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1. Analytic Correlation Filtration: A New Tool to Reduce Analytical Complexity of Metabolomic Datasets

Author

Stephanie Monnerie, Melanie Petera, Bernard Lyan, Pierrette Gaudreau, Blandine Comte, and Estelle Pujos-Guillot

Subjects
metabolomics, data filtration, high-resolution mass spectrometry, Microbiology, QR1-502
Abstract

Metabolomics generates massive and complex data. Redundant different analytical species and the high degree of correlation in datasets is a constraint for the use of data mining/statistical methods and interpretation. In this context, we developed a new tool to detect analytical correlation into datasets without confounding them with biological correlations. Based on several parameters, such as a similarity measure, retention time, and mass information from known isotopes, adducts, or fragments, the algorithm principle is used to group features coming from the same analyte, and to propose one single representative per group. To illustrate the functionalities and added-value of this tool, it was applied to published datasets and compared to one of the most commonly used free packages proposing a grouping method for metabolomics data: ‘CAMERA’. This tool was developed to be included in Galaxy and is available in Workflow4Metabolomics.

Published
2019
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2. Harmonized procedures lead to comparable quantification of total oxylipins across laboratories

Author

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

Subjects
eicosanoids, lipidomics, liquid chromatography, mass spectrometry, quantitation, harmonization, Biochemistry, QD415-436
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
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3. Untargeted metabolomics confirms the association between plasma branched chain amino acids and residual feed intake in beef heifers.

Author

Ezequiel Jorge-Smeding, Sergio Polakof, Muriel Bonnet, Stephanie Durand, Delphine Centeno, Mélanie Pétéra, Sébastien Taussat, and Gonzalo Cantalapiedra-Hijar

Subjects
Medicine, Science
Abstract

This study explored plasma biomarkers and metabolic pathways underlying feed efficiency measured as residual feed intake (RFI) in Charolais heifers. A total of 48 RFI extreme individuals (High-RFI, n = 24; Low-RFI, n = 24) were selected from a population of 142 heifers for classical plasma metabolite and hormone quantification and plasma metabolomic profiling through untargeted LC-MS. Most efficient heifers (Low-RFI) had greater (P = 0.03) plasma concentrations of IGF-1 and tended to have (P = 0.06) a lower back fat depth compared to least efficient heifers. However, no changes were noted (P ≥ 0.10) for plasma concentrations of glucose, insulin, non-esterified fatty acids, β-hydroxybutyrate and urea. The plasma metabolomic dataset comprised 3,457 ions with none significantly differing between RFI classes after false discovery rate correction (FDR > 0.10). Among the 101 ions having a raw P < 0.05 for the RFI effect, 13 were putatively annotated by using internal databases and 6 compounds were further confirmed with standards. Metabolic pathway analysis from these 6 confirmed compounds revealed that the branched chain amino acid metabolism was significantly (FDR < 0.05) impacted by the RFI classes. Our results confirmed for the first time in beef heifers previous findings obtained in male beef cattle and pointing to changes in branched-chain amino acids metabolism along with that of body composition as biological mechanisms related to RFI. Further studies are warranted to ascertain whether there is a cause-and-effect relationship between these mechanisms and RFI.

Published
2022
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4. Multiplatform metabolomics for an integrative exploration of metabolic syndrome in older men

Author

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

Subjects
Metabolic syndrome, Metabolomics, Deep phenotyping, Lipidomics, Metabolic signature, Medicine, Medicine (General), R5-920
Abstract

Background: Metabolic syndrome (MetS), a cluster of factors associated with risks of developing cardiovascular diseases, is a public health concern because of its growing prevalence. Considering the combination of concomitant components, their development and severity, MetS phenotypes are largely heterogeneous, inducing disparity in diagnosis. Methods: A case/control study was designed within the NuAge longitudinal cohort on aging. From a 3-year follow-up of 123 stable individuals, we present a deep phenotyping approach based on a multiplatform metabolomics and lipidomics untargeted strategy to better characterize metabolic perturbations in MetS and define a comprehensive MetS signature stable over time in older men. Findings: We characterize significant changes associated with MetS, involving modulations of 476 metabolites and lipids, and representing 16% of the detected serum metabolome/lipidome. These results revealed a systemic alteration of metabolism, involving various metabolic pathways (urea cycle, amino-acid, sphingo- and glycerophospholipid, and sugar metabolisms…) not only intrinsically interrelated, but also reflecting environmental factors (nutrition, microbiota, physical activity…). Interpretation: These findings allowed identifying a comprehensive MetS signature, reduced to 26 metabolites for future translation into clinical applications for better diagnosing MetS.

Published
2021
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5. Identification of Pre-frailty Sub-Phenotypes in Elderly Using Metabolomics

Author

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

Subjects
pre-frailty, biomarkers, elderly, sub-phenotypes, gender differences, untargeted metabolomics, Physiology, QP1-981
Abstract

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

Published
2019
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6. WiPP: Workflow for Improved Peak Picking for Gas Chromatography-Mass Spectrometry (GC-MS) Data

Author

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

Subjects
gas chromatography-mass spectrometry (GC-MS), metabolomics, peak detection, peak classification, pre-processing workflow, parameter optimisation, machine learning, support vector machine, Microbiology, QR1-502
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
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7. Milk metabolome reveals variations on enteric methane emissions from dairy cows fed a specific inhibitor of the methanogenesis pathway

Author

Diego P. Morgavi, Hamid Boudra, Bénédict Yanibada, Stéphanie Durand, Ulli Hohenester, Fabien Jourdan, Cécile Canlet, Anne Ferlay, Mélanie Pétéra, LESUR, Hélène, 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), Plateforme Exploration du Métabolisme-MetaboHUB (PFEM-MetaboHUB), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), 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), Métabolisme et Xénobiotiques (ToxAlim-MeX), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), 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), Unité de Nutrition Humaine (UNH), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Clermont Auvergne (UCA), Analyse de Xénobiotiques, Identification, Métabolisme (E20 Metatoul-AXIOM), 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), Plateforme Exploration du Métabolisme (PFEM), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-MetaboHUB-Clermont, MetaboHUB-MetaboHUB, 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 Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3)

Subjects
Rumen, multiplatform metabolomics, [SDV.BA] Life Sciences [q-bio]/Animal biology, Methanogenesis, [SDV]Life Sciences [q-bio], Gas Chromatography-Mass Spectrometry, 03 medical and health sciences, Metabolomics, Lactation, Genetics, Metabolome, medicine, Animals, [SDV.SA.SPA] Life Sciences [q-bio]/Agricultural sciences/Animal production studies, Food science, microbial metabolite, 030304 developmental biology, 2. Zero hunger, milk, 0303 health sciences, methane biomarker, Chemistry, dairy cow, [SDV.BA]Life Sciences [q-bio]/Animal biology, 0402 animal and dairy science, 04 agricultural and veterinary sciences, Metabolism, 040201 dairy & animal science, Diet, [SDV] Life Sciences [q-bio], Metabolic pathway, medicine.anatomical_structure, 13. Climate action, [SDV.SA.SPA]Life Sciences [q-bio]/Agricultural sciences/Animal production studies, Fermentation, Cattle, Female, Animal Science and Zoology, Gas chromatography, Methane, Food Science
Abstract

International audience; Metabolome profiling in biological fluids is an interesting approach for exploring markers of methane emissions in ruminants. In this study, a multiplatform metabolomics approach was used for investigating changes in milk metabolic profiles related to methanogenesis in dairy cows. For this purpose, 25 primiparous Holstein cows at similar lactation stage were fed the same diet supplemented with (treated, n = 12) or without (control, n = 13) a specific antimethanogenic additive that reduced enteric methane production by 23% with no changes in intake, milk production, and health status. The study lasted 6 wk, with sampling and measures performed in wk 5 and 6. Milk samples were analyzed using 4 complementary analytical methods, including 2 untargeted (nuclear magnetic resonance and liquid chromatography coupled to a quadrupole-time-of-flight mass spectrometer) and 2 targeted (liquid chromatography-tandem mass spectrometry and gas chromatography coupled to a flame ionization detector) approaches. After filtration, variable selection and normalization data from each analytical platform were then analyzed using multivariate orthogonal partial least square discriminant analysis. All 4 analytical methods were able to differentiate cows from treated and control groups. Overall, 38 discriminant metabolites were identified, which affected 10 metabolic pathways including methane metabolism. Some of these metabolites such as dimethylsulfoxide, dimethylsulfone, and citramalic acid, detected by nuclear magnetic resonance or liquid chromatography-mass spectrometry methods, originated from the rumen microbiota or had a microbial-host animal co-metabolism that could be associated with methanogenesis. Also, discriminant milk fatty acids detected by targeted gas chromatography were mostly of ruminal microbial origin. Other metabolites and metabolic pathways significantly affected were associated with AA metabolism. These findings provide new insight on the potential role of milk metabolites as indicators of enteric methane modifications in dairy cows.

Published
2021

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

Author

Céline Dalle, Jérémy Tournayre, Malwina Mainka, Alicja Basiak-Rasała, Mélanie Pétéra, Sophie Lefèvre-Arbogast, Jessica Dalloux-Chioccioli, Mélanie Deschasaux-Tanguy, Lucie Lécuyer, Emmanuelle Kesse-Guyot, Léopold K. Fezeu, Serge Hercberg, Pilar Galan, Cécilia Samieri, Katarzyna Zatońska, Philip C. Calder, Mads Fiil Hjorth, Arne Astrup, André Mazur, Justine Bertrand-Michel, Nils Helge Schebb, Andrzej Szuba, Mathilde Touvier, John W. Newman, Cécile Gladine, Unité de Nutrition Humaine (UNH), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Clermont Auvergne (UCA), University of Wuppertal, Wrocław Medical University, Plateforme Exploration du Métabolisme (PFEM), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-MetaboHUB-Clermont, MetaboHUB-MetaboHUB, 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), 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), Institut des Maladies Métaboliques et Casdiovasculaires (UPS/Inserm U1297 - I2MC), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM), Equipe 3: EREN- Equipe de Recherche en Epidémiologie Nutritionnelle (CRESS - U1153), Université Sorbonne Paris Nord-Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University Hospital Southampton NHS Foundation Trust, University of Southampton, Novo Nordisk Inc, NOVO NORDISK PARK, University of California [Davis] (UC Davis), University of California (UC), and Western Human Nutrition Research Center

Subjects
metabolic phenotyping, oxylipins, lipid mediators, Catalysis, metabolic syndrome, Inorganic Chemistry, Clinical Research, Genetics, Humans, 2.1 Biological and endogenous factors, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Physical and Theoretical Chemistry, Aetiology, Molecular Biology, Spectroscopy, Nutrition, Metabolic Syndrome, Chemical Physics, Organic Chemistry, lipidomics, General Medicine, Computer Science Applications, Good Health and Well Being, Cardiovascular Diseases, Case-Control Studies, Other Biological Sciences, Other Chemical Sciences, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition
Abstract

International audience; 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

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

Author

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

Abstract

BackgroundMetabolic syndrome (MetS) is a complex condition encompassing a constellation of cardiometabolic abnormalities. Integratively phenotyping the molecular pathways involved in MetS would help to deeply characterize its pathophysiology and to better stratify the risk of cardiometabolic diseases. Oxylipins are a superfamilly of lipid mediators regulating most biological processes involved in cardiometabolic health.MethodsA high-throughput validated mass spectrometry method allowing the quantitative profiling of over 130 oxylipins was applied to identify and validate the oxylipin signature of MetS in two independent case/control studies involving 476 participants.ResultsWe have uncovered and validated an oxylipin signature of MetS (coined OxyScore) including 23 oxylipins and having high performances of classification and replicability (cross-validated AUCROCof 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 both the oxylipins and the MetS criteria showed that the oxylipin signature brings consistent and complementary information supporting its clinical utility. Moreover, the OxyScore provides a unique mechanistic signature of MetS regarding the activation and/or negative feedback regulation of crucial molecular pathways that may help identify patients at higher risk of cardiometabolic diseases.ConclusionOxylipin profiling identifies a mechanistic signature of metabolic syndrome that may help to enhance MetS phenotyping and ultimately to better predict the risk of cardiometabolic diseasesviaa better patient stratification.

