LA MÉTABOLOMIQUE 20 ANS DÉJÀ - Dominique Rolin Université de Bordeaux - INRA - Université de Lille
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LA MÉTABOLOMIQUE 20 ANS DÉJÀ…. Dominique Rolin Université de Bordeaux - INRA Lille 15 octobre 2019 ANR-INBS-0010
Menu du jour ➡ Métabolomique versus lipidomique et fluxomique ➡ Une approche analytique • récente • adaptée aux enjeux de la biologie du XXI° siècle • à haut débit et à haute densité • avec un workflow complexe ➡ Enjeux et défis de la métabolomique • Quels étaient les enjeux au début des années 2000 ? • Bilan de ces 20 années • Que reste-t-il à faire ? MetaboHUB
Life sciences: Facing waves Synthetic biology of technological revolutions System biology Integrative biology Life Molecular Sequencing Functionnal Cell Biology observation Biology genome genomics XVII° XVIII° XIX° XX° XXI° Century No science without progress in technology No progress in technology without science MetaboHUB 4
Metabolomics versus Fluxomics Metabolomics & Fluxomics are 2 technological TOOLS to carry out Life Science Metabolomics Fluxomics Metabolic network Metabolism Metabolite identification Dynamic analysis of & quantification metabolic fluxes Flux quantification Fingerprinting M Profiling M. Movie: at which speed Pictures: which cars are Traffic on drive the cars driving on the roads road network MetaboHUB Metabolomics & Fluxomics are more complicated than a simple biochemical analysis ?
What do we mean by metabolomics and metabolome ? Metabolome (lipidome): all small molecules named polar metabolites (apolar metabolites) occurring in a biological system. Fluxome: All quantified metabolite fluxes occurring in a biological system. Metabolomics (lipidomics): Tools and strategy for determination of metabolite levels occurring in a biological system and their changes over time as a consequence of stimuli Fluxomics: Tools and strategy for determination of metabolite fluxes occurring in a biological system and their changes over time as a consequence of stimuli MetaboHUB
The multi-OMICS family and their biological significations • ce qui peut arriver • ce qui semble se passer • ce qui fait que ça arrive • ce qui s'est passé et ce qui se passe MetaboHUB
Flux maps provide more real informations Same pools of metabolites but different metabolic flux Metabolomics 100 100 40 20 40 20 60 80 10 5 60 75 60 40 75 30 20 45 20 45 20 45 Fluxomics Condition 1 Condition 2 MetaboHUB
Une approche analytique récente
« Metabolome » terme first appeared in 1998 Trends Biotechnol. 16, 373–378 ( 1998). The Metabolome Steven G. Oliver MetaboHUB
25471 publications depuis 2000 Web of Science DB du 29/09/19 interrogée sur la période 1956-2019 Web of science DB: Metabolomics MetaboHUB
Plus de 185 domaines d’application de la métabolomique Les 25 domaines qui publient le plus en métabolomique sur la période 2001-2019 MetaboHUB
Les 10 publications les plus citées entre 2001 et 2005 Total Year Title Authors Source Title Citations PLANT Metabolomics - the link between 2079 2002 genotypes and phenotypes Fiehn, O Strategy MOLECULAR BIOLOGY A functional genomics strategy that NATURE 732 2001 uses metabolome data to reveal the Raamsdonk et al. Strategy BIOTECHNOLOGY phenotype of silent mutations Plant metabolomics: large-scale 632 2003 phytochemistry in the functional Sumner, LW et al. Strategy PHYTOCHEMISTRY genomics era Metabolomics by numbers: acquiring TRENDS IN 753 2004 and understanding global metabolite Goodacre, R et al. Strategy BIOTECHNOLOGY data THERAPEUTIC METLIN - A metabolite mass 1079 2005 spectral database Smith, CA et al. Data base DRUG MONITORING GMD@CSB.DB: the Golm 750 2005 Metabolome Database Kopka,J et al. Data base BIOINFORMATICS The Orbitrap: a new mass JOURNAL OF MASS 734 2005 spectrometer Hu, QZ; et al Equipment SPECTROMETRY Quantitative metabolome analysis JOURNAL OF 588 2003 using capillary electrophoresis mass Soga, T; et al. Equipment PROTEOME spectrometry RESEARCH TRAC-TRENDS IN Metabolomics: Current analytical 626 2005 platforms and methodologies Dunn, WB; Ellis, DI Methodology ANALYTICAL CHEMISTRY Mitochondrial dysfunction in schizophrenia: evidence for Prabakaran, S; et MOLECULAR 624 2004 compromised brain metabolism and al. Science PSYCHIATRY oxidative stress MetaboHUB
