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Journal article

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

From

Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark1

Reconstruction, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark2

Strain Design Teams, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark3

University of California at San Diego4

DTU Microbes Initiative, Centers, Technical University of Denmark5

Natural Products Genome Mining, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark6

Center for Microbial Secondary Metabolites, Centers, Technical University of Denmark7

Bacterial Synthetic Biology, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark8

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.

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 13C 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. 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.

Language: English
Publisher: BioMed Central
Year: 2022
Pages: 566
ISSN: 14712105
Types: Journal article
DOI: 10.1186/s12859-022-05108-9
ORCIDs: van ‘t Hof, Max , Mohite, Omkar S. , Weber, Tilmann , Palsson, Bernhard O. and Sommer, Morten O. A.

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