces membrane fluidity, so it could be that the pharmacological and genetic manipulations done in the manuscript results in altered mitochondrial membrane fluidity. As such, it is not a result of increased ceramide per se, but overall alterations of mitochondrial membrane properties. If I was a reviewer, I would suggest the authors to perform additional rescue experiments, particularly using membrane - fluidising fatty acids, such as oleate, EPA or DHA. I suspect that they might equally rescue palmitate-induced CoQ depletion and insulin resistance.

Other than that, it is amazing work providing great insight into the core biological mechanisms driving insulin resistance.

On 2023-05-04 15:56:59, user Jordi Torres-Rosell wrote:

This manuscript has been published and is available at:

https://doi.org/10.1016/j.c...

On 2023-05-03 18:29:18, user Tessa Pierce Ward wrote:

Hi folks,

Neat paper. I don't see your Github repository, though -- can you make it public or fix the link, please? (https://github.com/MGXlab/v... is not accessible). I'm especially curious about your sourmash usage and parameters.

cheers,

Tessa

On 2023-05-03 13:45:00, user UTK Micr603 wrote:

Hello. Below is a review compiled by the MICR603 "Journal club in immunology" at the University of Tennessee Knoxville:

UTK MICR603 “Journal club in immunology” review of the paper by Gül et al. “Intraluminal neutrophils limit epithelium damage by reducing pathogen assault on intestinal epithelial cells during Slamonella gut infection”

Summary:

The work of Gül et al. investigates the role of neutrophil recruitment and activity on epithelial cell damage during Salmonella infection. They investigate this using several techniques including several in vivo models and microscopy. The authors investigate this from different angles by utilizing germ free mice that lack a resident gut microbiota and by investigating epithelial integrity and shedding during normal and neutrophil-depleted infections.

Positive feedback:

The authors provide an in detail review of what is understood during Salmonella infection and what is not during the different stages of infection in several different models. This provides solid reasoning for their use of several different model systems in this paper. Additionally, they are commended for their use of not only different Salmonella strains (wild-type vs mutants) but their use of different host models and antibiotic treatments to strengthen their claims and understanding of Salmonella infection and the role of neutrophils during infection. The authors use several different controls/treatments/previous studies to further back up their results and claims seen in this paper. For example, they further confirm their neutrophil depletion results by comparing against their controls as well as previous study results with neutrophil and monocyte depletions which takes away uncertainties that their results could be due to monocyte presence in the neutrophil depleted mice. Additionally, the use of experimental diagrams/design in figures is very useful when referencing other data in the figure. The authors are also commended on their use of microscopy to back up their data quantification and their flow and organization of figure panels.

Major Concerns:

• 4a- Antibiotic pre-treatment can greatly influence Salmonella invasion… every other figure/model uses streptomycin model and this one is using ampicillin (inconsistency with pre-treatment). Could you compare streptomycin and ampicillin results? Can they comment on what happens with ampicillin+WT treated mice?<br /> • Were mice being placed in clean cages after gentamicin treatment? <br /> • Gentamicin treatment: clarify if it was just in drinking water or during cecal tissue plating. A few sentences clarifying this is needed. <br /> • Germ free mice: they interpret these as no bacteria in the gut, but they also have weird immune systems because of this. Would it be better to pretreat WT mice with antibiotic cocktail to deplete the residential microbiota without perturbing the immune system? <br /> • How do you know the pad4 drug is working? Some confirmation here is needed.

Minor concerns:

• The authors use the abbreviation “mLN” in multiple figures and their writing without defining what this is. It may not be clear to some readers what this is referring to. <br /> • When referencing P values in the figure captions, it would be beneficial to state the actual P value and not just >/< in order to add more impact to the statistics. <br /> • 5e- different microscopy planes : control is a cross section and neutrophil depletion is from a top plane of view <br /> • 5d - levels spelled incorrectly <br /> • Why use pad4 inhibitor instead of pad4 deficient mice <br /> • Pad4 inhibitor IP injection vs oral administration – reasoning for the use of one over the other could be better described. <br /> • Controls in Fig1 are incomplete - no uninfected group and no isotype control group <br /> • What is the dashed line in 1B? – Some further clarification is needed <br /> • Conclusion: limitations of study needed <br /> • 1e: instead of quantifying with a 63X field of view they could use area metrics instead (more quantifiable) <br /> • 4 - B&C y axis - connect these units to whole organ so you can compare the bacterial load in lumen vs epithelial tissue <br /> • Mention division time of salmonella in vivo / in vitro <br /> • Speculate mechanism of expulsion? <br /> • Do we know that these are intact cells in imaging

On 2023-05-03 06:39:42, user Felipe G. Grazziotin wrote:

Dear Peter,<br /> This dataset doesn't include several taxa that are key to evaluate the taxonomy of Dipsadini. A more comprehensive analysis regarding that was recently published by Oliveira et al. in Syst. and Biodiversity (https://doi.org/10.1080/147....

On 2023-04-19 00:11:43, user Peter H Uetz wrote:

Can the authors please comment on some taxonomic implications? I see that Ninia atrata nests within Sibon and Tropidodipsas is paraphyletic with Sibon in their tree. There is probably other stuff like this ... (haven't checked carefully).

On 2023-05-03 05:04:01, user Prof. T. K. Wood wrote:

Rather than primarily self-references, should cite first report of toxin/antitoxins inhibiting phage: https://journals.asm.org/do..., in which Hok/Sok inhibit T4 phage (1996).

On 2023-05-02 18:08:52, user Priscilla wrote:

The comment below is a review written by two undergraduates for a course project:

Summary and Strengths:

This manuscript by Hanly et. al. seeks to explain continuous color variation found in Colias philodice and C. eurytheme sulfur butterflies. Pterin pigments create the color variation observed across butterflies as well as other species. However, there has been limited study of pterin and genetic variation in wild butterfly populations. Male C. philodice butterflies are observed to have yellow pterin expression while C. eurytheme displays orange pterin expression. Males are also known to have variation in UV-iridescence in both species. Both female C. philodice and C. eurytheme express a white phenotype since their pterin expression is reduced. QTL mapping across F2 hybrid males revealed a candidate gene red on chromosome 18 for pterin pigmentation variation. Mosaic knockout experiments on this gene revealed a decrease in pterin content, causing males to look more similar to the female phenotype, and significant disorganization of pterin granule structures. QTL mapping also revealed a second gene interacting with red, the transcription factor bab on chromosome Z, which had been previously documented to cause an increase in UV-iridescence, especially in females. Knockout experiments for bab in females showed a visible increase in UV-iridescence when viewed from a 30-degree angle, and increased orange pigmentation on the dorsal region and a variety of effects on other areas of the wings. The interaction between these loci controls an estimated 70% of pigmentation variation. Wing size is another trait that the authors hypothesized could be controlled by the sex chromosome. PCA analysis of male butterfly wing size distinguished between the UV-iridescent males and non-UV-iridescent males. When a QTL analysis was performed, the authors found significant markers at chromosomes Z and 2. Through two-QTL analyses, they found that 18% of size variation across males could be explained by an interaction between these loci. The combined data suggest that the Z chromosome plays a key role in intraspecific variation and distinguishing traits between C. philodice and C. eurytheme.

The authors did an exceptional job of connecting pertinent and creative analyses to the conclusions drawn toward the end of each subsection, as well as the overall logical flow and organization of the manuscript. In particular, their usage of QTL analyses all supported the conclusions drawn with regard to the sex-linked component of the color trait. This was significant because it set up additional experiments on this Z-chromosome, such as the male UV iridescence experiments studying the bab transcription factor. This made for a much easier read and allowed the paper to build on itself through each of its analyses. Additionally, the presentation of the automated phenotyping experiments before any other data created a much needed visual background for the subsequent molecular analyses. Understanding the phenotypic variation under scrutiny was valuable for the comprehension of what followed, so this was certainly a major strength and a clever way to present information. The described logical flow is also rooted in the paragraph structure throughout the manuscript which made the paper much more accessible to non-expert audiences. For example, most paragraphs began with a broad introductory sentence describing a rundown of what was done in the section, followed by a more in-depth description of the experimentation and interpretation of their results.

