The cis-regulatory codes of response to combined heat and drought stress in Arabidopsis thaliana

Abstract

Plants respond to their environment by dynamically modulating gene expression. A powerful approach for understanding how these responses are regulated is to integrate information about <i>cis-</i>regulatory elements (CREs) into models called <i>cis-</i>regulatory codes. Transcriptional response to combined stress is typically not the sum of the responses to the individual stresses. However, <i>cis-</i>regulatory codes underlying combined stress response have not been established. Here we modeled transcriptional response to single and combined heat and drought stress in <i>Arabidopsis thaliana</i>. We grouped genes by their pattern of response (independent, antagonistic and synergistic) and trained machine learning models to predict their response using putative CREs (pCREs) as features (median F-measure = 0.64). We then developed a deep learning approach to integrate additional omics information (sequence conservation, chromatin accessibility and histone modification) into our models, improving performance by 6.2%. While pCREs important for predicting independent and antagonistic responses tended to resemble binding motifs of transcription factors associated with heat and/or drought stress, important synergistic pCREs resembled binding motifs of transcription factors not known to be associated with stress. These findings demonstrate how <i>in silico</i> approaches can improve our understanding of the complex codes regulating response to combined stress and help us identify prime targets for future characterization.