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  • 1.
    Beger, Richard D.
    et al.
    Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, USA.
    Dunn, Warwick
    School of Biosciences, Phenome Centre Birmingham and Institute of Metabolism and Systems Research (IMSR), University of Birmingham, Birmingham, UK.
    Schmidt, Michael A.
    Advanced Pattern Analysis and Countermeasures Group, Research Innovation Center, Colorado State University, Fort Collins, USA.
    Gross, Steven S.
    Department of Pharmacology, Weill Cornell Medical College, New York, USA.
    Kirwan, Jennifer A.
    School of Biosciences, University of Birmingham, Birmingham, UK.
    Cascante, Marta
    Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain; Institute of Biomedicine of Universitat de Barcelona (IBUB) and CSIC-Associated Unit, Barcelona, Spain.
    Brennan, Lorraine
    UCD Institute of Food and Health, UCD, Belfield, Ireland.
    Wishart, David S.
    Departments of Computing Science and Biological Sciences, University of Alberta, Edmonton, Canada.
    Oresic, Matej
    Turku Centre for Biotechnology, University of Turku, Turku, Finland.
    Hankemeier, Thomas
    Division of Analytical Biosciences and Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research, Leiden University & Netherlands Metabolomics Centre, Leiden, The Netherlands.
    Broadhurst, David I.
    School of Science, Edith Cowan University, Perth, Australia.
    Lane, Andrew N.
    Center for Environmental Systems Biochemistry, Department Toxicology and Cancer Biology, Markey Cancer Center, Lexington, USA.
    Suhre, Karsten
    Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Doha, Qatar.
    Kastenmüller, Gabi
    Institute of Bioinformatics and Systems Biology, Helmholtz Center Munich, Oberschleißheim, Germany.
    Sumner, Susan J.
    Discovery Sciences, RTI International, Research Triangle Park, Durham, USA.
    Thiele, Ines
    University of Luxembourg, Luxembourg Centre for Systems Biomedicine, Campus Belval, Esch-Sur-Alzette, Luxembourg.
    Fiehn, Oliver
    West Coast Metabolomics Center, UC Davis, Davis, USA; Biochemistry Department, King Abdulaziz University, Jeddah, Saudi Arabia.
    Kaddurah-Daouk, Rima
    Psychiatry and Behavioral Sciences, Duke Internal Medicine and Duke Institute for Brain Sciences and Center for Applied Genomics and Precision Medicine, Duke University Medical Center, Durham, USA.
    Metabolomics enables precision medicine: "A White Paper, Community Perspective"2016In: Metabolomics, ISSN 1573-3882, E-ISSN 1573-3890, Vol. 12, no 10, article id 149Article in journal (Refereed)
    Abstract [en]

    INTRODUCTION BACKGROUND TO METABOLOMICS: Metabolomics is the comprehensive study of the metabolome, the repertoire of biochemicals (or small molecules) present in cells, tissues, and body fluids. The study of metabolism at the global or "-omics" level is a rapidly growing field that has the potential to have a profound impact upon medical practice. At the center of metabolomics, is the concept that a person's metabolic state provides a close representation of that individual's overall health status. This metabolic state reflects what has been encoded by the genome, and modified by diet, environmental factors, and the gut microbiome. The metabolic profile provides a quantifiable readout of biochemical state from normal physiology to diverse pathophysiologies in a manner that is often not obvious from gene expression analyses. Today, clinicians capture only a very small part of the information contained in the metabolome, as they routinely measure only a narrow set of blood chemistry analytes to assess health and disease states. Examples include measuring glucose to monitor diabetes, measuring cholesterol and high density lipoprotein/low density lipoprotein ratio to assess cardiovascular health, BUN and creatinine for renal disorders, and measuring a panel of metabolites to diagnose potential inborn errors of metabolism in neonates.

