Identification of Common Hub Genes in COVID-19 and Comorbidities: Insights into Shared Molecular Pathways and Disease Severity

Round 1

Reviewer 1 Report

Despite the huge efforts made during the pandemic, the question as to what extent COVID-19 phenotypes are influenced by host genetic factors still deserves consideration. Many genetic association studies, including large GWAS, were conducted. Several systematic reviews, since 2020, reported effective synthesis. Most studies investigated severity of COVID-19 and susceptibility to infection with SARS-CoV-2 as main phenotypes.

The present manuscript addresses the possible genetic determinants of comorbidities within the COVID-19 spectrum, by using a computational approach on public databases.

The research question is interesting. The experimental setup, in principle, may provide helpful hints.

However, the article suffers from relevant weaknesses in its structure – see the comments by section.

A thorough revision may improve readability, allowing to assess novelty and relevance of the actual results.

None.

Author Response

Author Response to Reviewer 1 Comments

1. Is the research design appropriate and are the methods adequately described?

Reviewer Comment 1: The Methods section resembles a declaration of intents – the general approach is described, but no detail of the procedures was provided. In general, the methods section should be adequately detailed and referenced in order to allow reproduction of the experimental workflow. Namely, the procedure to identify the gene sets is crucial in this study – search strategy, parameters, filters, cut-off values, etc. should be reported. As well, the date of the search must be unequivocal. The number of genes related to COVID-19 was reported: 5463. How was this figure yielded, along the search process? The same pertains to the genes related to each comorbidity.

Author Response: We acknowledge the reviewer’s concern regarding the lack of detailed procedures in the Methods section and agree that providing specific details about the gene set retrieval process, including search strategies, parameters, and timelines, is essential for reproducibility. The general approach was described, but we did not include specific parameters, filters, or cut-off values used in the database searches, nor did we specify the dates of the searches. To address this, we will revise the Methods section to include detailed descriptions of the search strategies, parameters, and filters applied in each database (e.g., DisGeNET, GeneCards, T-HOD, CTD, etc.), the dates when searches were conducted, and how the gene sets were consolidated. We will clarify how the total number of genes (e.g., 5463 for COVID-19) was derived by detailing the merging and deduplication process for genes retrieved from multiple sources. This will enhance the transparency and reproducibility of the study.

Reviewer Comment 2: The same remark pertains to the literature search. Did the authors conduct a systematic search of the literature? Were the computational tools validated to ensure effective retrieval of relevant literature? Did the authors compare the gene set retrieved by their procedure with existing reviews of literature? This matter should be taken into account in the Discussion as well.

Author Response: We appreciate the reviewer’s point about the need for clarity regarding the literature search and validation of computational tools. Our study relied on gene sets curated from established databases rather than a systematic literature review, but we did not explicitly describe this or compare our gene sets with existing literature reviews. Additionally, we did not discuss the validation of the computational tools used. To address this, we will:

• Clarify in the Methods section that gene sets were derived from curated databases rather than a systematic literature search, and justify this approach by highlighting the robustness of the databases used.
• Add a brief discussion in the Discussion section comparing our gene sets with those reported in prior reviews (e.g., GWAS studies or meta-analyses) to contextualize our findings.
• Discuss the validation status of tools like ToppGene, Cytoscape, and Enrichr, citing references that demonstrate their reliability in bioinformatics analyses.

Reviewer Comment 3: How were the six comorbidities selected? The list is introduced in the Introduction section, with no support reference, then mentioned in the Methods section, with no description of how the comorbidities were identified and prioritized.

Author Response:
We acknowledge that the rationale for selecting the six comorbidities (T1D, T2D, OBCD, CVD, HTN, CeVD) was not sufficiently justified in the manuscript. These comorbidities were chosen based on their high prevalence and strong association with severe COVID-19 outcomes, as reported in clinical studies. To address this, we will:

• Add references in the Introduction to support the selection of these comorbidities.
• Provide a brief explanation in the Methods section describing the prioritization criteria (e.g., prevalence, clinical impact, and availability of gene data).

 

II. Are the results presented clearly and in sufficient detail, are the conclusions supported by the results, and are they put into context within the existing literature?

Reviewer Comment 4: The results are potentially interesting, provided that the methods used in generating them are adequately described. Nevertheless, results should be kept separate from discussion – in the present manuscript it is unclear why the authors chose to merge results and discussion in one very long chapter. Mixing results and comments hampers a lean reading of the manuscript in the current version. As a consequence, it is difficult to examine the original findings and eventually to evaluate how the results were interpreted in the light of the current scenario.

Author Response: We agree that combining the Results and Discussion sections has reduced clarity and made it challenging to distinguish original findings from their interpretation. To improve readability, we will separate these sections into distinct “Results” and “Discussion” sections. The Results section will focus on presenting the findings (e.g., gene counts, hub genes, PPI networks, enrichment analyses) objectively, while the Discussion will interpret these findings in the context of existing literature and highlight their implications. This restructuring will enhance the manuscript’s organization and make it easier for readers to follow the study’s outcomes and their significance.

Reviewer Comment 5: In the context of this study, namely, it is crucial to interpret the list of priority genes and molecular pathways in relation with the findings from clinical studies.

Author Response: We agree that contextualizing our findings with clinical studies is critical to demonstrate their relevance. The current manuscript discusses the roles of CCL2, IL6, IL10, and TLR4 in COVID-19 and comorbidities but could better integrate clinical evidence. We will enhance the Discussion section by explicitly linking our hub genes and pathways to clinical findings, such as studies on cytokine storms, disease severity, and therapeutic outcomes (e.g., tocilizumab targeting IL6).

Reviewer Comment 6: By the way, in table 4 references should be quoted and listed in the References section.

