Nov. 4, 2004 Potential antimicrobial resistance in the bacteria that cause gonorrhea can be detected without culturing the organism, thanks to a rapid test developed by researchers at Johns Hopkins.
Key to the usefulness of the new test is that it does not require collection, culture or testing of the bacteria themselves - called Neisseria gonorrhea. Instead, the genes linked to resistance can be identified in urine samples or in leftover products from other commonly used diagnostic techniques, the Hopkins team reports.
This new application of probe technology should help public health officials study the spread of antibiotic-resistant gonorrhea by simplifying analysis of samples that cannot be used for culturing organisms, the Hopkins group added.
The Hopkins team developed the test by using an existing diagnostic technology called nucleic acid amplification tests (NAATs). These are FDA-approved tests that detect gonorrhea DNA in urine samples. Using leftover DNA from NAATs, the team performed a polymerase chain reaction (PCR) to make copies of genes linked to resistance. They then performed a melt curve analysis to detect mutations in these areas. In melt curve analysis, a short DNA sequence that matches the bacterial sequence in question is labeled with a fluorescent dye. The labeled sequence, called a probe, emits light only when bound to its target.
The probe and the DNA copies produced by PCR are dissolved together and the solution is slowly heated and cooled. This lets the probe bind to its target. Then the solution is slowly heated until the probe is melted from its target. A special instrument measures the temperature at which the probe melts from the DNA by identifying the temperature at which the probe ceases to emit light. The probe melts at a lower temperature if the target contains mutations and at a higher temperature if there are no mutations in that region of the gene. As a result, the lower melt temperature indicates potential resistance.
Rapid detection and characterization of gonococcal resistance determinants in NAAT samples. Julie Giles, Justin Hardick, Jeffrey Yuenger, Charlotte Gaydos, Jonathan Zenilman
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