Machine Learning-Guided Multi-Omics Suggests Iron-Dependent Hormonal Signaling Drives Root Morphological Plasticity in Wheat Under Temperature Stress

This study explores how wheat roots adapt morphologically to temperature stress through an iron-dependent hormonal signaling network. Using machine learning-driven multi-omics analysis, researchers found that low-temperature stress causes physiological iron deficiency, leading to an auxin surge that promotes root hair elongation. In contrast, high-temperature stress results in jasmonic acid accumulation, which helps maintain root hair growth and manage iron overload. The study identifies iron homeostasis as a key metabolic hub linking environmental perception, hormonal regulation, and root plasticity, offering insights for breeding climate-adaptive crops with optimized root systems.