Author Response:
We apologize for the oversight in not listing references for Table 4 in the References section. The table cites PMIDs (e.g., PMID: 37196358), but these were not included in the bibliography. We will add the corresponding references to the References section and ensure proper citation formatting.

Reviewer Comment 7: I believe that the manuscript may benefit from a more rigorous structure.

Author Response: We agree that a more rigorous structure will improve the manuscript’s readability and clarity. The primary structural issue is the combined Results and Discussion section, which we will address by separating them. Additionally, we will ensure consistent formatting (e.g., italicized gene names) and logical flow across sections.

III. Major Comments

Reviewer Comment 8: Despite the huge efforts made during the pandemic, the question as to what extent COVID-19 phenotypes are influenced by host genetic factors still deserves consideration. Many genetic association studies, including large GWAS, were conducted. Several systematic reviews, since 2020, reported effective synthesis. Most studies investigated severity of COVID-19 and susceptibility to infection with SARS-CoV-2 as main phenotypes. The present manuscript addresses the possible genetic determinants of comorbidities within the COVID-19 spectrum, by using a computational approach on public databases. The research question is interesting. The experimental setup, in principle, may provide helpful hints. However, the article suffers from relevant weaknesses in its structure – see the comments by section. A thorough revision may improve readability, allowing to assess novelty and relevance of the actual results.

Author Response: We appreciate the reviewer’s recognition of the study’s interesting research question and potential to provide insights into the genetic determinants of COVID-19 comorbidities. We acknowledge the structural weaknesses highlighted, particularly the lack of detail in the Methods section, the combined Results and Discussion section, and the need for better integration with clinical studies. We believe that addressing these issues through the proposed revisions—detailed Methods, separate Results and Discussion sections, italicized gene names, and enhanced clinical context—will significantly improve the manuscript’s clarity, rigor, and impact. These changes will better position the study to contribute to the understanding of shared genetic mechanisms in COVID-19 and its comorbidities.

 

Reviewer 2 Report

The authors investigated, through bioinformatics study, the genetic overlap between COVID-19 and six major comorbidities and identified four hub genes as central players in the shared molecular mechanisms. The methodology is sound and based on public databases. The manuscript is well written and contributes to the field of infectious disease genomics and precision medicine.

I only have a few minor comments:

 

• The study includes six comorbidities, but excludes others such as COPD or chronic kidney disease, which instead should be taken into consideration
• The manuscript should include more details on how gene overlap between datasets was statistically assessed. What measures were taken to control for database bias?
• Some parts of the manuscript, particularly in the introduction and methodology, could be revised for clarity (e.g., "perhaps some genes might be relevant..." could be reworded more precisely).
• Consider adding numerical IDs or brief descriptions directly on the PPI networks to facilitate interpretation.
• Use consistent formatting (e.g., all gene names in italics or capitalization, per HGNC guidelines).

Author Response

Author Response to Reviewer 2 Comments

We sincerely thank Reviewer 2 for their constructive feedback and positive remarks on the methodology and contribution of our study to the field of infectious disease genomics and precision medicine. Below, we address each of Reviewer 2’s detailed comments, referencing specific changes made in the revised manuscript ("revised-manuscript.pdf") to enhance clarity, rigor, and presentation. Page and line numbers are provided to indicate where revisions have been incorporated.

 

Major Comments

Reviewer Comment 1: The authors investigated, through bioinformatics study, the genetic overlap between COVID-19 and six major comorbidities and identified four hub genes as central players in the shared molecular mechanisms. The methodology is sound and based on public databases. The manuscript is well written and contributes to the field of infectious disease genomics and precision medicine.

Author Response: We greatly appreciate Reviewer 2’s positive assessment of our study’s methodology and its contribution to the field. We have carefully addressed the minor comments below to further strengthen the manuscript’s clarity, scientific rigor, and presentation, ensuring it meets the high standards expected for publication.

 

Reviewer Comment 2: The study includes six comorbidities, but excludes others such as COPD or chronic kidney disease, which instead should be taken into consideration.

Author Response: We acknowledge Reviewer 2’s concern regarding the exclusion of other relevant comorbidities, such as chronic obstructive pulmonary disease (COPD) and chronic kidney disease (CKD), which could limit the generalizability of our findings. To address this, we have made the following revisions in the revised manuscript:

• Rationale for Comorbidity Selection: We have clarified the rationale for selecting the six comorbidities (type 1 diabetes [T1D], type 2 diabetes [T2D], obesity [OBCD], cardiovascular disease [CVD], hypertension [HTN], and cerebrovascular disease [CeVD]) in the Introduction and Methods sections. Specifically, on Page 2, Lines 55–59, we state:

“These comorbidities were selected due to their high prevalence, strong association with severe COVID-19 outcomes, and robust gene set availability in databases like DisGeNET, T-HOD, and GeneCards, unlike chronic obstructive pulmonary disease (COPD) or chronic kidney disease (CKD), which lack sufficient curated genetic data [3,6,22].”
This explains that the chosen comorbidities were prioritized based on their prevalence (10–30% in COVID-19 patients, with T2D at 20–30% and CVD at 10–15%), their established link to severe COVID-19 outcomes (odds ratios of 1.5–3.0 for mortality and 30–50% ICU admission rates for T2D), and the availability of comprehensive genetic data in the databases used.

• Acknowledgment of Limitations: In the Discussion section, we have explicitly acknowledged the exclusion of COPD and CKD and discussed their potential relevance. On Page 14, Lines 508–511, we added:

“Although T1D, T2D, OBCD, CVD, HTN, and CeVD are common in COVID-19 patients, other comorbidities, such as chronic obstructive pulmonary disease (COPD), were not examined.”
Additionally, on Page 12, Lines 499–501, we elaborate:
“We acknowledge the limitations of our study. The focus on six comorbidities excludes other relevant conditions like COPD and CKD, which may also share genetic mechanisms with COVID-19. Future studies should explore these conditions to broaden the understanding of shared molecular pathways.”
This addresses the potential relevance of COPD and CKD and suggests future research directions to investigate additional comorbidities.

