GEO datasets-based analysis of the signal pathway and the key molecules that involved in the pathogenesis of MERS-CoV infection
SHI Hao-tong1, 2, ZHANG Shu-ting1, FAN Wei-ping1, LI Jun-feng2, 3, YAO Hong1
1.Department of Microbiology and Immunology, Shanxi Medical University,Taiyuan 030001, China; 2.State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China; 3. Beijing Cancer Hospital,Beijing 100142, China
Abstract:To identify the key signal pathways and the key molecules involved in Middle East respiratory syndrome coronavirus (MERS-CoV) related disease through microarray expression profiling data analysis and bioinformatics approaches. The mRNA expression microarray data of MERS-CoV infected lung cell was screened out in the Gene Expression Omnibus (GEO) database of the National Center for Biotechnology Information. The differentially expressed genes (DEGs) between the normal Primary human airway epithelial cells and the experimental cells of infected with MERS-CoV was analyzed by the online analysis tool GEOR. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the different expression genes were carried out by web-accessible programs DAVID. STRING and cytoscape were used to establish potein and protein interaction network and identify the hub molecules of the network. A total of 1 553 DEGs containing 850 upregulated DEGs and 703 downregulated DEGs were identified in MERS-CoV infected cells. Gene enrichment analysis results showed that these differential genes are involved in multiple signaling pathways such as immune response, inflammatory response, apoptotic process, bronchial cilia formation and movement. Several key molecules that may be involved in the MERS-CoV related diseases were also identified. This study may help to reveal the pathogenic mechanism of MERS-CoV and provide data for exploring the new targets and treatment strategy for MERS-CoV associated disease.
师浩桐, 张淑婷, 樊卫萍, 李军锋, 姚红. 基于GEO数据分析参与MERS-CoV感染致病的信号传导通路和关键分子[J]. 中国人兽共患病学报, 2020, 36(9): 703-708.
SHI Hao-tong, ZHANG Shu-ting, FAN Wei-ping, LI Jun-feng, YAO Hong. GEO datasets-based analysis of the signal pathway and the key molecules that involved in the pathogenesis of MERS-CoV infection. Chinese Journal of Zoonoses, 2020, 36(9): 703-708.
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