Core Genome Multilocus Sequence Typing for Identification of Globally Distributed Clonal Groups and Differentiation of Outbreak Strains of Listeria monocytogenes

Abstract

Many listeriosis outbreaks are caused by a few globally distributed clonal groups, designated clonal complexes or epidemic clones, of Listeria monocytogenes, several of which have been defined by classic multilocus sequence typing (MLST) schemes targeting 6 to 8 housekeeping or virulence genes. We have developed and evaluated core genome MLST (cgMLST) schemes and applied them to isolates from multiple clonal groups, including those associated with 39 listeriosis outbreaks. The cgMLST clusters were congruent with MLST-defined clonal groups, which had various degrees of diversity at the whole-genome level. Notably, cgMLST could distinguish among outbreak strains and epidemiologically unrelated strains of the same clonal group, which could not be achieved using classic MLST schemes. The precise selection of cgMLST gene targets may not be critical for the general identification of clonal groups and outbreak strains. cgMLST analyses further identified outbreak strains, including those associated with recent outbreaks linked to contaminated French-style cheese, Hispanic-style cheese, stone fruit, caramel apple, ice cream, and packaged leafy green salad, as belonging to major clonal groups. We further developed lineage-specific cgMLST schemes, which can include accessory genes when core genomes do not possess sufficient diversity, and this provided additional resolution over species-specific cgMLST. Analyses of isolates from different common-source listeriosis outbreaks revealed various degrees of diversity, indicating that the numbers of allelic differences should always be combined with cgMLST clustering and epidemiological evidence to define a listeriosis outbreak. IMPORTANCE Classic multilocus sequence typing (MLST) schemes targeting internal fragments of 6 to 8 genes that define clonal complexes or epidemic clones have been widely employed to study L. monocytogenes biodiversity and its relation to pathogenicity potential and epidemiology. We demonstrated that core genome MLST schemes can be used for the simultaneous identification of clonal groups and the differentiation of individual outbreak strains and epidemiologically unrelated strains of the same clonal group. We further developed lineage-specific cgMLST schemes that targeted more genomic regions than the species-specific cgMLST schemes. Our data revealed the genome-level diversity of clonal groups defined by classic MLST schemes. Our identification of U.S. and international outbreaks caused by major clonal groups can contribute to further understanding of the global epidemiology of L. monocytogenes.

Recent Technology of Clinical Microbiology; Whole Genome Sequencing (WGS) and Matrix-Assisted, Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS)

“…The cgMLST approach uses in silico analysis which analyzes more than dozens of housekeeping genes, and is an SNP-based technique, which results in higher discriminatory power and easier standardization. SeqSphere+ bioinformatic software (Ridom GmbH) has been used for cgMLST [5]. In outbreak cases, the specific ST can be determined through wgMLST, cgMLST or rMLST and rare STs can be detected owing to the high discriminatory power of the SNP assay.…”

“…Resistome analysis, analysis of horizontal transfer of mobile genomic islands, and recombination analysis greatly assist the development of antimicrobial therapeutics, particularly in the context of multidrug-resistant (MDR) strains containing extended-spectrum betalactamases (ESBLs) or carbapenemases [4]. Moreover, transmission tracking is enabled by phylogenetic mapping, and the analysis of clonality or lineage support infection surveillance and control [5,6].…”

Commentary: Next-Generation Epidemiology: Using Real-Time Core Genome Multilocus Sequence Typing To Support Infection Control Policy

“…The cgMLST methods address this problem by comparing only genes common to a set of bacterial isolates (2). These methods are now mature and have been applied successfully to the analysis of a number of bacterial outbreaks (8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21).Given the developments in WGS-based strain typing described above, there is much excitement about applying these methods to real-time hospital epidemiology, but the actual cost-benefit equation, the feasibility of achieving results on an actionable time scale, and the technical requirements for implementation in a "real world" clinical microbiology laboratory are difficult to study. In work published in this issue of the Journal of Clinical Microbiology, …”

“…The cgMLST methods address this problem by comparing only genes common to a set of bacterial isolates (2). These methods are now mature and have been applied successfully to the analysis of a number of bacterial outbreaks (8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21).…”

Molecular typing of bacteria for epidemiological surveillance and outbreak investigation / Molekulare Typisierung von Bakterien für die epidemiologische Überwachung und Ausbruchsabklärung

“…Several studies on a variety of bacterial species have already shown that WGS-based typing, based either on single nucleotide variants (SNVs) (Turabelidze et al, 2013;Pightling et al, 2015) or on gene-by-gene allelic profiling of core genome genes, frequently named core genome MLST (cgMLST) or MLST + (Laing et al, 2010;Mellmann et al, 2011;Köser et al, 2012;Maiden et al, 2013;Antwerpen et al, 2015;de Been et al, 2015;Moran-Gilad et al, 2015;Chaudhari et al, 2016;Moura et al, 2016), currently represents the ultimate diagnostic typing tool that have been successfully applied for outbreak investigations (Figure 9) (den Bakker et al, 2014;Schmid et al, 2014;Ruppitsch et al, 2015b; Lepuschitz, 2015). Molecular typing of bacteria for epidemiological surveillance and outbreak investigation Burckhardt et al, 2016;Chen et al, 2016;Jackson et al, 2016). Backward compatibility of NGS to former methods can be maintained by the extraction of the respective information from NGS data (Hyden et al, 2016).…”