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

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.

Enhanced Pharmaceutically Active Compounds Productivity from Streptomyces SUK 25: Optimization, Characterization, Mechanism and Techno-Economic Analysis

“…The most upregulated genes for the molecular function level were under categories of RNA binding, transferase activity, structural constituent of ribosome, oxidoreductase activity, and catalytic activity. These results are similar to the previous research conducted by Alam et al [ 49 ].…”

“…The flasks were plugged with foam caps and enclosed with aluminum foil, before being autoclaved at 121 °C for 15 min. The autoclaved flasks were left to cool, then 100 microliter (μL) of SUK 25 spore suspension were inoculated, as described by Thakur et al [ 49 ]. The 37 runs of the fermentation process were carried out according to the RSM design, as presented in Table S1 .…”

A Bioinformatics Approach to Identifying Potential Biomarkers for Cryptosporidium parvum: A Coccidian Parasite Associated with Fetal Diarrhea

“…We also used the Bioconductor Package (Lumi pipeline), designed exclusively for Illumine data analysis (BeadChip). It verifies data consistency and normalization while also lowering data volatility [23,24].…”

Identification of key regulators in parathyroid adenoma using an integrative gene network analysis

“…Since all the centrality measurements (centrality scores) were calculated using CytoNCA [ 32 ] for each node in the network. The details of network topological properties and centrality measures were already given in our previous article by Aftab et al [ 33 ].…”

“…In the hierarchical network, to determine the behaviour of modular and its properties; It is crucial to know about the activities of the network at different levels of hierarchy and to know the principle on which the network is self-organized. In this study, the Leading Eigen Vector method (LEV) [ 33 ] was employed to identify the communities in the main network using 'igraph' package in R [ 36 ]. For community detection, Leading Eigenvector (LEV) is the most promising method, as it measures the eigenvalue epitomizing the importance of the individual link.…”