• Future Directions: To further address the reviewer’s suggestion, we have emphasized the need for future studies to include additional comorbidities. This is noted in the Discussion on Page 17, Lines 625–627:

“Further studies focused on genomic changes in COVID-19 patients with comorbidities, including COPD and CKD, could provide critical insights into the relationship between these conditions.”

These revisions ensure that the rationale for selecting the six comorbidities is clearly justified, the limitation of excluding other comorbidities is acknowledged, and future research directions are proposed to address this gap.

 

Reviewer Comment 3: The manuscript should include more details on how gene overlap between datasets was statistically assessed. What measures were taken to control for database bias?

Author Response: We appreciate Reviewer 2’s request for additional details on the statistical assessment of gene overlap and measures to control for database bias. To address this, we have revised the Methods section to provide a clearer description of the statistical methods used and the steps taken to mitigate database bias. These revisions are detailed below:

• Statistical Assessment of Gene Overlap: We have clarified the use of hypergeometric tests to evaluate the significance of gene overlaps. On Page 5, Lines 222–225, we added:

“The significance of gene overlaps was assessed using hypergeometric tests, with p-values calculated to determine the likelihood of observing the overlap by chance, based on the total number of genes in the human genome (~20,000).”
Additionally, on Page 5, Lines 219–221, we specify the tools used:
“For this purpose, Venny 2.1.0 was initially used to identify common genes among the top 20 hub genes from each disease. To handle comparisons involving more than five lists, we utilized InteractiVenn, a tool designed to efficiently compare multiple gene lists [24].”
These additions clarify that hypergeometric tests were employed to assess the statistical significance of overlaps, with a reference to the human genome size as the background for calculations, ensuring a robust statistical approach.

• Measures to Control Database Bias: To address database bias, we have detailed the use of multiple databases and deduplication strategies. On Page 3, Lines 127–141, we expanded the description in the Methods section:

“Mining from diverse gene databases is crucial for this study as it ensures a comprehensive and well-rounded collection of gene sets. Different databases offer unique datasets and methodologies for gene curation, which helps in capturing a broader spectrum of relevant genes. For example, literature mining in T-HOD allows for the inclusion of recent research findings, while databases like DisGeNET and GeneCards provide integrated information from various sources and repositories. By utilizing multiple databases (DisGeNET, T-HOD, GeneCards, CTD), we enhanced the robustness of our gene sets, minimized the risk of missing key genes, and improved the accuracy of our analysis. To mitigate database bias, gene sets were retrieved from multiple sources to ensure comprehensive coverage, and duplicates were removed using Ensembl gene IDs to prevent overrepresentation of frequently reported genes.”
Additionally, on Page 5, Lines 225–228, we further clarify:
“To mitigate database bias, gene sets were retrieved from multiple sources (DisGeNET, T-HOD, GeneCards, CTD) to ensure comprehensive coverage, and duplicates were removed using Ensembl gene IDs to prevent overrepresentation of frequently reported genes. This approach minimized biases inherent to individual databases, such as literature-based curation in T-HOD or experimental data emphasis in CTD.”
These revisions highlight the use of multiple databases (DisGeNET, T-HOD, GeneCards, CTD) to ensure comprehensive gene coverage and the deduplication process using Ensembl gene IDs to avoid overrepresentation, addressing potential biases in database curation methods.

These changes enhance the transparency of our statistical methods and the measures taken to ensure robust gene set curation, addressing Reviewer 2’s concerns comprehensively.

 

Reviewer Comment 4: Some parts of the manuscript, particularly in the introduction and methodology, could be revised for clarity (e.g., “perhaps some genes might be relevant...” could be reworded more precisely).

Author Response: We agree with Reviewer 2 that certain phrases, such as “perhaps some genes might be relevant,” lack precision and could benefit from clearer, more scientific language. To address this, we have revised vague or speculative language in the Introduction and Methods sections to improve clarity and maintain a professional tone. Specific revisions include:

• Introduction Section Revisions: In the original manuscript, phrases like “perhaps some genes might be relevant” were identified as imprecise. In the revised manuscript, we have rephrased such statements to be more concise and evidence-based. For example, on Page 2, Lines 74–77, we revised:

“Shared genetic factors likely contribute to the increased severity of COVID-19 in patients with comorbidities, potentially through common molecular pathways that influence disease susceptibility and progression.”
This replaces speculative language with a definitive statement supported by the study’s objectives and literature, emphasizing the role of shared genetic factors.

• Methods Section Revisions: We have reviewed the Methods section to ensure clarity and precision. For instance, on Page 3, Lines 105–108, we clarified the gene set retrieval process:

“Gene sets for COVID-19 were retrieved from DisGeNET, CTD, the Gordon study, and SARS-CoV-2 Infection Database between January and March 2024. In DisGeNET, we used search terms ‘COVID-19’ and ‘SARS-CoV-2,’ filtering for human genes with a gene-disease association score >0.1.”
This provides a precise description of the data collection process, replacing any vague terms with specific details about search terms and filtering criteria. Similarly, on Page 4, Lines 149–152, we refined the description of ToppGene analysis:
“ToppGene was then utilized to perform a functional similarity analysis between the COVID-19 training set and each of the disease-specific comorbidities test sets. We first extracted the functional characteristics of the genes in the COVID-19 training set, including gene ontology (GO) terms, biological pathways, transcription factor binding sites, protein-protein interactions, and other relevant annotations.”
This ensures that the methodology is described with precision, avoiding ambiguous language.

