Zhang Guangyin, Xu Shixin, Yuan Zhuo, Shen Li
Department of Psychosomatic Medicine, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, People's Republic of China.
Tianjin Key Laboratory of Traditional Research of TCM Prescription and Syndrome; Medical Experiment Center, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, People's Republic of China.
Neuropsychiatr Dis Treat. 2020 Mar 12;16:703-713. doi: 10.2147/NDT.S244452. eCollection 2020.
Despite advances in characterizing the neurobiology of emotional disorders, there is still a significant lack of scientific understanding of the pathophysiological mechanisms governing major depressive disorder (MDD). This study attempted to elucidate the molecular circuitry of MDD and to identify more potential genes associated with the pathogenesis of the disease.
Microarray data from the GSE98793 dataset were downloaded from the NCBI Gene Expression Omnibus (GEO) database, including 128 patients with MDD and 64 healthy controls. Weighted gene coexpression network analysis (WGCNA) was performed to find modules of differentially expressed genes (DEGs) with high correlations followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to obtain further biological insight into the top three key modules. The protein-protein interaction (PPI) network, the modules from the PPI network, and the gene annotation enrichment of modules were analyzed, as well.
We filtered 3276 genes that were considered significant DEGs for further WGCNA analysis. By performing WGCNA, we found that the turquoise, blue and brown functional modules were all strongly correlated with MDD development, including immune response, neutrophil degranulation, ribosome biogenesis, T cell activation, glycosaminoglycan biosynthetic process, and protein serine/threonine kinase activator activity. Hub genes were identified in the key functional modules that might have a role in the progression of MDD. Functional annotation showed that these modules primarily enriched such KEGG pathways as the TNF signaling pathway, T cell receptor signaling pathway, primary immunodeficiency, Th1, Th2 and Th17 cell differentiation, autophagy and RNA degradation and oxidative phosphorylation. These results suggest that these genes are closely related to autophagy and cellular immune function.
The results of this study may help to elucidate the pathophysiology of MDD development at the molecular level and explore the potential molecular mechanisms for new interventional strategies.
尽管在情绪障碍神经生物学特征方面取得了进展，但对于重度抑郁症（MDD）发病的病理生理机制仍缺乏科学认识。本研究试图阐明MDD的分子通路，并鉴定更多与该疾病发病机制相关的潜在基因。
从NCBI基因表达综合数据库（GEO）下载GSE98793数据集的微阵列数据，包括128例MDD患者和64例健康对照。进行加权基因共表达网络分析（WGCNA）以寻找具有高度相关性的差异表达基因（DEG）模块，随后进行基因本体（GO）和京都基因与基因组百科全书（KEGG）通路富集分析，以进一步深入了解前三个关键模块的生物学特性。还分析了蛋白质-蛋白质相互作用（PPI）网络、PPI网络中的模块以及模块的基因注释富集情况。
我们筛选出3276个被认为是显著DEG的基因用于进一步的WGCNA分析。通过进行WGCNA，我们发现绿松石色、蓝色和棕色功能模块均与MDD的发展密切相关，包括免疫反应、中性粒细胞脱颗粒、核糖体生物发生、T细胞活化、糖胺聚糖生物合成过程以及蛋白质丝氨酸/苏氨酸激酶激活剂活性。在关键功能模块中鉴定出可能在MDD进展中起作用的枢纽基因。功能注释表明，这些模块主要富集于TNF信号通路、T细胞受体信号通路、原发性免疫缺陷、Th1、Th2和Th17细胞分化、自噬以及RNA降解和氧化磷酸化等KEGG通路。这些结果表明这些基因与自噬和细胞免疫功能密切相关。
本研究结果可能有助于在分子水平阐明MDD发生发展的病理生理学，并探索新干预策略的潜在分子机制。
