Abstract
Pyrazinamide plays an important role in tuberculosis treatment; however, its use is complicated by side-effects and challenges with reliable drug susceptibility testing. Resistance to pyrazinamide is largely driven by mutations in pyrazinamidase (pncA), responsible for drug activation, but genetic heterogeneity has hindered development of a molecular diagnostic test. We proposed to use information on how variants were likely to affect the 3D structure of pncA to identify variants likely to lead to pyrazinamide resistance. We curated 610 pncA mutations with high confidence experimental and clinical information on pyrazinamide susceptibility. The molecular consequences of each mutation on protein stability, conformation, and interactions were computationally assessed using our comprehensive suite of graph-based signature methods, mCSM. The molecular consequences of the variants were used to train a classifier with an accuracy of 80%. Our model was tested against internationally curated clinical datasets, achieving up to 85% accuracy. Screening of 600 Victorian clinical isolates identified a set of previously unreported variants, which our model had a 71% agreement with drug susceptibility testing. Here, we have shown the 3D structure of pncA can be used to accurately identify pyrazinamide resistance mutations. SUSPECT-PZA is freely available at: http://biosig.unimelb.edu.au/suspect_pza/.
Highlights
Tuberculosis (TB), caused by Mycobacterium tuberculosis, is the leading cause of infectious disease death worldwide
PZA therapy has been linked to improved outcomes for both non-MDR and multi-drug-resistant tuberculosis (MDR-TB), and is being considered as part of the future regimens in combinations with bedaquiline, delamanid, PA-824 and moxifloxacin, which are currently in phase three trials[4,5]
PZA is a structural analog of nicotinamide and is a pro-drug that needs to be converted into its active form, pyrazinoic acid (POA), by the non-essential enzyme pyrazinamidase, encoded by the pncA gene[14,15]
Summary
Tuberculosis (TB), caused by Mycobacterium tuberculosis, is the leading cause of infectious disease death worldwide. In vitro drug susceptibility testing (DST) is challenging, expensive and time-consuming as PZA is effective against M. tuberculosis only at acidic pH, leading to false resistance rates of up to 70%7–13 This has led to the WHO recommending the development of molecular genetics tests. To solve the problem of a reliable DST for PZA, we previously showed that protein structural information can be used in a clinical setting to rapidly, accurately and pre-emptively predict drug resistant mutations in pncA23 This showed that mutations that affected protein folding, flexibility, stability and activity were strongly associated with resistance. We have used a comprehensive combination of structure and sequence-based features to develop a predictive tool to characterize novel PncA mutations, which we tested on novel mutations from the Victorian Tuberculosis Program, CRyPTIC24 and Miotto et al dataset[25] This highlights the potential of using structural information to guide the genetic detection of resistance. We have implemented our model through the webserver SUSPECT-PZA (http://biosig.unimelb.edu.au/suspect_pza/), which will enable the rapid structural evaluation of the molecular and phenotypic consequences of any pncA nonsynonymous mutation to support informed clinical decisions
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