A thorough understanding of the complex signal transduction processes in monocots requires appropriate tools, as many aspects of their development and physiology are different from those of dicots. Rice, because of its diploid genetics, small genome size, extensive genetic map, available genome sequence, and relative ease of transformation, is considered a model monocot. Therefore the structural and functional analysis of rice has broad practical implications for the other economically important cereals. Rice is one of the few higher eukaryotic genomes that have been fully sequenced. The convergence of genomic sequence, informatics, and protein-protein interaction technologies has created the opportunity to dramatically enhance our understanding of cell signaling in the cereals, using rice as a model system. Development and application of these technologies will augment traditional approaches to create higher yielding varieties of cereal species.
We are extending this work through collaboration with Edward Marcotte (U. Texas, Austin) and Inusk Lee (Yonsei University) to develop a probabilistic functional network (RiceNet) for rice and to test its predictive capacity. Probabilistic functional gene networks provide frameworks for integrating heterogeneous functional genomics and proteomics data into objective models of cellular systems. We expect this work to facilitate generation of testable hypotheses regarding specific rice gene functions and associations with abiotic (and biotic) stress responses. The project integrates the computational and proteomics expertise of Marcotte's and Lee's laboratories with the biological and rice functional genomics expertise available in my laboratory.
Rice Interactomics -
Rice (Oryza sativa) is a staple food for more than half the world and a model for studies of monocotyledonous species, which include cereal crops and candidate bioenergy grasses. A major limitation of crop production is imposed by a suite of abiotic and biotic stresses resulting in 30-60% yield losses globally each year. To elucidate stress response signaling networks, we constructed an interactome of 100 proteins by yeast two-hybrid (Y2H) assays around key regulators of the rice biotic and abiotic stress responses. We validated the interactome using protein-protein interaction (PPI) assays, co-expression of transcripts, and phenotypic analyses. Using this interactome-guided prediction and phenotype validation, we identified ten novel regulators of stress tolerance, including two from protein classes not previously known to function in stress responses. Several lines of evidence support cross-talk between biotic and abiotic stress responses. The combination of focused interactome and systems analyses described here represents significant progress toward elucidating the molecular basis of traits of agronomic importance.