Dear Dr. Crovella,

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PLOS ONE

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Additional Editor Comments:

I would like to thank you for your work and for the significant contribution it makes in the field of genomics and the integration of multiomic data. However, I have some observations and questions that could help contribute to a greater clarity and understanding of your study. Additionally, I believe that Major revisions are necessary, and it is important to expand the manuscript by addressing the reviewers' comments in detail.

1. Clarification of the Objective: Clearly define whether the main objective is the introduction of the InterOmics tool or the analysis of Hartnup syndrome (HS).
2. Details on InterOmics: Provide information on where to access and install InterOmics, such as a GitHub repository.
3. Explanation of the Algorithm Steps: Offer a detailed description of the logic and objectives of each phase of the protocol in the methods section.
4. Clarity on TPM Cutoff: Explain the choice of the 10 TPM expression cutoff and its relevance to the results.
5. Comparison with Other Tools: Discuss the advantages and differences of InterOmics compared to other existing technologies in the multiomic landscape.
6. Risks of False Positives: Elaborate on how the risks of false positives are managed, especially regarding the increase in identified variants.
7. Connection to HS Pathogenesis: Provide more detailed explanations on how the results contribute to a better understanding of the mechanisms of HS.
8. Data Accessibility: Offer key guidance on how readers can access patient data and the requirements for consultation.
9. Correction of References and Proofreading: Review inconsistencies in the text and correct any language and writing errors.
10. These points can help the authors improve the clarity and quality of their manuscript.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

Reviewer #2: Partly

Reviewer #3: Yes

Reviewer #4: Yes

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2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: I Don't Know

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: No

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3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

Reviewer #4: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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Reviewer #1: Please see the PDF attachment for the formatted version of my review.

==============

I find the goal of this study extremely hard to understand. Is this a paper about (1) a new tool, InterOmics, which happen to be applied to HS as a proof of concept, or about (2) a HS study and its findings, which happen to use InterOmics to obtain the results?

It the answer is (1), what is the tool? Where is the tool? Is there a GitHub repository from which one can install the tool? Is there a tool at all, or is the analysis pipeline the series of bash scripts in the supplementary document? Presumably, this paper was written to encourage other researchers and medical practitioners to use multiomic tools to improve clinical practice – how should they do this?

What are the purposes of the various steps of the algorithm? What is the reasoning behind various choices? The methods section enumerates various steps which were performed, and the supplementary document has some non-commented code that shows how these steps may have been performed in shell. But why these steps and what do they accomplish? Since the tool is presented as a relatively user-friendly toolbox targeted at clinicians, it would be helpful not to assume the reader already knows all the complexities of the process, and to step through the logic.

“Beyond the basic normalization steps, our InterOmics protocol specifically identified genes with a Transcripts Per Million (TPM) expression rate above 10 as a benchmark for significant genetic activity” – what does this mean, precisely? If I understand correctly, the sentence just reports an arbitrary TPM cutoff. Does the protocol (algorithm?) relate to this cutoff choice? This is unclear.

There is an example workflow in document. But at the minimum, it seems natural to have 1. the actual workflow that was used to perform the analysis of the dataset discussed in the paper and 2. an example workflow that shows that the method works on some small synthetic or toy dataset and produces some expected results. How does the MySQL management step play into this? It is mentioned but not characterized.

For articles that characterize tools, some characterization or discussion of advantages over/ comparison to existing tools for analogous purposes is expected. There is certainly a variety of previous tools that combine WES and RNA-seq. It would be appropriate to explain how these tools are different, and what advantages this tool offers.

“The combined use of WES and RNA-seq in clinical approaches increased the number of variants by an average of 20.89%” – would it be possible to characterize the risks of false positives here in more detail? More does not necessarily mean better. Is this good? Is this bad? What performance do other tools achieve? “We enhance confidence by considering only RNAe and ASE variants with a read depth (DP) of 5 or greater in the alternative allele, thus mitigating discrepancies arising from sequencing errors or other potential technical issues” seems like it would account for sequencing errors, but not account for RT or early PCR cycle errors. How would you make the reader confident that an interesting variant is not just the consequence of a single, incidentally highly amplified wrong cDNA? How would you QC for such an issue?

