In silico aided metaoblic engineering of Saccharomyces cerevisiae for improved bioethanol production

1. In silico aided metaoblic engineering of Saccharomyces cerevisiae for improved bioethanol production Christoffer Bro et al. 2005

2. The problem • Under anaerobic conditions, S. cerevisiae produces only four major products from glucose: • CO2, ethanol, biomass and glycerol • To increase the ethanol yield, the flow of carbon going to biomass or glycerol should be redirected towards ethanol.

3. Overview of main fluxes

4. Previous work • Some of the carbon flowing to biomass can be redirected towards ethanol by increasing the consumption of ATP for biomass production or reducing the amount of ATP formed in association with ethanol production. (Nissen et al. 2000) • Deletion of the structural genes in glycerol biosynthesis is not a successful strategy. • The maximum specific growth rate is severely lowered in such strains • Formation of glycerol is necessary for maintaining the redox balance by oxidizing NADH

5. Strategy #1 • Substitution of NADPH-oxidizing reactions in biomass formation with NADH-oxidizing reactions

6. Strategy #2 • Substitution of NAD+-reducing reactions in biomass formation by NADP+-reducing reactions.

7. Strategy #3 • Introduction of a reaction which either directly or via a cycle converts NADH into NADPH.

8. Strategy #4 • Substitution of the glycerol production with production of ethanol, which has a net oxidation of NADH.

9. In silico model • iFF708 (Forster et al., 2003) • 708 genes • 584 metabolites • 1175 reactions

10. Method • A database of 3800 biochemical reactions is derived from the LIGAND database of KEGG. • Each gene (corresponding to a specific biochemical reaction) was inserted one at a time into the genome-scale metabolic model, and the performance of the engineered strain was evaluated. • Two other engineered strains: • Heterologous expression of a non-phosphorylating, NADP+-dependent D-GAPN • Deletion of GDH1 combined with simultaneous overexpression of GDH2 or GLN1 and GLT1. • GDH1: AKG + NH3 + NADPH -> GLU + NADP • GDH2: GLU + NAD -> AKG + NH3 + NADH • GLN1: GLU + NH3 + ATP -> GLN + ADP + PI • GLT1:AKG + GLN + NADH -> NAD + 2 GLU

11. Eight best strains predicted

12. The best strategy

13. In vivo testing of the best strategy • Ethanol production increased by 3% • Reasons for disagreement between experiment and model: • Limited GAPN activity in vivo • Low intracellular NADP+ concentrations compared with NADPH

14. Discussion • “The success of the strategies is due to the tight linking of the different parts of the metabolic network through the common usage of co-factors like NADH, NADPH and ATP, and the genome-scale metabolic model represents a valuable tool for studying how these co-factors link the different parts of the metabolism in a quantitative fashion.”

15. Efficiency of amino acid production in Escherichia coli Anthony Burgard & Costas Maranas, 2001

16. iJR904 20 8.2609 15.68 18.627 9.4501 11.322 11.81 26.122 7.8728 7.4219 7.5 7.8512 5.7951 5.4913 10.074 20 12.601 4.4907 5.6886 10

17. Universal model • The universal model is constructed by incorporating 3400 cellular reactions from the KEGG into the modified Keasling stoichiometric model.

18. Arginine production