EGRINs (Environmental Gene Regulatory Influence Networks) in Rice That Function in the Response to Water Deficit, High Temperature, and Agricultural Environments. Issue 10 (21st September 2016) - Wilkins, Olivia; Hafemeister, Christoph; Plessis, Anne; Holloway-Phillips, Meisha-Marika; Pham, Gina M.; Nicotra, Adrienne B.; Gregorio, Glenn B.; Jagadish, S.V. Krishna; Septiningsih, Endang M.; Bonneau, Richard; Purugganan, Michael
Abstract : Multiple genome-scale measurements in stress-treated and field-grown tropical Asian rice cultivars were integrated to uncover environmental gene regulatory influence networks during stress. Abstract: Environmental gene regulatory influence networks (EGRINs) coordinate the timing and rate of gene expression in response to environmental signals. EGRINs encompass many layers of regulation, which culminate in changes in accumulated transcript levels. Here, we inferred EGRINs for the response of five tropical Asian rice ( Oryza sativa ) cultivars to high temperatures, water deficit, and agricultural field conditions by systematically integrating time-series transcriptome data, patterns of nucleosome-free chromatin, and the occurrence of known cis -regulatory elements. First, we identified 5447 putative target genes for 445 transcription factors (TFs) by connecting TFs with genes harboring known cis -regulatory motifs in nucleosome-free regions proximal to their transcriptional start sites. We then used network component analysis to estimate the regulatory activity for each TF based on the expression of its putative target genes. Finally, we inferred an EGRIN using the estimated transcription factor activity (TFA ) as the regulator. The EGRINs include regulatory interactions between 4052 target genes regulated by 113 TFs. We resolved distinct regulatory roles for members of the heat shock factor family, including a putative regulatory connection between abiotic stress and the circadian clock. TFA estimation using network component analysis is an effective way of incorporating multiple genome-scale measurements into network inference.