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Metabolomics of the interaction between PPAR-alpha and age in the PPAR-alpha- mouse.

Atherton HJ, Gulston MK, Bailey NJ, Cheng KK, Zhang W, Clarke K, Griffin JL - Mol. Syst. Biol. (2009)

Bottom Line: Expression of the receptor is high in the liver, heart and skeletal muscle, but decreases with age.Hepatic glycogen and glucose also decreased with age for both genotypes.Furthermore, the combined metabolomic and multivariate statistics approach provides a robust method for examining the interaction between age and genotype.

View Article: PubMed Central - PubMed

Affiliation: Department of Biochemistry & Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK.

ABSTRACT
Regulation between the fed and fasted states in mammals is partially controlled by peroxisome proliferator-activated receptor-alpha (PPAR-alpha). Expression of the receptor is high in the liver, heart and skeletal muscle, but decreases with age. A combined (1)H nuclear magnetic resonance (NMR) spectroscopy and gas chromatography-mass spectrometry metabolomic approach has been used to examine metabolism in the liver, heart, skeletal muscle and adipose tissue in PPAR-alpha- mice and wild-type controls during ageing between 3 and 13 months. For the PPAR-alpha- mouse, multivariate statistics highlighted hepatic steatosis, reductions in the concentrations of glucose and glycogen in both the liver and muscle tissue, and profound changes in lipid metabolism in each tissue, reflecting known expression targets of the PPAR-alpha receptor. Hepatic glycogen and glucose also decreased with age for both genotypes. These findings indicate the development of age-related hepatic steatosis in the PPAR-alpha- mouse, with the normal metabolic changes associated with ageing exacerbating changes associated with genotype. Furthermore, the combined metabolomic and multivariate statistics approach provides a robust method for examining the interaction between age and genotype.

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(A) A section of 1H-NMR spectra (δ 0.5–2.7) showing an increase in resonances corresponding to fatty acid moieties in 13-month PPAR-α- liver tissue (black) relative to age-matched control tissue (grey). (B) PLS-DA plot showing the clustering of 13-month PPAR-α- liver samples (▪) from controls (○) for the fatty acids detected by GC-MS. The corresponding significant metabolite changes are labelled. (C) PLS-DA plot of the fatty acids analysed by GC-MS for the entire liver tissue (key as in (C)). (D) A PLS plot of the regression between the fatty acid profile (as measured along the x-axis) and the age of the animal (y-axis) (key as in (C)). (E) Predicted age versus actual age for the PLS model in (D). Each point represents the mean±standard deviation. (F) Validation plot of PLS model in (E). Filled triangles predict the R2 score and filled squares are Q2 scores. Values to the right were the actual values for the PLS model, whereas those on the left were formed by random permutation of the Y variable. (G) PLS-DA of free fatty acid profiles in the liver tissues from PPAR-α- and control mice at 3 months of age. (H) Validation of PLS-DA model in (G).
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f2: (A) A section of 1H-NMR spectra (δ 0.5–2.7) showing an increase in resonances corresponding to fatty acid moieties in 13-month PPAR-α- liver tissue (black) relative to age-matched control tissue (grey). (B) PLS-DA plot showing the clustering of 13-month PPAR-α- liver samples (▪) from controls (○) for the fatty acids detected by GC-MS. The corresponding significant metabolite changes are labelled. (C) PLS-DA plot of the fatty acids analysed by GC-MS for the entire liver tissue (key as in (C)). (D) A PLS plot of the regression between the fatty acid profile (as measured along the x-axis) and the age of the animal (y-axis) (key as in (C)). (E) Predicted age versus actual age for the PLS model in (D). Each point represents the mean±standard deviation. (F) Validation plot of PLS model in (E). Filled triangles predict the R2 score and filled squares are Q2 scores. Values to the right were the actual values for the PLS model, whereas those on the left were formed by random permutation of the Y variable. (G) PLS-DA of free fatty acid profiles in the liver tissues from PPAR-α- and control mice at 3 months of age. (H) Validation of PLS-DA model in (G).

