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1. 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

2. 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

3. Plant genome-scale metabolic networks

Author

Léo Gerlin, Sylvain Prigent, Clément Frainay, Fabien Jourdan, and Caroline Baroukh

Subjects
0303 health sciences, Computer science, Genome scale, Context (language use), 04 agricultural and veterinary sciences, 15. Life on land, Data science, Suspension culture, Visualization, Omics data, Model integration, 03 medical and health sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Host plants, Plant metabolism, 030304 developmental biology
Abstract

With the development of plant metabolomics, projects like the 10,000 plants genome sequencing project and the growth of other omics, the amount of data describing plant metabolism has never been so big. Genome-scale metabolic models (GEMs) are widely used to integrate and study the available information, and to better understand global responses to metabolic changes. In the first part of this chapter, we will focus on the existing plant GEMs, their history, and the biological questions associated with their reconstruction. Those GEMs were initially reconstructed based on cell suspension systems, but current reconstructions are widely focused on multiorgans and multitissues models, requiring some model integration. Model integration is the focus of the second part of this chapter, with the study of biotic interactions with the integration of GEMs coming from plant-interacting microorganisms and their host plants, modeling either pathogenic and symbiotic interactions. Emphasis will be placed on modeling quantitative interaction between plants and microorganisms. Finally, the third part of this chapter describes how the visualization of networks could be used to improve the understanding of the models and the integration of different omics data, and how good representation of metabolic networks can help the user to perform diverse tasks and put knowledge in context.

Published
2021

4. 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

5. 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, Daniel Zalko, Cabaton, Nicolas, and Poupin, Nathalie

Subjects
0301 basic medicine, HepG2, multivariate statistics, Endocrinology, Diabetes and Metabolism, Estrogen receptor, Metabolic network, Pharmacology, lcsh:Diseases of the endocrine glands. Clinical endocrinology, 03 medical and health sciences, Endocrinology, Metabolomics, metabolic network, Detoxification, Original Research, lcsh:RC648-665, Chemistry, Mechanism (biology), metabolomics, BPA, 17 beta-estradiol, endocrine disruption, 17β-estradiol, 3. Good health, Metabolic pathway, 030104 developmental biology, Toxicity, Hormone
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 (H-1-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(-6) M, 10(-9) M, and 10(-)(12) M), vs. the female reference hormone 17 beta-estradiol (E2, 10(-9) M, 10(-)(12) M, and 10(-)(15) M). 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

6. IDH1 Mutation Enhances Catabolic Flexibility and Mitochondrial Dependencies to Favor Drug Resistance in Acute Myeloid Leukemia

Author

Héléna Boutzen, Clément Larrue, Pierre Millard, Laurent Fernando, Laurent Le Cam, Laurie Gayte, Noémie Gadaud, Tony Kaoma, Pierre-Luc Mouchel, Evgenia Turtoi, Jean-Charles Portais, Nesrine Aroua, Justine Bertrand-Michel, Courtney Dinardo, Tony Lionel Palama, Yves Collette, Camille Montersino, Laure Tonini, Yves Gibon, Madi Y. Cissé, Cédric Cassan, Koichi Takahashi, Florence Castelli, Pierre Bories, Audrey Bidet, Joe Marszalek, Martin Carroll, Guillaume Cazals, Claire Calmettes, Arnaud Pigneux, Thomas Farge, Lucille Stuani, Feng Wang, Fabien Jourdan, Laetitia K. Linares, Mary A. Selak, Lara Gales, Andrew M. Futreal, Mathilde Gotanègre, Maud Heuillet, Kiyomi Morita, Jean-Emmanuel Sarry, Andrei Turtoi, Nicolas Broin, Christian Recher, Marina Konopleva, Mohsen Hosseini, Christophe Junot, Claudie Bosc, Emeline Chu-Van, Marine Fraisse, Nathalie Saint-Laurent, Lindsay Peyriga, Rémy Castellano, Estelle Saland, Natalia Baran, Norbert Vey, Frédéric Lopez, Floriant Bellvert, Marie Sabatier, and Nathalie Poupin

