Author: "Fabien Jourdan" / Topic: metabolomics - Searchworks@Jio Institute Digital Library Search Results

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1. 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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2. Gaining Insights Into Metabolic Networks Using Chemometrics and Bioinformatics: Chronic Kidney Disease as a Clinical Model

Author: Julien Boccard, Domitille Schvartz, Santiago Codesido, Mohamed Hanafi, Yoric Gagnebin, Belén Ponte, Fabien Jourdan, and Serge Rudaz
Subjects: metabolomics, chemometrics, bioinformatics, integrative data analysis, chronic kidney disease, metabolic networks, Biology (General), QH301-705.5
Abstract: Because of its ability to generate biological hypotheses, metabolomics offers an innovative and promising approach in many fields, including clinical research. However, collecting specimens in this setting can be difficult to standardize, especially when groups of patients with different degrees of disease severity are considered. In addition, despite major technological advances, it remains challenging to measure all the compounds defining the metabolic network of a biological system. In this context, the characterization of samples based on several analytical setups is now recognized as an efficient strategy to improve the coverage of metabolic complexity. For this purpose, chemometrics proposes efficient methods to reduce the dimensionality of these complex datasets spread over several matrices, allowing the integration of different sources or structures of metabolic information. Bioinformatics databases and query tools designed to describe and explore metabolic network models offer extremely useful solutions for the contextualization of potential biomarker subsets, enabling mechanistic hypotheses to be considered rather than simple associations. In this study, network principal component analysis was used to investigate samples collected from three cohorts of patients including multiple stages of chronic kidney disease. Metabolic profiles were measured using a combination of four analytical setups involving different separation modes in liquid chromatography coupled to high resolution mass spectrometry. Based on the chemometric model, specific patterns of metabolites, such as N-acetyl amino acids, could be associated with the different subgroups of patients. Further investigation of the metabolic signatures carried out using genome-scale network modeling confirmed both tryptophan metabolism and nucleotide interconversion as relevant pathways potentially associated with disease severity. Metabolic modules composed of chemically adjacent or close compounds of biological relevance were further investigated using carbon transfer reaction paths. Overall, the proposed integrative data analysis strategy allowed deeper insights into the metabolic routes associated with different groups of patients to be gained. Because of their complementary role in the knowledge discovery process, the association of chemometrics and bioinformatics in a common workflow is therefore shown as an efficient methodology to gain meaningful insights in a clinical context.
Published: 2021
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3. Postprandial NMR-Based Metabolic Exchanges Reflect Impaired Phenotypic Flexibility across Splanchnic Organs in the Obese Yucatan Mini-Pig

Author: Marie Tremblay-Franco, Nathalie Poupin, Aurélien Amiel, Cécile Canlet, Didier Rémond, Laurent Debrauwer, Dominique Dardevet, Fabien Jourdan, Isabelle Savary-Auzeloux, and Sergio Polakof
Subjects: arteriovenous differences, postprandial, high fat-high sucrose diet, mini-pigs, energy metabolism, metabolomics, Nutrition. Foods and food supply, TX341-641
Abstract: The postprandial period represents one of the most challenging phenomena in whole-body metabolism, and it can be used as a unique window to evaluate the phenotypic flexibility of an individual in response to a given meal, which can be done by measuring the resilience of the metabolome. However, this exploration of the metabolism has never been applied to the arteriovenous (AV) exploration of organs metabolism. Here, we applied an AV metabolomics strategy to evaluate the postprandial flexibility across the liver and the intestine of mini-pigs subjected to a high fat–high sucrose (HFHS) diet for 2 months. We identified for the first time a postprandial signature associated to the insulin resistance and obesity outcomes, and we showed that the splanchnic postprandial metabolome was considerably affected by the meal and the obesity condition. Most of the changes induced by obesity were observed in the exchanges across the liver, where the metabolism was reorganized to maintain whole body glucose homeostasis by routing glucose formed de novo from a large variety of substrates into glycogen. Furthermore, metabolites related to lipid handling and energy metabolism showed a blunted postprandial response in the obese animals across organs. Finally, some of our results reflect a loss of flexibility in response to the HFHS meal challenge in unsuspected metabolic pathways that must be further explored as potential new events involved in early obesity and the onset of insulin resistance.
Published: 2020
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4. An Untargeted Metabolomics Approach to Investigate the Metabolic Modulations of HepG2 Cells Exposed to Low Doses of Bisphenol A and 17β-Estradiol

Author: Nicolas J. Cabaton, Nathalie Poupin, Cécile Canlet, Marie Tremblay-Franco, Marc Audebert, Jean-Pierre Cravedi, Anne Riu, Fabien Jourdan, and Daniel Zalko
Subjects: HepG2, metabolomics, BPA, 17β-estradiol, endocrine disruption, metabolic network, Diseases of the endocrine glands. Clinical endocrinology, RC648-665
Abstract: The model xeno-estrogen bisphenol A (BPA) has been extensively studied over the past two decades, contributing to major advances in the field of endocrine disrupting chemicals research. Besides its well documented adverse effects on reproduction and development observed in rodents, latest studies strongly suggest that BPA disrupts several endogenous metabolic pathways, with suspected steatogenic and obesogenic effects. BPA's adverse effects on reproduction are attributed to its ability to activate estrogen receptors (ERs), but its effects on metabolism and its mechanism(s) of action at low doses are so far only marginally understood. Metabolomics based approaches are increasingly used in toxicology to investigate the biological changes induced by model toxicants and chemical mixtures, to identify markers of toxicity and biological effects. In this study, we used proton nuclear magnetic resonance (1H-NMR) based untargeted metabolite profiling, followed by multivariate statistics and computational analysis of metabolic networks to examine the metabolic modulation induced in human hepatic cells (HepG2) by an exposure to low and very low doses of BPA (10−6M, 10−9M, and 10−12M), vs. the female reference hormone 17β-estradiol (E2, 10−9M, 10−12M, and 10−15M). Metabolomic analysis combined to metabolic network reconstruction highlighted different mechanisms at lower doses of exposure. At the highest dose, our results evidence that BPA shares with E2 the capability to modulate several major metabolic routes that ensure cellular functions and detoxification processes, although the effects of the model xeno-estrogen and of the natural hormone can still be distinguished.
Published: 2018
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5. 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

6. The future of metabolomics in ELIXIR [version 2; referees: 3 approved]

Author: Merlijn van Rijswijk, Charlie Beirnaert, Christophe Caron, Marta Cascante, Victoria Dominguez, Warwick B. Dunn, Timothy M. D. Ebbels, Franck Giacomoni, Alejandra Gonzalez-Beltran, Thomas Hankemeier, Kenneth Haug, Jose L. Izquierdo-Garcia, Rafael C. Jimenez, Fabien Jourdan, Namrata Kale, Maria I. Klapa, Oliver Kohlbacher, Kairi Koort, Kim Kultima, Gildas Le Corguillé, Pablo Moreno, Nicholas K. Moschonas, Steffen Neumann, Claire O’Donovan, Martin Reczko, Philippe Rocca-Serra, Antonio Rosato, Reza M. Salek, Susanna-Assunta Sansone, Venkata Satagopam, Daniel Schober, Ruth Shimmo, Rachel A. Spicer, Ola Spjuth, Etienne A. Thévenot, Mark R. Viant, Ralf J. M. Weber, Egon L. Willighagen, Gianluigi Zanetti, and Christoph Steinbeck
Subjects: Opinion Article, Articles, Chemical Biology of the Cell, metabolomics, databases, bioinformatics infrastructure, data standards, computational workflows, cloud computing, training, multi-omics approaches
Abstract: Metabolomics, the youngest of the major omics technologies, is supported by an active community of researchers and infrastructure developers across Europe. To coordinate and focus efforts around infrastructure building for metabolomics within Europe, a workshop on the “Future of metabolomics in ELIXIR” was organised at Frankfurt Airport in Germany. This one-day strategic workshop involved representatives of ELIXIR Nodes, members of the PhenoMeNal consortium developing an e-infrastructure that supports workflow-based metabolomics analysis pipelines, and experts from the international metabolomics community. The workshop established metabolite identification as the critical area, where a maximal impact of computational metabolomics and data management on other fields could be achieved. In particular, the existing four ELIXIR Use Cases, where the metabolomics community - both industry and academia - would benefit most, and which could be exhaustively mapped onto the current five ELIXIR Platforms were discussed. This opinion article is a call for support for a new ELIXIR metabolomics Use Case, which aligns with and complements the existing and planned ELIXIR Platforms and Use Cases.
Published: 2017
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7. The future of metabolomics in ELIXIR [version 1; referees: 2 approved, 1 approved with reservations]

Author: Merlijn van Rijswijk, Charlie Beirnaert, Christophe Caron, Marta Cascante, Victoria Dominguez, Warwick B. Dunn, Timothy M. D. Ebbels, Franck Giacomoni, Alejandra Gonzalez-Beltran, Thomas Hankemeier, Kenneth Haug, Jose L. Izquierdo-Garcia, Rafael C. Jimenez, Fabien Jourdan, Namrata Kale, Maria I. Klapa, Oliver Kohlbacher, Kairi Koort, Kim Kultima, Gildas Le Corguillé, Nicholas K. Moschonas, Steffen Neumann, Claire O’Donovan, Martin Reczko, Philippe Rocca-Serra, Antonio Rosato, Reza M. Salek, Susanna-Assunta Sansone, Venkata Satagopam, Daniel Schober, Ruth Shimmo, Rachel A. Spicer, Ola Spjuth, Etienne A. Thévenot, Mark R. Viant, Ralf J. M. Weber, Egon L. Willighagen, Gianluigi Zanetti, and Christoph Steinbeck
Subjects: Opinion Article, Articles, Chemical Biology of the Cell, metabolomics, databases, bioinformatics infrastructure, data standards, computational workflows, cloud computing, training, multi-omics approaches
Abstract: Metabolomics, the youngest of the major omics technologies, is supported by an active community of researchers and infrastructure developers across Europe. To coordinate and focus efforts around infrastructure building for metabolomics within Europe, a workshop on the “Future of metabolomics in ELIXIR” was organised at Frankfurt Airport in Germany. This one-day strategic workshop involved representatives of ELIXIR Nodes, members of the PhenoMeNal consortium developing an e-infrastructure that supports workflow-based metabolomics analysis pipelines, and experts from the international metabolomics community. The workshop established metabolite identification as the critical area, where a maximal impact of computational metabolomics and data management on other fields could be achieved. In particular, the existing four ELIXIR Use Cases, where the metabolomics community - both industry and academia - would benefit most, and which could be exhaustively mapped onto the current five ELIXIR Platforms were discussed. This opinion article is a call for support for a new ELIXIR metabolomics Use Case, which aligns with and complements the existing and planned ELIXIR Platforms and Use Cases.
Published: 2017
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8. PeakForest: a multi-platform digital infrastructure for interoperable metabolite spectral data and metadata management

