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

Author

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

Published
2020
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5. Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds

Author

Low, Dorrain Yanwen, primary, Micheau, Pierre, additional, Koistinen, Ville Mikael, additional, Hanhineva, Kati, additional, Abrankó, László, additional, Rodriguez-Mateos, Ana, additional, da Silva, Andreia Bento, additional, van Poucke, Christof, additional, Almeida, Conceição, additional, Andres-Lacueva, Cristina, additional, Rai, Dilip K., additional, Capanoglu, Esra, additional, Tomás Barberán, Francisco A., additional, Mattivi, Fulvio, additional, Schmidt, Gesine, additional, Gürdeniz, Gözde, additional, Valentová, Kateřina, additional, Bresciani, Letizia, additional, Petrásková, Lucie, additional, Dragsted, Lars Ove, additional, Philo, Mark, additional, Ulaszewska, Marynka, additional, Mena, Pedro, additional, González-Domínguez, Raúl, additional, Garcia-Villalba, Rocío, additional, Kamiloglu, Senem, additional, de Pascual-Teresa, Sonia, additional, Durand, Stéphanie, additional, Wiczkowski, Wieslaw, additional, Bronze, Maria Rosário, additional, Stanstrup, Jan, additional, and Manach, Claudine, additional

Published
2021
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6. Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds

Author

European Cooperation in Science and Technology, European Commission, European Research Council, Nanyang Technological University, Prime Minister's Office Singapore, Agence Nationale de la Recherche (France), Czech Science Foundation, Universidade Nova de Lisboa, Fundação para a Ciência e a Tecnologia (Portugal), Lantmännen Research Foundation, Academy of Finland, University of Eastern Finland, Instituto de Salud Carlos III, Ministerio de Economía y Competitividad (España), Generalitat de Catalunya, Agencia Estatal de Investigación (España), Ministerio de Ciencia, Innovación y Universidades (España), Institución Catalana de Investigación y Estudios Avanzados, Consejo Superior de Investigaciones Científicas (España), Fundación Séneca, Gobierno de la Región de Murcia, Norwegian Institute of Food, Fisheries and Aquaculture Research, Carlsberg Foundation, Hungarian Academy of Sciences, Low, Dorrain Yanwen, Micheau, Pierre, Koistinen, Ville Mikael, Hanhineva, Kati, Abrankó, László, Rodríguez-Mateos, Ana, Silva, Andreia Bento da, Poucke, Christof J. van, Almeida, Conceição, Andrés-Lacueva, Cristina, Rai, Dilip K., Capanoglu, Esra, Tomás Barberán, Francisco, Mattivi, Fulvio, Schmidt, Gesine, Gürdeniz, Gözde, Valentová, Kateřina, Brescian, Letizia, Petrásková, Lucie, Dragsted, Lars, Philo, Mark, Ulaszewska, Marynka, Mena, Pedro, González-Domínguez, Raúl, García-Villalba, Rocío, Kamiloglu, Senem, Pascual-Teresa, Sonia de, Durand, Stéphanie, Wiczkowski, Wieslaw, Bronze, Maria do Rosário, Stanstrup, Jan, Manach, Claudine, European Cooperation in Science and Technology, European Commission, European Research Council, Nanyang Technological University, Prime Minister's Office Singapore, Agence Nationale de la Recherche (France), Czech Science Foundation, Universidade Nova de Lisboa, Fundação para a Ciência e a Tecnologia (Portugal), Lantmännen Research Foundation, Academy of Finland, University of Eastern Finland, Instituto de Salud Carlos III, Ministerio de Economía y Competitividad (España), Generalitat de Catalunya, Agencia Estatal de Investigación (España), Ministerio de Ciencia, Innovación y Universidades (España), Institución Catalana de Investigación y Estudios Avanzados, Consejo Superior de Investigaciones Científicas (España), Fundación Séneca, Gobierno de la Región de Murcia, Norwegian Institute of Food, Fisheries and Aquaculture Research, Carlsberg Foundation, Hungarian Academy of Sciences, Low, Dorrain Yanwen, Micheau, Pierre, Koistinen, Ville Mikael, Hanhineva, Kati, Abrankó, László, Rodríguez-Mateos, Ana, Silva, Andreia Bento da, Poucke, Christof J. van, Almeida, Conceição, Andrés-Lacueva, Cristina, Rai, Dilip K., Capanoglu, Esra, Tomás Barberán, Francisco, Mattivi, Fulvio, Schmidt, Gesine, Gürdeniz, Gözde, Valentová, Kateřina, Brescian, Letizia, Petrásková, Lucie, Dragsted, Lars, Philo, Mark, Ulaszewska, Marynka, Mena, Pedro, González-Domínguez, Raúl, García-Villalba, Rocío, Kamiloglu, Senem, Pascual-Teresa, Sonia de, Durand, Stéphanie, Wiczkowski, Wieslaw, Bronze, Maria do Rosário, Stanstrup, Jan, and Manach, Claudine

