Miren: An Optimization Tool for Data-Driven Discovery of Global Regulatory Phenomena Used to Elucidate the Heat Stress Response Mechanism in Rice Seed

Plants use a suite of strategies to respond to abiotic stresses, including changing the abundance of stress-responsive genes/proteins that ultimately lead to the large-scale changes in gene expression levels, protein abundances, and metabolites. Complex gene-protein-reaction associations as well as regulatory mechanisms constitute a challenge to elucidate stress response mechanisms in plants. This research aims to understand the heat stress response mechanisms in developing rice seed using temporal transcriptomic analyses in control and stress conditions and in silico metabolic analyses.

Rice plants were exposed to heat stress at 12 and 36 hours after fertilization with a 16h-light/8h-dark cycle, and young developing seeds were collected from control and stressed plants. Total RNA isolated from developing seeds was used for differential gene expression analysis, which yielded ~7000 significantly stress-responsive genes. Clustering analysis was used to develop a minimal gene interaction network and identify global regulators. The highly connected “hub” genes included previously-identified MADS-box genes as well as a large number of novel regulatory genes. MiReN, an MILP optimization-based tool was developed to decipher the minimal regulatory network using the time-series transcriptomic data. MiReN identified important regulatory relationships for stress-responsive rice transcription factors (e.g., OsMYB, OsbZIP, OsMADS etc.) and predicted the minimal global regulatory network for rice seed in control and stress conditions. A comparative analysis of the network topology reveals the shift in regulatory mechanisms in presence of stressors and allows for integration of transcriptomic data with a genome-scale metabolic model of rice seed. Work on other rice tissues and modeling the interactions between them using multi-level and multi-objective modeling frameworks to develop a robust plant-scale rice model is underway. Our predictive mathematical model will identify biologically important and non-intuitive solutions to questions related to stress response mechanisms and accelerate the development of tolerant plant varieties in an efficient and accurate fashion.