Literature DB >> 21763447

Genome-scale metabolic network modeling results in minimal interventions that cooperatively force carbon flux towards malonyl-CoA.

Peng Xu1, Sridhar Ranganathan, Zachary L Fowler, Costas D Maranas, Mattheos A G Koffas.   

Abstract

Malonyl-coenzyme A is an important precursor metabolite for the biosynthesis of polyketides, flavonoids and biofuels. However, malonyl-CoA naturally synthesized in microorganisms is consumed for the production of fatty acids and phospholipids leaving only a small amount available for the production of other metabolic targets in recombinant biosynthesis. Here we present an integrated computational and experimental approach aimed at improving the intracellular availability of malonyl-CoA in Escherichia coli. We used a customized version of the recently developed OptForce methodology to predict a minimal set of genetic interventions that guarantee a prespecified yield of malonyl-CoA in E. coli strain BL21 Star™. In order to validate the model predictions, we have successfully constructed an E. coli recombinant strain that exhibits a 4-fold increase in the levels of intracellular malonyl-CoA compared to the wild type strain. Furthermore, we demonstrate the potential of this E. coli strain for the production of plant-specific secondary metabolites naringenin (474mg/L) with the highest yield ever achieved in a lab-scale fermentation process. Combined effect of the genetic interventions was found to be synergistic based on a developed analysis method that correlates genetic modification to cell phenotype, specifically the identified knockout targets (ΔfumC and ΔsucC) and overexpression targets (ACC, PGK, GAPD and PDH) can cooperatively force carbon flux towards malonyl-CoA. The presented strategy can also be readily expanded for the production of other malonyl-CoA-derived compounds like polyketides and biofuels.
Copyright © 2011 Elsevier Inc. All rights reserved.

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Year:  2011        PMID: 21763447     DOI: 10.1016/j.ymben.2011.06.008

Source DB:  PubMed          Journal:  Metab Eng        ISSN: 1096-7176            Impact factor:   9.783


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