Urine metabolic profiles of patients with inborn errors of metabolism were examined with nuclear magnetic resonance (NMR) and desorption electrospray ionization mass spectrometry (DESI-MS) methods. Spectra obtained from the study of urine samples from individual patients with argininosuccinic aciduria (ASA), classic homocystinuria (HCY), classic methylmalonic acidemia (MMA), maple syrup urine disease (MSUD), phenylketonuria (PKU) and type II tyrosinemia (TYRO) were compared with six control patient urine samples using principal component analysis (PCA). Target molecule spectra were identified from the loading plots of PCA output and compared with known metabolic profiles from the literature and metabolite databases. Results obtained from the two techniques were then correlated to obtain a common list of molecules associated with the different diseases and metabolic pathways. The combined approach discussed here may prove useful in the rapid screening of biological fluids from sick patients and may help to improve the understanding of these rare diseases.
We report a chemical derivatization method that selects a class of metabolites from a complex mixture and enhances their detection by 13 C NMR. Acetylation of amines directly in aqueous medium with 1,1 -13 C2 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 13 C 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 13 C 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.carbon-13 ͉ inborn errors of metabolism ͉ metabolite profiling ͉ metabolomics ͉ metabonomics T he metabolic profile in biofluids represents a snapshot of ongoing biological processes in the human body. The presence of a particular metabolite, panel of metabolites, or a certain ratio of metabolites can indicate normal homeostasis, a response to biological stress, or even a specific disease state. Conventional medical diagnostic methods are typically based on the selective detection of a single or a few biochemical parameters that are associated with a given disease. A major challenge in the present practice of clinical medicine is the lack of suitable biomarkers and bioanalytical technologies for earlier detection of numerous diseases. Metabolic profiling or metabolomics, defined as the analysis of multiple biofluid metabolites in parallel, holds the promise of earlier disease detection and improved understanding of systems biology (1-3). Early indications of this potential have been reported for the detection of several diseases, including inborn errors of metabolism, cardiovascular diseases, and cancer (4-7).NMR and mass spectrometry are the two most often used analytical methods for metabolite profiling because of their high resolution and rich data content (8)(9)(10)(11)(12)(13)(14). Although mass spectrometry is the more sensitive technique, NMR provides broad coverage of the metabolome by detecting all of the (hydrogencontaining) metabolites present in the biofluid simultaneously, with excellent reproducibility and only limited sample pretreatment. Thus, for example, many classic inborn errors of metabolism (IEM) can be diagnosed by the use of 1 H NMR spectroscopy of body fluids (15-17). Several of the IEM are associated with the accumulation of amino acids as metabolites in serum an...
Metabolic profiling of urine provides a fingerprint of personalized endogenous metabolite markers that correlate to a number of factors such as gender, disease, diet, toxicity, medication, and age. It is important to study these factors individually, if possible to unravel their unique contributions. In this study, age-related metabolic changes in children of age 12 years and below were analyzed by 1H NMR spectroscopy of urine. The effect of age on the urinary metabolite profile was observed as a distinct age-dependent clustering even from the unsupervised principal component analysis. Further analysis, using partial least squares with orthogonal signal correction regression with respect to age, resulted in the identification of an age-related metabolic profile. Metabolites that correlated with age included creatinine, creatine, glycine, betaine/TMAO, citrate, succinate, and acetone. Although creatinine increased with age, all the other metabolites decreased. These results may be potentially useful in assessing the biological age (as opposed to chronological) of young humans as well as in providing a deeper understanding of the confounding factors in the application of metabolomics.
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