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Whole genome sequencing for drug resistance profile prediction in Mycobacterium tuberculosis


Gygli, Sebastian M; Keller, Peter M; Ballif, Marie; Blöchliger, Nicolas; Hömke, Rico; Reinhard, Miriam; Loiseau, Chloé; Ritter, Claudia; Sander, Peter; Borrell, Sonia; Loo, Jimena Collantes; Avihingsanon, Anchalee; Gnokoro, Joachim; Yotebieng, Marcel; Egger, Matthias; Gagneux, Sebastien; Böttger, Erik C (2019). Whole genome sequencing for drug resistance profile prediction in Mycobacterium tuberculosis. Antimicrobial Agents and Chemotherapy, 63(4):Epub ahead of print.

Abstract

Whole genome sequencing allows rapid detection of drug-resistant isolates. However, the availability of high-quality data linking quantitative phenotypic drug susceptibility testing (DST) and genomic data has thus far been limited.We determined drug resistance profiles of 176 genetically diverse clinical isolates from 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 73-99 % of cases, depending on the drug. Our data suggests that the established critical concentration (5 mg/L) for ethambutol resistance (MGIT 960 system) is too high and may misclassify strains as susceptible, compared to 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.

Abstract

Whole genome sequencing allows rapid detection of drug-resistant isolates. However, the availability of high-quality data linking quantitative phenotypic drug susceptibility testing (DST) and genomic data has thus far been limited.We determined drug resistance profiles of 176 genetically diverse clinical isolates from 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 73-99 % of cases, depending on the drug. Our data suggests that the established critical concentration (5 mg/L) for ethambutol resistance (MGIT 960 system) is too high and may misclassify strains as susceptible, compared to 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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Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Institute of Medical Microbiology
Dewey Decimal Classification:570 Life sciences; biology
610 Medicine & health
Language:English
Date:4 February 2019
Deposited On:15 Mar 2019 13:36
Last Modified:17 Sep 2019 20:15
Publisher:American Society for Microbiology
ISSN:0066-4804
OA Status:Green
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1128/AAC.02175-18
PubMed ID:30718257

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