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      Standardizing the experimental conditions for using urine in NMR-based metabolomic studies with a particular focus on diagnostic studies: a review

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          Abstract

          The metabolic composition of human biofluids can provide important diagnostic and prognostic information. Among the biofluids most commonly analyzed in metabolomic studies, urine appears to be particularly useful. It is abundant, readily available, easily stored and can be collected by simple, noninvasive techniques. Moreover, given its chemical complexity, urine is particularly rich in potential disease biomarkers. This makes it an ideal biofluid for detecting or monitoring disease processes. Among the metabolomic tools available for urine analysis, NMR spectroscopy has proven to be particularly well-suited, because the technique is highly reproducible and requires minimal sample handling. As it permits the identification and quantification of a wide range of compounds, independent of their chemical properties, NMR spectroscopy has been frequently used to detect or discover disease fingerprints and biomarkers in urine. Although protocols for NMR data acquisition and processing have been standardized, no consensus on protocols for urine sample selection, collection, storage and preparation in NMR-based metabolomic studies have been developed. This lack of consensus may be leading to spurious biomarkers being reported and may account for a general lack of reproducibility between laboratories. Here, we review a large number of published studies on NMR-based urine metabolic profiling with the aim of identifying key variables that may affect the results of metabolomics studies. From this survey, we identify a number of issues that require either standardization or careful accounting in experimental design and provide some recommendations for urine collection, sample preparation and data acquisition.

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          Increase in signal-to-noise ratio of > 10,000 times in liquid-state NMR.

          A method for obtaining strongly polarized nuclear spins in solution has been developed. The method uses low temperature, high magnetic field, and dynamic nuclear polarization (DNP) to strongly polarize nuclear spins in the solid state. The solid sample is subsequently dissolved rapidly in a suitable solvent to create a solution of molecules with hyperpolarized nuclear spins. The polarization is performed in a DNP polarizer, consisting of a super-conducting magnet (3.35 T) and a liquid-helium cooled sample space. The sample is irradiated with microwaves at approximately 94 GHz. Subsequent to polarization, the sample is dissolved by an injection system inside the DNP magnet. The dissolution process effectively preserves the nuclear polarization. The resulting hyperpolarized liquid sample can be transferred to a high-resolution NMR spectrometer, where an enhanced NMR signal can be acquired, or it may be used as an agent for in vivo imaging or spectroscopy. In this article we describe the use of the method on aqueous solutions of [13C]urea. Polarizations of 37% for 13C and 7.8% for 15N, respectively, were obtained after the dissolution. These polarizations correspond to an enhancement of 44,400 for 13C and 23,500 for 15N, respectively, compared with thermal equilibrium at 9.4 T and room temperature. The method can be used generally for signal enhancement and reduction of measurement time in liquid-state NMR and opens up for a variety of in vitro and in vivo applications of DNP-enhanced NMR.
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            Is Open Access

            HMDB: a knowledgebase for the human metabolome

            The Human Metabolome Database (HMDB, http://www.hmdb.ca) is a richly annotated resource that is designed to address the broad needs of biochemists, clinical chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community. Since its first release in 2007, the HMDB has been used to facilitate the research for nearly 100 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 2.0) has been significantly expanded and enhanced over the previous release (version 1.0). In particular, the number of fully annotated metabolite entries has grown from 2180 to more than 6800 (a 300% increase), while the number of metabolites with biofluid or tissue concentration data has grown by a factor of five (from 883 to 4413). Similarly, the number of purified compounds with reference to NMR, LC-MS and GC-MS spectra has more than doubled (from 380 to more than 790 compounds). In addition to this significant expansion in database size, many new database searching tools and new data content has been added or enhanced. These include better algorithms for spectral searching and matching, more powerful chemical substructure searches, faster text searching software, as well as dedicated pathway searching tools and customized, clickable metabolic maps. Changes to the user-interface have also been implemented to accommodate future expansion and to make database navigation much easier. These improvements should make the HMDB much more useful to a much wider community of users.
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              Is Open Access

