SysNatMed: rational natural medicine discovery by systems genetics

Area of Science:

• Systems genetics
• Pharmacology
• Computational biology

Background:

• Natural medicine (NM) is an important complement to modern medicine but remains under-exploited.
• A novel data-driven approach for natural medicine discovery is needed.

Purpose of the Study:

• To develop a transparent, data-driven framework for systems genetics-based natural medicine discovery.
• To identify and evaluate natural medicine candidates for Alzheimer's disease (AD) management and prevention.

Main Methods:

• Collected GWAS summary statistics for AD and quantitative trait loci.
• Ranked disease-gene associations using summary-based Mendelian randomization.
• Compiled hierarchical relationships among ingredients, natural products, and target genes using the BATMAN-TCM v2.0 database.
• Prioritized NM candidates for AD and validated efficacy in rat models.

Main Results:

• Developed a transparent, data-driven framework for NM discovery.
• Identified 139 prioritized NM candidates for AD management.
• Demonstrated the efficacy of Dang Gui (Angelicae Sinensis Radix, ASR) and Dang Shen (Codonopsis Pilosula, CP) for AD prevention in rat models.
• Showed ASR prevents AD-related damage via neural cell protection and inhibition of microglia, angiogenesis, inflammation, and extracellular matrices.

Conclusions:

• The developed method offers potential for new complementary medicine strategies for disease treatment and prevention.
• This approach is particularly promising for complex, multi-gene conditions.