Pyrazinamide (PZA) is a prodrug that is converted to pyrazinoic acid by the enzyme pyrazinamidase, encoded by the pncA gene in Mycobacterium tuberculosis. Molecular identification of mutations in pncA offers the potential for rapid detection of pyrazinamide resistance (PZA(r)). However, the genetic variants are highly variable and scattered over the full length of pncA, complicating the development of a molecular test. We performed a large multicenter study assessing pncA sequence variations in 1,950 clinical isolates, including 1,142 multidrug-resistant (MDR) strains and 483 fully susceptible strains. The results of pncA sequencing were correlated with phenotype, enzymatic activity, and structural and phylogenetic data. We identified 280 genetic variants which were divided into four classes: (i) very high confidence resistance mutations that were found only in PZA(r) strains (85%), (ii) high-confidence resistance mutations found in more than 70% of PZA(r) strains, (iii) mutations with an unclear role found in less than 70% of PZA(r) strains, and (iv) mutations not associated with phenotypic resistance (10%). Any future molecular diagnostic assay should be able to target and identify at least the very high and high-confidence genetic variant markers of PZA(r); the diagnostic accuracy of such an assay would be in the range of 89.5 to 98.8%. Importance: Conventional phenotypic testing for pyrazinamide resistance in Mycobacterium tuberculosis is technically challenging and often unreliable. The development of a molecular assay for detecting pyrazinamide resistance would be a breakthrough, directly overcoming both the limitations of conventional testing and its related biosafety issues. Although the main mechanism of pyrazinamide resistance involves mutations inactivating the pncA enzyme, the highly diverse genetic variants scattered over the full length of the pncA gene and the lack of a reliable phenotypic gold standard hamper the development of molecular diagnostic assays. By analyzing a large number of strains collected worldwide, we have classified the different genetic variants based on their predictive value for resistance which should lead to more rapid diagnostic tests. This would assist clinicians in improving treatment regimens for patients.
Copyright © 2014 Miotto et al.