Forecasting Root Rot Disease through Predictive Microbial Functional Profiling

Abstract

Early diagnosis of soil-borne diseases like root rot is a long-standing challenge in agriculture. While microbial functional genes are recognized as potent indicators of soil healthy, their application has been primarily limited to current or past soil conditions. Here, we demonstrate that microbial functional genes can transition from descriptive indicators to reliable predictive biomarkers. By analyzing 199 paired metagenomes from healthy and diseased medicinal plants rhizosphere soil samples, we identified a conserved core set of functional genes, specifically those governing biofilm formation, stress response, and plant-microbe mutualism that are robustly associated with root rot disease. To bridge the gap between discovery and field application, we developed a framework that integrates cost-effective qPCR assay for these key genes and fused their abundance data with machine learning. This model achieved over 80% accuracy in predicting disease onset from independent, pre-symptomatic soil samples, identifying risks long before visible symptoms of infection appeared. Our findings suggest a practical path for moving beyond simple microbial correlations toward an active forecasting tool. By positioning microbial functional genes at the core of disease management, this framework provides a targeted approach for mitigating soil-borne risks and supporting sustainable agricultural practices.