Title : Plant systems biology: Application to rice for understanding metabolic and regulatory characteristics under different abiotic and biotic stress conditions
Abstract:
Rice is one of the major food crops and staple food in South-East Asia. Although the overall yield of rice has been increasing, the growing population and adverse climatic changes pose huge challenges for their sustained production in the future. Therefore, systematic approaches are highly required to explore their effects on cereal crops’ phenotypic and cellular responses. It could be achieved by combining the available multiple high throughput data such as genomics, metabolomics, proteomics and transcriptomics, thereby analyzing the possible biochemical adaptations to several abiotic stresses, and subsequently improving the crop yield. Concurrently, the advent of constraint-based metabolic reconstruction and analysis paves way to characterize cellular physiology under various stresses via the mathematical network models. The first plant metabolic modeling studies have started in Arabidopsis followed by cereals such as, rice, maize and barley. We have employed similar systems biology approach, and initially developed a core mathematical model of rice to characterize cellular behaviour and metabolic states under various abiotic stress conditions. The core model was then further expanded to reconstruct a fully compartmentalized genome scale metabolic model. Subsequently, transcriptomics and metabolomics data were systematically integrated with the model to identify the potential transcription factors. For the first time, we have developed this integrative system for identification of potential candidate regulatory genes as new breeding targets for improving rice production. In addition, we have reconstructed a genome-scale metabolic model of Xanthomonas oryzae pathovar oryzae (Xoo), a vascular pathogen that causes leaf blight in rice leading to severe yield losses. In future, the current in silico model-guided framework can be further extended by including comprehensive genome-scale model of rice and its leaf microbiome for characterizing their interactions with Xoo and host. As such, this will allow us to systematically devise new strategies to effectively control leaf blight in rice.
