JOURNAL ARTICLE

Evaluation of the recovery effects of antibiotic-resistant Lactiplantibacillus plantarum subsp. plantarum ATCC14917 on the antibiotic-disturbed intestinal microbiota using a mice model.

  • Published In: Journal of Applied Microbiology, 2025, v. 136, n. 2. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Wang, Yiwei; Wang, Bini; Huo, Zhenquan; Zhang, Fuxin; Liu, Yufang 3 of 3

Abstract

This article focuses on developing a cephalexin-resistant strain of Lactiplantibacillus plantarum subsp. plantarum ATCC14917 and evaluating its effects on antibiotic-disturbed intestinal microbiota in a mouse model. Using laboratory evolution, the minimum inhibitory concentration (MIC) of L. plantarum to cephalexin increased 512-fold, and genomic analysis showed that resistance-associated mutations were not located on mobile genetic elements, suggesting low risk of horizontal gene transfer. Mice treated concurrently with cephalexin and the resistant L. plantarum strain exhibited improved gut microbial diversity, increased beneficial bacteria, reduced Firmicutes/Bacteroidetes ratio, and decreased abundance of potential pathogens compared to those treated with cephalexin alone or with the original strain. The findings indicate that co-administration of antibiotics with antibiotic-resistant probiotics like L. plantarum may help restore gut microbiota balance and mitigate antibiotic-associated dysbiosis.

Additional Information

  • Source:Journal of Applied Microbiology. 2025/02, Vol. 136, Issue 2, p1
  • Document Type:Article
  • Subject Area:Complementary and Alternative Medicine
  • Publication Date:2025
  • ISSN:1364-5072
  • DOI:10.1093/jambio/lxaf020
  • Accession Number:183431209
  • 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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