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Dynamics and control of the central carbon metabolism in hepatoma cells.

Maier K, Hofmann U, Reuss M, Mauch K - BMC Syst Biol (2010)

Bottom Line: 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).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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Insilico Biotechnology AG, Nobelstrasse 15, 70569 Stuttgart, Germany.

ABSTRACT

Background: The 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.

Results: Dynamic 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).

Conclusions: Based 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.

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Metabolic network model. Extra- and intracellular metabolites: blue ellipses. Enzymatic reactions and transportation steps: red circles. Non-balanced compounds: within gray, round-edged rectangles. Directions of arrows reflect the direction of the steady state fluxes. System boundary: dashed line. Extra- and intracellular space: white and gray. Some links were omitted for reasons of clarity (cf. Table 1 for the complete reaction stoichiometry)
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Figure 1: Metabolic network model. Extra- and intracellular metabolites: blue ellipses. Enzymatic reactions and transportation steps: red circles. Non-balanced compounds: within gray, round-edged rectangles. Directions of arrows reflect the direction of the steady state fluxes. System boundary: dashed line. Extra- and intracellular space: white and gray. Some links were omitted for reasons of clarity (cf. Table 1 for the complete reaction stoichiometry)

Mentions: In the present study, a stimulus response experiment was performed with HepG2 cells. After growing HepG2 cells on a glucose-containing medium, they were incubated with fresh medium for two hours and then exposed to a medium lacking glucose. Metabolite time-series data were determined and used to parameterize a dynamic network model of the central carbon metabolism. The model takes into account 49 reactions (including 5 transportation steps) that convert 45 balanced compounds (40 intracellular and 5 extracellular metabolites). The metabolic network is depicted in figure 1 and the reaction stoichiometry is listed in Table 1 (see also the model reconstruction in a subsection of the Methods section).


Dynamics and control of the central carbon metabolism in hepatoma cells.

Maier K, Hofmann U, Reuss M, Mauch K - BMC Syst Biol (2010)

Metabolic network model. Extra- and intracellular metabolites: blue ellipses. Enzymatic reactions and transportation steps: red circles. Non-balanced compounds: within gray, round-edged rectangles. Directions of arrows reflect the direction of the steady state fluxes. System boundary: dashed line. Extra- and intracellular space: white and gray. Some links were omitted for reasons of clarity (cf. Table 1 for the complete reaction stoichiometry)
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2874527&req=5

Figure 1: Metabolic network model. Extra- and intracellular metabolites: blue ellipses. Enzymatic reactions and transportation steps: red circles. Non-balanced compounds: within gray, round-edged rectangles. Directions of arrows reflect the direction of the steady state fluxes. System boundary: dashed line. Extra- and intracellular space: white and gray. Some links were omitted for reasons of clarity (cf. Table 1 for the complete reaction stoichiometry)
Mentions: In the present study, a stimulus response experiment was performed with HepG2 cells. After growing HepG2 cells on a glucose-containing medium, they were incubated with fresh medium for two hours and then exposed to a medium lacking glucose. Metabolite time-series data were determined and used to parameterize a dynamic network model of the central carbon metabolism. The model takes into account 49 reactions (including 5 transportation steps) that convert 45 balanced compounds (40 intracellular and 5 extracellular metabolites). The metabolic network is depicted in figure 1 and the reaction stoichiometry is listed in Table 1 (see also the model reconstruction in a subsection of the Methods section).

Bottom Line: 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).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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Insilico Biotechnology AG, Nobelstrasse 15, 70569 Stuttgart, Germany.

ABSTRACT

Background: The 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.

Results: Dynamic 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).

Conclusions: Based 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.

Show MeSH
Related in: MedlinePlus