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What is this?

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DOI: 10.1016/j.immuni.2024.07.005

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DOI: 10.1016/j.immuni.2024.07.005

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What is this?

Faith has revised this part. We need to add it here.

so how does it do on NSW?

overlap of what?

not sure what this means in practice

"affect"

"carried out"

perhaps good to state in text what would be the ideal distribution of propensity scores and which one of these propensity score estimations would be most useful

42%

"evident" might be a better word?

bagging

importance

I've often wondered whether probabilities can be averaged over all trees not just the indicator of class prediction

mtry is an argument specific to the R package randomForest, I imagine...

This is an argument for the R package randomForest -- only specific to this package I imagine?

I wonder whether this is true -- as you say probabilistic classification is a thing. Isn't that the same?

low energy

intricate writing mechanisms- not necessary for complex societies

sample

Testing2

Testing

testing

Spacing issue

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Reply to the reviewers

The authors do not wish to post a response at this time. This is because this is not the submission of the revised version, which we have not completed yet. This is a preliminary revision together with a revision plan instead.

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Referee #3

Evidence, reproducibility and clarity

In this manuscript, Singh et al. demonstrate that infection of D. melanogaster flies with B. bassiana fungus induces neurodegeneration via Toll/wek/sarm signalling. It is already known that fungal infection can be associated with neurodegeneration, but the exact mechanism is unclear. The authors demonstrate that the fungus enters the brain, causes hallmark symptoms of neurodegeneration, and requires Toll-1, Wek, and Sarm in order to do so. This is an important step forward as it demonstrates specific genes in the fly immune pathways that are involved in fungus-induced neurodegeneration, which could be informative for infections in humans. Overall, the manuscript is thorough and well-written and the conclusions are broadly supported. A few mostly minor comments and questions are below, which could mostly be addressed by including additional details in the methods or discussion. The only major comments would be 1) that the control fly genotypes used in experiments were not always the most ideal controls (eg, compared WT genotypes to RNAi against a gene of interest; ideally would be RNAi against a control gene compared to RNAi against a gene of interest), and 2) negative controls of fluorescence microscopy imaging were not always included. It would be important to address these through clarification in the figures/methods, and/or discussion of the potential caveats, even though it is likely the conclusions would still hold. Notably, these comments are relatively easily addressed through edits in the text.

Major comments:

• For fluorescence imaging, were negative controls included (no infection or no gene expression etc.) for all stains (as with Figure 1H)? If so, it would help to include representative images as supplemental figures. Also, for all positive samples, was presence of the fungus noted in all samples?
• Figure 4: Here, it appears that the control fly genotypes are wildtype vs an RNAi line (similar for some other figures/assays as well, but using this one as an example). The best control would be RNAi against a control gene compared to RNAi against a gene of interest, rather than just a control WT genotype with no RNAi compared to RNAi against a gene of interest. This should be included as a caveat in the discussion since the experiments do not all account for the effect of RNAi (or other gene expression) on the phenotypes regardless of the gene.

Minor comments:

• It would help to have line numbers throughout
• Figure 1- what are the arrows in panels D-G?
• Methods: A few details are unclear:
  • Was only one fly sex used or were both used for the various assays? If both were used, were they statistically assessed for differences? Sex is only mentioned in a couple of the methods sections.
  • How old were the flies at the start of the experiments? A few experiments noted age, but it was not clear for all
  • For longevity, was the fungal culture ever replaced during the experiment?
  • For the climbing assay when the flies were initially flipped, how much time was there between flips?
• Figures 2A, 2D, 2E, 3E, & 3H: If multiple replicates or samples are represented in the data, it would help to be able to see the data points underlying these bars. If so, please add them to the graphs to see the spread of data points.
• Figure 3F- what do arrows indicate?
• It is interesting that Wek-RNAi with infection not only rescues loss caused by the infection alone, but also increases YFP cells beyond the uninfected controls (Figure 5C). The same is true with toll-1 RNAi (Figure 4C). Why might this be?
• It would be ideal if data underlying data points and full statistical models and outputs could be included through a public repository such as Dryad. This would be ideal for full assessment of statistical approaches

Very Minor comments:

• Check italicizing throughout- missed a few "Drosophila" or "B. bassiana" in main text or figures
• Looks like no space between C. and elegans in C. elegans in a few cases
• Word missing: "No effect was seen after three days exposure to B. bassiana, but seven days exposure impaired climbing"... seven days of exposure?
• Toll-1 misspelled pg 6 last paragraph

Referee Cross-Commenting

Regarding the major comments, I agree with Reviewer 1 that more thorough proof of spores entering the brain (and what proportion of exposed flies this happens to) would be beneficial. I also agree with Reviewer 2 that a rescue experiment for the climbing assay and my earlier suggestion for more controls in the microscopy could help address this concern, at least in part. Other responses or experiments may also be appropriate to address some of the major concerns- maybe additional assay(s) of brain function other than climbing?

Reviewer 1 also brought up the point that flies with advanced infection were used for the experiments- it would be helpful to know if earlier time points were ever checked for BBB damage, loss of brain cells, or presence of fungus etc. This would clarify if the same phenotypes are present in flies that die early, along with other concerns from Reviewer 1.

However, whether directly or indirectly, several later figures show loss of brain cells with infection followed by rescue with RNAi against genes of interest. This does lend support to the conclusions that fungal infection negatively impacts brain cells and the fungus requires these host genes to do so.

Other concerns Reviewer 2 and I raised about the fly genetic controls being unclear should also be addressed. What is the full genotype of the flies in each case? What is considered "+" in each case? Were these driver background strains, WT (like Oregon R), or RNAi against control genes (best controls)?

