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1. Multiplatform metabolomics for an integrative exploration of metabolic syndrome in older men

Author

Comte, Blandine, Monnerie, Stéphanie, Brandolini-Bunlon, Marion, Canlet, Cécile, Castelli, Florence, Chu-Van, Emeline, Colsch, Benoit, Fenaille, François, Joly, Charlotte, Jourdan, Fabien, Lenuzza, Natacha, Lyan, Bernard, Martin, Jean-François, Migné, Carole, Morais, José A., Pétéra, Mélanie, Poupin, Nathalie, Vinson, Florence, Thevenot, Etienne, Junot, Christophe, Gaudreau, Pierrette, and Pujos-Guillot, Estelle

Published
2021
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2. PeakForest: a multi-platform digital infrastructure for interoperable metabolite spectral data and metadata management

Author

Paulhe, Nils, Canlet, Cécile, Damont, Annelaure, Peyriga, Lindsay, Durand, Stéphanie, Deborde, Catherine, Alves, Sandra, Bernillon, Stephane, Berton, Thierry, Bir, Raphael, Bouville, Alyssa, Cahoreau, Edern, Centeno, Delphine, Costantino, Robin, Debrauwer, Laurent, Delabrière, Alexis, Duperier, Christophe, Emery, Sylvain, Flandin, Amelie, Hohenester, Ulli, Jacob, Daniel, Joly, Charlotte, Jousse, Cyril, Lagree, Marie, Lamari, Nadia, Lefebvre, Marie, Lopez-Piffet, Claire, Lyan, Bernard, Maucourt, Mickael, Migne, Carole, Olivier, Marie-Francoise, Rathahao-Paris, Estelle, Petriacq, Pierre, Pinelli, Julie, Roch, Léa, Roger, Pierrick, Roques, Simon, Tabet, Jean-Claude, Tremblay-Franco, Marie, Traïkia, Mounir, Warnet, Anna, Zhendre, Vanessa, Rolin, Dominique, Jourdan, Fabien, Thévenot, Etienne, Moing, Annick, Jamin, Emilien, Fenaille, François, Junot, Christophe, Pujos-Guillot, Estelle, and Giacomoni, Franck

Published
2022
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4. FragHub: A mass spectral libraries data integration workflow

Author

Dablanc, Axel, primary, Hennechart, Solweig, additional, Perez, Amélie, additional, Cabanac, Guillaume, additional, Guitton, Yann, additional, Jamin, Emilien, additional, Lyan, Bernard, additional, Paulhe, Nils, additional, Giacomoni, Franck, additional, and Marti, Guillaume, additional

Published
2024
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5. Untargeted plasma metabolomic profiles associated with overall diet in women from the SU.VI.MAX cohort

Author

Lécuyer, Lucie, Dalle, Céline, Micheau, Pierre, Pétéra, Mélanie, Centeno, Delphine, Lyan, Bernard, Lagree, Marie, Galan, Pilar, Hercberg, Serge, Rossary, Adrien, Demidem, Aicha, Vasson, Marie-Paule, Partula, Valentin, Deschasaux, Mélanie, Srour, Bernard, Latino-Martel, Paule, Druesne-Pecollo, Nathalie, Kesse-Guyot, Emmanuelle, Durand, Stéphanie, Pujos-Guillot, Estelle, Manach, Claudine, and Touvier, Mathilde

Published
2020
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7. Vers une meilleure efficacité et rapidité dans l'identification des métabolites dans le cadre d'études de métabolomique non ciblées ?

Author

Lyan, Bernard, Centeno, Delphine, Massias, Justine, Guitton, Yann, Pujos-Guillot, Estelle, Durand, Stéphanie, Plateforme Exploration du Métabolisme (PFEM), MetaboHUB-Clermont, MetaboHUB-MetaboHUB-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Clermont Auvergne (UCA), Laboratoire d'étude des Résidus et Contaminants dans les Aliments (LABERCA), École nationale vétérinaire, agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Réseau Francophone de Métabolomique et Fluxomique (RFMF)

Subjects
Métabolomique non ciblée, Sirius, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, identification
Abstract

National audience

Published
2023

12. Can we trust untargeted metabolomics? Results of the metabo-ring initiative, a large-scale, multi-instrument inter-laboratory study

Author

Martin, Jean-Charles, Maillot, Matthieu, Mazerolles, Gérard, Verdu, Alexandre, Lyan, Bernard, Migné, Carole, Defoort, Catherine, Canlet, Cecile, Junot, Christophe, Guillou, Claude, Manach, Claudine, Jabob, Daniel, Bouveresse, Delphine Jouan-Rimbaud, Paris, Estelle, Pujos-Guillot, Estelle, Jourdan, Fabien, Giacomoni, Franck, Courant, Frédérique, Favé, Gaëlle, Le Gall, Gwenaëlle, Chassaigne, Hubert, Tabet, Jean-Claude, Martin, Jean-Francois, Antignac, Jean-Philippe, Shintu, Laetitia, Defernez, Marianne, Philo, Mark, Alexandre-Gouaubau, Marie-Cécile, Amiot-Carlin, Marie-Josephe, Bossis, Mathilde, Triba, Mohamed N., Stojilkovic, Natali, Banzet, Nathalie, Molinié, Roland, Bott, Romain, Goulitquer, Sophie, Caldarelli, Stefano, and Rutledge, Douglas N.

Published
2015
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22. Whole-grain and refined wheat flours show distinct metabolic profiles in rats as assessed by a [sup.1.H] NMR-based metabonomic approach

Author

Fardet, Anthony, Canlet, Cecile, Gottardi, Gaelle, Lyan, Bernard, Llorach, Rafael, Remesy, Christian, Mazur, Andre, Paris, Alain, and Scalbert, Augustin

Subjects
Nuclear magnetic resonance -- Usage, Flour -- Nutritional aspects, Rats -- Physiological aspects, Rattus -- Physiological aspects, Food/cooking/nutrition
Abstract

The protection against diabetes and cardiovascular disease provided by whole-grain cereal consumption has been attributed to the fiber and micronutrients present in the bran. But exactly how this occurs remains unclear due to both diversity of bran constituents and the complexity of the metabolic responses to each of these constituents. We investigated the metabolic responses of 2 groups of rats (n = 10/group) fed 2 diets, for 2 wk each, in a crossover design. One diet contained 60 g/100 g whole-grain wheat flour (WGF) and the other contained 60 g/100 g refined wheat flour (RF). Markers of oxidative stress [urinary isoprostanes and malondialdehydes (MDA), plasma ferric-reducing ability of plasma, MDA, and vitamins E and C] and lipid status (liver and plasma triglycerides and cholesterol) were measured. Urine samples collected during the feeding periods and plasma and liver samples collected at the end of experiment were analyzed by [sup.1.H] NMR spectroscopy. Metabonomic analyses showed that each group reached a new metabolic balance within 48 h of changing the diet. Urinary excretion of some tricarboxylic acid cycle intermediates, aromatic amino acids, and hippurate was significantly greater in rats fed the WGF diet. Although the diets did not affect conventional lipid and oxidative stress markers, there were decreases in some liver lipids and increases in liver reduced glutathione and betaine as shown by metabonomic analyses. These suggest that the WGF diet improved the redox and lipid status. J. Nutr. 137: 923-929, 2007.