              MetaboLights—an open-access general-purpose repository for metabolomics studies and associated meta-data

              MetaboLights (http://www.ebi.ac.uk/metabolights) is the first general-purpose, open-access repository for metabolomics studies, their raw experimental data and associated metadata, maintained by one of the major open-access data providers in molecular biology. Metabolomic profiling is an important tool for research into biological functioning and into the systemic perturbations caused by diseases, diet and the environment. The effectiveness of such methods depends on the availability of public open data across a broad range of experimental methods and conditions. The MetaboLights repository, powered by the open source ISA framework, is cross-species and cross-technique. It will cover metabolite structures and their reference spectra as well as their biological roles, locations, concentrations and raw data from metabolic experiments. Studies automatically receive a stable unique accession number that can be used as a publication reference (e.g. MTBLS1). At present, the repository includes 15 submitted studies, encompassing 93 protocols for 714 assays, and span over 8 different species including human, Caenorhabditis elegans, Mus musculus and Arabidopsis thaliana. Eight hundred twenty-seven of the metabolites identified in these studies have been mapped to ChEBI. These studies cover a variety of techniques, including NMR spectroscopy and mass spectrometry.
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                Author and article information

                Contributors
                abdelhamid.emwas@kaust.edu.sa
                luchinat@cerm.unifi.it
                turano@cerm.unifi.it
                tenori@cerm.unifi.it
                roy@cbmr.res.in
                rms72@cam.ac.uk
                dryan@csu.edu.au
                jasmeen.merzaban@kaust.edu.sa
                rima.kaddurahdaouk@duke.edu
                ana.carolina.zeri@gmail.com
                ngowda@uw.edu
                draftery@uw.edu
                yulan.wang@wipm.ac.cn
                lorraine.brennan@ucd.ie
                dwishart@ualberta.ca
                Journal
                Metabolomics
                Metabolomics
                Metabolomics
                Springer US (New York )
                1573-3882
                1573-3890
                21 November 2014
                21 November 2014
                2015
                : 11
                : 4
                : 872-894
                Affiliations
                [ ]Imaging and Characterization Core Lab, King Abdullah University of Science and Technology, KSA, Thuwal, Saudi Arabia
                [ ]Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology, KSA, Thuwal, Saudi Arabia
                [ ]Centro Risonanze Magnetiche – CERM, University of Florence, Florence, Italy
                [ ]FiorGen Foundation, 50019 Sesto Fiorentino, Florence, Italy
                [ ]Centre of Biomedical Research, Formerly known as Centre of Biomedical Magnetic Resonance, Sanjay Gandhi Post-Graduate Institute of Medical Sciences Campus, Lucknow, India
                [ ]Department of Biochemistry & Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK
                [ ]European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, CB10 1SD UK
                [ ]School of Agricultural and Wine Sciences, Charles Sturt University, Wagga Wagga, Australia
                [ ]Pharmacometabolomics Center, School of Medicine, Duke University, Durham, USA
                [ ]Brazilian Biosciences National Laboratory, LNBio, Campinas, SP Brazil
                [ ]Department of Anethesiology and Pain Medicine, Northwest Metabolomics Research Center, University of Washington, 850 Republican St., Seattle, WA 98109 USA
                [ ]Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Beijing, China
                [ ]Institute of Food and Health and Conway Institute, School of Agriculture & Food Science, Dublin 4, Ireland
                [ ]Department of Computing Science, University of Alberta, Edmonton, Alberta Canada
                Article
                746
                10.1007/s11306-014-0746-7
                4475544
                26109927
                fd265589-077a-4c6a-8153-5483bc27dbdb
                © The Author(s) 2014

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

                History
                : 29 May 2014
                : 27 October 2014
                Categories
                Review Article
                Custom metadata
                © Springer Science+Business Media New York 2015

                Molecular biology
                nmr,metabolomics,metabonomics,metabolites profiling,urine,biomarker,human diseases,standardization,diagnosis,recommendations

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