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

Brochado AR, Matos C, Møller BL, Hansen J, Mortensen UH, Patil KR - Microb. Cell Fact. (2010)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Center for Microbial Biotechnology, Technical University of Denmark, DK - 2800 Kgs. Lyngby, Denmark.

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.

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Related in: MedlinePlus

Minimum turnover of selected metabolites from the central carbon metabolism and from the VG biosynthetic pathway (including cofactors). Metabolites from the central carbon metabolism: glucose-6-phosphate, erythrose-4-phosphate, pyruvate and ethanol; Metabolites from the VG biosynthetic pathway (ATP, NADPH, SAM and UDP-glucose). Metabolites for which minimum turnover was calculated are represented by filled circles, metabolites for which no minimum turnover was calculated are represented by open rings. Reactions are represented as arrows. Qualitative variation of the minimum turnovers relatively to the reference (VG0) is shown by the arrows next to each metabolite; yellow corresponds to VG2 while red corresponds to VG4.
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Figure 6: Minimum turnover of selected metabolites from the central carbon metabolism and from the VG biosynthetic pathway (including cofactors). Metabolites from the central carbon metabolism: glucose-6-phosphate, erythrose-4-phosphate, pyruvate and ethanol; Metabolites from the VG biosynthetic pathway (ATP, NADPH, SAM and UDP-glucose). Metabolites for which minimum turnover was calculated are represented by filled circles, metabolites for which no minimum turnover was calculated are represented by open rings. Reactions are represented as arrows. Qualitative variation of the minimum turnovers relatively to the reference (VG0) is shown by the arrows next to each metabolite; yellow corresponds to VG2 while red corresponds to VG4.

Mentions: Besides NADPH and ATP, the minimum turnover of some other relevant metabolites was also calculated (Figure 6). The minimum turnover for PAC is lower in the strains VG2 and VG4 than for VG0; while for PAL and VG the opposite trend is verified, in agreement with the flux variability analysis. An increase in the glucose uptake rate will result in an increase in glycolysis and pentose phosphate pathway, which is reflected in the increased minimum turnover of pyruvate and erythrose-4-phosphate in the VG4 strain. On the other hand, the VG2 strain exhibits a decreased glucose uptake rate and consequently less flux through glycolysis and pentose phosphate pathway. The same trends apply to ATP, implying that most ATP available in the cell is being produced from glycolysis. NADPH, S-adenosylmethionine and UDP-glucose minimum turnovers are increased for both simulated phenotypes, reflecting the increased flux through ACAR, hsOMT and UGT leading to PAL and VG. The analysis above provides insight into the intracellular flux changes and pinpoints metabolites that play a role in the engineered strains.


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

Brochado AR, Matos C, Møller BL, Hansen J, Mortensen UH, Patil KR - Microb. Cell Fact. (2010)

Minimum turnover of selected metabolites from the central carbon metabolism and from the VG biosynthetic pathway (including cofactors). Metabolites from the central carbon metabolism: glucose-6-phosphate, erythrose-4-phosphate, pyruvate and ethanol; Metabolites from the VG biosynthetic pathway (ATP, NADPH, SAM and UDP-glucose). Metabolites for which minimum turnover was calculated are represented by filled circles, metabolites for which no minimum turnover was calculated are represented by open rings. Reactions are represented as arrows. Qualitative variation of the minimum turnovers relatively to the reference (VG0) is shown by the arrows next to each metabolite; yellow corresponds to VG2 while red corresponds to VG4.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2992047&req=5

Figure 6: Minimum turnover of selected metabolites from the central carbon metabolism and from the VG biosynthetic pathway (including cofactors). Metabolites from the central carbon metabolism: glucose-6-phosphate, erythrose-4-phosphate, pyruvate and ethanol; Metabolites from the VG biosynthetic pathway (ATP, NADPH, SAM and UDP-glucose). Metabolites for which minimum turnover was calculated are represented by filled circles, metabolites for which no minimum turnover was calculated are represented by open rings. Reactions are represented as arrows. Qualitative variation of the minimum turnovers relatively to the reference (VG0) is shown by the arrows next to each metabolite; yellow corresponds to VG2 while red corresponds to VG4.
Mentions: Besides NADPH and ATP, the minimum turnover of some other relevant metabolites was also calculated (Figure 6). The minimum turnover for PAC is lower in the strains VG2 and VG4 than for VG0; while for PAL and VG the opposite trend is verified, in agreement with the flux variability analysis. An increase in the glucose uptake rate will result in an increase in glycolysis and pentose phosphate pathway, which is reflected in the increased minimum turnover of pyruvate and erythrose-4-phosphate in the VG4 strain. On the other hand, the VG2 strain exhibits a decreased glucose uptake rate and consequently less flux through glycolysis and pentose phosphate pathway. The same trends apply to ATP, implying that most ATP available in the cell is being produced from glycolysis. NADPH, S-adenosylmethionine and UDP-glucose minimum turnovers are increased for both simulated phenotypes, reflecting the increased flux through ACAR, hsOMT and UGT leading to PAL and VG. The analysis above provides insight into the intracellular flux changes and pinpoints metabolites that play a role in the engineered strains.

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Center for Microbial Biotechnology, Technical University of Denmark, DK - 2800 Kgs. Lyngby, Denmark.

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.

Show MeSH
Related in: MedlinePlus