학술논문

101 Dothideomycetes genomes: A test case for predicting lifestyles and emergence of pathogens.
Document Type
article
Source
Studies in mycology. 96(96)
Subject
Aulographales Crous
Spatafora
Haridas & Grigoriev
Coniosporiaceae Crous
Spatafora
Haridas & Grigoriev
Coniosporiales Crous
Spatafora
Haridas & Grigoriev
Eremomycetales Crous
Spatafora
Haridas & Grigoriev
Fungal evolution
Genome-based prediction
Lineolataceae Crous
Spatafora
Haridas & Grigoriev
Lineolatales Crous
Spatafora
Haridas & Grigoriev
Machine-learning
New taxa
Rhizodiscinaceae Crous
Spatafora
Haridas & Grigoriev
Aulographales Crous
Spatafora
Haridas & Grigoriev
Coniosporiaceae Crous
Coniosporiales Crous
Eremomycetales Crous
Lineolataceae Crous
Lineolatales Crous
Rhizodiscinaceae Crous
Microbiology
Mycology & Parasitology
Language
Abstract
Dothideomycetes is the largest class of kingdom Fungi and comprises an incredible diversity of lifestyles, many of which have evolved multiple times. Plant pathogens represent a major ecological niche of the class Dothideomycetes and they are known to infect most major food crops and feedstocks for biomass and biofuel production. Studying the ecology and evolution of Dothideomycetes has significant implications for our fundamental understanding of fungal evolution, their adaptation to stress and host specificity, and practical implications with regard to the effects of climate change and on the food, feed, and livestock elements of the agro-economy. In this study, we present the first large-scale, whole-genome comparison of 101 Dothideomycetes introducing 55 newly sequenced species. The availability of whole-genome data produced a high-confidence phylogeny leading to reclassification of 25 organisms, provided a clearer picture of the relationships among the various families, and indicated that pathogenicity evolved multiple times within this class. We also identified gene family expansions and contractions across the Dothideomycetes phylogeny linked to ecological niches providing insights into genome evolution and adaptation across this group. Using machine-learning methods we classified fungi into lifestyle classes with >95 % accuracy and identified a small number of gene families that positively correlated with these distinctions. This can become a valuable tool for genome-based prediction of species lifestyle, especially for rarely seen and poorly studied species.