Profiling of dynamic changes in hypermetabolic livers

Abstract

The liver plays an important role in the overall negative nitrogen balance leading to muscle wasting commonly observed in patients following many conditions, including severe injury, cancer, and diabetes. In order to study changes in liver metabolism during the establishment of such catabolic states, we used a rat skin burn injury model that induces hypermetabolism and muscle wasting. At various times during the first week following the injury, livers were isolated and perfused in a recirculating system under well-defined conditions. We applied a steady-state metabolic flux analysis model of liver metabolism and then used k-means clustering to objectively group together reaction flux time profiles. We identified six distinct groups of reactions that were differentially responsive: (1) pentose phosphate pathway (PPP); (2) amino acid oxidation reactions leading to the formation of tricarboxylic acid (TCA) cycle intermediates; (3) gluconeogenesis; (4) TCA-cycle and mitochondrial oxidation; (5) lipolysis, beta-oxidation, and ketone body formation; and (6) urea-cycle. Burn injury sequentially upregulated the urea-cycle, the PPP, and the TCA-cycle, in order, while beta-oxidation and gluconeogenesis remained unchanged. The upregulation of the PPP was transient, whereas the rise in urea- and TCA-cycle fluxes was sustained. An ATP balance predicted an increased production of ATP and energy expenditure starting on day 3 post-burn, which correlated with the induction of the oxidative phosphorylation uncoupler uncoupling protein-2. We conclude that metabolic profiling using flux analysis and clustering analysis is a useful methodology to characterize the differential activation of metabolic pathways in perfused organs and to identify specific key pathways that are sensitive to a stimulus or insult without making a priori assumptions.

Integrated Energy and Flux Balance Based Multiobjective Framework for Large-Scale Metabolic Networks

“…4e and 6e), the changes in flux required to move from points I to J along the Pareto frontier significantly differed between gluconeogenic and glycolytic hepatocytes (Figs. 5e and 7e), mainly with respect to gluconeogenesis fluxes (2-6), and TCA cycle fluxes (8)(9)(10)(11)(12)(13). In the gluconeogenesis mode, TCA cycle fluxes are higher because of increased demand to produce ATP (gluconeogenesis consumes ATP too), since glycolysis itself produces ATP (2 molecules of ATP for 1 molecule of glucose consumed).…”

“…1,[5][6][7][8][11][12][13][14]24,25 Flux balance analysis (FBA) uses stoichiometric and mass balance constraints to compute the intracellular fluxes. Recently, energy balance analysis (EBA) was developed to ensure the thermodynamic feasibility of the computed fluxes.…”

Modular decomposition of metabolic reaction networks based on flux analysis and pathway projection

“…Measurements on the perfusion medium consisted of assays on 25 major intermediates of liver central carbon metabolism: glucose, lactate, urea, oxygen, carbon dioxide, ketone bodies and amino acids. Experimental details on the metabolite measurements have been reported previously (Lee et al, 2003).…”

“…The number of pathways (P) were 217 and 582 for the liver and adipocyte model, respectively. For the liver, a unique set of fluxes was calculated for each of the six time points (days 0, 1, 2, 3, 4 and 7 post-burn), where the v obs k for each time point represented the averaged result of at least four separate replicate perfusions (Lee et al, 2003). For the adipocyte, flux distributions were calculated for four different time points (days 4, 8, 12 and 16 post-induction), where the v obs k for each time point was the averaged result of six independent replicate cultures.…”

“…We develop both the partition and scoring methods against two relatively simple, but distinct model systems for which detailed, experimentally derived metabolic flux data were available. The first model described the impact of fasting and whole body inflammation on the energy metabolism of the rat liver (Lee et al, 2003). The second model represented the metabolic changes underlying de novo adipose tissue formation (Si et al, 2007).…”