Published
2022

10. Dietary switch to Western diet induces hypothalamic adaptation associated with gut microbiota dysbiosis in rats

Author

Carole Migné, Gaëlle Boudry, M. Fouesnard, Mélanie Pétéra, Fabienne Devime, Johanna Zoppi, Veronique Douard, Léa Le Gleau, Stéphanie Durand, Samuel Chaffron, A. Benani, Nutrition, Métabolismes et Cancer (NuMeCan), Université de Rennes (UR)-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 en Transplantation et Immunologie (U1064 Inserm - CRTI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Nantes - UFR de Médecine et des Techniques Médicales (UFR MEDECINE), Université de Nantes (UN)-Université de Nantes (UN), Unité de Nutrition Humaine (UNH), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Clermont Auvergne (UCA), MICrobiologie de l'ALImentation au Service de la Santé (MICALIS), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre des Sciences du Goût et de l'Alimentation [Dijon] (CSGA), Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Combinatoire et Bioinformatique (LS2N - équipe COMBI), Laboratoire des Sciences du Numérique de Nantes (LS2N), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), ANR-11-INBS-0010,METABOHUB,Développement d'une infrastructure française distribuée pour la métabolomique dédiée à l'innovation(2011), Chaffron, Samuel, Développement d'une infrastructure française distribuée pour la métabolomique dédiée à l'innovation - - METABOHUB2011 - ANR-11-INBS-0010 - INBS - VALID, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Combinatoire et Bioinformatique (COMBI), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Male, medicine.medical_specialty, 030309 nutrition & dietetics, Endocrinology, Diabetes and Metabolism, Hypothalamus, SOD2, Medicine (miscellaneous), 030209 endocrinology & metabolism, Inflammation, Gut flora, medicine.disease_cause, digestive system, 03 medical and health sciences, 0302 clinical medicine, Internal medicine, Brain-Gut Axis, Gene expression, medicine, Metabolome, Animals, 030212 general & internal medicine, Rats, Wistar, 2. Zero hunger, [SDV.MHEP.EM] Life Sciences [q-bio]/Human health and pathology/Endocrinology and metabolism, 0303 health sciences, Nutrition and Dietetics, biology, business.industry, digestive, oral, and skin physiology, [SDV.MHEP.EM]Life Sciences [q-bio]/Human health and pathology/Endocrinology and metabolism, biology.organism_classification, medicine.disease, Gastrointestinal Microbiome, Rats, [SDV.AEN] Life Sciences [q-bio]/Food and Nutrition, Endocrinology, Diet, Western, Cytokines, Dysbiosis, medicine.symptom, business, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, Homeostasis, Oxidative stress
Abstract

International audience; Background: Early hyperphagia and hypothalamic inflammation encountered after Western diet (WD) are linked to rodent propensity to obesity. Inflammation in several brain structures has been associated with gut dysbiosis. Since gut microbiota is highly sensitive to dietary changes, we hypothesised that immediate gut microbiota adaptation to WD in rats is involved in inflammation-related hypothalamic modifications. Methods: We evaluated short-term impact of WD consumption (2 h, 1, 2 and 4 days) on hypothalamic metabolome and caecal microbiota composition and metabolome. Data integration analyses were performed to uncover potential relationships among these three datasets. Finally, changes in hypothalamic gene expression in absence of gut microbiota were evaluated in germ-free rats fed WD for 2 days. Results: WD quickly and profoundly affected the levels of several hypothalamic metabolites, especially oxidative stress markers. In parallel, WD consumption reduced caecal microbiota diversity, modified its composition towards pro-inflammatory profile and changed caecal metabolome. Data integration identified strong correlations between gut microbiota sub-networks, unidentified caecal metabolites and hypothalamic oxidative stress metabolites. Germ-free rats displayed reduced energy intake and no changes in redox homoeostasis machinery expression or pro-inflammatory cytokines after 2 days of WD, in contrast to conventional rats, which exhibited increased SOD2, GLRX and IL-6 mRNA levels. Conclusion: A potentially pro-inflammatory gut microbiota and an early hypothalamic oxidative stress appear shortly after WD introduction. Tripartite data integration highlighted putative links between gut microbiota sub-networks and hypothalamic oxidative stress. Together with the absence of hypothalamic modifications in germ-free rats, this strongly suggests the involvement of the microbiota-hypothalamus axis in rat adaptation to WD introduction and in energy homoeostasis regulation.

Published
2021

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

Author

Mélanie Pétéra, Lucie Lécuyer, Aicha Demidem, Stéphanie Durand, Delphine Centeno, Nathalie Druesne-Pecollo, Emmanuelle Kesse-Guyot, Mélanie Deschasaux, Valentin Partula, Pilar Galan, Adrien Rossary, Bernard Lyan, Marie-Paule Vasson, Bernard Srour, Serge Hercberg, Mathilde Touvier, Marie Lagree, Thomas Ferreira, Estelle Pujos-Guillot, Paule Latino-Martel, Céline Dalle, Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM), Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), Plateforme d'Exploration du Métabolisme, MetaboHUB, Institut de Chimie, Université Clermont Auvergne (UCA), Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Plateforme Exploration du Métabolisme (PFEM), MetaboHUB-Clermont, MetaboHUB-MetaboHUB-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Clermont Auvergne (UCA), and Université Clermont Auvergne [2017-2020] (UCA [2017-2020])

Subjects
Adult, 0301 basic medicine, Oncology, medicine.medical_specialty, Epidemiology, Breast Neoplasms, [SDV.CAN]Life Sciences [q-bio]/Cancer, Mass Spectrometry, 03 medical and health sciences, 0302 clinical medicine, Breast cancer, Risk Factors, Internal medicine, Biomarkers, Tumor, medicine, Humans, Metabolomics, Prospective Studies, Prospective cohort study, Cell Proliferation, Cause of death, business.industry, Blood Proteins, Environmental exposure, Middle Aged, medicine.disease, 3. Good health, Glutamine, Oxidative Stress, 030104 developmental biology, Case-Control Studies, 030220 oncology & carcinogenesis, Etiology, Female, Energy Metabolism, business, Chromatography, Liquid, Follow-Up Studies, Cohort study, Blood drawing
Abstract

Background: Breast cancer is a major cause of death in occidental women. The role of metabolism in breast cancer etiology remains unclear. Metabolomics may help to elucidate novel biological pathways and identify new biomarkers to predict breast cancer long before symptoms appear. The aim of this study was to investigate whether untargeted metabolomic signatures from blood draws of healthy women could contribute to better understand and predict the long-term risk of developing breast cancer. Methods: A nested case–control study was conducted within the SU.VI.MAX prospective cohort (13 years of follow-up) to analyze baseline plasma samples of 211 incident breast cancer cases and 211 matched controls by LC/MS. Multivariable conditional logistic regression models were computed. Results: A total of 3,565 ions were detected and 1,221 were retained for statistical analysis. A total of 73 ions were associated with breast cancer risk (P < 0.01; FDR ≤ 0.2). Notably, we observed that a lower plasma level of O-succinyl-homoserine (OR = 0.70, 95%CI = [0.55-0.89]) and higher plasma levels of valine/norvaline [1.45 (1.15–1.83)], glutamine/isoglutamine [1.33 (1.07–1.66)], 5-aminovaleric acid [1.46 (1.14–1.87)], phenylalanine [1.43 (1.14–1.78)], tryptophan [1.40 (1.10–1.79)], γ-glutamyl-threonine [1.39 (1.09–1.77)], ATBC [1.41 (1.10–1.79)], and pregnene-triol sulfate [1.38 (1.08–1.77)] were associated with an increased risk of developing breast cancer during follow-up. Conclusion: Several prediagnostic plasmatic metabolites were associated with long-term breast cancer risk and suggested a role of microbiota metabolism and environmental exposure. Impact: After confirmation in other independent cohort studies, these results could help to identify healthy women at higher risk of developing breast cancer in the subsequent decade and to propose a better understanding of the complex mechanisms involved in its etiology.

Published
2019

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

Author

Paule Latino-Martel, Pietro Ferrari, Céline Dalle, Emmanuelle Kesse-Guyot, Mathilde Touvier, Pierre Micheau, Adrien Rossary, Sophie Lefèvre-Arbogast, Pilar Galan, Cécilia Samieri, Mélanie Deschasaux, Delphine Centeno, Lucie Lécuyer, Bernard Lyan, Marie-Paule Vasson, Valentin Partula, Marie Lagree, Bernard Srour, Nada Assi, Serge Hercberg, Mélanie Pétéra, Nathalie Druesne-Pecollo, Vivian Viallon, Aicha Demidem, Stéphanie Durand, Claudine Manach, Estelle Pujos-Guillot, Equipe 3: EREN- Equipe de Recherche en Epidémiologie Nutritionnelle (CRESS - U1153), Université Sorbonne Paris Nord-Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), 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), 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), Plateforme d'Exploration du Métabolisme, MetaboHUB, Institut de Chimie de Clermont-Ferrand (ICCF), SIGMA Clermont (SIGMA Clermont)-Institut de Chimie du CNRS (INC)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), Hôpital Avicenne [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Centre de Recherche en Cancérologie de Lyon (UNICANCER/CRCL), Centre Léon Bérard [Lyon]-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Centre Jean Perrin [Clermont-Ferrand] (UNICANCER/CJP), UNICANCER, ANR-11-INBS-0010,METABOHUB,Développement d'une infrastructure française distribuée pour la métabolomique dédiée à l'innovation(2011), Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM), Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), UMR 1219, Centre de Recherche Inserm-Université de Bordeaux, Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Bordeaux, Institut de Chimie, Université Clermont Auvergne (UCA), International Agency for Research on Cancer, Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)-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), and Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)

Subjects
Adult, 0301 basic medicine, Oncology, medicine.medical_specialty, Epidemiology, Metabolite, education, Breast Neoplasms, [SDV.CAN]Life Sciences [q-bio]/Cancer, Risk Assessment, Mass Spectrometry, LEHA, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Metabolomics, Breast cancer, Risk Factors, Internal medicine, Biomarkers, Tumor, medicine, Humans, Steroid sulfate, Risk factor, Randomized Controlled Trials as Topic, Penalized regression, business.industry, Feeding Behavior, Middle Aged, medicine.disease, 3. Good health, Logistic Models, 030104 developmental biology, Clinical Trials, Phase III as Topic, chemistry, Case-Control Studies, 030220 oncology & carcinogenesis, Piperine, Cohort, Female, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, business, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition
Abstract

Background: Diet has been recognized as a modifiable risk factor for breast cancer. Highlighting predictive diet-related biomarkers would be of great public health relevance to identify at-risk subjects. The aim of this exploratory study was to select diet-related metabolites discriminating women at higher risk of breast cancer using untargeted metabolomics. Methods: Baseline plasma samples of 200 incident breast cancer cases and matched controls, from a nested case–control study within the Supplémentation en Vitamines et Minéraux Antioxydants (SU.VI.MAX) cohort, were analyzed by untargeted LC-MS. Diet-related metabolites were identified by partial correlation with dietary exposures, and best predictors of breast cancer risk were then selected by Elastic Net penalized regression. The selection stability was assessed using bootstrap resampling. Results: 595 ions were selected as candidate diet–related metabolites. Fourteen of them were selected by Elastic Net regression as breast cancer risk discriminant ions. A lower level of piperine (a compound from pepper) and higher levels of acetyltributylcitrate (an alternative plasticizer to phthalates), pregnene-triol sulfate (a steroid sulfate), and 2-amino-4-cyano butanoic acid (a metabolite linked to microbiota metabolism) were observed in plasma from women who subsequently developed breast cancer. This metabolomic signature was related to several dietary exposures such as a “Western” dietary pattern and higher alcohol and coffee intakes. Conclusions: Our study suggested a diet-related plasma metabolic signature involving exogenous, steroid metabolites, and microbiota-related compounds associated with long-term breast cancer risk that should be confirmed in large-scale independent studies. Impact: These results could help to identify healthy women at higher risk of breast cancer and improve the understanding of nutrition and health relationship.

