TB Research

Genome wide differential methylation analysis reveals aberrant methylation of T cell immune response related genes in tuberculosis patients

Ankit Kumar, Indu Verma, Ashutosh N. Aggarwal, Jyotdeep Kaur

Abstract

<bold>Background:</bold> In the last few years, there has been increasing evidence in the literature for promoter hypermethylation of various host genes following infection with various pathogens. Therefore, in the present study, the role of epigenetic reprogramming of host cells through DNA methylation was evaluated. <bold>Materials and methods:</bold> To decipher the methylation profile of PBMCs in PTB patients whole genome bisulphite sequencing was performed using 4 DNA samples from each study group i.e. PTB, healthy controls and diseased controls.GEOR2 online server was used to investigate the mRNA expression of selected genes associated with hypermethylated DMRs in promoter region. <bold>Results:</bold> Differential methylation region analysis was performed for the following comparisons. TB v/s Diseased (Hypermethylated=1756; Hypomethylated=1886), TB v/s Healthy(Hypermethylated=2203; Hypomethylated=2466). Further gene set enrichment analysis showed that the hypermetylated regions belonged to genes that are involved in immune responses mainly T cell functioning and T cell mediated immune processes. There were 8 DMR belonging to TAF8,FZD5, HLA-DRB, MIR483, PVRIG, SH2B2, ZAP70, TNFRSF13C simultaneously (n=10 from each study group). mRNA expression analysis with GEOR2 (Datasets used n= 12) revealed significant downregulation of TNFRSF13C, ZAP70, PVRIG Among the selected genes associated with hypermethylated DMRs in promoter region. <bold>Conclusion:</bold> The altered differential methylation profile of PBMCs from TB patients shows that on onset the active disease there occurs aberrant methylation of expression regulating regions of genes related to T cell functions.

MeSH terms

  • Methylation
  • Gene
  • Immune system
  • DNA methylation
  • Biology
  • Genome
  • Genetics
  • Tuberculosis
  • Computational biology