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Silencing of glycolysis in muscle: experimental observation and numerical analysis - PubMed

Silencing of glycolysis in muscle: experimental observation and numerical analysis

Joep P J Schmitz et al. Exp Physiol. 2010 Feb.

Abstract

The longstanding problem of rapid inactivation of the glycolytic pathway in skeletal muscle after contraction was investigated using (31)P NMR spectroscopy and computational modelling. Accumulation of phosphorylated glycolytic intermediates (hexose monophosphates) during cyclic contraction and subsequent turnover during metabolic recovery was measured in vivo in human quadriceps muscle using dynamic (31)P NMR spectroscopy. The concentration of hexose monophosphates in muscle peaked 40 s into metabolic recovery from maximal contractile work at 6.9 +/- 1.3 mm (mean +/- s.d.; n = 8) and subsequently declined at a rate of 0.009 +/- 0.001 mm s(1). It was next tested whether the current knowledge of the kinetic controls in the glycolytic pathway in muscle integrated in the Lambeth and Kushmerick computational model of skeletal muscle glycolysis explained the experimental data. It was found that the model underestimated the magnitude of deactivation of the glycolytic pathway in resting muscle, resulting in depletion of glycolytic intermediates and substrate for oxidative ATP synthesis. Numerical analysis of the model identified phosphofructokinase and pyruvate kinase as the kinetic control sites involved in deactivation of the glycolytic pathway. Ancillary 100-fold inhibition of both phosphofructokinase and pyruvate kinase was found necessary to predict glycolytic intermediate and ADP concentrations correctly in resting human muscle. Incorporation of this information into the model resulted in highly improved agreement between predicted and measured in vivo dynamics of hexose monophosphates in muscle following contraction. We concluded that silencing of the glycolytic pathway in muscle following contraction is most likely to be mediated by phosphofructokinase and pyruvate kinase inactivation on a time scale of seconds and minutes, respectively, and is necessary to prevent depletion of vital cellular substrates.

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Figures

Figure 1
Figure 1. Schematic representation of the computational model

Recovery condition model (A), original Lambeth and Kushmerick model (B), resting condition model (C).

Figure 2
Figure 2. Selection of 31P NMR spectra recorded during the rest - exercise – recovery protocol

The spectrum at rest (A), end of exercise (B), 40s into recovery (C) and 240s into recovery (D) are shown. Spectra were apodized with a 10Hz lorentzian function. Signal intensity is expressed in arbitrary units.

Figure 3
Figure 3. Quantified metabolite dynamics during the rest – exercise – recovery protocol

The vertical solid black lines separate the data points of the three different workloads and recovery period. The error bars indicate the standard deviation (n=8). The [PCr] • and [Pi] □ (A), [PME] Δ (B), [ATP] ○ and [total phosphate pool] ▪ (C), pH ♦ (D), are shown. The part of the data analyzed with the computational model is indicated by a black bar.

Figure 4
Figure 4. Simulations of recovery dynamics of glycolytic intermediate metabolites performed with the original model

The solution space is indicated by the mean ± SD of the 5000 simulations that were run in a Monte Carlo approach.

Figure 5
Figure 5. HMP recovery dynamics according to predictions of the original model and experimental data ▲

Model predictions of HMP dynamics were calculated by summation of G1P, G6P and F6P dynamics. The solution space is indicated by the mean ± SD of the 5000 simulations that were run in a Monte Carlo approach. Experimental data represent the pooled results of all eight subjects, error bars indicate standard deviation (n=8).

Figure 6
Figure 6. Effect of inhibition of individual enzyme activity on resting muscle steady state [G6P] (A), [F-1,6P2] (B) and [ADP] (C)

Inhibition of the enzyme activity was modeled by setting Vmax values to 5%. The predictions of the original model are shown as a darker bar. The shaded area represents the physiological range and the horizontal solid black line indicates the mean value, as listed in table 2.

Figure 7
Figure 7. Simulations of recovery dynamics of glycolytic intermediate metabolites performed with the model that included PFK and PK inhibition

The solution space is indicated by the mean ± SD of the 5000 simulations that were run in a Monte Carlo approach.

Figure 8
Figure 8. Simulations of recovery dynamics of glycolytic intermediate metabolites performed with the model that included PFK inhibition

The solution space is indicated by the mean ± SD deviation of the 5000 simulations that were run in a Monte Carlo approach.

Figure 9
Figure 9. HMP recovery dynamics (A, B) and steady state G6P, F-1,6P2 and ADP relative to literature values (C, D), predicted for the case of both PFK and PK inhibition (A, C), and only PFK inhibition (B, D)

Model predictions of HMP dynamics were calculated by summation of G1P, G6P and F6P dynamics. The solution space is indicated by the mean ± SD of the 5000 simulations that were run in a Monte Carlo approach. Experimental data (▲) represent the pooled results of all eight subjects (n=8), error bars were omitted for clarity of presentation. The value of the steady state G6P, F-1,6P2 and ADP relative to literature values were calculated by dividing steady state model predictions by the mean value reported in literature as listed in table 2.

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