Gong Wenxia, Wang Kexin, Wang Xueyuan, Chen Yupeng, Qin Xuemei, Lu Aiping, Guan Daogang
Modern Research Center for Traditional Chinese Medicine of Shanxi University, Taiyuan, Shanxi, China.
Key Laboratory of Chemical Biology and Molecular Engineering of Ministry of Education, Taiyuan, Shanxi, China.
Front Cell Dev Biol. 2022 Aug 22;10:937621. doi: 10.3389/fcell.2022.937621. eCollection 2022.
Depression, a complex epidemiological mental disorder, affects around 350 million people worldwide. Despite the availability of antidepressants based on monoamine hypothesis of depression, most patients suffer side effects from these drugs, including psychomotor impairment and dependence liability. Traditional Chinese medicine (TCM) is receiving more and more attention due to the advantages of high therapeutic performance and few side effects in depression treatment. However, complex multicomponents and multi-targets in TCM hinder our ability to identify the functional components and molecular mechanisms of its efficacy. In this study, we designed a novel strategy to capture the functional components and mechanisms of TCM based on a mathematical algorithm. To establish proof of principle, the TCM formula Danggui-Shaoyao-San (DSS), which possesses remarkable antidepressant effect but its functional components and mechanisms are unclear, is used as an example. According to the network motif detection algorithm, key core function motifs (CIM) of DSS in treating depression were captured, followed by a functional analysis and verification. The results demonstrated that 198 pathways were enriched by the target genes of the CIM, and 179 coincided with the enriched pathways of pathogenic genes, accounting for 90.40% of the gene enrichment pathway of the C-T network. Then the functional components group (FCG) comprising 40 components was traced from CIM based on the target coverage accumulation algorithm, after which the pathways enriched by the target genes of FCG were selected to elucidate the potential mechanisms of DSS in treating depression. Finally, the pivotal components in FCG of DSS and the related pathways were selected for experimental validation . Our results indicated good accuracy of the proposed mathematical algorithm in sifting the FCG from the TCM formula, which provided a methodological reference for discovering functional components and interpreting molecular mechanisms of the TCM formula in treating complex diseases.
抑郁症是一种复杂的流行病学精神障碍,全球约有3.5亿人受其影响。尽管基于抑郁症单胺假说的抗抑郁药已广泛应用,但大多数患者会出现这些药物的副作用,包括精神运动障碍和成瘾倾向。由于中医在抑郁症治疗中具有疗效高、副作用少的优势,正受到越来越多的关注。然而,中药的多成分、多靶点特性阻碍了我们识别其有效功能成分和分子机制的能力。在本研究中,我们设计了一种基于数学算法的新策略来捕捉中药的功能成分和作用机制。为了建立原理证明,以具有显著抗抑郁作用但其功能成分和机制尚不清楚的中药方剂当归芍药散(DSS)为例。根据网络基序检测算法,捕捉DSS治疗抑郁症的关键核心功能基序(CIM),随后进行功能分析和验证。结果表明,CIM的靶基因富集了198条通路,其中179条与致病基因富集通路一致,占C-T网络基因富集通路的90.40%。然后基于靶点覆盖累积算法从CIM中追踪出由40种成分组成的功能成分组(FCG),选择FCG靶基因富集的通路来阐明DSS治疗抑郁症的潜在机制。最后,选择DSS的FCG中的关键成分和相关通路进行实验验证。我们的结果表明,所提出的数学算法在从中药方剂中筛选FCG方面具有良好的准确性,为发现中药方剂治疗复杂疾病的功能成分和解释分子机制提供了方法学参考。