Published
2007

25. Identification of Pre-frailty Sub-Phenotypes in Elderly Using Metabolomics

Author

Pujos-Guillot, Estelle, Pétéra, Mélanie, Jacquemin, Jérémie, Centeno, Delphine, Lyan, Bernard, Montoliu, Ivan, Madej, Dawid, Pietruszka, Barbara, Fabbri, Cristina, Santoro, Aurelia, Brzozowska, Anna, Franceschi, Claudio, Comte, Blandine, Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), Plateforme d'Exploration du Métabolisme, Institut National de la Recherche Agronomique (INRA), Nestlé Institute of Health Sciences SA [Lausanne, Switzerland], Human Nutrition, Szkoła Główna Gospodarstwa Wiejskiego w Warszawie, Warsaw University of Life Sciences (SGGW), Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Centro Interdipartimentale « L. Galvani» (CIG), Institute of Neurological Sciences of Bologna IRCCS, European Union's Seventh Framework Program 266486, MetaboHUB French infrastructure ANR-INBS-0010, Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), European Project: 266486,EC:FP7:KBBE,FP7-KBBE-2010-4,NU-AGE(2011), Plateforme Exploration du Métabolisme (PFEM), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-MetaboHUB-Clermont, MetaboHUB-MetaboHUB, Alma Mater Studiorum Università di Bologna [Bologna] (UNIBO), Pujos-Guillot, Estelle, Pétéra, Mélanie, Jacquemin, Jérémie, Centeno, Delphine, Lyan, Bernard, Montoliu, Ivan, Madej, Dawid, Pietruszka, Barbara, Fabbri, Cristina, Santoro, Aurelia, Brzozowska, Anna, Franceschi, Claudio, and Comte, Blandine

Subjects
untargeted metabolomics, pre-frailty, sub-phenotypes, Physiology, gender differences, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, gender difference, [SDV.MHEP.PHY]Life Sciences [q-bio]/Human health and pathology/Tissues and Organs [q-bio.TO], biomarker, biomarkers, sub-phenotype, elderly, Original Research
Abstract

Aging is a dynamic process depending on intrinsic and extrinsic factors and its evolution is a continuum of transitions, involving multifaceted processes at multiple levels. It is recognized that frailty and sarcopenia are shared by the major age-related diseases thus contributing to elderly morbidity and mortality. Pre-frailty is still not well understood but it has been associated with global imbalance in several physiological systems, including inflammation, and in nutrition. Due to the complex phenotypes and underlying pathophysiology, the need for robust and multidimensional biomarkers is essential to move toward more personalized care. The objective of the present study was to better characterize the complexity of pre-frailty phenotype using untargeted metabolomics, in order to identify specific biomarkers, and study their stability over time. The approach was based on the NU-AGE project (clinicaltrials.gov , NCT01754012) that regrouped 1,250 free-living elderly people (65-79 y.o., men and women), free of major diseases, recruited within five European centers. Half of the volunteers were randomly assigned to an intervention group (1-year Mediterranean type diet). Presence of frailty was assessed by the criteria proposed by Fried et al. (2001). In this study, a sub-cohort consisting in 212 subjects (pre-frail and non-frail) from the Italian and Polish centers were selected for untargeted serum metabolomics at T0 (baseline) and T1 (follow-up). Univariate statistical analyses were performed to identify discriminant metabolites regarding pre-frailty status. Predictive models were then built using linear logistic regression and ROC curve analyses were used to evaluate multivariate models. Metabolomics enabled to discriminate sub-phenotypes of pre-frailty both at the gender level and depending on the pre-frailty progression and reversibility. The best resulting models included four different metabolites for each gender. They showed very good prediction capacity with AUCs of 0.93 (95% CI = 0.87-1) and 0.94 (95% CI = 0.87-1) for men and women, respectively. Additionally, early and/or predictive markers of pre-frailty were identified for both genders and the gender specific models showed also good performance (three metabolites; AUC = 0.82; 95% CI = 0.72-0.93) for men and very good for women (three metabolites; AUC = 0.92; 95% CI = 0.86-0.99). These results open the door, through multivariate strategies, to a possibility of monitoring the disease progression over time at a very early stage.

Published
2019
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26. Diet-Related Metabolomic Signature of Long-Term Breast Cancer Risk Using Penalized Regression: An Exploratory Study in the SU.VI.MAX Cohort

Author

Lécuyer, Lucie, primary, Dalle, Céline, additional, Lefevre-Arbogast, Sophie, additional, Micheau, Pierre, additional, Lyan, Bernard, additional, Rossary, Adrien, additional, Demidem, Aicha, additional, Petera, Mélanie, additional, Lagree, Marie, additional, Centeno, Delphine, additional, Galan, Pilar, additional, Hercberg, Serge, additional, Samieri, Cecilia, additional, Assi, Nada, additional, Ferrari, Pietro, additional, Viallon, Vivian, additional, Deschasaux, Mélanie, additional, Partula, Valentin, additional, Srour, Bernard, additional, Latino-Martel, Paule, additional, Kesse-Guyot, Emmanuelle, additional, Druesne-Pecollo, Nathalie, additional, Vasson, Marie-Paule, additional, Durand, Stéphanie, additional, Pujos-Guillot, Estelle, additional, Manach, Claudine, additional, and Touvier, Mathilde, additional

Published
2020
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27. INPUT OF DEEP PHENOTYPING IN THE METABOLIC SYNDROME STRATIFICATION

Author

Monnerie, Stéphanie, Pétéra, Mélanie, Canlet, Cécile, Castelli, Florence, Colsch, Benoit, Fenaille, Francois, Joly, Charlotte, Jourdan, Fabien, Lenuzza, Natacha, Lyan, Bernard, Martin, Jean-Francois, Migné, Carole, Morais, José A, Poupin, Nathalie, Vinson, Florence, Thevenot, Etienne, Junot, Christophe, Gaudreau, Pierrette, Comte, Blandine, Pujos-Guillot, Estelle, Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), Plateforme d’Exploration du Métabolisme, Institut National de la Recherche Agronomique (INRA), ToxAlim (ToxAlim), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), MetaboHUB, Service de Pharmacologie et Immunoanalyse (SPI), Laboratoire d'Etude du Métabolisme des Médicaments (LEMM), Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), Institut National de Recherche en Agriculture, Alimentation et Environnement (INRAE), Université Paris Saclay (COMUE), Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Division de Gériatrie, Centre de recherche du Centre universitaire de santé McGill, McGill University, Département de médecine, Centre de Recherche du Centre hospitalier de l’Université de Montréal, Université de Montréal (UdeM), Unité de Nutrition Humaine (UNH), Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Plateforme Exploration du Métabolisme (PFEM), MetaboHUB-Clermont, MetaboHUB-MetaboHUB-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Clermont Auvergne (UCA), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-MetaboHUB-Clermont, MetaboHUB-MetaboHUB, MetaToul AXIOM (E20), Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Ecole d'Ingénieurs de Purpan (INP - PURPAN), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-MetaboHUB-MetaToul, MetaboHUB-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-MetaboHUB-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Médicaments et Technologies pour la Santé (MTS), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Métabolisme et Xénobiotiques (ToxAlim-MeX), Université de Toulouse (UT)-Université de Toulouse (UT), Laboratoire Sciences des Données et de la Décision (LS2D), Département Métrologie Instrumentation & Information (DM2I), Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, McGill University = Université McGill [Montréal, Canada], Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CR CHUM), Centre Hospitalier de l'Université de Montréal (CHUM), Université de Montréal (UdeM)-Université de Montréal (UdeM)-Centre Hospitalier de l'Université de Montréal (CHUM), Université de Montréal (UdeM)-Université de Montréal (UdeM), Analyse de Xénobiotiques, Identification, Métabolisme (E20 Metatoul-AXIOM), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-MetaToul-MetaboHUB, Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Plateforme d'Exploration du Métabolisme PFEM, Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-MetaToul-MetaboHUB, Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées

Subjects
[SDV.OT]Life Sciences [q-bio]/Other [q-bio.OT], [CHIM.ANAL]Chemical Sciences/Analytical chemistry
Abstract

IntroductionMetabolic syndrome (MetS), defined as a cluster of cardiometabolic factors, is a public health challenge because of its growing prevalence. In the context of personalized medicine, new tools are necessary to bring additional knowledge about MetS etiology, better stratify populations and customise strategies for prevention. The objective of this study was to characterize the MetS phenotypic spectrum using complementary untargeted metabolomics platforms (HRMS, RMN).Technological and methodological innovationA case-control study was designed within the Quebec NuAge cohort1. Six complementary untargetedmetabolomic/lipidomic approaches were performed on serum samples collected at recruitment and 3 years later. Procedures were set up to guaranty the inter-laboratory standardisation from sample preparation to data processing, performed using reproducible online Galaxy workflows. A full feature selection strategy was developed to build a comprehensive molecular MetS signature, stable over time.Results and impactA wide range of metabolites (lipids, carbohydrates, amino-acids, peptides…) reflecting subject stability and providing new insights about underlying mechanisms, were found to be modulated. An optimized reduced signature was proposed, allowing good prediction performances (12% misclassification, AUC=0.95, CI:[0.92-0.98]). These results demonstrated the interest of a multidimensional molecular phenotyping as part of the next generation of medicine tools in the frame of noncommunicable diseases.References[1] Gaudreau P et al., 2007. Rejuvenation Res.10(3):377-386.

