Chemical energy available for the heart to do work, in the form of the ATP hydrolysis potential, is diminished in heart failure as a result of an altered metabolic profile. In fact, metabolic dysfunction can precede and may play a role in initiating structural remodeling and mechanical malfunction in the heart. While 31phosphate spectroscopy reveals significant changes in the cardiac phosphate metabolite profile in heart disease, heart failure, and hypertrophic cardiomyopathy, and a variety of metabolically targeted therapies are applied to improve cardiac metabolic function clinically, the full potential of these technologies have not been realized. The overall goals of this proposed study are to apply cardiac tissue computer modeling tools to quantify the physiological mechanisms controlling metabolic fluxes in the working heart, to determine how these mechanisms fail in a variety of pathophysiological settings, and to analyze how cardiac energetics may be observed and manipulated based on available technology. Our approach is to develop computer models that simulate oxygen and substrate transport in cardiac tissue and intracellular energy metabolism to serve as quantitatively testable hypotheses regarding the regulation of energy metabolism in health and disease. The basic modeling framework (developed under Aim 1) will extend our integrated model of microvascular transport and cardiac oxidative metabolism to account for uptake and handling of primary substrates and cytoplasmic and mitochondrial transport and metabolism of related compounds. The developed models will be parameterized and validated in Aim 2 based on data on metabolic fluxes and concentrations from healthy controls and rat models of cardiovascular disease. Based on these data, we will evaluate the roles of established physiological control mechanisms-including malonyl-CoA-mediated regulation of intracellular fatty acid transport and citrate-mediated regulation of glycolysis-in controlling in vivo substrate metabolism. In addition, hypotheses regarding a number of poorly understood mechanisms will be formulated in the model to test against experimental data.
In Aim 3 we propose to use the developed and validated models for a series of clinically relevant applications. Specifically, we will analyze data on phosphoenergetics and oxygenation derived from magnetic resonance spectroscopy to predict the metabolic state in normal and failing hearts and predict the sensitivity at which noninvasive 31P-MRS imaging data can diagnose a pathophysiological metabolic state in the heart;we will predict how chronic shifts in metabolic gene expression and substrate availability impact the energetic and oxidative state of the heart;and we will evaluate how certain current and proposed metabolic strategies for treatment of heart disease are expected to affect energy metabolism.
Metabolic dysfunction in the diseased heart limits the rate at which primary substrates can be oxidized to synthesize ATP necessary for ionic homeostasis and cardiac contraction. The potential consequences of a diminished energetic state include an impaired the ability of the heart to work and respond to acute and chronic stresses. We propose to develop validated simulation-based tools to understand and diagnosis of metabolic dysfunction that may be used in concert with noninvasive imaging of energy metabolites. In addition, our proposed tools for simulation of the pathophysiological operation of metabolic control mechanisms in heart disease may be used to guide clinical interventions aimed at modulation of cardiac energy metabolism.
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