Scholarlink

<p>The rapid evolution of antifungal resistance in <i>Candidozyma auris</i> (formerly <i>Candida auris</i>)&#xa0;presents significant challenges for conventional public&#xa0;health surveillance methods, particularly in detecting emergent and highly transmissible drug-resistant variants.&#xa0;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 <i>C. auris</i> infections and resistance patterns. Our evaluation across Southern Nevada demonstrates that upstream sewage monitoring at healthcare facilities provides significant sensitivity (<i>p</i> &lt; 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 &gt;90% genomic concordance between 443 wastewater-derived genomes and 2945 clinical isolates. We also detect previously unreported subclades and resistance mutations, including <i>FKS1</i> Phe635Leu and co-occurring <i>ERG11</i>/<i>FKS1</i> 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&#xa0;health approaches for early detection and monitoring of <i>C. auris</i> outbreaks and antifungal resistance.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月18日

查看全文
  • Ching-Lan Chang,
  • Michael A. Moshi,
  • Quang-Huy Nguyen,
  • Junghun Oh,
  • Ha Nguyen,
  • Pratik Paranjape,
  • Mohammed Abushanab,
  • Austin J. Tang,
  • Jose Yani Itorralba,
  • Lauryn Massic,
  • Eakalak Khan,
  • Cassius Lockett,
  • Horng-Yuan Kan,
  • Mark Pandori,
  • Daniel Gerrity,
  • Van Vo,
  • Tin Nguyen,
  • David Hess,
  • Edwin C. Oh

摘要

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.