Abstract

Prediction of retention times (RTs) is increasingly considered in untargeted metabolomics to complement MS/MS matching for annotation of unidentified peaks. We tested the performance of PredRet (http://predret.org/) to predict RTs for plant food bioactive metabolites in a data sharing initiative containing entry sets of 29–103 compounds (totalling 467 compounds, >30 families) across 24 chromatographic systems (CSs). Between 27 and 667 predictions were obtained with a median prediction error of 0.03–0.76 min and interval width of 0.33–8.78 min. An external validation test of eight CSs showed high prediction accuracy. RT prediction was dependent on shape and type of LC gradient, and number of commonly measured compounds. Our study highlights PredRet’s accuracy and ability to transpose RT data acquired from one CS to another CS. We recommend extensive RT data sharing in PredRet by the community interested in plant food bioactive metabolites to achieve a powerful community-driven open-access tool for metabolomics annotation.

Published
2021

7. Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds

Author

Low, Dorrain Yanwen, Micheau, Pierre, Koistinen, Ville Mikael, Hanhineva, Kati, Abrankó, László, Rodriguez-Mateos, Ana, da Silva, Andreia Bento, van Poucke, Christof, Almeida, Conceição, Andres-Lacueva, Cristina, Rai, Dilip K, Capanoglu, Esra, Barberán, Francisco A Tomás, Mattivi, Fulvio, Schmidt, Gesine, Gürdeniz, Gözde, Valentová, Kateřina, Bresciani, Letizia, Petrásková, Lucie, Dragsted, Lars Ove, Philo, Mark, Ulaszewska, Marynka, Mena, Pedro, González-Domínguez, Raúl, Garcia-Villalba, Rocío, Kamiloglu, Senem, de Pascual-Teresa, Sonia, Durand, Stéphanie, Wiczkowski, Wieslaw, Rosário Bronze, Maria, Stanstrup, Jan, Manach, Claudine, Low, Dorrain Yanwen, Micheau, Pierre, Koistinen, Ville Mikael, Hanhineva, Kati, Abrankó, László, Rodriguez-Mateos, Ana, da Silva, Andreia Bento, van Poucke, Christof, Almeida, Conceição, Andres-Lacueva, Cristina, Rai, Dilip K, Capanoglu, Esra, Barberán, Francisco A Tomás, Mattivi, Fulvio, Schmidt, Gesine, Gürdeniz, Gözde, Valentová, Kateřina, Bresciani, Letizia, Petrásková, Lucie, Dragsted, Lars Ove, Philo, Mark, Ulaszewska, Marynka, Mena, Pedro, González-Domínguez, Raúl, Garcia-Villalba, Rocío, Kamiloglu, Senem, de Pascual-Teresa, Sonia, Durand, Stéphanie, Wiczkowski, Wieslaw, Rosário Bronze, Maria, Stanstrup, Jan, and Manach, Claudine

Abstract

Prediction of retention times (RTs) is increasingly considered in untargeted metabolomics to complement MS/MS matching for annotation of unidentified peaks. We tested the performance of PredRet (http://predret.org/) to predict RTs for plant food bioactive metabolites in a data sharing initiative containing entry sets of 29–103 compounds (totalling 467 compounds, >30 families) across 24 chromatographic systems (CSs). Between 27 and 667 predictions were obtained with a median prediction error of 0.03–0.76 min and interval width of 0.33–8.78 min. An external validation test of eight CSs showed high prediction accuracy. RT prediction was dependent on shape and type of LC gradient, and number of commonly measured compounds. Our study highlights PredRet’s accuracy and ability to transpose RT data acquired from one CS to another CS. We recommend extensive RT data sharing in PredRet by the community interested in plant food bioactive metabolites to achieve a powerful community-driven open-access tool for metabolomics annotation.

Published
2021

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

Author

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

Published
2020
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10. Diet-Related Metabolites Associated with Cognitive Decline Revealed by Untargeted Metabolomics in a Prospective Cohort

Author

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

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

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

Published
2019
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11. P1‐011: UNTARGETED METABOLOMICS IN A PROSPECTIVE COHORT TO IDENTIFY DIET‐RELATED METABOLITES ASSOCIATED WITH AGE‐RELATED COGNITIVE DECLINE