Subjects
Citric acid cycle, IDH1, Chemistry, Catabolism, Mutant, Myeloid leukemia, Metabolism, Oxidative phosphorylation, Reprogramming, Cell biology
Abstract

Isocitrate dehydrogenases (IDH) are involved in redox control and central metabolism. We hypothesized that key metabolic fluxes are selectively reprogrammed to maintain biosynthetic homeostasis and lower drug responses in IDH mutant acute myeloid leukemia cells. Here we show that metabolic reprogramming initiated by IDH1 mutation leads to marked increases in glucose, glutamine and fatty acid catabolism that along with enhancement of wild-type IDH enzyme activity contribute to provision of α-KG required for 2-HG synthesis and to replenish Krebs cycle intermediates for biosynthetic reactions, oxygen consumption and ATP production. Mechanistically, this occurs through both methylation-driven CEBPα activation of FAO and reprogramming of systemic metabolic fluxes through other pathways that augment catabolic flexibility. Consequently, this catabolic flexibility enhances Krebs cycle and OxPHOS activities that are not necessarily rescued by IDH mutant inhibitors or 2-HG reduction. This renders IDH1 mutant cells more resistant to chemotherapeutics but more susceptible to mitochondrial inhibition. Our findings provide a scientific rationale for innovative combinatory targeted therapies to treat this subgroup of patients, especially those unresponsive to or relapsing from IDH mutant-specific inhibitors.

Published
2018

7. 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

8. Understanding Protozoan Parasite Metabolism and Identifying Drug Targets through Constraint-Based Modeling

Author

Glenn A. McConkey, Francis Isidore Totanes, Sanu Shameer, David R. Westhead, Fabien Jourdan, University of Leeds, 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), 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
Drug, 0303 health sciences, media_common.quotation_subject, 030302 biochemistry & molecular biology, Computational biology, Biology, Bioinformatics, Protozoan parasite, Flux balance analysis, 03 medical and health sciences, Parasitology, Constraint based modeling, [SDV.MP.PAR]Life Sciences [q-bio]/Microbiology and Parasitology/Parasitology, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, media_common
Abstract

International audience

Published
2016

9. 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

10. 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

11. Flux Analysis of the Trypanosoma brucei Glycolysis Based on a Multiobjective-Criteria Bioinformatic Approach

Author

Fabien Jourdan, Amine Ghozlane, Isabelle Dutour, Frédéric Bringaud, Hayssam Soueidan, Patricia Thebault, Ghozlane, Amine, and Thébault, Patricia

Subjects
0303 health sciences, Flux distribution, Optimization problem, Article Subject, biology, 030302 biochemistry & molecular biology, Biomedical Engineering, Metabolic network, Computational biology, Trypanosoma brucei, biology.organism_classification, Biochemistry, Genetics and Molecular Biology (miscellaneous), Protozoan parasite, Computer Science Applications, 03 medical and health sciences, Metabolic Model, lcsh:Biology (General), Biochemistry, Glycolysis, Computational analysis, lcsh:QH301-705.5, lcsh:Statistics, lcsh:HA1-4737, 030304 developmental biology, Research Article
Abstract

Trypanosoma brucei is a protozoan parasite of major of interest in discovering new genes for drug targets. This parasite alternates its life cycle between the mammal host(s) (bloodstream form) and the insect vector (procyclic form), with two divergent glucose metabolism amenable to in vitro culture. While the metabolic network of the bloodstream forms has been well characterized, the flux distribution between the different branches of the glucose metabolic network in the procyclic form has not been addressed so far. We present a computational analysis (called Metaboflux) that exploits the metabolic topology of the procyclic form, and allows the incorporation of multipurpose experimental data to increase the biological relevance of the model. The alternatives resulting from the structural complexity of networks are formulated as an optimization problem solved by a metaheuristic where experimental data are modeled in a multiobjective function. Our results show that the current metabolic model is in agreement with experimental data and confirms the observed high metabolic flexibility of glucose metabolism. In addition, Metaboflux offers a rational explanation for the high flexibility in the ratio between final products from glucose metabolism, thsat is, flux redistribution through the malic enzyme steps.