    OBJECTIVES OF WHITE PAPER—EXPECTED TREATMENT OUTCOMES AND METABOLOMICS ENABLING TOOL FOR PRECISION MEDICINE: We anticipate that the narrow range of chemical analyses in current use by the medical community today will be replaced in the future by analyses that reveal a far more comprehensive metabolic signature. This signature is expected to describe global biochemical aberrations that reflect patterns of variance in states of wellness, more accurately describe specific diseases and their progression, and greatly aid in differential diagnosis. Such future metabolic signatures will: (1) provide predictive, prognostic, diagnostic, and surrogate markers of diverse disease states; (2) inform on underlying molecular mechanisms of diseases; (3) allow for sub-classification of diseases, and stratification of patients based on metabolic pathways impacted; (4) reveal biomarkers for drug response phenotypes, providing an effective means to predict variation in a subject's response to treatment (pharmacometabolomics); (5) define a metabotype for each specific genotype, offering a functional read-out for genetic variants: (6) provide a means to monitor response and recurrence of diseases, such as cancers: (7) describe the molecular landscape in human performance applications and extreme environments. Importantly, sophisticated metabolomic analytical platforms and informatics tools have recently been developed that make it possible to measure thousands of metabolites in blood, other body fluids, and tissues. Such tools also enable more robust analysis of response to treatment. New insights have been gained about mechanisms of diseases, including neuropsychiatric disorders, cardiovascular disease, cancers, diabetes and a range of pathologies. A series of ground breaking studies supported by National Institute of Health (NIH) through the Pharmacometabolomics Research Network and its partnership with the Pharmacogenomics Research Network illustrate how a patient's metabotype at baseline, prior to treatment, during treatment, and post-treatment, can inform about treatment outcomes and variations in responsiveness to drugs (e.g., statins, antidepressants, antihypertensives and antiplatelet therapies). These studies along with several others also exemplify how metabolomics data can complement and inform genetic data in defining ethnic, sex, and gender basis for variation in responses to treatment, which illustrates how pharmacometabolomics and pharmacogenomics are complementary and powerful tools for precision medicine.

    CONCLUSIONS KEY SCIENTIFIC CONCEPTS AND RECOMMENDATIONS FOR PRECISION MEDICINE: Our metabolomics community believes that inclusion of metabolomics data in precision medicine initiatives is timely and will provide an extremely valuable layer of data that compliments and informs other data obtained by these important initiatives. Our Metabolomics Society, through its "Precision Medicine and Pharmacometabolomics Task Group", with input from our metabolomics community at large, has developed this White Paper where we discuss the value and approaches for including metabolomics data in large precision medicine initiatives. This White Paper offers recommendations for the selection of state of-the-art metabolomics platforms and approaches that offer the widest biochemical coverage, considers critical sample collection and preservation, as well as standardization of measurements, among other important topics. We anticipate that our metabolomics community will have representation in large precision medicine initiatives to provide input with regard to sample acquisition/preservation, selection of optimal omics technologies, and key issues regarding data collection, interpretation, and dissemination. We strongly recommend the collection and biobanking of samples for precision medicine initiatives that will take into consideration needs for large-scale metabolic phenotyping studies.

