Abstract
In 2019, Candida auris became the first fungal pathogen included in the list of the urgent antimicrobial threats by the Centers for Disease Control (CDC). Short tandem repeat (STR) analysis and whole-genome sequencing (WGS) are considered the gold standard, and can be complemented by other molecular methods, for the genomic surveillance and clade classification of this multidrug-resistant yeast. However, these methods can be expensive and require time and expertise that are not always available. The long turnaround time is especially not compatible with the speed needed to manage clonal transmission in healthcare settings. Fourier transform infrared (FTIR) spectroscopy, a biochemical fingerprint approach, has been applied in this study to a set of 74 C. auris isolates belonging to the five clades of C. auris (I-V) in combination with an artificial neural network (ANN) algorithm to create and validate "CLADE-FINDER", a tool for C. auris clade determination. The CLADE-FINDER classifier allowed us to discriminate the four primary C. auris clades (I-IV) with a correct classification for 96% of the samples in the validation set. This newly developed genotyping scheme can be reasonably applied for the effective epidemiological monitoring and management of C. auris cases in real time.