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A Candida auris cgMLST Scheme for Enhanced Outbreak Surveillance and Global Epidemiological Tracking

Year: 2026

Type: preprint

Abstract: This study presents a Candida auris core genome multi-locus sequence typing (cgMLST) scheme designed from a comprehensive dataset of public genomes. Representatives across all 6 global clades were used to select 3,538 core genome loci. The proposed scheme was benchmarked with 3,165 genomes, 97.0% (n = 3072) meeting a > = 95% “good targets” threshold, successfully recapitulating the global clade delineations, and further validated against published SNP-based studies of epidemiologically-linked outbreaks. Pairwise cgMLST to SNP linear regression (R2 = 0.98) averaged 2.92 polymorphisms per allele. This scheme was successfully utilized for detecting a real-world outbreak with confirmed epidemiological links between patients and hospital environments.

Source: Research Square

DOI: 10.21203/rs.3.rs-9975114/v1

Authors: Jason Stam, Casey W. Harless, German G. Vargas-Cuebas, Brendan W. Corey, Emily Nguyen, Brendan T. Jones, Maya Polashenski, Jamil Muqtadir, Jennifer Gutowski, Ana Ong, Yoon I. Kwak, Jason W. Bennett, Keith A. Jolley, Emil Lesho, Patrick T. Mc Gann, Francois Lebreton

Institutions: Walter Reed Army Institute of Research, Unity Health System, University of Oxford

Language: English

Topic: Antifungal resistance and susceptibility

Subfield: Infectious Diseases

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7165175496

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