High-Resolution Quantitative Metabolome Analysis of Urine by Automated Flow Injection NMR

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• 作者：Laeticia Da Silva ; Markus Godejohann ; Fran莽ois-Pierre J. Martin ; Sebastiano Collino ; Alexander B眉rkle ; Mar铆a Moreno-Villanueva ; J眉rgen Bernhardt ; Olivier Toussaint ; Beatrix Grubeck-Loebenstein ; Efstathios S. Gonos ; Ewa Sikora ; Tilman Grune ; Nicolle Breusing ; Claudio Franceschi ; Antti Hervonen ; Manfred Spraul ; Sofia Moco
• 刊名：Analytical Chemistry
• 出版年：2013
• 出版时间：June 18, 2013
• 年：2013
• 卷：85
• 期：12
• 页码：5801-5809
• 全文大小：405K
• 年卷期：v.85,no.12(June 18, 2013)
• ISSN：1520-6882

文摘

Metabolism is essential to understand human health. To characterize human metabolism, a high-resolution read-out of the metabolic status under various physiological conditions, either in health or disease, is needed. Metabolomics offers an unprecedented approach for generating system-specific biochemical definitions of a human phenotype through the capture of a variety of metabolites in a single measurement. The emergence of large cohorts in clinical studies increases the demand of technologies able to analyze a large number of measurements, in an automated fashion, in the most robust way. NMR is an established metabolomics tool for obtaining metabolic phenotypes. Here, we describe the analysis of NMR-based urinary profiles for metabolic studies, challenged to a large human study (3007 samples). This method includes the acquisition of nuclear Overhauser effect spectroscopy one-dimensional and J-resolved two-dimensional (J-Res-2D) ¹H NMR spectra obtained on a 600 MHz spectrometer, equipped with a 120 渭L flow probe, coupled to a flow-injection analysis system, in full automation under the control of a sampler manager. Samples were acquired at a throughput of 20 (or 40 when J-Res-2D is included) min/sample. The associated technical analysis error over the full series of analysis is 12%, which demonstrates the robustness of the method. With the aim to describe an overall metabolomics workflow, the quantification of 36 metabolites, mainly related to central carbon metabolism and gut microbial host cometabolism, was obtained, as well as multivariate data analysis of the full spectral profiles. The metabolic read-outs generated using our analytical workflow can therefore be considered for further pathway modeling and/or biological interpretation.