Understanding the regulation of aspartate metabolism using a model based on measured kinetic parameters
Gilles Curien1,2,3,4, Olivier Bastien4, Mylène Robert-Genthon1,2,3,4, Athel Cornish-Bowden5, María Luz Cárdenas5 & Renaud Dumas1,2,3,4
- CNRS, UMR 5168, 17 rue des Martyrs, Grenoble, France
- CEA, DSV, iRTSV, Laboratoire de Physiologie Cellulaire Végétale, Grenoble, France
- Université Joseph Fourier, Grenoble, France
- INRA, UMR 1200, Grenoble, France
- CNRS-BIP, 31 chemin Joseph-Aiguier, Marseille Cedex 20, France
Correspondence to: Gilles Curien1,2,3,4 IRTSV, CNRS, UMR 5168, 17 rue des Martyrs, Grenoble 38054, France. Tel.: +33 4 38 782 364; Fax: +33 4 38 785 091; Email: gcurien@cea.fr
Received 20 August 2008; Accepted 16 April 2009; Published online 19 May 2009
Article highlights
- A key result from the quantitative model of the aspartate pathway from Arabidopsis is the identification of feedback controls whose function is not to couple demand and supply but to maintain a high independence between fluxes in competing pathways.
- The model shows that enzyme isoforms are not functionally redundant, because they contribute unequally to the flux and/or to its regulation
- Another result is the identification of threonine concentration as the most sensitive variable in the system, suggesting a regulatory role for threonine at a higher level of integration
Synopsis
A challenging goal of systems biology is to develop detailed kinetic models for simulating and predicting the dynamic responses of metabolic networks. However, very few published models have been based on kinetic measurements on enzymes in conditions relevant to those in vivo. In addition, the few kinetic models of real systems that have been published do not address the action of effectors in branched pathways. As feedback regulation often nullifies the effects of genetic manipulations, it is important to understand the molecular mechanism of robustness to genetic perturbations of biochemical networks. Until now the functioning of allosteric controls in a branched metabolic system has only been studied theoretically (e.g. Savageau, 1974; Cornish-Bowden et al, 1995). In addition, branch-point enzymes often exist as isoforms that respond unequally to cooperative inhibition (or sometimes activation) by allosteric effectors. The physiological role of isoforms has been discussed for many years (Ureta, 1978), and study of the flux distribution between isoforms in a real pathway should shed light on the need for them. The aspartate-derived amino-acid pathway from plants constitutes an excellent model system for understanding regulatory mechanisms in branched metabolic pathways. This pathway is responsible for the distribution of the carbon flux from aspartate into the branches for synthesis of lysine, threonine, methionine and isoleucine. There are several branch-points, many enzyme isoforms and different allosteric control mechanisms (inhibition, activation, antagonism and synergism), as illustrated in Figure 1B. Much is known about the individual components of the systems but little about their role in the economy of the ensemble, as until now no large-scale detailed kinetic model of this system has been available either in plants or in microorganisms, though small parts of it have been modelled in bacteria (Chassagnole et al, 2001; Yang et al, 2005) and in plants (Curien et al, 2003).
Figure 1
The central enzymes of the Asp-derived amino-acid pathway in chloroplasts and allosteric regulation. (A) The amino acids Lys, Met, Thr and Ile, and the methylating agent AdoMet are synthesized from aspartate. Simple and double arrows indicate reactions treated as irreversible and reversible, respectively. Enzyme names are indicated in italics. (B) Regulatory map of the Asp-derived amino-acid pathway in Arabidopsis leaf mesophyll cell. The 13 enzymes constituting the core of the system are co-expressed in Arabidopsis mesophyll cell chloroplasts. Isoforms exist for monofunctional AKs (AK1, AK2), bifunctional AKs (AKI–HSDH I and AKII–HSDH II) and DHDPS (DHDPS1 and 2). AK3 and TS2 are not expressed in mesophyll cell chloroplasts (see Supplementary data). Genes for ASADH, HSK, CGS and allosteric TD are present as single copies in the Arabidopsis genome. Enzymes shown against a white background are non-allosteric, and those with a yellow background are allosteric. Bifunctional AK–HSDH proteins are symbolized with a linker between the two domains. Continuous lines symbolize interactions of high apparent affinity, and broken lines symbolize interactions of low apparent affinity. Boxed metabolites appear explicitly in the enzyme equation models. Other metabolite concentrations were taken into account but are subsumed in the apparent rate constants (see Supplementary data).
Full figure and legend (173K)Figures & Tables indexA mathematical model of the core of the pathway in the chloroplasts of the model plant Arabidopsis has been constructed with a view to understanding and quantifying the short-term regulatory capabilities of the system. Flux values and metabolite concentration calculated with the model (Figure 3) were very close to in vivo values. In addition, the metabolic pattern of several mutants could be reproduced. The present work represents the first detailed kinetic model of a real branched metabolic pathway in which all the allosteric controls and isoforms are taken into account. Kinetic characterization in conditions that mimic the in vivo metabolic context, together with the use of purified recombinant enzymes, explains the predictive power of the model. A key result from this model is the identification of allosteric interactions whose function is not to couple demand and but to maintain a high independence between fluxes in competing pathways. Specifically, it shows that proximal controls of dihydrodipicolinate synthase (DHDPS) by Lys and of threonine synthase (TS1) by S-adenosylmethionine (AdoMet) are involved in the coupling between demand and supply. Distal controls (synergistic inhibition of aspartate kinase 1, inhibition of aspartate kinase 2 by lysine) may also contribute to this function but are much less efficient. Their main function is rather to attenuate flux changes in the branches where demand remains constant, and they thus participate in the independence between pathways. The existence of isoforms with different regulatory patterns also contributes to this independence indicating that enzyme isoforms are not functionally redundant. Another result is the explanation of threonine concentration instability, suggesting a regulatory role for threonine at a higher level of integration. Finally, important information useful for later work is the estimation of the half-time of the system (400 s). This metabolic system is a slow responsive one, but kinetic controls are still slightly faster than protein concentration control mechanisms.
Figure 3
Reference steady state. Each enzyme is represented by a green disk of sizes that roughly indicate the abundance of the corresponding protein (Table V). Each flux is symbolized by a gray line of thickness proportional to its magnitude, its value in 
M s-1 being shown in blue. Metabolites shown against a yellow background (Asp, AdoMet, Val, Cys) have fixed concentrations; those with white backgrounds have the steady-state concentrations indicated, which were obtained by the simulation. The limiting rate VAaRS for lysyl, threonyl and isoleucyl tRNA synthetases was set at 0.43 M s-1. The concentrations of pyruvate, ATP, ADP, NADPH, NADP, Pi, all set at physiological values (see Materials and methods), are omitted from the scheme for the sake of clarity. Histograms for the common flux (ASADH), the demand flux for AdoMet, and the demand fluxes for the three aminoacyl-tRNA synthetases indicate the flux control coefficients for these fluxes (see Table II for numerical values). In each histogram the ordinate range is from –1 to +1, and the black bar (not visible in the ASADH histogram because the control coefficient concerned is very small) refers to the enzyme that catalyzes the reaction concerned. Most other control coefficients are very small, but non-trivial ones are labeled with the corresponding enzymes.
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Full figure and legend (160K)Figures & Tables index
