Bioinformatics and computational genomics of host-pathogen interaction in plants

* 许多微生物是植物病原体，导致植物组织疾病，阻碍植物生长和繁殖。作为回应，植物通过两层防御来抵御病原体的攻击。第一层是PAMP（病原体相关分子模式）触发免疫（PTI），即植物细胞表面模式识别受体（PRRs）检测PAMP激发子。第二层防御是植物抗性（R）蛋白识别特定的病原体效应物并引发效应物触发的免疫（ETI）。这些R蛋白含有核苷酸结合位点（NBS）-富含亮氨酸重复序列（LRR）结构域，用于识别病原体无毒（Avr）基因编码的不同效应子，从而导致R蛋白与效应子之间的不同结合特异性。在所有已知的涉及植物防御的基因家族中，NBS-LRR或NLR基因家族是数量最多的，迄今为止在分析的每个植物基因组中鉴定了数百个NLR编码基因。然而，由于作物基因组庞大、复杂，以及昂贵、劳动密集型的实验，只有几十个基因被克隆和功能特征化。即使病原体基因组较小，病原体方面也存在类似的挑战。近年来，生物信息学方法的应用已经可以预测植物R基因和病原体Avr基因，但很少关注植物-病原体蛋白质-蛋白质相互作用。在这个提议中，我将开发新的生物信息学方法来全基因组预测编码禾谷类作物（小麦和大麦）中具有NLR结构域的蛋白质的R基因、锈菌中的效应子编码基因以及宿主-病原体蛋白质-蛋白质相互作用。我的发展将包括：（i）建立谷物宿主和锈菌的蛋白质组数据源;（ii）预测和分类谷物宿主中的NLR蛋白;（iii）预测锈菌中的效应蛋白;和（iv）预测谷物-锈菌病理系统中的蛋白质-蛋白质相互作用。我的预测将大大有助于利用基因组资源（例如，同源DNA和蛋白质序列）的模式植物（拟南芥和二穗短柄草）及其病原体的充分注释或表征的基因组。该建议允许使用生物信息学和计算基因组学工具检测可能发生在整个谷类植物/锈菌基因组中的所有可能的基因-基因/蛋白质-蛋白质相互作用。它代表了一个显着的进步，目前的方法，有有限的识别R基因在宿主植物和病原体中的Avr基因，只有目标基因组区域。这一进展将有助于为在谷类作物植物中具有持久抗性的新品种中引入抗锈基因铺平新的道路，从而更有效地对抗加拿大西部锈病病原体的常年流行和严重的产量损失。

***Many microorganisms are plant pathogens, causing diseases on plant tissues and impeding plant growth and reproduction. In response, plants defend themselves from pathogen attack through two layers of defense. The first layer is the PAMP (pathogen-associated molecule pattern)-triggered immunity (PTI), that is, plant cell surface pattern-recognition receptors (PRRs) detect PAMP elicitors. The second layer of defense is that plant resistance (R) proteins recognize specific pathogen effectors and elicit an effector-triggered immunity (ETI). These R proteins contain nucleotide-binding site (NBS)-leucine-rich repeat (LRR) domains for recognition of different effectors encoded by pathogen avirulence (Avr) genes, leading to different binding specificities between the R proteins and effectors. Of all known gene families involving plant defense, the NBS-LRR or NLR gene family is the most numerous with hundreds of NLR-coding genes being identified in each plant genome analyzed thus far. Yet, only a few dozens of these genes have been cloned and functionally characterized due to large, complex crop genomes, and costly, labor-intensive experiments. Similar challenges occur on the pathogen side even with smaller pathogen genomes. Recent uses of bioinformatics approaches have allowed for prediction of plant R genes and pathogen Avr genes, but little attention is paid to plant-pathogen protein-protein interactions. In this proposal, I will develop new bioinformatic approaches to genome-wide prediction of R genes encoding the proteins with NLR domains in cereal crops (wheat and barley), effector-coding genes in rust fungi and host-pathogen protein-protein interactions. My developments will include: (i) building proteome data sources of cereal hosts and rust fungi; (ii) prediction and classification of NLR proteins in cereal hosts; (iii) prediction of effector proteins in rust fungi; and (iv) prediction of protein-protein interactions in cereal-rust pathosystems. My prediction will be greatly facilitated by leveraging genomic resources (e.g., homologous DNA and protein sequences) of well annotated or characterized genomes for model plants (Arabidopsis thaliana and Brachypodium distachyon) and their pathogens. This proposal allows for detection of all possible gene-gene/protein-protein interactions that may occur across the whole cereal plant/rust fungus genomes using bioinformatics and computational genomics tools. It represents a significant advance over the current approaches that have limited identification of R genes in host plants and Avr genes in pathogens only to targeted genomic regions. Such advance will help pave a novel way towards pyramiding R genes in new cultivars with durable resistance in cereal crop plants, thereby more effectively combating perennial epidemics of rust pathogens and severe yield loss in western Canada.