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AuFe crystallizes in the trigonal R-3m space group. The structure is three-dimensional. Fe is bonded to six equivalent Au atoms to form a mixture of distorted corner and edge-sharing FeAu6 cuboctahedra. All Fe–Au bond lengths are 2.77 Å. Au is bonded to six equivalent Fe atoms to form a mixture of distorted corner and edge-sharing AuFe6 cuboctahedra.
Abstract 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.
Domestication of plant species has affected the evolutionary dynamics of plant pathogens in agriculture and forestry. A model system for studying the consequences of plant domestication on the evolution of an emergent plant disease is the fungal pathogen Sphaerulina musiva. This ascomycete causes leaf spot and stem canker disease of Populus spp. and their hybrids. A population genomics approach was used to determine the degree of population structure and evidence for selection on the North American population of S. musiva. In total, 122 samples of the fungus were genotyped identifying 120,016 single-nucleotide polymorphisms after quality filtering. In North America, S. musiva has low to moderate degrees of differentiation among locations. Three main genetic clusters were detected: southeastern United States, midwestern United States and Canada, and a new British Columbia cluster (BC2). Population genomics suggest that BC2 is a novel genetic cluster from central British Columbia, clearly differentiated from previously reported S. musiva from coastal British Columbia, and the product of a single migration event. Phenotypic measurements from greenhouse experiments indicate lower aggressiveness of BC2 on Populus trichocarpa. In summary, S. musiva has geographic structure across broad regions indicative of gene flow among clusters. The interconnectedness of the North American S. musiva populations across large geographic distances further supports the hypothesis of anthropogenic-facilitated transport of the pathogen.