Bioinformatics and computational genomics of host-pathogen interaction in plants

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***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.