09/10/2009. A metabolome experiment: 6 steps. Objectif. 1. Experiment design to sampling. 2. Metabolite extraction. 3. Biochemical analyses

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1 PC (%) 9//9 Transcriptome Proteome Metabolome Intégrations de données de génomique fonctionnelle Modèles «Plantes» Annick Moing UMR69 Biologie du Fruit Plateforme Métabolome-Fluxome Bordeaux 6 octobre 8 Objectif Stratégies «simples» pour lesquelles des compétences pointues en statistiques ne sont pas indispensables (stratégie, résultats, quelques outils logiciels/web) Point de départ pour discuter avec un biostatisticien Potentiel de quelques autres stratégies d analyse des données Stratégies émergentes: domaine biostats/bioinfo actif Exemples de stratégies d analyse combinée - Projets auxquels la PMF Bordeaux a participé - Projets de l UMR Biologie du Fruit - Publications A metabolome experiment: 6 steps. Experiment design to sampling. Metabolite extraction. Biochemical analyses 4. Data pre-processing 5. Data visualization and mining PC PC (6%) PCA ACP PC 6. Data integration

2 9//9 Au menu Rappels sur le pré-traitement des données AnalyseS en Composantes Principales Classification, Clustering Utilisation des corrélations entre variables Outils de visualisation combinée sur les voies métaboliques 4 Pré-traitement des données 5 Pré traitement des données Data preprocessing To correct for chromatographic or instrumental shifts Baseline correction Spectral alignment, deconvolution Noise reduction Automatisation ANOVA, FDR correction for multiple comparisons Data normalisation Before statistical analysis Data reduction Elimination of redundancy Mean centring, Pareto or unit variance Normalisation against total intensity LN, log of ratio with control Weckwerth et al. 4 PNAS Jacob pers. com. 6

3 9//9 Rem: Analyse combinée de données métabolome et d autres données prétraitées Mêmes outils stats que pour le métabolome seul (une exp métabolome= plusieurs technologies analytiques) - Analyses multivariées tq ACP classique ou pas (PCA, ICA, block PCA) - Classification (HCA, KMC, SOMs, BL-SOMs) Stratégies plus spécifiques - Corrélations - Reconstruction de réseaux de corrélations - Outils de visualisation /voies métaboliques 7 Analyses en Composantes Principales ACP/PCA 8 RAPPEL: ACP Multidimensional space: [metabolites] x samples [metabolite ] Optimization of variance Transformation of original variables into a small number of variables (PCs) [metabolite ] [metabolite ] PC: combination of original variables highlighting sample variability CP CP Visualization of sample groups Identification of discriminant variables Powerful if the biological question is related to the highest variance in the data set 9

4 9//9 PCA example : on going study on melon metabolomics Metabolome data received from 5/6 different analytical partners/technologies Total 9 variables Fig Polar compounds by H NMR Polar compounds by GC MS Isoprenoids by LC DAD Secondary metabolites by LC MS Volatiles by GC MS 46 Microelements by ICP MS PCA example : on going study on melon metabolomics PCA for 69 out of 9 analytes (ANOVA) Scores A PC (5.%) stage stage stage Loadings B.4 PC. NMR ICP MS LC DAD GC MS LC MS. GC MS vol 4 5 PC (4.8%) Idem pour des données issues de différentes approches omiques Séparément ou pas.4.6 PC PCA : study on tomato transcriptome and metabolome One PCA per omics technology EX: Regulatory Features Underlying Pollination-Dependent and -Independent Tomato Fruit Set Revealed by Transcript and Primary Metabolite Profiling Wang et al. The Plant Cell : 48 IAA9 a negative auxin response regulator 4

5 9//9 Multiblock PCA One PCA for all omics technologies Ex: GC-EI-TOF-MS of melon tissue extracts, varieties, 4 positions Due to the high dimensionality and noise of GC-EI-TOF-MS data compared to H-NMR data, standard PCA routines were unsuccessful to separate positions for each var. so a more advanced approach known as block hierarchical (H)PCA was applied Biais et al. (9) Anal Chem 8:884 Multiblock PCA Variété ou technologie Même importance à chaque bloc indépendamment de son nombre de variables Les composantes se correspondent entre les blocs Maximiser la variance et la corrélation avec différentes méthodes Ex. multiblock HPCA Split PCA J.W. Allwood 4 Multiblock PCA H/CPCA has ability to enhance common trend between different blocks and show it in super scores. It provides more interpretable block scores and block loadings compared to PCA models on each block separately. Block weights provide further information about connections between different blocks. Biais et al. (9) Anal Chem 8:

