Rational design of 13C-labeling experiments for metabolic flux analysis in mammalian cells

BMC Systems Biology

Maciek R Antoniewicz¹  Woo Suk Ahn¹  Scott B Crown¹ 

[1] Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE, 19716, USA

关键词: Mass spectrometry;    Free fluxes;    Mammalian cells;    Confidence intervals;    Statistical analysis;    Pathway analysis;    Experiment design;    Stable-isotope tracers;    Metabolic flux analysis;   

Others  :  1144477
DOI  :  10.1186/1752-0509-6-43

 received in 2011-11-11, accepted in 2012-04-17,  发布年份 2012

【 摘 要 】

Background

¹³C-Metabolic flux analysis (¹³C-MFA) is a standard technique to probe cellular metabolism and elucidate in vivo metabolic fluxes. ¹³C-Tracer selection is an important step in conducting ¹³C-MFA, however, current methods are restricted to trial-and-error approaches, which commonly focus on an arbitrary subset of the tracer design space. To systematically probe the complete tracer design space, especially for complex systems such as mammalian cells, there is a pressing need for new rational approaches to identify optimal tracers.

Results

Recently, we introduced a new framework for optimal ¹³C-tracer design based on elementary metabolite units (EMU) decomposition, in which a measured metabolite is decomposed into a linear combination of so-called EMU basis vectors. In this contribution, we applied the EMU method to a realistic network model of mammalian metabolism with lactate as the measured metabolite. The method was used to select optimal tracers for two free fluxes in the system, the oxidative pentose phosphate pathway (oxPPP) flux and anaplerosis by pyruvate carboxylase (PC). Our approach was based on sensitivity analysis of EMU basis vector coefficients with respect to free fluxes. Through efficient grouping of coefficient sensitivities, simple tracer selection rules were derived for high-resolution quantification of the fluxes in the mammalian network model. The approach resulted in a significant reduction of the number of possible tracers and the feasible tracers were evaluated using numerical simulations. Two optimal, novel tracers were identified that have not been previously considered for ¹³C-MFA of mammalian cells, specifically [2,3,4,5,6-¹³C]glucose for elucidating oxPPP flux and [3,4-¹³C]glucose for elucidating PC flux. We demonstrate that ¹³C-glutamine tracers perform poorly in this system in comparison to the optimal glucose tracers.

Conclusions

In this work, we have demonstrated that optimal tracer design does not need to be a pure simulation-based trial-and-error process; rather, rational insights into tracer design can be gained through the application of the EMU basis vector methodology. Using this approach, rational labeling rules can be established a priori to guide the selection of optimal ¹³C-tracers for high-resolution flux elucidation in complex metabolic network models.