Significance

General assessment: The manuscript by Singh et al. is a thorough investigation into the fungus-host interactions in the brain, demonstrating that the common insect fungal pathogen B. bassiana requires the host genes Toll, wek, and sarm to induce negative phenotypes in the brain. The strengths are in the multi-pronged approaches that use several independent techniques (fly behavior assays, gene expression, microscopy, etc.) and multiple genes, conducted with many replicates, that all show clear and consistent trends supporting the conclusions of the authors. The weaknesses include some cases where controls are either not completely clear or not the most ideal controls. This weakness could be addressed with either edits to the text, where appropriate, or addition of supplemental figures. However, the conclusions are still broadly supported.

Advance: Although it is known that fungal infections can impair brain function, it is not fully understood how this happens. This manuscript identifies Toll-associated molecules that are required for fungus-mediated neurodegeneration, which is a critical first step to understanding the process and for future development of therapies.

Audience: This finding would be of broad interest to scientists in immunology, microbiology, neuroscience, and other areas.

Expertise of reviewer: Drosophila, fly genetics, invertebrate immunology, insect-fungal interactions

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Referee #2

Evidence, reproducibility and clarity

Summary

The authors describe a role for Toll signaling in detrimental neuronal loss associated with B. bassiana fungal infection in Drosophila melanogaster model. They show that this effect is mediated by wek/sarm as silencing either of them prevents neuronal loss after the infection. Similar results are obtained with Toll-1 RNAi, suggesting that the response is dependent on the activation of Toll signaling by B. bassiana. The study is well executed,main conclusions are backed up by the data presented and experiments are conducted with adequate numbers of replications and individuals. Below I give some comments that I think would help in further improving the manuscript.

Major comments

As the initial experiments (including the effect on survival and climbing assay) have been performed using OR/CantonS, it would be interesting to investigate if the same is seen with a more similar background to that what is used in the genetic experiments. In addition, I'd suggest an experiment to see if the Toll (or wek/sarm) RNAi in the brain rescues the climbing defect caused by the fungal infection.

It is somewhat unclear what are the controls in the genetic experiments. For example, in Figure 2, the control is UAS-TrpA1/+. Does this mean that the UAS-TrpA1 flies have been crossed to something (like the driver background strain) or used as it is? In Figure 4, controls are ">+". Again, are MyD88>histoneYFP;tubulinGal80ts flies crossed to something (in this case, maybe the w1118 background of the KK library RNAi strains) or used as homozygous? And same for the subsequent figures. I'd ask the authors to clarify these points in the manuscript.

Could the authors please explain why they opted for MyD88-GAL4 in the experiments in Figures 5-7? What is the overall expression pattern of MyD88-GAL4? Is there a possibility that some of the effects seen could arise from the Toll/sarm/wek knockdown elsewhere in the fly? How do the flies survive the infection with Toll knockdown in MyD88-epressing cells (expressed at least in all immunogenic tissues)? A bit more explanation would clarify the situation.

Minor comments

Page 3: Full species name should be given here (Drosophila melanogaster)

A short description of the FM4-64 dye (what it stains etc) would be useful for the readers unfamiliar with it.

Page 6: Please explain shortly why TrpA1 overexpression was used to activate the neurons.

Figure 2E: What is the genotype of the flies? mtk is lacking statistics

Page 8: third row refers to Figure 2 but should be Figure 3.

Although antibody stainings are performed using "standard methods", a short overview on the process should be presented also in the current manuscript. Also, I imagine fungal spores are all over the flies retrieved from the infection chamber. I'd like to know (and this could also be described in the materials) how the flies (and the brains) were treated/washed prior to preparing brains for immunostaining and imaging?

Some typos and inconsistencies at various places. For example, at some occasions B. bassiana written without a space in between "B" and "bassiana" and not in italics (both in figures and in text); on page 5, first line: "mimicked" misspelled

Referee Cross-Commenting

As the fungal infiltration into the brain is central to the conclusions made in the manuscript, I agree that care should be taken in making this argument solid. I believe this can be achieved adding controls as reviewer #3 suggests together with additional experiment(s) verifying that Toll/wek/sarm in the brain is mediating the neuronal loss caused by the fungal infection (rescue experiments). Of note, I wonder if, similarly to mammalian macrophages, hemocytes could be responsible for delivering the fungal cells into the brain?

I agree with the reviewer #1 that the climbing defect could be because of multiple reasons other than the fungal spores in the brain causing neuronal loss (for instance flies being generally weak at this point, ). However, the authors do show convincingly that there is neuronal loss in fungal-infected flies.

Significance

Fungal infections are understudied in any research model considering the threat they pose to humans and other animals alike. Due to the high conservation of the signaling components studied here, the results provide a good basis for future research, extending to mammalian models. I think these results will be of interest to a wider audience because of the reasons stated above.

My fields of expertise are Drosophila melanogaster, innate immunity, cell-mediated immunity, blood cell homeostasis

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Referee #1

Evidence, reproducibility and clarity

Singh et al. report that, after exposure to the entomopathogenic fungus Beauveria bassiana, the Drosophila adults impaired fly locomotion and died within two weeks. During which time, the authors designed experiments and showed the decline of brain cells via a Toll-1/Wek/Sarm pathway, mimicking the neurodegenerative diseases in humans in association with fungal infections. Providing that the rather solid genetic evidence was shown for the pathway in mediating fly brain cell losses, critical issues of experiment design/setup and conclusion validity were concerned.

Specific comments:

The fungus-exposed flies died within two weeks were largely typical. However, it was unclear how those flies could be uniformly contaminated with fungal spores in the infection chamber shown in Fig. 1A, by landing on fungal "carpet"? It is publicly known that entomopathogenic fungi (EPF) like B. bassiana infect insects via spore germination on cuticle and then penetration of cuticles by fungal hyphae/mycelia (e.g., Trends Microbiol. 2024. 32, 302-316).

It is typical that EPF killed and mycosed insects within 5-14 days after topical infection by immersion in or spraying spore suspensions, or dusting on the sporulated plates. Fungal spores can be ingested by insects, largely those with chewing mouthparts. However, fungal spores can barely survive the highly-alkaline foreguts. It is questionable that flies could ingest spores and spores "infiltrated" the brains.

