Dataset Information

Overcoming the pitfalls of automatic interpretation of whole genome sequencing data by online tools for the prediction of pyrazinamide resistance in Mycobacterium tuberculosis.

ABSTRACT: OBJECTIVES:Automated online software tools that analyse whole genome sequencing (WGS) data without the need for bioinformatics expertise can motivate the implementation of WGS-based molecular drug susceptibility testing (DST) in routine diagnostic settings for tuberculosis (TB). Pyrazinamide (PZA) is a key drug for current and future TB treatment regimens; however, it was reported that predictive power for PZA resistance by the available tools is low. Therefore, this low predictive power may make users hesitant to use the tools. This study aimed to elucidate why and to uncover the real performance of the tools when taking into account their variation calling lists (manual inspection), not just their automated reporting system (default setting) that was evaluated by previous studies. METHODS:WGS data from 191 datasets comprising 108 PZA-resistant and 83 susceptible strains were used to evaluate the potential performance of the available online tools (TB Profiler, TGS-TB, PhyResSE, and CASTB) for predicting phenotypic PZA resistance. RESULTS:When taking into consideration the variation calling lists, 73 variants in total (47 non-synonymous mutations and 26 indels) in pncA were detected by TGS-TB and PhyResSE, covering all mutations for the 108 PZA-resistant strains. The 73 variants were confirmed by Sanger sequencing. TB Profiler also detected all but three complete loss, two large deletion at the 3'-end, and one relatively large insertion of pncA. On the other hand, many of the 73 variants were lacking in the automated reporting systems except by TGS-TB; of these variants, CASTB detected only 20. By applying the 'non-wild type sequence' approach for predicting PZA resistance, accuracy of the results significantly improved compared with that of the automated results obtained by each tool. CONCLUSION:Users can obtain more accurate predictions for PZA resistance than previously reported by manually checking the results and applying the 'non-wild type sequence' approach.

SUBMITTER: Iwamoto T

PROVIDER: S-EPMC6394917 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Overcoming the pitfalls of automatic interpretation of whole genome sequencing data by online tools for the prediction of pyrazinamide resistance in Mycobacterium tuberculosis.

Iwamoto Tomotada T Murase Yoshiro Y Yoshida Shiomi S Aono Akio A Kuroda Makoto M Sekizuka Tsuyoshi T Yamashita Akifumi A Kato Kengo K Takii Takemasa T Arikawa Kentaro K Kato Seiya S Mitarai Satoshi S

PloS one 20190228 2

<h4>Objectives</h4>Automated online software tools that analyse whole genome sequencing (WGS) data without the need for bioinformatics expertise can motivate the implementation of WGS-based molecular drug susceptibility testing (DST) in routine diagnostic settings for tuberculosis (TB). Pyrazinamide (PZA) is a key drug for current and future TB treatment regimens; however, it was reported that predictive power for PZA resistance by the available tools is low. Therefore, this low predictive power ...[more]

PMID: 30817803

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Project description: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.

Dataset Information

Overcoming the pitfalls of automatic interpretation of whole genome sequencing data by online tools for the prediction of pyrazinamide resistance in Mycobacterium tuberculosis.

Publications

Overcoming the pitfalls of automatic interpretation of whole genome sequencing data by online tools for the prediction of pyrazinamide resistance in Mycobacterium tuberculosis.

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