Biotic interactions shape infection outcomes in Arabidopsis

Abstract

The plant microbiome protects plants from stresses, including pathogen attacks. However, identifying microbes that provide plant protection remains challenging in complex microbial communities. In this study, we analysed samples from natural A. thaliana populations, including both plants infected with the pathogenic oomycete Albugo laibachii and uninfected plants, over six years. Using machine learning classification models, we achieved high accuracy in distinguishing infected and uninfected plants based on microbiome abundance. We identified 80 key taxa associated with health and disease. Among the health-associated microbes (HCom), we selected bacteria, fungi, and cercozoa that effectively reduced pathogen presence in co-inoculation assays. In comparison, disease-associated microbes (DCom) were less effective in conferring protection. Our findings highlight the complexity of plant-microbe interactions and advance our understanding of microbial roles in plant disease ecology. By integrating ecological insights with machine learning, we take a significant step towards designing robust microbial consortia that enhance plant resilience against pathogens.