Identification of Hub Genes in Tuberculosis via Bioinformatics Analysis

Abstract

Background. Tuberculosis (TB) is a serious chronic bacterial infection caused by Mycobacterium tuberculosis (MTB). It is one of the deadliest diseases in the world and a heavy burden for people all over the world. However, the hub genes involved in the host response remain largely unclear. Methods. The data set GSE11199 was studied to clarify the potential gene network and signal transduction pathway in TB. The subjects were divided into latent tuberculosis and pulmonary tuberculosis, and the distribution of differentially expressed genes (DEGs) was analyzed between them using GEO2R. We verified the enriched process and pathway of DEGs by making use of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). The construction of protein-protein interaction (PPI) network of DEGs was achieved through making use of the Search Tool for the Retrieval of Interacting Genes (STRING), aiming at identifying hub genes. Then, the hub gene expression level in latent and pulmonary tuberculosis was verified by a boxplot. Finally, through making use of Gene Set Enrichment Analysis (GSEA), we further analyzed the pathways related to DEGs in the data set GSE11199 to show the changing pattern between latent and pulmonary tuberculosis. Results. We identified 98 DEGs in total in the data set GSE11199, 91 genes upregulated and 7 genes downregulated included. The enrichment of GO and KEGG pathways demonstrated that upregulated DEGs were mainly abundant in cytokine-mediated signaling pathway, response to interferon-gamma, endoplasmic reticulum lumen, beta-galactosidase activity, measles, JAK-STAT signaling pathway, cytokine-cytokine receptor interaction, etc. Based on the PPI network, we obtained 4 hub genes with a higher degree, namely, CTLA4, GZMB, GZMA, and PRF1. The box plot showed that these 4 hub gene expression levels in the pulmonary tuberculosis group were higher than those in the latent group. Finally, through Gene Set Enrichment Analysis (GSEA), it was concluded that DEGs were largely associated with proteasome and primary immunodeficiency. Conclusions. This study reveals the coordination of pathogenic genes during TB infection and offers the diagnosis of TB a promising genome. These hub genes also provide new directions for the development of latent molecular targets for TB treatment.

Transcriptional analysis of human peripheral blood mononuclear cells stimulated by Mycobacterium tuberculosis antigen

“…KEGG enrichment results showed that DEGs stimulated by Mtb-Ag are mainly related to cytokine-cytokine receptor interactions, the JAK-STAT signaling pathway, the TNF signaling pathway, the IL-17 signaling pathway, the NF-κB signaling pathway, and the C-type lectin receptor signaling pathway. These pathways are closely related to tuberculosis’s pathogenesis ( Turrin and Plata-Salamán, 2000 ; Rawlings et al., 2004 ; Wu et al., 2021 ; Zhang T et al., 2021 ). This suggests that GO and KEGG were practical ways to further investigate the mechanism of action of Mtb-Ag in tuberculosis.…”

In-depth systems biological evaluation of bovine alveolar macrophages suggests novel insights into molecular mechanisms underlying Mycobacterium bovis infection

“…Other hub-central gene of the brown module, including CSF1 ( Chatterjee et al, 2021 ), PLAUR ( McLoughlin et al, 2021b ), ITGB1 ( Yang et al, 2017 ), CCND1 ( Koo et al, 2012 ; Looney et al, 2021 ), CSF2 ( Marsay et al, 2013 ; Shukla et al, 2017 ; Abdelaal et al, 2022 ), CTLA4 ( Zhang et al, 2021 ), FLNA ( Xu et al, 2015 ), CCND2 ( Lavalett et al, 2017 ), WEE1 ( Jayaswal et al, 2010 ), MMP9 ( Blanco et al, 2012 ; McLoughlin et al, 2014 ), CDC25A ( Shapira et al, 2020 ), CSF2RB ( Benmerzoug et al, 2018 ), TIMP1 ( Sun et al, 2020 ), CD69 ( Li et al, 2011 ; Chen et al, 2020 ), PTPN22 ( Boechat et al, 2013 ), CD38 ( Silveira-Mattos et al, 2019 ), IL7R ( Jenum et al, 2016 ; Alsulaimany et al, 2022 ), IL1RN ( Alcaraz-López et al, 2020 ), and IDO1 ( Weiner et al, 2012 ; Gautam et al, 2018 ) were also involved in host-pathogen interactions as well as suppression of host immune response. For example, the CTLA4 hub-central gene encodes an inhibitor of T cell-mediated response ( Schneider et al, 2006 ), and the upregulation of this gene in response to M. bovis infection may reflect a mechanism of immunomodulation used by M. bovis to subvert a host T-cell response ( Killick et al, 2011 ).…”

Molecular Insight into Mycobacterium tuberculosis Resistance to Nitrofuranyl Amides Gained through Metagenomics-like Analysis of Spontaneous Mutants

“…We additionally applied gene/protein enrichment and network analysis to the set of the above six mutated genes by making use of the STRING tool. This kind of analysis was shown to be useful in recent studies aimed to decipher the emergence of drug resistance, and to elucidate the potential gene network and signaling pathways in pulmonary TB through gene/protein network analysis [22][23][24].…”