Department of Infection Control and Preparedness, Norwegian Institute of Public Health, Oslo, Norway.
ECDC Fellowship Programme, Public Health Microbiology path (EUPHEM), European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden.
J Clin Microbiol. 2024 Aug 14;62(8):e0004024. doi: 10.1128/jcm.00040-24. Epub 2024 Jul 11.
() 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.
() 是耶尔森菌病最常见的病因,在挪威和其他国家引发了几起全国性疫情。需要一种标准化的高分辨率方法,如核心基因组多位点序列分型 (cgMLST),以便在国家和国际层面进行病原体溯源。在这项研究中,我们为 开发并实施了一种 cgMLST 方案。我们在 SeqSphere + 中使用不同生物型亚系的高质量基因组设计了 cgMLST 方案。该方案在经过验证后,如果在所有测试的 中发现了超过 95%的目标:2012 年至 2022 年间收集的 563 株挪威基因组和来自公共数据集的 327 株基因组。我们将该方案应用于已知的疫情,以确定基于等位基因差异数量识别主要复合类型 (CT) 的阈值。最终的 cgMLST 方案包括 2582 个基因,中位数为 97.9%(四分位距 97.6%-98.8%),在所有测试的基因组中发现了目标。通过在四个等位基因差异处进行单链接聚类分析疫情,识别出所有疫情菌株。该阈值在挪威确定了 311 个独特的 CT,其中 CT18、CT12 和 CT5 被确定为与疫情最相关的 CT。该 cgMLST 方案在使用不同数据源进行分型方面表现出非常好的性能,能够识别出疫情集群。我们建议在国家和国际上实施该方案,以促进国家和跨境疫情的监测并改善疫情应对。