Published
2020

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

Author

Mélanie Deschasaux, Pierre Micheau, Mathilde Touvier, Nathalie Druesne-Pecollo, Adrien Rossary, Pilar Galan, Marie-Paule Vasson, Delphine Centeno, Bernard Srour, Aicha Demidem, Bernard Lyan, Paule Latino-Martel, Valentin Partula, Mélanie Pétéra, Céline Dalle, Lucie Lécuyer, Serge Hercberg, Emmanuelle Kesse-Guyot, Stéphanie Durand, Claudine Manach, Estelle Pujos-Guillot, Marie Lagree, Equipe 3: EREN- Equipe de Recherche en Epidémiologie Nutritionnelle (CRESS - U1153), Université Sorbonne Paris Nord-Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), 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), Institut de Chimie de Clermont-Ferrand (ICCF), SIGMA Clermont (SIGMA Clermont)-Institut de Chimie du CNRS (INC)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), Hôpital Avicenne [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Centre Jean Perrin [Clermont-Ferrand] (UNICANCER/CJP), UNICANCER, Institut National du Cancer (INCA) France INCa_8085 INCa_11323Federative Institute for Biomedical Research IFRB Paris 13 Region Ile-de-France, Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), and HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
0301 basic medicine, [SDV]Life Sciences [q-bio], Population, Medicine (miscellaneous), 030209 endocrinology & metabolism, Cohort Studies, Decile, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Metabolomics, Vegetables, medicine, Humans, Food science, Carnitine, Dietary patterns, education, 2. Zero hunger, education.field_of_study, 030109 nutrition & dietetics, Nutrition and Dietetics, Overall diet, Mass spectrometry, business.industry, Cholic acid, 24-h dietary records, Plasma levels, Diet, 3. Good health, Cross-Sectional Studies, chemistry, Cohort, Female, Alcohol intake, business, medicine.drug
Abstract

Dietary intakes are reflected in plasma by the presence of hundreds of exogenous metabolites and variations in endogenous metabolites. The exploration of diet-related plasma metabolic profiles could help to better understand the impact of overall diet on health. Our aim was to identify metabolomic signatures reflecting overall diet in women from the French general population. This cross-sectional study included 160 women in the SU.VI.MAX cohort with detailed dietary data (≥ 10 24-h dietary records) selected according to their level of adherence to the French dietary recommendations, represented by the validated score mPNNS-GS; 80 women from the 10th decile of the score were matched with 80 women from the 1st decile. Plasma metabolomic profiles were acquired using untargeted UPLC-QToF mass spectrometry analysis. The associations between metabolomic profiles and the mPNNG-GS, its components and Principal Component Analyses-derived dietary patterns were investigated using multivariable conditional logistic regression models and partial correlations. Adherence to the dietary recommendations was positively associated with 3-indolepropionic acid and pipecolic acid (also positively associated with fruit and vegetable intake and a healthy diet)—2 metabolites linked to microbiota and inversely associated with lysophosphatidylcholine (LysoPC(17:1)), acylcarnitine C9:1 (also inversely associated with a healthy diet), acylcarnitine C11:1 and 2-deoxy-D-glucose. Increased plasma levels of piperine and Dihydro4mercapto-3(2H) furanone were observed in women who consumed a Western diet and a healthy diet, respectively. Ethyl-β-D-glucopyranoside was positively associated with alcohol intake. Plasma levels of LysoPC(17:1), cholic acid, phenylalanine-phenylalanine and phenylalanine and carnitine C9:1 decreased with the consumption of vegetable added fat, sweetened food, milk and dairy products and fruit and vegetable intakes, respectively. This study highlighted several metabolites from both host and microbial metabolism reflecting the long-term impact of the overall diet. SU.VI.MAX, clinicaltrials.gov NCT00272428. Registered 3 January 2006, https://clinicaltrials.gov/show/NCT00272428

Published
2020

14. A hybrid and exploratory approach to knowledge discovery in metabolomic data

Author

Estelle Pujos-Guillot, Blandine Comte, Mélanie Pétéra, Dhouha Grissa, Amedeo Napoli, 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), Knowledge representation, reasonning (ORPAILLEUR), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Unité de Nutrition Humaine - Clermont Auvergne (UNH), and Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA)

Subjects
Formal Concept Analysis, knowledge discovery, 0211 other engineering and technologies, Context (language use), 0102 computer and information sciences, 02 engineering and technology, Machine learning, computer.software_genre, 01 natural sciences, knoledge discovery, Domain (software engineering), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Knowledge extraction, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Formal concept analysis, Discrete Mathematics and Combinatorics, Preprocessor, visualization, interpretation, Mathematics, Complex data type, business.industry, Applied Mathematics, data and pattern exploration, 021107 urban & regional planning, metabolomic data, Hybrid knowledge discovery, Small set, Visualization, ComputingMethodologies_PATTERNRECOGNITION, classification, 010201 computation theory & mathematics, hybrid data mining algorithms, Artificial intelligence, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, computer, pattern mining
Abstract

International audience; In this paper, we propose a hybrid and exploratory knowledge discovery approach for analyzing metabolomic complex data based on a combination of supervised classifiers, pattern mining and Formal Concept Analysis (FCA). The approach is based on three main operations, preprocessing, classification, and postprocessing. Classifiers are applied to datasets of the form individuals×features and produce sets of ranked features which are further analyzed. Pattern mining and FCA are used to provide a complementary analysis and support for visualization. A practical application of this framework is presented in the context of metabolomic data, where two interrelated problems are considered, discrimination and prediction of class membership. The dataset is characterized by a small set of individuals and a large set of features, in which predictive biomarkers of clinical outcomes should be identified. The problems of combining numerical and symbolic data mining methods, as well as discrimination and prediction, are detailed and discussed. Moreover, it appears that visualization based on FCA can be used both for guiding knowledge discovery and for interpretation by domain analysts.

Published
2020

15. 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

Cécile Canlet, Franck Giacomoni, Sophie Goulitquer, Jean-François Martin, Patrick Tardivel, Rémi Servien, Etienne A. Thévenot, Alexis Delabrière, Marie Tremblay-Franco, Yann Guitton, Mélanie Pétéra, Gildas Le Corguillé, Christophe Duperier, Pierrick Roger-Mele, Christophe Caron, Misharl Monsoor, 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), Institut de Recherche en Génie Civil et Mécanique (GeM), Université de Nantes - Faculté des Sciences et des Techniques, Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-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-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), 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), Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université, Plateforme d'Exploration du Métbolisme, MetaboHUB, Laboratoire d'analyse des données et d'intelligence des systèmes (LADIS), Département Métrologie Instrumentation & Information (DM2I), Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Université Paris-Saclay-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Génétique, génomique fonctionnelle et biotechnologies (UMR 1078) (GGB), EFS-Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), Laboratoire Universitaire de Biodiversité et Ecologie Microbienne (LUBEM), Ingenum, Institut National de la Recherche Agronomique (INRA), ANR-11-INBS-0013/11-INBS-0013,ReNaBi-IFB,Institut français de bioinformatique(2011), ANR-11-INBS-0010/11-INBS-0010,METABOHUB,Développement d’une infrastructure française distribuée pour la métabolomique dédiée à l’innovation(2011), ANR-10-BTBR-04-01/10-BTBR-0004,IDEALG,IDEALG(2010), Institut National de la Recherche Agronomique (INRA)-École nationale vétérinaire, agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), 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), 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), 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 Clermont Auvergne [2017-2020] (UCA [2017-2020]), 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, Université de Toulouse (UT)-Université de Toulouse (UT), Laboratoire Sciences des Données et de la Décision (LS2D), French Institute of Bioinformatics (IFB) ANR-11-INBS-0013, French Infrastructure for Metabolomics and Fluxomics (MetaboHUB) ANR-11-INBS-0010, IDEALG project ANR-10-BTBR-04, ANR-11-INBS-0013,IFB (ex Renabi-IFB),Institut français de bioinformatique(2011), ANR-11-INBS-0010,METABOHUB,Développement d'une infrastructure française distribuée pour la métabolomique dédiée à l'innovation(2011), ANR-10-BTBR-0004,IDEALG,Biotechnologies pour la valorisation des macroalgues(2010), Analyse de Xénobiotiques, Identification, Métabolisme (E20 Metatoul-AXIOM), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), 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 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), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, 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), 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), Institut Brestois Santé Agro Matière (IBSAM), and Université de Brest (UBO)-Université de Brest (UBO)-EFS-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
MASS SPECTROMETRY, 0301 basic medicine, Web server, Magnetic Resonance Spectroscopy, Computer science, Data analysis, CHROMATOGRAPHY, spectrum analysis, Bioinformatics, computer.software_genre, Quantitative Biology - Quantitative Methods, Biochemistry, Workflow, 03 medical and health sciences, Annotation, Resource (project management), [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Digital Object Identifier, Animals, Humans, Metabolomics, [INFO.INFO-DL]Computer Science [cs]/Digital Libraries [cs.DL], Preprocessor, signal processing, Quantitative Methods (q-bio.QM), Electronic Data Processing, ADULT URINARY METABOLOME, IDENTIFICATION, BIOINFORMATICS, MASS-SPECTROMETRY, Cell Biology, COMPUTATIONAL METABOLOMICS, Data science, GENOME, Galaxy, Identification (information), 030104 developmental biology, FOS: Biological sciences, WEB SERVER, E-infrastructure, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], computer, Software, REPOSITORY, STANDARDS
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., The International Journal of Biochemistry \& Cell Biology, Elsevier, 2017

Published
2017

16. WiPP: workflow for improved peak picking for gas chromatography-mass spectrometry (GC-MS) data

Author

Yann Guitton, Emilie Sicard, Jennifer A. Kirwan, Nico Borgsmüller, Bruno Le Bizec, Carole Migné, Franck Giacomoni, Mélanie Pétéra, Eric Blanc, Yoann Gloaguen, Anne-Lise Royer, Tobias Opialla, Dieter Beule, Stéphanie Durand, Estelle Pujos-Guillot, Core Unit Bioinformatics, Berlin Institute of Health (BIH), Berlin Institute of Health Metabolomics Platform, Max Delbrück Center for Molecular Medicine [Berlin] (MDC), Helmholtz-Gemeinschaft = Helmholtz Association, Berlin Institute for Medical Systems Biology, Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), MetaboHUB, Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), 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, Charité - Universitätsmedizin Berlin / Charite - University Medicine Berlin, 1019, unité de Nutrition Humaine, Institut National de la Recherche Agronomique (INRA), Université Clermont Auvergne (UCA), Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), and Institut National de la Recherche Agronomique (INRA)-École nationale vétérinaire, agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)

