Unlocking the Genetic Basis of Abiotic Stress Tolerance in Wheat: Insights from Dif- ferential Expression Analysis and Machine Learning

Abstract

Abiotic stresses such as heat and cold temperatures, salinity, and drought are threatening global food security by affecting crop quality and reproductivity. Wheat is the most essential staple crop in the world, its complex genome is the main barrier to finding valuable genes responsive to different stresses. Thus, in our study we conducted differential RNA-seq analysis to identify Differentially Expressed Genes (DEGs) involved in 4 different stresses such as drought, heat, freeze resistance, and water-deficit stress, then applied two machine learning models; the "Extra-tree regressor" and LIME algorithms to accurately predict and select the highly significant genes. Our findings identified a set of 36 significant genes, many of which play important roles in various molecular functions, cellular components, and biological processes related to the response or resistance to abiotic stress in wheat. For example, Hsp101b is a member of the heat shock protein family, which protects cells against stress by stabilizing proteins. BADH, an enzyme involved in the synthesis of stress hormones, is important for the plant’s response to different stresses. AGL14 is a member of the AGL protein family, which regulates gene expression and is involved in the plant’s response to drought, cold, and salinity stresses. This study demonstrates the prospects of the integration of bioinformatics tools as well as machine learning models to assess the genes responsible for wheat stress resistance, genes’ regulatory networks, and their functions in order to save time and cost to improve wheat productivity.