On 2023-03-16 01:54:59, user HELEN THOME wrote:

Hello! I wanted to say that this paper was so fascinating and exciting to read. The novel approach to defining fibrosis in post-MI models is so interesting, and is a great build upon your previous findings. The mathematical modeling of cold vs. hot fibrosis was an impressive approach. A couple comments regarding the experimental approach:

1 - Considering how different myocardial infarction progresses in male and female humans, and how differently each sex responds post-MI, the lack of differentiation between sexes in the murine and porcine models was concerning. All kinds of factors related to sex can influence post-MI condition, and identifying samples by sex may have explained some of the variability (as seen in Figure 2E).

2 - While the GO analysis refinement of the bulk RNA-seq data is intriguing and provokes question of further study, establishing continuity in using scRNA-seq for both the murine and porcine models would have made for a better comparison of the data.

3 - The UMAP in Supplemental Figure 1A shows a much different gene expression profile in the fibroblasts and myofibroblasts at the end of the healing period in comparison with the early healing period. This would suggest that the ending fibroblasts have much a different identity in comparison to the starting ones, and calls into question whether the two populations can be deemed to be the same. Perhaps further study into differential gene expression of these two populations could be beneficial to the project.

On 2023-02-21 20:06:19, user Pauline Young wrote:

Hi, great paper with lots of interesting findings! A couple comments:

1. For figure 1B, it might be useful to include figure S1A instead of just showing figure 1B. Before looking at S1A it was very confusing to understand why the macrophage population was said to return to baseline while myofibroblast population remained constant when the graph seemed to show both leveling off at around 10%. Looking at figure S1A helped clear up that confusion so it might be helpful to just include that in1B instead.

2. For figures 1J-1M, the results and data are very exciting, however there seemed to be a sample size of just one per time point and therefore no standard deviations were provided. The data might be more convincing if there could be a larger sample size.

3. Lastly, the idea of using both mice and pigs as model organisms seems to be a good system. However, it is a little unclear why for animals the threshold for an "adult" was 3 months when pigs seem to take longer to mature compared to mice.

On 2023-03-15 18:03:08, user John Moreau wrote:

The following 6 comments were submitted via Arcadia, and are reproduced along with the authors’ responses here:

Text: To investigate the evolutionary history of HgcA, we further enlarged the sample size by retrieving HgcA homologs in UniProt Reference Proteomes database v2022_03 at 75% cutoff (RP75). Two other datasets, including one containing 700 representative prokaryotic proteomes constructed by Moody et al. (2022) and another containing several novel hgc-carriers published by Lin et al. (2021), were retrieved and incorporated into the RP75 dataset. Totally 169 HgcA sequences were collected after removing redundancies

Comment: I might have missed something, but it appears that you have included hgcAB sequences that are either included in the PF03599 protein family or MAGs from Lin et al. Are the HgcA protein sequences from the large curation efforts from McDaniel et al. 2020, Capo et al. 2022, and Gionfriddo et al. 2020 for example integrated into this uniprot release? It would seem easier in this case to pull directly from the Capo et al. database since those are curated sequences and metadata to link back to, unless I'm missing how Uniprot accessions work with incorporating data from MAGs

Response: Thank you for pointing this out. We considered using the MATE-Hg database from Capo et al. 2022 for this study at first, but some reasons made us turn to the UniProt Reference Proteomes (RP) database eventually. First, this work considered the gene tree (HgcA tree) and the species tree (house-keeping genes tree) together to resolve the evolutionary history. Since the MATE-Hg database does not contain the house-keeping gene information, we decided to use the RP database. Second, to build the species tree, high-quality genomes are required to extract enough house-keeping marker genes. Some host genomes in MATE-Hg database are not in high quality; they might not contain enough marker genes or might be produced from mistaken/biased binning processes. Third, in order to confirm the true hgcB genes to elaborate their evolution history, we used more strict criteria than the MATE-Hg database. Our study determined hgcB genes not only according to the HMM profile of HgcB, but also required they are located close to hgcA genes. Finally, the RP set we used is sampled from all UniProt proteomes containing most genomes in the MATE-Hg database. Therefore, the final dataset we used is representative and should be ideal to study the evolution of Hg methylation.

Text: A few putative HGT events could be inferred from the larger clade of the HgcA tree e.g., Marinimicrobia-HgcA clustered with Euryarchaeota-HgcA in the archaeal cluster,

Comment: Was the inference made by position in the tree or analyzing the pairwise sequence identity similarity of the proteins from this Archaea/Marinimicrobia? I am curious because in McDaniel et al. 2020 mSystems we also found only a few potentially clear cases of HGT but did this through pairwise sequence analysis, for example for a case of Deltaproteobacteria/Acidobacteria/Verrucomicrobia/Actinobacteria in permafrost

Response: This inference was made by position in the tree and also the sequence identity. Marinimicrobia is a bacterial phylum, but the hgcA genes they carry are clustered with other archaeal HgcA in the HgcA tree. Also, the closest sequence with Marinimicrobia-HgcA in the nr database is the HgcA sequence from Theionarchaea archaeon DG-70-1, which has 98% coverage, 56% identity, and 72% similarity by blastp. We have also discussed this in our previous paper Lin et al., 2021 ISMEJ.

Text Nevertheless, several hgcA+ genomes did not carry neighbouring hgcB genes, including all Nitrospina and a few Deltaproteobactiera and Firmicutes, potentially because of gene loss during evolution or incomplete transfer events (i.e., only hgcA genes were acquired during the HGT events).

Comment: I wanted to clarify something from the methods - were just the hgcAB proteins from uniprot pulled down or the entire genome sequences for these hgcAB+ representatives? If you did have the entire genome, did you check for the cases where hgcB was missing if hgcA fell close to the end of a contig or not? I think Peterson et al. 2020 ES&T had a couple cases that hgcA was at the end of a contig

Response: We have the entire genome sequences. We have ruled out the situation where hgcB was missing because of incomplete assembly. We confirmed that hgcA was not at the end of a contig and genes other than hgcB were located downstream or upstream of the hgcA.

Text: Figure 3.

Comment: Are these trees rooted with either cdh outgroups or fused hgcAB? I see the symbol for fused hgcAB but in Gionfriddo et al. 2020 fused sequences are usually used to root the tree for accurate topology inference

Response: The HgcA tree in Figure 2 was rooted according to the MAD- and outgroup-rooting analyses shown in Figure 1. In that analysis, the root of the gene family tree was placed between CdhD and CdhE, with the "small clade" emerging at the base of HgcA. Therefore, fused HgcA is not appropriate to be used as an outgroup alone.

Text: Our study reveals an ancient origin for microbial mercury methylation, evolving from LUCA to radiate extensively throughout the tree of life both vertically, albeit with extensive loss, and to a lesser extent horizontally.

Comment: I think to make a statement like this you would need more extensive analyses quantitatively calculating gene transfer rates and using tree dating methods such as in https://journals.asm.org/do...

Response: The evidence from our analysis that HgcA was already present in LUCA is its sister relationship to the CdhE clade, a group of genes which both our tree and those of others (e.g., Adam et al., 2018) suggest was already present in LUCA. If this hypothesis is correct, then the gene must have been lost extensively during subsequent evolution because it is less widespread across Bacteria and Archaea than are the CdhE (and CdhD) families today. We agree it would be interesting to know precisely when these events occurred during evolution, but this is a difficult problem for single gene trees, especially ones containing ancient splits. Note that the study cited as an example dates a species tree (of Streptomyces), not a gene family tree (as we would need to do to date the events within the CdhD/E/HgcA family. Published dated species trees of Archaea and Bacteria suggest that LUCA might have lived close to the formation of the planet ~4.5Ga (e.g., Betts et al. 2018, Moody et al. 2022), suggesting the deepest divergences between CdhE, D and HgcA occurred prior to that date, although these ancient nodes are difficult to estimate. Timing the subsequent loss of lineages within the HgcA clade would be interesting, but is impeded because it is not straightforward to determine when, or how many, losses took place along the long stem branch.

Text: We mapped the presence/absence of merB and hgc genes onto the Tree of Life

Comment: If I interpreted the methods correctly, this tree only includes ribosomal proteins from genomes that either have hgcAB/merB or both of them. This isn't exactly overlaying hgcAB/merB presence/absence onto the "tree of life" because to accurately portray these relationships you would also want to include genomes that have neither or these operons. To do this accurately you would want to overlay the information you have here including genomes that are in Hug et al. 2016 for example

Response: Thanks for pointing this out. Yes, we agree that this is not the ‘tree of life’. The tree only includes genomes that either have hgcAB or merB or both of them. So this is a species tree rather than a tree of life. We will modify the sentence in the next version to clarify. The modification will not affect the results that we deduced from the tree

On 2023-03-15 16:51:15, user CJ San Felipe wrote:

Summary:<br /> Molecular dynamics simulations (MD) have emerged as an important tool in drug discovery. Their application to prospectively discover binding sites and to screen compounds interacting at those sites, remains a frontier application, even for compounds with high potency. Here, simulations are used to run a “virtual fragment screen”/”swimming experiment” for two compounds with very weak binding affinity that they identified as potential hits by SPR. In some sense, because of the weak affinity and limited interactions, these results are much more impressive and surprising than the landmark “How does a drug molecule find its target binding site” paper from the Shaw group a decade ago (https://pubmed.ncbi.nlm.nih....

The poses are validated with crystal structures of these fragments bound to PTP1b. The major strength of this paper is showing that MD simulations can identify weakly bound fragments at allosteric sites (even those not previously highlighted extensively in fragment screens) with binding poses that closely resemble experimentally obtained structures. The major weakness of this paper is that the authors only performed “swimming” on two fragments, which does not allow us to generalize how well MD simulations both predict other allosteric sites and how well MD simulations predict fragment poses of larger chemical spaces of varying fragment sizes. Given the large number of negative controls available from the SPR screen and the relatively large number of positive (ish? - as affinities weren’t measured) controls from Keedy et al fragment screens, a larger study could be conducted - but for now, this work remains a tantalizing glimpse into the capabilities and potential of the swimming method for PTP1b.

Major points:<br /> The authors initially performed SPR to identify two fragments for MD simulation and crystallization but this appears to be a very small number of fragments for MD. Could they discuss how feasible (or not) would it be to perform MD simulations of the whole library to identify potentially weaker fragments that may not have been detectable by SPR? Additionally, how might the “swimming” approach compare to a more conventional pipeline (e.g. identifying binding sites and fragment pose compare to mixed solvent MD with virtual docking)?<br /> The authors describe two binding sites for the fragments they identified through SPR, one that was previously identified (DES-4884) and a second site that they report as an allosteric site (DES-4779) but do not describe or provide structures of any broader structural changes to PTP1b that occur as a result of fragment binding at either site. The authors should show an alignment between apo and bound states of PTP1b to highlight allosterically induced structural changes. <br /> The authors highlight the DES-4884 fragment inducing two phenylalanine rearrangements (Phe196 and Phe280). While Phe196 conformational change has also been reported in fragment screens, they say that the Phe280 also swings out but don’t explain whether that could be of significance to future fragment screens (does Phe280 create new binding opportunities, increase/decrease fragment affinity, induce broader changes to the structure of PTP1B?) It would be helpful if the authors could contextualize the phenylalanine rearrangements with what is previously known about this allosteric site for future fragment screens.

Minor points:<br /> We feel that the crystallography section of the methods and materials is incomplete. It would be helpful if the authors: 1) explained what PTP1b construct they used, 2) the crystallization conditions, 3) the structure resolutions, 4) method of obtaining fragment bound structure (fragment soaking vs co-crystallization) and 5) how they identified the fragments in their crystal structures. Further information about how crystal diffraction data was collected and processed would be helpful.<br /> Figure 3e: The authors show the DES-6016 variant of DES-4884 makes hydrogen bonds with at least two water molecules. Do the authors propose that this allosteric site requires coordination with water molecules for fragment binding? How stable are the water molecules in the MD simulation to support this?<br /> The authors mention that for each fragment there were additional minor binding sites from the MD simulations. During the crystallization experiments, were fragments detected at these binding sites as well?

Reviewed by - CJ San Felipe (UCSF) and James Fraser (UCSF)

On 2023-03-15 06:42:05, user Davidski wrote:

Hello authors,

Yamnaya is definitely not a 50/50 mixture between EHG and CHG/Iranian ancients.

Rather, it's a mixture between Progress Eneolithic-like and Ukraine Neolithic-like populations, probably with a few per cent of ancestry from European farmers. The ratios might be roughly 80/15/5.

Yamnaya is basically Progress Eneolithic with some extra western ancestry.

Progress Eneolithic is dated to ~4,300 BCE, so a Yamnaya-like population already existed on the steppe at that time, and may have existed much earlier.

So the reality is more complex than your assumptions, and I think that this complexity must be taken into account in your model for it to make sense.

On 2023-03-13 08:07:03, user Toman Bortecen wrote:

A PDF with detailed LC-MS method summary has been added to the GitHub repository (https://github.com/krijgsve....

On 2023-03-10 14:26:08, user Giulia Zerbini wrote:

Here the link to the peer-reviewed published article:

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

On 2023-03-10 04:42:43, user 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 Gomes et al. “Shigella induces epigenetic reprogramming of zebrafish neutrophils

Summary.

Innate immunity training is a growing field of immunological research. Mycobacterium bovis Bacille Calmette-Guérin (BCG) and β-glucan are the two most studied triggers in innate immunity, but research using other triggers such as metabolites and inflammatory cytokines is much more understudied and primarily focuses on macrophage training. The authors of this paper aim to fill in the gaps of neutrophil training in a host system using the human pathogen Shigella flexneri. Gomes et al. use a zebrafish model to study trained innate immunity, and they demonstrate that neutrophil priming with Shigella successfully generates a protective immune response against reinfection. They also dive deeper into the factors that could contribute to this immune response, such as host immune factors, bacterial lipopolysaccharide, and Shigella effector proteins. The authors also show that this neutrophil priming induces an increase in the histone 3 lysine 4 trimethylation epigenetic marker in neutrophils, and they show that primed neutrophils show greater antimicrobial capabilities via mitochondrial reactive oxygen species.

Positive feedback.

This study broaches a much needed subject of research on the development of strengthening host immune responses to a human pathogen that is lacking any effective vaccines or preventative treatments. A positive for this paper is the reasoning behind using zebrafish as a model organism for this study. As the authors state, zebrafish lack a trained immunity until 4 weeks post-infection, causing them to rely solely on their innate immunity. Zebrafish are also effective models for studying human pathogens, as they share greater than 80% of disease associated genes with humans. The authors do a great job of showing that their method of neutrophil priming does indeed induce a trained immune response and that it is non-specific and long lasting. They use multiple infections with Shigella and other bacterial components throughout the paper to establish the validity of using their system with this bacterial pathogen. The use of different pathogenesis screens (cell bacterial cell counts and zebrafish survival) and different techniques (imaging, survival screens, FACS sorting, etc.) also enhances the strength of their methods. The authors also do well with showing that Shigella-induced trained immunity can be protective against other bacteria other than Shigella, specifically Pseudomonas aeruginosa and Staphylococcus aureus. Another positive component of this paper is the use of ample supplementary figures to support claims they are making in their main text.

Major Concerns

There is some conversation lacking on prior research on what is known about how S. flexneri interacts with the host immune system. Adding in some more information on the current knowledge, or lack thereof, of how this pathogen interacts with the immune system would strengthen the reasoning behind this study.

The authors should use a methyltransferase inhibitor, such as PF-06726304 acetate to confirm that histone methylation leads to immune training by Shigella.

The authors are looking at histone h3 lysine 4 Histone h3 lysine 4 trimethylation as an epigenetic marker in this system, however, the reasoning for using this epigenetic marker in favor of others is unclear. For example, lysine 27 acetylation is a major transcription activator for human cells, and it would be beneficial to discuss why something like this was not explored. This epigenetic pathway could be playing a role in infection as well.

Detection of cross-protection of trained innate immunity to different bacteria is interesting. Does that immunity work against viruses or fungi? Can this be tested or perhaps discussed (if testing it is too expensive currently)?

Most of the analyses are presented for mRNA (e.g., for gene expression). It would be good to provide evidence for proteins too when possible and relevant.

There is a bit of misinterpretation of the impact of Shigella immunization on neutrophils. The way the text is phrased currently is that immunization changes neutrophils. That is incorrect as “current” neutrophils are terminally differentiated cells and should die soon (within days in mammals). So, training is probably happening in stem cells that result in production of new neutrophils. This nuance must be carefully stated. - Or are neutrophils in fish long-lived and can be “trained” after leaving the bone marrow?

We know that neutrophils may have their impact on bacteria more than just phagocytosis. How do authors know that i) suppression of growth, ii) NETs generation are not important in control of Shigella?

Minor concerns

The lethal dose of S. flexneri should be introduced as soon as the non-lethal dose is discussed for ease of comparison.

Figures 1C &D - How were these cells quantified? Stating the exact method in the figure legend would make it more clear.

Figure 1H & I -Including 24 and 48 hour time points like the ones included in C and D to these graphs would help readers fully compare the first and second infections.

Figure 2F - These two data sets do not look significantly different. The figure legend just states that the P value is <0.05, but what is it specifically? Stating the actual P value would give readers the ability to see how close to the cut off the value actually, and it will solidify the significance.

Figure 5A- FACS sorting neutrophils can sometimes change the physiology of the cells, as they often spontaneously NETose during sorting. This physiological change could have an impact on the ChIP-seq results, and a disclaimer stating this fact would be beneficial.

Gating strategies for flow cytometry analysis (such as in Figure S5A) should be shown.

Stating the doubling time of Shigella in zebrafish would be useful for data interpretation.

