Identification of COVID-19 and COPD common key genes and pathways using a protein-protein interaction approach

  1. Thiviya S. Thambiraja
  2. Kalimuthu Karuppanan
  3. Gunasekaran Subramaniam
  4. Suresh Kumar

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Abstract

Coronavirus disease (COVID-19) is an extremely contagious and cognitive disease that could cause immense hypoxemia. The rise in critically ill patients in epidemic regions has put enormous pressure on hospitals. There is a need to define extreme COVID-19 clinical determinants to optimize clinical diagnosis and the management system is strong. Chronic obstructive pulmonary disease (COPD) is linked to a rapidly increasing risk of death rates in population pneumonia. In this research, a network of protein-protein interaction (PPI) was developed using constructed datasets of COVID-19 and COPD genes to define the interrelationship between COVID-19 and COPD, how it affects each other, and the genes that are responsible for the process. The PPI network shows the top 10 common overlapping genes, which include IL10, TLR4, TNF, IL6, CXCL8, IL4, ICAM1, IFNG, TLR2, and IL18. These are the genes that COVID-19 and high-risk COPD patients are known to be expressed. These important genes shared by COVID-19 and COPD are involved in pathways such as malaria, African trypanosomiasis, inflammatory bowel disease, Chagas disease, influenza, and tuberculosis.

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    SciScore for 10.1101/2021.10.28.466298: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    2.1 COVID-19 gene dataset construction: To construct the COVID-19 dataset, the COVID-19 curated geneset has been downloaded from the Comparative Toxicogenomics Database (CTD) (http://ctdbase.org/) [19] DisGeNET (a database of gene-disease associations) (https://www.disgenet.org/) [20] and the study of Gordon et al [21].
    http://ctdbase.org/
    suggested: (Comparative Toxicogenomics Database, RRID:SCR_006530)
    https://www.disgenet.org/
    suggested: (DisGeNET, RRID:SCR_006178)
    The common overlapping 248 genes COVID-19 and COPD genes were obtained from Venny tools (https://bioinfogp.cnb.csic.es/tools/venny/) [22].
    https://bioinfogp.cnb.csic.es/tools/venny/
    suggested: (Venny 2.1, RRID:SCR_016561)
    2.4 Protein-protein network construction using Cytoscape: To construct a protein-protein interaction (PPI) network, the 248 common overlapping genes were input into the String app [23] in Cytoscape (v 3.8.0) [24] downloaded through the App manager option to build an interaction network.
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)
    The Maximal Clique Centrality (MCC) algorithm is used in Cytohubba to calculate the top 10 ranked nodes.
    Cytohubba
    suggested: (cytoHubba, RRID:SCR_017677)
    2.5 Gene Ontology of top 10 common overlapping genes in COVID-19 and COPD: WebGestalt (WEB-based Gene SeT Analysis Toolkit) http://www.webgestalt.org/ [26] have been used to find the gene enrichments and gene ontology of the top 10 common overlapping genes in COVID-19 and COPD.
    Gene SeT Analysis Toolkit
    suggested: (WebGestalt: WEB-based GEne SeT AnaLysis Toolkit, RRID:SCR_006786)
    2.6 Gene pathway analysis of top 10 common overlapping genes in COVID-19 and COPD: WebGestalt (WEB-based Gene SeT Analysis Toolkit) http://www.webgestalt.org/ has been used to find the gene pathway of the top 10 common overlapping genes in COVID-19 and COPD.
    WebGestalt
    suggested: None
    Homosapiens have been used as the organism of interest, Over-Representation Analysis (ORA) have been selected for the method of interest, and for the functional database, the KEGG pathway has been selected.
    KEGG
    suggested: (KEGG, RRID:SCR_012773)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

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  2. Version published to 10.1101/2021.10.28.466298v1 on bioRxiv