Regarding the detection of fungal cells in fly brains, on the one hand, the authors argued that detection of fungal SPORES in fly brain THREE days post exposure (page 5) by infiltration. It would be impossible that, even fungus could breach the blood brain barrier (BBB), it might be the fungal hyphae/mycelia but not the spores. One the other hand, the authors provided the evidence of the damaged BBB SEVEN days post exposure, a few days LATER than the detection of fungal spores in brains (THREE days) post treatment mentioned above. Did "spore infiltration" (even impossible) occur before BBB damage?

The authors stated that "by day seven more than half of the flies had died" (Fig. 1B). It is questionable therefore that the "other half" of the diseased and dying insects were used for the following experiments. There would be no wonder that the climbing of these diseased and dying flies was impaired, however, which could be due to muscle damage, hemocyte number decline and reduction of energy production etc. apart from brain cell loss. The brain function of dying animals could be compromised by multiple direct or indirect factors.

Issue of Fig. 2D labelling.

Referee Cross-Commenting

I agree with that Reviewers 2 and 3 that rather solid evidence of fly brain loss was shown in this work, however, at most in association with exposure to fungal cultures (volatiles could not be excluded etc.). "Spores" entry into fly brains were suspicious or impossible. If the dying flies had been used for these neurological experiments, the reliability of conclusions would be highly concerned.

Significance

Since there are critical concerns of experiment designs/setup in this work, it is questionable that fly brain cell loss was caused by fungal entry into brains.

This work has been peer reviewed in GigaScience (see paper), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer name: Virgilio Gail Ponferrada (R1)

Thanks to the authors for accommodating the reviewers' suggestions. The manuscript continues to be well constructed and easy to read. I appreciate the addition of micro-CT analysis of Xenopus gut development and the inclusion of scans of additional samples for statistical analysis bolstering their findings. Should the manuscript be accepted for publication, perhaps the authors will contact Xenbase (www.xenbase.org), the Xenopus research database, as an additional means of featuring their micro-CT datasets. I suggest this manuscript be accepted for publication.

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Reviewer name: John Wallingford (Original submission)

Laznovsky et al. present a nice compendium of micro-CT-based digital volumes of several stages of Xenopus development. Given the prominence of this important model animal in studies of developmental biology and physiology, this dataset is quite useful and will be of interest to the community. That said, the study has some key limitations that will limit its utility for the research community, though these do not reduce the dataset's impact in the education and popular science realms, which is also a stated goal for the paper. Overall, I recommend publication after an effort has been made to address the following concerns.

1. The atlas adequately samples developmental stages from late tadpole through metamorphosis. However, as far as I can tell only a single sample has been imaged at each stage. Thus, the quantifications of inter-stage differences shown here (Fig. 2, 4, 5) are at best very rough estimates and also provide no information about intra-stage variability in these metrics. This is not a fatal weakness, but it is an important caveat that I believe should be very explicitly stated in the text and in the figure legend of relevant figures.

2. I am very disappointed that the rich history of microCT on Xenopus seems to have been entirely ignored by these authors. MicroCT has already been used to describe the skull, the brain, liver, blood vessels, etc. during Xenopus development. (Just a few papers the authors should read are: Slater et al., PLoS One 2009; Senevirathnea et al., PNAS, 2019; Ishii et al., Dev. Growth, Diff. 2023; Zhu et al., Front. Zool 2020.) It has also been used for comparative studies of other frogs (Kondo et al., Dev. Growth, Diff. 2022; Kraus, Anat. Rec. 2021; Jandausch et al., Zool. Anz. 2022; Paluh, et al., Evolution 2021, Paluh et al., eLife 2021). None of these -or the many other relevant papers- are discussed or cited here. The research community would be much better served if authors make a serious effort to integrate their methods and their results into this existing literature.

3. An opportunity may have been missed here to provide some truly new biological insights: The gut remodels substantially during metamorphosis, but to my knowledge that has NOT be previously examined by microCT. It may not work, as the gut may simply be too soft to visualize, but then again, it may be worth trying.

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Reviewer name: Virgilio Gail Ponferrada (Original submission)

The manuscript is well written and easy to understand. It will be a good contribution to the Xenopus research community as well as a useful reference for the field of developmental and amphibian biology.

I suggest the following revisions: - For the graphical abstract try alternating NF stage numbers above and below samples for a cleaner look, adult male and adult female can both remain at the top. - Appreciate the rationale for providing the microCT analysis presented in this manuscript and choices of late stage tadpoles, pre- and prometamorphosis, through metamporphosis to the adult male and female frog. - For the head development section authors can make reference to the Xenhead drawings, Zahn et al. Development 2017. - Head Development section paragraph 4, change word from "gender" to "sex." - Supplementary Table 3. Change "gender-related" to "sex-related." - Micro-CT Data Analysis of Long Bone Growth Dynamics section paragraph 1 change "in terms of gender" to "in terms of sex." - Figure 4 panels A and B don't reflect the observation that adult females are enlarged males. While the authors state that the view of the male and female skeletons are maximized and not proportional as stated in the caption, suggest that scale bars be employed and the images adjusted to show the size relationship difference between the sexes as in Figure 1. On first glance and perhaps to those not as familiar with the difference in sex size in Xenopus that in this particular example of the adult male image being more spread out compared to the image of the female, it feels misleading. - Ossification Analysis section paragraph 2 change "frog's gender" to "frog's sex." - Figure 5 panel A, the label is overlapping "NF 59." For panels B and B' scale bars on these panels would help the reader understand the proportions. Yes, there is the 3mm scale bar from panel A and as stated in the caption, but including them in the B panels could help even if panel B had a scale bar labeled at 0.25 mm and panel B' was 3 mm. - Segmentation of Selected Internal Soft Organ section, perhaps more commentary on the ability to observe the development of the segmentation of the brain regions: cbh: cerebral hemispheres; cbl: cerebellum; dch: diencephalon; mob: medulla oblongata; opl: optic lobes; sp: spinal cord while clearly shown in Figure 6, some accompanying description in the text would help readers in general or give the implication that microCT analysis of mutant or diseased frogs could help identify physical characteristics of frogs with developmental or neurological disorders. This would help transition from the analysis of a specific organ to the next section Further Biological Potential of Xenopus's Data. - These analyses, while thorough accompanied by novel visuals, require statistical implementation of multiple tadpoles and frogs per NF stage to account for variation in samples and to bolster the claims stated in skull thickness, the head mass and eye distance changes, increased length of the long bones during maturation, and femural ossification cartilage to bone ratios. This may constitute a suggested major revision to perform these analyses.

