High-quality genome-scale metabolic network reconstruction of probiotic bacterium Escherichia coli Nissle 1917.
Constraint-based flux analysis
Genome-scale metabolic models
Nissle 1917
Phenotype microarray
Probiotic
Secondary metabolites
Journal
BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194
Informations de publication
Date de publication:
30 Dec 2022
30 Dec 2022
Historique:
received:
24
03
2022
accepted:
12
12
2022
entrez:
30
12
2022
pubmed:
31
12
2022
medline:
4
1
2023
Statut:
epublish
Résumé
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 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.
Sections du résumé
BACKGROUND
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
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
CONCLUSION
CONCLUSIONS
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.
Identifiants
pubmed: 36585633
doi: 10.1186/s12859-022-05108-9
pii: 10.1186/s12859-022-05108-9
pmc: PMC9801561
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
566Subventions
Organisme : Novo Nordisk Fonden
ID : NNF17CO0028232
Organisme : Novo Nordisk Fonden
ID : NNF16OC0021746
Organisme : Novo Nordisk Fonden
ID : NNF16OC0021746
Organisme : Novo Nordisk Fonden
ID : NNF17CO0028232
Informations de copyright
© 2022. The Author(s).
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