Principal component analysis of untargeted metabolomics data from tissues collected using different methods of anesthesia.

<p>Two-dimensional PCA score plots reveal separation in metabolite profiles induced by different methods of anesthesia and euthanasia in C57BL/6J mice. Tissues analyzed were a) skeletal muscle, b) heart, c) liver, d) white adipose and e) serum. Methods of anesthesia and euthanasia were: CD, cervical dislocation euthanasia (red); CO2, Carbon dioxide euthanasia (green); Iso-Cont, continuous isoflurane anesthesia (dark blue); Iso-OD, isoflurane overdose euthanasia (light blue); Ket, ketamine anesthesia (pink); Pent, pentobarbital anesthesia (orange). Ellipses represent the 95% confidence interval.</p

Methods workflow.

<p>C57BL/6J mice were anesthetized or euthanized using six different standard laboratory methods as described in the text. Skeletal muscle, serum, liver, heart and adipose tissue were then rapidly collected and frozen by immersion in liquid nitrogen. Tissue samples were pulverized under liquid nitrogen, then sonicated in extraction solvent. Samples were centrifuged and the supernatant was analyzed by LC-MS. Targeted and untargeted metabolomics were used to identify metabolites which differed in abundance between methods of anesthesia or euthanasia.</p

Annotated features most responsible for differentiating methods of anesthesia and euthanasia in each tissue as determined by PLS-DA analysis.

<p>Annotated features most responsible for differentiating methods of anesthesia and euthanasia in each tissue as determined by PLS-DA analysis.</p

Heatmap illustrating alterations in metabolite levels in tissues collected using different methods of anesthesia.

<p>Data are expressed as fold change versus cervical dislocation. n = 8 mice per method of anesthesia or euthanasia. • indicates p < 0.05 versus cervical dislocation after false discovery rate correction.</p

Impact of anesthesia or euthanasia on selected metabolites.

<p>Data are presented as mean ± standard error of the mean and expressed as peak area relative to cervical dislocation (CD) for each method of anesthesia or euthanasia. n = 8 mice per method of euthanasia. * indicates p < 0.05 versus cervical dislocation after false discovery rate correction.</p

Histograms of LCR enzyme activity (X_LCR) sensitivities to fuel selection ranked from multiple LCR parameter sets.

<p>A 10³ parameter sets, each composed of 86 enzyme activities, individually fitted to LCR were used to obtain 10³ sensitivity coefficients for each activity. Each histogram panel in the figure represents a distribution of one of the 86 enzyme activity sensitivity coefficients. Each panel is annotated per the shortened enzyme identifier defined in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005982#pcbi.1005982.s001" target="_blank">S1 Table</a>. In each histogram panel, the y-axis represents the observed occurrence, or frequency, of a sensitivity coefficient and the x-axis represents the rank, or order of sensitivity coefficient magnitude out of 86. For instance, rank 1 is the most sensitive activity (X_LCR) whereas as rank 86 is the least sensitive activity. Histogram panels are ordered per their median rank value from top left to bottom right in the figure. Thus, the most sensitive activities are ordered from top left to bottom right in the figure.</p

Ratios of HCR/LCR enzyme activities for minimal LCR simulation.

<p>The enzyme activities used to fit the HCR and LCR data in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005982#pcbi.1005982.g004" target="_blank">Fig 4</a> were used to create the ratio X_HCR/X_LCR. Enzyme activities are labeled by shortened identifiers, described in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005982#pcbi.1005982.s001" target="_blank">S1 Table</a>, and further grouped with brackets into major pathways defined in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005982#pcbi.1005982.g001" target="_blank">Fig 1</a>.</p

