(598c) Isotopomer Model For Metabolic Flux Profiling Of Adipocyte Differentiation And Growth
Obesity has become a major health concern in most industrialized countries. Of particular concern is the strong correlation between adiposity and type 2 diabetes, as well as the ?metabolic syndrome? associated with insulin resistance. Adipocytes are one of the most highly insulin-responsive cell types. Adipose tissue has long been known to house the largest energy reserves in the form of triacylglycerol in the body. There is now compelling evidence that adipocytes have another role, that of regulating energy metabolism through paracrine and endocrine mechanisms. A thorough and quantitative understanding of adipose tissue metabolism would open new avenues to develop therapies aimed at controlling adiposity.
Metabolic flux analysis (MFA) is an important methodology for cell physiology and metabolic engineering. In particular, isotope labeling has become an attractive technique for determining metabolic flux distributions in complex, multi-compartmental models for cells. One major drawback of an isotopomer model is that it has a very large number of nonlinear equations that need to be solved. Very recently, Antoniewicz and co-workers have developed a new framework, termed elementary metabolite units (EMUs), that significantly reduces the number of isotopomer model variables. In this work, we have compared the EMU approach to another novel framework that combines metabolite balances with pathway thermodynamics. These methods were used to quantitatively profile the metabolic flux distributions of differentiation 3T3-L1 (pre)adipocytes.
Our model included all of major pathways of primary metabolism that are known to be expressed in adipocytes: glycolysis, pentose phosphate pathway, TCA cycle, fatty acid and TG synthesis, and amino acid metabolism. The model dimensions were 45 metabolites by 74 reactions, and underdetermined even with measurement on 26 external fluxes. Applying inequality constrains derived from pathway thermodynamics yielded robust estimates of the net intracellular fluxes. To explore whether an improved resolution may be obtained through isotopic labeling, we recast the problem using EMU equations. We set [1,2-13C] glucose as the labeled input substrate and again utilized the measured external flues as mass balance constrains. Based on the previously estimated net fluxes, we then calculated the mass isotopomer distributions of glyceraldehyde 3-phosphate and citrate. These metabolites were chosen, because they represent key junctions of glycolysis, the pentose phosphate pathway and the TCA cycle. The simulation results of the EMU model were quantitatively consistent with the results of the flux balance model with pathway inequality constraints. However, several of the calculated reaction fluxes were unstable, as they were sensitive to even small random errors (5% or less) in the mass isotopomer distribution data. Based on these findings, we conclude that the EMU model is a promising new tool for determining the intracellular reaction fluxes in complex mammalian cells. The calculations depend on an appropriate selection of labeled inputs and measured metabolites. Future work will systematically explore the space of feasible input substrates and mass isotopomer measurements.