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Metabolic Control Theory and the genetics and evolution of metabolic fluxes UMR de Génétique Végétale, INRA/UPS/CNRS/INA PG Ferme du Moulon, 91190 Gif-sur-Yvette,

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Presentation on theme: "Metabolic Control Theory and the genetics and evolution of metabolic fluxes UMR de Génétique Végétale, INRA/UPS/CNRS/INA PG Ferme du Moulon, 91190 Gif-sur-Yvette,"— Presentation transcript:

1 Metabolic Control Theory and the genetics and evolution of metabolic fluxes UMR de Génétique Végétale, INRA/UPS/CNRS/INA PG Ferme du Moulon, Gif-sur-Yvette, France Christine Dillmann, Julie Fievet, Sébastien Lion, Frédéric Gabriel, Grégoire Talbot, Delphine Sicard, Dominique de Vienne

2 Metabolic Control Theory and the genetics and evolution of metabolic fluxes - Metabolic fluxes as model quantitative traits and the relationship between genotype and phenotype - Experimental validation of the Metabolic Control Theory - The metabolic bases of dominance and heterosis - Evolution of enzyme concentrations in natural populations

3 Quantitative traits Most phenotypic traits … Growth rate, flowering date, fruit pH, behaviour traits, morphological traits, blood pressure, metabolic flux, enzyme activity, mRNA/protein concentrations, etc. « Quantitative » genetics … Display a continuous variation within populations :

4 Continuous variation can be maintained by independent segregation of multiple factors. George Udny Yule, G. J. Mendel, 1865 « Recherche sur les hybrides d autres plantes » A AB Ba ab b x A ab B F2 aabb Aabb aaBb AaBb aaBB AAbb AABb AaBB AABB N Number of «Capital letter » alleles

5 E 1 E 2 E j E n X 0 S 1 … S j 1 S j … X n Enzymes The genotype : all the genes that determine enzyme activities (concentrations and kinetic parameters, genetically variable) The metabolic fluxes as model quantitative traits The phenotype : the flux Kacser H. and Burns J.A., The molecular basis of dominance. Genetics, 97,

6 E1E1 E2E2 EjEj E j+1 EnEn S n-1 SjSj … S1S1 S0S0 SnSn … E n-1 Stationary phase : v 1 = v 2 = … = v j = … = v n = J Kacser and Burns, 1973 Heinrich and Rappoport, 1974 Metabolic control theory (1) Michaelis-Menten enzymes Enzymes far from saturation Enzyme concentrations are independent

7 E1E1 E2E2 EjEj E j+1 EnEn S n-1 SjSj … S1S1 S0S0 SnSn … E n-1 At stationary phase : v 1 = v 2 = … = v j = … = v n = J Kacser and Burns, 1973 Heinrich and Rappoport, 1974 Metabolic control theory (2)

8 AjAj Kinetic parameters : Enzyme efficiency : EjEj Enzyme cellular concentration QjQj = A j Q j Metabolic control theory (3) : genetically variable parameters

9 Genetic variability of kinetic parameters - Few in vivo data - Slightly variable Wang & Dykhuisen, Pathway of gluconate metabolism in E. coli. Evolution, 55:897.

10 fba1 IPG SDS Number of molecules per cell Genetic variability of enzyme concentrations - Highly variables Fiévet et al, 2004

11 Flux J Q j or A j or E j = Q j x A j J max n enzymes 1 enzyme Relationship between enzymes and flux : independent enzymes non linear relationship between the flux and the concentration of on enzyme of the pathway the flux tends asymptotically towards a maximum which depends on the concentrations of all the enzymes of the pathway

12 Metabolic Control Theory and the genetics and evolution of metabolic fluxes - Metabolic fluxes as model quantitative traits and the relationship between genotype and phenotype - Experimental validation of the Metabolic Control Theory - The metabolic bases of dominance and heterosis - Evolution of enzyme concentrations in natural populations

13 Experimental validation : in vivo Kacser and Burns, Genetics 97:639

14 Relationship RubisCO-photosynthesis A typical example: dependence of carbon assimilation flux on rubisco levels in transgenic tobacco plants. Laurer et al, Planta (1993).

15 Experimental validation : in vitro glucosefructose 1,6 bisP GAP DHAP glycérol 3 P NADH NAD + GPI FBA TPI Créatine-P + ADPCréatine + ATP Créatine kinase ATP ADP PFK1 ATP ADP HK glucose 6 Pfructose 6 P First part of glycolysis Julie Fievet et Gilles Curien Temps Concentration du NADH Etat stationnaire

16 Mixing enzymes, substrates, cofactors Enzymes HXK+PGI+PFK + FBA+TPI+G3D H+CK Substrates Glucose+ Creatine P+ NADH+ buffer ATP One tube one « genotype »

17 Experimental validation HXK concentration (µM) Each enzyme vary at a turn, the other being kept constant = Titration curves non linear relationship between the flux and the concentration of on enzyme of the pathway the flux tends asymptotically towards a maximum which depends ont the concentrations of all the enzymes of the pathway

18 Complex equations Many parameters Estimation of kinetic parameters : explicit modelling

19 Estimation of kinetic parameters : MCT-based modelling A i composite activity parameter p i dispensability p i =0 pi0pi0pi0pi0 J0J0 J max The maximum value for the flux is estimated from titration curves :

