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1. The Plasma Oxylipin Signature Provides a Deep Phenotyping of Metabolic Syndrome Complementary to the Clinical Criteria

Author

Dalle, Céline, Tournayre, Jérémy, Mainka, Malwina, Basiak-Rasała, Alicja, Pétéra, Mélanie, Lefèvre-Arbogast, Sophie, Dalloux-Chioccioli, Jessica, Deschasaux-Tanguy, Mélanie, Lécuyer, Lucie, Kesse-Guyot, Emmanuelle, Fezeu, Léopold K, Hercberg, Serge, Galan, Pilar, Samieri, Cécilia, Zatońska, Katarzyna, Calder, Philip C, Hjorth, Mads Fiil, Astrup, Arne, Mazur, André, Bertrand-Michel, Justine, Schebb, Nils Helge, Szuba, Andrzej, Touvier, Mathilde, Newman, John W, and Gladine, Cécile

Subjects
Nutrition, Clinical Research, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Cardiovascular Diseases, Case-Control Studies, Humans, Metabolic Syndrome, Oxylipins, oxylipins, lipid mediators, lipidomics, metabolic syndrome, metabolic phenotyping, Other Chemical Sciences, Genetics, Other Biological Sciences, Chemical Physics
Abstract

Metabolic syndrome (MetS) is a complex condition encompassing a constellation of cardiometabolic abnormalities. Oxylipins are a superfamily of lipid mediators regulating many cardiometabolic functions. Plasma oxylipin signature could provide a new clinical tool to enhance the phenotyping of MetS pathophysiology. A high-throughput validated mass spectrometry method, allowing for the quantitative profiling of over 130 oxylipins, was applied to identify and validate the oxylipin signature of MetS in two independent nested case/control studies involving 476 participants. We identified an oxylipin signature of MetS (coined OxyScore), including 23 oxylipins and having high performances in classification and replicability (cross-validated AUCROC of 89%, 95% CI: 85-93% and 78%, 95% CI: 72-85% in the Discovery and Replication studies, respectively). Correlation analysis and comparison with a classification model incorporating the MetS criteria showed that the oxylipin signature brings consistent and complementary information to the clinical criteria. Being linked with the regulation of various biological processes, the candidate oxylipins provide an integrative phenotyping of MetS regarding the activation and/or negative feedback regulation of crucial molecular pathways. This may help identify patients at higher risk of cardiometabolic diseases. The oxylipin signature of patients with metabolic syndrome enhances MetS phenotyping and may ultimately help to better stratify the risk of cardiometabolic diseases.

Published
2022

2. Harmonized procedures lead to comparable quantification of total oxylipins across laboratories

Author

Mainka, Malwina, Dalle, Céline, Pétéra, Mélanie, Dalloux-Chioccioli, Jessica, Kampschulte, Nadja, Ostermann, Annika I, Rothe, Michael, Bertrand-Michel, Justine, Newman, John W, Gladine, Cécile, and Schebb, Nils Helge

Subjects
Medical Biochemistry and Metabolomics, Biomedical and Clinical Sciences, Clinical Research, Calibration, Chromatography, Liquid, Humans, Oxylipins, Quality Control, Tandem Mass Spectrometry, eicosanoids, lipidomics, liquid chromatography, mass spectrometry, quantitation, harmonization, plasma lipids, interlaboratory comparison, oxidized fatty acids, lipid mediators, Biochemistry and Cell Biology, Biochemistry & Molecular Biology, Biochemistry and cell biology, Medical biochemistry and metabolomics
Abstract

Oxylipins are potent lipid mediators involved in a variety of physiological processes. Their profiling has the potential to provide a wealth of information regarding human health and disease and is a promising technology for translation into clinical applications. However, results generated by independent groups are rarely comparable, which increases the need for the implementation of internationally agreed upon protocols. We performed an interlaboratory comparison for the MS-based quantitative analysis of total oxylipins. Five independent laboratories assessed the technical variability and comparability of 133 oxylipins using a harmonized and standardized protocol, common biological materials (i.e., seven quality control plasmas), standard calibration series, and analytical methods. The quantitative analysis was based on a standard calibration series with isotopically labeled internal standards. Using the standardized protocol, the technical variance was within ±15% for 73% of oxylipins; however, most epoxy fatty acids were identified as critical analytes due to high variabilities in concentrations. The comparability of concentrations determined by the laboratories was examined using consensus value estimates and unsupervised/supervised multivariate analysis (i.e., principal component analysis and partial least squares discriminant analysis). Interlaboratory variability was limited and did not interfere with our ability to distinguish the different plasmas. Moreover, all laboratories were able to identify similar differences between plasmas. In summary, we show that by using a standardized protocol for sample preparation, low technical variability can be achieved. Harmonization of all oxylipin extraction and analysis steps led to reliable, reproducible, and comparable oxylipin concentrations in independent laboratories, allowing the generation of biologically meaningful oxylipin patterns.

Published
2020

3. Create, run, share, publish, and reference your LC-MS, FIA-MS, GC-MS, and NMR data analysis workflows with the Workflow4Metabolomics 3.0 Galaxy online infrastructure for metabolomics

Author

Guitton, Yann, Tremblay-Franco, Marie, Corguillé, Gildas Le, Martin, Jean-François, Pétéra, Mélanie, Roger-Mele, Pierrick, Delabrière, Alexis, Goulitquer, Sophie, Monsoor, Misharl, Duperier, Christophe, Canlet, Cécile, Servien, Remi, Tardivel, Patrick, Caron, Christophe, Giacomoni, Franck, and Thévenot, Etienne

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Metabolomics is a key approach in modern functional genomics and systems biology. Due to the complexity of metabolomics data, the variety of experimental designs, and the variety of existing bioinformatics tools, providing experimenters with a simple and efficient resource to conduct comprehensive and rigorous analysis of their data is of utmost importance. In 2014, we launched the Workflow4Metabolomics (W4M, http://workflow4metabolomics.org) online infrastructure for metabolomics built on the Galaxy environment, which offers user-friendly features to build and run data analysis workflows including preprocessing, statistical analysis, and annotation steps. Here we present the new W4M 3.0 release, which contains twice as many tools as the first version, and provides two features which are, to our knowledge, unique among online resources. First, data from the four major metabolomics technologies (i.e., LC-MS, FIA-MS, GC-MS, and NMR) can be analyzed on a single platform. By using three studies in human physiology, alga evolution, and animal toxicology, we demonstrate how the 40 available tools can be easily combined to address biological issues. Second, the full analysis (including the workflow, the parameter values, the input data and output results) can be referenced with a permanent digital object identifier (DOI). Publication of data analyses is of major importance for robust and reproducible science. Furthermore, the publicly shared workflows are of high-value for e-learning and training. The Workflow4Metabolomics 3.0 e-infrastructure thus not only offers a unique online environment for analysis of data from the main metabolomics technologies, but it is also the first reference repository for metabolomics workflows., Comment: The International Journal of Biochemistry \& Cell Biology, Elsevier, 2017

Published
2017
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4. Workflow4Metabolomics (W4M): a user-friendly metabolomics platform for analysis of MS and NMR data

