Machine learning-guided multi-omics suggests iron-dependent hormonal signaling drives root morphological plasticity...

Root plasticity is crucial for crop survival under climate change. However, the coordinated regulatory network between metabolic disturbances and hormonal signaling that drives morphological adaptation under temperature fluctuations remains unclear. This study integrated machine learning-driven multi-omics analysis, in situ histochemical localization, and pharmacological validation to decipher the root adaptation strategies of wheat under temperature gradients. Wheat roots exhibited convergent morphological plasticity under temperature stress. However, this convergence was associated with distinct hormonal signaling pathways linked to iron homeostasis. Transcriptome data indicated that temperature stress generally down-regulated genes associated with iron acquisition strategy II. Low-temperature stress induced physiological iron deficiency, which triggered an auxin surge to promote compensatory root hair elongation, coinciding with the transcriptional upregulation of iron acquisition Strategy I. Conversely, high-temperature stress induced jasmonic acid accumulation, which contributed to maintaining root hair growth while potentially mitigating iron overload by promoting iron compartmentalization and restricting local accumulation. Our findings support an 'iron-dependent hormonal trade-off' model and identified iron homeostasis as the core metabolic hub connecting environmental perception, hormonal regulation, and root architectural plasticity. This study highlights the powerful role of multi-omics integration approaches in uncovering hidden metabolic targets, providing a theoretical basis for breeding climate-adaptive crops with optimized root systems.