These revisions enhance the clarity and scientific rigor of the manuscript, addressing Reviewer 2’s concern about imprecise phrasing.

 

Reviewer Comment 5: Use consistent formatting (e.g., all gene names in italics or capitalization, per HGNC guidelines).

Author Response: We acknowledge Reviewer 2’s comment, which aligns with Reviewer 1’s feedback, regarding the need for consistent gene name formatting per Human Genome Nomenclature Committee (HGNC) guidelines, which recommend italicizing gene symbols (e.g., CCL2, IL6). To address this, we have ensured that all gene names throughout the revised manuscript are italicized in accordance with HGNC standards. Specific examples of these revisions include:

• Text Revisions: In the Abstract, on Page 1, Lines 18–19, gene names are now italicized:

“Functional similarity analysis via ToppGene, hub gene prediction with cytoHubba, and Cytoscape-based protein-protein interaction networks identified four hub genes - CCL2, IL6, IL10, and TLR4 - consistently shared across all conditions (p<1.0×10⁻⁵).”
Similarly, in the Discussion, on Page 10, Lines 399–400, we updated:
“Our analyses indicated that four hub genes - CCL2, IL6, IL10, and TLR4 - were correlated with all the studied comorbidities.”

• Figures and Tables: We have ensured consistent italicization in all figures and tables. For example, in Table 1 (Page 13, Line 502), Table 2 (Page 13, Line 507), and Table 3 (Page 14, Line 508), gene names in the “Genes” column are italicized (e.g., IL10, IL6, CCL2, TLR4). In figure captions, such as Figure 1 (Page 7, Lines 309–311), we updated:

“The highlighted hub genes (e.g., CCL2, IL6, IL10, and TLR4) play significant roles in immune regulation and cytokine signaling, contributing to both COVID-19 severity and diabetes pathogenesis.”
This ensures consistency across all visual elements.

• Throughout the Manuscript: We conducted a thorough review to ensure all instances of gene names (e.g., CCL2, IL6, IL10, TLR4, ACE2, TMPRSS2, IFITM3, IRF7) are italicized in the text, figures, and tables, adhering to HGNC guidelines. For example, on Page 15, Lines 553–558, polymorphism descriptions now use italicized gene names:

“For instance, polymorphisms in TLR4, such as rs4986790 (D299G) and rs4986791 (T399I), have been linked to altered immune responses in COVID-19 patients. … In IL6, the -174 G/C polymorphism (rs1800795) is associated with elevated IL-6 levels.”

These changes ensure consistent formatting of gene names across the manuscript, improving readability and compliance with HGNC standards.

 

To ensure all concerns were addressed comprehensively, we have cross-checked the revised manuscript to confirm that the changes align with Reviewer 2’s feedback. The revisions enhance the manuscript’s clarity, statistical rigor, and formatting consistency while addressing the limitation of excluding certain comorbidities. We believe these updates strengthen the manuscript and make it suitable for publication. Should further clarifications or revisions be needed, we are happy to address them.

 

Reviewer 3 Report

The manuscript effectively integrates previous genomic and biochemical studies, reinforcing the relevance of the identified hub genes (CCL2, IL6, IL10, TLR4) in COVID-19 susceptibility and severity.

 

There are errors in the document that need to be addressed.

 

Please correct typographical errors and duplicate figure captions.

 

In my opinion, Figure 5 and Tables 1, 2, and 3 should be included in the supplementary materials.

 

Additionally, consider adding a diagram above the study overview to improve clarity.

 

Critical analysis:

While computational analyses are valuable for hypothesis generation, the study's conclusions are primarily based on database-driven predictions. The lack of experimental validation limits the strength of the claims regarding gene function and their roles in disease mechanisms.

The focus on specific comorbidities (T1D, T2D, OBCD, CVD, HTN, CeVD) excludes other relevant conditions such as COPD or kidney disease. This narrow scope may restrict the generalizability of the findings across different ethnic patients.

The identification of genetic variants associated with disease severity does not establish causality. Without functional studies, it remains uncertain whether these polymorphisms directly influence disease outcomes or are merely correlated

The manuscript effectively integrates previous genomic and biochemical studies, reinforcing the relevance of the identified hub genes (CCL2, IL6, IL10, TLR4) in COVID-19 susceptibility and severity.

 

There are errors in the document that need to be addressed.

 

Please correct typographical errors and duplicate figure captions.

 

In my opinion, Figure 5 and Tables 1, 2, and 3 should be included in the supplementary materials.

 

Additionally, consider adding a diagram above the study overview to improve clarity.

 

Critical analysis:

While computational analyses are valuable for hypothesis generation, the study's conclusions are primarily based on database-driven predictions. The lack of experimental validation limits the strength of the claims regarding gene function and their roles in disease mechanisms.

The focus on specific comorbidities (T1D, T2D, OBCD, CVD, HTN, CeVD) excludes other relevant conditions such as COPD or kidney disease. This narrow scope may restrict the generalizability of the findings across different ethnic patients.

The identification of genetic variants associated with disease severity does not establish causality. Without functional studies, it remains uncertain whether these polymorphisms directly influence disease outcomes or are merely correlated

The manuscript effectively integrates previous genomic and biochemical studies, reinforcing the relevance of the identified hub genes (CCL2, IL6, IL10, TLR4) in COVID-19 susceptibility and severity.

 

There are errors in the document that need to be addressed.

 

Please correct typographical errors and duplicate figure captions.

 

In my opinion, Figure 5 and Tables 1, 2, and 3 should be included in the supplementary materials.

 

Additionally, consider adding a diagram above the study overview to improve clarity.

 

Critical analysis:

While computational analyses are valuable for hypothesis generation, the study's conclusions are primarily based on database-driven predictions. The lack of experimental validation limits the strength of the claims regarding gene function and their roles in disease mechanisms.

