The Utility of High-Resolution Melting Analysis of SNP Nucleated PCR Amplicons-An MLST Based Staphylococcus aureus Typing Scheme
AuthorLilliebridge, RA; Tong, SYC; Giffard, PM; Holt, DC
Source TitlePLoS One
PublisherPUBLIC LIBRARY SCIENCE
University of Melbourne Author/sTong, Steven
Document TypeJournal Article
CitationsLilliebridge, R. A., Tong, S. Y. C., Giffard, P. M. & Holt, D. C. (2011). The Utility of High-Resolution Melting Analysis of SNP Nucleated PCR Amplicons-An MLST Based Staphylococcus aureus Typing Scheme. PLOS ONE, 6 (6), https://doi.org/10.1371/journal.pone.0019749.
Access StatusOpen Access
High resolution melting (HRM) analysis is gaining prominence as a method for discriminating DNA sequence variants. Its advantage is that it is performed in a real-time PCR device, and the PCR amplification and HRM analysis are closed tube, and effectively single step. We have developed an HRM-based method for Staphylococcus aureus genotyping. Eight single nucleotide polymorphisms (SNPs) were derived from the S. aureus multi-locus sequence typing (MLST) database on the basis of maximized Simpson's Index of Diversity. Only G↔A, G↔T, C↔A, C↔T SNPs were considered for inclusion, to facilitate allele discrimination by HRM. In silico experiments revealed that DNA fragments incorporating the SNPs give much higher resolving power than randomly selected fragments. It was shown that the predicted optimum fragment size for HRM analysis was 200 bp, and that other SNPs within the fragments contribute to the resolving power. Six DNA fragments ranging from 83 bp to 219 bp, incorporating the resolution optimized SNPs were designed. HRM analysis of these fragments using 94 diverse S. aureus isolates of known sequence type or clonal complex (CC) revealed that sequence variants are resolved largely in accordance with G+C content. A combination of experimental results and in silico prediction indicates that HRM analysis resolves S. aureus into 268 "melt types" (MelTs), and provides a Simpson's Index of Diversity of 0.978 with respect to MLST. There is a high concordance between HRM analysis and the MLST defined CCs. We have generated a Microsoft Excel key which facilitates data interpretation and translation between MelT and MLST data. The potential of this approach for genotyping other bacterial pathogens was investigated using a computerized approach to estimate the densities of SNPs with unlinked allelic states. The MLST databases for all species tested contained abundant unlinked SNPs, thus suggesting that high resolving power is not dependent upon large numbers of SNPs.
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