TY - JOUR
T1 - Metabolic modeling of dynamic brain 13C NMR multiplet data
T2 - Concepts and simulations with a two-compartment neuronal-glial model
AU - Shestov, Alexander A.
AU - Valette, Julien
AU - Deelchand, Dinesh K.
AU - Ugurbil, Kǎmil
AU - Henry, Pierre Gilles
N1 - Funding Information:
Acknowledgments This work was supported by NIH grants P41R R008079, P41EB015894, P30NS057091 and R01NS038672 (P.G.H.).
PY - 2012/11
Y1 - 2012/11
N2 - Metabolic modeling of dynamic 13C labeling curves during infusion of 13C-labeled substrates allows quantitative measurements of metabolic rates in vivo. However metabolic modeling studies performed in the brain to date have only modeled time courses of total isotopic enrichment at individual carbon positions (positional enrichments), not taking advantage of the additional dynamic 13C isotopomer information available from finestructure multiplets in 13C spectra. Here we introduce a new 13C metabolic modeling approach using the concept of bonded cumulative isotopomers, or bonded cumomers. The direct relationship between bonded cumomers and 13C multiplets enables fitting of the dynamic multiplet data. The potential of this new approach is demonstrated using Monte- Carlo simulations with a brain two-compartment neuronalglial model. The precision of positional and cumomer approaches are compared for two different metabolic models (with and without glutamine dilution) and for different infusion protocols ([1,6-13C2]glucose, [1,2-13C2]acetate, and double infusion [1,6-13C2]glucose + [1,2-13C2]acetate) . In all cases, the bonded cumomer approach gives better precision than the positional approach. In addition, of the three different infusion protocols considered here, the double infusion protocol combined with dynamic bonded cumomer modeling appears the most robust for precise determination of all fluxes in the model. The concepts and simulations introduced in the present study set the foundation for taking full advantage of the available dynamic 13C multiplet data in metabolic modeling.
AB - Metabolic modeling of dynamic 13C labeling curves during infusion of 13C-labeled substrates allows quantitative measurements of metabolic rates in vivo. However metabolic modeling studies performed in the brain to date have only modeled time courses of total isotopic enrichment at individual carbon positions (positional enrichments), not taking advantage of the additional dynamic 13C isotopomer information available from finestructure multiplets in 13C spectra. Here we introduce a new 13C metabolic modeling approach using the concept of bonded cumulative isotopomers, or bonded cumomers. The direct relationship between bonded cumomers and 13C multiplets enables fitting of the dynamic multiplet data. The potential of this new approach is demonstrated using Monte- Carlo simulations with a brain two-compartment neuronalglial model. The precision of positional and cumomer approaches are compared for two different metabolic models (with and without glutamine dilution) and for different infusion protocols ([1,6-13C2]glucose, [1,2-13C2]acetate, and double infusion [1,6-13C2]glucose + [1,2-13C2]acetate) . In all cases, the bonded cumomer approach gives better precision than the positional approach. In addition, of the three different infusion protocols considered here, the double infusion protocol combined with dynamic bonded cumomer modeling appears the most robust for precise determination of all fluxes in the model. The concepts and simulations introduced in the present study set the foundation for taking full advantage of the available dynamic 13C multiplet data in metabolic modeling.
KW - Bonded cumomer
KW - Brain
KW - C Magnetic resonance spectroscopy
KW - Isotopomer
KW - Metabolic modeling
KW - Monte-Carlo simulation
KW - Neuronal-glial metabolism
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U2 - 10.1007/s11064-012-0782-5
DO - 10.1007/s11064-012-0782-5
M3 - Article
C2 - 22528840
AN - SCOPUS:84871404862
SN - 0364-3190
VL - 37
SP - 2388
EP - 2401
JO - Neurochemical Research
JF - Neurochemical Research
IS - 11
ER -