Metabolòmica Marta Cascante Integrative Systems Biology, Metabolomics and Cancer lab Department of Biochemistry and Molecular Biology Institute of Biomedicine University of Barcelona (IBUB) E-mail: [email protected] http://www.bq.ub.es/bioqint/arecerca.html Genomics DNA RNA Transcriptomics Protein Proteomics Proteins Biochemicals Metabolomics (Metabolites) FROM MOLECULAR BIOLOGY TO SYSTEMS BIOLOGY Cytomics Genomics Information Proteomics Metabolomics Fluxomics SYSTEMS BIOLOGY Highest capacity to predict phenotype Metabolomics and fluxomics in the systems Biology approach Driving force: Development of high-throughput data-collection techniques, e.g. microarrays, protein chips, NMR, LC/MS-GC/MS…. allow to simultaneously interrogate all cell components at any given time. From molecules to networks: transcription/regulatory network ... - protein-protein interaction network - signaling network - metabolic network -These networks are not independent but form “network of networks“ -Metabolic network crosstalk with other networks must be considered in a systems biology approach Metabolomics and fluxomics in the systems Biology approach Central dogma of Gene molecular biology: mRNA Proteines Metabolites -Biological processes regulation is a complex phenomena more “democratic” than “hierarchical” Metabolites are not only the “end point” also the “driving force”? • The study of the total metabolite pools (metabolome) in a cell-organism at one particular point in time. • Metabolomics allows direct measurement of multiple low-molecular-weight metabolites from a biological sample. • Metabonomics (often named metabolomics) The study of the systemic biochemical profiles and regulation of function in whole organisms by analyzing a metabolome in biofluids and tissues Complementary approaches: • Highthroughput metabolite profiling: the identification of the specific metabolic profile that characterizes a given sample, i.e. the set of all of the metabolites or derivative products (identified or unknown) detected by analysing a sample using a particular technique. Biomarkers identification etc..... • Target metabolomics: Selected known metabolites are analysed: Biological question, biomedical hypothesis... drives the analysis of a set of compounds that are related to specific pathways. Challenges : all metabolic activity has to be stopped in the moment of sampling • Rapid sampling and fast quenching needed (much faster than the turnover times of the metabolite pools) • Complete extraction • No metabolite degradation during extraction/processing/storage • No enzymatic conversion during sample processing Obtaining proper “snapshots” of the metabolome in time requires Standard Operation Protocols. Validation for each experiment necessary. Metabolome analysis: The metabolome does not consist of a limited number of building blocks…. we are far away to have a “microarray”! -Large differences in: Physicochemical properties (polarity, hydrophobicity), structure, concentration range…… Combination of techniques is necessary Metabolomics experimental approaches: • Enzymatic assays, HPLC, Capillary electrophoresis-mass spectrometry (CE-MS/MS) • Liquid chromatography-mass spectrometry (LC-MS/MS) • Gas chromatography-mass spectrometry (GC-MS) Currently most used methods • NMR Analytical Technologies NMR spectroscopy Solution state (plasma, urine, extracts) MAS (tissue extracts) in vivo spectroscopy Relatively robust GC- and LC-Mass spectroscopy More analytically sensitive Potentially truly global Problems with ionisation Van der Greef et al. “The Role of Metabolomics in Systems What is needed? Biology” In: Metabolic Profiling, Kluwer (2003). - Catalogue of all metabolites that can potentially be found in human tissues. -Purified metabolites to be used as standards and/or spectral libraries. -SOPs for different platforms and appropriate chemometrics tools Metabolomics: diagnostic, mechanism, biomarkers.... Tissue or biofluid sample 1. Mass spectrometry Bioanalytical tools 2. 1H NMR spectroscopy Measure the metabolite profile e.g. by NMR Statistical bioinformatic tools Explore profile to determine Treat profile as ‘fingerprint’ mechanism and potential for diagnostic purposes biomarkers E.g. plasma samples randomly selected from 12 students…. 2 abnormal profiles - too much alcohol? - diseased? 