It is also worth noting that many of the experiments were very creative and yielded visually appealing analyses and highly convincing results. For example, the SEM analyses conducted on the red crispant scales added a fascinating visual component of microscopic phenotypic differences that correlate on a larger scale to observed variation in scale pigmentation. Having multiple levels of visual data lends well to the conclusion that the intactness of the red gene is necessary for most (not all, however, as in Alba individuals) wild-type pigmentation in C. eurytheme and C. philodice. Overall, the combination of experimental creativity, thorough organization, and logical flow all contribute to an interesting and well-executed manuscript.

Major Point:

As a broad note, one of the aims could have been tied together with the others more comprehensively. Specifically, we suggest that a more thorough justification of the wing size analysis is provided. While it was included in the introduction, it jumps out in the sense that it is only marginally related to the main focus of continuous color variation. Deepening the connection would serve as a much needed transition because, in the current draft, the wing size data appear to be added onto the end rather than an integral and interconnected part of the study. Part of the reason for this is that the other aims flow so well and appear in a logical order, while this experiment is almost ancillary. The experiment and accompanying analysis are still valuable and absolutely warranted in the paper, but strengthening the justification for their inclusion would go a long way in making the transition more seamless.

Minor Points:

Figure 1, legend, section H-I: “Agricultural site”, we suggest that these sites are specified.

Figure 1J, legend: we suggest the inclusion of a p-value on the graph or in the legend.

Page 6, paragraph 3: A period is missing at the end of the last sentence.

Page 8, paragraph 5: “Marginally significant interaction”, we suggest a p-value be specified.

Figure 3B: We suggest a p-value be included on the graph or in the legend.

Page 10, paragraph 1: “...fused with other chitinous elements”, we suggested that the fusion be expanded on, as it is unclear without background insect knowledge.

Page 12, paragraph 2: “Of note”, we recommend this be deleted or the wording changed.

Page 12, paragraph 3: “...pterin pigmentation in females in a complex fashion…” we suggest the deletion of “in a complex fashion.”

Figure 6, A-K: We recommend the use of the alternative transformed data from the supplement (using the purple, blue and pink color scale) or the inclusion of specific color swatches sampled from the images to to highlight the extent of the color differences that are not as visible to the untrained eye.

In general, the alternative transformed data was more visually striking and helped us observe the differences more readily, so perhaps these can be incorporated throughout the paper while the raw images are placed in the supplement.

Figure 6D: We recommend that the wild-type and knock-out regions are better highlighted by using a thicker black line to highlight the boundaries between the two.

Finding ways to increase the amount of contrast in all of the figures containing wing images would be helpful, as there are a lot of the same colors next to each other.

Figure 7A: We suggest a P-value be included either on the graph or in the legend.

Page 16, paragraph 1: We suggest considering a rewrite of the first three sentences in the discussion section. There are a few run-on sentences that have too much additional punctuation, making it a little difficult to connect the sentences together without having to reread. Shortening the sentences would also make them a bit punchier which is more advisable for driving home a point.

Page 22, methods on butterfly mapping broods: We suggest including the reason for selection of summer conditions, as well as perhaps the inclusion of how the winter conditions may affect the observed results.

Seasonal variation is mentioned several times during the manuscript as being an observable and testable phenotypically plastic trait, so this justification would be consistent with the claims that have been made in the text

On 2023-05-02 11:37:02, user Lukas Palatinus wrote:

Unfortunately, the preprint does not provide correct references about the origins of the MicroED techique. It is not correct that the technique called MicroED "was initially developed for studying protein structures from very small microcrystals", as stated in the manuscript. The technique, albeit under different name(s), was developed long before that to study all kinds of crystals, first applied to inorganic materials, then organic, MOFs etc. and also to proteins. All these developments and corresponding publications predate the publication of the two references given in the preprint (Nannenga et al., 2014; Shi et al., 2013).

The key references are:<br /> Kolb et al. Ultramicroscopy 107, (2007) doi.org/10.1016/j.ultramic.... (experimental aspects of the method, first real use of what is now called "MicroED")<br /> Mugnaioli et al. Ultramicroscopy 109, (2009) doi.org/10.1016/j.ultramic.... (first ab initio stucture solution)<br /> Zhang et al. Z. Krist. 225, (2010) doi.org/10.1524/zkri.2010.1202 (fine slicing of reciprocal space - a predecessor of continuous rotation)<br /> Denysenko et al. Chem. Eur. J. 17, (2011) doi.org/10.1002/chem.201001872 (first experiments in cryo-conditions)<br /> Nederlof et al. Acta Cryst. A69, (2013) doi.org/10.1107/S0907444913... (experiment on a protein, continuous rotation)

Please do not ignore the contribution of these scientists, who were the real pioneers and developers of the method, to the field.

On 2023-05-01 23:16:32, user Sam Lord wrote:

Really nice demonstration of the importance of testing FPs in the biological system of interest! I have a couple points:

1) It would be nice to know the expression level of the different FPs.

2) I think the best photobleaching comparison would be to set the illumination power differently for each FP to have each sample start at the same brightness, then rank the brightnesses after 30 s. That eliminates the confounding variable that some FPs absorb photons at a higher rate (and thus might bleach sooner) at a particular wavelength.

Thanks again for this interesting preprint!

On 2023-05-01 20:20:56, user Julian King wrote:

How can ISG65 bind covalently to C3b but still have an off-rate in SPR? This does not make sense.

On 2023-05-01 09:27:13, user Julian King wrote:

Just found a similar study already published in Nature Communications.

For a more detailed structural and functional analysis:

https://www.nature.com/arti...

The authors show the cryoEM structures of C3 and C3b with ISG65 and also show that ISG65 inactivates the C5 convertase of the alternative pathway.

On 2023-05-01 16:50:10, user Dr. Mahnoor Pervez wrote:

This is a very interesting and comprehensive study. It will provide new aspects for future research. Author describes each step of the work very efficiently ????

On 2023-04-30 15:24:14, user Gul Zerze wrote:

I sincerely thank both Emil Thomasen and Kresten Lindorff-Larsen for their time, careful reading, and comments on the manuscript. Below, I attach my responses to each point with reproduction of the comment. Since these commentary is not capable of pasting modified visuals, added/modified visuals can be seen in the published version of the manuscript (doi: 10.1021/acs.jctc.2c01273)

Comment:<br /> The manuscript by Zerze reports on molecular dynamics simulations of the intrinsically disordered low complexity domain (LCD) of FUS using a beta version of the coarse-grained force field Martini 3. The author performed simulations to study the formation of FUS LCD condensates under varying protein-water interaction strengths (in the Martini force field) and at different NaCl concentrations, and concludes that strengthening protein-water interactions by a factor of 1.03 improves the agreement with experimental transfer free energies between the dilute and dense phases. Additionally, the author concludes that the NaCl concentration affects condensate morphology and protein-protein interactions in the condensate, and that the effect of NaCl concentration on protein-protein interactions in the condensate is sensitive to rescaling of the protein-water interactions. The manuscript provides an interesting and novel benchmark of the (beta) Martini 3 model in predicting phase separation of IDPs, and reveals potential short-comings of the model in predicting protein concentrations in (or volumes of) the condensed and dilute phases. This benchmark will be useful for readers who wish to simulate liquid-liquid phase separation of IDPs with Martini 3, and the work will be interesting to a wider audience interested in the biophysics of IDPs and their condensates.<br /> Below we outline some questions and comments that the author might take into account when revising the manuscript. Our main comment regards a clearer assessment of the convergence of the simulations and correspondingly the lack of error estimates for observables calculated from the simulations. We also suggest a clearer presentation of the experimental data used to validate the simulations. While some of these changes are mostly textual, in other cases we suggest additional simulations. We realize that some of these simulations require substantial resources; if these are beyond what is available, we suggest at least to clarify caveats as per the points below.