It would be beneficial to explore other experimental methods such as Hi-C and A/B compartment switching analysis to get a further understanding of the influence Shigella is having on host chromatin structure and epigenetic changes.

A diagram in Figure 1 of zebrafish anatomy and the regions of inoculation would be beneficial for people that are not as familiar with this system to better understand where you are doing your infections.

It is difficult to read panel A of each figure in the bioRxiv format, as the disclaimer at the top covers parts of the figures.

On 2023-03-09 21:58:40, user Michel Raymond wrote:

To access the supplementary file, go to version 4 of the preprint.

On 2023-03-08 21:13:53, user Elizabeth Duncan wrote:

We read your impressive study as part of a student journal club at the Markey Cancer Center (University of Kentucky). We appreciate how many controls and orthogonal experiments you did to test your hypotheses rigorously.

One question we have is regarding the genetic background of the cell lines used e.g., A2780, A2058, HCT116, AGS, etc. Given that you made an important point about the mutual exclusivity between the 1q aneuploidy and TP53 mutations in patient cancer samples, we wondered if the cell lines used to manipulate chromosome ploidy were wild-type for TP53. Upon searching the literature and ATTC webpage, it seems like all of the above lines are WT for TP53 except for melanoma line A2058. Notably, this is also the line in which you show significant regain of chromosome 1q after deletion. We see that you tested the role of p53 in aneuploid addiction by mutating TP53 in the TP53-WT line A2780, but what role might the TP53 mutant status of A2058 cells play in their robust re-acquisition of 1q aneuploidy? Have you tried restoring the TP53 gene to its WT sequence in this cell line and comparing their ability to regain chromosome 1q?

On 2023-03-08 15:17:22, user Tom Crocker wrote:

It should be noted that this paper mis-cites <br /> Crocker TF, Brown L, Lam N, Wray F, Knapp P, Forster A. Information provision for stroke survivors and their carers. Cochrane Database Syst Rev. 2021;11:CD001919. [https://doi.org/10.1002/14651858.CD001919.pub4](https%3A%2F%2Fdoi.org%2F10.1002%2F14651858.CD001919.pub4%3Afco64gGMK_P8N9jzOER9pPHw-a0&cuid=2634513 "https://doi.org/10.1002/14651858.CD001919.pub4") <br /> as 23 in the following sentence.

While the intrinsic pathogenic potential of Omicron remains uncertain (21), its antigenic divergence leads to a loss of activity of most therapeutic monoclonal antibodies (22) and failure of current first-generation vaccines to protect from infection (23, 24).

As the author of the previous paper, I request that this citation is removed.<br /> Regards, Tom Crocker

On 2023-03-07 20:01:07, user Andy wrote:

Really impressive work!<br /> I'd suggest citing the work of Betsuyaku et al - it's a really cool paper looking at ETI in Arabidopsis and the area of SA response surrounded by a JA response ring -- this is during incompatible reaction (vs your work in susceptible interaction), but is a cool example of spatial response <br /> https://pubmed.ncbi.nlm.nih...

On 2023-03-07 16:40:41, user William Martin wrote:

The role of prokaryotic outer membrane vesicles at eukaryote origin is discussed, in depth, in

Gould SB, Garg S G, Martin W F. 2016. Bacterial vesicle secretion and the evolutionary origin of the eukaryotic endomembrane system. Trends Microbiol, 24: 525-534.

The bacterial vesicles are made of bacterial lipids (the right kind for eukaryotes) and produced in the cytosol by mitochondria (mitochondrial derived vesicles).

On 2023-03-07 12:51:34, user LS wrote:

Super interesting- the authors could do a cool extension if the authors examine the possibility that Spike is activating transcytosis through caveolae or a transcellular pathway across endothelium allowing the infection to establish within stromal cells in lung and other tissues. Of interest is the capture and potential efflux dependent on region within the microvasculature.

On 2023-03-06 19:55:02, user Elena Cruz wrote:

• Dosage of MAO-inhibitors not defined in the patient selection, and no discussion of how the genetic MAO model mimics the reduced MAO activity achieved with MAO inhibitors.<br /> • Were the statistics performed on the single cardiomyocyte number or the number of animals. The P values of some comparisons are very close to being non-significant, therefore this should be clarified. If single cardiomyocytes were used, this should be justified since it can significantly inflate the potential for false-positive results.<br /> • Scale bars were inconsistent within figures, and the format of presentation changed.<br /> • No discussion of the PLB pentamers or monomers  what is the physiological significance?<br /> • Why is there is no western blot confirmation of MAO knockdown? Also, did the expression of the MAO-B isoform stay the same with the MAO-A knockout.<br /> • Why are the results of this study clinically meaningful if MAO-inhibitors are not prescribed much anymore

On 2023-03-04 04:52:18, user UTK - Journal Club 603 wrote:

Summary. <br /> The bacterium Bordetella bronchiseptica causes respiratory infections, atrophic rhinitis, and kennel cough. B. bronchiseptica is widely transmitted via respiratory droplets between animals. This study investigates the dynamics of how B. bronchiseptica induces an immune response and, more specifically, on how eosinophils may provide long-term immunity against Bordetlla spp. after B. bronchiseptica infection. Using RNA sequencing data, animal disease models, microscopy, and cytokine analysis, the report confirms that eosinophils play a larger role than previously thought in generating a more robust adaptive immune response.

Positive feedback. <br /> There are several good things about the paper. Eosinophils are an understudied and potentially misunderstood immune cell type, and the paper sheds light on their role in a bacterial infection (while it is typical to think of eosinophils to be important in worm infections). The use of mutated Bordetella btrs and RB50 strains was a significant strength of the paper. In comparison to the immune response in the murine models, they provided solid evidence collectively. In addition, the repetition of screening methods for the effects of the mutant on immune suppression provides stronger evidence of immunological variation. Using two strains of mice (Balb/c and B6 with two different mutants) is also a strength.

Major Concerns

The authors do not talk about the specificity of the GATA-1-deficient mice for depletion of eosinophils. It has previously shown that GATA-1 regulates basophil development and function of basophils (https://www.pnas.org/doi/10.... Perhaps as a confirmatory experiment, the authors should perform eosinophil depletion with mAbs as was previously published (https://www.jacionline.org/.... This will allow to confirm that eosinophils are directly involved in the process of bacterial control and better establish causality.<br /> When looking at lymphocytes in the lung, authors do not discriminate between cells in the lung vasculature vs. lung parenchyma. This may be important to determine which cell population is actually in the lung and involved in bacterial control. How intravascular staining could be used to detect cells in the blood vs. tissue is described here: https://www.nature.com/arti... <br /> The authors need to add figure/panels on the gating strategies for detecting T cells and B cells, along with histograms for major panels. <br /> When determining if eosinophils are required to promote a TH17 microenvironment, Figure 7D shows a possible false positive - i.e., detection of IL17 in naive lungs. These tissues should not have IL-17.<br /> Measurement of immune responses are not numerically consistent between different panels. For example, Fig 34D states 200 million T cells to be detected which is likely impossible. Please check ALL numbers and make them correct.<br /> In most cases it would be useful to measure Ag-specific immune response. Is IFNg+ T cells detected specific to the bacteria?<br /> Side note: B. bronchiseptica rarely infects humans. It is a clinical concern in animals such as canines, felines, livestock, and mice. iBALT formation may correlate to tissue damage within the lungs of humans. I understand that the infection with B. bronchiseptica may provide resistance to B. pertussis, but vaccinations are already in place to provide resistance. Do these vaccines stimulate a similar initial response as RB50 and RB50∆btrs?

Minor concerns

The paper did not give sufficient context for some of the employed models. Comparing BALB/c and C57BL/6 to the eosinophil-deficient EPX/MBP, for example, two mice models were used: BALB/c and eosinophil-deficient EPX/MBP. Prior to conducting study on eosinophils, I am unsure of the meanings of the acronyms, however I understand why they were used.<br /> Would experiments with infection and then antibiotic treatment be informative?<br /> Eosinophils have been shown to play a role in TB in mice, e.g., PMID: 34347010, 35905725. Perhaps this should be mentioned.<br /> The author should increase the resolution of the figures used in the paper, some axes labels are very tiny and impossible to read. The current state could lead to confusion or misinterpretation of the data provided.<br /> Throughout the paper, somewhat inappropriate language is employed. For instance, the term novel and the opening sentence of the abstract. Check if the author can also adjust the usage of these terms when describing findings. A less biased observation is the result.<br /> With a computer and printout, microscopy images proved tough to observe. For improved processing, the writers should raise the exposure of their photos. While discussing exposure, the writers should modify figures to make them more accessible to colorblind readers.<br /> The authors should quantify the relevance of the graphs' statistical significance. The values would aid the reader in comprehending which facts are significant.<br /> Some figures are difficult to comprehend because they contain too many or too few data points. Figure 5C is a case of insufficient specificity. The reader cannot grasp the data in the CD4+IL17+ graph. <br /> In many places conclusions are reached only by looking at mRNA levels. Can these be confirmed with ELISA?<br /> Statement that pathogens evolve to suppress immunity lacks evidence. Some pathogens may actually want inflammation for transmission, e.g., Mtb.<br /> Selection of differentially expressed genes should be corrected for false positive detection, e.g., using FDR (e.g., https://en.wikipedia.org/wi... <br /> The author should add extra identifiers to Figure 6's figures. There are two D's on display. This makes it difficult to explain and read in the figure explanation. Figure 6's micrograph likewise lacks a visible or understandable scale bar. These photos may be difficult to decipher for the reader.<br /> For all the microscopy, the author should state that the images were taken with the same exposure.<br /> The authors should be more thorough in distinguishing that lungs are complex organs, with many results in different results where the organism tries to colonize.

On 2023-03-02 22:22:51, user Evan Saitta wrote:

Congratulations on the study! It is very interesting and plays an important role in collating this useful data!

I have explored sexual dimorphism, including in body mass, in extinct organisms (https://academic.oup.com/bi.... I am jealous of your extant research subjects!

If you are looking for feedback on your preprint, then I am happy to give my thoughts (for whatever those are worth).

I think your second figure is a more apt portrayal of the data than your first, because it presents the data with a mind towards effect size statistics (i.e., it reports the estimated magnitude of dimorphism and the uncertainty in that estimate without additional interpretation).

Namely, I think that the secondary methodological step of designating each species into a categorization of dimorphic or monomorphic might obscure the excellent data you have amassed.

I certainly understand and appreciate your use of objective criteria to assign a monomorphic label (i.e., when the 95% confidence interval straddles zero in estimated dimorphism magnitude). However, any finite population of males and females is not expected to have an effect size of precisely zero, even if just for stochastic reasons rather than reasons of sexual selection (or lack thereof!).

So, what does the "same size" category actually include then?

Those species that are labelled as "same size" between males and females could be those with relatively modest magnitudes of dimorphism (i.e., near, but not exactly, zero) and/or those with small sample sizes and therefore higher uncertainty (i.e., larger confidence intervals).

For example, if you assume that these 39% of species that fall into the "same size" category are roughly equally likely to sit either just barely above or below zero effect size, then that would mean about 63.5% of species in orders with 10 or more taxa have an estimated effect size that places average male size greater than average female size -- albeit that many of those species have modest dimorphism and/or high uncertainty.

That would seem (to me at least) to differ from the conclusion that males are not larger than females in most mammals, which I assume is derived from the "larger males" category being less than 50%, at 44%.

I applaud your use of effect sizes and confidence intervals! However, I worry that by using these confidence intervals to then make a dichotomous (or trichotomous?) categorization, the method then becomes prone to the same shortcomings as does binary significance testing based on p-values (an approach that is becoming more and more criticized: https://www.nature.com/arti....

Of course... I could be wrong!

Did I understand your work correctly? Do my comments make sense? Am I totally mistaken about something here?

PS. I was Princeton EEB undergraduate class of 2014 (Advisor: Gould) and will be attending reunions this year. Perhaps we can meet up at some point to discuss your fascinating work, and maybe you can give me some advice about how to deal with these pesky fossils!

Go Tigers!<br /> Evan Saitta

On 2023-03-02 02:02:03, user Michael Laub wrote:

Our work shares a similar approach with Andersson and colleagues (BioRxiv, April 2022) to studying questions about de novo gene birth. However, it is clear that the texts, results, and their presentation are very different. Our work was initiated following the publication of a paper from the Tautz group (doi:<br /> 10.1038/s41559-017-0127) suggesting that random peptides could have beneficial functions. As any member of my lab can attest, Idan Frumkin, the first author, pursued his work carefully, rigorously, and *independently* in recent years. Additionally,<br /> I will note that the approach used in our paper was detailed in fellowship applications submitted by Idan in 2017, prior to any papers from Andersson's group on random proteins. Idan has also presented some of the results described in our paper at several prior events (e.g. the Wadsworth Center in February 2021; Lambda Lunch at the National Institutes of Health in March 2022, and the Molecular Genetics of Bacteria and Phages Meeting in August 2022). Moreover, as detailed in our paper, one of the random peptides we identified as inhibiting MazF and that we focused on functions through cellular chaperones to affect MazF proteolysis. There is nothing related to MazF or chaperone/protease-based control in the BioRxiv paper referred to by Andersson. Similarly, I would note that the other paper mentioned (doi:10.1038/srep04807) does not involve the screening of random peptides, nor does it involve MazF, chaperones, or proteases.

We are happy to see that studies of random proteins examined in different contexts and done by different groups are providing exciting results, and that this nascent field is growing. We look forward to learning more about what these proteins can do and to understanding how genes can emerge de novo.

On 2023-02-13 10:31:25, user Dan Andersson wrote:

The text is eerily similar to:<br /> doi: https://doi.org/10.1101/202...

On 2023-03-01 13:52:17, user Richard Edwards wrote:

Hi. Have you tried DepthSizer (https://github.com/slimsuit... in addition to GenomeScope for the sequence-based prediction? For the cane toad, we found that the kmer approach was a massive underestimate, whereas the depth-based approach employed by DepthSizer appears to be much more consistent and accurate. I'd be happy to help you get it working if interested. (It should be OK with Python3 now, though this is not yet in the docs.) Rich

On 2023-03-01 11:05:33, user KS wrote:

Dear author, <br /> I attempted to access PyCalibrate, but received an error message indicating that the processing frame on the web page failed to load. I attempted to resolve the issue by trying multiple browsers and devices, but without success.<br /> If you could kindly check the frame, it may resolve the issue. However, if the issue still persists, please let me know, and I would be happy to help you troubleshoot the issue further.<br /> Best,

On 2023-02-28 18:13:34, user Bruno Ghersi wrote:

can you indicate the primers used for Influenza A and what primers were used for H5 N1 identification? what protocols were used? What CTs were observed?

On 2023-02-28 13:34:03, user Mehdi Ghram wrote:

Hi, <br /> This project research has been published in The EMBO journal:<br /> https://doi.org/10.15252/em...<br /> Thanks

On 2023-02-28 04:25:51, user Jesse wrote:

The sustained engraftment of KO cells here is remarkable. In our published work we also consistently observed >95% KO in CD34+ cells using RNPs with nucleofector program DZ-100, which worked better for us than DS-150. https://ashpublications.org.... Detailed protocols at www.plateletlab.com.

On 2023-02-27 15:17:45, user Ramon Crehuet wrote:

This is a very nice papers studying the physics of protein phase separation. It deals with two questions that are well-known for homo-polymers but not so obvious for proteins 1) to which extent single-molecule properties correlate with phase-separation propensity 2) how does multi-valency lead to phase separation and why is it required? Non-computational scientists should read beyond the comparisons of the HP and HP+ models to understand the connections between strength of interactions and number of them (multivalency). <br /> The only thing I would change from this work is the size of the protein chains used in the simulations. In my opinion, using N=20 is a bit too small, and leads to "discretization" errors for low and high fractions of polar vs hydrophobic residues. I think the behaviour at values above to 0.85 would be better described if they had chosen N=50, which is also more realistic in the case of intrinsically disordered proteins that phase-separate.

On 2023-02-27 00:24:30, user Phil Bird wrote:

This is a comprehensive study. It should be noted that Ebrahimnezhaddarzi et al (PMID: 35471730 cited as Ref 31 in the preprint) also reported a defect in antigen presentation in Mpeg1knockout mice carrying a different mutant allele to the one described here. Similarities and/or differences in findings between the two studies should be acknowledged and discussed.

Details of the Mpeg1 CRISPR mutant strain's genetic background should be provided, and how control animals were generated and used for experiments. Recent studies have shown that so-called C57 mice may have a mixed C57BL/6J and C57BL/6N genetic background, and that care needs to be taken to match mutant and wt control strain backgrounds (PMID: 35119362).

On 2023-02-25 01:22:56, user Kostas Tsirigos wrote:

It's interesting to see that papers publishing topology prediction methods in 2023 only compare themselves to one (1) method (!) and, of all methods, this is TMHMM (published in 2001). You could at least compare to DeepTMHMM, that has replaced it.

Not sure how that signifies any progress in the field of TM topology prediction methods.

Also, where are the training data? Could not see them on Github. These should be made available to the community.

On 2023-02-24 17:20:49, user QuiPrimusAbOris wrote:

Very refreshing to read a paper that has the open mind and courage to publish a counterintuitive finding: that chemo-therapy not only can immunize the tumor (readying it for combo with check-point inhibitors, in the theory of "ICD"=immunogenic cell death) but can also promote immuno-suppression - hence, immune evasion. <br /> In fact, chemo-indcued cell death which overwhelms the tissue with sterile debris may equally trigger the "baked-in" tissue reponse of immunosuppression that naturally accompanies injury and regeneration.<br /> One has to consider both sides of the equation.

