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Genome-Wide Analysis in Three Fusarium Pathogens Identifies Rapidly Evolving Chromosomes and Genes Associated with Pathogenicity

Pathogens and hosts are in an ongoing arms race and genes involved in host–pathogen interactions are likely to undergo diversifying selection. Fusarium plant pathogens have evolved diverse infection strategies, but how they interact with their hosts in the biotrophic infection stage remains puzzling...

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Detalles Bibliográficos
Autores principales: Sperschneider, Jana, Gardiner, Donald M., Thatcher, Louise F., Lyons, Rebecca, Singh, Karam B., Manners, John M., Taylor, Jennifer M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4494044/
https://www.ncbi.nlm.nih.gov/pubmed/25994930
http://dx.doi.org/10.1093/gbe/evv092
Descripción
Sumario:Pathogens and hosts are in an ongoing arms race and genes involved in host–pathogen interactions are likely to undergo diversifying selection. Fusarium plant pathogens have evolved diverse infection strategies, but how they interact with their hosts in the biotrophic infection stage remains puzzling. To address this, we analyzed the genomes of three Fusarium plant pathogens for genes that are under diversifying selection. We found a two-speed genome structure both on the chromosome and gene group level. Diversifying selection acts strongly on the dispensable chromosomes in Fusarium oxysporum f. sp. lycopersici and on distinct core chromosome regions in Fusarium graminearum, all of which have associations with virulence. Members of two gene groups evolve rapidly, namely those that encode proteins with an N-terminal [SG]-P-C-[KR]-P sequence motif and proteins that are conserved predominantly in pathogens. Specifically, 29 F. graminearum genes are rapidly evolving, in planta induced and encode secreted proteins, strongly pointing toward effector function. In summary, diversifying selection in Fusarium is strongly reflected as genomic footprints and can be used to predict a small gene set likely to be involved in host–pathogen interactions for experimental verification.