The author’s response: I thank the reviewer for their scrutiny and thoughtful comments that greatly helped substantiating the optimization analysis in the revised version of the manuscript.

Comment: We have the following suggestions for revisions to the manuscript:<br /> 1)<br /> Fig. 1 and 2: The finding of non-spherical droplets is interesting and intriguing. To examine whether the formation of these shapes in the simulations with higher salt and λ-values represent stable states or perhaps trapped metastable states of the system, we suggest that:<br /> 1a) The author runs simulations with the parameters that give rise to non-spherical morphologies (e.g. λ=1.025 and 50 mM NaCl) starting from the structure of the spherical droplet (for example formed with λ=1.0 and no salt) and observe whether the non-spherical morphology is recovered or the droplet remains stable. If the droplet remains stable, then the effect of salt concentration on the inter-chain contacts (Fig. 6) could be assessed without potentially confounding factors from different dense phase morphologies.

The author’s response: Following the reviewer’s suggestion, I have performed an additional set of simulations for all λ values (1, 1.01, 1.02, 1.025, 1.03) at 50 mM salt concentration starting from a preformed spherical droplet. The initial condition with the preformed droplet is obtained from the last saved frame of the λ=1 simulation for 0 mM salt. We ran the simulations for 10 microseconds each. Within the given time frame the droplet remained stable for λ values 1, 1.01, 1.02, and 1.025 without a dilute phase concentration. I now added these findings into the supporting information (Figure S5).<br /> I also modified the main text (Page 9 last paragraph and Page 10 first paragraph) as follows:

“Recent studies from independent groups show that the nonspherical droplet formation might be a kinetic arrest, playing an important role in droplet maturation and aging [51–53]. To test whether the nonspherical morphologies we observed are impacted by the initial conditions, we rerun 50 mM at all λ values starting from a preformed droplet (last saved configuration of 0 mM salt, λ = 1 condition). We simulated each λ for 10 μs and presented the analysis in Figure S5. Within the given simulation time, the initially spherical droplets stayed intact and spherical, except for λ=1.03, which had one copy of the FUS LC protein exchange back and forth between the dense and dilute phases). The enlarged droplet in the case of λ=1.03 also deviated from its initially spherical shape. These findings show that the nonspherical morphology was not reproducible for λ values less than 1.03 when starting from a preformed spherical droplet. We argue that the strength of effective protein-protein interactions at low λ are largely<br /> responsible from the initial spherical droplet staying intact.”

Since the droplets stayed nearly spherical, I also analyzed the contact formation in these simulations (50 mM added salt, initially starting from a spherical preformed droplet) and presented the findings in Figure S7.

I also discussed these findings in the main text as follows (Page 19, 20, the last paragraph before Conclusions):

“Finally, we also examined the contact formation for the case of 50 mM added salt that starts from a preformed droplet (see Identification of condensate formation subsection for the description). As presented in Figure S5, we found that the initially spherical droplet remains largely spherical within the simulation time (never forms rod-like percolated structures) for this case. Therefore, this case helps us assess the effect of salt concentration on the inter-chain contacts without potentially confounding factors from different dense phase morphologies. Figure S7 shows both the contact propensity (A.) and the effect of salt concentration (B.) on the contact propensity. Figure S7A shows that the contact propensity decreases as the λ parameter increases, similar to the findings in Figure 5. Figure S7B shows, however, that the change in contact fraction with respect to 0 mM salt at λ = 1 is weaker (resembling λ = 1.02 at 50 mM salt in Figure 6A) although the salting out effect at high λ (λ = 1.025 and 1.03) are more prominent and stronger compared to those in Figure 6A.”

Comment: 1b) The author shows time-series or distributions of an observable that reports on the dynamics of the proteins in the non-spherical droplet (e.g. Rg, mean square displacement, residue-residue contacts) and/or of an observable that reports on the dynamics of the droplet shape (e.g. the x-, y-, and z-components of the gyration tensor).

The author’s response: Following the reviewer’s suggestion, we added the analysis of observables that reports on dynamics of shape fluctuations and size and presented them in Figure S4.

We also modified the main text (Page 9, second half of the second paragraph) to discuss these findings: <br /> “We also investigated the time dependence of the size and shape of these morphologies by quantifying the radius of gyration (Rg) and the ratio of the smallest and largest eigenvalues of the gyration tensor (Figure S4). The latter offers a measure of sphericity of droplets. We found that low λ cases (λ = 1, 1.01, 1.02) at 0 mM salt have the most spherical morphologies. Beyond λ = 1.025 at no salt, the cluster formation is not tight (as evident from the Rg) so it also loses its sphericity. The condition that shows percolation (λ = 1 at 50 mM salt) has the largest deviation from the sphericity (it is rod-like instead) combined with a large Rg.”

Comment: 1c) Additionally, independent replicas of droplet formation for each condition and parameter set would be ideal, but we realize that this would be expensive in computational resources and may be infeasible.

The author’s response: We agree with the reviewer that the molecular simulations presented in this work are highly computationally demanding (e.g., a 10-microsecond simulation of one of these simulations at given salt and given λ takes about 25 days in terms of walk-clock time, occupying 28 CPUs and 4 GPUs) While it certainly is computationally demanding to replicate all λ parameters at all salt concentrations, we now rerun 50 mM salt concentration at all λ parameters where we start from a completely different initial condition (preformed droplet) for each. And we found that the morphology was not reproducible within the given simulation time at low λ, highlighting the initial condition dependence at low λ conditions. We now discussed this in the main text (Page 21, Conclusions).

“We also note that we observed an initial condition dependence of the morphology at low λ conditions at 50 mM salt. This finding emphasizes the necessity of future work for exploring condensate morphology with proper advanced sampling techniques.”

Comment: 2)<br /> “As λ increases, the volume of the dense phase increases (and condensed phase concentration decreases accordingly) until the system is not capable of forming a dense phase (λ >1.03)”: From Fig. 1 it seems that the rate of cluster formation decreases as λ increases. Is it not then possible that droplet formation at λ>1.03 is stable at equilibrium, but occurs on time-scales greater than those tested in the simulations? To support the statement that no droplets are stable at λ>1.03, we suggest that the author runs simulations with a higher value of λ starting from the structure of the spherical droplet (formed with λ=1.0 and no salt) to observe whether the droplet is dissolved or remains stable.

The author’s response: Following the reviewer’s suggestion, we have performed a simulation for λ=1.04 at no salt condition starting from the preformed spherical droplet (last saved configuration of λ=1.0 at 0mM salt) and we found that the droplet quickly dissolves for λ=1.04. This finding is now presented in Figure S3.

The main text is also modified as follows (Page 9, end of the first paragraph):

“To further verify that no droplets are stable beyond λ = 1.03, we also ran λ = 1.04 simulations<br /> at no salt conditions starting from a preformed spherical droplet (last saved configuration of<br /> λ = 1 at 0 mM salt). We then analyzed the cluster formation as a function of time (Figure<br /> S3) and found that the initial droplet dissolves quickly (at a timescale shorter than that of<br /> the formation of the droplets).”

Comment: 3)<br /> Figure 3: The use of the radial distribution does not seem ideal for the droplets that have a non-spherical morphology, as certain distances will report on an average over the dense and dilute phases. This should at a minimum be discussed.

