Changing the game: using integrative genomics to probe virulence mechanisms of the stem rust pathogen Puccinia graminis f. sp. tritici

The recent resurgence of wheat stem rust caused by new virulent races of Puccinia graminis f. sp. tritici (Pgt) poses a threat to food security. These concerns have catalyzed an extensive global effort towards controlling this disease. Substantial research and breeding programs target the identification and introduction of new stem rust resistance (Sr) genes in cultivars for genetic protection against the disease. Such resistance genes typically encode immune receptor proteins that recognize specific components of the pathogen, known as avirulence (Avr) proteins. A significant drawback to deploying cultivars with single Sr genes is that they are often overcome by evolution of the pathogen to escape recognition through alterations in Avr genes. Thus, a key element in achieving durable rust control is the deployment of multiple effective Sr genes in combination, either through conventional breeding or transgenic approaches, to minimize the risk of resistance breakdown. In this situation, evolution of pathogen virulence would require simultaneous changes in multiple Avr genes in order to bypass recognition. However, choosing the optimal Sr gene combinations to deploy is a challenge that requires detailed knowledge of the pathogen Avr genes with which they interact and the virulence phenotypes of Pgt existing in nature. Identifying specific Avr genes from Pgt will provide screening tools to enhance pathogen virulence monitoring, assess heterozygosity and propensity for mutation in pathogen populations, and confirm individual Sr gene functions in crop varieties carrying multiple effective resistance genes. Towards this goal, much progress has been made in assembling a high quality reference genome sequence for Pgt, as well as a Pan-genome encompassing variation between multiple field isolates with diverse virulence spectra. In turn this has allowed prediction of Pgt effector gene candidates based on known features of Avr genes in other plant pathogens, including the related flax rust fungus. Up regulation of gene expression in haustoria and evidence for diversifying selection are two useful parameters to identify candidate Avr genes. Recently, we have also applied machine learning approaches to agnostically predict candidate effectors. Here, we review progress in stem rust pathogenomics and approaches currently underway to identify Avr genes recognized by wheat