Candidate metabolic network states in human mitochondria. Impact of diabetes, ischemia, and diet.

TitleCandidate metabolic network states in human mitochondria. Impact of diabetes, ischemia, and diet.
Publication TypeJournal Article
Year of Publication2005
AuthorsThiele I, Price ND, Vo TD, Palsson BØ
JournalJ Biol Chem
Volume280
Issue12
Pagination11683-95
Date Published2005 Mar 25
ISSN0021-9258
KeywordsAdenosine Triphosphate, Diabetes Mellitus, Diet, Energy Metabolism, Humans, Ischemia, Mitochondria, Heart, Oxygen Consumption
Abstract

The human mitochondrial metabolic network was recently reconstructed based on proteomic and biochemical data. Linear programming and uniform random sampling were applied herein to identify candidate steady states of the metabolic network that were consistent with the imposed physico-chemical constraints and available experimental data. The activity of the mitochondrion was studied under four metabolic conditions: normal physiologic, diabetic, ischemic, and dietetic. Pairwise correlations between steady-state reaction fluxes were calculated in each condition to evaluate the dependence among the reactions in the network. Applying constraints on exchange fluxes resulted in predictions for intracellular fluxes that agreed with experimental data. Analyses of the steady-state flux distributions showed that the experimentally observed reduced activity of pyruvate dehydrogenase in vivo could be a result of stoichiometric constraints and therefore would not necessarily require enzymatic inhibition. The observed changes in the energy metabolism of the mitochondrion under diabetic conditions were used to evaluate the impact of previously suggested treatments. The results showed that neither normalized glucose uptake nor decreased ketone body uptake have a positive effect on the mitochondrial energy metabolism or network flexibility. Taken together, this study showed that sampling of the steady-state flux space is a powerful method to investigate network properties under different conditions and provides a basis for in silico evaluations of effects of potential disease treatments.

DOI10.1074/jbc.M409072200
PubMed URLhttp://www.ncbi.nlm.nih.gov/pubmed/15572364?dopt=Abstract
Alternate TitleJ. Biol. Chem.
PubMed ID15572364