ABSTRACT Candida auris is an emerging pathogen known for causing invasive, multidrug-resistant infections and healthcare-associated outbreaks. Measuring the relatedness of C. auris strains is critical for detecting and preventing healthcare-associated transmission. Whole genome sequencing (WGS) and analysis of single-nucleotide polymorphisms (SNPs) offer high-resolution assessments of genetic relatedness and antifungal susceptibility; however, interpretation requires an understanding of inter- and intra-patient C. auris diversity and genotypic markers of resistance, respectively. This study aimed to define SNP thresholds for assessing C. auris relatedness in healthcare-associated outbreak investigations, predict antifungal resistance via genotypic markers, and evaluate patient risk factors to inform future surveillance strategies. WGS was performed on 68 C. auris isolates (clades I and III) obtained from 31 hospitalized patients across a healthcare system between 2021 and 2024. Using MycoSNP analysis, we observed a maximum intra-patient variation of 14 SNPs. Five probable transmission clusters were identified based on epidemiologic links, with patient isolates differing by a median of 5 SNPs (range: 0−12). Analysis with a commercial pipeline (refMLST, BugSeq) showed similar clustering patterns. All new detections occurring more than 1 month after admission were linked to a cluster, representing a highly specific indicator of healthcare-associated infection. WGS detected known genotypic markers for fluconazole (ERG11 Y132F, F126L, and MRR1 N647T) and micafungin (FKS1 F635C) resistance, with the latter emerging during antimicrobial therapy. In addition, putative FUR1 mutations (G207R and Q16::STOP) associated with flucytosine resistance were identified. These findings emphasize the utility of WGS for identifying healthcare-associated clusters of C. auris and predicting antifungal resistance. IMPORTANCE Candida auris is a difficult-to-treat yeast that causes invasive infections in vulnerable patient populations. Healthcare exposure is a key risk factor for becoming colonized or infected with C. auris , and infection prevention groups focus on curbing the spread of this organism within the healthcare environment. Whole genome sequencing approaches are key for supporting these efforts, as they can help define clusters of C. auris transmission and can also provide insight into antifungal resistance. Our work provides practical guidance for interpreting genomic data in this setting, helping infection prevention teams respond more effectively to outbreaks and expanding the use of genotypic predictions for antifungal resistance.
Murphy et al. (2026) studied this question.