The author’s response: Following the reviewer’s suggestion, we have added further discussion related radial density distribution to the main text (Page 12, first paragraph):

“This approach works reasonably well for droplets that have spherical/ellipsoidal shapes. However, since the condensates for the conditions with finite salt concentrations significantly deviate from a sphere (they do not show a clear plateau as the center is approached), we used a surface reconstruction method [54] to estimate the volume and concentration instead of fitting the radial density profiles/using the limiting values.”

Comment: 4)<br /> Table 1: It seems that the discrepancy between the sigmoidal fit approach and the surface reconstruction approach increases with λ, possibly due to sensitivity to the shape of the droplets, illustrating that there might be significant uncertainty associated with the reported dense phase volumes. We think it would be useful to have an error estimate for the reported dense phase volumes (e.g. an error over volume calculation approaches and/or over different probe sizes).

The author’s response: The volume obtained by surface reconstruction is definitely highly sensitive to the probe size. To justify the size of the probe that I used, I directly compared the sigmoidal fit protein concentrations and the surface construction protein concentration calculated by different probe sizes (Figure S3 in the old SI, Figure S6 in the revised SI). Based on that comparison, probe radius 10 A was the size that minimized the differences considering all lambda values. That’s how I justified the probe size I used. For the uncertainty/error estimates, I performed block averaging analyses (please also see the response to the point 7).

Comment: 5)<br /> Table 2 and Fig. 4: We suggest that the author more explicitly states which experimental data was used for comparison with the simulations in Fig. 4. We also suggest a more direct comparison with experimental data points where possible (e.g. by showing the experimental values of csat as a function of NaCl concentration).

The author’s response: We used two experimental papers to extract the experimental data, one is reference 36. In reference 36, the authors state: “Using incubation on ice to increase the driving force for droplet formation followed by centrifugation to fuse the droplets due to their higher density, our 15 ml samples of 1 mM FUS LC phase-separated to form an ∼400 μl viscous, protein-dense phase stable for weeks at room temperature. FUS LC concentration in the phase is approximately 7 mM (120 mg/ml FUS LC) as determined by spectrophotometry.“

We note that the salt concentration is not specified in this case (or the authors obtained approximately the same protein concentration in the dense phase regardless of the salt concentration). Also, the thermodynamic conditions defined here does not exactly correspond to those in our simulations. That’s partly the reason why we looked for multiple sources of experimental data. The other experimental work that we used is reference 39. In reference 39, the authors state that “The relative intensity of the glutamine side chain residue NMR resonances in the condensed phase compared to a standard concentration (100 μM) dispersed phase FUS LC suggests a concentration of 27.8 mM = 477 mg/ml in the condensed phase.”

The salt concentration in the corresponding NMR experiments were carried out at 25 °C in 50 mM MES, 150 mM NaCl pH 5.5. The conditions do not exactly correspond to our thermodynamic conditions, either. Since an exact match is not available in the conditions, we did not prefer to present a direct comparison of dense phase concentrations, instead, we preferred to show a range in Figure 4. We now modified the main text (Page 15, right above the Contact Maps subsection) to more explicitly state the source of the data:

“The experimental data range is referenced from the work by Fawzi and coworkers; [36,39] where reference [36] measures the FUS LC concentration in the dense phase as approximately 120 mg/mL (spectroscopically) and in reference [39], a 477 mg/mL FUS LC concentration is deduced from the relative intensity of the glutamine side chain residue NMR resonances in the condensed phase (compared to a standard protein concentration in the dispersed phase, which is given as 100 μM, or 1.71 mg/mL). 477 mg/mL FUS LC dense phase has been obtained from 15 ml samples of 1 mM FUS LC solutions [36] (from which we calculated the dilute phase concentration as approximately 14.3 mg/mL). We used these dense phase and their respective dilute phase concentrations to calculate the experimental range of transfer free energy (gray-shaded areas in Figure 4).”

Comment: 6)<br /> “We used the “tiny” bead type (TQ1) both for Na+ and Cl- ions”: The author should clarify the reason for and possible effects of choosing the TQ1 bead type, as TQ5 is, we think, the standard bead type for Na+ and Cl- ions in Martini 3.

The author’s response: We would like to clarify that tiny refers to the bead type being Txx. We then also would like to clarify that TQ5 type was not available in the MARTINI version that we used. Ion topology file in the version that we used only had TQ1 types as the ion type. We are pasting the contents of “martini_v3.0_ions.itp” file below:

;;; IONS<br /> ;

;;;;;; SODIUM ION

[moleculetype]<br /> ; molname nrexcl<br /> TNA 1

[atoms]<br /> ;id type resnr residu atom cgnr charge<br /> 1 TQ1 1 ION NA 1 1.0

;;;;;; CHLORIDE ION

[moleculetype]<br /> ; molname nrexcl<br /> TCL 1

[atoms]<br /> ;id type resnr residu atom cgnr charge<br /> 1 TQ1 1 ION CL 1 -1.0

;;;;;; CHOLINE ION

[moleculetype]<br /> ; molname nrexcl<br /> NC3 1

[atoms]<br /> ;id type resnr residu atom cgnr charge<br /> 1 Q0 1 ION NC3 1 1.0

;;;;;; CALCIUM ION

[moleculetype]<br /> ; molname nrexcl<br /> SCA 1

[atoms]<br /> ;id type resnr residu atom cgnr charge<br /> 1 SQ2 1 ION CA 1 2.0

Since we understand that this is causing a confusion, we modified the sentence as below (Page 6, right above the Simulation Details section):

“We used the relevant TQ bead types for Na+ and Cl- ions and kept the ion-water and ion-protein interactions unmodified.”

For further details of the parameters (e.g., epsilon-sigma), we made our topology and run parameter files publicly available (please see the response to the point 10).

Comment: 7)<br /> We suggest that the author, where possible, reports error estimates for the various observables, for example from block error analysis and/or repeated simulations.

The author’s response: We performed block averaging analysis (using two block) for volume estimation (accordingly, the protein concentration in the dense phase) and included the error estimates in Table 1 (Page 12). We note that for most ???? parameters, the error was less than 1%. But we now added the errors larger than 1% in Figure 4. We modified the Table 1 caption as:<br /> “…. Statistical errors calculated by block averaging of the data (dividing the equilibrated data into two equal blocks) are less than 1% at low ???? conditions. Errors larger than 1% are reported.”

Comment: 8)<br /> It would be useful to include a discussion of the effects of simulation convergence and simulation starting configurations on the reported results.

The author’s response: We added a discussion of the reproducibility issue and the initial condition dependence both to the Results and Discussion section and the Conclusions section (please also see the responses to the point 1a and 1c).

Comment: 9)<br /> A discussion of the potential differences in the effect of non-bonded cut-offs in the dilute and dense phase would also be useful.

The author’s response: We used a fairly large cutoff distance (1.1 nm) for short-range treatment of vdW and electrostatics but a potential nonbonded cutoff effect that I can think of is the long-range treatment of electrostatics. While vdW interactions are large power of r in denominator (therefore, negligible contribution to the potential at large r), we may argue that the long-range treatment of electrostatics might be a concern in general. It is well known that the simple cutoff of electrostatic interactions introduces artifacts on phase behavior of anomalous liquids that has two distinct phases [e.g., J. Chem. Phys. 131, 104508 (2009)]. Here, we applied the reaction field method for long-range treatment of electrostatics. In this method, a given particle is assumed to be surrounded by a spherical cavity of finite radius within which the electrostatic interactions are calculated explicitly. Outside the cavity, the system is treated as a dielectric continuum. Any net dipole within the cavity induces a polarization in the dielectric, which in turn interacts with the given molecule. The reaction field method allows the replacement of the infinite Coulomb sum by a finite sum plus the reaction field. One caveat of this approach might be the nonuniform distribution of the particles within the system (i.e., one protein-dense phase and one protein-dilute phase), which may jeopardize the assumption that outside the cavity is a uniform continuum dielectric. While this caveat may make the Ewald summation (or particle mesh Ewald, faster version of Ewald sum) look more preferable, we note that Ewald sum and reaction field techniques yield nearly identical phase behavior for liquid crystals (also nonuniform in nature) (see, Molecular Physics 92(4), 723-734 (1997)). We discussed some of these points in the main text as follows (Page 6, third from the last sentence):

“Long-range electrostatic interactions were calculated using a generalized reaction field method [45]. We note that a long-range treatment of electrostatic interactions is essential to obtain accurate phase behavior [46].”

