基于表观基因组学数据分析阿尔茨海默病中差异基因表达的研究  

Research on differential gene expression in Alzheimer’s disease based on epigenomic data analysis

作　　者：于倩 赵琳琳[1] 任彦学 何敏 汪煜楠 黄山[2] 张惊宇[1] YU Qian;ZHAO Linlin;REN Yanzue;HE Min;WANG Yunan;HUANG Shan;ZHANG Jingyu(Department of Neurology,the Fourth Affiliated Hospital of Harbin Medical University,Harbin 15o001,China;Department of Neurology,the Second Affiliated Hospital of Harbin Medical University,Harbin 150086,China)

机构地区：[1]哈尔滨医科大学附属第四医院神经内科,黑龙江哈尔滨150001 [2]哈尔滨医科大学附属第二医院神经内科,黑龙江哈尔滨150086

出　　处：《中国实验诊断学》2024年第11期1344-1350,共7页Chinese Journal of Laboratory Diagnosis

基　　金：国家自然科学基金面上项目(项目编号:62272138);国家自然科学基金青年项目(项目编号:62002087);横向课题(项目编号:HX2020-12)。

摘　　要：目的 利用表观基因组学数据探究阿尔茨海默病(Alzheimer’s disease, AD)中差异基因的表达。方法 从GEO(gene expression omnibus, GEO)数据库中下载mRNA表达微阵列数据集GSE1297和GSE4757,以及DNA甲基化450 K芯片数据集GSE125895,利用Bioconductor包进行数据预处理,使用R中的limma包筛选出差异表达基因(differentially expressed genes, DEGs)和差异甲基化基因(differentially methylated genes, DMGs)。利用jvenn在线软件从DEGs和DMGs中识别重叠基因,对DEGs和DMGs进行基因本体论(gene ontology, GO)和京都基因与基因组百科全书(Kyoto encyclopedia of genes and genomes, KEGG)富集分析。利用数据库BioGRID和Reactome构建DEGs和DMGs的蛋白质互作网络。利用受试者工作(receiver operating characteristic, ROC)曲线验证DEGs和DMGs的诊断效能。结果 通过对数据集GSE4757和GSE1297的DEGs取交集得到178个关键的DEGs,在DNA甲基化芯片数据集GSE125895中识别出59个DMGs;通过对DEGs和DMGs的重叠得到17个低甲基化高表达的基因主要富集在干扰素γ信号通路和白细胞激活的调节通路上;利用统计学分析及绘制ROC曲线,最终筛选出5个低甲基化高表达的基因HLA-DPB1、MYT1L、PARP12、REPIN1、CHL1对AD的诊断效能更高。结论 AD中表观遗传修饰的异常改变影响了基因的表达,其中低甲基化高表达的基因HLA-DPB1、MYT1L、PARP12、REPIN1、CHL1可能与AD的发病相关,干扰素γ信号通路可能参与AD的发病机制。Objective To explore differential gene expression in Alzheimer's disease(AD) using epigenomic data.Methods Download mRNA expression microarray datasets GSE1297 and GSE4757,as well as DNA methylation 450 K chip dataset GSE125895 from the GEO(Gene Expression Omnibus, GEO) database, and preprocess the data using the Bioconductor package.Use the limma package in R to screen differentially expressed genes(DEGs) and differentially methylated genes(DMGs).Use jvenn online software to identify overlapping genes from DEGs and DMGs, and perform gene ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment analysis on DEGs and DMGs.Construct protein interaction networks between DEGs and DMGs using the BioGRID and Reactome databases.Verify the diagnostic efficacy of DEGs and DMGs using receiver operating characteristic(ROC) curves.Results By taking the intersection of DEGs from datasets GSE4757 and GSE1297,178 key DEGs were identified, and 59 DMGs were identified in the DNA methylation chip dataset GSE125895;By overlapping DEGs and DMGs, 17 genes with low methylation and high expression were identified, mainly enriched in the interferon γ signaling pathway and the regulatory pathway of leukocyte activation;By using statistical analysis and drawing ROC curves, five genes with low methylation and high expression, HLA-DPB1,MYT1L,PARP12,REPIN1,and CHL1,were ultimately selected for their higher diagnostic efficacy in AD.Conclusion The abnormal changes in epigenetic modifications in AD affect gene expression, among which genes HLA-DPB1,MYT1L,PARP12,REPIN1,and CHL1 with low methylation and high expression may be associated with the pathogenesis of AD,and the interferon γ signaling pathway may be involved in the pathogenesis of AD.

关 键 词：阿尔茨海默病 DNA甲基化 基因表达 发病机制 

分 类 号：R741.05[医药卫生—神经病学与精神病学]