Author: Nils Paulhe, Cécile Canlet, Annelaure Damont, Lindsay Peyriga, Stéphanie Durand, Catherine Deborde, Sandra Alves, Stephane Bernillon, Thierry Berton, Raphael Bir, Alyssa Bouville, Edern Cahoreau, Delphine Centeno, Robin Costantino, Laurent Debrauwer, Alexis Delabrière, Christophe Duperier, Sylvain Emery, Amelie Flandin, Ulli Hohenester, Daniel Jacob, Charlotte Joly, Cyril Jousse, Marie Lagree, Nadia Lamari, Marie Lefebvre, Claire Lopez-Piffet, Bernard Lyan, Mickael Maucourt, Carole Migne, Marie-Francoise Olivier, Estelle Rathahao-Paris, Pierre Petriacq, Julie Pinelli, Léa Roch, Pierrick Roger, Simon Roques, Jean-Claude Tabet, Marie Tremblay-Franco, Mounir Traïkia, Anna Warnet, Vanessa Zhendre, Dominique Rolin, Fabien Jourdan, Etienne Thévenot, Annick Moing, Emilien Jamin, François Fenaille, Christophe Junot, Estelle Pujos-Guillot, Franck Giacomoni, Unité de Nutrition Humaine (UNH), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Clermont Auvergne (UCA), Metatoul AXIOM (E20 ), MetaboHUB-MetaToul, MetaboHUB-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-MetaboHUB-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-ToxAlim (ToxAlim), Université de Toulouse (UT)-Ecole d'Ingénieurs de Purpan (INP - PURPAN), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Ecole d'Ingénieurs de Purpan (INP - PURPAN), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), ToxAlim (ToxAlim), Médicaments et Technologies pour la Santé (MTS), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université 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), Plateforme Exploration du Métabolisme (PFEM), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-MetaboHUB-Clermont, MetaboHUB-MetaboHUB, Biologie du fruit et pathologie (BFP), Université de Bordeaux (UB)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Plateforme Bordeaux Metabolome, Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-MetaboHUB-Bordeaux, MetaToul FluxoMet (TBI-MetaToul), 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)-Toulouse Biotechnology Institute (TBI), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-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)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), The PeakForest project is supported by the French National Facility in Metabolomics & Fluxomics, MetaboHUB (11-INBS-0010), launched by the French Ministry of Research and Higher Education and the French ANR funding agency within the Programme 'Investissements d’Avenir', and ANR-11-INBS-0010,METABOHUB,Développement d'une infrastructure française distribuée pour la métabolomique dédiée à l'innovation(2011)
Subjects: Metadata, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Endocrinology, Diabetes and Metabolism, [SDV]Life Sciences [q-bio], Clinical Biochemistry, Metabolite identification, Spectral library, Interoperability, Biochemistry, Mass Spectrometry, Curation, Database, Metabolome, [CHIM]Chemical Sciences, Metabolomics, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Data Curation, FAIR
Abstract: Introduction Accuracy of feature annotation and metabolite identification in biological samples is a key element in metabolomics research. However, the annotation process is often hampered by the lack of spectral reference data in experimental conditions, as well as logistical difficulties in the spectral data management and exchange of annotations between laboratories. Objectives To design an open-source infrastructure allowing hosting both nuclear magnetic resonance (NMR) and mass spectra (MS), with an ergonomic Web interface and Web services to support metabolite annotation and laboratory data management. Methods We developed the PeakForest infrastructure, an open-source Java tool with automatic programming interfaces that can be deployed locally to organize spectral data for metabolome annotation in laboratories. Standardized operating procedures and formats were included to ensure data quality and interoperability, in line with international recommendations and FAIR principles. Results PeakForest is able to capture and store experimental spectral MS and NMR metadata as well as collect and display signal annotations. This modular system provides a structured database with inbuilt tools to curate information, browse and reuse spectral information in data treatment. PeakForest offers data formalization and centralization at the laboratory level, facilitating shared spectral data across laboratories and integration into public databases. Conclusion PeakForest is a comprehensive resource which addresses a technical bottleneck, namely large-scale spectral data annotation and metabolite identification for metabolomics laboratories with multiple instruments. PeakForest databases can be used in conjunction with bespoke data analysis pipelines in the Galaxy environment, offering the opportunity to meet the evolving needs of metabolomics research. Developed and tested by the French metabolomics community, PeakForest is freely-available at https://github.com/peakforest.
Published: 2022

9. Osteogenic Response of Human Mesenchymal Stem Cells Analysed Using Combined Intracellular and Extracellular Metabolomic Monitoring

Author: Fabien Jourdan, Dong-Hyun Kim, Amal Surrati, Sergey Evseev, Virginie Sottile, University of Nottingham, UK (UON), Métabolisme et Xénobiotiques (ToxAlim-MeX), 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)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and University of Pavia
Subjects: Physiology, Metabolite, Cellular differentiation, Metabolomics profiling, [SDV]Life Sciences [q-bio], QD415-436, Biochemistry, 03 medical and health sciences, chemistry.chemical_compound, Metabolomics, Osteogenesis, medicine, Extracellular, Humans, QP1-981, Progenitor cell, 030304 developmental biology, Cell Line, Transformed, 0303 health sciences, Chemistry, 030302 biochemistry & molecular biology, Mesenchymal stem cell, Cell Differentiation, Cell biology, LC-MS, medicine.anatomical_structure, Differentiation, Metabolome, Mesenchymal stem cells, Bone marrow, Stem cell
Abstract: International audience; Background/Aims: The skeleton is a metabolically active organ undergoing continuous remodelling initiated by mesenchymal progenitors present in bone and bone marrow. Under certain pathological conditions this remodelling balance shifts towards increased resorption resulting in weaker bone microarchitecture, and there is consequently a therapeutic need to identify pathways that could inversely enhance bone formation from stem cells. Metabolomics approaches recently applied to stem cell characterisation could help identify new biochemical markers involved in osteogenic differentiation. Methods: Combined intra- and extracellular metabolite profiling was performed by liquid chromatography-mass spectrometry (LC-MS) on human mesenchymal stem cells (MSCs) undergoing osteogenic differentiation in vitro. Using a combination of univariate and multivariate analyses, changes in metabolite and nutrient concentration were monitored in cultures under osteogenic treatment over 10 days. Results: A subset of differentially detected compounds was identified in differentiating cells, suggesting a direct link to metabolic processes involved in osteogenic response. Conclusion: These results highlight new metabolite candidates as potential biomarkers to monitor stem cell differentiation towards the bone lineage.
Published: 2021

10. Pathway analysis in metabolomics: pitfalls and best practice for the use of over-representation analysis

Author: Clément Frainay, Pablo Rodriguez-Mier, Juliette Cooke, Fabien Jourdan, Florence Vinson, Cecilia Wieder, Timothy M. D. Ebbels, Nathalie Poupin, Rachel P. J. Lai, Jacob G. Bundy, Métabolisme et Xénobiotiques (ToxAlim-MeX), 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)-Université Toulouse III - Paul Sabatier (UT3), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Subjects: 2. Zero hunger, 0303 health sciences, Computer science, pathway enrichment, Computational biology, bioinformatics, 3. Good health, pathway analysis, Biological pathway, 03 medical and health sciences, 0302 clinical medicine, Metabolomics, model interpretation, Profiling (information science), Identification (biology), over-representation analysis, KEGG, Biomarker discovery, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Set (psychology), 030217 neurology & neurosurgery, Organism, 030304 developmental biology
Abstract: Over-representation analysis (ORA) is one of the commonest pathway analysis approaches used for the functional interpretation of metabolomics datasets. Despite the widespread use of ORA in metabolomics, the community lacks guidelines detailing its best-practice use. Many factors have a pronounced impact on the results, but to date their effects have received little systematic attention in the field. We developed in-silico simulations using five publicly available datasets and illustrated that changes in parameters, such as the background set, differential metabolite selection methods, and pathway database choice, could all lead to profoundly different ORA results. The use of a non-assay-specific background set, for example, resulted in large numbers of false-positive pathways. Pathway database choice, evaluated using three of the most popular metabolic pathway databases: KEGG, Reactome, and BioCyc, led to vastly different results in both the number and function of significantly enriched pathways. Metabolomics data specific factors, such as reliability of compound identification and assay chemical bias also impacted ORA results. Simulated metabolite misidentification rates as low as 4% resulted in both gain of false-positive pathways and loss of truly significant pathways across all datasets. Our results have several practical implications for ORA users, as well as those using alternative pathway analysis methods. We offer a set of recommendations for the use of ORA in metabolomics, alongside a set of minimal reporting guidelines, as a first step towards the standardisation of pathway analysis in metabolomics.Author summaryMetabolomics is a rapidly growing field of study involving the profiling of small molecules within an organism. It allows researchers to understand the effects of biological status (such as health or disease) on cellular biochemistry, and has wide-ranging applications, from biomarker discovery and personalised medicine in healthcare to crop protection and food security in agriculture. Pathway analysis helps to understand which biological pathways, representing collections of molecules performing a particular function, are involved in response to a disease phenotype, or drug treatment, for example. Over-representation analysis (ORA) is perhaps the most common pathway analysis method used in the metabolomics community. However, ORA can give drastically different results depending on the input data and parameters used. In this work, we have established the effects of these factors on ORA results using computational simulations applied to five real-world datasets. Based on our results, we offer the research community a set of best-practice recommendations applicable not only to ORA but also to other pathway analysis methods to help ensure the reliability and reproducibility of results.
Published: 2021

11. Postprandial NMR-Based Metabolic Exchanges Reflect Impaired Phenotypic Flexibility across Splanchnic Organs in the Obese Yucatan Mini-Pig

