Publications

• Publications
• Seminars

Publications

2008

• 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 (In Press) [Show Abstract] [pdf] (486KB)
  Abstract
  We previously reported that glutamine was a major source of carbon for de novo fatty acid synthesis in a brown adipocyte cell line. The pathway for fatty acid synthesis from glutamine may follow either of two distinct pathways after it enters the citric acid cycle. The glutaminolysis pathway follows the citric acid cycle while the reductive carboxylation pathway travels in reverse of the citric acid cycle from alpha-ketoglutarate to citrate. To quantify fluxes in these pathways we incubated brown adipocyte cells in [U-(13)C]glutamine or [5-(13)C]glutamine and analyzed the mass isotopomer distribution of key metabolites using models that fit the isotopomer distribution to fluxes. We also investigated inhibitors of NADP dependent isocitrate dehydrogenase and mitochondrial citrate export. The results indicated that one third of glutamine entering the citric acid cycle travels to citrate via reductive carboxylation while the remainder is oxidized through succinate. The reductive carboxylation flux accounted for 90% of all flux of glutamine to lipid. The inhibitor studies were compatible with reductive carboxylation flux through mitochondrial isocitrate dehydrogenase. Total cell citrate and alpha-ketoglutarate were near isotopic equilibrium as expected if rapid cycling exists between these compounds involving the mitochondrial membrane NAD/NADP transhydrogenase. Taken together, these studies demonstrate a new role for glutamine as a lipogenic precursor and proposes an alternative to the glutaminolysis pathway where flux of glutamine to lipogenic acetyl-CoA occurs via reductive carboxylation. These findings were enabled by a new modeling tool and software implementation (Metran) for global flux estimation.
  
  
• Young JD, Walther JL, Antoniewicz MR, Yoo H, Stephanopoulos G.
  An Elementary Metabolite Unit (EMU) based method of isotopically nonstationary flux analysis.
  Biotechnol Bioeng 99(3): 686-699, 2008 [Show Abstract] [pdf] (538KB)
  Abstract
  Nonstationary metabolic flux analysis (NMFA) is at present a very computationally intensive exercise, especially for large reaction networks. We applied elementary metabolite unit (EMU) theory to NMFA, dramatically reducing computational difficulty. We also introduced block decoupling, a new method that systematically and comprehensively divides EMU systems of equations into smaller subproblems to further reduce computational difficulty. These improvements led to a 5000-fold reduction in simulation times, enabling an entirely new and more complicated set of problems to be analyzed with NMFA. We simulated a series of nonstationary and stationary GC/MS measurements for a large E. coli network that was then used to estimate parameters and their associated confidence intervals. We found that fluxes could be successfully estimated using only nonstationary labeling data and external flux measurements. Addition of near-stationary and stationary time points increased the precision of most parameters. Contrary to prior reports, the precision of nonstationary estimates proved to be comparable to the precision of estimates based solely on stationary data. Finally, we applied EMU-based NMFA to experimental nonstationary measurements taken from brown adipocytes and successfully estimated fluxes and some metabolite concentrations. By using NFMA instead of traditional MFA, the experiment required only 6 h instead of 50 (the time necessary for most metabolite labeling to reach 99% of isotopic steady state).

2007

• Antoniewicz MR, Kelleher JK, Stephanopoulos G.
  Accurate assessment of amino acid mass isotopomer distributions for metabolic flux analysis.
  Anal Chem 79(19):7554-9, 2007 [Show Abstract] [pdf] (275KB)
  Abstract
  Metabolic flux analysis based on stable-isotope labeling experiments and analysis of mass isotopomer distributions (MID) of cellular metabolites is a tool of great significance for metabolic engineering and study of human disease. This method relies on accurate and precise measurements of mass isotopomers by gas chromatography/mass spectrometry. To improve flux estimates, we assessed potential errors in determining MID of tert-butyldimethylsilyl-derivatized amino acids, which were attributed to (i) the choice of integration algorithm, (ii) concentration effects, and (iii) overlapping fragments. We report 29 amino acid fragments that are useful for flux analysis and another 18 fragments that should be rejected, most importantly Val-302, Leu-200, Leu-302, Ile-302, Ser-302, and Asp-316. In addition, we provide a protocol to minimize errors for determining MID to less than 0.4 mol % for accepted fragments.
  