Subjects
[SPI.OTHER]Engineering Sciences [physics]/Other, Cancer Research, Computer science, peak detection, lcsh:QR1-502, computer.software_genre, 01 natural sciences, lcsh:Microbiology, Article, 03 medical and health sciences, Metabolomics, parameter optimisation, support vector machine, peak classification, 030304 developmental biology, 0303 health sciences, 010401 analytical chemistry, metabolomics, 0104 chemical sciences, pre-processing workflow, Workflow, machine learning, Cardiovascular and Metabolic Diseases, gas chromatography-mass spectrometry (GC-MS), Data mining, Gas chromatography–mass spectrometry, Technology Platforms, Classifier (UML), computer
Abstract

High false positive rates in GC-MS metabolomics peak detection is a common issue that impedes automated analysis of large-scale datasets. There is a growing need for improving the reliability and scalability of data analysis workflows. Many algorithms are available for peak detection [1], a crucial step for the data analysis, but performance and outcome can differ widely depending on both algorithmic approach and data acquisition method. This makes it difficult to compare and contrast between algorithms without extensive manual intervention.nnWe present a workflow for improved peak picking (WiPP), a parameter optimizing, multi-algorithm peak detection workflow for GC-MS metabolomics, which automatically evaluates the quality of detected peaks using machine learning-based classification. First, the classifier is trained to distinguish between real compound related peaks and false positive peaks. Then the algorithm parameters are scored based on the quality of detected peaks and optimized accordingly. This procedure is repeated for two peak detection algorithms and subsequently both algorithms are run in parallel on the entire data set with the optimized parameters. The qualitative information returned by the classifier for every peak is then used to merge individual algorithm results into one final high confidence peak set.nnUsing this approach, we show that automated detection and evaluation of peak quality is improved. The additional quantitative and qualitative information generated by the classifier allows:nnO_LIa novel way to classify peaks based on seven classes and thus objectively to assess their qualitynC_LIO_LIimpartial performance comparison of different peak picking algorithmsnC_LIO_LIautomated parameter optimization for each individual peak picking algorithmnC_LIO_LIa final, improved high quality peak list to be generated for statistical or further analyses.nC_LInnIt achieves this while minimising the operator-time required by packaging this within a fully automated workflow. The modular design allows extension, adjustment and improvement of the workflow using different or additional peak detection algorithms and classifiers. Importantly, due to the fully automated implementation, the workflow is suitable for large-scale studies.nnThe pipeline supports mzML, mzData and NetCDF formats and is implemented in python using snakemake, a reproducible and scalable workflow management system, it is available on GitHub (https://github.com/bihealth/WiPP).

Published
2019

17. P1‐011: UNTARGETED METABOLOMICS IN A PROSPECTIVE COHORT TO IDENTIFY DIET‐RELATED METABOLITES ASSOCIATED WITH AGE‐RELATED COGNITIVE DECLINE

Author

Paul J. Lucassen, Cristina Andres-Lacueva, Cécilia Samieri, Stéphanie Durand, Boris P. Hejblum, Delphine Centeno, Sandrine Thuret, Dorrain Yanwen Low, Mireia Urpi-Sarda, Sophie Lefèvre-Arbogast, Raúl González-Domínguez, Mélanie Pétéra, Cécile Proust-Lima, Pierre Micheau, Aniko Korosi, Ludwig Aigner, Claudine Manach, Estelle Pujos-Guillot, and Catherine Helmer

Subjects
Oncology, Age-related cognitive decline, medicine.medical_specialty, Epidemiology, business.industry, Health Policy, Psychiatry and Mental health, Cellular and Molecular Neuroscience, Untargeted metabolomics, Developmental Neuroscience, Internal medicine, Medicine, Neurology (clinical), Geriatrics and Gerontology, business, Prospective cohort study
Published
2019

18. Workflow4Metabolomics: an international computing infrastructure for Metabolomics

Author

Mélanie Pétéra, Romain Dallet, Christophe Dupérier, Franck Giacomoni, Yann GUITTON, Jean-François Martin, Pierrick Roger-Mele, Julien Saint-Vanne, Etienne Thévenot, Marie Tremblay Franco, Gildas Le Corguillé, Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), UMS 3601, Institut Français de Bioinformatique (IFB), Centre National de la Recherche Scientifique (CNRS), 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), Université Nantes Angers Le Mans (LUNAM), Ecole Nationale Vétérinaire Agroalimentaire et de l'Alimentation Nantes Atlantique (ONIRIS), 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), LIST/LADIS, Laboratoire d'Intégration de Systèmes et des technologies, Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), MetaboHUB, FR2424, ABiMS Station Biologique, Université Pierre et Marie Curie (Paris 6), Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Institut Français de Bioinformatique - UMS CNRS 3601 (IFB-CORE), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), LUNAM Université [Nantes Angers Le Mans], Analyse de Xénobiotiques, Identification, Métabolisme (E20 Metatoul-AXIOM), 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), 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), 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), 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), Laboratoire Sciences des Données et de la Décision (LS2D), Département Métrologie Instrumentation & Information (DM2I), Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-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), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, 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), 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), 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-Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-MetaToul-MetaboHUB, Fédération de recherche de Roscoff (FR2424), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-École nationale vétérinaire, agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS), 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), ABiMS - Informatique et bioinformatique = Analysis and Bioinformatics for Marine Science (ABIMS), 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), and Université de Toulouse (UT)-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan)

Subjects
[SDV.OT]Life Sciences [q-bio]/Other [q-bio.OT], bioinformatics, spectrum analysis, artificial intelligence, metabolomics, spectrum, machine learning, classification, annotations, [INFO.INFO-OS]Computer Science [cs]/Operating Systems [cs.OS], [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], signal processing, data processing, mass spectrometry
Abstract

Thursday 4th July - Talks I & Trainings Proteomics & Metabolomics. Moderator: P. Jagtap Thursday 4th July - Talks I & Trainings Proteomics & Metabolomics. Moderator: P. JagtapThursday 4th July - Talks I & TrainingsProteomics & Metabolomics. Moderator: P. Jagtap; The Workflow4Metabolomics (W4M) project aims to develop full LC/MS, GC/MS, FIA/MS and NMR pipelines using Galaxy framework for dataanalysis including preprocessing, normalization, quality control, statistical analysis and annotation steps. Our current developments aim to provide a set of interactive visualization tools in order to make ease the results interpretations. The development of Shiny applications will allows interactions from graphical features and dataset filters with graphical outputs like chromatograms, RMN spectra, heatmaps or PCA. In parallel, one of the major issue of the metabolomic approach is the compounds identification. To facilitate this annotation step, tandem mass spectrometry (MS/MS) is able to provide informations about the compounds structure. For that reason, an MS/MS data processing workflow based on msPurity, and two identification tools, metFrag and Sirius-CSI: FingerID, will be available in W4M.

Published
2019

19. Multi-block PLS discriminant analysis for the joint analysis of metabolomic and epidemiological data

Author

Pierrette Gaudreau, Blandine Comte, Mélanie Pétéra, Stéphanie Bougeard, Marion Brandolini-Bunlon, Estelle Pujos-Guillot, 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], Laboratoires de Ploufragan-Plouzané, Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES), 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
Computer science, Epidemiology, Endocrinology, Diabetes and Metabolism, Clinical Biochemistry, computer.software_genre, 01 natural sciences, Biochemistry, 03 medical and health sciences, Multiblock PLS discriminant analysis, Partial least squares regression, Discrimination, Humans, Metabolomics, Relevance (information retrieval), Least-Squares Analysis, 030304 developmental biology, Block (data storage), Complex data type, 0303 health sciences, 010401 analytical chemistry, Discriminant Analysis, Linear discriminant analysis, Data structure, 0104 chemical sciences, Metadata, Discriminant, Epidemiological Monitoring, Multi-block, Data mining, [INFO.INFO-OS]Computer Science [cs]/Operating Systems [cs.OS], computer, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, Algorithms
Abstract

INTRODUCTION: Metabolomics is a powerful phenotyping tool in nutrition and health research, generating complex data that need dedicated treatments to enrich knowledge of biological systems. In particular, to investigate relations between environmental factors, phenotypes and metabolism, discriminant statistical analyses are generally performed separately on metabolomic datasets, complemented by associations with metadata. Another relevant strategy is to simultaneously analyse thematic data blocks by a multi-block partial least squares discriminant analysis (MBPLSDA) allowing determining the importance of variables and blocks in discriminating groups of subjects, taking into account data structure. OBJECTIVE: The present objective was to develop a full open-source standalone tool, allowing all steps of MBPLSDA for the joint analysis of metabolomic and epidemiological data. METHODS: This tool was based on the mbpls function of the ade4 R package, enriched with 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. RESULTS: To illustrate the potential of this tool, MBPLSDA was applied to a real case study involving metabolomics, nutritional and clinical data from a human cohort. The availability of 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. CONCLUSION: 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

20. Metabolomics reveals changes in urea cycle associated to residual feed intake in growing heifers

Author

Delphine Centeno, Mélanie Pétéra, G. Renand, Gonzalo Cantalapiedra-Hijar, S. Polakof, E. Jorge-Smeding, Stéphanie Durand, ProdInra, Archive Ouverte, 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 la Recherche Agronomique (INRA)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Génétique Animale et Biologie Intégrative (GABI), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Unité Mixte de Recherches 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)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Recherche Agronomique (INRA), Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), and Institut National de la Recherche Agronomique (INRA)-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

Subjects
feed efficiency, metabolomics, cattle, RFI, bovin, [SDV]Life Sciences [q-bio], education, Biology, 03 medical and health sciences, Metabolomics, Animal science, health care economics and organizations, 030304 developmental biology, rfid, radio frequency identification, 0303 health sciences, efficacité alimentaire, 0402 animal and dairy science, 04 agricultural and veterinary sciences, 040201 dairy & animal science, [SDV] Life Sciences [q-bio], Urea cycle, sense organs, Residual feed intake, métabolomique
Abstract

Metabolomics reveals changes in urea cycle associated to residual feed intake in growing heifers. 6. International Symposium on Energy and Protein Metabolism and Nutrition (ISEP)

Published
2019

21. Signatures métabolomiques associées à des profils alimentaires spécifiques dans la cohorte su.vi.max

Author

Pierre Micheau, Valentin Partula, Estelle Pujos-Guillot, Bernard Srour, Mélanie Deschasaux, Marie-Paule Vasson, S. Hercberg, Adrien Rossary, Mélanie Pétéra, Pilar Galan, Aicha Demidem, Claudine Manach, L. Lécuyer, Delphine Centeno, T. Mathilde, Bernard Lyan, Paule Latino-Martel, Céline Dalle, Emmanuelle Kesse-Guyot, Christine Morand, Stéphanie Durand, Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM), Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Département de Santé Publique, Hôpital Avicenne [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), CHU Clermont-Ferrand, Equipe 3: EREN- Equipe de Recherche en Epidémiologie Nutritionnelle (CRESS - U1153), Institut National de la Recherche Agronomique (INRA)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Université Paris 13 (UP13)-Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM), département de santé publique, Unité de Recherche en Epidémiologie Nutritionnelle (UREN), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Sorbonne Paris Cité (USPC)-Université Paris 13 (UP13)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Institut National de la Recherche Agronomique (INRA), Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC), Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), Institut National de la Recherche Agronomique (INRA)-Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université, Université Paris 13 (UP13)-Institut National de la Recherche Agronomique (INRA)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC), Hôpital Avicenne, Assistance Publique - Hôpitaux de Paris (AP-HP), Clermont Université-Université d'Auvergne - Clermont-Ferrand I (UdA)-Institut National de la Recherche Agronomique (INRA), Université Paris 13 (UP13)-Institut National de la Recherche Agronomique (INRA)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM), Lécuyer, Lucie, ProdInra, Migration, Université Paris 13 (UP13)-Institut National de la Recherche Agronomique (INRA)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), and HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
2. Zero hunger, 0303 health sciences, Nutrition and Dietetics, 030309 nutrition & dietetics, Endocrinology, Diabetes and Metabolism, [SDV]Life Sciences [q-bio], 030209 endocrinology & metabolism, signatures métabolomiques, [SDV] Life Sciences [q-bio], 03 medical and health sciences, 0302 clinical medicine, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, Internal Medicine, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, ComputingMilieux_MISCELLANEOUS, su.vi.max
Abstract