Comment: 10)<br /> It would be very useful if the inputs/settings (including starting configurations) used for simulation and code for analysis were available.

The author’s response: Following the reviewer’s suggestion, we uploaded the initial configurations and run files for all lambda values for 0 mM salt and 100 mM to GitHub and made it publicly available. We now noted in the availability of the data in the main text by modifying the last paragraph of Modeling subsection as follows:

“Equilibrated initial conditions, topology files, and run parameter files for all λ values of 0 mM and 100 mM salt are publicly available on GitHub (https://github.com/gzerze/m...

Comment: We also have the following suggestions for minor revisions to the manuscript:<br /> 1)<br /> “We kept the protein-protein interactions unmodified (and no additional elastic backbone constraints were applied)”: The author should clarify whether this includes assignment of secondary structure and/or side chain angle and dihedral restraints (ss and scfix in Martinize).

The author’s response: Yes, this would apply for any restraints (i.e., they would remain unmodified). This particular protein, FUS LC, is left fully flexible, without any backbone/side chain structure. We clarified this in the main text by modifying the relevant part in the Modeling subsection:

“No elastic backbone (or side chain) constraints were applied (i.e., FUS LC is kept fully flexible). We kept the protein-protein interactions unmodified but systematically tested a range of scaled protein-water interactions.”

Comment: 2)<br /> “All simulations were performed using GROMACS MD engine (version 2016.3).”: Error in references.

The author’s response: The references are fixed.

Comment: 3)<br /> In the Cluster Formation Analysis section: We suggest that the author cites the specific package used (e.g. SciPy).

The author’s response: Following the reviewer’s suggestion, we added the name of the routine related references by modifying the relevant part in Cluster Formation Analysis subsection as follows:

“Any two protein molecules are considered to be in the same cluster if any two beads of the molecules are within 0.5 nm (or less) distance from each other. Based on this criterion, we built adjacency matrices and then found the connected components by using the compressed sparse graph routines of public Python libraries [50]”

Comment: 4)<br /> Fig. 2: There are small red dots on the droplets, which should either be explained in the figure text or removed.

The author’s response: Following the reviewer’s suggestion, we remade the Figure 2 by removing the red dots.

Comment: 5)<br /> Fig. 3: It would be useful for the reader if the NaCl concentration was labelled at the top of each column. Additionally, the radial distribution of the ion concentration is shown as two separate rows, which we assume corresponds to Na+ and Cl- ions. This should be clearly labelled.

The author’s response: Following the reviewer’s suggestion, we updated Figure 3 with proper labels.

Comment: 6)<br /> “We found the largest water fraction For the ionic species…”: Typo?

The author’s response: We removed that incomplete sentence now.

Comment: 7)<br /> Fig. 4: Depending on how the plot is updated with more details on the experiments, perhaps the range shown on the y-axis could be made smaller.

The author’s response: Figure 4 is updated as presented above (please see the response to point 7 above).

Comment: 8)<br /> Fig. 5: May be clearer with a colourmap with three colours, as in figure 6.

The author’s response: Figure 5 uses a color scale that changes the colors uniformly from black to white. For contact maps (like Figure 5), since the range of change is sequential growth of fraction, we thought a perceptually uniform sequential color scale fits better as opposed to a divergent color scale (e.g. the color scale in Figure 6).

On 2023-04-30 08:38:44, user Jamie Bojko wrote:

Really interesting read! I’ve not conducted a full review, but could I suggest that you use Astathelohania, which replaced crayfish infecting Thelohania in your genomic tree. You may also benefit from reading through https://www.cell.com/trends...

On 2023-04-29 17:21:44, user Ashraya Ravikumar wrote:

Note: We reviewed an updated version of this preprint for a journal. The comments are posted with this preprint in the hopes the authors post the updated version that we commented on here as the journal we are reviewing for does not place limits on updating preprints during the peer review process.

Ashraya Ravikumar and James Fraser

Summary:

The traditional Ramachandan plot uses the ϕ and ψ torsion angles about the N-C???? bond and C????-C bond respectively to represent aspects of the three dimensional protein backbone structure in two dimensions. Some of the atoms involved in the calculation of ϕ and ψ torsion angles of a residue come from the adjacent residues in the protein chain. In this work, the authors consider the ψ angle of residue i and ϕ angle of residue i+1 as an entity and analyze the distribution of these amino acid pairs. Their approach has the advantage of the torsion angle pair being fully contained in an amino acid pair and the ease of representation of these pairs in the familiar Ramachandran like plot. The authors show that their cross peptide bond plot covers more area than the traditional ϕ,ψ plot and identifies certain structural elements that are “recurring outliers” using the traditional plot. They also show some differences in conformational preference between thermophilic and mesophilic proteins. There is an initial attempt at experimental validation, with small stability changes (measured by melting temperature) upon point mutations to amino acids more favored for that specific region of the cross plot; however, this validation is limited and would benefit from examples intended to be neutral and destabilizing. The major strength of the paper is a new concept that is very simple yet also powerful for identifying regions of conformational space that should be considered “valid”, not outliers. In doing so, their method provides a lot of scope for some interesting future work and new ways of validating protein structures, refinement procedures, and structure predictions. The major recurring issue in the manuscript however has been lack of clarity and lack of attention to detail, which can be improved in a future (third?) iteration of the manuscript. The major and minor points of concern are expanded below:

Major points:

1. In Figure 1, the authors claim that “Standard secondary structures (such as left and right ????-helices, ????-strands and turns) are clearly recognizable” from the gray stick plots. While the ????-helices are recognizable especially in clusters 12 and 19, it is not possible to identify ????-strands and turns from these images. More amino acids may have to be added to the visualization on either side of the pair to make this clear.
2. Their analysis on the correlation of cross bond angles using MD simulations need more details and discussion. The text points to Figure 2 and Table S1 to show the correlation but Figure 2 simply shows the five structures which were simulated along with the amino acid pairs and the cross peptide bond plot. It is unclear how to interpret the correlation between ϕ(k) and ψ(k+1) from this figure. Also, their choice of proteins for this analysis seems arbitrary. What do they mean by “small” protein? What is the dataset of structures they started out with before randomly picking these five structures?
3. How do the authors claim that cluster 9 and 10 shown in Figure 4 represent a transition into helix? The ϕ(k+1),ψ(k+1) distribution is not indicative of being part of a helix.
4. The authors refer to Figure 5 in their discussion about the cluster 15 representing the Type II ???? turn. The representatives of the cluster do appear in different structural contexts. But, are they all indeed ???? turns? Do they satisfy other criteria to be called a beta turn - either distance between C???? atoms of i and i+3 residue or H bond between carbonyl oxygen of i and amide hydrogen of i+3. This information is needed to support the statement that type II ???? turns are also common in random coil regions. Also, Figure 5 caption says the representatives are from cluster 6, but we are assuming the authors mean cluster 15.
5. The authors have not mentioned what is the color scale used to color the nodes in Figure 6. We are assuming that warmer color means higher probability of the cluster being occupied by thermophiles. The clusters that are part of the most prominent transitions are very close to each other. Maybe many of the amino acid pairs from thermophiles that are classified into cluster 12 could be part of cluster 19 with some minor change in ψ or ϕ angle. Are the alphafold predicted structures accurate enough to distinguish between such close ψ,ϕ angles? These observations could also be a result of inherent biases in alphafold. Perhaps the authors could also analyze experimental structures of mesophiles and thermophiles to see if these trends hold.
6. The statement “Visual inspection of the most prominent cases suggests that the preferred clusters in thermophiles, presumably the more thermostable ones, are those which appear more ordered” is not well supported. If this statement is being made purely based on the cartoon representation of the two cluster transitions shown as inset in Figure 6, then it does not look convincing, especially 1 to 11. Perhaps the authors could analyze the extent of disorder in a more systematic way by comparing preferred clusters of thermophiles and mesophiles and quantitatively look at the difference in disorder, if any. The authors can also show specific examples of transition matrix along with pictorial representations of differences in angles/planes so that the reader can understand them better.
7. In the context specific mutation analysis, the differences in ΔTm observed are very small and the raw DSF data is not shown in the supplemental figure. Are these changes significant enough to conclude about the effect of these context specific mutations on protein stability? Additional experiments are likely needed to place these ΔTm changes in context. What is the typical change in ΔTm for a predicted neutral or deleterious mutation? Can anything be inferred based on prior deep mutational scans for GFP or another protein to help give this analysis more power?
8. Over the years, Ramachandran angle restraints have become part of structure refinement protocols, which when applied inappropriately, could lead to over-optimization of ϕ-ψ angles. For such cases a simple Ramachandran validation will fail to identify issues in the structure and needs a more global approach such as the Ramachandran Z-score (https://www.sciencedirect.c.... From the way the authors’ approach is designed, it could have potential for a similar application. They have shown in Figure 3 how outliers of Ramachandran plot fall into acceptable regions in their cross-peptide bond plot. Along these lines, the authors should discuss about global validation metrics derivable from their method (perhaps related to the normalized marginal distribution distance shown in Figure 7)

