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

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-null mice and wild-type controls during ageing between 3 and 13 months. For the PPAR-alpha-null 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-null 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.

Figure 1

(A) ¹H-NMR spectra showing the difference in glucose and glycogen concentration between PPAR-α-null liver tissue samples (black) and controls (blue) at 3 and 13 months. (B) PCA plot showing the clusterings of 3m (open circles), 5m (open diamonds), 7m (stars), 9m (open triangles), 11m (black squares) and 13m (crosses) liver tissue across principal component 1. Note the x-axis is the order of samples in terms of age and does not represent a principal component. (C) ¹H-NMR spectra showing the difference in glucose and glycogen concentration between 3 and 13 months for liver tissue extracts from PPAR-α-null mice. Each spectrum is the average of the five spectra obtained from all animals at that age. Key: red, 3 months; blue, 5 months; black, 11 months; green, 13 months. (D) peak area of the anomeric ¹H α-glucose (δ 5.24) and glycogen (δ 5.40) for spectra from the extracts of liver tissue from PPAR-α-null mice (▪) and control mice (◊) (E) PLS plot regressing age of animal (y-axis) against the metabolic profile of the liver tissue (x-axis) in control mice as measured by ¹H-NMR spectroscopy. PPAR-α-null mice were then mapped on to the same model. (F) Validation plot of PLS model in (E). 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) Predicted age compared with actual age for a PLS plot regressing age of the animal against the metabolic profile of the liver tissue in control mice as measured by ¹H-NMR spectroscopy. PPAR-α-null mice were then mapped on to the same model. Each point represents the mean±standard deviation. (H) PLS plot showing the age-related perturbations in aqueous soluble metabolites occurring in the PPAR-α-null liver (3–13 months) measured by GC-MS with corresponding significant metabolic changes annotated. (I) Percentage glucose in selected PPAR-α-null tissues relative to age-matched control tissues (error bars represent standard error) ^*P<0.05; ^**P<0.01; ^***P<0.001. (J) PLS plot showing the age-related perturbations in aqueous soluble metabolites occurring in diaphragm tissue from PPAR-α-null and control mice (3–13 months) measured by NMR spectroscopy. (K) PLS plot showing the age-related perturbations in aqueous soluble metabolites occurring in soleus tissue from PPAR-α-null and control mice (3–13 months) measured by NMR spectroscopy. Key for all panels: ◊ control mice; ▪ PPAR-α-null mice.

Figure 2

(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-null liver tissue (black) relative to age-matched control tissue (grey). (B) PLS-DA plot showing the clustering of 13-month PPAR-α-null 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-null and control mice at 3 months of age. (H) Validation of PLS-DA model in (G).

Figure 3

PLS-DA plots showing the differentiation of 3–13 month PPAR-α-null (▪) and control (○) samples in (A) heart tissue and (B) white adipose tissue following methyl esterification of the fatty acid complement, and analysis by GC-MS. (C) PLS plot showing the age-related perturbations in lipid metabolites occurring in the PPAR-α-null WAT (3–13 months) with corresponding significant metabolic changes annotated. (D) PLS coefficient values showing the contribution of selected metabolites to age-related metabolic trends in the gastrocnemius, soleus, heart and diaphragm in the PPAR-α-null mouse. All coefficients shown are significant at the 95% confidence limit. Error bars indicate standard error.

Figure 4

A summary figure of the key changes between wild-type and PPAR-α-null mice and associated with ageing in both mouse genotypes.