Published
2020

28. From Molecular Profiling to Precision Medicine in Metabolic Syndrome

Author

Comte, Blandine, Monnerie, Stéphanie, Brandolini-Bunlon, Marion, Canlet, Cécile, Castelli, Florence, Colsch, Benoit, Fenaille, Francois, Joly, Charlotte, Lenuzza, Natacha, Lyan, Bernard, Martin, Jean-Francois, Migné, Carole, Morais, José A., Pétéra, Mélanie, Thévenot, Etienne, Gaudreau, Pierrette, Junot, Christophe, Pujos-Guillot, Estelle, Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Analyse de Xénobiotiques, Identification, Métabolisme (E20 Metatoul-AXIOM), ToxAlim (ToxAlim), Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-MetaToul-MetaboHUB, Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CR CHUM), Centre Hospitalier de l'Université de Montréal (CHUM), Université de Montréal (UdeM)-Université de Montréal (UdeM), Département de médecine, Centre Léon Bérard [Lyon], MetaboHUB, Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), Centre de Recherche du CHUM, MetaToul AXIOM (E20), Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Ecole d'Ingénieurs de Purpan (INP - PURPAN), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-MetaboHUB-MetaToul, MetaboHUB-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-MetaboHUB-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-MetaToul-MetaboHUB, Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
[SDV.OT]Life Sciences [q-bio]/Other [q-bio.OT], [CHIM.ANAL]Chemical Sciences/Analytical chemistry
Abstract

Session: From Molecular Profiling to Precision Medicine in Metabolic Syndrome Session: From Molecular Profiling to Precision Medicine in Metabolic SyndromeSession: From Molecular Profiling to Precision Medicine in Metabolic Syndrome; Metabolic syndrome (MetS), defined as a cluster of cardio-metabolic factors including obesity, hypertension, dysglycemia, and dyslipidemia, and mostly affecting older adults, is now a public health challenge because of its growing prevalence. In the context of personalized medicine/nutrition, new tools are necessary to bring additional knowledge about MetS etiology, better stratify populations and customise strategies for prevention. The objective of this study was to investigate the integration of data from complementary untargeted metabolomics platforms (HRMS, RMN) and technologies to characterize the MetS phenotypic spectrum. A case-control study was designed within the Quebec NuAge cohort1. Six complementary untargeted metabolomic/lipidomic approaches, available within the MetaboHUB infrastructure2, were performed on serum samples collected at recruitment and 3 years later. Standard operating procedures were designed to guaranty the inter-laboratory standardisationfrom sample preparation to data processing. Data analyses were performed using reproducible online Galaxy workflows3. A full feature selection strategy was developed to build a comprehensive molecular MetS signature, stable over time. Consistent cross-sectional and longitudinal data were observed with a wide range of metabolites (lipids, carbohydrates, amino-acids, peptides…) reflecting subject stability regarding MetS, and providingnew insights about underlying mechanisms. Correlation network analysis contributed to explore the links between the molecular signature and clinical parameters. Additionally, an optimized reduced signature was proposed, allowing good prediction performances (12% misclassification, AUC=0.96, CI:[0.94-0.98]), for future clinical application. These results demonstrated the interest of a multidimensional molecular phenotyping aspart of the next generation of medicine tools in the frame of non-communicable diseases.

Published
2019

29. Mass spectral databases for metabolomics: How to build a consistently annotated mass spectral database from pure reference compounds analyzed under electrospray ionization conditions?

Author

Damont, A., Olivier, M. F., Warnet, A., Lyan, Bernard, Pujos-Guillot, Estelle, Jamin, Emilien, Debrauwer, Laurent, Bernillon, Stéphane, Junot, C., Tabet, J. C., Fenaille, F., Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), MetaboHUB, Université Paris Saclay (COmUE), Institut National de la Recherche Agronomique (INRA), Service de Pharmacologie et Immunoanalyse (SPI), Médicaments et Technologies pour la Santé (MTS), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), Plateforme d'Exploration du Métabolisme, ToxAlim (ToxAlim), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), Analytical Platform for Metabolomics and Toxicology (MetaToul-AXIOM), Biologie du fruit et pathologie (BFP), Université Sciences et Technologies - Bordeaux 1-Institut National de la Recherche Agronomique (INRA)-Université Bordeaux Segalen - Bordeaux 2, Institut Parisien de Chimie Moléculaire (IPCM), Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU), and Université Bordeaux Segalen - Bordeaux 2-Institut National de la Recherche Agronomique (INRA)-Université Sciences et Technologies - Bordeaux 1

Subjects
High-resolution mass spectrometry, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, Annotation, Mass spectra, Metabolomics, MS databases
Abstract

Epub ahead of print; Nowadays, high-resolution mass spectrometry is widely used for metabolomic studies. Thanks to its high sensitivity, it enables the detection of a large range of metabolites. In metabolomics, the continuous quest for a metabolite identification as complete and accurate as possible has led during the last decade to an ever increasing development of public MS databases (including LC-MS data) concomitantly with bioinformatic tool expansion. To facilitate the annotation process of MS profiles obtained from biological samples, but also to ease data sharing, exchange and exploitation, the standardization and harmonization of the way to describe and annotate mass spectra seemed crucial to us. Indeed, under electrospray (ESI) conditions, a single metabolite does not produce a unique ion corresponding to its protonated or deprotonated form but could lead to a complex mixture of signals. These MS signals result from the existence of different natural isotopologues of the same compound and also to the potential formation of adduct ions, homo and hetero-multimeric ions, fragment ions resulting from "prompt" in-source dissociations. As a joint reflection process within the French Infrastructure for Metabolomics and Fluxomics (MetaboHUB) and with the purpose of developing a robust and exchangeable annotated MS database made from pure reference compounds (chemical standards) analysis, it appeared to us that giving the metabolomics community some clues to standardize and unambiguously annotate each MS feature, was a prerequisite to data entry and further efficient querying of the database. The use of a harmonized notation is also mandatory for inter-laboratory MS data exchange. Additionally, thorough description of the variety of MS signals arising from the analysis of a unique metabolite might provide greater confidence on its annotation.

Published
2019
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30. Analytic correlation filtration: A new tool to reduce analytical complexity of metabolomic datasets

Author

Monnerie, Stéphanie, Pétéra, Mélanie, Lyan, Bernard, Gaudreau, Pierrette, Comte, Blandine, Pujos-Guillot, Estelle, Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), MetaboHUB, Centre de Recherche du CHUM, Département de médecine, Centre Léon Bérard [Lyon], Centre Hospitalier Universitaire de Montréal, Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CR CHUM), Centre Hospitalier de l'Université de Montréal (CHUM), and Université de Montréal (UdeM)-Université de Montréal (UdeM)

Subjects
filtration, [SDV.OT]Life Sciences [q-bio]/Other [q-bio.OT], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, analytical redundancies, workflow, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, lcsh:QR1-502, high-resolution mass spectrometry, data filtration, metabolomics, lcsh:Microbiology, Article
Abstract

Session: Analytic correlation filtration: A new tool to reduce analytical complexity of metabolomic datasets Poster P-501* (*AWARD WINNERS) Session: Analytic correlation filtration: A new tool to reduce analytical complexity of metabolomic datasets Poster P-501* (*AWARD WINNERS)Session: Analytic correlation filtration: A new tool to reduce analytical complexity of metabolomic datasetsPoster P-501* (*AWARD WINNERS); Metabolomics generates complex data that need dedicated workflows to extract the meaningful information. For biological interpretation, experts are mainly focusing on metabolites rather than on the redundant different analytical species. Moreover, the high degree of correlation in datasets is a constraint for the use of statistical methods. In this context, we developed a new tool to detect analytical correlation into datasets.The algorithm principle is to group features from the same analyte and to propose one single representative per group. The user can define grouping criteria with various options including correlation coefficient, retention time, mass defect information. The representative feature can be determined following four methods according to the analytical technology. The present tool was compared to one of the most commonly used free package proposing a grouping method: ‘CAMERA’, using its Galaxy version ‘CAMERA.annotate’ available in workflow4Metabolomics (W4M; http://workflow4metabolomics.org). To illustrate its functionalities, a published dataset available on W4M (Thevenot et al., 2015) was used as an example. Within the 3,120 ions of the dataset, the tool allowed creating 2,651 groups, meaning that 15% of ions are proposed to be filtered because of analytical redundancies. The proposed tool subdivided more than 20 groups of more than 10 ions into smaller ones corresponding to individualannotated metabolites, thus demonstrating the efficiency and relevance of the present approach. As a key element in metabolomics data analysis, the tool will be available via the web-based galaxy platform W4M with different output files for network vizualisation and for further data analysis within workflows.

