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      NMR and Metabolomics—A Roadmap for the Future

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          Abstract

          Metabolomics investigates global metabolic alterations associated with chemical, biological, physiological, or pathological processes. These metabolic changes are measured with various analytical platforms including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance spectroscopy (NMR). While LC-MS methods are becoming increasingly popular in the field of metabolomics (accounting for more than 70% of published metabolomics studies to date), there are considerable benefits and advantages to NMR-based methods for metabolomic studies. In fact, according to PubMed, more than 926 papers on NMR-based metabolomics were published in 2021—the most ever published in a given year. This suggests that NMR-based metabolomics continues to grow and has plenty to offer to the scientific community. This perspective outlines the growing applications of NMR in metabolomics, highlights several recent advances in NMR technologies for metabolomics, and provides a roadmap for future advancements.

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          Most cited references135

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          NMRPipe: a multidimensional spectral processing system based on UNIX pipes.

          The NMRPipe system is a UNIX software environment of processing, graphics, and analysis tools designed to meet current routine and research-oriented multidimensional processing requirements, and to anticipate and accommodate future demands and developments. The system is based on UNIX pipes, which allow programs running simultaneously to exchange streams of data under user control. In an NMRPipe processing scheme, a stream of spectral data flows through a pipeline of processing programs, each of which performs one component of the overall scheme, such as Fourier transformation or linear prediction. Complete multidimensional processing schemes are constructed as simple UNIX shell scripts. The processing modules themselves maintain and exploit accurate records of data sizes, detection modes, and calibration information in all dimensions, so that schemes can be constructed without the need to explicitly define or anticipate data sizes or storage details of real and imaginary channels during processing. The asynchronous pipeline scheme provides other substantial advantages, including high flexibility, favorable processing speeds, choice of both all-in-memory and disk-bound processing, easy adaptation to different data formats, simpler software development and maintenance, and the ability to distribute processing tasks on multi-CPU computers and computer networks.
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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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              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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                Author and article information

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                Journal
                METALU
                Metabolites
                Metabolites
                MDPI AG
                2218-1989
                August 2022
                July 23 2022
                : 12
                : 8
                : 678
                Article
                10.3390/metabo12080678
                35893244
                2ff16e03-a378-4a34-b64b-fc0f241e30bd
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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