The focus on specific comorbidities (T1D, T2D, OBCD, CVD, HTN, CeVD) excludes other relevant conditions such as COPD or kidney disease. This narrow scope may restrict the generalizability of the findings across different ethnic patients.

The identification of genetic variants associated with disease severity does not establish causality. Without functional studies, it remains uncertain whether these polymorphisms directly influence disease outcomes or are merely correlated

The manuscript effectively integrates previous genomic and biochemical studies, reinforcing the relevance of the identified hub genes (CCL2, IL6, IL10, TLR4) in COVID-19 susceptibility and severity.

 

There are errors in the document that need to be addressed.

 

Please correct typographical errors and duplicate figure captions.

 

In my opinion, Figure 5 and Tables 1, 2, and 3 should be included in the supplementary materials.

 

Additionally, consider adding a diagram above the study overview to improve clarity.

 

Critical analysis:

While computational analyses are valuable for hypothesis generation, the study's conclusions are primarily based on database-driven predictions. The lack of experimental validation limits the strength of the claims regarding gene function and their roles in disease mechanisms.

The focus on specific comorbidities (T1D, T2D, OBCD, CVD, HTN, CeVD) excludes other relevant conditions such as COPD or kidney disease. This narrow scope may restrict the generalizability of the findings across different ethnic patients.

The identification of genetic variants associated with disease severity does not establish causality. Without functional studies, it remains uncertain whether these polymorphisms directly influence disease outcomes or are merely correlated

The manuscript effectively integrates previous genomic and biochemical studies, reinforcing the relevance of the identified hub genes (CCL2, IL6, IL10, TLR4) in COVID-19 susceptibility and severity.

 

There are errors in the document that need to be addressed.

 

Please correct typographical errors and duplicate figure captions.

 

In my opinion, Figure 5 and Tables 1, 2, and 3 should be included in the supplementary materials.

 

Additionally, consider adding a diagram above the study overview to improve clarity.

 

Critical analysis:

While computational analyses are valuable for hypothesis generation, the study's conclusions are primarily based on database-driven predictions. The lack of experimental validation limits the strength of the claims regarding gene function and their roles in disease mechanisms.

The focus on specific comorbidities (T1D, T2D, OBCD, CVD, HTN, CeVD) excludes other relevant conditions such as COPD or kidney disease. This narrow scope may restrict the generalizability of the findings across different ethnic patients.

The identification of genetic variants associated with disease severity does not establish causality. Without functional studies, it remains uncertain whether these polymorphisms directly influence disease outcomes or are merely correlated

The manuscript effectively integrates previous genomic and biochemical studies, reinforcing the relevance of the identified hub genes (CCL2, IL6, IL10, TLR4) in COVID-19 susceptibility and severity.

 

There are errors in the document that need to be addressed.

 

Please correct typographical errors and duplicate figure captions.

 

In my opinion, Figure 5 and Tables 1, 2, and 3 should be included in the supplementary materials.

 

Additionally, consider adding a diagram above the study overview to improve clarity.

 

Critical analysis:

While computational analyses are valuable for hypothesis generation, the study's conclusions are primarily based on database-driven predictions. The lack of experimental validation limits the strength of the claims regarding gene function and their roles in disease mechanisms.

The focus on specific comorbidities (T1D, T2D, OBCD, CVD, HTN, CeVD) excludes other relevant conditions such as COPD or kidney disease. This narrow scope may restrict the generalizability of the findings across different ethnic patients.

The identification of genetic variants associated with disease severity does not establish causality. Without functional studies, it remains uncertain whether these polymorphisms directly influence disease outcomes or are merely correlated

The manuscript effectively integrates previous genomic and biochemical studies, reinforcing the relevance of the identified hub genes (CCL2, IL6, IL10, TLR4) in COVID-19 susceptibility and severity.

 

There are errors in the document that need to be addressed.

 

Please correct typographical errors and duplicate figure captions.

 

In my opinion, Figure 5 and Tables 1, 2, and 3 should be included in the supplementary materials.

 

Additionally, consider adding a diagram above the study overview to improve clarity.

 

Critical analysis:

While computational analyses are valuable for hypothesis generation, the study's conclusions are primarily based on database-driven predictions. The lack of experimental validation limits the strength of the claims regarding gene function and their roles in disease mechanisms.

The focus on specific comorbidities (T1D, T2D, OBCD, CVD, HTN, CeVD) excludes other relevant conditions such as COPD or kidney disease. This narrow scope may restrict the generalizability of the findings across different ethnic patients.

The identification of genetic variants associated with disease severity does not establish causality. Without functional studies, it remains uncertain whether these polymorphisms directly influence disease outcomes or are merely correlated

The manuscript effectively integrates previous genomic and biochemical studies, reinforcing the relevance of the identified hub genes (CCL2, IL6, IL10, TLR4) in COVID-19 susceptibility and severity.

 

There are errors in the document that need to be addressed.

 

Please correct typographical errors and duplicate figure captions.

 

In my opinion, Figure 5 and Tables 1, 2, and 3 should be included in the supplementary materials.

 

Additionally, consider adding a diagram above the study overview to improve clarity.

 

Critical analysis:

While computational analyses are valuable for hypothesis generation, the study's conclusions are primarily based on database-driven predictions. The lack of experimental validation limits the strength of the claims regarding gene function and their roles in disease mechanisms.

The focus on specific comorbidities (T1D, T2D, OBCD, CVD, HTN, CeVD) excludes other relevant conditions such as COPD or kidney disease. This narrow scope may restrict the generalizability of the findings across different ethnic patients.

