Identification and Classification of Differentially Expressed Genes and Network Meta-Analysis Reveals Potential Molecular Signatures Associated With Tuberculosis - Alam, Aftab; Imam, Nikhat; Ahmed, Mohd Murshad; Tazyeen, Safia; Tamkeen, Naaila; Farooqui, Anam; Malik, Md. Zubbair; Ishrat, Romana (Frontiers in Genetics, 2019)
Tuberculosis (TB) is one of deadly transmissible disease that causes death worldwide; however, only 10% of people infected with Mycobacterium tuberculosis develop disease, indicating that host genetic factors may play key role in determining susceptibility to TB disease. In this way, the analysis of gene expression profiling of TB infected individuals can give us a snapshot of actively expressed genes and transcripts under various conditions. In the present study, we have analysed microarray data set and compared the gene expression profiles of patients with different datasets of healthy control, latent infection and active TB. We observed the transition of genes from normal condition to different stages of the TB and identified, annotated those genes/pathways/processes that have important roles in TB disease during its cyclic interventions in the human body. We identified 488 genes that were differentially expressed at various stages of TB and allocated to pathways and gene set enrichment analysis. These pathways as well as GSEA’s importance were evaluated according to the number of DEGs presents in both. In addition, we studied the gene regulatory networks that may help to further understand the molecular mechanism of immune response against the tuberculosis infection and provide us a new angle for future biomarker and therapeutic targets. In this study, we identified 26 leading hubs which are deeply rooted from top to bottom in the gene regulatory network and work as the backbone of the network. These leading hubs contains 31 key regulator genes, of which 14 genes were up regulated 17 genes were down regulated. The proposed approach is based on gene-expression profiling and network analysis approaches predicts some unknown TB-associated genes, that can be considered (or can be tested) as reliable candidates for further (in-vivo/vitro) studies.