Metabotyping of long-lived mice using 1H NMR spectroscopy - PubMed (original) (raw)

. 2012 Apr 6;11(4):2224-35.

doi: 10.1021/pr2010154. Epub 2012 Feb 27.

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Metabotyping of long-lived mice using 1H NMR spectroscopy

Anisha Wijeyesekera et al. J Proteome Res. 2012.

Abstract

Significant advances in understanding aging have been achieved through studying model organisms with extended healthy lifespans. Employing 1H NMR spectroscopy, we characterized the plasma metabolic phenotype (metabotype) of three long-lived murine models: 30% dietary restricted (DR), insulin receptor substrate 1 null (Irs1-/-), and Ames dwarf (Prop1df/df). A panel of metabolic differences were generated for each model relative to their controls, and subsequently, the three long-lived models were compared to one another. Concentrations of mobile very low density lipoproteins, trimethylamine, and choline were significantly decreased in the plasma of all three models. Metabolites including glucose, choline, glycerophosphocholine, and various lipids were significantly reduced, while acetoacetate, d-3-hydroxybutyrate and trimethylamine-N-oxide levels were increased in DR compared to ad libitum fed controls. Plasma lipids and glycerophosphocholine were also decreased in Irs1-/- mice compared to controls, as were methionine and citrate. In contrast, high density lipoproteins and glycerophosphocholine were increased in Ames dwarf mice, as were methionine and citrate. Pairwise comparisons indicated that differences existed between the metabotypes of the different long-lived mice models. Irs1-/- mice, for example, had elevated glucose, acetate, acetone, and creatine but lower methionine relative to DR mice and Ames dwarfs. Our study identified several potential candidate biomarkers directionally altered across all three models that may be predictive of longevity but also identified differences in the metabolic signatures. This comparative approach suggests that the metabolic networks underlying lifespan extension may not be exactly the same for each model of longevity and is consistent with multifactorial control of the aging process.

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Figures

Figure 1

Figure 1

Aliphatic region (δ1H 0.5–4.5) of exemplar 1H NMR plasma spectra. Acquired using (A) standard 1D pulse sequence with presaturation suppression of the water peak, (B) the Carr–Purcell–Meiboom–Gill sequence to attenuate broad signals from proteins and lipoproteins that may overlap signals from low molecular weight metabolites, also using presaturation and (C) a diffusion edited pulse sequence to analyze high molecular weight molecules such as lipids and proteins. *Ethanol contaminant.

Figure 2

Figure 2

Results of pairwise supervised multivariate modeling performed on CPMG plasma spectroscopic data for each long-lived model vs respective control: O-PLS-DA cross validated scores scatter plot showing the clustering of samples according to metabotype; corresponding O-PLS-DA loadings coefficients plot back-projected with _p_-values, showing the plasma metabolites discriminating between metabotype (models computed using 1 predictive component, 2 orthogonal components in X, 0 orthogonal components in Y, 7-fold cross-validation). (A) O-PLS-DA cross-validated scores of DR and AL mice; (B) O-PLS-DA loadings of DR and AL mice (R2Y = 0.91, Q2Y = 0.85). (C) O-PLS-DA cross-validated scores of Irs1 –/– and WT control mice; (D) O-PLS-DA loadings of Irs1 –/– and WT control mice (R2Y = 0.93, Q2Y = 0.86). (E) O-PLS-DA cross-validated scores of Ames dwarf and WT control mice; (F) O-PLS-DA loadings of Ames dwarf and WT control mice (R2Y = 0.95, Q2Y = 0.84). Refer to Table 1 for assignments of discriminatory metabolites.

Figure 3

Figure 3

Results of pairwise supervised multivariate modeling performed on CPMG plasma spectroscopic data for each long-lived model vs long-lived model (models computed using 1 predictive component, 2 orthogonal components in X, 0 orthogonal components in Y, 7-fold cross-validation). (A) O-PLS-DA cross-validated scores of DR and Irs1 –/– mice; (B) O-PLS-DA loadings of DR and Irs1 –/– mice (R2Y = 0.99, Q2Y = 0.98). (C) O-PLS-DA cross-validated scores of DR and Ames dwarf mice; (D) O-PLS-DA loadings of DR and Ames dwarf mice (R2Y = 0.95, Q2Y = 0.90). (E) O-PLS-DA cross-validated scores of Irs1 –/– and Ames dwarf mice; (F) O-PLS-DA loadings of Irs1 –/– and Ames dwarf (R2Y = 0.99, Q2Y = 0.86). Refer to Table 1 for assignments of discriminatory metabolites.

Figure 4

Figure 4

Results of pairwise supervised multivariate modeling performed on Diffusion Edited plasma spectroscopic data for each long-lived model vs respective control. (A) O-PLS-DA cross-validated scores of DR and AL mice; (B) O-PLS-DA loadings of DR and AL mice (R2 = 0.88, Q2 = 0.85). (C) O-PLS-DA cross-validated scores of Irs1 –/– and WT control mice; (D) O-PLS-DA loadings of Irs1 –/– and WT control mice (R2 = 0.86, Q2 = −0.51). (E) O-PLS-DA cross-validated scores of Ames dwarf and WT control mice; (F) O-PLS-DA loadings of Ames dwarf and WT control mice (R2 = 0.87, Q2 = 0.75). Lipoproteins discriminating between models are labeled in Table 1.

Figure 5

Figure 5

Results of pairwise supervised multivariate modeling performed on Diffusion Edited plasma spectroscopic data for each long-lived model vs long-lived model. (A) O-PLS-DA cross-validated scores of DR and Irs1 –/– mice; (B) O-PLS-DA loadings of DR and Irs1 –/– mice (R2 = 0.89, Q2 = 0.42). (C) O-PLS-DA cross-validated scores of DR and Ames dwarf mice; (D) O-PLS-DA loadings of DR and Ames dwarf mice (R2 = 0.96, Q2 = 0.89). (E) O-PLS-DA cross-validated scores of Irs1 –/– and Ames dwarf mice; (F) O-PLS-DA loadings of Irs1 –/– and Ames dwarf (R2 = 0.93, Q2 = 0.86). Lipoproteins discriminating between models are labeled in Table 1.

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