Integrative Metabolomics and Machine Learning Approach to Uncover Scab Resistance Markers in Mature Pecan Trees

Scab, caused by Venturia effusa, is the most destructive disease affecting pecan (Carya illinoinensis). Developing scab-resistant cultivars offers a sustainable solution for disease management. To elucidate the metabolic basis of resistance, we applied a pathway-based metabolomics that examines metabolites within pathways relevant to plant-pathogen interactions across 15 orchard-grown pecan cultivars with varying resistance levels. Machine learning models including support vector machine, random forest, and linear regression identified biomarkers predictive of resistance, achieving over 97% classification accuracy. Resistant cultivars showed elevated levels of metabolic precursors supporting lignin biosynthesis, energy metabolism, and antioxidant turnover, such as syringin, proline, and galactono-1,4-lactone, indicating a proactive and coordinated defense strategy. In contrast, susceptible trees accumulated defense-related end-products (e.g., salicylate glucoside, ascorbic acid) and nitrogen-rich amino acids, but showed limited availability of precursors, potentially contributing to increased susceptibility. These metabolic traits will support breeding of disease-resilient cultivars and improve disease management strategies in pecan orchards.