P2385 An algorithmic approach to identifying fungal species using multiple sequence barcodes

Yael Shachor-Meyouhas*1, Ana Novikov2, Edna Bash3, Ronen Ben-Ami3

1Rambam Hospital, Haifa, Israel, 2Tel Aviv Medical Center, Tel Aviv-Yafo, Israel, 3Tel Aviv Medical Center, Tel Aviv, Israel

Background: Accurate identification of pathogenic fungal species has important implications for clinical decisions and epidemiological surveillance. Sequence-based identification has the potential to enhance identification, but the choice of genomic targets, reference databases and similarity breakpoints are poorly defined. We prospectively tested an algorithmic scheme for sequence-based fungal identification.

Materials/methods: Clinical fungal isolates (n=433) collected at the Tel Aviv Sourasky Medical Center (TASMC) Reference Mycology Laboratory from January, 2014 through December 2016, and reference strains (n=27) were identified using standard phenotypic and sequence-based (barcoding) methods. A barcoding algorithm was formulated using four sequencing targets: the ribosomal DNA (rDNA) internal transcribed spacer (ITS)-1 and ITS2 (ITS1-5.8s-ITS2), rDNA D1/D2 large subunit (LSU), β-tubulin for Aspergillus species, and rDNA intergenic spacer (IGS) for Cryptococcus species.

Results: A total of 460 fungal isolates were tested: 402 Ascomycota (including 72 Saccharomycetes, 180 Aspergillus spp., 27 cryptic Aspergillus spp. 44 Fusarium spp), 34 Basidiomycota (including 14 Cryptococcus spp.) and 23 Zygomycota. Compared with phenotypic identification, algorithmic barcoding yielded significantly higher rates of species-level identification across all major fungal taxa: overall 91.3% versus 57.4% with molecular and phenotypic methods, respectively. The majority of isolates identified with sequencing (81.3%) were identified with a single barcode, and almost all (99.7%) were identified using 2 barcodes. Concordance between different barcodes was high (98.8%). Complementary information provided by different barcodes enhanced species identification, notably the combination of ITS and β-tubulin for the identification of Aspergillus species. Cryptococcus spp. identification was achieved for all 14 isolates using IGS sequencing. Algorithmic barcoding helped identify outbreaks with the emerging fungi Candida auris (bloodstream infections) and Microsporum audouinii (tinea capitis).

Conclusions: An algorithmic approach can be used to optimize the use of barcode sequencing to identify clinically important fungi.