Department of Psychological Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
Department of Psychological Medicine, Xiamen Branch, Zhongshan Hospital, Fudan University, Xiamen, 361015, China.
BMC Med Genomics. 2021 Feb 23;14(1):55. doi: 10.1186/s12920-021-00908-z.
Major depressive disorder (MDD) is a leading psychiatric disorder that involves complex abnormal biological functions and neural networks. This study aimed to compare the changes in the network connectivity of different brain tissues under different pathological conditions, analyzed the biological pathways and genes that are significantly related to disease progression, and further predicted the potential therapeutic drug targets.
Expression of differentially expressed genes (DEGs) were analyzed with postmortem cingulate cortex (ACC) and prefrontal cortex (PFC) mRNA expression profile datasets downloaded from the Gene Expression Omnibus (GEO) database, including 76 MDD patients and 76 healthy subjects in ACC and 63 MDD patients and 63 healthy subjects in PFC. The co-expression network construction was based on system network analysis. The function of the genes was annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Human Protein Reference Database (HPRD, http://www.hprd.org/ ) was used for gene interaction relationship mapping.
We filtered 586 DEGs in ACC and 616 DEGs in PFC for further analysis. By constructing the co-expression network, we found that the gene connectivity was significantly reduced under disease conditions (P = 0.04 in PFC and P = 1.227e-09 in ACC). Crosstalk analysis showed that CD19, PTDSS2 and NDST2 were significantly differentially expressed in ACC and PFC of MDD patients. Among them, CD19 and PTDSS2 have been targeted by several drugs in the Drugbank database. KEGG pathway analysis demonstrated that the function of CD19 and PTDSS2 were enriched with the pathway of Glycerophospholipid metabolism and T cell receptor signaling pathway.
Co-expression network and tissue comparing analysis can identify signaling pathways and cross talk genes related to MDD, which may provide novel insight for understanding the molecular mechanisms of MDD.
重度抑郁症（MDD）是一种主要的精神疾病，涉及复杂的异常生物功能和神经网络。本研究旨在比较不同病理条件下不同脑组织网络连接的变化，分析与疾病进展显著相关的生物途径和基因，并进一步预测潜在的治疗药物靶点。
从基因表达综合数据库（GEO）下载的死后扣带回皮质（ACC）和前额叶皮质（PFC）mRNA 表达谱数据集分析差异表达基因（DEGs）的表达，包括 76 例 MDD 患者和 76 例健康对照者的 ACC 以及 63 例 MDD 患者和 63 例健康对照者的 PFC。基于系统网络分析构建共表达网络。通过京都基因与基因组百科全书（KEGG）途径分析注释基因功能。人类蛋白质参考数据库（HPRD，http://www.hprd.org/）用于基因相互作用关系映射。
我们筛选了 ACC 中的 586 个 DEG 和 PFC 中的 616 个 DEG 进行进一步分析。通过构建共表达网络，我们发现疾病状态下基因连接性显著降低（PFC 中 P=0.04，ACC 中 P=1.227e-09）。串扰分析显示，CD19、PTDSS2 和 NDST2 在 MDD 患者的 ACC 和 PFC 中差异表达显著。其中，CD19 和 PTDSS2 已被 Drugbank 数据库中的几种药物靶向。KEGG 途径分析表明，CD19 和 PTDSS2 的功能富集于甘油磷脂代谢和 T 细胞受体信号通路。
共表达网络和组织比较分析可以鉴定与 MDD 相关的信号通路和串扰基因，这可能为理解 MDD 的分子机制提供新的见解。