20 Q ref J max J0J0 Sp i SA i hxk0,118, ,93 pgi0,1513, ,5 pfk0,2917, ,02 fba1,5418,540 0 tpi0,8412,6110,4661,3559,79 Activity parameters are estimated in systemo. They are different from what can be estimated on isolated enzymes The global equation can be used to predict the flux for other enzyme concentrations : Predicting the flux Fievet et al, submitted

21 Predicted flux (µM/s) Flux measured in vitro (µM/s) r = 0,94 Testing the predictor for the flux on 122 genotubes Fievet et al, submitted

22 Metabolic Control Theory and the genetics and evolution of metabolic fluxes (1) Based on MCT, we validated a simple model which describes the relationship between flux and enzyme concentrations (2) Composite kinetic parameters can be estimated « in vivo » from titration curves (3) It should also work for more complex networks like … S1S1 S2S2 S3S3 S4S4 S5S5 S6S6 S7S7 S8S8 E1E1 E2E2 E3E3 E5E5 E4E4 E6E6 E7E7 E8E8 E9E9 E 10 … with one stable stationary state

23 Metabolic Control Theory and the genetics and evolution of metabolic fluxes - Metabolic fluxes as model quantitative traits and the relationship between genotype and phenotype - Experimental validation of the Metabolic Control Theory - The metabolic bases of dominance and heterosis - Evolution of enzyme concentrations in natural populations

24 Genetical consequences of ~hyperbolic relationships : dominance - Most deleterious mutations are recessive - There is ~ additivity between highly deleterious mutations Three observations - There is ~ additivity between slightly deleterious mutations

25 - R. A. Fisher (1928, 1931, 1958) : «modifiers» of dominance relationship between alleles arise due to natural selection - S. Wright (1934) : dominance can be explained by the non linear genotype-phenotype relationship. Two hypothesis to explain dominance

26 Dominance : Fishers model does not work Population genetics models : mutations are eliminated before they become recessive. Mutations in Chlamydomonas reinhardtii (Orr, 1991).

27 Recessive mutations occur in Chlamydomonas as frequently as in drosophila Dominance : Fishers model does not work

28 Dominance E i Flux A1A1A1A1 A1A2A1A2 A2A2A2A2 Kacser H. and Burns J.A., The molecular basis of dominance. Genetics, 97, E i Flux Weak dominance Dominance : S. Wright was right

29 Généralisation : several variables enzymes E. Coli - Dykhuisen et al., 1987, Genetics, 115, 25 Flux

30 Generalization : metabolic model for heterosis i ii iii hybrid Levure Huître P 1 Homozygous line Maïs F 1 hybrid Increased vigor F 1 > (P 1,P 2 ) P 2 Homozygous line

31 J EjEj EiEi EjEj EiEi Line 1 x Line 2Hybrid F1 Metabolic heterosis due to dominance at different loci

32 JP1JP1 JP2JP2 Heterosis in vitro Tube 1 Tube 2 Tube (1+2)/2 Flux Simulations Fievet et al, in prep

33 Metabolic Control Theory and the genetics and evolution of metabolic fluxes (4) Dominance and heterosis arise as emergent properties of metabolic systems (5) Heterosis can be explained by antagonistic epistatic relationships between enzymes

34 Metabolic Control Theory and the genetics and evolution of metabolic fluxes - Metabolic fluxes as model quantitative traits and the relationship between genotype and phenotype - Experimental validation of the Metabolic Control Theory - The metabolic bases of dominance and heterosis - Evolution of enzyme concentrations in natural populations

35 Evolution of enzyme concentration under selection for increasing the flux W= Monte-Carlo simulations Analytical predictions Natural selection shapes the sharing out of the control of the flux Talbot et al, in prep

36 Dominique de Vienne Bruno Bost Julie Fiévet Frédéric Gabriel Sébastien Lion Delphine Sicard Grégoire Talbot Gilles Curien Olivier Martin

37 Heterosis and epistasis -A substitution at one locus changes the effects of a substitution at another locus -The effect of a substituion depends on the genetic background E A1 E B1 E B2 E A2 Flux

38 Heterosis and epistasis Tryptophane flux Synergistic epistasis in tryptophane metabolic pathway Niederberger et al., 1992, Biochem. J. 287, 473.

39 Epistasis index Antagonistic Synergistic Additivity I = 1 J hyb J J H = Heterosis and epistasis Heterosis index Fievet et al, in prep

40 Autres axes de recherche La concentration denzymes allouée à une chaîne est nécessairement finie ( « compétition » corrélations négatives) Corrélations physiologiques, positives ou négatives Matrice n x n des E j / E i E total fixé, ou fonction de coût 1- Les concentrations des enzymes ne sont pas nécessairement indépendantes

41 Autres axes de recherche 2- Comment agit la sélection pour maximiser/optimiser un flux ? - Approche expérimentale : variabilité des paramètres enzymatiques et évolution expérimentale chez la levure (modèle : glycolyse) - Evolution des flux avec ou sans contraintes sur les concentrations denzymes. J EjEj Red curve: No co-regulation Blue curves: Co-regulations


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