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High aerobic capacity mitigates changes in the plasma metabolic profile associated with aging Oluyemi S. Falegan, Hans J. Vogel, Dustin S. Hittel, Lauren G Koch, Steven L Britton, Russ T Hepple, and Jane Shearer J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00796 • Publication Date (Web): 12 Dec 2016 Downloaded from http://pubs.acs.org on December 13, 2016

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Journal of Proteome Research

Manuscript ID pr-2016-00796h.R1

High aerobic capacity mitigates changes in the plasma metabolomic profile associated with aging

Oluyemi S. Falegan1, Hans J. Vogel1,2, Dustin S. Hittel2,3, Lauren G. Koch4, Steven L. Britton4,5, Russ. T. Hepple6, Jane Shearer2,3*

1

Department of Biological Sciences, Faculty of Science, University of Calgary, Calgary,

AB, Canada 2

Department of Biochemistry & Molecular Biology, Faculty of Medicine, University of

Calgary. Calgary, AB, Canada 3

Faculty of Kinesiology, University of Calgary. Calgary, AB, Canada

4

Department of Anesthesiology, University of Michigan, Ann Arbor, MI, USA

5

K.G. Jebsen Center for Exercise in Medicine, Department of Circulation and Medical

Imaging, Norwegian University of Science and Technology, Trondheim. Norway 6

Department of Physical Therapy, University of Florida, Gainesville, FL, USA

Number of Figures: 5 Number of Tables: 2 Word Count: 3201

*Corresponding Author. Telephone: +1 403 220 3431, Facsimile: +1 403 284 3553, Email: [email protected]

Keywords: aging, aerobic capacity, metabolomics, nuclear magnetic resonance, metabolism

ACS Paragon Plus Environment


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Abstract ( 1 were considered potentially relevant.

Pathway analysis. Significantly differential and co-regulated metabolites found in supervised OPLS-DA models were used for pathway enrichment analysis using MetaboAnalyst (version 3.0)16,17. This web-based tool relies on the knowledgebase of high-quality KEGG metabolic pathways for mapping altered metabolites to their corresponding pathways. Raw (not adjusted) values are reported due to the small number of pathways highlighted by the program for this dataset.

Results Animal Characteristics. At the time of study young and old rats were 13 and 26 months old and animal characteristics are shown in Table 1. Animals were all examined at 11 weeks for body mass, best running time, best running distance and best running speed. As they aged, old animals underwent these tests again at 52 and 83 weeks of age. Results of these tests are depicted in Figure 1. Results show that at 11 weeks of age, HCR and LCR animals diverged by 25% in mass, with LCR being heavier (Figure 1A). As the animals aged, this difference became more exaggerated with LCR more than


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doubling HCR mass at 83 weeks. Lower running times, distance and speed (Figure 1BD) were also noted for LCR at all time points. Of interest, declines with age in LCR were minimal over time suggesting that these animals were already close their lowest aerobic capacity at a young age. In contrast, sharp declines in all three parameters were noted in HCR and at the last testing (83 weeks), yet all measures were still significantly greater than in their old LCR counterparts.

Whole Body and Tissue Specific Glucose Disposal. To examine metabolic indices of insulin stimulated glucose uptake in old LCR and HCR animals, euglycemichyperinsulinemic clamps were performed in conscious animals. During the last 30 min of the insulin clamp, glucose levels were held constant (p>0.05, data not shown) and a radioactive glucose tracer was used to assess whole body and tissue specific insulin sensitivity. The glucose infusion rate, the amount of variable glucose required to maintain glycemia (6 mM) was assessed for each animal. Glucose infusion rates for LCR and HCR were 13.6 ± 4.0 and 8.4 ± 2.7 mg/kg/min respectively (p>0.05, Table 1). Indices of glucose uptake into skeletal and cardiac muscle were also examined (Figure 2). Results show LCR to display impaired glucose utilization in skeletal muscle, especially in the soleus (p0.05)(Figure 2D).

Predictive Models Based on Age and Aerobic Capacity. A minimum of 50 metabolites were identified and quantified in each sample. The most differential plasma metabolites


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identified based on VIP values > 1 were used for subsequent supervised multivariate analysis, OPLS-DA. To investigate the underlying metabolic relationships within the groups, an OPLS-DA model that incorporated all four groups was built (Figure 3A). The resultant model has variability, R2 of 0.46 and a predictive value Q2 of 0.20. The loadings plot of the individual metabolites contributing to the model is shown in Figure 3B. Metabolites closest to the line of identity change with both age and genotype (e.g. valine) while off-axis metabolites are more influenced by either age or aerobic capacity (e.g. citrate).

