Logo image
Methicillin-resistant Staphylococcus aureus genotyping using a small set of polymorphisms
Journal article   Peer reviewed

Methicillin-resistant Staphylococcus aureus genotyping using a small set of polymorphisms

A J Stephens, F Huygens, J Inman-Bamber, Erin P Price, G R Nimmo, J Schooneveldt, W Munckhof and P M Giffard
Journal of Medical Microbiology, Vol.55(1), pp.43-51
2006
url
https://doi.org/10.1099/jmm.0.46157-0View
Published Version

Abstract

The aim of this study was to identify a set of genetic polymorphisms that efficiently divides methicillin-resistant Staphylococcus aureus (MRSA) strains into groups consistent with the population structure. The rationale was that such polymorphisms could underpin rapid real-time PCR or low-density array-based methods for monitoring MRSA dissemination in a cost-effective manner. Previously, the authors devised a computerized method for identifying sets of single nucleotide polymorphisms (SNPs) with high resolving power that are defined by multilocus sequence typing (MLST) databases, and also developed a real-time PCR method for interrogating a seven-member SNP set for genotyping S. aureus. Here, it is shown that these seven SNPs efficiently resolve the major MRSA lineages and define 27 genotypes. The SNP-based genotypes are consistent with the MRSA population structure as defined by eBURST analysis. The capacity of binary markers to improve resolution was tested using 107 diverse MRSA isolates of Australian origin that encompass nine SNP-based genotypes. The addition of the virulence-associated genes cna, pvl and bbp/sdrE, and the integrated plasmids pT181, pI258 and pUB110, resolved the nine SNP-based genotypes into 21 combinatorial genotypes. Subtyping of the SCCmec locus revealed new SCCmec types and increased the number of combinatorial genotypes to 24. It was concluded that these polymorphisms provide a facile means of assigning MRSA isolates into well-recognized lineages. © 2006 SGM.

Details

Metrics

2 File views/ downloads
274 Record Views

InCites Highlights

These are selected metrics from InCites Benchmarking & Analytics tool, related to this output

Collaboration types
Domestic collaboration
Web Of Science research areas
Microbiology

UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

Source: InCites

Logo image