Mechanistic insights into bacterial metabolic reprogramming from omics-integrated genome-scale models.

Détails

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Etat: Public
Version: Final published version
Licence: CC BY 4.0
ID Serval
serval:BIB_AED3DDBE9005
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Mechanistic insights into bacterial metabolic reprogramming from omics-integrated genome-scale models.
Périodique
NPJ systems biology and applications
Auteur(s)
Hadadi N., Pandey V., Chiappino-Pepe A., Morales M., Gallart-Ayala H., Mehl F., Ivanisevic J., Sentchilo V., Meer JRV
ISSN
2056-7189 (Electronic)
ISSN-L
2056-7189
Statut éditorial
Publié
Date de publication
2020
Peer-reviewed
Oui
Volume
6
Numéro
1
Pages
1
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: epublish
Résumé
Understanding the adaptive responses of individual bacterial strains is crucial for microbiome engineering approaches that introduce new functionalities into complex microbiomes, such as xenobiotic compound metabolism for soil bioremediation. Adaptation requires metabolic reprogramming of the cell, which can be captured by multi-omics, but this data remains formidably challenging to interpret and predict. Here we present a new approach that combines genome-scale metabolic modeling with transcriptomics and exometabolomics, both of which are common tools for studying dynamic population behavior. As a realistic demonstration, we developed a genome-scale model of Pseudomonas veronii 1YdBTEX2, a candidate bioaugmentation agent for accelerated metabolism of mono-aromatic compounds in soil microbiomes, while simultaneously collecting experimental data of P. veronii metabolism during growth phase transitions. Predictions of the P. veronii growth rates and specific metabolic processes from the integrated model closely matched experimental observations. We conclude that integrative and network-based analysis can help build predictive models that accurately capture bacterial adaptation responses. Further development and testing of such models may considerably improve the successful establishment of bacterial inoculants in more complex systems.
Mots-clé
Environmental sciences, Systems analysis
Pubmed
Web of science
Open Access
Oui
Création de la notice
17/01/2020 17:09
Dernière modification de la notice
30/04/2021 7:13
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