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

主要研究者：Pamela罗纳德（加州-戴维斯大学）CoPI：Edward Marcotte（德克萨斯大学奥斯汀分校）合作者：Roger Wise（USDA-ARS）和Jorge Dubcovsky（加州-戴维斯大学）植物和动物通过位于质膜和细胞质的受体感知保守的微生物特征。这些受体，通常称为模式识别受体（PRR），通常携带或与非精氨酸天冬氨酸（非RD）激酶，启动复杂的信号网络累积在广谱抗性。虽然现在人们普遍认识到PRR在植物和动物的免疫反应中起着关键作用，但对控制这些反应的信号通路知之甚少。这在单子叶植物物种中尤其如此，据估计，与双子叶植物相比，单子叶植物物种含有10倍多的PRR。该项目利用先前实验验证的水稻基因的基因组规模功能基因网络RiceNet，在全基因组规模上探索谷物中PRR介导的免疫反应。利用相同基因的遗传修饰剂经常聚集在基因网络中的观察结果，可以基于这种网络中的局部连通性有效地识别和优先化PRR介导的免疫应答的新调节剂。该项目将识别和验证控制水稻、大麦和小麦PRR介导的免疫反应的子网络（即基因组），基于创新的蛋白质组学方法生成水稻的新数据集，并建立小麦的基因组规模功能网络。这些研究的结果将导致在理解谷物作物的PRR介导的免疫反应方面取得重大进展。由于水稻中控制PRR介导的反应的途径与其他植物和动物中的相似，因此预期结果将与其他物种相关，这些物种将作为开发谷类作物工程抗性新策略的起点。各级研究人员，从本科生实习生到博士后科学家，将接受基因组学，蛋白质组学，病理学和系统生物学的基本培训。该项目将组织和举办免疫网络（NetI）研讨会，促进这一重要学科越来越多的研究人员之间的合作。研究结果将广泛提供，从而产生新的假设和生物技术应用，并作为比较基因组学研究的基础。所有经验证的相互作用将存入中央蛋白质-蛋白质相互作用数据库，如IntAct、MINT、DIP或bioGRID。所有原始质谱数据将保存在开放蛋白质组学数据库（http：//bioinformatics.icmb.utexas.edu/OPD/）中，原始蛋白质组学数据集将提交给公共存储库PeptideAtlas（http：www.peptideatlas.org）或Tranche（https：//proteomecommons.org/tranche/）。 最后，基因表达数据集将存放在NCBI的基因表达综合数据库（GEO）公共数据库中。

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.