Strategy for NMR metabolomic analysis of urine in mouse models of obesity--from sample collection to interpretation of acquired data
Jazyk angličtina Země Velká Británie, Anglie Médium print-electronic
Typ dokumentu časopisecké články, práce podpořená grantem
PubMed
26263053
DOI
10.1016/j.jpba.2015.06.036
PII: S0731-7085(15)30053-4
Knihovny.cz E-zdroje
- Klíčová slova
- Mouse, NMR metabolomics, Obesity, Urine,
- MeSH
- analýza hlavních komponent MeSH
- biologické markery moč MeSH
- inbrední kmeny myší MeSH
- interpretace statistických dat MeSH
- metabolom * MeSH
- metabolomika metody MeSH
- modely nemocí na zvířatech MeSH
- novorozená zvířata MeSH
- nukleární magnetická rezonance biomolekulární metody MeSH
- obezita metabolismus MeSH
- odběr biologického vzorku MeSH
- zvířata MeSH
- Check Tag
- mužské pohlaví MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- biologické markery 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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