Metatranscriptomics as a tool to identify fungal species and subspecies in mixed communities - a proof of concept under laboratory conditions

• Published in: IMA fungus Vol. 10; no. 1; p. 12
• Main Authors: Marcelino, Vanesa R, Irinyi, Laszlo, Eden, John-Sebastian, Meyer, Wieland, Holmes, Edward C, Sorrell, Tania C
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 08.08.2019; BioMed Central; BMC
• Subjects: Antifungal agents; Biodiversity; Contaminants; Cryptococcus; Deoxyribonucleic acid; DNA; DNA sequencing; Fungi; Genes; Genomes; Genomics; Laboratories; Meta-transcriptomics; Metagenomics; Microbiome; Next-generation sequencing; Nucleotide sequence; RNA; Species; Taxonomy; Virulence; Fungi; Metagenomics; Meta-transcriptomics; Microbiome
• ISSN: 2210-6340; 2210-6359; 2210-6359
• DOI: 10.1186/s43008-019-0012-8

High-throughput sequencing (HTS) enables the generation of large amounts of genome sequence data at a reasonable cost. Organisms in mixed microbial communities can now be sequenced and identified in a culture-independent way, usually using amplicon sequencing of a DNA barcode. Bulk RNA-seq (metatranscriptomics) has several advantages over DNA-based amplicon sequencing: it is less susceptible to amplification biases, it captures only living organisms, and it enables a larger set of genes to be used for taxonomic identification. Using a model mock community comprising 17 fungal isolates, we evaluated whether metatranscriptomics can accurately identify fungal species and subspecies in mixed communities. Overall, 72.9% of the RNA transcripts were classified, from which the vast majority (99.5%) were correctly identified at the species level. Of the 15 species sequenced, 13 were retrieved and identified correctly. We also detected strain-level variation within the species complexes: 99.3% of transcripts assigned to were classified as one of the four strains used in the mock community. Laboratory contaminants and/or misclassifications were diverse, but represented only 0.44% of the transcripts. Hence, these results show that it is possible to obtain accurate species- and strain-level fungal identification from metatranscriptome data as long as taxa identified at low abundance are discarded to avoid false-positives derived from contamination or misclassifications. This study highlights both the advantages and current challenges in the application of metatranscriptomics in clinical mycology and ecological studies.