Xiaocui Wu1, Rongliang Gao2, Xin Shen3, Yinjuan Guo4, Jinghui Yang5, Zheyuan Wu6, Guangkun Tan7, Hongxiu Wang8, Fangyou Yu9. 1. Department of Clinical Laboratory, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China; Shanghai Key Laboratory of Tuberculosis, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China. Electronic address: wuxiaocui1210@163.com. 2. Department of Clinical Laboratory, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China; Shanghai Key Laboratory of Tuberculosis, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China. Electronic address: 1980gaooag@sina.com. 3. Shanghai Municipal Center for Disease Control and Prevention. Electronic address: shenxin@scdc.sh.cn. 4. Department of Clinical Laboratory, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China; Shanghai Key Laboratory of Tuberculosis, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China. Electronic address: 402873391@qq.com. 5. Department of Clinical Laboratory, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China; Shanghai Key Laboratory of Tuberculosis, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China. Electronic address: yangjh918@163.com. 6. Shanghai Municipal Center for Disease Control and Prevention. Electronic address: wuzheyuan@scdc.sh.cn. 7. Department of Clinical Laboratory, Shanghai University of Traditional Chinese Medical Attached Shuguang Hospital, Shanghai, China. Electronic address: tanguangkun1988@163.com. 8. Department of Clinical Laboratory, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China; Shanghai Key Laboratory of Tuberculosis, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China. Electronic address: polerterwang@163.com. 9. Department of Clinical Laboratory, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China; Shanghai Key Laboratory of Tuberculosis, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China. Electronic address: wzjxyfy@163.com.
Abstract
OBJECTIVE: To evaluate the performance of whole-genome sequencing (WGS) for predicting Mycobacterium tuberculosis (MTB) drug resistance. METHODS: 276 rifampin-resistance tuberculosis (RR-TB) and 30 rifampicin-sensitive clinical isolates were randomly selected from patients with tuberculosis in Shanghai Pulmonary Hospital (SPH). Phenotypic drug susceptibility testing (DST) against six anti-TB drugs was performed, and WGS was used to predict the drug resistance using an online 'TB-Profiler' tool. RESULTS: Using phenotypic susceptibility as the gold standard, the overall sensitivities and specificities for WGS were 94.53% and 92.00% for isoniazid, 97.10% and 100.00% for rifampicin, 97.46% and 64.36% for ethambutol, 97.14% and 95.83% or streptomycin, 93.02% and 98.87% for ofloxacin, and 75.00% and 100.00% for amikacin, respectively. The concordances of WGS-based DST and phenotypic DST were: isoniazid (94.12%), rifampicin (97.39%), ethambutol (77.12%), streptomycin (96.73%), ofloxacin (96.41%) and amikacin (97.06%). CONCLUSIONS: WGS could be a promising approach to predict resistance to isoniazid, rifampicin, ethambutol, streptomycin, ofloxacin, and amikacin.
OBJECTIVE: To evaluate the performance of whole-genome sequencing (WGS) for predicting Mycobacterium tuberculosis (MTB) drug resistance. METHODS: 276 rifampin-resistance tuberculosis (RR-TB) and 30 rifampicin-sensitive clinical isolates were randomly selected from patients with tuberculosis in Shanghai Pulmonary Hospital (SPH). Phenotypic drug susceptibility testing (DST) against six anti-TB drugs was performed, and WGS was used to predict the drug resistance using an online 'TB-Profiler' tool. RESULTS: Using phenotypic susceptibility as the gold standard, the overall sensitivities and specificities for WGS were 94.53% and 92.00% for isoniazid, 97.10% and 100.00% for rifampicin, 97.46% and 64.36% for ethambutol, 97.14% and 95.83% or streptomycin, 93.02% and 98.87% for ofloxacin, and 75.00% and 100.00% for amikacin, respectively. The concordances of WGS-based DST and phenotypic DST were: isoniazid (94.12%), rifampicin (97.39%), ethambutol (77.12%), streptomycin (96.73%), ofloxacin (96.41%) and amikacin (97.06%). CONCLUSIONS: WGS could be a promising approach to predict resistance to isoniazid, rifampicin, ethambutol, streptomycin, ofloxacin, and amikacin.
Authors: Matúš Dohál; Věra Dvořáková; Miluše Šperková; Martina Pinková; Andrea Spitaleri; Anders Norman; Andrea Maurizio Cabibbe; Erik Michael Rasmussen; Igor Porvazník; Mária Škereňová; Ivan Solovič; Daniela Maria Cirillo; Juraj Mokrý Journal: Sci Rep Date: 2022-05-03 Impact factor: 4.996