Les 10 publications les plus citées entre 2001 et 2019 (25471 publications) Total Year Title Authors Source Title Citations HMDB 3.0-The Human Wishart, David S. NUCLEIC ACIDS 1608 2013 Metabolome Database in 2013 et al. Data base RESEARCH HMDB: the human metabolome Wishart, David S. NUCLEIC ACIDS 1394 2007 database et al. Data base RESEARCH HMDB: a knowledgebase for the Wishart, David NUCLEIC ACIDS 1095 2009 human metabolome et al. Data base RESEARCH THERAPEUTIC METLIN - A metabolite mass 1079 2005 spectral database Smith, CA et al. Data base DRUG MONITORING Xia, Jianguo; S MetaboAnalyst 3.0-making NUCLEIC ACIDS 1368 2015 metabolomics more meaningful et al. (Wishart, Methodology RESEARCH David) Proposed minimum reporting Sumner, Lloyd 1209 2007 METABOLOMICS standards for chemical analysis W. et al. Methodology PLANT Metabolomics - the link between 2079 2002 genotypes and phenotypes Fiehn, O Strategy MOLECULAR BIOLOGY Metabolite profiling for plant NATURE 1314 2000 functional genomics Fiehn, O; et al. Strategy BIOTECHNOLOGY Gut flora metabolism of 1811 2011 phosphatidylcholine promotes Wang, et al. Science NATURE cardiovascular disease Metabolite profiles and the risk Wang, Thomas 1333 2011 of developing diabetes J.; et al. MetaboHUB Science NATURE MEDICINE
Une approche analytique adaptée aux enjeux de la biologie du XXI° siècle
XXI century: new biology The National Institutes of Health The National Research Council’s The National Science Foundation Board on Life Science The Department of Energy (2008-2009) Report A New Biology for the 21st Century http://www.nap.edu/catalog/12764.html 1- to examine the current state of biological research in the United States 2- recommend how best to capitalize on recent technological and scientific advances Human Health Food Science Energy Environment MetaboHUB
XXI century: new biology Interconnected problems need Interconnected solutions The challenge cannot be met in isolation Metabolomics needs all these scientific domains MetaboHUB
Une approche analytique avec des technologies diverses à haut débit et à haute densité
Where are the Challenges ? MetaboHUB
Une diversité de technologie pour identifier et quantifier les molécules Spectrométrie de masse: une technique de choix couplée ou non à la chromatographie gazeuse ou liquide RESOLUTION Faible Moyenne Haute Ultra-haute Quantité d’information, durée de traitement de l’information, moyens de stockage MetaboHUB
Profils Empreintes X métabolomiques métabolomiques Analyse - ciblée Moyens d’obtenir + Quantité d’information, durée de traitement de l’information, moyens de stockage l’information Moyens pour stocker l’information Moyens pour traiter l’information MetaboHUB
Faire la différence entre la Métabolomique à haute densité – la Métabolomique à haut débit Métabolomique à haute densité Nombre de métabolites FT-ICR-MS détectés Orbitrap LC - MS non targeted GC - MS RMN LC - MS targeted Enzymatic M. Nombre d’échantillons MetaboHUB Métabolomique à haut débit
Une approche analytique avec un workflow complexe
Une démarche commune avec de très très nombreuses options Information initiale Information analysable Intensité des ions au sein des échantillons MetaboHUB
