Niche-specific metabolic phenotypes can be used to identify antimicrobial targets in pathogens.

Autor: Glass EM; Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America., Dillard LR; Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America.; Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America., Kolling GL; Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America.; Division of Infectious Diseases & International Health, Department of Medicine, University of Virginia, Charlottesville, Virginia, United States of America., Warren AS; Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, Virginia, United States of America., Papin JA; Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America.; Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America.; Division of Infectious Diseases & International Health, Department of Medicine, University of Virginia, Charlottesville, Virginia, United States of America.
Jazyk: angličtina
Zdroj: PLoS biology [PLoS Biol] 2024 Nov 18; Vol. 22 (11), pp. e3002907. Date of Electronic Publication: 2024 Nov 18 (Print Publication: 2024).
DOI: 10.1371/journal.pbio.3002907
Abstrakt: Bacterial pathogens pose a major risk to human health, leading to tens of millions of deaths annually and significant global economic losses. While bacterial infections are typically treated with antibiotic regimens, there has been a rapid emergence of antimicrobial resistant (AMR) bacterial strains due to antibiotic overuse. Because of this, treatment of infections with traditional antimicrobials has become increasingly difficult, necessitating the development of innovative approaches for deeply understanding pathogen function. To combat issues presented by broad- spectrum antibiotics, the idea of narrow-spectrum antibiotics has been previously proposed and explored. Rather than interrupting universal bacterial cellular processes, narrow-spectrum antibiotics work by targeting specific functions or essential genes in certain species or subgroups of bacteria. Here, we generate a collection of genome-scale metabolic network reconstructions (GENREs) of pathogens through an automated computational pipeline. We used these GENREs to identify subgroups of pathogens that share unique metabolic phenotypes and determined that pathogen physiological niche plays a role in the development of unique metabolic function. For example, we identified several unique metabolic phenotypes specific to stomach pathogens. We identified essential genes unique to stomach pathogens in silico and a corresponding inhibitory compound for a uniquely essential gene. We then validated our in silico predictions with an in vitro microbial growth assay. We demonstrated that the inhibition of a uniquely essential gene, thyX, inhibited growth of stomach-specific pathogens exclusively, indicating possible physiological location-specific targeting. This pioneering computational approach could lead to the identification of unique metabolic signatures to inform future targeted, physiological location-specific, antimicrobial therapies, reducing the need for broad-spectrum antibiotics.
Competing Interests: JP has financial interest in Cerillo, Inc. that manufactured the microplate reader used in some validation experiments.
(Copyright: © 2024 Glass et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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