Occurrence of paradoxical or sustained control by an enzyme when overexpressed: necessary conditions and experimental evidence with regard to hepatic glucokinase

Control analysis in the identification of key enzymes driving metabolic adaptations: Towards drug target discovery

Metabolic Control Analysis (MCA) marked a turning point in understanding the design principles of metabolic network control by establishing control coefficients as a means to quantify the degree of control that an enzyme exerts on flux or metabolite concentrations. MCA has demonstrated that control of metabolic pathways is distributed among many enzymes rather than depending on a single rate-limiting step. MCA also proved that this distribution depends not only on the stoichiometric structure of the network but also on other kinetic determinants, such as the degree of saturation of the enzyme active site, the distance to thermodynamic equilibrium, and metabolite feedback regulatory loops. Consequently, predicting the alterations that occur during metabolic adaptation in response to strong changes involving a redistribution in such control distribution can be challenging. Here, using the framework provided by MCA, we illustrate how control distribution in a metabolic pathway/network depends on enzyme kinetic determinants and to what extent the redistribution of control affects our predictions on candidate enzymes suitable as targets for small molecule inhibition in the drug discovery process. Our results uncover that kinetic determinants can lead to unexpected control distribution and outcomes that cannot be predicted solely from stoichiometric determinants. We also unveil that the inference of key enzyme-drivers of an observed metabolic adaptation can be dramatically improved using mean control coefficients and ruling out those enzyme activities that are associated with low control coefficients. As the use of constraint-based stoichiometric genome-scale metabolic models (GSMMs) becomes increasingly prevalent for identifying genes/enzymes that could be potential drug targets, we anticipate that incorporating kinetic determinants and ruling out enzymes with low control coefficients into GSMM workflows will facilitate more accurate predictions and reveal novel therapeutic targets.

Paradoxical performance of tryptophan synthase gene trp1 (+) in transformations of the basidiomycete Coprinopsis cinerea

Several transformation strains of Coprinopsis cinerea carry the defective tryptophan synthase allele trp1-1,1-6 which can be complemented by introduction of the trp1 (+) wild-type gene. Regularly in C. cinerea, single-trp1 (+)-vector transformations yield about half the numbers of clones than cotransformations with a non-trp1 (+)-plasmid done in parallel. The effect is also observed with the orthologous Schizophyllum commune trpB (+) gene shown here to function as a selection marker in C. cinerea. Parts of single-trp1 (+) - or single-trpB (+) -vector transformants are apparently lost. This paradoxical phenomenon relates to de-regulation of aromatic amino acid biosynthesis pathways. Adding tryptophan precursors to protoplast regeneration agar or feeding with other aromatic amino acids increases loss of single-trp1 (+)-vector transformants and also sets off loss of clones in cotransformation with a non-trp1 (+)-plasmid. Feedback control by tryptophan and cross-pathway control by tyrosine and phenylalanine are both active in the process. We deduce from the observations that more cotransformants than single-vector transformants are obtained by in average less disturbance of the tryptophan biosynthesis pathway. DNA in C. cinerea transformation usually integrates into the genome at multiple ectopic places. Integration events for a single vector per nucleus should statistically be 2-fold higher in single-vector transformations than in cotransformations in which the two different molecules compete for the same potential integration sites. Integration of more trp1 (+) copies into the genome might more likely lead to sudden tryptophan overproduction with subsequent rigid shut-down of the pathway. Blocking ectopic DNA integration in a Δku70 mutant abolished the effect of doubling clone numbers in cotransformations due to preferred single trp1 (+) integration by homologous recombination at its native genomic site.

Glucokinase and molecular aspects of liver glycogen metabolism

Conversion of glucose into glycogen is a major pathway that contributes to the removal of glucose from the portal vein by the liver in the postprandial state. It is regulated in part by the increase in blood-glucose concentration in the portal vein, which activates glucokinase, the first enzyme in the pathway, causing an increase in the concentration of glucose 6-P (glucose 6-phosphate), which modulates the phosphorylation state of downstream enzymes by acting synergistically with other allosteric effectors. Glucokinase is regulated by a hierarchy of transcriptional and post-transcriptional mechanisms that are only partially understood. In the fasted state, glucokinase is in part sequestered in the nucleus in an inactive state, complexed to a specific regulatory protein, GKRP (glucokinase regulatory protein). This reserve pool is rapidly mobilized to the cytoplasm in the postprandial state in response to an elevated concentration of glucose. The translocation of glucokinase between the nucleus and cytoplasm is modulated by various metabolic and hormonal conditions. The elevated glucose 6-P concentration, consequent to glucokinase activation, has a synergistic effect with glucose in promoting dephosphorylation (inactivation) of glycogen phosphorylase and inducing dephosphorylation (activation) of glycogen synthase. The latter involves both a direct ligand-induced conformational change and depletion of the phosphorylated form of glycogen phosphorylase, which is a potent allosteric inhibitor of glycogen synthase phosphatase activity associated with the glycogen-targeting protein, GL [hepatic glycogen-targeting subunit of PP-1 (protein phosphatase-1) encoded by PPP1R3B]. Defects in both the activation of glucokinase and in the dephosphorylation of glycogen phosphorylase are potential contributing factors to the dysregulation of hepatic glucose metabolism in Type 2 diabetes.

