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Whole-Genome Sequencing for Drug Resistance Profile Prediction in Mycobacterium tuberculosis
Author(s) -
Sebastian M. Gygli,
Peter M. Keller,
Marie Ballif,
Nicolas Blöchliger,
Rico Hömke,
Miriam Reinhard,
Chloé Loiseau,
Claudia Ritter,
Peter Sander,
Sònia Borrell,
Jimena Collantes Loo,
Anchalee Avihingsa,
Joachim Gnokoro,
Marcel Yotebieng,
Matthias Egger,
Sébastien Gagneux,
Erik C. Böttger
Publication year - 2019
Publication title -
antimicrobial agents and chemotherapy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.07
H-Index - 259
eISSN - 1070-6283
pISSN - 0066-4804
DOI - 10.1128/aac.02175-18
Subject(s) - mycobacterium tuberculosis , tuberculosis , drug resistance , biology , whole genome sequencing , genome , dna sequencing , mycobacterium tuberculosis complex , computational biology , genetics , microbiology and biotechnology , gene , medicine , pathology
Whole-genome sequencing allows rapid detection of drug-resistant Mycobacterium tuberculosis isolates. However, the availability of high-quality data linking quantitative phenotypic drug susceptibility testing (DST) and genomic data have thus far been limited. We determined drug resistance profiles of 176 genetically diverse clinical M. tuberculosis isolates from the Democratic Republic of the Congo, Ivory Coast, Peru, Thailand, and Switzerland by quantitative phenotypic DST for 11 antituberculous drugs using the BD Bactec MGIT 960 system and 7H10 agar dilution to generate a cross-validated phenotypic DST readout. We compared DST results with predicted drug resistance profiles inferred by whole-genome sequencing. Classification of strains by the two phenotypic DST methods into resistotype/wild-type populations was concordant in 73 to 99% of cases, depending on the drug. Our data suggest that the established critical concentration (5 mg/liter) for ethambutol resistance (MGIT 960 system) is too high and misclassifies strains as susceptible, unlike 7H10 agar dilution. Increased minimal inhibitory concentrations were explained by mutations identified by whole-genome sequencing. Using whole-genome sequences, we were able to predict quantitative drug resistance levels for the majority of drug resistance mutations. Predicting quantitative levels of drug resistance by whole-genome sequencing was partially limited due to incompletely understood drug resistance mechanisms. The overall sensitivity and specificity of whole-genome-based DST were 86.8% and 94.5%, respectively. Despite some limitations, whole-genome sequencing has the potential to infer resistance profiles without the need for time-consuming phenotypic methods.

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