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Tuberculosis (Edinb). 2018 May;110:44-51. doi: 10.1016/j.tube.2018.03.009. Epub 2018 Mar 27.

Dissecting whole-genome sequencing-based online tools for predicting resistance in Mycobacterium tuberculosis: can we use them for clinical decision guidance?

Author information

1
National Reference Laboratory for Mycobacteria, Department of Infectious Diseases, National Institute of Health, Lisbon, Portugal. Electronic address: rita.macedo@insa.min-saude.pt.
2
Bioinformatics Unit, Department of Infectious Diseases, National Institute of Health, Lisbon, Portugal. Electronic address: alexandra.nunes@insa.min-saude.pt.
3
iMed.ULisboa-Research Institute for Medicines, University of Lisbon, Lisbon, Portugal. Electronic address: iportugal@ff.ulisboa.pt.
4
Innovation and Technology Unit, National Institute of Health, Lisbon, Portugal. Electronic address: silvia.duarte@insa.min-saude.pt.
5
Innovation and Technology Unit, National Institute of Health, Lisbon, Portugal; Centre for Toxicogenomics and Human Health (ToxOmics), Genetics, Oncology and Human Toxicology, Nova Medical School, New University of Lisbon, Lisbon, Portugal. Electronic address: luis.vieira@insa.min-saude.pt.
6
Bioinformatics Unit, Department of Infectious Diseases, National Institute of Health, Lisbon, Portugal. Electronic address: j.paulo.gomes@insa.min-saude.pt.

Abstract

Whole-genome sequencing (WGS)-based bioinformatics platforms for the rapid prediction of resistance will soon be implemented in the Tuberculosis (TB) laboratory, but their accuracy assessment still needs to be strengthened. Here, we fully-sequenced a total of 54 multidrug-resistant (MDR) and five susceptible TB strains and performed, for the first time, a simultaneous evaluation of the major four free online platforms (TB Profiler, PhyResSE, Mykrobe Predictor and TGS-TB). Overall, the sensitivity of resistance prediction ranged from 84.3% using Mykrobe predictor to 95.2% using TB profiler, while specificity was higher and homogeneous among platforms. TB profiler revealed the best performance robustness (sensitivity, specificity, PPV and NPV above 95%), followed by TGS-TB (all parameters above 90%). We also observed a few discrepancies between phenotype and genotype, where, in some cases, it was possible to pin-point some "candidate" mutations (e.g., in the rpsL promoter region) highlighting the need for their confirmation through mutagenesis assays and potential review of the anti-TB genetic databases. The rampant development of the bioinformatics algorithms and the tremendously reduced time-frame until the clinician may decide for a definitive and most effective treatment will certainly trigger the technological transition where WGS-based bioinformatics platforms could replace phenotypic drug susceptibility testing for TB.

KEYWORDS:

Multidrug-resistant tuberculosis; Mykrobe predictor; PhyResSE; TB profiler; TGS-TB; Whole-genome sequencing

PMID:
29779772
DOI:
10.1016/j.tube.2018.03.009
[Indexed for MEDLINE]

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