Acetyl coenzyme A (AcCoA) is the central biosynthetic precursor for fatty acid synthesis and protein acetylation. In the conventional view of mammalian cell metabolism, AcCoA is primarily generated from glucose-derived pyruvate through the citrate shuttle and adenosine triphosphate citrate lyase (ACL) in the cytosol1-3. However, proliferating cells that exhibit aerobic glycolysis and those exposed to hypoxia convert glucose to lactate at near stoichiometric levels, directing glucose carbon away from the tricarboxylic acid cycle (TCA) and fatty acid synthesis4. Although glutamine is consumed at levels exceeding that required for nitrogen biosynthesis5, the regulation and utilization of glutamine metabolism in hypoxic cells is not well understood. Here we show that human cells employ reductive metabolism of alpha-ketoglutarate (αKG) to synthesize AcCoA for lipid synthesis. This isocitrate dehydrogenase 1 (IDH1) dependent pathway is active in most cell lines under normal culture conditions, but cells grown under hypoxia rely almost exclusively on the reductive carboxylation of glutamine-derived αKG for de novo lipogenesis. Furthermore, renal cell lines deficient in the von Hippel-Lindau (VHL) tumor suppressor protein preferentially utilize reductive glutamine metabolism for lipid biosynthesis even at normal oxygen levels. These results identify a critical role for oxygen in regulating carbon utilization in order to produce AcCoA and support lipid synthesis in mammalian cells.

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 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 elementary metabolite units, 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 2 million isotopomers.

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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.

SUMMARYMetabolic 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 E. 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. KeywordsInstationary fluxes; 13C flux analysis; elementary metabolite units (EMU); gas chromatography mass spectrometry (GC/MS); statistical analysis

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, whereas the reductive carboxylation pathway travels in reverse of the citric acid cycle from ␣-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 ␣-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 propose 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.Glutamine is utilized at a high rate by rapidly growing cells, including almost all cultured cell lines, where it is required at super-physiological concentrations of 2-4 mM for optimal growth (1). Recently, we evaluated the role of glutamine as a substrate for lipogenesis in a transformed wild type (WT) 5 and IRS-1 knock-out brown adipose cell lines developed by Kahn and co-workers (2). Using isotopomer spectral analysis (ISA) we found that WT cells utilized glutamine for over 40% of their lipogenic acetyl-CoA (3). Glutamine was the largest precursor for lipogenic carbon, supplying more acetyl-CoA units than glucose or any other single source. This unexpected result led us to investigate glutamine metabolism in brown fat cell lines in more detail.The pathway for glutamine utilization in rapidly dividing cell is generally described as "glutaminolysis" where glutamine enters the citric acid cycle (CAC) as ␣-ketoglutarate traversing the cycle to oxaloacetate and exits as pyruvate or aspartate (1, 4). Pyruvate then could be converted to acetyl-CoA and citrate in the lipogenic pathway. A major argument for glutaminolysis is the need for a large supply of anaplerotic substrates for rapidly growing cells, which explains the high rate of glutamine utilization (5). An alternative to glutaminolysis is the reductive carboxylation path...

Systems level tools for the quantitative analysis of metabolic networks are required to engineer metabolism for biomedical and industrial applications. While current metabolomics techniques enable high-throughput quantification of metabolites, these methods provide minimal information on the rates and connectivity of metabolic pathways. Here we present a new method, nontargeted tracer fate detection (NTFD), that expands upon the concept of metabolomics to solve the above problems. Through the combined use of stable isotope tracers and chromatography coupled to mass spectrometry, our computational analysis enables the quantitative detection of all measurable metabolites derived from a specific labeled compound. Without a priori knowledge of a reaction network or compound library, NTFD provides information about relative flux magnitudes into each metabolite pool by determining the mass isotopomer distribution for all labeled compounds. This novel method adds a new dimension to the metabolomics tool box and provides a framework for global analysis of metabolic fluxes.

We developed a simple and accurate method for determining deuterium enrichment of glucose hydrogen atoms by electron impact gas chromatography mass spectrometry (GC-MS). First, we prepared 18 derivatives of glucose and screened over 200 glucose fragments to evaluate the accuracy and precision of mass isotopomer data for each fragment. We identified three glucose derivatives that gave six analytically useful ions: (1) glucose aldonitrile pentapropionate (m/z 173 derived from C4–C5 bond cleavage; m/z 259 from C3–C4 cleavage; m/z 284 from C4–C5 cleavage; and m/z 370 from C5–C6 cleavage); (2) glucose 1,2,5,6-di-isopropylidene propionate (m/z 301, no cleavage of glucose carbon atoms); and (3) glucose methyloxime pentapropionate (m/z 145 from C2–C3 cleavage). Deuterium enrichment at each carbon position of glucose was determined by least squares regression of mass isotopomer distributions. The validity of the approach was tested using labeled glucose standards and carefully prepared mixtures of standards. Our method determines deuterium enrichment of glucose hydrogen atoms with an accuracy of 0.3 mol%, or better, without the use of any calibration curves or correction factors. The analysis requires only 20 μL of plasma, which makes the method applicable for studying gluconeogenesis using deuterated water in cell culture and animal experiments.