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Daniel Beard Medical College of Wisconsin

Emergent and Critical Phenomena in Cardiac Energy Metabolism in the Healthy Heart, in Ischemia, and in the Development of Heart Failure. Daniel Beard Medical College of Wisconsin. IMAG 13March09. The Conventional Wisdom “Metabolic stability hypothesis”

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Daniel Beard Medical College of Wisconsin

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  1. Emergent and Critical Phenomena in Cardiac Energy Metabolism in the Healthy Heart, in Ischemia, and in the Development of Heart Failure Daniel Beard Medical College of Wisconsin IMAG 13March09

  2. The Conventional Wisdom “Metabolic stability hypothesis” data from intact tissues with high oxidative phosphorylation capacities (i.e., heart, brain, and kidney) indicate that the cytosolic concentration of ADP and Pi do not change significantly with work. These data imply that [a] simple feedback model is not adequate to explain the regulation of energy metabolism in these tissues. Balaban, R.S., Am J Physiol, 1990. 258:C377-89. Emergent and Critical Phenomena in Cardiac Energy Metabolism in the Healthy Heart, in Ischemia, and in the Development of Heart Failure Question 1: What mechanism(s) control oxidative phosphorylation? Approach: 1. Devising alternative hypotheses; 2. Devising a crucial experiment (or several of them), with alternative possible outcomes, each of which will, as nearly as possible, exclude one or more of the hypotheses; 3. Carrying out the experiment so as to get a clean result; 1’. Recycling the procedure, making subhypotheses or sequential hypotheses to refine the possibilities that remain; and so on. Platt. Strong Inference. Science, 1964. 146:347-353

  3. Emergent and Critical Phenomena in Cardiac Energy Metabolism in the Healthy Heart, in Ischemia, and in the Development of Heart Failure Question 2: What is going on with energy metabolism in the heart in heart failure? Conventional Wisdom: The failing heart is “energy starved”. “[PCr] decreases in hypertrophy and failure because of a mismatch in ATP supply and demand.” “Based on analysis of human biopsy specimens, we now know that [ATP] is 25% to 30% lower in the failing human heart…Why [ATP] decreases by only 25% is not known...” -Ingwall & Weiss. Circ Res, 2004. 95:135-45 How? Why? Not necessarily! A putative explanation emerges from our simulations.

  4. The Mitochondrion: A coupled biochemical & electrophysiological system • Important Features: • Ionic currents influence biochemical species distributions (e.g., [ATP] = [ATP4-] + [HATP3-] + … • Biochemical species influence ionic concentrations and fluxes. • Several components operate near equilibrium. • 4. Whole system approaches equilibrium during ischemia/hypoxia. • Model Requirements: • Capture species distribution of biochemical reactants. • Capture influence of species distribution on biochemical kinetics and thermodynamics. Matrix Inter-Membrane space External Space

  5. Biochemical equilibria measurements (obtained under different in vitro conditions) Functional models of enzymes and transporters In vitro data on enzyme kinetics Ion dissociation data (obtained under different in vitro conditions) Theoretical/Computational Tools Tools for simulating integrated biochemical systems Biochemical thermodynamics Modeling/simulation of enzyme kinetics Database of derived thermodynamic properties (Df Go’s, Df Ho’s) Integrated systems models Integrated systems data Database of ion dissociation constants Raw data Computational models and components Derived data Our Approach to Simulating Biochemical Systems Beard et al. CellML metadata: Standards, tools, and repositories. Trans. Roy. Soc. (in press), 2008.

  6. Simulating Purified Mitochondria Fan Wu Postdoctoral Fellow Medical College of Wisconsin Kathryn LaNoue Distinguished Professor Cellular and Molecular Physiology Penn State Hershey Medical Center Wu et al., Computer modeling of mitochondrial TCA cycle, oxidative phosphorylation, metabolite transport, and electrophysiology. J Biol Chem. 282:24525-24537, 2007.

  7. CIT = CIT3- + HCIT2- + MgCIT- + … OAA = OAA2- + HOAA- + MgOAA + … The model to simulate an experiment to characterize TCA cycle kinetics based on data from isolated mitochondria

  8. 1. At t = 0 add some substrate(s) 2. At t = t quench reactions 3. Measure concentrations of metabolic intermediates Model Parameterization: Kinetic measurements using purified mitochondria LaNoue et al. (JBC, 245:102-111, 1970)

  9. Parameterization Data Set #1 Data Set #2 State 2 State 3 LaNoue et al. (JBC, 245:102-111, 1970)

  10. Parameterization: Data Set #3 Validation(against in vitro data) steady-state: kinetic: Bose et al. (JBC, 278:39155-39165, 2003)

  11. Biochemical equilibria measurements (obtained under different in vitro conditions) Functional models of enzymes and transporters In vitro data on enzyme kinetics Ion dissociation data (obtained under different in vitro conditions) Theoretical/Computational Tools Tools for simulating integrated biochemical systems Biochemical thermodynamics Modeling/simulation of enzyme kinetics Database of derived thermodynamic properties (Df Go’s, Df Ho’s) Integrated systems models Integrated systems data Database of ion dissociation constants Raw data Computational models and components Derived data Our Approach to Simulating Biochemical Systems Beard et al. CellML metadata: Standards, tools, and repositories. Trans. Roy. Soc. (in press), 2008.

