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1.中国中医科学院 中药研究所,道地药材与品质保障全国重点实验室,北京 100700
2.中国中医科学院 中医临床基础医学研究所,北京 100700
3.中国中医科学院 中医基础理论研究所,北京 100700
Received:10 July 2024,
Accepted:30 September 2024,
Published Online:09 October 2024,
Published:05 December 2024
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田思玮,宗文静,刘骏等.基于靶点模块组效关系的丹红注射液治疗心绞痛疗效驱动机制[J].中国实验方剂学杂志,2024,30(23):121-128.
TIAN Siwei,ZONG Wenjing,LIU Jun,et al.Efficacy-driving Mechanism of Danhong Injection for Stable Angina Pectoris Based on Composition-activity Relationship of Target Modules[J].Chinese Journal of Experimental Traditional Medical Formulae,2024,30(23):121-128.
田思玮,宗文静,刘骏等.基于靶点模块组效关系的丹红注射液治疗心绞痛疗效驱动机制[J].中国实验方剂学杂志,2024,30(23):121-128. DOI: 10.13422/j.cnki.syfjx.20241515.
TIAN Siwei,ZONG Wenjing,LIU Jun,et al.Efficacy-driving Mechanism of Danhong Injection for Stable Angina Pectoris Based on Composition-activity Relationship of Target Modules[J].Chinese Journal of Experimental Traditional Medical Formulae,2024,30(23):121-128. DOI: 10.13422/j.cnki.syfjx.20241515.
目的
2
基于靶点模块组效关系,探究丹红注射液(DHI)治疗慢性稳定型心绞痛(SAP)的疗效驱动机制,以此阐明DHI的药理作用。
方法
2
该研究根据前期临床试验获得的西雅图心绞痛问卷(SAQ)评分中的心绞痛频率数值(AF),划分DHI治疗前后的疗效组别,以治疗前和治疗后第30天最佳疗效组患者的转录组数据为数据来源,采用加权基因共表达网络(WGCNA)分析,构建共表达网络,划分网络模块,并与临床特征进行关联;此外,通过Zsummary识别药效应答模块,将Z值<0的模块确定为药效应答模块;其次,采用网络密度、中心性、聚类系数等网络拓扑指标在网络层次、模块层次分别探究DHI疗效的网络模块动态变化;通过Personalized network control algorithm(PNC)算法筛选驱动基因;最后,采用大鼠H9C2细胞建立缺氧/复氧(H/R)模型对驱动基因进行实验验证,证实DHI治疗慢性SAP的潜在疗效靶点,为DHI治疗慢性SAP的药理机制提供科学依据。
结果
2
DHI治疗慢性SAP的最佳疗效组中包含19个模块;分别与基线(Day0)和治疗30 d后相比,得到12个药效应答模块;通过网络和模块等不同层次的网络指标变化,证实了DHI治疗SAP后网络呈现聚合状态;最终,采用H9C2细胞缺氧/复氧模型验证了驱动基因Klotho和成纤维细胞生长因子22(FGF22)在DHI治疗SAP中的关键作用。
结论
2
该研究基于临床转录组数据,确定了DHI治疗SAP的靶点模块组效关系,为DHI治疗SAP的疗效驱动机制提供了科学依据。
Objective
2
To explore the efficacy-driving mechanism of Danhong injection (DHI) in the treatment of stable angina pectoris (SAP) based on the composition-activity relationship of target modules and clarify the pharmacological effects of DHI.
Method
2
According to the angina frequency (AF) in the Seattle Angina Questionnaire (SAQ) that was obtained in the previous clinical trial, the patients before and after DHI treatment were grouped based on efficacy. The transcriptomic data of the patients before treatment and in the best efficacy group 30 days post-treatment were selected as the data source, and then weighted gene co-expression network analysis (WGCNA) was employed to construct the co-expression network. Relevant modules in the network were identified and associated with clinical features. In addition, the On-modules (Z value below 0) were identified by Zsummary. The topological indicators such as density, centrality, and clustering coefficient were adopted to explore the dynamics of DHI efficacy at the network level and module level, respectively. In addition, the driver genes were screened by the personalized network control (PNC) algorithm. Finally, rat H9C2 cells were used to establish the model of hypoxia/reoxygenation (H/R), which was used to confirm the potential therapeutic target of DHI for SAP and provide a scientific basis for revealing the therapeutic mechanism of DHI.
Result
2
We identified 19 modules in the best efficacy group of DHI for SAP, and the comparison between day 0 and day 30 revealed 12 On-modules. The changes of network topological indicators at the network and module levels confirmed the correlation between the best efficacy of DHI treatment and topological dynamics. Finally, the driver genes, Klotho and fibroblast growth factor 22 (FGF22), in DHI treatment of SAP were verified by the H9C2 cell model of H/R.
Conclusion
2
Based on clinical transcriptome data, this study determined the composition-activity relationship of target modules of DHI for SAP, which provided a scientific basis for deciphering the efficacy-driven mechanism of DHI for SAP.
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