JOURNAL ARTICLE

Isolation and identification of antimicrobial multicyclic terpenoids from the medicinal plant Salvia officinalis and development of a formulation against clinical Staphylococcus aureus strains.

  • Published In: Letters in Applied Microbiology, 2024, v. 77, n. 8. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Purgato, Gislaine Aparecida; Píccolo, Mayra Soares; Moreira, Maria Aparecida Scatamburlo; Pizziolo, Virgínia Ramos; Diaz-Muñoz, Gaspar; Rossi, Ciro César; Diaz, Marisa Alves Nogueira 3 of 3

Abstract

This article focuses on the antimicrobial potential of Salvia officinalis (common sage) extracts against Staphylococcus aureus strains responsible for bovine mastitis, a significant disease in dairy farming complicated by multidrug resistance. The study identified three key multicyclic terpenoids—ferruginol, sugiol, and sclareol—in the hexane extract of sage leaves, which exhibited strong antimicrobial, anti-adhesive, and antibiofilm activities at low concentrations, comparable to or better than pure compounds. A topical formulation incorporating this extract effectively inhibited biofilm formation and disrupted established biofilms of clinical S. aureus strains, while demonstrating no cytotoxicity to mammary epithelial cells at therapeutic doses. These findings suggest that S. officinalis-derived compounds offer a promising, safe alternative for managing bovine mastitis caused by drug-resistant S. aureus, with potential applications in veterinary medicine and surface decontamination.

Additional Information

  • Source:Letters in Applied Microbiology. 2024/08, Vol. 77, Issue 8, p1
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2024
  • ISSN:0266-8254
  • DOI:10.1093/lambio/ovae077
  • Accession Number:179483959
  • Copyright Statement:Copyright of Letters in 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.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.