A benchmark dataset for validating FKS1 mutations in Candida auris.

Area of Science:

• Clinical Mycology
• Antimicrobial Resistance
• Genomic Epidemiology

Background:

• Candida auris is a multidrug-resistant yeast causing invasive infections globally.
• Echinocandins are first-line therapy, but resistance is an emerging threat.
• Rapid detection of echincoandin resistance is critical for patient management.

Purpose of the Study:

• To develop a benchmark dataset of whole-genome sequenced Candida auris isolates.
• To validate the utility of the MycoSNP-nf bioinformatics pipeline for detecting echincoandin resistance-conferring FKS1 mutations.
• To assess the concordance between genomic detection and antifungal susceptibility testing (AFST) results.

Main Methods:

• Whole-genome sequencing of 100 Candida auris isolates (53 susceptible, 47 resistant).
• Implementation of the MycoSNP-nf pipeline for clade typing and FKS1 hotspot mutation detection.
• Phylogenetic analysis to classify isolates into clades.

Main Results:

• The benchmark dataset comprises 100 isolates categorized by AFST.
• MycoSNP-nf identified FKS1 hotspot mutations in 44 of 47 resistant isolates.
• Mutations were located in known and a potential third hotspot region of FKS1.

Conclusions:

• The developed benchmark dataset is a valuable resource for validating genomic tools.
• MycoSNP-nf effectively detects echincoandin resistance-conferring FKS1 mutations in C. auris.
• This work supports the development of sequencing-based methods for rapid resistance detection.