SysNatMed: rational natural medicine discovery by systems genetics

Although 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.

GWAS 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.

We 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.

Our 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.