JOURNAL ARTICLE
Correlation between rrs gene mutations and amikacin resistance in Mycobacterium abscessus: implications for fitness cost and clinical prevalence.
Published In: Journal of Antimicrobial Chemotherapy (JAC), 2025, v. 80, n. 3. P. 746 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Ding, Jie; Hameed, H M Adnan; Long, Lihua; Zhang, Jingran; Fang, Cuiting; Tian, Xirong; Zhang, Han; Li, Lijie; Li, Chunyu; Yang, Ruhao; Gao, Yamin; Wang, Shuai; Zhang, Tianyu 3 of 3
Abstract
This article focuses on the role of specific mutations in the rrs gene of Mycobacterium abscessus (Mab) in conferring resistance to amikacin, a key aminoglycoside antibiotic used to treat Mab infections. Using CRISPR/Cas12a-assisted recombineering, the study precisely engineered Mab strains with five distinct rrs mutations (T1373A, A1375G, C1376T, G1458T, and T1465A) and demonstrated that all mutations confer high-level resistance to amikacin and cross-resistance to other aminoglycosides. The mutation A1375G was the most frequent in spontaneous mutants and clinical isolates and showed no fitness cost, while mutations T1373A and T1465A, which had higher fitness costs, were not found in clinical strains. These findings suggest that the prevalence of certain rrs mutations in clinical settings may be influenced by their spontaneous mutation frequency and associated fitness costs, providing insights for molecular diagnostics and antibiotic resistance management in Mab infections.
Additional Information
- Source:Journal of Antimicrobial Chemotherapy (JAC). 2025/03, Vol. 80, Issue 3, p746
- Document Type:Article
- Subject Area:Health and Medicine
- Publication Date:2025
- ISSN:0305-7453
- DOI:10.1093/jac/dkae468
- Accession Number:184039988
- Copyright Statement:Copyright of Journal of Antimicrobial Chemotherapy (JAC) is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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