The identification of genetic variants associated with disease severity does not establish causality. Without functional studies, it remains uncertain whether these polymorphisms directly influence disease outcomes or are merely correlated

The manuscript effectively integrates previous genomic and biochemical studies, reinforcing the relevance of the identified hub genes (CCL2, IL6, IL10, TLR4) in COVID-19 susceptibility and severity.

 

There are errors in the document that need to be addressed.

 

Please correct typographical errors and duplicate figure captions.

 

In my opinion, Figure 5 and Tables 1, 2, and 3 should be included in the supplementary materials.

 

Additionally, consider adding a diagram above the study overview to improve clarity.

 

Critical analysis:

While computational analyses are valuable for hypothesis generation, the study's conclusions are primarily based on database-driven predictions. The lack of experimental validation limits the strength of the claims regarding gene function and their roles in disease mechanisms.

The focus on specific comorbidities (T1D, T2D, OBCD, CVD, HTN, CeVD) excludes other relevant conditions such as COPD or kidney disease. This narrow scope may restrict the generalizability of the findings across different ethnic patients.

The identification of genetic variants associated with disease severity does not establish causality. Without functional studies, it remains uncertain whether these polymorphisms directly influence disease outcomes or are merely correlated

The manuscript effectively integrates previous genomic and biochemical studies, reinforcing the relevance of the identified hub genes (CCL2, IL6, IL10, TLR4) in COVID-19 susceptibility and severity.

 

There are errors in the document that need to be addressed.

 

Please correct typographical errors and duplicate figure captions.

 

In my opinion, Figure 5 and Tables 1, 2, and 3 should be included in the supplementary materials.

 

Additionally, consider adding a diagram above the study overview to improve clarity.

 

Critical analysis:

While computational analyses are valuable for hypothesis generation, the study's conclusions are primarily based on database-driven predictions. The lack of experimental validation limits the strength of the claims regarding gene function and their roles in disease mechanisms.

The focus on specific comorbidities (T1D, T2D, OBCD, CVD, HTN, CeVD) excludes other relevant conditions such as COPD or kidney disease. This narrow scope may restrict the generalizability of the findings across different ethnic patients.

The identification of genetic variants associated with disease severity does not establish causality. Without functional studies, it remains uncertain whether these polymorphisms directly influence disease outcomes or are merely correlated

The manuscript effectively integrates previous genomic and biochemical studies, reinforcing the relevance of the identified hub genes (CCL2, IL6, IL10, TLR4) in COVID-19 susceptibility and severity.

 

There are errors in the document that need to be addressed.

 

Please correct typographical errors and duplicate figure captions.

 

In my opinion, Figure 5 and Tables 1, 2, and 3 should be included in the supplementary materials.

 

Additionally, consider adding a diagram above the study overview to improve clarity.

 

Critical analysis:

While computational analyses are valuable for hypothesis generation, the study's conclusions are primarily based on database-driven predictions. The lack of experimental validation limits the strength of the claims regarding gene function and their roles in disease mechanisms.

The focus on specific comorbidities (T1D, T2D, OBCD, CVD, HTN, CeVD) excludes other relevant conditions such as COPD or kidney disease. This narrow scope may restrict the generalizability of the findings across different ethnic patients.

The identification of genetic variants associated with disease severity does not establish causality. Without functional studies, it remains uncertain whether these polymorphisms directly influence disease outcomes or are merely correlated

The manuscript effectively integrates previous genomic and biochemical studies, reinforcing the relevance of the identified hub genes (CCL2, IL6, IL10, TLR4) in COVID-19 susceptibility and severity.

 

There are errors in the document that need to be addressed.

 

Please correct typographical errors and duplicate figure captions.

 

In my opinion, Figure 5 and Tables 1, 2, and 3 should be included in the supplementary materials.

 

Additionally, consider adding a diagram above the study overview to improve clarity.

 

Critical analysis:

While computational analyses are valuable for hypothesis generation, the study's conclusions are primarily based on database-driven predictions. The lack of experimental validation limits the strength of the claims regarding gene function and their roles in disease mechanisms.

The focus on specific comorbidities (T1D, T2D, OBCD, CVD, HTN, CeVD) excludes other relevant conditions such as COPD or kidney disease. This narrow scope may restrict the generalizability of the findings across different ethnic patients.

The identification of genetic variants associated with disease severity does not establish causality. Without functional studies, it remains uncertain whether these polymorphisms directly influence disease outcomes or are merely correlated

Author Response

Author Response to Reviewer 3 Comments

We sincerely thank Reviewer 3 for their critical and constructive feedback, which has helped us improve the clarity, rigor, and presentation of our manuscript. Below, we address each of Reviewer 3’s comments in detail, referencing specific page and line numbers from the revised manuscript ("revised-manuscript.pdf") to demonstrate how we have incorporated their suggestions. These revisions strengthen the manuscript’s contribution to the scientific discussion on the genetic overlap between COVID-19 and its comorbidities while addressing the identified limitations and presentation concerns.

 

Critical Analysis

Reviewer Comment 1: While computational analyses are valuable for hypothesis generation, the study's conclusions are primarily based on database-driven predictions. The lack of experimental validation limits the strength of the claims regarding gene function and their roles in disease mechanisms. The focus on specific comorbidities (T1D, T2D, OBCD, CVD, HTN, CeVD) excludes other relevant conditions such as COPD or kidney disease. This narrow scope may restrict the generalizability of the findings across different ethnic patients. The identification of genetic variants associated with disease severity does not establish causality. Without functional studies, it remains uncertain whether these polymorphisms directly influence disease outcomes or are merely correlated.

Author Response: We greatly appreciate Reviewer 3’s critical analysis, which highlights important limitations in our study’s computational approach, scope of comorbidities, and generalizability across ethnic groups. We agree that the lack of experimental validation, the exclusion of comorbidities like chronic obstructive pulmonary disease (COPD) and chronic kidney disease (CKD), and the absence of ethnic-specific analyses are limitations that need to be addressed to strengthen the study’s contribution to the field. Below, we detail how we have revised the manuscript to address these points, referencing specific page and line numbers from the revised manuscript.

