Tissue Capacity for Mitochondrial Oxidative Phosphorylation and its Adaptation to Stress

Metabolic homeostasis: oxidative phosphorylation and the metabolic requirements of higher plants and animals

A model of oxidative phosphorylation and its regulation is presented, which is consistent with the experimental data on metabolism in higher plants and animals. The variables that provide real-time control of metabolic status are: intramitochondrial [NAD+]/[NADH], energy state ([ATP]/[ADP][Pi]), and local oxygen concentration ([O2]). ATP consumption and respiratory chain enzyme content are tissue specific (liver vs. heart muscle), and the latter is modulated by chronic alterations in ATP consumption (i.e., endurance training etc.). ATP consumption affects the energy state, which increases or decreases as necessary to match synthesis with consumption. [NAD+]/[NADH], local [O2], and respiratory chain content determine the energy state at which match of synthesis and utilization is achieved. Tissues vary widely in their ranges of ATP consumption. Expressed as the turnover of cytochrome c, the rates may change little (7 to 12/s) (liver) or a lot (1 to >300/s) (flight muscle of birds, bats, and insects). Ancillary metabolic pathways, including creatine or arginine kinase, glycerol phosphate shuttle, fatty acid, and citric acid cycle dehydrogenases, are responsible for meeting tissue-specific differences in maximal rate and range in ATP utilization without displacing metabolic homeostasis. Intramitochondrial [NAD+]/[NADH], [ATP], and [Pi] are adjusted to keep [ADP] and [AMP] similar for all tissues despite large differences in ranges in ATP utilization. This is essential because [ADP] and [AMP], particularly the latter, have major roles in regulating the activity of many enzymes and signaling pathways (AMP deaminase, AMP dependent protein kinases, etc.) common to all higher plants and animals.

NEW & NOTEWORTHY Oxidative phosphorylation has an intrinsic program that sets and stabilizes cellular energy state ([ATP]/[ADP][Pi]), and thereby metabolic homeostasis. A computational model consistent with regulation of oxidative phosphorylation in higher plants and animals is presented. Focus is on metabolism ancillary to oxidative phosphorylation by which it was integrated into preexisting metabolic regulation and adapted by evolution to develop cells and tissues with differing rates of ATP utilization: i.e., liver versus brain versus muscle.

The thermodynamic basis of glucose-stimulated insulin release: a model of the core mechanism

A model for glucose sensing by pancreatic β-cells is developed and compared with the available experimental data. The model brings together mathematical representations for the activities of the glucose sensor, glucokinase, and oxidative phosphorylation. Glucokinase produces glucose 6-phosphate (G-6-P) in an irreversible reaction that determines glycolytic flux. The primary products of glycolysis are NADH and pyruvate. The NADH is reoxidized and the reducing equivalents transferred to oxidative phosphorylation by the glycerol phosphate shuttle, and some of the pyruvate is oxidized by pyruvate dehydrogenase and enters the citric acid cycle. These reactions are irreversible and result in a glucose concentration-dependent reduction of the intramitochondrial NAD pool. This increases the electrochemical energy coupled to ATP synthesis and thereby the cellular energy state ([ATP]/[ADP][Pi]). ATP and Pi are 10-100 times greater than ADP, so the increase in energy state is primarily through decrease in ADP The decrease in ADP is considered responsible for altering ion channel conductance and releasing insulin. Applied to the reported glucose concentration-dependent release of insulin by perifused islet preparations (Doliba et al. 2012), the model predicts that the dependence of insulin release on ADP is strongly cooperative with a threshold of about 30 μmol/L and a negative Hill coefficient near -5.5. The predicted cellular energy state, ADP, creatine phosphate/creatine ratio, and cytochrome c reduction, including their dependence on glucose concentration, are consistent with experimental data. The ability of the model to predict behavior consistent with experiment is an invaluable resource for understanding glucose sensing and planning experiments.