Additional OPLS-DA models were designed to test age and aerobic capacity relationships. Figure 4 shows relationships between age and aerobic capacity groups along their orthogonal partial least squares (OPLS) and partial least squares components (PLS) when all animals are compared. These scatter plots show separation in the plasma metabolic profile of old vs young (Figure 4A) and HCR vs. LCR (Figure 4B). Analysis of model metrics reveals that old rats differ vividly from young rats with metrics R2 = 0.80, Q2 = 0.63 (Figure 4A), while the separation between HCR and LCR rats was not as strong R2 = 0.64, Q2 = 0.48 (Figure 4B). Individual comparisons (e.g. Young vs. Old within HCR) were also constructed and model metrics are shown in Table S1.

Individual Metabolites Contributing to Model Separation. Specific metabolites increased or decreased with either age or aerobic capacity and a VIP greater than 1 are shown in Figure 5. Positive coefficient values (upper portion of the plot) indicate increased metabolite concentrations while negative values (the lower part of diagram) show a


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decrease in metabolite concentrations. Two comparisons are shown, old compared to young rats (Figure 5A) and HCR vs. LCR (Figure 5B). Note that all animals are included in the plots (e.g. both LCR and HCR young rats vs. LCR and HCR old rats).

Metabolic Biopatterns. To illustrate metabolic biopatterns between and within the age and aerobic capacity, pathway analysis was performed (Table 2). Four pathways were identified in the LCR vs. HCR comparison while only two pathways were identified in the analysis of age. These common pathways mainly implicated pathways related to amino acids. When all factors (age and aerobic capacity) were examined, common pathways converged on energy metabolism, namely tricarboxylic acid cycle (TCA) cycle intermediates.

Discussion Intrinsic aerobic capacity is predictive of survivability and health; however this relationship in aged populations is not conclusive2,18. We therefore sought to examine the divergence in metabolic patterns between young and old rats selectively bred for aerobic capacity using a system-based, metabolomics approach. LCR and HCR animals display strikingly distinct phenotypes that go well beyond aerobic capacity. HCR display excellent health, resistance to environmental challenges and protection from oxidative damage while the LCR show characteristics indicative of numerous metabolic disease states including hyperlipidemia, impaired glucose tolerance and hypertension19.

In the present study, rats selectively bred for aerobic capacity were studied at either 13 or 26 months of age, equivalent to middle and late age for these animals. Age selection


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at the time of study (26 months) was based on previous data showing survival rates of the same generation of rats to be 20% and 90% for LCR and HCR respectively20. Exercise testing, consisting of running time, distance and speed revealed that differences between LCR and HCR were evident at a young age (11 weeks) and continued to persist into late age. In other words, aging did not mitigate the differences in aerobic capacity between the two groups. Furthermore, declines in LCR animals were minimal with age due to the low starting aerobic capacity of these animals. While old HCR animals exhibited a 38% decline in running distance and time, LCR only declined by 17% between the first and last testing sessions at 11 and 83 weeks. These data indicate that aerobic capacity in HCR animals was better maintained with age despite no additional exercise training.

Our primary results show metabolomics analysis to predict age better than aerobic capacity when all animals are compared, a somewhat unexpected finding considering aerobic capacity is such a critical determinant of healthy aging. Separation between all groups could be attributed to major metabolites, some of which were significantly differential. Dysregulation in the levels of TCA intermediates succinate and citrate were the most prevalent metabolic alteration between all animals and specifically within the HCR group. This would suggest that selection for higher aerobic capacity increases energy metabolism in older rats, and is consistent with findings of previous studies showing mice artificially selected for maximal aerobic metabolic rate have increased amino acid and TCA cycle metabolites in gastrocnemius muscle21.


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Examination of whole body and tissue specific insulin sensitivity employing the hyperinsulinemic-euglycemic clamp procedure combined with sensitive glucose tracers revealed impaired tissue glucose disposal in the old LCR animals. The glucose infusion rate, the amount of exogenous glucose required to maintain glycemia at a constant level was highly variable, and is therefore not significantly different between LCR and HCR animals in the present study. Moreover, the glucose infusion rates were more than twice that of what is required in younger animals11. This response has also been noted in humans were elderly individuals have lower rates of glucose disappearance due to reduced insulin secretion and action22,23. In agreement with this observation, we show profound impairments in the metabolic indexes of tissue-specific glucose utilization in both slow (soleus) and fast twitch (vastus lateralis) muscles, but not the heart. This finding replicates previous studies showing LCR animals lack cardiac insulin resistance despite systemic and skeletal muscle insulin resistance24. Insulin resistance and hyperinsulinemia in LCR animals also arises from impaired hepatic insulin clearance and progressive liver steatosis that is accompanied by elevated serum triglyceride levels25, 26. This is likely caused by lower rates of hepatic fatty acid oxidation and mitochondrial respiratory capacity in the LCR animals27. In contrast, the HCR animals appear to be protected from this response because of an enhanced mitochondrial oxidative capacity28.