Author

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

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

Author

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

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

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

Published
2017

14. PhytoHub, an online platform to gather expert knowledge on polyphenols and other dietary phytochemicals

Author

Giacomoni , Franck, Bento Da Silva , Andreia Leonor, Bronze , Maria, Gladine , Cécile, Hollman , Peter, Kopec , R, Low Yanwen , Dorrain, Micheau , Pierre, Nunes Dos Santos , Maria Claudia, Pavot , Balthazar, Schmidt , G, Morand , Christine, Urpi Sarda , Mireia, Vazquez Manjarrez , Natalia, Verny , Marie-Anne, Wiczkowski , Wieslaw, Knox , C., Manach , Claudine, Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), RIKILT, Wageningen University and Research [Wageningen] (WUR), Ohio State University [Columbus] (OSU), Norwegian Institute of Food,Fisheries and Aquaculture Research (NOFIMA), OMx, Support: ANR Project ALIA-007-PhenoMeNEp, COST Action FA 1403 POSITIVe, JPI HDHL FoodBAll project ANR-14-HDHL-0002-02, Agreenskills fellowship to DL, Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), Wageningen University, Nofima Norwegian Institute of Food,Fisheries and Aquaculture Research, 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 ), and Ohio State University [Columbus] ( OSU )

Subjects
[SDV.AEN] Life Sciences [q-bio]/Food and Nutrition, polyphénol, [CHIM.ANAL] Chemical Sciences/Analytical chemistry, alimentation végétale, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, [ SDV.AEN ] Life Sciences [q-bio]/Food and Nutrition, [ INFO.INFO-BI ] Computer Science [cs]/Bioinformatics [q-bio.QM], [ CHIM.ANAL ] Chemical Sciences/Analytical chemistry, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, database, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], analyse métabolomique
Abstract

SESSION 9 Analytical tools SESSION 9 Analytical toolsSESSION 9Analytical tools; PhytoHub, an online platform to gather expert knowledge on polyphenols and other dietary phytochemicals. 8. International Conference on Polyphenols and Health (ICPH 2017)

Published
2017

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

Author

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

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

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

Published
2017

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

Author

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

Published
2018
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17. Identification of dietary modulators of cognitive function in ageing using metabolomics discovery

Author

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

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

18. PhytoHub, an online platform to gather expert knowledge on polyphenols and other dietary phytochemicals

Author

Giacomoni, Franck, Bento Da Silva, Andreia Leonor, Bronze, Maria, Gladine, Cécile, Hollman, Peter, Kopec, R, Low Yanwen, Dorrain, Micheau, Pierre, Nunes Dos Santos, Maria Claudia, Pavot, Balthazar, Schmidt, G, Morand, Christine, Urpi Sarda, Mireia, Vazquez Manjarrez, Natalia, Verny, Marie-Anne, Wiczkowski, Wieslaw, Knox, C., and Manach, Claudine

Subjects
polyphénol, Bioinformatics, alimentation végétale, Alimentation et Nutrition, Chimie analytique, Food and Nutrition, Bio-informatique, Analytical chemistry, database, analyse métabolomique
Published
2017

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

Author

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

Abstract

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

Published
2017

20. Transcriptomic adaptation of [i]staphylococcus xylosus[/i] to a cheese model

Author

Even, Sergine, Leroy, Sabine, Micheau, Pierre, De La Foye, Anne, Le Loir, Yves, Talon, Régine, Science et Technologie du Lait et de l'Oeuf (STLO), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Microbiologie Environnement Digestif Santé - Clermont Auvergne (MEDIS), Université Clermont Auvergne (UCA)-INRA Clermont-Ferrand-Theix, 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), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Unité de Microbiologie (MIC), Institut National de la Recherche Agronomique (INRA), Unité Mixte de Recherche sur les Herbivores - UMR 1213 (UMRH), 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, INRA Clermont-Ferrand-Theix-Université Clermont Auvergne (UCA), Science et Technologie du Lait et de l'Oeuf ( STLO ), Institut National de la Recherche Agronomique ( INRA ) -AGROCAMPUS OUEST, Microbiologie Environnement Digestif Santé - Clermont Auvergne ( MEDIS ), Université Clermont Auvergne ( UCA ) -INRA Clermont-Ferrand-Theix, Unité Mixte de Recherches sur les Herbivores ( UMR 1213 Herbivores ), VetAgro Sup ( VAS ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Recherche Agronomique ( INRA ), Microbiologie Environnement Digestif Santé (MEDIS), and INRA Clermont-Ferrand-Theix-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])

Subjects
[SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, [ SDV.MP ] Life Sciences [q-bio]/Microbiology and Parasitology, [ SDV.AEN ] Life Sciences [q-bio]/Food and Nutrition, transcriptomique, [SDV.IDA]Life Sciences [q-bio]/Food engineering, [ SDV.IDA ] Life Sciences [q-bio]/Food engineering, fromage, staphylococcus xylosus, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, produit laitier
Abstract