Author: Isabelle Savary-Auzeloux, Cécile Canlet, Nathalie Poupin, Dominique Dardevet, Marie Tremblay-Franco, Aurélien Amiel, Fabien Jourdan, Sergio Polakof, Laurent Debrauwer, Didier Rémond, Metatoul AXIOM (E20 ), MetaboHUB-MetaToul, MetaboHUB-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-MetaboHUB-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-ToxAlim (ToxAlim), Université de Toulouse (UT)-Ecole d'Ingénieurs de Purpan (INP - PURPAN), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Ecole d'Ingénieurs de Purpan (INP - PURPAN), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), MetaToul FluxoMet (TBI-MetaToul), 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)-Toulouse Biotechnology Institute (TBI), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-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)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Métabolisme et Xénobiotiques (ToxAlim-MeX), ToxAlim (ToxAlim), 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), 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), INRAE AlimH department funding French Infrastructure for Metabolomics and Fluxomics MetaboHUB-ANR-11-INBS-0010, Analyse de Xénobiotiques, Identification, Métabolisme (E20 Metatoul-AXIOM), MetaToul-MetaboHUB, Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-ToxAlim (ToxAlim), 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 Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Métabolomique et Fluxomique (MetaToul) (TBI-MetaToul), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Toulouse Biotechnology Institute (TBI), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and 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)
Subjects: 0301 basic medicine, Blood Glucose, Magnetic Resonance Spectroscopy, Swine, high fat-high sucrose diet, [SDV]Life Sciences [q-bio], postprandial, mini-pigs, chemistry.chemical_compound, 0302 clinical medicine, Dietary Sucrose, energy metabolism, Glucose homeostasis, Homeostasis, Intestinal Mucosa, Nutrition and Dietetics, Glycogen, Postprandial Period, metabolomics, Postprandial, Swine, Miniature, Animal Nutritional Physiological Phenomena, Female, Splanchnic, lcsh:Nutrition. Foods and food supply, arteriovenous differences, medicine.medical_specialty, 030209 endocrinology & metabolism, lcsh:TX341-641, Biology, Diet, High-Fat, liver, Article, 03 medical and health sciences, Metabolomics, Insulin resistance, Internal medicine, medicine, Metabolome, Animals, Obesity, intestine, phenotypic flexibility, medicine.disease, 030104 developmental biology, Endocrinology, chemistry, Insulin Resistance, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, Food Science
Abstract: The postprandial period represents one of the most challenging phenomena in whole-body metabolism, and it can be used as a unique window to evaluate the phenotypic flexibility of an individual in response to a given meal, which can be done by measuring the resilience of the metabolome. However, this exploration of the metabolism has never been applied to the arteriovenous (AV) exploration of organs metabolism. Here, we applied an AV metabolomics strategy to evaluate the postprandial flexibility across the liver and the intestine of mini-pigs subjected to a high fat&ndash, high sucrose (HFHS) diet for 2 months. We identified for the first time a postprandial signature associated to the insulin resistance and obesity outcomes, and we showed that the splanchnic postprandial metabolome was considerably affected by the meal and the obesity condition. Most of the changes induced by obesity were observed in the exchanges across the liver, where the metabolism was reorganized to maintain whole body glucose homeostasis by routing glucose formed de novo from a large variety of substrates into glycogen. Furthermore, metabolites related to lipid handling and energy metabolism showed a blunted postprandial response in the obese animals across organs. Finally, some of our results reflect a loss of flexibility in response to the HFHS meal challenge in unsuspected metabolic pathways that must be further explored as potential new events involved in early obesity and the onset of insulin resistance.
Published: 2020

12. Improving lipid mapping in Genome Scale Metabolic Networks using ontologies

Author: Pierre van Delft, Laetitia Fouillen, Maxime Chazalviel, Nathalie Poupin, Corinne Pouyet, Jessica Dalloux-Chioccioli, Pauline Le Faouder, Benoit Colsch, Spiro Khoury, Florence Vinson, Fanny Viars, Justine Bertrand-Michel, Sarah Guez, Aurélie Batut, Anthony Tournadre, Fabien Jourdan, Arthur Moreau, Métabolisme et Xénobiotiques (ToxAlim-MeX), ToxAlim (ToxAlim), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Ecole d'Ingénieurs de Purpan (INP - PURPAN), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), MetaToul Lipidomics, Institut des Maladies Métaboliques et Cardiovasculaires (I2MC), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-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)-Ecole Nationale Vétérinaire de Toulouse (ENVT), 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), MedDay Pharmaceuticals, Médicaments et Technologies pour la Santé (MTS), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire de biogenèse membranaire (LBM), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), 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), This work was supported by the French Ministry of Research and National Research Agency as part of the French MetaboHUB, the national metabolomics and fluxomics infrastructure (Grant ANRINBS-0010) and by PhenoMeNal project, European Commission’s Horizon 2020 program (Grant Agreement No. 654241). AM was funded by INRA Human Nutrition division (project Halomics coordinated by Nicolas Cabaton). MC is unded by MedDay Pharmaceuticals, Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Plateau MetaToul-LIPIDOMIQUE = MetaToul-Lipidomics, Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-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)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Bordeaux Segalen - Bordeaux 2-Centre National de la Recherche Scientifique (CNRS), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-MetaToul-MetaboHUB, Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), and Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Ecole Nationale Vétérinaire de Toulouse (ENVT)
Subjects: Computer science, Endocrinology, Diabetes and Metabolism, Clinical Biochemistry, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], Metabolic network, Context (language use), Computational biology, Metabolic networks, Ontology (information science), Biochemistry, 03 medical and health sciences, 0302 clinical medicine, Metabolomics, Lipidomics, 030304 developmental biology, computer.programming_language, 0303 health sciences, Ontology, Network mapping, Python (programming language), Lipids, Identifier, Gene Ontology, Mapping, 030220 oncology & carcinogenesis, Original Article, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], computer, Metabolic Networks and Pathways
Abstract: Introduction To interpret metabolomic and lipidomic profiles, it is necessary to identify the metabolic reactions that connect the measured molecules. This can be achieved by putting them in the context of genome-scale metabolic network reconstructions. However, mapping experimentally measured molecules onto metabolic networks is challenging due to differences in identifiers and level of annotation between data and metabolic networks, especially for lipids. Objectives To help linking lipids from lipidomics datasets with lipids in metabolic networks, we developed a new matching method based on the ChEBI ontology. The implementation is freely available as a python library and in MetExplore webserver. Methods Our matching method is more flexible than an exact identifier-based correspondence since it allows establishing a link between molecules even if a different level of precision is provided in the dataset and in the metabolic network. For instance, it can associate a generic class of lipids present in the network with the molecular species detailed in the lipidomics dataset. This mapping is based on the computation of a distance between molecules in ChEBI ontology. Results We applied our method to a chemical library (968 lipids) and an experimental dataset (32 modulated lipids) and showed that using ontology-based mapping improves and facilitates the link with genome scale metabolic networks. Beyond network mapping, the results provide ways for improvements in terms of network curation and lipidomics data annotation. Conclusion This new method being generic, it can be applied to any metabolomics data and therefore improve our comprehension of metabolic modulations.
Published: 2020

13. 1H-NMR metabolomics response to a realistic diet contamination with the mycotoxin deoxynivalenol: Effect of probiotics supplementation

Author: Mathieu Castex, Marie Tremblay-Franco, Caroline Achard, Philippe Pinton, Ana Paula Frederico Rodrigues Loureiro Bracarense, Fabien Jourdan, Anne Marie Cossalter, Cécile Canlet, Imourana Alassane-Kpembi, Maxime Chalzaviel, Sylvie Combes, Isabelle P. Oswald, 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), Université d'Abomey-Calavi, University of Abomey Calavi (UAC), Analyse de Xénobiotiques, Identification, Métabolisme (E20 Metatoul-AXIOM), MetaToul-MetaboHUB, Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-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), MedDay Pharmaceuticals, Biosynthèse & Toxicité des Mycotoxines (ToxAlim-BioToMyc), Plateforme Ezop (Ezop), Lallemand S.A.S., Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Lab. Patologia Animal, and State University of Londrina = Universidade Estadual de Londrina
Subjects: pig, Biology, Toxicology, law.invention, histology, 03 medical and health sciences, chemistry.chemical_compound, Probiotic, 0404 agricultural biotechnology, Metabolomics, law, mycotoxins, Metabolome, Food science, Mycotoxin, intestine, 030304 developmental biology, Subclinical infection, 2. Zero hunger, 0303 health sciences, Saccharomyces cerevisiae boulardii, 04 agricultural and veterinary sciences, General Medicine, Metabolism, 040401 food science, metabolomics, Yeast, 3. Good health, chemistry, [SDV.TOX]Life Sciences [q-bio]/Toxicology, Toxicity, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, Food Science
Abstract: International audience; Low-level contamination of food and feed by deoxynivalenol (DON) is unavoidable. We investigated the effects of subclinical treatment with DON, and supplementation with probiotic yeast Saccharomyces cerevisiae boulardii I1079 as a preventive strategy in piglets. Thirty-six animals were randomly assigned to either a control diet, a diet contaminated with DON (3 mg/kg), a diet supplemented with yeast (4 × 109 CFU/kg), or a DON-contaminated diet supplemented with yeast, for four weeks. Plasma and tissue samples were collected for biochemical analysis, 1H-NMR untargeted metabolomics, and histology. DON induced no significant modifications in biochemical parameters. However, lesion scores were higher and metabolomics highlighted alterations of amino acid and 2-oxocarboxylic acid metabolism. Administering yeast affected aminoacyl-tRNA synthesis and amino acid and glycerophospholipid metabolism. Yeast supplementation of piglets exposed to DON prevented histological alterations, and partial least square discriminant analysis emphasised similarity between the metabolic profiles of their plasma and that of the control group. The effect on liver metabolome remained marginal, indicating that the toxicity of the mycotoxin was not eliminated. These findings show that the 1H-NMR metabolomics profile is a reliable biomarker to assess subclinical exposure to DON, and that supplementation with S. cerevisiae boulardii increases the resilience of piglets to this mycotoxin.
Published: 2020

