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Original Research

Identification of Hub Genes Associated with Diabetes Mellitus and Tuberculosis Using Bioinformatic Analysis

ORCID Icon, , , &
Pages 4061-4072 | Published online: 30 Jul 2021

References

  • World Health Organization. Global Tuberculosis Report 2020. Geneva; 2020. Available from: https://www.who.int/tb/publications/global_report/en/. Accessed July 20, 2021.
  • Cho NH, Shaw JE, Karuranga S, et al. IDF diabetes atlas: global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract. 2018;138:271–281. doi:10.1016/j.diabres.2018.02.023
  • Gubitosi-Klug RA. The diabetes control and complications trial/epidemiology of diabetes interventions and complications study at 30 years: summary and future directions: figure 1. Diabetes Care. 2014;37(1):44–49. doi:10.2337/dc13-2148
  • Arnold M, Beran D, Haghparast-Bidgoli H, et al. Coping with the economic burden of diabetes, TB and co-prevalence: evidence from Bishkek, Kyrgyzstan. BMC Health Serv Res. 2016;16(1):118. doi:10.1186/s12913-016-1369-7
  • Al-Rifai RH, Pearson F, Critchley JA, et al. Association between diabetes mellitus and active tuberculosis: a systematic review and meta-analysis. PLoS One. 2017;12(11):e187967. doi:10.1371/journal.pone.0187967
  • Chumburidze-Areshidze N, Kezeli T, Avaliani Z, et al. The relationship between type-2 diabetes and tuberculosis. Georgian Med News. 2020;300:69–74.
  • Baker MA, Harries AD, Jeon CY, et al. The impact of diabetes on tuberculosis treatment outcomes: a systematic review. BMC Med. 2011;9(1):81. doi:10.1186/1741-7015-9-81
  • Singhal A, Jie L, Kumar P, et al. Metformin as adjunct antituberculosis therapy. Sci Transl Med. 2014;6(263):159r–263r. doi:10.1126/scitranslmed.3009885
  • Yu X, Li L, Xia L, et al. Impact of metformin on the risk and treatment outcomes of tuberculosis in diabetics: a systematic review. BMC Infect Dis. 2019;19(1):859. doi:10.1186/s12879-019-4548-4
  • Eckold C, Kumar V, Weiner J, et al. Impact of Intermediate Hyperglycemia and Diabetes on Immune Dysfunction in Tuberculosis. Clin Infect Dis. 2021;72(1):69–78. doi:10.1093/cid/ciaa751
  • Lachmandas E, Eckold C, Bohme J, et al. Metformin alters human host responses to Mycobacterium tuberculosis in healthy subjects. J Infect Dis. 2019;220(1):139–150. doi:10.1093/infdis/jiz064
  • Clough E, Barrett T. The gene expression omnibus database. Methods Mol Biol. 2016;1418:93–110.
  • Xia J, Gill EE, Hancock RE. NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nat Protoc. 2015;10:823–844. doi:10.1038/nprot.2015.052
  • Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4:44–57. doi:10.1038/nprot.2008.211
  • Ashburner M, Ball CA, Blake JA, et al. Gene ontology: tool for the unification of biology. The gene ontology consortium. Nat Genet. 2000;25(1):25–29. doi:10.1038/75556
  • Kanehisa M. The KEGG database. Novartis Found Symp. 2002;247:91–101, 101–103, 119–128, 244–252.
  • Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47:D607–D613. doi:10.1093/nar/gky1131
  • Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–2504. doi:10.1101/gr.1239303
  • Chin C, Chen S, Wu H, et al. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 2014;8(Suppl 4):S11. doi:10.1186/1752-0509-8-S4-S11
  • Corsello SM, Bittker JA, Liu Z, et al. The drug repurposing hub: a next-generation drug library and information resource. Nat Med. 2017;23(4):405–408. doi:10.1038/nm.4306