Under what conditions chemo-indcued cell death STIMULATS or SUPPRESSES the immune response remains to be worked out.

On 2023-02-24 02:32:18, user markyz wrote:

Hello I like your article anything that makes data analysis more straightforward is going to be useful to many! In line with PMID:36750393 and the literature cited there, the tool should accept a background list, otherwise the enrichment test results could be invalid. Moreover the enrichment p-values should be corrected with FDR or similar, otherwise there could be many false positive results.

On 2023-02-23 17:56:44, user Cadhla Firth wrote:

The Discussion contains the following statement, which I presume is the main evidence used to link HKU4 to WIV:

"We note that Oryza sativa cultivar:japonica (rice) sequencing BioProject PRJNA601977 was registered on NCBI on 2020-01-17 by the WIV, two days before the registration of the Oryza sativa japonica BioProject PRJNA602160 by the HZAU (containing the novel HKU4-related CoV clone), indicating the two projects may be related, however this observation cannot be confirmed as no data has been published for PRJNA601977 by the WIV."

BioProject PRJNA601977 was not registered on NCBI by WIV, but by Wuhan University.

On 2023-02-23 14:01:41, user Andreas Martin Lisewski wrote:

In their preprint (https://doi.org/10.1101/202... version 2), Jones et al conclude:

"As the MERS-CoV RBD binds more efficiently to hDPP4 than known HKU4r-CoVs, and as the MERS-CoV S protein has the demonstrated capability of utilizing human cell proteases for mediating cell entry, the HKU4r-HZAU-2020+S(MERS) chimera appears to constitute enhanced potential pandemic pathogen (gain-of-function) research."

However, as they directly observe, their bioinformatics analyzed sequencing data does not cover a 33 nt stretch between genomic nucleotides 23,908 and 23,940 of MERS HCoV-EMC/2012, which corresponds to amino acid residues 818EQLLREYGQFCS829 in MERS S.

This missing sequence is at the N-terminus of the "upstream helix" (UH, MERS S residues 816-851) - a critical spike ectodomain scaffold structure that is extremely conserved among betacoronaviruses [1-4].

Without this local 818EQLLREYGQFCS829 sequence, the ectodomain would be structurally unstable and the resulting mutant MERS S glycoprotein most likely functionally inactive [3,4].

It is therefore incorrect to conclude that "HKU4r-HZAU-2020+S(MERS)" is a representative of "enhanced potential pandemic pathogen (gain-of-function) research", because the sequencing data presented by Jones et al probably does not constitute a functional MERS S glycoprotein.

In the context of biosafety and biosecurity, extreme care must therefore be taken with specific statements about "enhanced potential pandemic pathogen" and "gain-of-function research". The authors and/or bioRxiv content editors should have moderated the above statements and conclusions before publication.

Also, it is unclear if "HKU4r-HZAU-2020" itself (the proposed backbone) corresponds to an actual virus; and any experimental attempt to bioactively resurrect a putative viral bioinformatics sequence of unknown origin poses considerable biosafety and biosecurity risks.

References

1.Yuan Y, Cao D, Zhang Y, Ma J, Qi J, Wang Q, Lu G, Wu Y, Yan J, Shi Y, Zhang X, Gao GF. Cryo-EM structures of MERS-CoV and SARS-CoV spike glycoproteins reveal the dynamic receptor binding domains. Nat Commun. 2017 Apr 10;8:15092. doi: 10.1038/ncomms15092.

1. Barnes CO, West AP Jr, Huey-Tubman KE, Hoffmann MAG, Sharaf NG, Hoffman PR, Koranda N, Gristick HB, Gaebler C, Muecksch F, Lorenzi JCC, Finkin S, Hägglöf T, Hurley A, Millard KG, Weisblum Y, Schmidt F, Hatziioannou T, Bieniasz PD, Caskey M, Robbiani DF, Nussenzweig MC, Bjorkman PJ. Structures of Human Antibodies Bound to SARS-CoV-2 Spike Reveal Common Epitopes and Recurrent Features of Antibodies. Cell. 2020 Aug 20;182(4):828-842.e16. doi: 10.1016/j.cell.2020.06.025.

2. Sorokina M, Belapure J, Tüting C, Paschke R, Papasotiriou I, Rodrigues JPGLM, Kastritis PL. An Electrostatically-steered Conformational Selection Mechanism Promotes SARS-CoV-2 Spike Protein Variation. J Mol Biol. 2022 Jul 15;434(13):167637. doi: 10.1016/j.jmb.2022.167637.

3. Walls AC, Tortorici MA, Snijder J, Xiong X, Bosch BJ, Rey FA, Veesler D. Tectonic conformational changes of a coronavirus spike glycoprotein promote membrane fusion. Proc Natl Acad Sci U S A. 2017 Oct 17;114(42):11157-11162. doi: 10.1073/pnas.1708727114.

On 2023-02-21 13:00:41, user Flo Débarre wrote:

The authors of this preprint, Jones et al., write that

There are no known publications documenting a reverse genetic system for a HKU4-related CoV strain.

but this is incorrect: a reverse genetics system for a HKU4-related CoV strain was previously identified by Zhang et al. (2021) https://arxiv.org/abs/2104..... Jones et al. cannot ignore that previous publication because three of the co-authors signed the previous work. Unfortunately, Zhang et al. (2021) does not appear in the list of references of Jones et al. (2023). This omission is surprising given that Zhang et al. (2021) was cited in previous publications by the authors (see this Google Scholar link for an exhaustive list).

In addition to the discovery of the novel merbecovirus, Jones et al. (2023) [hereafter J2023] present as new many results which were already published by Zhang et al. (2021) [hereafter Z2021]. <br /> For instance, <br /> - Figure 6 in J2023 is Figure 11 in Z2021, <br /> - Table 3 in J2023 is Table 2 in Z2021, <br /> - Figures 8-9 in J2023 correspond to Figures 12-13 in Z2021.

Some paragraphs were also copied with no or only few modifications, e.g.:

In an attempt to find an obvious signature of genetic engineering in the sequence of the HKU4r-HZAU-2020 genome, we performed a restriction enzyme mapping of the sequence using SnapGene using the set of all type II and type IIS restriction endonucleases [...]. For comparison, we also obtained and performed similar restriction site mapping of two related coronaviruses: BtTp-BetaCoV/GX2012 (KJ473822.1) (Fig. 9) and HKU4 CZ07 (MH002338.1) (Fig. 10).

(J2023), vs.

In an attempt to find an obvious signature of genetic engineering in the sequence of this HKU4-r CoV purported infectious clone, we performed a restriction enzyme mapping of the sequence using SnapGene Viewer (SnapGene® software) using the set of all type II restriction endonucleases [...]. For comparison, we also obtained and performed restriction site mapping of two related coronaviruses: BtTp-BetaCoV/GX2012(accession KJ473822.1) (Fig. 13) and HKU4-1 (accession NC_009019.1) (Fig. 14).

(Z2021)

On 2023-02-23 11:27:49, user Ahmet E. YETİMAN wrote:

The revised version of this preprint has been published at Probiotics and antimicrobial proteins. The link of the published article is as follows: <br /> https://link.springer.com/a...

On 2023-02-22 21:28:14, user Christian Diener wrote:

Great evaluation and overview, just wanted to point out some small things with the MICOM part. First you said you divided by the gDW but I think you meant multiplied, right? Since fluxes are in mmol/(gDW*h) and division would just give you (mmol/(gDW^2*h). Also the growth rates are normalized, so they are in 1/h so they should not be scaled as those are independent of the abundance (like all growth rates are). And finally, we would usually recommend to run cooperative tradeoff with a tradeoff of 0.5, instead of just "optimize" which is more similar tot the original cFBA or what MMT does.

On 2023-02-22 14:29:46, user Marija Bežulj wrote:

In section Spatial domain-specific saliency map, sentence " We denoted the saliency map of the m-th auto-encoder and the corresponding MLP classifier across all spots as S(m) ∈ Rn×p, where the i-th column of is computed by (5).", should it say "..where the i-th row of Sm is computed by (5)"?<br /> Thank you!

On 2023-02-22 11:57:34, user Hanne Poulsen wrote:

Beautiful!! Should the supplementary tables be (somew)here?

On 2023-02-22 11:10:16, user Mohammad Amin Abazari wrote:

The URL to the published version of this paper is: https://aip.scitation.org/doi/abs/10.1063/5.0130259. Please note that this paper has now been published in Physics of Fluids and can be accessed through the link provided. Thank you for your interest!

On 2023-02-21 15:44:18, user Sophie Kellaway wrote:

Your data regarding Nfe2 is very interesting! In our recent paper https://www.life-science-al... we expressed the R201Q missense RUNX1 in multipotent progenitors. One thing this analysis showed that did not make it into the paper as we did not know what it meant was that R201Q led to a huge increase in Gata1 binding, particularly at the Nfe2 promoter and enhancers.

On 2023-02-21 13:25:17, user Giorgio Cattoretti wrote:

We read with much interest your evaluation and comparison of dimensionality reduction (DR) algorithms, and we are intrigued by your finding that CYTOF data are somewhat “continuous”, or at least “have a much larger range than those of scRNA-seq and will be pre-processed in various steps, which loses their discrete count nature.”<br /> Including IMC (in situ multiplex) data in your analysis may not be appropriate because in situ antibody-based data are even more broadly spread, because of imperfect cell segmentation (and bleeding from neighbors), partial cell sectioning, specimen thickness, etc. etc.<br /> Because of the continuous nature of in situ data, we devised a data pre-processing step, Lognormal Shrinkage (see our publication BRAQUE, https://www.mdpi.com/1099-4... ), which dramatically helps the clustering and the cell identification steps.<br /> Bayesian Reduction for Amplified Quantization in UMAP Embedding results in a more granular an accurate cell identification, pointing at data pre-processing as a crucial step for continuous type of data.<br /> It would be interesting to analogously pre-process CYTOF data as we did and then compare DR algorithms. By the same token, we made available in the supplementary BRAQUE materials, in situ multiplex data, obtained with the MILAN technology ( https://www.researchsquare.... ), comprising 80 markers and up to more than half a million cells.

Prof. Giorgio Cattoretti

On 2023-02-20 19:30:25, user Banksinoma spinifera wrote:

This is an interesting study about the mechanisms behind fructose-induced ER stress in the liver that promote NAFLD. This study is heavily based on Western Blots, but there are no molecular weight markers or raw blots. That would help the reproducibility of the results found in this study and help discern between very similar band profiles, such as PERK, Vinculin, and mTOR, in Figure 4, before peer-reviewed publication.

On 2023-02-20 18:47:32, user Donald R. Forsdyke wrote:

Despite this feedback, the paper was published in Physics Reports. The authors have continued to disregard the feedback. A new paper in eLife has passed peer-review and is now formally accepted for publication. I have added a brief comment to the corresponding bioRxiv preprint.

On 2023-02-20 18:30:00, user Donald R. Forsdyke wrote:

While there are some interesting observations, the confusion of the authors, which has been remarked upon previously, is maintained here. Potential T cells auditioning in the thymus either die from "neglect" (zero or very weakly self-reactive) or are selected either for survival (positive selection - weakly self-reactive) or inhibition or destruction (negative selection - strongly self-reactive). The summary perpetuates this misinformation. There is no indication in the text that they have discovered some flaw in the now generally agreed roles of the two types of selection. The paper has been formally accepted for publication in eLife (20 Jan 2023).

On 2023-02-20 14:12:38, user Thorsten Pfirrmann wrote:

To me it is unclear how one can write a publication about the Gid-complex without citing any of the various publications of Prof. Dieter Wolf, who discovered and named the Gid-complex in S. cerevisiae. He also was first to recognize the importance of Gid4 in substrate binding and degradation and was first to describe the importance of N-terminal proline residues in yeast substrates. These important pieces of work should be at least honoured by citing them!

On 2023-02-20 09:49:57, user Jheronimus wrote:

1st paragraph: the possessive form of [it] doesn’t have an apostrophe.

On 2023-02-19 07:00:01, user Dario Strbenac wrote:

Interesting reanalysis but all of it is based on Kraken data processing. Didn't the senior author previously claim that Kraken is a substandard method which is polluting the microbiome research literature?

... serious issues in microbiome data analysis based on sequence abundances, which are typically produced by DNA-to-DNA methods and have been applied in thousands of published microbiome studies (for example, Kraken: 1,438 citations; Kraken2: 204 citations; Bracken: 202 citations, by March 2021, according to their official websites).

Also,, another benchmarking study found that Kraken had high sensitivity but low specificity (Critical Assessment of Metagenome Interpretation: The Second Round of Challenges, Nature Methods, Figure 4, labelled as Bracken). How would the results change if the top-scoring methods (i.e. high sensitivity and high specificity) such as mOTUs 2 or MetaPhlAn 3 were used instead which seem to have good control of false discoveries?

On 2023-02-18 16:18:05, user Guest wrote:

Review of the paper by Ilanges et al. “Microbiota-stimulated Interleukin-22 regulates brain neurons and protects against stress-induced anxiety” [part of the MICR603 “Journal club in Immunology”]

Summary. Stress is a very common public health problem, leading to numerous health concerns and reduced societal impact. For a while, it has been thought that the host immune system plays an important role in the development and progression of anxiety disorders, however, there are few studies which address this. In the same note, it is now being appreciated the link between gut and brain, through the gut-brain axis. Yet, there are limited studies which link physiological stress to gut permeability. This study aims to link the host immune system to anxious disorders, primarily looking at response to stress of mice. The authors utilized mice deficient in ɑβ T cells (TCRb-/-) and observed lowered anxiety behavior following exposure to stress. Additionally, the authors found that there is an inverse correlation between IL-22 and anxiety behavior. By injecting IL-22 into mice undergoing stress, the authors found that these IL22-injected mice exhibited lower neuronal activation in the septal area, that resulted in less stressed behavior. As the gut is a major source of IL-22, they additionally demonstrate that stressed mice have increased gut permeability, resulting in TH17 differentiation through microbiota-stimulated IL-1β, and resultant IL-22 production. As such, IL-22 is an attractive therapeutic target for addressing anxious disorders.

Positive feedback. This study addresses a much needed topic of research, understanding how the host immune system modulates psychological anxious disorders. Through cutting edge immunostaining and sequencing techniques, the study identified specific immune molecules and how the host immune system regulates anxious behavior in specific areas of the brain. Furthermore, with the addition of experiments demonstrating the therapeutic capabilities of their findings were impressive and were much needed experiments to validate their findings. Another important element of the paper is the sheer amount of data that includes behavioral experiments, brain imaging, immunity-related parameters, and microbiome manipulations.

Major Concerns

It is not clear from the materials and methods whether measurements of mouse behavior have been randomized. Because handling the animals can introduce stress too, operators performing cage experiments must not know the mouse group. Additionally, more information should be provided on conditions under which behavioral tests have been performed - e.g., at night (typical time for activity of mice)? If not, perhaps experiments during night time must be repeated.

There is a need to have other, more objective ways to measure stress level in animals including those treated with IL-22. For example, measuring levels of stress hormones could strengthen the argument. <br /> Some experiments need controls. For example, having some positive controls could be useful - these could include different doses of IL22 injected to see proportional changes in behavior, using known behavior-influencing (stress-reducing) drugs could help to better interpret the data. Also, the experiments with T cell transfer into T cell-deficient mice, having sham-operated animals (transfer PBS or B cells) could be a useful negative control. <br /> There does not seem to be much discussion about figure 1B. This is a major dataset, so the authors should spend more time going over what the figure is showing and how to interpret the dataset. <br /> Figure 2F - the authors should split up the microscopy into individual channels and then do a merged image. It was difficult to view specifically where Fos was located. The authors should additionally show an unstressed mouse as a negative control to show the difference between staining. <br /> Figure 2H - Throughout the manuscript, the number of mice per treatment was 5, however, for this experiment, it was only done in duplicate. The authors do not mention this, so justification for this low of a number must be done.<br /> When the authors are talking about IL-22 suppressing neuron activation, they do not seem to mention microglia activation in the brain. León-Rodríguez et al., Nature Communications, 2022 look at microglia activation in anxiety-like behavior, so the authors should consider experiments investigating microglia activation or discuss these findings. <br /> Figure 4A - When mentioning the TLR4 reporter, in the results, it is mentioned as “microbial exposure in mice treated with our repeated mild stress paradigm”. This is not inherently true. It is simply how many TLR4 ligands (lipopolysaccharides), which have not entered circulation due to a leaky gut. The wording for this needs to be changed.<br /> The authors mention IL-1β playing a key role in this model system, however, they do not mention inflammasome activation for how IL-1β is produced. Which cell types in the gut are hypothesized to be the source of IL-1β? Could these experiments be repeated in inflammasome deficient mice (NAIP, NLRC4, or NLRP3 perhaps)?<br /> Figure 5H - The hypothesized model for the system is much appreciated, as there are many players at play. However, with the current state of the model, it is overtly simple. If the authors could have this more broad model, but have separate panels for what they believe to be occurring within the gut and brain, respectively, it would be more clear to the reader.

Minor concerns

Continuous line numbers would assist with the review process. It appears the line numbers start over each new page, so having the line numbers continuous could assist speed up the review process.

If the authors could talk about why they chose to perform single-nuclei RNA-sequencing over bulk single-cell RNA sequencing, that would make the data/story more clear.

Figure 3A - The microscopy cannot be seen on printed paper. If the authors increase the exposure, it would help with the resolution of the microscopy.

Figure 4A - The TLR4 reporter needs to be discussed further. The authors do not discuss it enough for a non-expert to understand the meaning of these findings.

Figure 5A - The microscopy cannot be seen on printed paper. If the authors increase the exposure, it would help with the resolution of the microscopy.

