Genome-Scale Metabolic Modeling of Halomonas elongata 153B Explains Polyhydroxyalkanoate and Ectoine Biosynthesis in Hypersaline Environments.
Extremophiles
Halomonas Elongata
Metabolic Engineering
Metabolic Modelling
Omics
Systems Biology
Journal
Biotechnology journal
ISSN: 1860-7314
Titre abrégé: Biotechnol J
Pays: Germany
ID NLM: 101265833
Informations de publication
Date de publication:
Oct 2024
Oct 2024
Historique:
revised:
22
08
2024
received:
22
04
2024
accepted:
09
09
2024
medline:
9
10
2024
pubmed:
9
10
2024
entrez:
9
10
2024
Statut:
ppublish
Résumé
Halomonas elongata thrives in hypersaline environments producing polyhydroxyalkanoates (PHAs) and osmoprotectants such as ectoine. Despite its biotechnological importance, several aspects of the dynamics of its metabolism remain elusive. Here, we construct and validate a genome-scale metabolic network model for H. elongata 153B. Then, we investigate the flux distribution dynamics during optimal growth, ectoine, and PHA biosynthesis using statistical methods, and a pipeline based on shadow prices. Lastly, we use optimization algorithms to uncover novel engineering targets to increase PHA production. The resulting model (iEB1239) includes 1534 metabolites, 2314 reactions, and 1239 genes. iEB1239 can reproduce growth on several carbon sources and predict growth on previously unreported ones. It also reproduces biochemical phenotypes related to Oad and Ppc gene functions in ectoine biosynthesis. A flux distribution analysis during optimal ectoine and PHA biosynthesis shows decreased energy production through oxidative phosphorylation. Furthermore, our analysis unveils a diverse spectrum of metabolic alterations that extend beyond mere flux changes to encompass heightened precursor production for ectoine and PHA synthesis. Crucially, these findings capture other metabolic changes linked to adaptation in hypersaline environments. Bottlenecks in the glycolysis and fatty acid metabolism pathways are identified, in addition to PhaC, which has been shown to increase PHA production when overexpressed. Overall, our pipeline demonstrates the potential of genome-scale metabolic models in combination with statistical approaches to obtain insights into the metabolism of H. elongata. Our platform can be exploited for researching environmental adaptation, and for designing and optimizing metabolic engineering strategies for bioproduct synthesis.
Identifiants
pubmed: 39380500
doi: 10.1002/biot.202400267
doi:
Substances chimiques
Amino Acids, Diamino
0
ectoine
7GXZ3858RY
Polyhydroxyalkanoates
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e202400267Subventions
Organisme : Alan Turing Institute
ID : TNDC2-100022
Organisme : Alan Turing Institute
ID : D-ELA-013
Informations de copyright
© 2024 The Author(s). Biotechnology Journal published by Wiley‐VCH GmbH.
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