6 9//9 Multiblock PCA: variétés de melon Classical PCA Superscores plot Multiblock HPCA Superscores plot GC-EI-TOF-MS Multiblock HPCA Scores plots Idem pour différentes stratégies analytiques GC/MS H-RMN Biais et al. (9) Anal Chem 8:884 6 Pour les statisticiens:hierarchical PLS model sans contraintes sur n ni les données manquantes New Mode A for blocks X,,X J and for super-block X J+ Factorial p can be > n <=> J Maximize Cov a j = j = X jaj X J + aj + (, ) PLS Regression of F = X J+ a J+ on X j X X F Super-block X J+ X X M F F J+ X L X J X J X J F J X,J+ Deflated Block (residual) (M. Tenenhaus) Deflated Block (residual) Approches supervisées/modélisation OPLS (cf J.C. Martin) Random forest (Enot et al. 6 PNAS : 4865) 8 6

7 - - - Stage Stage Stage //9 Classification Clustering 9 Classification hiérarchique ) Echantillons Average linkage, euclidian distance ) Variables Nodule Lat. root Prim. root Devel. leaf Mature leaf Flower HCA of polar extracts (GC-MS) from different organs of Lotus japonicus (Desbrosses et al. 5 Plant Physiology) MetaGeneAlyse Profiles of known metabolites and EPCLUST hierarchical clustering of differentially MeV expressed genes during rice germination (Howell et al. 9 Plant Physiol 49:96) Variables issues de différentes stratégies omiques Classification des variables: K-means clustering Melon tissue for 69 analytes grouped by spatial and/or developmental patterns Cluster Cluster Cluster 8 Cluster 4 Cluster 4 5 Cluster Cluster Cluster 9 Cluster 6 Choisir la distance utilisée Puis analyse/interprétation pour chaque cluster ou groupe de clusters: Cluster 5 Réseau de corrélations Données de la littérature Visualisation sur les voies métaboliques Cluster Cluster 7 7

8 9//9 Un autre outil de classification: Cartes auto-organisatrices de Kohonen Self-organizing map (SOM) Kohonen () Self organizing maps, Third edition, Springer, Berlin. A particular application of artificial neural networks (ANN) The input neuron layer connected to each vector of the data set (i.e. one vector is a real sample with n standardized variables) The output neuron layer (the Kohonen map), forms a -D array of neurons. In each hexagonal cell a reference vector is considered that corresponds to virtual samples with variables to be computed. The units of the grid give a representation of the distribution of the samples in an ordered way. The modifications of the virtual tomato samples are achieved through an ANN and computed during a training phase by iterative adjustments. - Several maps - The chemical composition of each virtual tomato sample can be used to display the distribution of each metabolite on the organized map on which real samples were plotted: a component sliced version of the SOM Classifying methods: SOM Metabolic comparison of tomato flesh and seed Kohonen s SOMs on 44 metabolites Flesh Seed 45Seeds 5Seeds 45 Flesh Seeds 45Seeds 5Seeds 45 Flesh 5 Flesh 45Seeds 5Seeds Flesh Flesh 5 Flesh 5 Flesh Seeds Flesh Flesh 45 Flesh Seeds Seeds 8 Seeds 8 Flesh Seeds Seeds 8 Seeds 8 Flesh 8 Seeds 8 Flesh Flesh Flesh (9 units) (5 units) ( units) Mounet et al. (7) Metabolomics :7 Matlab Classifying methods: SOM Metabolic comparison of tomato flesh and seed Kohonen s SOMs on 44 metabolites Flesh Seed Component plane representation Metabolites accumulated in flesh 45Seeds 5Seeds Flesh Flesh 5 Flesh 45 Flesh Fructose Seeds Seeds 8 Seeds 8 Flesh Glucose Mounet et al. (7) Metabolomics :7 4 8

