Validation of virulence and epidemiology DNA microarray for identification and characterization of Staphylococcus aureus isolates

Abstract

The human pathogen Staphylococcus aureus is isolated and characterized using traditional culture and sensitivity methodologies that are slow and offer limited information on the organism. In contrast, DNA microarray technology can provide detailed, clinically relevant information on the isolate by detecting the presence or absence of a large number of virulence-associated genes simultaneously in a single assay. We have developed and validated a novel, cost-effective multiwell microarray for the identification and characterization of Staphylococcus aureus. The array comprises 84 gene targets, including species-specific, antibiotic resistance, toxin, and other virulence-associated genes, and is capable of examining 13 different isolates simultaneously, together with a reference control strain. Analysis of S. aureus isolates whose complete genome sequences have been determined (Mu50, N315, MW2, MRSA252, MSSA476) demonstrated that the array can reliably detect the combination of genes known to be present in these isolates. Characterization of a further 43 S. aureus isolates by the microarray and pulsed-field gel electrophoresis has demonstrated the ability of the array to differentiate between isolates representative of a spectrum of S. aureus types, including methicillin-susceptible, methicillin-resistant, community-acquired, and vancomycin-resistant S. aureus, and to simultaneously detect clinically relevant virulence determinants.

FIG. 1.

Dendrogram of 43 S. aureus isolates examined by PFGE, produced with Bionumerics (version 2.0) software using the Dice coefficient and UPGMA. Isolates were clustered using the criteria of Tenover et al., where 80% similarity is the cutoff for differentiating closely related isolates (31). Clusters 1 through 7 are shown.

FIG. 2.

Dendrogram of 43 clinical S. aureus isolates examined by the VirEp microarray, produced with Bionumerics (version 2.0) software using the Dice coefficient and UPGMA. A cutoff value of 93.5% was used to distinguish genotypes. Genotypes A through E were distinguished.