The pathogenetic influence of smoking on SARS-CoV-2 infection: Integrative transcriptome and regulomics analysis of lung epithelial cells

Md Ali Hossain; Tania Akter Asa; Md Rabiul Auwul; Md Aktaruzzaman; Md Mahfizur Rahman; M Zahidur Rahman; Mohammad Ali Moni

doi:10.1016/j.compbiomed.2023.106885

The pathogenetic influence of smoking on SARS-CoV-2 infection: Integrative transcriptome and regulomics analysis of lung epithelial cells

Comput Biol Med. 2023 Jun:159:106885. doi: 10.1016/j.compbiomed.2023.106885. Epub 2023 Mar 31.

Authors

Md Ali Hossain¹, Tania Akter Asa², Md Rabiul Auwul³, Md Aktaruzzaman⁴, Md Mahfizur Rahman⁵, M Zahidur Rahman⁶, Mohammad Ali Moni⁷

Affiliations

¹ Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh; Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.
² Department of Electrical and Electronics Engineering, Islamic University, Kushtia, Bangladesh.
³ Department of Statistics, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh.
⁴ Department of Computer Science and Engineering, Islamic University, Kushtia, Bangladesh.
⁵ Biotechnology and Genetic Engineering, Islamic University, Kushtia, Bangladesh.
⁶ Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh.
⁷ Artificial Intelligence & Data Science, Faculty of Health and Behavioral Sciences, The University of Queensland, Australia. Electronic address: m.moni@uq.edu.au.

Abstract

Corona virus disease (COVID-19) has been emerged as pandemic infectious disease. The recent epidemiological data suggest that the smokers are more vulnerable to infection with COVID-19; however, the influence of smoking (SMK) on the COVID-19 infected patients and the mortality is not known yet. In this study, we aimed to discern the influence of SMK on COVID-19 infected patients utilizing the transcriptomics data of COVID-19 infected lung epithelial cells and transcriptomics data smoking matched with controls from lung epithelial cells. The bioinformatics based analysis revealed the molecular insights into the level of transcriptional changes and pathways which are important to identify the impact of smoking on COVID-19 infection and prevalence. We compared differentially expressed genes (DEGs) between COVID-19 and SMK and 59 DEGs were identified as consistently dysregulated at transcriptomics levels. The correlation network analyses were constructed for these common genes using WGCNA R package to see the relationship among these genes. Integration of DEGs with network analysis (protein-protein interaction) showed the presence of 9 hub proteins as key so called "candidate hub proteins" overlapped between COVID-19 patients and SMK. The Gene Ontology and pathways analysis demonstrated the enrichment of inflammatory pathway such as IL-17 signaling pathway, Interleukin-6 signaling, TNF signaling pathway and MAPK1/MAPK3 signaling pathways that might be the therapeutic targets in COVID-19 for smoking persons. The identified genes, pathways, hubs genes, and their regulators might be considered for establishment of key genes and drug targets for SMK and COVID-19.

Keywords: COVID-19; Comorbidity; Drugs; Pathway; Protein–protein interaction; SMOKING.

MeSH terms

COVID-19* / genetics
Computational Biology
Epithelial Cells
Humans
Lung
SARS-CoV-2
Smoking / adverse effects
Smoking / genetics
Transcriptome / genetics