TITLE

Improved vanillin production in baker's yeast through in silico design

AUTHOR(S)
Brochado, Ana Rita; Matos, Claudia; Møller, Birger L.; Hansen, Jørgen; Mortensen, Uffe H.; Patil, Kiran Raosaheb
PUB. DATE
January 2010
SOURCE
Microbial Cell Factories;2010, Vol. 9, p84
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Background: Vanillin is one of the most widely used flavouring agents, originally obtained from cured seed pods of the vanilla orchid Vanilla planifolia. Currently vanillin is mostly produced via chemical synthesis. A de novo synthetic pathway for heterologous vanillin production from glucose has recently been implemented in baker's yeast, Saccharamyces cerevisiae. In this study we aimed at engineering this vanillin cell factory towards improved productivity and thereby at developing an attractive alternative to chemical synthesis. Results: Expression of a glycosyltransferase from Arabidopsis thaliana in the vanillin producing S. cerevisiae strain served to decrease product toxicity. An in silico metabolic engineering strategy of this vanillin glucoside producing strain was designed using a set of stoichiometric modelling tools applied to the yeast genome-scale metabolic network. Two targets (PDC1 and GDH1) were selected for experimental verification resulting in four engineered strains. Three of the mutants showed up to 1.5 fold higher vanillin β-D-glucoside yield in batch mode, while continuous culture of the Δpdc1 mutant showed a 2-fold productivity improvement. This mutant presented a 5- fold improvement in free vanillin production compared to the previous work on de novo vanillin biosynthesis in baker's yeast. Conclusion: Use of constraints corresponding to different physiological states was found to greatly influence the target predictions given minimization of metabolic adjustment (MOMA) as biological objective function. In vivo verification of the targets, selected based on their predicted metabolic adjustment, successfully led to overproducing strains. Overall, we propose and demonstrate a framework for in silico design and target selection for improving microbial cell factories.
ACCESSION #
55598020

 

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