Staphylococcus xylosus belongs to the food-related species of coagulase-negative staphylococci. Thisspecies is present in the natural microbiota of traditional cheeses. It is also commercially available as an adjunct culture to enhance the aroma and texture of cheeses, as well as the color of the surface of smeared cheeses. The molecular mechanisms allowing the growth and adaptation of S. xylosus in the dairy products are still poorly understood. A microarray representing the S. xylosus C2a genome was used to determine how the gene expression profile was modified during growth and adaptation to a cheese model. The C2a strain was inoculated at 5.6 log CFU/g in ultra filtered milk concentrated 5.5-fold and supplemented with UHT cream and salt. Then rennet was added. The temperature of incubation was decreased from 30°C to 12°C after 10 hours of culture, to mimic the temperature shift generally encountered during cheese making. The growth of S. xylosus in the cheese model was exponential until 5 hours and reached a maximum of 8.7 log CFU/g at 24h. S. xylosus population remained almost at this population level after 48 hours of incubation. Total RNA extractions were performed directly from the solid dairy matrix inoculated with S. xylosus after 6, 24 and 48 hours of incubation. Compared to the 6 hours time point sampling, more than 1000 genes of S. xylosuswere differentially expressed at 24 and/or 48 hours of incubation. In particular, extensive upregulation of genes involved in the biosynthesis of purines, pyrimidines, aromatic amino acids and arginine was observed.This work generates a comprehensive genome-wide picture of what genes are modulated during S. xylosus growth and adaptation to a cheese model. Our study increases the understanding of the physiology of adjunct culture in the first step of cheese making process.

Published
2016

21. UNTARGETED METABOLOMICS IN A PROSPECTIVE COHORT TO IDENTIFY DIET-RELATED METABOLITES ASSOCIATED WITH AGE-RELATED COGNITIVE DECLINE

Author

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

Published
2019
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25. Numerical convergence of vortex method for a high Reynolds number 2D bluff-body flow

Author

Mortazavi, Iraj, Micheau, Pierre, and Giovannini, André

Subjects
*PARAMETER estimation, *STOCHASTIC convergence
Abstract

In this paper the convergence of the RVM for a complex flow is studied, in function of three discretization parameters. Two of these parameters are related to the spatial discretization of the vorticity Γ (sheet or blob strength) and h (sheet length or core radius) and the third one to the time discretization Δt. Two main events are observed: first, the computation works but the convergence is not attained, secondly the computation fails. The first behaviour is attributed to a lack of accuracy and the second to a lack of numerical stability. Once the stability conditions are satisfied, decreasing the value of parameters always leads to convergence. To cite this article: I. Mortazavi et al., C. R. Mecanique 330 (2002) 409–416. [Copyright &y& Elsevier]

Published
2002
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26. Diet-Related Metabolites Associated with Cognitive Decline Revealed by Untargeted Metabolomics in a Prospective Cohort.

Author

Low DY, Lefèvre-Arbogast S, González-Domínguez R, Urpi-Sarda M, Micheau P, Petera M, Centeno D, Durand S, Pujos-Guillot E, Korosi A, Lucassen PJ, Aigner L, Proust-Lima C, Hejblum BP, Helmer C, Andres-Lacueva C, Thuret S, Samieri C, and Manach C

Subjects
Aged, Aged, 80 and over, Blood Chemical Analysis, Case-Control Studies, Coffea, Cognitive Dysfunction metabolism, Dementia metabolism, Eating, Female, Fish Products, Humans, Longitudinal Studies, Male, Metabolomics methods, Blood metabolism, Cognitive Dysfunction blood, Dementia blood, Diet
Abstract

Scope: Untargeted metabolomics may reveal preventive targets in cognitive aging, including within the food metabolome., Methods and Results: A case-control study nested in the prospective Three-City study includes participants aged ≥65 years and initially free of dementia. A total of 209 cases of cognitive decline and 209 controls (matched for age, gender, education) with slower cognitive decline over up to 12 years are contrasted. Using untargeted metabolomics and bootstrap-enhanced penalized regression, a baseline serum signature of 22 metabolites associated with subsequent cognitive decline is identified. The signature includes three coffee metabolites, a biomarker of citrus intake, a cocoa metabolite, two metabolites putatively derived from fish and wine, three medium-chain acylcarnitines, glycodeoxycholic acid, lysoPC(18:3), trimethyllysine, glucose, cortisol, creatinine, and arginine. Adding the 22 metabolites to a reference predictive model for cognitive decline (conditioned on age, gender, education and including ApoE-ε4, diabetes, BMI, and number of medications) substantially increases the predictive performance: cross-validated Area Under the Receiver Operating Curve = 75% [95% CI 70-80%] compared to 62% [95% CI 56-67%]., Conclusions: The untargeted metabolomics study supports a protective role of specific foods (e.g., coffee, cocoa, fish) and various alterations in the endogenous metabolism responsive to diet in cognitive aging., (© 2019 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Published
2019
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