Fungal spores and mycelium fragments are particles which become and remain airborne and have been subjects of aerobiological studies. The presence and the abundance of taxa in aerobiological samples can be very variable and impaired by changeable climatic conditions. Because many fungi produce mycotoxins and both their mycelium fragments and spores are potential allergens, monitoring the presence of these taxa is of key importance. So far data on exposure and sensitization to fungal allergens are mainly based on the assessment of few, easily identifiable taxa and focused only on certain environments. The microscopic method used to analyze aerobiological samples and the inconspicuous fungal characters do not allow a in depth taxonomical identification. Here, we present a first assessment of fungal diversity from airborne samples using a DNA metabarcoding analysis. The nuclear ITS2 region was selected as barcode to catch fungal diversity in mixed airborne samples gathered during two weeks in four sites of North-Eastern and Central Italy. We assessed the taxonomic composition and diversity within and among the sampled sites and compared the molecular data with those obtained by traditional microscopy. The molecular analyses provide a tenfold more comprehensive determination of the taxa than the traditional morphological inspections. Our results prove that the metabarcoding analysis is a promising approach to increases quality and sensitivity of the aerobiological monitoring. The laboratory and bioinformatic workflow implemented here is now suitable for routine, high-throughput, regional analyses of airborne fungi.
DNA metabarcoding uncovers fungal diversity of mixed airborne samples in Italy
Elisa Banchi,Claudio G. Ametrano,D. Stanković,Pierluigi Verardo,O. Moretti,Francesca Gabrielli,Stefania Lazzarin,M. F. Borney,F. Tassan,M. Tretiach,A. Pallavicini,L. Muggia
Published 2018 in PLoS ONE
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- Publication year
2018
- Venue
PLoS ONE
- Publication date
2018-03-20
- Fields of study
Biology, Medicine, Environmental Science
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Semantic Scholar, PubMed
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