JOURNAL ARTICLE
Characterization of antibiotic resistance genes and mobile elements in extended-spectrum β-lactamase-producing Escherichia coli strains isolated from hospitalized patients in Guangdong, China.
Published In: Journal of Applied Microbiology, 2023, v. 134, n. 7. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Shafiq, Muhammad; Bilal, Hazrat; Permana, Budi; Xu, Danhong; Cai, Gengzhong; Li, Xin; Zeng, Mi; Yuan, Yumeng; Jiao, Xiaoyang; Yao, Fen 3 of 3
Abstract
This study focused on the phenotypic and genotypic characterization of extended-spectrum β-lactamase (ESBL)-producing Escherichia coli isolates from hospitalized patients in a tertiary care hospital in Guangdong Province, China. Among 409 E. coli isolates collected from various clinical specimens between May and September 2021, 216 (52.8%) were ESBL producers, predominantly from urine and blood samples. The most common ESBL genes identified were blaCTX-M, blaTEM, and blaSHV, with additional detection of resistance genes blaNDM, mcr-1, and fosA3 linked to last-resort antibiotics. Mobile genetic elements (MGEs) such as IS26 and integrons were prevalent, facilitating horizontal gene transfer, as confirmed by conjugation assays. Whole-genome sequencing of eight multidrug-resistant isolates revealed diverse sequence types, multiple plasmid replicons, and various virulence factors, underscoring the genetic diversity and potential for dissemination of resistance within hospital settings. The findings highlight the high prevalence of multidrug-resistant ESBL-producing E. coli and emphasize the need for continuous molecular surveillance to inform infection control and antimicrobial stewardship.
Additional Information
- Source:Journal of Applied Microbiology. 2023/07, Vol. 134, Issue 7, p1
- Document Type:Article
- Subject Area:Health and Medicine
- Publication Date:2023
- ISSN:1364-5072
- DOI:10.1093/jambio/lxad125
- Accession Number:171853638
- Copyright Statement:Copyright of Journal of Applied Microbiology 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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