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Date : 29/6/2010
Internship proposal for : Master 2
Laboratory
Laboratoire de biomathématiques - Faculté de Pharmacie
EA 4466 Paris Descartes
4 avenue de l'Observatoire 75006 Paris
Director : Unit Luc Cunober ; Lab Simone Bénazeth
Website : http://www.univ-paris5.fr/cgi-bin/WebObjects/Labs.woa/wa/showInfoLabo?cle=20101763#ficheLabo
Main discipline : Molecular biologySystems Biology
Supervisor
Ioannis Nicolis
email :
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phone : +33 153739778
Subjects / Tools-Methodologies
1 : Amino acid metabolism/Mathematical modeling
2 : Arginine citrulline conversion/Mathematica software
3 : Enzyme kinetics/Ordinary differntial equation systems
Summary of lab's interests
The EA 4466 name is "STRESS CELLULAIRE : PHYSIOPATHOLOGIE, STRATEGIES NUTRITIONNELLES ET TH…RAPEUTIQUES INNOVANTES". A number of pathological situations is characterized by a cellular stress with a common underlying mechanism. Typically, this is the case of low grade (e.g. aging, obesity), or acute (e.g. ischemia-reperfusion injury) inflammation, associated with oxidative and nitrosative stress. In this context, nitric oxide (∞NO) plays a key role both directly by limiting the extent of the injury (i.e. quenching free radicals) and indirectly as a signaling molecule. In these situations, ∞NO production is blunted either as a result of decreased availability of its endogenous precursors (direct: arginine, indirect: citrulline), or a decreased expression or activity of the enzymes. Therefore, there is a considerable interest in developing therapeutic strategies aiming at decreasing ∞NO dysfunction-related cellular stress. The approaches that we propose may be nutritional (i.e. arginine, citrulline, resveratrol), based on drug use (polyhydroxyl derivatives of resveratrol, statins) or the combination of both. Our aim is to gain a better insight in the mechanisms of action of these nutrients and drugs on ∞NO production in order to propose these new therapeutic strategies. In the unit research program, attention is focused: - on the nutritional and metabolic control of the homeostasis of endogenous ∞NO precursors: arginine and citrulline; - on the metabolic and antioxidative properties of citrulline, an amino acid largely ignored up to now, and the assessment of its therapeutic interest - on the influence of resveratrol and its derivatives on the control of ∞NO production at the ∞NO synthase level; - on the ability of associations of nutrients (arginine, citrulline) and drugs (resveratrol derivatives, statins) to promote ∞NO production in some pathological situations. In silico modelling project therein presented for this project is a part of the first one of these objectives.
Summary of project
Citrulline/arginine interconversion plays a pivotal role in nitrogen homeostasis. Arginine is converted to citrulline in the enterocyte and citrulline is converted back to arginine in the kidney. This interconversion avoids arginine liver retention, making it available to other tissues. In order to establish a mathematical model of those amino-acids inter-organ flow kinetics, our team applied a modular approach[1-7]. We developed a hepatic urea cycle model, as well as a model of the enterocytic glutamate/arginine/citrulline equilibrium. These models have been calibrated and validated using experimental data provided by the EA 4466. This project focuses in the development of the renal part of this metabolic pathway. Two of the urea cycle enzymes exist in kidney: argininosuccinate synthase (ASS, EC 6.3.4.5) and argininosuccinate lyase (ASL, EC 4.3.2.1), catalysing respectively the reactions: ATP + L-citrulline + L-aspartate <-> AMP + diphosphate + omega-N-(L-arginino)succinate omega-N-(L-arginino)succinate <-> fumarate + L-arginine The ASS mechanism follows an ordered addition of MgATP, citrulline and aspartate followed by an ordered release of argininosuccinate, MgPPi and AMP[8]. ASL, follows a uni-bi (one substrate, two products) mechanism complexing first argininosuccinate followed by a release (ordered or random depending on species) of fumarate and arginine[9-11]. The proposed subject consists on the modelling of these two-reaction cascade, applying three different methodologies, and comparison of these methodologies under different conditions. The model can be enriched taking into account the renal citrulline absorption and arginine release. The reactions will be modelled via a macroscopic approach, derived from the Michaelis Menten assumptions, and also by two different microscopic approaches: writing-down the equations system or generating it using the kMech[12] virtual toolbox. All modelling will be carried on under the Mathematica software environment. Subsequently, predicted concentration profiles and pseudo-equilibrium conditions of these models will be compared under different assumptions (initial substrate and product concentrations, inhibition, reaction reversibility, kinetic constant variations) in order to define the validity regions of each approach and the best-suited methodology. This work can be extended to the assembly of this renal metabolism model with our previous hepatocyte and enterocyte models, to achieve a systemic approach of the whole inter-organ flow of this metabolic pathway.
1. E. Curis, I. Nicolis, C. Moinard, S. Osowska, N. Zerrouk, S. Bénazeth , L. Cynober Amino acids, (2005), 3, 177-205
2. C. Moinard, I. Nicolis, N. Neveux, S. Darquy, S. Bénazeth, L. Cynober Br.J.nutr., (2007), (Oct 22), 1-8
3. E. Curis, I. Nicolis, J. Bensaci, P. Deschamps , S. Bénazeth Biochimie, (2009), 91, 1238-1254
4. I. Nicolis, E. Curis, P. Deschamps, L. Cynober, S. Bénazeth, Nutrition Clinique et MÈtabolisme, (2006), 20, S113
5. P. Deschamps, I. Nicolis, E. Curis, , L. Cynober, S. Bénazeth, Nutrition Clinique et MÈtabolisme, (2007), 21, S50
6. J. Bensaci, E. Curis, P. Deschamps, I. Nicolis, L. Cynober, J.P. de Bandt, S. Bénazeth, Nutrition Clinique et Métabolisme, (2008), 22, Suppl. 1, 137-138
7. J. Bensaci, E. Curis, I. Nicolis, J.P. de Bandt, L. Cynober, S. Bénazeth, Nutrition Clinique et Métabolisme, (2009), 23, Suppl. 1, 34-35
8. F.M. Raushell, J.L. Seiglie, Arch. Biochem. Biophys. (1983) 225, 979-985
9. V. Bulusu, B. Srinivasan, M. Ponnappa Bopanna, H. Balaram, Biochimica et Biophysica Acta (BBA) - Proteins & Proteomics, (2009), 1794(4), 642-654
10. S.H. Chiou, H.J. Lee, H. Chu, T.A. Lai, G.G. Chang, Biochem. Int., (1991), 25, 705-713
11. F.M. Raushel, R. Nygaard, Arch. Biochem. Biophys. (1983) 221, 143-147
12. C.R. Yang, B.E. Shapiro, E.D. Mjolsness and G. W. Hatfield, Bioinformatics, (2005), 21(6), 774-780