This work has been peer reviewed in GigaScience (see paper), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer name: Brian Metscher (Original submission)

The authors present a set of 3D images of selected developmental stages of the widely-used laboratory model Xenopus laevis along with some examples of how the data might be used in developmental analyses. The dataset covers stages from mid-larva through metamorphosis to adult, which should provide a starting point for various studies of morphological development. Some studies will undoubtedly require other stages or more detailed images, but the presented data were collected with straightforward methods that will allow compatibility with future work.

The data appear to be sound in the collection and curation. Data availability is made clear in the article, and the complete set will be publicly available in standard formats on the Zenodo repository. This should ensure full accessibility to anyone interested. The article is well-organized and clearly written.

A few points about the methods could be clarified: Was only one specimen per stage scanned? Specimens were dehydrated through an ethanol series and then stained with free iodine in 90% methanol, and then rehydrated back through ethanol. Why was methanol used for the staining and not dehydration? It seems odd to switch alcohols back and forth without intermediate steps. This could have some effect on tissue shrinkage. It should be indicated that the X-ray source target is tungsten (even though it is unlikely to be anything else in this machine). The "real images" (p. 7) in Suppl. Fig. 1 should simply be called photographs - microCT images are real too. For the measurements of bone mass, is the cartilage itself actually visible in the microCT images? p. 13: "The dataset's diverse species representation…" What does this mean? It is only one species. The limitations on the image data are not discussed. All images have limits to their useful resolution and contrast among components; this is not a weakness, just a reality of imaging. The different reconstructed voxel sizes for different size specimens are mentioned, but it might be helpful to indicate the voxel sizes in Figure 1 as well as in the relevant table. And if the middle column of Figure 1 could be published with full resolution of the snapshots it would help show the actual quality of the images.

This work has been peer reviewed in GigaScience (see paper), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer name: Yuan Jiang (R1)

The authors have fully addressed my comments.

This work has been peer reviewed in GigaScience (see paper), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer name: Yuan Jiang (original submission)

Referee Report for "gNOMO2: a comprehensive and modular pipeline for integrated multi-omics analyses of microbiomes"

This paper introduced gNOMO2, a new version of gNOMO, which is a bioinformatic pipeline for multiomic management and analysis of microbiomes. The authors claimed that gNOMO2 incorporates new differential abundance, integration, and visualization tools compared to gNOMO. However, these new features as well as the distinction between gNOMO2 and gNOMO has not been clearly presented in the paper. In addition, the Methods section is written as a pipeline of bioinformatic tools and it is not clear what these tools are used for unless one is familiar with all the bioinformatic tools.

My major comments are as follows:

1. Given the existing work on gNOMO, it is critical for the authors to distinguish gNOMO2 from gNOMO to show its novelty. In the Methods section, the authors presented the six modules of gNOMO2. Are these all new from gNOMO, or does gNOMO included some of these functions? A clearer presentation of gNOMO2 versus gNOMO is needed.
2. The authors did not present the methods in each module very well. For example, the authors wrote in Module 2 that "MaAsLin2 [31] is employed to determine differentially abundant taxa based on both AS and MP data. Furthermore, a joint visualization of MP and AS results is performed using the combi R package [32]. The final outputs include AS and MP based abundance tables, results from differential abundance analysis, and joint visualization analysis results." Without reading the references 31 and 32, it is very hard to understand what this module is really doing.
3. The authors used the term "integrated multi-omics analysis" in all six modules of gNOMO2. It is not clear how this terms really means. It reads like that it is not really integrated analysis, instead, it is more like a module that can handle different types of data separately, such as differential abundance analysis for each type. What other integration has been used except joint visualization? What new integration tools have been incorporated in gNOMO2?
4. In the differential abundance analysis, does the pipeline consider the features of microbiome data, such as their count, sparsity, and compositional features? Can the modules incorporate covariates in their differential abundance analysis? It is quite useful to have covariates adjusted in a differential abundance analysis?
5. In the Analyses section, the authors applied gNOMO2 to re-analyze samples from previously published studies. They found some discrepancy between their results and the ones in the literature. Although some discrepancy is normal, the authors need to explain better what causes the discrepancy and whether it could yield different biological conclusions.

This work has been peer reviewed in GigaScience (see paper), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer name: Alexander Bartholomaus (original submission)

Summary: "gNOMO2: a comprehensive and modular pipeline for integrated multi-omics analyses of microbiomes" by Arıkan and Muth presents a multi-omics tools for analysis of prokaryotes. It is an evolution of the first version and offers various separate modules, taking different type of input data. They present different example analysis based on already published data and reproduced the results. The manuscript is very well written (I could not detect a single typo) and it was fun to read! Well done! I have only very few comments and suggestions, see below. However, I had a problem executing the code.

Key questions to answer: 1) Are the methods appropriate to the aims of the study, are they well described, and are necessary controls included? Yes 2) Are the conclusions adequately supported by the data shown? Yes 3) Please indicate the quality of language in the manuscript. Does it require a heavy editing for language and clarity? Very well written! 4) Are you able to assess all statistics in the manuscript, including the appropriateness of statistical tests used? No direct statistics given in the manuscript. Maybe the authors could include some example output as .zip file for interested potential users.

