Network analysis of toxin production in Clostridioides difficile identifies key metabolic dependencies.

Autor: Powers DA; Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, Virginia, United States of America., Jenior ML; Biomedical Engineering, School of Engineering, University of Virginia, Charlottesville, Virginia, United States of America., Kolling GL; Biomedical Engineering, School of Engineering, University of Virginia, Charlottesville, Virginia, United States of America., Papin JA; Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, Virginia, United States of America.; Biomedical Engineering, School of Engineering, University of Virginia, Charlottesville, Virginia, United States of America.
Jazyk: angličtina
Zdroj: PLoS computational biology [PLoS Comput Biol] 2023 Apr 26; Vol. 19 (4), pp. e1011076. Date of Electronic Publication: 2023 Apr 26 (Print Publication: 2023).
DOI: 10.1371/journal.pcbi.1011076
Abstrakt: Clostridioides difficile pathogenesis is mediated through its two toxin proteins, TcdA and TcdB, which induce intestinal epithelial cell death and inflammation. It is possible to alter C. difficile toxin production by changing various metabolite concentrations within the extracellular environment. However, it is unknown which intracellular metabolic pathways are involved and how they regulate toxin production. To investigate the response of intracellular metabolic pathways to diverse nutritional environments and toxin production states, we use previously published genome-scale metabolic models of C. difficile strains CD630 and CDR20291 (iCdG709 and iCdR703). We integrated publicly available transcriptomic data with the models using the RIPTiDe algorithm to create 16 unique contextualized C. difficile models representing a range of nutritional environments and toxin states. We used Random Forest with flux sampling and shadow pricing analyses to identify metabolic patterns correlated with toxin states and environment. Specifically, we found that arginine and ornithine uptake is particularly active in low toxin states. Additionally, uptake of arginine and ornithine is highly dependent on intracellular fatty acid and large polymer metabolite pools. We also applied the metabolic transformation algorithm (MTA) to identify model perturbations that shift metabolism from a high toxin state to a low toxin state. This analysis expands our understanding of toxin production in C. difficile and identifies metabolic dependencies that could be leveraged to mitigate disease severity.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2023 Powers 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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