Machine learning reveals biocontrol agents shaping disease outcome in natural Arabidopsis populations.

Abstract

Plants recruit antagonistic microbes to defend against phytopathogens, offering a route to rational biocontrol beyond empirical screening. Here, using six generations of leaf-microbiome data from natural Arabidopsis populations infected by the oomycete Albugo laibachii, we show that microbial diversity is driven by infection, site, and host genotype, and that infected plants form modular networks with increased inter-kingdom antagonism. We train four machine-learning models to discriminate infected from uninfected plants by microbiota composition and identify microbes enriched in diseased (disease-associated) or healthy (health-associated) plants. Testing the most predictive bacteria, fungi, and cercozoa in planta, we find all confer varying protection against Albugo, with health-associated microbes outperforming disease-associated taxa. The best candidate, a Cystofilobasidium fungus, is validated in a synthetic community, where genomic and community assays indicate biocontrol acts mainly through microbe-microbe interactions rather than plant immune activation. This work shows that pairing microbiome data with machine learning identifies effective biocontrol agents.