Author

Delporte, Cédric, Tremblay-Franco, Marie, Guitton, Y., Canlet, Cecile CC, Weber, Ralf J. M., Hecht, Helge, Price, Elliott James, Klanova, Jana, Joly, Charlotte, Dalle, Céline, Saint-Vanne, Julien, Thévenot, Etienne E.A., Schmitz, Isabelle, Chéreau, Sylvain, Dechaumet, Sylvain, Diémé, Binta, Giacomoni, Franck FG, Le Corguillé, Gildas G C, Pétéra, Mélanie MP, Souard, Florence, Delporte, Cédric, Tremblay-Franco, Marie, Guitton, Y., Canlet, Cecile CC, Weber, Ralf J. M., Hecht, Helge, Price, Elliott James, Klanova, Jana, Joly, Charlotte, Dalle, Céline, Saint-Vanne, Julien, Thévenot, Etienne E.A., Schmitz, Isabelle, Chéreau, Sylvain, Dechaumet, Sylvain, Diémé, Binta, Giacomoni, Franck FG, Le Corguillé, Gildas G C, Pétéra, Mélanie MP, and Souard, Florence

Abstract

info:eu-repo/semantics/inPress

Published
2024

6. 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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10. A project-scale map of metadata to improve future data management

Author

Pétéra, Mélanie, Cabasson, Cecile, Comte, Blandine, Duperier, Christophe, Filangi, Olivier, Prigent, Sylvain, Pujos-Guillot, Estelle, Giacomoni, Franck, Plateforme Exploration du Métabolisme (PFEM), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-MetaboHUB-Clermont, MetaboHUB-MetaboHUB, Biologie du fruit et pathologie (BFP), Université de Bordeaux (UB)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Plateforme Bordeaux Metabolome, Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-MetaboHUB-Bordeaux, Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Université de Rennes (UR)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Rennes Angers, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), ANR-11-INBS-0010,METABOHUB,Développement d'une infrastructure française distribuée pour la métabolomique dédiée à l'innovation(2011), Petera, Mélanie, and Développement d'une infrastructure française distribuée pour la métabolomique dédiée à l'innovation - - METABOHUB2011 - ANR-11-INBS-0010 - INBS - VALID

Subjects
[CHIM.OTHE] Chemical Sciences/Other, Gestion de données de la recheche, [CHIM.OTHE]Chemical Sciences/Other, Métabolomique, FAIR, Méta-données
Abstract

International audience; Today, the intra-lab application of best practices in the metabolomics field usually guarantees an adequate data exploitation within a single lab. However, the growing interest in multi-analyses designs (e.g. complementary analytical platforms, variety of matrices, multi-omics), as well as the need of data sharing and reuse, increase the difficulty of data management. Indeed, managing the multiplicity and the heterogeneity of information involved is required to achieve relevant knowledge extraction from metabolomics data. Within the MetaboHUB national infrastructure, one objective is to optimize data handling, especially metadata, to facilitate large-scale analyses, multi-platforms studies, and data FAIRisation (Findability, Accessibility, Interoperability, Reusability). In particular, this fits in the MetaboHUB scientific roadmap that promotes the open science development in the field of metabolomics.In the context of metabolomic and lipidomic studies, data production and analysis come along with a large diversity of metadata (data of the data). To identify clearly-defined bottlenecks and targets for future improvement in data management, the objective of this work was to build a metadata map at the scale of a scientific project. Aiming for completeness, this map was constructed in a collaborative and multidisciplinary way involving chemists, biologists, data stewards as well as computer scientists, combining their respective experience and knowledge. Based on the resulting metadata map, targets (areas and topics) to be further investigated were identified, enabling the construction of transversal working groups at the consortium scale. In particular, this work enables to focus efforts on clearly defined issues to improve standardisation of practices regarding data management and metadata documentation. In conclusion, this collaborative map construction has been shown to be an efficient tool to draw a clear « where do we stand / where do we go » picture inside a national infrastructure like MetaboHUB regarding project-scale metadata. This facilitates the definition of a precise data management. Such an approach could be translated within other infrastructures, consortia and/or communities.

Published
2022

11. Update on technological developments and opportunities with Workflow4Metabolomics

Author

Pavot, Lain, Pétéra, Mélanie, Paulhe, Nils, Guitton, Yann, Le Corguillé, Gildas, Saint-Vanne, Julien, Tremblay-Franco, Marie, Canlet, Cécile, Martin, Jean-François, Weber, Ralf, Delporte, Cédric, Souard, Florence, Dalle, Céline, Giacomoni, Franck, Plateforme Exploration du Métabolisme (PFEM), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-MetaboHUB-Clermont, MetaboHUB-MetaboHUB, 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), ABiMS - Informatique et bioinformatique = Analysis and Bioinformatics for Marine Science (ABIMS), Fédération de recherche de Roscoff (FR2424), Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), ToxAlim (ToxAlim), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), 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 Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Metatoul AXIOM (E20 ), 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)-ToxAlim (ToxAlim), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Ecole d'Ingénieurs de Purpan (INP - PURPAN), University of Birmingham [Birmingham], Université libre de Bruxelles (ULB), Institut de Recherche Biomédicale des Armées [Brétigny-sur-Orge] (IRBA), Plateforme Exploration du Métabolisme-MetaboHUB (PFEM-MetaboHUB), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), 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 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)-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)-ToxAlim (ToxAlim), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Institut de Recherche Biomédicale des Armées (IRBA), and Petera, Mélanie

Subjects
Métabolomique non ciblée, [CHIM.ANAL] Chemical Sciences/Analytical chemistry, Galaxy web interface, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, [CHIM.CHEM] Chemical Sciences/Cheminformatics, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Traitement de données, [CHIM.CHEM]Chemical Sciences/Cheminformatics, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract

International audience; IntroductionMetabolomics data analysis is a complex and multistep process, which is constantly evolving with the development of new analytical technologies, mathematical methods, bioinformatics tools and databases. The Workflow4Metabolomics (W4M) infrastructure[1] provides tools in a single online web interface (through Galaxy[1]). During the last two years, W4M has evolved with new upgrades for LC-MS, LC-MSMS, GC-MS and NMR pipelines, including preprocessing, quality control, statistical analysis and annotation tools. W4M also proposes new community resources promoting open science in metabolomics.Technological and methodological innovationW4M major updates include: Specific Galaxy interactive tools with NMRPro[2] for NMR spectra visualization and Xseeker for visualization and annotation of LC-MSMS data (In collaboration with CHOPIN ANR project[3]); An improved MSMS pipeline; New annotation tools suite allowing connexion with PeakForest[4], a new spectral data manager infrastructure for laboratories; New functionalities regarding mixed model computation for repeated measure designs, and improvements in annotation of complex mixture bidimensional NMR spectra. New training resources through the Galaxy Training Network (GTN) are also proposed, and Training Infrastructure as a Service (TIass) is available through the new usegalaxy.fr host.Results and impactW4M’s improvements increase raw data input management and LC/GC-MS(MS) workflow efficiency. We highlight how current advances, along with community training as through the yearly international school Workflow4Experimenters, contribute to open data analysis practices worldwide. In addition the W4M organization on Github repository aims to review code, annotate tools and propose a showcase for contributors from the metabolomics community.References[1] Giacomoni F., Le Corguillé et al (2015). Workflow4Metabolomics: A collaborative research infrastructure for computational metabolomics. Bioinformatics 2015, May 1;31(9):1493-5. doi: 10.1093/bioinformatics/btu813[2] Mohamed A, Nguyen CH, Mamitsuka H. NMRPro: an integrated web component for interactive processing and visualization of NMR spectra. Bioinformatics. 2016 Jul 1;32(13):2067-8. doi: 10.1093/bioinformatics/btw102. Epub 2016 Feb 26. PMID: 27153725.[3] https://anr.fr/ProjetIA-16-RHUS-0007[4] Paulhe, N., Canlet, C., Damont, A. et al. PeakForest: a multi-platform digital infrastructure for interoperable metabolite spectral data and metadata management. Metabolomics 18, 40 (2022). doi: 10.1007/s11306-022-01899-3[5] Afgan E. et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update. Nucleic Acids Res. 2016 Jul 8;44(W1):W3-W10. doi: 10.1093/nar/gkw343

