4.6 Article

Comprehensive metabolic modeling of multiple 13C-isotopomer data sets to study metabolism in perfused working hearts

出版社

AMER PHYSIOLOGICAL SOC
DOI: 10.1152/ajpheart.00428.2016

关键词

metabolic flux; C-13-MFA; parallel labeling experiments; heart metabolism; perfusion

资金

  1. National Institute of Diabetes and Digestive and Kidney Diseases [R01-DK-089312, P01-DK-058398]

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In many forms of cardiomyopathy, alterations in energy substrate metabolism play a key role in disease pathogenesis. Stable isotope tracing in rodent heart perfusion systems can be used to determine cardiac metabolic fluxes, namely those relative fluxes that contribute to pyruvate, the acetylCoA pool, and pyruvate anaplerosis, which are critical to cardiac homeostasis. Methods have previously been developed to interrogate these relative fluxes using isotopomer enrichments of measured metabolites and algebraic equations to determine a predefined metabolic flux model. However, this approach is exquisitely sensitive to measurement error, thus precluding accurate relative flux parameter determination. In this study, we applied a novel mathematical approach to determine relative cardiac metabolic fluxes using C-13-metabolic flux analysis (C-13-MFA) aided by multiple tracer experiments and integrated data analysis. Using C-13-MFA, we validated a metabolic network model to explain myocardial energy substrate metabolism. Four different C-13-labeled substrates were queried (i.e., glucose, lactate, pyruvate, and oleate) based on a previously published study. We integrated the analysis of the complete set of isotopomer data gathered from these mouse heart perfusion experiments into a single comprehensive network model that delineates substrate contributions to both pyruvate and acetyl-CoA pools at a greater resolution than that offered by traditional methods using algebraic equations. To our knowledge, this is the first rigorous application of C-13-MFA to interrogate data from multiple tracer experiments in the perfused heart. We anticipate that this approach can be used widely to study energy substrate metabolism in this and other similar biological systems.

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