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

Max van ‘t Hof, Omkar S. Mohite, Jonathan M. Monk, Tilmann Weber, Bernhard O. Palsson, Morten O. A. Sommer*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

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.
Original languageEnglish
Article number566
JournalBMC Bioinformatics
Volume23
Issue number1
Number of pages23
ISSN1471-2105
DOIs
Publication statusPublished - 2022

Keywords

  • Genome-scale metabolic models
  • Probiotic
  • Nissle 1917
  • Phenotype microarray
  • Secondary metabolites
  • Constraint-based flux analysis

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