Published
2022

14. Oxylipin profiling identifies a mechanistic signature of metabolic syndrome: results from two independent cohorts

Author

Dalle, Céline, primary, Tournayre, Jérémy, additional, Mainka, Malwina, additional, Basiak-Rasała, Alicja, additional, Pétéra, Mélanie, additional, Lefèvre-Arbogast, Sophie, additional, Dalloux-Chioccioli, Jessica, additional, Deschasaux-Tanguy, Mélanie, additional, Lécuyer, Lucie, additional, Kesse-Guyot, Emmanuelle, additional, Fezeu, Léopold, additional, Hercberg, Serge, additional, Galan, Pilar, additional, Samieri, Cécilia, additional, Zatońska, Katarzyna, additional, Calder, Philip C., additional, Hjorth, Mads Fiil, additional, Astrup, Arne, additional, Mazur, André, additional, Bertrand-Michel, Justine, additional, Schebb, Nils H., additional, Szuba, Andrzej, additional, Touvier, Mathilde, additional, Newman, John W., additional, and Gladine, Cécile, additional

Published
2022
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15. Developments and opportunities with Workflow4Metabolomics

Author

Pavot, Lain, Pétéra, Mélanie, Paulhe, Nils, Guitton, Yann, Le Corguillé, Gildas, Saint-Vanne, Julien, Tremblay-Franco, Marie, Canlet, Cécile, Martin, Jean-François, Weber, Ralf, Delporte, Cédric, Souard, Florence, Dalle, Céline, Giacomoni, Franck, LESUR, Hélène, Unité de Nutrition Humaine (UNH), 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), ABiMS - Informatique et bioinformatique = Analysis and Bioinformatics for Marine Science (ABIMS), Fédération de recherche de Roscoff (FR2424), Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), ToxAlim (ToxAlim), 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 Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Metatoul AXIOM (E20 ), MetaboHUB-MetaToul, 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)-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)-ToxAlim (ToxAlim), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), University of Birmingham [Birmingham], Faculté de Pharmacie [Bruxelles] (ULB), Université libre de Bruxelles (ULB), Institut de Recherche Biomédicale des Armées (IRBA), 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 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)-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)-ToxAlim (ToxAlim), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Ecole d'Ingénieurs de Purpan (INP - PURPAN), and Institut de Recherche Biomédicale des Armées [Brétigny-sur-Orge] (IRBA)

Subjects
NMR pipelines, [SDV] Life Sciences [q-bio], [SDV]Life Sciences [q-bio], New W4M LC-MS, LC-MSMS, GC-MS, [PHYS.PHYS.PHYS-DATA-AN] Physics [physics]/Physics [physics]/Data Analysis, Statistics and Probability [physics.data-an], [PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis, Statistics and Probability [physics.data-an]
Abstract

International audience; Metabolomics data analysis is a complex, multistep process, constantly evolving with the development of new analytical technologies, mathematical methods, bioinformatics tools and databases. Workflow4Metabolomics[1] is a collaborative portal dedicated to metabolomics data processing, analysis and annotation for the Metabolomics community. In the latest version of W4M, the core team proposes new upgrades for LC-MS, GC-MS and NMR pipelines, including new preprocessing steps, as well as enhancement of statistical analysis and annotation tools. W4M aims to promote open science in Metabolomics and facilitate knowledge dissemination by providing community resources.

Published
2022

16. Spot urine collection: A valid alternative to total urine collection for metabolomic studies in dairy cattle

Author

Boudra, Hamid, Noziere, Pierre, Cantalapiedra-Hijar, Gonzalo, Traïkia, Mounir, Martin, Jean-Francois, Pétéra, Mélanie, Lagree, Marie, DOREAU, M., Morgavi, Diego, Unité Mixte de Recherche sur les Herbivores - UMR 1213 (UMRH), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Unité de Nutrition Humaine (UNH), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Clermont Auvergne (UCA), Metatoul AXIOM (E20 ), MetaboHUB-MetaToul, MetaboHUB-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), 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)-ToxAlim (ToxAlim), Université de Toulouse (UT)-Ecole d'Ingénieurs de Purpan (INP - PURPAN), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Ecole d'Ingénieurs de Purpan (INP - PURPAN), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), ToxAlim (ToxAlim), This work was funded by the seventh EU framework project 'Innovative and practical management approaches to reduce nitrogen excretion by ruminants (REDNEX),'FP7, KBB-2007-1., European Project: 211606,EC:FP7:KBBE,FP7-KBBE-2007-1,REDNEX(2008), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Analyse de Xénobiotiques, Identification, Métabolisme (E20 Metatoul-AXIOM), 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-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), 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), 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)-ToxAlim (ToxAlim), 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 Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and 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)

Subjects
Rumen, Nitrogen, [SDV]Life Sciences [q-bio], lactating dairy cow, Animal Feed, metabolomics, urine, nitrogen diet, Diet, spot collection, Milk, Metabolome, Animals, Lactation, Cattle, Female, Urine Specimen Collection
Abstract

International audience; Urine is a highly suitable biological matrix for metabolomics studies. Total collection for 24-h periods is the gold standard as it ensures the presence of all metabolites excreted throughout the day. However, in animal studies, it presents limitations related to animal welfare and also due to alterations of the metabolome originating from the use of acid for preventing microbial growth or microbial contamination. In this study, we investigated whether spot urine collection is a practical alternative to total collection for metabolomic studies in lactating cows. For this purpose, we collected urine samples from 4 lactating Holstein cows fed 4 diets in a 4 × 4 Latin square design. Urine was collected for 24 h using a collecting device (i.e., total collection) or collected once per day 4 h after the morning feeding (i.e., spot urine collection). Dietary treatments differed by the amount of nitrogen content (high vs. low) and by the nature of the energy (starch vs. fiber). Urine metabolome was analyzed by 2 untargeted complementary methods, nuclear magnetic resonance and hydrophilicinteraction liquid chromatography (HILIC) coupled to a time-of-flight mass spectrometer, and by 1 targeted method, HILIC–tandem mass spectrometry. Although sampling technique had an effect on the abundance of metabolites detected, spot urine samples were equally capable of showing differences in urine metabolomethan samples from total collection. When considering nitrogen levels in the diet, the robustness and precision for discriminating high- and low-nitrogen dietswas equally achieved with both sampling techniques. A total of 22 discriminant metabolites associated with the N level of diets were identified from untargeted HILIC coupled to a time-of-flight mass spectrometer (n = 9) and nuclear magnetic resonance (n = 11), and 2 from targeted HILIC–tandem mass spectrometry. Alternatively, starch or fiber in the diet induced less changes in the metabolome that were not clearly discriminated independently of the sampling technique. We concluded that spot urine collection can successfully reveal differences in the urine metabolome elicited by dietary N levels and be used as a substitute of total urinary 24-h collection for metabolomic studies.