Mentions: By 13 months, both GC-MS and 1H-NMR spectroscopy indicated fatty acid accumulation in the liver. Figure 2A shows the increase in resonances corresponding to fatty acids in the 1H-NMR spectrum of 13-month liver tissue relative to age-matched control tissue. Although this represents only a partial partition of the more water-soluble free fatty acids, as determined by subsequent GC-MS analysis, these fats were consistently increased in the extracts from PPAR-α- mice. GC-MS analysis of the fatty acid content of the total lipid extracts of 13-month PPAR-α- mice livers in conjunction with PLS-DA revealed that there was a large increase in linoleic acid (18:2), oleic acid (18:1) and di-homo-γ-linolenic acid (20:3) (Figure 2B; R2=0.88, Q2=0.45). As these models were formed from a relatively small number of biological repeats, the analysis was also repeated using the Student's t-test with a Bonferroni correction (Supplementary Table 7). The changes in oleic acid and di-homo-γ-linolenic acid were also detected in younger mice. Applying PLS-DA to the entire fatty acid data set produced a model with five components that separated the wild-type and PPAR-α- mice (R2=0.80; Q2=0.61; Figure 2C). On examining the loadings plot for component 1, the PPAR-α- mice showed increased concentrations of oleic acid (18:1), linoleic acid (9c, 12c-18:2) and arachidonic acid (20:4) as determined by those fatty acids that contributed significantly to the loadings plot (P<0.05 for each metabolite according to a jack knifing procedure). Given that the separation was over five components, the variable importance to projection (VIP) scores were also considered. VIPs examine the contribution made to the model by each variable over all five components. The three fats that drove the separation in PLS component 1 were also the three fats with the highest VIP score and were also deemed to be significant according to jack knifing cross-validation of their VIP scores. In a similar manner, orthogonal-PLS produced similar results (data not shown).


Metabolomics of the interaction between PPAR-alpha and age in the PPAR-alpha- mouse.

Atherton HJ, Gulston MK, Bailey NJ, Cheng KK, Zhang W, Clarke K, Griffin JL - Mol. Syst. Biol. (2009)

(A) A section of 1H-NMR spectra (δ 0.5–2.7) showing an increase in resonances corresponding to fatty acid moieties in 13-month PPAR-α- liver tissue (black) relative to age-matched control tissue (grey). (B) PLS-DA plot showing the clustering of 13-month PPAR-α- liver samples (▪) from controls (○) for the fatty acids detected by GC-MS. The corresponding significant metabolite changes are labelled. (C) PLS-DA plot of the fatty acids analysed by GC-MS for the entire liver tissue (key as in (C)). (D) A PLS plot of the regression between the fatty acid profile (as measured along the x-axis) and the age of the animal (y-axis) (key as in (C)). (E) Predicted age versus actual age for the PLS model in (D). Each point represents the mean±standard deviation. (F) Validation plot of PLS model in (E). Filled triangles predict the R2 score and filled squares are Q2 scores. Values to the right were the actual values for the PLS model, whereas those on the left were formed by random permutation of the Y variable. (G) PLS-DA of free fatty acid profiles in the liver tissues from PPAR-α- and control mice at 3 months of age. (H) Validation of PLS-DA model in (G).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2683722&req=5