On 2023-02-15 19:03:12, user Elisabeth Bik wrote:

Several panels in Figure 3 appear to contain small repetitive areas. I have shared my concerns here on PubPeer: https://pubpeer.com/publica.... These repeats are also visible in the published version of this paper, DOI: 10.1016/j.scitotenv.2021.152345, where this figure is Figure 4.

On 2023-02-15 12:03:16, user Faraz wrote:

Unfortunately, the provided link of the model (https://mlaep.cbrc.kaust.ed... is not working. Not able to use. Even following the provided documentation did not help (https://mlaep.cbrc.kaust.ed....

On 2023-02-13 13:48:56, user Hrishikesh Kulkarni wrote:

The manuscript has been published: https://pubmed.ncbi.nlm.nih.... A direct link between the preprint to the journal site is forthcoming.

On 2023-02-13 00:52:12, user John Barry Gallagher wrote:

The article as it stands makes it not possible to verify their results or conclusions: 1) there is no data or presentation of the 210Pb geochronology or independent validation, especially important in these not ideal dynamic depositional environments; 2) no disentanglement between seaweed and their epiphyte remaining deposits and allochthonous deposits, importantly that have been consumed before deposition; 3) While the title focusses on deposits under the farm, there should thus be a discussion or quasi estimation of the amount of export that survives consumption and the role of calcareous epibionts and benthic fauna on the sequestration rate the article implies from organic carbon soil accumulation; 4) how does sequestrtaion of biomass related to atmospheric flux, driven but not equivalent.

On 2023-02-12 18:12:26, user Stefan Kirov wrote:

Excellent and very necessary work! Probably you have thought about it, but have you also checked if in some cases the target was an alternative sequence arising from a frameshift rather than SNPs which you have checked (as specified in the methods section).

On 2023-02-11 22:49:23, user Vitaly V. Ganusov wrote:

Review of the paper by Rahmberg et al. “Ongoing Production of Tissue-Resident Macrophages from Hematopoietic Stem Cells in Healthy Adult Macaques” [part of the MICR603 “Journal club in Immunology”]

Summary. Tissue-resident macrophage are cells playing an important role in homeostasis of tissues by removing dying cells as well as responding to local exposures to microbes. The origin of tissue-resident macrophages has been under intense investigation; studies primarily done in laboratory mice suggest that some macrophages (e.g., microglia) are derived from yolk sac during embryonic development while other macrophages (e.g., in the gut) are primarily derived from circulating in the blood monocytes. Laboratory mice are typically kept in clean conditions with little to no exposure to many pathogens, and whether the conclusions found studies with mice extend to other animals including humans remains unclear. This study takes an advantage of using GFP-expressing hematopoietic stem cell (HPSC) transplantation of monkeys with barcoded stem cells to track the dynamics of barcoded, bone marrow-derived cells in the tissues. Interestingly, the authors found that macrophages in liver, spleen/LN, and GI tract have high frequency of the GFP expressing cells and suggesting high turnover of these tissue-resident macrophages from bone marrow-derived precursors. Authors performed several additional analyses to ensure robustness of their findings including imaging of GFP-expressing macrophages, analysis of barcode sequences between different bone marrow-derived T cells, and using classical pulse-chase experiments to detect division of macrophages in the tissues.

Positive feedback. The study is a timely addition to the debate on the origins of tissue-resident macrophages in animals that live in environments with larger exposure to environmental antigens. The use of barcoded bone marrow-derived cells to track origin of cells in the peripheral tissues is innovative and interesting. The analysis of imaging data to ensure that GFP-expressing macrophages are not phagocytosing other cells is important, and PCA analysis of the barcodes is important to establish relatedness between different cell types. Labeling with BrdU is also a useful way to look at cell turnover that had been forgotten and may be revived by this study. Yet, because of some missing controls and not fully rigorous analysis of the data the conclusion that tissue-resident macrophages in monkeys undergo relatively rapid replacement from bone marrow-derived cells remains to be determined.

Major Concerns

It seems that several controls are missing in the paper. In particular, having the data in which samples are done very early after HPSC transplantation would be very important - it is expected that no GFP+ cells would be then found in tissues - so, presenting data for GFP+ cells (e.g., for key populations) as these change over time would be most useful for interpretation (some of that is present with barcodes but comes too late in the paper). This is a major limitation of the study that does not allow to fully interpret the data.

Most tissues are filled with blood. How do you know that you did not sample cells in the blood that have features of macrophages (e.g., MHCII+)? Work with serial intravascular staining by Mario Roederer’s group clearly showed contamination of tissue samples (e.g., LNs) by blood-derived cells (PMID: 33441422). Some type of control is needed (e.g., intravascular staining).

Are these “myeloid cells” macrophages? Can the authors show some other types of data, e.g., imaging that clearly identifies those isolated cells as macrophages?

Gut-resident macrophages are known to be primarily monocyte-derived, so for these cells, finding high frequency of GFP+ cells is not surprising. What about brain-associated macrophages in monkeys? Are these HPSC-derived? (In mice, they seem to be yolk sac-derived). Another site to look at may be skin and other skin cells (e.g., Langerhans cells?)

It is hard to interpret what the data on percent of GFP+ cells mean. For example, if one finds 20% of cells with irradiation of monkeys in 2-3 months after transplantation, what does this suggest in terms of kinetics of macrophage turnover? It seems that the authors would benefit from some type of mathematical modeling-based analysis to generate a baseline prediction on expectations.

PCA analysis lacks rigor. Finding that some points are “clustered” and some are not must be done with some statistical tests. For example, resampling the data and reclustering may provide some evidence of robustness of clusters.

Interpretation of BrdU data can be made better. There have been a wealth of mathematical models aim at inferring cell division and death rates from pulse chase experiments (e.g., 9469816, 10799860, 12737664, 23034350), perhaps authors could use those methods to provide some boundaries of the macrophage division rate and/or differentiation-from-monocyte rates. Also, including some data - if available - on labeling during pulse dynamics - could be very informative.

A comparison to some other cells that we know do not have long residency time in tissues may be useful - e.g., neutrophils. THese are thought to be relatively short-lived cells in the blood (but this is again debated) and in the tissues. What is the GFP/barcode kinetics in neutrophils?

Minor concerns

Authors should better describe experimental design and specifically sampling details. For example, Fig 1A does not fully explain when the animals were sampled. Is 46m is the time of 1st sample for JM82? Is 49m when the animal was sacrificed? Only looking at Fig 3 it is possible to see sampling times but this is too late in the paper. Captions must be improved to explain that all and be more detailed.

There are sometimes too generic statements that may not be fully supported by the presented data, e.g., turnover of ALL macrophages being rapid (e.g., last paragraph in Discussion). Yet, authors only sampled some tissues.

Paper organization could be improved. For example, it is easier to review the paper when figures and figure captions are located on the same page. Numbering the lines in the paper can help to provide comments to specific parts of the paper.

Better formatting of the references would be helpful - .e.g, more space between individual entries or numbering the entries.

Figure 2D could include control with macrophages that may carry high levels of of TCRg, e.g., from the thymus.

Figure 2 figure legend could be more descriptive about which panels are representative of which sample

Figure 2 could have info on the tissues where samples were coming from (e.g., Macrophage/liver), etc. GFP staining in these images is hard to interpret - perhaps adding a membrane stain and DAPI (nuclear stain) could help to tell that GFP is in the cytoplasm.

Table 1 could also include symbols/shapes used in graphs to identify animals.

Would be very interesting to see spleen confocal to compare macrophages and their GFP staining - will them have the scattered signal due to taking up dying cells?

Adding shapes used in Figure 1A &D to differentiate each group to Table 1 would be helpful

It may be useful to discuss potential reasons for macrophage dynamics differences between mice and monkeys - is that because species are different or because they live in different environments. Would experiments in dirty mice be useful to tell if the environment is what is the main driver of (perhaps) different macrophage dynamics in some tissues?

For necropsied animals, is it possible to show data for macrophages in tissues that show little evidence of link to hematopoietic system (e.g., microglia)?

It may be useful to more thoroughly discuss the efficiency of lentivirus infection. Does this efficiency impact interpretation of GFP/barcode dynamics, e.g., does one need to normalize the data in some way?

The number of samples per time point (e.g., Fig 5) is varied per animal and tissue. Perhaps some justification of this could be useful.

In Figure 3, would it be possible to better describe how to interpret the PCA plots? Also, it would be good to use other colors besides green and red as green-red color blindness is the most common form.

On 2023-02-11 20:25:43, user Patrick Deelen wrote:

When we used PGS to predicted the phenotype in order to identify sample mixups we mentioned in the discussion that in some case misalignment might be the result of rare variants [https://academic.oup.com/bioinformatics/article/38/4/1059/6430970]. Great to see evidence for this now!

On 2023-02-11 18:38:25, user Zach Hensel wrote:

This method to more rapidly characterizing SARS-CoV-2 variants across the genome is especially valuable as recombination starts to play a more obvious role in on-going SARS-CoV-2 evolution with the fraction of sequences globally being from designated recombinant lineages rising from approximately 10 to 40% in past two months. It is particularly interesting to see a low-risk experimental framework to quickly evaluate some of the risks posed by possible future recombination when diverse variants are co-circulating.

I am curious about the rationale to define "Delta" as 90% GISAID consensus for B.1.617.2 and define "Omicron" as 90% consensus for BA.1. Omicron by this definition is a genome that has been observed, but it is unclear that Delta as defined in Fig 3A has been observed, with a previous report defining 5 Delta clades [1] that each include multiple non-synonymous ORF1a mutations. Are we sure that Delta as defined here was "naturally occuring" and that, if it was, how relevant is this genome to those previously compared to Omicron?

Specifically with respect to nsp6, Delta sequences include one of H11Q, T77A, or V149A (the latter with or without T181I). Notably, nsp6 T77A is defining for B.1.617.1. Searching COV-SPECTRUM for March-May 2021, 39,726 sequences include Delta consensus mutations in Fig 3a. There are 21 sequences within that set with nsp6 lacking any of these mutations and brief examination of placements on the UShER tree indicate imperfect sequences (reversions, etc).

Additionally, Omicron BA.2/3/4/5 have nsp6 differing from that in BA.1, but the same as that observed in previous variants (e.g. alpha, beta, and gamma). It would be very interesting to see apples-to-apples comparisons of these nsp6 and hopefully this methodology can help unravel the significance of recurrent nsp6 deletions and subsequent nsp6 adaptation in future work.

While Delta lineages were eventually dominated by those containing nsp6 T77A (~97% by the end of 2021), a Delta lineage with nsp6 V149A/T181I contributed its nsp6 to the XBC lineage, with derivative lineages adding nsp6 I50T. This is one of the few "Deltacron" lineages with a Delta nsp6 and may be of special interest.

Lastly, Fig 3A does not include S:G142D in its definition of Delta; absence in over 10% of sequences is a dropout artifact [2].

[1] Stern et al. medRxiv 2021. https://www.medrxiv.org/con...<br /> [2] Sanderson & Barrett. Wellcome Open Res 2021. https://doi.org/10.12688/we...

On 2023-02-10 18:09:13, user Robert Laroche wrote:

The final, much revised version of this initial preprint "Size-associated energetic constraints on the seasonal onset of reproduction in a species with indeterminate growth" is published in Oikos and available now! doi: 10.1111/oik.09739

On 2023-02-10 18:05:05, user Karl Kaiyala wrote:

Energy expenditure is reported using the traditional ratio method whereby whole animal EE is divided by body mass, i.e., ml/kg/min. If the goal is to adjust EE for the confounding effect of differing body mass, this method is inappropriate; it essentially mathematically forces lighter animals to have higher 'adjusted' EE and vice versa.

There are many many papers on this, dating back to 1949 and more recently emphazed in the NIH-supported biomedical realm; see, e.g., Kaiyala and Schwartz's Toward a More Complete (and Less Controversial) Understanding of Energy Expenditure and Its Role in Obesity Pathogenesis published in Diabetes or papers by John Speakman, Mattias Tshop, or a host of others.

On 2023-02-09 17:54:11, user Sam Lord wrote:

Beautiful work!

For your plots, you say n_exp = 4 (for example) but show many more dots on the plot. I presume those are repeated measurements within each experiment? The n for the statistical tests should be the n_exp not the total number of repeated measurements.

https://doi.org/10.1085/jgp...<br /> https://doi.org/10.1083/jcb...

On 2023-02-09 15:07:21, user Leyla Slamti wrote:

The topic of this manuscript is of<br /> great interest for the Bacillus cereus community. The authors present a lot of<br /> data and the results about temperature-dependent PapR maturation reveal new information<br /> about the mechanisms underlying quorum-sensing in these bacteria. However, in my<br /> opinion, the conclusions regarding expression heterogeneity are based on data<br /> that present a major flaw. Plasmid pHT315 is not appropriate for the measurement<br /> of fluorescence intensity because it induces heterogeneity in itself, probably<br /> because of its copy number. The authors should have verified this by using a<br /> control such as the promoter of a constitutively-expressed gene. They would<br /> have seen the same kind of result as they see with PlcR-regulated genes. Plasmid<br /> pHT304 should have been used instead. The reference for plasmid pHT315 is wrong.<br /> It should be Arantes and Lereclus Gene. 108 (1991)115-l 19. The difference in<br /> cell morphology between strains that only differed by the reporter fusion they<br /> carry is also puzzling (chains or filaments, it's difficult to say, versus individualized<br /> cells ; Figure 2). Are the bacteria sick? Could this influence the expression<br /> results? It would have been helpful to show the growth curves corresponding to<br /> each strain at each temperature to determine if bacterial growth was affected<br /> by the reporters used.

On 2023-02-09 14:31:48, user Dan Pack wrote:

below eq (2): <br /> "where μ, kbind0, and kdeath,T are, respectively, the growth, binding, and death kinetic constants of infected cells."<br /> Should this say UNinfected cells?

On 2023-02-08 14:37:20, user Mégane wrote:

Very interesting and exciting results !! You might be interested in our recent work about the formation of invadosomes by IPF fibroblasts: https://www.mdpi.com/1422-0...

Congratulations!!

On 2023-02-07 16:49:51, user Tanai Cardona Londoño wrote:

Really interesting! Thank you!

It turns out that Gloeobacterales strain do contain PsaJ and PsaI. PsaJ is just downstram from the PsaF gene, just like in most other cyanoabcteria, but for some reason the gene is not discovered/detected by annotation methods. I found it in G. violaceus and Anthocerotibacter manually, and it is indeed annotated in G. kilaueensis, I believe... it was also seen in the G. violaceus PSI structure published recently, but not annotated properly. The PsaI subunit is also found, at least in some Gloeobacter strains, and it was originally annotated as PsaZ. The new structure of the PSI from G. violaceus show that PsaZ is PsaI, it's just that they are such small genes that sequece identity become unreliable for orthology searches...

It's possible that some of the other "missing" genes of the photosystems and cyt b6f, which are mostly very small genes, encoding a ~40 amino acid proteins, suffer of the same detectability issues.

On 2023-02-06 22:17:47, user Fraser Lab wrote:

The following is an anonymous review, edited by James Fraser, that is largely concordant with the reviews of this manuscript posted by eLife. James is posting it on behalf of the author (who wishes to remain anonymous) to provide additional background context on the issue of how Orf3a could be misidentified as an ion channel.

This paper is welcome because the biophysical aspects of previous viroporin work are problematic. The virology and other aspects of those works are presumably technically correct. But this speaks to the problems of silos, and that at some point virology and biophysics really need to sit down and have a talk.

Below, I highlight how the papers from the virology labs are weak in the biophysical aspects. Consequently, the field is replete with exceedingly poorly executed and reviewed biophysics work that has proven to be irreproducible. A nice review (https://pubmed.ncbi.nlm.nih... by Colin Nichols and Conor McClenaghan outlines the problems with the interpretations in previous work using bilayers, oocytes or mammalian cells.

This paper by Miller/Clapham sets the record straight with regard to SARS CoV 2 protein 3a. It builds on Steve Grant in Henry Lester's lab at Caltech work (https://pubmed.ncbi.nlm.nih..., which has shown that there is no evidence for 3a forming functional channels in oocyte plasma membranes. The Grant/Lester paper did a nice control using Spike and nsp2, which have never been claimed to have ion channel activity.

These papers are necessary because of previous, poor-quality papers, which claimed channel activity. For example, Toft-Bertelsen et al. (https://www.ncbi.nlm.nih.go... suggested that 3a was an ion channel. This paper claimed that numerous other viral accessory proteins - 7 or 8 of them also acted as ion channels, which prompted some skepticism in the field. That paper lacked important controls (e.g. no controls for proteins known not to be an ion channel, like Spike as done in the Grant/Lester paper). The major issue is that it was not properly controlled for the issue of endogenous background channels, sadly ( which is should be pointed out by reviewers as electrophysiology 101).

Importantly, the Toft-Bertelsen work didn't even demonstrate that the proteins were in the plasma membrane. Without engineering the expression construct, it is almost certainly all in the ER-Golgi intermediate compartment, ERGIC). Sixteen biophysicists recently commented in Communications Biology (https://www.nature.com/arti... on the shortcomings of the Todt-Bertelsen paper, explained several possible sources of artifact, and outlined experimental steps that could be followed to claim ion channel function.

There are also papers on the E and 3a proteins that use only bilayer recordings. Two papers on SARS-CoV-1 from the Enjaunes lab show channel records that are scaled multiples of one another. In other words, the E channel (Figure 3a of Verdia-Baguena https://www.ncbi.nlm.nih.go... ) and the 3a channels (Figure 3 of Castaño-Rodriguez https://www.ncbi.nlm.nih.go... ) appear to be exact scaled copies of the same trace. This example has no rational explanation, but it is illustrative of the problems in the field. It doesn't reflect well on the reviewers and editors of those papers.

