Rapid sequence evolution driven by transposable elements at a virulence locus in a fungal wheat pathogen

Abstract

Plant pathogens cause substantial crop losses in agriculture production and threaten food security. Plants evolved the ability to recognize virulence factors and pathogens have repeatedly escaped recognition due rapid evolutionary change at pathogen virulence loci (i.e. effector genes). The presence of transposable elements (TEs) in close physical proximity of effector genes can have important consequences for gene regulation and sequence evolution. Species-wide investigations of effector gene loci remain rare hindering our ability to predict pathogen evolvability. Here, we performed genome-wide association studies (GWAS) on a highly polymorphic mapping population of 120 isolates of Zymoseptoria tritici, the most damaging pathogen of wheat in Europe. We identified a major locus underlying significant variation in reproductive success of the pathogen and damage caused on the wheat cultivar Claro. The most strongly associated locus is intergenic and flanked by genes encoding a predicted effector and a serine type protease, respectively. The center of the locus contained a highly dynamic region consisting of multiple families of TEs. Based on a large global collection of assembled genomes, we show that the virulence locus has undergone substantial recent sequence evolution. Large insertion and deletion events generated length variation between the flanking genes by a factor of seven (5-35 kb). The locus showed also strong signatures of genomic defenses against TEs (i.e. RIP) contributing to the rapid diversification of the locus. In conjunction, our work highlights the power of combining GWAS and population-scale genome analyses to investigate major effect loci in pathogens.

A devil's bargain with transposable elements in plant pathogens

“…What is known is that effectors are some of the most rapidly evolving genes in genomes [7,12]. In some cases, TEs have been shown to drive rapid sequence evolution of regions encoding effectors or rapidly evolving pathogen minichromosomes [13,14]. The mostly young effector genes may have arisen recently in such repetitive regions, more easily tolerate the insertion of TEs, or have been translocated into such regions.…”

Combined reference-free and multi-reference approaches uncover cryptic variation underlying rapid adaptation in microbial pathogens

“…The extensive gains in the power of GWAS analyses that take into account structural variation reveals a greater proportion of the complexity inherent in adaptive genetic variation within microbial species. SNP-based GWAS based on a single reference genome dataset have been successful in describing the genetic basis of complex pathogen traits (Mohd-Assaad et al 2016;Pereira et al 2020b;Caseys et al 2021;Singh et al 2021). By expanding the number of reference genome SNP datasets used for GWAS, we identified substantially more independent loci than what was previously identified using the same phenotype dataset (Dutta et al 2021).…”

“…SNP-based GWAS based on a single reference genome dataset have been successful in describing the genetic basis of complex pathogen traits (Mohd-Assaad et al 2016; Pereira et al 2020b; Caseys et al 2021; Singh et al 2021). By expanding the number of reference genome SNP datasets used for GWAS, we identified substantially more independent loci than what was previously identified using the same phenotype dataset (Dutta et al 2021).…”

“…Structural rearrangements and deletion events were found to be associated with host adaptation (Hartmann et al 2017; Meile et al 2018). GWAS based on single reference genomes was successful in discerning the genetic underpinnings of pathogen virulence and fungicide resistance (Hartmann et al 2021; Singh et al 2021). The recent pangenome constructed for Z. tritici based on 19 different isolates from six continents showed that the pathogen harbors a substantially larger gene repertoire than the canonical reference genome (Badet et al 2020).…”

“…For plant studies, powerful approaches were recently proposed to associate SVs to causal genes controlling trait variation (Todesco et al 2020; Guo et al 2020). However, our understanding of SVs governing trait variation in microorganisms is limited by approaches focused on SNPs (Laabei et al 2014; Pereira et al 2020b; Singh et al 2021). Microbial genomes are highly plastic in terms of gene content and associated SVs.…”