2026-04-21 東京大学
植物遺伝子、微生物、代謝物の相互関係
＜関連情報＞
- https://www.a.u-tokyo.ac.jp/topics/topics_20260421-1.html
- https://link.springer.com/article/10.1186/s40793-026-00883-x
解釈可能なマルチオミクス機械学習により、干ばつによって引き起こされる植物と微生物の相互作用の変化が明らかになる Interpretable multi-omics machine learning reveals drought-driven shifts in plant-microbe interactions
Hayato Yoshioka,Pavla Debeljak,Soizic Prado,Yushiro Fuji,Yasunori Ichihashi & Hiroyoshi Iwata
BMC Environmental Microbiome Published:10 April 2026
DOI:https://doi.org/10.1186/s40793-026-00883-x Unedited version
Abstract
Background
Plant-microbe interactions in the rhizosphere are central to plant growth, nutrient acquisition, and stress resilience. Although multi-omics approaches enable comprehensive profiling of different biological layers, integrating these data to understand the mechanisms underlying plant-microbe symbiosis, particularly under drought stress, remains a challenge.
Results
Genomic, metabolomic, and microbiome data from 198 soybean accessions grown under both control and drought conditions were integrated to identify environment-specific predictive features of the plant phenotypes. We compared best linear unbiased prediction (BLUP), genome-wide association study (GWAS), and a nonlinear machine learning model to evaluate their ability to detect informative features. The machine learning models provided flexible variable selection and outperformed linear models in capturing nonlinear dependencies. Model interpretation using SHapley Additive exPlanations (SHAP) indicated that the isoflavone derivative, daidzin, and the drought-tolerant Candidatus Nitrosocosmicus, were major contributors to phenotypic variation, specifically under drought stress. SHAP-based interaction networks indicated cross-omics links, including connections between daidzin, gamma-aminobutyric acid (GABA), and Paenibacillus.
Conclusion
The proposed interpretable machine learning approach for plant phenotype prediction identified multi-omics biomarkers and interactions, providing insights into plant adaptation to drought stress through environment-dependent rhizosphere networks and symbiotic associations.