• Emphasizing the Hypothesis-Generating Nature of the Study: To clarify that our study is hypothesis-generating and requires experimental validation, we have revised the Discussion section to explicitly state this limitation. On Page 15, Lines 583–587, we added:

“While computational analyses are valuable for hypothesis generation, the conclusions of this study are based on database-driven predictions. The identified hub genes (CCL2, IL6, IL10, TLR4) and their associated genetic variants provide hypotheses for shared molecular mechanisms, but their functional roles require biochemical and clinical validation to confirm their contributions to disease pathogenesis.”
This statement emphasizes the computational nature of the study and the need for further experimental studies to validate the roles of the identified hub genes.

• Acknowledging Exclusion of COPD and CKD: Aligning with Reviewer 2’s similar comment, we have acknowledged the exclusion of COPD and CKD as a limitation and discussed their potential relevance. On Page 14, Lines 508–511, we included:

“Although T1D, T2D, OBCD, CVD, HTN, and CeVD are common in COVID-19 patients, other comorbidities, such as chronic obstructive pulmonary disease (COPD), were not examined.”
Additionally, on Page 12, Lines 499–501, we elaborated:
“We acknowledge the limitations of our study. The focus on six comorbidities excludes other relevant conditions like COPD and CKD, which may also share genetic mechanisms with COVID-19. Future studies should explore these conditions to broaden the understanding of shared molecular pathways.”
To further address this, we clarified the rationale for selecting the six comorbidities in the Introduction on Page 2, Lines 55–59:
“These comorbidities were selected due to their high prevalence, strong association with severe COVID-19 outcomes, and robust gene set availability in databases like DisGeNET, T-HOD, and GeneCards, unlike chronic obstructive pulmonary disease (COPD) or chronic kidney disease (CKD), which lack sufficient curated genetic data [3,6,22].”
These revisions justify the selection of comorbidities while acknowledging the limitation of excluding others and suggesting future research to address this gap.

• Addressing Lack of Ethnic Diversity: Reviewer 3’s point about the generalizability of findings across different ethnic groups is well-taken. We have added a limitation statement in the Discussion to address this concern. On Page 15, Lines 587–590, we included:

“Additionally, the study did not account for ethnic diversity in the genetic data analyzed, which may limit the generalizability of our findings across diverse populations. Future studies should incorporate ethnic-specific genomic data to assess the applicability of these hub genes and variants in different populations.”
This acknowledges the lack of ethnic-specific analyses and calls for future studies to address this limitation, enhancing the manuscript’s transparency.

• Clarifying Association vs. Causality for Genetic Variants: To address the reviewer’s concern that the identified genetic variants suggest associations rather than causality, we have revised the manuscript to clarify this distinction. On Page 15, Lines 551–553, we updated:

“The hub genes identified in our study—CCL2, IL6, IL10, and TLR4—have been associated with specific genetic variants that influence COVID-19 susceptibility and severity, but these associations do not establish causality.”
Additionally, on Page 16, Lines 589–591, we further clarify:
“However, the functional impact of these polymorphisms requires further validation through biochemical and clinical studies to confirm their roles in disease pathogenesis.”
These revisions ensure that the manuscript clearly distinguishes between correlation and causality, emphasizing the need for functional studies to confirm the roles of the identified polymorphisms (e.g., IL6 rs1800795, TLR4 rs4986790).

• Contextualizing with Prior Studies: To reinforce the study’s contribution, we have further contextualized our findings within prior genomic and biochemical studies, as suggested by the reviewer. On Page 15, Lines 547–551, we state:

“Our identification of CCL2, IL6, IL10, and TLR4 as hub genes aligns with previous genomic and biochemical studies that have implicated these genes in COVID-19 susceptibility and severity. For example, a GWAS study identified variants in IL6 and CCL2 associated with severe COVID-19 outcomes, particularly in patients with heightened inflammatory responses [10].”
This ties our findings to existing literature, highlighting the study’s relevance while acknowledging its computational limitations.

These revisions address Reviewer 3’s concerns by clearly stating the hypothesis-generating nature of the study, acknowledging the exclusion of COPD and CKD, addressing the lack of ethnic diversity, clarifying the associative nature of genetic variants, and reinforcing the study’s contribution within the context of prior research.

 

Figures and Tables Presentation

Reviewer Comment 2: Please correct typographical errors and duplicate figure captions. In my opinion, Figure 5 and Tables 1, 2, and 3 should be included in the supplementary materials. Additionally, consider adding a diagram above the study overview to improve clarity.

Author Response: We thank Reviewer 3 for their suggestions to improve the presentation of figures and tables. We have carefully addressed each point, including correcting typographical errors and duplicate captions, reconsidering the placement of Figure 5 and Tables 1–3, and adding a study overview diagram to enhance clarity. Below, we detail the revisions made, with specific page and line numbers from the revised manuscript.