【 授权许可】

   
2012 Crown et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Sauer U: Metabolic networks in motion: 13C-based flux analysis. Mol Syst Biol 2006, 2:62.
• [2]Zamboni N, Fendt SM, Ruhl M, Sauer U: (13)C-based metabolic flux analysis. Nat Protoc 2009, 4(6):878-892.
• [3]Stephanopoulos G: Metabolic fluxes and metabolic engineering. Metab Eng 1999, 1(1):1-11.
• [4]Crown SB, Indurthi DC, Ahn WS, Choi J, Papoutsakis ET, Antoniewicz MR: Resolving the TCA cycle and pentose-phosphate pathway of Clostridium acetobutylicum ATCC 824: Isotopomer analysis, in vitro activities and expression analysis. Biotechnol J 2011, 6(3):300-305.
• [5]Reed JL, Senger RS, Antoniewicz MR, Young JD: Computational approaches in metabolic engineering. J Biomed Biotechnol 2010, 2010:207414.
• [6]Szyperski T: Biosynthetically directed fractional 13C-labeling of proteinogenic amino acids, An efficient analytical tool to investigate intermediary metabolism. Eur J Biochem 1995, 232(2):433-448.
• [7]Antoniewicz MR, Kelleher JK, Stephanopoulos G: Accurate assessment of amino acid mass isotopomer distributions for metabolic flux analysis. Anal Chem 2007, 79(19):7554-7559.
• [8]Wittmann C: Metabolic flux analysis using mass spectrometry. Adv Biochem Eng Biotechnol 2002, 74:39-64.
• [9]Antoniewicz MR, Kelleher JK, Stephanopoulos G: Measuring deuterium enrichment of glucose hydrogen atoms by gas chromatography/mass spectrometry. Anal Chem 2011, 83:3211-3216.
• [10]Choi J, Antoniewicz MR: Tandem mass spectrometry: A novel approach for metabolic flux analysis. Metab Eng 2011, 13:225-233.
• [11]Jeffrey FM, Roach JS, Storey CJ, Sherry AD, Malloy CR: 13C isotopomer analysis of glutamate by tandem mass spectrometry. Anal Biochem 2002, 300(2):192-205.
• [12]Wiechert W, Mollney M, Petersen S, de Graaf AA: A universal framework for 13C metabolic flux analysis. Metab Eng 2001, 3(3):265-283.
• [13]Antoniewicz MR, Kelleher JK, Stephanopoulos G: Elementary metabolite units (EMU): a novel framework for modeling isotopic distributions. Metab Eng 2007, 9(1):68-86.
• [14]Leighty RW, Antoniewicz MR: Dynamic metabolic flux analysis (DMFA): A framework for determining fluxes at metabolic non-steady state. Metab Eng 2011, 13(6):745-755.
• [15]Ahn WS, Antoniewicz MR: Metabolic flux analysis of CHO cells at growth and non-growth phases using isotopic tracers and mass spectrometry. Metab Eng 2011, 13(5):598-609.
• [16]Boghigian BA, Seth G, Kiss R, Pfeifer BA: Metabolic flux analysis and pharmaceutical production. Metab Eng 2010, 12(2):81-95.
• [17]Metallo CM, Walther JL, Stephanopoulos G: Evaluation of 13C isotopic tracers for metabolic flux analysis in mammalian cells. J Biotechnol 2009, 144(3):167-174.
• [18]Niklas J, Heinzle E: Metabolic Flux Analysis in Systems Biology of Mammalian Cells. Adv Biochem Eng Biotechnol 2011, 127:109-132.
• [19]Niklas J, Schneider K, Heinzle E: Metabolic flux analysis in eukaryotes. Curr Opin Biotechnol 2010, 21(1):63-69.
• [20]Quek LE, Dietmair S, Kromer JO, Nielsen LK: Metabolic flux analysis in mammalian cell culture. Metab Eng 2010, 12(2):161-171.
• [21]Noguchi Y, Young JD, Aleman JO, Hansen ME, Kelleher JK, Stephanopoulos G: Effect of anaplerotic fluxes and amino acid availability on hepatic lipoapoptosis. J Biol Chem 2009, 284(48):33425-33436.
• [22]Yoo H, Antoniewicz MR, Stephanopoulos G, Kelleher JK: Quantifying reductive carboxylation flux of glutamine to lipid in a brown adipocyte cell line. J Biol Chem 2008, 283(30):20621-20627.