  
• Antoniewicz MR, Kraynie DF, Laffend LA, González-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 9(3): 277-292, 2007 [Show Abstract] [Scopus] [pdf] (1209KB)
  Abstract
  Metabolic fluxes estimated from stable-isotope studies provide a key to understanding cell physiology and regulation of metabolism. A limitation of the classical method for metabolic flux analysis (MFA) is the requirement for isotopic steady state. To extend the scope of flux determination from stationary to nonstationary systems, we present a novel modeling strategy that combines key ideas from isotopomer spectral analysis (ISA) and stationary MFA. Isotopic transients of the precursor pool and the sampled products are described by two parameters, D and G parameters, respectively, which are incorporated into the flux model. The G value is the fraction of labeled product in the sample, and the D value is the fractional contribution of the feed for the production of labeled products. We illustrate the novel modeling strategy with a nonstationary system that closely resembles industrial production conditions, i.e. fed-batch fermentation of Escherichia coli that produces 1,3-propanediol (PDO). Metabolic fluxes and the D and G parameters were estimated by fitting labeling distributions of biomass amino acids measured by GC/MS to a model of E. coli metabolism. We obtained highly consistent fits from the data with 82 redundant measurements. Metabolic fluxes were estimated for 20 time points during course of the fermentation. As such we established, for the first time, detailed time profiles of in vivo fluxes. We found that intracellular fluxes changed significantly during the fed-batch. The intracellular flux associated with PDO pathway increased by 10%. Concurrently, we observed a decrease in the split ratio between glycolysis and pentose phosphate pathway from 70/30 to 50/50 as a function of time. The TCA cycle flux, on the other hand, remained constant throughout the fermentation. Furthermore, our flux results provided additional insight in support of the assumed genotype of the organism.
  
  
• Antoniewicz MR, Kelleher JK, Stephanopoulos G.
  Elementary Metabolite Units (EMU): A novel framework for modeling isotopic distributions.
  Metab Eng 9(1): 68-86, 2007 [Show Abstract] [Scopus] [pdf] (1397KB) highly cited!!
  Abstract
  Metabolic flux analysis (MFA) has emerged as a tool of great significance for metabolic engineering and mammalian physiology. An important limitation of MFA, as carried out via stable isotope labeling and GC/MS and nuclear magnetic resonance (NMR) measurements, is the large number of isotopomer or cumomer equations that need to be solved, especially when multiple isotopic tracers are used for the labeling of the system. This restriction reduces the ability of MFA to fully utilize the power of multiple isotopic tracers in elucidating the physiology of realistic situations comprising complex bioreaction networks. Here, we present a novel framework for the modeling of isotopic labeling systems that significantly reduces the number of system variables without any loss of information. The elementary metabolite unit (EMU) framework is based on a highly efficient decomposition method that identifies the minimum amount of information needed to simulate isotopic labeling within a reaction network using the knowledge of atomic transitions occurring in the network reactions. The functional units generated by the decomposition algorithm, called EMUs, form the new basis for generating system equations that describe the relationship between fluxes and stable isotope measurements. Isotopomer abundances simulated using the EMU framework are identical to those obtained using the isotopomer and cumomer methods, however, require significantly less computation time. For a typical (13)C-labeling system the total number of equations that needs to be solved is reduced by one order-of-magnitude (100s EMUs vs. 1000s isotopomers). As such, the EMU framework is most efficient for the analysis of labeling by multiple isotopic tracers. For example, analysis of the gluconeogenesis pathway with (2)H, (13)C, and (18)O tracers requires only 354 EMUs, compared to more than two million isotopomers.

2006

• Antoniewicz MR, Kelleher JK, Stephanopoulos G.
  Determination of confidence intervals of metabolic fluxes estimated from stable isotope measurements.
  Metab Eng 8(4): 324-337, 2006 [Show Abstract] [Scopus] [pdf] (276KB)
  Abstract
  Metabolic fluxes, estimated from stable isotope studies, provide a key to quantifying physiology in fields ranging from metabolic engineering to the analysis of human metabolic diseases. A serious drawback of the flux estimation method in current use is that it does not produce confidence limits for the estimated fluxes. Without this information it is difficult to interpret flux results and expand the physiological significance of flux studies. To address this shortcoming we derived analytical expressions of flux sensitivities with respect to isotope measurements and measurement errors. These tools allow the determination of local statistical properties of fluxes and relative importance of measurements. Furthermore, we developed an efficient algorithm to determine accurate flux confidence intervals and demonstrated that confidence intervals obtained with this method closely approximate true flux uncertainty. In contrast, confidence intervals approximated from local estimates of standard deviations are inappropriate due to inherent system nonlinearities. We applied these methods to analyze the statistical significance and confidence of estimated gluconeogenesis fluxes from human studies with [U-13C]glucose as tracer and found true limits for flux estimation in specific human isotopic protocols.
  