Minor points:

1. In the introduction section, the authors mention the disadvantage of the (ϕ,ψ)2 method. But apart from mentioning the protein blocks (PB) method, they don’t explain how their approach improves over the PB method. It’s important to place this work in context to PB since PB’s have been used for several structure related applications. Another related work is TERMs (https://www.sciencedirect.c... where the protein structure is broken down into smaller structural entities which are then used to assess model quality, sequence/structure compatibility, conformational transitions, etc. The authors could discuss about the relationship of between their work and TERMs
2. The authors should add labels to panels within figures instead of addressing them as top, middle, etc.
3. What is the correct resolution cut off used to extract structures from PDB? Results and discussion says 1.8Å but methods say 1.5Å. Also, the authors need to provide the list of PDB structures used.
4. The authors have probably cited the wrong reference for the proteomes of mesophile/thermophile bacterial pairs in materials and methods
5. Page 5, para 2, text says Figure 7 while it’s actually referring to Figure 6.
6. Page 5, para 3, Fig S3 should be changed to Fig S4
7. Page 6, para 1, unclear if authors are referring to Fig S2 or Figure 7 since Figure S2 does not have the FH and YH distribution. Same para, reference to Figure 6 instead of Figure 7.
8. In Materials and Methods, page 7, under “Calculation of correlation coefficients between dihedral angles”, it should be Figure 2

Ashraya Ravikumar and James Fraser

On 2023-04-28 23:25:26, user Charles Warden wrote:

Hi,

Thank you very much for posting this preprint.

For the 2nd author affiliation, I believe that there is a minor typo:

Current: Sothern

Corrected: Southern

Thank you again!

Sincerely,<br /> Charles

On 2023-04-28 13:14:03, user Jason Bubier wrote:

Peer Reviewed version available here https://onlinelibrary.wiley...

On 2023-04-28 04:02:48, user Alexis Rohou wrote:

I was asked to review this manuscript for a journal. My comments are below:

Sweeny and colleagues describe a method which identifies the location and conformation of small molecule ligands within cryoEM volumetric maps of ligand:target complexes in an automated manner, given an atomic model of the macromolecular target only. The method, named ChemEM, is shown to match or exceed the performance of existing, commonly-used methods in its ability to successfully locate and model energetically favorable and structurally accurate conformations of ligands. Such a tool will be of keen interest to structural biologists working on small molecule ligands, for example in the context of drug discovery projects supported by cryoEM. In such projects, protein:ligand co-structures typically have resolutions in the 2.5 to 3.6 Å range, leaving some ambiguity as to the exact position of ligand atoms, and thus the conformation of the small molecule and the details of its interaction with the protein binding site. <br /> The authors achieved impressive performance, as judged by well-defined benchmarks, by introducing several key features, including:<br /> (1) a carefully calibrated function to score the geometric and chemical plausibility of ligand and ligand-protein conformations, termed ChemDock<br /> (2) a measure of quality-of-fit carefully weighted relative to ChemDock as as to optimize performance in benchmarks<br /> (3) the use of mutual information (MI) rather than the more commonly-used cross-correlation coefficient (CCC) as a quality-of-fit measure<br /> (4) the use of difference maps rather than full cryoEM maps as input for the docking and initial conformational search

In addition to point (3) above, the manuscript is replete with technical nuggets that will be of interest to those working directly in this field (e.g. the use of full vs difference map, the relative weighting of model-only vs model-map scoring functions and its calibration, the creation of cryoEM benchmarking sets).

Overall, I found no major technical issues with the description of the work. I found the methodological details are well described, as are their tuning, benchmarking and validation. Most of the claims are well supported by evidence, and the results do appear impressive. The manuscript appears close to publishable to my eyes.

I do have some suggestions for improvements:

(1) I found the following were missing: A description of the software and end-user experience. What inputs are needed? What is the interface like? The authors claim that ChemEM is automated... is it truly fully automated in a robust way? How many parameters does the user need to adjust? For example, are difference maps calculated automatically by ChemEM? Are the maps filtered, or their spectra flattened or normalized in any way, before computing difference maps and MI? Also, what is the performance like? Relatedly, the authors really should specify: How is the software available? How is it distributed? Under what license? Oh and also: does ChemEM handle covalent ligands?

(2) Q scores (or equivalent metric) should be included when making comparative statements about the quality of (ligand) model-to-map agreement (e.g. Fig 6A, but Figs 5 and 6 in general, and references to them in the text, e.g. "markedly better fit to the density than the cryoEM deposited structure")

(3) The abstract claims that SM docking into medium resolution maps is "unexplored territory". I don't think this is accurate. For example, the glideEM paper treats EMD-0488 (3.4 Å).

(4) Introduction: the sentence which cites reference 2 seems inaccurate to me. As far as I could tell from a brief review of the cited work, it does not identify a novel drug target. GABA receptors were already known to be the targets of the small molecules studied in that paper. In general, I fail to see how higher resolution structures by cryoEM would help identify new targets (proteins) for small molecule drugs.

In my opinion the suggestions above really should be addressed before publication. In addition the suggestions below would improve the manuscript further and may be acted upon if the authors/editors agree:

(5) Up to the editor, but this is a very technical paper. It might be worth investing a few sentences here and there to help non-specialist readers along. For example, when referring to CASF-2016's "funnel" and the related decoy or "RMSD" ligand sets, or when first referring to mutual information. The non-expert reader likely will not know exactly what these are and some short descriptions may help. Generally, given the target journal, a bit more effort to write for a broader audience might be warranted.

(6) Figure 4: perhaps label/title the figure panels themselves with "Different maps" (left column) / "Full maps" (right column) and "Low-resolution maps" (bottom row) / "High-resolution maps" (top row). And also, in the MI-only case with different maps - what was the initial conformation of the ligand (which I assume was held constant)? Was it a minimized-in-vacuum conformation?

(7) A detail: I have a preference for avoiding the word "density" when referring to cryoEM maps, since they do not map electron density like x-ray maps do.