Discipline Epidemiologie. Introduction et but de l’etude Notre alimentation se traduit au niveau plasmatique par la presence de divers metabolites exogenes et endogenes. L’identification de profils de metabolites plasmatiques lies a la nutrition permettrait d’ameliorer les mesures d’exposition alimentaire en epidemiologie nutritionnelle et de mieux comprendre les effets biologiques de certains composants alimentaires et notamment ceux de la qualite de l’alimentation sur la sante. Notre objectif etait donc de determiner les associations entre differents profils alimentaires et les profils de metabolites plasmatiques. Materiel et methodes Cette etude incluait 160 femmes de la cohorte SU.VI.MAX (≥10 R24). Ces femmes ont ete selectionnees selon leur niveau d’adequation aux recommandations du Programme National Nutrition Sante ; 80 femmes au sein du 10e decile de la distribution du score PNNS-GS, appariees avec 80 femmes du 1er decile. Les echantillons de plasma recoltes a l’inclusion, ont ete analyses en spectrometrie de masse non ciblee. Les donnees ont ete analysees avec des methodes de regressions logistiques conditionnelles (1e vs 10e decile du score PNNS-GS) et des analyses de correlations avec les composantes du score PNNS-GS et avec des patterns issus d’une Analyse en Composante Principale (ACP) realisee sur 29 groupes d’aliments. Resultats et analyse statistique Au total, 30 ions etaient associes aux donnees nutritionnelles, l’identification de ces ions est en cours. 10 ions etaient associes au score PNNS-GS (FDR |0,2|) ; 4 associes negativement au profil alimentaire « Western », dont l’acide pipecolique ; 5 associes positivement au profil alimentaire « Healthy », dont l’acide pipecolique et 1 associe negativement a ce dernier profil ; 1 associe negativement au profil alimentaire « Produits sucres ». L’acide pipecolique etait egalement associe (FDR |0,2|) a la composante « fruits et legumes » du score PNNS-GS. Conclusion Cette etude met en evidence des associations entre profils metabolomiques et profils alimentaires obtenus par deux methodes complementaires. Les premiers resultats permettent de reveler des signatures metabolomiques liees a des profils alimentaires differents. L’identification de ces signatures pourrait contribuer a terme a l’amelioration de l’evaluation des expositions nutritionnelles dans les etudes epidemiologiques et a une meilleure comprehension des mecanismes impliques dans les relations nutrition-sante.

Published
2018

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

Author

C.I. Mack, Pierre Micheau, Marynka Ulaszewska, Sabine E. Kulling, Natalia Vázquez-Manjarrez, Bub Achim, Delphine Centeno, Stéphanie Durand, Lars O. Dragsted, Mélanie Pétéra, Charlotte Joly, Christopher Weinert, Estelle Pujos-Guillot, and Claudine Manach

Subjects
Meal, Metabolomics, Untargeted metabolomics, Clinical study design, Dietary intake, Healthy subjects, food and beverages, Biomarker (medicine), Urine, Food science, Biology
Abstract

Background: Accurate assessment of dietary intake is crucial for nutritional and health research. However, the dietary assessment tools currently used, such as dietary records or food frequency questionnaires, are subject to different factors that result in inaccurate information. The use of biomarkers of intake to determine dietary exposure offers more objective and potentially more precise information compared to the currently used dietary assessment tools. The identification of biomarkers of intake for highly consumed foods i.e. banana, may promote further research on their impact on human health. Banana is a widely consumed fruit in different countries. However, it has been widely neglected by the research community. Thus, identifying the biomarkers of intake of this fruit may promote further investigation on its impact on human health. Objective: To discover and validate urinary intake biomarkers of banana by applying an untargeted metabolomics approach using two different platforms, UPLC-QTOF-MS and GC×GC-MS, to analyze urine samples from two different study designs. Methods: In order to discover new biomarkers of banana intake, n=12 healthy subjects were recruited for a three arm, crossover, randomized, controlled meal study. The dietary interventions consisted of: 1) 240 g of banana, 2) 300 g of tomato and 3) 250 ml of control drink; each intervention phase was separated by a washout period of 3 days minimum. Urine samples obtained from the meal intervention study were analyzed by UPLC-QTOF-MS and GC×GC-MS. Following data-analysis, the identification of the relevant features was performed with MS/MS experiments in an Orbitrap-LTQ-XL MS instrument. To confirm the identity of the compounds in both systems, standards were acquired and conjugated when needed. In addition, banana samples were analyzed to look for compounds recovered in urine profiles. Finally, to validate the candidate biomarkers of banana, n=78 samples from an observational study, The Karlsruhe Metabolomics and Nutrition Study (KarMeN), were selected based on the volunteers’ declared amount of banana consumption using 24 h dietary recalls. Samples were grouped based on recorded intakes ( 1)high consumers of banana, 2) low consumers of banana and 3) non consumers of banana) and analysed on both platforms. Results: The discriminating compounds identified by both platforms in the meal intervention were cross-validated in the observational study. Among the highly discriminant compounds biogenic amine metabolites, methoxyphenols as well as tryptophan and carbohydrate metabolites were observed. The combination of two metabolites, methoxyeugenol glucuronide and 6-hydroxy-1-methyl-1,2,3,4-tetrahydro-b-carboline-sulfate, were validated as a parsimonious biomarker of banana intake with excellent ability to predict the intake of banana, exhibiting a ROC curve AUC (CV) of 0.92 (p

Published
2018

23. Correction to exploration of biological markers of feed efficiency in young bulls

Author

Gonzalo Cantalapiedra-Hijar, Isabelle Cassar-Malek, Hamid Boudra, Mélanie Pétéra, Anne-Marie Schiphorst, Donato Andueza, Diego P. Morgavi, Benoît Graulet, Richard Robins, Carole Migné, Sophie Laverroux, Isabelle Ortigues-Marty, S. J. Meale, Unité Mixte de Recherches 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)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Recherche Agronomique (INRA), Chimie Et Interdisciplinarité : Synthèse, Analyse, Modélisation (CEISAM), Université de Nantes - Faculté des Sciences et des Techniques, Université de Nantes (UN)-Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS), Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), 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 la Recherche Agronomique (INRA)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC), Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), and Université de Nantes (UN)-Université de Nantes (UN)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, endocrine system, animal structures, business.industry, urogenital system, marqueur biologique, animal diseases, efficacité alimentaire, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, General Chemistry, Biology, Feed conversion ratio, GeneralLiterature_MISCELLANEOUS, Biotechnology, ComputingMilieux_GENERAL, bull calf, taurillon, [SDV.IDA]Life Sciences [q-bio]/Food engineering, ComputingMethodologies_GENERAL, General Agricultural and Biological Sciences, business, reproductive and urinary physiology, biological markers
Abstract

Correction to exploration of biological markers of feed efficiency in young bulls

Published
2018

24. Systems metabolomics for prediction of metabolic syndrome

Author

Eléonore Herquelot, Mélanie Pétéra, Bernard Lyan, Charlotte Joly, Marcel Goldberg, Dhouha Grissa, Marion Brandolini, Blandine Comte, Marie Zins, Sébastien Czernichow, Estelle Pujos-Guillot, 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), UMS 011, Institut National de la Santé et de la Recherche Médicale (INSERM), Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Centre de Recherche en Nutrition Humaine d'Auvergne (CRNH d'Auvergne), 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), Cohortes épidémiologiques en population (CONSTANCES), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay-Université Paris Cité (UPCité), Département de Nutrition, Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Université Paris Descartes - Paris 5 (UPD5), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), UMR 1168, ANR-INBS-0010, INRA DID'IT Metaprogramme, MetaboHUB, UMS011 Cohortes épidémiologiques en population (CONSTANCES), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut National de la Santé et de la Recherche Médicale (INSERM), 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), ProdInra, Archive Ouverte, Unité de Nutrition Humaine - Clermont Auvergne ( UNH ), Université Clermont Auvergne ( UCA ) -Institut national de la recherche agronomique [Auvergne/Rhône-Alpes] ( INRA Auvergne/Rhône-Alpes ), Centre de Recherche en Nutrition Humaine d'Auvergne ( CRNH d'Auvergne ), UMS 011 - Cohortes épidémiologiques en population, Institut National de la Santé et de la Recherche Médicale ( INSERM ), Centre Hospitalier Régional Universitaire [Lille] ( CHRU Lille ), Université Paris Descartes - Paris 5 ( UPD5 ), Université de Versailles Saint-Quentin-en-Yvelines ( UVSQ ), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-MetaboHUB-Clermont, MetaboHUB-MetaboHUB, and Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)

Subjects
0301 basic medicine, Male, approche métabolomique, [SDV]Life Sciences [q-bio], mass spectrum analysis, Disease, Bioinformatics, Biochemistry, Human health, systems approach, spectrométrie de masse, database, syndrome métabolique, Systems Biology, metabolic x syndrome, cohort, [ SDV.MHEP.EM ] Life Sciences [q-bio]/Human health and pathology/Endocrinology and metabolism, Middle Aged, [SDV.MHEP.EM]Life Sciences [q-bio]/Human health and pathology/Endocrinology and metabolism, metabolomics, 3. Good health, [SDV] Life Sciences [q-bio], Phenotype, Disease Progression, Biomarker (medicine), biomarker, France, biomarqueur, Metabolic profile, Validation study, Food habits, Computational biology, Biology, metabolic syndrome, analyse métabolomique, 03 medical and health sciences, Metabolomics, Predictive Value of Tests, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, [ SDV.MHEP ] Life Sciences [q-bio]/Human health and pathology, cohorte, medicine, Humans, General Chemistry, medicine.disease, 030104 developmental biology, Case-Control Studies, [ CHIM.ANAL ] Chemical Sciences/Analytical chemistry, Metabolic syndrome, Biomarkers, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology
Abstract

