Deletion of the pyk gene (1428 bp) was obtained by allelic replacement with a shortened DNA fragment containing only the two flanking regions, but not the coding sequence of the gene. The resulting excision of the whole nucleotide sequence of pyk was verified by PCR analysis. Using a site specific primer set of forward and reverse primer (Table 1), the obtained DNA fragment was shortened by about 1500 bp when genomic DNA of lysC^fbr Δpyk was used as template instead of genomic DNA of the reference strain C. glutamicum ATCC 13032.

C. glutamicum lysC^fbr Δpyk revealed complete absence of in vitro pyruvate kinase activity (< 0.6 mU mg^-1), whereas the parent strain C. glutamicum lysC^fbr showed a strong specific activity of 1099 mU mg^-1 for this enzyme. PCR analysis and in vitro measurement of enzyme activity in crude cell extracts ensured the deletion of the pyk gene and further demonstrated that there is no remaining pyruvate kinase like activity in C. glutamicum lysC^fbr Δpyk. In the deletion strain, direct conversion of PEP into pyruvate thus is restricted to glucose uptake by the PTS.

To investigate quantitative physiological effects of the pyruvate kinase deletion, lysine producing C. glutamicum lysC^fbr and its pyruvate kinase-deficient derivative C. glutamicum lysC^fbr Δpyk were grown in batch culture (Figure 1A–D). While both strains exhibited relatively similar growth characteristics, e.g. concerning specific growth rate, specific glucose uptake rate or biomass yield, yield and specific rate of lysine production appeared to be slightly lower in C. glutamicum lysC^fbr Δpyk (Table 2, 3). Moreover, lack of pyruvate kinase activity resulted in the formation of the overflow metabolites dihydroxyacetone (DHA) and glycerol whereas these compounds were not produced by the parent strain. Trehalose formation was slightly reduced. No significant difference was observed concerning the secretion of lactate which, however, was formed only in very low amount. Major kinetic and stoichiometric parameters, including specific growth rate or yield for biomass, lysine and by-products remained constant throughout the cultivation (Figure 1A – D). This clearly shows that both strains were in metabolic steady-state. In this context, the dissolved oxygen level was above 20 % throughout the whole cultivation so that sufficient oxygen supply for the cells was ensured. The pH remained constant in a range of 7.0 ± 0.2.

In the present work, complete resolution of the fluxes through the different carboxylating and decarboxylating enzymes, i.e. PEPC, PC, phosphoenolpyruvate carboxykinase (PEPCK) and malic enzyme, was required, in order to study the exact influence of the pyruvate kinase deletion to these closely linked reactions. First, the metabolic network of C. glutamicum was extended by considering PEP and pyruvate as separate pools and PEPC, PC, PEPCK and malic enzyme as separate metabolic reactions. For this scenario, a previous approach utilizing labeling data from a single tracer study with [1-¹³C] glucose for C. glutamicum was significantly extended, since this is not capable to completely resolve all these fluxes, but provides lumped carboxylation and decarboxylation flux 17. Concerning the central metabolic pathways, [1-¹³C] glucose is valuable for resolving the upper part of metabolism, in particular the oxidative PP pathway, glycolysis and the Entner-Doudoroff pathway, whereas the use of a mixture of [¹³C₆] glucose and unlabeled glucose is particularly useful to resolve fluxes downstream of PEP, especially at the pyruvate node 2324. Due to this the experimental strategy was based on two parallel tracer studies with (i) [1-¹³C] glucose and (ii) a mixture of [¹³C₆] glucose and unlabeled glucose to combine the information content available from the GC-MS labelling analysis of metabolites from the different tracer substrates for the flux calculation. Since it is known, that more detailed information for flux calculation can be obtained with GC-MS via additional analysis of fragment ions which contain only specific parts of the carbon skeleton of the analyte, we additionally considered a number of fragment ions from the proteinogenic amino acids which have previously proven useful for flux analysis in complex metabolic networks of prokaryotes 25 and eukaryotes 26. Overall, 197 different mass isotopomer fractions were considered here for each strain, whereas the original simplified approach with only one single tracer experiment and less fragments measured considered only 29 mass isotopomer fractions. The extended approach was tested by computer based simulation studies to see, weather the additionally considered labeling information can be utilized to determine the additionally introduced free fluxes around the pyruvate node. For this purpose, sensitivities for the available mass isotopomer fractions and the flux parameters of interest were derived from partial derivatives as previously described 24. The labeling patterns of a number of newly considered fragment ions were affected by variation of the free fluxes around the pyruvate node and thus contain sensitive information to determine these flux parameters of interest. This is exemplified for the study with [¹³C₆] glucose and unlabeled glucose as tracer substrate and variation of the free flux parameters Φ_PEPC (flux partitioning between PEPC and PC), ζ_PEPC/PEPCK (exchange flux by PEPC and PEPCK) and ζ_PC/MAE (exchange flux by PC and malic enzyme) which strongly influences the labelling pattern of analyzed metabolites (Figure 2). Using synthetic labelling data sets, a unique solution for the free fluxes was obtained in multiple parameter estimations with randomized varied starting values, demonstrating the observability and identifiability of all free fluxes in the network and the suitability of the developed extended approach.

Metabolic flux analysis, as applied here, requires a metabolic and isotopic steady-state of the investigated culture. The presence of metabolic steady-state was ensured from the constant growth and production behavior of the two strains (Figures 1A–D). Exemplified for different proteinogenic amino acids from the two parallel tracer studies sampled at different time points of the cultivation, also the ¹³C labeling patterns of the metabolites remained constant over time (Figure 3). This indicated isotopic steady-state during the cultivation, so that the flux distributions obtained can be taken as representative for each strain covering the whole cultivation period. The calculation of the metabolic fluxes was based on minimizing the deviation between the experimentally measured and the simulated mass isotopomer fractions. The approach comprised metabolic balancing during each step considering stoichiometric data on growth and product formation from three parallel cultivations (Table 2) and on anabolic demand for biomass precursors 27. Obviously, an excellent fit was achieved (Table 4). The set of intracellular fluxes that gave minimum deviation between experimental and simulated labelling patterns was taken as best estimate for the intracellular flux distribution (Figure 4).