(8) Abstract: to claim that automatically docking ligands is of "utmost importance" I think is an overblown claim. Automation is important in high throughput applications, such as fragment screening, but otherwise automation is just as desirable in ligand docking & refining as it is in protein structure building, which is to say (in my opinion) not of utmost importance. Accuracy is.

(9) Abstract: "ChemEM is a novel method". I'm not dogmatic about this, I think it's OK to claim novelty from time to time, but in this case, only some aspects of the method are really novel, and I think the claim of novelty sounds a bit hollow because it is applied in such a broad stroke. In view of the quality of the results, it's not even necessary.

(10) Abstract: The last sentence "ChemEM unlocks the potential of medium-resolution cryo-EM structures for drug discovery"... Again this claim is actually really quite strong but sounds quite hollow in this case, at least to someone who has been doing medium-resolution cryoEM for drug discovery for some years. I would strike such boombastic language unless some more specific statement can be made about some type of experiment of projects that was not previously possible until ChemEM came along and is now "unlocked".

(11) Signicance statement: is ChemEM already used in drug discovery?? Is it even available for download from anywhere? Maybe just say "which can be used used in drug discovery"; "In the last decade Cryo-electron microscopy": the capitalization is unecessary here. Also, because the electrons are not cryogenic, I recommend using "cryogenic electron microscopy (cryoEM or cryo-EM)".

(12) Introduction, second paragraph: "high- and low-resolution": Hyphenation is not warranted here. I would say "at both high and low resolutions". If you really like the hyphen, try something like "in both high- and low-resolution regimes".

(13) Introduction, penultimate paragraph: "there is no data that compares" should be "there are no data that compare"

(14) Evaluation of the ChemDock scoring function: "and of these a correct pose": Is correct pose different from correct conformation? The way the sentence is written suggests so, but I suspect not. Perhaps simplify the syntax to avoid confusion?

(15) Analysis of specific examples. Last paragraph: If I understood correctly, the authors know this is not the correct solution because it doesn't match the high-resolution control structure. I guess the top panel of Fig 6D shows PDB 6T24. Did the authors consider adding a panel showing the "correct" conformation form the high-resolution PDB?

(16) Discussion. First sentence. The need for automation is dominant perhaps only during fragment screening campaigns and the like. My view is that the accuracy (RMSD to ground truth) & quality (low strain) of the poses found by ChemEM are impressive, even without considering the automation. I would suggest "accurately and automatically".

(17) "our benchmark only cases better than 4.5Å": "only includes cases" (word missing)?

(18) M&M. Computational datasets. First sentence: "Data to train the ChemDock scoring function was taken" should be "were taken"

(19) M&M. Computational datasets. First paragraph. Of the remaining 3,281 complexes, did any of them have close matches in the CASF-2016 dataset? For example, small molecule ligands bound to the same protein target, in the same pocket and with similar (but not identical) chemical structure to one in the CASF-2016 dataset? If so, then the training and test datasets may not be sufficiently independent.

(20) Page 14. "The data was split" should be "The data were split"

(21) Page 18. "For molecular docking experiments smiles strings for the 32 ligands", I think smiles should be capitalized: "SMILES"

Alexis Rohou<br /> Genentech<br /> April 2023

On 2023-04-27 15:17:01, user jfhmast wrote:

Is that ERFE fc-tagged? That would explain why it doesn't seem to inhibit anything in the pSmad5 western. ERFE primarily exists as a trimer and a dimeric FC just won't do the job as well. ERFE binding doesn't always lead to inhibition, sadly.

On 2023-04-27 01:29:07, user Ava Bignell wrote:

This was a great paper to read, and I appreciate the biomedical applications it has! Here are some suggestions that I have:

1) The statistics in this paper should be represented differently, using a more consistent and coherent system. In addition, due to some of the data seeming to be non-normally distributed, a different statistical test should be used. A Friedman One Way Repeated Measure Analysis of Variance by Ranks may be a test to look into, as I believe this would allow for comparison between all groups while taking into account the distribution of the data. The statistical analysis should also be elaborated on more in the methods section.

2) The methods for administering the shear stress should be reconsidered, using more consistent controls. For example, the control (static condition) should incorporate the cone on top of the cells to control for any effects this may have on the cell phenotypes. The addition of medium to the cells may also impact the results. If possible, a different set up could be considered to more accurately reflect the in vivo conditions of blood flowing through the veins/arteries/heart since these are the biomedical applications discussed in the paper. For example, flowing fluid through a tube with cells to cause shear stress rather than spinning the fluid to cause shear stress.

Overall, I thought this was a very interesting paper, and I think it is great that this is the first paper to address this specific topic in vitro!

On 2023-04-25 23:31:20, user Sean wrote:

Review of the paper by Mai et al. “Whole mouse body histology using standard IgG antibodies” [part of the MICR603 “Journal club in Immunology”]

Summary. Most diseases involve the connection of numerous coordinated host systems. While it is known that numerous host systems, including the nervous systems, are involved with distal disease sites, research has been limited to investigate single organs. In the present manuscript, the researchers sought to develop an imaging method (wildDISCO) in whole mice bodies to visualize the distribution and association of cell types within hosts. Using wildDISCO, researchers were able to develop a method to investigate through microscopy the nervous, immune, and lymphatic systems. In addition to validating the methodology, the researchers demonstrated the nerve connections between different organs, a previous limitation to whole-organ microscopy. Additionally, they demonstrate that there is co-localization of immune cells with vagus and sympathetic nerves in the inferior mesenteric plexus and intestinal wall, respectively. Finally, using germ-free mice, the study determined the nerve lattice structure is less dense compared to mice harboring a gut microbiota, highlighting the importance of the gut microbiota in the development of the mesenteric plexus. Therefore, wildDISCO is a useful technology that can not only be used for physiological research, but also fully understand the cellular dynamics within whole mice during various diseases.

Positive feedback. <br /> The present study addresses a long standing limitation to the field of whole-organ microscopy, allowing researchers to now address organ connectivity of various systems. As such, the manuscript was well organized and the microscopy was clear in demonstrating the various principles investigated. Specifically, the microscopy for PGP9.5 and LYVE1 truly demonstrates the distribution and connectivity of nerves within whole mice, a tremendous feature not previously investigated before. In addition to the physiological questions addressed in the manuscript, the in vivo experiment demonstrating the influence of the gut microbiota in the development of the sympathetic nervous system was particularly exciting. The results present in the manuscript provide crucial insights into the interconnection of host systems within mice and allow other researchers to begin to address these insights.

Major Concerns<br /> The graphical abstract needs to be more clarified for where cholesterol depletion is occurring within these mice<br /> Throughout the manuscript, analysis of the results is often missing. If the authors could include their interpretation of the results at the end of each figure, it would help readers understand. <br /> Figure 1C - The methylene blue staining of mouse brains should be quantified. The present microscopy is difficult to see on paper and staining looks similar across treatment groups. <br /> Figure 4A - the authors state there is substantial co-localization of immune cells with vagus nerves and sympathetic nerves, could this be quantified by the authors? Perhaps the percentage of CD45+ signal which localize to the nerves?<br /> The results section for figure 5 is missing from the manuscript. This should be added to the manuscript.

Minor concerns<br /> The gut-brain axis has been investigated heavily. In the germ free mice, were there changes in the development of nerves in the brains?<br /> If the authors could talk more about this technology in the discussion section and how this could be applicable to other physiological systems or diseases, I think that would help the manuscript. <br /> Figure 1B - CD2 appears to perform cholesterol depletion, but is not mentioned in the text. The authors should discuss this finding. <br /> Figure 5L - Your graph needs to be bigger and shift the Y axis label, as this cut off the T axis numbers.

