Trypanosoma brucei is a protozoan parasite of major of interest in discovering new genes for drug targets. This parasite alternates its life cycle between the mammal host(s) (bloodstream form) and the insect vector (procyclic form), with two divergent glucose metabolism amenable to in vitro culture. While the metabolic network of the bloodstream forms has been well characterized, the flux distribution between the different branches of the glucose metabolic network in the procyclic form has not been addressed so far. We present a computational analysis (called Metaboflux) that exploits the metabolic topology of the procyclic form, and allows the incorporation of multipurpose experimental data to increase the biological relevance of the model. The alternatives resulting from the structural complexity of networks are formulated as an optimization problem solved by a metaheuristic where experimental data are modeled in a multiobjective function. Our results show that the current metabolic model is in agreement with experimental data and confirms the observed high metabolic flexibility of glucose metabolism. In addition, Metaboflux offers a rational explanation for the high flexibility in the ratio between final products from glucose metabolism, thsat is, flux redistribution through the malic enzyme steps. 1. Introduction Trypanosomes are unicellular protozoa that are ubiquitous parasites of higher eukaryotes, including insects, plants, and mammals. Among the numerous species belonging to the trypanosomatid family, Trypanosoma brucei, Trypanosoma cruzi, and Leishmania spp. are responsible for Human diseases. Most of these parasites live in more than one host over their life cycle and encounter very different environments, such as insect vectors’ gut and vertebrate bloodstream. Consequently, the different parasitic forms have developed distinct morphologies and metabolisms. We will consider here T. brucei, which belongs to the group of parasites responsible for sleeping sickness in Africa. T. brucei belongs to the only group of organisms that performs glycolysis in a peroxisome-like organelle, called glycosome [1]. It is widely considered that this compartmentalized glycolysis requires impermeability of glycosomal membrane to cofactors, such as NAD(P)+ and NAD(P)H, and nucleotides (ATP, ADP, etc.) [2]. As a consequence, the intraglycosomal NAD+/NADH and ATP/ADP balances need to be maintained, which implies that each NAD+ or ATP molecules consumed during the first glycolytic steps have to be regenerated inside the organelle (see Figure 1). Figure 1: Metabolic
References
[1]
F. R. Opperdoes and P. Borst, “Localization of non glycolytic enzymes in a microbody like organelle in Trypanosoma brucei: the glycosome,” FEBS Letters, vol. 80, no. 2, pp. 360–364, 1977.
[2]
B. M. Bakker, F. I. C. Mensonides, B. Teusink, P. Van Hoek, P. A. M. Michels, and H. V. Westerhoff, “Compartmentation protects trypanosomes from the dangerous design of glycolysis,” Proceedings of the National Academy of Sciences of the United States of America, vol. 97, no. 5, pp. 2087–2092, 2000.
[3]
L. Rivière, S. W. H. Van Weelden, P. Glass et al., “Acetyl:succinate CoA-transferase in procyclic Trypanosoma brucei. Gene identification and role in carbohydrate metabolism,” Journal of Biological Chemistry, vol. 279, no. 44, pp. 45337–45346, 2004.
[4]
Y. Millerioux, P. Mor, M. Biran et al., “ATP synthesis-coupled, -uncoupled acetate production from acetyl-CoA by the mitochondrial acetate:succinate CoA-transferase, acetyl-CoA thioesterase in Trypanosoma,” Journal of Biological Chemistry, vol. 287, no. 21, pp. 17186–17197, 2012.
[5]
S. Besteiro, M. P. Barrett, L. Rivière, and F. Bringaud, “Energy generation in insect stages of Trypanosoma brucei: metabolism in flux,” Trends in Parasitology, vol. 21, no. 4, pp. 185–191, 2005.
[6]
V. Coustou, S. Besteiro, L. Rivière et al., “A mitochondrial NADH-dependent fumarate reductase involved in the production of succinate excreted by procyclic Trypanosoma brucei,” Journal of Biological Chemistry, vol. 280, no. 17, pp. 16559–16570, 2005.
[7]
F. Bringaud, L. Rivière, and V. Coustou, “Energy metabolism of trypanosomatids: adaptation to available carbon sources,” Molecular and Biochemical Parasitology, vol. 149, no. 1, pp. 1–9, 2006.
[8]
F. Bringaud, C. Ebikeme, and M. Boshart, “Acetate and succinate production in amoebae, helminths, diplomonads, trichomonads and trypanosomatids: common and diverse metabolic strategies used by parasitic lower eukaryotes,” Parasitology, vol. 137, no. 9, pp. 1315–1331, 2010.
[9]
V. Lacroix, L. Cottret, P. Thébault, and M. F. Sagot, “An introduction to metabolic networks and their structural analysis,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 5, no. 4, pp. 594–617, 2008.
[10]
D. A. Fell and J. R. Small, “Fat synthesis in adipose tissue. An examination of stoichiometric constraints,” Biochemical Journal, vol. 238, no. 3, pp. 781–786, 1986.
[11]
J. D. Orth, I. Thiele, and B. O. Palsson, “What is flux balance analysis?” Nature Biotechnology, vol. 28, no. 3, pp. 245–248, 2010.
[12]
N. D. Price, J. L. Reed, and B. O. Palsson, “Genome-scale models of microbial cells: evaluating the consequences of constraints,” Nature Reviews Microbiology, vol. 2, no. 11, pp. 886–897, 2004.
[13]
M. J. Herrg？rd, S. S. Fong, and B. O. Palsson, “Identification of genome-scale metabolic network models using experimentally measured flux profiles,” PLoS Computational Biology, vol. 2, no. 7, article e72, 2006.
