In silico profiling of Escherichia coli and Saccharomyces cerevisiae as terpenoid factories

Abstract

Background: Heterologous microbial production of rare plant terpenoids of medicinal or industrial interest is attracting more and more attention but terpenoid yields are still low. Escherichia coli and Saccharomyces cerevisiae are the most widely used heterologous hosts; a direct comparison of both hosts based on experimental data is difficult though. Hence, the terpenoid pathways of E. coli (via 1-deoxy-D-xylulose 5-phosphate, DXP) and S. cerevisiae (via mevalonate, MVA), the impact of the respective hosts metabolism as well as the impact of different carbon sources were compared in silico by means of elementary mode analysis. The focus was set on the yield of isopentenyl diphosphate (IPP), the general terpenoid precursor, to identify new metabolic engineering strategies for an enhanced terpenoid yield.

Results: Starting from the respective precursor metabolites of the terpenoid pathways (pyruvate and glyceraldehyde-3-phosphate for the DXP pathway and acetyl-CoA for the MVA pathway) and considering only carbon stoichiometry, the two terpenoid pathways are identical with respect to carbon yield. However, with glucose as substrate, the MVA pathway has a lower potential to supply terpenoids in high yields than the DXP pathway if the formation of the required precursors is taken into account, due to the carbon loss in the formation of acetyl-CoA. This maximum yield is further reduced in both hosts when the required energy and reduction equivalents are considered. Moreover, the choice of carbon source (glucose, xylose, ethanol or glycerol) has an effect on terpenoid yield with non-fermentable carbon sources being more promising. Both hosts have deficiencies in energy and redox equivalents for high yield terpenoid production leading to new overexpression strategies (heterologous enzymes/pathways) for an enhanced terpenoid yield. Finally, several knockout strategies are identified using constrained minimal cut sets enforcing a coupling of growth to a terpenoid yield which is higher than any yield published in scientific literature so far.

Conclusions: This study provides for the first time a comprehensive and detailed in silico comparison of the most prominent heterologous hosts E. coli and S. cerevisiae as terpenoid factories giving an overview on several promising metabolic engineering strategies paving the way for an enhanced terpenoid yield.

Figure 1

Comparison of the DXP and MVA pathway. A section of the central carbon metabolism from glucose (GLC) to IPP: A: of E. coli including glycolysis and DXP pathway, and B: of S. cerevisiae including glycolysis and the MVA pathway. The maximal IPP yields based on carbon stoichiometry and ignoring energy and redox equivalent requirements (5/6 = 0.83 and 5/9 = 0.56 C-mol/C-mol) differ due to different carbon loss via CO₂ in the formation of the precursors pyruvate (PYR) and glyceraldehyde-3-phosphate (GAP) or acetyl-CoA (AcCoA). The sum shows energy requirements and redox equivalents that have to be balanced by the organism’s metabolism. *: Note that cytosolic NADP⁺-dependent aldehyde dehydrogenase (ALD6) is constitutive while cytosolic NAD⁺-dependent aldehyde dehydrogenases (ALD2, ALD3) are stress-induced, glucose-repressed [51] and were not considered.

Figure 2

Comparison of the solution space spanned by elementary modes. A: Wild type network of E. coli (36,590 EMs). B: Wild type network of S. cerevisiae (9,844 EMs). The biomass yield on glucose is plotted against the IPP yield on glucose [C-mol/C-mol] for all computed elementary modes. Elementary modes on the axes represent modes producing either only biomass (x-axis) or only IPP (y-axis). Theoretical maximal IPP yields at zero growth as well as with biomass formation are highlighted.

Figure 3

Optimal flux distribution of E. coli. Wild type (DXP pathway) on glucose as single carbon source without biomass formation (one of two modes with maximum theoretical IPP carbon yield). Numbers indicate relative molar fluxes (mmol/(gCDW h), CDW = cell dry weight) normalized to glucose uptake. Grey reactions are not active (flux = 0). Variable fluxes within two optimal modes are marked with an asterisk (*). Bold green arrows indicate biomass precursors.

Figure 4

Optimal flux distribution of S. cerevisiae. Wild type (MVA pathway) on glucose as single carbon source without biomass formation (one out of five modes with maximum theoretical IPP carbon yield). Numbers indicate relative molar fluxes (mmol/(gCDW h), CDW = cell dry weight) normalized to glucose uptake. Grey reactions are not active (flux = 0). Variable fluxes within the 5 optimal modes are marked with an asterisk (*). Bold green arrows indicate biomass precursors.

Figure 5

Concept of cMCSs illustrated with an example of E. coli. A: Solution space formed by the elementary modes of the wild type network of E. coli (36,590 EMs; compare Figure 2). The biomass yield on glucose is plotted against the IPP yield on glucose [C-mol/C-mol] of all computed elementary modes. The deletion task is defined by collecting all modes exhibiting an IPP yield lower than 0.25 C-mol/C-mol in the set of target modes $\tilde{\mathbf{T}}$ (33,087 EMs). The set of desired modes $\tilde{\mathbf{D}}$ comprises all EMs having (i) a minimum IPP yield of 0.25 C-mol/C-mol; and (ii) a simultaneous biomass yield strictly higher than zero (268 EMs). B: Example of one specific cMCS. The in silico knockout of transhydrogenase and phosphoglycerate mutase (strategy 2 in Table 3) leads to a reduction of elementary modes. Remaining elementary modes (191) display a minimum IPP yield of 0.28 and a maximum of 0.62 C-mol/C-mol as well as a maximum biomass yield of 0.41 C-mol/C-mol on glucose. In those modes, biomass formation is coupled to a minimum yield of IPP production.