We thank Yunfei Liu for his contribution concerning correlation network analyses. All metabolomics analyses were performed within the metaboHUB French infrastructure (ANR-INBS-0010). This study was supported by the INRA DID'IT Metaprogramme. The GAZEL cohort received approvals from the National Commission for Data Processing and Freedoms (CNIL), the National Medical Council and the National Consultative Committee of Ethics, and the INSERM IRB. The study protocol was approved by the GAZEL Scientific Committee. All volunteers gave written and informed consent for this study. All the experiments conformed to the principles set out in the WMA Declaration of Helsinki and the Department of Health and Human Services Belmont Report; The evolution of human health is a continuum of transitions, involving multifaceted processes at multiple levels, and there is an urgent need for integrative biomarkers that can characterize and predict progression toward disease development. The objective of this work was to perform a systems metabolomics approach to predict metabolic syndrome (MetS) development. A case-control design was used within the French occupational GAZEL cohort (n =112 males: discovery study n = 94: replication/validation study). Our integrative strategy was to combine untargeted metabolomics with clinical, sociodemographic, and food habit parameters to describe early phenotypes and build multidimensional predictive models. Different models were built from the discriminant variables, and prediction performances were optimized either when reducing the number of metabolites used or when keeping the associated signature. We illustrated that a selected reduced metabolic profile was able to reveal subtle phenotypic differences 5 years before MetS occurrence. Moreover, resulting metabolomic markers, when combined with clinical characteristics, allowed improving the disease development prediction. The validation study showed that this predictive performance was specific to the MetS component. This work also demonstrates the interest of such an approach to discover subphenotypes that will need further characterization to be able to shift to molecular reclassification and targeting of MetS

Published
2017

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

Author

Aicha Demidem, Mathilde Touvier, Emmanuelle Kesse-Guyot, Lucie Lécuyer, Pilar Galan, Bernard Lyan, Thomas Ferreira, Claudine Manach, Marie Lagree, Paule Latino-Martel, Valentin Partula, Céline Dalle, Mélanie Deschasaux, Stéphanie Durand, Bernard Srour, Estelle Pujos-Guillot, Marie-Paule Vasson, Mélanie Pétéra, S. Hercberg, Adrien Rossary, Delphine Centeno, Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM), Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Centre de Recherche en Nutrition Humaine, Département de Santé Publique, Hôpital Avicenne [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), CHU Clermont-Ferrand, Fondation ARC., Réseau National Alimentation Cancer Recherche (réseau NACRe). FRA., ProdInra, Archive Ouverte, département de santé publique, Centre Jean Perrin [Clermont-Ferrand] (UNICANCER/CJP), UNICANCER, Societe Francaise de Nutrition Enterale et Parenterale (SFNEP). et Societe Francaise de Nutrition (SFN), FRA., Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), Hôpital Avicenne, Assistance Publique - Hôpitaux de Paris (AP-HP), Centre Jean Perrin - Centre Régional de Lutte contre le Cancer d'Auvergne, and Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)

Subjects
0303 health sciences, Nutrition and Dietetics, [CHIM.ANAL] Chemical Sciences/Analytical chemistry, 030309 nutrition & dietetics, Endocrinology, Diabetes and Metabolism, cancer du sein, mass spectrum analysis, étude prospective, 030209 endocrinology & metabolism, [SDV.CAN]Life Sciences [q-bio]/Cancer, mammary malignant tumor, 3. Good health, 03 medical and health sciences, [SDV.AEN] Life Sciences [q-bio]/Food and Nutrition, 0302 clinical medicine, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, spectrométrie de masse, Internal Medicine, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, métabolomique, plasma
Abstract

Remerciements : Institut National du Cancer (INCa) / Institut Fédératif de Recherche biomédicale (IFRB) / Canceropôle Ilede- France / Réseau NACReRemerciements : Institut National du Cancer (INCa) / Institut Fédératif de Recherche biomédicale (IFRB) / Canceropôle Ilede-France / Réseau NACRe; Introduction et but de l’étude : La métabolomique permet d’étudier l’ensemble des métabolites présents dans unbiofluide. Cette étude basée sur une analyse non ciblée en spectrométrie de masse a pour but d’étudier si des profils métabolomiques établis à partir d’un prélèvement sanguin sur des femmes à priori en bonne santé, pourrait contribuer à prédire le risque à long terme de développer le cancer du sein et d’améliorer la compréhension de son étiologie.Matériel et méthodes : Un cas-témoin niché prospectif a été mené dans la cohorte SU.VI.MAX, incluant 211 cas de cancer du sein et 211 témoins appariés. Les profils métabolomiques ont été établis sur des échantillons de plasma prélevés à l’inclusion, avant l’apparition du cancer, grâce à une analyse non ciblée par spectrométrie de masse (LC-MS). Des modèles de régressions logistiques conditionnelles multivariées ont été calculés sur chaque ion présélectionné grâce à une ANOVA et sur les combinaisons de potentiels biomarqueurs issus d’une Analyse en Composante Principale. Une correction de test multiple de type FDR (False Discovery Rate) a été appliquée.Résultats et Analyse statistique : Plusieurs métabolites étaient associés au risque de cancer du sein. La finalisation de l’identification des métabolites discriminants est en cours. Au vu des résultats actuels, les femmes caractérisées par un niveau plasmatique plus élevé de valine (OR=1,45 [1,15-1,83]; p= 0.009), de phénylalanine (OR=1,43 [1,14-1,78]; p=0,009), de tryptophane (OR=1,4 [1,1-1,79]; p=0,01), et de glutamine (OR=1,33 [1,07-1,66]; p=0,01) et d’un niveau plus bas d’OSuccinyl-L-homoserine (OR=0,7 [0,55-0,89]; p=0,01) auraient un risque plus important de développer le cancer du sein dans la décennie qui suit.Conclusion : Cette étude suggère plusieurs associations entre profils métabolomiques à l’inclusion et risque à long terme de développer le cancer du sein et confirme certains résultats que nous avons mis en évidence par une analyse RMN. Cette étude pourrait aider à améliorer l’identification des femmes à risque de développer un cancer du sein, bien avant l’apparition des symptômes. L’étude des biomarqueurs nutritionnels associés au risque de développer cette pathologie (actuellement en cours) pourra permettre de préciser le rôle de la nutrition (facteur modifiable) dans l’étiologie du cancer du sein et d’améliorer les stratégies préventives.

Published
2017

26. New metabolites of dietary terpenoids identified using in silico prediction of metabolism and high-resolution mass spectrometry

Author

Yannick Djoumbou Feunang, Céline Dalle, Mélanie Pétéra, Jarlei Fiamoncini, Stéphanie Durand, Claudine Manach, and David S. Wishart

Subjects
Terpene, chemistry.chemical_compound, Limonene, chemistry, Biochemistry, Myrcene, Perillyl alcohol, Pharmacometabolomics, Metabolome, Geraniol, Terpenoid
Abstract

Despite being well absorbed and displaying a range of biological properties, dietary terpenoids have been little studied. Better knowledge about their metabolism will help understanding the health effects of plant foods and provide information on the food metabolome. As part of the FoodBAll project we are investigating the metabolism of terpenes, identifying metabolites and biotransformations involved in their metabolism. PhytoHub (database that compiles all known metabolites of dietary phytochemicals, including terpenes) and Nexus Meteor (in silico prediction of metabolism), were used to identify biotransformations involved in the metabolism of dietary terpenoids. Selected biotransformations were used to predict the metabolism of camphene, camphor, carvacrol, carvone, caryophyllene, 1,4-cineole, 1,8-cineole, citral, citronellal, cuminaldehyde, p-cymene, fenchone, geraniol, limonene, linalool, menthol, myrcene, nootkatone, perillyl alcohol, pinene, pulegone, terpinen-4-ol and thymol. The metabolism prediction tool “BioTransformer” that is under development at Dr. Wishart’s lab was also used to generate predicted metabolites of the mentioned compounds. The urine of Wistar rats was collected before and after 5 days of the exposure to the dietary monoterpenes (given in the dose of 0.05% of diet). Untargeted metabolomics analysis was performed in urine using high-resolution mass spectrometry (UPLC-QToF). We identified twenty-two enzymatic reactions involved in the synthesis of terpenoid metabolites described in the literature. In average, 10 metabolites per compound were identified in rat urine, including new and known ones. Identification of metabolites was based on monoisotopic mass and formula match, presence of adducts and specific mass losses indicative of glucuronidation, sulphation and conjugation to amino acids. Validation of identification is being done using MS/MS experiments. The combination of in silico predictions and in vivo experiment allowed the identification of known and new metabolites of different dietary terpenoids. Predicted metabolites of terpenes will be added in databases such as PhytoHub to complement the database of known metabolites. The validations of the metabolism predictions are helping the development of BioTransformer. Funding: Grant from the Agence Nationale de la Recherche (#ANR-14-HDHL-0002-02) for FoodBAll project (JPI HDHL). JF is an AgreenSkills fellow.

Published
2017

27. EPMA world congress : traditional forum in predictive, preventive and personalised medicine for multi-professional consideration and consolidation

Author

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

Subjects
Personalized medicine -- Abstracts, business.industry, Health Policy, Biochemistry (medical), Computational biology, Integrated approach, Cancer -- Chemotherapy -- Congresses, Proteomics, medicine.disease, Precision medicine -- Abstracts, 3. Good health, Cancer -- Prevention -- Congresses, Systems medicine, 03 medical and health sciences, 0302 clinical medicine, Metabolomics, 030220 oncology & carcinogenesis, Drug Discovery, medicine, Identification (biology), Metabolic syndrome, business, Medicine, Preventive -- Abstracts, Patient stratification, 030217 neurology & neurosurgery
Abstract

EPMA was created in 2009 as the Forum for Predictive, Preventive and Personalised Medicine (PPPM). During the 8 years since then, EPMA meetings – in the form of world congresses and international summits [1- 3] – have traditionally included multi-professional presentations, discussions, considerations, publications, and consolidation in the field of PPPM. The EPMA Journal releases over 90% of publications dedicated to PPPM worldwide, acting as the leading journal in the field. EPMA currently comprises a network of over 50 countries globally, actively contributing to the innovative concepts of PPPM. For more information, please see www.epmanet.eu The EPMAWorld Congress 2017 in Malta is specifically focused on the following topics: PPPM in Cancer, PPPM in Neurodegenerative Neurological Neuropsychiatric Disorders (NNND), PPPM in Cardiovascular Disease, PPPM in Diabetes Mellitus and related pathologies, PPPM in Dentistry, Innovative Technologies in PPPM, and Innovative PPPM Strategies in Healthcare. [Excerpt], peer-reviewed

Published
2017

28. Identification de biomarqueurs nutritionnels par une approche métabolomique en spectrométrie de masse

Author

Pilar Galan, Mélanie Deschasaux, Pierre Micheau, L. Lécuyer, Mathilde Touvier, Claudine Manach, Delphine Centeno, Valentin Partula, Mélanie Pétéra, Bernard Srour, Estelle Pujos-Guillot, Bernard Lyan, Serge Hercberg, Paule Latino-Martel, Emmanuelle Kesse-Guyot, Christine Morand, Céline Dalle, Stéphanie Durand, Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM), Département de Santé Publique, Hôpital Avicenne [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Université Paris Diderot - Paris 7 (UPD7)-Université Sorbonne Paris Cité (USPC)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA), Equipe 3: EREN- Equipe de Recherche en Epidémiologie Nutritionnelle (CRESS - U1153), Université Paris 13 (UP13)-Institut National de la Recherche Agronomique (INRA)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Université Paris Diderot - Paris 7 (UPD7)-Université Sorbonne Paris Cité (USPC)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Sorbonne Paris Cité (USPC)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA), Unité de Recherche en Epidémiologie Nutritionnelle (UREN), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Sorbonne Paris Cité (USPC)-Université Paris 13 (UP13)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Institut National de la Recherche Agronomique (INRA), Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC), Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC), Université Paris 13 (UP13)-Institut National de la Recherche Agronomique (INRA)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Université Paris 13 (UP13)-Institut National de la Recherche Agronomique (INRA)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
2. Zero hunger, 0303 health sciences, étude épidémiologique, Nutrition and Dietetics, 030309 nutrition & dietetics, Endocrinology, Diabetes and Metabolism, Chimie analytique, 030209 endocrinology & metabolism, rôle nutritionnel, 3. Good health, 03 medical and health sciences, Santé publique et épidémiologie, 0302 clinical medicine, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, Alimentation et Nutrition, Internal Medicine, Food and Nutrition, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, Analytical chemistry, biomarqueur, ComputingMilieux_MISCELLANEOUS, métabolomique
Abstract

