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Microbial plant pathogens, including fungi and oomycetes, are a severe threat to food security, with five major fungal species damaging crop stocks capable of feeding more than 600 million people. Effector proteins secreted by fungal pathogens mediate crop disease through specific interactions with cognate host receptors. Accurate identification of effectors is an important step in selecting and breeding disease-resistant crop cultivars. In this project, we are focussing on integrating the latest 'pathogenomic' tools and developing new methods in the form of a bioinformatics toolkit for mining pan-genomes with the aim of;

(i) providing an in-depth understanding of host-pathogen interactions and informative crop disease diagnostic and surveillance

(ii) developing of novel effector prediction method - "EffectorFisher" - which mines low-cost pathogen pan-genomic data for protein isoform profiles and integrates these with disease phenotype

(iii) informing the optimal selection of cultivars for disease resistance

We have developed an initial framework for the Genome-Based Disease Diagnostic and Surveillance Toolkit based on state and global Parastagonospora nodorum (a model cereal necrotroph) populations. This toolkit incorporates and provides summarised outputs of pathogenomic data, including the time and geographic locations of isolates, population structure, mating type, fungicide resistance allele profiles, effector haplotypes/isoforms across evolving pathogen populations, and other pathogenicity-relevant genomic features, along with disease phenotype (severity) scores versus a cultivar panel where available - all of which have direct implications for crop disease management.

The main outputs of this project are: firstly, step-change genome-based disease diagnostic and surveillance capabilities - it will enable us to identify pathogens at the sub-race level rather than just at the species or genus level (where current field diagnostic methods i.e. PCR-based assays do not account for). This toolkit will also provide isolate-specific effector and fungicide-resistance profiling and enable spatio-temporal monitoring of aggressive or resistant pathogen lineages using phylogeographic analysis. Secondly, developing a novel effector prediction method ("EffectorFisher") is another major output that enables accurate identification of new cultivar-specific effectors and provides fast-tracked information to support pre-breeding for disease resistance. Lastly, "EffectorFisher" has provided the basis for the development of new diagnostic capabilities ("CultSelect") that will enable us to recommend optimally resistant cultivars for any given pathogen isolate.

Overall, this toolkit will enable complex bioinformatic/pathogenomic data to be contextualised, making it more accessible and interpretable to a range of grain industry stakeholders with different backgrounds. Thus, it will offer new decision-support options that can be used in specific crop-growing regions based on the pathogen isolates present in that area.