Literature DB >> 22426117

The dynamic range of the human metabolome revealed by challenges.

Susanne Krug1, Gabi Kastenmüller, Ferdinand Stückler, Manuela J Rist, Thomas Skurk, Manuela Sailer, Johannes Raffler, Werner Römisch-Margl, Jerzy Adamski, Cornelia Prehn, Thomas Frank, Karl-Heinz Engel, Thomas Hofmann, Burkhard Luy, Ralf Zimmermann, Franco Moritz, Philippe Schmitt-Kopplin, Jan Krumsiek, Werner Kremer, Fritz Huber, Uwe Oeh, Fabian J Theis, Wilfried Szymczak, Hans Hauner, Karsten Suhre, Hannelore Daniel.   

Abstract

Metabolic challenge protocols, such as the oral glucose tolerance test, can uncover early alterations in metabolism preceding chronic diseases. Nevertheless, most metabolomics data accessible today reflect the fasting state. To analyze the dynamics of the human metabolome in response to environmental stimuli, we submitted 15 young healthy male volunteers to a highly controlled 4 d challenge protocol, including 36 h fasting, oral glucose and lipid tests, liquid test meals, physical exercise, and cold stress. Blood, urine, exhaled air, and breath condensate samples were analyzed on up to 56 time points by MS- and NMR-based methods, yielding 275 metabolic traits with a focus on lipids and amino acids. Here, we show that physiological challenges increased interindividual variation even in phenotypically similar volunteers, revealing metabotypes not observable in baseline metabolite profiles; volunteer-specific metabolite concentrations were consistently reflected in various biofluids; and readouts from a systematic model of β-oxidation (e.g., acetylcarnitine/palmitylcarnitine ratio) showed significant and stronger associations with physiological parameters (e.g., fat mass) than absolute metabolite concentrations, indicating that systematic models may aid in understanding individual challenge responses. Due to the multitude of analytical methods, challenges and sample types, our freely available metabolomics data set provides a unique reference for future metabolomics studies and for verification of systems biology models.

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Year:  2012        PMID: 22426117     DOI: 10.1096/fj.11-198093

Source DB:  PubMed          Journal:  FASEB J        ISSN: 0892-6638            Impact factor:   5.191


  123 in total

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