10 of the profiles are very similar (“normal”) Use computerized pattern recognition methods alcoholic diseased normal E.g. plasma samples randomly selected from 12 students…. histidine citrate Insight into mechanism of disease/toxicant Example: applications in cancer The Metabolome of an organism is the result of the in vivo function of gene products and is, is closely tied to its physiology and its environment (what is eat or breath). Analysis of human samples Blood – serum or plasma Urine Biomarker Discovery Tumour samples Biopsies Cell based model studies Systems Biology Approach: Choice of cell line Cell content or - Drug target discovery secreted metabolites Fluxomics • The distinct metabolic processes involved in metabolites production and degradation are dynamic and finely regulated and interconnected. • Knowledge of the metaboloma is not enough to predict the phenotype as give only an instant 'snapshot' of the physiology of that cell. • For a characterization of metabolic networks and their functional operation quantitative knowledge of intracellular metabolic fluxes is required. Fluxomics is the field of “omics” research dealing with the dynamic changes of metabolites over time, i.e. the quantitative analysis of fluxes through metabolic pathways Methods Intracellular fluxes can be estimated through: • Knowledge of network stoichiometry •Quantitative measurements of metabolites at different times and/or incubation of cells/organisms with labeled substrates (i.e. 13C) •Interpretation of stable isotope patterns in metabolites using appropriate software packages. Metabolomics and fluxomics in Cancer Systems Biology • Transcriptomics and proteomic analysis do not tell the whole story of what might be happening in a cell. • Metabolomics anf fluxomics offers a unique opportunity to look at relationships between genotype and phenotype as well as with environment. -Metabolomics and fluxomics in cancer: -From tumor metabolome to new therapies targeting tumor metabolome? CANCER Changes in GENOME Changes in PROTEOME Oncogenes and tumor supressor Signaling pathways, transcription genes… factors… Activated growth signalling Tissue invasion and metastasis Evading cell death and senescence Sustained angiogenesis Limitless replicative potential Evading immune surveillance DNA damage and DNA replication stress Metabolic stress Mitotic stress Genomic instability (modified from Negrini et al., 2010) Accelerated, disordered and decontrolled proliferation of tissue cells that invades, moves and destroys as well as in a local level as in distance, other health tissues of the organism. CANCER Changes in GENOME Changes in PROTEOME Oncogenes and tumor supressor Signaling pathways, transcription genes… factors… Satisfy energetic tumor requirements Creation of acidic environment Alterations in METABOLISM Insensibility to O2 Decrease of pyruvate oxidation in the mitochondria TUMOR METABOLOME General increase of glycolytic intermediates Is metabolic network reorganization a consequence or a cause of tumor progression? Could metabolism be used as therapeutic target against tumor progression? TUMOR METABOLOME High glucose consumption and lactate production. Warburg effect Activation of biosynthetic pathways Expression of isoforms, changes in enzymatic activities and affinities NADPH NADPH HK II Glucose G6P 6PGL 6PGT Ru5P F6P E4P Fatty acids F1,6BP - M2-Pyruvate kinase (M2-PK) TKTL1 DHAP S7P Palmitate - Transketolase-like 1 (TKTL1) 1,3BPG GAP 3PG R5P X5P - Hexokinase I and II (HK) 2PG Nucleotide Acetyl-CoA Malonyl-CoA synthesis PEP M2-PK Acetyl-CoA Citrate Pyr Lactate Lactate Citrate Pyr CO2 See as a review: Robust metabolic adaptation underlying tumor progression Vizan P, Mazurek S and Cascante M, Metabolomics (2008) 4:1–12 CANCER AS A METABOLIC ALTERATION Cancer cells are perfect systems to invade and parasite other tissues Robust metabolic profile FRAGILITY unexpected perturbations Exploitable Target for CANCER THERAPY? MULTIPLE HIT CANCER THERAPY AT METABOLIC LEVEL Tumor metabolism robustness counteracts single hits Multiple hit strategies can avoid bypass of single inhibitions Tumor metabolism response to multiple inhibition is unpredictable Rational design of new therapeutical combinations is necessary 1 In series Synergyreactions A C 2 E F Addition 3 Antagonism B D Parallel reactions MULTIPLE HIT CANCER THERAPY AT METABOLIC LEVEL Tumor metabolism robustness counteracts single hits Multiple hit strategies can avoid bypass of single inhibitions Tumor metabolism response to multiple inhibition is unpredictable Rational design of new therapeutical combinations is necessary 1 Synergy A C 2 KNOWLEDGEE F Addition OF THE 3 Antagonism B DMETABOLIC NETWORK FLUXOMICS FOR THE ANALYSIS OF TUMOR METABOLOME Metabolomics and Fluxomics are necessary for rational design of new therapeutical combinations TRACER-BASED METABOLOMICS Pyruvate dehydrogenase Pyruvate [2,3-13C]-pyr carboxylase [1,2-13C]-glucose Metabolome Fatty acid synthesis [1,2-13C]-acetylCoA [2,3-13C]-OAA Metabolic Pathways [5,6-13C]-citrate FLUXOME 13 13 Glutamate [2,3- C]-- [4,5- C]-- Glutamate ketoglutarate ketoglutarate AN ALGORITHM FOR DYNAMICS ANALYSIS OF THE ISOTOPE TRACER DISTRIBUTION IN METABOLITES EXPERIMENTAL