Published
2022

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

Author

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

Published
2021
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19. 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

20. A new tool for multi-block PLS discriminant analysis: application to metabolomic data in systems epidemiology

Author

Brandolini-Bunlon, Marion, Pétéra, Mélanie, Gaudreau, Pierrette, Comte, Blandine, Bougeard, Stéphanie, Pujos-Guillot, Estelle, 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), MetaboHUB, 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), Laboratoire de Ploufragan-Plouzané-Niort [ANSES], and Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES)

Subjects
Multi-block PLS discriminant analysis, epidemiology, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, [INFO.INFO-OS]Computer Science [cs]/Operating Systems [cs.OS], metabolomics, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2020

21. 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

22. Machine learning-based classification to improve Gas Chromatography-Mass spectrometry data processing

Author

Gloaguen, Yoann, Borgsmüller, Nico, Opialla, Tobias, Blanc, Eric, Sicard, Emilie, Royer, Anne Lise, Le Bizec, Bruno, Durand, Stéphanie, Migné, Carole, Pétéra, Mélanie, Pujos-Guillot, Estelle, Giacomoni, Franck, Guitton, Yann, Beule, Dieter, Kirwan, Jennifer, Core Unit Bioinformatics, Berlin Institute of Health (BIH), Berlin Institute of Health Metabolomics Platform, DEU, Max Delbrück Center for Molecular Medicine, Charité - Universitätsmedizin Berlin / Charite - University Medicine Berlin, Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), MetaboHUB, Laboratoire d'étude des Résidus et Contaminants dans les Aliments (LABERCA), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS), Max Delbrück Center for Molecular Medicine [Berlin] (MDC), Helmholtz-Gemeinschaft = Helmholtz Association, Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], 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), Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and École nationale vétérinaire, agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
[SDV.OT]Life Sciences [q-bio]/Other [q-bio.OT], [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [CHIM.OTHE]Chemical Sciences/Other
Abstract

Methodological & Technological developments Methodological & Technological developmentsMethodological & Technological developments; IntroductionLack of reliable peak detection impedes automated analysis of large-scale gas chromatography-mass spectrometry (GCMS) metabolomics datasets. Performance and outcome of individual peak-picking algorithms can differ widely depending on both algorithmic approach and parameters, as well as data acquisition method. Therefore, comparing and contrasting between algorithms is difficult.Technological and methodological innovationWe present part of the work published in [1] and implemented in our workflow for improved peak picking (WiPP),focusing on the use of machine learning-based classification to optimize and improve different steps of the common GC-MS metabolomics data processing workflow. Our approach evaluates the quality of detected peaks using a machine learning based classification scheme based on seven peak classes. The quality information returned by the classifier for each individual peak is merged with results from different peak detection algorithms to create one final high-quality peak set for immediate down-stream analysis.Results and impactWe benchmarked our workflow to standard compound mixes and a complex biological dataset, demonstrating that peak detection is improved. Furthermore, the approach can provide an impartial performance comparison of different peak picking algorithms. We also discuss the applicability of the approach to liquid chromatography-mass spectrometry data.References[1] Gloaguen, Y.; Borgsmüller, N. et al. WiPP: Workflow for Improved Peak Picking for Gas Chromatography-MassSpectrometry (GC-MS) Data. Metabolites 2019, 9, 171.

Published
2020

23. Technological developments and opportunities with Workflow4Metabolomics

Author

Pétéra, Mélanie, Canlet, Cecile, Dallet, Romain, Guitton, Yann, Le Corguillé, Gildas, Martin, Jean-François, Monnerie, Stéphanie, Pujos-Guillot, Estelle, Saint-Vanne, Julien, Tremblay Franco, Marie, W4M Core Team, ., Giacomoni, Franck, MetaboHUB, 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), Analyse de Xénobiotiques, Identification, Métabolisme (E20 Metatoul-AXIOM), MetaToul-MetaboHUB, Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), 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-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)-ToxAlim (ToxAlim), 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 Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), ABiMS - Informatique et bioinformatique = Analysis and Bioinformatics for Marine Science (FR2424), Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC), Laboratoire d'étude des Résidus et Contaminants dans les Aliments (LABERCA), Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), 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), Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), ToxAlim (ToxAlim), 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), Analytical Platform for Metabolomics and Toxicology (MetaToul-AXIOM), ABiMS, Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS), Institut National de Recherche en Agriculture, Alimentation et Environnement (INRAE), Ecole Nationale Vétérinaire Agroalimentaire et de l'Alimentation Nantes Atlantique (ONIRIS), Metatoul AXIOM (E20 ), MetaboHUB-MetaToul, MetaboHUB-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), 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)-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)-ToxAlim (ToxAlim), Université de Toulouse (UT)-Ecole d'Ingénieurs de Purpan (INP - PURPAN), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Ecole d'Ingénieurs de Purpan (INP - PURPAN), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), ABiMS - Informatique et bioinformatique = Analysis and Bioinformatics for Marine Science (ABIMS), Fédération de recherche de Roscoff (FR2424), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and École nationale vétérinaire, agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
[SDV.OT]Life Sciences [q-bio]/Other [q-bio.OT], [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract

Technological developments and opportunities with Workflow4Metabolomics. European RFMF Metabomeeting 2020

Published
2020

25. Machine learning-based classification to improve Gas Chromatography-Mass spectrometry data processing

Author

Borgsmüller, Nico, Opialla, Tobias, Blanc, Eric, Sicard, Emilie, Royer, Anne Lise, Le Bizec, Bruno, Durand, Stéphanie, Migné, Carole, Pétéra, Mélanie, Pujos-Guillot, Estelle, Giacomoni, Franck, Guitton, Yann, Beule, Dieter, Kirwan, Jennifer, and Gloaguen, Yoann

Subjects
Bioinformatics, Autre (Chimie), Bio-informatique, Other, Autre (Sciences du Vivant)
Abstract

Introduction Lack of reliable peak detection impedes automated analysis of large-scale gas chromatography-mass spectrometry (GCMS) metabolomics datasets. Performance and outcome of individual peak-picking algorithms can differ widely depending on both algorithmic approach and parameters, as well as data acquisition method. Therefore, comparing and contrasting between algorithms is difficult. Technological and methodological innovation We present part of the work published in [1] and implemented in our workflow for improved peak picking (WiPP), focusing on the use of machine learning-based classification to optimize and improve different steps of the common GC-MS metabolomics data processing workflow. Our approach evaluates the quality of detected peaks using a machine learning based classification scheme based on seven peak classes. The quality information returned by the classifier for each individual peak is merged with results from different peak detection algorithms to create one final high-quality peak set for immediate down-stream analysis. Results and impact We benchmarked our workflow to standard compound mixes and a complex biological dataset, demonstrating that peak detection is improved. Furthermore, the approach can provide an impartial performance comparison of different peak picking algorithms. We also discuss the applicability of the approach to liquid chromatography-mass spectrometry data. References [1] Gloaguen, Y.; Borgsmüller, N. et al. WiPP: Workflow for Improved Peak Picking for Gas Chromatography-Mass Spectrometry (GC-MS) Data. Metabolites 2019, 9, 171.

