Detection of Isoniazid-, Fluoroquinolone-, Amikacin-, and Kanamycin-Resistant Tuberculosis in an Automated, Multiplexed 10-Color Assay Suitable for Point-of-Care Use.

ABSTRACT

Extensively drug-resistant (XDR) tuberculosis (TB) cannot be easily or quickly diagnosed. We developed a rapid, automated assay for the detection of XDR-TB plus resistance to the drug isoniazid (INH) for point-of-care use. Using a simple filter-based cartridge with an integrated sample processing function, the assay identified a wide selection of wild-type and mutant sequences associated with XDR-TB directly from sputum. Four new large-Stokes-shift fluorophores were developed. When these four Stokes-shift fluorophores were combined with six conventional fluorophores, 10-color probe detection in a single PCR tube was enabled. A new three-phase, double-nested PCR approach allowed robust melting temperature analysis with enhanced limits of detection (LODs). Finally, newly designed sloppy molecular beacons identified many different mutations using a small number of probes. The assay correctly distinguished wild-type sequences from 32 commonly occurring mutant sequences tested in gyrA, gyrB, katG, and rrs genes and the promoters of inhA and eis genes responsible for resistance to INH, the fluoroquinolone (FQ) drugs, amikacin (AMK), and kanamycin (KAN). The LOD was 300 CFU of Mycobacterium tuberculosis in 1 ml sputum. The rate of detection of heteroresistance by the assay was equivalent to that by Sanger sequencing. In a blind study of 24 clinical sputum samples, resistance mutations were detected in all targets with 100% sensitivity, with the specificity being 93.7 to 100%. Compared to the results of phenotypic susceptibility testing, the sensitivity of the assay was 75% for FQs and 100% each for INH, AMK, and KAN and the specificity was 100% for INH and FQ and 94% for AMK and KAN. Our approach could enable testing for XDR-TB in point-of-care settings, potentially identifying highly drug-resistant TB more quickly and simply than currently available methods.