14. Pathway analysis in metabolomics: Recommendations for the use of over-representation analysis

Author: Pablo Rodriguez-Mier, Cecilia Wieder, Fabien Jourdan, Nathalie Poupin, Rachel P. J. Lai, Timothy M. D. Ebbels, Clément Frainay, Florence Vinson, Jacob G. Bundy, Juliette Cooke, Imperial College London, Métabolisme et Xénobiotiques (ToxAlim-MeX), 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)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Métabolomique et Fluxomique (MetaToul) (TBI-MetaToul), MetaToul-MetaboHUB, Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Toulouse Biotechnology Institute (TBI), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), This research was funded in whole, or in part, by the Wellcome Trust [222837/Z/21/Z]. CW is supported by a Wellcome Trust PhD Studentship [222837/Z/21/Z]. RPJL receives support from the UK Medical Research Council (MR/R008922/1). JC is supported by a state-funded PhD contract (MESRI (Minister of Higher Education, Research and Innovation)). FJ is supported by the French Ministry of Research and National Research Agency as part of the French MetaboHUB, the national metabolomics and fluxomics infrastructure (Grant ANR-INBS-0010), and MetClassNet project (ANR-19-CE45-0021 and DFG: 431572533), ANR-19-CE45-0021,MetClassNet,Nouvelles approches pour combiner les réseaux métaboliques et la métabolomique non ciblée(2019), ANR-11-INBS-0010,METABOHUB,Développement d'une infrastructure française distribuée pour la métabolomique dédiée à l'innovation(2011), 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)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), MetaToul FluxoMet (TBI-MetaToul), 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)-Toulouse Biotechnology Institute (TBI), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-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)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), LESUR, Hélène, Nouvelles approches pour combiner les réseaux métaboliques et la métabolomique non ciblée - - MetClassNet2019 - ANR-19-CE45-0021 - AAPG2019 - VALID, Développement d'une infrastructure française distribuée pour la métabolomique dédiée à l'innovation - - METABOHUB2011 - ANR-11-INBS-0010 - INBS - VALID, 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)-Toulouse Biotechnology Institute (TBI), Institut National des Sciences Appliquées (INSA)-Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées (INSA)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), and Institut National des Sciences Appliquées (INSA)-Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Subjects: Physiology, Computer science, [SDV]Life Sciences [q-bio], Datasets as Topic, Biochemistry, 0302 clinical medicine, Metabolites, Biology (General), Protein Metabolism, 0303 health sciences, Ecology, Genomics, Pathway analysis, 3. Good health, [SDV] Life Sciences [q-bio], Chemistry, Identification (information), Physiological Parameters, Computational Theory and Mathematics, 030220 oncology & carcinogenesis, Modeling and Simulation, Physical Sciences, Metabolic Pathways, Selection method, Transcriptome Analysis, Metabolic Networks and Pathways, Network Analysis, Research Article, Chemical Elements, Computer and Information Sciences, QH301-705.5, MEDLINE, Computational biology, Over representation, Metabolic Networks, 03 medical and health sciences, Cellular and Molecular Neuroscience, Metabolomics, Genetics, KEGG, Set (psychology), Molecular Biology, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, Body Weight, Computational Biology, Reproducibility of Results, Biology and Life Sciences, Genome Analysis, Metabolism
Abstract: Over-representation analysis (ORA) is one of the commonest pathway analysis approaches used for the functional interpretation of metabolomics datasets. Despite the widespread use of ORA in metabolomics, the community lacks guidelines detailing its best-practice use. Many factors have a pronounced impact on the results, but to date their effects have received little systematic attention. Using five publicly available datasets, we demonstrated that changes in parameters such as the background set, differential metabolite selection methods, and pathway database used can result in profoundly different ORA results. The use of a non-assay-specific background set, for example, resulted in large numbers of false-positive pathways. Pathway database choice, evaluated using three of the most popular metabolic pathway databases (KEGG, Reactome, and BioCyc), led to vastly different results in both the number and function of significantly enriched pathways. Factors that are specific to metabolomics data, such as the reliability of compound identification and the chemical bias of different analytical platforms also impacted ORA results. Simulated metabolite misidentification rates as low as 4% resulted in both gain of false-positive pathways and loss of truly significant pathways across all datasets. Our results have several practical implications for ORA users, as well as those using alternative pathway analysis methods. We offer a set of recommendations for the use of ORA in metabolomics, alongside a set of minimal reporting guidelines, as a first step towards the standardisation of pathway analysis in metabolomics., Author summary Metabolomics is a rapidly growing field of study involving the profiling of small molecules within an organism. It allows researchers to understand the effects of biological status (such as health or disease) on cellular biochemistry, and has wide-ranging applications, from biomarker discovery and personalised medicine in healthcare to crop protection and food security in agriculture. Pathway analysis helps to understand which biological pathways, representing collections of molecules performing a particular function, may be involved in response to a disease phenotype, or drug treatment, for example. Over-representation analysis (ORA) is perhaps the most common pathway analysis method used in the metabolomics community. However, ORA can give drastically different results depending on the input data and parameters used. Here, we have established the effects of these factors on ORA results using computational modifications applied to five real-world datasets. Based on our results, we offer the research community a set of best-practice recommendations applicable not only to ORA but also to other pathway analysis methods to help ensure the reliability and reproducibility of results.
Published: 2021

15. PhenoMeNal: processing and analysis of metabolomics data in the cloud

Author: Daniel J. Jacob, Noureddin Sadawi, Kristian Peters, Reza M. Salek, Christoph Ruttkies, Kim Kultima, Susanna-Assunta Sansone, James Bradbury, Rico Rueedi, Steffen Neumann, Ralf J. M. Weber, Kenneth Haug, Sven Bergmann, Merlijn van Rijswijk, Evangelos Handakas, Marco Capuccini, Alina Peluso, Pedro de Atauri, Bita Khalili, Michelle A.C. Reed, Claire O'Donovan, Ulrich L. Günther, Namrata Kale, Philippe Rocca-Serra, Vitaly A. Selivanov, Alejandra Gonzalez-Beltran, Antonio Rosato, Massimiliano Izzo, Pablo Moreno, Pierrick Roger, Christoph Steinbeck, Etienne A. Thévenot, Luca Pireddu, Gianluigi Zanetti, Michael van Vliet, Timothy M. D. Ebbels, Fabien Jourdan, Jon Ander Novella, Anders Larsson, Robert C. Glen, Ibrahim Karaman, Jake T M Pearce, Payam Emami Khoonsari, Stephanie Herman, Marco Enrico Piras, Petr Holub, Mattia Tomasoni, Marta Cascante, Christian Ludwig, Daniel Schober, Thomas Hankemeier, David Johnson, Samuel Lampa, Ola Spjuth, Carles Foguet, Mark R. Viant, European Molecular Biology Laboratory, Imperial College Healthcare NHS Trust- BRC Funding, National Institutes of Health, Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry (IPB), School of Biosciences, University of Birmingham [Birmingham], Université de Lausanne (UNIL), Swiss Institute of Bioinformatics [Lausanne] (SIB), Uppsala University, Department of Pharmaceutical Biosciences, Department of Biochemistry and Molecular Biomedicine, Faculty of Science and Technology, Biochemistry and Molecular Biology, Instituto de Salud Carlos III [Madrid] (ISC), Department of Surgery & Cancer, Imperial College London, University of Cambridge [UK] (CAM), Department of Engineering Science, University of Oxford [Oxford], University of Birmingham, Leiden University, European Molecular Biology Laboratory European Bioinformatics Institute, Department of Medical Sciences, Clinical Chemistry, BBMRI-ERIC, Partenaires INRAE, Biologie du fruit et pathologie (BFP), Université Bordeaux Segalen - Bordeaux 2-Institut National de la Recherche Agronomique (INRA)-Université Sciences et Technologies - Bordeaux 1, Department of Informatics and Media, Brandenburg University of Applied Sciences, Métabolisme et Xénobiotiques (ToxAlim-MeX), 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)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), School of Public Health - Department of Epidemiology and Biostatistics, German Centre for Integrative Biodiversity Research (iDiv), CRS4 Bioinformat, 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, Univ Florence, Magnet Resonance Ctr CERM, I-50019 Florence, Italy, Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (CIRMMP), Università degli Studi di Siena = University of Siena (UNISI)-Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI)-University of Bologna-Partenaires INRAE, Univ Florence, Dept Chem, I-50019 Florence, Italy, Brunel University London [Uxbridge], Netherlands Metabolomics Centre, Life Sciences, Institute of Analytical Chemistry, European Project: 654241,H2020,H2020-EINFRA-2014-2,PhenoMeNal(2015), Université de Lausanne = University of Lausanne (UNIL), University of Oxford, Universiteit Leiden, European research infrastructure for biobanking (BBMRI-ERIC), Université Bordeaux Segalen - Bordeaux 2-Institut National de la Recherche Agronomique (INRA)-Université Sciences et Technologies - Bordeaux 1 (UB), Plateforme Bordeaux Metabolome, Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-MetaboHUB-Bordeaux, MetaboHUB-MetaboHUB, 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), 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)), Università degli Studi di Firenze = University of Florence (UniFI), Università degli Studi di Siena = University of Siena (UNISI)-Università degli Studi di Firenze = University of Florence (UniFI)-University of Bologna/Università di Bologna-Partenaires INRAE, Glen, Robert [0000-0003-1759-2914], and Apollo - University of Cambridge Repository
Subjects: Life Sciences & Biomedicine - Other Topics, Computer science, Interoperability, data analysis, Cloud computing, METABOLITES, ANNOTATION, Field (computer science), Workflow, NMR, cloud computing, computational workflows, e-infrastructures, mass spectrometry, metabolomics, standardization, Technical Note, TOOL, Orchestration (computing), 0303 health sciences, 030302 biochemistry & molecular biology, Small molecule, Multidisciplinary Sciences, Open data, Statistical analysis, statistics, [SDE]Environmental Sciences, Science & Technology - Other Topics, User interface, Life Sciences & Biomedicine, INTEGRATION, STANDARDS, REPOSITORY, Process (engineering), Phenome, 03 medical and health sciences, Metabolomics, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Metabolome, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, SPECTRA, Humans, Adaptation (computer science), signal processing, Biology, 030304 developmental biology, Reproducibility, Science & Technology, business.industry, PLATFORM, Metabolism, MASS-SPECTROMETRY, Omics, Data science, Spectrum analysis, galaxy, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, Cloud Computing, Metabolomics/methods, Software, SYSTEM
Abstract: International audience; Background Metabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological, and many other applied biological domains. Its computationally intensive nature has driven requirements for open data formats, data repositories, and data analysis tools. However, the rapid progress has resulted in a mosaic of independent, and sometimes incompatible, analysis methods that are difficult to connect into a useful and complete data analysis solution. Findings PhenoMeNal (Phenome and Metabolome aNalysis) is an advanced and complete solution to set up Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open-source tools that are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated, and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi, and Pachyderm. Conclusions PhenoMeNal constitutes a keystone solution in cloud e-infrastructures available for metabolomics. PhenoMeNal is a unique and complete solution for setting up cloud e-infrastructures through easy-to-use web interfaces that can be scaled to any custom public and private cloud environment. By harmonizing and automating software installation and configuration and through ready-to-use scientific workflow user interfaces, PhenoMeNal has succeeded in providing scientists with workflow-driven, reproducible, and shareable metabolomics data analysis platforms that are interfaced through standard data formats, representative datasets, versioned, and have been tested for reproducibility and interoperability. The elastic implementation of PhenoMeNal further allows easy adaptation of the infrastructure to other application areas and omics research domains.
Published: 2019

