Redox balance is key to explaining full vs. partial switching to low-yield metabolism

BMC Systems Biology

Roeland MH Merks¹  Milan JA van Hoek² 

[1] Mathematical Institute, Leiden University, P.O. Box 9512, 2300 RA Leiden, The Netherlands;Netherlands Consortium for Systems Biology, Amsterdam, The Netherlands

关键词: Saccharomyces cerevisiae;    Lactococcus lactis;    Escherichia coli;    Redox balance;    Overflow metabolism;    Flux Balance Analysis with Molecular Crowding;    Genome-scale metabolic model;    Metabolic switching;   

Others  :  1144629
DOI  :  10.1186/1752-0509-6-22

 received in 2012-01-13, accepted in 2012-03-24,  发布年份 2012

【 摘 要 】

Background

Low-yield metabolism is a puzzling phenomenon in many unicellular and multicellular organisms. In abundance of glucose, many cells use a highly wasteful fermentation pathway despite the availability of a high-yield pathway, producing many ATP molecules per glucose, e.g., oxidative phosphorylation. Some of these organisms, including the lactic acid bacterium Lactococcus lactis, downregulate their high-yield pathway in favor of the low-yield pathway. Other organisms, including Escherichia coli do not reduce the flux through the high-yield pathway, employing the low-yield pathway in parallel with a fully active high-yield pathway. For what reasons do some species use the high-yield and low-yield pathways concurrently and what makes others downregulate the high-yield pathway? A classic rationale for metabolic fermentation is overflow metabolism. Because the throughput of metabolic pathways is limited, influx of glucose exceeding the pathway's throughput capacity is thought to be redirected into an alternative, low-yield pathway. This overflow metabolism rationale suggests that cells would only use fermentation once the high-yield pathway runs at maximum rate, but it cannot explain why cells would decrease the flux through the high-yield pathway.

Results

Using flux balance analysis with molecular crowding (FBAwMC), a recent extension to flux balance analysis (FBA) that assumes that the total flux through the metabolic network is limited, we investigate the differences between Saccharomyces cerevisiae and L. lactis that downregulate the high-yield pathway at increasing glucose concentrations, and E. coli, which keeps the high-yield pathway functioning at maximal rate. FBAwMC correctly predicts the metabolic switching mode in these three organisms, suggesting that metabolic network architecture is responsible for differences in metabolic switching mode. Based on our analysis, we expect gradual, "overflow-like" switching behavior in organisms that have an additional energy-yielding pathway that does not consume NADH (e.g., acetate production in E. coli). Flux decrease through the high-yield pathway is expected in organisms in which the high-yield and low-yield pathways compete for NADH. In support of this analysis, a simplified model of metabolic switching suggests that the extra energy generated during acetate production produces an additional optimal growth mode that smoothens the metabolic switch in E. coli.

Conclusions

Maintaining redox balance is key to explaining why some microbes decrease the flux through the high-yield pathway, while other microbes use "overflow-like" low-yield metabolism.

