JOURNAL ARTICLE

Metabolomic and molecular insights into adenosine triphosphate synthase inhibitors from Micromonospora sp. as novel antimicrobial agents against multidrug-resistant Gram-negative pathogens of enteric origin.

  • Published In: Letters in Applied Microbiology, 2025, v. 78, n. 5. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: El-Shatoury, Sahar A; Megawer, Hanan K; Hanora, Amro M; Makharita, Rabab R; Riesco, Raúl; Trujillo, Martha E; Nafie, Mohamed S 3 of 3

Abstract

This article focuses on evaluating the antimicrobial potential of metabolic extracts from Micromonospora spp., particularly strain 65SH, against multidrug-resistant (MDR) Gram-negative bacteria of enteric origin. The study identified Micromonospora sp. 65SH, closely related to Micromonospora fluminis, as producing six bioactive compounds—melibiose, oligomycin A, queuine, heptelidic acid, diethyl phthalate, and 2′-deoxyguanosine—that inhibit bacterial ATP synthase and ATP-dependent topoisomerases, essential enzymes for bacterial proliferation. Using non-targeted metabolomics, molecular docking, and 16S rRNA gene sequencing, the research demonstrated significant antimicrobial activity of the strain’s extracts against clinical MDR isolates of Enterobacter aerogenes and Escherichia coli, with minimum inhibitory concentrations as low as 12.5 µg/ml. The findings highlight Micromonospora-derived metabolites as promising candidates for novel antibacterial agents targeting energy metabolism in resistant pathogens, with future work planned for structural elucidation and experimental validation.

Additional Information

  • Source:Letters in Applied Microbiology. 2025/05, Vol. 78, Issue 5, p1
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2025
  • ISSN:0266-8254
  • DOI:10.1093/lambio/ovaf064
  • Accession Number:185678935
  • 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.)

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