Metabarcoding of insect-associated fungal communities: a comparison of internal transcribed spacer (ITS) and large-subunit (LSU) rRNA markers
File(s)
Published version
Author(s)
Ceballos Escalera Fernandez, Angelina
Richards, John
Arias, Maria Belen
Inward, Daegan JG
Vogler, Alfried
Type
Journal Article
Abstract
Full taxonomic characterisation of fungal communities is necessary for establishing ecological associations and early detection of pathogens and invasive species. Complex communities of fungi are regularly characterised by metabarcoding using the Internal Transcribed Spacer (ITS) and the Large-Subunit (LSU) gene of the rRNA locus, but reliance on a single short sequence fragment limits the confidence of identification. Here we link metabarcoding from the ITS2 and LSU D1-D2 regions to characterise fungal communities associated with bark beetles (Scolytinae), the likely vectors of several tree pathogens. Both markers revealed similar patterns of overall species richness and response to key variables (beetle species, forest type), but identification against the respective reference databases using various taxonomic classifiers revealed poor resolution towards lower taxonomic levels, especially the species level. Thus, Operational Taxonomic Units (OTUs) could not be linked via taxonomic classifiers across ITS and LSU fragments. However, using phylogenetic trees (focused on the epidemiologically important Sordariomycetes) we placed OTUs obtained with either marker relative to reference sequences of the entire rRNA cistron that includes both loci and demonstrated the largely similar phylogenetic distribution of ITS and LSU-derived OTUs. Sensitivity analysis of congruence in both markers suggested the biologically most defensible threshold values for OTU delimitation in Sordariomycetes to be 98% for ITS2 and 99% for LSU D1-D2. Studies of fungal communities using the canonical ITS barcode require corroboration across additional loci. Phylogenetic analysis of OTU sequences aligned to the full rRNA cistron shows higher success rate and greater accuracy of species identification compared to probabilistic taxonomic classifiers.
Editor(s)
dal Grande, Francesco
Date Issued
2022-03-08
Date Acceptance
2022-01-25
Citation
MycoKeys, 2022, 88, pp.1-33
ISSN
1314-4057
Publisher
Pensoft Publishers
Start Page
1
End Page
33
Journal / Book Title
MycoKeys
Volume
88
Copyright Statement
Copyright Angelina Ceballos-Escalera et al. This is an open access article distributed under the terms of the Creative Commons Attribution License
(CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
(CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
License URL
Identifier
https://mycokeys.pensoft.net/article/77106/
Subjects
Science & Technology
Life Sciences & Biomedicine
Mycology
clustering
fungi
ITS
LSU
metabarcoding
pathogens
phylogeny
Scolytinae
BAYESIAN CLASSIFIER
AMBROSIA BEETLE
BARK
IDENTIFICATION
TREE
ARTHROPODS
DATABASES
SOFTWARE
REGION
GENUS
Publication Status
Published
Date Publish Online
2022-03-08