Multilocus sequence typing for global surveillance of meningococcal disease.
The global surveillance of bacterial pathogens is particularly important for bacteria with diverse and dynamic populations that cause periodic epidemics or pandemics. The isolate characterization methods employed for surveillance should: (1) generate unambiguous data; (2) be readily implemented in a variety of scenarios and be reproducible among laboratories; (3) be scalable and preferably available in a high throughput format; and (4) be cost effective. Multilocus sequence typing (MLST) was designed to meet these criteria and has been implemented effectively for a wide range of microorganisms. The 'Impact of meningococcal epidemiology and population biology on public health in Europe (EU-MenNet)' project had amongst its objectives: (1) to disseminate meningococcal MLST and sequence-based typing throughout Europe by establishing a centre for training and data generation, and (2) to produce a comprehensive Europe-wide picture of meningococcal disease epidemiology for the first time. Data produced from the project have shown the distribution of a relatively small number of STs, clonal complexes and PorA types that account for a large proportion of the disease-associated isolates in Europe. The project demonstrates how molecular typing can be combined with epidemiological data via the Internet for global disease surveillance.
Brehony
Carina
C
The Peter Medawar Building for Pathogen Research and Department of Zoology, University of Oxford, Oxford, UK.
Jolley
Keith A
KA
Maiden
Martin C J
MC
D019295:Computational Biology / Q000379:methods*; D004269:DNA, Bacterial / Q000737:chemistry / Q000235:genetics; D030561:Databases, Nucleic Acid; D005060:Europe / Q000453:epidemiology; D006801:Humans; D008589:Meningococcal Infections / Q000175:diagnosis / Q000453:epidemiology / Q000382:microbiology*; D009345:Neisseria meningitidis / Q000145:classification* / Q000235:genetics; D016133:Polymerase Chain Reaction; D011159:Population Surveillance
D004269:DNA, Bacterial