16. Large-Scale Modeling Approach Reveals Functional Metabolic Shifts during Hepatic Differentiation

Author: Anne Corlu, Sandrine Bruel, Nicolas J. Cabaton, Fabien Jourdan, Florence Vinson, Hélène Dubois-Pot-Schneider, Clément Frainay, Cécile Canlet, Daniel Zalko, Elodie Person, Fabrice Morel, Marie-Anne Robin, Florence Maurier, Nathalie Poupin, Bernard Fromenty, Métabolisme et Xénobiotiques (ToxAlim-MeX), 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)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), Nutrition, Métabolismes et Cancer (NuMeCan), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), Analyse de Xénobiotiques, Identification, Métabolisme (E20 Metatoul-AXIOM), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-MetaToul-MetaboHUB, Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de la Santé et de la Recherche Médicale (INSERM), This work was supported in part by the French Ministry of Research and the National Research Agency, as part of the French MetaboHUB infrastructure (the National Metabolomics and Fluxomics Infrastructure, Grant ANR-INBS-0010) and the NISTEC project (Grant ANR-09-CESA-003)., ANR-09-CESA-0003,NISTEC,Nouvelles stratégies in vitro pour l'évaluation de la cytotoxicite et la genotoxicite des contaminants de l'environnement(2009), 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 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, Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-MetaToul-MetaboHUB, Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-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 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), Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM), MetaToul AXIOM (E20), 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), and 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)
Subjects: 0301 basic medicine, genome-scale metabolic modeling, [SDV]Life Sciences [q-bio], Metabolic network, Network structure, Computational biology, Biology, Models, Biological, Biochemistry, Cell Line, Omics data, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, HepaRG cell line, hepatic differentiation, Humans, Metabolomics, ComputingMilieux_MISCELLANEOUS, transcriptomics and metabolomics, Cell Differentiation, [SDV.MHEP.HEG]Life Sciences [q-bio]/Human health and pathology/Hépatology and Gastroenterology, General Chemistry, Phenotype, Metabolic pathway, 030104 developmental biology, Cell metabolism, Liver, Flux (metabolism), Metabolic Networks and Pathways, 030217 neurology & neurosurgery, global metabolic shifts
Abstract: International audience; Being able to explore the metabolism of broad metabolizing cells is of critical importance in many research fields. This article presents an original modeling solution combining metabolic network and omics data to identify modulated metabolic pathways and changes in metabolic functions occurring during differentiation of a human hepatic cell line (HepaRG). Our results confirm the activation of hepato-specific functionalities and newly evidence modulation of other metabolic pathways, which could not be evidenced from transcriptomic data alone. Our method takes advantage of the network structure to detect changes in metabolic pathways that do not have gene annotations and exploits flux analyses techniques to identify activated metabolic functions. Compared to the usual cell-specific metabolic network reconstruction approaches, it limits false predictions by considering several possible network configurations to represent one phenotype rather than one arbitrarily selected network. Our approach significantly enhances the comprehensive and functional assessment of cell metabolism, opening further perspectives to investigate metabolic shifts occurring within various biological contexts.
Published: 2019

17. Genome-scale metabolic networks

Author: Clément Frainay, Fabien Jourdan, Métabolisme et Xénobiotiques (ToxAlim-MeX), ToxAlim (ToxAlim), Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées
Subjects: 0303 health sciences, Computer science, food and beverages, Context (language use), Computational biology, [SDV.TOX.TCA]Life Sciences [q-bio]/Toxicology/Toxicology and food chain, Set (abstract data type), 03 medical and health sciences, Metabolic pathway, 0302 clinical medicine, Metabolomics, Graph drawing, Global network, DECIPHER, Graph (abstract data type), 030217 neurology & neurosurgery, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology
Abstract: This chapter discusses the benefits of interpreting metabolomics data in the context of genome-scale metabolic networks. These networks gather all the metabolic reactions occuring in an organism, a tissue or a cell, thus providing a biological background to decipher the role of metabolites of interest. This chapter first introduces how this list of biochemical reactions is built using genomic or metabolomic data. This knowledge can then be used in a targeted way, by mapping metabolomics data onto metabolic pathways, or, in a more global manner, using the entire network. Both approaches are described in this chapter, revealing how pathway analysis can be achieved using statistical methods and how global network analysis can be performed using graph representation. Turning the network into a metabolic graph consists of modeling metabolites and reactions by a set of nodes connected by edges. Various graph algorithms presented in this chapter can then be used to extract sub-networks of interest based on a list of identified metabolites. Finally, the benefits of using network visualization for data interpretation are discussed.
Published: 2019

18. Targeted versus untargeted omics — the CAFSA story

Author: Emmanuel Roze, Frédéric Sedel, Christel Depienne, Ron A. Wevers, Christophe Junot, Agnès Rastetter, Clément Frainay, Claude Jardel, Farid Ichou, F. Lamari, Maria del Mar Amador, Benoit Colsch, Fabien Jourdan, Fanny Mochel, CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Service de Pharmacologie et Immunoanalyse (SPI), Médicaments et Technologies pour la Santé (MTS), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Unité de Recherche sur les Maladies Cardiovasculaires, du Métabolisme et de la Nutrition = Research Unit on Cardiovascular and Metabolic Diseases (ICAN), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Institut de Cardiométabolisme et Nutrition = Institute of Cardiometabolism and Nutrition [CHU Pitié Salpêtrière] (IHU ICAN), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-CHU Pitié-Salpêtrière [AP-HP], Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), MedDay Pharmaceuticals, Métabolisme et Xénobiotiques (ToxAlim-MeX), ToxAlim (ToxAlim), 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), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Ecole d'Ingénieurs de Purpan (INP - PURPAN), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT), Radboud University Medical Center [Nijmegen], Les Hôpitaux Universitaires de Strasbourg (HUS), French Ministry of Health (PHRC SPECTMET-II) [RCB 2010-A01395-34], Service de Pharmacologie et d'Immunoanalyse (SPI), Institut National de la Recherche Agronomique (INRA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Unité de Recherche sur les Maladies Cardiovasculaires, du Métabolisme et de la Nutrition = Institute of cardiometabolism and nutrition (ICAN), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), 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), 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)-Université Toulouse III - Paul Sabatier (UT3), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)
Subjects: 0301 basic medicine, Male, Folate, DNA Mutational Analysis, 01 natural sciences, Mass Spectrometry, chemistry.chemical_compound, Exome, Genetics (clinical), Exome sequencing, Tetrahydrofolates, Cerebellar ataxia, Genomics, Disorders of movement Donders Center for Medical Neuroscience [Radboudumc 3], 3. Good health, DNA Polymerase gamma, Cerebrospinal fluid, Female, medicine.symptom, medicine.drug, Adult, Mitochondrial disease, DNA, Mitochondrial, 03 medical and health sciences, Metabolomics, All institutes and research themes of the Radboud University Medical Center, Exome Sequencing, Genetics, medicine, Humans, Carnitine, business.industry, Siblings, 010401 analytical chemistry, medicine.disease, N-Acetylneuraminic Acid, Sialic acid, 0104 chemical sciences, 030104 developmental biology, Targeted mass spectrometry, chemistry, Case-Control Studies, Cancer research, business, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology
Abstract: International audience; Background In 2009, untargeted metabolomics led to the delineation of a new clinico-biological entity called cerebellar ataxia with elevated cerebrospinal free sialic acid, or CAFSA. In order to elucidate CAFSA, we applied sequentially targeted and untargeted omic approaches. Methods and results First, we studied five of the six CAFSA patients initially described. Besides increased CSF free sialic acid concentrations, three patients presented with markedly decreased 5-methyltetrahydrofolate (5-MTHF) CSF concentrations. Exome sequencing identified a homozygous POLG mutation in two affected sisters, but failed to identify a causative gene in the three sporadic patients with high sialic acid but low 5-MTHF. Using targeted mass spectrometry, we confirmed that free sialic acid was increased in the CSF of a third known POLG-mutated patient. We then pursued pathophysiological analyses of CAFSA using mass spectrometry-based metabolomics on CSF from two sporadic CAFSA patients as well as 95 patients with an unexplained encephalopathy and 39 controls. This led to the identification of a common metabotype between the two initial CAFSA patients and three additional patients, including one patient with Kearns-Sayre syndrome. Metabolites of the CSF metabotype were positioned in a reconstruction of the human metabolic network, which highlighted the proximity of the metabotype with acetyl-CoA and carnitine, two key metabolites regulating mitochondrial energy homeostasis. Conclusion Our genetic and metabolomics analyses suggest that CAFSA is a heterogeneous entity related to mitochondrial DNA alterations either through POLG mutations or a mechanism similar to what is observed in Kearns-Sayre syndrome.
Published: 2018

19. Mind the gap: mapping mass spectral databases in genome-scale metabolic networks reveals poorly covered Areas

Author: Reza M. Salek, Steffen Neumann, Oscar Yanes, Fabien Jourdan, Clément Frainay, Emma L. Schymanski, Benjamin Merlet, Métabolisme et Xénobiotiques (ToxAlim-MeX), ToxAlim (ToxAlim), 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), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Ecole d'Ingénieurs de Purpan (INP - PURPAN), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT), Swiss Federal Insitute of Aquatic Science and Technology [Dübendorf] (EAWAG), Université du Luxembourg (Uni.lu), Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry (IPB), German Centre for Integrative Biodiversity Research (iDiv), International Agency for Research on Cancer (IARC), Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III [Madrid] (ISC), Universitat Rovira i Virgili, Ministerio de Economia y Competitividad (MINECO) [BFU2014-57466-P], Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), an initiative of Instituto de Investigacion Carlos III (ISCIII), French Ministry of Research and National Research Agency, French MetaboHUB [ANR-INBS-0010], PhenoMeNal project, European Commission's Horizon 2020 programme [654241]., European Project: 603437,EC:FP7:ENV,FP7-ENV-2013-two-stage,SOLUTIONS(2013), 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), 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)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (Ciberdem), and Yanes, Oscar
Subjects: 0301 basic medicine, Computer science, Endocrinology, Diabetes and Metabolism, [SDV]Life Sciences [q-bio], Genome scale, lcsh:QR1-502, metabolomics data mapping, Metabolic network, mass spectral libraries, Multidisciplinary, general & others [F99] [Life sciences], computer.software_genre, Mass spectrometry, 01 natural sciences, Biochemistry, lcsh:Microbiology, 03 medical and health sciences, Multidisciplinaire, généralités & autres [F99] [Sciences du vivant], Metabolomics, Betweenness centrality, Metabolome, Molecular Biology, Reference standards, Database, metabolite annotation, 010401 analytical chemistry, metabolic networks, 0104 chemical sciences, 030104 developmental biology, Bile acid metabolism, computer
Abstract: International audience; The use of mass spectrometry-based metabolomics to study human, plant and microbial biochemistry and their interactions with the environment largely depends on the ability to annotate metabolite structures by matching mass spectral features of the measured metabolites to curated spectra of reference standards. While reference databases for metabolomics now provide information for hundreds of thousands of compounds, barely 5% of these known small molecules have experimental data from pure standards. Remarkably, it is still unknown how well existing mass spectral libraries cover the biochemical landscape of prokaryotic and eukaryotic organisms. To address this issue, we have investigated the coverage of 38 genome-scale metabolic networks by public and commercial mass spectral databases, and found that on average only 40% of nodes in metabolic networks could be mapped by mass spectral information from standards. Next, we deciphered computationally which parts of the human metabolic network are poorly covered by mass spectral libraries, revealing gaps in the eicosanoids, vitamins and bile acid metabolism. Finally, our network topology analysis based on the betweenness centrality of metabolites revealed the top 20 most important metabolites that, if added to MS databases, may facilitate human metabolome characterization in the future.
Published: 2018