  12. The Mitochondrion: A coupled biochemical & electrophysiological system Matrix Inter-Membrane space External Space

  13. Studies on in vivo Cardiac Energetics Fan Wu Postdoctoral Fellow Medical College of Wisconsin Jay Zhang Professor of Medicine University of Minnesota Wu et al., Phosphate metabolite concentrations and ATP hydrolysis potential in normal and ischaemic hearts. J Physiol. 586:4193-4208, 2008.

  14. data from intact tissues with high oxidative phosphorylation capacities (i.e., heart, brain, and kidney) indicate that the cytosolic concentration of ADP and Pi do not change significantly with work. These data imply that [a] simple feedback model is not adequate to explain the regulation of energy metabolism in these tissues. Balaban, R.S., Am J Physiol, 1990. 258:C377-89. Dataset #1: Data from Zhang, Bache, Ugurbil, et al., 1999-2005

  15. Integrated Simulation of Mitochondrial Energy Metabolism and Oxygen Transport in the Heart

  16. Integrated Simulation of Mitochondrial Energy Metabolism and Oxygen Transport in the Heart

  17. Hypothesis #1: ADP is critical feedback controller

  18. Hypothesis #2: Pi is critical feedback controller

  19. The metabolic stability hypothesis data from intact tissues with high oxidative phosphorylation capacities (i.e., heart, brain, and kidney) indicate that the cytosolic concentration of ADP and Pi do not change significantly with work. These data imply that [a] simple feedback model is not adequate to explain the regulation of energy metabolism in these tissues. is disproven. Summary so far… * Specifically, a (simple) feedback model is adequate to explain the data.

  20. DGCRIT≈-63 kJ mol-1 Hypothesis #3: Cardiac work rate is limited by DGCRIT

  21. Experiment to Test Hypothesis #3: Transient (Acute) Ischemia and Recovery

  22. Transient (Acute) Ischemia and Recovery Model prediction using baseline ATPase activity

  23. with Transient (Acute) Ischemia and Recovery Model prediction accounting for DGCRIT

  24. Studies on Cardiac Energetics in Heart Failure Fan Wu Postdoctoral Fellow Medical College of Wisconsin Jay Zhang Professor of Medicine University of Minnesota

  25. Depletion of Metabolic Pools in LVH Early LVH: Bache et al., 1994. Am J Physiol. 266:H1959-H1970. Mod LVH: Bache et al., 1999. Cardiovasc Res. 42:616-626.

  26. Model Predictions for “Early LVH” Case Data from Bache et al., 1994. Am J Physiol. 266:H1959-H1970. Solid lines are model predictions. (Model assumptions?)

  27. Model Predictions for “Mod. LVH” Case Data from Mod LVH: Bache et al., 1999. Cardiovasc Res. 42:616-626. Solid lines are model predictions.

  28. Cardiac Energetics During Evolution of Heart Failure Assume steady depletion of metabolites: Data from Zhang et al., 1993. J Clin Invest. 92:993-1003.

  29. Cardiac Energetics During Evolution of Heart Failure Canine model of Ingwall and coworkers Shen et al. 1999. Circ Res. 100:2113-2118.

  30. Cardiac Energetics During Evolution of Heart Failure Max Basal EDP < 15 mmHg EDP > 15 mmHg Max Max Data from Zhang et al., 1993. J Clin Invest. 92:993-1003.

  31. Predicted DGATP at MVO2max TAN, TEP, and CRtot in units of mmol s-1 (l cell)-1

  32. Predicted DGATP at MVO2max “Based on analysis of human biopsy specimens, we now know that [ATP] is 25% to 30% lower in the failing human heart…Why [ATP] decreases by only 25% is not known...” Ingwall & Weiss. Circ Res, 2004. 95:135-45

  33. Downstream signaling tied to metabolic remodeling.Potential effects on AMPK and oxidative stress?

  34. Possible metabolic therapy for heart failure?“creatine and PCr levels are…maintained within a narrow range in the healthy…myocardium. We postulate that any disturbance of this fine balance, irrespective of the direction of change, leads to energetic and subsequentlyfunctional impairment.”Wallis et al. (2005) Circulation 112, 3131-3139.

  35. Key Findings—Physiological Control of OxPhos The metabolic stability hypothesis is disproved. In vitro (purified mitochondria) and in vivo data are consistent with the hypothesis that cardiac energy metabolism is primarily regulated through feedback of substrates for oxidative phosphorylation. Inorganic phosphate is key signal. Maximal cardiac oxygen consumption is an emergent property of the integrated metabolic and transport system. DGCRIT≈-63 kJ mol-1.

  36. Key Findings—Progression of Heart Failure There are two distinct phases associated with the gradual depletion of adenine nucleotides and creatine from the heart: and early “adaptive” phase and a later “maladaptive” phase. Metabolic reserve and ATP hydrolysis potential are maintained (or improved) in the adaptive phase and are significantly diminished in the maladaptive phase. The critical transition between the adaptive and maladaptive phases occurs when the adenine nucleotide pools is approximately 30% depleted compared to baseline. Inorganic phosphate concentration decreases in the heart failure models we have examined.

  37. Thanks MCW Fan Wu Kalyan Vinnakota Feng Qi Ranjan Dash Muscle Metabolism Jeroen Jeneson, Eindhoven Jay Zhang, Minnesota Kay LaNoue, Penn State Marty Kushmerick, Washington Cardiac Systems Simulation Nic Smith, Oxford Peter Hunter, Auckland

  38. http://biocoda.org

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