Published
2020

26. Metabolomics data analysis I & II

Author

Le Corguillé, Gildas, Pétéra, Mélanie, Weber, Ralf, Lawson, Thomas, ABiMS - Informatique et bioinformatique = Analysis and Bioinformatics for Marine Science (FR2424), Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC), Centre National de la Recherche Scientifique (CNRS), Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Phenomene Centre Birmingham, University of Birmingham, School of Biosciences, ABiMS - Informatique et bioinformatique = Analysis and Bioinformatics for Marine Science (ABIMS), Fédération de recherche de Roscoff (FR2424), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), FR2424, ABiMS, Université Pierre et Marie Curie (Paris 6), Unité de Nutrition Humaine - Clermont Auvergne (UNH), and Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA)

Subjects
[SDV.OT]Life Sciences [q-bio]/Other [q-bio.OT], [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract

This workshop will introduce W4M and how to use it for metabolomics data analysis.This workshop will introduce W4M and how to use it for metabolomics data analysis.; Metabolomics data analysis is a complex, multistep process, which is constantly evolving with the development of new analytical technologies, mathematical methods, and bioinformatics tools and databases. The Workflow4Metabolomics (W4M) project aims to develop full LC/MS, GC/MS, FIA/MS and NMR pipelines using Galaxy framework for data analysis including preprocessing, normalization, quality control, statistical analysis and annotation steps.This workshop will introduce W4M and how to use it for metabolomics data analysis.

Published
2019

27. 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

28. A new tool for multi-block PLS discriminant analysis of metabolomic data: application to systems epidemiology

Author

Brandolini-Bunlon, Marion, Pétéra, Mélanie, Gaudreau, Pierrette, Comte, Blandine, Bougeard, Stephanie, Pujos-Guillot, Estelle, Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), MetaboHUB, 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 Universitaire de Montréal, Département de médecine, Centre Léon Bérard [Lyon], Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES), Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), and Centre de Recherche du CHUM

Subjects
multi block PLS discriminant analysis, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, epidemiology, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, metabolomics
Abstract

Metabolomics is a powerful phenotyping tool in nutrition and health research, generating massive and complex data that need dedicated treatments to enrich our knowledge of biological systems. In particular, to deeper investigate relations between environmental factors, phenotypes and metabolism, discriminant statistical analyses performed separately on metabolomic datasets, are often complemented by associations with metadata (anthropometric, clinical, nutritional and physical activity data…). Another relevant strategy is to perform a multi-block partial least squares discriminant analysis (MBPLSDA) that simultaneously analyses data available from different sources, allowing determining the importance of variables and variable blocks in discriminating groups of subjects, taking into account data structure in thematic blocks.In order to propose a full open-source standalone tool, the present objective was to develop an R package allowing all steps of MBPLSDA analysis for the joint analysis of metabolomic and additional data.The tool was based on the mbpls function of the ade4 R package, enriched with different functionalities, including some dedicated to discriminant analysis. Provided indicators help to determine the optimal number of components, to check the MBPLSDA model validity, and to evaluate the variability of its parameters and predictions. To illustrate the potential of the proposed tool and the associated procedure, MBPLSDA was applied to a real case study involving metabolomics, nutritional and clinical data from a human cohort.The availability of the different functionalities in a single R package allowed optimizing parameters for an efficient joint analysis of metabolomics and epidemiological data to obtain new insights into multidimensional phenotypes. In particular, we highlighted the impact of filtering the metabolomic variables beforehand, and the relevance of a MBPLSDA approach in comparison to a standard PLS-discriminant analysis method.

Published
2019

29. WiPP: Workflow for Improved Peak Picking for Gas Chromatography-Mass Spectrometry (GC-MS) Data

Author

Borgsmüller, N., Gloaguen, Y., Opialla, T., Blanc, E., Sicard, E., Royer, Anne-Lise, Le Bizec, Bruno, Durand, Stéphanie, Migné, Carole, Pétéra, Mélanie, Pujos-Guillot, Estelle, Giacomoni, Franck, Guitton, Yann, Beule, D., and Kirwan, J.

Subjects
Autre (Sciences de l'ingénieur), gas chromatography-mass spectrometry (GC-MS), machine learning, metabolomics, parameter optimisation, peak classification, peak detection, pre-processing workflow, support vector machine
Abstract

Lack of reliable peak detection impedes automated analysis of large-scale gas chromatography-mass spectrometry (GC-MS) metabolomics datasets. Performance and outcome of individual peak-picking algorithms can differ widely depending on both algorithmic approach and parameters, as well as data acquisition method. Therefore, comparing and contrasting between algorithms is difficult. Here we present a workflow for improved peak picking (WiPP), a parameter optimising, multi-algorithm peak detection for GC-MS metabolomics. WiPP evaluates the quality of detected peaks using a machine learning-based classification scheme based on seven peak classes. The quality information returned by the classifier for each individual peak is merged with results from different peak detection algorithms to create one final high-quality peak set for immediate down-stream analysis. Medium- and low-quality peaks are kept for further inspection. By applying WiPP to standard compound mixes and a complex biological dataset, we demonstrate that peak detection is improved through the novel way to assign peak quality, an automated parameter optimisation, and results in integration across different embedded peak picking algorithms. Furthermore, our approach can provide an impartial performance comparison of different peak picking algorithms. WiPP is freely available on GitHub (https://github.com/bihealth/WiPP) under MIT licence.