Context: Metabolomics & Fluxomics Multidisciplinary approaches Biological questions Experimental Design Sampling acquisition Numerous worflows for data analysis Metadata acquisition in DB Analytical Sample preparation tools Numerous analytical methods 27 Metabolomics fingerprints or profilings
Context: Metabolomics & Fluxomics Multidisciplinary approaches Interpretation Identification Statistical analysis ID RetTime Mass Significance sample 1 sample 2 70.1@1.2 1.2228 70.0695 0.0023 2.2385 1.8719 72.1@1.4 1.4379 72.0796 0.0106 9.4194 6.9449 80.9@0.9 0.9096 80.9474 0.0144 11.8372 8.0128 81@1 1.0163 80.9533 0.0006 0.3338 0.1262 83@0.9 0.9206 82.9588 0.0044 4.1611 3.3217 84.9@0.8 0.818 84.9451 0.0924 60.7749 32.0214 86.1@1.9 86.1@3.7 91@0.9 1.8814 3.7238 0.9038 86.0886 86.0925 90.9733 Data extraction & 0.0142 13.3605 13.2928 0.0363 29.7443 20.0236 0.007 6.3339 4.7593 normalisation 96.9@0.9 0.9312 96.9269 0.0027 2.0042 1.2272 100.1@4.3 4.3137 100.0674 0.0264 25.1952 20.3527 100.1@4.5 4.5163 100.069 0.0139 6.1492 1.4764 100.1@4.2 4.2209 100.0691 0.0387 12.1114 22.6781 100.1@19.5 19.4586 100.1@21.6 21.5638 100.0751 100.0759 0.011 0.0035 0.4926 1.3987 3.9759 2.2989 Need to visualise metabolic networks RAW conversion T1_220909_101109AFAMM 375 (11.814) Cm (280:402) 100 289.2036 33362 465.2295 32595 1: TOF MS ES+ 3.34e4 to solve biological questions 363.2011 23490 301.2049 21124 317.2080 17148 % 335.2192 13449 79.0247 Urgent needs for DB to store reference spectra 5850 393.2261 255.1689 271.1941 5939 466.2465 4556 5124 4619 Analytical methods 159.0724 181.1131 253.1821 111.0227 1498 2203 3009 394.2391 431.2523 467.2661 1052 807 493 773 492.3099 230.0992;787 9 0 m/z 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480 500
une démarche commune avec de trés nombreuses options Question Acquisition Question Question de l’ préparation Profil ou biologique échantillonnage des empreintes échantillons Traitement Sélection recherche des Analyses des formules brutes données statistiques signaux correspondantes brutes Integration des Elucidation Validation et Interprétation métabolites dans structurale suivi des bio biologique les réseaux des signaux marqueurs métaboliques Traitement des échantillons, besoin d’équipements Traitement l’information, besoin de serveurs, de soft, de base de données, de stockage MetaboHUB
Metabolite Metabolic Lipidomics and identification and fingerprinting high the lipid world quantification density Sample preparation Data integration Sampling data mining acquisition Infrared technology Data What is the reduction Biology question INFORMATICS Analytical Pipetting BEFORE Robots Tools GC-MS AFTER Expertises NMR Experimental LC-MS Statistic Design analysis Acquisition of Metadata Network analysis in database and modeling Metabolomic flux Using stable isotopes for quantification metabolomics Metabolomics and fluxomics much more complicated than a simple metabolite analysis
A real need to exchange and Other communicate NMR Which Where can I found Analytical these needed analytical tools ? GC-MS tools ? LC-MS What do you mean by experimental design ? Where can I found a Where and how can I chemist or biochemist ? store my data (big) ? Which statistical tools or softwares can I use ? What do you mean by metadata ? Biologists face to metabolomics How can I integrate and visualise my data in the metabolic network ? MetaboHUB
A need to exchange and Other communicate between experts NMR LC-MS Where can I found Which GC-MS Specialists GC-MS Analytical the needed analytical tools ? Specialists tools ? NMR LC-MSSpecialists What do you mean Computer By experimentalSpecialists design ? Biochemist Platform Concept Where can I found a Specialists Psychologist Where and how can I chemist or biochemist ? to share store myBiology data ? Specialists information Which statistical tools or Chemist Specialists can I use ? Biologist face to metabolomics What do you mean softwares by metadata ? Metabolism How can I integrate Specialists Biostatistician Specialists my data in the meta- Modelling Specialists? bolic network MetaboHUB
