×

Metabolic network analysis of perfused livers under fed and fasted states: incorporating thermodynamic and futile-cycle-associated regulatory constraints. (English) Zbl 1307.92086

Summary: Isolated liver perfusion systems have been extensively used to characterize intrinsic metabolic changes in liver under various conditions, including systemic injury, hepatotoxin exposure, and warm ischemia. Most of these studies were performed utilizing fasted animals prior to perfusion so that a simplified metabolic network could be used in order to determine intracellular fluxes. However, fasting induced metabolic alterations might interfere with disease related changes. Therefore, there is a need to develop a “unified” metabolic flux analysis approach that could be similarly applied to both fed and fasted states. In this study we explored a methodology based on elementary mode analysis in order to determine intracellular fluxes and active pathways simultaneously. In order to decrease the solution space, thermodynamic constraints, and enzymatic regulatory properties for the formation of futile cycles were further considered in the model, resulting in a mixed integer quadratic programming problem. Given the published experimental observations describing the perfused livers under fed and fasted states, the proposed approach successfully determined that gluconeogenesis, glycogenolysis and fatty acid oxidation were active in both states. However, fasting increased the fluxes in gluconeogenic reactions whereas it decreased fluxes associated with glycogenolysis, TCA cycle, fatty acid oxidation and electron transport reactions. This analysis further identified that more pathways were found to be active in fed state while their weight values were relatively lower compared to fasted state. Glucose, lactate, glutamine, glutamate and ketone bodies were also found to be important external metabolites whose extracellular fluxes should be used in the hepatic metabolic network analysis. In conclusion, the mathematical formulation explored in this study is an attractive tool to analyze the metabolic network of perfused livers under various disease conditions. This approach could be simultaneously applied to both fasted and fed data sets.

MSC:

92C40 Biochemistry, molecular biology
92C45 Kinetics in biochemical problems (pharmacokinetics, enzyme kinetics, etc.)
92C42 Systems biology, networks

Software:

YANA
PDF BibTeX XML Cite
Full Text: DOI

References:

[1] Arai, K.; Lee, K.; Berthiaume, F.; Tompkins, R.G.; Yarmush, M.L., Intrahepatic amino acid and glucose metabolism in a {\scd}-galactosamine-induced rat liver failure model, Hepatology, 34, 360-371, (2001)
[2] Banta, S.; Yokoyama, T.; Berthiaume, F.; Yarmush, M.L., Effects of dehydroepiandrosterone administration on rat hepatic metabolism following thermal injury, J. surg. res., 127, 93-105, (2005)
[3] Banta, S.; Vemula, M.; Yokoyama, T.; Jayaraman, A.; Berthiaume, F.; Yarmush, M.L., Contribution of gene expression to metabolic fluxes in hypermetabolic livers induced through burn injury and cecal ligation and puncture in rats, Biotechnol. bioeng., 97, 118-137, (2007)
[4] Beard, D.A.; Qian, H., Thermodynamic-based computational profiling of cellular regulatory control in hepatocyte metabolism, Am. J. physiol.-endocrinol. metab., 288, E633-E644, (2005)
[5] Boghigian, B.; Shi, H.; Lee, K.; Pfeifer, B., Utilizing elementary mode analysis, pathway thermodynamics, and a genetic algorithm for metabolic flux determination and optimal metabolic network design, BMC syst. biol., 4, 49, (2010)
[6] Boyd, M.E.; Albright, E.B.; Foster, D.W.; Denis McGarry, J., In vitro reversal of the fasting state of liver metabolism in the rat. reevaluation of the roles of insulin and glucose, J. clin. invest., 68, 142-152, (1981)
[7] Chan, C.; Berthiaume, F.; Lee, K.; Yarmush, M.L., Metabolic flux analysis of cultured hepatocytes exposed to plasma, Biotechnol. bioeng., 81, 33-49, (2003)
[8] Covert, M.W.; Palsson, B.O., Transcriptional regulation in constraints-based metabolic models of Escherichia coli, J. biol. chem., 277, 28058-28064, (2002)
[9] Covert, M.W.; Schilling, C.H.; Palsson, B., Regulation of gene expression in flux balance models of metabolism, J. theor. biol., 213, 73-88, (2001)
[10] De Koning, T.J.; Snell, K.; Duran, M.; Berger, R.; Poll-The, B.T.; Surtees, R., {\scl}-serine in disease and development, Biochem. J., 371, 653-661, (2003)
[11] Iyer, V.V.; Yang, H.; Ierapetritou, M.G.; Roth, C.M., Effects of glucose and insulin on hepg2-C3A cell metabolism, Biotechnol. bioeng., 107, 347-356, (2010)
[12] Iyer, V.V.; Ovacik, M.A.; Androulakis, I.P.; Roth, C.M.; Ierapetritou, M.G., Transcriptional and metabolic flux profiling of triadimefon effects on cultured hepatocytes, Toxicol. appl. pharmacol., 248, 165-177, (2010)
[13] Kaleta, C.; de Figueiredo, L.F.; Werner, S.; Guthke, R.; Ristow, M.; Schuster, S., Evidence for gluconeogenesis from fatty acids in humans, Plos comput. biol., 7, e1002116, (2011)
[14] Klamt, S.; Stelling, J., Two approaches for metabolic pathway analysis?, Trends biotechnol., 21, 64-69, (2003)
[15] Klamt, S.; Saez-Rodriguez, J.; Gilles, E.D., Structural and functional analysis of cellular networks with cellnetanalyzer, BMC syst. biol., 1, (2007)
[16] Laffel, L., Ketone bodies: a review of physiology, pathophysiology and application of monitoring to diabetes, Diabetes-metab. res. rev., 15, 412-426, (1999)
[17] Lee, K.; Berthiaume, F.; Stephanopoulos, G.N.; Yarmush, M.L., Profiling of dynamic changes in hypermetabolic livers, Biotechnol. bioeng., 83, 400-415, (2003)
[18] Lee, K.; Berthiaume, F.; Stephanopoulos, G.N.; Yarmush, D.M.; Yarmush, M.L., Metabolic flux analysis of postburn hepatic hypermetabolism, Metab. eng., 2, 312-327, (2000)
[19] Maskow, T.; von Stockar, U., How reliable are thermodynamic feasibility statements of biochemical pathways?, Biotechn. bioeng., 92, 223-230, (2005)
[20] Nolan, R.P.; Fenley, A.P.; Lee, K., Identification of distributed metabolic objectives in the hypermetabolic liver by flux and energy balance analysis, Metab. eng., 8, 30-45, (2006)
[21] Nookaew, I.; Meechai, A.; Thammarongtham, C.; Laoteng, K.; Ruanglek, V.; Cheevadhanarak, S.; Nielsen, J.; Bhumiratana, S., Identification of flux regulation coefficients from elementary flux modes: a systems biology tool for analysis of metabolic networks, Biotechnol. bioeng., 97, 1535-1549, (2007)
[22] Orman, M.A.; Berthiaume, F.; Androulakis, I.P.; Ierapetritou, M.G., Pathway analysis of liver metabolism under stressed condition, J. theor. biol., 272, 131-140, (2011) · Zbl 1405.92062
[23] Orman, M.A.; Arai, K.; Yarmush, M.L.; Androulakis, I.P.; Berthiaume, F.; Ierapetritou, M.G., Metabolic flux determination in perfused livers by mass balance analysis: effect of fasting, Biotechnol. bioeng., 107, 825-835, (2010)
[24] Qian, H.; Beard, D.A., Thermodynamics of stoichiometric biochemical networks in living systems far from equilibrium, Biophys. chem., 114, 213-220, (2005)
[25] Qian, H.; Beard, D.A.; Liang, S-d., Stoichiometric network theory for nonequilibrium biochemical systems, Eur. J. biochem., 270, 415-421, (2003)
[26] Rutter, M.T.; Zufall, R.A., Pathway length and evolutionary constraint in amino acid biosynthesis, J. mol. evol., 58, 218-224, (2004)
[27] Schilling, C.H.; Letscher, D.; Palsson, B.O., Theory for the systemic definition of metabolic pathways and their use in interpreting metabolic function from? A pathway-oriented perspective, J. theor. biol., 203, 229-248, (2000)
[28] Schwartz, J.M.; Kanehisa, M., A quadratic programming approach for decomposing steady-state metabolic flux distributions onto elementary modes, Bioinformatics, 21, 204-205, (2005)
[29] Schwartz, J.M.; Kanehisa, M., Quantitative elementary mode analysis of metabolic pathways: the example of yeast glycolysis, BMC bioinf., 7, (2006)
[30] Schwarz, R.; Musch, P.; von Kamp, A.; Engels, B.; Schirmer, H.; Schuster, S.; Dandekar, T., YANA—a software tool for analyzing flux modes, gene-expression and enzyme activities, BMC bioinf., 6, (2005)
[31] Stelling, J.; Klamt, S.; Bettenbrock, K.; Schuster, S.; Gilles, E.D., Metabolic network structure determines key aspects of functionality and regulation, Nature, 420, 190-193, (2002)
[32] Trinh, C.T.; Wlaschin, A.; Srienc, F., Elementary mode analysis: a useful metabolic pathway analysis tool for characterizing cellular metabolism, Appl. microbiol. biotechnol., 81, 813-826, (2009)
[33] Wagner, A.; Fell, D.A., The small world inside large metabolic networks, Proc. R. soc. London ser. B, 268, 1803-1810, (2001)
[34] Watford, M., Glutamine and glutamate metabolism across the liver sinusoid, J. nutr., 130, 983S-987S, (2000)
[35] Wiback, S.J.; Mahadevan, R.; Palsson, B.Ø., Reconstructing metabolic flux vectors from extreme pathways: defining the [alpha]-spectrum, J. theor. biol., 224, 313-324, (2003)
[36] Xu, M.; Smith, R.; Sadhukhan, J., Optimization of productivity and thermodynamic performance of metabolic pathways, Ind. eng. chem. res., 47, 5669-5679, (2008)
[37] Yamaguchi, Y.; Yu, Y.M.; Zupke, C.; Yarmush, D.M.; Berthiaume, F.; Tompkins, R.G.; Yarmush, M.L., Effect of burn injury on glucose and nitrogen metabolism in the liver: preliminary studies in a perfused liver system, Surgery, 121, 295-303, (1997)
[38] Yang, H., Roth, C.M., Ierapetritou, M.G., 2011. Analysis of amino acid supplementation effects on hepatocyte cultures using flux balance analysis. OMICS: J. Integr. Biol. doi:10.1089/omi.2010.0070.
[39] Yokoyama, T.; Banta, S.; Berthiaume, F.; Nagrath, D.; Tompkins, R.G.; Yarmush, M.L., Evolution of intrahepatic carbon, nitrogen, and energy metabolism in a {\scd}-galactosamine-induced rat liver failure model, Metab. eng., 7, 88-103, (2005)
[40] Yoon, J.; Si, Y.; Nolan, R.; Lee, K., Modular decomposition of metabolic reaction networks based on flux analysis and pathway projection, Bioinformatics, 23, 2433-2440, (2007)
[41] Zhao, Q.Y.; Kurata, H., Maximum entropy decomposition of flux distribution at steady state to elementary modes, J. biosci. bioeng., 107, 84-89, (2009)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.