On 2023-04-25 15:45:25, user CDSL JHSPH wrote:

Thank you for submitting your abstract on engineering a functional variant of Dengue virus (DENV) with a mutated fusion-loop to potentially improve the safety and efficacy of DENV vaccines.<br /> The abstract provides a clear background on the antibody-dependent enhancement issue in DENV pathogenesis and its impact on vaccine development. The objectives of the study are also well-defined. However, I recommend adding a brief explanation of saturation mutagenesis and directed evolution for readers who may not be familiar with these techniques.<br /> It is interesting to note the development of the D2-FL and D2-FLM variants that evade both pre-membrane and fusion-loop antibodies. I suggest providing more information on the functional analysis and characterization of these engineered variants, including any potential impact on viral fitness, replication, or virulence.<br /> The observation of lower neutralization titers against D2-FL and D2-FLM in heterotypic serum compared to isogenic wildtype DENV2 is an important finding. However, it would be helpful to include some quantitative data or statistical analysis to support this observation.<br /> In the conclusion, you propose the use of D2-FL and D2-FLM as tools for delineating cross-reactive antibody subtypes and as a platform for safer live attenuated DENV vaccines. While this is a promising application, it would be valuable to discuss any preliminary data or experiments that demonstrate the safety and efficacy of these engineered viruses in vivo.<br /> In summary, the abstract presents an innovative approach to address a significant challenge in DENV vaccine development. With some additional details and clarification, it will provide a strong foundation for a compelling research article. I look forward to seeing the full paper and the outcomes of this exciting work.

On 2023-04-25 12:08:57, user Damien HUZARD wrote:

Great system, congrats !<br /> I have a question concerning the system that was used to correct the identity of each mice in case of miss-identification (following fighting or nesting for example) ?<br /> thank you,<br /> Damien

On 2023-04-25 10:49:22, user Juan Rodriguez Vita wrote:

A peer-reviewed version of this manuscript is in press in Hepatology (doi: 10.1097/HEP.0000000000000407). For your information, we have outlined a list of the changes introduced:<br /> • Literature confirming SMEA3C unique expression in hepatic stellate cells.<br /> • Immunofluorescence of NRP2 in isolated HSCs showing that SM22αCRE/NRP2fl mice effectively eliminate NRP2.<br /> • Overexpression of human SEMA3C in GRX cells.<br /> • Reproduction of most relevant data from the CCL4 fibrotic model in TAA injected mice.

On 2023-04-25 10:47:11, user Juan Rodriguez Vita wrote:

A peer-reviewed version of this manuscript has been published in Cancer Research (doi: 10.1158/0008-5472.CAN-22-0076). For your information, we have outlined a list of the changes introduced:<br /> • Longer time point for our animal model for metastatic EOC<br /> • Subcutaneous tumor model for Lewis lung carcinoma cells<br /> • Bioinformatic analysis of patient prognosis<br /> • Lipid raft staining by flow cytometry<br /> • Tissue microarray of patients suffering ovarian cancer showing the correlation between Notch activity and myeloid cell infiltration.<br /> • Determination of CXCL2 levels in vivo upon Rbpj deletion<br /> • Experiments to determine the receptor involved in CD44 regulation

On 2023-04-25 10:44:48, user Juan Rodriguez Vita wrote:

A peer-reviewed version of this has been published in Nature Communications (doi: 10.1038/s41467-023-38064-w). For your information, we have outlined a list of the changes introduced:<br /> • Patient data where different cell compartments are analyzed separately to address the differential expression of HAPLN1<br /> • Bioinformatic analysis of additional patient databases<br /> • Evaluation of recombinant HAPLN1 action on KPC cells<br /> • Identification of TNFα as the upstream regulator for HAPLN1-induced differences in vivo.<br /> • Validation of TNFα as the mediator of HAPLN1-induced invasion in vitro.<br /> • In vivo luminescence of the model.

On 2023-04-25 07:14:24, user Sai Li wrote:

Dear Readers,

We have recently published this work in PNAS (https://www.pnas.org/doi/10.... Conclusions regarding Figure 1 have been changed. We found electron-beam inactivated virions have less invaginations than PFA-inactivated ones.

Sai Li

On 2023-04-24 22:54:50, user Jeffrey Ross-Ibarra wrote:

We read this manuscript with great interest, and were uniformly intrigued by the network approach to thinking about polyploidy evolution and the possibilities it offers. In particular, it was nice to see how these approaches provide results that largely match with the gene balance model and other ideas about how polyploids might evolve, and we liked that comparison to results in mammals suggests the uniformity of these processes across the tree of life. Nonetheless, our discussion led to several suggestions that we hope might help improve the manuscript. We stress, however, that we recognize the authors have thought about these ideas much longer than we have, and that some of our comments may stem from our own misunderstanding of the work.

In the manuscript the authors recognize that comparison to null networks is important for distinguishing intriguing features of real networks. The authors generate a good null expectation for motifs in a GRN by modeling random degree-preserving unscaled networks and counting motifs. We wondered, however, whether random unduplicated networks are the appropriate null. Simple duplication of all the nodes in a network, where each node maintains its links, will create a network with bilateral symmetry. This symmetry will give rise to symmetrical motifs – for example two nodes connected by a directed link will generate a bifan motif when duplicated. Over time, or after random perturbations to the links and nodes in the network, this bifan could be degraded to form triangular motifs like the V and lambda motifs. Because duplication creates new motifs, the expected number of motifs should be much higher in a duplicated GRN created by whole genome duplication. A symmetrical null model should capture this higher motif frequency. Testing hypotheses with a revised null model could yield exciting results and address additional questions: . Is the observation of motif enrichment an inevitable consequence of network duplication? Is the search for motifs with bilateral symmetry why patterns in Solanum – the result of a triplication – are an exception to the observed patterns?

A fair bit of the discussion focuses on the timing of polyploidy. Here we felt it would help readers to introduce them earlier to the ages of the different polyploidy events studied. We also felt the authors could do more to distinguish between the age and number of polyploidy events – in their examples, species with “newer” polyploidy events were also those with the largest number of total events, for example. And even the newest events are many millions of years old – is a 5myo polyploidy event expected to be much different from a 15 or 30myo event?

Throughout, we felt there were places where explicit hypotheses would help the reader. What are the predictions made by different models of duplication and evolution? Do we expect these to differ qualitatively or quantitatively between GRNs and PPI? Do we have predictions about which GO terms might be significant and why? (e.g. why should stress genes hang around longer?). Laying these out would especially be helpful for biologists less familiar with networks and the gene balance hypothesis and their comparisons.

The authors compare duplicates from WGD to those from SSD, but neither group is homogeneous. Whole genome duplications may be the result of allopolyploidy or autopolyploidy, which could impact the detection of different motifs. It would be useful for the authors to discuss the implications of the type of ancestral WGD event and how it could bias their detection of motifs and subsequent findings. Additionally, the software developed by the authors’ research group, doubletrouble, appears to separate SSDs into 4 categories (tandem, proximal, transposed, and dispersed duplications). Would we expect patterns for the various types of SSDs to differ? Is the variation depicted in Figure 2 for the SSD-derived gene pairs a result of variation between SSD classes or other types of noise?

Finally, we wondered how large-scale duplications of multiple genes or even aneuploidy might lead to different results, especially if there is any clustering of genes by function.

Elli Cryan, Alyssa Phillips, Jeffrey Ross-Ibarra, Ayelet Salman-Minkov

On 2023-04-24 13:53:52, user Peter Pearman wrote:

Please take the time to make a comment, or write a short review. We will appreciate your effort greatly.

On 2023-04-22 15:21:21, user Taslima Haque wrote:

This has been published recently as: https://www.nature.com/arti...

On 2023-04-21 15:31:52, user Vassili Kouvelis wrote:

the revised manuscript has been published at "Scientific Reports" (https://doi.org/10.1038/s41...

On 2023-04-21 12:57:57, user Guilherme Campos Tavares wrote:

https://pubmlst.org/organis...