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重度抑郁症发病机制相关基因的生物信息学分析

高丽娟^1,2, 赵欣^1,2, 李建国^1,2, 徐勇³, 张宇^1,2,*

¹山西医科大学生理学系，太原 030001；²山西医科大学细胞生理学教育部重点实验室，太原 030001；³山西医科大学第一医院精神科，太原 030001

摘要

本文旨在采用生物信息学方法筛选重度抑郁症发病机制相关基因。以美国国家生物技术信息中心(NCBI)网站GEO公共数据库中GSE98793芯片数据为研究对象，用R语言limma包筛选出116个差异表达基因(differentially expressed genes, DEGs)，其中上调基因66个，下调基因50个。Gene Ontology (GO)基因功能注释分析结果显示，DEGs主要分布于线粒体内膜、线粒体内，参与铜离子结合、半胱氨酸肽链内切酶活性、白细胞介素-1的细胞应答、蛋白质加工等生物过程。KEGG通路富集分析结果显示，DEGs主要富集于氧化磷酸化反应、帕金森病、非酒精性脂肪肝病、阿尔茨海默病和亨廷顿氏舞蹈病等发病机制。蛋白质相互作用网络分析结果显示，54个DEGs编码的蛋白之间存在相互作用。结合上述多种方法的分析结果，UQCRC1、GZMB、NDUFB9、NSF、SLC17A5、CTSH、NDUFB10、UQCR10、ATOX1、CST7和CTSW共11个关键基因被筛选出来，可作为诊断和治疗重度抑郁症的候选基因。综上，本研究通过对重度抑郁症芯片及其DEGs进行深入分析得到关键基因，为揭示抑郁症的分子机制和临床靶向治疗提供了重要的线索。

关键词： GEO数据库; 重度抑郁症; 生物信息学

分类号：R749

Bioinformatics analysis of genes related to pathogenesis of major depression disorder

GAO Li-Juan^1,2, ZHAO Xin^1,2, LI Jian-Guo^1,2, XU Yong³, Zhang Yu^1,2,*

¹Department of Physiology, Shanxi Medical University, Taiyuan 030001, China；²Key Laboratory for Cellular Physiology of Ministry of Education, Shanxi Medical University, Taiyuan 030001, China；³Department of Psychiatry, First Hospital, Shanxi Medical University, Taiyuan 030001, China

Abstract

The aim of this study was to screen the genes related to the pathogenesis of major depression disorder (MDD) by bioinformatics. Taking GSE98793 chip data from GEO public database of National Biotechnology Information Center (NCBI) website as the research object, 116 differentially expressed genes (DEGs) were screened by R language limma package. Among the 116 DEGs, 66 genes were up-regulated and 50 down-regulated. The results of gene functional annotation analysis of Gene Ontology (GO) showed that the DEGs were mainly distributed in mitochondria intima and mitochondria. They were involved in copper ion binding, cysteine- type endopeptidase activity, the cell response of interleukin-1, protein processing and other biological processes. KEGG pathway enrichment analysis results showed that the DEGs were mainly concentrated in oxidative phosphorylation, Parkinson’s disease, non-alcoholic fatty liver disease, Alzheimer’s disease and Huntington’s disease etc. The results of protein interaction network analysis showed that there were interactions among proteins encoded by 54 DEGs. Combined with the analysis results of the above methods, 11 key genes were screened out, including UQCRC1, GZMB, NDUFB9, NSF, SLC17A5, CTSH, NDUFB10, UQCR10, ATOX1, CST7 and CTSW, which could be used as candidate genes for the diagnosis and treatment of MDD. Taken together, the key genes were obtained by analyzing the microarray and the DEGs of MDD in the present study, which would provide important clues for revealing the molecular mechanism and clinical targeted therapy of depression.

Key words: GEO database; major depression disorder; bioinformatics

收稿日期：2017-12-26　　录用日期：2018-07-12

通讯作者：张宇　　E-mail: zhyucnm@163.com

DOI: 10.13294/j.aps.2018.0053

引用本文：

高丽娟, 赵欣, 李建国, 徐勇, 张宇. 重度抑郁症发病机制相关基因的生物信息学分析[J]. 生理学报 2018; 70 (4): 361-368.

GAO Li-Juan, ZHAO Xin, LI Jian-Guo, XU Yong, Zhang Yu. Bioinformatics analysis of genes related to pathogenesis of major depression disorder. Acta Physiol Sin 2018; 70 (4): 361-368 (in Chinese with English abstract).