Dataset Information

Gene network in pulmonary tuberculosis based on bioinformatic analysis.

ABSTRACT: BACKGROUND:Pulmonary tuberculosis (PTB) is one of the serious infectious diseases worldwide; however, the gene network involved in the host response remain largely unclear. METHODS:This study integrated two cohorts profile datasets GSE34608 and GSE83456 to elucidate the potential gene network and signaling pathways in PTB. Differentially expressed genes (DEGs) were obtained for Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis using Metascape database. Protein-Protein Interaction (PPI) network of DEGs was constructed by the online database the Search Tool for the Retrieval of Interacting Genes (STRING). Modules were identified by the plug-in APP Molecular Complex Detection (MCODE) in Cytoscape. GO and KEGG pathway of Module 1 were further analyzed by STRING. Hub genes were selected for further expression validation in dataset GSE19439. The gene expression level was also investigated in the dataset GSE31348 to display the change pattern during the PTB treatment. RESULTS:Totally, 180 shared DEGs were identified from two datasets. Gene function and KEGG pathway enrichment revealed that DEGs mainly enriched in defense response to other organism, response to bacterium, myeloid leukocyte activation, cytokine production, etc. Seven modules were clustered based on PPI network. Module 1 contained 35 genes related to cytokine associated functions, among which 14 genes, including chemokine receptors, interferon-induced proteins and Toll-like receptors, were identified as hub genes. Expression levels of the hub genes were validated with a third dataset GSE19439. The signature of this core gene network showed significant response to Mycobacterium tuberculosis (Mtb) infection, and correlated with the gene network pattern during anti-PTB therapy. CONCLUSIONS:Our study unveils the coordination of causal genes during PTB infection, and provides a promising gene panel for PTB diagnosis. As major regulators of the host immune response to Mtb infection, the 14 hub genes are also potential molecular targets for developing PTB drugs.

SUBMITTER: Li L

PROVIDER: S-EPMC7436983 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Gene network in pulmonary tuberculosis based on bioinformatic analysis.

Li Lili L Lv Jian J He Yuan Y Wang Zhihua Z

BMC infectious diseases 20200818 1

<h4>Background</h4>Pulmonary tuberculosis (PTB) is one of the serious infectious diseases worldwide; however, the gene network involved in the host response remain largely unclear.<h4>Methods</h4>This study integrated two cohorts profile datasets GSE34608 and GSE83456 to elucidate the potential gene network and signaling pathways in PTB. Differentially expressed genes (DEGs) were obtained for Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis using Metascape database. Pr ...[more]

PMID: 32811479

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Project description:Coronavirus disease 2019 (COVID-19) seriously threatens human health and has been disseminated worldwide. Although there are several treatments for COVID-19, its control is currently suboptimal. Therefore, the development of novel strategies to treat COVID-19 is necessary. Ion channels are located on the membranes of all excitable cells and many intracellular organelles and are key components involved in various biological processes. They are a target of interest when searching for drug targets. This study aimed to reveal the relevant molecular features of ion channel genes in COVID-19 based on bioinformatic analyses. The RNA-sequencing data of patients with COVID-19 and healthy subjects (GSE152418 and GSE171110 datasets) were obtained from the Gene Expression Omnibus (GEO) database. Ion channel genes were selected from the Hugo Gene Nomenclature Committee (HGNC) database. The RStudio software was used to process the data based on the corresponding R language package to identify ion channel-associated differentially expressed genes (DEGs). Based on the DEGs, Gene Ontology (GO) functional and pathway enrichment analyses were performed using the Enrichr web tool. The STRING database was used to generate a protein-protein interaction (PPI) network, and the Cytoscape software was used to screen for hub genes in the PPI network based on the cytoHubba plug-in. Transcription factors (TF)-DEG, DEG-microRNA (miRNA) and DEG-disease association networks were constructed using the NetworkAnalyst web tool. Finally, the screened hub genes as drug targets were subjected to enrichment analysis based on the DSigDB using the Enrichr web tool to identify potential therapeutic agents for COVID-19. A total of 29 ion channel-associated DEGs were identified. GO functional analysis showed that the DEGs were integral components of the plasma membrane and were mainly involved in inorganic cation transmembrane transport and ion channel activity functions. Pathway analysis showed that the DEGs were mainly involved in nicotine addiction, calcium regulation in the cardiac cell and neuronal system pathways. The top 10 hub genes screened based on the PPI network included KCNA2, KCNJ4, CACNA1A, CACNA1E, NALCN, KCNA5, CACNA2D1, TRPC1, TRPM3 and KCNN3. The TF-DEG and DEG-miRNA networks revealed significant TFs (FOXC1, GATA2, HINFP, USF2, JUN and NFKB1) and miRNAs (hsa-mir-146a-5p, hsa-mir-27a-3p, hsa-mir-335-5p, hsa-let-7b-5p and hsa-mir-129-2-3p). Gene-disease association network analysis revealed that the DEGs were closely associated with intellectual disability and cerebellar ataxia. Drug-target enrichment analysis showed that the relevant drugs targeting the hub genes CACNA2D1, CACNA1A, CACNA1E, KCNA2 and KCNA5 were gabapentin, gabapentin enacarbil, pregabalin, guanidine hydrochloride and 4-aminopyridine. The results of this study provide a valuable basis for exploring the mechanisms of ion channel genes in COVID-19 and clues for developing therapeutic strategies for COVID-19.

Project description:Pulmonary tuberculosis (TB) is a chronic infectious disease that is caused by respiratory infections, principally Mycobacterium tuberculosis. Increasingly, studies have shown that circular (circ)RNAs play regulatory roles in different diseases through different mechanisms. However, their roles and potential regulatory mechanisms in pulmonary TB remain unclear. In this study, we analyzed circRNA sequencing data from adjacent normal and diseased tissues from pulmonary TB patients and analyzed the differentially expressed genes. We then constructed machine learning models and used single-factor analysis to identify hub circRNAs. We downloaded the pulmonary TB micro (mi)RNA (GSE29190) and mRNA (GSE83456) gene expression datasets from the Gene Expression Omnibus database and performed differential expression analysis to determine the differentially expressed miRNAs and mRNAs. We also constructed a circRNA-miRNA-mRNA interaction network using Cytoscape. Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis were used to predict the biological functions of the identified RNAs and determine hub genes. Then, the STRING database and cytoHubba were used to construct protein-protein interaction networks. The results showed 125 differentially expressed circRNAs in the adjacent normal and diseased tissues of pulmonary TB patients. Among them, we identified three hub genes associated with the development of pulmonary TB: hsa_circ_0007919 (upregulated), hsa_circ_0002419 (downregulated), and hsa_circ_0005521 (downregulated). Through further screening, we determined 16 mRNAs of potential downstream genes for hsa-miR-409-5p and hsa_circ_0005521 and established an interaction network. This network may have important roles in the occurrence and development of pulmonary TB. We constructed a model with 100% prediction accuracy by machine learning and single-factor analysis. We constructed a protein-protein interaction network among the top 50 hub mRNAs, with FBXW7 scoring the highest and SOCS3 the second highest. These results may provide a new reference for the identification of candidate markers for the early diagnosis and treatment of pulmonary TB.

Dataset Information

Gene network in pulmonary tuberculosis based on bioinformatic analysis.

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Gene network in pulmonary tuberculosis based on bioinformatic analysis.

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