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Metabolic Profiling of Accelerated Aging ERCC1d/− Mice

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Biomolecular Mass Spectrometry Unit, Department of Parasitology, Leiden University Medical Center, Leiden, The Netherlands, Erasmus Medical Center, Rotterdam, Netherlands, and DNage BV, Leiden, The Netherlands
* To whom correspondence should be addressed. Ekaterina Nevedomskaya, Biomolecular Mass Spectrometry Unit, Dept. of Parasitology, Leiden University Medical Center, Albinusdreef 2, 2333ZA, Leiden, The Netherlands, tel. +31715265078.
†Leiden University Medical Center.
‡Erasmus Medical Center.
§DNage BV.
Cite this: J. Proteome Res. 2010, 9, 7, 3680–3687
Publication Date (Web):May 27, 2010
https://doi.org/10.1021/pr100210k
Copyright © 2010 American Chemical Society

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    Abstract

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    Aging is a fundamental biological process for which the mechanism is still largely unknown due to its complex and multifactorial nature. Animal models allow us to simplify this complexity and to study individual factors separately. As there are many causative links between DNA repair deficiency and aging, we studied the ERCC1d/− mouse, which has a modified ERCC1 gene, involved in the Nucleotide Excision Repair, and as a result has a premature aging phenotype. Profiling of these mice on different levels can give an insight into the mechanisms underlying the aging phenotype. In the current study, we have performed metabolic profiling of serum and urine of these mice in comparison to wild type and in relation to aging by 1H NMR spectroscopy. Analysis of metabolic trajectories of animals from 8 to 20 weeks suggested that wild type and ERCC1d/− mutants have similar age-related patterns of changes; however, the difference between genotypes becomes more prominent with age. The main differences between these two genetically diverse groups of mice were found to be associated with altered lipid and energy metabolism, transition to ketosis, and attenuated functions of the liver and kidney.

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    Supplementary Table 1. Mean body weight, g (SD) of mice with different genetic background and at different ages. Supplementary Figure 1. Metabolic compounds of serum related to age and their changes with age. Supplementary Figure 2. Validation plot of PLS-DA model for mouse serum NMR data. Supplementary Figure 3. One-dimensional STOCSY analysis for the selected variable. Details on absolute glucose quantification. Supplementary Figure 4. Comparison of deconvoluted TSP signals. Supplementary Figure 5. Glucose concentration in blood. Supplementary Figure 6. Spectral region of 3-hydroxybutyric acid doublet in urine samples. This material is available free of charge via the Internet at http://pubs.acs.org.

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