Abstract

Plants and their pathogens are engaged in continuous evolutionary battles, with pathogens evolving to circumvent plant defense mechanisms and plants responding through enhanced protection to prevent or mitigate damage induced by pathogen attack. Managed ecosystems are composed of genetically identical populations of crop plants with few changes from year to year. These environments are highly conducive to the emergence and dissemination of pathogens and they exert selective pressure for both qualitative virulence factors responsible for fungal pathogenicity, and quantitative traits linked to pathogen fitness, such as aggressiveness. In this study, we used a comparative genome-wide approach to investigate the genomic basis underlying the pathogenicity and aggressiveness of the fungal coffee pathogen Colletotrichum kahawae infecting green coffee berries. The pathogenicity was investigated by comparing genomic variation between C. kahawae and its non-pathogenic sibling species, while the aggressiveness was studied by a genome-wide association approach with groups of isolates with different phenotypic profiles. High genetic differentiation was observed between C. kahawae and the most closely related species with 5,560 diagnostic SNPs identified, in which a significant enrichment of non-synonymous mutations was detected. Functional annotation of these non-synonymous mutations revealed a significant enrichment mainly in two gene ontology categories, “oxidation–reduction process” and “integral component of membrane.” Finally, the annotation of several genes potentially under-selection revealed that C. kahawae’s pathogenicity may be a complex biological process, in which important biological functions, such as, detoxification and transport, regulation of host and pathogen gene expression, and signaling are involved. On the other hand, the genome-wide association analyses for aggressiveness were able to identify 10 SNPs and 15 SNPs of small effect in single and multi-association analysis, respectively, from which 7 were common, giving in total 18 SNPs potentially associated. The annotation of these genomic regions allowed the identification of four candidate genes encoding F-box domain-containing, nitrosoguanidine resistance, Fungal specific transcription factor domain-containing and C6 transcription factor that could be associated with aggressiveness. This study shed light, for the first time, on the genetic mechanisms of C. kahawae host specialization.

Highlights

  • Plant diseases have become one of the most challenging threats to modern agriculture, for their huge economic impact caused by severe production losses, and due to a global food security problem

  • Illumina RAD-seq of 30 C. kahawae isolates, collected from almost all coffee regions where Coffee Berry Disease (CBD) occurs, and 10 isolates from several closely related species of the C. gloeosporioides complex, generated an average of 3.76 × 106 reads per sample, amounting to a total of 150.41 × 106 of 85 bp single-end reads after barcode trimming

  • This work took the first step toward the understanding of the genetic mechanisms underlying the ability of C. kahawae to infect green coffee berries and its aggressiveness

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Summary

Introduction

Plant diseases have become one of the most challenging threats to modern agriculture, for their huge economic impact caused by severe production losses, and due to a global food security problem. In managed ecosystems, crops evolve through artificial selection, in which agriculturally desired traits are favored and the genetic heterogeneity of the host is severely reduced (Möller and Stukenbrock, 2017) In such homogeneous environments, the pathogen has a selective advantage, and newly pathogenic strains can quickly increase in frequency and spread across the fields (Zhan et al, 2014). It is well-known that the host is the strongest driver of pathogen evolution, as a successful infection is required for pathogen reproduction and dispersal In this sense, genes related to pathogenicity are expected to be under strong selective pressure, and genomic signatures of selection can be used to identify candidate genes involved in host–pathogen interactions (Möller and Stukenbrock, 2017). These traits are likely to be under selection, resulting in differential adaptive patterns according to the environment (Pariaud et al, 2009)

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