An example of the steps for Ion channel function being met is observed for the E protein, which is likely to be a channel similar to the M2 of influenza (https://www.ncbi.nlm.nih.go.... Problematic aspects of other previous studies contrasting E and 3a are also reviewed here: https://www.ncbi.nlm.nih.go... .

The structure of Orf3a is described by Kern et al (https://www.nature.com/arti.... Key evidence for the channel function is provided by its sensitivity to ruthenium red, which blocks quite a few ubiquitous channels including those found in intracellular organelles. This results provide the main reason for skepticism of an Orf3a-specific result for this due to the high protein ratios. It is possible this is real, but it is unproven. One should keep in mind that ions can get across membranes in multiple ways, so this doesn't have to be by conduction?

The current Miller et al manuscript (https://www.biorxiv.org/con...<br /> ) argues strongly against Orf3a channel function and provides an explanation for the results in Kern et al. Miller et al, only saw channels in proteoliposome patch-clamp recordings under the highest protein conditions used. These channel recordings were sensitive to DIDS and therefore likely to be due to contamination by VDAC or something similar.

On 2023-02-06 21:36:18, user CJ San Felipe wrote:

Cryo-crystallography is one of the most common techniques used in x-ray data collection of macromolecules. This is because cryogenically freezing crystals helps minimize radiation damage and extend crystal lifetime which allows for complete data sets collected from single crystals. However, proteins are not rigid and exist as a conformational ensemble. This is relevant for studying protein-protein and protein-ligand interactions because some binding/interaction sites may require alternative side-chain conformations or subtle backbone changes that may be inaccessible under cryogenic conditions due to shifting the energy landscape. In this paper, the authors sought to address the protein-ligand interactions at room temperature (RT) by performing a large crystallographic fragment screen. They utilized the therapeutic target PTP1B using 2 RT screens which consist primarily of fragments that have been tested previously at cryogenic temperatures. The main outcome of this paper reveals novel ligand binding poses in allosteric sites which is relevant for understanding allosteric mechanisms, however, at RT relatively fewer weakly-binding fragments are found. Along with this, a new fragment-binding site is also reported in the paper - importantly this site was identified at RT, but not by any fragments at cryo temperature. The last section of the paper also describes the identification of a fragment bound to the Lys197 of the allosteric site. The binding pose of the fragment at this position is similar to a covalent inhibitor to the mutant protein. The major strength of this paper is demonstrating that RT screening has the potential to reveal additional allosteric binding sites that may otherwise be inaccessible at cryogenic temperatures. The major limitation of this paper is the low coverage of chemical space due to focusing on previous fragments that were detected under cryo conditions and could be strengthened with a broader screen of chemical space for fragments that were not previously tested. Further, the lower hit rate compared to cryo screening may limit the utility of RT screening in a structure based drug-design approach. The paper is written concisely with a clear focus on the comparison of fragment binding at RT and cryo temperatures, however lacking few things which we would like to highlight below:

Major points:<br /> In Figure 5, the authors point out that cryo and RT fragments can bind at the same site but in different poses to suggest relevance in allosteric inhibitor design but don’t fully explore the chemical rationale for alternate binding poses. It would be beneficial if the authors could describe the environment around the alternate binding poses to potentially explain if both conformations are relevant or if one is more likely than the other.<br /> In Figure 6A, the authors claim that a previously modeled cryo pose does not correspond well with the RT event map, however we feel that the event map appears to plausibly support both conformations. The only conclusion we can draw from here is the presence of water molecule in cryo and not in RT density. It may be worthwhile to try paired refinement to determine if the absence of the water at RT is due to resolution differences between the two data sets. <br /> In Figure 7A, the authors claim that the RT event map supports a flipped pose for fragments compared to cryo, however, the cryo event map is ambiguous and the absence of density (owing to lower resolution for this dataset at cryo?) suggest that the cryo ligand may be modeled incorrectly.

Minor:<br /> Fig S4-H: Does the RT event map support alternate conformation for the fragment/ too ambiguous? Or is it a case of a new binding pose at the same site?<br /> In Figure 8A: 38A in legend or 40A in text?<br /> In Figure 9, the authors explain that the isatin-based fragment is covalently linked to Lys197 at RT but not cryo and suggest that the absence of this fragment from cryo may be due to insufficient soaking. It seems reasonable to follow up this “cryo-non-hit” by examining the effect soaking duration has on ligand binding for cryo. The manuscript would also benefit from a chemical schematic showing the covalent mechanism.

NOTE - This paper was selected by the lab members for a group discussion. JSF was the postdoctoral advisor of Keedy and graduate advisor for Biel, who are authors on this paper - and is acknowledged in the manuscript for helpful discussions prior to posting the preprint. Thus, there is a conflict of interest that may color others' assessment of the review, which was driven by group members. Noting that, we have still attempted to provide helpful commentary and suggestions on the paper.

-CJ San Felipe, Pooja Asthana, James Fraser - UCSF

On 2023-02-06 18:23:45, user Ralf Koebnik wrote:

We have discovered that an error has crept into our bioRxiv manuscript 456806v1 regarding an experimental detail that may cause unintended confusion. The problem is in Table 2, where we inadvertently duplicated the sequence of primer PANAN_gyrB_fwd for primer PANAG_infB_fwd. The correct primers are given in our Plant Disease paper, Table 2 (doi: 10.1094/PDIS-07-20-1474-RE), and have the following sequences: 5’-TGTCCGGCGTGCCGGCTG (PANAG_infB_fwd) and 5’-CCAACGCGAACGTCGTTGT (PANAG_infB_rev). We also recommend using a slightly longer sequence for primer 16S_907R: CCCCGTCAATTCMTTTRAGTTT.

We apologize for any inconvenience this error may have caused.

On 2023-02-06 17:39:11, user Gael Cristofari wrote:

The published book chapter can be found here:<br /> https://doi.org/10.1007/978-1-0716-2883-6_10

On 2023-02-06 12:35:58, user Pablo MR wrote:

This paper was published on 31st December in Harvard Papers in Botany and is freely available here: https://huh.harvard.edu/sit.... The DOI has not been activated yet.

On 2023-02-06 12:17:52, user Iain Wilson wrote:

Interesting paper. One comment on the abstract: the sentence "B3GALT6 covalently attaches glycosaminoglycans (GAGs) to proteins to generate proteoglycans and its germline loss-of-function causes skeletal dysplasias." is not quite correct. B3GALT6 is involved in the biosynthesis of the tetrasaccharide linker carrying glycosaminoglycans (GAGs) on proteoglycan core proteins; the sentence sounds as if B3GALT6 enzymatically transfers the whole GAG chain to the core protein.

On 2023-02-06 02:08:53, user Susan C Kandarian wrote:

1. page 13, line 6, "(as in Figure 3C)" please correct to "(shown in Figure 4J)"
2. please discuss implications of increased TLR4 and TLR5 in monocytes from patients. Are these gene expression changes responsible for the difference in the "DNA sensing pathway" indicated in the figures?
3. The "TNFalpha signaling via NF-kappaB" is plotted in 3 figures but it is not referred to anywhere in the text. Is this what is meant by the few text references to inflammatory signals? Please expand on which gene expression changes were strongest in calling out this pathway by the gene cluster analysis program.
4. Related to the above comments, it would be helpful (where possible) to state some major gene expression changes that likely caused the bioinformatics programs (KEGG or GSEA) to call out pathways such as TNFalpha signaling, IFNalpha response, etc)
5. If you search the dataset for individual genes, like other PRRs (besides the 2 TLRs found), are there others whose expression was changed in patients such as the RIG-I like receptors that can be found on monocytes?

I think this work on scRNAseq in different immune cell types is an important advance, but realize that scRNA data alone are difficult for making conclusions about biological processes. The monocyte data (normal vs diseased) are compelling, and they are consistent with other data indicating a problem in innate immune cell function (detection of PAMPs or DAMPS and/or an innate immune cell signaling loop error?). Perhaps it is triggered by exertion products since even ATP or ischemia-reperfusion are known activators of the PRR called NLRP3.

I have no ideas about how to understand the messaging to macrophages since they are mostly in tissues. Lymph node biopsies?

On 2023-02-04 09:14:39, user Karen Lange wrote:

This preprint was a pleasure to read; very well written.

I have a suggestion if you are going to be modelling more variants in the future. I have developed a PCR based method to identify engineered missense variants and this is faster/cheaper/easier than using restriction digest. Essentially you design a PCR primer that is complementary to the engineered mutations and this will specifically amplify in the mutant. Typically there are 3-6 mutations (missense + silent) in this region so it is specific enough to not amplify wild-type.

I detail this approach in Supplemental Figure 1 of this paper https://doi.org/10.1242/dmm.046631

On 2023-02-03 22:42:59, user Miles Markus wrote:

The authors of this interesting article correctly refer to the malariological dogma that frequently, the majority of Plasmodium vivax infections in human populations are the result of activation of dormant liver stages. Some figures exceeding 80% appear in the literature. As is mentioned in the article, these parasite forms are known as "hypnozoites", a term I coined 45 years ago [1].

This hypnozoitophilic dogma is, entirely logically, based on extrapolation from the results of drug treatment of patients with P. vivax malaria. However, the idea does not make sense to me parasitologically. It is not necessarily correct, for reasons explained elsewhere [2,3].

The point is that a proportion of relapse-like P. vivax malarial recurrences might originate from concealed merozoites (a recently confirmed parasite reservoir), as opposed to hypnozoites [3]. The suggestion was originally made 12 years ago [4]. It is not so much a question of why would these merozoites be a source of recrudescent P. vivax malaria but, rather, why would they not be? Luckily, evidence one way or the other should soon be forthcoming from drug-related experimentation using humanized mice [2].

Following on from the novel research concerning inhibition of hypnozoite and hepatic schizont activity that is reported in this bioRxiv paper, perhaps the authors could consider investigating, in addition, what might inhibit non-circulating P. vivax asexual stages (they did include P. falciparum blood stages in their study), such as occur in vast numbers in bone marrow and the spleen. I.e. if feasible. These hidden merozoites may be responsible for numerous clinical malarial episodes and it is possible that they are at least as problematic as hypnozoites. But this remains to be determined.

REFERENCES:<br /> 1. Markus MB. 2011. The hypnozoite concept, with particular reference to malaria. Parasitology Research 108 (1): 247–252. https://doi.org/10.1007/s00...<br /> 2. Markus MB. 2022. How does primaquine prevent Plasmodium vivax malarial recurrences? Trends in Parasitology 38 (11): 924–925. https://doi.org/10.1016/j.p...<br /> 3. Markus MB. 2022. Theoretical origin of genetically homologous Plasmodium vivax malarial recurrences. Southern African Journal of Infectious Diseases 37 (1): 369. https://doi.org/10.4102/saj...<br /> 4. Markus MB. 2011. Origin of recurrent Plasmodium vivax malaria – a new theory. South African Medical Journal 101 (10): 682–684. http://www.samj.org.za/inde...

On 2023-02-03 08:53:18, user Marc RobinsonRechavi wrote:

Nice work. Checking the LEUTX gene in our Bgee database, I noticed that its top expression is in male germ line, maybe this is relevant to the fast evolution of this gene?

https://bgee.org/gene/ENSG0...

On 2023-02-03 02:42:45, user Nikolay Kandul wrote:

Dear Dr. Wang,

Please note that the same genetic circuit design was already used to report an active miRNA in eukaryotes and published in the open-access peer reviewed paper.

Here it is for your references: <br /> https://peerj.com/articles/...

Somehow your paper failed to cite the previous publication. You need to acknowledge the previous 5.5-year-old design and refer to the original publication.

it is highly unlikely that you arrived to the same design after 5.5 years, while our paper has been open-access published.

Nikolay Kandul

On 2023-02-02 20:04:27, user Francesca Day wrote:

Hello! We selected your paper to discuss in our undergraduate Journal Club seminar and I wanted to share some of the thoughts and suggestions that came up as we were discussing it. Firstly, thank you for sharing your science and for posting it here! The subject matter of this paper was extremely interesting, and we all enjoyed reading your paper. The background information was presented in a manner that was clear, concise, and detailed; this allowed us (non-experts) to easily engage with your research. Additionally, we came up with some suggestions for your paper:

1) In Figure 5, we thought it might be nice to present the gene-network and GSEAs in another format. For individuals that are not familiar with these types of analyses, it is rather difficult to infer what’s going on within the figure from just the paper alone. Therefore, an alternative presentation of the data (perhaps in a table or heatmap format) may make it even more accessible.

2) It seems that the same control was used within Figure 3 and 4 (as the control brain image is the same across both figures); therefore, we suggest that these be combined into one large figure.

3) Additionally, we noted that single-end RNA sequencing was employed; however, due to the limitations with this technique, in terms of reconstructing transcripts, it may be nice to repeat this experiment with pair-end RNA sequencing. Single-cell RNA sequencing would be even better than pair-end RNA sequencing; however, we acknowledge the immense cost of this technique.

4) In terms of the RNA experiments, we suggest that the average length of transcripts be noted within the methods section as this would give the reader an indication of the quality of transcripts.

5) Lastly, we also suggest that the plunger plots within Figure 2 be replaced with a violin plot or a box and whisker plot. We suggest this because within Figure 2b, you can see that there is a lot of variation within the Core group. This variation would be best illustrated with a different plot type and would provide a reader with a visualization of the skew in the data.

Overall, we really enjoyed reading this paper and thank you for sharing it here

On 2023-02-02 19:51:11, user Oliver Xu wrote:

Hi, thank you for sharing the research on Biorxiv. I enjoyed reading your paper which intrigued me so much. I have some experience with glioblastoma, so knowing more about them are helpful for me. After reading through the paper, I like how you are bringing ideas on the frontline. From your paper, I learned that FGFR1 and 2 are expressed in both in vitro and in vivo but may have spatial heterogeneity in regions of tumor, also FGFR receptors have antagonist effect on tumor prognosis or potentially on invasion. I also like how the networks presented in the last figure yield some more interesting markers that could be studies further.

Here is something I like about this paper:<br /> 1. The paper uses clear languages and explanation that could be easily understand. <br /> 2. I like how you incorporate xenograft model for in vivo study. <br /> 3. I like how you incorporate different types of controls: loading control and positive control ShCo on WB and validation of antibodies / xenograft models. <br /> 4. Additionally, I like how you refer back to in vitro study in your paper, while also mentioning the in vivo results.

Here is something I think you could think further / consider for improvements. <br /> 1. For figure 1, even though you test the antibodies and xenograft model, it is interesting that you put failed antibody as your first panel. I don’t usually see this in published papers, I think putting these failed ones in supplementary details are better. <br /> 2. For figure 2, I think the way data is presented could be improved. From my perspective, the data wasn’t normally distributed, thus using mean and standard deviation, as well as plunger plots, are not good graphical choices. Better choices are violin plots and box-and whisker plots, since they show variance and raw data distribution (whether it is skewed).<br /> 3. For study design, you mentioned that you measured percentage of FCFRs in vitro in your previous paper, would you consider also measuring the amount of FCFRs in vivo models? It might be interesting to compare how much percentage of cells express in vivo model, which may provide insights on whether targeting these receptor is good for regulating/inhibiting global tumor invasion.<br /> 4. For figure 3 and 4, I think you also use the same control for both, which may be better to combine into one figure. Otherwise, two sets of control should be used, as the condition may have changed as you run the experiments at different times. I think using ANOVA here would be better. I am also wondering why brain slices shown are incomplete, I think for publication quality, it would be better to put full brain slice in the panel.<br /> 5. For figure 5, I think it is hard to interpret it without some experience with bulk-rnaseq. I hope it could be explained better in the results or method section. Also, putting software generated figures is not a wise choice, because it is not annotated. I think it would be better to make your own plots, or flowchart to show how they work for clarity. My collogue also pointed out the lack of FGFR2 data here, despite it being nonsignificant.<br /> 6. The supplementary figure is extremely blurry, which is hard to read.

Overall, thank you for doing the research and sharing it here. Love the passion and hope to see more research on this. I hope my comment somewhat helps to discern some confusion from someone with a bit of cancer experience.

On 2023-01-30 03:30:59, user Nicole Agranonik wrote:

Thank you, Drs. Alshahrany, Jimenez-Pascual, and Siebzehnrubl, for your work in this study. As an undergraduate student reading for a journal club course, I found the pre-print well-written with clear language and thorough explanations of background information, making it an excellent introduction to cancer biology and neuroscience research methods for someone with minimal experience in the field, like myself. However, in my opinion, there are several areas in which this publication could be made more easily digestible.

In figures 3 and 4, I found some in vivo data presentation choices to be unnecessarily confusing. For instance, in figures 3b and 4b, the coronal slices are in disarray, with large portions of the mouse brain missing, and the use of gray-scale nuclear stain makes it difficult to distinguish hGBM xenografts from the surrounding neural architecture; in the shFGFR1 slice of figure 3b, the xenograft appears indistinguishable from the adjacent lateral ventricle. Moreover, the identical shCo slices presented in both figures 3b and 4b lead the reader to believe that both experimental conditions, shFGFR1 and shFGFR2, were compared against the same shCo group, in which case, the data for all 3 conditions should have been statistically analyzed together and presented in the same figure for clarity.

More generally, the use of plunger plots throughout this paper conceals the spread of the data set. An alternative statistical approach, such as box/whisker or violin plots, would have demonstrated the variance in the data. For example, in figure 2b, the mean fluorescence intensity of the tumor core appears to have a more skewed distribution than the invasive edge. Perhaps, this wide range of values reflects the heterogeneity of FGFR1 expression within the tumor core such that there are discrete populations of cells in the core with higher or lower FGFR1 expression. Perhaps, in future experiments, single-cell RNA sequencing with 10X Genomics could be used to identify populations with variable FGFR1 expression within the hGBM xenograft.

