Fungi in the class Leotiomycetes are ecologically diverse, including mycorrhizas, endophytes of roots and leaves, plant pathogens, aquatic and aero-aquatic hyphomycetes, mammalian pathogens, and saprobes. These fungi are commonly detected in cultures from diseased tissue and from environmental DNA extracts. The identification of specimens from such character-poor samples increasingly relies on DNA sequencing. However, the current classification of Leotiomycetes is still largely based on morphologically defined taxa, especially at higher taxonomic levels. Consequently, the formal Leotiomycetes classification is frequently poorly congruent with the relationships suggested by DNA sequencing studies. Previous class-wide phylogenies of Leotiomycetes have been based on ribosomal DNA markers, with most of the published multi-gene studies being focussed on particular genera or families. In this paper we collate data available from specimens representing both sexual and asexual morphs from across the genetic breadth of the class, with a focus on generic type species, to present a phylogeny based on up to 15 concatenated genes across 279 specimens. Included in the dataset are genes that were extracted from 72 of the genomes available for the class, including 10 new genomes released with this study. To test the statistical support for the deepest branches in the phylogeny, an additional phylogeny based on 3156 genes from 51 selected genomes is also presented. To fill some of the taxonomic gaps in the 15-gene phylogeny, we further present an ITS gene tree, particularly targeting ex-type specimens of generic type species. A small number of novel taxa are proposed: Marthamycetales ord. nov., and Drepanopezizaceae and Mniaeciaceae fams. nov. The formal taxonomic changes are limited in part because of the ad hoc nature of taxon and specimen selection, based purely on the availability of data. The phylogeny constitutes a framework for enabling future taxonomically targeted studies using deliberate specimen selection. Such studies will ideally include designation of epitypes for the type species of those genera for which DNA is not able to be extracted from the original type specimen, and consideration of morphological characters whenever genetically defined clades are recognized as formal taxa within a classification.

DNA metabarcoding is widely used to study prokaryotic and eukaryotic microbial diversity. Technological constraints limit most studies to marker lengths below 600 base pairs (bp). Longer sequencing reads of several thousand bp are now possible with third-generation sequencing. Increased marker lengths provide greater taxonomic resolution and allow for phylogenetic methods of classification, but longer reads may be subject to higher rates of sequencing error and chimera formation. In addition, most bioinformatics tools for DNA metabarcoding were designed for short reads and are therefore unsuitable. Here, we used Pacific Biosciences circular consensus sequencing (CCS) to DNA-metabarcode environmental samples using a ca. 4,500 bp marker that included most of the eukaryote SSU and LSU rRNA genes and the complete ITS region. We developed an analysis pipeline that reduced error rates to levels comparable to short-read platforms. Validation using a mock community indicated that our pipeline detected 98% of chimeras de novo. We recovered 947 OTUs from water and sediment samples from a natural lake, 848 of which could be classified to phylum, 397 to genus and 330 to species. By allowing for the simultaneous use of three databases (Unite, SILVA and RDP LSU), long-read DNA metabarcoding provided better taxonomic resolution than any single marker. We foresee the use of long reads enabling the cross-validation of reference sequences and the synthesis of ribosomal rRNA gene databases. The universal nature of the rRNA operon and our recovery of >100 nonfungal OTUs indicate that long-read DNA metabarcoding holds promise for studies of eukaryotic diversity more broadly.

Many species of fungi can cause disease in plants, animals and humans. Accurate and robust detection and quantification of fungi is essential for diagnosis, modeling and surveillance. Also direct detection of fungi enables a deeper understanding of natural microbial communities, particularly as a great many fungi are difficult or impossible to cultivate. In the last decade, effective amplification platforms, probe development and various quantitative PCR technologies have revolutionized research on fungal detection and identification. Examples of the latest technology in fungal detection and differentiation are discussed here.

No abstract