20. Integrated transcriptomics and metabolomics reveal signatures of lipid metabolism dysregulation in HepaRG liver cells exposed to PCB 126

Author: Eva Wozniak, Clément Frainay, Nathalie Poupin, Charles A. Mein, Robin Mesnage, Fabien Jourdan, Sucharitha Balu, Theodoros Xenakis, Michael Antoniou, Martina Biserni, Department of Medical and Molecular Genetics, King‘s College London, Métabolisme et Xénobiotiques (ToxAlim-MeX), ToxAlim (ToxAlim), Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Barts and The London School of Medicine and Dentistry, and Sustainable Food Alliance (USA)
Subjects: 0301 basic medicine, Health, Toxicology and Mutagenesis, 010501 environmental sciences, Toxicology, medicine.disease_cause, liver, 01 natural sciences, Cell Line, Transcriptome, 03 medical and health sciences, Metabolomics, Molecular Toxicology, Non-alcoholic Fatty Liver Disease, NAFLD, Basic Helix-Loop-Helix Transcription Factors, medicine, Metabolome, Humans, 030304 developmental biology, 0105 earth and related environmental sciences, chemistry.chemical_classification, 0303 health sciences, PCB, Liver cell, Gene Expression Profiling, Fatty liver, Lipid metabolism, General Medicine, Lipid Metabolism, medicine.disease, Polychlorinated Biphenyls, 3. Good health, 030104 developmental biology, Receptors, Aryl Hydrocarbon, Biochemistry, chemistry, 13. Climate action, [SDV.TOX]Life Sciences [q-bio]/Toxicology, Inactivation, Metabolic, metabolome, Steatosis, HepaRG, transcriptome, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, Oxidative stress, Polyunsaturated fatty acid
Abstract: Chemical pollutant exposure is a risk factor contributing to the growing epidemic of non-alcoholic fatty liver disease (NAFLD) affecting human populations that consume a western diet. Although it is recognized that intoxication by chemical pollutants can lead to NAFLD, there is limited information available regarding the mechanism by which typical environmental levels of exposure can contribute to the onset of this disease. Here, we describe the alterations in gene expression profiles and metabolite levels in the human HepaRG liver cell line, a validated model for cellular steatosis, exposed to the polychlorinated biphenyl (PCB) 126, one of the most potent chemical pollutants that can induce NAFLD. Sparse partial least squares classification of the molecular profiles revealed that exposure to PCB 126 provoked a decrease in polyunsaturated fatty acids as well as an increase in sphingolipid levels, concomitant with a decrease in the activity of genes involved in lipid metabolism. This was associated with an increased oxidative stress reflected by marked disturbances in taurine metabolism. A gene ontology analysis showed hallmarks of an activation of the AhR receptor by dioxin-like compounds. These changes in metabolome and transcriptome profiles were observed even at the lowest concentration (100 pM) of PCB 126 tested. A decrease in docosatrienoate levels was the most sensitive biomarker. Overall, our integrated multi-omics analysis provides mechanistic insight into how this class of chemical pollutant can cause NAFLD. Our study lays the foundation for the development of molecular signatures of toxic effects of chemicals causing fatty liver diseases to move away from a chemical risk assessment based on in vivo animal experiments. Electronic supplementary material The online version of this article (10.1007/s00204-018-2235-7) contains supplementary material, which is available to authorized users.
Published: 2018

21. Telling metabolic stories to explore metabolomics data: a case study on the yeast response to cadmium exposure

Author: Fabien Jourdan, Pierluigi Crescenzi, Christophe Junot, Andrea Marino, Michele Borassi, Etienne Birmelé, Marie-France Sagot, Ludovic Cottret, Leen Stougie, Vicente Acuña, Cecilia C. Klein, Vincent Lacroix, Alberto Marchetti-Spaccamela, Paulo Vieira Milreu, An algorithmic view on genomes, cells, and environments (BAMBOO), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés (LISBP), Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Recherche Agronomique (INRA), Mathomics, Universidad de Chile = University of Chile [Santiago] (UCHILE), Université d'Évry-Val-d'Essonne (UEVE), Scuola Normale Superiore di Pisa (SNS), Laboratoire d'Etude du Métabolisme des Médicaments (LEMM), Service de Pharmacologie et Immunoanalyse (SPI), Médicaments et Technologies pour la Santé (MTS), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Médicaments et Technologies pour la Santé (MTS), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Dipartimento di Informatica e Sistemistica 'Antonio Ruberti' (DIS), Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome], Dipartimento di Sistemi e Informatica (DSI), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Department of mathematics and computing science [Eindhoven], Eindhoven University of Technology [Eindhoven] (TU/e), Métabolisme et Xénobiotiques (ToxAlim-MeX), 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)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), European Research Council under the European Community [(247073)10], French project [ANR MIRI BLAN08-1335497], ANR, Plateforme Bioinformatique de Toulouse, ANR-BBSRC Systryp, CIRIC-INRIA Chile line Natural Resources, NWO-CLS MEMESA project, 'DISCO' PRIN National Research Project, ANR-08-BLAN-0293,MIRI,Combinatorial exploration of the molecular landscape and evolution of intimate species relations(2008), European Project: 247073,EC:FP7:ERC,ERC-2009-AdG,SISYPHE(2010), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome] (UNIROMA), Università degli Studi di Firenze = University of Florence (UniFI), 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 (INPT - EI 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), Université de Toulouse (UT)-Ecole d'Ingénieurs de Purpan (INP - PURPAN), Institut National de la Recherche Agronomique (INRA), University of Chile [Santiago], Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Médicaments et Technologies pour la Santé (MTS), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Università degli Studi di Roma 'La Sapienza' [Rome], Università degli Studi di Firenze [Firenze], ANR-08-BLAN-1335497,MIRI,Combinatorial exploration of the molecular landscape and evolution of intimate species relations(2008), Econometrics and Operations Research, Amsterdam Business Research Institute, Evolutionary Intelligence, Milreu, Paulo Vieira, Lacroix, Vincent, and Sagot, Marie-France
Subjects: Statistics and Probability, Computer science, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], Saccharomyces cerevisiae, Machine learning, computer.software_genre, Biochemistry, 03 medical and health sciences, chemistry.chemical_compound, Cadmium, Enzyme Activation, Glutathione, Metabolomics, Medicine (all), Molecular Biology, Computer Science Applications1707 Computer Vision and Pattern Recognition, Computational Theory and Mathematics, Computational Mathematics, Statistics, Organism, 030304 developmental biology, 0303 health sciences, business.industry, 030302 biochemistry & molecular biology, Glutathione biosynthesis, Original Papers, Structural Bioinformatics, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Computer Science Applications, Variety (cybernetics), Enzyme inhibition, CADMIUM EXPOSURE, chemistry, sense organs, Artificial intelligence, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, computer
Abstract: Motivation: The increasing availability of metabolomics data enables to better understand the metabolic processes involved in the immediate response of an organism to environmental changes and stress. The data usually come in the form of a list of metabolites whose concentrations significantly changed under some conditions, and are thus not easy to interpret without being able to precisely visualize how such metabolites are interconnected. Results: We present a method that enables to organize the data from any metabolomics experiment into metabolic stories. Each story corresponds to a possible scenario explaining the flow of matter between the metabolites of interest. These scenarios may then be ranked in different ways depending on which interpretation one wishes to emphasize for the causal link between two affected metabolites: enzyme activation, enzyme inhibition or domino effect on the concentration changes of substrates and products. Equally probable stories under any selected ranking scheme can be further grouped into a single anthology that summarizes, in a unique subnetwork, all equivalently plausible alternative stories. An anthology is simply a union of such stories. We detail an application of the method to the response of yeast to cadmium exposure. We use this system as a proof of concept for our method, and we show that we are able to find a story that reproduces very well the current knowledge about the yeast response to cadmium. We further show that this response is mostly based on enzyme activation. We also provide a framework for exploring the alternative pathways or side effects this local response is expected to have in the rest of the network. We discuss several interpretations for the changes we see, and we suggest hypotheses that could in principle be experimentally tested. Noticeably, our method requires simple input data and could be used in a wide variety of applications. Availability and implementation: The code for the method presented in this article is available at http://gobbolino.gforge.inria.fr. Contact: pvmilreu@gmail.com; vincent.lacroix@univ-lyon1.fr; marie-france.sagot@inria.fr Supplementary information: Supplementary data are available at Bioinformatics online.
Published: 2013

22. Computational methods to identify metabolic sub-networks based on metabolomic profiles

Author: Clément Frainay, Fabien Jourdan, 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), This work was supported by the French Ministry of Research and National Research Agency (ANR) as part of the French MetaboHUB, the national metabolomics and fluxomics infrastructure (Grant ANR-INBS-0010), Métabolisme et Xénobiotiques (ToxAlim-MeX), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3)
Subjects: 0301 basic medicine, Computer science, multi-objective particle swarm optimization (mopso), [SDV]Life Sciences [q-bio], 0206 medical engineering, Metabolic network, 02 engineering and technology, computer.software_genre, path search, modelling, 03 medical and health sciences, Metabolomics, metabolic network, graph algorithm, Path search, Graph algorithms, Molecular Biology, bioinformatique, modélisation, sub-network extraction, Computational Biology, Graph theory, 030104 developmental biology, Untargeted metabolomics, réseau métabolique, Graph (abstract data type), Data mining, computer, 020602 bioinformatics, métabolomique, algorithme, Algorithms, Metabolic Networks and Pathways, Information Systems
Abstract: Untargeted metabolomics makes it possible to identify compounds that undergo significant changes in concentration in different experimental conditions. The resulting metabolomic profile characterizes the perturbation concerned, but does not explain the underlying biochemical mechanisms. Bioinformatics methods make it possible to interpret results in light of the whole metabolism. This knowledge is modelled into a network, which can be mined using algorithms that originate in graph theory. These algorithms can extract sub-networks related to the compounds identified. Several attempts have been made to adapt them to obtain more biologically meaningful results. However, there is still no consensus on this kind of analysis of metabolic networks. This review presents the main graph approaches used to interpret metabolomic data using metabolic networks. Their advantages and drawbacks are discussed, and the impacts of their parameters are emphasized. We also provide some guidelines for relevant sub-network extraction and also suggest a range of applications for most methods.
Published: 2015