[14]
T. Shlomi, O. Berkman, and E. Ruppin, “Regulatory on/off minimization of metabolic flux changes after genetic perturbations,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 21, pp. 7695–7700, 2005.
[15]
D. Segrè, D. Vitkup, and G. M. Church, “Analysis of optimality in natural and perturbed metabolic networks,” Proceedings of the National Academy of Sciences of the United States of America, vol. 99, no. 23, pp. 15112–15117, 2002.
[16]
R. Mahadevan and C. H. Schilling, “The effects of alternate optimal solutions in constraint-based genome-scale metabolic models,” Metabolic Engineering, vol. 5, no. 4, pp. 264–276, 2003.
[17]
D. A. Beard, S. D. Liang, and H. Qian, “Energy balance for analysis of complex metabolic networks,” Biophysical Journal, vol. 83, no. 1, pp. 79–86, 2002.
[18]
K. Yizhak, T. Benyamini, W. Liebermeister, E. Ruppin, and T. Shlomi, “Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model,” Bioinformatics, vol. 26, no. 12, Article ID btq183, pp. i255–i260, 2010.
[19]
M. W. Covert, N. Xiao, T. J. Chen, and J. R. Karr, “Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli,” Bioinformatics, vol. 24, no. 18, pp. 2044–2050, 2008.
[20]
A. M. Feist and B. O. Palsson, “The biomass objective function,” Current Opinion in Microbiology, vol. 13, no. 3, pp. 344–349, 2010.
[21]
Y. G. Oh, D. Y. Lee, S. Y. Lee, and S. Park, “Multiobjective flux balancing using the NISE method for metabolic network analysis,” Biotechnology Progress, vol. 25, no. 4, pp. 999–1008, 2009.
[22]
D. Nagrath, M. Avila-Elchiver, F. Berthiaume, A. W. Tilles, A. Messac, and M. L. Yarmush Martin, “Soft constraints-based multiobjective framework for flux balance analysis,” Metabolic Engineering, vol. 12, no. 5, pp. 429–445, 2010.
[23]
S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by simulated annealing,” Science, vol. 220, no. 4598, pp. 671–680, 1983.
[24]
N. Bochud-Allemann and A. Schneider, “Mitochondrial substrate level phosphorylation is essential for growth of procyclic Trypanosoma brucei,” Journal of Biological Chemistry, vol. 277, no. 36, pp. 32849–32854, 2002.
[25]
V. Coustou, S. Besteiro, M. Biran et al., “ATP generation in the Trypanosoma brucei procyclic form: cytosolic substrate level is essential, but not oxidative phosphorylation,” The Journal of biological chemistry, vol. 278, no. 49, pp. 49625–49635, 2003.
[26]
V. Coustou, M. Biran, M. Breton et al., “Glucose-induced remodeling of intermediary and energy metabolism in procyclic Trypanosoma brucei,” Journal of Biological Chemistry, vol. 283, no. 24, pp. 16343–16354, 2008.
[27]
S. W. H. Van Weelden, B. Fast, A. Vogt et al., “Procyclic Trypanosoma brucei do not use Krebs cycle activity for energy generation,” Journal of Biological Chemistry, vol. 278, no. 15, pp. 12854–12863, 2003.
[28]
N. Lamour, L. Rivière, V. Coustou, G. H. Coombs, M. P. Barrett, and F. Bringaud, “Proline metabolism in procyclic Trypanosoma brucei is down-regulated in the presence of glucose,” Journal of Biological Chemistry, vol. 280, no. 12, pp. 11902–11910, 2005.
[29]
M. Heiner, I. Koch, and J. Will, “Model validation of biological pathways using Petri nets—demonstrated for apoptosis,” BioSystems, vol. 75, no. 1–3, pp. 15–28, 2004.
[30]
C. Chaouiya, “Petri net modelling of biological networks,” Briefings in Bioinformatics, vol. 8, no. 4, pp. 210–219, 2007.
[31]
M. A. Marsan, G. Conte, and G. Balbo, “A class of generalized stochastic Petri Nets for the performance evaluation of multiprocessor systems,” ACM Transactions on Computer Systems, vol. 2, pp. 93–122, 1984.
[32]
S. Bandyopadhyay, S. Saha, U. Maulik, and K. Deb, “A simulated annealing-based multiobjective optimization algorithm: AMOSA,” IEEE Transactions on Evolutionary Computation, vol. 12, no. 3, pp. 269–283, 2008.
[33]
M. Galassi, J. Davies, J. Theiler et al., GNU Scientific Library Reference Manual, 3rd edition, 2009.
[34]
B. M. Bakker, P. A. M. Michels, F. R. Opperdoes, and H. V. Westerhoff, “Glycolysis in bloodstream form Trypanosoma brucei can be understood in terms of the kinetics of the glycolytic enzymes,” Journal of Biological Chemistry, vol. 272, no. 6, pp. 3207–3215, 1997.
[35]
B. M. Bakker, P. A. M. Michels, F. R. Opperdoes, and H. V. Westerhoff, “What controls glycolysis in bloodstream form Trypanosoma brucei?” Journal of Biological Chemistry, vol. 274, no. 21, pp. 14551–14559, 1999.
[36]
V. Coustou, M. Biran, S. Besteiro et al., “Fumarate is an essential intermediary metabolite produced by the procyclic Trypanosoma brucei,” Journal of Biological Chemistry, vol. 281, no. 37, pp. 26832–26846, 2006.
[37]
E. Grafahrend-Belau, C. Klukas, B. H. Junker, and F. Schreiber, “FBA-SimVis: interactive visualization of constraint-based metabolic models,” Bioinformatics, vol. 25, no. 20, pp. 2755–2757, 2009.
