Development and implementation of a core genome multilocus sequence typing scheme for Yersinia enterocolitica: a tool for surveillance and outbreak detection

• Published in: Journal of clinical microbiology 2024-08, Vol.62 (8), p.e0004024
• Main Authors: Pires, Joao, Brandal, Lin T, Naseer, Umaer
• Format: Article
• Language: English
• Subjects: Bacterial Typing Techniques - methods; Bacteriology; Disease Outbreaks; Epidemiological Monitoring; Genome, Bacterial - genetics; Genotype; Humans; Molecular Epidemiology - methods; Multilocus Sequence Typing - methods; Norway - epidemiology; Yersinia enterocolitica - classification; Yersinia enterocolitica - genetics; Yersinia enterocolitica - isolation & purification; Yersinia Infections - diagnosis; Yersinia Infections - epidemiology; Yersinia Infections - microbiology
• ISSN: 0095-1137; 1098-660X; 1098-660X
• DOI: 10.1128/jcm.00040-24

( ) is the most frequent etiological agent of yersiniosis and has been responsible for several national outbreaks in Norway and elsewhere. A standardized high-resolution method, such as core genome Multilocus Sequence Typing (cgMLST), is needed for pathogen traceability at the national and international levels. In this study, we developed and implemented a cgMLST scheme for . We designed a cgMLST scheme in SeqSphere + using high-quality genomes from different biotype sublineages. The scheme was validated if more than 95% of targets were found across all tested : 563 Norwegian genomes collected between 2012 and 2022 and 327 genomes from public data sets. We applied the scheme to known outbreaks to establish a threshold for identifying major complex types (CTs) based on the number of allelic differences. The final cgMLST scheme included 2,582 genes with a median of 97.9% (interquartile range 97.6%-98.8%) targets found across all tested genomes. Analysis of outbreaks identified all outbreak strains using single linkage clustering at four allelic differences. This threshold identified 311 unique CTs in Norway, of which CT18, CT12, and CT5 were identified as the most frequently associated with outbreaks. The cgMLST scheme showed a very good performance in typing using diverse data sources and was able to identify outbreak clusters. We recommend the implementation of this scheme nationally and internationally to facilitate surveillance and improve outbreak response in national and cross-border outbreaks.