On 2023-02-02 17:27:12, user JeRui Chang wrote:

We apologized that there is a typo in the name of the github repo. This link should work. https://github.com/jrchang6...

On 2023-02-02 09:25:55, user Sebastian Van Blerk wrote:

Dear authors,

There seems to be a mistake with the primers described in the methods section.<br /> The 515F-806R primer pair only amplify the V4 region. They do not amplify V3-V4.

If these primers were indeed the ones used for sequencing then this is a comparison of V1-V3 vs V4.

Kind regards,<br /> Sebastian Van Blerk

On 2023-02-01 17:38:19, user Alessandro Bonfini wrote:

Hi, Nice pre-print!<br /> I just wanted to mention a small details: I think you misplaced a citation to our publication! (Bonfini et al. 2021).<br /> You wrote: "So far, most starvationrefeeding studies have been performed within 2 days of eclosion, when progenitor cells are still sparse (Lucchetta and Ohlstein 2017; O'Brien et al. 2011; Bonfini et al. 2021), or involves prolonged periods of protein starvation of 7-15 days , which triggers high levels of apoptosis and dramatically reduces total numbers of EBs and ECs (McLeod et al. 2010)."

We did 7 days protein starvation refeeding experiments and later on in the life of the flies (Fig. 4A, C,D), so probably the citation should go after 7-15 days instead of where it is now.

Good luck with your submission!<br /> Alessandro

On 2023-01-31 22:59:50, user Bruce Kirkpatrick wrote:

The data presented in Figures 1C and 1D seems to internally conflict — it would be unusual for the mesh size to increase past the size of the soft, non-degradable condition without the modulus decreasing correspondingly (i.e., it is odd that a mesh size 50% greater in the stiff vs. soft condition could be achieved in the context of a G' that is 3-fold greater in the stiff than soft condition).

On 2023-01-31 18:53:04, user Marco Incarbone wrote:

Pre-print review for Nielsen et al, bioRxiv 2023 <br /> (doi: 10.1101/2023.01.10.523395)

The manuscript by Nielsen and colleagues proposes a novel function of Arabidopsis DCL2 as a sensor of double-stranded RNA (dsRNA) that activates innate immunity through NLR receptors. This immune function of DCL2 is independent of its canonical function in RNA interference through small RNA. The strength of the study is in the genetic dissection of the autoimmune phenotype caused by DCL2 that was previously described in literature. The authors use phenotypic analysis combined with RNA sequencing in Arabidopsis to show that DCL2 triggers activation of immunity and an autoimmune phenotype through at least two NLR proteins (L5 and RPP9). This is a novel and important discovery. They show that this activity is cytoplasmic and not nuclear. Interestingly, immunity is also activated in tomato by DCL2 and moss by DCL3, suggesting this mechanism is evolutionarily highly conserved. These conclusions are well supported by data and well presented.<br /> However, the evidence for an antiviral function of this pathway is not sufficient for the strong claims made in this paper. References to virus are made in the title, the abstract, results and conclusions, and are an important part of the paper, so major concerns need to be addressed. These are listed below, with suggested experiments.

• The virus infection results are not conclusive. For example, in Fig 4A dcl4/l5 line 1 shows more virus accumulation than dcl4/l5 line 2 (which in turn accumulates as much virus as the dcl4 single mutant), while in Fig 4B the opposite is true. In fact, in Fig 4B the l5 mutation is not additive to dcl4 mutation in terms of antiviral defense. In Fig 4B the dcl2 mutant shows significantly less accumulation than WT, which is not the case in Fig 4A and in literature. All this points to high stochastic variability between samples, which can be expected when working with pathogens. It is highly appreciated that the authors show conflicting results between experiments, but these results need to be robust given their importance for the conclusions of the paper. One possible explanation for this variability could be the use of labile in vitro transcripts as inoculum. This could be solved by either randomizing the plants when infecting (not one genotype after the other, if this wasn’t done) and/or use systemically infected tissues from inoculated dcl2/dcl4 mutants as inoculum for rub inoculation (although this TCV clone has no CP, which could be a problem). In any case, the infection experiments need to be repeated with more biological replicates per genotype, in the key genotypes Col, dcl4, l5, rpp9, dcl4/l5, dcl4/rpp9.

• A VSR-impaired virus should not be used to test whether DCL2 has an RNAi-independent function. As this virus is very sensitive to RNAi, in this experimental setup this pathway cannot be uncoupled from the proposed dsRNA-DCL2-NLR sensing pathway. In addition, the P38 VSR is also the viral coat protein, loss of which could impact the viral life cycle and accessibility of dsRNA structures in vivo. On the other hand, the use of wild-type TCV, encoding a very efficient VSR, would reliably show whether DCL2 has a small RNA/AGO-independent antiviral function. In this case WT, dcl4, l5 and rpp9 should show similar levels of virus accumulation, while dcl4/l5 and dcl4/rpp9 should show increased accumulation, if the hypothesis is valid. As above, several replicates should be analyzed.

• A central conclusion in this paper is that DCL2 is a sensor of dsRNA that activates innate immunity through NLRs. While the fact that DCL2 is an RNAseIII strongly suggests that it senses dsRNA, there is limited experimental evidence for this (the knock-out of RNA decay factors in literature and the experiments with TCV-deltaP38 - see comments above). Two experimental approaches would significantly strengthen these conclusions. (1) Treatment with a dsRNA analogue such as poly I:C of the various dcl/NLR mutants described in the paper. Activation of immunity can be assessed by quantifying expression of immune-responsive genes as in Supplementary Figure 1 or as described in literature (DOI: 10.1111/pbi.13327, DOI: 10.1042/EBC20210100). Performing this experiment on WT, dcl2, l5, rpp9, dcl2/l5 and dcl2/rpp9 should genetically determine whether DCL2 activates innate immunity through L5 and RPP9 NLR receptors upon detection of dsRNA. (2) Perform infection experiments with phylogenetically distant RNA viruses, and test activation of immunity as in (1). Available and routinely used in Arabidopsis are, among others, Cucumber mosaic virus (CMV), Turnip mosaic virus (TuMV), Tobacco rattle virus (TRV), Turnip yellow mosaic virus (TYMV), Tomato bushy stunt virus (TBSV) and Turnip crinkle virus (TCV – see above). These virus species have very different proteomes, replicate on different organelles, have different capsid shapes, etc. If several of these activate immunity in a DCL2/NLR-dependent fashion, the case for DCL2 being a sensor for dsRNA in vivo would be far more robust. Using these viruses to infect Col, dcl4, l5, rpp9, dcl4/l5, dcl4/rpp9, then assessing viral accumulation, would indicate that in addition to activating immunity, this mechanism also has antiviral activity.

If these points are not addressed, I believe that claims of immune activation by viruses and antiviral activity should not be present as major conclusions in the paper. They could be suggested in the discussion. In addition, conclusions regarding dsRNA as the trigger for immunity should be tempered.<br /> It is my hope that these experiments will be conducted, and if the current conclusions of the authors are substantiated, this is a very significant discovery and advancement in plant virology and immunity.

Next are listed some minor points to address.

• The authors refer to antiviral RNAi function of DCL2 as being dependent on the genetic abrogation of DCL4. The authors state more than once that DCL4 is the main antiviral Dicer, with DCL2 dicing only when DCL4 is somehow overwhelmed by viral dsRNA. This view is not up to date, as more recent studies have shown that in WT plants DCL2 can be an active antiviral RNAi player, depending on virus species and/or tissue (e.g.: doi: 10.1038/nplants.2017.94; doi: 10.1093/nar/gkab802), suggesting that precise function and context of DCL2 in antiviral RNAi remain poorly understood. This should at least be mentioned.
• A whole section in the middle of the manuscript delineates the model proposed by the authors, while describing some of the results. In my opinion this interrupts the flow of the manuscript. The results should be presented as in the rest of the manuscript, while the model should be built in the discussion or at the end of the results. A partial model is also presented in Figure 2, taking away space for data.
• The authors end the final paragraph of the results with “when RNAi has been abrogated by mutation of DCL4”. KO of DCL4 does not abrogate antiviral RNAi, it reduces its efficacy. This should be corrected.

On 2023-01-30 16:55:38, user r.virgili wrote:

I congratulate with the authors for the effort and the impressive species accounted in this study.<br /> However, I should point out that the study is apparently lacking information from the most up to date records on the matter. In particular, the present work is not considering relevant outcomes by Virgili et al. (2022) on the same species treated here. For instance, the records and molecular data of Botrylloides niger, Symplegma brakenhielmi, and Polyclinum constellatum.<br /> Regarding P. constellatum, in fact neither Aydin-Önen et al. (2018) nor Montesanto et al. (2022) addressed this species as a cryptogenic. <br /> This species has undoubtedly recently arrived in the Mediterranean, although Virgili et al. (2022) has backdated its arrival and spread since 2014. Being the Mediterranean one of the most studied basin both regarding ascidians taxonomy and introduced species, it is odd that such an invasive species could have originated here and being totally overlooked in the past. Although I agree that relationships with congeneric species in the native areas are unclear and should be revised in the future. I may also add that the low intraspecific divergence of COI gene among different localities it is not a sign of cryptogenicity, and other markers/genes should be used to investigate this issue (see Hudson et al. 2022).<br /> I hope these comments may be helpful and I am looking forward to see your work published.

R. Virgili

On 2023-01-30 14:42:31, user Leduc cécile wrote:

"Vincente et al. have measured the periodicity between C-to-C terminal and N-to-N terminal from ULF inside cells, but, here, we observe this periodicity in the actual filaments which is formed by the WT vimentin, not the mutant."<br /> We did it in the filaments too. Just check the figure 3 of Vicente et al.

On 2023-01-30 14:31:40, user Leduc cécile wrote:

"In vitro, they used a fixed vimentin-Y117L mutant, which stopspolymerizing at ULF stage." No, we used this mutant in cells, not in vitro. Please read more carefully the paper from Vicente et al and correct your statements which are not accurate.

On 2023-01-30 14:29:31, user Leduc cécile wrote:

"Recently, Vincente et al. have also reported similar axial repeats in vimentin in fixed cells where they found a pairwise distance between C-to-C terminal was ~50 nm and between N-to-N terminal was ~34 nm, measured by 2D STORM imaging. " Please read again the paper from Vicente et al, we reported a distance of ~50 nm for both C-toC and N- to N terminal distances within filaments. 34 nm corresponds to N- to N distance within ULFs.

On 2023-01-30 12:42:03, user JVollm wrote:

minor comment: in Figure2, for both a & b the X-axis is labelled "Error in Completeness Prediction". But, it seems for 2b it should actually be "Error in Contamination Prediction"...

On 2023-01-30 12:20:05, user Zdravko Odak wrote:

There are a few ways the research could be improved:

Larger sample size: This study used cells collected from only two patients, and a larger sample size could provide more robust results and increase the generalizability of the findings.

Comparison with other treatments: Comparing the efficacy of mifepristone to other treatments could provide a more comprehensive understanding of its potential as a treatment option.

In vivo validation: Although the 3D organotypic model provides a closer representation of the in vivo environment, validating the findings in animal models or clinical trials would further increase the reliability of the results.

Long-term effects: Studying the long-term effects of mifepristone on cancer cells and its potential for preventing recurrence would be valuable for its clinical implications.

Mechanism of action: Further investigation into the mechanism of action of mifepristone on cancer cells could provide a deeper understanding of how it inhibits their metastatic abilities.

Best regards,

Zdravko Odak

On 2023-01-30 08:29:42, user Luisa wrote:

Dear authors, I have just selected one of your proteins from your website based on high prediction value. The name shown in your database says it is identified as <br /> MGYP003555391488 (https://esmatlas.com/explor... . However, as I looked for the sequence in the MGnify database, their website says that there is no record of such identifier (https://www.ebi.ac.uk/metag... . I think something might be still buggy in your website.

On 2022-11-13 15:18:53, user Hong wrote:

Thanks for the interesting paper. RE contact head fitting, given that L*K is ~100s, I'm wondering whether the training size 20 is too small and the deviation 0.0028 is due to overfitting? Also in github it seam the head is co-trained within the NN, rather than separately trained using sklearn?

On 2023-01-27 10:34:19, user IanL wrote:

This is a lovely paper and a very powerful tool.

Apologies if I have misread or misinterpreted the following. As far as I can understand the fitness scores for individual genes are calculated using an array of 4 guides against that gene, and the fitness score of a gene in combination with a putative genetic interactor is calculated using 2 guides against that gene combined with 2 guides against the interactor.

Would a more fair comparitor to use for the single gene fitness score not be 2 gene-targetting guides + 2 non-essential guides? Or is there no meaningful additional fitness effect seen above 2 guides?

On 2023-01-07 18:22:56, user Doyonlab wrote:

I would suggest to add the following reference:

-Multiplex gene editing by CRISPR-Cpf1 using a single crRNA array https://pubmed.ncbi.nlm.nih...

"The Cas12a system enables multiplexing beyond two targets 12,16, XYZ"

On 2023-01-26 09:44:19, user Juri Rappsilber wrote:

We discussed your manuscript in our lab and enjoyed its balanced tone and inquisition of an important aspect of the search. Thank you for sharing your search parameters for xiSEARCH and as a developer of that tool I second your choice of parameters completely. However, to answer the question “STY or not STY?” your current search experiment seems insufficient to us for reasons detailed below, and we would like to suggest a number of controls and changes. When allowing either KSTY or KGVL you disregard an important element of link assignment based on the often-incomplete MS data. Indeed, the MS2 data often do not unambiguously assign the linked residue by neighbouring backbone fragments and the likely link site is chosen based on the predefined chemistry of the linker. Fig 2G is a nice example of this. Depending on what you define as chemical preference of the crosslinker, you either report S or G. In all those cases, any residue is equally likely based on the MS data and your linker definition determines the reported site. If STY are disregarded and the abundant GVL are pre-defined, obviously, they will be reported. To put this to an extreme, why do you not disregard K as a link possibility and only allow GVL? We would like to suggest a couple of changes to your experiment:<br /> (1) To determine if STY are targets of NHS crosslinkers, only evaluate sites that were reported unambiguously based on MS data, i.e, with neighbouring fragments. Maybe run a completely indiscriminate crosslinker and look for enriched amino acids. Open modification search analyses linear peptides which differ from crosslinked peptides in not having a second reaction step in a spatially confined setting. The first reaction of a crosslinker brings it into the protein, the second reaction takes place with a spatially proximal residue of appropriate chemistry. The second step can only be assessed by looking into crosslinked peptides.<br /> (2) To determine if the data support STY as well or as poorly as the chemically meaningless GVL, define a linker KSTYGVL to let STY and GVL compete. Note that xiSEARCH currently will take the more C-terminal residue in any ambiguity window and thus have some random component in link site assignment. If an ambiguity window contains a K near STY, xiSEARCH has a preference setting for K, though. Consequently, if a STY is reported in favour of a nearby K, there must be supporting MS2 evidence in the spectrum for this report. <br /> (3) Do the GVL linker experiment (no KSTY). I would expect you to find many crosslinked peptides, nearly all the same peptide pairs as with K or KSTY. <br /> (4) Do KYST versus KGVL and match the YST with the GVL peptides where the are the identical peptide matches and compare the score. Plot a 2D scatter plot with score(YST) versus score(GVL). My expectation is that you will have many equal scoring peptides (ambiguity window based on insufficient MS2 data) and many cases where the score(YST) will be larger. <br /> As a final note, link site assignment is currently an open issue of crosslinking, and you are right to point this out. Curiously, speaking to Jan Kosinski as a modeller, he did not care about link sites at all. Our own work on using photo-crosslinking (where link site assignment is even more problematic because of the wider reactivity) did not show difference in protein structure model quality using link sites with a +/- 5 residue scatter (PMID: 26385339). So far, it seems to be unclear if link site precision offers any structural value. I hope it does… albeit it might not, considering the length of NHS crosslinkers and the flexibility of proteins. From a mass spectrometry side, I would, nonetheless, prefer to see link sites reported with a measure of precision.

On 2023-01-25 22:02:12, user Liu wrote:

It is naïve to assume that these practices are a result of CIVID. Australian research community works under these unwritten rules since before COVID. These dishonest practices have probably been increasing with time. There are many corruption cases hidden under the rug and need to be exposed.

On 2023-01-24 22:48:46, user Jackie wrote:

I really wish they gave better info about their demographics. In particular, where the respondents were located. Is this truly an Australian issue? Or were the majority from say Brisbane or Sydney? Also, the happy folks are less likely to agree to do a survey...This is an important study, but the limitations could be better discussed

On 2023-01-24 20:12:49, user Lupin wrote:

Interesting work. However, I am not sure how this "Perhaps those from another country valued a job in Australia more than one in their country of origin" can be concluded from "while those who do not speak English at home appeared to be less pessimistic, they also appeared to be less satisfied with the workplace culture (χ2= 9, df=2, P= 0.011)." This comment sounds condescendent to me (but maybe that was not the intention). I lived and worked in academia in Australia and my view of that answer is somewhat different. As a non-Australian ERC you could be happy with your job, but not with the workplace culture, especially when you realise that the Australian Science system puts you in a disadvantage just because you are a foreigner (e.g., no access to many funding sources). Add to that, the draconian VISA system and the fact, that even if you are paying taxes, you do not have access to the health care system unless you pay private insurance, and might start understanding the lower satisfaction with the workplace culture for the non-Australian ERC in Australia.