If the answer is (2), then what are the findings? The abstract says “The integration of this data not only advances our comprehension of HS's underlying mechanisms but also demonstrates the potential of multiomics to improve personalized medical approaches.” The conclusions say “The capability of InterOmics to integrate multiomic data has the great potential to elucidate the pathogenesis of complex diseases, such as HS, from an omic standpoint. Therefore, for conditions like HS, which involve intricate molecular and environmental interactions, such a comprehensive analysis is important.” In what way does the paper accomplish this? I understand that the procedure characterizes a diversity of variants. But the authors do not take the final step and connect show that the some or all of these results are sensible in explaining the pathogenesis of HS.

In other words: presenting the tool also requires explaining how a user should interpret its results, and I do not think this last essential connection to the disease is really explained; the paper jumps from presenting tables of variants – which can reasonably be called “personalized genomic/transcriptomic profiles” – to saying the tool “offer[s] deep insights into the unique genetic landscapes of these individuals.” I am not clear on what these insights are, or how one should obtain such insights from applying the workflow to their own data.

Where are the data? New patient data were collected for the study, but this is not discussed in the manuscript or submission disclosures, which state “all data are fully available without restriction.” I realize that releasing human patient data involves various challenges. But the reader should be able to obtain the data the conclusions are based on, or at the very minimum understand what they would need to do to obtain the data.

Could the authors comment on what seems to be a contradiction between using GRCh38 to quantify RNA but also exploring complex differences between the patient genome and GRCh38. I would intuitively think that if the pipeline is generating an individual genomic profile, the quantification would be with reference to that profile, instead of something else. Does it make a difference?

Some broken references e.g. line 260, 283. Various proofreading issues e.g. line 266. “InterOMICs” or “InterOmics”? If “GRChg38” is the name of a genome, it is unexpected and should be explained/cited. WSX = unexplained abbreviation? TBS = typo of TFBS?

Some oddities in table S2 findings. nhr-6, a C. elegans gene? AT5G05790, an Arabidopsis gene? YKL222C, a yeast gene? Maybe there is some homology argument, but it is not really clear how his should be interpreted in terms of human genetics, and the authors do not explain.

TF2DNA is a bare link to an inaccessible website. Was this database published somewhere? Could other databases be used?

Various other bare links. (The TCAG one may even be blocked by modern browsers because it is a download from a non-secure http website). Is this zip file versioned? Is it identical to the file linked in the 2018 paper? Have there been updates in the last six years? When was it accessed for this paper’s analysis? Will the file be available and identical in the future? Etc. Various other bare links, databases, R packages discussed without version information.

Fig 3 is very hard to understand without carefully reading the caption. I expect that there is a better way to illustrate these distinctions – perhaps by showing the sort of data these variants can generate? Or distinguishing the different chromosomes? As it reads, it conflates technology and biology (whether some variant is not present or merely not observed because of a lack of reads) and the distinction between e.g. DNA-only and [rare recovery] RNA editing is unclear.

This is partially a stylistic point but I think crucial to the paper’s scope: there is a tremendous amount of superlative language. “innovative”, “promises to uncover previously unrecognized molecular signatures”, “profound insight”, “evidence to the potential… in advancing personalized medicine”, “delve deep”, “paving the way for novel insights and novel potential therapeutic strategies”, “a transformative advancement”, “pivotal, not merely for its technical value but for its profound impact on expanding our genomic insight”, “paradigm shift”, “its contributions to the field of genomics are expected to grow, marking a promising era of discovery and innovation”, “designed to revolutionize” and so on. There are all specific claims and conclusions about the method’s impact and utility.

The review is not based on impact. But it is based on making sure that the claims in the paper are supported, e.g.:

• What are the main claims of the paper and how significant are they for the discipline?