23. Dynamic metabolic disruption in rats perinatally exposed to low doses of bisphenol-A

Author: Ana M. Soto, Fabien Jourdan, Marie Tremblay-Franco, Nicolas J. Cabaton, Daniel Zalko, Florence Vinson, Carlos Sonnenschein, Cécile Canlet, Cheryl M. Schaeberle, Roselyne Gautier, Analyse de Xénobiotiques, Identification, Métabolisme (E20 Metatoul-AXIOM), 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)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-MetaToul-MetaboHUB, Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Métabolisme et Xénobiotiques (ToxAlim-MeX), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), Tufts University School of Medicine, National Institute of Environmental Health Sciences [R01ES08314], [RC2ES018822], Zalko, Daniel, Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-MetaToul-MetaboHUB, Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Tufts University School of Medicine [Boston], Centre Cavaillès, La République des savoirs : Lettres, Sciences, Philosophie, Collège de France (CdF (institution))-Centre National de la Recherche Scientifique (CNRS)-Département de Philosophie - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Collège de France (CdF (institution))-Centre National de la Recherche Scientifique (CNRS)-Département de Philosophie - ENS Paris, and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)
Subjects: Male, medicine.medical_specialty, Proton Magnetic Resonance Spectroscopy, [SDV]Life Sciences [q-bio], lcsh:Medicine, 010501 environmental sciences, Biology, Carbohydrate metabolism, 01 natural sciences, 03 medical and health sciences, chemistry.chemical_compound, Metabolomics, Phenols, Pregnancy, Internal medicine, medicine, Metabolome, Endocrine system, Animals, Benzhydryl compounds, Estrogens, Non-Steroidal, Benzhydryl Compounds, lcsh:Science, Toxicologie, 030304 developmental biology, 0105 earth and related environmental sciences, 0303 health sciences, Chemicals, Multidisciplinary, Perinatal Exposure, lcsh:R, Metabolism, Rats, Xenoestrogen, Endocrinology, chemistry, Liver, lcsh:Q, Female, Energy Metabolism, Research Article
Abstract: International audience; Along with the well-established effects on fertility and fecundity, perinatal exposure to endocrine disrupting chemicals, and notably to xeno-estrogens, is strongly suspected of modulating general metabolism. The metabolism of a perinatally exposed individual may be durably altered leading to a higher susceptibility of developing metabolic disorders such as obesity and diabetes; however, experimental designs involving the long term study of these dynamic changes in the metabolome raise novel challenges. H-1-NMR-based metabolomics was applied to study the effects of bisphenol-A (BPA, 0; 0.25; 2.5, 25 and 250 mu g/kg BW/day) in rats exposed perinatally. Serum and liver samples of exposed animals were analyzed on days 21, 50, 90, 140 and 200 in order to explore whether maternal exposure to BPA alters metabolism. Partial Least Squares-Discriminant Analysis (PLS-DA) was independently applied to each time point, demonstrating a significant pair-wise discrimination for liver as well as serum samples at all time-points, and highlighting unequivocal metabolic shifts in rats perinatally exposed to BPA, including those exposed to lower doses. In BPA exposed animals, metabolism of glucose, lactate and fatty acids was modified over time. To further explore dynamic variation, ANOVA-Simultaneous Component Analysis (A-SCA) was used to separate data into blocks corresponding to the different sources of variation (Time, Dose and Time*Dose interaction). A-SCA enabled the demonstration of a dynamic, time/age dependent shift of serum metabolome throughout the rats' lifetimes. Variables responsible for the discrimination between groups clearly indicate that BPA modulates energy metabolism, and suggest alterations of neurotransmitter signaling, the latter finding being compatible with the neurodevelopmental effect of this xenoestrogen. In conclusion, long lasting metabolic effects of BPA could be characterized over 200 days, despite physiological (and thus metabolic) changes connected with sexual maturation and aging.
Published: 2015

24. ProbMetab: an R package for Bayesian probabilistic annotation of LC-MS-based metabolomics

Author: Emilien L. Jamin, Diego M. Salvanha, Carlos Alberto Labate, Simone Guidetti-Gonzalez, Ricardo Silva, Fabien Jourdan, Fabien Létisse, Ricardo Z. N. Vêncio, FFCLRP-USP, Department of Computing and Mathematics, LabPIB, University of São Paulo (USP), Toxicologie Alimentaire (UTA), Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés (LISBP), Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Recherche Agronomique (INRA), Institute for Systems Biology, ESALQ-USP, Department of Genetics, Laboratório Nacional de Ciência e Tecnologia do Bioetanol (CTBE), ESALQ USP, Department of Genetics, Microsoft Research, FAPESP [09/53161-6], Métabolisme et Xénobiotiques (ToxAlim-MeX), 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)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), Analyse de Xénobiotiques, Identification, Métabolisme (E20 Metatoul-AXIOM), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-MetaToul-MetaboHUB, Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University of São Paulo ( USP ), Toxicologie Alimentaire ( UTA ), Institut National de la Recherche Agronomique ( INRA ) -Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés ( LISBP ), Institut National de la Recherche Agronomique ( INRA ) -Institut National des Sciences Appliquées - Toulouse ( INSA Toulouse ), Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratório Nacional de Ciência e Tecnologia do Bioetanol ( CTBE ), Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-MetaToul-MetaboHUB, Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), and Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Subjects: Statistics and Probability, Computer science, [SDV]Life Sciences [q-bio], Bayesian probability, Mass spectrometry, Bayesian inference, computer.software_genre, Quantitative Biology - Quantitative Methods, Biochemistry, Mass Spectrometry, Annotation, Metabolomics, spectrométrie de masse, Liquid chromatography–mass spectrometry, Metabolome, Biochemical reactions, Quantitative Biology - Genomics, Molecular Biology, Quantitative Methods (q-bio.QM), Automation, Laboratory, Genomics (q-bio.GN), [ SDV ] Life Sciences [q-bio], modèle probabiliste, Systems Biology, Probabilistic logic, Chromatography liquid, Bayes Theorem, Applications Notes, modèle bayésien, 3. Good health, Computer Science Applications, Computational Mathematics, annotation, Computational Theory and Mathematics, FOS: Biological sciences, chromatographie liquide, Graph (abstract data type), Data mining, computer, métabolomique, Software, Chromatography, Liquid
Abstract: We present ProbMetab, an R package which promotes substantial improvement in automatic probabilistic LC-MS based metabolome annotation. The inference engine core is based on a Bayesian model implemented to: (i) allow diverse source of experimental data and metadata to be systematically incorporated into the model with alternative ways to calculate the likelihood function and; (ii) allow sensitive selection of biologically meaningful biochemical reactions databases as Dirichlet-categorical prior distribution. Additionally, to ensure result interpretation by system biologists, we display the annotation in a network where observed mass peaks are connected if their candidate metabolites are substrate/product of known biochemical reactions. This graph can be overlaid with other graph-based analysis, such as partial correlation networks, in a visualization scheme exported to Cytoscape, with web and stand alone versions. ProbMetab was implemented in a modular fashion to fit together with established upstream (xcms, CAMERA, AStream, mzMatch.R, etc) and downstream R package tools (GeneNet, RCytoscape, DiffCorr, etc). ProbMetab, along with extensive documentation and case studies, is freely available under GNU license at: http://labpib.fmrp.usp.br/methods/probmetab/., Comment: Manuscript to be submitted very soon. 7 pages, 3 color figures. There is a companion material, the two case studies, which are going to be posted here together with the main text in next updated version
Published: 2014

25. Qualitative Modelling of Metabolic Networks

Author: Fabien Jourdan, Métabolisme et Xénobiotiques (ToxAlim-MeX), 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)-Université Toulouse III - Paul Sabatier (UT3), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)
Subjects: 0303 health sciences, Single model, [SDV]Life Sciences [q-bio], Systems biology, 030302 biochemistry & molecular biology, Experimental data, Metabolic network, Biology, Bioinformatics, Data science, Visualization, Metabolic network modelling, 03 medical and health sciences, Metabolomics, Organism, 030304 developmental biology
Abstract: Chapitre 12; International audience; Global and untargeted approaches such as metabolomics require analysis of experimental data with no assumption regarding the organism's functions affected by any environmental or genetic stress. From a metabolism perspective, it means that the metabolic shift induced by the studied perturbation may span several pathways. The challenge is then to incorporate and analyze simultaneously all the metabolic reactions. This is achieved by using genome-scale metabolic networks since they gather in a single model all the anabolic and catabolic reactions a cell or an organism can perform. This information is generally retrieved by processing genome sequences and applying validation methods. In this chapter, we will discuss how they are built and raise some issues on databases and network exchanges between different platforms. Then, we will show how graph modelling can be used to model and process this qualitative information. In particular, we will focus on the way to identify reaction paths between metabolites and how it can be used to create sub-networks based on metabolomics data. Finally, since these networks often contain thousands of reactions and metabolites, we will illustrate how visualization can be used to enhance this qualitative mining of metabolism.
Published: 2013

26. Intuitive Visualization and Analysis of Multi-Omics Dataand Application to Escherichia coli Carbon Metabolism

Author: Jean-Charles Portais, Fabien Jourdan, Brice Enjalbert, Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés (LISBP), Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Recherche Agronomique (INRA), Toxicologie Alimentaire (UTA), Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, French 'Agence Nationale de la Recherche' (ANR) ANR-06-BYOS-0003-03, Enjalbert, Brice, Jourdan, Fabien, Portais, Jean-Charles, Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés ( LISBP ), Institut National de la Recherche Agronomique ( INRA ) -Institut National des Sciences Appliquées - Toulouse ( INSA Toulouse ), Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ) -Centre National de la Recherche Scientifique ( CNRS ), Toxicologie Alimentaire ( UTA ), Institut National de la Recherche Agronomique ( INRA ) -Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Xénobiotiques, 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-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), Institut National de la Recherche Agronomique (INRA)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), and Université de Toulouse (UT)-Université de Toulouse (UT)
Subjects: Computer science, Microarrays, [SDV]Life Sciences [q-bio], Statistics as Topic, Protein metabolism, lcsh:Medicine, Bioinformatics, Central carbon metabolism, computer.software_genre, Biochemistry, Transcriptome, chemistry.chemical_compound, Transcriptional regulation, lcsh:Science, Regulation of gene expression, 0303 health sciences, Multidisciplinary, Systems Biology, 030302 biochemistry & molecular biology, Representation (systemics), Genomics, Complex network, Functional Genomics, Carbohydrate Metabolism, Data mining, Metabolic Pathways, Metabolic Networks and Pathways, Research Article, Carbon metabolism, Biological Data Management, External Data Representation, Microbiology, Set (abstract data type), 03 medical and health sciences, Metabolic Networks, Metabolomics, Data visualization, Escherichia coli, Biology, 030304 developmental biology, Microbial Metabolism, Regulatory Networks, Internet, [ SDV ] Life Sciences [q-bio], business.industry, lcsh:R, Computational Biology, Omics, Carbon, Visualization, Metabolic pathway, Metabolism, chemistry, lcsh:Q, Metagenomics, business, Genome Expression Analysis, computer, Biological network, Software, Transcription Factors
Abstract: Combinations of 'omics' investigations (i.e, transcriptomic, proteomic, metabolomic and/or fluxomic) are increasingly applied to get comprehensive understanding of biological systems. Because the latter are organized as complex networks of molecular and functional interactions, the intuitive interpretation of multi-omics datasets is difficult. Here we describe a simple strategy to visualize and analyze multi-omics data. Graphical representations of complex biological networks can be generated using Cytoscape where all molecular and functional components could be explicitly represented using a set of dedicated symbols. This representation can be used i) to compile all biologically-relevant information regarding the network through web link association, and ii) to map the network components with multi-omics data. A Cytoscape plugin was developed to increase the possibilities of both multi-omic data representation and interpretation. This plugin allowed different adjustable colour scales to be applied to the various omics data and performed the automatic extraction and visualization of the most significant changes in the datasets. For illustration purpose, the approach was applied to the central carbon metabolism of Escherichia coli. The obtained network contained 774 components and 1232 interactions, highlighting the complexity of bacterial multi-level regulations. The structured representation of this network represents a valuable resource for systemic studies of E. coli, as illustrated from the application to multi-omics data. Some current issues in network representation are discussed on the basis of this work.
Published: 2011

