AN ALGORITHMIC APPROACH TO FUNGAL BARCODING ENHANCES SPECIES IDENTIFICATION
2Infectious Diseases, Tel Aviv Medical Center, Tel Aviv, Israel
3Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
4Clinical Microbiology Laboratory, Tel Aviv Medical Center, Tel Aviv, Israel
Background
Accurate identification of pathogenic fungi by phenotypic methods requires considerable expertise and may be limited by the absence of differentiating features and non-sporulating strains. The intergenic spacer (ITS) region has been suggested as a fungal barcode, but is not universally applicable. We tested an algorithmic approach to sequencing of specific fungal targets to achieve precise species identification.
Methods
Algorithmic molecular barcoding (AMBar) was performed by PCR and sequencing of ITS1-5.8S-ITS2 and/or large subunit (LSU) ribosomal DNA regions for all isolates. Aspergillus species were further typed using a segment of the beta-tubulin gene. Differentiation among Cryptococcus species was done using intergenic sequence amplification and sequencing. Sequences were aligned in MycoBank. Species and genus designation was determined by the level of similarity to the reference sequence and separation from other sequences.
Results
We tested 124 fungal strains (108 clinical, 10 environmental, 6 reference) using both phenotypic and algorithmic molecular methods (113 Ascomycota: 51 yeasts, 62 moulds; 6 Basidiomycota; and 5 Zygomycota). Phenotypic methods achieved species-level identification in 50 (40%) and genus-level identification in 61 (49%) of strains. AMBar achieved species-level identification in 104 (84%) and genus-level identification in 12 (9.7%) of strains. AMBar identified to the species-level 77% of isolates identified to genus-level and 77% of isolates not identified by phenotypic methods. AMBar was responsible for the identification of 2 novel pathogens in Israel: Microsporum audouinii as a cause of tinea capitis in immigrant children and Candida auris as a cause of multidrug-resistant fungemia in hospitalized patients.
Conclusions
An algorithmic approach allows economical and efficient use of specific fungal targets to enhance accurate species-level identification. Such identification may have significant epidemiological and clinical implications.
Powered by Eventact EMS