Stephens Alex J, Huygens Flavia, Giffard Philip M
Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, QLD, Australia.
Antimicrob Agents Chemother. 2007 Aug;51(8):2954-64. doi: 10.1128/AAC.01323-06. Epub 2007 May 21.
The aim of this study was to identify optimized sets of genotyping targets for the staphylococcal cassette chromosome mec (SCCmec). We analyzed the gene contents of 46 SCCmec variants in order to identify minimal subsets of targets that provide useful resolution. This was achieved by firstly identifying and characterizing each available SCCmec element based on the presence or absence of 34 binary targets. This information was used as input for the software "Minimum SNPs," which identifies the minimum number of targets required to differentiate a set of genotypes up to a predefined Simpson's index of diversity (D) value. It was determined that 22 of the 34 targets were required to genotype the 46 SCCmec variants to a D of 1. The first 6, 9, 12, and 15 targets were found to define 21, 29, 35, and 39 SCCmec variants, respectively. The genotypes defined by these marker subsets were largely consistent with the relationships between SCCmec variants and the accepted nomenclature. Consistency was made virtually complete by forcing the computer program to include ccr1 and ccr5 in the target set. An alternative target set biased towards discriminating abundant SCCmec variants was derived by analyzing an input file in which common SCCmec variants were repeated, thus ensuring that markers that discriminate abundant variants had a large effect on D. Finally, it was determined that mecA single nucleotide polymorphisms (SNPs) can increase the overall genotyping resolution, as different mecA alleles were found in otherwise identical SCCmec variants.
本研究的目的是确定葡萄球菌盒式染色体mec(SCCmec)基因分型靶点的优化集。我们分析了46种SCCmec变体的基因内容,以确定能提供有效分辨率的最小靶点子集。这首先通过基于34个二元靶点的有无来识别和表征每个可用的SCCmec元件来实现。该信息被用作软件“Minimum SNPs”的输入,该软件可识别将一组基因型区分到预定义的辛普森多样性指数(D)值所需的最小靶点数量。已确定对46种SCCmec变体进行基因分型至D值为1需要34个靶点中的22个。发现前6、9、12和15个靶点分别可定义21、29、35和39种SCCmec变体。这些标记子集所定义的基因型在很大程度上与SCCmec变体之间的关系以及公认的命名法一致。通过强制计算机程序将ccr1和ccr5纳入靶点集,一致性几乎达到了完全一致。通过分析一个重复常见SCCmec变体的输入文件,得出了一个偏向于区分丰富SCCmec变体的替代靶点集,从而确保区分丰富变体的标记对D值有很大影响。最后,确定mecA单核苷酸多态性(SNP)可提高整体基因分型分辨率,因为在其他方面相同的SCCmec变体中发现了不同的mecA等位基因。