International audience; Introduction et but de l’étude: Ce travail s’inscrit dans le cadre du projet Metabo-KS (2015-2017, INCa, PI : M Touvier), qui vise à: découvrir les biomarqueurs prédictifs du cancer du sein, de la qualité du régime alimentaire et d’aliments spécifiques en utilisant la métabolomique, puis à relier ces biomarqueurs pour améliorer la compréhension du rôle de la nutrition sur le risque de cancer du sein. Le présent travail porte sur la découverte de biomarqueurs plasmatiques des apports alimentaires usuels par l'exploration du métabolome alimentaire (« food metabolome ») par spectrométrie de masse non ciblée. Matériel et méthodes: Les femmes de la cohorte SU.VI.MAX ayant rempli au moins dix enregistrements alimentaires de 24h ont été stratifiées en déciles selon leur niveau d'adhésion aux recommandations du Programme français National Nutrition Santé. Au total, 80 femmes ont été sélectionnées aléatoirement dans le 10e décile de la distribution PNNS-GS et ont été appariées sur 80 femmes du 1er décile. Les échantillons de plasma prélevés à l’inclusion ont été analysés en spectrométrie de masse non ciblée en utilisant UPLC-ESI-QToF. 1575 et 601 signaux ont été détectés (mode positif et négatif). Les profils métabolomiques ont été comparés en utilisant des méthodes statistiques univariées et multivariées pour sélectionner les ions associés à la consommation de 58 aliments / groupes d’aliments. Résultats et Analyse statistique : 84 et 30 ions (mode positif et négatif) étaient associés à des aliments spécifiques / des groupes alimentaires (r> 0,3, valeur p avec BH

Published
2017

29. Exploration of biological markers of feed efficiency in young bulls

Author

Sarah J, Meale, Diego P, Morgavi, Isabelle, Cassar-Malek, Donato, Andueza, Isabelle, Ortigues-Marty, Richard J, Robins, Anne-Marie, Schiphorst, Carole, Migné, Mélanie, Pétéra, Sophie, Laverroux, Benoit, Graulet, Hamid, Boudra, Gonzalo, Cantalapiedra-Hijar, 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 la Recherche Agronomique (INRA)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Université de Nantes (UN), European Union's Horizon research and innovation program under the Marie Sklodowska-Curie grant 658126, and APIS-GENE

Subjects
0301 basic medicine, Vitamin b, Male, [SDV.OT]Life Sciences [q-bio]/Other [q-bio.OT], Meat, Silage, animal diseases, energetic efficiency, Fractionation, Biology, Poaceae, Feed conversion ratio, vitamin B, 03 medical and health sciences, Feces, Animal science, Animals, Dry matter, Amino Acids, amino acid catabolism, 2. Zero hunger, nitrogen fractionation, Spectroscopy, Near-Infrared, business.industry, 0402 animal and dairy science, Animal Structures, 04 agricultural and veterinary sciences, General Chemistry, Vitamins, 040201 dairy & animal science, Animal Feed, Biotechnology, 030104 developmental biology, Cattle, Residual feed intake, General Agricultural and Biological Sciences, business, Biomarkers, metabolomic
Abstract

International audience; The efficiency with which ruminants convert feed to desirable products is difficult to measure under normal commercial settings. We explored the use of potential biological markers from easily obtainable samples, that is, blood, hair, and feces, to characterize potential causes of divergent efficiency when considered as residual feed intake (RFI) or feed conversion efficiency (FCE). A total of 54 Charolais bulls, 20 in period 1 and 34 in period 2, were examined for individual dry matter intake (DMI) and growth. Bulls were offered a diet of 70:30 wrapped grass silage to concentrate for 99 d. At the conclusion of the test period, blood samples were collected for the determination of vitamins B2 and B6, and plasma used for the determination of metabolites, natural isotopic 15N abundance (15N NIA, expressed as δ15N ‰) and fractionation (Δ15Nplasma proteins‑diet and Δ13Cplasma proteins‑diet) and near-infrared spectroscopy (NIRS). Feces were analyzed by NIRS. Bulls were slaughtered at 15−17 months of age and carcass characteristics determined. Bulls were ranked according to RFI with extremes (SD ± 0.5; n = 31) classified as either efficient (Neg-RFI) or inefficient (Pos-RFI). Extreme bulls were then classified for FCE (high vs low FCE), changing the groups. Pos-RFI bulls consumed 14% more feed than Neg-RFI bulls for the same level of weight gain. Low FCE bulls tended to eat more, but had lower weight gains than high FCE bulls. No differences were detected in carcass conformation, fat scores, hot carcass weight, or dressing percentage. Yet, heart and bladder weights were heavier in Pos-RFI, and rumen weight tended to be heavier in Pos-RFI bulls. RFI did not affect bulk 15N or 13C fractionation. A negative correlation was observed between FCE and Δ15Nplasma proteins‑diet. Inefficient bulls (Pos-RFI) had higher δ15N in glycine compared to Neg-RFI bulls. Similarly, metabolomic analysis showed a tendency for concentrations of glycine and sarcosine to be elevated in Pos-RFI bulls, whereas aspartic acid and carnosine tended to be elevated, and serine tended to be lower in High FCE. Among vitamins, only flavin adenine dinucleotide concentration was higher in the blood of bulls with High FCE. These results suggest that the two feed efficiency metrics differ in the underlying mechanisms of metabolism, where RFI is driven by differences in the energetic requirements of visceral organs and the extent of AA catabolism.

Published
2017

30. Weight for gestational age and metabolically healthy obesity in adults from the Haguenau cohort

Author

Mélanie Pétéra, Claire Carette, Estelle Pujos-Guillot, Joane Matta, Marie Zins, Sébastien Czernichow, Claire Levy Marchal, Blandine Comte, Julien Bertrand, ProdInra, Migration, UMS011, Institut National de la Santé et de la Recherche Médicale (INSERM), Département Nutrition, Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Unité d'épidémiologie clinique, Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université, and Université Paris Descartes - Paris 5 (UPD5)

Subjects
Blood Glucose, Male, Pediatrics, Epidemiology, body-mass index, cardiovascular-disease, 030204 cardiovascular system & hematology, Cohort Studies, 0302 clinical medicine, Risk Factors, Metabolically healthy obesity, Medicine, health care economics and organizations, reproductive and urinary physiology, 2. Zero hunger, education.field_of_study, Gestational age, General Medicine, female genital diseases and pregnancy complications, Santé publique et épidémiologie, trouble métabolique, Quartile, Alimentation et Nutrition, Cohort, Infant, Small for Gestational Age, Female, France, PUBLIC HEALTH, diabète, Cohort study, Adult, medicine.medical_specialty, education, Population, NUTRITION & DIETETICS, 030209 endocrinology & metabolism, Gestational Age, 03 medical and health sciences, Young Adult, Food and Nutrition, Humans, patient obèse, Obesity, Metabolically Benign, business.industry, Research, Body Weight, Infant, Newborn, [SDV.AEN] Life Sciences [q-bio]/Food and Nutrition, âge gestationnel, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, Relative risk, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Insulin Resistance, business, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, Body mass index
Abstract

Background An obesity subphenotype, named ‘metabolically healthy obese’ (MHO) has been recently defined to characterise a subgroup of obese individuals with less risk for cardiometabolic abnormalities. To date no data are available on participants born with small weight for gestational age (SGA) and the risk of metabolically unhealthy obesity (MUHO). Objective Assess the risk of MUHO in SGA versus appropriate for gestational age (AGA) adult participants. Methods 129 young obese individuals (body mass index ≥30 kg/m²) from data of an 8-year follow-up Haguenau cohort (France), were identified out of 1308 participants and were divided into 2 groups: SGA (n=72) and AGA (n=57). Metabolic characteristics were analysed and compared using unpaired t-test. The HOMA-IR index was determined for the population and divided into quartiles. Obese participants within the first 3 quartiles were considered as MHO and those in the fourth quartile as MUHO. Relative risks (RRs) and 95% CI for being MUHO in SGA versus AGA participants were computed. Results The SGA-obese group had a higher risk of MUHO versus the AGA-obese group: RR=1.27 (95% CI 1.10 to 1.6) independently of age and sex. Conclusions In case of obesity, SGA might confer a higher risk of MUHO compared with AGA.

Published
2016

31. Feature selection methods for early predictive biomarker discovery using untargeted metabolomic data

Author

Estelle Pujos-Guillot, Dhouha Grissa, Blandine Comte, Marion Brandolini, Mélanie Pétéra, Amedeo Napoli, Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université, Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), INRA DID’IT Metaprogramme, Clermont Université-Université d'Auvergne - Clermont-Ferrand I (UdA)-Institut National de la Recherche Agronomique (INRA), Knowledge representation, reasonning (ORPAILLEUR), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Métaprogramme INRA, Projet 'Diapason', Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université-Institut National de la Recherche Agronomique (INRA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), and Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
0301 basic medicine, Computer science, méthode de prédiction, [SDV]Life Sciences [q-bio], Feature selection, Context (language use), Overfitting, feature selection, untargeted metabolomics, biomarker discovery, prediction, computer.software_genre, 01 natural sciences, Biochemistry, Genetics and Molecular Biology (miscellaneous), Biochemistry, analyse métabolomique, 03 medical and health sciences, formal concept analysis, Knowledge extraction, spectrométrie de masse, biomarqueur métabolique, Resampling, phénotypage, Food and Nutrition, Molecular Biosciences, [INFO]Computer Science [cs], Biomarker discovery, bioinformatique, Molecular Biology, visualization, Original Research, univariate statistics, 010401 analytical chemistry, Univariate, metabolomics, 0104 chemical sciences, 030104 developmental biology, Workflow, machine learning, ComputingMethodologies_PATTERNRECOGNITION, chemométrie, Alimentation et Nutrition, biomarker, Data mining, pathologie humaine, computer, biomarqueur, métabolomique
Abstract

International audience; Untargeted metabolomics is a powerful phenotyping tool for better understanding biological mechanisms involved in human pathology development and identifying early predictive biomarkers. This approach, based on multiple analytical platforms, such as mass spectrometry (MS), chemometrics and bioinformatics, generates massive and complex data that need appropriate analyses to extract the biologically meaningful information. Despite various tools available, it is still a challenge to handle such large and noisy datasets with limited number of individuals without risking overfitting. Moreover, when the objective is focused on the identification of early predictive markers of clinical outcome, few years before occurrence, it becomes essential to use the appropriate algorithms and workflow to be able to discover subtle effects among this large amount of data. In this context, this work consists in studying a workflow describing the general feature selection process, using knowledge discovery and data mining methodologies to propose advanced solutions for predictive biomarker discovery. The strategy was focused on evaluating a combination of numeric-symbolic approaches for feature selection with the objective of obtaining the best combination of metabolites producing an effective and accurate predictive model. Relying first on numerical approaches, and especially on machine learning methods (SVM-RFE, RF, RF-RFE) and on univariate statistical analyses (ANOVA), a comparative study was performed on an original metabolomic dataset and reduced subsets. As resampling method, LOOCV was applied to minimize the risk of overfitting. The best k-features obtained with different scores of importance from the combination of these different approaches were compared and allowed determining the variable stabilities using Formal Concept Analysis. The results revealed the interest of RF-Gini combined with ANOVA for feature selection as these two complementary methods allowed selecting the 48 best candidates for prediction. Using linear logistic regression on this reduced dataset enabled us to obtain the best performances in terms of prediction accuracy and number of false positive with a model including 5 top variables. Therefore, these results highlighted the interest of feature selection methods and the importance of working on reduced datasets for the identification of predictive biomarkers issued from untargeted metabolomics data.

