ArticleBMC bioinformatics2022
High-quality genome-scale metabolic network reconstruction of probiotic bacterium Escherichia coli Nissle 1917.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Development of an efficient genome editing system using recombinase-mediated cassette exchange inSynthetic and systems biotechnology · 2026Article
- An Antibiotic-Free Cryptic Plasmid Platform for Modular Pathway Coupling in Probiotic Escherichia Coli Nissle 1917.Biotechnology and bioengineering · 2026Article
- Incorporation of Cryptic Plasmid Energetics Improves Genome-Scale Metabolic Predictions in Probiotic E. coli Nissle 1917.Microbial biotechnology · 2026Article
- A multi-way SMILES-based hypergraph inference network for metabolic model reconstruction.Communications biology · 2026Article
- Evaluating transcriptomic integration for cyanobacterial constraint-based metabolic modelling.Frontiers in bioinformatics · 2026Article
- Article
- Determination of the genome-scale metabolic network ofFrontiers in bioengineering and biotechnology · 2025Article
- Bacterial microcompartment utilization in the human commensalJournal of bacteriology · 2024Article
- Review
- Engineering a Novel Probiotic Toolkit inACS synthetic biology · 2024Article
- Unveiling the impact of cryptic plasmids curing on Escherichia coli Nissle 1917: massive increase in Ag43c expression.AMB Express · 2024Article
- Applications of genome-scale metabolic models to investigate microbial metabolic adaptations in response to genetic or environmental perturbations.Briefings in bioinformatics · 2023Review
- Genome-scale metabolic models consistently predictFrontiers in bioinformatics · 2023Article
- Benefits and concerns of probiotics: an overview of the potential genotoxicity of the colibactin-producingGut microbesReview
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Authors and funding
6 authors.
Funding
Abstract
backgroundEscherichia 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.
resultsAn 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
conclusioniHM1533 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.
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