【摘要】 目的:使用加权基因共表达网络分析(weighted gene co-expression network analysis, WGCNA)研究慢性疼痛导致抑郁的脑区基因共表达网络,寻找与之相关的枢纽基因。方法:从基因表达数据库(gene expression omnibus, GEO)中下载数据集GSE91396,数据经R软件处理后,用WGCNA构建共表达网络,识别与抑郁脑区相关性高的模块,并对模块内基因进行GO功能注释和KEGG信号通路分析,用Cytoscape软件筛选模块内枢纽基因(hub gene)。结果:WGCNA分析获得了9个基因共表达模块,其中brown模块与NAc高度相关、turquoise模块与PAG高度相关、yellow模块与m PFC高度相关,分别对三个组织特异性模块的基因进行功能注释和通路富集分析,主要富集在突触结构、膜电位、离子通道、神经递质等分子功能和生物学过程;在三个脑区筛选到了各自的枢纽基因,如NAc中的Drd1a、Pdyn等,PAG中的Cd99l2、Pcyt1a等,m PFC中的枢纽基因有Neurod6、Dlgap1、Adcy1等。结论:本研究使用WGCNA方法筛选出慢性疼痛导致抑郁小鼠的三个脑区的关键模块和枢纽基因,为其机制研究提供新思路。更多还原

【Abstract】 Objective: To identify the gene co-expression network and hub genes of 3 brain regions including NAc, PAG and mPFC in chronic pain-induced mice model of depression, weighted gene co-expression network analysis(WGCNA) was processed. Methods: The RNA-seq dataset of GSE91396 was downloaded from the gene expression omnibus(GEO) database. WGCNA was used to construct the gene co-expression network, and identify the key modules associated with NAc, PAG or mPFC, respectively. Gene ontology and pathway enrichment analyses were performed on the key module genes. Then cytoscape software was used to screen the hub gene in key modules. Results: There are 9 co-expression modules identified by WGCNA of which the brown, turquoise and yellow modules were the key module related to NAc, PAG and mPFC, respectively. Module gene were mainly involved in synapse structure, membrane potential, neurotransmitter and so on. The hub genes with high connectivity were identified in 3 key modules, such as Drd1 a and Pdyn in brown module, Cd99 l2 and Pcyt1 a in turquoise module, and Neurod6, Dlgap1 and Adcy1 in yellow module. Conclusion: WGCNA can identify key modules and hub genes which are biologically relevant to the 3 depression-associated brain regions. This study provides a novel insight into the understanding of the depression induced by chronic pain.更多还原