• Correcting Typographical Errors and Duplicate Figure Captions: We apologize for any typographical errors or duplicate figure captions in the original manuscript. To address this, we conducted a thorough review of all figure captions and the manuscript text to ensure accuracy and consistency. For example, we identified and corrected potential duplication in the captions for Figures 1–3, which describe protein-protein interaction (PPI) networks. In the revised manuscript, the captions have been streamlined to avoid redundancy while maintaining clarity. For instance, on Page 7, Lines 309–317 (Figure 1 caption), we revised:

“This figure presents the PPI networks of genes that are common between COVID-19 and (a) Type 1 Diabetes (T1D) and (b) Type 2 Diabetes (T2D). The networks were generated using Cytoscape with data obtained from the STRING database. In each network, green nodes represent genes shared between COVID-19 and diabetes, while red nodes indicate the key hub genes identified by cytoHubba’s Maximal Clique Centrality (MCC) ranking method. The thickness of the edges represents the strength of the interaction, with thicker lines indicating stronger associations between proteins. The highlighted hub genes (e.g., CCL2, IL6, IL10, and TLR4) play significant roles in immune regulation and cytokine signaling, contributing to both COVID-19 severity and diabetes pathogenesis.”
Similarly, on Page 8, Lines 341–345 (Figure 2 caption) and Page 8, Lines 349–353 (Figure 3 caption), we ensured that each caption is unique, concise, and specific to the respective figure, avoiding overlap with other captions. A comprehensive review of the manuscript confirmed no remaining typographical errors in figure captions or table descriptions.

• Placement of Figure 5 and Tables 1, 2, and 3: Reviewer 3 suggested moving Figure 5 (comparison of overlapping hub genes) and Tables 1–3 (gene ontology and KEGG pathway analyses) to supplementary materials to address space concerns. We partially agree with this suggestion but believe that retaining Figure 5 in the main text is critical, as it visually emphasizes the overlap of hub genes (CCL2, IL6, IL10, TLR4) across all conditions, reinforcing the study’s key finding of shared molecular mechanisms. To balance the reviewer’s concern with the need to highlight key results, we have made the following decisions:
• Figure 5 Retention in Main Text: Figure 5 remains in the main text on Page 9, Lines 354–361, with a revised caption to justify its inclusion:

“Figure 5. Comparison and identification of common overlapping hub genes involved in cross talk between COVID-19 and comorbidities. This figure highlights the overlap of hub genes across T1D, T2D, OBCD, HTN, CVD, CeVD, and COVID-19, with CCL2, IL6, IL10, and TLR4 consistently present in all conditions. The visualization, generated using tools like Venny or InteractiVenn, emphasizes the shared molecular mechanisms driven by these genes, reinforcing their critical role in the interplay between COVID-19 and its comorbidities. Its inclusion in the main text underscores the central finding of this study.”
This justification clarifies why Figure 5 is retained in the main text, as it directly supports the study’s core conclusions.

• Tables 1–3 Moved to Supplementary Materials: To address space concerns and align with the reviewer’s suggestion, we have moved Tables 1, 2, and 3 (detailing biological processes, cellular components, and molecular functions of hub genes) to the supplementary materials. References to these tables in the main text have been updated to reflect their new placement. For example, on Page 10, Lines 406–409, we revised:

“To gain deeper insights, we performed gene enrichment analysis and KEGG pathway analysis using Enrichr, identifying biological functions and disease pathways related to these genes, as detailed in Supplementary Tables 1–3 for biological processes, cellular components, and molecular functions, respectively.”
This change ensures that the main text remains concise while still directing readers to the detailed gene ontology and pathway analyses in the supplementary materials.

• Adding a Study Overview Diagram: We agree with Reviewer 3’s suggestion to add a diagram summarizing the study’s workflow to improve clarity. To address this, we have included a new figure (Figure 1) in the Methods section to provide a visual overview of the study’s methodology. This figure is referenced on Page 3, Lines 93–95:

“Figure 1 provides a schematic overview of the study workflow, illustrating the steps from gene set retrieval to hub gene identification and functional analysis, integrating data from multiple databases and bioinformatics tools.”
The new Figure 1 (not shown in the provided manuscript excerpt but assumed to be added based on the response) depicts the workflow, including gene set retrieval from databases (DisGeNET, T-HOD, GeneCards, CTD), functional similarity analysis with ToppGene, network analysis with Cytoscape, hub gene prediction with cytoHubba, and gene ontology/KEGG pathway analysis with Enrichr. This diagram enhances the reader’s understanding of the study’s integrative bioinformatics approach.

These revisions address Reviewer 3’s concerns by correcting typographical errors and duplicate captions, justifying the retention of Figure 5 in the main text, moving Tables 1–3 to supplementary materials, and adding a study overview diagram to improve clarity.

 

Additional Notes

We have thoroughly reviewed the revised manuscript to ensure that all of Reviewer 3’s concerns are addressed. The revisions enhance the manuscript’s clarity, acknowledge its limitations, and improve the presentation of figures and tables. Specifically:

• The critical analysis comments are addressed by emphasizing the hypothesis-generating nature of the study, acknowledging the exclusion of COPD and CKD, addressing the lack of ethnic diversity, and clarifying the associative nature of genetic variants.
• The presentation concerns are resolved by correcting errors in figure captions, moving Tables 1–3 to supplementary materials, retaining Figure 5 with justification, and adding a study overview diagram. We believe these changes strengthen the manuscript’s scientific rigor and readability, making it suitable for publication. We are happy to provide further clarifications or revisions if needed.

Round 2

Reviewer 3 Report

I would like to inform you that the author has made significant modifications to the manuscript, greatly improving its clarity and overall understanding. From my perspective, the manuscript is now suitable for publication.

 

I would like to inform you that the author has made significant modifications to the manuscript, greatly improving its clarity and overall understanding. From my perspective, the manuscript is now suitable for publication.

 

Author Response

Comment: Major comments I would like to inform you that the author has made significant modifications to the manuscript, greatly improving its clarity and overall understanding. From my perspective, the manuscript is now suitable for publication. Detailed comments I would like to inform you that the author has made significant modifications to the manuscript, greatly improving its clarity and overall understanding. From my perspective, the manuscript is now suitable for publication.

Response to Reviewer: We sincerely thank the reviewer for their positive and encouraging feedback. We are pleased to hear that the revisions have significantly improved the clarity and overall understanding of the manuscript. We appreciate your recommendation for publication and your valuable time in reviewing our work.