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 TOOLS COMPUTATIONAL TOOLS
ObtainExperimental dynamic tools data EvaluateThey should metabolic permit for Tracer-based to fluxesevaluate metabolomics permit metabolic fluxes to obtain dynamic under non-steady data that need to be METABOLICstate in situ analyzed and can be conditions and to TRACER-BASED FLUX MAP METABOLOMICSused for fluxes provide insight to the estimation kinetic mechanisms which govern the ALGORITHM metabolic networks Able to analyze: in vivo Data generated on different platforms (GC-LC/MS, NMR) on the metabolites levels and isotopic isomer distributions obtained by incubation with stable labeled substrates the non-steady state of metabolism (time courses) By using enzyme kinetic idata in combination with in vitro or in vivo metabolomic data Useful to: Analyze and understand the metabolic adaptations supporting cell functions Design metabolic interventions in drug development Selivanov et al, 2004 Bioinformatics Selivanov et al, 2005 Bioinformatics Selivanov et al, 2006 Bioinformatics DEVELOPING DRUGS FOR NEW THERAPEUTICAL STRATEGIES AIMING TO DISRUPT METABOLIC ROBUSTNESS OF CANCER CELLS
Exploiting tumoral metabolic adaptation of adenocarcinoma cancer cells for new antitumoral therapies
Pentose-phosphate Glucose pathways enhanced oxidative Purine G6P ribose Pyrimidine
F6P non oxidative oxidative ROBUSTNESS GAP
Pyruvate Lactate ?
non oxidative AcetylCoA Phase Plane Analysis FRAGILITY DEVELOPING DRUGS FOR NEW THERAPEUTICAL STRATEGIES AIMING TO DISRUPT METABOLIC ROBUSTNESS OF CANCER CELLS
Multiple hit target strategy to disrupt this balance 1-Control DHEA 2-MTX MTX 3-DHEA+MTX G6P 4-OT+DHEA+MTX G6PDH R5P PRPP 0,35 TKT Purine 0,3 1 Biosynthesis RNA 3 dUMP dTMP 4 0,25
UMP dTTP 2
Timidilate DNA sintase 0,2 OT N6,N10-methylene Pyrimidine tetrahydrofolate Dihydrofolate Biosynthesis 0,15 + Glicine NADPH + H oxidative DHFR 0,1 Serine + Tetrahydrofolate NADP 0,05 120 MTX 1 0
100 0 0,05 0,1 0,15
MTX nM 80 MTX + 20 mM DHEA non oxidative MTX + 2 mM OT + 60
2 20 mM DHEA
ViabilidadViabilidad Viabilidad Viability 40 3
20 4 0 0 10 20 30 40 50 60 70 MTXMTX (nM)(nM (nM ) ) 4 3 2 Oxidative/non-oxidative balance is essential to cancer cells and is a possible new target within the cancer metabolic network for novel therapies. Ramos-Montoya et al., 2006. Int J Cancer; 119(12):2733-41 MODULATION OF PPP DURING CELL CYCLE PROGRESSION IN HUMAN COLON ADENOCARCINOMA CELL LINE HT29
Cells do not have nucleotide reservoirs, so PPP must be regulated during cell cycle G6PDH activity SD %G1 %S %G2 80 Cell Cycle (IC50 inhibitors) G1 rich G1 rich population 457,41 14,22 population S-G2 rich populationS -G2 rich population 554,96 14,81 60 40 TKT activity SD 20 G1 rich population 29,79 1,10
S-G2 rich population 35,03 1,24 0 Ct OT+DH Ct OT+DH Ct OT+DH Ct OT+DH % Increase in ribose enrichment (Smn)/hour 0h 10h 15h 20h 0,03 %G1 %S %G2 0,02 0h 81,7 12,7 5,6 5h 82,8 9,8 7,4 0,01 10h 58,1 35,4 6,5 0 15h 35,2 59,3 5,5 0-5h 5-10h 10-15h 15-20h 20h 21,8 60,2 18,1
G6PDH and TKT activities depend on cell cycle progression and are higher in S-G2 phases. This increase correlates with an augment in ribose phosphate synthesis in late G1-S phase. Avoiding pentose phosphate production G6PDH and TKT inhibitors are able to slow down cell cycle. Vizan et al., 2009. Int J Cancer; 124(12):2789-96 Characterization of metabolic adaptation underlying growth factor . angiogenic activation: Identification of potential therapeutic targets
Exploiting angiogenesis metabolic adaptation of HUVEC cells for new antiangiogenic therapies
1.The activation of HUVEC cells produced by VEGF or FGF produced a similar pattern of glucose usage.
