Neisseria meningitidis - Eurosurveillance

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Review articles Automated extraction of typing information for bacterial pathogens from whole genome sequence data: Neisseria meningitidis as an exemplar K A Jolley 1 , M C Maiden (martin.maiden@zoo.ox.ac.uk) 1 1. Department of Zoology, University of Oxford, Oxford, United Kingdom Citation style for this article: Jolley KA, Maiden MC. Automated extraction of typing information for bacterial pathogens from whole genome sequence data: Neisseria meningitidis as an exemplar. Euro Surveill. 2013;18(4):pii=20379. Available online: http://www.eurosurveillance.org/ViewArticle.aspx?ArticleId=20379 Whole genome sequence (WGS) data are increasingly used to characterise bacterial pathogens. These data provide detailed information on the genotypes and likely phenotypes of aetiological agents, enabling the relationships of samples from potential disease outbreaks to be established precisely. However, the generation of increasing quantities of sequence data does not, in itself, resolve the problems that many microbiological typing methods have addressed over the last 100 years or so; indeed, providing large volumes of unstructured data can confuse rather than resolve these issues. Here we review the nascent field of storage of WGS data for clinical application and show how curated sequence-based typing schemes on websites have generated an infrastructure that can exploit WGS for bacterial typing efficiently. We review the tools that have been implemented within the PubMLST website to extract clinically useful, straincharacterisation information that can be provided to physicians and public health professionals in a timely, concise and understandable way. These data can be used to inform medical decisions such as how to treat a patient, whether to instigate public health action, and what action might be appropriate. The information is compatible both with previous sequence-based typing data and also with data obtained in the absence of WGS, providing a flexible infrastructure for WGSbased clinical microbiology. Introduction The application of whole genome sequencing (WGS) technology to clinical microbiology has been described as revolutionary: the opportunities are certainly immense, but so too are the challenges of implementing this technology effectively [1]. Above all, clinical microbiology and epidemiology are pragmatic sciences, which require accurate and understandable information to be delivered to those who need to make medical judgements in real time. Often these judgements have to be made in the absence of complete information, and it is essential that widely understood, accepted and reproducible typing methods are employed to guide these decisions [2]. Just as the advent of molecular www.eurosurveillance.org Article submitted on 07 December 2012 / published on 24 January 2013 techniques challenged phenotypic methodologies over a decade ago – replacing imperfect but at least widely accepted techniques with a plethora of non-standardised alternatives [3] – the high volumes of sequence data have to be carefully managed if they are to provide enlightenment rather than confusion. The multilocus sequence typing (MLST) paradigm was established in 1998 [4], a time when molecular techniques were beginning to be widely used in the clinical laboratory, but when there was no universally agreed way forward [5]. It was intended as a standardised, reproducible and portable approach that could replace and enhance previous methods, particularly multilocus enzyme electrophoresis (MLEE) [6]. MLST was the first sequence-based approach to the genome-wide characterisation of bacterial isolates to be widely adopted and automated methods for performing the reactions and extracting the sequence information have subsequently been developed [7-9]. At the time MLST was introduced, it was impractical to sequence whole genomes on very large numbers of isolates and early analyses showed that in many cases this was not required. The first MLST scheme, for example, was designed to identify major clones within populations of Neisseria meningitidis, the meningococcus, and was able to do this reliably and reproducibly with just seven gene fragments, totalling only 3,284 bp or about 0.15% of the whole genome [10,11]. Similar numbers and sizes of loci have been successful for MLST schemes covering a wide range of organisms, which is an indication of the high degree of structuring present in many bacterial populations. For many bacteria, including the meningococcus, the extent of genetic diversity present even in this small number of genes under stabilising selection is extensive [12]: as of November 2012, each of the gene fragments used as meningococcal MLST loci had between 424 to 675 distinct alleles recorded on the PubMLST Neisseria website [13], with 54–94% (mean: 71%) sites that were polymorphic. Furthermore, in the representative abcZ locus, all four bases were present at a given site over the known population in 54/433 (12%) of the nucleotide positions (Figure 1). Much of this variation is at low frequency and transitory, but 29
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Acknowledgments We gratefully thank
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