Published
2019
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31. Analytic correlation filtration: A new tool to reduce analytical complexity of metabolomic datasets

Author

Pétéra, Mélanie, Lyan, Bernard, Gaudreau, Pierrette, Comte, Blandine, Pujos-Guillot, Estelle, and Monnerie, Stéphanie

Subjects
Chimie analytique, Signal and Image Processing, analytical redundancies, filtration, workflow, Analytical chemistry, Autre (Sciences du Vivant), Traitement du signal et de l'image (Informatique)
Abstract

Metabolomics generates complex data that need dedicated workflows to extract the meaningful information. For biological interpretation, experts are mainly focusing on metabolites rather than on the redundant different analytical species. Moreover, the high degree of correlation in datasets is a constraint for the use of statistical methods. In this context, we developed a new tool to detect analytical correlation into datasets. The algorithm principle is to group features from the same analyte and to propose one single representative per group. The user can define grouping criteria with various options including correlation coefficient, retention time, mass defect information. The representative feature can be determined following four methods according to the analytical technology. The present tool was compared to one of the most commonly used free package proposing a grouping method: ‘CAMERA’, using its Galaxy version ‘CAMERA.annotate’ available in workflow4Metabolomics (W4M; http://workflow4metabolomics.org). To illustrate its functionalities, a published dataset available on W4M (Thevenot et al., 2015) was used as an example. Within the 3,120 ions of the dataset, the tool allowed creating 2,651 groups, meaning that 15% of ions are proposed to be filtered because of analytical redundancies. The proposed tool subdivided more than 20 groups of more than 10 ions into smaller ones corresponding to individual annotated metabolites, thus demonstrating the efficiency and relevance of the present approach. As a key element in metabolomics data analysis, the tool will be available via the web-based galaxy platform W4M with different output files for network vizualisation and for further data analysis within workflows.

Published
2019

32. Plasma metabolomic signatures associated with long-term breast cancer risk in the SU.VI.MAX prospective cohort

Author

LECUYER, Lucie, Dalle, Céline, Lyan, Bernard, Demidem, Aïcha, Rossary, Adrien, VASSON, Marie-Paule, Petera, Mélanie, Lagree, Marie, Ferreira, Thomas, Centeno, Delphine, Zelek, Laurent, Galan, Pilar, Hercberg, Serge, DESCHASAUX, Mélanie, PARTULA, Valentin, SROUR, Bernard, Latino Martel, Paule, Kesse-Guyot, Emmanuelle, Druesne Pecollo, Nathalie, Manach, Claudine, Durand, Stéphanie, Pujos-Guillot, Estelle, Touvier, Mathilde, Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM), Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), Université Clermont Auvergne (UCA), Unité de Nutrition Humaine (UNH), Clermont Université-Université d'Auvergne - Clermont-Ferrand I (UdA)-Institut National de la Recherche Agronomique (INRA), Equipe 3: EREN- Equipe de Recherche en Epidémiologie Nutritionnelle (CRESS - U1153), Université Paris 13 (UP13)-Institut National de la Recherche Agronomique (INRA)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM), Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam UMC - Academic medical center, Unité de Recherche en Epidémiologie Nutritionnelle (UREN), Université Paris 13 (UP13)-Institut National de la Recherche Agronomique (INRA)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC), Institut National de la Recherche Agronomique (INRA)-Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université, Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Sorbonne Paris Cité (USPC)-Université Paris 13 (UP13)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Institut National de la Recherche Agronomique (INRA), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Université Paris 13 (UP13)-Institut National de la Recherche Agronomique (INRA)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM), Lécuyer, Lucie, and Institut National de la Recherche Agronomique (INRA)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Université Paris 13 (UP13)-Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153))

Subjects
[SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie
Abstract

International audience; Purpose: Breast cancer is a major cause of death in occidental women. Mechanisms involved in its etiology remain misunderstood. Metabolomics is a powerful tool which may help elucidating novel biological pathways and identify new biomarkers in order to predict breast cancer well before symptoms appear. The aim of this study was to investigate whether untargeted metabolomic signatures from blood draws of healthy women could contribute to better understand and predict the long-term risk of developing breast cancer. Methods: A nested case-control study was conducted within the SU.VI.MAX prospective cohort (13 years of follow-up) to analyze baseline plasma samples of 211 incident breast cancer cases and 211 matched controls by LC-MS mass spectrometry. Multivariable conditional logistic regression models were computed. Results: 83 ions were significantly associated (corrected-pvalue

Published
2018

33. Metabolomics enabled the identification of pre-frailty sub-phenotypes in elderly

Author

Pujos-Guillot , Estelle, Pétéra , Mélanie, Jacquemin , Jérémie, Centeno , Delphine, Lyan , Bernard, Montoliu , Ivan, Madej , Dawid, Pietruszka , Barbara, Fabbri , Christina, Santoro , Aurelia, Brzozowska , Anna, Comte , Blandine, Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), MetaboHUB, Nestlé, Department of Human Nutrition, Faculty of Life Science [Copenhagen], University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU), Alma Mater Studiorum Università di Bologna [Bologna] (UNIBO), Nestlé S.A., University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH), Unité de Nutrition Humaine - Clermont Auvergne ( UNH ), Université Clermont Auvergne ( UCA ) -Institut national de la recherche agronomique [Auvergne/Rhône-Alpes] ( INRA Auvergne/Rhône-Alpes ), Nestlé Institute of Health Sciences, WULS-SGGW, Department of Experimental, Diagnostic and Specialty Medicine ( DIMES ), University of Bologna, and Interdepartmental Centre 'L. Galvani' ( GIG )

Subjects
[CHIM.ANAL]Chemical Sciences/Analytical chemistry, [ SDV.AEN ] Life Sciences [q-bio]/Food and Nutrition, [ CHIM.ANAL ] Chemical Sciences/Analytical chemistry, [ SDV.SPEE ] Life Sciences [q-bio]/Santé publique et épidémiologie, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition
Abstract

International audience; Context: Ageing is a dynamic process depending on intrinsic and extrinsic factors and its evolution is a continuum of transitions, involving multifaceted processes at multiple levels. It is recognized that frailty and sarcopenia are shared by the major age-related diseases thus contributing to elderly morbidity and mortality. They are major health issues in aging populations, given their high prevalence and association with several adverse outcomes. Pre-frailty is still not well understood but it has been associated with changes in several physiological systems, including inflammation as well as changes in the balance of micronutrients and vitamins. Due to the complex phenotypes and underlying pathophysiology, the need for robust and multidimensional biomarkers is now essential to move towards more personalized care and prevention. Objective: The objective of the present study was to better characterize the complexity of pre-frailty phenotype using untargeted metabolomics in order to identify specific biomarkers, and study their stability over time. Research design and methods: The approach was based on the NU-AGE project (FP7 EU programme; clinicaltrials.gov, NCT01754012) that regrouped 1,250 free-living elderly people (65-79 y.o., men and women), free of major diseases, recruited within five European centres. Half of the volunteers were randomly assigned to an intervention group (1-year Mediterranean type diet). Presence of frailty was assessed by the criteria proposed by Fried et al (Fried et al. J Gerontol A Biol Sci Med Sci, 2001). In this study, a sub-cohort consisting in 212 subjects (pre-frail and non-frail) from the Italian and Polish centres were selected for mass spectrometry-based untargeted metabolomics. Metabolic profiles were determined from serum samples at T0 (baseline) and T1 (follow-up). All data were processed under the Galaxy web-based platform Worflow4metabolomics, guaranteeing their reproducibility (Giacomoni et al. Bioinformatics, 2015). Univariate statistical analyses were performed to identify discriminant metabolites regarding pre-frailty status. Predictive models were then built using linear logistic regression and ROC curve analyses were used to evaluate multivariate biomarkers. Results: Presence of sub-phenotypes of pre-frailty both at the gender level and depending on the pre-frailty progression and reversibility were revealed by untargeted metabolomics. Additionally, early markers, able to predict the evolution towards pre-frailty within one year, were identified for both genders. Moreover, some of these early biomarkers were found to be still relevant for classification of a ‘light pre-frail’ phenotype after its clinical appearance. Conclusion: These results open the door, through multivariate strategies, to the possibility of monitoring the disease progression over time at a very early stage. Longitudinal analysis of individual time trajectories to detect early deviations of health status would indeed contribute to a better disease prevention.

