Filamentous pathogens, including fungi and oomycetes, pose major threats to global food security. Crop pathogens cause damage by secreting effectors that manipulate the host to the pathogen's advantage. Genes encoding such effectors are among the most rapidly evolving genes in pathogen genomes. Here, we review how the major characteristics of the emergence, function, and regulation of effector genes are tightly linked to the genomic compartments where these genes are located in pathogen genomes. The presence of repetitive elements in these compartments is associated with elevated rates of point mutations and sequence rearrangements with a major impact on effector diversification. The expression of many effectors converges on an epigenetic control mediated by the presence of repetitive elements. Population genomics analyses showed that rapidly evolving pathogens show high rates of turnover at effector loci and display a mosaic in effector presence-absence polymorphism among strains. We conclude that effective pathogen containment strategies require a thorough understanding of the effector genome biology and the pathogen's potential for rapid adaptation.

Transposable elements (TEs) are drivers of genome evolution and affect the expression landscape of the host genome. Stress is a major factor inducing TE activity; however, the regulatory mechanisms underlying de-repression are poorly understood. Plant pathogens are excellent models to dissect the impact of stress on TEs. The process of plant infection induces stress for the pathogen, and virulence factors (i.e., effectors) located in TE-rich regions become expressed. To dissect TE de-repression dynamics and contributions to virulence, we analyzed the TE expression landscape of four strains of the major wheat pathogen Zymoseptoria tritici. We experimentally exposed strains to nutrient starvation and host infection stress. Contrary to expectations, we show that the two distinct conditions induce the expression of different sets of TEs. In particular, the most highly expressed TEs, including miniature inverted-repeat transposable element and long terminal repeat-Gypsy element, show highly distinct de-repression across stress conditions. Both the genomic context of TEs and the genetic background stress (i.e., different strains harboring the same TEs) were major predictors of de-repression under stress. Gene expression profiles under stress varied significantly depending on the proximity to the closest TEs and genomic defenses against TEs were largely ineffective to prevent de-repression. Next, we analyzed the locus encoding the Avr3D1 effector. We show that the insertion and subsequent silencing of TEs in close proximity likely contributed to reduced expression and virulence on a specific wheat cultivar. The complexity of TE responsiveness to stress across genetic backgrounds and genomic locations demonstrates substantial intraspecific genetic variation to control TEs with consequences for virulence.

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Epidemics caused by fungal plant pathogens pose a major threat to agro-ecosystems and impact global food security. High-throughput sequencing enabled major advances in understanding how pathogens cause disease on crops. Hundreds of fungal genomes are now available and analyzing these genomes highlighted the key role of effector genes in disease. Effectors are small secreted proteins that enhance infection by manipulating host metabolism. Fungal genomes carry 100s of putative effector genes, but the lack of homology among effector genes, even for closely related species, challenges evolutionary and functional analyses. Furthermore, effector genes are often found in rapidly evolving chromosome compartments which are difficult to assemble. We review how population and comparative genomics toolsets can be combined to address these challenges. We highlight studies that associated genome-scale polymorphisms with pathogen lifestyles and adaptation to different environments. We show how genome-wide association studies can be used to identify effectors and other pathogenicity-related genes underlying rapid adaptation. We also discuss how the compartmentalization of fungal genomes into core and accessory regions shapes the evolution of effector genes. We argue that an understanding of genome evolution provides important insight into the trajectory of host-pathogen co-evolution.

Accurate, high-throughput phenotyping for quantitative traits is a limiting factor for progress in plant breeding. We developed an automated image analysis to measure quantitative resistance to septoria tritici blotch (STB), a globally important wheat disease, enabling identification of small chromosome intervals containing plausible candidate genes for STB resistance. 335 winter wheat cultivars were included in a replicated field experiment that experienced natural epidemic development by a highly diverse but fungicide-resistant pathogen population. More than 5.4 million automatically generated phenotypes were associated with 13,648 SNP markers to perform the GWAS. We identified 26 chromosome intervals explaining 1.9-10.6% of the variance associated with four independent resistance traits. Sixteen of the intervals overlapped with known STB resistance intervals, suggesting that our phenotyping approach can identify simultaneously (i.e., in a single experiment) many previously defined STB resistance intervals. Seventeen of the intervals were less than 5 Mbp in size and encoded only 173 genes, including many genes associated with disease resistance. Five intervals contained four or fewer genes, providing high priority targets for functional validation. Ten chromosome intervals were not previously associated with STB resistance, perhaps representing resistance to pathogen strains that had not been tested in earlier experiments. The SNP markers associated with these chromosome intervals can be used to recombine different forms of quantitative STB resistance that are likely to be more durable than pyramids of major resistance genes. Our experiment illustrates how high-throughput automated phenotyping can accelerate breeding for quantitative disease resistance.

Meiosis is one of the most conserved molecular processes in eukaryotes. The fidelity of pairing and segregation of homologous chromosomes has a major impact on the proper transmission of genetic information. Aberrant chromosomal transmission can have major phenotypic consequences, yet the mechanisms are poorly understood. Fungi are excellent models to investigate processes of chromosomal transmission, because many species have highly polymorphic genomes that include accessory chromosomes. Inheritance of accessory chromosomes is often unstable and chromosomal losses have little impact on fitness. We analyzed chromosomal inheritance in 477 progeny coming from two crosses of the fungal wheat pathogen Zymoseptoria tritici. For this, we developed a high-throughput screening method based on restriction site-associated DNA sequencing that generated dense coverage of genetic markers along each chromosome. We identified rare instances of chromosomal duplications (disomy) in core chromosomes. Accessory chromosomes showed high overall frequencies of disomy. Chromosomal rearrangements were found exclusively on accessory chromosomes and were more frequent than disomy. Accessory chromosomes present in only one of the parents in an analyzed cross were inherited at significantly higher rates than the expected 1:1 segregation ratio. Both the chromosome and the parental background had significant impacts on the rates of disomy, losses, rearrangements, and distorted inheritance. We found that chromosomes with higher sequence similarity and lower repeat content were inherited more faithfully. The large number of rearranged progeny chromosomes identified in this species will enable detailed analyses of the mechanisms underlying chromosomal rearrangement.

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Species harbor extensive structural variation underpinning recent adaptive evolution. However, the causality between genomic features and the induction of new rearrangements is poorly established. Here, we analyze a global set of telomere-to-telomere genome assemblies of a fungal pathogen of wheat to establish a nucleotide-level map of structural variation. We show that the recent emergence of pesticide resistance has been disproportionally driven by rearrangements. We use machine learning to train a model on structural variation events based on 30 chromosomal sequence features. We show that base composition and gene density are the major determinants of structural variation. Retrotransposons explain most inversion, indel and duplication events. We apply our model to Arabidopsis thaliana and show that our approach extends to more complex genomes. Finally, we analyze complete genomes of haploid offspring in a four-generation pedigree. Meiotic crossover locations are enriched for new rearrangements consistent with crossovers being mutational hotspots. The model trained on species-wide structural variation accurately predicts the position of >74% of newly generated variants along the pedigree. The predictive power highlights causality between specific sequence features and the induction of chromosomal rearrangements. Our work demonstrates that training sequence-derived models can accurately identify regions of intrinsic DNA instability in eukaryotic genomes.