2. The inhibition of the VEGF receptor caused a decrease in the proliferation rate accompanied by a decrease in the pentose phosphate pathway activity and glycogen metabolism.
3. The Direct inhibition of key enzymes of glycogen metabolism and pentose phosphate pathways reduced HUVEC cell viability and migration.
The inhibition of pentose-phosphate pathway and glycogen metabolism offers a novel and powerful therapeutic approach, which simultaneously inhibits tumor cell proliferation and tumor-induced angiogenesis. Vizan et al., 2009. Carcinogenesis; 30(6):946-52 TKTL1 AS BIOMARKER FOR TUMOR PROGRESSION IN COLORECTAL CANCER
TNM classification of colon cancer according to American Joint Committee on Cancer (AJCC)
Primary Tumor (T) Tis: Carcinoma in situ. T1-4: depending on local growth degree. Regional Lymph Nodes (N) N0: No regional lymph node metastasis. N1-2: depending on the number of regional lymph nodes affected. Distant Metastasis (M) M0: No distant metastasis. M1: Distant metastasis. Stage grouping according to AJCC
STAGE T N M 0 Tis N0 M0 I T1-2 N0 M0 IIA T3 N0 M0 IIB T4 N0 M0 IIIA T1-2 N1 M0 IIIB T3-4 N1 M0 IIIC T1-4 N2 M0 IV T1-4 N0-2 M1
• TKT, and its isoenzyme TKTL1, play a key role for tumor cell metabolism. Stage No. Samples I 9 • 46 men + 17 women (69 12 years) with colorectal cancer in different II 21 III 16 stages were included to confirm TKTL1 as a biomarker for tumor progression. IV 17
• TKTL1 Immunohistochemical staining of 2 mm thick sections of tumors was performed. (collaboration with Dr. Antoni Castells, Hospital Clinic) RESULTS Diaz-Moralli et al. Plos One 2011; 11;6(9) e25323. 50 *** P = 0.000008 Stage Mean SD 40 I 13,3 7,9 II 20,8 9,9 30 III 32,9 11,5
IV 13,7 8,7 20
TKTL1TKTL1expression expression (Relative value x 1000) x value (Relative Stage III tumors present the highest levels 1000) x value (Relative 10 of TKTL1 expression (p=0,000008). 0 Stage I Stage II Stage III Stage IV
40
35
30
25 TKTL1 levels decrease 20 P = 0.0004 15 *** significantly (p=0,0004) when
10 distant metastasis appears (M).
5
(Relative (Relative value x 1000) TKTL1 expressionTKTL1 0 M0 M1
50
40
45 P = 0.0014 P = 0.029 ** 35 * 40 30 35
30 25
25 20
20 15 15 10 10 5
5
(Relative (Relative value x 1000)
TKTL1 expressionTKTL1 (Relative (Relative value x 1000)
0 expression TKTL1 0 N 0 N 1-2 T 1-2 T 3-4 TKTL1 increase correlates significantly There is a slightly correlation (p=0,0014) with regional lymph node between TKTL1 levels and local affection degree (N). growth (p=0,029). METABOLIC CHANGES ACCOMPANYING TUMOR CELL DIFFERENTATION
Histone deacetylase enzymes downregulate genes that cause or induce cell differentiation.
Deacetylated chromatin HDI no gene expression
N
HAT HDAC HDI NHOH TSA
Acetylated chromatin O O
O Gene expresion ONa Butyrate (NaB)
Differentiation HT29 TSA HT29 Butyrate (NaB) METABOLIC CHANGES ACCOMPANYING TUMOR CELL DIFFERENTATION GLUCOSE GLUCOSE NADPH NADPH G6P RIBOSE G6P RIBOSE G6PDH
F6P F6P
GAP GAP
LACTATE PYR LACTATE PYR PDH
OAA Ac-CoA OAA Ac-CoA
ButyrateButirato oro TSA TSA
- cetoglutarate - cetoglutarate HDAC GLUTAMATE GLUTAMATE Differentiation HT29 HT29 Transformation
Butyrate and TSA show similar effects on HT29 cells. Other fatty acids that are not able to induce differentiation not induce this changes -> The metabolics effects induce are due to histone deacetylase inhibition. Alcarraz-Vizan et al 2010 Metabolomics EARLY METABOLIC CHANGES PRECEED EDELFOSINE (ET-18-OCH3 ) INDUCED APOPTOSIS
GLUCOSE GLUCOSE NADPH NADPH G6P RIBOSE G6P RIBOSE G6PDH G6PDH F6P F6P
GAP GAP
LACTATE PYR LACTATE PYR PDH PDH
OAA Ac-CoA OAA Ac-CoA
- cetoglutarate - cetoglutarate
Jurkat cells without edelfosine Jurkat cells + low dosis edelfosine (apoptosis < 5%)
•Low edelfosine (before apoptosis) : Krebs cycle and RNA synthesis increase , PPP decrease •Higher dosis (apoptosis): enhanced metabolic effects and ROS production Selivanov et al. BMC Systems Biology 2010, 4:135 EXTENDING METABOLIC MODELS TO ROS PRODUCTION:
Important component of redox status is the level of reactive oxygen species (ROS) produced in mitochondria.