Published
2018

34. Plasma Metabolomic Signatures Associated with Long-term Breast Cancer Risk in the SU.VI.MAX Prospective Cohort

Author

Lécuyer, Lucie, primary, Dalle, Céline, additional, Lyan, Bernard, additional, Demidem, Aicha, additional, Rossary, Adrien, additional, Vasson, Marie-Paule, additional, Petera, Mélanie, additional, Lagree, Marie, additional, Ferreira, Thomas, additional, Centeno, Delphine, additional, Galan, Pilar, additional, Hercberg, Serge, additional, Deschasaux, Mélanie, additional, Partula, Valentin, additional, Srour, Bernard, additional, Latino-Martel, Paule, additional, Kesse-Guyot, Emmanuelle, additional, Druesne-Pecollo, Nathalie, additional, Durand, Stéphanie, additional, Pujos-Guillot, Estelle, additional, and Touvier, Mathilde, additional

Published
2019
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35. Proposal for a chemically consistent way to annotate ions arising from the analysis of reference compounds under ESI conditions: A prerequisite to proper mass spectral database constitution in metabolomics

Author

Damont, Annelaure, primary, Olivier, Marie-Françoise, additional, Warnet, Anna, additional, Lyan, Bernard, additional, Pujos-Guillot, Estelle, additional, Jamin, Emilien L., additional, Debrauwer, Laurent, additional, Bernillon, Stéphane, additional, Junot, Christophe, additional, Tabet, Jean-Claude, additional, and Fenaille, François, additional

Published
2019
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38. Metabolomics enabled the identification of pre-frailty sub-phenotypes in elderly

Author

Pujos-Guillot, Estelle, Pétéra, Mélanie, Jacquemin, Jérémie, Centeno, Delphine, Lyan, Bernard, Montoliu, Ivan, Madej, Dawid, Pietruszka, Barbara, Fabbri, Christina, Santoro, Aurelia, Brzozowska, Anna, and Comte, Blandine

Subjects
Santé publique et épidémiologie, Chimie analytique, Alimentation et Nutrition, Food and Nutrition, Analytical chemistry
Abstract

Context: Ageing is a dynamic process depending on intrinsic and extrinsic factors and its evolution is a continuum of transitions, involving multifaceted processes at multiple levels. It is recognized that frailty and sarcopenia are shared by the major age-related diseases thus contributing to elderly morbidity and mortality. They are major health issues in aging populations, given their high prevalence and association with several adverse outcomes. Pre-frailty is still not well understood but it has been associated with changes in several physiological systems, including inflammation as well as changes in the balance of micronutrients and vitamins. Due to the complex phenotypes and underlying pathophysiology, the need for robust and multidimensional biomarkers is now essential to move towards more personalized care and prevention. Objective: The objective of the present study was to better characterize the complexity of pre-frailty phenotype using untargeted metabolomics in order to identify specific biomarkers, and study their stability over time. Research design and methods: The approach was based on the NU-AGE project (FP7 EU programme; clinicaltrials.gov, NCT01754012) that regrouped 1,250 free-living elderly people (65-79 y.o., men and women), free of major diseases, recruited within five European centres. Half of the volunteers were randomly assigned to an intervention group (1-year Mediterranean type diet). Presence of frailty was assessed by the criteria proposed by Fried et al (Fried et al. J Gerontol A Biol Sci Med Sci, 2001). In this study, a sub-cohort consisting in 212 subjects (pre-frail and non-frail) from the Italian and Polish centres were selected for mass spectrometry-based untargeted metabolomics. Metabolic profiles were determined from serum samples at T0 (baseline) and T1 (follow-up). All data were processed under the Galaxy web-based platform Worflow4metabolomics, guaranteeing their reproducibility (Giacomoni et al. Bioinformatics, 2015). Univariate statistical analyses were performed to identify discriminant metabolites regarding pre-frailty status. Predictive models were then built using linear logistic regression and ROC curve analyses were used to evaluate multivariate biomarkers. Results: Presence of sub-phenotypes of pre-frailty both at the gender level and depending on the pre-frailty progression and reversibility were revealed by untargeted metabolomics. Additionally, early markers, able to predict the evolution towards pre-frailty within one year, were identified for both genders. Moreover, some of these early biomarkers were found to be still relevant for classification of a ‘light pre-frail’ phenotype after its clinical appearance. Conclusion: These results open the door, through multivariate strategies, to the possibility of monitoring the disease progression over time at a very early stage. Longitudinal analysis of individual time trajectories to detect early deviations of health status would indeed contribute to a better disease prevention.

Published
2018

39. Metabolomics applied to nutritional epidemiology to identify biomarkers of food intake in the framework of the Metabo-Breast cancer project, SU.VI.MAX cohort

Author

LECUYER, Lucie, Dalle, Céline, Petera, Mélanie, Centeno, Delphine, Lyan, Bernard, Durand, Stéphanie, Pujos-Guillot, Estelle, Micheau, Pierre, Morand, Christine, Galan, Pilar, Hercberg, Serge, PARTULA, Valentin, DESCHASAUX, Mélanie, Srour, Bernard, Latino Martel, Paule, Kesse, Emmanuelle, Touvier, Mathilde, Manach, Claudine, Equipe 3: EREN- Equipe de Recherche en Epidémiologie Nutritionnelle (CRESS - U1153), Université Paris 13 (UP13)-Institut National de la Recherche Agronomique (INRA)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC), Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), Hôpital avicenne, Université Paris 13 (UP13)-Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-Hôpital Avicenne, Unité de Recherche en Epidémiologie Nutritionnelle (UREN), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Sorbonne Paris Cité (USPC)-Université Paris 13 (UP13)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Institut National de la Recherche Agronomique (INRA), Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM), French National Cancer Institute [INCa_8085, INCa_11323], French network for Nutrition And Cancer Research (NACRe), Centre de Recherche en Nutrition Humaine (CRNH). FRA., Lécuyer, Lucie, Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Paris 13 (UP13)-Institut National de la Recherche Agronomique (INRA)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Paris 13 (UP13)-Institut National de la Recherche Agronomique (INRA)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Academic Medical Center - Academisch Medisch Centrum [Amsterdam] (AMC), University of Amsterdam [Amsterdam] (UvA), Hôpital Avicenne [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de la Recherche Agronomique (INRA)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Université Paris 13 (UP13)-Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC), Université Paris Diderot - Paris 7 (UPD7)-Université Sorbonne Paris Cité (USPC)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA), Université Paris Diderot - Paris 7 (UPD7)-Université Sorbonne Paris Cité (USPC)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Sorbonne Paris Cité (USPC)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA), and ProdInra, Migration

Subjects
[SDV] Life Sciences [q-bio], [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, [SDV]Life Sciences [q-bio], [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie
Abstract

International audience; The work presented is part of the Metabo-Breast cancer project (2015-2017, INCa, P.I. M. Touvier), which aims at 1) discovering predictive biomarkers of breast cancer using metabolomics 2) identifying biomarkers of the quality of the usual diet and of specific foods with putative health effects and 3) relating these biomarkers to enhance our understanding of the role of nutrition and specific dietary factors on breast cancer. Here we focus on the objective of discovering biomarkers of food intake by the exploration of the food metabolome in serum samples from the SU.VI.MAX cohort, using high-resolution mass spectrometry. Untargeted metabolomics is a holistic, data-driven approach that has proved efficient to discover dietary biomarkers through the comparison of the comprehensive profiles of plasma or urine metabolites from subjects differing according to their dietary habits or recent food consumption (Scalbert et al., AJCN 2014). SU.VI.MAX female subjects who filled at least ten 24h dietary records during the first 2 years of follow-up were stratified in deciles according to their level of adherence to the guidelines of the Programme National Nutrition Santé, assessed by the score PNNS-GS previously described (Estaquio et al., JADA 2009) but not taking into account the physical activity component. A total of 80 women, aged 48±6.4 years old was randomly selected in the 10th decile of the PNNS-GS distribution and 80 women matched for age, baseline menopausal status, BMI, smoking and season of blood draw were selected in the 1st decile. Plasma samples collected at baseline in the SU.VI.MAX study were analyzed using Ultra Performance Liquid Chromatography (UPLC) coupled with a quadrupole time of flight mass spectrometer (QToF, Impact II Bruker), equipped with an electrospray ionization source. Metabolic profiles were acquired in both positive and negative modes with a scan range from 50 to 1,000 mass-to-charge ratio. Data were pre-processed using Galaxy workflow4metabolomics. A total of 1575 and 601 signals (ions) were detected in positive and negative mode, respectively. Metabolomics profiles were compared using univariate (with Benjamini-Hochberg (BH) correction) and multivariate statistical methods (ANOVA, multivariable conditional logistic regression, PCA, HCA, PLS andcorrelation analyses) to determine the ions associated with the PNNS-GS, some specific components of the score and with the level of consumption of 58 foods/food groups assessed with the 24h dietary records.84 ions in positive mode and 30 ions in negative mode were found correlated with specific foods/food groups (r>0.3, p-value after BH correction

