An experimental set-up for acquiring metabolite and transient (13)C-labeling data in mammalian cells is presented. An efficient sampling procedure was established for hepatic cells cultured in six-well plates as a monolayer attached to collagen, which allowed simultaneous quenching of metabolism and extraction of the intracellular intermediates of interest. Extracellular concentrations of glucose, amino acids, lactate, pyruvate, and urea were determined by GC-MS procedures and were used for estimation of metabolic uptake and excretion rates. Sensitive LC-MS and GC-MS methods were used to quantify the intracellular intermediates of tricarboxylic acid cycle, glycolysis, and pentose phosphate pathway and for the determination of isotopomer fractions of the respective metabolites. Mass isotopomer fractions were determined in a transient (13)C-labeling experiment using (13)C-labeled glucose as substrate. The absolute amounts of intracellular metabolites were obtained from a non-labeled experiment carried out in exactly the same way as the (13)C-labeling experiment, except that the media contained naturally labeled glucose only. Estimation of intracellular metabolic fluxes from the presented data is addressed in part II of this contribution.

This contribution addresses the identification of metabolic fluxes and metabolite concentrations in mammalian cells from transient (13)C-labeling experiments. Whilst part I describes experimental set-up and acquisition of required metabolite and (13)C-labeling data, part II focuses on setting up network models and the estimation of intracellular fluxes. Metabolic fluxes were determined in glycolysis, pentose-phosphate pathway (PPP), and citric acid cycle (TCA) in a hepatoma cell line grown in aerobic batch cultures. In glycolytic and PPP metabolite pools isotopic stationarity was observed within 30 min, whereas in the TCA cycle the labeling redistribution did not reach isotopic steady state even within 180 min. In silico labeling dynamics were in accordance with in vivo (13)C-labeling data. Split ratio between glycolysis and PPP was 57%:43%; intracellular glucose concentration was estimated at 101.6 nmol per 10(6) cells. In contrast to isotopic stationary (13)C-flux analysis, transient (13)C-flux analysis can also be applied to industrially relevant mammalian cell fed-batch and batch cultures.

Even though the epileptogenesis in glioma is multifactorial, it is conceivable that a down-regulation of GS may have an important pro-epileptogenic role in GBM, through the slowing of glutamate-glutamine cycle. This study suggests that seizures in GBM are coupled with a highly localized enzyme deficiency. The manipulation of GS activity might constitute a novel principle for inhibiting seizures in patients with glioma epilepsy.

BackgroundThe liver plays a major role in metabolism and performs a number of vital functions in the body. Therefore, the determination of hepatic metabolite dynamics and the analysis of the control of the respective biochemical pathways are of great pharmacological and medical importance. Extra- and intracellular time-series data from stimulus-response experiments are gaining in importance in the identification of in vivo metabolite dynamics, while dynamic network models are excellent tools for analyzing complex metabolic control patterns. This is the first study that has been undertaken on the data-driven identification of a dynamic liver central carbon metabolism model and its application in the analysis of the distribution of metabolic control in hepatoma cells.ResultsDynamic metabolite data were collected from HepG2 cells after they had been deprived of extracellular glucose. The concentration of 25 extra- and intracellular intermediates was quantified using HPLC, LC-MS-MS, and GC-MS. The in silico metabolite dynamics were in accordance with the experimental data. The central carbon metabolism of hepatomas was further analyzed with a particular focus on the control of metabolite concentrations and metabolic fluxes. It was observed that the enzyme glucose-6-phosphate dehydrogenase exerted substantial negative control over the glycolytic flux, whereas oxidative phosphorylation had a significant positive control. The control over the rate of NADPH consumption was found to be shared between the NADPH-demand itself (0.65) and the NADPH supply (0.38).ConclusionsBased on time-series data, a dynamic central carbon metabolism model was developed for the investigation of new and complex metabolic control patterns in hepatoma cells. The control patterns found support the hypotheses that the glucose-6-phosphate dehydrogenase and the Warburg effect are promising targets for tumor treatment. The systems-oriented identification of metabolite dynamics is a first step towards the genome-based assessment of potential risks posed by nutrients and drugs.