On 2023-01-25 20:02:33, user Stefan wrote:

Nice work! I cannot find the Supplementary Tables. Am I missing them or could you please also upload them?

On 2023-01-25 13:44:52, user Furkan Gökçe wrote:

Dear readers,<br /> Thank you for your interest in our preprint.<br /> There is a typo in the abstract in v3 of our manuscript, which we did not notice until it was accepted for publication in Advanced Healthcare Materials. Unfortunately, now that it is accepted, we can no longer update or revise the preprint here. However, this typo has been corrected in the published version, which can be accessed here: https://doi.org/10.1002/adh...<br /> The corrected sentence in the abstract should read as follows:

...Drug sensitivity and resistance testing on patient-derived leukemia samples provide important information to tailor treatments for high-risk patients. However, currently used well-based drug screening platforms have limitations in predicting the effects of prodrugs, a class of therapeutics that require metabolic activation to become effective...

On 2023-01-25 03:38:51, user Santiago Claramunt wrote:

It looks very interesting. I would be interested in seeing how the new method compares with the discrete rates mixture model described by Paradis 2013 <br /> https://doi.org/10.1016/j.y...

On 2023-01-24 22:32:37, user Nozomu Yachie wrote:

After discussing the first version with Aziz Al'Khafaji, who developed COLBERT, we realized that the high-copy CRISPRa experiments presented in the paper were not performed the same way as COLBERT/ClonMapper. We revised the texts related to this and provided additional discussions. The new version can be found here: https://www.biorxiv.org/con...

On 2023-01-24 09:48:58, user Nam Hoang wrote:

While waiting for a direct link to appear, here is the link to the published version on The Plant Cell https://doi.org/10.1093/plc...

On 2023-01-23 20:42:50, user Elizabeth Cooper wrote:

This paper is now published in a special issue Frontiers in Plant Science dedicated to resources for bioenergy crops. The published version can be found at: https://www.frontiersin.org...

On 2023-01-23 18:11:04, user Jinoh Kim wrote:

A link to the accepted paper: https://www.molbiolcell.org...

On 2023-01-23 14:56:20, user Benjamin Himes wrote:

Manuscript review<br /> dated January 23, 2023:

by: Benjamin A. Himes

“A robust normalized local filter to estimate occupancy directly from cryo-EM maps.”

The version posted January 20, 2023, to biorxiv https://doi.org/10.1101/202....

The problem being investigated:

The interpretation and utility of cryo-EM reconstructions [maps hereafter] is often<br /> made challenging by spatially localized degradation that may arise from several<br /> sources. To this end, many tools exist to estimate and/or modulate cryo-EM maps<br /> non-uniformly. These tools are generally sensitive to artifacts in the cryo-EM<br /> maps, user-selected processing parameters like the local window size, and image<br /> intensity distributions that deviate from those generated by well-isolated<br /> globular proteins.

The proposed solution:

Forsberg, Shah, and Burt propose a non-linear filter based on the maximal value in a sliding<br /> window. While existing tools lean toward estimating local resolution or<br /> signal-to-noise ratios, the authors aim to avoid problems this may introduce by<br /> starting from the premise that the real-space image intensities should be<br /> relatively uniform at moderate-to-low resolution unless there is flexibility or<br /> compositional heterogeneity. By starting from this simple premise and selecting<br /> the max-value filter, the method aims to be robust as well as fast

The results:

The filter is implemented in Python. The authors have developed a clean and well-designed GUI that is<br /> easy to install, intuitive to use, well-documented, and interfaces beautifully<br /> with a USCF-Chimerx, a staple visualization tool in the field. The manuscript<br /> is well written, and the results clearly show they can measure non-uniformity<br /> in real-space cryo-EM maps. Beyond visualization, they demonstrate that this<br /> statistic can also be used to modify the cryo-EM map; however, the full utility<br /> of such modifications is somewhat less convincing. That is, of course, no<br /> concern, as the improved visualization should already be beneficial in cryo-EM<br /> map interpretation.

Major concerns:<br /> None

Minor concerns:

1. Several minor phrasing issues result in<br /> statements that could be understood as factually incorrect if read out of<br /> context—these I’ve sent to the authors directly.

2. “So-called ab-initio 3D reconstructions can<br /> now be made without user input bias.” It is worth<br /> noting that template-based particle picking or even blob-based picking combined<br /> with 2d classification can introduce model bias that persists even when<br /> ab-initio 3D reconstruction is used. Even the selection of 2D classes can<br /> introduce model bias from the users mental model of the target. See for<br /> example, Superstitious Perceptions Reveal Properties of Internal<br /> Representations, Gosselin and Schyns 2003 Psychological Science.

3. Resolution in cryo-EM is not a contested term.<br /> It is the spatial frequency at which the reconstruction is no longer<br /> statistically reliable. The definition of where exactly that point is, however,<br /> has been contested in the past.

4. A few relative qualifiers should be specific—Eg.<br /> “reasonably sized input”, “…reasonably set lower…” etc.

5. In the methods section, the authors point out<br /> that the CDF in eq 6 is only valid for statistically independent voxels. While<br /> it is often the case that these conditions can be relaxed, I think it is<br /> reasonable to ask for an analysis of or justification for using this CDF, given<br /> that the core problem the authors address is one that, by definition, results<br /> from statistical dependence between pixels. As a simple example, consider a<br /> loop flipping back and forth between two positions, resulting in lower average<br /> occupancy. At any given time, the intensity measured in one of those positions<br /> is correlated with the intensity in the other.

6. The value of 449’260 CPU seconds in table S1<br /> seems unreasonably high. Are these measurements the average of several repeated<br /> experiments? Please double-check.

Final thoughts:

The authors have presented an algorithm that is robust to common characteristics in cryo-EM maps<br /> and developed a tool to execute that algorithm that is easy to use and open to<br /> modify. Well done! I could install this and immediately use it in a project I<br /> was working on.

On 2023-01-21 23:37:14, user Donovan Parks wrote:

Hi,

I much enjoyed reading your preprint and look forward to playing with skani. Do you have results showing how alignment fraction (AF) compares between skani, FastANI, ANIu, and/or ANIm? AF is a key criteria for applications such as assigning genomes to species clusters. An application dear to me as a maintainer of the GTDB.

Thanks,<br /> Donovan

On 2023-01-21 09:39:04, user Karen Lange wrote:

I have been able to successfully replicate the method described in this manuscript. In the future, I will be using this approach for all of my large knock-in CRISPR experiments.

In my hands, CRISPR using a ssDNA repair template for a large insertion (~1Kbp, GFP) was 30% more efficient than the corresponding dsDNA template. The protocol described in the supplemental methods was thorough and easy to follow. I appreciated the comments that added extra context. Using a single phosphorylated primer and lambda-exonuclease to generate the ssDNA template from a PCR product was simple and only added an extra hour or so to my usual protocol. The enzyme was cheap enough (~€70) and will be sufficient for 100s of reactions. The phosphorylated primer was about 5x more expensive than a standard primer but I think this added cost is worth the increase in efficiency. Important note - I highly recommend using the Monarch PCR & DNA Cleanup Kit mentioned in the article to purify the ssDNA.

On 2023-01-20 10:48:18, user Nicola Festuccia wrote:

Based on current data, and the genetic model used in our study, we do not have evidence of remaining NR5A2 in maternal KOs. However, we will explore this question further, by immunostaining or other suitable means.

Thank you for the advice,

Nicola Festuccia

Epigenomics, Proliferation, and the Identity of Cells Unit,<br /> Dept. of Developmental and Stem Cell Biology, <br /> Institut Pasteur, Université Paris Cité, CNRS UMR3738, Paris, France

On 2023-01-18 16:38:19, user Kikue Tachibana wrote:

The morula arrest observed after deletion of the Nr5a2 gene is interesting. But based on the data presented in this preprint, can the authors really conclude that Nr5a2 protein is not required for zygotic genome activation (ZGA)? The detection of Nr5a2 protein by immunofluorescence is technically challenging. For this reason, and because Nr5a2 is maternally provided (Gassler et al., Science 2022), can the authors rule out that Nr5a2 knockout zygotes still contain maternally provided Nr5a2 protein, which is sufficient for ZGA? We recommend using our optimized protocol, with which maternally provided Nr5a2 protein can be detected in oocytes, zygotes and 2-cell embryos (Gassler et al., Science 2022) to quantify how much Nr5a2 protein is left in conditional Nr5a2 knockout embryos.

Kikue Tachibana<br /> Max Planck Institute of Biochemistry<br /> Martinsried/Munich, Germany<br /> tachibana@biochem.mpg.de

On 2023-01-18 22:36:30, user Biophysical Paris @Biophysics@ wrote:

Nice work. I have been through it rapidly. What about the EPS production around the droplets? Do you see any matrix?

On 2023-01-18 18:20:58, user Nick Bauer wrote:

I'm disappointed to see that the code for this has not been made public yet. The preprint states that the code will be made available with formal publication, but it has been three years since the preprint was posted and I can find no formal published version.

On 2023-01-18 15:45:40, user Leonardo Giannantoni wrote:

This preprint has since been published in the proceedings of the 22nd <br /> International Work-Conference on Bioinformatics and Biomedical <br /> Engineering (IWBBIO) - https://link.springer.com/c....

On 2023-01-17 21:10:46, user Gregory Way wrote:

Hi Daniel,

It was our pleasure to review. Thank you for posting!

Here are some comments regarding your very speedy reply:

1. Glad to hear your repo will be made public!
2. The LINCS dataset, which includes official access instructions, is detailed in our recent paper (DOI: 10.1016/j.cels.2022.10.001)
3. We include level 3 data in that resource, but I'd advise strongly against using level 3 data. They contain unnormalized CellProfiler features that are incompatible with standard distance metrics. My assumption is that a like-to-like comparison is more suited if the full standard approaches are compared (i.e. I don't think people use the level 3 data all that often, so benchmarking against it is less valuable). I think MOAProfiler is likely to still outperform, but at a lower margin.
4. I agree that testing the generalizability is a very exciting application. I think that for my lab to use MOAProfiler, I would need to see this trade-off.

Thanks again!<br /> Greg

On 2023-01-09 15:10:22, user Daniel Wong wrote:

Hi Greg!<br /> Thanks for reviewing the preprint — it will definitely help to make the manuscript better and improve the science. For the minor comments, I will go through them individually and make changes as necessary to the paper before its official publication. For the major comments:<br /> - The repo will be made public soon depending on how quickly Pfizer’s IP department can review and approve it — apologies for the delays.<br /> - For the CP embeddings, we actually derived from two sources as described in Methods:<br /> - JUMP Pilot: We downloaded from the repo https://github.com/jump-cel.... To our understanding, this repo was created by the same people who made the JUMP Pilot dataset, and they provided the source and instructions for downloading the CP embeddings used in their study here: https://www.biorxiv.org/con.... If you have another source for embeddings, I’m happy to analyze them!<br /> - LINCS: We downloaded them from the repo: https://github.com/broadins.... To our understanding, this repo was created by one of the main developers and maintainers of DeepProfiler for use in benchmarking DeepProfiler against CellProfiler, so we thought that it was satisfactory but we could be mistaken! As with the JUMP Pilot, if there is a better source for embeddings, I’m also happy to analyze them if you can provide a CSV. <br /> - We purposefully chose level 3 (un-normalized) embeddings because the MP profiles are also un-normalized and we wanted to compare like to like. MP's embeddings are pulled directly from the model's hidden state without any post-processing or plate / DMSO normalization. Hence if there are better embedding sources, it would be helpful if you could provide level 3 ones. <br /> - We did not try combining datasets — that’s part of a potential future work which I anticipate would boost performance as we expose the model to more diversity. Although interesting, we saw this as out of scope for supporting the main claims of the paper. For this paper at least, we did not do a cross analysis (i.e. Model trained on A but evaluated on Dataset B). It might be an interesting supplemental analysis, but we thought that prediction on held-out compounds was sufficient to prove the point we were trying to make. Strong model generalizability to other datasets instead of generalizability to new compounds (as in the current study) will be left as a future (and very exciting!) exercise. <br /> Thanks for taking the time to review and help improve the work. I admire what your group is doing for advancing open science!<br /> With gratitude,<br /> Daniel

On 2023-01-05 20:51:34, user Gregory Way wrote:

Wong et al. present a deep learning approach called MOAProfiler (MP) to specifically predict compound mechanism of action (MOA) from Cell Painting images. They benchmark MP against CellProfiler (the standard image-based profiling approach) and DeepProfiler (an emerging image-based profiling approach also based on deep learning) using two publicly-available datasets (JUMP-pilot and LINCS). They evaluate these approaches using precision, recall, and f1 score at k for held out MOA predictions and by comparing similarity between same-MOA and different-MOA profiles. They report an astounding 1,000% performance increase for MP over CellProfiler and DeepProfiler in grouping like MOAs. We thank the authors for posting their work as a preprint - thank you!

The primary innovation in MP is the specific training approach. MP uses the same architectural backbone as DeepProfiler (EfficientNet), but trains the model directly to predict compound MOA (instead, DeepProfiler uses EfficientNet to derive representations). Additionally, MP does not perform single-cell segmentation, instead training using full field of views and a series of data augmentations. The authors use the last layer as the per-compound feature embedding in their performance benchmarks. The authors also include several convincing supplementary analyses that further support their claims.

Overall, the paper presents a very interesting observation and pushes against a commonly held mindset of analyzing Cell Painting data with generalist/universal approaches. Instead, the paper suggests that fine-tuned models for specific applications are vastly superior for specifically tailored tasks.

However, we have two major concerns and several relatively minor comments that the authors might clarify in order to strengthen their findings and claims.

Major concerns:

• Our primary concern involves publicly-available resources. Namely, the github url is not public: https://github.com/pfizer-r.... Because we were unable to access the code, we were not able to perform a detailed code review. Additionally, the authors link to the CellProfiler and DeepProfiler embeddings they used to benchmark. These embeddings were derived from https://github.com/broadins.... These are not the official LINCS and JUMP resources, and at least one of the links pointed to level 3 profiles, which are not normalized. This could at least partially explain the exceptionally poor performance for CellProfiler and DeepProfiler.
• Second, the authors train two separate MP models for both datasets. Did the authors try applying a trained-MP on the alternative dataset? The authors state: “To simulate the real-world use case of identifying MOAs of unknown held-out compounds, we performed an analysis where we split the dataset by compound instead of by wells (Methods).” We imagine that analyzing future compounds using embeddings of a pre-trained MP is also a common real-world application. This analysis would also reveal the level of overfitting occurring in each independently trained dataset. Would combining datasets improve performance?

Minor comments and concerns:

• The authors state: “​​Although traditional computer vision techniques have proved useful, they often require much fine-tuning and require human intelligence and intuition for deciding which phenotypic features and their parameters are important to measure.” We think this is a really good point, and we are glad someone else brought up the parameters and all the fine-tuning that typically needs to happen, even for generalist approaches.

• The authors state: “In contrast, deep learning has emerged as a tool for learning and encoding meaningful representations (i.e. embeddings) without requiring humans to know beforehand what features may be useful for the task of interest.” We may have missed this, but the authors might decide to mention the deep learning limitation of having unlabeled and difficult-to-interpret features.

• Figure 1B needs a scale bar

• The authors state: “We divided the dataset such that 60% of the wells were assigned to training, 10% to validation, and 30% to test (Methods).” What does “class-balanced the training set” mean? Is this during cross validation? The authors should clarify.

• The authors state: “We also ensured each MOA’s test wells spanned multiple plates (at least seven, Figure 2D, left)”. However, Figure 2D shows that most MOAs in LINCS spanned fewer than 7 plates, what did the authors do with those?

• The authors state: “We also included the negative DMSO as a class to learn but excluded it from all performance metrics because of its overrepresentation in the dataset”. It would be helpful for the authors to clarify how they handled positive controls. Also related, the authors state: “we performed four analyses to assess how well the embeddings captured MOA-specific features.” How did DMSO perform? It would be interesting to see the distribution of DMSO probabilities across classes, which could point to classes with no effect or how often DMSO features might be influenced by batch effects.

• For Figure 3A, the authors should clarify that their supervised learning architecture was multi-class. This is not explicitly stated.

• The authors state: “On the held-out test set, the model achieved an area under the precision recall curve (AUPRC) of 0.46 (random AUPRC = 0.006) for image field classification over 176 MOA classes (Figure 3A)”. How are the authors calculating this random AUPRC? If this is theoretical, the authors should compare performance with a model trained with a randomly shuffled baseline.

• Additionally, the authors state elsewhere: “it was able to correctly predict MOAs for 10.2-13.6% of the compounds in a space of 176 possible MOAs. Compared to a random baseline of 0.6%, this is a 17.9-23.9x improvement.” This begs another question of how the authors formed the baselines. Also, why did the authors choose to not include DP and CP in this eval?

• The Figure 4B plate map might be wrong. There are more DMSO and what are the NAs?

• How did the authors determine the categories “strongly correlated” and “weakly correlated”? At different thresholds did MP still outperform?

• The authors state: “Performance varied depending upon whether we predicted MOA by the neural network’s classification output or by a compound’s latent similarity to training compound embeddings”. The authors should clarify how they determined these classification outputs and latent similarities as they are introduced.

• The authors state: “(delta = 0.44 for MP, Figure 3C). For both CP and DP, the difference was smaller (delta = 0.03 for CP, 0.03 for DP).” The authors define delta in the figure legend, but this should also be clearly delineated in the methods.

• We were confused by the legend in figure 4D - why are each of the models showing a different k? Is this the optimal k? MP doesn’t look optimal at k=4.

