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Medical Science Monitor Basic Research


eISSN: 1643-3750

Integrated Bioinformatics Analysis Reveals Key Candidate Genes and Cytokine Pathways Involved in COVID-19 After Rhinovirus Infection in Asthma Patients

Wenjuan Dai, Dawei Cao, Wei Zhang, Yangyang Wei, Daqing Ding, Bei Li, Yan Gao, Lixuan Zhao, Yi Jiang, Xiaomei Kong

Department of Respiratory and Critical Care Medicine, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China (mainland)

Med Sci Monit 2020; 26:e928861

DOI: 10.12659/MSM.928861

Available online: 2020-11-16

Published: 2020-12-14


BACKGROUND: Rhinovirus (RV) is the most common pathogen involved in asthma, and COVID-19, caused by SARS-COV-2, may be more severe in asthma patients. Here, we applied integrated bioinformatics to identify potential key genes and cytokine pathways after RV infection in asthma, and analyzed changes in angiotensin-converting enzyme 2 (ACE2), the cellular receptor of SARS-COV-2.
MATERIAL AND METHODS: The gene expression profile dataset GSE149273 was downloaded from NCBI-GEO, which included 90 samples of non-infected, RVA, and RVC. Differentially expressed genes (DEGs) were identified using t tests in the limma R package, and subsequently investigated by GO, KEGG, and DO analysis. Moreover, the expression of ACE2 and the proportion of immune cells were further analyzed to determine the effects of RV on cytokines.
RESULTS: A total of 555 DEGs of RVA and 421 of RVC were identified. There were 415 DEGs in RVA and RVC, of which 406 were upregulated and 9 were downregulated. The functional enrichment analysis showed that most DEGs were obviously enriched in cytokines, and were mainly enriched in “influenza” and “hepatitis C, chronic”. In addition, the expression of ACE2 increased significantly and the proportion of immune cytokines significantly changed after RV infection. Our results suggest that RV can activate the cytokine pathway associated with COVID-19 by increasing ACE2.
CONCLUSIONS: The DEGs and related cytokine pathways after asthma RV infection identified using integrated bioinformatics in this study elucidate the potential link between RV and COVID-19.

Keywords: Computational Biology, COVID-19, Rhinovirus