• Are the claims properly placed in the context of the previous literature? Have the authors treated the literature fairly?

• Do the data and analyses fully support the claims? If not, what other evidence is required?

If the authors repeatedly claim broad significance and revolutionary novelty, then please provide evidence, which must necessarily address superiority over the rest of the field and concrete impact im advancing personalized medicine. I do not really see this in the current version.

Reviewer #2: In this article Brandao et al. introduce an analytical framework for the integrative multi-omic analysis of HS using RNA-seq and WES data. The authors illustrate the technique they develop to build bioinformatic evidence for the molecular underpinnings of disease exploring alterations in DNA and RNA. They derive a clever schema by which they create classifications of WES-RNA variants broken down into 6 distinct types. Determination of multi-modal proof for variants responsible for disease is a worthwhile effort. However, there are several concerns that must be addressed.

Major points:

- The publication of the steps performed in a word document is not sufficient currently for tool development and publication. This must be standardized and ideally containerized using any number of pipeline management tools (SnakeMake, Nextflow, etc.) or at least using a wrapper script. Furthermore, this could be suppoted using Docker, bioconda, or any number of frameworks for computational environments.

- There is not nearly enough evidence for clinical relevance. Were the distribution of variants related to outcome in patients? Which classes were over or underrepresented in which populations? There needs to be substantially more support for clinical relevance.

Minor points:

- For figure 2: It would be interesting and more informative to show this break down for all 7 patients, and not just the one (AUT101) as an example. The dataset is limited so every effort should be made to show as much of the data as possible. Possible figures could be series of venns, or stacked bar plots

- The order of your categories as they are introduced in writing does not follow the image, which is confusing, please fix so they match (RNA-only is 4th in image and 3rd in writing)

- Please fix this typo or spell out the citation: Line 185 “Transcription isoform expressions were calculated for each individual according to [26].”

- Please fix: Line 259: “The frequency of each MultiOmic Variant Categories from a given individual is reported in Error! Reference source not found.2.”

- Line 283: “sample are listed in Error! Reference source not found.3). Beyond”

- Line 291: “transcript, as shown in Error! Reference source not found.4 and supplementary”

I commend the authors on a clever analysis and analytical framework that is worthwhile of development and exploration. However, at this time the tool and analysis is not ready for publication.

Reviewer #3: Thank you for submitting your manuscript.

Unfortunately, the manuscript does not meet my expectations. The authors have made significant efforts to explain the novelty of InterOmics and the methodology used, but they have put very little effort into explaining how the detected gene variants and altered gene expression are relevant to HS pathogenesis. It would be better to also explain the candidate genes suggested for targeted therapy and personalized treatment. I think the manuscript, with slight modifications, could be easily published as a protocol. However, the results and discussion sections require more work to make a meaningful contribution as explained above.

Reviewer #4: 1.The manuscript highlights the novelty of the InterOmics pipeline but does not compare its performance with existing tools for multiomic integration . A benchmarking analysis or qualitative discussion of its advantages over existing approaches would significantly enhance the manuscript's impact.

2.The categorization of variants into Multi-Omic Variant Categories is well-executed; however, their functional significance, particularly for RNA editing and ASE (allele-specific expression), is underexplored. Expanding on how these categories contribute to our understanding of HS pathogenesis would provide greater depth to the analysis.

3.The manuscript does not provide sufficient details on certain statistical aspects, such as how differential expression analyses were corrected for multiple testing. Additionally, more information on the criteria for variant filtering and thresholds used (e.g., DP > 5 for RNAe and ASE) should be included to enhance transparency and reproducibility.

4.The legends for some figures (e.g., Fig. 1 and Fig. 3) are not detailed enough to allow readers to fully understand the content without referring back to the main text. Please provide more comprehensive descriptions for all figures and tables.

