A core-genome multilocus sequence typing scheme for the detection of genetically related Streptococcus pyogenes clusters

The recently observed increase in invasive Streptococcus pyogenes infections causes concern in Europe. However, conventional molecular typing methods lack discriminatory power to aid investigations of outbreaks caused by S. pyogenes. Therefore, there is an urgent need for high-resolution molecular typing methods to assess genetic relatedness between S. pyogenes isolates. In the current study, we aimed to develop a novel high-resolution core-genome multilocus sequence typing (cgMLST) scheme for S. pyogenes and compared its discriminatory power to conventional molecular typing methods. The cgMLST scheme was designed with the commercial Ridom SeqSphere+ software package. To define a cluster threshold, the scheme was evaluated using publicly available data from nine defined S. pyogenes outbreaks in the United Kingdom. The cgMLST scheme was then applied to 23 isolates from a suspected S. pyogenes outbreak and 117 S. pyogenes surveillance isolates both from the Netherlands. MLST and emm-typing results were used for comparison to cgMLST results. The allelic differences between isolates from defined outbreaks ranged between 6 and 31 for isolates with the same emm-type, resulting in a proposed cluster threshold of <5 allelic differences out of 1,095 target loci. Seven out of twenty-three (30%) isolates from the suspected outbreak had an allelic difference of <2, thereby identifying a potential cluster that could not be linked to other isolates. The proposed cgMLST scheme shows a higher discriminatory ability when compared to conventional typing methods. The rapid and simple analysis workflow allows for extended detection of clusters of potential outbreak isolates and surveillance and may facilitate the sharing of sequencing results between (inter)national laboratories.

Implementation of routine genomic surveillance provided insights into a locally acquired outbreak caused by a rare clade of Salmonella enterica serovar Enteritidis in Queensland, Australia

Salmonellosis is a significant public health problem globally. In Australia, Salmonella enterica serovar Enteritidis is one of the main causes of salmonellosis. This study reports how the implementation of routine genetic surveillance of isolates from human S. Enteritidis cases enabled identification of the likely source of an outbreak that occurred in a remote town in Far North Queensland, Australia. This study included patient, food and water samples collected during an outbreak investigation. S. Enteritidis of the novel sequence type 5438 was isolated from all seven patient samples and one bore water sample but not any of the food samples. Both whole-genome single nucleotide polymorphism (SNP) and core-genome multilocus sequence typing analysis revealed that S. Enteritidis isolated from outbreak-related patient samples and the bore water isolates clustered together with fewer than five SNP differences and ten allelic differences. This genetic relatedness informed the outbreak response team around public health interventions and no further cases were identified post-treatment of the bore water. This disease cluster was identified through the routine sequencing of S. Enteritidis performed by the state public health laboratory in an actionable time frame. Additionally, genomic surveillance captured a case with unknown epidemiological links to the affected community, ruled out a simultaneous outbreak in an adjacent state as the source and provided evidence for the likely source preventing further transmission. Therefore, this report provides compelling support for the implementation of whole-genome sequencing based genotyping methods in public health microbiology laboratories for better outbreak detection and management.

Isolate-Based Surveillance of Bordetella pertussis, Austria, 2018-2020

Pertussis is a vaccine-preventable disease, and its recent resurgence might be attributable to the emergence of strains that differ genetically from the vaccine strain. We describe a novel pertussis isolate-based surveillance system and a core genome multilocus sequence typing scheme to assess Bordetella pertussis genetic variability and investigate the increased incidence of pertussis in Austria. During 2018–2020, we obtained 123 B. pertussis isolates and typed them with the new scheme (2,983 targets and preliminary cluster threshold of <6 alleles). B. pertussis isolates in Austria differed genetically from the vaccine strain, both in their core genomes and in their vaccine antigen genes; 31.7% of the isolates were pertactin-deficient. We detected 8 clusters, 1 of them with pertactin-deficient isolates and possibly part of a local outbreak. National expansion of the isolate-based surveillance system is needed to implement pertussis-control strategies.