【 授权许可】

   
2012 van Hoek and Merks; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

【 参考文献 】

• [1]van Dijken JP, Weusthuis RA, Pronk JT: Kinetics of growth and sugar consumption in yeasts. Antonie Van Leeuwenhoek 1993, 63(3-4):343-352.
• [2]Varma A, Palsson BØ: Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl Environ Microbiol 1994, 60(10):3724-3731.
• [3]Dauner M, Storni T, Sauer U: Bacillus subtilis metabolism and energetics in carbon-limited and excess-carbon chemostat culture. J Bacteriol 2001, 183(24):7308-7317.
• [4]Thomas TD, Ellwood DC, Longyear VM: Change from homo- to heterolactic fermentation by Streptococcus lactis resulting from glucose limitation in anaerobic chemostat cultures. J Bacteriol 1979, 138:109-117.
• [5]Teusink B, Wiersma A, Molenaar D, Francke C, de Vos WM, Siezen RJ, Smid EJ: Analysis of growth of Lactobacillus plantarum WCFS1 on a complex medium using a genome-scale metabolic model. J Biol Chem 2006, 281(52):40041-40048.
• [6]Kim JW, Dang CV: Cancer's molecular sweet tooth and the Warburg effect. Cancer Res 2006, 66(18):8927-8930.
• [7]Robergs RA, Ghiasvand F, Parker D: Biochemistry of exercise-induced metabolic acidosis. Am J Physiol Regul Integr Comp Physiol 2004, 287(3):R502-R516.
• [8]Westerhoff HV, Hellingwerf KJ, Dam KV: Thermodynamic efficiency of microbial growth is low but optimal for maximal growth rate. P Natl Acad Sci USA 1983, 80:305-309.
• [9]Tempest DW, Neijssel OM: Physiological and energetic aspects of bacterial metabolite overproduction. FEMS Microbiol Lett 1992, 79(1-3):169-176.
• [10]Russell JB, Cook GM: Energetics of bacterial growth: balance of anabolic and catabolic reactions. Microbiol Rev 1995, 59:48-62.
• [11]Russell JB: The energy spilling reactions of bacteria and other organisms. J Mol Microbiol Biotechnol 2007, 13(1-3):1-11.
• [12]Molenaar D, van Berlo R, de Ridder D, Teusink B: Shifts in growth strategies reflect tradeoffs in cellular economics. Mol Syst Biol 2009, 5:323.
• [13]Andersen KB, Von Meyenburg K: Are growth rates of Escherichia coli in batch cultures limited by respiration? J Bacteriol 1980, 144:114-123.
• [14]Holms H: Flux analysis and control of the central metabolic pathways in Escherichia coli. FEMS Microbiol Rev 1996, 19(2):85-116.
• [15]Heinrich R, Montero F, Klipp E, Waddell TG, Melendez-Hevia E: Theoretical approaches to the evolutionary optimization of glycolysis: thermodynamic and kinetic constraints. Eur J Biochem 1997, 243(1-2):191-201.
• [16]Schuster S, Pfeiffer T, Fell DA: Is maximization of molar yield in metabolic networks favoured by evolution? J Theor Biol 2008, 252(3):497-504.
• [17]Vazquez A, Beg QK, Demenezes MA, Ernst J, Bar-Joseph Z, Barabasi AL, Boros LG, Oltvai ZN: Impact of the solvent capacity constraint on E. coli metabolism. BMC Syst Biol 2008, 2:7. BioMed Central Full Text
• [18]Vazquez A, Liu J, Zhou Y, Oltvai ZN: Catabolic efficiency of aerobic glycolysis: the Warburg effect revisited. BMC Syst Biol 2010, 4:58. BioMed Central Full Text
• [19]Merico A, Sulo P, Piskur J, Compagno C: Fermentative lifestyle in yeasts belonging to the Saccharomyces complex. FEBS J 2007, 274(4):976-989.
• [20]Fuhrer T, Fischer E, Sauer U: Experimental identification and quantification of glucose metabolism in seven bacterial species. J Bacteriol 2005, 187(5):1581-1590.
• [21]Edwards JS, Palsson BØ: The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. P Natl Acad Sci USA 2000, 97(10):5528-5533.
• [22]Beg QK, Vazquez A, Ernst J, de Menezes MA, Bar-Joseph Z, Barabasi AL, Oltvai ZN: Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity. P Natl Acad Sci USA 2007, 104(31):12663-12668.
• [23]Shlomi T, Benyamini T, Gottlieb E, Sharan R, Ruppin E: Genome-scale metabolic modeling elucidates the role of proliferative adaptation in causing the Warburg effect. PLoS Comput Biol 2011, 7(3):e1002018.
• [24]Oliveira AP, Nielsen J, Forster J: Modeling Lactococcus lactis using a genome-scale flux model. BMC Microbiol 2005, 5:39. BioMed Central Full Text
• [25]Feist AM, Henry CS, Reed JL, Krummenacker M, Joyce AR, Karp PD, Broadbelt LJ, Hatzimanikatis V, Palsson BØ: A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol Syst Biol 2007, 3:121.
• [26]Duarte NC, Herrgard MJ, Palsson BØ: Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model. Genome Res 2004, 14(7):1298-1309.
• [27]Caspi R, Foerster H, Fulcher CA, Kaipa P, Krummenacker M, Latendresse M, Paley S, Rhee SY, Shearer AG, Tissier C, Walk TC, Zhang P, Karp PD: The MetaCyc Database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res 2008, 36(Database):D623-D631.
• [28]Chang A, Scheer M, Grote A, Schomburg I, Schomburg D: BRENDA, AMENDA and FRENDA the enzyme information system: new content and tools in 2009. Nucleic Acids Res 2009, 37:(Database issue):D588-D592.
• [29]Hoek PV, Dijken JPV, Pronk JT: Effect of specific growth rate on fermentative capacity of baker's yeast. Appl Environ Microbiol 1998, 64(11):4226-4233.
• [30]Causey TB, Zhou S, Shanmugam KT, Ingram LO: Engineering the metabolism of Escherichia coli W3110 for the conversion of sugar to redox-neutral and oxidized products: homoacetate production. P Natl Acad Sci USA 2003, 100(3):825-832.
• [31]Mey MD, Lequeux GJ, Beauprez JJ, Maertens J, Horen EV, Soetaert WK, Vanrolleghem PA, Vandamme EJ: Comparison of different strategies to reduce acetate formation in Escherichia coli. Biotechnol Progr 2007, 23(5):1053-1063.
• [32]Vemuri GN, Eiteman MA, McEwen JE, Olsson L, Nielsen J: Increasing NADH oxidation reduces overflow metabolism in Saccharomyces cerevisiae. P Natl Acad Sci USA 2007, 104(7):2402-2407.
• [33]Vemuri GN, Altman E, Sangurdekar DP, Khodursky AB, Eiteman MA: Overflow metabolism in Escherichia coli during steady-state growth: transcriptional regulation and effect of the redox ratio. Appl Environ Microbiol 2006, 72(5):3653-3661.
• [34]Bull JJ, Wang IN: Optimality models in the age of experimental evolution and genomics. J Evolution Biol 2010, 23(9):1820-1838.
• [35]Pfeiffer T, Schuster S, Bonhoeffer S: Cooperation and competition in the evolution of ATP-producing pathways. Science 2001, 292(5516):504-507.
• [36]MacLean RC, Gudelj I: Resource competition and social conflict in experimental populations of yeast. Nature 2006, 441(7092):498-501.
• [37]Pfeiffer T, Schuster S: Game-theoretical approaches to studying the evolution of biochemical systems. Trends Biochem Sci 2005, 30:20-25.
• [38]Zhuang K, Vemuri GN, Mahadevan R: Economics of membrane occupancy and respiro-fermentation. Mol Syst Biol 2011, 7:500.
• [39]Schellenberger J, Park JO, Conrad TM, Palsson BØ: BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions. BMC Bioinformatics 2010, 11:213. BioMed Central Full Text
• [40]Becker SA, Feist AM, Mo ML, Hannum G, Palsson BØ, Herrgard MJ: Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protoc 2007, 2(3):727-738.