| Summary: | Shengsheng Liu,1– 3,* Weicong Ren,1,* Jiajia Yu,1,2 Chuanyou Li,1 Shenjie Tang2 1Department of Bacteriology and Immunology, Beijing Tuberculosis and Thoracic Tumor Research Institute/Beijing Chest Hospital, Capital Medical University, Beijing, 101149, People’s Republic of China; 2Multidisciplinary Diagnosis and Treatment Centre for Tuberculosis, Beijing Tuberculosis and Thoracic Tumor Research Institute/Beijing Chest Hospital, Capital Medical University, Beijing, 101149, People’s Republic of China; 3Department of Tuberculosis, Anhui Chest Hospital, Anhui, 230022, People’s Republic of China*These authors contributed equally to this workCorrespondence: Shenjie Tang; Chuanyou Li Tel +8613621028338; +8613683140887Email tangsj1106@hotmail.com; lichuanyou6688@hotmail.comPurpose: To investigate the potential pathophysiological association between tuberculosis (TB) and diabetes mellitus (DM) using bioinformatic analyses.Patients and Methods: Gene expression datasets for healthy controls (HCs), TB patients, DM patients, TB+DM patients (GSE114192), and metformin-treated cells (GSE102677) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified from pairwise dataset comparisons TB vs HCs and DM vs HCs. DEGs were verified by comparing them to DEGs for TB+DM vs HCs. Enrichment analysis of DEGs common to all three dataset comparisons was conducted using DAVID. The protein–protein interaction (PPI) network was established via STRING and visualised in Cytoscape. Hub genes were identified using the Cytoscape plug-in cytoHubba and then were verified using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis. Targeted miRNA prediction analysis identified metformin treatment-induced gene expression changes in peripheral blood mononuclear cells.Results: A total of 422 DEGs were common to all three dataset comparisons. Functional enrichment analysis revealed these DEGs were enriched for functional terms of type I interferon signaling pathway, innate immune response, inflammatory response, and infectious diseases. Ten hub genes identified using PPI network analysis were screened for interactions with metformin target gene INS using cytoHubba based on maximal clique centrality (MCC) score. Subsequently, five hub genes were predicted to functionally interact with INS, including STAT1, IFIT3, RSAD2, IFI44L, and XAF1, as verified by RT-qPCR. Meanwhile, seven miRNAs (miR-3680-3p, miR-3059-5p, miR-629-3p, miR-29b-2-5p, miR-514b-5p, miR-4755-5p, miR-4691-3p) were associated with regulation of hub genes. Notably, six hub genes (STAT1, IFIT3, RSAD2, ISG15, IFI44, IFI6) were down-regulated in cells exposed to both metformin and Mycobacterium tuberculosis antigens.Conclusion: Network hub genes hold promise as disease status biomarkers and as metformin treatment targets for alleviating TB and DM. This study describes a strategy for exploring pathogenic mechanisms of diseases such as TB and DM.Keywords: diabetes mellitus, tuberculosis, differentially expressed gene, metformin, bioinformatics
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