Dongxin Liu1, Fei Huang2, Guoliang Zhang3, Wencong He2, Xichao Ou2, Ping He2, Bing Zhao2, Baoli Zhu4, Fei Liu4, Zhiyuan Li4, Chunfa Liu2, Hui Xia2, Shengfen Wang2, Yang Zhou2, Timothy M Walker5, Lei Liu3, Derrick W Crook5, Yanlin Zhao6. 1. Chinese Centre for Disease Control and Prevention, Beijing, China; National Clinical Research Centre for Infectious Diseases, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China. 2. Chinese Centre for Disease Control and Prevention, Beijing, China. 3. National Clinical Research Centre for Infectious Diseases, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China. 4. Institute of Microbiology, Chinese Academy of Sciences, Beijing, China. 5. Nuffield Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK. 6. Chinese Centre for Disease Control and Prevention, Beijing, China. Electronic address: zhaoyl@chinacdc.cn.
Abstract
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 MICs of anti-TB drugs. METHODS: We used WGS to measure the susceptibility of 4880 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 was highly accurately predicted with at least 92.92% (95% confidence interval [CI], 88.19-97.65) sensitivity, resistance to pyrazinamide with 50.52% (95% CI, 40.57-60.47) sensitivity, and resistance to six second-line drugs with 85.05% (95% CI, 80.27-89.83) to 96.01% (95% CI, 93.89-98.13) sensitivity. The rpoB S450L, katG S315T and gyrA D94G mutations always confer high-level resistance, while rpoB L430P, rpoB L452P, fabG1 C-15T and embB G406S often confer low-level resistance or sub-epidemiological cutoff (ECOFF) MIC elevation. CONCLUSION: WGS can predict phenotypic susceptibility with high accuracy and could be a valuable tool for drug-resistance surveillance and allow the detection of drug-resistance level; It can be an important approach in TB drug-resistance surveillance and for determining therapeutic schemes. Crown
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 MICs of anti-TB drugs. METHODS: We used WGS to measure the susceptibility of 4880 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 was highly accurately predicted with at least 92.92% (95% confidence interval [CI], 88.19-97.65) sensitivity, resistance to pyrazinamide with 50.52% (95% CI, 40.57-60.47) sensitivity, and resistance to six second-line drugs with 85.05% (95% CI, 80.27-89.83) to 96.01% (95% CI, 93.89-98.13) sensitivity. The rpoB S450L, katG S315T and gyrA D94G mutations always confer high-level resistance, while rpoB L430P, rpoB L452P, fabG1 C-15T and embB G406S often confer low-level resistance or sub-epidemiological cutoff (ECOFF) MIC elevation. CONCLUSION: WGS can predict phenotypic susceptibility with high accuracy and could be a valuable tool for drug-resistance surveillance and allow the detection of drug-resistance level; It can be an important approach in TB drug-resistance surveillance and for determining therapeutic schemes. Crown
Authors: Iris Finci; Audrey Albertini; Matthias Merker; Sönke Andres; Nino Bablishvili; Ivan Barilar; Tatiana Cáceres; Valeriu Crudu; Eduardo Gotuzzo; Nchimunya Hapeela; Harald Hoffmann; Christine Hoogland; Thomas A Kohl; Katharina Kranzer; Anna Mantsoki; Florian P Maurer; Mark P Nicol; Ecaterina Noroc; Sara Plesnik; Timothy Rodwell; Morten Ruhwald; Theresa Savidge; Max Salfinger; Elizabeth Streicher; Nestani Tukvadze; Robin Warren; Widaad Zemanay; Anna Zurek; Stefan Niemann; Claudia M Denkinger Journal: Lancet Microbe Date: 2022-07-27