Review articleIn vivo 13C NMR spectroscopy and metabolic modeling in the brain: a practical perspective
Introduction
Carbon-13 NMR spectroscopy is a powerful tool to investigate intermediary metabolism. The high chemical specificity of 13C NMR, which can distinguish 13C isotope incorporation not only into different molecules, but also into specific carbon positions within the same molecule (13C isotopomers), allows one to follow the fate of 13C label through multiple metabolic pathways. However, the interpretation of 13C NMR data to derive quantitative metabolic fluxes requires analysis with a metabolic model. This metabolic modeling is particularly complex in the brain due to the highly organized interaction between different cell types corresponding to different metabolic compartments.
The potential of 13C NMR spectroscopy to study metabolic pathways was demonstrated using suspensions of microorganisms [1], [2]. The first attempt to model the flow of 13C label into the TCA cycle was made in 1983 by Chance et al. [3] in the heart, at a time when such analysis required some of the fastest computers available. Initial studies in the brain in vivo were reported in the late 1980s and early 1990s [4], [5], [6], [7]. Since then, the steady increase in magnetic fields available for in vivo studies and progress in NMR methodology have allowed detection of 13C labeling time courses in localized regions of the brain with constantly improving sensitivity. In parallel, methods for metabolic modeling have evolved from relatively simple models into complex two-compartment models. Together, these improvements have allowed 13C metabolic modeling studies to make important contributions to our understanding of brain metabolism and compartmentation, showing for example that the glutamate–glutamine cycle is a major metabolic pathway in the brain [8], [9], that the neuronal TCA cycle rate increases with neuronal activity [10], [11], [12], that glial TCA cycle is significant in the brain [13] and that anaplerotic pyruvate carboxylase activity is significant in the brain [13] and increases with neuronal activity [14].
The aim of this review is to provide the reader with an overview of the experimental design for a 13C metabolic modeling study. This is not intended as a comprehensive review of the field of 13C metabolic modeling, but rather as a practical guide for readers (not only NMR spectroscopists, but also neuroscientists in general) interested in understanding the experimental details of such studies. The emphasis has been placed on metabolic modeling rather than NMR spectroscopy, and NMR spectroscopy methodology is described only when it is relevant to metabolic modeling. We refer the reader to recent reviews for further details on NMR methodology [15], [16].
We have chosen to focus in this review on metabolic modeling which uses a one-compartment model and [1-13C]glucose or [1,6-13C2]glucose as a metabolic substrate. Other tracers and more complex metabolic models can be used, such as two-compartment (neuron–astrocyte) models. However the one-compartment model is relatively simple and is well suited for explaining the principles of metabolic modeling. We have also chosen to focus on “dynamic” metabolic modeling, meaning modeling of 13C labeling time courses. Therefore other analysis methods such as isotopomer analysis will not be considered in this review.
13C metabolic studies commonly involve four steps (Fig. 1):
- (1)
choice of 13C-labeled substrate and infusion protocol
- (2)
detection of 13C label incorporation into brain metabolites during infusion of 13C labeled substrate
- (3)
quantitation of 13C spectra to obtain 13C turnover curves
- (4)
metabolic modeling of 13C turnover curves to obtain quantitative fluxes through specific biochemical pathways
In the following sections, each of these steps will be examined separately, with a focus on the relevance of each aspect to metabolic modeling.
Section snippets
Choice of 13C-labeled substrate
The first step in the design of a 13C metabolic study is the choice of a metabolic substrate. Since glucose is the main fuel for the brain, 13C-labeled glucose has been the preferred substrate for metabolic studies in the brain. Most in vivo metabolic studies have been performed using [1-13C]glucose or [1,6-13C2]glucose. Both substrates lead to the formation of [3-13C]pyruvate, with [1-13C]glucose generating one unlabeled pyruvate and one labeled pyruvate per molecule of glucose, while [1,6-13C2
Detection and quantification of 13C label. What can be measured from NMR spectra in vivo?
The next step in the design of a 13C metabolic study is the detection of 13C label incorporation into brain metabolites during infusion of the 13C-labeled substrates and the quantification of the resulting NMR spectra. Since most TCA cycle intermediates are not concentrated enough to be detected by NMR in vivo, one relies on detection of 13C label in the larger pools of brain amino acids such as glutamate, glutamine, aspartate and (if sensitivity is sufficient) GABA.
A fundamental choice in the
Metabolic modeling
The fourth and final step in a 13C metabolic study is the analysis of 13C labeling time courses with a metabolic model to derive quantitative metabolic fluxes. Time courses of 13C labeling obtained after measurement and quantitation of 13C NMR spectra offer only limited metabolic information unless they are further analyzed using a metabolic model. For example, the rate of labeling of glutamate C4 depends not only on the TCA cycle rate (VTCA), but also on the exchange rate between
Conclusion
13C metabolic modeling is still an evolving field. Although this review has focused on the one-compartment model to illustrate the key points in metabolic modeling, much progress can be expected in the next few years as two-compartment models are being refined and validated. We expect Monte Carlo simulations to play a key role in the assessment of the robustness of two-compartment models in a variety of experimental conditions.
With the availability of very high magnetic fields for human studies
Acknowledgments
We would like to thank Dee Koski for technical assistance. This work was supported in part by NIH grants P41-RR08079, R01-NS38672, the Keck Foundation and the MIND Institute. The General Clinical Research Center was supported by NIH grant M01-RR00400.
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