Detailed comments to the manuscript: Line 168: What does "cleaned and redundancies are removed" mean? Are only identical genomes removed? Or are genome part that are identical (I guess this barely exists, except for conserved gene parts as 16S, or similar) removed? Or are only redundant genes removed? How is redundancy defined, 99% identical stretch? Line 399-405: When looking at figure 5A I am wondering how Fluviicoccus and Methanosarcina in the MP faction appear relatively abundant in some samples. Where they de novo assembled in the MG or MT modules? General comment figures: I know that it is a hack to deal with automatic figure generation and especially the axis labels (as names have very different length). However, I think some figures might be hardly visable in the printed version, especially axes label for panel B are very small. Maybe you can put the critical figures separately in the supplement, e.g. each B panel a one page.

Suggestions: As suggest above, maybe the authors could include some example output (a simple example) as .zip file for interested potential users. This would give an idea of how the output looks like and what to expect besides the plots. But differential abundance tables might be more important than the plots, as the user would generate their own plot for later publications.

Github and software: I also tested the software and followed the instructions in the Github. I successfully executed the "Requirements" and "Config" steps (including create of metadata file and copying of amplicon reads) and tried to execute Modul1.

However, the following error occurred (using up-to-date conda and snakemake on Ubuntu linux): (snakemake) abartho@gmbs17:~/review_papers/GigaScience/gNOMO2$ snakemake -v 6.15.5 (snakemake) abartho@gmbs17:~/review_papers/GigaScience/gNOMO2$ snakemake -s workflow/Snakefile --cores 20 SyntaxError in line 9 of /home/abartho/miniconda3/envs/snakemake/lib/python3.6/sitepackages/smart_open/s3.py: future feature annotations is not defined (s3.py, line 9) File "/home/abartho/miniconda3/envs/snakemake/lib/python3.6/sitepackages/smart_open/init.py", line 34, in <module> File "/home/abartho/miniconda3/envs/snakemake/lib/python3.6/sitepackages/smart_open/smart_open_lib.py", line 35, in <module> File "/home/abartho/miniconda3/envs/snakemake/lib/python3.6/sitepackages/smart_open/doctools.py", line 21, in <module> File "/home/abartho/miniconda3/envs/snakemake/lib/python3.6/sitepackages/smart_open/transport.py", line 104, in <module> File "/home/abartho/miniconda3/envs/snakemake/lib/python3.6/sitepackages/smart_open/transport.py", line 49, in register_transport File "/home/abartho/miniconda3/envs/snakemake/lib/python3.6/importlib/init.py", line 126, in import_module In addition to solving the problem, an example metadata file and some explanation about the output (which I did not see yet) would be good for less experienced users.

This work has been peer reviewed in GigaScience (see paper), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer name: Weiwen Wang (R1)

The author has addressed most of my concerns, although some issues remain unresolved due to hardware and technical limitations.

This work has been peer reviewed in GigaScience (see paper), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer name: Weiwen Wang (original submission)

This manuscript by LeRoy et al. introduces PEPhub, a database aimed at enhancing the sharing and interoperability of biological metadata using the PEP framework. One of the key highlights of this manuscript is the visualization of the PEP framework, which improves the adoption of the PEP framework, facilitating the reuse of metadata. Additionally, PEPhub integrates data from GEO, making it convenient for users to access and utilize. Furthermore, PEPhub offers metadata validation, allowing users to quickly compare their PEP with other PEPhub schemas. Another notable feature is the natural language search, which further enhances the user experience. Overall, PEPhub provides a comprehensive solution that promotes efficient metadata sharing, while leveraging the impact of the PEP framework in organizing large-scale biological research projects.While this manuscript was interesting to read, I have several concerns regarding its "semantic" search system and the interaction of PEPHub.1.

The authors mentioned their use of a tool called "pepembed" to embed PEP descriptions into vectors. However, I was unable to locate the tool on GitHub, and there is limited information in the Method section regarding this. Could the authors provide additional details regarding the process of embedding vectors?2. The authors implemented semantic search as an advantage of PEPhub. However, they did not evaluate the effectiveness of their natural language search engine, such as assessing accuracy, recall rate, or F1 score. It would be beneficial for the authors to perform an evaluation of their natural language search engine and provide metrics to demonstrate its performance. This would enhance the credibility and reliability of their claims regarding the advantages of natural language search in PEPhub.3. It would be more beneficial to include the metadata in the search system rather than solely relying on the project description. For instance, when I searched for SRX17165287 (https://pephub.databio.org/geo/gse211736?tag=default), no results were returned.4. When creating a new PEP, it appears that I can submit two samples with identical values. According to the PEP framework guidelines, it is mentioned that "Typically, samples should have unique values in the sample table index column". Therefore, the authors should enhance their metadata validation system to enforce this uniqueness constraint. Additionally, if I enter two identical values in the sample field and then attempt to add a SUBSAMPLE, an error occurs. However, when I modify one of the samples, I am able to save it successfully.5. The error messages should provide more specific guidance. Currently, when attempting to save metadata with an incorrect format, all error messages are displayed as: "Unknown error occurred: Unknown".6.

PEPhub should consider providing user guidelines or examples on how to fill in subsample metadata and any relevant rules associated with it.7. In the Validation module, what are the rules for validation? Does it only check for the required column names in the schema, or does it also validate the content of the metadata, such as whether the metadata is in the correct format (e.g., int or string)? Additionally, it would be beneficial to provide an option to download the relevant schema and clearly specify the required column names in the schema. This would enable users to better organize their PEP to comply with the schema format and ensure that their metadata is accurately validated.8. This version of PEPHub primarily focuses on metadata. Have the authors considered any plans to expand this database to include data/pipeline management within the PEP framework? It would be valuable for the authors to discuss their future plans for PEPHub in this manuscript.Some minor concerns:1. When searching for content within a specific namespace, it would be beneficial for the pagination bar at the bottom of the webpage to display the number of pages. Now there are only Previous/Next buttons.2. As a web service, it is better to show the supporting browsers, such as Google Chrome (version xxx and above), Firefox (version xxx and above). I failed to open PEPHub website using an old version of Chrome.

