目的:基于转录组水平探索抑郁症的病因学机制.方法:检测157例抑郁症患者及135名健康对照者的外周血转录组学数据.使用加权基因共表达网络分析,寻找与抑郁症相关的基因共表达模块,利用基因本体论(gene ontology,GO)和京都基因与基因组百科全书(Kyoto encyclopedia of genes and genomes,KEGG)进行功能富集,使用蛋白质间相互作用(protein-protein interaction,PPI)分析寻找关键基因.结果:发现了 2个与抑郁症相关的共表达网络,主要与模式识别受体、肿瘤坏死因子和Notch信号通路及帕金森病等神经退行性疾病的生物学过程相关,识别出细胞肿瘤抗原p53(cell tumor antigen p53,TP53)、肿瘤坏死因子受体相关因子 6(tumor necrosis factor receptor-associated factors 6,TRAF6)和核糖体蛋白L5(ribosomal protein L5,RPL5)等8个关键基因.结论:抑郁症患者的炎症、免疫、代谢以及核糖体翻译修饰相关的基因在转录过程中存在异常表达.
Weighted gene co-expression network analysis for depression transcriptomics
Objective:To find the etiological mechanisms of depression based on the transcriptomic data.Method:Transcriptomic data from peripheral blood of 157 depression patients and 135 healthy controls were examined.Weighted gene co-expression network analysis was used to find gene modules associated with depression.Gene ontology(GO)and Kyoto encyclopedia of genes and genomes(KEGG)enrichment analyses were used to annotate the function of genes in identified modules,and protein-protein interaction(PPI)analysis was used to find key genes in the module.Results:Two co-expression modules were found be associated with depression,which were mainly related to biological processes such as pattern recognition receptors,tumor necrosis factor,Notch signaling pathway and neurodegenerative diseases such as Parkinson's disease.Eight key genes,such as cell tumor antigen p53(TP53),tumor necrosis factor receptor-associated factor 6(TRAF6),and ribosomal protein L5(RPL5),were identified in the two modules.Conclusion:Genes related to immunity,inflammation,metabolism,and ribosome translation and modification are abnormally expressed during transcription in patients with depression.
万方数据
维普