Enjeux et défis de la métabolomique Quels étaient les besoins au début des années 2000 Bilan de l’actif
Les besoins et les défis technologiques, Question computationnels et humains sont biologique presque infinis Traitements Organiser les Echantillons Equipements des données échanges • Questions Masse • Standardisation • développement biologiques • + de résolution (workflow, format des des plateformes • Dépendant du • + de sensibilité fichiers, protocoles, etc) • besoin de domaine • + de répétabilité • Bases de données structurer les • Cohorte de plus • + rapide • Interoperabilité échanges à de 100 -10000 • + automatisation • Identifier les molécules l’échelle régionale, échantillons • + robuste • Extraire l’information nationale et • Automatisation • - cher + cher biologique (statistiques) européenne • Procédures • etc • Développer des • développer d'extraction ORBITRAP solutions softs l’enseignement • Standardisation • La question du stockage universitaire • MetaData RMN des données (brutes, • développer les • etc. • + de sensibilité nettoyées, etc.) formations • etc. • + automatisation spécialisées •MetaboHUB + serveurs, icloud
Two initiatives to promote metabolomics & fluxomics MetaboHUB a French governmental initiative aimed to set up a French Infrastructure devoted to the M & F in France Top down initiative 2013 Bottom up 2005 initiative French-speaking Network for Metabolomics and Fluxomics This is a "bottom up" initiative aimed at facilitating and promoting sustainable development of the M &F in France MetaboHUB
Two initiatives to promote metabolomics & fluxomics French-speaking Metabolomics and Fluxomics network Created in 2005, affiliated to metabolomics society since 2013 Currently ≈300 members Aims: ∗ to make an inventory and promote French skills in the fields of metabolomics and fluxomics ∗ to provide and support scientific meetings or workshops in metabolomics and fluxomics ∗ to facilitate knowledge transfer to students and newcomers in the field and help students to promote their work ■ MetaboHUB
MetaboHUB impacts (2013-2017) Why not a MétaNORD ?? French-speaking Network Networking and structuring on Metabolomics & Fluxomics French community First contact Les Lipidomystes: Identified PF • French cluster working on lipidomics In progress MTH platforms in France 37 MetaboHUB
French National Infrastructure in Biology and Health (2013-2025) Distributed & coordinated infrastructure for metabolomics & fluxomics devoted to innovation, training and technology transfer MetaboHUB v1: 4 platforms HR in full-time permanents Nantes-Rennes Saclay-Paris Clermont-Ferrand Bordeaux Toulouse MetaboHUB v2 (in 2020): Corsaire PF 2 universities + 1 National Engineer School 5 National institutes 4 Universities MetaboHUB
MetaboHUB activities 1 Technology: Developing generic tools, analytical and computer solutions 2 Sciences: Proof of concept projects (Health, Plants, Biotechnology) 3 Training and teaching M & F 4 Web portal for national access to MetaboHUB services 5 Technology transfer to French community 6 Structuring French community through networking 7 Strengthening French position within European community MetaboHUB