Published
2019

30. Diet-Related Metabolites Associated with Cognitive Decline Revealed by Untargeted Metabolomics in a Prospective Cohort

Author

Low, Dorrain, Lefèvre-Arbogast, Sophie, Dominguez-Gonzalez, Raúl, Urpi Sarda, Mireia, Micheau, Pierre, Pétéra, Mélanie, Centeno, Delphine, Durand, Stéphanie, Pujos-Guillot, Estelle, Korosi, Aniko, Lucassen, Paul J., Aigner, Ludwig., Proust-Lima, Cecile, Manach, Claudine, Hejblum, Boris P., Helmer, Catherine, Andres-Lacueva, Cristina, Thuret, Sandrine, Samieri, Cecilia, Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Nanyang Technological University [Singapour], Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de la Santé et de la Recherche Médicale (INSERM), University of Barcelona, Instituto de Salud Carlos III [Madrid] (ISC), Plateforme Exploration du Métabolisme (PFEM), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-MetaboHUB-Clermont, MetaboHUB-MetaboHUB, University of Amsterdam [Amsterdam] (UvA), Paracelsus Medizinische Privatuniversität = Paracelsus Medical University (PMU), Statistics In System biology and Translational Medicine (SISTM), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)- Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), King‘s College London, Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), Instituto de Salud Carlos III (ISC), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Epidémiologie et Biostatistique [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Epidémiologie et Biostatistique [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Structural and Functional Plasticity of the nervous system (SILS, FNWI)

Subjects
Male, Aging, Cognitive decline, Coffea, Coffee, Eating, Fish Products, Metabolites, Humans, Metabolomics, Cognitive Dysfunction, Longitudinal Studies, Research Articles, Aged, Aged, 80 and over, Dietary biomarkers, Untargeted metabolomics, Metabòlits, Diet, Blood, Case-Control Studies, Dieta, Dementia, Female, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, Blood Chemical Analysis, Research Article
Abstract

Scope Untargeted metabolomics may reveal preventive targets in cognitive aging, including within the food metabolome. Methods and results A case‐control study nested in the prospective Three‐City study includes participants aged ≥65 years and initially free of dementia. A total of 209 cases of cognitive decline and 209 controls (matched for age, gender, education) with slower cognitive decline over up to 12 years are contrasted. Using untargeted metabolomics and bootstrap‐enhanced penalized regression, a baseline serum signature of 22 metabolites associated with subsequent cognitive decline is identified. The signature includes three coffee metabolites, a biomarker of citrus intake, a cocoa metabolite, two metabolites putatively derived from fish and wine, three medium‐chain acylcarnitines, glycodeoxycholic acid, lysoPC(18:3), trimethyllysine, glucose, cortisol, creatinine, and arginine. Adding the 22 metabolites to a reference predictive model for cognitive decline (conditioned on age, gender, education and including ApoE‐ε4, diabetes, BMI, and number of medications) substantially increases the predictive performance: cross‐validated Area Under the Receiver Operating Curve = 75% [95% CI 70–80%] compared to 62% [95% CI 56–67%]. Conclusions The untargeted metabolomics study supports a protective role of specific foods (e.g., coffee, cocoa, fish) and various alterations in the endogenous metabolism responsive to diet in cognitive aging., The Three‐City cohort of older persons with a 12‐year follow‐up for cognition is leveraged to identify, in the serum of initially non‐demented participants, metabolites associated with subsequent cognitive decline. Untargeted metabolomics reveals a signature of 22 metabolites improving cognitive decline prediction. It includes diet‐derived metabolites (e.g., from coffee, citrus juice, cocoa, fish), as well as endogenous metabolites responsive to diet.

Published
2019

31. 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

32. 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

33. A new tool for multi-block PLS discriminant analysis of metabolomic data: application to systems epidemiology

Author

Pétéra, Mélanie, Gaudreau, Pierrette, Comte, Blandine, Bougeard, Stephanie, Pujos-Guillot, Estelle, and Brandolini-Bunlon, Marion

Subjects
Santé publique et épidémiologie, Chimie analytique, multi block PLS discriminant analysis, metabolomics, epidemiology, Analytical chemistry
Abstract

Metabolomics is a powerful phenotyping tool in nutrition and health research, generating massive and complex data that need dedicated treatments to enrich our knowledge of biological systems. In particular, to deeper investigate relations between environmental factors, phenotypes and metabolism, discriminant statistical analyses performed separately on metabolomic datasets, are often complemented by associations with metadata (anthropometric, clinical, nutritional and physical activity data…). Another relevant strategy is to perform a multi-block partial least squares discriminant analysis (MBPLSDA) that simultaneously analyses data available from different sources, allowing determining the importance of variables and variable blocks in discriminating groups of subjects, taking into account data structure in thematic blocks. In order to propose a full open-source standalone tool, the present objective was to develop an R package allowing all steps of MBPLSDA analysis for the joint analysis of metabolomic and additional data. The tool was based on the mbpls function of the ade4 R package, enriched with different functionalities, including some dedicated to discriminant analysis. Provided indicators help to determine the optimal number of components, to check the MBPLSDA model validity, and to evaluate the variability of its parameters and predictions. To illustrate the potential of the proposed tool and the associated procedure, MBPLSDA was applied to a real case study involving metabolomics, nutritional and clinical data from a human cohort. The availability of the different functionalities in a single R package allowed optimizing parameters for an efficient joint analysis of metabolomics and epidemiological data to obtain new insights into multidimensional phenotypes. In particular, we highlighted the impact of filtering the metabolomic variables beforehand, and the relevance of a MBPLSDA approach in comparison to a standard PLS-discriminant analysis method.

Published
2019

34. 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

35. 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

36. WiPP: Workflow for improved Peak Picking for Gas Chromatography-Mass Spectrometry (GC-MS) data

Author

Borgsmüller, Nico, primary, Gloaguen, Yoann, additional, Opialla, Tobias, additional, Blanc, Eric, additional, Sicard, Emilie, additional, Royer, Anne-Lise, additional, Le Bizec, Bruno, additional, Durand, Stéphanie, additional, Migné, Carole, additional, Pétéra, Mélanie, additional, Pujos-Guillot, Estelle, additional, Giacomoni, Franck, additional, Guitton, Yann, additional, Beule, Dieter, additional, and Kirwan, Jennifer, additional

Published
2019
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37. Identification of dietary modulators of cognitive function in ageing using metabolomics discovery

Author

Low Yanwen, Dorrain, Lefèvre-Arbogast, Sophie, Pétéra, Mélanie, Micheau, Pierre, Centeno, Delphine, Durand, Stéphanie, Proust-Lima, Cécile, Hejblum, Boris P., Urpi Sarda, Mireia, Gonzalez-Dominguez, Raul, Andres-Lacueva, Cristina, Thuret, Sandrine, Samieri, Cecilia, Manach, Claudine, Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), UMR 1219, Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Bordeaux, Nutrition and Food Science Department, University of Barcelona, Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institut, King‘s College London-King‘s College London, Centre de Recherche en Nutrition Humaine (CRNH). FRA., ProdInra, Archive Ouverte, Université de Bordeaux (UB), Unité de Nutrition Humaine - Clermont Auvergne (UNH), and Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA)

Subjects
[SDV.AEN] Life Sciences [q-bio]/Food and Nutrition, aliment d'origine végétale, prévention santé, food and beverages, déclin cognitif, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, profil métabolique
Abstract

Health Break SESSION 3 Cognition and Gut-BrainHealth Break SESSION 3Cognition and Gut-Brain; Identification of dietary modulators of cognitive function in ageing using metabolomics discovery. 8. International Conference on Polyphenols and Health ICPH 2017

Published
2017

38. Optimized selection process to identify a metabolic syndrome metabolomic/lipidomic signature in older adults of the NuAge cohort

