Metatranscriptomics as a tool to identify fungal species and subspecies in mixed communities – a proof of concept under laboratory ... View Full Text


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Article Info

DATE

2019-08-08

AUTHORS

Vanesa R. Marcelino, Laszlo Irinyi, John-Sebastian Eden, Wieland Meyer, Edward C. Holmes, Tania C. Sorrell

ABSTRACT

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 Cryptococcus species complexes: 99.3% of transcripts assigned to Cryptococcus 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. More... »

PAGES

12

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    28 schema:description 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 Cryptococcus species complexes: 99.3% of transcripts assigned to Cryptococcus 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.
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    35 Cryptococcus species complex
    36 DNA
    37 DNA barcodes
    38 RNA transcripts
    39 RNA-seq
    40 abundance
    41 accurate species
    42 advantages
    43 amount
    44 amplicon sequencing
    45 amplification biases
    46 application of metatranscriptomics
    47 applications
    48 barcodes
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    50 bulk RNA-seq
    51 challenges
    52 clinical mycology
    53 community
    54 complexes
    55 concept
    56 conditions
    57 contaminants
    58 contamination
    59 cost
    60 culture-independent way
    61 current challenges
    62 data
    63 ecological studies
    64 fungal identification
    65 fungal isolates
    66 fungal species
    67 generation
    68 genes
    69 genome sequence data
    70 high-throughput sequencing
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    72 isolates
    73 laboratory conditions
    74 laboratory contaminants
    75 large amount
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    77 levels
    78 low abundance
    79 majority
    80 metatranscriptome data
    81 metatranscriptomics
    82 microbial communities
    83 misclassification
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