Machine Learning-Guided Stress Atlases Reveal Co-Expression Rewiring and Divergent Cellular Deployment of Abiotic Stress Programs in Rice and Wheat.

Abiotic stress limits cereal productivity, yet whether conserved stress-responsive genes retain similar transcriptional network organization and cellular deployment across cereal species remains unknown. Here, we developed a machine learning-guided comparative framework to integrate public leaf transcriptomes of rice (Oryza sativa) and wheat (Triticum aestivum) under major abiotic perturbations. Using harmonized compendia comprising 787 rice and 337 wheat samples, we found that rice samples resolved into more discrete stress-associated transcriptional states, whereas wheat samples formed a more continuous landscape with partial overlap among stress responses. Supervised learning prioritized compact sets of stress-predictive candidate genes, including heat-shock/chaperone-related, ABA-biosynthetic and membrane-associated features. Orthology-guided co-expression analysis further showed that homologous stress-predictive genes, particularly heat-associated candidates, can retain stress responsiveness while occupying divergent module neighborhoods in rice and wheat. Leaf single-cell projection resolved this divergence at cellular resolution: several rice predictors showed compartmentalized expression in parenchyma and vascular parenchyma, whereas their wheat counterparts were more broadly deployed across mesophyll, epidermal and vascular cell types. Our findings reveal context-dependent redeployment of homologous stress-associated genes in cereals and offer a scalable strategy for prioritizing candidates for functional validation and climate-resilience breeding.