  • American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2013;36(Supplement_1):S67–S74. doi:10.2337/dc13-S067
  • Sticht C, De La Torre C, Parveen A, et al. miRWalk: an online resource for prediction of microRNA binding sites. PLoS One. 2018;13(10):e0206239. doi:10.1371/journal.pone.0206239
  • Pedraza-Sanchez S, Lezana-Fernandez JL, Gonzalez Y, et al. Disseminated tuberculosis and chronic mucocutaneous candidiasis in a patient with a gain-of-function mutation in signal transduction and activator of transcription 1. Front Immunol. 2017;8:1651. doi:10.3389/fimmu.2017.01651
  • Yi X-H, Zhang B, Fu Y-R, et al. STAT1 and its related molecules as potential biomarkers in Mycobacterium tuberculosis infection. J Cell Mol Med. 2020;24(5):2866–2878. doi:10.1111/jcmm.14856
  • Yao K, Chen Q, Wu Y, Liu F, Chen X, Zhang Y. Unphosphorylated STAT1 represses apoptosis in macrophages during Mycobacterium tuberculosis infection. J Cell Sci. 2017;130:1740–1751.
  • Kim S, Kim HS, Chung KW, et al. Essential role for signal transducer and activator of transcription-1 in pancreatic -cell death and autoimmune type 1 diabetes of nonobese diabetic mice. Diabetes. 2007;56(10):2561–2568. doi:10.2337/db06-1372
  • Cox AR, Chernis N, Bader DA, et al. STAT1 dissociates adipose tissue inflammation from insulin sensitivity in obesity. Diabetes. 2020;69(12):2630–2641. doi:10.2337/db20-0384
  • Plenchette S, Cheung HH, Fong WG, LaCasse EC, Korneluk RG. The role of XAF1 in cancer. Curr Opin Investig Drugs. 2007;8:469–476.
  • Andreu N, Phelan J, de Sessions PF, Cliff JM, Clark TG, Hibberd ML. Primary macrophages and J774 cells respond differently to infection with Mycobacterium tuberculosis. Sci Rep. 2017;7(1):42225. doi:10.1038/srep42225
  • de Oyarzabal E, Garcia-Garcia L, Rangel-Escareno C, et al. Expression of USP18 and IL2RA is increased in individuals receiving latent tuberculosis treatment with isoniazid. J Immunol Res. 2019;2019:1297131. doi:10.1155/2019/1297131
  • Tsuruta M, Iwashita M, Shinjo T, Matsunaga H, Yamashita A, Nishimura F. Metabolic endotoxemia-activated macrophages promote pancreatic β cell death via IFNβ-Xaf1 pathway. Horm Metab Res. 2018;50(2):160–167. doi:10.1055/s-0043-121467
  • Hare NJ, Chan B, Chan E, Kaufman KL, Britton WJ, Saunders BM. Microparticles released from Mycobacterium tuberculosis-infected human macrophages contain increased levels of the type I interferon inducible proteins including ISG15. Proteomics. 2015;15(17):3020–3029. doi:10.1002/pmic.201400610
  • Sambarey A, Devaprasad A, Mohan A, et al. Unbiased identification of blood-based biomarkers for pulmonary tuberculosis by modeling and mining molecular interaction networks. EBioMedicine. 2017;15:112–126. doi:10.1016/j.ebiom.2016.12.009
  • Maji A, Misra R, Kumar MA, et al. Expression profiling of lymph nodes in tuberculosis patients reveal inflammatory milieu at site of infection. Sci Rep. 2015;5(1):15214. doi:10.1038/srep15214
  • Gupta MK, Vadde R. Identification and characterization of differentially expressed genes in type 2 diabetes using in silico approach. Comput Biol Chem. 2019;79:24–35. doi:10.1016/j.compbiolchem.2019.01.010
  • Shukla GC, Singh J, Barik S. MicroRNAs: processing, maturation, target recognition and regulatory functions. Mol Cell Pharmacol. 2011;3:83–92.