cgMLST: Core Genome Typing for Outbreaks

Typing Outbreaks by the Whole Core Genome

The study: Moura A, Criscuolo A, Pouseele H, Maury MM, Leclercq A, Tarr C, Björkman JT, Dallman T, Reimer A, Enouf V, et al. 2016. “Whole genome-based population biology and epidemiological surveillance of Listeria monocytogenes.” Nature Microbiology 2:16185. DOI 10.1038/nmicrobiol.2016.185. Analyzing 1,696 Listeria monocytogenes isolates from clinical, food, and environmental sources, the team defined a core-genome MLST scheme — 1,748 loci — and showed it could structure the species’ population biology and power global epidemiological surveillance.

Classical MLST types bacteria by seven housekeeping genes — a huge advance in its day, but seven genes is a thin lens. cgMLST keeps the MLST idea (compare allele sequences at defined loci, assign portable type designations) and scales it to the core genome: the 1,748 genes shared by essentially all Listeria monocytogenes, distilled from a larger set of candidate core genes after the genes failing the scheme’s quality criteria were set aside. Instead of asking whether seven genes match, you ask whether seventeen hundred do. The resolution jump is enormous, and the nomenclature stays shareable between laboratories — the property that made MLST powerful, now at genomic scale and ready for routine surveillance use.

What the 1,696 isolates showed

The scale of the analysis is what made it definitive. Across nearly seventeen hundred isolates from around the world, the cgMLST scheme resolved the species into sublineages that corresponded to the clonal complexes of classical MLST — but with far finer discrimination within them. Isolates that conventional typing called identical split into distinct cgMLST types; isolates from different continents carrying the same cgMLST type flagged international clusters, the same strain turning up in food systems oceans apart.

That last finding is the surveillance payoff. A portable, sequence-based typing scheme means a laboratory in one country can compare its isolates against a global database and recognize that its cluster is part of something bigger — a contaminated ingredient shipped internationally, a piece of equipment sold to plants on two continents. Before cgMLST, those connections were nearly impossible to see; national databases spoke incompatible dialects. The paper created exactly that framework: a global, genome-based language for Listeria epidemiology, with the sublineage structure giving every isolate an interpretable address.

Why cgMLST won the typing wars

The method hit the sweet spot between two extremes. PFGE gave band patterns that were hard to standardize between labs — the same isolate could produce subtly different patterns in different hands, and exchanging patterns electronically was clumsy. Whole-genome SNP analysis gives maximal resolution but requires bioinformatics judgment calls (reference choice, filtering) that complicate sharing. cgMLST sits in the middle: gene-by-gene allele calls against a defined scheme, producing type designations as portable as MLST’s but with near-SNP resolution. For surveillance networks like PulseNet and its international counterparts, that combination — resolution plus shareability — is exactly what made cgMLST the workhorse of modern outbreak investigation.

For industry, the implication mirrors the WGS transition: the surveillance net now speaks cgMLST, and your environmental isolates get typed in the same language as the clinical cases. A persistent strain in your plant isn’t just “Listeria-positive” anymore — it’s a defined cgMLST type that can be matched against the global database, tracked across your facilities, and linked to illnesses with a precision PFGE never offered. When regulators ask whether your environmental isolate matches the outbreak strain, the answer now comes back as a number of allelic differences across 1,748 loci — quantitative, reproducible, and very hard to argue with. The Moura paper is the reference that built the dictionary.

Source: Moura A et al. 2016. Whole genome-based population biology and epidemiological surveillance of Listeria monocytogenes. Nature Microbiology 2:16185.