NDLI: Class selection of amino acid metabolites in body fluids using chemical derivatization and their enhanced 13C NMR

Content Provider	PubMed Central
Author	Shanaiah, Narasimhamurthy Desilva, M. Aruni Gowda, G. A. Nagana Raftery, Michael A. Hainline, Bryan E. Raftery, Daniel
Copyright Year	2007
Abstract	We report a chemical derivatization method that selects a class of metabolites from a complex mixture and enhances their detection by 13C NMR. Acetylation of amines directly in aqueous medium with 1,1′-13C2 acetic anhydride is a simple method that creates a high sensitivity and quantitative label in complex biofluids with minimal sample pretreatment. Detection using either 1D or 2D 13C NMR experiments produces highly resolved spectra with improved sensitivity. Experiments to identify and compare amino acids and related metabolites in normal human urine and serum samples as well as in urine from patients with the inborn errors of metabolism tyrosinemia type II, argininosuccinic aciduria, homocystinuria, and phenylketonuria demonstrate the method. The use of metabolite derivatization and 13C NMR spectroscopy produces data suitable for metabolite profiling analysis of biofluids on a time scale that allows routine use. Extension of this approach to enhance the NMR detection of other classes of metabolites has also been accomplished. The improved detection of low-concentration metabolites shown here creates opportunities to improve the understanding of the biological processes and develop improved disease detection methodologies.
Related Links	http://dx.doi.org/10.1073/pnas.0704449104
Ending Page	11544
Page Count	5
Starting Page	11540
File Format	PDF
ISSN	00278424
e-ISSN	10916490
Journal	Proceedings of the National Academy of Sciences of the United States of America
Issue Number	28
Volume Number	104
Language	English
Publisher	National Academy of Sciences
Publisher Date	2007-07-10
Access Restriction	Open
Rights Holder	National Academy of Sciences
Subject Keyword	General Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Multidisciplinary

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