All reviews of published articles are made public. This includes manuscript files, peer review comments, author rebuttals and revised materials. Note: This was optional for articles submitted before 13 February 2023.
Peer reviewers are encouraged (but not required) to provide their names to the authors when submitting their peer review. If they agree to provide their name, then their personal profile page will reflect a public acknowledgment that they performed a review (even if the article is rejected). If the article is accepted, then reviewers who provided their name will be associated with the article itself.
The authors have addressed all the comments this reviewer made in the previous round of review.
[# PeerJ Staff Note - this decision was reviewed and approved by Vladimir Uversky, a PeerJ Section Editor covering this Section #]
-
-
-
The authors have addressed all my comments.
The authors have addressed all the comments this reviewer made in the previous round of review.
-
-
**PeerJ Staff Note:** Please ensure that all review and editorial comments are addressed in a response letter and that any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.
**Language Note:** The review process has identified that the English language must be improved. PeerJ can provide language editing services - please contact us at [email protected] for pricing (be sure to provide your manuscript number and title). Alternatively, you should make your own arrangements to improve the language quality and provide details in your response letter. – PeerJ Staff
1. Language and Clarity
• Strengths: Most of the manuscript is written in standard scientific English with professional tone.
• Issues:
o Numerous grammatical and typographical errors, e.g., "expert" instead of "except" (line 67), “welafre” instead of “welfare” (line 69), and awkward phrases like "the central for their biological event".
o Inconsistent tense usage and occasional unclear sentence construction.
Recommendation: A comprehensive English language editing is needed to meet international publishing standards.
2. Literature References and Background
• Strengths:
o The introduction provides a solid overview of sepsis and RCD, citing relevant and recent studies.
o Methodological tools and concepts are well-referenced.
• Recommendation: Expand discussion around clinical implications of the identified biomarkers and provide a clearer knowledge gap in prior studies.
Overall, the research question is well-defined. The gap—insufficient systematic understanding of RCD in sepsis—is adequately stated. Ethical guidelines are followed (line 302-306), with IRB approval and adherence to animal welfare standards. The computational and experimental methods are described in enough detail for replication, including software and R packages used, animal model procedures, qRT-PCR protocols.
However, minor clarification is needed on the details of machine learning method implementation and model selection. As the authors are not building machine learning algorithms for prediction but using them for feature selection to get the best genes that explain the variance of RRDs. The authors should explain the reason of using those three machine learning models and how they make the feature selection robust and reliable.
Figure 2E-G showed some hyperparameters tuning in LASSO, RMSE in SVM model and top 30 variations in RF model. However, it is not related to the major information to deliver in this figure. The authors should explain in the context that why they use the genes selected through those machine learning models for downstream analysis, and consider moving those figures to supplementary.
The study investigates how various forms of regulatory cell death (RCD) contribute to the pathophysiology of sepsis. Specifically, it seeks to identify sepsis-related RCD pathways and genes that may serve as diagnostic biomarkers or therapeutic targets. The research question is important because sepsis is a major global health concern with high morbidity and mortality. Understanding the role of RCD in sepsis could reveal new mechanisms underlying organ dysfunction and immune dysregulation, potentially leading to more effective diagnostics or interventions. To address this question, the authors collected bulk RNA-seq and single-cell RNA-seq datasets from GEO and compiled genes associated with 18 RCD pathways. Main findings of this manuscript include:
• Identified five core RCD-related DEGs (RRDs): ZDHHC3, CLIC1, GSTO1, BLOC1S1, and TLR5
• ZDHHC3 and TLR5 were confirmed as independent risk factors for sepsis.
• Monocytes and neutrophils were the main immune cells implicated.
• The mRNA expression levels of Zdhhc3 and Tlr5 were significantly increased in a murine model of sepsis.
• Authors propose these genes as novel diagnostic biomarkers and potential therapeutic targets in sepsis.
Overall, this manuscript presents a valuable and methodologically sound investigation into the regulatory cell death pathways involved in sepsis. With improved language quality and clearer methodological reporting, it will be a strong contribution to the field.
Some major recommendations for Improvement include:
1. Language Editing: Engage a professional editing service.
2. Clarify Experimental Design: Describe the utility of machine learning methods and details of MR analysis.
3. Biological Discussion: Expand on the role of ZDHHC3 and TLR5 beyond expression levels—e.g., potential mechanisms.
Cao et al. presented a comprehensive investigation of the roles of a set of selected regulatory cell death (RCD) genes in sepsis. The authors utilized a variety of bioinformatics and machine learning methods to determine the relevance of RCD in sepsis from public bulk and one single-cell RNAseq data. The article is overall written in professional, unambiguous English and organized in a structural, logical format. The figures are nicely presented. All sections are self-contained to the topic of discussion.
The reviewer has some minor suggestions on several word uses in the first half of the manuscript. Particularly,
(1) The authors should consider de-capitalize "regulatory cell death" and "bulk and single-cell" in the title.
(2) The authors claim on Line 20 that LASSO, SVM, and RF are "machine learning languages" which is inappropriate, as these are "methods", not "languages".
(3) The authors called monocytes and neutrophils to be "the central of their biological event." To the reviewer, it appears that the authors are trying to convey the idea that these are the two key cell types that over-express the core RRDs Yet, the current wording is unconventional and should be modified.
Overall, the research question was well-defined. The experimental setups are described sufficiently and the results were analyzed in depth. However, the manuscript fell short in introducing the public bulk RNAseq datasets in greater details. The authors should add the total number of samples included in the batch-corrected final dataset, and if any inclusion-exclusion event occurred. Furthermore, it is worth investigate whether any other confounding variables exist between sepsis and healthy patients, such as other clinical (comorbidity, drug and chronic disease history, etc) or demographic (sex, race, age, etc) information that may bias the differential gene calling.
No other comments other than the minor suggestions above.
All text and materials provided via this peer-review history page are made available under a Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.