Published
2017

40. Untargeted metabolomics reveals pre-frailty sub-phenotypes in elderly

Author

Pujos-Guillot, Estelle, Petera, Mélanie, Centeno, Delphine, Lyan, Bernard, Pietruszka, B, Santoro, A, Brzozowska, A, Franceschi, Emanuele, Comte, Blandine, Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Plateforme Exploration du Métabolisme (PFEM), MetaboHUB-Clermont, MetaboHUB-MetaboHUB-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Clermont Auvergne (UCA), Department of Human Nutrition, Faculty of Life Science [Copenhagen], University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH), Department of Experimental Diagnostic and Specialty Medicine, Alma Mater Studiorum Università di Bologna [Bologna] (UNIBO), MetaboHUB, European Union Geriatric Medicine Society (EUGMS). GBR., Plateforme d'Exploration du Métabolisme, Institut National de la Recherche Agronomique (INRA), University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU), Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-MetaboHUB-Clermont, and MetaboHUB-MetaboHUB

Subjects
sarcopenia, effet de l'âge, sarcopenie, fragilité des os, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, méthode de prévention, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition
Abstract

Poster presentations / European Geriatric Medicine 8S1 (2017) S40–S247; National audience; Introduction: Human ageing is a dynamic process depending on intrinsic and extrinsic factors and its evolution is a continuum of transitions, involving multifaceted processes at multiple levels. It is recognized that frailty and sarcopenia are shared by the major age-related diseases, thus contributing to elderly morbidity and mortality. They are major health issues in elderly populations, given their high prevalence and association with several adverse outcomes. Due to their complex phenotypes and underlying pathophysiology, the need for robust and multidimensional biomarkers is now essential to move towards a more personalized care and prevention. Methods: The NU-AGE project [1] regroups 1250 free-living elderly people (65–79 y.o., men and women), free of major diseases, recruited within 5 European centres. Twenty percent of the subjects were pre-frail as defined by the criteria proposed by Fried et al.[2]. Six hundred twenty five volunteers were randomly assigned to an intervention group (1-year Mediterranean diet). A sub-cohort consisting in first, 120 subjects, half pre-frail randomly selected from the Italian and Polish centres, and secondly, 92 subjects shifting their frailty status were included for untargeted serum metabolomics at T0 (recruitment) and T1 (after diet intervention). Results: Metabolomics enables to discriminate sub-phenotypes of pre-frailty both at the gender level and depending on the pre-frailty progression and reversibility. Additionally, early and/or predictive markers of pre-frailty were identified in both populations. Conclusion: These results open the door, through multivariate strategies, to a possibility of monitoring the disease progression over time and/or in response to interventions at a very early stage

Published
2017

41. Signatures métabolomiques par spectrométrie de masse et risque de cancer du sein

Author

Dalle, Céline, Lyan, Bernard, Petera, Mélanie, Lagree, Marie, Rossary, Adrien, Demidem, Aïcha, Ferreira, Tom, Centeno, Delphine, Galan, Pilar, Hercberg, Serge, Deschasaux, Mélanie, Partula, Valentin, Srour, Bernard, Latino Martel, Paule, Kesse Guyot, Emmanuelle, Manach, Claudine, Vasson, Marie-Paule, Durand, Stéphanie, Pujos Guillot, Estelle, Touvier, Mathilde, and Lécuyer, Lucie

Subjects
métabolomique, cancer du sein, spectrométrie de masse, plasma, étude prospective, Cancer
Abstract

Contexte et objectifs : La métabolomique permet d’étudier l’ensemble des métabolites présents dans un biofluide. Cette étude basée sur une analyse non ciblée en spectrométrie de masse a pour but d’étudier si des profils métabolomiques établis à partir d’un prélèvement sanguin sur des femmes a priori en bonne santé, pourrait contribuer à prédire le risque à long terme de développer le cancer du sein et d’améliorer la compréhension de son étiologie. Méthodes : Un cas-témoin niché prospectif a été mené dans la cohorte SU.VI.MAX, incluant 211 cas de cancer du sein et 211 témoins appariés. Les profils métabolomiques ont été établis sur des échantillons de plasma prélevés à l’inclusion, avant l’apparition du cancer, grâce à une analyse non ciblée par spectrométrie de masse (LC-MS). Des modèles de régressions logistiques conditionnelles multivariées ont été calculés sur chaque ion présélectionné grâce à une ANOVA et sur les combinaisons de potentiels biomarqueurs issus d’une Analyse en Composante Principale. Une correction de test multiple de type FDR (False Discovery Rate) a été appliquée. Résultats : Plusieurs métabolites étaient associés au risque de cancer du sein. La finalisation de l’identification des métabolites discriminants est en cours. Au vu des résultats actuels, les femmes caractérisées par un niveau plasmatique plus élevé de valine (OR=1,45 [1,15-1,83] ; p= 0,009), de phénylalanine (OR=1,43 [1,14-1,78] ; p=0,009), de tryptophane (OR=1,4 [1,1-1,79] ; p=0,01), et de glutamine (OR=1,33 [1,07-1,66] ; p=0,01) et d’un niveau plus bas d’O-succinyl-L-homosérine (OR=0,7 [0,55-0,89] ; p=0,01) auraient un risque plus important de développer un cancer du sein dans la décennie qui suit. Conclusions et perspectives : Cette étude suggère plusieurs associations entre profils métabolomiques à l’inclusion et risque à long terme de développer le cancer du sein et confirme certains résultats que nous avons mis en évidence par une analyse RMN. Cette étude pourrait aider à améliorer l’identification des femmes à risque de développer un cancer du sein, bien avant l’apparition des symptômes. L’étude des biomarqueurs nutritionnels associés au risque de développer cette pathologie (actuellement en cours) pourra permettre de préciser le rôle de la nutrition (facteur modifiable) dans l’étiologie du cancer du sein et d’améliorer les stratégies préventives.

Published
2017

42. Metabolomics applied to nutritional epidemiology to identify biomarkers of food intake in the framework of the Metabo-Breast cancer project, SU.VI.MAX cohort

Author

Pétéra, Mélanie, Centeno, Delphine, Lyan, Bernard, Durand, Stéphanie, Pujos Guillot, Estelle, Micheau, Pierre, Morand, Christine, Galan, Pilar, Hercberg, Serge, Partula, Valentin, Deschasaux, Mélanie, Srour, Bernard, Latino Martel, Paule, Kesse Guyot, Emmanuelle, Dalle, Céline, Lécuyer, Lucie, Touvier, Mathilde, and Manach, Claudine

Abstract

The work presented is part of the Metabo-Breast cancer project (2015-2017, INCa, P.I. M. Touvier), which aims at 1) discovering predictive biomarkers of breast cancer using metabolomics 2) identifying biomarkers of the quality of the usual diet and of specific foods with putative health effects and 3) relating these biomarkers to enhance our understanding of the role of nutrition and specific dietary factors on breast cancer. Here we focus on the objective of discovering biomarkers of food intake by the exploration of the food metabolome in serum samples from the SU.VI.MAX cohort, using high-resolution mass spectrometry. Untargeted metabolomics is a holistic, data-driven approach that has proved efficient to discover dietary biomarkers through the comparison of the comprehensive profiles of plasma or urine metabolites from subjects differing according to their dietary habits or recent food consumption (Scalbert et al., AJCN 2014). SU.VI.MAX female subjects who filled at least ten 24h dietary records during the first 2 years of follow-up were stratified in deciles according to their level of adherence to the guidelines of the Programme National Nutrition Santé, assessed by the score PNNS-GS previously described (Estaquio et al., JADA 2009) but not taking into account the physical activity component. A total of 80 women, aged 48±6.4 years old was randomly selected in the 10th decile of the PNNS-GS distribution and 80 women matched for age, baseline menopausal status, BMI, smoking and season of blood draw were selected in the 1st decile. Plasma samples collected at baseline in the SU.VI.MAX study were analyzed using Ultra Performance Liquid Chromatography (UPLC) coupled with a quadrupole time of flight mass spectrometer (QToF, Impact II Bruker), equipped with an electrospray ionization source. Metabolic profiles were acquired in both positive and negative modes with a scan range from 50 to 1,000 mass-to-charge ratio. Data were pre-processed using Galaxy workflow4metabolomics. A total of 1575 and 601 signals (ions) were detected in positive and negative mode, respectively. Metabolomics profiles were compared using univariate and multivariate statistical methods (ANOVA with Benjamini-Hochberg (BH) correction, PCA, HCA, PLS, correlation analyses adjusted for energy intake) to determine the ions associated with the PNNS-GS, some specific components of the score and with the level of consumption of 58 foods/food groups assessed with the FFQ. 84 ions in positive mode and 30 ions in negative mode were found correlated with specific foods/food groups (r>0.3, p-value with BH

