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

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.

Recent Advanced Technologies for the Characterization of Xenobiotic-Degrading Microorganisms and Microbial Communities

“…Metatranscriptomic analysis of this interaction model demonstrated genes encoding the transcription of 1,2-bis(4-hydroxyphenyl)-2-propanol (1-BP) into 4-hydroxybenzaldehyde (4-HBD) and 4-hydroxy-acetophenone (4-HAP) via 3,4-dihydroxybenzoate (3,4-DHB) and 3-oxoadipate (3-ODP), respectively, to the tricarboxylic acid cycle (TCA)cycle. Marcelino et al ( 2019 ) identified fungal species and subspecies in a mixed community by using metatranscriptomics. This study suggested a strain-level discrepancy between the Cryptococcus fungal species and their in-situ mock communities.…”

CCMetagen: comprehensive and accurate identification of eukaryotes and prokaryotes in metagenomic data

“…We validated the CCMetagen pipeline with a fungal community previously generated in vitro by culturing, processing, and sequencing 15 fungal species ( [34], Additional file 5). The analyses were performed using the NCBI nt collection as a reference.…”

“…We validated the CCMetagen pipeline using two biological data sets: one defined fungal community (biological data set 1) and one set of environmental samples (biological data set 2). The fungal community was constructed by culturing, pooling, and sequencing the same 15 fungal species used in the metatranscriptome simulated in silico (SRA Bio-Project number PRJNA521097) [34].…”

“…25910/5cc7cd40fca8e [59] (simulated_datasets.zip). The biological data is available on GenBank (SRA BioProject numbers PRJNA521097 [34] and PRJNA472212 [37]). The nt and RefSeq-bf databases indexed to function with KMA and CCMetagen are hosted in two sites, at https://doi.org/10.25910/5cc7cd40fca8e [59] (Australia) and http://www.cbs.…”

Multiplex Sequencing of SARS-Cov-2 genome directly from clinical samples using the Ion Personal Genome Machine (PGM)

“…The rst SARS-CoV-2 complete genome sequence, Wuhan-Hu-1 (MN908947), was derived from RNA extracted directly from the patient's bronchoalveolar lavage uid using the whole genome meta-transcriptomics approach [4]. This unbiased sequencing approach allows culture-free detection of novel [4] or known pathogens [9]. The metatranscriptomics approach, however, is not always able to produce the entire genome sequence due in part to the presence of host RNAs that contribute to the majority of the generated sequencing reads [4].…”