Published
2016

32. Comparaison des apports alimentaires de sujets nés avec un petit poids de naissance versus ceux de poids normal dans la cohorte Haguenau

Author

Julien Bertrand, Estelle Pujos-Guillot, Blandine Comte, Sébastien Czernichow, Claire Levy-Marchal, Claire Carette, Joane Matta, Marie Zins, Mélanie Pétéra, UMS 011, Institut National de la Santé et de la Recherche Médicale (INSERM), Nutrition, Nestlé Institute of Health Sciences SA [Lausanne, Switzerland], Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), Vieillissement et Maladies chroniques : approches épidémiologique et de santé publique (VIMA), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut National de la Santé et de la Recherche Médicale (INSERM), ProdInra, Archive Ouverte, Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université, Société Francophone Nutrition Clinique et Métabolisme (SFNEP). FRA., Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université-Institut National de la Recherche Agronomique (INRA), and Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)

Subjects
0303 health sciences, adulte, Nutrition and Dietetics, syndrome métabolique, 030309 nutrition & dietetics, Endocrinology, Diabetes and Metabolism, adult, poids à la naissance, metabolic x syndrome, birth weight, 03 medical and health sciences, [SDV.AEN] Life Sciences [q-bio]/Food and Nutrition, 0302 clinical medicine, 030220 oncology & carcinogenesis, Alimentation et Nutrition, Internal Medicine, Food and Nutrition, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, apport alimentaire
Abstract

Introduction et but de l’étude: Contexte : Le petit poids de naissance a été associé à un risque accru de syndrome métabolique (SM) à l’âge adulte. Il a aussi été montré que les apports alimentaires pouvaient moduler le risque de SM. Cependant, il n’existe aucune donnée décrivant l’alimentation à l’âge adulte de sujets nés avec un petit poids de naissance (SGA) versus des sujets nés de poids normal (AGA). Objectif : Comparer l’apport alimentaire de sujets adultes nés SGA versus de sujets nés AGA. Matériel et méthodes: Design : Analyse transversale de données issues de la cohorte Haguenau. Participants : 172 adultes nés SGA et 229 adultes nés AGA parmi les 536 adultes de la cohorte ayant des données alimentaires disponibles. Les sujets étaient âgés de 22 ± 3,8 ans; 46% de sexe masculin avec un IMC moyen de 22,5 ± 3,9 kg/m². Méthodes: L’apport alimentaire a été calculé en se basant sur un questionnaire alimentaire de 19 items portant sur différents groupes d’aliments. Des analyses univariées ont été réalisées sur ces aliments. Des analyses de correspondances multiples ont été utilisées pour déterminer des typologies alimentaires. Un score d’adéquation alimentaire au Programme National Nutrition Santé (PNNS) a été calculé. Le score inclut 8 items, la valeur la plus basse correspondant à un apport alimentaire le moins en adéquation avec les recommandations. Les risques relatifs (IC 95%) dans le quartile 1 du score versus les quartiles 2, 3, et 4 chez les sujets nés SGA versus AGA ont été calculés. Résultats et Analyse statistique: Résultats : En analyses univariées, les sujets nés SGA consommaient moins de poisson (p=0.045) et plus de viande (p=0.034) et dessert (p=0.035) que les sujets nés AGA. Les analyses de correspondances multiples n’ont pas donné de résultats significatifs. Le risque relatif d’appartenir au quartile PNNS le plus bas chez les sujets SGA n’était pas significativement différent comparé à celui des sujets nés AGA ; RR= 0,94 (0,72; 1,23, avec et sans ajustement pour l’âge et le sexe). Conclusion: Conclusions : Cette étude est la première à comparer les apports alimentaires à l’âge adulte de sujets nés avec un petit poids de naissance versus ceux avec un poids normal. En dehors de différences minimes observées en analyse univariée, il n’a pas été retrouvé de différence sur les scores alimentaires définis a priori ou a posteriori. Ces résultats suggèrent que les paramètres associés à l’empreinte fœtale joueraient un rôle prépondérant dans le développement du SM à l’âge adulte. Cependant, d’autres études seraient nécessaires pour comprendre l’impact des apports alimentaires et de l’activité physique sur le statut métabolique des sujets nés SGA

Published
2016

33. Dietary intake in young adults born small or appropriate for gestational age: data from the Haguenau cohort

Author

Claire Carette, Blandine Comte, Claire Levy Marchal, Julien Bertrand, Estelle Pujos-Guillot, Marie Zins, Joane Matta, Mélanie Pétéra, Sébastien Czernichow, UMS 011, Institut National de la Santé et de la Recherche Médicale (INSERM), Département Nutrition, Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Unité d'épidémiologie clinique, Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université, Université Paris Descartes - Paris 5 (UPD5), Hôpital Européen Georges Pompidou, Assistance Publique – Hôpitaux de Paris (AP-HP), Clermont Université - Université d'Auvergne - Clermont-Ferrand I (UdA) - Institut National de la Recherche Agronomique (INRA), Université Paris Descartes (Paris 5), and ProdInra, Archive Ouverte

Subjects
Male, 0301 basic medicine, Pediatrics, Multivariate analysis, Child Development, 0302 clinical medicine, Epidemiology, 030212 general & internal medicine, Young adult, Infant Nutritional Physiological Phenomena, reproductive and urinary physiology, 2. Zero hunger, Univariate analysis, [SDV.MHEP] Life Sciences [q-bio]/Human health and pathology, syndrome métabolique, public health, metabolic x syndrome, General Medicine, Programme National Nutrition Santé, epidemiology, nutrition & dietetics, female genital diseases and pregnancy complications, 3. Good health, Santé publique et épidémiologie, Quartile, Alimentation et Nutrition, Infant, Small for Gestational Age, Cohort, Female, France, medicine.medical_specialty, Médecine humaine et pathologie, Gestational Age, 03 medical and health sciences, medicine, Food and Nutrition, Humans, 030109 nutrition & dietetics, business.industry, Research, questionnaire, Infant, Newborn, Infant, medicine.disease, [SDV.AEN] Life Sciences [q-bio]/Food and Nutrition, Cross-Sectional Studies, âge gestationnel, Socioeconomic Factors, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, Relative risk, Small for gestational age, Human health and pathology, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Energy Intake, business, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, Demography
Abstract

This study was funded by the Fondation Francophone pour la recherche sur le diabete; Objectives Compare the dietary intake of young adults born small for gestational age (SGA) versus those born appropriate for gestational age (AGA).Design Cross-sectional analysis.Setting Data at the 8-year follow-up Haguenau cohort (France). Data from 229 AGA-born adults and 172 SGA-born adults with available dietary information are presented.Methods Dietary intake was based on a food questionnaire including 19 items. The χ2 test was run to compare intake between SGA-born and AGA-born individuals. An a priori score was calculated based on the adherence to recommendations from the French Nutrition and Health Program and included 8 components with the lowest value indicating a lower adherence to recommendations. The score was then divided into quartiles. Relative risks and 95% CIs, controlling for age and sex in multivariate analysis, were calculated in order to determine the risk of belonging to the first versus the second to the fourth quartiles in SGA-born and AGA-born individuals.Results Intakes of SGA-born adults indicated that they consumed more meat, sugar and less fish than AGA-born individuals (all p

Published
2016

34. Variations in the metabolome in response to disease activity of rheumatoid arthritis

Author

Carole Migné, Mélanie Pétéra, Jacques Tebib, Hubert Marotte, Martin Soubrier, Estelle Pujos Guillot, Thierry Lequerré, Zuzana Tatar, Philippe Gaudin, Rhumatologie, CHU Strasbourg, Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), CHU Bois-Guillaume, U 1059, Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Lyon, Hospital and Clinical Research Program, Passerelle 2012 founding (Pfizer), Unité de Nutrition Humaine (UNH), Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université-Institut National de la Recherche Agronomique (INRA), CHU Clermont-Ferrand, CHU Grenoble, CHU Rouen, Normandie Université (NU), Physiopathologie et biothérapies des maladies inflammatoires et autoimmunes, Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Normandie Université (NU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Lyon (COMUE), Centre Hospitalier Universitaire de Lyon, Tatar, Zuzana, Institut National de la Recherche Agronomique (INRA)-Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université, and ProdInra, Archive Ouverte

Subjects
0301 basic medicine, Male, rheumatoid arthritis, Necrosis, [SDV]Life Sciences [q-bio], Bioinformatics, Mass Spectrometry, drug response biomarkers, Etanercept, Arthritis, Rheumatoid, 0302 clinical medicine, Orthopedics and Sports Medicine, anti-TNF-alpha therapy, ComputingMilieux_MISCELLANEOUS, Chromatography, Reverse-Phase, [SDV.MHEP] Life Sciences [q-bio]/Human health and pathology, facteur de nécrose tumorale, Discriminant Analysis, drug treatment, échantillon de plasma, Middle Aged, Prognosis, metabolomics, 3. Good health, Treatment Outcome, Rheumatoid arthritis, Antirheumatic Agents, Alimentation et Nutrition, Metabolome, Tumor necrosis factor alpha, Female, medicine.symptom, metabolomics and rheumatoid arthritis (RA), profil métabolique, Research Article, Adult, medicine.medical_specialty, anti-TNF therapy, Médecine humaine et pathologie, Disease activity, 03 medical and health sciences, Metabolomics, Rheumatology, Internal medicine, medicine, Humans, Food and Nutrition, In patient, Aged, 030203 arthritis & rheumatology, business.industry, Tumor Necrosis Factor-alpha, Adalimumab, biomarkers, medicine.disease, Infliximab, Surgery, [SDV.AEN] Life Sciences [q-bio]/Food and Nutrition, 030104 developmental biology, Human health and pathology, business, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology
Abstract

Background Anti-Tumor Necrosis Factor (TNF) therapies are able to control rheumatoid arthritis (RA) disease activity and limit structural damage. Yet no predictive factor of response to anti-TNF has been identified. Metabolomic profile is known to vary in response to different inflammatory rheumatisms so determining it could substantially improve diagnosis and, consequently, prognosis. The aim of this study was to use mass spectrometry to determine whether there is variation in the metabolome in patients treated with anti-TNF and whether any particular metabolomic profile can serve as a predictor of therapeutic response. Methods Blood samples were analyzed in 140 patients with active RA before initiation of anti-TNF treatment and after 6 months of Anti-TNF treatment (100 good responders and 40 non-responders). Plasma was deproteinized, extracted and analyzed by reverse-phase chromatography–QToF mass spectrometry. Extracted and normalized ions were tested by univariate and ANOVA analysis followed by partial least-squares regression-discriminant analysis (PLS-DA). Orthogonal Signal Correction (OSC) was also used to filter data from unwanted non-related effects. Disease activity scores (DAS 28) obtained at 6 months were correlated with metabolome variation findings to identify a metabolite that is predictive of therapeutic response to anti-TNF. Results After 6 months of anti-TNF therapy, 100 patients rated as good responders and 40 patients as non-responders according to EULAR criteria. Metabolomic investigations suggested two different metabolic fingerprints splitting the good-responders group and the non-responders group, without differences in anti-TNF therapies. Univariate analysis revealed 24 significant ions in positive mode (p

Published
2016

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