Strategy for NMR metabolomic analysis of urine in mouse models of obesity--from sample collection to interpretation of acquired data
Language English Country England, Great Britain Media print-electronic
Document type Journal Article, Research Support, Non-U.S. Gov't
PubMed
26263053
DOI
10.1016/j.jpba.2015.06.036
PII: S0731-7085(15)30053-4
Knihovny.cz E-resources
- Keywords
- Mouse, NMR metabolomics, Obesity, Urine,
- MeSH
- Principal Component Analysis MeSH
- Biomarkers urine MeSH
- Mice, Inbred Strains MeSH
- Data Interpretation, Statistical MeSH
- Metabolome * MeSH
- Metabolomics methods MeSH
- Disease Models, Animal MeSH
- Animals, Newborn MeSH
- Nuclear Magnetic Resonance, Biomolecular methods MeSH
- Obesity metabolism MeSH
- Specimen Handling MeSH
- Animals MeSH
- Check Tag
- Male MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Names of Substances
- Biomarkers MeSH
The mouse model of monosodium glutamate induced obesity was used to examine and consequently optimize the strategy for analysis of urine samples by NMR spectroscopy. A set of nineteen easily detectable metabolites typical in obesity-related studies was selected. The impact of urine collection protocol, choice of (1)H NMR pulse sequence, and finally the impact of the normalization method on the detected concentration of selected metabolites were investigated. We demonstrated the crucial effect of food intake and diurnal rhythms resulting in the choice of a 24-hour fasting collection protocol as the most convenient for tracking obesity-induced increased sensitivity to fasting. It was shown that the Carr-Purcell-Meiboom-Gill (CPMG) experiment is a better alternative to one-dimensional nuclear Overhauser enhancement spectroscopy (1D-NOESY) for NMR analysis of mouse urine due to its ability to filter undesirable signals of proteins naturally present in rodent urine. Normalization to total spectral area provided comparable outcomes as did normalization to creatinine or probabilistic quotient normalization in the CPMG-based model. The optimized approach was found to be beneficial mainly for low abundant metabolites rarely monitored due to their overlap by strong protein signals.
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