A multilayered success Development of 4 online reference platforms Workflow4Metabolomics.org data analysis in Galaxy (Giacomoni et al. 2015; Guitton et al. 2017) à in collaboration with IFB (French Institute of Bioinformatics) NMRProcFlow.org interactive data processing (Jacob et al. 2017) PeakForest.org database for metabolite identification (Damont et al. 2019) MetExplore network analysis (Cottret et al. 2018) World-wide use by academia and industry RHU cohort projects (e.g. CHOPIN ANR-16-RHUS-0007, Bioart Lung ANR-15-RHUS-0002-07, QUID ANR-17-RHUS-0009) Collaboration with MedDay pharmaceuticals « Bring Your Own Data » annual courses Interoperability with the European Science Cloud (PHENOMENAL H2020 project) Integrated offer at the national level • Joint proteomics and metabolomics data analysis pipeline developed with ProFI, IFB, PHENOMIN and France Génomique for systems phenotyping (ProMetIS project) MetaboHUB
MetaboHUB outputs Science •Proof of concept projects (Biotech., Health, Plants, etc.) (2013-2018) Aim: Deep phenotyping tools for analyzing large epidemiological cohorts Services for future collaborative projects with cohorts ➢ Identify specific signatures of diseases ➢ Characterize the phenotypic spectrum of pathologies ➢ New tools for patient stratification • Proof-of-concept project on Metabolic Syndrome Nutrition, Geriatrics, Aging, Epidemiology • Project on the development of immune-related disorder. Early life nutrition, allergy • E.U. project on microbiome based treatments for liver diseases (Personalized medicine) • Implementation within a European COST Action. Open Multiscale System Medicine CA15120 41 MetaboHUB
MetaboHUB outputs: National & international training (2013-2018) •Training in-house: on the platforms •Training out: workshops, webinar, National & international teaching •University in France & all around the world •MOOC on metabolomics (in French) •Open courses (in English) Training Total In-house Trainees •3066 registered in 77 countries (63% in France, 7% Masters 202 MOOC in Morocco) PhD’s 254 Training Total •602 participated in evaluated activities (quizzes, Trainees (National + Post Doc 45 Out International trainings) weekly evaluations, peer reviews) •214 participants were awarded the certificate of Invited W4M 295 (8+5) success 51 scientists MetExplore 514 (12+ 18) Others 40 Lipidomics 384 (8) Total Two open courses: usemetabo.org 592 Total trainees 1193 (28+ 23) trainees NMRProc 3726 web users Flow 6882 web sessions 42 MetaboHUB
MetaboHUB outputs: Service (2013-2018) • a unique web portal for national access to MetaboHUB services (MTH Analyses Manager: MAMA) • For academic communities and private compagnies MetaboHUB contribution to science : Publications at high IF 1. Lenarčič T. et al. (2017) Eudicot plant-specific sphingolipids determine host selecevity of microbial NLP cytolysins. Science 2. Asiliauskaite-Brooks, I. et al. (2018). Structure of a human intramembrane ceramidase explains enzymaec dysfunceon found in leukodystrophy. Nature Communicaoons 3. Perez-Berezo, T. et al. (2017). Ideneficaeon of an analgesic lipopepede produced by the probioec Escherichia coli strain Nissle 1917. Nature Communicaoons Service 4. Clària J, Moreau R, Fenaille F et al. Orchestraeon of Tryptophan- contracts Publications Kynurenine Pathway, Acute Decompensaeon, and Acute-on-Chronic Liver Failure in Cirrhosis. Hepatology. 2019 2013 5. Despres, C. et al. (2017). Ideneficaeon of the Tau phosphorylaeon 267 99 pajern that drives its aggregaeon. Proceedings of the Naoonal Academy of Sciences of the United States of America 2014 407 113 6. Tabet, R. et al. (2016). Fragile X Mental Retardaeon Protein (FMRP) controls diacylglycerol kinase acevity in neurons. Proceedings of the 2015 330 98 Naoonal Academy of Sciences of the United States of America 7. Liu, R. et al. (2015). A DEMETER-like DNA demethylase governs tomato 2016 247 82 fruit ripening. Proceedings of the Naoonal Academy of Sciences of the United States of America 2017 320 8. Cojret L. et al. (2018). MetExplore: collaboraeve edieon and 104 exploraeon of metabolic networks. Nucleic Acids Research 2018 390 129 TOT AL 1961 625 43 MetaboHUB
Quels sont les défis technologiques et computationnels dans un futur proche?
