Data Sheet 1_SysNatMed: rational natural medicine discovery by systems genetics.pdf

BackgroundAlthough acknowledged as an important complement to modern medicine, the utility of natural medicine (NM) remains under-exploited. We aimed to develop a novel data-driven approach for natural medicine discovery.
MethodsGWAS summary statistics of disease (Alzheimer’s disease, i.e., AD, for the case study) and quantitative trait loci were collected from public sources. The ranking of disease-gene associations was established using summary-based Mendelian randomization. The comprehensive hierarchical relationships among ingredients, natural products, and target genes were compiled from the BATMAN-TCM v2.0 database. Based on the ranking of disease-gene associations and the comprehensive hierarchical relationships among ingredients, natural products, and target genes, we prioritized NM ingredients as potential candidates for AD management and examined the efficacy for AD prevention using rat AD models.
ResultsWe developed a non-trivial transparent data-driven framework for systems genetics-based NM discovery. Among the 139 prioritized candidates for AD management, we demonstrated the efficacy of Dang Gui (Angelicae Sinensis Radix, ASR) and Dang Shen (Codonopsis Pilosula, CP) for AD prevention using rat models. Mechanistically, we showed that ASR may prevent AD-related damage through protection of neural cells, as well as inhibition of microglia, angiogenesis, inflammation, and extracellular matrices.
ConclusionOur method holds potential for the development of new strategies of complementary medicine for disease treatment and prevention, especially for complex conditions involving a number of genes.

背景：尽管天然药物（natural medicine, NM）已被公认为现代医学的重要补充，但其应用潜力仍未得到充分发掘。本研究旨在开发一种新颖的数据驱动方法，用于天然药物的发现研究。
方法：本研究从公共数据源收集了疾病（本案例研究选用阿尔茨海默病，Alzheimer’s disease, AD）的全基因组关联分析（Genome-Wide Association Study, GWAS）汇总统计数据与数量性状位点（quantitative trait loci, QTL）数据。采用基于汇总数据的孟德尔随机化（summary-based Mendelian randomization）方法构建疾病-基因关联的排序体系。从BATMAN-TCM v2.0数据库中整合得到有效成分、天然产物与靶基因之间的完整层级关联关系。基于疾病-基因关联排序结果以及有效成分、天然产物与靶基因间的完整层级关联关系，我们将天然药物有效成分优先筛选为阿尔茨海默病管理的潜在候选药物，并利用阿尔茨海默病大鼠模型验证其预防阿尔茨海默病的药效。
结果：本研究开发了一种兼具严谨性与透明性的、基于系统遗传学的天然药物发现数据驱动框架。在139个优先筛选出的阿尔茨海默病管理候选天然药物成分中，我们通过阿尔茨海默病大鼠模型证实了当归（Angelicae Sinensis Radix, ASR）与党参（Codonopsis Pilosula, CP）预防阿尔茨海默病的药效。机制研究表明，当归可通过保护神经细胞、抑制小胶质细胞活化、血管生成、炎症反应以及细胞外基质重塑，从而预防阿尔茨海默病相关的组织损伤。
结论：本研究方法有望为疾病治疗与预防的补充医学新策略开发提供新思路，尤其适用于涉及多基因的复杂疾病。