Globally, fungal plant pathogens are a significant threat to food security as they rapidly adapt to disease management strategies. One aspect of improving fungal disease outcomes is developing a better understanding of effector proteins. Effectors are secreted by plant pathogens to facilitate infection and are the primary determinants of fungal crop disease outcomes.

Therefore, accurate effector profiling can be used for improving crop disease resistance. However, low sequence homology between effector proteins and their location in highly plastic regions of the genome makes this challenging. Fungal pathogens often possess genomes compartmentalised into core conserved regions and variable accessory regions.

Accessory regions are subjected to high mutation rates and may contain several effector genes. The variation observed in the accessory genome is driven by a variety of mutagenesis mechanisms such as repeat-induced point (RIP) mutation, mesosyntenic rearrangements, and lateral gene transfers (LGT).

The number of available fungal genomes has increased exponentially over the last 20 years, improving the feasibility of using genomics to better understand the molecular mechanisms that drive fungal adaptation.

Currently, we have a wide range of datasets available - that include closely-related species with differing host ranges, and pathogenic lifestyles – allowing broad comparative/pan-genomics to be performed across a wide range of pathosystems spanning multiple hosts (wheat, barley, grasses, peas, chickpea, beans, lentil, lupin, pistachio, pomegranate, pear) and fungal pathogen genera (Parastagonospora, Pyrenophora, Fusarium, Blumeria, Ascochyta, Septoria, Botrytis, Sclerotinia and Rhizoctonia).

This will enable the discovery and further study of sets of genes associated with host specificity. In particular, pan-genomic datasets of Pyrenophora spp. (tritici-repentis (n=163), teres f. sp. teres (n=307) and teres f. sp. maculata (n=59)) and Parastagonospora nodorum (n=650), both infecting wheat and barley hosts, will provide new insight into wheat-, barley- and cereal-specific pathogenicity gene sets.

These comparisons will also improve our understanding of the mechanisms, taxonomic ranges and frequencies of pathogenicity-associated genome features including accessory compartmentalisation, LGT, RIP mutation, and mesosynteny.

New effector prediction methods will be applied - which rank candidates based on physicochemical properties and disease phenotype association - and functionally tested using synthetic effector constructs.