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Antimicrobial agents and chemotherapy

Evaluation of pyrosequencing for detecting extensively drug-resistant Mycobacterium tuberculosis among clinical isolates from four high-burden countries.


PMID 25367911

Abstract

Reliable molecular diagnostics, which detect specific mutations associated with drug resistance, are promising technologies for the rapid identification and monitoring of drug resistance in Mycobacterium tuberculosis isolates. Pyrosequencing (PSQ) has the ability to detect mutations associated with first- and second-line anti-tuberculosis (TB) drugs, with the additional advantage of being rapidly adaptable for the identification of new mutations. The aim of this project was to evaluate the performance of PSQ in predicting phenotypic drug resistance in multidrug- and extensively drug-resistant tuberculosis (M/XDR-TB) clinical isolates from India, South Africa, Moldova, and the Philippines. A total of 187 archived isolates were run through a PSQ assay in order to identify M. tuberculosis (via the IS6110 marker), and to detect mutations associated with M/XDR-TB within small stretches of nucleotides in selected loci. The molecular targets included katG, the inhA promoter and the ahpC-oxyR intergenic region for isoniazid (INH) resistance; the rpoB core region for rifampin (RIF) resistance; gyrA for fluoroquinolone (FQ) resistance; and rrs for amikacin (AMK), capreomycin (CAP), and kanamycin (KAN) resistance. PSQ data were compared to phenotypic mycobacterial growth indicator tube (MGIT) 960 drug susceptibility testing results for performance analysis. The PSQ assay illustrated good sensitivity for the detection of resistance to INH (94%), RIF (96%), FQ (93%), AMK (84%), CAP (88%), and KAN (68%). The specificities of the assay were 96% for INH, 100% for RIF, FQ, AMK, and KAN, and 97% for CAP. PSQ is a highly efficient diagnostic tool that reveals specific nucleotide changes associated with resistance to the first- and second-line anti-TB drug medications. This methodology has the potential to be linked to mutation-specific clinical interpretation algorithms for rapid treatment decisions.

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