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

Mengjun Tian, Guangnan Xing, Junjie Tan, Xiangchao Gan, Zhenchuan Ma, Shaoyan Lin, Bingcui Zhang, Yizhan Wang, Wenwen Huang, Yaru Sun, Yufei Xu, Guodong Zhang, Yu Chen, Jiang Zhu, Ming Wang, Fang Zhang, Zhiyuan Li, Suhua Li, Junyi Gai, Yuanchao Wang

Journal: Proceedings of the National Academy of Sciences of the United States of America 2025;122(34):e2505532122

PMID: 40828026

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Abstract

e-induced root rot poses a major threat to soybean production. While the molecular mechanisms underlying soybean- 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 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 () 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.

Address: State Key Laboratory of Agricultural and Forestry Biosecurity, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China.; Department of Plant Pathology, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China.; Key Laboratory of Soybean Disease and Pest Control (Ministry of Agriculture and Rural Affairs), Nanjing Agricultural University, Nanjing 210095, China.; Jiangsu Key Laboratory for the Research and Utilization of Plant Resources, Jiangsu Province Engineering Research Center of Eco-cultivation and High-value Utilization of Chinese Medicinal Materials, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, Jiangsu, China.; Key Laboratory of Biology and Genetics Improvement of Soybean, Ministry of Agriculture and Rural Affairs, Nanjing Agricultural University, Nanjing 210095, China.; Zhongshan Biological Breeding Laboratory, No. 50 Zhongling Street, Nanjing 210014, China.; National Innovation Platform for Soybean Breeding and Industry-Education Integration, Nanjing Agricultural University, Nanjing 210095, China.; State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China.; College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China.; Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518000, China.; State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China.; Zhongshan Biological Breeding Laboratory, No. 50 Zhongling Street, Nanjing 210014, China.; State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China.

DOI: 10.1073/pnas.2505532122

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MeSH Terms: Glycine max, Metabolomics, Acrolein, Plant Diseases, Secondary Metabolism, Phytophthora, Coumestrol, Plant Roots, Host-Pathogen Interactions