The genetic basis of rapid plant pathogen evolution
Project responsable Daniel Croll
Abstract Plants and pathogens are locked in arms races to detect invasion and to disable host resistance, respectively. A key evolutionary step for pathogens is to evolve effectors, which are small proteins that specifically target and disable the plant immune system. The effector content is a major determinant of the pathogen's host range and evolutionary potential. The ability of hosts to detect specific pathogen effectors is expected to lead to strong directional selection on pathogen populations. However, for most plant pathogenic fungi, the content in effector genes and their evolutionary trajectory are poorly known. Plants in agricultural ecosystems are attacked by a multitude of microbial pathogens. Rapid evolution in pathogens poses a significant threat to food security. What enables pathogens to overcome disease resistance of crops and cause damage is poorly understood. In this project, we will use the highly polymorphic pathogen of wheat Zymoseptoria tritici as a model. The wheat genome encodes a large number of uncharacterized resistance factors against the pathogen. The very large population sizes of the pathogen enabled the rapid evolution of fungicide resistance and virulence on previously resistant wheat cultivars. However, it is largely unknown what loci in the pathogen genome contribute to virulence on wheat. We also lack an understanding how selection, imposed by the host’s ability to detect the pathogen, impacts the evolutionary trajectory of pathogen populations. The major goal of the proposed research is to establish a comprehensive understanding of the loci in the pathogen genome that contribute to virulence evolution. The first set of experiments will map phenotypic traits of the pathogen to loci in the genome using genome-wide association studies (GWAS). For this, we will create a highly diverse mapping population from an experimental wheat field site. We will measure the ability of fungal strains to cause disease on a series of different wheat cultivars in greenhouse experiments. We will also assay the mapping population for the ability to tolerate abiotic stress factors and quantify the secretion of secondary metabolites, which likely play a role in ecological interactions during infection. Whole genome sequencing will provide a highly dense set of genetic markers for association mapping. The second set of experiments takes a “reverse ecology” approach to identify targets of selection in pathogen populations. For this, we will collect pathogen strains in replicated plots of multiple wheat cultivars grown at the same experimental wheat field site. Pathogen genotypes better adapted to cause disease on a specific wheat cultivar are expected to accumulate over the growing season. We will perform a large-scale sequencing study to detect responses to selection in pathogen populations by identifying consistent allele frequency changes over time. Finally, we will combine knowledge gained from the first and second part of this project. Importantly, we will be able to disentangle loci segregating adaptive genetic variation to cause disease from loci responding to selection pressure to colonize the same host. In principle, pathogen loci contributing to virulence on a specific host (identified by GWAS) should match the targets of selection on the same host in the field experiments. However, mismatches in the identity of loci recovered by the two approaches will provide important insights into how ecological factors impact the evolution of host specialization. This project will identify key mechanisms driving rapid plant pathogen evolution. Knowledge generated in this project will advance the functional understanding of fungal pathogenesis and inform sustainable strategies to manage disease in agricultural ecosystems.
Keywords Plant pathogens, genome-wide association studies, population genomics, Zymoseptoria tritici, fungi, selection scans
Type of project Fundamental research project
Research area Evolution de pathogènes dans des écosystèmes agricoles
Method of financing FNS
Status Completed
Start of project 1-7-2017
End of project 31-3-2020
Overall budget 549'680 CHF
Contact Daniel Croll