Identification and Classification of Differentially Expressed Genes and Network Meta-Analysis Reveals Potential Molecular Signatures Associated With Tuberculosis

Aftab Alam; Nikhat Imam; Mohd Murshad Ahmed; Safia Tazyeen; Naaila Tamkeen; Anam Farooqui; Md Zubbair Malik; Romana Ishrat

doi:10.3389/fgene.2019.00932

Identification and Classification of Differentially Expressed Genes and Network Meta-Analysis Reveals Potential Molecular Signatures Associated With Tuberculosis

Front Genet. 2019 Nov 4:10:932. doi: 10.3389/fgene.2019.00932. eCollection 2019.

Authors

Aftab Alam¹, Nikhat Imam^{1

2}, Mohd Murshad Ahmed¹, Safia Tazyeen¹, Naaila Tamkeen³, Anam Farooqui¹, Md Zubbair Malik⁴, Romana Ishrat¹

Affiliations

¹ Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India.
² Department of Mathematics, Institute of Computer Science & Information Technology, Magadh University, Bodh Gaya, India.
³ Department of Biosciences, Jamia Millia Islamia, New Delhi, India.
⁴ School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi, India.

Abstract

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 analyzed 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 and 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 TB 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 and 17 genes were down-regulated. The proposed approach is based on gene-expression profiling, and network analysis approaches predict some unknown TB-associated genes, which can be considered (or can be tested) as reliable candidates for further (in vivo/in vitro) studies.

Keywords: DEGs; KEGG; LCP; gene knock-out; gene ontology; meta-analysis; network analysis.