Identifying Key Regulator Genes for Tuberculosis by Differential Co- Expression Analysis of Gene Expression Profiling

Title:Identifying Key Regulator Genes for Tuberculosis by Differential Co- Expression Analysis of Gene Expression Profiling

Volume: 12 Issue: 2

Author(s): Jingming Liu, Wei Wang, Joy Fleming, Lijun Bi, Mengqiu Gao*Chuanyou Li

Affiliation:

• Beijing Key Laboratory of Drug Resistant Tuberculosis Research, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, No. 97 Ma Chang, Tongzhou District, 101149 Beijing,China

Keywords: Tuberculosis, gene expression profiling, co-expression analysis, regulator genes, regulated links.

Abstract: Introduction: Tuberculosis (TB) is a major global public health problem. Its pathogenesis, however, is not fully understood. The purpose of this study was to identify key genes for TB by a bioinformatics analysis of gene expression profiles.

Methods: We downloaded the gene expression profiles of TB from the Gene Expression Omnibus and identified differentially-expressed genes (DEGs) and highly-enriched pathways between TB patients and healthy controls. We then identified differentially co-expressed genes (DCGs), differentially coexpressed links (DCLs), differentially-regulated genes (DRGs) and differentially-regulated links (DRLs) using Differential Co-Expression Analysis (DCEA) and Differential Regulation Analysis (DRA). In addition, we constructed a TF bridged DCL-centered network by mapping the DCGs to known regulatory data between transcription factors (TFs) and target genes. We then calculated the TED, TDD and regulatory impact factor (RIF) of each TF.

Results: A total of 5540 DEGs, 61DCGs, 3915 DCLs, 59 DRGs and 1139 DRLs were identified between TB patients and healthy controls. KEGG pathway enrichment analysis identified the lysosome as the most significantly-enriched pathway. Based on their TED, TDD and RIF scores, the REL, TAL1, RELA, NFKB1, NF-kappaB2, Cart-1, TCF3, MZF1, POU2F2 and EPAS1 transcription factors may play key roles in tuberculosis. Of these genes, REL and TAL1 were the only two among the top 20 genes of the three algorithms and may therefore paly more significant roles in tuberculosis.

Conclusions: REL and TAL1 may play more significant roles in tuberculosis. However, more laboratory work is needed to validate our results.

Cite this article as:

Liu Jingming, Wang Wei, Fleming Joy, Bi Lijun, Gao Mengqiu*, Li Chuanyou, Identifying Key Regulator Genes for Tuberculosis by Differential Co- Expression Analysis of Gene Expression Profiling, Current Bioinformatics 2017; 12 (2) . https://dx.doi.org/10.2174/1574893610666151015212742

DOI https://dx.doi.org/10.2174/1574893610666151015212742	Print ISSN 1574-8936
Publisher Name Bentham Science Publisher	Online ISSN 2212-392X