Multiplex real-time SYBR Green I PCR assay for detection of tetracycline efflux genes of Gram-negative bacteria

2007 
Abstract In an effort to find a rapid, efficient, and reliable method for screening and classifying large numbers of tetracycline-resistant bacterial isolates, we developed a multiplex, real-time PCR assay using SYBR Green I and the Roche LightCycler. The assay can rapidly identify eight genes encoding tetracycline resistance efflux pumps including tet (A), tet (B), tet (C), tet (D), tet (E), tet (G), tet (H) and tet (J). Primers were selected for PCR amplification of these eight tetracycline resistance determinant ( tet ) genes commonly found in Gram-negative organisms. We combined primer pairs together to make a single-tube multiplex PCR reaction followed by melting curve analysis. Amplification of the expected tet gene products was confirmed by both agarose gel electrophoresis and DNA sequence analysis. Based on melting temperature differences, we could identify the different classes of tet genes. To test the multiplex PCR, the assay was used on 107 tetracycline-resistant clinical isolates of various Gram-negative organisms isolated in several locations around the world. About 49.5% of those strains carried a tet (A) gene, 35.5% carried a tet (B), 7.5% carried a tet (J), 5.6% carried a tet (C) and 1.9% carried a tet (D) gene. DNA sequence analysis of the amplicons confirmed that the specificity of the test was 100%. The sensitivity of the multiplex test varied from 10 to 1000 CFU per PCR reaction. Our real time PCR assay utilizing SYBR Green I and melting point analysis on the Lightcycler system showed not only a high confidence level in differentiation of the classes of tet genes but also precise reproducibility. Our multiplex PCR tet gene class identification assay offers a significant savings of time and labor in the analysis of large numbers of clinical strains compared with assays using individual gene PCR or traditional phenotype methods.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    32
    References
    47
    Citations
    NaN
    KQI
    []