Metabolomics navigates natural variation in pathogen-induced secondary metabolism across soybean cultivar populations

Phytophthora soja e-induced root rot poses a major threat to soybean production. While the molecular mechanisms underlying soybean– P. sojae interactions have been extensively studied, their biochemical basis remains largely unexplored. Previous research has identified key metabolic modules involved in pathogen defense, but structural diversity has largely been constrained by studies on single soybean accessions. Here, we broadened the chemical search space to a diverse soybean germplasm collection using high-throughput metabolomics as a powerful tool for comprehensive metabolic profiling. Chemical classes of lipids and phenylpropanoids again retrieved the most pronounced responses upon P. sojae infection in general. A two-layer analytical strategy further finely resolved metabolites into pathogenesis-, resistance-, and tolerance-type accumulation patterns, leading to the identification of cinnamaldehyde and coumestrol as potent defense metabolites. Bioassays validated cinnamaldehyde directly and strongly inhibited cyst germination and mycelial growth, and coumestrol, a benzofuran-type metabolite, exhibited broad-spectrum activity against spore germination as an identified phytoalexin. Multiomics analyses nailed down the candidate of coumestrol biosynthesis genes, and genetically overexpression of regulatory genes ( Dir2a/4a/4b ) in hairy root systems increased coumestrol accumulation thus positively correlating with improved host resistance. Interestingly, tolerance-type compounds may serve distinct ecological roles, as exemplified by daidzein, which, despite being classified as a tolerance-type metabolite, recruits more zoospores facilitating secondary infection in fact. This study highlights a systematic approach for population-level investigations and emphasizes the necessity of integrating bioinformatics with experimental validation to accurately predict metabolite or gene ecological functions.