Invasive plant pathogenic fungi have a global impact, with devastating economic and environmental effects on crops and forests. Biosurveillance, a critical component of threat mitigation, requires risk prediction based on fungal lifestyles and traits. Recent studies have revealed distinct genomic patterns associated with specific groups of plant pathogenic fungi. We sought to establish whether these phytopathogenic genomic patterns hold across diverse taxonomic and ecological groups from the Ascomycota and Basidiomycota, and furthermore, if those patterns can be used in a predictive capacity for biosurveillance. Using a supervised machine learning approach that integrates phylogenetic and genomic data, we analyzed 387 fungal genomes to test a proof-of-concept for the use of genomic signatures in predicting fungal phytopathogenic lifestyles and traits during biosurveillance activities. Our machine learning feature sets were derived from genome annotation data of carbohydrate-active enzymes (CAZymes), peptidases, secondary metabolite clusters (SMCs), transporters, and transcription factors. We found that machine learning could successfully predict fungal lifestyles and traits across taxonomic groups, with the best predictive performance coming from feature sets comprising CAZyme, peptidase, and SMC data. While phylogeny was an important component in most predictions, the inclusion of genomic data improved prediction performance for every lifestyle and trait tested. Plant pathogenicity was one of the best-predicted traits, showing the promise of predictive genomics for biosurveillance applications. Furthermore, our machine learning approach revealed expansions in the number of genes from specific CAZyme and peptidase families in the genomes of plant pathogens compared to non-phytopathogenic genomes (saprotrophs, endo- and ectomycorrhizal fungi). Such genomic feature profiles give insight into the evolution of fungal phytopathogenicity and could be useful to predict the risks of unknown fungi in future biosurveillance activities.

© 2018 Pomraning et al. The yeast Yarrowia lipolytica undergoes a morphological transition from yeast-to-hyphal growth in response to environmental conditions. A forward genetic screen was used to identify mutants that reliably remain in the yeast phase, which were then assessed by whole-genome sequencing. All the smooth mutants identified, so named because of their colony morphology, exhibit independent loss of DNA at a repetitive locus made up of interspersed ribosomal DNA and short 10- to 40-mer telomere-like repeats. The loss of repetitive DNA is associated with downregulation of genes with stress response elements (5'-CCCCT-3') and upregulation of genes with cell cycle box (5'-ACGCG-3') motifs in their promoter region. The stress response element is bound by the transcription factor Msn2p in Saccharomyces cerevisiae. We confirmed that the Y. lipolytica msn2 (Ylmsn2) ortholog is required for hyphal growth and found that overexpression of Ylmsn2 enables hyphal growth in smooth strains. The cell cycle box is bound by the Mbp1p/Swi6p complex in S. cerevisiae to regulate G1-to-S phase progression. We found that overexpression of either the Ylmbp1 or Ylswi6 homologs decreased hyphal growth and that deletion of either Ylmbp1 or Ylswi6 promotes hyphal growth in smooth strains. A second forward genetic screen for reversion to hyphal growth was performed with the smooth-33 mutant to identify additional genetic factors regulating hyphal growth in Y. lipolytica. Thirteen of the mutants sequenced from this screen had coding mutations in five kinases, including the histidine kinases Ylchk1 and Ylnik1 and kinases of the high-osmolarity glycerol response (HOG) mitogen-activated protein (MAP) kinase cascade Ylssk2, Ylpbs2, and Ylhog1. Together, these results demonstrate that Y. lipolytica transitions to hyphal growth in response to stress through multiple signaling pathways.

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.