The Xpert MTB/RIF assay (Xpert) is a rapid test for tuberculosis (TB) and rifampin resistance (RIF-R) suitable for point-of-care testing. However, it has decreased sensitivity in smear-negative sputum, and false identification of RIF-R occasionally occurs. We developed the Xpert MTB/RIF Ultra assay (Ultra) to improve performance. Ultra and Xpert limits of detection (LOD), dynamic ranges, and RIF-R rpoB mutation detection were tested on Mycobacterium tuberculosis DNA or sputum samples spiked with known numbers of M. tuberculosis H37Rv or Mycobacterium bovis BCG CFU. Frozen and prospectively collected clinical samples from patients suspected of having TB, with and without culture-confirmed TB, were also tested. For M. tuberculosis H37Rv, the LOD was 15.6 CFU/ml of sputum for Ultra versus 112.6 CFU/ml of sputum for Xpert, and for M. bovis BCG, it was 143.4 CFU/ml of sputum for Ultra versus 344 CFU/ml of sputum for Xpert. Ultra resulted in no false-positive RIF-R specimens, while Xpert resulted in two false-positive RIF-R specimens. All RIF-R-associated M. tuberculosis rpoB mutations tested were identified by Ultra. Testing on clinical sputum samples, Ultra versus Xpert, resulted in an overall sensitivity of 87.5% (95% confidence interval [CI], 82.1, 91.7) versus 81.0% (95% CI, 74.9, 86.2) and a sensitivity on sputum smear-negative samples of 78.9% (95% CI, 70.0, 86.1) versus 66.1% (95% CI, 56.4, 74.9). Both tests had a specificity of 98.7% (95% CI, 93.0, 100), and both had comparable accuracies for detection of RIF-R in these samples. Ultra should significantly improve TB detection, especially in patients with paucibacillary disease, and may provide more-reliable RIF-R detection.

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.KEYWORDS 10-color assay, three-phase PCR, XDR-TB, point-of-care test

Molecular surveillance of rifampin-resistant Mycobacterium tuberculosis can help to monitor the transmission of the disease. The Xpert MTB/RIF Ultra assay detects mutations in the rifampin resistance-determining region (RRDR) of the rpoB gene by the use of melting temperature (Tm) information from 4 rpoB probes which can fall in one of the 9 different assay-specified Tm windows. The large amount of Tm data generated by the assay offers the possibility of an RRDR genotyping approach more accessible than whole-genome sequencing. In this study, we developed an automated algorithm to specifically identify a wide range of mutations in the rpoB RRDR by utilizing the pattern of the Tm of the 4 probes within the 9 windows generated by the Ultra assay. The algorithm builds a RRDR mutation-specific “Tm signature” reference library from a set of known mutations and then identifies the RRDR genotype of an unknown sample by measuring the Tm distances between the test sample and the reference Tm values. Validated using a set of clinical isolates, the algorithm correctly identified RRDR genotypes of 93% samples with a wide range of rpoB single and double mutations. Our analytical approach showed a great potential for fast RRDR mutation identification and may also be used as a stand-alone method for ruling out relapse or transmission between patients. The algorithm can be further modified and optimized for higher accuracy as more Ultra data become available.