基于GEO数据库线粒体脑肌病伴高乳酸血症和卒中样发作芯片数据的生物信息学分析

doi:10.3969/j.issn.1672-6731.2020.04.015

摘要/Abstract

摘要：

目的从分子水平探讨线粒体脑肌病伴高乳酸血症和卒中样发作（MELAS）的可能发病机制和临床表现。方法自GEO数据库中获取野生型细胞系和高表达mtDNA A3243G位点突变细胞系的芯片信息，采用R语言程序分析获得差异表达基因，行GO富集分析和KEGG通路富集分析并通过多组芯片的两两比较筛选出共同的差异表达基因，蛋白质相互作用网络分析关键调控基因与MELAS发病机制和临床症状之间可能的关联。结果自GEO数据库中筛选获得563个差异表达基因，250个基因表达上调、313个基因表达下调；GO富集分析显示，差异表达基因的主要功能是细胞外基质（ECM）粘附的生物学过程；KEGG通路富集分析包括磷脂酰肌醇3-激酶（PI3K）-丝氨酸/苏氨酸激酶（AKT）信号通路、细胞因子-细胞因子受体相互作用通路、转化生长因子-β（TGF-β）信号通路、ECM受体相互作用通路等。STRING数据库筛选出GNG2、SDC2、ANXA1、FN1、TNC、CYR61、IGFBP3、LTBP1、SERPIND1共9个关键基因。3组芯片进行两两比较筛选出PAMR1、GLRX5、SNCA等共同的差异表达基因。结论 ECM粘附的生物学过程以及PI3K-AKT信号通路、TGF-β信号通路、ECM受体相互作用通路均参与线粒体能量代谢过程，可能与MELAS的发病机制有关；GNG2、TNC、LTBP1、PAMR1、GLRX5、SNCA等作为关键基因可能与MELAS的临床表现有关。

关键词: MELAS综合征, 基因, 蛋白质阵列分析, 计算生物学

Abstract:

Objective To investigate the possible pathogenesis and clinical feature of mitochondrial encephalomyopathy with lactic acidemia and stroke-like episodes (MELAS) on the molecular level by bioinformatics analysis of differential expression genes. Methods The microarray information of the wild-type cell lines and the mutant cell line with high expression of mtDNA A3243G locus was downloaded from the GEO database, and the differential expression genes were obtained by the R platform. Then Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed, and paired comparison of gene microarray analysis to screen out the same differently expressed genes, and the protein-protein interaction (PPI) network was used to find the relation of key genes between MELAS and clinical symptoms. Results A total of 563 differential expression genes were obtained, of which 250 genes were up-regulated and 313 genes were down-regulated. GO enrichment analysis showed that the differential expression genes were mainly involved in the extracellular matrix (ECM) binding biological process, and KEGG pathway involved in phosphatidylinositol 3-kinase (PI3K)-serine/threonine kinase (AKT) signaling pathway, tranforming growth factor-β (TGF-β) signaling pathway, ECM receptor interaction pathway. Using the STRING platform, 9 hubs genes including GNG2, SDC2, ANXA1, FN1, TNC, CYR61, IGFBP3, LTBP1, SERPIND1 had been found. PAMR1, GLRX5, SNCA and other common differential genes were obtained by paired comparison of 3 groups of gene microarrays. Conclusions According to the above results, the PI3K-AKT signaling pathway, TGF-β signaling pathway, ECM receptor interaction pathway and the biological processes of ECM binding were involved in the process of the mitochondrial energy metabolism, which might be related to the pathogenesis of MELAS. GNG2, TNC, LTBP1, PAMR1, GLRX5, SNCA and other hubs genes might be related to the clinical manifestations of MELAS.

Key words: MELAS syndrome, Genes, Protein array analysis, Computational biology

许谦, 高阳, 李蕾, 严峻, 王礼玲, 任桃杰. 基于GEO数据库线粒体脑肌病伴高乳酸血症和卒中样发作芯片数据的生物信息学分析[J]. 中国现代神经疾病杂志, 2020, 20(4): 347-353.

XU Qian, GAO Yang, LI Lei, YAN Jun, WANG Li-ling, REN Tao-jie. Bioinformatics analysis of mitochondrial encephalomyopathy with lactic acidemia and stroke-like episodes geneome microarray based on GEO database[J]. Chinese Journal of Contemporary Neurology and Neurosurgery, 2020, 20(4): 347-353.

http://journal11.magtechjournal.com/cjcnn/CN/Y2020/V20/I4/347

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2 基于GEO数据库线粒体脑肌病伴高乳酸血症和卒中样发作芯片数据的生物信息学分析

Bioinformatics analysis of mitochondrial encephalomyopathy with lactic acidemia and stroke-like episodes geneome microarray based on GEO database

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