9 9//9 Classifying methods: SOM Metabolic comparison of tomato flesh and seed Kohonen s SOMs on 44 metabolites Flesh Seed Component plane representation Metabolites accumulated in seed Metabolites accumulated in flesh 45Seeds 5Seeds Flesh Flesh 5 Flesh Lignoceric acid (C4:) 45 Flesh Fructose Seeds Seeds 8 Seeds 8 Flesh Arachidic acid (C:) Glucose Mounet et al. (7) Metabolomics :7 5 Classification SOMs: BL-SOMs (Batch learning SOMs) «Elucidation of gene-to-gene and metabolite-to-gene networks in Arabidopsis by integration of metabolomics and transcriptomics» Hirai et al. JBC (5) 7: ~ metabolites (FT-ICR-MS, LC, CE),5 transcripts classified by the shape of the time-dependent changing pattern -Elucidation of metabolites and transcripts with strong correlations: Identification of regulatory metabolites and function of uncharacterized genes. One cell ~ metabolites/genes. Glucosinolates Isothiocyanates SOM GLS genes Myrosinase genes - Revealed coordinated regulation of glucosinolate metabolism - Determination of function of candidate genes - O-acetylserine a metabolite regulating sulfur assimilation 6 Utilisation des corrélations entre variables 7 9

10 9//9 Ya t il un lien/ une relation entre des variables issues de différentes approches omiques? Un outil simple: Corrélations Prérequis distribution selon le type choisi variance pas d auto corrélation Remarques: Ne prouve pas un mécanisme Ne donne ni cause, ni conséquence (Szymanski et al., 7 Phytochemistry 68:6) exciter = the sulphur molecule itself (Nikiforova et al., 5 J Exp Bot 56: 887) effect = the last metabolites to accumulate in the ripening tomato fruit, isoprenoids (Carrari et al., 6 Plant Physiol 4: 8) - La relation reste à valider 8 Coefficients de corrélation Pearson r= COV(var,var)/ET(var)*ET(var) Coefficient de corrélation «linéaire» Spearman Rangs, non paramétrique, moins sensible aux valeurs aberrantes (outliers) test de signification t = r.(n-).5 /(-r ).5 avec n nombre de paires et (n-) DDL Correction de Bonferroni r = coefficient de détermination, PVE= r % de variance que perd une variable quand l autre est fixée ou, % de variabilité d une variable expliquée par l autre 9 Exemples d utilisation des corrélations : Tomate Metabolic profiling of tomato fruit tissues 5 plants batches of 6 or fruits A B C Mesocarp Locular tissue DPA DPA 5DPA Amino Acids Organic Acids 5 Soluble Sugars 7 Quaternary amines Polar phenolic compounds Starch Isoprenoids Unknowns 5 H-NMR Enzymatic analysis HPLC-DAD - Absolute quantification of 4 metabolites - Relative quantification of ~ ions Mounet et al. (9) Plant Physiol LC-MS Plant Metabolomics Yokohama July 8

11 9//9 Exemples d utilisation des corrélations : Tomate Gene expression profiles along tomato development Mesocarp DPA Mesocarp DPA Mesocarp 5 DPA TOM microarray (cdna 44 spots 8 genes) Mesocarp Locular tissue Locular tissue DPA Locular tissue DPA Locular tissue 5 DPA fold variation of expression for 58 spots / 9 (F test, James-Stein shrinkage, P<. and FDR <.5 ) Locular tissue Mesocarp Differential : 45 Non Diff: 74 Differential : 6 Non Diff : 9 Plant Metabolomics Yokohama July 8 Exemples d utilisation des corrélations : Tomate Liens entre les métabolites/ions et les transcrits Matrice de corrélations (86 variables) MBin 9 - Pearson Bin Heat-map 9 Ala Exemples d utilisation des corrélations : Tomate Quelle corrélation: Pearson ou Spearman? Pearson Spearman Ex: ATP dependent Clp protease ATP binding subunit AT5g59 et Alanine NS. L M r= Ex: Disease resistant protein ATG6587. et Alanine. Ala P<. 7 P<. NS. 6 L M r= Ala r= Les deux types de corrélations sont potentiellement intéressants Ala 5 4 r=