Kinetic properties required for sustained or paradoxical control of metabolic fluxes under large changes in enzyme activities

The effect that an increase in the activity of an enzyme has on its flux normally decreases with activity increase. To achieve a large increase in flux by manipulating a single step would therefore require a high initial effect that maintains or increases when the activity is increased, what has been called sustained or paradoxical control. Using metabolic control analysis for large responses, we derive conditions for sustained or paradoxical control in terms of elasticity coefficients. These are used to characterise types of rate laws contributing to this behaviour. The result that simple pathways, with normal kinetics, subject to large activity changes can lead to paradoxical control behaviour suggests that this type of pattern may be much more ubiquitous than could have, in principle, been suspected.

Contributions of glucokinase and phosphofructokinase-2/fructose bisphosphatase-2 to the elevated glycolysis in hepatocytes from Zucker fa/fa rats

The insulin-resistant Zucker fa/fa rat has elevated hepatic glycolysis and activities of glucokinase and phosphofructokinase-2/fructose bisphosphatase-2 (PFK2). The latter catalyzes the formation and degradation of fructose-2,6-bisphosphate (fructose-2,6-P2) and is a glucokinase-binding protein. The contributions of glucokinase and PFK2 to the elevated glycolysis in fa/fa hepatocytes were determined by overexpressing these enzymes individually or in combination. Metabolic control analysis was used to determine enzyme coefficients on glycolysis and metabolite concentrations. Glucokinase had a high control coefficient on glycolysis in all hormonal conditions tested, whereas PFK2 had significant control only in the presence of glucagon, which phosphorylates PFK2 and suppresses glycolysis. Despite the high control strength of glucokinase, the elevated glycolysis in fa/fa hepatocytes could not be explained by the elevated glucokinase activity alone. In hepatocytes from fa/fa rats, glucokinase translocation between the nucleus and the cytoplasm was refractory to glucose but responsive to glucagon. Expression of a kinase-active PFK2 variant reversed the glucagon effect on glucokinase translocation and glucose phosphorylation, confirming the role for PFK2 in sequestering glucokinase in the cytoplasm. Glucokinase had a high control on glucose-6-phosphate content; however, like PFK2, it had a relative modest effect on the fructose-2,6-P2 content. However, combined overexpression of glucokinase and PFK2 had a synergistic effect on fructose-2,6-P2 levels, suggesting that interaction of these enzymes may be a prerequisite for formation of fructose-2,6-P2. Cumulatively, this study provides support for coordinate roles for glucokinase and PFK2 in the elevated hepatic glycolysis in fa/fa rats.

Metabolic control analysis aimed at the ribose synthesis pathways of tumor cells: a new strategy for antitumor drug development

Metabolic control analysis predicts that effects on tumor growth are likely to be obtained with lower concentrations of drug, if an enzyme with a high control coefficient on tumor growth is being inhibited. Here we measure glucose-6-phosphate dehydrogenase (G6PDH) control coefficient on in vivo tumor growth using mice bearing Ehrlich ascites tumor cells. We used dehydroepiandrosterone-sulphate (DHEA-S), an uncompetitive inhibitor of this enzyme and the in situ cytochemical method to measure the enzyme activity changes that accompany changes on tumor cell growth. This method ensures that the enzyme activity determined is the one existing in the in situ conditions and enables computing a control coefficient in in situ conditions. From the data obtained on tumor cell number and the in situ enzyme activities in absence and presence of DHEA-S, a control coefficient of 0.41 for G6PDH on tumor cell growth was computed. This value is approximately the half of the transketolase control coefficient value of 0.9 previously reported. Moreover, the use of in situ methods to assess enzyme activities, applied for first time for the calculation of control coefficients in this study, opens new avenues to the use of uncompetitive inhibitors for the measurement of in situ control coefficients.

Elasticity analysis and design for large metabolic responses produced by changes in enzyme activities

Metabolic control analysis has been extensively used to describe how the sensitivity properties of the component enzymes in a metabolic pathway (represented by the elasticity coefficients) determine the way in which metabolic variables respond (described by the control coefficients). Similarly, metabolic control design addresses the inverse problem of obtaining the sensitivity properties of the component enzymes that are required for the system to show a pre-established pattern of responses. These formalisms, including what is called elasticity analysis and design, were developed for small, strictly speaking infinitesimal, changes. Here we extend them to large metabolic responses. The new approach can be applied to simple two-step pathways or to any arbitrary metabolic system divided into two groups linked by one intermediate. General expressions that relate control and elasticity coefficients for large changes are derived. Concentration and flux connectivity relationships are obtained. The relationships for large changes indicate that the pattern of responses is not necessarily the same as the one obtained with the traditional infinitesimal approach, in some cases the patterns being qualitatively different. The general analysis is used to study the control of ketogenesis in rat liver mitochondria, starting from data available in the literature. The control profile of the pathway subject to large changes shows both quantitative and qualitative differences from the one obtained from an analysis that is performed with infinitesimal coefficients. This exemplifies the type of errors that may be introduced when drawing conclusions about large metabolic responses from results obtained with an infinitesimal treatment.