Predicting transcriptional responses to cold stress across plant species

• DOI: 10.1073/pnas.2026330118
• Publication Date: 2021-03-04T01:00:55Z
• Authors: Xiaoxi Meng; Zhikai Liang; Xiuru Dai; Yang Zhang; Samira Mahboub; Daniel W. Ngu; Rebecca L. Roston; James C. Schnable
• Keywords: 580; 0301 basic medicine; 570; Models, Genetic; Transcription, Genetic; Cold-Shock Response; Gene Expression Profiling; Plant Sciences; Life Sciences; Plant Biology; Plant Breeding and Genetics; comparative genomics; Plant Pathology; Biological Sciences; 15. Life on land; Poaceae; 03 medical and health sciences; machine learning; Species Specificity; Gene Expression Regulation, Plant; cold stress; transcriptional regulation; transcriptional regulation | comparative genomics | machine learning | cold stress

SignificanceThe same gene is often regulated differently in response to stress in even closely related plant species. Directly measuring stress-responsive gene expression can be financially and logistically challenging in nonmodel species. Here, we show that models trained using data on which genes respond to cold in one species can predict which genes will respond to cold in related species, even when the training and target species vary in their degree of tolerance to cold. The prediction models we used require only genomic sequence and gene models. As a result, data from well-studied model species may be used to predict which genes will respond to stress in less-studied species with sequenced genomes.