JOURNAL ARTICLE

The antibiotic resistome in Escherichia coli isolated from human, food, and animal sources.

  • Published In: Journal of Applied Microbiology, 2023, v. 134, n. 2. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Rodrigues, Isadora de Alcântara; Ferrari, Rafaela Gomes; Panzenhagen, Pedro; Pereira dos Santos, Anamaria Mota; Rodrigues, Grazielle Lima; Junior, Carlos Adam Conte; Mano, Sergio Borges 3 of 3

Abstract

This study analyzes the prevalence and distribution of antimicrobial resistance genes (ARGs) in Escherichia coli genomes isolated from human invasive clinical samples (ICH) and animal-based foods (ABF) worldwide, stratified by countries' Human Development Index (HDI). Using 7,123 genomes from the NCBI Pathogen Detection Database, the research found that in high-HDI countries (HDI ≥ 0.850), ARGs conferring resistance to cephalosporins (notably blaCTX-M-134 and blaCTX-M-27) predominated in clinical isolates, while aminoglycoside resistance genes (aadA12, ant(3"), aac(3)-IV) were highly prevalent in E. coli from animal-based foods. In lower-HDI countries (HDI ≤ 0.849), a greater diversity of ARGs was observed, including genes linked to resistance to macrolides, fluoroquinolones, β-lactams (e.g., blaCTX-M-15), and aminoglycosides, with some overlap between clinical and food sources. The findings highlight the role of antimicrobial use in both human medicine and animal production in shaping resistance profiles and underscore the need for targeted policies to address antimicrobial resistance, especially concerning aminoglycosides and cephalosporins, across different socioeconomic contexts.

Additional Information

  • Source:Journal of Applied Microbiology. 2023/02, Vol. 134, Issue 2, p1
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2023
  • ISSN:1364-5072
  • DOI:10.1093/jambio/lxac059
  • Accession Number:162823954
  • 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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