(176v) Miren: An Optimization Tool for Transcriptomic Data-Driven Discovery of Global Regulatory Phenomena during Heat Stress in Rice Seed

Abiotic stressors such as cold, heat, drought, salt and high concentrations of heavy metals significantly affect the plant vigor and crop yields. In order to adapt to these stress conditions, plants use a suite of strategies such as changing the levels of relative abundance of stress responsive genes and/or proteins that ultimately lead to the system-wide changes in the levels of gene expressions (i.e., transcriptome), protein abundances (i.e., proteome), and metabolite levels (i.e., metabolome) ¹. However, due to posttranslational mechanism and complex gene to protein associations (among many other factors), expression patterns at the transcript as well as protein levels and metabolite abundances in response to environmental cues do not always correlate linearly. This constitutes a challenge to understand and then utilize the mechanisms involved in stress response in plants for crop improvement.

Heat stress during early seed development stage adversely affect the seed size and quality at maturity in rice plants, which in turn affect the nutrition of approximately half of the world’s population. This justifies the need for a comprehensive systems-level effort to elucidate the phenotypic differences and/or plasticity of rice under stress. This research project is aimed at understanding the heat stress response mechanisms in developing rice seed using spatiotemporal transcriptomic analyses and optimization-based tools. In this work, 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 approximately 7000 significantly stress-responsive genes. Clustering analysis was used to develop a minimal gene interaction network and to 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 predicted important regulatory relationships for a total of 228 stress-responsive rice transcription factors (e.g., OsMYB, OsbZIP, OsMADS etc.) and the minimal global regulatory network for rice seed in control and stress conditions. MiReN predictions were validated against published gene regulatory information for multiple global regulators in rice, including the stress-responsive gene Slender Rice 1 (slr1) and the disease resistance gene Xa21. A comparative analysis of the network topology revealed the shift in regulatory mechanisms in presence of stressors and allowed for integration of transcriptomic data with a genome-scale metabolic model of rice seed currently in development. Informed from regulatory predictions and transcriptomic data, the rice seed metabolic model will serve as a useful tool for in silicophenotyping analyses. Metabolic reconstructions of 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.

References:

1. Singh R, Jwa NS. Understanding the responses of rice to environmental stress using proteomics. J Proteome Res. Nov 1 2013;12(11):4652-4669.