Distinct patterns of insulin resistance were also observed in the metabolomic profiles of both LCR and old animals. Key differential metabolites contributing to the group separation between HCR and LCR included glutamine, o-acetylcarnitine, citrate and proline that were decreased in HCR compared to LCR. Acetylcarnitine is particularly


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interesting as it is implicated in skeletal muscle and liver insulin resistance29. Insulin resistance is linked to several underlying mechanisms; including depressed TCA cycling in favour of fatty acid oxidation leaving the accumulation of lipid intermediates such as acetylcarnitine in its trail, blocking insulin sensitivity in the process30. Low levels of acetylcarnitine in HCR and high levels in LCR is in agreement with previous studies that reveal an association between elevation of products of incomplete fatty acid oxidation and lower aerobic capacity31. Interestingly, it appears that in the LCR, this change in acetylcarnitine is less conspicuous with the advancement in age suggesting that animals are already insulin resistant when young and that aging does not result in significant further impairment. In addition, Morris et al28 have shown that when HCR and LCR animals are metabolically challenged by administration of a high fat diet, the HCR animals are able to respond by increasing fatty acid oxidation rates, where as LCR cannot. This finding is in agreement with the present results and shows that LCR animals have a limited metabolic flexibility as demonstrated by accumulation of acetylcarnitine suggesting an impaired capacity for lipid metabolism32. As a result, HCR animals display lower weight gain and feeding efficiency, greater brown adipose mass as well as greater expression of genes involved in the regulation of thermogenesis compared to LCR after only one week of high fat diet exposure32,33.

Pathway analysis of HCR and LCR animals showed distinct patterns of metabolites that were not apparent when age was considered. Specifically, age resulted in changes to taurine metabolism as well as branched chain amino acids (BCAA; valine, leucine, isoleucine). As with acetylcarnitine, the presence of elevated BCAA in the circulation are known to be highly predictive of insulin resistance34. Convincingly, metabolomic profiles


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enriched in BCAA are predictive of developing type 2 diabetes 12 years in advance of onset in adults35. BCAA are also important predictors of cardiovascular dysfunction35 In agreement, previous work shows that the LCR animals have impairments in left ventricular function, cardiomyocyte morphology and impaired intracellular Ca2+ handling6. It is this cardiovascular dysfunction that likely contributes to the premature mortality observed in the LCR animals. The appearance of taurine in the pathway analysis of LCR and HCR animals is also relevant as this semi-essential amino acid has been implicated in the regulation of lipid homeostasis, insulin secretion, glucose uptake, and antioxidant defence36. Taurine metabolism is known to be downregulated in obese mice and may be caused by increased adiposity noted in the LCR animals37.

Conclusion In summary, metabolomics analysis better predicts age rather than aerobic capacity, shows age-related differences and establishes a relationship between aerobic capacity and some features of insulin resistance in selectively bred rats. This observation highlights the importance of age when attempting to isolate metabolic changes in aerobic capacity and their relation to chronic disease risk. Furthermore, metabolite patterns showing variance in energy metabolism along the age divide reiterates the influence of exercise on indicators of insulin sensitivity.


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Acknowledgements Supported by Natural Sciences and Engineering Research Council of Canada (NSERC) (JS) and the Lance Armstrong chair in molecular cancer epidemiology (HJV). The LCRHCR rat model system was funded by the National Center for Research Resources grant R24 RR017718 and is currently supported by the Office of Research Infrastructure Programs/OD grant ROD012098A (to L.G.K. and S.L.B.) from the National Institutes of Health. S.L.B. was also supported by National Institutes of Health grant RO1 DK077200. The LCR and HCR model can be made available for collaborative study (contact: [email protected] or [email protected]).

Associated Content Available OPLS model statistics of Figures 3 and 4 as well as the results of additional models generated in data analysis depicting individual groups is shown in Table S1 - Additional Model Statistics.

Author contributions LGH and SLB generated the LCR and HCR animals and performed all exercise testing. JS, DH and RTH designed, performed and supervised the study. OSF and HJV performed all metabolomics analysis. All authors contributed to the writing and review of this manuscript.

Conflicts of Interest The authors have no conflicts to declare.


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Figure Legends Figure 1. Impact of age on body mass, running time, distance run and running speed in aging LCR and HCR rats. Measures were obtained at 11, 52 and 83 weeks of age. Data represents mean ± SE, n=7 animals per group. *Indicates significant difference (p

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