Management of early meningococcal disease

Multilocus sequence typing

Multilocus sequence typing (MLST) was first proposed in 1998 as a typing approach that enables the unambiguous characterization of bacterial isolates in a standardized, reproducible, and portable manner using the human pathogen Neisseria meningitidis as the exemplar organism. Since then, the approach has been applied to a large and growing number of organisms by public health laboratories and research institutions. MLST data, shared by investigators over the world via the Internet, have been successfully exploited in applications ranging from molecular epidemiological investigations to population biology and evolutionary analyses. This chapter describes the practical steps in the development and application of an MLST scheme and some of the common tools and techniques used to obtain the maximum benefit from the data. Considerations pertinent to the implementation of high-capacity MLST projects (i.e., those involving thousands of isolates) are discussed.

Multilocus sequence typing of bacteria

Multilocus sequence typing (MLST) was proposed in 1998 as a portable, universal, and definitive method for characterizing bacteria, using the human pathogen Neisseria meningitidis as an example. In addition to providing a standardized approach to data collection, by examining the nucleotide sequences of multiple loci encoding housekeeping genes, or fragments of them, MLST data are made freely available over the Internet to ensure that a uniform nomenclature is readily available to all those interested in categorizing bacteria. At the time of writing, over thirty MLST schemes have been published and made available on the Internet, mostly for pathogenic bacteria, although there are schemes for pathogenic fungi and some nonpathogenic bacteria. MLST data have been employed in epidemiological investigations of various scales and in studies of the population biology, pathogenicity, and evolution of bacteria. The increasing speed and reduced cost of nucleotide sequence determination, together with improved web-based databases and analysis tools, present the prospect of increasingly wide application of MLST.