Sample preparation method to minimize chemical shift variability for NMR-based urinary metabonomics of genetically hypertensive rats

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• 作者：Kazuki Akira ; ^{akira@cc.matsuyama-u.ac.jp} ; Hiroyuki Hichiya ; Maki Shuden ; Mayu Morita ; Hidemichi Mitome
• 关键词：Metabonomics ; Urine ; pH ; Chemical shift ; NMR
• 刊名：Journal of Pharmaceutical and Biomedical Analysis
• 出版年：2012
• 期刊代码：65_07317085
• 类别：ch
• 出版时间：July, 2012
• 卷：66
• 期：Complete
• 页码：339-344
• 文件大小：266 K

摘要

Urinary metabonomics based on proton nuclear magnetic resonance (¹H NMR) has been widely employed to study metabolic differences associated with gene function and pathophysiological and toxicological stimuli. However, the chemical shift variability of ¹H NMR signals, which is due to differences in pH and ionic strength among urine samples, remains an outstanding problem for efficient data mining. Thus, we have proposed an improved sample preparation method where urine samples are lyophilized and reconstituted in a buffer solution (pH 7.40) so that the extent of urine concentration becomes constant based on creatinine concentration. In order to examine the usefulness of the proposed method, urine samples taken from spontaneously hypertensive rats (SHR) and stroke-prone SHR (SHRSP) were treated not only by the proposed method but also by the usual method where urine with various concentrations is mixed with an equivalent volume of buffer solution (pH 7.40). Consequently, the pH of the urine samples prepared by the proposed method was precisely controlled to 6.89-7.01, whereas the pH of samples by the usual method was in the range of 6.81-7.18. The chemical shift variations of various metabolites having ionizable groups such as succinate, 伪-ketoglutarate, cis-aconitate, taurine, and glycine were significantly reduced with decreases in pH variability. A preliminary multivariate statistical analysis was carried out for the ¹H NMR spectral data obtained by the proposed method, where the metabolic profiles were distinguished between the SHR and SHRSP. The proposed sample preparation method will be particularly useful to closely inspect NMR-based urinary metabonomic data for the exploration of metabolic changes.