Transcriptomic and Physiological Profiling of Enhanced Drought Tolerance in a Gamma-Ray-Induced Colored Wheat Mutant
Abstract
Drought stress poses a major threat to global wheat (Triticum aestivum L.) productivity by impairing physiological processes and inducing oxidative damage. Mutation breeding provides a valuable approach to generate novel genetic variation and identify stress-tolerant germplasms. In this study, phenotypic, physiological, and transcriptomic analyses were integrated to elucidate the drought adaptation mechanisms of a gamma-ray-induced mutant wheat line, PL6, alongside its wild-type parent, PL1. Under osmotic stress and soil drought conditions, PL6 exhibited an enhanced germination rate and higher photosynthetic efficiency (Fv/Fm). Furthermore, PL6 maintained lower malondialdehyde (MDA) accumulation, which was supported by elevated activities of antioxidant enzymes including SOD, APX, and CAT. Time-series transcriptomic analysis via WGCNA and GSEA revealed that PL6 actively maintains environmental sensing, transmembrane transport, and photosynthetic processes under PEG-induced osmotic stress. Conversely, pathways associated with the cell cycle and DNA metabolism were transiently suppressed. To isolate key regulatory genes without computational bias, a multi-algorithm machine learning framework—combining Random Forest, LightGBM, and LASSO—was applied to variance-stabilizing transformed (VST) expression profiles. This approach successfully identified 45 consensus core drought-responsive genes enriched in targeted protein turnover, redox balance, cell wall restructuring, and lipid metabolism, from which ten representative candidate genes were experimentally validated via qRT-PCR. Collectively, this study demonstrates an effective analytical framework for selection of high-confidence transcripts, providing candidate targets for future targeted gene editing and molecular breeding in wheat.
