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Host Factor Interaction Networks Identified by Integrative Bioinformatics Analysis Reveals Therapeutic Implications in COPD Patients With COVID-19.

ABSTRACT: Background: The COVID-19 pandemic poses an imminent threat to humanity, especially for those who have comorbidities. Evidence of COVID-19 and COPD comorbidities is accumulating. However, data revealing the molecular mechanism of COVID-19 and COPD comorbid diseases is limited. Methods: We got COVID-19/COPD -related genes from different databases by restricted screening conditions (top500), respectively, and then supplemented with COVID-19/COPD-associated genes (FDR<0.05, |LogFC|≥1) from clinical sample data sets. By taking the intersection, 42 co-morbid host factors for COVID-19 and COPD were finally obtained. On the basis of shared host factors, we conducted a series of bioinformatics analysis, including protein-protein interaction analysis, gene ontology and pathway enrichment analysis, transcription factor-gene interaction network analysis, gene-microRNA co-regulatory network analysis, tissue-specific enrichment analysis and candidate drug prediction. Results: We revealed the comorbidity mechanism of COVID-19 and COPD from the perspective of host factor interaction, obtained the top ten gene and 3 modules with different biological functions. Furthermore, we have obtained the signaling pathways and concluded that dexamethasone, estradiol, progesterone, and nitric oxide shows effective interventions. Conclusion: This study revealed host factor interaction networks for COVID-19 and COPD, which could confirm the potential drugs for treating the comorbidity, ultimately, enhancing the management of the respiratory disease.

SUBMITTER: Zheng W

PROVIDER: S-EPMC8733735 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Purpose:To explore the potential mechanism underpinning the development of chronic obstructive pulmonary disease (COPD) and to investigate the role of the Roundabout signaling pathway in COPD. Methods:Three microarray datasets (GSE1650, GSE38974 and GSE76925) including 139 cases of severe COPD and 52 cases of normal smokers without carcinoma, were integrated to screen differentially expressed genes (DEGs) using bioinformatics methods. Gene ontology (GO) annotations and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of the DEGs were performed by a DAVID online tool. Finally, a cigarette smoke (CS)- induced emphysema mice model was established, the lung mRNA expression levels of genes associated with Slit guidance ligand 2 (SLIT2) -Roundabout (ROBO) signaling pathway were detected by reverse transcription quantitative polymerase chain reaction (RT-qPCR), and the protein level of SLIT2 was examined by immunohistochemistry staining. Results:A total of 315 DEGs were identified in three databases. GO and KEGG pathway analyses suggested that the inflammatory response, extracellular matrix disassembly, immune response, the apoptotic signaling pathway, ubiquitination and the Roundabout signaling pathway all together were involved in the development of COPD. The genes SLIT2 and ROBO2 were decreased in patients with COPD and these decreases were significantly negatively correlated with the disease stages of COPD. Consistently, the mRNA expression levels of SLIT2, ROBO1 and ROBO2, and the protein level of SLIT2 were revealed to be lower in the lungs of CS-induced emphysema mice compared with the air-exposed control mice. In addition, the SLIT2 protein level was negatively associated with alveolar mean linear intercept. Conclusion:Integrated bioinformatics analysis may provide novel insights into the complicated pathogenesis of COPD, and to the best of our knowledge, this study is the first to provide evidence to suggest that the Roundabout signaling pathway may be involved in the pathogenesis of COPD.

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Host Factor Interaction Networks Identified by Integrative Bioinformatics Analysis Reveals Therapeutic Implications in COPD Patients With COVID-19.

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