Wang Kexin, Li Kai, Chen Yupeng, Wei Genxia, Yu Hailang, Li Yi, Meng Wei, Wang Handuo, Gao Li, Lu Aiping, Peng Junxiang, Guan Daogang
National Key Clinical Specialty/Engineering Technology Research Center of Education Ministry of China, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Neurosurgery Institute, Department of Neurosurgery, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangzhou, China.
Institute of Integrated Bioinformedicine and Translational Science, Hong Kong Baptist University, Hong Kong, China.
Front Pharmacol. 2021 Nov 12;12:782060. doi: 10.3389/fphar.2021.782060. eCollection 2021.
Traditional Chinese medicine (TCM) usually plays therapeutic roles on complex diseases in the form of formulas. However, the multicomponent and multitarget characteristics of formulas bring great challenges to the mechanism analysis and secondary development of TCM in treating complex diseases. Modern bioinformatics provides a new opportunity for the optimization of TCM formulas. In this report, a new bioinformatics analysis of a computational network pharmacology model was designed, which takes Chai-Hu-Shu-Gan-San (CHSGS) treatment of depression as the case. In this model, effective intervention space was constructed to depict the core network of the intervention effect transferred from component targets to pathogenic genes based on a novel node importance calculation method. The intervention-response proteins were selected from the effective intervention space, and the core group of functional components (CGFC) was selected based on these intervention-response proteins. Results show that the enriched pathways and GO terms of intervention-response proteins in effective intervention space could cover 95.3 and 95.7% of the common pathways and GO terms that respond to the major functional therapeutic effects. Additionally, 71 components from 1,012 components were predicted as CGFC, the targets of CGFC enriched in 174 pathways which cover the 86.19% enriched pathways of pathogenic genes. Based on the CGFC, two major mechanism chains were inferred and validated. Finally, the core components in CGFC were evaluated by experiments. These results indicate that the proposed model with good accuracy in screening the CGFC and inferring potential mechanisms in the formula of TCM, which provides reference for the optimization and mechanism analysis of the formula in TCM.
中医通常以方剂的形式对复杂疾病发挥治疗作用。然而，方剂的多成分、多靶点特性给中医治疗复杂疾病的作用机制分析和二次开发带来了巨大挑战。现代生物信息学为优化中药方剂提供了新机遇。在本报告中，设计了一种新的计算网络药理学模型的生物信息学分析方法，以柴胡疏肝散治疗抑郁症为例。在该模型中，基于一种新的节点重要性计算方法构建有效干预空间，以描绘从成分靶点到致病基因的干预效应核心网络。从有效干预空间中筛选出干预反应蛋白，并基于这些干预反应蛋白筛选出功能成分核心组（CGFC）。结果表明，有效干预空间中干预反应蛋白的富集通路和基因本体（GO）术语分别覆盖了对主要功能治疗效果有反应的常见通路和GO术语的95.3%和95.7%。此外，从1012个成分中预测出71个成分作为CGFC，CGFC的靶点富集在174条通路中，覆盖了致病基因富集通路的86.19%。基于CGFC，推断并验证了两条主要作用机制链。最后，通过实验对CGFC中的核心成分进行了评估。这些结果表明，所提出的模型在筛选CGFC和推断中药方剂潜在作用机制方面具有良好的准确性，为中药方剂的优化和作用机制分析提供了参考。
