Cis-regulatory code for predicting plant cell-type specific high salinity response

Abstract

Multicellular organisms have diverse cell types with distinct roles in development and responses to the environment. At the transcriptional level, the differences in environmental response between cell types are due to differences in regulatory programs. In plants, although cell-type environmental responses have been examined, details on how these responses are regulated remain spotty. Here, we identify a set of putative cis-regulatory elements (pCREs) enriched in the promoters of genes responsive to high salinity stress in six Arabidopsis thaliana root cell types. Using machine learning with pCREs as predictors, we establish cis-regulatory codes, i.e. models predicting whether a gene is responsive to high salinity for each cell type. These pCRE-based models outperform models utilizing in vitro binding data of 758 A. thaliana transcription factors. Surprisingly, organ pCREs identified based on whole root high salinity response can predict cell-type responses as well as pCREs derived from cell-type data -because organ and cell-type pCREs predict complementary subsets of high salinity response genes. Our findings not only advance our understanding of the regulatory mechanisms of plant spatial transcriptional response through cis-regulatory codes, but also suggest broad applicability of the approach to any species, particularly those with little or no trans regulatory data.