An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR)

Abstract

Background: Diverse datasets, including genomic, transcriptomic, proteomic and metabolomic data, are becoming readily available for specific organisms. There is currently a need to integrate these datasets within an in silico modeling framework. Constraint-based models of Escherichia coli K-12 MG1655 have been developed and used to study the bacterium's metabolism and phenotypic behavior. The most comprehensive E. coli model to date (E. coli iJE660a GSM) accounts for 660 genes and includes 627 unique biochemical reactions.

Results: An expanded genome-scale metabolic model of E. coli (iJR904 GSM/GPR) has been reconstructed which includes 904 genes and 931 unique biochemical reactions. The reactions in the expanded model are both elementally and charge balanced. Network gap analysis led to putative assignments for 55 open reading frames (ORFs). Gene to protein to reaction associations (GPR) are now directly included in the model. Comparisons between predictions made by iJR904 and iJE660a models show that they are generally similar but differ under certain circumstances. Analysis of genome-scale proton balancing shows how the flux of protons into and out of the medium is important for maximizing cellular growth.

Conclusions: E. coli iJR904 has improved capabilities over iJE660a. iJR904 is a more complete and chemically accurate description of E. coli metabolism than iJE660a. Perhaps most importantly, iJR904 can be used for analyzing and integrating the diverse datasets. iJR904 will help to outline the genotype-phenotype relationship for E. coli K-12, as it can account for genomic, transcriptomic, proteomic and fluxomic data simultaneously.

Figure 1

Principles of constraint-based modeling. A three-dimensional flux space for a given metabolic network is depicted here. Without any constraints the fluxes can take on any real value. After application of stoichiometric, thermodynamic and enzyme capacity constraints, the possible solutions are confined to a region in the total flux space, termed the allowable solution space. Any point outside of this space violates one or more of the applied constraints. Linear optimization can then be applied to identify a solution in the allowable solution space that maximizes or minimizes a defined objective, for example ATP or biomass production [3-6].

Figure 2

Representation of gene to protein to reaction (GPR) associations. Each gene included in the model is associated with at least one reaction. Examples of different types of associations are shown, where the top layer is the gene locus, the second layer is the translated peptide, the third layer is the functional protein and the bottom layer is the reaction (shown as its corresponding abbreviation listed in the additional data file). Subunits (for example, sdhABCD and gapC_1C_2) and enzyme complexes (for example, xylFGH) are connected to reactions with '&' associations, indicating that all have to be expressed for the reaction to occur. For sdhABCD, the '&' is shown above the functional protein level, denoting that all of these gene products are needed for the functional enzyme. With xylFGH the '&' association is shown above the reaction level, indicating that the different proteins form a complex that carries out the reaction. Isozymes (for example, gapC_1C_2 and gapA) are independent proteins which carry out identical reactions where only one of the isozymes needs to be present for the reaction to occur. Isozymes are shown as two or more arrows leaving different proteins but impinging on the same reaction.

Figure 3

Quinone specificity for electron donors and terminal acceptors. E. coli iJR904 also accounts for the quinone specificity of different reactions. Electron donors are listed on the left, terminal electron acceptors are on the right, and the three types of quinones (DMK, MK, Q; note that these abbreviations differ from those listed in the additional data files) that serve as carriers are shown in the middle. The electron donors shown in red were able to donate their electrons to fumarate in iJE660a, but are now unable to do so. DMK, demethylmenaquinone; DMSO, dimethyl sulfoxide; MK, menaquinone; Q, ubiquinone; TMAO, trimethylamine N-oxide.

Figure 4

Effect of proton balancing on predicted growth rate. The figure shows how limiting the exchange of protons between the cell and the medium affects the predicted growth rates under aerobic conditions. The relative growth rate (y-axis) is the ratio of the predicted growth rate when the proton exchange flux is limited over the predicted growth rate when the proton exchange flux is not limited (at its optimal value). These calculations are for the conditions of aerobic growth on minimal media; the carbon source uptake rates were set to 5 mmol/g DW-hr and the maximum oxygen uptake rate was set to 20 mmol/g DW-hr. Carbon sources resulting in an outward flux of protons (lines to the right of the y-axis) would make the medium more acidic, while carbon sources resulting in an inward flux of protons (lines to the left of the y-axis) would make the medium more basic.

Figure 5

Comparisons between phenotypic phase planes (PhPP) calculated using iJR904 and iJE660a. (a-e) The PhPP for growth on (a) malate, (b) glucose, (c) acetate, (d) glycerol, and (e) α-ketoglutarate (αKG) are shown, where the red lines show the PhPP calculated from iJR904 and the blue lines the PhPP calculated from iJE660a. The line of optimality (LO) corresponds to maximal biomass yield. (e) With αKG as the carbon source, the phase planes calculated from iJR904 (red line) and iJE660a (blue line) are drastically different in the oxygen-limited region (area below the LO). (f) The different metabolic routes (along with their associated genes) added to the network in iJR904 that enable fermentative growth on αKG are shown in red, blue and green. AC, acetate; CIT, citrate; GLYCLT, glycolate; GLYC-R, R-glycerate; GLX, glyoxylate; ICIT, isocitrate; αKG, α-ketoglutarate, OAA, oxaloacetate; 3PG, 3-phospho-D-glycerate; SUCC, succinate; SUCCOA, succinyl-CoA.

Figure 6

Flux map of TCA cycle and citrate lyase. During oxygen-limited growth on αKG maximal biomass will be made by utilizing the citrate lyase reaction. As oxygen becomes more limiting, the reactions shown in red, culminating in the production of oxaloacetate (OAA), were predicted by iJR904 to be used more heavily than the forward direction of the TCA cycle reactions (shown in black). iJE660a does not include the citrate lyase reaction so carbon flow is directed only in the forward direction of the TCA cycle; these reactions produce more reduced redox carriers, which are difficult for the cell to oxidize in an oxygen-limited environment. The '+' sign indicates that the cofactor is produced by the reaction in the direction shown and the '-' sign indicates that the cofactor is consumed. For abbreviations see Figure 5, with the following additions: ACCOA, acetyl-CoA; FUM, fumarate; MAL, malate, PYR, pyruvate.