12 9//9 Pairwise correlations Métabolites et transcrits de gènes de régulation Glutamine Glutamine e.g. Amino acids &Transcription factors Isoleucine 4 Correlation networks «spring embedding» d= abs(r), different algorithmes Metabolites and regulation genes threshold P<. TF Leucine-zipper transcription factor Trig Cit Val Asp UDPG Cho Mal Starch Xant NAC-NOR transcription Chlb Gln Unk S5-5 diminuto Tol Chla Car Tom TG Ile Unk S5-4 UnkSD5. Ala Pajek Mounet et al. 9 Plant Physiol 49:55 o Metabolite o Hormones and polyamine related transcripts (MapMan BIN 7, BIN ) o Redox related transcripts (BIN ) o Transcription related transcripts (BIN 7.) o Transcripts related with regulation of protein activity (BIN 9., BIN 9.4, BIN 9.5) o Epigenetic related transcripts (BIN 8.) o Signalling related transcripts (BIN ) 5 Correlation networks Using a network hub as a bite to highlight metabolism regulation Ex: TF hub Metabolites Photosynthesis N-AA metabolism Minor CHO metabolism Lipid metabolism Transport Secondary metabolism Correlation network of TF a leucine-zipper TF with metabolites and metabolism genes malate citrate TF choline Direct correlations with TF XTH5 PhospholipaseD alpha Homecysteine-S-methyltransferase Chalcone reductase Cytochrome P45 86A Short chain dehydrogenase Tic Carnitine/acylcarnitine translocase Phospholipid transporting ATPase Networks hubs > 5 connections carotene Vacuolar invertase Aldehyde DH β mannan endohydrolase XTH5 Homocysteine-S-methyltransferase Serine acetyltransferase (S)-N-methylcoclaurine -hydrolase Cytochromes P45 Purple acid phosphatase Short chain dehydrogenase Tic lutein Pajek d= abs(r) P<. TF, a potential candidate TF gene for regulation of the metabolic shifts occuring during early development Metabomeeting Norwich July 9 6

13 9//9 Correlation networks Reconstruction of gene-metabolite response networks in Arabodopsis Path from sulphur to sulphur responding metabolites A hormone regulated network fragment Extracted fragments of gene-metabolite response networks in Arabidopsis Gene vertices are depicted as circles, metabolites as squares 7 Nikiforova et al. 5 Journal of Experimental Botany Correlation networks Reconstruction of networks with or without modules Sugar-responsive gene network 94 accessions Arabidopsis Combined metabolic and morphological network of the tomato (Schauer et al. 6 Nature Biotechnology) Genes : o, green sugar-induced and orange sugarrepressed genes. Blue, positive and red, negative correlations. Metabolic traits : squares. Mutual information ED, end of day; EN, end of night. Cf Usadel et al. 9 Plant cell Environ (in press) (Sulpice et al. PNAS 9 6:48) 8 Outils de visualisation combinées liés ou non aux microarrays 9

14 9//9 Combined vizualisation of metabolome and transcriptome data on «processes» Regulatory Features Underlying Pollination-Dependent and -Independent Tomato Fruit Set Revealed by Transcript and Primary Metabolite Profiling Wang et al. The Plant Cell : 48 PageMan/MapMan ontologies for metabolic and regulatory pathways Bin and sub-bin wise 4 Combined vizualisation of metabolome and transcriptome data by processes Regulatory Features Underlying Pollination-Dependent and -Independent Tomato Fruit Set Revealed by Transcript and Primary Metabolite Profiling Wang et al. The Plant Cell : 48 wild type IAA9 antisense line color-coded results of a Wilcoxon test for a consistent upregulation (blue) or downregulation (red) of individual processes for the whole data set individual metabolites or the averages of individual enzyme classes 4 Combined vizualisation of metabolome and transcriptome data on metabolic maps MapMan une application à télécharger initialement pour Arabidopsis et transcriptome Rotter et al. (9) Gene expression profiling in susceptible interaction of grapevine with its fungal pathogen Eutypa lata: Extending MapMan ontology for grapevine. Bmc Plant Biology 9 Sreenivasulu et al. (8) Barley grain maturation and germination: Metabolic pathway and regulatory network commonalities and differences highlighted by new MapMan/PageMan profiling tools. Plant Physiology 46: Urbanczyk-Wochniak et al. (6) Conversion of MapMan to allow the analysis of transcript data from Solanaceous species: Effects of genetic and environmental alterations in energy metabolism in the leaf. Plant Molecular Biology 6: Usadel et al. (9) A guide to using MapMan to visualize and compare Omics data in plants: a case study in the crop species, Maize. Plant Cell and Environment :