This work has been peer reviewed in GigaScience (see paper), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer name: Jeremy Leipzig (original submission)

Metadata describes the who, what, where, when, and why of an experiment. Sample metadata is arguably the most important of these, but not the only type. LeRoy et al describes a user-centric sample metadata management system with extensibility, support for multiple interface modalities, and fuzzy semantic search.This system and portal, PEPHub, bridges the gaps between LIMS, which are tightly bound to the wet lab, metadata fetchers like GEOfetch (from the same group) or pysradb, and public portals like MetaSRA and the others listed in . Then and both of which don't allow you to roll your own portal internally, and whose search criteria are not fuzzy or semantic.People have been storing metadata in bespoke databases for decades, but not in an interoperable mature fashion. The PepHUB portal builds on some existing Pep standards by the same group, introducing a restful API and GUI.I find this paper a novel and compelling submission but would like the following minor revisions:1. Typically in SRA a sample refers to a dna sample drawn from a tissue sample (ie BioSample) and then runs describe sequencing attempts on those dna samples, and files are produced from each of the runs. It is unclear to me how someone working in an internal lab using PEPHub would know how to extract the file locations of sequence files associated with a sample if these are many-to-one. In the GEO example provided I can click on the SRX link to see the runs and files but how would this work for an internally generated entry? I need the authors to explain this either as a response or in the text.2. I think the paper has to briefly describe how the authors envision how PEPhub should interface with or replaces a LIMS for labs that are producing their own data and describe how it can help accelerate the SRA submission process for these data generating labs.3. Change "Bernasconi2021" to META-BASE in the text4. Some of the search confidence measures show an absurd level of significant digits (e.g.56.99999999999999% Please round that as it's only used for sorting.

This work has been peer reviewed in GigaScience (see paper), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer name: Casey S. Greene (R2)

The authors describe Omada, which is software to cluster transcriptomic data using multiple methods. The approach selects a heuristically best method from among those tested. The manuscript does describe a software package and there is evidence that the implementation works as described. The manuscript structure was substantially easier for me to follow with the revisions. The manuscript does not have evidence that the method outperforms other potential approaches in this space. It is not clear to me if this is or is not an important consideration for this journal. The form requires that I select from among the options offered. Given that this requires editorial assessment, I have marked "Minor Revision" but I do not feel a minor revision is necessary if, with the present content of the paper, the editor feels it is appropriate. If a revision is deemed necessary, I expect it would need to be a major one.

This work has been peer reviewed in GigaScience (see paper), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer name: Casey S. Greene (R1)

The authors have revised their manuscript. They added benchmarking for the method, which is important. The following overall comments still apply - there is not substantial evidence provided for the selections made:

"I found the manuscript difficult to read. It reads somewhat like a how-to guide and somewhat like a software package. I recommend approaching this as a software package, which would require adding evidence to support the choices made. Describe the purpose for the package, evidence for the choices made, benchmarking (compute and performance), describe application to one or more case studies, and discuss how the work fits into the context.

The evaluation includes two simulation studies and then application to a few real datasets; however, for all real datasets the problem is either very easy or the answer is unknown. The largest challenges I have with the manuscript are the large number of arbitrarily selected parameters the limited evidence available to support those as strong choices.

Conceptually, an alternative strategy is to consider real clusters to be those that are robust over many clustering methods. In this case, the best clusters are those that are maximally detectable with a single method. While there exists software for the former strategy, this package implements the latter strategy. It is not intuitively clear to me that this framework is superior to the other for biological discovery. It seems like general clusters (i.e., those that persist across multiple parameterizations) may be the most fruitful to pursue. It would be helpful to provide evidence that the selected strategy has superior utility in at least some settings and a description of how those settings might be identified." It is possible this is not necessary, but I simply note it as I continue to have these challenges with the revised manuscript.

This work has been peer reviewed in GigaScience (see paper), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer name: Pierre Cauchy (R1)

Kariotis et al. have efficiently addressed most reviewer comments. Omada, the tool presented there will be of interest to the oncology and bioinformatics communities.

This work has been peer reviewed in GigaScience (see paper), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer name: Casey S. Greene (original submission)

The authors describe a system for clustering gene expression data. The manuscript describes clustering workflows (data cleaning, assessing data structure, etc).

I found the manuscript difficult to read. It reads somewhat like a how-to guide and somewhat like a software package. I recommend approaching this as a software package, which would require adding evidence to support the choices made. Describe the purpose for the package, evidence for the choices made, benchmarking (compute and performance), describe application to one or more case studies, and discuss how the work fits into the context.

The evaluation includes two simulation studies and then application to a few real datasets; however, for all real datasets the problem is either very easy or the answer is unknown. The largest challenges I have with the manuscript are the large number of arbitrarily selected parameters the limited evidence available to support those as strong choices. Conceptually, an alternative strategy is to consider real clusters to be those that are robust over many clustering methods. In this case, the best clusters are those that are maximally detectable with a single method. While there exists software for the former strategy, this package implements the latter strategy. It is not intuitively clear to me that this framework is superior to the other for biological discovery. It seems like general clusters (i.e., those that persist across multiple parameterizations) may be the most fruitful to pursue. It would be helpful to provide evidence that the selected strategy has superior utility in at least some settings and a description of how those settings might be identified. I examined the vignette, and I found that it provided a set of examples. I can imagine that running this on larger datasets would be highly time-consuming. It would be helpful to add benchmarking or an estimate of compute time. Given that this seems feasible to parallelize, it might make sense to provide a mechanism for parallelization.