f2: (A) A section of 1H-NMR spectra (δ 0.5–2.7) showing an increase in resonances corresponding to fatty acid moieties in 13-month PPAR-α- liver tissue (black) relative to age-matched control tissue (grey). (B) PLS-DA plot showing the clustering of 13-month PPAR-α- liver samples (▪) from controls (○) for the fatty acids detected by GC-MS. The corresponding significant metabolite changes are labelled. (C) PLS-DA plot of the fatty acids analysed by GC-MS for the entire liver tissue (key as in (C)). (D) A PLS plot of the regression between the fatty acid profile (as measured along the x-axis) and the age of the animal (y-axis) (key as in (C)). (E) Predicted age versus actual age for the PLS model in (D). Each point represents the mean±standard deviation. (F) Validation plot of PLS model in (E). Filled triangles predict the R2 score and filled squares are Q2 scores. Values to the right were the actual values for the PLS model, whereas those on the left were formed by random permutation of the Y variable. (G) PLS-DA of free fatty acid profiles in the liver tissues from PPAR-α- and control mice at 3 months of age. (H) Validation of PLS-DA model in (G).
Mentions: By 13 months, both GC-MS and 1H-NMR spectroscopy indicated fatty acid accumulation in the liver. Figure 2A shows the increase in resonances corresponding to fatty acids in the 1H-NMR spectrum of 13-month liver tissue relative to age-matched control tissue. Although this represents only a partial partition of the more water-soluble free fatty acids, as determined by subsequent GC-MS analysis, these fats were consistently increased in the extracts from PPAR-α- mice. GC-MS analysis of the fatty acid content of the total lipid extracts of 13-month PPAR-α- mice livers in conjunction with PLS-DA revealed that there was a large increase in linoleic acid (18:2), oleic acid (18:1) and di-homo-γ-linolenic acid (20:3) (Figure 2B; R2=0.88, Q2=0.45). As these models were formed from a relatively small number of biological repeats, the analysis was also repeated using the Student's t-test with a Bonferroni correction (Supplementary Table 7). The changes in oleic acid and di-homo-γ-linolenic acid were also detected in younger mice. Applying PLS-DA to the entire fatty acid data set produced a model with five components that separated the wild-type and PPAR-α- mice (R2=0.80; Q2=0.61; Figure 2C). On examining the loadings plot for component 1, the PPAR-α- mice showed increased concentrations of oleic acid (18:1), linoleic acid (9c, 12c-18:2) and arachidonic acid (20:4) as determined by those fatty acids that contributed significantly to the loadings plot (P<0.05 for each metabolite according to a jack knifing procedure). Given that the separation was over five components, the variable importance to projection (VIP) scores were also considered. VIPs examine the contribution made to the model by each variable over all five components. The three fats that drove the separation in PLS component 1 were also the three fats with the highest VIP score and were also deemed to be significant according to jack knifing cross-validation of their VIP scores. In a similar manner, orthogonal-PLS produced similar results (data not shown).

Bottom Line: Expression of the receptor is high in the liver, heart and skeletal muscle, but decreases with age.Hepatic glycogen and glucose also decreased with age for both genotypes.Furthermore, the combined metabolomic and multivariate statistics approach provides a robust method for examining the interaction between age and genotype.

View Article: PubMed Central - PubMed

Affiliation: Department of Biochemistry & Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK.

ABSTRACT
Regulation between the fed and fasted states in mammals is partially controlled by peroxisome proliferator-activated receptor-alpha (PPAR-alpha). Expression of the receptor is high in the liver, heart and skeletal muscle, but decreases with age. A combined (1)H nuclear magnetic resonance (NMR) spectroscopy and gas chromatography-mass spectrometry metabolomic approach has been used to examine metabolism in the liver, heart, skeletal muscle and adipose tissue in PPAR-alpha- mice and wild-type controls during ageing between 3 and 13 months. For the PPAR-alpha- mouse, multivariate statistics highlighted hepatic steatosis, reductions in the concentrations of glucose and glycogen in both the liver and muscle tissue, and profound changes in lipid metabolism in each tissue, reflecting known expression targets of the PPAR-alpha receptor. Hepatic glycogen and glucose also decreased with age for both genotypes. These findings indicate the development of age-related hepatic steatosis in the PPAR-alpha- mouse, with the normal metabolic changes associated with ageing exacerbating changes associated with genotype. Furthermore, the combined metabolomic and multivariate statistics approach provides a robust method for examining the interaction between age and genotype.

Show MeSH
Related in: MedlinePlus