Profiling of dynamic changes in hypermetabolic livers
- 4 June 2003
- journal article
- research article
- Published by Wiley in Biotechnology & Bioengineering
- Vol. 83 (4) , 400-415
- https://doi.org/10.1002/bit.10682
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, β‐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 β‐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. Biotechnol Bioeng 83: 400–415, 2003.Keywords
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