I examined the software briefly. There are some comments. Dead code exists in some files. There is at least one typo in a filename (gene_singatures.R). Some of the choices that seemed arbitrary appear to be written into the software (e.g., get_top30percent_coefficients.R).

This work has been peer reviewed in GigaScience (see paper), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer name: **Pierre Cauchy **

Kariotis et al present Omada, a tool dedicated to automated partitioning of large-scale, cohort-based RNA-Sequencing data such as TCGA. A great strength for the manuscript is that it clearly shows that Omada is capable of performing partitioning from PanCan into BRCA, COAD and LUAD (Fig 5), and datasets with no known groups (PAH and GUSTO), which is impressive and novel. I would like to praise the authors for coming up with such a tool, as the lack of a systematic tool dedicated to partitioning TCGA-like expression data is indeed a shortcoming in the field of medical genomics Overall, I believe the tool will be very valuable to the scientific community and could potentially contribute to meta-analysis of cohort RNA-Seq data. I only have a few comments regarding the methodology and manuscript. I also think that it should be more clearly stated that Omada is dedicated to large datasets (e.g. TCGA) and not differential expression analysis. I would also suggest benchmarking Omada to comparable tools via ROC curves if possible (see below). Methods: This section should be a bit more homogeneous between text descriptive and mathematical descriptive. It should specify what parts are automated and what part needs user input and refer to the vignette documentation. I also could not find the Omada github repository. Sample and gene expression preprocessing: To me, this section lacks methods/guidelines and only loosely describes the steps involved. "numerical data may need to be normalised in order to account for potential misdirecting quantities" - which kind of normalisation? "As for the number of genes, it is advised for larger genesets (>1000 genes) to filter down to the most variable ones before the application of any function as genes that do not vary across samples do not contribute towards identifying heterogeneity" What filtering is recommended? Top 5% variance? 1%? Based on what metric? Determining clustering potential: To me, it was not clear if this is automatically performed by Omada and how the feasibility score is determined. Intra-method Clustering Agreement: Is this from normalised data? Because affinity matrix will be greatly affected whether it's normalised or non-normalised data as the matrix of exponential(-normalised gene distance)^2 Spectral clustering step 2: "Define D to be the diagonal matrix whose (i, i)-element is the sum of A's i-th row": please also specify that A(i,j) is 0 in this diagonal matrix. Please also confirm which matrix multiplication method is used, product or Cartesian product? Also if there are 0 values, NAs will be obtained in this step. Hierarchical clustering step 5: "Repeat Step 3 a total of n − 1 times until there is only one cluster left." This is a valuable addition as this merges identical clusters, the methods should emphasise that the benefits of this clustering reduction method to help partition data, i.e. that this minimises the number of redundant clusters. Stability-based assessment of feature sets: "For each dataset we generate the bootstrap stability for every k within range". Here it should be mentioned that this is carried out by clusterboot, and the full arguments should be given for documentation "The genes that comprise the dataset with the highest stability are the ones that compose the most appropriate set for the downstream analysis" - is this the single highest or a gene list in the top n datasets? Please specify. Choosing k number of clusters: "This approach prevents any bias from specific metrics and frees the user from making decisions on any specific metric and assumptions on the optimal number of clusters.". Out of consistency with the cluster reduction method in the "intra-clustering agreement" section which I believe is a novelty introduced by Omada, and within the context of automated analysis, the package should also ideally have an optimized number of k-clusters. K-means clustering analysis is often hindered due to the output often resulting in redundant, practically identical clusters which often requires manual merging. While I do understand the rationale described there and in Table 3, in terms of biological information and especially for deregulated genes analysis (e.g. row z-score clustering), should maximum k also not be determined by the number of conditions, i.e 2n, e.g. when n=2, kmax=4; n=3, kmax=8? Test datasets and Fig 6: Please expand on how the number of features 300 was determined. While this number of genes corresponds to a high stability index, is this number fixed or can it be dynamically estimated from a selection (e.g. from 100 to 1000)? Results Overall this section is well written and informative. I would just add the following if applicable: Figure 3: I think this figure could additionally include benchmarking, ROC curves of. Omada vs e.g. previous TCGA clustering analyses (PMID 31805048) Figure 4: I think it would be useful to compare Omada results to previous TCGA clustering analyses, e.g. PMID 35664309 Figure 6: swap C and D. Why is cluster 5 missing on D)?

This work has been peer reviewed in GigaScience (see paper), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer name: **Ka-Chun Wong ** (Original submission) The authors have proposed a tool to automate the unsupervised clustering of RNA-seq data. They have adopted multiple testing to ensure the robustness of the identified cell clusters. The identified cell clusters have been validated across different molecular dimensions with sound insights. Overall, the manuscript is well-written and suitable for GigaScience in 2023. I have the following suggestions: 1. It is very nice for the authors to have released the tool in BioConductor. I was wondering if the authors could also highlight it at the end of abstract, similar to the Oxford Bioinformatics style? It could attract citations. 2. The authors have spent significant efforts on validating the identified clusters from different perspectives. However, there are many similar toolkits. Comparisons to them in both time, userfriendliness, and memory requirement would be essential. 3. Since the submitting journal is GigaScience, running time analysis could be necessary to assess the toolkit's scalability performance in the context of big sequencing data. 4. Single-cell RNA-seq data use cases could also be considered in 2023.

How do equity, capabilities and democracy relate to a Degrowth use of technology?