  
• Antoniewicz MR, Stephanopoulos G, Kelleher JK.
  Evaluation of regression models in metabolic physiology: Predicting fluxes from isotopic data without knowledge of the pathway.
  Metabolomics 2(1): 41-52, 2006 [Show Abstract] [pdf] (304KB)
  Abstract
  This study explores the ability of regression models, with no knowledge of the underlying physiology, to estimate physiological parameters relevant for metabolism and endocrinology. Four regression models were compared: multiple linear regression (MLR), principal component regression (PCR), partial least-squares regression (PLS) and regression using artificial neural networks (ANN). The pathway of mammalian gluconeogenesis was analyzed using [U-(13)C]glucose as tracer. A set of data was simulated by randomly selecting physiologically appropriate metabolic fluxes for the 9 steps of this pathway as independent variables. The isotope labeling patterns of key intermediates in the pathway were then calculated for each set of fluxes, yielding 29 dependent variables. Two thousand sets were created, allowing independent training and test data. Regression models were asked to predict the nine fluxes, given only the 29 isotopomers. For large training sets (>50) the artificial neural network model was superior, capturing 95% of the variability in the gluconeogenic flux, whereas the three linear models captured only 75%. This reflects the ability of neural networks to capture the inherent non-linearities of the metabolic system. The effect of error in the variables and the addition of random variables to the data set was considered. Model sensitivities were used to find the isotopomers that most influenced the predicted flux values. These studies provide the first test of multivariate regression models for the analysis of isotopomer flux data. They provide insight for metabolomics and the future of isotopic tracers in metabolic research where the underlying physiology is complex or unknown.

2001-2005

• vanGulik WM, Antoniewicz MR, deLaat WT, Vinke JL, Heijnen JJ.
  Energetics of growth and penicillin production in a high-producing strain of Penicillium chrysogenum.
  Biotechnol Bioeng 72(2): 185-193, 2001 [Show Abstract] [pdf] (127KB)
  Abstract
  The results of a large number of carbon-limited chemostat cultures of Penicillium chrysogenum carried out on glucose, ethanol, and acetate as the growth limiting substrate have been used to obtain an estimation of the adenosine triphosphate (ATP) costs for mycelium growth, penicillin production, and maintenance and the overall stoichiometry of oxidative phosphorylation of the fungus. It was found that penicillin production was accompanied by a significant additional energy drain (73 mol of ATP per mole of penicillin-G) from primary metabolism. This finding has been confirmed in independent experiments and has been shown to result in a significantly lower estimate for the maximum theoretical yield of penicillin-G on the carbon source.

In preparation

• Antoniewicz MR, Yoo H, Kelleher JK, Stephanopoulos G.
  Global flux quantification and statistical analysis in stable-isotope studies quantifies effects of IRS-1 knockout in brown adipose cells.
  
• Moxley JF, Jewett MC, Antoniewicz MR, Villas-Boas SG, Alper H, Wheeler RT, Stephanopoulos G, Hinnebusch AG, Ideker T, Nielsen J, Stephanopoulos G.
  Linking transcriptional regulation and high resolution metabolic phenotypes modulated by the global regulator Gcn4p.
  
• Antoniewicz MR, Alemán JO, Kelleher JK, Stephanopoulos G.
  Quantification of net and reversible fluxes in primary hepatocytes by integrated use of 13C and 2H tracers.
  
• Alemán JO, Antoniewicz MR, Wong M, Kelleher JK, Stephanopoulos G.
  Gluconeogenesis as a system: flux analysis and metabolomics of glucose production in primary liver cells.
  
• Antoniewicz MR, Kelleher JK, Stephanopoulos G.
  Accurate assessment of deuterium labeling of glucose by mass isotopomer analysis of novel glucose derivatives.
  
  

The reprints of articles are provided for personal and educational use only, and are subject to the copyright by their respective publishers.

Seminars

1. University of Delaware, Chemistry-Biology Interface (CBI) Seminar. Newark, DE. April 2008
   
2. Delaware Biotechnology Institute, DBI Seminar Series. Newark, DE. December 2007 [view video]
3. E. I. DuPont de Nemours, Horizons in Biotechnology Seminar. Wilmington, DE. December 2007
4. 2nd International Metabolomics Symposium. Louisville, KY. March 2007
   
5. University of Maryland, Dept. of Chemical and Biomolecular Engineering. College Park, MD. March 2007
   
6. University of Delaware, Department of Chemical Engineering. Newark, DE. February 2007
   
7. National Institutes of Health, Brain Physiology and Metabolism Section. Bethesda, MD. January 2007
   
8. E. I. DuPont de Nemours, Central Research & Development. Wilmington, DE. June 2006
   
9. Duke University, Sarah W. Stedman Nutrition & Metabolism Center. Durham, NC. October 2005
   
10. Broad Institute of MIT and Harvard, Broad Metabolism Initiative. Cambridge, MA. January 2005
    
11. 7th International Meeting of the Microarray Gene Expression Data Society. Toronto, Canada. Sep. 2004
    
12. E. I. DuPont de Nemours, Central Research & Development. Wilmington, DE. November 2003

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