Anti-cardiac fibrosis (CF) is one of the key therapeutic strategies for the treatment of various heart diseases. Therefore, development of drugs targeting CF is promising. However, there are very few studies that systemically explore effective drugs for CF. It has been known that many natural compounds display antifibrosis effects. In this work, we aim to build an integrated model for systematic pursuit of anti-CF agents from natural compounds. We first constructed a heart-specific CF marker-gene-centered functional gene module (HCFM) that represents a set of genes specifically involved in CF based on the CF marker genes and known gene coexpression knowledge. Then, we extracted transcriptional data induced by natural compounds from the Gene Expression Omnibus database. The anti-CF effects of compounds were evaluated by the correlation of HCFM in the compound-induced gene expression profiles by gene set enrichment analysis. Finally, the anti-CF effect of a top-predicted natural monomer, schisantherin A, was experimentally validated in the myocardial infarction animal model. This strategy integrating different types of technologies is expected to help create new opportunities for development of drugs targeting CF.

中文翻译：

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通过基于转录组的功能基因模块参考方法发现用于心脏纤维化的天然化合物

抗心脏纤维化（CF）是治疗各种心脏病的关键治疗策略之一。因此，开发针对 CF 的药物是有希望的。然而，很少有研究系统探索CF的有效药物。众所周知，许多天然化合物具有抗纤维化作用。在这项工作中，我们的目标是建立一个综合模型，从天然化合物中系统地寻找抗 CF 剂。我们首先基于 CF 标记基因和已知的基因共表达知识构建了一个心脏特异性 CF 标记基因为中心的功能基因模块 (HCFM)，它代表一组专门参与 CF 的基因。然后，我们从基因表达综合数据库中提取了由天然化合物诱导的转录数据。通过基因集富集分析，通过HCFM在化合物诱导的基因表达谱中的相关性来评估化合物的抗CF作用。最后，顶级预测的天然单体五味子甲苷 A 的抗 CF 作用在心肌梗塞动物模型中得到了实验验证。这种整合不同类型技术的策略有望为开发针对 CF 的药物创造新的机会。