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High-quality genome-scale metabolic network reconstruction of probiotic bacterium Escherichia coli Nissle 1917

BACKGROUND: Escherichia coli Nissle 1917 (EcN) is a probiotic bacterium used to treat various gastrointestinal diseases. EcN is increasingly being used as a chassis for the engineering of advanced microbiome therapeutics. To aid in future engineering efforts, our aim was to construct an updated meta...

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Autores principales: van ‘t Hof, Max, Mohite, Omkar S., Monk, Jonathan M., Weber, Tilmann, Palsson, Bernhard O., Sommer, Morten O. A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9801561/
https://www.ncbi.nlm.nih.gov/pubmed/36585633
http://dx.doi.org/10.1186/s12859-022-05108-9
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author van ‘t Hof, Max
Mohite, Omkar S.
Monk, Jonathan M.
Weber, Tilmann
Palsson, Bernhard O.
Sommer, Morten O. A.
author_facet van ‘t Hof, Max
Mohite, Omkar S.
Monk, Jonathan M.
Weber, Tilmann
Palsson, Bernhard O.
Sommer, Morten O. A.
author_sort van ‘t Hof, Max
collection PubMed
description BACKGROUND: Escherichia coli Nissle 1917 (EcN) is a probiotic bacterium used to treat various gastrointestinal diseases. EcN is increasingly being used as a chassis for the engineering of advanced microbiome therapeutics. To aid in future engineering efforts, our aim was to construct an updated metabolic model of EcN with extended secondary metabolite representation. RESULTS: An updated high-quality genome-scale metabolic model of EcN, iHM1533, was developed based on comparison with 55 E. coli/Shigella reference GEMs and manual curation, including expanded secondary metabolite pathways (enterobactin, salmochelins, aerobactin, yersiniabactin, and colibactin). The model was validated and improved using phenotype microarray data, resulting in an 82.3% accuracy in predicting growth phenotypes on various nutrition sources. Flux variability analysis with previously published (13)C fluxomics data validated prediction of the internal central carbon fluxes. A standardised test suite called Memote assessed the quality of iHM1533 to have an overall score of 89%. The model was applied by using constraint-based flux analysis to predict targets for optimisation of secondary metabolite production. Modelling predicted design targets from across amino acid metabolism, carbon metabolism, and other subsystems that are common or unique for influencing the production of various secondary metabolites. CONCLUSION: iHM1533 represents a well-annotated metabolic model of EcN with extended secondary metabolite representation. Phenotype characterisation and the iHM1533 model provide a better understanding of the metabolic capabilities of EcN and will help future metabolic engineering efforts. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-05108-9.
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spelling pubmed-98015612022-12-31 High-quality genome-scale metabolic network reconstruction of probiotic bacterium Escherichia coli Nissle 1917 van ‘t Hof, Max Mohite, Omkar S. Monk, Jonathan M. Weber, Tilmann Palsson, Bernhard O. Sommer, Morten O. A. BMC Bioinformatics Research BACKGROUND: Escherichia coli Nissle 1917 (EcN) is a probiotic bacterium used to treat various gastrointestinal diseases. EcN is increasingly being used as a chassis for the engineering of advanced microbiome therapeutics. To aid in future engineering efforts, our aim was to construct an updated metabolic model of EcN with extended secondary metabolite representation. RESULTS: An updated high-quality genome-scale metabolic model of EcN, iHM1533, was developed based on comparison with 55 E. coli/Shigella reference GEMs and manual curation, including expanded secondary metabolite pathways (enterobactin, salmochelins, aerobactin, yersiniabactin, and colibactin). The model was validated and improved using phenotype microarray data, resulting in an 82.3% accuracy in predicting growth phenotypes on various nutrition sources. Flux variability analysis with previously published (13)C fluxomics data validated prediction of the internal central carbon fluxes. A standardised test suite called Memote assessed the quality of iHM1533 to have an overall score of 89%. The model was applied by using constraint-based flux analysis to predict targets for optimisation of secondary metabolite production. Modelling predicted design targets from across amino acid metabolism, carbon metabolism, and other subsystems that are common or unique for influencing the production of various secondary metabolites. CONCLUSION: iHM1533 represents a well-annotated metabolic model of EcN with extended secondary metabolite representation. Phenotype characterisation and the iHM1533 model provide a better understanding of the metabolic capabilities of EcN and will help future metabolic engineering efforts. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-05108-9. BioMed Central 2022-12-30 /pmc/articles/PMC9801561/ /pubmed/36585633 http://dx.doi.org/10.1186/s12859-022-05108-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
van ‘t Hof, Max
Mohite, Omkar S.
Monk, Jonathan M.
Weber, Tilmann
Palsson, Bernhard O.
Sommer, Morten O. A.
High-quality genome-scale metabolic network reconstruction of probiotic bacterium Escherichia coli Nissle 1917
title High-quality genome-scale metabolic network reconstruction of probiotic bacterium Escherichia coli Nissle 1917
title_full High-quality genome-scale metabolic network reconstruction of probiotic bacterium Escherichia coli Nissle 1917
title_fullStr High-quality genome-scale metabolic network reconstruction of probiotic bacterium Escherichia coli Nissle 1917
title_full_unstemmed High-quality genome-scale metabolic network reconstruction of probiotic bacterium Escherichia coli Nissle 1917
title_short High-quality genome-scale metabolic network reconstruction of probiotic bacterium Escherichia coli Nissle 1917
title_sort high-quality genome-scale metabolic network reconstruction of probiotic bacterium escherichia coli nissle 1917
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9801561/
https://www.ncbi.nlm.nih.gov/pubmed/36585633
http://dx.doi.org/10.1186/s12859-022-05108-9
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