• The authors state: “From a low-dimensional t-distributed stochastic neighbor embedding (TSNE) visualization of embeddings from three example MOAs, we could see that different compounds with the same MOA were clustered together with different MOAs inhabiting different areas in latent space (Figure 3G).” How did the authors choose these three example MOAs? Why not include all of them? It would be nice to visualize all embeddings for both datasets, and the TSNE plots look a bit strange, with highly similar distances between points.

• The authors state: “However, the model created embeddings that were clustered by MOA despite each MOA being represented by multiple compounds (Supplemental Figure 1).” Supplementary Figure 1 is not a specific enough reference - there are multiple panels and it is unclear which panel the reader should focus on.

• The authors state: “We found minor differences in classification accuracy (0.54 vs .50) suggesting that the model was not leveraging much confounding edge-specific features for its learning” Given the number of NA’s (especially in LINCS platemap in Figure 4), normalization to remove batch effects or TSNE/UMAP to suggest no batch effects would be more convincing.

• Figure 6G mentions different shapes in the legend but all look like circles in the image (they are different but it's very hard to tell). The authors also forgot to include the letter g in the figure legend.

• Does supplementary figure 3 show MP embeddings? This is not explicitly stated.

• Performance across MOA counts for MP is impressive! Very strong performance at low n

• In the discussion, the authors state: “Second, all the analyses were performed on compounds with just one known MOA. Understanding drugs that are associated with multiple MOAs is an important task, but our study did not address this question.” The authors seem to avoid explaining this in-depth throughout the article. Why is it an important task and is their justification for not including drugs with multiple MOAs good enough? They mention that they didn’t include compounds with multiple MOAs to simplify the compound space and limit polypharmacology intricacies. Did they try to include compounds with multiple MOAs? If so, I think they should report the results. If the results are bad, then that could give insight into how we can improve performance.

• In the discussion, the authors state: “Although DP is another deep learning based approach to phenotypic profiling that also uses an EfficientNet backbone architecture, we observed larger performance gains with MP.” What was the authors’ rationale for using EfficientNet? Also, “architecture” here and in other sentences appears to have a broad meaning. Could another word be substituted for greater specificity? We think it would be helpful to include a diagram of their model architecture.

• The authors state: “We permuted each channel’s brightness and contrast independently by a random factor in the range of 0 to 0.30 (just for the LINCS dataset).” This seems non-traditional, the authors should provide a citation. Why not perform this in the JUMP dataset?

• The authors state: “As a final training augmentation step, we performed random 90-degree rotations on each image, along with random horizontal flips.” The authors should specify how many augmentations they performed, how did this expand the dataset, were any specific augmentations particularly helpful?

• The authors state: “We kept only the compound data that had no more than one known MOA according to the CLUE Connectivity Map…” How often does compound data have more than one MOA from the CLUE Connectivity Map? Would it create a significant difference in results if others were included? How was CLUE connectivity data joined with or used as a filter for JUMP1?

• The authors state: “We trained for 100 epochs and selected the model that had the highest accuracy on the validation set. We used a learning rate of 0.1, a weight decay of 0.0001, a dropout rate of 0.2, a learning momentum of 0.9, a learning rate scheduler with a gamma decay of 0.1 at epoch 50 and 75, and batch size of 56 for training.” Did the authors perform any sort of hyperparameter optimization? How did they select these hyperparameters?

• For their CellProfiler pipelines, the authors do not explain why they used specific modules. The pipeline utilizes various different modules that I haven’t seen in other pipelines so it would help to know what is being done if there were notes.

Reviewed by:<br /> Gregory P. Way, PhD<br /> Jenna Tomkinson<br /> Roshan Kern<br /> Dave Bunten<br /> Parker Hicks<br /> Rose Doss<br /> Keenan Manpearl

On 2023-01-16 18:59:16, user Lucie Zinger wrote:

Nice results, congrats! We found some years ago similar trends across water dephts down to the sea floor for prokaryotes. You might be interested in the two following papers for further discussion/points of comparison? https://journals.plos.org/p..., https://onlinelibrary.wiley...

On 2023-01-13 14:37:31, user G Ferraz wrote:

This work offers a potentially useful contribution by identifying which tree cavity features seem to be selected by breeding Ara ambiguus pairs, as well as which features are more evidently associated with breeding success. The observation that success and selection point to the same features is reassuring. It suggests that this population is not walking into an ecological trap, which a very interesting point. One problem with the work, though, is that an undisclosed proportion of the cavity-feature data was imputed (i.e. not actually measured in the field but inferred after the fact based on the distribution of measurements that were made - see section 2.5 of ms). Imputation may be a reasonable approach if it applies to a small enough proportion of the data. The authors should disclose that proportion, so that readers can evaluate the implications of imputation. I also would have liked to see mathematical formulae of the models compared by WAIC, as well as the outcome of that comparison. How did it affect the results? What was the structure of the models?

On 2023-01-11 09:20:19, user Holger Stark wrote:

This paper reveals densities that are in total disagreement with how a 1.1 Angstrom map should look like. Densities of individual C atoms in aromatic side chains are entirely missing, other densities are misplaced and distorted. This map is entirely useless, the resolution claims are wrong and the paper should be retracted and removed from the BioRxiv server as soon as possible to avoid damage to the author, the institution and the cryo-EM community.

On 2023-01-11 08:50:00, user Stepanka Vanacova wrote:

This whole preprint looks fake. The single author is not listed at the Buchmann Institute. Was this created by AI or a failed PhD student?

On 2023-01-10 13:07:55, user rdrighetto wrote:

I suspect the results presented in this manuscript are a consequence of overfitting or some other issue(s) in the data processing:

--The density presented in Fig 1 looks quite noisy. Compare for example with the cryo-EM densities of apoferritin at 1.22 Å resolution (EMD-11638), apoferritin at 1.15 Å (EMD-11668), or even the maps of Beta-gal at 1.9 Å (EMD-0153 and EMD-7770). The zoomed-in densities for aminoacids presented in Figures 2d and 3a,d seem to be missing features.

--While the half-map FSC extends all the way to Nyquist at a high correlation (~0.3), it starts decreasing quite early, which looks suspicious for an allegedly very high quality map. Furthermore the model-map FSC only correlates (0.5 criterion) to ~3 Å, which is quite a difference to the half-map FSC.

--All figures provided in the manuscript are of very low quality, making it difficult to read the graphs and assess the results in more detail.

--The author should clarify why were external templates used for particle picking and for initializing the 3D refinement. Such a dataset should easily warrant the use of a reference determined ab initio, as well as templates generated from the own data. It should also be stated to which resolution the reference 3D map was low-pass filtered to exactly. The use of external references could be the source of overfitting (model bias).

--The pixel size of the acquired micrographs, as well as the defocus range used during acquisition should be stated in the manuscript. The manuscript is missing a table summarizing the cryo-EM data collection parameters.

--The atomic modelling is clearly not on par with the claimed data quality. A clashscore of 42 is abnormally high, especially for a claimed resolution of 1.1 Å. The Molprobity score of 3.32 is also an indication of wrong or incomplete modelling and refinement (for a claimed resolution of 1.1 Å).

--Most importantly: the main map, half-maps and masks resulting from this study should be made available to the community on EMDB for proper assessment of the results. The raw data should be deposited to EMPIAR as well.

Kind regards,<br /> Ricardo D. Righetto

On 2023-01-11 08:55:55, user kamounlab wrote:

I don’t think “inspiration” in the title is the correct word. It doesn’t mean anything in this context. Better say impact or influence.

On 2023-01-11 05:02:41, user Joshua Heazlewood wrote:

In the Results / Discussion, I assume you mean the "xylulose kinase gene (xylB)" rather than "xylose kinase".

https://www.uniprot.org/uni...

On 2023-01-10 20:25:41, user Brad Rikke wrote:

Not clear why Figure 1b shows the death of a control animal prior to the red arrow indicating the start of treatment.<br /> There should have been a baseline measure of the frailty index for the mice at the start of treatment.

On 2023-01-07 22:12:55, user Francisco W. G. Paula-Silva wrote:

This manuscript has been published. Please find the reference as follows: Lorencetti-Silva F, Arnez MFM, Thomé JPQ, Carvalho MS, Carvalho FK, Queiroz AM, Faccioli LH, Paula-Silva FWG. Leukotriene B4 Loaded in Microspheres Inhibits Osteoclast Differentiation and Activation. Braz Dent J. 2022 Sep-Oct;33(5):35-45. doi: 10.1590/0103-6440202204827. PMID: 36287497; PMCID: PMC9645171.

On 2023-01-07 14:23:47, user Mia wrote:

Good evening, very interesting study. It is known that many reserachers were struggling to find right compound to provide better care for COVID-19 patients. When You say that You assessed safety and distribution of compound by intranasal, intravenous and intraperitoneal methods, in which dosages You did that?<br /> Also, what are the characteristics of animals used in this study? Best, Mia

On 2023-01-06 16:17:13, user M_Niemeyer wrote:

Isn't this already published , mainly in Prigge et al. 2020 and discussed there together with the publicaitons by Matyas Fendrych?

On 2023-01-05 16:50:33, user ingokeck wrote:

The authors should clearly state in the summary that they only looked at (fully) vaccinated persons. Also the infected persons were vaccinated.

On 2023-01-04 17:25:33, user S Kalkunte wrote:

Excellent piece of work!! Congratulation to the entire team!! Desiccation triggers frugal use of biochemical resources. Are the physical attributes including size affected by desiccation?

On 2023-01-04 15:27:36, user Soraya Juarbe-Diaz wrote:

Am I understanding correctly that this study simulated one, and only one, complex maneuver, jumping in a straight line, to determine the tail's function in athletic movement? Is this not like trying to analyze steering by looking at motion in a straight line only? Did they model turning behaviors at all, or balancing behaviors while walking a narrow plank or similar obstacle? What am I missing here? And as to the claim of less than one degree of effect in change in "centre of mass movement," such minute corrections may be sufficient to determine success or failure in agility or coursing dog sports, a MWD successfully apprehending his or her target or a sheepdog gathering livestock. I suggest the researchers step out of their math lab for an opportunity to observe working dogs in the real world, so they might rework their approach to computational modeling of live subjects.

On 2023-01-03 17:36:36, user parodeez wrote:

Juat because a CBM probe isn't cytotoxic doesn't mean it will be suitable for real-time imaging. The authors cute the finding that the presence of calcofluor affects the synthesis of cellulose without fully graspong or addressing the implications. Having anything bind to cellulose while it's being synthesized can (and likely will) significantly alter it's biosynthesis, final structure, and interactions with other cell wall moieties. The authors present no evidence that the native cellulose structure is maintained in the presence of their CBM(s).

On 2023-01-03 09:02:26, user Michal Hocek wrote:

Interesting work. We have come to similar conclusion from the chemical point of view - see: https://doi.org/10.1002/che... - glucosylation of 5hmC protects from cleavage by restriction endonucleases but does not prevent transcription.

Michal Hocek

On 2023-01-03 08:55:04, user Pustelny Katarzyna wrote:

Thank you. Currently, we have MS data confirming Tyr273 phosphorylation in the activation loop and it is also clearly visible on the electron density map. Detailed analysis of pTyr273 is on-going.

On 2023-01-01 23:09:32, user Krisztian Magori wrote:

Very interesting and important study! I found the methods, the results and the discussion quite convincing, with just a few questions and observations:

1. I understand that you used 11 additional JEV positive locations for external validation of model performance. 8 of these locations were additional piggeries, similarly to the 54 outbreaks at piggeries that you used to train your model. However, 3 of these were positive mosquito pools, which are very different, could have been collected far away from piggeries, and could be impacted by other factors. However, you didn't mention if model performance against these 3 positive pools was different than against the other 8. Can you comment on that? I understand that these are extremely low sample sizes, but it would be interesting to know if the model predicted those mostly correctly as well.

2. On page 8-9, you describe the habitat suitability modeling for Ardeidae birds. However, I don't see a similar descripticion for the habitat suitability modeling for feral pigs. Was that done the same way?

3. I understand your description that the stacked composite of ardeid suitability as a proxy for regional species richness, but that it only describes the fundamental, not the realized niche. Does this have to do with either alpha, beta or gamma diversity?

4. You're citing a paper by Furlong et al. describing the distribution of JEV mosquito vectors based on environmental niche modeling. Would it be possible to include maps of those distributions in your inhomogeneous point process framework as another covariate? Would it further improve the fit and the performance of the models, or would it be confounded with existing covariates?

5. On line 312, you mention sentinel chicken surveillance data. Was that also included? If yes, I missed it in the methods.

6. Figure 3 seems to indicate that the model was restricted to Queesnland, New South Wales, Victoria and South Australia. Is that correct? You don't mention that is the text.

7. On page 12, you mention that the model demonstrated strong associations between JEV outbreaks and proximity to waterways. However, the methods or Table S2 does not include "proximity to waterways" as a specific predictor. It only includes "distances to inland wetland and riparian wetland systems", and "distances to rivers and streams". Which one of these is "proximity to waterways"? Or is it a combination of those?

8. I find it very interesting that increased precipitation and decreased temperature associated with La Nina were both associated with increased risk in univariate models, but not after accounting for landscape structure and ardeid suitability. I do wonder if this could be because of only looking at a single season where there wasn't much variation spatially across the three states studied, since La Nina affected the entire area. I also wonder if there was collinearity between increased precipitation, decreased temperature and landscape structure and ardeid suitability, which might explain the different results in the univariate vs the multivariate models.

9. I noticed that in the multivariate models you used both Ardeid species richness as well as the square of Ardeid species richness to incorporate a non-linear response. Why did you incorporate the quadratic response for this particular predictor variable, based on what analysis? And why haven't you done the same for the other predictors?

10. I understand that the relationship between JEV risk and Ardeid species richness was non-linear, such that the highest risk was at intermediate Ardeid richness. Were there particular Ardeid species for which the suitability was high at those intermediate Ardeid richness levels particularly, or was it pretty much random, or just based on the distribution of specific species? You do mention the dilution effect in the discussion, and I understand that there is limited information on the host competence of specific Ardeid species in Australia, but it would be interesting to know if there's a consistent species assemblage predicted to correspond with the highest JEV risk.

11. I'm not sure I've seen the results for the relationship between feral pig distribution and JEV risk. I'm not seeing it in Table 1, or in the text, but I see that it had a negative univariate association with JEV risk in Table S2. However, I don't see it in Table S4. Was the decision made to remove it from the list of predictors, even from the full model? If yes, why?

Thank you very much for posting such an interesting study, and I'm looking forward to seeing your answers to my questions!

Krisztian Magori

On 2022-12-29 06:52:04, user Giorgio Cattoretti wrote:

The acronym/title "Stardust" has been already taken. See doi:10.1093/gigascience/giac075 (Stardust: improving spatial transcriptomics data analysis through space-aware modularity optimization-based clustering). In a small, very focused field of dimensionality reduction, it is curious how some names tend to recur.

On 2022-12-28 19:21:36, user Donald R. Forsdyke wrote:

Many seeking evolutionary explanations have followed Darwin’s approach. He collected facts and then explored how they might relate. Having already made major additions to these facts, Noboru Sueoka (1995) considered one of Chargaff’s four “rules” – PR2 – in terms of base mutation rates and a “directional mutation pressure,” with little consideration of other facts. With more sequences available, Pflughaupt and Sahakyan (2022) have now extended Sueoka’s work in a “completely assumption free” manner. Supporting Sueoka, their study “reinstates the mutation rates as the major drivers behind the emergence of PR-2.”

Furthermore, they have deduced that PR2 gained its universality very early in evolution. This accords well with prior considerations of PR2 using a fact-based approach (Forsdyke 1995). Here an important underlying fact was that effective information systems, whatever their nature, must gain error-correcting ability at an early stage. Accordingly, when seeking to explain introns, it was thought that nucleic acid sequences might contain error-correcting codes (Hamming 1980; Forsdyke 1981).

However, as the facts of DNA structure expanded, a genome-wide recombination-based correction process that predicted PR2 compliancy, became more plausible. In a primordial “RNA world” (prior to duplex DNA and PR1) sequences were deemed to be enriched in stem-loop structures capable of mediating error-correction. Into these intron-like sequences, genes would later have been able to elbow their way, and base content would have approached equilibria as Chargaff’s other rules evolved (Forsdyke 2013, 2021).

Since some readers of Pflughaupt and Sahakyan (2022) may also read Sueoka (1995), the authors should mention that they use the term “PR2” in the widely accepted historical sense (i.e., it was preceded by “PR1”), rather in the sense used by Sueoka. Furthermore, having downplayed Forsdyke (1995) by citing the works of Chen and Zhao (2005) and Zhang and Huang (2009), they should consider referring readers to his webpage rebuttals (see [or search the Wayback Machine (archive-it.org)).

Chen L, Zhao H (2005) Negative correlation between compositional symmetries and local<br /> recombination rates. Bioinformatics 21:3951-3958.

Forsdyke DR (1981) Are introns in-series error detecting sequences? Journal of Theoretical Biology 93:861-866.

Forsdyke DR (2013) Introns first. Biological Theory 7:196-203.

Forsdyke DR (2021) Neutralism versus selectionism: Chargaff's second parity rule revisited. Genetica 149:81-88.

Hamming RW (1980) Coding and Information Theory. Prentice-Hall, Englewood Cliffs.

Pflughaupt P, Sahakyan AB (2022) Generalised interrelations among mutation rates drive the genomic compliance of Chargaff’s second parity rule. bioRxiv doi:](https%3A%2F%2Fwww.queensu.ca%2Facademia%2Fforsdyke%2Fbioinfo9.htm%3AwSdnicm9t7E6LP8InSMngVCM7k4&cuid=2634513 "https://www.queensu.ca/academia/forsdyke/bioinfo9.htm") [.