Pecan scab ( Venturia effusa ) is a devastating fungal disease affecting commercial pecan production. Developing resistant cultivars remains the most sustainable strategy, but this has been hindered by limited mechanistic understanding of resistance and the presence of diverse pathogen pathotypes. Current resistance phenotyping methods rely on microscopic assessments, which are labor-intensive and prone to inconsistency due to sample preparation and interpretation variability. A pathway-based metabolomics approach combined with machine learning was employed to identify early biomarkers of resistance in pecan. Metabolite profiles were obtained from the pecan cultivar 'Desirable' in response to the virulent scab isolate De-Tif-11 (susceptible reaction) and the avirulent scab isolate Pa-OK-11 (resistant reaction) from 0 to 7 days post inoculation (DPI). Logistic regression with L 1 regularization followed by linear regression identified potential marker compounds with high sensitivity and specificity for resistant and susceptible reactions. Three major defense mechanisms were identified that differentiate scab-resistant from susceptible reactions. In the resistant reaction, salicylic acid and 3-hydroxy-3-methylglutaryl-coenzyme A were rapidly upregulated at 1–2 DPI, prioritizing immune activation over energy metabolism. Once defense signaling was initiated, the resistant reaction shifted towards biochemical defense by accumulating flavonoids at 3–4 DPI, reinforcing pathogen restriction. The susceptible reaction exhibited delayed and ineffective defense responses throughout the infection period. These results reveal early functional shifts in hormone signaling and secondary metabolism that differentiate resistant from susceptible reactions. The identified biomarkers may serve as robust indicators of resistance and facilitate accurate, early-stage screening in pecan breeding programs.
Kang et al. (Mon,) studied this question.