  • 2.
    Rocca-Serra, Philippe
    et al.
    Oxford e-Research Centre, University of Oxford, Oxford, UK.
    Salek, Reza M.
    European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, UK.
    Arita, Masanori
    National Institute of Genetics, Mishima, Japan; RIKEN Center for Sustainable Resource Science, Yokohama, Japan.
    Correa, Elon
    Centre for Endocrinology and Diabetes, University of Manchester, Manchester, UK; School of Chemistry, Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK.
    Dayalan, Saravanan
    Metabolomics Australia, The University of Melbourne, Parkville, Australia.
    Gonzalez-Beltran, Alejandra
    Oxford e-Research Centre, University of Oxford, Oxford, UK.
    Ebbels, Tim
    Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London, UK.
    Goodacre, Royston
    School of Chemistry, Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK.
    Hastings, Janna
    European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, UK.
    Haug, Kenneth
    European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, UK.
    Koulman, Albert
    Elsie Widdowson Laboratory, MRC Human Nutrition Research, Cambridge, UK.
    Nikolski, Macha
    Bordeaux Bioinformatics Center, Universite´ de Bordeaux, Bordeaux, France; CNRS/LaBRI, Universite´ de Bordeaux, Talence, France.
    Oresic, Matej
    Steno Diabetes Center, Gentofte, Denmark.
    Sansone, Susanna-Assunta
    Oxford e-Research Centre, University of Oxford, Oxford, UK.
    Schober, Daniel
    Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Halle, Germany.
    Smith, James
    Elsie Widdowson Laboratory, MRC Human Nutrition Research, Cambridge, UK; Department of Applied Mathematics and Theoretical Physics, Cambridge Computational Biology Institute, University of Cambridge, Cambridge, UK.
    Steinbeck, Christoph
    European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, UK.
    Viant, Mark R.
    School of Biosciences, University of Birmingham, Birmingham, UK.
    Neumann, Steffen
    Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Halle, Germany.
    Data standards can boost metabolomics research, and if there is a will, there is a way2016In: Metabolomics, ISSN 1573-3882, E-ISSN 1573-3890, Vol. 12, article id 14Article in journal (Refereed)
    Abstract [en]

    Thousands of articles using metabolomics approaches are published every year. With the increasing amounts of data being produced, mere description of investigations as text in manuscripts is not sufficient to enable re-use anymore: the underlying data needs to be published together with the findings in the literature to maximise the benefit from public and private expenditure and to take advantage of an enormous opportunity to improve scientific reproducibility in metabolomics and cognate disciplines. Reporting recommendations in metabolomics started to emerge about a decade ago and were mostly concerned with inventories of the information that had to be reported in the literature for consistency. In recent years, metabolomics data standards have developed extensively, to include the primary research data, derived results and the experimental description and importantly the metadata in a machine-readable way. This includes vendor independent data standards such as mzML for mass spectrometry and nmrML for NMR raw data that have both enabled the development of advanced data processing algorithms by the scientific community. Standards such as ISA-Tab cover essential metadata, including the experimental design, the applied protocols, association between samples, data files and the experimental factors for further statistical analysis. Altogether, they pave the way for both reproducible research and data reuse, including meta-analyses. Further incentives to prepare standards compliant data sets include new opportunities to publish data sets, but also require a little "arm twisting" in the author guidelines of scientific journals to submit the data sets to public repositories such as the NIH Metabolomics Workbench or MetaboLights at EMBL-EBI. In the present article, we look at standards for data sharing, investigate their impact in metabolomics and give suggestions to improve their adoption.