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

<div><p>High capacity and low capacity running rats, HCR and LCR respectively, have been bred to represent two extremes of running endurance and have recently demonstrated disparities in fuel usage during transient aerobic exercise. HCR rats can maintain fatty acid (FA) utilization throughout the course of transient aerobic exercise whereas LCR rats rely predominantly on glucose utilization. We hypothesized that the difference between HCR and LCR fuel utilization could be explained by a difference in mitochondrial density. To test this hypothesis and to investigate mechanisms of fuel selection, we used a constraint-based kinetic analysis of whole-body metabolism to analyze transient exercise data from these rats. Our model analysis used a thermodynamically constrained kinetic framework that accounts for glycolysis, the TCA cycle, and mitochondrial FA transport and oxidation. The model can effectively match the observed relative rates of oxidation of glucose versus FA, as a function of ATP demand. In searching for the minimal differences required to explain metabolic function in HCR versus LCR rats, it was determined that the whole-body metabolic phenotype of LCR, compared to the HCR, could be explained by a ~50% reduction in total mitochondrial activity with an additional 5-fold reduction in mitochondrial FA transport activity. Finally, we postulate that over sustained periods of exercise that LCR can partly overcome the initial deficit in FA catabolic activity by upregulating FA transport and/or oxidation processes.</p></div

HCR and LCR metabolic constraint-based solutions.

<p>(A) HCR constraint-based solution: <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005982#pcbi.1005982.e003" target="_blank">Eq 1</a> (in Methods) was solved at each time point for internal reaction fluxes using given carbohydrate, FA, O₂, and CO₂ transport fluxes. Transport fluxes were derived from HCR O₂ and CO₂ flux data [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005982#pcbi.1005982.ref030" target="_blank">30</a>] shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005982#pcbi.1005982.g002" target="_blank">Fig 2</a>. (Inset) Zoomed in view of HCR mitochondrial FA transport and beta-oxidation enzyme fluxes. (B) LCR constraint-based solution: <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005982#pcbi.1005982.e003" target="_blank">Eq 1</a> (in Methods) was solved at each time point for internal reaction fluxes using given carbohydrate, FA, O₂, and CO₂ fluxes [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005982#pcbi.1005982.ref030" target="_blank">30</a>] shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005982#pcbi.1005982.g002" target="_blank">Fig 2</a>. (Inset) Zoomed in view of LCR mitochondrial FA transport and beta-oxidation enzyme fluxes. (C) The ATPase (ATP → ADP + Pi) flux (circles) from the HCR constraint-based solution in panel A was used to estimate the ATPase rate with time for subsequent simulations.</p

Simulation of HCR and minimal simulation of LCR rat exercise data.

<p>HCR (blue circles) and LCR (red circles) rat data were collected previously [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005982#pcbi.1005982.ref030" target="_blank">30</a>] during a graded treadmill experiment. Major pathways (87 reactions) of glucose, FA transport, oxidation, and bioenergetics (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005982#pcbi.1005982.g001" target="_blank">Fig 1</a>) were simulated using an ordinary differential equation system (98 state variables). Enzyme activities (<i>X</i>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005982#pcbi.1005982.e012" target="_blank">Eq 4</a>) were adjusted to fit the HCR data (blue circles), while a change in HCR enzyme activities were used to fit the LCR data (red circles). Simulations are represented by lines for HCR (blue) and LCR (red). LCR: solid lines were simulated by decreasing HCR total mitochondrial and FAO activities (error function value = 2.68), red dash-dot lines were simulated by decreasing total HCR mitochondrial enzyme activities only (error function value = 2.89), and dashed lines were simulated by decreasing HCR FAO enzyme activities only (error function value = 4.03). Total acyl-carnitine concentrations shown in panels (E-J) were derived from HCR and LCR gastrocnemius muscle [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005982#pcbi.1005982.ref030" target="_blank">30</a>]. Error bars represent standard error of the mean, while error bars in panel D were calculated from the propagation of error using errors in JCO₂ and JO₂ fluxes shown in panel A and B, respectively. (A) HCR and LCR rat carbon dioxide flux (JCO₂). (B) HCR and LCR rat molecular oxygen flux (JO₂). (C) Plasma lactate from HCR and LCR. (D) Respiratory quotient (JCO2/JO2) for HCR and LCR. (E) Total C16-carnitine muscle concentration for HCR and LCR rats. (F) Total C14-carnitine muscle concentration for HCR and LCR rats. (G) Total C8-carnitine muscle concentration for HCR and LCR rats. (H) Total C4-carnitine muscle concentration for HCR and LCR rats. (I) Total Acetyl-carnitine muscle concentration for HCR and LCR. (J) Total Carnitine muscle concentration for HCR and LCR rats.</p