• [23]Burgess SC, He T, Yan Z, Lindner J, Sherry AD, Malloy CR, Browning JD, Magnuson MA: Cytosolic phosphoenolpyruvate carboxykinase does not solely control the rate of hepatic gluconeogenesis in the intact mouse liver. Cell Metab 2007, 5(4):313-320.
• [24]Burgess SC, Hausler N, Merritt M, Jeffrey FM, Storey C, Milde A, Koshy S, Lindner J, Magnuson MA, Malloy CR, et al.: Impaired tricarboxylic acid cycle activity in mouse livers lacking cytosolic phosphoenolpyruvate carboxykinase. J Biol Chem 2004, 279(47):48941-48949.
• [25]Previs SF, Brunengraber H: Methods for measuring gluconeogenesis in vivo. Curr Opin Clin Nutr Metab Care 1998, 1(5):461-465.
• [26]Noguchi Y, Nishikata N, Shikata N, Kimura Y, Aleman JO, Young JD, Koyama N, Kelleher JK, Takahashi M, Stephanopoulos G: Ketogenic essential amino acids modulate lipid synthetic pathways and prevent hepatic steatosis in mice. PLoS One  , 5(8):e12057.
• [27]Walther JL, Metallo CM, Zhang J, Stephanopoulos G: Optimization of 13C isotopic tracers for metabolic flux analysis in mammalian cells. Metab Eng 14(2):162-171.
• [28]Antoniewicz MR, Kraynie DF, Laffend LA, Gonzalez-Lergier J, Kelleher JK, Stephanopoulos G: Metabolic flux analysis in a nonstationary system: fed-batch fermentation of a high yielding strain of E. coli producing 1,3-propanediol. Metab Eng 2007, 9(3)):277-292.
• [29]Sengupta N, Rose ST, Morgan JA: Metabolic flux analysis of CHO cell metabolism in the late non-growth phase. Biotechnol Bioeng 2011, 108(1):82-92.
• [30]Noh K, Wahl A, Wiechert W: Computational tools for isotopically instationary 13C labeling experiments under metabolic steady state conditions. Metab Eng 2006, 8(6):554-577.
• [31]Henry O, Durocher Y: Enhanced glycoprotein production in HEK-293 cells expressing pyruvate carboxylase. Metab Eng 2011, 13(5):499-507.
• [32]Yang TH, Wittmann C, Heinzle E: Respirometric 13C flux analysis, Part I: design, construction and validation of a novel multiple reactor system using on-line membrane inlet mass spectrometry. Metab Eng 2006, 8(5):417-431.
• [33]Crown SB, Antoniewicz MR: Selection of tracers for 13C-metabolic flux analysis using elementary metabolite units (EMU) basis vector methodology. Metab Eng 2012, 14(2):150-161.
• [34]Ahn WS, Antoniewicz MR: Towards dynamic metabolic flux analysis in CHO cell cultures. Biotechnol J 2012, 7(1):61-74.
• [35]Schellenberger J, Zielinski DC, Choi W, Madireddi S, Portnoy V, Scott DA, Reed JL, Osterman AL, Palsson BO: Predicting outcomes of steady-state 13C isotope tracing experiments with Monte Carlo sampling. BMC Syst Biol 2012, 6(1):9. BioMed Central Full Text
• [36]Mollney M, Wiechert W, Kownatzki D, de Graaf AA: Bidirectional reaction steps in metabolic networks: IV. Optimal design of isotopomer labeling experiments. Biotechnol Bioeng 1999, 66(2)):86-103.
• [37]Yang TH, Heinzle E, Wittmann C: Theoretical aspects of 13C metabolic flux analysis with sole quantification of carbon dioxide labeling. Comput Biol Chem 2005, 29(2):121-133.
• [38]Noh K, Wiechert W: Experimental design principles for isotopically instationary 13C labeling experiments. Biotechnol Bioeng 2006, 94(2):234-251.
• [39]Wahrheit J, Nicolae A, Heinzle E: Eukaryotic metabolism: measuring compartment fluxes. Biotechnol J 2011, 6(9):1071-1085.
• [40]Young JD, Walther JL, Antoniewicz MR, Yoo H, Stephanopoulos G: An elementary metabolite unit (EMU) based method of isotopically nonstationary flux analysis. Biotechnol Bioeng 2008, 99(3):686-699.
• [41]Antoniewicz MR, Kelleher JK, Stephanopoulos G: Determination of confidence intervals of metabolic fluxes estimated from stable isotope measurements. Metab Eng 2006, 8(4):324-337.