Network-Guided Predictions and Characterization of Genes Governing Pattern Recognition Receptor-Mediated Immunity in Cereals

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PI: Pamela Ronald (University of California - Davis)CoPI: Edward Marcotte (University of Texas at Austin)Collaborators: Roger Wise (USDA-ARS) and Jorge Dubcovsky (University of California - Davis)Plants and animals sense conserved microbial signatures through receptors localized to the plasma membrane and cytoplasm. These receptors, often called pattern recognition receptors (PRRs), typically carry or associate with non-arginine-aspartate (non-RD) kinases that initiate complex signaling networks cumulating in broad-spectrum resistance. While it is now widely appreciated that PRRs play a key role in the immune response of plants and animals, little is known about the signaling pathways governing these responses. This is especially true in monocotyledonous species, which are estimated to contain a ten-fold larger number of PRRs as compared to dicots. This project exploits RiceNet, a previously experimentally validated genome-scale functional gene network of rice genes, to explore PRR-mediated immune responses in cereals on a genome-wide scale. Taking advantage of the observation that genetic modifiers of the same gene often cluster in gene networks, novel regulators of PRR-mediated immune responses can be effectively identified and prioritized based on local connectivity in such networks. The project will identify and validate subnetworks (i.e. sets of genes) governing rice, barley and wheat PRR-mediated immune responses, generate a new dataset for rice based on an innovative proteomics approach, and establish a genome-scale functional network for wheat. Results from these studies will lead to major advances in understanding the PRR-mediated immune response of cereal crops. Because pathways controlling PRR-mediated responses in rice are similar to those in other plants and animals, the expected results will be relevant to other species that will serve as a starting point to develop new strategies for engineering resistance in cereal crops. Researchers at all levels, from undergraduate student interns through postdoctoral scientists, will receive essential training in genomics, proteomics, pathology, and systems biology. The project will organize and hold a Networks in Immunity (NetI) workshop that will facilitate collaborations between increasing numbers of researchers in this important discipline. Results will be made broadly available, allowing for generation of novel hypotheses and biotechnological applications, and serve as a basis for comparative genomics studies. All validated interactions will be deposited into central protein-protein interaction databases such as IntAct, MINT, DIP, or bioGRID. All raw mass spectrometry data will be deposited into the Open Proteomics Database (http://bioinformatics.icmb.utexas.edu/OPD/) and raw proteomics datasets will be submitted to the public repositories PeptideAtlas (http://www.peptideatlas.org) or Tranche (https://proteomecommons.org/tranche/). Finally, gene expression datasets will be deposited in the Gene Expression Omnibus (GEO) public database at the NCBI.