Research article
CardioNet: A human metabolic network suited for the study of cardiomyocyte metabolism
1 Institute of Biochemistry, Charité-Universitätsmedizin Berlin, 10117 Berlin, Charitéplatz 1/ Virchowweg 6, Germany
2 Center for Cardiovascular Research, Charité-Universitätsmedizin Berlin, 10115 Berlin, Hessische Straße 3-4, Germany
BMC Systems Biology 2012, 6:114 doi:10.1186/1752-0509-6-114
Published: 29 August 2012Additional files
Additional file 1:
Metabolites of the metabolic network. Metabolites listed in this table occur in the metabolic network. For each entry a unique network identifier is given and provided with information of metabolite title, title synonym, metabolite sum formula and assigned compartment. Additionally cross-references to other databases are given and refer to the following databases: UniProtKB (UniProtKB entry), KEGG (Compound ID), Lipid Maps (LM ID), Pub Chem (CID) and Human Metabolome Database (HMDB ID). Abbreviations used in the table for compartments are as following, ext: external, cyto: cytosol, mito: mitochondrion, lyso: lysosome, peroxy: peroxisome and micro: microsome.
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Additional file 2:
References. During the network reconstruction additional evidence for occurrence of metabolic reactions in the cardiomyocyte were obtained from previously reported studies. This table gives a full list of cross-references to PubMed identifier (PMID) providing evidence for included reactions of the metabolic network. Furthermore, directionality of reactions was set according to Gibbs energy (ΔG, kJ/mol) and is provided with this table.
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Additional file 3:
Definition and overview of objectives and constraints for simulation of metabolic and physiological functions of the cardiomyocyte. To ensure consistency and full functionality of the metabolic network, we performed a critical testing of physiological functions based on knowledge of the cardiac metabolism by using flux balance analysis. The table lists all objectives and applied constraints as used in the optimization problem. Furthermore, constraints as used in functional pruning of the network are given. Abbreviations for constraints as used in simulations with FASIMU software are as follows: (+), secretion of the metabolite is allowed or the metabolite is product; (-), uptake of the metabolite is allowed or metabolite is substrate and (=), secretion and uptake of the metabolite is allowed or metabolites is either product or substrate.
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Additional file 4:
Flux distributions of metabolic and physiological functions of cardiomyocyte. To ensure consistency and full functionality of the metabolic network, we performed a critical testing of physiological functions based on knowledge of the cardiac metabolism by using flux balance analysis. Flux distributions listed in this table have been predicted for each metabolic objective as defined in Additional file 5. Abbreviations for compartments: ext - external, cyto - cytosol, mito - mitochondrion, lyso - lysosome, peroxy - peroxisome, micro - microsome.
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Additional file 5:
Metabolic network of the human cardiomyocyte in SBML format.
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Additional file 6:
Testing functionality of Human heart model. A comparison of the metabolic network to a previously reported genome-scale reconstruction of the human heart [25] was performed. The presented physiological functions of the cardiomyocyte (see Additional file 13) were applied to test the functionality of the partial network of the human heart and compare the performance of both networks. From 110 tested functions 53 were found to have no feasible solution, this included important cellular functions such as the citric acid cycle.
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Additional file 7:
Definition and overview of constraints for simulations as used in the optimization problems for varied substrate availability. Constraints listed in this table were applied in simulations for varied substrate availability. For all three simulation settings the corresponding target function and applied constraints are given. The simulation settings include, first, simulations of substrate uptake rates for four different substrates and oxygen demands while satisfying a baseline ATP consumption rate. Second, simulations of substrate uptake rates for four different substrates and oxygen demands as under experimental conditions while satisfying the same baseline ATP consumption rate. Finally, simulations of substrate uptake rates for nine different substrates and oxygen demands while satisfying a predefined metabolic target function.
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Additional file 8:
Predicted metabolic fluxes of substrate uptake and oxygen demand for ATP expenditure
in varied substrate availability,
. We simulated a varied substrate availability for four selected substrates, including
glucose, oleate, acetoacetate and lactate while demanding a baseline ATP consumption
rate (vATPase) of 21.6 mmol·min−1·(l cell)−1. This table lists uptake rates for oxygen, glucose, oleate, acetoacetate, lactate
and the resulting total substrate uptake rate for each simulated substrate composition.
Efficiency indices (
) were separately calculated for each simulation. Results are shown for calculated
efficiency values (
) greater than 0.6 and given in descending order.
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Additional file 9:
Predicted metabolic fluxes of substrate uptake and oxygen demand for ATP expenditure
in varied substrate availability,
. See caption of Additional file
8 but results are shown for calculated efficiency values (
) equal or less than 0.6.
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Additional file 10:
Alternate optima. To identify alternate flux solutions that can equally satisfy the problem, i.e. yield
the same optimal solution, we performed additional simulations. The MILP was re-solved
after adding a constraint (z*) for a single flux of the original flux distribution
which was set to either 1.01-fold (
) or 0.99-fold (
) of its original calculated flux value (v0). The optimization problem was repeated
with substrate combinations which were identified with the highest or lowest efficiency
value while satisfying (1) a baseline ATP consumption rate and (2) a target function
of the cardiomyocyte. This table includes all calculated flux solutions yielding the
same optimal solution as with the original optimization problem. Furthermore, an overview
is given of alternate flux solutions for fluxes representing external substrate and
oxygen uptake. Statistical significance between flux solutions for the analysis of
alternate flux solutions was determined by use of 1-way ANOVA.
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Additional file 11:
Predicted metabolic fluxes of substrate uptake and oxygen demand for fulfilling the
metabolic target function in varied substrate availability,
. We simulated a varied substrate availability for nine selected substrates, including
glucose, palmitate, stearate, oleate, alpha-linoleate, eicosapentaenoate, docosahexaenoate,
acetoacetate and lactate. During the simulations, we demanded an ATP expenditure (vATPase) of 21.6 mmol·min−1·(l cell)−1 and metabolic target flux, as specified in Additional file
6. This table lists results for substrate combination for which efficiency values (
) greater than 0.8 were calculated. Uptake rates for all nine substrates, the resulting
total substrate uptake rate and oxygen consumption rate for all simulated substrate
compositions which fulfilled the metabolic objective are given. Furthermore solutions
for glycogen synthesis and glycogenolysis as determined during simulations are shown.
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Additional file 12:
Predicted metabolic fluxes of substrate uptake and oxygen demand for fulfilling the
metabolic target function in varied substrate availability,
. See caption of Additional file
7 but results are shown for calculated efficiency values (
) equal or less than 0.8.
Format: CSV Size: 14MB Download file
Additional file 13:
Gene expression annotation. The identification of human heart tissue specific reactions requires a tissue specific gene expression profile. We obtained gene expression samples from different gene expression data available from Gene Expression Omnibus, including GDS181 and GSE1145. This table provides gene expression information annotated to metabolic reactions of the cardiomyocyte network. Each reaction identifier refers to a compartment localisation of the respective metabolic reaction. Furthermore, each entry in the table provides information about annotated Ensemble Gene ID, Geo Dataset ID, Geo Sample ID, Probeset ID, Gene ID, gene expression value and detection call. The information of gene expression status can be obtained from the column “Detection call”. Each expression is either categorized as present (P), absent (A) or M (marginal). We further considered genes as expressed for gene expression values with a cut-off greater than 100.
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