Abstract:

Angelica biserrata (Shan et Yuan) Yuan et Shan (A. biserrata) roots, a widely distributed medicinal crop with intraspecific diversity, exhibits significant variability in coumarin content across habitats. This study integrated metabolomics and transcriptomics to dissect the spatial heterogeneity in metabolite profiles and gene expression, revealing the mechanisms driving coumarin biosynthesis divergence. By synthesizing climate-related big data with machine learning and Bayesian-optimized deep learning models, we identified key environmental drivers and predicted optimal cultivation conditions. The key findings were as follows: (1) differential regions most strongly influenced coumarin; (2) upstream genes (such as PAL-1, PAL-2, BGLU44, etc.) modulated downstream coumarin metabolites; (3) elevation (Elev) and warmest quarter temperature (Bio10) dominated coumarin variation, whereas May solar radiation (Srad5) and precipitation seasonality (Bio15) controlled transcriptomic reprogramming; (4) the optimized environment for bioactive compounds included mean annual temperature (Bio1) = 9.99 °C, annual precipitation (Bio12) = 1493 mm, Elev = 1728 m, cumulative solar radiation = 152,643 kJ·m^-2·day^-1, and soil organic carbon = 11,883 g·kg^-1. This study aimed to clarify the biological characteristics and differential regulatory mechanisms of A. biserrata roots in different habitats, establish a theoretical framework for understanding the molecular mechanisms controlling metabolic changes under various habitats, and contribute to elucidating the formation of active constituents while facilitating their effective utilization.