Metabolòmica Marta Cascante

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
DNA
Genomics
RNA
Transcriptomics
Protein
Proteomics Proteins
Biochemicals
(Metabolites)
Metabolomics
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
molecular biology:
Gene
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)
Currently most
• Gas chromatography-mass spectrometry (GC-MS)
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
What is needed?
Van der Greef et al. “The Role of Metabolomics in Systems
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
Bioanalytical tools
1. Mass spectrometry
2. 1H NMR spectroscopy
Measure the
metabolite profile
e.g. by NMR
Statistical bioinformatic tools
Treat profile as ‘fingerprint’
for diagnostic purposes
Explore profile to determine
mechanism and potential
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
 Tumour samples
 Biopsies
Cell based model studies
 Choice of cell line
 Cell content or
secreted metabolites
Biomarker Discovery
Systems Biology Approach:
- Drug target discovery
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
Oncogenes and tumor supressor
genes…
Changes in PROTEOME
Signaling pathways, transcription
factors…
Activated growth signalling
Evading cell death and senescence
Tissue invasion and metastasis
Limitless replicative potential
Sustained angiogenesis
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
Oncogenes and tumor supressor
genes…
Changes in PROTEOME
Signaling pathways, transcription
factors…
 Satisfy energetic tumor requirements
 Creation of acidic environment
Alterations in METABOLISM
TUMOR METABOLOME
 Insensibility to O2
 Decrease of pyruvate oxidation in the
mitochondria
 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
6PGL
G6P
Ru5P
6PGT
F6P
- M2-Pyruvate kinase (M2-PK)
Fatty acids
F1,6BP
TKTL1
S7P
DHAP
- Transketolase-like 1 (TKTL1)
R5P
2PG
PEP
Palmitate
GAP
1,3BPG
3PG
- Hexokinase I and II (HK)
E4P
X5P
Acetyl-CoA
Nucleotide
synthesis
M2-PK
Acetyl-CoA
Malonyl-CoA
Citrate
Pyr
Lactate
Citrate
Lactate
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
Exploitable Target
for
CANCER THERAPY?
unexpected
perturbations
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
A
1
In series Synergy
reactions
C
E
B
3
D
2
F
Addition
Antagonism
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
A
B
1
3
Synergy
C
2
KNOWLEDGE
E
F
OF THE
DMETABOLIC NETWORK
Addition
Antagonism
FLUXOMICS FOR THE ANALYSIS OF TUMOR METABOLOME
Metabolomics and Fluxomics are necessary for rational design of new
therapeutical combinations
TRACER-BASED METABOLOMICS
Pyruvate
[1,2- 13C]-glucose
carboxylase
Pyruvate dehydrogenase
[2,3- 13C]-pyr
Fatty acid synthesis
Metabolome
[1,2- 13C]-acetylCoA
[2,3-13C]-OAA
Metabolic Pathways
[5,6- 13C]-citrate
FLUXOME
Glutamate
[2,3-13C]-ketoglutarate
[4,5- 13C]-ketoglutarate
Glutamate
AN ALGORITHM FOR DYNAMICS ANALYSIS OF THE
ISOTOPE TRACER DISTRIBUTION IN METABOLITES
EXPERIMENTAL TOOLS
COMPUTATIONAL TOOLS
Experimental
Obtain
dynamictools
data
for Tracer-based
metabolomics permit
to obtain dynamic
data that need to be
analyzed
and can be
TRACER-BASED
used for fluxes
METABOLOMICS
estimation
ALGORITHM
 Able to analyze:
They should
permit
Evaluate
metabolic
to fluxes
evaluate
metabolic fluxes
under non-steady
state in situ
METABOLIC
conditions
to
FLUXand
MAP
provide insight to the
kinetic mechanisms
which govern the
metabolic networks
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
Selivanov et al, 2004 Bioinformatics
Design metabolic interventions in drug development
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
pathways enhanced
Glucose
G6P
oxidative
Purine
ribose
Pyrimidine
F6P
non oxidative
oxidative
ROBUSTNESS
GAP
Pyruvate
non oxidative
Phase Plane Analysis
Lactate
?
