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

ABSTRACT: 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.

SUBMITTER: Moxley MA

PROVIDER: S-EPMC5841818 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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

Moxley Michael A MA Vinnakota Kalyan C KC Bazil Jason N JN Qi Nathan R NR Beard Daniel A DA

PLoS computational biology 20180223 2

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 mi ...[more]

PMID: 29474500

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Project description:IntroductionTo date, no study has compared anaerobic capacity (AnC) estimates computed with the maximal accumulated oxygen deficit (MAOD) method and the gross energy cost (GEC) method applied to treadmill running exercise.PurposeFour different models for estimating anaerobic energy supply during treadmill running exercise were compared.MethodsFifteen endurance-trained recreational athletes performed, after a 10-min warm-up, five 4-min stages at ∼55-80% of peak oxygen uptake, and a 4-min time trial (TT). Two linear speed-metabolic rate (MR) regression models were used to estimate the instantaneous required MR during the TT (MR TT_req ), either including (5+Y LIN ) or excluding (5-Y LIN ) a measured Y-intercept. Also, the average GEC (GEC AVG ) based on all five submaximal stages, or the GEC based on the last submaximal stage (GEC LAST ), were used as models to estimate the instantaneous MR TT_req . The AnC was computed as the difference between the MR TT_req and the aerobic MR integrated over time.ResultsThe GEC remained constant at ∼4.39 ± 0.29 J⋅kg-1⋅m-1 across the five submaximal stages and the TT was performed at a speed of 4.7 ± 0.4 m⋅s-1. Compared with the 5-Y LIN , GEC AVG , and GEC LAST models, the 5+Y LIN model generated a MR TT_req that was ∼3.9% lower, with corresponding anaerobic capacities from the four models of 0.72 ± 0.20, 0.74 ± 0.16, 0.74 ± 0.15, and 0.54 ± 0.14 kJ⋅kg-1, respectively (F 1.07,42 = 13.9, P = 0.002). The GEC values associated with the TT were 4.22 ± 0.27 and 4.37 ± 0.30 J⋅kg-1⋅m-1 for 5+Y LIN and 5-Y LIN , respectively (calculated from the regression equation), and 4.39 ± 0.28 and 4.38 ± 0.27 J⋅kg-1⋅m-1 for GEC AVG and GEC LAST , respectively (F 1.08,42 = 14.6, P < 0.001). The absolute typical errors in AnC ranged between 0.03 and 0.16 kJ⋅kg-1 for the six pair-wise comparisons and the overall standard error of measurement (SEM) was 0.16 kJ⋅kg-1.ConclusionThese findings demonstrate a generally high disagreement in estimated anaerobic capacities between models and show that the inclusion of a measured Y-intercept in the linear regression (i.e., 5+Y LIN ) is likely to underestimate the MR TT_req and the GEC associated with the TT, and hence the AnC during maximal 4-min treadmill running.

Project description:BackgroundColorectal cancer is one of the most prevalent causes of cancer death. It has been studied extensively for a long time, and numerous genetic and epigenetic events have been associated with the disease. However, its molecular mechanisms are still unclear. High-throughput metabolomics data, combined with customized computational systems modeling, can assist our understanding of some of these mechanisms by revealing connections between alterations in enzymatic activities and their consequences for a person's metabolic profile. Of particular importance in this context is purine metabolism, as it provides the nucleotides needed for cell proliferation.Methods and findingsWe employ a computational systems approach to infer molecular mechanisms associated with purine metabolism in colorectal carcinoma. The approach uses a dynamic model of purine metabolism as the simulation system and metabolomics data as input. The execution of large-scale Monte Carlo simulations and optimization with the model permits a step-wise reduction in possibly affected enzyme mechanisms, from which likely targets emerge.ConclusionsAccording to our results, some enzymes in the purine pathway system are very unlikely the targets of colorectal carcinoma. In fact, only three enzymatic steps emerge with statistical confidence as most likely being affected, namely: amidophosphoribosyltransferase (ATASE), 5'-nucleotidase (5NUC), and the xanthine oxidase/dehydrogenase (XD) reactions. The first of these enzymes catalyzes the first committed step of de novo purine biosynthesis, while the other two enzymes are associated with critical purine salvage pathways. The identification of these enzymes is statistically significant and robust. In addition, the results suggest potential secondary targets. The computational method cannot discern whether the inferred mechanisms constitute symptoms of colorectal carcinoma, or whether they might be causative and critical components of the uncontrolled cellular growth in cancer. The inferred molecular mechanisms present testable hypotheses that suggest targeted experiments for future studies of colorectal carcinoma and might eventually lead to improved diagnosis and treatment.