27. MetExplore: a web server to link metabolomic experiments and genome-scale metabolic networks

Author: Marie-France Sagot, Ludovic Cottret, David Wildridge, Florence Vinson, Michael P. Barrett, Fabien Jourdan, Hubert Charles, Xénobiotiques, Institut National de la Recherche Agronomique (INRA)-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-Université Fédérale Toulouse Midi-Pyrénées, University of Glasgow, An algorithmic view on genomes, cells, and environments (BAMBOO), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Baobab, Département PEGASE [LBBE] (PEGASE), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Biologie Fonctionnelle, Insectes et Interactions (BF2I), Institut National de la Recherche Agronomique (INRA)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon, Société Française de Bio-Informatique (SFBI). Marseille, FRA., Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA), Institut National de Recherche en Informatique et en Automatique (Inria), Université de Lyon (COMUE), Université de Lyon, Institut National de la Recherche Agronomique (INRA), ANR REGLIS NT05-3_45205 MIRI BLAN08-1335497, ANR-BBSRC ANR-BBSRC MetNet4SysBio ANR-07-BSYS 003 02 ANR-BBSRC SysTryp, and Biotechnology and Biological Sciences Research Council BB/F005679/1
Subjects: Web server, [SDV.BIO]Life Sciences [q-bio]/Biotechnology, Metabolic network, Context (language use), Computational biology, Biology, MetExplore, Bioinformatics, computer.software_genre, 03 medical and health sciences, MESH: Software, Metabolomics, métabolite, Computer Graphics, Genetics, Metabolome, MESH: Genome, MESH: Metabolomics, Organism, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, Internet, 0303 health sciences, Genome, génome, 030302 biochemistry & molecular biology, Articles, Pipeline (software), Metabolic pathway, MESH: Internet, serveur web, MESH: Metabolic Networks and Pathways, MESH: Computer Graphics, computer, Metabolic Networks and Pathways, Software, [SDV.EE.IEO]Life Sciences [q-bio]/Ecology, environment/Symbiosis
Abstract: International audience; High-throughput metabolomic experiments aim at identifying and ultimately quantifying all metabolites present in biological systems. The metabolites are interconnected through metabolic reactions, generally grouped into metabolic pathways. Classical metabolic maps provide a relational context to help interpret metabolomics experiments and a wide range of tools have been developed to help place metabolites within metabolic pathways. However, the representation of metabolites within separate disconnected pathways overlooks most of the connectivity of the metabolome. By definition, reference pathways cannot integrate novel pathways nor show relationships between metabolites that may be linked by common neighbours without being considered as joint members of a classical biochemical pathway. MetExplore is a web server that offers the possibility to link metabolites identified in untargeted metabolomics experiments within the context of genome-scale reconstructed metabolic networks. The analysis pipeline comprises mapping metabolomics data onto the specific metabolic network of an organism, then applying graph-based methods and advanced visualization tools to enhance data analysis. The MetExplore web server is freely accessible at http://metexplore.toulouse.inra.fr.
Published: 2010

28. MetaNetter: inference and visualization of high-resolution metabolomic networks

Author: David Gilbert, Rainer Breitling, Fabien Jourdan, Michael P. Barrett, Xénobiotiques, 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-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), Bioinformatics, Groningen Biomolecular Sciences and Biotechnology, and Theoretical Philosophy
Subjects: Statistics and Probability, Proteome, Computer science, Inference, High resolution, Mass spectrometry, computer.software_genre, Models, Biological, Sensitivity and Specificity, 01 natural sciences, Biochemistry, User-Computer Interface, 03 medical and health sciences, Metabolomics, Protein Interaction Mapping, Computer Simulation, Molecular Biology, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, 0303 health sciences, Gene Expression Profiling, 010401 analytical chemistry, Reproducibility of Results, 0104 chemical sciences, Computer Science Applications, Visualization, Computational Mathematics, Computational Theory and Mathematics, Data mining, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], computer, Software, Signal Transduction
Abstract: Summary: We present a Cytoscape plugin for the inference and visualization of networks from high-resolution mass spectrometry metabolomic data. The software also provides access to basic topological analysis. This open source, multi-platform software has been successfully used to interpret metabolomic experiments and will enable others using filtered, high mass accuracy mass spectrometric data sets to build and analyse networks. Availability: http://compbio.dcs.gla.ac.uk/fabien/abinitio/abinitio.html Contact: Fabien.Jourdan@toulouse.inra.fr Supplementary information: http://compbio.dcs.gla.ac.uk/fabien/abinitio/doc/Supplementary.pdf
Published: 2008

29. Use of reconstituted metabolic networks to assist in metabolomic data visualization and mining

Author: David G. Watson, David Wildridge, Fabien Jourdan, Ludovic Cottret, Richard A. Scheltema, Michael P. Barrett, Daniel Zalko, Laurent Debrauwer, Anne Hillenweck, Laurence Huc, Xénobiotiques, 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-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), University of Glasgow, University of Groningen, University of Strathclyde [Glasgow], and Bioinformatics
Subjects: Computer science, [SDV]Life Sciences [q-bio], Endocrinology, Diabetes and Metabolism, Clinical Biochemistry, Metabolic network, Context (language use), computer.software_genre, 01 natural sciences, Biochemistry, 03 medical and health sciences, Metabolomics, Data visualization, Metabolome, TOOL, Selection (genetic algorithm), 030304 developmental biology, 0303 health sciences, Gap filling, business.industry, 010401 analytical chemistry, Graph algorithm, PATHWAYS, MASS-SPECTROMETRY, 0104 chemical sciences, GENOME, Feature (computer vision), Gap-filling, Original Article, Data mining, business, computer
Abstract: International audience; Metabolomics experiments seldom achieve their aim of comprehensively covering the entire metabolome. However, important information can be gleaned even from sparse datasets, which can be facilitated by placing the results within the context of known metabolic networks. Here we present a method that allows the automatic assignment of identified metabolites to positions within known metabolic networks, and, furthermore, allows automated extraction of sub-networks of biological significance. This latter feature is possible by use of a gap-filling algorithm. The utility of the algorithm in reconstructing and mining of metabolomics data is shown on two independent datasets generated with LC-MS LTQ-Orbitrap mass spectrometry. Biologically relevant metabolic sub-networks were extracted from both datasets. Moreover, a number of metabolites, whose presence eluded automatic selection within mass spectra, could be identified retrospectively by virtue of their inferred presence through gap filling. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0196-9) contains supplementary material, which is available to authorized users.
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29 results on '"Fabien Jourdan"'

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

2. Gaining Insights Into Metabolic Networks Using Chemometrics and Bioinformatics: Chronic Kidney Disease as a Clinical Model

3. Postprandial NMR-Based Metabolic Exchanges Reflect Impaired Phenotypic Flexibility across Splanchnic Organs in the Obese Yucatan Mini-Pig

4. An Untargeted Metabolomics Approach to Investigate the Metabolic Modulations of HepG2 Cells Exposed to Low Doses of Bisphenol A and 17β-Estradiol

5. Milk metabolome reveals variations on enteric methane emissions from dairy cows fed a specific inhibitor of the methanogenesis pathway

6. The future of metabolomics in ELIXIR [version 2; referees: 3 approved]

7. The future of metabolomics in ELIXIR [version 1; referees: 2 approved, 1 approved with reservations]

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

9. Osteogenic Response of Human Mesenchymal Stem Cells Analysed Using Combined Intracellular and Extracellular Metabolomic Monitoring

10. Pathway analysis in metabolomics: pitfalls and best practice for the use of over-representation analysis

11. Postprandial NMR-Based Metabolic Exchanges Reflect Impaired Phenotypic Flexibility across Splanchnic Organs in the Obese Yucatan Mini-Pig

12. Improving lipid mapping in Genome Scale Metabolic Networks using ontologies

13. 1H-NMR metabolomics response to a realistic diet contamination with the mycotoxin deoxynivalenol: Effect of probiotics supplementation

14. Pathway analysis in metabolomics: Recommendations for the use of over-representation analysis

15. PhenoMeNal: processing and analysis of metabolomics data in the cloud

16. Large-Scale Modeling Approach Reveals Functional Metabolic Shifts during Hepatic Differentiation

17. Genome-scale metabolic networks

18. Targeted versus untargeted omics — the CAFSA story

19. Mind the gap: mapping mass spectral databases in genome-scale metabolic networks reveals poorly covered Areas

20. Integrated transcriptomics and metabolomics reveal signatures of lipid metabolism dysregulation in HepaRG liver cells exposed to PCB 126

21. Telling metabolic stories to explore metabolomics data: a case study on the yeast response to cadmium exposure

22. Computational methods to identify metabolic sub-networks based on metabolomic profiles

23. Dynamic metabolic disruption in rats perinatally exposed to low doses of bisphenol-A

24. ProbMetab: an R package for Bayesian probabilistic annotation of LC-MS-based metabolomics

25. Qualitative Modelling of Metabolic Networks

26. Intuitive Visualization and Analysis of Multi-Omics Dataand Application to Escherichia coli Carbon Metabolism

27. MetExplore: a web server to link metabolomic experiments and genome-scale metabolic networks

28. MetaNetter: inference and visualization of high-resolution metabolomic networks

29. Use of reconstituted metabolic networks to assist in metabolomic data visualization and mining

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29 results on '"Fabien Jourdan"'

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