Where are the M&F issues for the next 10 years ? t pu Int rtifi n gh e r o c ia s - nolo den h th tio a ftw es sity rou pe l int a ing - rn on hig ific rab ell Da y, kn nce, i -le at ilit ige at nt ta owl netw - e om on ide a t sci edg or ar - Au e n e m k an ati te es tic oli ce ana aly so gi ba tech gh an ab s ata al hi qu Met Metabolomics - D lytic & ge sis se & Fluxomics me Issues nt a , An & Standardization Harmonisation – Regulatory compliance Transfert to academic and industrial world MetaboHUB
Some areas of technological innovation Sample Creation of a FAIR preparation: from one cell to computational whole organism e-infrastructure Microfluidic Highly curated DB for technology for enhanced metabolite sample Infrared identification technology preparation (PeakForest DB) Analytical Pipetting BEFORE Robots Tools GC-MS AFTER Expertises NMR LC-MS Statistical, Acquisition of Metadata computational and in DB (domain mathematical tools dependent) (moving to AI) MS Imaging, Advanced MS based Faster NMR with fast 2D-NMR data acquisition Boosting the sensitivity of NMR (identification & with Dynamic Nuclear quantification) Polarization
Data sciences Interoperability, knowledge management, artificial intelligence, network analysis better DB for molecule identification
analytical challenge: need robust spectral reference data base Typical processing flow of MS data • Size and complexity of raw data file (1 Go and more) in the field of metabolomics. • Different file format and conversion difficulties. need Open access • Software heterogeneity and compatibility • Mathematic complexity of the used methods • High number of data treatment • Difficulty to identify automatically metabolites Sugimoto et al. 2012 Current Boinformatics MetaboHUB
Search databases for accurate mass February 2018 Master Biology Agrosciences: Metabolomics course MetaboHUB
PeakForest: a community-based spectral database Online MetaboHUB resource for identification of new compounds in biological matrices 1D-2D NMR GC-MS LC-MS LC-MSn
PeakForest spectral DB will be share MetaboHUB Paris-Saclay MetaboHUB Nantes-Rennes MetaboHUB Clermont-Ferrand MetaboHUB Bordeaux MetaboHUB Toulouse In France, many laboratories have their own DB without sharing their resources
Computational metabolomics challenge Jean-François Martin Mélanie Statistical Mélanie Petera Petera analysis eMetaboHUB The computational solution Raw files LC-MS, GC-MS, NMR Peak table Etienne Thevenot Identification n samples URINE30DIL4_CID20_endogènes #68 RT: 1.22 AV: 1 NL: 4.17E5 mz rt Db_015 ... Db_068 F: FTMS + p ESI Full ms2 173.09@cid20.00 [50.00-800.00] p variables 116.07041 100 75.0322 41.28 22162 ... 48575 95 90 75.0441 174.83 1371 ... 820 85 80 75.0634 56.23 49111 ... 91769 75 70 ... ... ... ... ... 65 time -C2H3ON Relative Abundance 60 999.6653 844.61 571 ... 636 55 50 999.6759 844.61 711 ... 665 45 [ 40 999.6865 844.61 698 ... 612 35 30 127.08652 -HCOOH 173.09190 Fabien Jourdan 25 20 - m/z 15 155.08136 M 10 80.49458 70.06497 169.67426 H2 5 86.06400 61.03990 102.71753 143.04167 184.86218 0 60 80 100 120 140 160 180 m/z O Signal processing Pathway analysis LC-MS, GC-MS, NMR + H] + MetaboHUB
Programme de la journée • 10h35-11h20: Workflow4metabolomics (W4M), portail dédié à l’analyse métabolomique: extraction de données, normalisation, analyses statistiques et annotations, Jean-François Martin, MTH-Toulouse • 11h20-11h40: Pause • 11h40-12h25: Experimental design and data treatment in metabolomics applied to nutrition and health, Mélanie Pétéra, MTH-Clermont, • 12h25-12h45: Metabolomics in Alzheimer's disease, Vincent Damotte, Inserm U1167, Institut Pasteur de Lille • 12h45-14h30: Déjeuner (RU Pariselle) • 14h30-15h15: Connecting the dots: metabolic networks for metabolome mining, Fabien Jourdan, MTH-Toulouse • 15h15-16h00: Data sciences for deep phenotyping and precision medicine, Etienne Thevenot, MTH-Saclay, CEA, LIST • 16h00-16h20: Métabolomique appliquée à l’identification de biomarqueurs en sélection variétale, Philippe Hance, Institut Charles Viollette, Université de Lille • 16h20-16h40: La bioinformatique des peptides nonribosomiques, des métabolites secondaires microbiens remarquables, Valérie Leclère, Institut Charles Viollette, Université de MetaboHUB Lille
Thank you for your attention Rennes Saclay-Paris Nantes Clermont Bordeaux Toulouse 54 MetaboHUB
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