Literature DB >> 33515599

Detailed evaluation of pyruvate dehydrogenase complex inhibition in simulated exercise conditions.

Bodhi A Jelinek1, Michael A Moxley2.   

Abstract

The mammalian pyruvate dehydrogenase complex (PDC) is a mitochondrial multienzyme complex that connects glycolysis to the tricarboxylic acid cycle by catalyzing pyruvate oxidation to produce acetyl-CoA, NADH, and CO2. This reaction is required to aerobically utilize glucose, a preferred metabolic fuel, and is composed of three core enzymes: pyruvate dehydrogenase (E1), dihydrolipoyl transacetylase (E2), and dihydrolipoyl dehydrogenase (E3). The pyruvate-dehydrogenase-specific kinase (PDK) and pyruvate-dehydrogenase-specific phosphatase (PDP) are considered the main control mechanism of mammalian PDC activity. However, PDK and PDP activity are allosterically regulated by several effectors fully overlapping PDC substrates and products. This collection of positive and negative feedback mechanisms confounds simple predictions of relative PDC flux, especially when all effectors are dynamically modulated during metabolic states that exist in physiologically realistic conditions, such as exercise. Here, we provide, to our knowledge, the first globally fitted, pH-dependent kinetic model of the PDC accounting for the PDC core reaction because it is regulated by PDK, PDP, metal binding equilibria, and numerous allosteric effectors. The model was used to compute PDH regulatory complex flux as a function of previously determined metabolic conditions used to simulate exercise and demonstrates increased flux with exercise. Our model reveals that PDC flux in physiological conditions is primarily inhibited by product inhibition (∼60%), mostly NADH inhibition (∼30-50%), rather than phosphorylation cycle inhibition (∼40%), but the degree to which depends on the metabolic state and PDC tissue source.
Copyright © 2021. Published by Elsevier Inc.

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Year:  2021        PMID: 33515599      PMCID: PMC8008327          DOI: 10.1016/j.bpj.2021.01.018

Source DB:  PubMed          Journal:  Biophys J        ISSN: 0006-3495            Impact factor:   4.033


  63 in total

1.  Steady state kinetics of rat brain pyruvate dehydrogenase multienzyme complex.

Authors:  T T Nigo; A Barbeau
Journal:  J Neurochem       Date:  1978-07       Impact factor: 5.372

Review 2.  The pyruvate dehydrogenase complexes: structure-based function and regulation.

Authors:  Mulchand S Patel; Natalia S Nemeria; William Furey; Frank Jordan
Journal:  J Biol Chem       Date:  2014-05-05       Impact factor: 5.157

3.  Derivation of rate equations for multisite ping-pong mechanisms with ping-pong reactions at one or more sites.

Authors:  W W Cleland
Journal:  J Biol Chem       Date:  1973-12-25       Impact factor: 5.157

4.  A simple method for derivation of rate equations for enzyme-catalyzed reactions under the rapid equilibrium assumption or combined assumptions of equilibrium and steady state.

Authors:  S Cha
Journal:  J Biol Chem       Date:  1968-02-25       Impact factor: 5.157

5.  Bovine heart pyruvate dehydrogenase kinase stimulation by alpha-ketoisovalerate.

Authors:  J G Robertson; L L Barron; M S Olson
Journal:  J Biol Chem       Date:  1990-10-05       Impact factor: 5.157

Review 6.  Regulation of pyruvate dehydrogenase (PDH) activity in human skeletal muscle during exercise.

Authors:  Lawrence L Spriet; George J F Heigenhauser
Journal:  Exerc Sport Sci Rev       Date:  2002-04       Impact factor: 6.230

7.  A pH-dependent kinetic model of dihydrolipoamide dehydrogenase from multiple organisms.

Authors:  Michael A Moxley; Daniel A Beard; Jason N Bazil
Journal:  Biophys J       Date:  2014-12-16       Impact factor: 4.033

8.  Elementary steps in the reaction of the pyruvate dehydrogenase complex from pig heart. Kinetics of thiamine diphosphate binding to the complex.

Authors:  B Sümegi; I Alkonyi
Journal:  Eur J Biochem       Date:  1983-11-02

9.  Computer simulation of metabolism in pyruvate-perfused rat heart. IV. Model behavior.

Authors:  M J Achs; M C Kohn; D Garfinkel
Journal:  Am J Physiol       Date:  1979-09

10.  Systems-level computational modeling demonstrates fuel selection switching in high capacity running and low capacity running rats.

Authors:  Michael A Moxley; Kalyan C Vinnakota; Jason N Bazil; Nathan R Qi; Daniel A Beard
Journal:  PLoS Comput Biol       Date:  2018-02-23       Impact factor: 4.475

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