15 9//9 Combined vizualisation of metabolome and transcriptome data on metabolic maps MapMan 4 Combined vizualisation of metabolome and transcriptome data on metabolic maps Mapman: pratique pour comparer «niveaux» Ex: AA-TCA cycle Comparison of tomato locular tissue and pericarp at DPA Mounet et al Combined vizualisation of metabolome and transcriptome data on metabolic maps Mapman Mapping Metabolic and Transcript Temporal Switches during Germination in Rice Highlights Specific Transcription Factors and the Role of RNA Instability in the Germination Process Starch-Suc metabolism, glycolysis, the TCA cycle, GABA shunt, mitochondrial respiratory chain, and amino acid metabolism. log transformed Metabolites: 6- versus 4-HAI comparison Transcripts: - versus -HAI comparison Howell et al. 9 Plant Physiol 49:

16 9//9 Combined vizualisation of metabolome and transcriptome data on metabolic maps KaPPA-view on the web Tokimatsu et al. (5) PLANT PHYSIOLOGY 8 : Combined vizualisation of metabolome and transcriptome data on metabolic maps EX KaPPA-view Light/dark regulation of metabolic activities Arabidopsis cell culture Expression profiles of Arabidopsis flavonoid biosynthetic pathway genes under the light/dark conditions. T87 cell transcript levels. Higher transcript levels in the light condition were shown in red, and higher expression levels in the dark were in green. Nakamura et al. (7) Planta 7 : Conclusions Toujours plusieurs analyses statistiques pour un jeu de données combinées (ne pas oublier les grands classiques) Le point de vue du biologiste Les solutions des chimiométriciens-statisticiens par ex. Mohamed Hanafi (ENITIAA-INRA, Nantes), Douglas Rutledge (AgroParisTec), Michel Tenenhaus (HEC) Les solutions des bioinformaticiens 48 6

17 co 45pe 45ge pe 5pe pe co 45ep ep 5ep PC (6%) co 5co ge 5ge ge 9//9 Plus d infos: quelques adresses web / logiciels (non exhaustif) ACP, HCA, K-means clustering, heat maps: Matlab, R, MetaGeneAlyse MeV BL-SOMs: Réseaux: Pathways: NAIST blsom_index Pajek Cytoscape MapMan KaPPA-View 49 Plus d infos: quelques références Riano-Pachon, et al. (9) GabiPD: the GABI primary database-a plant integrative 'omics' database. NUCLEIC ACIDS RESEARCH, 7: D954-D959 Bouveresse, D et al. (9) Two new extensions of principal component transform to compute a PLS model between two wide matrices: PCT-PLS and segmented PCT-PLS. ANALYTICA CHIMICA ACTA, 64 (-): 7-44 Weckwerth, W (8) Integration of metabolomics and proteomics in molecular plant physiology - coping with the complexity by data-dimensionality reduction. PHYSIOLOGIA PLANTARUM, (): Tenenhaus, M et Hanafi, M (7) A bridge between PLS path modelling and multi-block data analysis, Handbook on Partial Least Squares (PLS): Concepts, methods and applications, Springer Bylesjo, M et al. (7) Data integration in plant biology: The OPLS method for combined modeling of transcript and metabolite data. PLANT JOURNAL, 5 (6): 8-9 Enot et al. (6) Predicting interpretability of metabolome models based on behavior, putative identity, and biological relevance of explanatory signals PNAS, (4): Tokimatsu, T et al. (5) KaPPA-View, A web-based analysis tool for integration of transcript and metabolite data on plant metabolic pathway maps. PLANT PHYSIOLOGY, 8 (): 89- Westerhuis et al. (998) Analysis of multiblock and hierarchical PCA and PLS models. JOURNAL OF CHEMOMETRICS, 5 Mapman UMR69 Fruit Biology PF Métabolome-Fluxome Bordeaux Dominique Rolin Annick Moing Stéphane Bernillon Anne Bertrand Cécile Cabasson Catherine Deborde Mickaël Maucourt Yves Gibon Fabien Mounet PhD Philippe Gallusci Christophe Rothan Martine Lemaire-Chamley Virginie Garcia Johann Petit Patricia Coat Aurélie André ACP SOM ANN Réseaux HCA META-PHOR partners Univ. Manchester (JW Allwood) MPI MP Golm Weizmann Inst. Rehovot PRI Wageningen PC4 (8%) EPCA, ISM, UMR 555, Périgueux Jean-Luc Giraudel HEC, Paris Michel Tenenhaus 5 7