Integrative Analysis Reveals the Gene Regulatory Network of Rice Leaves in Response to Environmental Stress

Global regulatory networks (GRNs) coordinate the timing and rate of gene expression in response to environmental and developmental signals, genome-wide. The interactions between protein transcription factors (TFs) with conserved regulatory elements in genomic DNA that comprise the GRNs, are regulated through a variety of post-transcriptional and post-translational mechanisms. Learning GRN from single data types (e.g. gene expression, proteomics) has severe limitations as GRNs integrate signals on many levels. My research aims to learn GRNs associated with the response of tropical Asian rice (Oryza sativa), to high temperatures and water deficit, two of the main stresses affecting growth and yield. In model prokaryotic and eukaryotic systems, where much of the true architecture of many GRNs is known, methods that combined expression data and additional data types that define structure priors were able to infer more accurate regulatory networks that methods based on gene expression data alone. We therefore undertook to incorporate multiple genome scale measurements to construct a robust network prior and to learn our GRN. This approach not only overcomes some of the shortcomings implicit in transcriptome based network prediction (e.g. co-expression as a proxy for regulation), it also leverages a multi-factor experimental design, including controlled and agricultural field experiments, to increase the resolution of the inferred network.