Fungal taxonomy and sequence-based nomenclature

Abstract

The identification and proper naming of microfungi, in particular plant, animal and human pathogens, remains challenging. Molecular identification is becoming the default approach for many fungal groups, and environmental metabarcoding is contributing an increasing amount of sequence data documenting fungal diversity on a global scale. This includes lineages represented only by sequence data. At present, these taxa cannot be formally described under the current nomenclature rules. By considering approaches used in bacterial taxonomy, we propose solutions for the nomenclature of taxa known only from sequences to facilitate consistent reporting and communication in the literature and public sequence repositories.

Fig. 1 |. Fungal diversity.

Although Fungi and fungus-like organisms exhibit striking phenotypic diversity, accurate and precise identification often requires molecular approaches or specific tools such as metabolic profiling owing to widespread cryptic diversification and a lack of diagnostic features in microscopic vegetative structures. a, Albugo laibachii (Oomycota) sporogenous hyphae. b, Albugo candida oospore. c, Candida auris cells. d, Pyricularia oryzae conidiophore with conidia. e, Cryptococcus neoformans cells in tissue. f, Banana plant infected with fusarium wilt. g, Fusarium odoratissimum macroconidia. h, Colletotrichum siamense section of acervulus. i, Trypethelium purpurinum (also known as Marcelaria purpurina) physical type specimen. j, Chaetocapnodium tanzanicum culture. Credit: photographs courtesy of Young-Joon Choi (a,b), Nani Maryani (f,g), Min Fu (h) and Jafar Abdollahzadeh (j). Scale bars, 10 μm (a–e, g and h) or 10 mm (i and j).

Fig. 2 |. Timeline of important events in fungal taxonomy and nomenclature.

See Supplementary Information for detailed references regarding each event.

Fig. 3 |. Visualization of new lineages.

Environmental samples of ITS amplicons generated from the fungal microbiome of switchgrass (Panicum virgatum) at a site in North Carolina, USA (SRA accession number SRS7144269; see ref. for the corresponding paper). The approximately 60,000 original fungal ITS reads from the switchgrass sample (black dots) were analysed together with 84,000 ITS sequences (blue dots) from the UNITE General FASTA Release 8.2. (https://unite.ut.ee/repository.php). Visualization was done in the rgl package in R (https://r-forge.r-project.org/projects/rgl) after employing a sparse similarity matrix approach, with multidimensional ordination using LargeVis based on fast multilevel clustering (fMLC; thresholds of 0.95 and 0.98). The two ellipses indicate the approximate location of Ascomycota (separate smaller cluster represents Eurotiomycetes) and Basidiomycota sequences; the large centre cluster also contains representatives of the other phyla present in Fungi. The reads from the Panicum sample form two additional clusters (A and B) in the periphery of the UNITE reference sequences, which indicate phyla or classes only sparsely captured in the UNITE General FASTA Release.