Author

Lenuzza, Natacha, Thévenot, Etienne, Pétéra, Mélanie, Monnerie, Stéphanie, Morais, José A, Payette, Hélène, Gaudreau, Pierrette, Comte, Blandine, Pujos-Guillot, Estelle, Commissariat à l'énergie atomique et aux énergies alternatives (CEA), MetaboHUB, Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), McGill University = Université McGill [Montréal, Canada], Université de Sherbrooke (UdeS), Département de médecine, Le Centre de Recherche du Centre hospitalier de l' Université de Montréal, Plateforme d'Exploration du Métabolisme, European Union Geriatric Medicine Society (EUGMS). GBR., Plateforme Exploration du Métabolisme (PFEM), MetaboHUB-Clermont, and MetaboHUB-MetaboHUB-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Clermont Auvergne (UCA)

Subjects
[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: Metabolic syndrome (MetS) is characterized by a cluster of risk factors including obesity, metabolic dysregulations such as insulin resistance, hypertension, and dyslipidemia, raising the risk for type 2 diabetes development and its complications. It involves multifaceted processes at multiple levels that are still far from being understood. New tools are therefore necessary to bring new knowledge about MetS, better stratify populations and customise strategies for its prevention and/or reversal. Methods: The Quebec Longitudinal Study on Nutrition and Successful Aging (NuAge) regroups 853 men and 940 women, aged 68–82 at recruitment in 2003–2005 (T1) and followed up annually for three years (T2-T4). In the present study, a nested case-control study on MetS was designed to identify a metabolomic/lipidomic signature of MetS in older men, reflecting its phenotypic spectrum. An optimized participant selection strategy was developed based on presence and number of MetS criteria, including medication, their stability over 3 years, as well as the identification of outliers. Results: The final selection included 123 men, 61 cases and 62 controls, with similar age and partial overlap of values defining MetS. This design is necessary to precisely detect and estimate the amplitude of metabolic deviations among the massive data sets, at an individual metabolite level as well as for a multivariate description. Conclusion: This selection process, optimized to limit cofounding effects, will allow identifying specific metabolomic/lipidomic signatures along with significant features for sample classification. Thus, one complex molecular phenotyping will provide a new approach/tool for a better MetS stratification in elderly

Published
2017

39. A multilevel integrated approach for the identification of predictive markers of metabolic syndrome

Author

Pujos-Guillot, Estelle, Bertrand, Julien, Rambeau, Mathieu, Pétéra, Mélanie, Brandolini-Bunlon, Marion, Fernandes, Anthony, Matta, Joanne, Lévy-Marchal, Claire, Czernichow, Sébastien, and Comte, Blandine

Subjects
Santé publique et épidémiologie, Multilevel prediction, Metabolic syndrome, Metabolomics, Proteomics, Multidimensional biomarkers, Patient stratification, Alimentation et Nutrition, Food and Nutrition
Published
2017

40. Identification of dietary modulators of cognitive function in ageing using metabolomics discovery

Author

Lefèvre-Arbogast, Sophie, Pétéra, Mélanie, Micheau, Pierre, Centeno, Delphine, Durand, Stéphanie, Proust-Lima, Cécile, Hejblum, Boris, Urpi Sarda, Mireia, Gonzalez-Dominguez, Raul, Andres-Lacueva, Cristina, Thuret, Sandrine, Samieri, Cecilia, Manach, Claudine, and Low Yanwen, Dorrain

Subjects
aliment d'origine végétale, prévention santé, Alimentation et Nutrition, Food and Nutrition, déclin cognitif, profil métabolique
Published
2017

41. Untargeted metabolomic approach by GC-QTOF : From low to high resolution

Author

Pétéra, Mélanie, Migné, Carole, Paulhe, Nils, Guitton, Yann, Giacomoni, Franck, Durand, Stéphanie, and Pujos-Guillot, Estelle

Subjects
GQ-Q-Tof, métabolomique, spectrométrie de masse, traitement de données, Bioinformatics, Chimie analytique, Bio-informatique, Analytical chemistry
Abstract

Les approches métabolomiques non ciblées par GC-MS ont pu évoluer grâce au développement de nouveaux instruments de plus haute résolution, comme le GC-QToF. Cette approche est utilisée au laboratoire dans le cadre de projets scientifiques pour la recherche de biomarqueurs permettant la carac- térisation de phénotypes métaboliques. Une méthode d’analyse non ciblée pour la détermination de profils métaboliques de biofluides par GC- QToF a été adaptée d’une méthode basse résolution (Gao et al (1)) basée sur une double dérivation oximation /silylation. Cette technique, plus sensible et plus résolutive, nécessite des outils de traitement des données spéci- fiques dédiés. Aussi, nous avons dû adapter des outils développés par notre laboratoire pour le traitement de données métabolomiques à ce type de données. Ces outils comprennent l’extraction des données par xcms sous la plateforme Galaxy (W4M, (2)), ainsi que tout le workflow conduisant à l’annotation des ions extraits après filtration et correction des effets batch. Parallèlement, nous déployons une stratégie de déconvolution, à partir d’outils constructeurs afin de com- pléter les résultats obtenus sous Galaxy. A ce jour, les bibliothèques GC-MS (NIST, Golm, Massbank) restent très utilisées pour l’identification des métabolites mais ne contiennent aucun spectre avec des masses précises bien que certains provi- ennent de GC-EI-ToF. Par conséquent, nous constituons une bibliothèque interne en haute résolution avec des standards purs et en matrices biologiques qui alimentera la base de données PeakForest de l’infrastructure française MetaboHUB. La mesure des masses précises ainsi que le développement de nouveaux outils d’automatisation du traite- ment de données devraient permettre de lever certains verrous rencontrés dans la recherche de biomar- queurs concernant l’identification des métabolites.

Published
2017

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

Author

Pujos-Guillot, Estelle, Pétéra, Mélanie, Centeno, Delphine, Lyan, Bernard, Pietruszka, B, Santoro, A, Brzozowska, A, Franceschi, and Comte, Blandine

Subjects
effet de l'âge, Santé publique et épidémiologie, sarcopenie, Alimentation et Nutrition, Food and Nutrition, méthode de prévention, biomarqueur
Abstract

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

43. 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

44. Applying an untargeted metabolomics approach using two complementary platforms for the discovery and validation of banana intake biomarkers

Author

Manach, Claudine, primary, Vázquez-Manjarrez, Natalia, additional, Weinert, Christopher, additional, Ulaszewska, Marynka, additional, Pétéra, Mélanie, additional, Mack, Carina, additional, Kulling, Sabine, additional, Achim, Bub, additional, Micheau, Pierre, additional, Centeno, Delphine, additional, Joly, Charlotte, additional, Durand, Stephanie, additional, Pujos-Guillot, Estelle, additional, and Dragsted, Lars, additional

Published
2018
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45. Correction to Exploration of Biological Markers of Feed Efficiency in Young Bulls

