Evaluation of genetic correlation with fluoroquinolones resistance in rifampicin-resistant Mycobacterium tuberculosis isolates

Chong Teng; Ling Li; Dan Su; Hui Li; Bing Zhao; Hui Xia; Hui Teng; Yuanyuan Song; Yang Zheng; Xiaolong Cao; Huiwen Zheng; Yanlin Zhao; Xichao Ou

Heliyon (Jun 2024)

Evaluation of genetic correlation with fluoroquinolones resistance in rifampicin-resistant Mycobacterium tuberculosis isolates

Chong Teng,
Ling Li,
Dan Su,
Hui Li,
Bing Zhao,
Hui Xia,
Hui Teng,
Yuanyuan Song,
Yang Zheng,
Xiaolong Cao,
Huiwen Zheng,
Yanlin Zhao,
Xichao Ou

Affiliations

Chong Teng: Department of Tuberculosis, Beijing Dongcheng District Center for Disease Control and Prevention, Beijing, 100050, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China; Chinese Field Epidemiology Training Program, Chinese Center for Disease Control and Prevention, Beijing, 100050, China
Ling Li: Department of Clinical Laboratory, Ya'an People's Hospital, Sichuan, 625000, China
Dan Su: Department of Pathology, Capital Medical University Affiliated Beijing Chest Hospital, Beijing, 101149, China
Hui Li: Department of Tuberculosis, Beijing Dongcheng District Center for Disease Control and Prevention, Beijing, 100050, China
Bing Zhao: National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
Hui Xia: National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
Hui Teng: Centre of Health Management, Hunan Prevention and Treatment Institute for Occupational Diseases, Hunan, 410007, China
Yuanyuan Song: National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
Yang Zheng: National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
Xiaolong Cao: National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
Huiwen Zheng: Laboratory of Respiratory Diseases, Beijing Key Laboratory of Pediatric Respiratory Infection Diseases, Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, Key Laboratory of Major Diseases in Children, Ministry of Education, National Clinical Research Center for Respiratory Diseases, National Center for Children's Health, Beijing, 100045, China; Corresponding author.
Yanlin Zhao: National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China; Corresponding author.
Xichao Ou: National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China; Corresponding author.

Journal volume & issue: Vol. 10, no. 11
p. e31959

Abstract

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Objective: To detect levofloxacin (LFX) and moxifloxacin (MFX) resistance among rifampicin-resistant tuberculosis (RR-TB) isolates, and predict the resistance level based on specific mutations in gyrA and gyrB genes. Methods: A total of 686 RR-TB isolates were collected from Chinese Drug Resistance Surveillance Program from 2013 to 2020. The minimum inhibitory concentrations (MICs) of 12 anti-TB drugs were acquired using the broth microdilution method, followed by whole genome sequencing (WGS) analysis. Results: Among the 686 RR isolates, the most prevalent resistance was to isoniazid (80.5 %) and ethambutol (28.4 %), followed by LFX (26.1 %) and MFX (21.9 %). The resistance rate of LFX (26.1%–99.4 %) was higher than that of MFX (21.9%–83.3 %) across various drug resistance patterns. Of the 180 fluoroquinolones (FQs) resistant isolates, 168 (93.3 %) had mutations in quinolone-resistant determining regions (QRDRs) with 21 mutation types, and Asp94Gly (32.7 %, 55/168) was the predominant mutation. Isolates with mutations in Asp94Asn and Asp94Gly were associated with high levels of resistance to LFX and MFX. Using broth microdilution method as gold standard, the sensitivities of WGS for LFX and MFX were 93.3 % and 98.0 %, and the specificities were 98.6 % and 95.0 %, respectively. Conclusion: The resistance rate of LFX was higher than that of MFX among various drug resistance patterns in RR-TB isolates. The gyrA Asp94Gly was the predominant mutation type underlying FQs resistance. However, no significant difference was observed between mutation patterns in gyrA gene and resistance level of FQs.

Published in Heliyon

ISSN: 2405-8440 (Online)
Publisher: Elsevier
Country of publisher: United Kingdom
LCC subjects: Science: Science (General); Social Sciences: Social sciences (General)
Website: https://www.cell.com/heliyon/home

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