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抑郁症潜在生物标志物的鉴定及基于网络药理学方法探讨关键基因和治疗抑郁症的中药作用机制

Identification of Potential Biomarkers of Depression and Network Pharmacology Approach to Investigate the Mechanism of Key Genes and Therapeutic Traditional Chinese Medicine in the Treatment of Depression.

作者信息

Shi Yucong, Chen Dan, Ma Shengsuo, Xu Huachong, Deng Li

机构信息

College of Traditional Chinese Medicine, Jinan University, Guangzhou 510632, China.

Department of Obstetrics and Gynecology, Central Hospital of Wuhan, Affiliated to Huazhong University of Science and Technology, Wuhan 430014, China.

出版信息

Evid Based Complement Alternat Med. 2021 Dec 31;2021:2165632. doi: 10.1155/2021/2165632. eCollection 2021.

DOI:10.1155/2021/2165632
PMID:35003290
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8741373/
Abstract

BACKGROUND

To explore the potential target of depression and the mechanism of related traditional Chinese medicine in the treatment of depression.

METHOD

Differential gene expression in depression patients and controls was analyzed in the GEO database. Key genes for depression were obtained by searching the disease databases. The COREMINE Medical database was used to search for Chinese medicines corresponding to the key genes in the treatment of depression, and the network pharmacological analysis was performed on these Chinese medicines. Then, protein-protein interaction analysis was conducted. Prediction of gene phenotypes was based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment scores.

RESULTS

The total number of differentially expressed genes in the GEO database was 147. Combined with the GEO dataset and disease database, a total of 3533 depression-related genes were analyzed. After screening in COREMINE Medical, it was found that the top 4 traditional Chinese medicines with the highest frequency for depression were Pall., Crocus sativus L., DC., and L. The compound target network consisted of 24 compounds and 138 corresponding targets, and the key targets involved PRKACA, NCOA2, PPARA, and so on. GO and KEGG analysis revealed that the most commonly used Chinese medicine could regulate multiple aspects of depression through these targets, related to metabolism, neuroendocrine function, and neuroimmunity. Prediction and analysis of protein-protein interactions resulted in the selection of nine hub genes (ESR1, HSP90AA1, JUN, MAPK1, MAPK14, MAPK8, RB1, RELA, and TP53). In addition, a total of four ingredients (petunidin, isorhamnetin, quercetin, and luteolin) from this Chinese medicine could act on these hub genes.

CONCLUSIONS

Our research revealed the complicated antidepressant mechanism of the most commonly used Chinese medicines and also provided a rational strategy for revealing the complex composition and function of Chinese herbal formulas.

摘要

背景

探讨抑郁症潜在靶点及相关中药治疗抑郁症的机制。

方法

在基因表达综合数据库（GEO数据库）中分析抑郁症患者与对照组的差异基因表达。通过检索疾病数据库获取抑郁症关键基因。利用COREMINE医学数据库搜索治疗抑郁症的与关键基因对应的中药，并对这些中药进行网络药理学分析。然后，进行蛋白质-蛋白质相互作用分析。基于基因本体论（GO）和京都基因与基因组百科全书（KEGG）通路富集分数预测基因表型。

结果

GEO数据库中差异表达基因总数为147个。结合GEO数据集和疾病数据库，共分析了3533个与抑郁症相关的基因。在COREMINE医学数据库中筛选后发现，治疗抑郁症使用频率最高的前4味中药分别为[此处原文缺失中药名称]、藏红花、[此处原文缺失中药名称]、[此处原文缺失中药名称]。化合物-靶点网络由24种化合物和138个相应靶点组成，关键靶点涉及蛋白激酶A催化亚基α（PRKACA）、核受体辅激活因子2（NCOA2）、过氧化物酶体增殖物激活受体α（PPARA）等。GO和KEGG分析显示，最常用的中药可通过这些靶点调节抑郁症的多个方面，涉及代谢、神经内分泌功能和神经免疫。蛋白质-蛋白质相互作用的预测与分析筛选出9个枢纽基因（雌激素受体1（ESR1）、热休克蛋白90α家族成员1（HSP90AA1）、原癌基因c-Jun（JUN）、丝裂原活化蛋白激酶1（MAPK1）、丝裂原活化蛋白激酶14（MAPK14）、丝裂原活化蛋白激酶8（MAPK8）、视网膜母细胞瘤蛋白（RB1）、信号转导和转录激活因子3（RELA）和肿瘤蛋白p53（TP53））。此外，该中药共有4种成分（矮牵牛素、异鼠李素、槲皮素和木犀草素）可作用于这些枢纽基因。

结论

我们的研究揭示了最常用中药复杂的抗抑郁机制，也为揭示中药复方复杂的组成和功能提供了合理策略。

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