ArticleHeliyon2024
Discovering common pathogenetic processes between tuberculosis and COVID-19 by bioinformatics and system biology approach.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
7 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Background: SARS-CoV-2, the cause of the COVID-19 pandemic, poses a significant threat to humanity. Individuals with pulmonary tuberculosis (PTB) are at increased risk of developing severe COVID-19, due to long-term lung damage that heightens their susceptibility to full-blown disease. Methods: Three COVID-19 datasets (GSE157103, GSE166253, and GSE171110) and one PTB dataset (GSE83456) were obtained from the Gene Expression Omnibus databases. Subsequently, data were subjected to weighted gene co-expression network analysis(WGCNA)followed by functional enrichment analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway databases. These analyses revealed two overlapping disease-specific modules, each comprising co-regulated genes with potentially related biological functions. Using Cytoscape, we visualised the interaction network containing common disease-related genes found within the intersection between modules and predicted transcription factors (TFs). Real-time qPCR was conducted to quantify expression levels of these genes in blood samples from COVID-19 and PTB patients. Finally, DisGeNET and the Drug Signatures database were employed to analyze these common genes, unveiling their connections to clinical disease features and potential drug treatments. Results: Examination of the overlap between COVID-19 and PTB gene modules unveiled 11 common genes. Functional enrichment analyses using KEGG and GO shed light on potential functional relationships among these genes, providing insights into their potential roles in the heightened mortality of PTB patients due to SARS-CoV-2 infection. Furthermore, results of various bioinformatics-based analyses of common TFs and target genes led to identification of shared pathways and therapeutic targets for PTB patients with COVID-19, along with potential drug treatments for these patients. Conclusion: Our results unveiled a potential biological connection between COVID-19 and PTB, as supported by results of functional enrichment analysis that highlighted potential biological processes and signaling pathways shared by both diseases. Building on these findings, we propose potential drug treatments for PTB patients with COVID-19, pending verification of drug safety and efficacy through laboratory and multicentre studies before clinical use.
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