Identification of key pathways and genes involved in microglia inflammation by bioinformatics analysis of transcriptome sequencing / 中华骨科杂志

ABSTRACT

Objective:To explore the key pathways and genes involved in microglia inflammation through transcriptome sequencing and bioinformatics analysis.Methods:BV2 cells were stimulated by lipopolysaccharide to establish microglia inflammation model. The levels of IL-6 and TNF-α were detected by ELISA and RT-qPCR. The established microglia inflammation model was sequenced by transcriptome sequencing, and the differentially expressed genes were screened by bioinformatics method. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of differentially expressed genes were performed. The protein-protein interaction network of differentially expressed genes was constructed by using string database, and the protein-protein interaction network was visualized by using Cytoscape software. The protein interaction network module was extracted by using MCODE app. The hub gene was extracted by using cytohubba app and was verified through RT-qPCR. We conducted enrichment analysis of hub genes, predicted their targeted miRNAs and interacting drugs.Results:The microglia inflammation model was successfully established and verified by ELISA and RT-qPCR. We screened 434 differentially expressed genes by bioinformatics analysis of transcriptome sequencing results. GO analysis showed that these differentially expressed genes were mainly concentrated in cellular response to cytokine stimulus, inflammatory response, regulation of response to external stimulation. KEGG analysis showed that these differentially expressed genes were mainly concentrated in Chemokine signaling pathway, TNF signaling pathway, IL-17 signaling pathway. We constructed the protein interaction network of these differentially expressed genes, and carried out module analysis and extraction of hub genes. Most of hub genes are located in module 1, and the seed gene of module 1 is S1pr1. Hub genes include S1pr1, Cxcr4, Cx3cl1, Cx3cr1, Cxcl10, Cxcl2, Ccl4, Ccl5, Ccl9, Fpr1. RT-qPCR results showed that compared with the culture medium group, the mRNA expressions of S1pr1, Cxcr4, Cx3cl1 and Cx3cr1 were down-regulated, and the mRNA expressions of Cxcl10, Cxcl2, Ccl4, Ccl5, Ccl9 and Fpr1 were up-regulated in the LPS group. The enrichment analysis of hub genes mainly focused on chemokine-mediated signaling pathway, Class A/1 (Rhodopsin-like receptors), cell chemotaxis and so on. Drugs and miRNAs that may interact with hub genes were predicted. Conclusion:Through transcriptome sequencing and bioinformatics analysis of microglia inflammation model, differentially expressed genes were screened, hub genes and seed genes were extracted, which will help us further understand the molecular mechanism of microglia inflammation and provide potential targets for the treatment of related diseases.