The -regulatory codes of response to combined heat and drought stress in .

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 regulatory elements (CREs) into models called regulatory codes. Transcriptional response to combined stress is typically not the sum of the responses to the individual stresses. However, regulatory codes underlying combined stress response have not been established. Here we modeled transcriptional response to single and combined heat and drought stress in . 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 approaches can improve our understanding of the complex codes regulating response to combined stress and help us identify prime targets for future characterization.