JOURNAL ARTICLE

Restoring vancomycin activity against resistant Enterococcus faecalis using a transcription factor decoy as a vanA operon-inhibitor.

  • Published In: Journal of Antimicrobial Chemotherapy (JAC), 2024, v. 79, n. 11. P. 2999 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Abdelall, Loai M; Nagy, Yosra Ibrahim; Kashef, Mona T 3 of 3

Abstract

This article focuses on the development and evaluation of a transcription factor decoy (TFD) system targeting the vanA operon to inhibit vancomycin resistance in Enterococcus faecalis. The vanA operon encodes proteins responsible for acquired vancomycin resistance by altering cell wall precursors, reducing vancomycin binding. The study synthesized a TFD mimicking the VanR transcription factor binding site, loaded it onto cationic liposomes (TFD-lipoplexes), and demonstrated that these complexes significantly reduced vanA gene expression and lowered vancomycin minimum inhibitory concentrations (MIC) in resistant strains without affecting bacterial growth or causing cytotoxicity or hemolysis. In a systemic mouse infection model, co-administration of TFD-lipoplexes with vancomycin effectively eradicated vancomycin-resistant E. faecalis infection, restoring antibiotic efficacy in vivo. This approach offers a novel, gene-targeted therapeutic strategy against vancomycin-resistant enterococci and may be applicable to other resistance genes.

Additional Information

  • Source:Journal of Antimicrobial Chemotherapy (JAC). 2024/11, Vol. 79, Issue 11, p2999
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2024
  • ISSN:0305-7453
  • DOI:10.1093/jac/dkae320
  • Accession Number:180625963
  • 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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