Bioinformatics and computational genomics of host-pathogen interaction in plants

*许多微生物是植物病原体，会在植物组织上引起疾病，阻碍植物的生长和繁殖。作为回应，植物通过两层防御来保护自己免受病原体的攻击。第一层是PAMP(病原体相关分子模式)触发的免疫(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器，并引发效应器触发的免疫(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，用于识别由病原体无毒(AVR)基因编码的不同效应物，导致R蛋白与效应物之间存在不同的结合特异性。在所有已知的与植物防御相关的基因家族中，NBS-LRR或NLR基因家族是数量最多的，到目前为止在每个植物基因组中都发现了数百个NLR编码基因。然而，由于大量复杂的作物基因组和昂贵的劳动密集型实验，只有几十个这样的基因被克隆和功能鉴定。即使是较小的病原体基因组，病原体方面也存在类似的挑战。最近生物信息学方法的应用使得对植物R基因和病原菌AVR基因的预测成为可能，但对植物-病原菌蛋白-蛋白质相互作用的关注很少。在这项提议中，我将开发新的生物信息学方法来预测全基因组范围内编码谷类作物(小麦和大麦)中带有NLR结构域的蛋白质的R基因、锈菌中的效应编码基因以及宿主-病原体蛋白质-蛋白质的相互作用。我的进展将包括：(I)建立谷物寄主和锈菌的蛋白质组数据源；(Ii)谷物寄主中NLR蛋白的预测和分类；(Iii)锈菌中效应蛋白的预测；以及(Iv)谷物-锈病病理系统中蛋白质-蛋白质相互作用的预测。我的预测将极大地促进利用基因组资源(例如，同源DNA和蛋白质序列)，这些基因组资源是经过良好注释的或具有特征的模式植物(拟南芥和短柄青霉)及其病原体的基因组。这一建议允许利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物植物/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。与目前仅限于目标基因组区域识别寄主植物中的R基因和病原体中的AVR基因的方法相比，这是一个重大的进步。这一进展将有助于为将R基因聚合到具有持久抗性的谷类作物新品种中铺平道路，从而更有效地抗击加拿大西部锈病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.