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      Permanent link:
      https://www.ias.ac.in/article/fulltext/jbsc/047/0069

    • Keywords

       

      BioNSi; differential expression; simulation; text mining; tuberculosis; type 2 diabetes

    • Abstract

       

      In type 2 diabetes mellitus (T2DM) patients, chronic hyperglycemia and inflammation underlie susceptibility to tuberculosis (TB) and result in poor TB control. Here, an integrative pathway-based approach is used to investigate perturbed pathways in T2DM patients that render susceptibility to TB. We obtained 36 genes implicated in type 2 diabetes-associated tuberculosis (T2DMTB) from the literature. Gene expression analysis on T2DM patient data (GSE26168) showed that DEFA1 is differentially expressed at ${P}_{adj}$<0.05. The human host TB susceptibility genes TNFRSF10A, MSRA, GPR148, SLC37A3, PXK, PROK2, REV3L, PGM1, HIST3H2A, PLAC4, LETM2, and EMP2 and hsa-miR-146a microRNA were also differentially expressed at ${P}_{adj}$<0.05. We included all these genes and added the remaining 28 genes from the T2DMTB set and the remaining differentially expressed genes at ${P}_{adj}$<0.05 in STRING and obtained a well-connected network with high confidence score ($\geq$0.7). Further, we extracted the KEGG pathways at FDR<0.05 and retained only the diabetes and TB pathways. The network was simulated with BioNSi using gene expression data. It is evident from BioNSi analysis that the NF-kappa B and Toll-like receptor pathways are commonly perturbed with high ranking in multiple gene expression datasets of type 2 diabetes versus healthy controls. The other pathways, necroptosis pathway and FoxO signalling pathway, appear perturbed with high ranking in different gene expression datasets. These pathways likely underlie susceptibility to TB in T2DM patients.

    • Author Affiliations

       

      JYOTI RANI1 2 ANASUYA BHARGAV2 3 SURABHI SETH2 3 MALABIKA DATTA3 4 URMI BAJPAI1 SRINIVASAN RAMACHANDRAN2 3

      1. Department of Biomedical Science, Acharya Narendra Dev College, University of Delhi, New Delhi 110019, India
      2. Informatics and Big Data, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), New Delhi 110025, India
      3. Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
      4. Council of Scientific and Industrial Research-Institute of Genomics and Integrative Biology (CSIRIGIB), New Delhi 110007, India

    • Dates

       
      Published
      7 December 2022

    • Supplementary Material

       

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