Wastewater intelligence predicts the emergence of clinically-relevant and drug-resistant Candidozyma auris at healthcare facilities

Abstract

The rapid evolution of antifungal resistance in Candidozyma auris (formerly Candida auris) presents significant challenges for conventional public health surveillance methods, particularly in detecting emergent and highly transmissible drug-resistant variants. Here, using wastewater-based epidemiology tools initially developed during the COVID-19 pandemic, we implement a high-resolution, facility-level early warning system to monitor C. auris infections and resistance patterns. Our evaluation across Southern Nevada demonstrates that upstream sewage monitoring at healthcare facilities provides significant sensitivity (p < 0.001) compared to wastewater treatment plant sampling. By combining amplicon sequencing and MALDI-TOF mass spectrometry, we identify clinically-relevant, resistance-associated variants in wastewater samples, while whole-genome sequencing reveals >90% genomic concordance between 443 wastewater-derived genomes and 2945 clinical isolates. We also detect previously unreported subclades and resistance mutations, including FKS1 Phe635Leu and co-occurring ERG11/FKS1 variants in wastewater samples up to nearly five months before their appearance in clinical settings. Transcriptomic profiling of drug-resistant isolates under antifungal and stress conditions identifies previously uncharacterized adaptation mechanisms, including differential regulation of ribosomal assembly pathways and cell cycle checkpoints. These findings highlight how wastewater intelligence can enhance traditional public health approaches for early detection and monitoring of C. auris outbreaks and antifungal resistance.