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.

No 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.

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