Published
2012

12. Interactive Refinement of Multi-scale Network Clusterings

Author

Fabien Jourdan, David Auber, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), and Auber, David

Subjects
Pattern clustering, Small-world network, business.industry, Computer science, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], 020207 software engineering, Graph theory, 02 engineering and technology, User expectations, Machine learning, computer.software_genre, Visualization, [INFO.INFO-OH] Computer Science [cs]/Other [cs.OH], Linear algorithm, Data visualization, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, Data mining, business, Scale (map), computer
Abstract

International audience; Insight of multiscale networks could be accessed through the visualization of automatic multiscale clusterings. But results of these methods do not necessarily fulfill user expectations since they don't provide error prone clusterings. In this article we propose a way to refine interactively these results by the use of multiscale grouping and ungrouping interactions. This approach revealed to give very good results on common networks, especially on small world networks. Moreover, the linear algorithm makes that the method remains interactive on huge graphs with thousand of nodes.

Published
2006

13. Tool for metabolic and regulatory pathways visual analysis

Author

Fabien Jourdan and Guy Melançon

Subjects
Computer science, business.industry, Representation (systemics), Cognition, Machine learning, computer.software_genre, Visualization, Information visualization, ComputingMethodologies_PATTERNRECOGNITION, Knowledge extraction, Human–computer interaction, Artificial intelligence, Zoom, Set (psychology), business, computer
Abstract

The research activity in bio-informatics has now reached a new phase, called post-genomics. It aims at the description of gene products as part of global processes in cells. In this research area, the various tasks to be conducted by biologists call for methods inspired from knowledge extraction and representation, and from information visualization. We describe a system devoted to the visualization of metabolic pathways. This set of biological reactions describe product transformations in the cell. The analysis of various pathway visualization tools led us to qualitative assertions. First, it is essential that the visualization environment preserves the drawing conventions borrowed from biology. Second, it seems important to offer an environment in which the user can navigate while preserving cognitive continuity. Our system focuses on these interactive and navigational issues. It offers mechanisms such as interactive color mapping and semantic zooming of pathways through various levels of details. Our tool also aims at helping biologists in the analysis of experimental results measuring gene expression in various biological processes. Although our efforts have focused on the visualization of metabolic pathways, our system should help to visualize, analyze and discover other types of biological pathways (e.g. regulatory pathways).

Published
2003

14. Multiscale Visualization of Small World Networks

Author

Fabien Jourdan, David Auber, Yves Chiricota, Guy Melançon, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Auber, David, Université du Québec à Chicoutimi (UQAC), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), and IEEE Computer Society

Subjects
Connected component, Theoretical computer science, Small-world network, business.industry, Computer science, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], 020207 software engineering, Network science, 02 engineering and technology, Complex network, Multiscale graphs, Clustering metric, Visualization, [INFO.INFO-OH] Computer Science [cs]/Other [cs.OH], Information visualization, Data visualization, Evolving networks, Semantic zooming, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], 020201 artificial intelligence & image processing, business
Abstract

International audience; Many networks under study in Information Visualization are "small world" networks. These networks first appeared in the study social networks and were shown to be relevant models in other application domains such as software reverse engineering and biology. Furthermore, many of these networks actually have a multiscale nature: they can be viewed as a network of groups that are themselves small world networks. We describe a metric that has been designed in order to identify the weakest edges in a small world network leading to an easy and low cost filtering procedure that breaks up a graph into smaller and highly connected components. We show how this metric can be exploited through an interactive navigation of the network based on semantic zooming. Once the network is decomposed into a hierarchy of sub-networks, a user can easily find groups and subgroups of actors and understand their dynamics.

15. Reconstruction du réseau métabolique de la tomate

Author

Florence Maurier, Clément Frainay, Fabien Jourdan, Sophie Colombie, 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), Biologie du fruit et pathologie (BFP), Université Bordeaux Segalen - Bordeaux 2-Institut National de la Recherche Agronomique (INRA)-Université Sciences et Technologies - Bordeaux 1, Métabolisme et Xénobiotiques (ToxAlim-MeX), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), and Réseau Francophone de Métabolomique et Fluxomique (RFMF). FRA.

Subjects
[SDV.BV]Life Sciences [q-bio]/Vegetal Biology, ComputingMilieux_MISCELLANEOUS
Abstract

National audience

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