In silico environmental sampling of emerging fungal pathogens via big data analysis

Abstract

Yeast species in the Candida haemulonii complex (C. haemulonii, C. haemulonii var. vulnera, C. duobushaemulonii, C. pseudohaemulonii, and C. vulturna) and closely related species (C. auris, C. heveicola, and C. ruelliae) are of significant public health concern worldwide. Little is known about their natural habitat. To understand the worldwide emergence of new fungal pathogens, it is important to identify key environmental habitats. Showing the effectiveness of the primary fungal DNA barcode and leveraging big data archived in the Sequence Read Archive (SRA) database enabled the identification of novel reservoirs over a wide range of geographical areas for those yeasts. We identified 1209 datasets corresponding to species in the C. haemulonii complex and three closely related species. Our results imply that climate change is not the main driver for the emergence of pathogenic multidrug-resistant yeast species. This approach opens the door for further big data analysis using the accessible resources of such databases.