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

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

doi:10.3389/fgene.2019.00932

Aftab Alam, Romana Ishrat + Show 6 more

Open Access

https://doi.org/10.3389/fgene.2019.00932

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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.

Highlights

Tuberculosis (TB) is a communicable disease generally caused by the bacterium Mycobacterium tuberculosis (MTB)
All these predictive differentially expressed genes (DEGs) showed a broad range of protein classes which involved in various processes
Normal to Latent infections: In the early infection, we have identified 28 genes which are differentially expressed in the host system, of which 12 genes (IER5L, MS4A6A, DOK2, FZD2, NCKI-ASI, SNHG12, NLRC4, XPO7, SMA4, CD36, AFFI, and NDUFS8) were up-regulated, and 16 genes (IL1A, IL6, ACOD1, IL1B, ELOVL7, PTGS2, EREG, F3, IFIT1, TNF, KANK1, CCL4, CXCL11, PTX3, IRAK2, and AREG) were down-regulated

Summary

Introduction

Tuberculosis (TB) is a communicable disease generally caused by the bacterium Mycobacterium tuberculosis (MTB). India has the highest burden of both TB and advanced TB (like MDR TB) and second highest of HIV-associated TB. In India, the major challenge to curb the TB is poor primary healthcare system in rural areas due to deregulation of private health care leading to indiscriminate use of first- and second-line TB drugs, poverty, spreading HIV infection, and lack of administrative coordination among government functionary bodies. The concept of network theory is an imperative method to know the topological properties and the complex-system dynamics that correspond to their functional modules. We constructed the gene regulatory network and analyzed its topological properties, because it helps to understand the structure of a network which facilitates in understanding the hidden mechanisms.

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