Project description:Homogeneous Charge Compression Ignition (HCCI) combustion is a potential candidate for dealing with the stringent regulations on vehicle emissions while still providing very good energy efficiency. Despite the promising results obtained in preliminary studies, the lack of autoignition control has delayed its launch in the engine industry. In the development of the HCCI concept, the availability of reliable computer models has proved extremely valuable, due to their flexibility and lower cost compared with experiments using real engines. In order to obtain the best formulation of a fuel surrogate formulated with n-heptane, toluene and cyclohexane that efficiently estimate the autoignition behaviour, regression adjustments are made to the Root-Mean-Square Errors (RMSE) of experimental Starts of Combustion (SOC) from the modeled SOC. The canonical form of the Scheffé polynomials is widely used to fit the data from mixture experiments, however the experimenter might have only partial knowledge. In this paper we present the adaptation of the robust methodology for possibly misspecified blending model and an algorithm to obtain tailor-made optimal designs for mixture experiments, instead of using standard designs which are indiscriminately employed, to make good estimations of the parameters blending model. We maximize the determinant of the mean squared error matrix of the least square estimator over a realistic neighbourhood of the fitted regression mixture model. The maximized determinant is then minimized over the class of possible designs, yielding an optimal design. Thus, the computed desings are robust to the exact form of the true blending model. Standard mixture designs, as the simplex lattice, are around 25% efficient for estimation purposes compared with the designs obtained in this work when deviances from the considered model occur during the experiments. Once an optimal-robust design was selected (based on the level of certainty about model adequacy), we computed the optimal mixture that best reproduces the combustion property to be imitated. Optimal mixtures obtained when the considered model is inadequate agree with the results achieved in empirical studies, which validates the methodology proposed in this work.

Project description:Purpose: Aerobic capacity is a strong predictor of cardiovascular mortality. To determine the relationship between inborn aerobic capacity and soleus gene expression we examined genome-wide gene expression in soleus muscle of rats artificially selected for high and low running capacity (HCR and LCR, respectively) over 16 generations. The artificial selection of LCR caused accumulation of risk factors of cardiovascular disease similar to the metabolic syndrome seen in man, whereas HCR had markedly better cardiac function. We also studied alterations in gene expression in response to exercise training in the two groups, since accumulating evidence indicates that exercise has profound beneficial effects on the metabolic syndrome. Methods: Soleus gene expression of both sedentary and exercise trained HCR and LCR was characterized by microarray- and gene ontology analysis. Results: Although HCR and LCR had an inborn 347% difference in running capacity, only three genes were found differentially expressed in the soleus muscle between the two groups. Up-regulation of the mitochondrial enzyme leucyl-transferRNA synthetase (LARS2) was found in the sedentary LCR. Increased expression of LARS2 has been associated with a mitochondrial DNA mutation linked to maternally inherited diabetes and mitochondrial dysfunction. In line with our findings, a growing body of evidence suggests that LCR have compromised mitochondrial function. After exercise training, 58 genes were altered in the soleus muscle of HCR, in contrast to only one in the LCR group. This suggests that animals born with different levels of fitness respond different to the same type of exercise training. Adaptations to exercise in HCR seemed to be associated with increased lipid metabolism and fatty acid elongation in the mitochondria. Also, genes associated with the peroxisomes, seemed to be central in the adaptation to exercise. Conclusion: The results indicate that (i) LCR might have mitochondrial dysfunction, which may be a contributing factor of the low inborn aerobic capacity, (ii) animals born with different levels of fitness respond different to the same exercise program. Keywords: aerobic capacity, metabolic syndrome, soleus muscle, gene expression, metabolism

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

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

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