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OBJECTIVES: Phenotypic drug susceptibility testing for prediction of tuberculosis (TB) drug resistance is slow and unreliable, limiting individualized therapy and monitoring of national TB data. Our study evaluated whole genome sequencing (WGS) for its predictive accuracy, use in TB drug-resistance surveillance, and ability to quantify the effects of resistance-associated mutations on minimum inhibitory concentrations (MICs) of anti-TB drugs. METHODS: We used WGS to measure the susceptibility of 4,880 isolates to ten anti-TB drugs; for pyrazinamide, we used BACTEC MGIT 960. We determined the accuracy of WGS by comparing the prevalence of drug resistance, measured by WGS, with the true prevalence, determined by phenotypic susceptibility testing. We used the Student-Newman-Keuls test to confirm MIC differences of mutations. RESULTS: Resistance to isoniazid, rifampin, and ethambutol were highly accurately predicted with at least 92.29% sensitivity, resistance to pyrazinamide with 50.52% sensitivity, and resistance to six second-line drugs with 85.05% to 96.01% sensitivity. In addition to the large overlap in estimated drug resistance prevalence by WGS and phenotypic testing, WGS can detect low-level resistant or sub-ECOFF mutations which may be missed by phenotyping. For nearly all drugs, resistance-conferring mutations had varying levels of impact on MICs. CONCLUSION: WGS can predict phenotypic susceptibility with high accuracy and be a valuable tool for drug resistance surveillance and allow for detection of drug resistant level; as such, it can be a important approach in TB drug resistance surveillance and for determining the therapeutic schemes.

Original publication

DOI

10.1016/j.cmi.2021.09.014

Type

Journal article

Journal

Clin Microbiol Infect

Publication Date

29/09/2021

Keywords

Drug-resistance, Minimum inhibitory concentration, Tuberculosis, Wholegenome sequencing, surveillance