Whole-Genome Sequencing to Detect Numerous Campylobacter jejuni Outbreaks and Match Patient Isolates to Sources, Denmark, 2015-2017

In industrialized countries, the leading cause of bacterial gastroenteritis is Campylobacter jejuni. However, outbreaks are rarely reported, which may reflect limitations of surveillance, for which molecular typing is not routinely performed. To determine the frequency of genetic clusters among patients and to find links to concurrent isolates from poultry meat, broiler chickens, cattle, pigs, and dogs, we performed whole-genome sequencing on 1,509 C. jejuni isolates from 774 patients and 735 food or animal sources in Denmark during 2015–2017. We found numerous clusters; 366/774 (47.3%) clinical isolates formed 104 clusters of >2 isolates. A total of 41 patient clusters representing 199/366 (54%) patients matched a potential source, primarily domestic chickens/broilers. This study revealed serial outbreaks and numerous matches to concurrent food and animal isolates and highlighted the potential of whole-genome sequencing for improving routine surveillance of C. jejuni by enhancing outbreak detection, source tracing, and potentially prevention of human infections.

Analysis of the Global Population Structure of Paenibacillus larvae and Outbreak Investigation of American Foulbrood Using a Stable wgMLST Scheme

Paenibacillus larvae causes the American foulbrood (AFB), a highly contagious and devastating disease of honeybees. Whole-genome sequencing (WGS) has been increasingly used in bacterial pathogen typing, but rarely applied to study the epidemiology of P. larvae. To this end, we used 125 P. larvae genomes representative of a species-wide diversity to construct a stable whole-genome multilocus sequence typing (wgMLST) scheme consisting of 5745 loci. A total of 51 P. larvae isolates originating from AFB outbreaks in Slovenia were used to assess the epidemiological applicability of the developed wgMLST scheme. In addition, wgMLST was compared with the core-genome MLST (cgMLST) and whole-genome single nucleotide polymorphism (wgSNP) analyses. All three approaches successfully identified clusters of outbreak-associated strains, which were clearly separated from the epidemiologically unlinked isolates. High levels of backward comparability of WGS-based analyses with conventional typing methods (ERIC-PCR and MLST) were revealed; however, both conventional methods lacked sufficient discriminatory power to separate the outbreak clusters. The developed wgMLST scheme provides an improved understanding of the intra- and inter-outbreak genetic diversity of P. larvae and represents an important progress in unraveling the genomic epidemiology of this important honeybee pathogen.

Genus-wide Yersinia core-genome multilocus sequence typing for species identification and strain characterization

The genus Yersinia comprises species that differ widely in their pathogenic potential and public-health significance. Yersinia pestis is responsible for plague, while Yersinia enterocolitica is a prominent enteropathogen. Strains within some species, including Y. enterocolitica, also vary in their pathogenic properties. Phenotypic identification of Yersinia species is time-consuming, labour-intensive and may lead to incorrect identifications. Here, we developed a method to automatically identify and subtype all Yersinia isolates from their genomic sequence. A phylogenetic analysis of Yersinia isolates based on a core subset of 500 shared genes clearly demarcated all existing Yersinia species and uncovered novel, yet undefined Yersinia taxa. An automated taxonomic assignment procedure was developed using species-specific thresholds based on core-genome multilocus sequence typing (cgMLST). The performance of this method was assessed on 1843 isolates prospectively collected by the French National Surveillance System and analysed in parallel using phenotypic reference methods, leading to nearly complete (1814; 98.4 %) agreement at species and infra-specific (biotype and serotype) levels. For 29 isolates, incorrect phenotypic assignments resulted from atypical biochemical characteristics or lack of phenotypic resolution. To provide an identification tool, a database of cgMLST profiles and reference taxonomic information has been made publicly accessible (https://bigsdb.pasteur.fr/yersinia). Genomic sequencing-based identification and subtyping of any Yersinia is a powerful and reliable novel approach to define the pathogenic potential of isolates of this medically important genus.