5.While the manuscript briefly acknowledges challenges in RNA-seq variant detection (e.g., RNA editing), other potential limitations, such as biases introduced during sample preparation or sequencing, should also be discussed.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

Reviewer #4: No

**********

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Attachments

Attachment

Submitted filename: review.pdf

Dear Dr. Crovella,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================<pre aria-label="Testo tradotto: thanks to the authors because the manuscript is much improved" class="tw-data-text tw-text-large tw-ta" data-placeholder="Traduzione" data-ved="2ahUKEwiAtPPBsvCMAxVk2AIHHaGbIm8Q3ewLegQICBAV" dir="ltr" id="tw-target-text" style="font-size: 28px; line-height: 36px; background-color: rgb(248, 249, 250); border: none; padding: 2px 0.14em 2px 0px; position: relative; margin-top: -2px; margin-bottom: -2px; resize: none; font-family: inherit; overflow: hidden; width: 270px; text-wrap-mode: wrap; overflow-wrap: break-word; color: rgb(31, 31, 31);">We would like to thank the authors for their contribution, as the manuscript has significantly improved. We invite the authors to review the requests made by the reviewers.</pre>==============================

Please submit your revised manuscript by Jun 08 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

We look forward to receiving your revised manuscript.

Kind regards,

Donatella Mentino

Academic Editor

PLOS ONE

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Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

**********

Reviewer #1: I want to begin by saying that I think the authors did a superb job with this revision. The manuscript is now terse, clear, and focused. The improvement is considerable, and I am mostly satisfied with the changes made to address my concerns.

Still a few minor errors:

154 ", which consists on variants that it homozygous "

"GRCh38" not "GRChg38"

I think there are two remaining basic issues. The first is crucial to me, on the second one I will defer to the editor.

This paper is about a particular structured framework for thinking about DNA and RNA-seq collected from the same samples. In the response to the editor's query "Comparison with Other Tools: Discuss the advantages and differences of InterOmics compared to other existing technologies in the multiomic landscape.", the authors respond "due to the low number of samples we have not been able to use other OMICs integrated tools." I do not think this sufficiently addresses the key question, which is no more and no less than "when should a researcher use the framework vs. other tools?" I would argue this is not a new problem. There are other such frameworks: this is the entire purpose of eQTL analysis. But of course eQTL requires very large sample sizes, and applying it to data from six patients would be unreasonable. I think there is an opportunity for the authors to discuss the range of DNA/RNA-seq tools and frameworks in popular use, their assumptions (such as sample size recommendations), and their goals (for instance, some do eQTL analysis, whereas others attempt to refine SNV identification), and explain where InterOmics fits in. The authors are on the right track -- the emphasis should be on relatively small studies, probably on the scale of clinical or pilot investigations, where other tools are not going to be appropriate. But it is necessary to make this comparison and motivation explicit, which means a review of existing literature and methods.

The other concern I have is that, due to patient privacy concerns and the emphasis on the conceptual framework (rather than algorithmic implementation), there are no raw data or code. This makes it much more challenging for somebody to actually implement this framework in their own research. In addition, right now, the logic of some of the steps (e.g. what does it mean to do "gene quantification" with DESeq2?) is still unclear. I think the paper would be far stronger if it were accompanied by a (relatively brief) analogous example workflow applied to a public DNA/RNA-seq dataset on some reasonably well-characterized tissue, which would take the reader through the logic and essential steps of the analysis, going from raw data to processed tables to their joint analysis. Such a tutorial workflow could be done on just one or two patients/donors, and added as a supplement + uploaded to GitHub. But I recognize that what I am asking for is essentially a small new analysis, not necessarily related to the authors' work in HS.

**********

what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #1: No

**********

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Attachments

Attachment

Submitted filename: R1.pdf

Genomic Profiling in Hidradenitis Suppurativa: InterOmics Pipeline for DNA-RNA Sequencing Integration highlights HLA variants, keratin-associated mutations and extracellular matrix alterations as contributing factors to HS pathogenesis

PONE-D-24-41615R2

Dear Dr. Sergio Crovella,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments: