(194u) Effect of Heat Stress on Rice Seed Development: Discovering Global Regulatory Players and Modeling of Rice Metabolism

Increased global temperature is detrimental to plant growth and development and significantly reduce crop yields. In rice, heat stress during early seed development adversely affects the seed size and quality at maturity. 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 stress response mechanisms in developing rice seed using temporal transcriptomic analyses in control and stress conditions and using in silico metabolic analyses.

Rice plants were grown in a greenhouse under control conditions prior to flowering. A few days prior to flowering, plants were transferred to reach-in growth chambers with a 16h-light/8h-dark cycle at 28°C/ 25°C (control) . Fertilized seeds were marked on two consecutive days and plants were maintained under control conditions. Plants were exposed to heat stress (35˚C) at 12 and 36 hours after fertilization (HAF) on day 1 and day 2, respectively, and young developing seeds were collected from control plants and stressed plants having two biological replicates. Total RNA isolated from developing seeds was used for differential gene expression analysis, which yielded ~7000 significantly stress-responsive genes. Clustering analysis based on Pearson’s correlation was used to develop minimal regulatory network and sub-networks, and identifying global regulators. The highly connected “hub” genes in the co-regulatory network included previously identified MADS-box genes as well as a large number of novel regulatory genes. A comprehensive, genome-scale metabolic model of rice seed is under development using draft genome reconstruction in ModelSEED and KBase databases and previous models. The coexpression information obtained from our experiments will be incorporated as regulatory constraints in the model.

Work on other tissue types of rice and modeling the interactions between them is underway. The important interactions of the rhizobiome with the plant root not only affects root metabolism, but significantly affects the plant metabolism in all tissue types. We are using multi-level and multi-objective modeling framework to integrate different tissue-specific models into a robust plant-scale model. This systems level study will identify bottlenecks in the metabolic pathways and subsequently propose genetic intervention strategies improving crop yield. Our predictive mathematical model will come up with biologically important and non-intuitive solutions to problems related to stress response mechanisms, plant-microbiome interactions and developing tolerant plant species in an efficient and accurate fashion.