Dataset Information

In Silico Prediction of Antibiotic Resistance in Mycobacterium ulcerans Agy99 through Whole Genome Sequence Analysis.

ABSTRACT: Buruli ulcer is an emerging infectious disease caused by Mycobacterium ulcerans that has been reported from 33 countries. Antimicrobial agents either alone or in combination with surgery have been proved to be clinically relevant and therapeutic strategies have been deduced mainly from the empirical experience. The genome sequences of M. ulcerans strain AGY99, M. ulcerans ecovar liflandii, and three Mycobacterium marinum strains were analyzed to predict resistance in these bacteria. Fourteen putative antibiotic resistance genes from different antibiotics classes were predicted in M. ulcerans and mutation in katG (R431G) and pncA (T47A, V125I) genes were detected, that confer resistance to isoniazid and pyrazinamide, respectively. No mutations were detected in rpoB, gyrA, gyrB, rpsL, rrs, emb, ethA, 23S ribosomal RNA genes and promoter region of inhA and ahpC genes associated with resistance. Our results reemphasize the usefulness of in silico analysis for the prediction of antibiotic resistance in fastidious bacteria.

SUBMITTER: Gupta SK

PROVIDER: S-EPMC5590560 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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In Silico Prediction of Antibiotic Resistance in Mycobacterium ulcerans Agy99 through Whole Genome Sequence Analysis.

Gupta Sushim Kumar SK Drancourt Michel M Rolain Jean-Marc JM

The American journal of tropical medicine and hygiene 20170727 3

Buruli ulcer is an emerging infectious disease caused by Mycobacterium ulcerans that has been reported from 33 countries. Antimicrobial agents either alone or in combination with surgery have been proved to be clinically relevant and therapeutic strategies have been deduced mainly from the empirical experience. The genome sequences of M. ulcerans strain AGY99, M. ulcerans ecovar liflandii, and three Mycobacterium marinum strains were analyzed to predict resista ...[more]

PMID: 28749770

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Project description:Two billion people are infected with Mycobacterium tuberculosis, leading to 10 million new cases of active tuberculosis and 1.5 million deaths annually. Universal access to drug susceptibility testing (DST) has become a World Health Organization priority. We previously developed a software tool, Mykrobe predictor, which provided offline species identification and drug resistance predictions for M. tuberculosis from whole genome sequencing (WGS) data. Performance was insufficient to support the use of WGS as an alternative to conventional phenotype-based DST, due to mutation catalogue limitations. Here we present a new tool, Mykrobe, which provides the same functionality based on a new software implementation. Improvements include i) an updated mutation catalogue giving greater sensitivity to detect pyrazinamide resistance, ii) support for user-defined resistance catalogues, iii) improved identification of non-tuberculous mycobacterial species, and iv) an updated statistical model for Oxford Nanopore Technologies sequencing data. Mykrobe is released under MIT license at https://github.com/mykrobe-tools/mykrobe. We incorporate mutation catalogues from the CRyPTIC consortium et al. (2018) and from Walker et al. (2015), and make improvements based on performance on an initial set of 3206 and an independent set of 5845 M. tuberculosis Illumina sequences. To give estimates of error rates, we use a prospectively collected dataset of 4362 M. tuberculosis isolates. Using culture based DST as the reference, we estimate Mykrobe to be 100%, 95%, 82%, 99% sensitive and 99%, 100%, 99%, 99% specific for rifampicin, isoniazid, pyrazinamide and ethambutol resistance prediction respectively. We benchmark against four other tools on 10207 (=5845+4362) samples, and also show that Mykrobe gives concordant results with nanopore data. We measure the ability of Mykrobe-based DST to guide personalized therapeutic regimen design in the context of complex drug susceptibility profiles, showing 94% concordance of implied regimen with that driven by phenotypic DST, higher than all other benchmarked tools.

Dataset Information

In Silico Prediction of Antibiotic Resistance in Mycobacterium ulcerans Agy99 through Whole Genome Sequence Analysis.

Publications

In Silico Prediction of Antibiotic Resistance in <i>Mycobacterium ulcerans</i> Agy99 through Whole Genome Sequence Analysis.

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