Published
2017

43. PhytoMetaboBank, une nouvelle base de données sur les bioactifs végétaux et leurs métabolites chez l’homme

Author

Giacomoni, Franck, Rothwell, Joseph, Fillâtre, Johann, Knox, C., Cesaire, Daniel, Quintana, Mercedes, Lyan, Bernard, Sébédio, Jean-Louis, Comte, Blandine, Pujos-Guillot, Estelle, Manach, Claudine, Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université, In Siliflo Inc, Centre de Recherche en Nutrition Humaine (CRNH). FRA., and ProdInra, Migration

Subjects
[SDV.AEN] Life Sciences [q-bio]/Food and Nutrition, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition
Abstract

National audience; The “food metabolome” comprises all metabolites present in biological fluids that are directly derived from the digestion of food. A large proportion of the food metabolome consists of phytochemical metabolites, which are products of intestinal,hepatic or microbial metabolism of plant secondary metabolites such as polyphenols, terpenoids, alkaloids...Identification of unknowns in metabolome fingerprints is a laborious step-by -step process and often a bottleneck in biomarker discovery. One major limitation for the interpretation of the Food metabolome fingerprints is the incompleteness of existing databases regarding phytochemical metabolites. As part of the ANR PhenoMeNep project, we aim to construct a new database tailored to the study of the phytochemical component of the food metabolome. Provisionally named PhytoMetaboBank, the data base will be an inventory of known metabolites described in the literature for all dietary phytochemicals. It will also include the most likely metabolites predicted in silico for these dietary phytochemicals. Built with MySQL and Perl processing chains, an efficient elational design will underpin a powerful and intuitive web interface. For a queried monoisotopic mass or elemental formula, the database will return a list of possible metabolites, with their physicochemical properties, spectral data and possible dietary precursors linked to food sources. PhytoMetaboBank will be the first database to collate information on phytochemical metabolites from a metabolomics standpoint, and should improve the identification of discriminant ions in non-targeted profiling

Published
2016

44. BioBanaTom study: Metabolomics to dientidy biomarkers of banana and tomato intake

Author

Vazquez Manjarrez, Natalia, Lyan, Bernard, Petera, Mélanie, Dalle, Céline, Pujos-Guillot, Estelle, Durand, Stéphanie, Morand, Christine, Touvier, Mathilde, DRAGSTED, Laars Ove, Manach, Claudine, Unité de Nutrition Humaine (UNH), Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université-Institut National de la Recherche Agronomique (INRA), Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Université Paris Diderot - Paris 7 (UPD7)-Université Sorbonne Paris Cité (USPC)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA), Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Clermont Auvergne (UCA) - Institut national de la recherche agronomique [Auvergne/Rhône-Alpes] (INRA Auvergne/Rhône-Alpes), Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS), Institut National de la Recherche Agronomique (INRA) - Université Paris Diderot - Paris 7 (UPD7) - Université Paris Descartes - Paris 5 (UPD5) - Université Paris 13 - Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de la Recherche Agronomique (INRA)-Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université, and Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)

Subjects
approche métabolomique, [CHIM.ANAL] Chemical Sciences/Analytical chemistry, Chimie analytique, digestive, oral, and skin physiology, consommation alimentaire, food and beverages, tomato, banane, [SDV.AEN] Life Sciences [q-bio]/Food and Nutrition, tomate, banana, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, food consumption, Alimentation et Nutrition, impact sur la santé, Food and Nutrition, Analytical chemistry, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition
Abstract

Demonstrating the health benefits of food bioactives: challenges and opportunitiesDemonstrating the health benefits of food bioactives: challenges and opportunities; BioBanaTom study: Metabolomics to dientidy biomarkers of banana and tomato intake. 1. International Conference on Food Bioactive and Health

Published
2016

45. Two complementary metabolomics studies to identify biomarkers of banana intake

Author

Vazquez Manjarrez, Natalia, Lyan, Bernard, Petera, Mélanie, Dalle, Céline, Pujos-Guillot, Estelle, Durand, Stéphanie, Morand, Christine, Touvier, Mathilde, DRAGSTED, Laars Ove, Manach, Claudine, Unité de Nutrition Humaine (UNH), Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université-Institut National de la Recherche Agronomique (INRA), Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Université Paris Diderot - Paris 7 (UPD7)-Université Sorbonne Paris Cité (USPC)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA), JPI, FOODBALL, Nutrigenomics Organisation (NuGO). NLD., Institut National de la Recherche Agronomique (INRA)-Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université, Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM), Department of Nutrition, Exercise and Sports [Copenhagen], Faculty of Science [Copenhagen], University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH), Max Rubner-Institut (MRI). DEU., Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU), Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC), ProdInra, Archive Ouverte, Université Clermont Auvergne (UCA) - Institut national de la recherche agronomique [Auvergne/Rhône-Alpes] (INRA Auvergne/Rhône-Alpes), Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS), Institut National de la Recherche Agronomique (INRA) - Université Paris Diderot - Paris 7 (UPD7) - Université Paris Descartes - Paris 5 (UPD5) - Université Paris 13 - Institut National de la Santé et de la Recherche Médicale (INSERM), Department of Nutrition, Exercise and Sports, and University of Copenhagen (KU)

Subjects
intermediate consumption, approche métabolomique, [CHIM.ANAL] Chemical Sciences/Analytical chemistry, [SDV]Life Sciences [q-bio], Chimie analytique, education, consommation alimentaire, food and beverages, clinical study, banane, [SDV] Life Sciences [q-bio], [SDV.AEN] Life Sciences [q-bio]/Food and Nutrition, consommation, banana, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, food consumption, Alimentation et Nutrition, impact sur la santé, Food and Nutrition, biomarker, étude clinique, Analytical chemistry, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, biomarqueur, health care economics and organizations
Abstract

13 th NuGOwee: PHENOTYPES AND PREVENTION -THE INTERPLAY OF GENES, LIFE-STYLES FACTORS AND GUT ENVIRONMENT Session 1 board 2: Biomarkers13 th NuGOwee: PHENOTYPES AND PREVENTION -THE INTERPLAY OF GENES, LIFE-STYLES FACTORS AND GUT ENVIRONMENTSession 1 board 2: Biomarkers; Two complementary metabolomics studies to identify biomarkers of banana intake. NuGOweek 2016

Published
2016

46. An integrated approach for the identification of predictive markers of type 2 diabetes

Author

Pujos-Guillot, Estelle, Brandolini-Bunlon, Marion, Grissa, Dhouha, Liu, Yunfei, Pééra, Mélanie, Joly, Charlotte, Lyan, Bernard, Czernichow, Sébastien, Zins, M, Goldberg M, M, Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université, Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Metabolomics Society., and Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université-Institut National de la Recherche Agronomique (INRA)

Subjects
Alimentation et Nutrition, Food and Nutrition, biomarkers, prediction, cohort, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2016

47. Enrichissement de la base de données spectrale peakforest en LC-MS

Author

Emery, Sylvain, Lyan, Bernard, Joly, Charlotte, Paulhe, Nils, Giacomoni, Franck, Thévenot, Etienne, Junot, C., Pujos-Guillot, Estelle, Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université, Plateforme d'Exploration du Métabolisme, MetaboHUB, Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Laboratoire d'Etude du Métabolisme des Médicaments (LEMM), Service de Pharmacologie et Immunoanalyse (SPI), Médicaments et Technologies pour la Santé (MTS), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Médicaments et Technologies pour la Santé (MTS), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Réseau Francophone de Métabolomique et Fluxomique (RFMF). FRA., Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), Université Paris-Saclay-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Médicaments et Technologies pour la Santé (MTS), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Plateforme Exploration du Métabolisme (PFEM), MetaboHUB-Clermont, MetaboHUB-MetaboHUB-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Clermont Auvergne (UCA), and Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA))

