ArticleNature communications2023
Distributed genotyping and clustering of Neisseria strains reveal continual emergence of epidemic meningococcus over a century.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 11 citations in OpenAlex.
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- Molecular docking analysis of FAS II fromBioinformation · 2026Article
- Emergence and regional evolution of Klebsiella pneumoniae carbapenemase (KPC)-producing Klebsiella pneumoniae ST11 in China.BMC genomics · 2025Article
- CoDing Sequence Typer (CDST): a fast, simple, decentralized and interoperable solution for bacterial genomic typing and clustering.Microbial genomics · 2025Article
- Genome-wide approaches to bacterial strain typing: a history and review of recent methodological advances.Current opinion in infectious diseases · 2025Review
- Genetic landscape and evolution of Acinetobacter pittii, an underestimated emerging nosocomial pathogen.Communications biology · 2025Article
- Emergence and global spread of a dominant multidrug-resistant clade within Acinetobacter baumannii.Nature communications · 2025Article
- Current methods in the diagnosis of invasive meningococcal disease.Frontiers in pediatrics · 2025Review
- KleTy: integrated typing scheme for core genome and plasmids reveals repeated emergence of multi-drug resistant epidemic lineages in Klebsiella worldwide.Genome medicine · 2024Article
- Meningococcal meningitis in Spain in the Horizon 2030: A position paper.Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia · 2024Review
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Authors and funding
16 authors at 6 institutions in 2 countries.
Funding
Abstract
Core genome multilocus sequence typing (cgMLST) is commonly used to classify bacterial strains into different types, for taxonomical and epidemiological applications. However, cgMLST schemes require central databases for the nomenclature of new alleles and sequence types, which must be synchronized worldwide and involve increasingly intensive calculation and storage demands. Here, we describe a distributed cgMLST (dcgMLST) scheme that does not require a central database of allelic sequences and apply it to study evolutionary patterns of epidemic and endemic strains of the genus Neisseria. We classify 69,994 worldwide Neisseria strains into multi-level clusters that assign species, lineages, and local disease outbreaks. We divide Neisseria meningitidis into 168 endemic lineages and three epidemic lineages responsible for at least 9 epidemics in the past century. According to our analyses, the epidemic and endemic lineages experienced very different population dynamics in the past 100 years. Epidemic lineages repetitively emerged from endemic lineages, disseminated worldwide, and apparently disappeared rapidly afterward. We propose a stepwise model for the evolutionary trajectory of epidemic lineages in Neisseria, and expect that the development of similar dcgMLST schemes will facilitate epidemiological studies of other bacterial pathogens.
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