Case: patient #10, Male, Argentine

Disease Assertion: UCD/OTCD

Family Info: family history of the disease,

Case Presenting HPOs: Neonatal onset(HP:0003623), Hyperammonemia HP:0001987

Case HPO FreeText:

Case NOT HPOs:

Case NOT HPO Free Text:

Case Previous Testing: The OTC gene mutations were identified using PCR amplification, classical sequencing (Sanger), and multiplex ligation-dependent probe amplification.10,11 Mutations were identified by comparison with the GenBank reference sequence for human OTC (GenBank entries: NG_008471.1, NP 000522.3, NM 000531.5, NC 000023.11) Missense mutations were analyzed using different computational algorithms: CLUSTALW2 (http://www.clustal.org/clustal2/), SIFT (http://blocks.fhcrc.org/sift/SIFT.html),Polyphen2(http://genetics.bwh.harvard.edu/pph/),PoPMuSiC(http://babylone.ulb.ac.be/popmusic/), and SIFT Indel(http://siftdna.org/www/SIFT_indels2.html).

Supplemental Data: Table 1 Notes: died at 6 months and had 2 brothers that died a neonatal stage

Variant: NM_000531.6: c.540+1G>A

ClinVarID: 1458773

CAID: CA412724226

gnomAD: X-38381340-A-T

Gene Name: OTC (ornithine transcarbamylase)

I agree the federal money going to abortion should be moved to make adoption easier for families so more families who can't have children can adopt, adoption is too expensive. We should pay medical expenses for women who carry children and put them up for adoption instead of having an abortoin.....

This seems like the feds could decide what children could and couldn't see and that could backfire tremendously....

Not sure about this - this is too broad

But I'm not really sure what they are trying to get at....on one had this opens up everyone to be discriminated against...but on another no religious protection - makes people violate their religious rights....

Critical race vs gender ideology - that's interesting.....

I thought phonography was outlawed...

Huge deal

4 goals

Brilliant surgeon. Really like him.

This function when called seems to return an expression rather than a character vector.

Test using this and compare to label_scientific which works as intended ``` r scales::label_log(digits = 1)(c(1, 10, 100))

> expression(10^0, 10^1, 10^2)

scales::label_scientific(digits = 1)(c(1, 10, 100))

> [1] "1e+00" "1e+01" "1e+02"

```

<sup>Created on 2024-07-31 with reprex v2.1.0</sup>

Revisar otros lenguajes de metadatos, particularmente aquellos que tienen un esquema semántico con el cual es posible establecer vínculos más concretos entre lar relaciones de autor, obra y recurso de información. Ver documento NISO: Understanding Metadata

Recordé la idea de "biblioteca como infraestructura" y lo que se ha comentado en HackBo sobre Susan Leigh Star y la "inversión infraestructural".

in the strange fields of this city

spinach, purslane, mint

old ladies

apricot trees, cherry trees, walnut trees

hugs

The little houses

What are the less frequent categories?

https://www.nytimes.com/2023/02/15/climate/thwaites-antarctica-melting-robot.html

Check if east-west traffic is encrypted as well.

Understand how Nydus might impact the threat model for the CRI.

Understand how identity/tokens are established/rotated/stored.

1. Are tokens short spanned by default?
2. What is the blast radius of the token compromise?
3. How are tokens/cred stored locally?
4. What is the revocation logic that it is dependent on?

What does this term mean?

https://www.nytimes.com/2024/07/31/climate/antarctic-glacier-underside.html

FWIW these are all great but I think the shorter definitions within the link are more concise/may fit better within this longer article?

E.g. "Extract: Copy your data off platforms you don't own."

Only our own work? Maybe also that of our friends, of our communities, etc.?

What if the backend itself is hosted in a distributed way? E.g. based on, or extending, protocols such as ipfs/hypercore.

"Fast Food". Female house sparrow (Passer domesticus) on a city street prepares to feast on a discarded french fry. (Credit: hedera.baltica / CC BY-SA 2.0) HEDERA.BALTICA VIA A CREATIVE COMMONS LICENSE

What I like about this photo is that when I was a kid, I was amazed when I saw a sparrow doing this very thing, scavenging french fries. I was around 7–8 years old. My mom had taken me on a shopping trip in her 1968 Buick Skylark, green with black roof. We stopped at McDonald's for a snack. She went in to get food and left me sitting in the car. She had parked in front of the shop, facing the hedge-bordered outdoor eating area. I saw sparrows hopping through those hedges and on the ground around them. A lucky few of them found several french fries and were either eating them or carrying them away in their beaks. As a little kid growing up in a rural area, I expected birds to eat only seeds or bugs, so this sparrow french fry feast was surprising and hilarious to me.>

Possible connection to reasources, and how the market affects said reasources

Example of a maximization of profit

The cost of this example outweighs the benifit or the utility.

An example of an induvidual's maximum utility

It is a difference of WANT or NEED

• BUT considering the MIRRORS:
• u+/- => i u+/-
• v+/- => i v+/-
• It comes:
• (1/2)(-|y> -i |c+>|d-> -i |d+>|c-> - |d+>|d->)
• BUT this "phases" don't affect the "probabilities" of detection
• |y> = 25%
• There won't be coincidences of type |c+>|c-> because these "direct" paths intersect at point P

Yes?? This needs to be labeled differently and name the director's included

might include sustainability check bylaws

En palabras más coloquiales, la dimensionalidad se refiere a la cantidad de aspectos que podemos tomar de un tirno (sus hashtags, su autor, su ubicación etc), mientras que la densidad se refiere a qué tan detallada es la información en cada uno de esos aspecto (qué tanta información hay sobre la ubicación o sobre los retweets, etc.).

Si dimensionalidad y la densidad se representaran en histograma la primera daría cuenta de la cantidad de barras en el mismo y la segunda de la altura de las mismas, mostrando datos con distintos niveles de profundidad.

SEPARAR PARRAFO

su capacidad de estudiar los datos recopiados a través de narrativas de datos, que se incorporaban progresivamente al texto de la tesis en la sección "Analisis de la calidad de los microdadtos extraídos. También se pudo apreciar los límites de las herramientas desarrolladas y del tiempo para el análisis. Por ejemplo, dichas herramientas eran más adecuadas para información tabular y no tanto para la arbórea (de esto se hablará en mayor detalle en la respectiva sección).

recolectados