Subjects
chronic diseases, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, PeakForest, maladie chronique, identification metabolites, LC MSMS, banque de données, base de données spectrales, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, analyse métabolomique
Abstract

L’analyse métabolomique non ciblée est une approche puissante permettant la caractérisation du phénotype métabolique lié aux développements de maladies chroniques. L’identification des biomarqueurs qui y sont associés est devenue un enjeu majeur pour ce type d’approche. Il existe aujourd’hui un très large panel de banques de données en métabolomique, pouvant aider lors de cette étape d’identification, telles que MassBank, HMDB ou Lipidmaps, mais ces bases n’intègrent pas de données chromatographiques alors que le temps de rétention peut être un paramètre contribuant largement dans l’identification de molécules (Sumner et al, 2014).La base de données PeakForest est une banque de données de spectres de référence dédiée à l’annotation des données métabolomiques. Plus de 1000 composés standards (métabolites endogènes déjà décrits dans les biofluides) ont été analysés en LC-HRMS (Orbitrap, QTof) et selon des méthodes chromatographiques complémentaires au sein des quatre plateformes du consortium MetaboHUB. L’implémentation de PeakForest est réalisé via des fichiers ‘template’ qui intègrent les metadata et les peaklists annotées. L’originalité de cette base est qu’elle intègre également les conditions chromatographiques ainsi que les temps de rétention de chaque molécule, ce qui permet d’intégrer ce paramètre dans les requêtes.L’objectif sera de pouvoir utiliser cette ressource pour une annotation automatique des jeux de données. C’est pourquoi la base de données PeakForest est utilisable via des outils Galaxy, bientôt intégrés au sein de la plate-forme web Galaxy W4M (Workflow4Metabolomics ; Giacomoni et al, 2015).Une preuve de concept de l’utilisation de cet outil a été réalisée pour l’annotation d’un échantillon de plasma de référence du NIST. Au total plus de 70 métabolites ont été confirmés avec un score égal à 5 (Sumner et al, 2014). Ces résultats pourront à terme être intégrés au sein de PeakForest après curation effectuée par des experts dans le but de valider les résultats. Des données MS/MS viendront également complémenter la base.Cet outil dédié à l’annotation de métabolites en haut débit contribuera donc à terme à enrichir la caractérisation des métabolomes de différents systèmes biologiques

Published
2016

48. Metabolomics as part of an integrated approach for the identification of predictive markers of type 2 diabetes

Author

Pujos-Guillot , Estelle, Brandolini-Bunlon , Marion, Grissa , Dhouha, Liu , Yunfei, Petera , Mélanie, Joly , Charlotte, Lyan , Bernard, Czernichow , Sébastien, Zins , M., Goldberg , M., Comte , Blandine, Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université, Cohortes épidémiologiques en population (CONSTANCES), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay-Université Paris Cité (UPCité), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Université Paris Descartes - Paris 5 (UPD5), INRA DID’IT Metaprogramme, ProdInra, Archive Ouverte, Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), UMS011 Cohortes épidémiologiques en population (CONSTANCES), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut National de la Santé et de la Recherche Médicale (INSERM), Service de Nutrition, Université Pierre et Marie Curie - Paris 6 (UPMC)-Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-CHU Pitié-Salpêtrière [APHP], Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université-Institut National de la Recherche Agronomique (INRA), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay-Université de Paris (UP), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Unité de Nutrition Humaine - Clermont Auvergne ( UNH ), Université Clermont Auvergne ( UCA ) -Institut national de la recherche agronomique [Auvergne/Rhône-Alpes] ( INRA Auvergne/Rhône-Alpes ), Laboratoire Lorrain de Recherche en Informatique et ses Applications ( LORIA ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Université Henri Poincaré - Nancy 1 ( UHP ) -Université Nancy 2-Institut National Polytechnique de Lorraine ( INPL ) -Centre National de la Recherche Scientifique ( CNRS ), UMS11 Cohortes en population, Institut National de la Santé et de la Recherche Médicale ( INSERM ), Université de Versailles Saint-Quentin-en-Yvelines ( UVSQ ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Assistance publique - Hôpitaux de Paris (AP-HP)-CHU Pitié-Salpêtrière [APHP], and Université Paris Descartes - Paris 5 ( UPD5 )

Subjects
metabolomics, biomarkers, prediction, type 2 diabetes, cohort, diabète de type 2, [ SDV.AEN ] Life Sciences [q-bio]/Food and Nutrition, type ii diabetes, [SDV.AEN] Life Sciences [q-bio]/Food and Nutrition, cohorte, Alimentation et Nutrition, Food and Nutrition, biomarker, biomarqueur, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, métabolomique
Abstract

The trajectory and underlying mechanisms of human health are determined by a complex interplay between intrinsic and extrinsic factors. Its evolution is a continuum of transitions, involving multifaceted processes at multiple levels and there is an urgent need for integrative biomarkers that can characterize and predict health status evolution. The objective of the present study was to identify accurate and robust multidimensional markers, predictive of type 2 diabetes (T2D). A case-control approach was used within the French population-based cohort GAZEL (n~20,000) [1]. Male overweight subjects (n=112, 25≤BMI

Published
2016

49. Two complementary metabolomics studies to identify biomarkers of banana intake

Author

Lyan, Bernard, Pétéra, Mélanie, Dalle, Céline, Pujos-Guillot, Estelle, Durand, Stéphanie, Morand, Christine, Touvier, Mathilde, Dragsted, Laars Ove, Manach, Claudine, and Vazquez Manjarrez, Natalia

Subjects
approche métabolomique, consommation alimentaire, étude clinique, biomarqueur, banane
Published
2016

50. Enrichissement de la base de données spectrale peakforest en LC-MS

Author

Lyan, Bernard, Joly, Charlotte, Paulhe, Nils, Giacomoni, Franck, Thévenot, Etienne, Junot, C., Pujos-Guillot, Estelle, and Emery, Sylvain

Subjects
maladie chronique, Alimentation et Nutrition, Chimie analytique, Food and Nutrition, PeakForest, LC MSMS, identification metabolites, base de données spectrales, banque de données, Analytical chemistry, analyse métabolomique
Abstract

L’analyse métabolomique non ciblée est une approche puissante permettant la caractérisation du phénotype métabolique lié aux développements de maladies chroniques. L’identification des biomarqueurs qui y sont associés est devenue un enjeu majeur pour ce type d’approche. Il existe aujourd’hui un très large panel de banques de données en métabolomique, pouvant aider lors de cette étape d’identification, telles que MassBank, HMDB ou Lipidmaps, mais ces bases n’intègrent pas de données chromatographiques alors que le temps de rétention peut être un paramètre contribuant largement dans l’identification de molécules (Sumner et al, 2014). La base de données PeakForest est une banque de données de spectres de référence dédiée à l’annotation des données métabolomiques. Plus de 1000 composés standards (métabolites endogènes déjà décrits dans les biofluides) ont été analysés en LC-HRMS (Orbitrap, QTof) et selon des méthodes chromatographiques complémentaires au sein des quatre plateformes du consortium MetaboHUB. L’implémentation de PeakForest est réalisé via des fichiers ‘template’ qui intègrent les metadata et les peaklists annotées. L’originalité de cette base est qu’elle intègre également les conditions chromatographiques ainsi que les temps de rétention de chaque molécule, ce qui permet d’intégrer ce paramètre dans les requêtes. L’objectif sera de pouvoir utiliser cette ressource pour une annotation automatique des jeux de données. C’est pourquoi la base de données PeakForest est utilisable via des outils Galaxy, bientôt intégrés au sein de la plate-forme web Galaxy W4M (Workflow4Metabolomics ; Giacomoni et al, 2015). Une preuve de concept de l’utilisation de cet outil a été réalisée pour l’annotation d’un échantillon de plasma de référence du NIST. Au total plus de 70 métabolites ont été confirmés avec un score égal à 5 (Sumner et al, 2014). Ces résultats pourront à terme être intégrés au sein de PeakForest après curation effectuée par des experts dans le but de valider les résultats. Des données MS/MS viendront également complémenter la base. Cet outil dédié à l’annotation de métabolites en haut débit contribuera donc à terme à enrichir la caractérisation des métabolomes de différents systèmes biologiques

Published
2016

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