Algorithms developed for isotopomer analysis and study of cancer metabolism network adaptation can be used to cope with the complexity of modelling ROS production and energetic metabolism in muscle. Selivanov et al. 2009 PLOS Computational Biology, In Press CONCLUSIONS FROM ROS MODELLING
•The detailed modeling of electron transport in mitochondria identified two steady state modes of operation (bistability) of respiratory complex III at the same microenvironmental conditions.
•Normally complex III is in a low ROS producing mode, temporal anoxia could switch it to a high ROS producing state, which persists after the return to normal oxygen supply. .
•This prediction, which we qualitatively validated experimentally, explains the mechanism of anoxia-induced cell damage.
Recognition of complex III bistability may enable novel therapeutic strategies for oxidative stress
0: Q-Q-b -b -c -FeS-Q-Q 1: Q-Q-b -b -c -FeS-Q-Q h l 1 h l 1 2: Q-Q-bh-bl-c1-FeS-Q-Q ⇆ Fe3++ QH Fe2++ Q-+ 2H+ Fe2++ c ox Fe3++ c red Q-+ b ox⇆b red+ Q 2 ⇆ p 1 1 l l xxxxx011 ⇆xxxxx101+ 2H+ xxxx01xx ⇆xxxx10xx ⇆ p xxx0xx01 xxx1xx00
v = k ·C vf31 = kf31 ·Cxx01 vf32 = kf32 ·Cx0xx01 f30 f30 xxxxx011 2 vr30 =k r30 ·Cxxxxx101·Hp vr31 =kr31 ·Cxx10 vr32 =kr32 ·Cx1xx00
The scheme of reactions performed by complex III as it is generally accepted. One of two electrons taken from ubiquinol (QH2), which releases its two protons into the intermembrane space, recycles through cytochromes bh and bl reducing another quinone. The other electron continues its way to oxygen through cytochromes c1 and c and complex IV. Complexes I and II provide QH2. Selivanov et al., 2009. PLOS Comput. Biol. and Selivanov et al 2011, PLOS Comput. Biol. Developing a modelling environment, which links clinical characteristics with the redox status of cell
Gl y c o l y s i s NA D Gl c Clinical Data connection AD P uptake Mi to c h o n d r i a O2 Exhalates TC A cy c l e Ci t AT P NA D H Py r Ac C o A NA D OA A NA D H Su c c O2 transport AD P O NA D La c 2
RESPIRATION La c Omics AT P in Blood ROS antioxidant system Clinical Data connection cell damage ”OMICS” in biopsies signalling Integration of existing models
• Skeletal muscle bioenergetics – sub-cell • Mitochondrial reactive oxygen species (ROS) generation – sub-cell
• Central and peripheral O2 transport and utilization – organ system (heart, lung, hemoglobin, skeletal muscle) • Pulmonary gas exchange – organ (lungs) • Spatial heterogeneities of lung ventilation and perfusion – tissue
SYNERGY: Modeling and simulation environment for systems medicine: chronic obstructive pulmonary disease -COPD- as a use case (FP7) ACKNOWLEDGEMENTS Group of Integrative Biochemistry Department of Biochemistry and Molecular Biology, University of Barcelona Dr. Pedro de Atauri Dr. Vitaly Selivanov Dr. Josep Centelles Dr. Silvia Marín Dr. Gema Alcarraz-Vizán Miriam Zanuy Santiago Díaz-Moralli Susana Sánchez Adrián Benito Roldán Cortés Igor Marín Collaborators Hospital Clínic-IDIBAPS, University of Barcelona: Pneumology service, Institut del Torax,, directed by Dr. Josep Roca and Gastroenterology Department Dr Antoni Castells Financial support: SAF2005-01627, SAF2008-00164 from the Ministerio de Ciencia y Tecnologia of the Spanish Government SYNERGY, METAFLUX, ETHERPATHS from the European Union (FP7) ICREA ACADEMIA Award Autonomous Government of Catalonia