  • 3.
    Salek, Reza M
    et al.
    European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus Hinxton, Cambridge, United Kingdom; Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom.
    Neumann, Steffen
    Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Halle, Germany.
    Schober, Daniel
    Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Halle, Germany.
    Hummel, Jan
    Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.
    Billiau, Kenny
    Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.
    Kopka, Joachim
    Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.
    Correa, Elon
    School of Chemistry & Manchester Institute of Biotechnology, University of Manchester, Manchester, United Kingdom.
    Reijmers, Theo
    Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University, Leiden, Netherlands.
    Rosato, Antonio
    Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino FI, Italy.
    Tenori, Leonardo
    Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino FI, Italy; FiorGen Foundation, Sesto Fiorentin FI, Italy.
    Turano, Paola
    Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino FI, Italy.
    Marin, Silvia
    Department of Biochemistry and Molecular Biology, IBUB, Universitat de Barcelona, Barcelona, Spain.
    Deborde, Catherine
    INRA, Univ. Bordeaux, UMR1332 Fruit Biology and Pathology, Metabolome Facility of Bordeaux (MetaboHUB), Functional Genomics Center, IBVM, Centre INRA Bordeaux, Villenave d’Ornon, France.
    Jacob, Daniel
    INRA, Univ. Bordeaux, UMR1332 Fruit Biology and Pathology, Metabolome Facility of Bordeaux (MetaboHUB), Functional Genomics Center, IBVM, Centre INRA Bordeaux, Villenave d’Ornon, France.
    Rolin, Dominique
    INRA, Univ. Bordeaux, UMR1332 Fruit Biology and Pathology, Metabolome Facility of Bordeaux (MetaboHUB), Functional Genomics Center, IBVM, Centre INRA Bordeaux, Villenave d’Ornon, France.
    Dartigues, Benjamin
    Centre of bioinformatics of Bordeaux (CBiB), University of Bordeaux, Bordeaux, France.
    Conesa, Pablo
    European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus Hinxton, Cambridge, United Kingdom.
    Haug, Kenneth
    European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus Hinxton, Cambridge, United Kingdom.
    Rocca-Serra, Philippe
    University of Oxford e-Research Centre, Oxford, United Kingdom.
    O'Hagan, Steve
    School of Chemistry & Manchester Institute of Biotechnology, University of Manchester, Manchester, United Kingdom.
    Hao, Jie
    Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London, United Kingdom.
    van Vliet, Michael
    Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University, Leiden, Netherlands.
    Sysi-Aho, Marko
    Zora Biosciences OY, Espoo, Finland.
    Ludwig, Christian
    School of Cancer Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom.
    Bouwman, Jildau
    Microbiology & Systems Biology TNO, Zeist, Netherlands.
    Cascante, Marta
    Department of Biochemistry and Molecular Biology, IBUB, Universitat de Barcelona, Barcelona, Spain.
    Ebbels, Timothy
    Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London, United Kingdom.
    Griffin, Julian L
    Medical Research Council Human Nutrition Research, Cambridge, United Kingdom; Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom.
    Moing, Annick
    INRA, UMR1332 Fruit Biology and Pathology, Metabolome Facility of Bordeaux (MetaboHUB), Functional Genomics Center (IBVM), Centre INRA Bordeaux Villenave d’Ornon, Univ. Bordeaux, Bordeaux, France.
    Nikolski, Macha
    University of Bordeaux CBiB/LaBRI, Bordeaux, France.
    Oresic, Matej
    Örebro University, School of Medical Sciences. Zora Biosciences OY, Espoo, Finland.
    Sansone, Susanna-Assunta
    University of Oxford e-Research Centre, Oxford, United Kingdom.
    Viant, Mark R.
    School of Biosciences, University of Birmingham Edgbaston, Birmingham, United Kingdom.
    Goodacre, Royston
    School of Chemistry & Manchester Institute of Biotechnology, University of Manchester, Manchester, United Kingdom.
    Günther, Ulrich L
    School of Cancer Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom.
    Hankemeier, Thomas
    Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University, Leiden, Netherlands.
    Luchinat, Claudio
    Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino FI, Italy.
    Walther, Dirk
    Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.
    Steinbeck, Christoph
    European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus Hinxton, Cambridge, United Kingdom.
    COordination of Standards in MetabOlomicS (COSMOS): facilitating integrated metabolomics data access2015In: Metabolomics, ISSN 1573-3882, E-ISSN 1573-3890, Vol. 11, no 6, p. 1587-1597Article in journal (Refereed)
    Abstract [en]

    Metabolomics has become a crucial phenotyping technique in a range of research fields including medicine, the life sciences, biotechnology and the environmental sciences. This necessitates the transfer of experimental information between research groups, as well as potentially to publishers and funders. After the initial efforts of the metabolomics standards initiative, minimum reporting standards were proposed which included the concepts for metabolomics databases. Built by the community, standards and infrastructure for metabolomics are still needed to allow storage, exchange, comparison and re-utilization of metabolomics data. The Framework Programme 7 EU Initiative 'coordination of standards in metabolomics' (COSMOS) is developing a robust data infrastructure and exchange standards for metabolomics data and metadata. This is to support workflows for a broad range of metabolomics applications within the European metabolomics community and the wider metabolomics and biomedical communities' participation. Here we announce our concepts and efforts asking for re-engagement of the metabolomics community, academics and industry, journal publishers, software and hardware vendors, as well as those interested in standardisation worldwide (addressing missing metabolomics ontologies, complex-metadata capturing and XML based open source data exchange format), to join and work towards updating and implementing metabolomics standards.

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