Author

Meale, Sarah J., primary, Morgavi, Diego P., additional, Cassar-Malek, Isabelle, additional, Andueza, Donato, additional, Ortigues-Marty, Isabelle, additional, Robins, Richard J., additional, Schiphorst, Anne-Marie, additional, Migné, Carole, additional, Pétéra, Mélanie, additional, Laverroux, Sophie, additional, Graulet, Benoit, additional, Boudra, Hamid, additional, and Cantalapiedra-Hijar, Gonzalo, additional

Published
2018
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46. Create, run, share, publish, and reference your LC–MS, FIA–MS, GC–MS, and NMR data analysis workflows with the Workflow4Metabolomics 3.0 Galaxy online infrastructure for metabolomics

Author

Guitton, Yann, primary, Tremblay-Franco, Marie, additional, Le Corguillé, Gildas, additional, Martin, Jean-François, additional, Pétéra, Mélanie, additional, Roger-Mele, Pierrick, additional, Delabrière, Alexis, additional, Goulitquer, Sophie, additional, Monsoor, Misharl, additional, Duperier, Christophe, additional, Canlet, Cécile, additional, Servien, Rémi, additional, Tardivel, Patrick, additional, Caron, Christophe, additional, Giacomoni, Franck, additional, and Thévenot, Etienne A., additional

Published
2017
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47. Contribution of an integrative multi-omic approach in the metabolic syndrome prediction: a nested case-control study. Systems Medicine, personalised health and therapy

Author

Pujos-Guillot, Estelle, Bertrand, Julien, Rambeau, Mathieu, Pétéra, Mélanie, Brandolini, Marion, Fernandes, Anthony, Matta, Joane, Levy-Marchal, Claire, Czernichow, Sebastien, Comte, Blandine, Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université, Plate-Forme d'Exploration du Métabolisme, Institut National de la Recherche Agronomique (INRA), Université de Montpellier (UM), Institut National de la Santé et de la Recherche Médicale (INSERM), Département Nutrition, Centre Hospitalier Universitaire Ambroise Paré (CHU Ambroise Paré), Plateforme Exploration du Métabolisme (PFEM), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-MetaboHUB-Clermont, and MetaboHUB-MetaboHUB

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

Acknowledgments: Project supported by the Fondation Francophone de Recherche sur le Diabète; International audience; Background: The rising worldwide prevalence of metabolic syndrome (MetS), a cluster of cardiometabolic risk factors of predictive of type 2 diabetes, relates largely to increasing obesity and sedentary but also to early metabolic life events. Objective: The objective of the study was to identify predictive biomarkers of evolution toward MetS 8 years later, and to bring new knowledge about this pathological state using a multidisciplinary approach in an at-risk population (subjects with small birth weight). Design: This case-control study (subjects free of MetS at baseline (n=92 born small vs n=76 born adequate for gestational age (SGA vs SGA)) was nested in the French community-based Haguenau cohort. The control group was randomly matched for age and sex. Serum signatures were determined and compared at baseline (20 years old) to determine predictive biomarkers using both untargeted mass spectrometry metabolomics and targeted proteomics using microarrays. Individual predictive models were first built using linear logistic regressions from the omics datasets. Metabolomic and proteomic data were finally integrated using random forest to determine whether multidimensional models improve prediction. Results: Univariate statistical analyses allowed identifying 93 discriminant metabolites and 47 proteins between cases and controls at baseline, with in both cases, specific gender differences. The resulting models based on either 4 metabolites or 4 proteins showed good performances: 22% misclassification on training set, 25% on validation set vs 11% misclassification on training set, 33% on validation set, respectively. Multi-omic data integration improved performance and robustness of the prediction (11% misclassification on training set, 8% on validation set). Correlation analyses with other data (anthropometric, biochemical) contributed to better understand the role of these biomarkers in the pathological processes, and therefore to evaluate their potential clinical value. Conclusion: These results should provide new tools to better stratify at-risk populations, and additional knowledge on MetS development

Published
2016

48. 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

49. Systems Metabolomics for Prediction of Metabolic Syndrome

Author

Pujos-Guillot, Estelle, primary, Brandolini, Marion, additional, Pétéra, Mélanie, additional, Grissa, Dhouha, additional, Joly, Charlotte, additional, Lyan, Bernard, additional, Herquelot, Éléonore, additional, Czernichow, Sébastien, additional, Zins, Marie, additional, Goldberg, Marcel, additional, and Comte, Blandine, additional

Published
2017
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50. Des protéines sanguines marqueurs de prédiction du syndrome métabolique ? Une étude cas-témoins nichée dans la cohorte Haguenau

Author

Bertrand, Julien, Pétéra, Mélanie, Fernandes, Angélo, Matta, J., Levy-Marchal, C., Czernichow, S, Pujos-Guillot, Estelle, Comte, Blandine, Unité de Nutrition Humaine (UNH), Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université-Institut National de la Recherche Agronomique (INRA), Centre de Recherche en Nutrition Humaine, 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 de Paris (UP), Institut National de la Santé et de la Recherche Médicale (INSERM), Département Nutrition, Centre Hospitalier Universitaire Ambroise Paré (CHU Ambroise Paré), Centre de Recherche en Nutrition Humaine (CRNH). Clermont-Ferrand, FRA., ProdInra, Migration, Institut National de la Recherche Agronomique (INRA)-Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université, and 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)

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

National audience; La nutrition foetale est maintenant reconnue comme un facteur de risque pour le développement du syndrome métabolique (SMet) et du diabète de type 2 à l’âge adulte. Cette étude cas-témoins nichée au sein de la cohorte Haguenau a pour objectif d’identifier des biomarqueurs précoces et/ou prédictifs du SMet, associés ou non à l’empreinte foetale par une approche protéomique ciblée. Elle utilise les données anthropométriques et sociales d’individus nés à la maternité d’Haguenau et identifiés avec ou non un petit poids de naissance ajusté pour l’âge gestationnel (petit poids=SGA, poids normal=AGA). Nous avons sélectionné des sujets SGA, sains à 20 ans (T1, n=56), ayant développé (SMet30, n=27) ou non (non SMet30, n=29) un SMet à 30 ans (T2). Une analyse protéomique ciblée a été réalisée sur 56 échantillons biologiques prélevés à T1 en utilisant des biopuces à protéines. Les résultats ont été analysés pour mettre en évidence des modulations de protéines et identifier des biomarqueurs prédictifs de l’apparition du SMet à T2. Une étape de validation a été effectuée (n=12) pour tester la robustesse du modèle prédictif obtenu. Les résultats ont révélé la variation significative de 47 protéines entre cas et témoins à T1, soit plus de 7 ans avant l’apparition du SMet, et avec un ratio de variation supérieur à 1,5 (n=45) ou inférieur à 0,65 (n=2). Elles sont en majorité impliquées dans la régulation de l’angiogenèse (n=7), de l’inflammation (n=5) ou encore du métabolisme (n=3). D’autres participent à la prolifération cellulaire ou sont impliquées dans le développement foetal (n=5).Un modèle de régression logistique a permis de retenir 4 protéines avec une capacité prédictive. Ce projet va contribuer à renforcer nos connaissances sur le SMet et l’empreinte foetale; il va permettre l’identification précoce des sujets à risque pour améliorer leur prise en charge avec une meilleure stratégie préventive.

Published
2015

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