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      Improving sample classification by harnessing the potential of 1H-NMR signal chemical shifts

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          Abstract

          NMR spectroscopy is a technology that is widely used in metabolomic studies. The information that these studies most commonly use from NMR spectra is the metabolite concentration. However, as well as concentration, pH and ionic strength information are also made available by the chemical shift of metabolite signals. This information is typically not used even though it can enhance sample discrimination, since many conditions show pH or ionic imbalance. Here, we demonstrate how chemical shift information can be used to improve the quality of the discrimination between case and control samples in three public datasets of different human matrices. In two of these datasets, chemical shift information helped to provide an AUROC value higher than 0.9 during sample classification. In the other dataset, the chemical shift also showed discriminant potential (AUROC 0.831). These results are consistent with the pH imbalance characteristic of the condition studied in the datasets. In addition, we show that this signal misalignment dependent on sample class can alter the results of fingerprinting approaches in the three datasets. Our results show that it is possible to use chemical shift information to enhance the diagnostic and predictive properties of NMR.

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          Metabolomics--the link between genotypes and phenotypes.

          Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate response of biological systems to genetic or environmental changes. In parallel to the terms 'transcriptome' and proteome', the set of metabolites synthesized by a biological system constitute its 'metabolome'. Yet, unlike other functional genomics approaches, the unbiased simultaneous identification and quantification of plant metabolomes has been largely neglected. Until recently, most analyses were restricted to profiling selected classes of compounds, or to fingerprinting metabolic changes without sufficient analytical resolution to determine metabolite levels and identities individually. As a prerequisite for metabolomic analysis, careful consideration of the methods employed for tissue extraction, sample preparation, data acquisition, and data mining must be taken. In this review, the differences among metabolite target analysis, metabolite profiling, and metabolic fingerprinting are clarified, and terms are defined. Current approaches are examined, and potential applications are summarized with a special emphasis on data mining and mathematical modelling of metabolism.
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            Tumour acidosis: from the passenger to the driver's seat

            This Review by Corbet and Feron summarizes recent data showing that tumour acidosis influences cancer metabolism and contributes to cancer progression; it also highlights advances in therapeutic modalities aimed at either inhibiting or exploiting tumour acidification.
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              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
                daniel.canueto@urv.cat
                nicolau.canyellas@urv.cat
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                8 August 2018
                8 August 2018
                2018
                : 8
                : 11886
                Affiliations
                [1 ]ISNI 0000 0001 2284 9230, GRID grid.410367.7, Metabolomics Platform, IISPV, DEEEA, , Universitat Rovira i Virgili, Campus Sescelades, Carretera de Valls, s/n, ; 43007 Tarragona, Catalonia Spain
                [2 ]ISNI 0000 0000 9709 7726, GRID grid.225360.0, European Bioinformatics Institute (EMBL-EBI), , European Molecular Biology Laboratory, Wellcome Trust Genome Campus, Hinxton, ; Cambridge, CB10 1SD United Kingdom
                [3 ]GRID grid.430579.c, CIBERDEM, Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders, ; Madrid, Spain
                Author information
                http://orcid.org/0000-0003-2948-0127
                http://orcid.org/0000-0001-8604-1732
                Article
                30351
                10.1038/s41598-018-30351-7
                6082897
                30089873
                f797e525-ccb2-41d6-9ce9-9d4ad7d9f1ee
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 21 February 2018
                : 24 July 2018
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100003329, Ministerio de Economía y Competitividad (Ministry of Economy and Competitiveness);
                Award ID: TEC2015-69076-P
                Award Recipient :
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