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

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

Md Ali Hossain  1 , Tania Akter Asa  2 , Md Rabiul Auwul  3 , Md Aktaruzzaman  4 , Md Mahfizur Rahman  5 , M Zahidur Rahman  6 , Mohammad Ali Moni  7
Affiliations

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

Md Ali Hossain et al. Comput Biol Med. 2023 Jun.

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.

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Conflict of interest statement

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Flowchart used in this study. Using edgeR package on RNAseq data of COVID19 and SMK, DEGs of these diseases were identified, After that, common significant DEGs between diseases were identified. Correlation analysis using WGCNA for the common genes to see the characteristics of common genes. Then, identify common significant pathways and GO analysis through the pathway and go analysis on the common significant DEGs. Then, to identify the hub proteins, PPI network was constructed around the common genes, and to identify regulatory TFs and miRNA, DEGs–TFs and DEGs–miRNA networks were also constructed. Finally, the protein drug interaction network was constructed around the hub genes.
Fig. 2
Fig. 2
Protein–protein interaction network around 59 common significant genes in COVID-19 and SMK.
Fig. 3
Fig. 3
The gene–TFs interaction network obtained from JASPAR database.
Fig. 4
Fig. 4
The gene–microRNAs interaction network obtained from miRTarbase and Tarbase databases.
Fig. 5
Fig. 5
The Protein–Drug interaction network analysis.
Fig. 6
Fig. 6
Correlation analysis using WGCNA for the common significant genes in COVID-19 and SMK.
See this image and copyright information in PMC

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