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

The rapid evolution of antifungal resistance in Candida auris presents significant challenges for conventional public health surveillance methods, particularly in detecting emergent and highly transmissible drug-resistant variants. Using wastewater-based epidemiology (WBE) tools initially developed during the COVID-19 pandemic, we implemented a high-resolution facility-level early warning system to monitor C. auris infections and resistance patterns. Our comprehensive evaluation across Southern Nevada demonstrated that upstream sewage monitoring at healthcare facilities provided significant sensitivity (p<0.001) compared to wastewater treatment plant (WWTP) sampling. By combining amplicon sequencing and MALDI-TOF mass spectrometry, we identified clinically relevant resistance-associated variants in wastewater samples, while whole genome sequencing revealed >90% genomic concordance between 443 wastewater-derived genomes and 2,977 clinical isolates. We also detected novel 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. Further transcriptomic profiling of drug-resistant isolates under antifungal and stress conditions identified previously uncharacterized adaptation mechanisms, including differential regulation of ribosomal assembly pathways and cell cycle checkpoints. These findings highlight how wastewater intelligence can substantially enhance traditional public health surveillance approaches for the early and proactive detection and monitoring of C. auris outbreaks and antifungal resistance.