Reconstruction and analysis of a carbon-core metabolic network for Dunaliella salina.
Central carbon metabolism
Dunaliella salina
Flux balance analysis
Metabolic network reconstruction
Journal
BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194
Informations de publication
Date de publication:
02 Jan 2020
02 Jan 2020
Historique:
received:
17
05
2019
accepted:
17
12
2019
entrez:
4
1
2020
pubmed:
4
1
2020
medline:
19
3
2020
Statut:
epublish
Résumé
The green microalga Dunaliella salina accumulates a high proportion of β-carotene during abiotic stress conditions. To better understand the intracellular flux distribution leading to carotenoid accumulation, this work aimed at reconstructing a carbon core metabolic network for D. salina CCAP 19/18 based on the recently published nuclear genome and its validation with experimental observations and literature data. The reconstruction resulted in a network model with 221 reactions and 212 metabolites within three compartments: cytosol, chloroplast and mitochondrion. The network was implemented in the MATLAB toolbox CellNetAnalyzer and checked for feasibility. Furthermore, a flux balance analysis was carried out for different light and nutrient uptake rates. The comparison of the experimental knowledge with the model prediction revealed that the results of the stoichiometric network analysis are plausible and in good agreement with the observed behavior. Accordingly, our model provides an excellent tool for investigating the carbon core metabolism of D. salina. The reconstructed metabolic network of D. salina presented in this work is able to predict the biological behavior under light and nutrient stress and will lead to an improved process understanding for the optimized production of high-value products in microalgae.
Sections du résumé
BACKGROUND
BACKGROUND
The green microalga Dunaliella salina accumulates a high proportion of β-carotene during abiotic stress conditions. To better understand the intracellular flux distribution leading to carotenoid accumulation, this work aimed at reconstructing a carbon core metabolic network for D. salina CCAP 19/18 based on the recently published nuclear genome and its validation with experimental observations and literature data.
RESULTS
RESULTS
The reconstruction resulted in a network model with 221 reactions and 212 metabolites within three compartments: cytosol, chloroplast and mitochondrion. The network was implemented in the MATLAB toolbox CellNetAnalyzer and checked for feasibility. Furthermore, a flux balance analysis was carried out for different light and nutrient uptake rates. The comparison of the experimental knowledge with the model prediction revealed that the results of the stoichiometric network analysis are plausible and in good agreement with the observed behavior. Accordingly, our model provides an excellent tool for investigating the carbon core metabolism of D. salina.
CONCLUSIONS
CONCLUSIONS
The reconstructed metabolic network of D. salina presented in this work is able to predict the biological behavior under light and nutrient stress and will lead to an improved process understanding for the optimized production of high-value products in microalgae.
Identifiants
pubmed: 31898485
doi: 10.1186/s12859-019-3325-0
pii: 10.1186/s12859-019-3325-0
pmc: PMC6941287
doi:
Substances chimiques
Carotenoids
36-88-4
Carbon
7440-44-0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1Références
Biotechnol Bioeng. 2010 Jul 1;106(4):638-48
pubmed: 20229508
J Appl Phycol. 2012 Apr;24(2):253-266
pubmed: 22427720
PeerJ. 2018 Sep 3;6:e5528
pubmed: 30202653
BMC Syst Biol. 2009 Jan 07;3:4
pubmed: 19128495
Plant J. 2011 Aug;67(3):513-25
pubmed: 21501261
Bioresour Technol. 2011 Jan;102(1):142-9
pubmed: 20656476
Plant Physiol. 1983 Jul;72(3):593-7
pubmed: 16663050
Mol Syst Biol. 2011 Aug 02;7:518
pubmed: 21811229
Plant J. 2015 Dec;84(6):1239-56
pubmed: 26485611
BMC Genomics. 2011 Dec 22;12 Suppl 4:S5
pubmed: 22369158
Microb Cell Fact. 2011 Nov 02;10:91
pubmed: 22047615
Protein Expr Purif. 2003 Mar;28(1):151-7
pubmed: 12651119
Front Plant Sci. 2015 Jun 30;6:474
pubmed: 26175742
Microb Cell Fact. 2018 Mar 5;17(1):36
pubmed: 29506528
Plant Physiol. 1998 Apr;116(4):1239-48
pubmed: 9536040
Photosynth Res. 2013 Nov;118(1-2):167-79
pubmed: 24142039
Bioresour Technol. 2014 Dec;173:21-31
pubmed: 25280110
Plant Physiol. 2015 Feb;167(2):586-99
pubmed: 25511434
Nat Methods. 2009 Aug;6(8):589-92
pubmed: 19597503
Genome Announc. 2017 Oct 26;5(43):null
pubmed: 29074648
Plant Physiol. 1991 May;96(1):50-60
pubmed: 16668185
Bioresour Technol. 2011 Apr;102(8):5083-92
pubmed: 21324679
Metabolites. 2014 Aug 04;4(3):612-28
pubmed: 25093929
Mol Biol Evol. 2012 Dec;29(12):3625-39
pubmed: 22826458
J Exp Bot. 2012 Mar;63(6):2353-62
pubmed: 22207618
Microb Cell Fact. 2012 Jul 25;11:96
pubmed: 22830315
BMC Syst Biol. 2017 Jul 4;11(1):66
pubmed: 28676050
Biotechnol Bioeng. 2013 Mar;110(3):792-802
pubmed: 23055276
Eukaryot Cell. 2013 Jun;12(6):776-93
pubmed: 23543671
Biochem Eng J. 2000 Oct 1;6(2):87-102
pubmed: 10959082
Photosynth Res. 2011 Sep;109(1-3):133-49
pubmed: 21365258
Biotechnol Biofuels. 2016 Aug 04;9:165
pubmed: 27493687
J Biotechnol. 2012 Nov 30;162(1):21-7
pubmed: 22750089
Front Microbiol. 2015 Dec 15;6:1376
pubmed: 26696985
Plant Physiol. 2013 Oct;163(2):637-47
pubmed: 23926077
BMC Syst Biol. 2007 Jan 08;1:2
pubmed: 17408509
Plant Physiol. 2010 Feb;152(2):579-89
pubmed: 20044452
Trends Biotechnol. 2014 Dec;32(12):617-26
pubmed: 25457388
Bioresour Technol. 2011 Jan;102(1):111-7
pubmed: 20619638