AcetylCoA
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
2-MTX
3-DHEA+MTX
4-OT+DHEA+MTX
DHEA
MTX
G6P
G6PDH
R5P
0,35
PRPP
Purine
Biosynthesis
TKT
0,3
RNA
dUMP dTMP
10
,N -methylene
OT Pyrimidine Ntetrahydrofolate
Biosynthesis
Dihydrofolate
NADPH + H+
Glicine
Tetrahydrofolate
NADP+
120
MTX
1
Viabilidad
Viabilidad
Viability
0,2
0,15
0,05
0
100
MTX nM
MTX + 20 mM DHEA
MTX + 2 mM OT +
20 mM DHEA
80
2
60
2
0,1
DHFR
Serine
oxidative
6
DNA
Timidilate
sintase
1
4
0,25
dTTP
UMP
3
0
0,05
0,1
0,15
non oxidative
3
40
20
4
0
0
10
20
30
40
MTX (nM)
(nM
))
MTX
(nM
MTX
50
60
70
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 rich population
G1 rich
population
S-G2 rich populationS-G2
457,41
14,22
rich population 554,96
14,81
TKT activity SD
G1 rich population
29,79
1,10
S-G2 rich population
35,03
1,24
0,01
0
0-5h
5-10h
10-15h
15-20h
Cell Cycle (IC50 inhibitors)
%G1
%S
%G2
60
40
20
0
Ct
%
Increase in ribose enrichment (Smn)/hour
0,03
0,02
80
OT+DH
0h
Ct
OT+DH
10h
%G1
%S
%G2
0h
81,7
12,7
5,6
5h
82,8
9,8
7,4
10h
58,1
35,4
6,5
15h
35,2
59,3
5,5
20h
21,8
60,2
18,1
Ct
OT+DH
15h
Ct
OT+DH
20h
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
0
I
IIA
IIB
IIIA
IIIB
IIIC
IV
T
Tis
T1-2
T3
T4
T1-2
T3-4
T1-4
T1-4
N
N0
N0
N0
N0
N1
N1
N2
N0-2
M
M0
M0
M0
M0
M0
M0
M0
M1
• TKT, and its isoenzyme TKTL1, play a key role for tumor cell metabolism.
• 46 men + 17 women (69 12 years) with colorectal cancer in different
stages were included to confirm TKTL1 as a biomarker for tumor progression.
Stage
I
II
III
IV
No. Samples
9
21
16
17
• TKTL1 Immunohistochemical staining of 2 mm thick sections of tumors was performed.
(collaboration with Dr. Antoni Castells, Hospital Clinic)
Diaz-Moralli et al. Plos One 2011; 11;6(9) e25323.
RESULTS
Mean
13,3
20,8
32,9
13,7
SD
7,9
9,9
11,5
8,7
Stage III tumors present the highest levels
of TKTL1 expression (p=0,000008).
TKTL1 expression
Stage
I
II
III
IV
(Relative value x 1000)
50
***
40
30
20
10
0
Stage I
Stage II
Stage III
Stage IV
40
(Relative value x 1000)
TKTL1 expression
P = 0.000008
35
30
25
20
***
15
TKTL1
levels
decrease
significantly (p=0,0004) when
distant metastasis appears (M).
P = 0.0004
10
5
0
M1
40
P = 0.0014
35
30
25
20
15
10
5
0
N0
N 1-2
TKTL1 increase correlates significantly
(p=0,0014) with regional lymph node
affection degree (N).
40
(Relative value x 1000)
**
45
TKTL1 expression
(Relative value x 1000)
TKTL1 expression
M0
50
*
35
P = 0.029
30
25
20
15
10
5
0
T 1-2
 There is a slightly
between TKTL1 levels
growth (p=0,029).
T 3-4
correlation
and local
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
Acetylated chromatin
O
TSA
O
O
Gene expresion
ONa
Butyrate (NaB)
Differentiation
HT29
TSA
Butyrate (NaB)
HT29
METABOLIC CHANGES ACCOMPANYING TUMOR CELL
DIFFERENTATION
GLUCOSE
GLUCOSE
NADPH
NADPH
RIBOSE
G6P
LACTATE
RIBOSE
G6P
F6P
F6P
GAP
GAP
LACTATE
PYR
G6PDH
PYR
PDH
OAA
OAA
Ac-CoA
Ac-CoA
o TSA
Butirato or
TSA
Butyrate
-cetoglutarate
-cetoglutarate
HDAC
GLUTAMATE
GLUTAMATE
Differentiation
HT29
Transformation
HT29
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
G6PDH
G6PDH
LACTATE
F6P
F6P
GAP
GAP
LACTATE
PYR
PYR
PDH
PDH
OAA
RIBOSE
G6P
RIBOSE
G6P
OAA
Ac-CoA
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
mechanism of anoxia-induced cell damage.
validated
experimentally,
explains
the
Recognition of complex III bistability may enable novel therapeutic
strategies for oxidative stress
0: Q-Q-bh-bl-c1-FeS-Q-Q 1: Q-Q-bh-bl-c1-FeS-Q-Q 2: Q-Q-bh-bl-c1-FeS-Q-Q
Fe3++ QH2 ⇆Fe2++ Q-+ 2H+p
Fe2++ c1ox ⇆Fe3++ c1red
Q-+ blox⇆blred+ Q
xxxxx011 ⇆xxxxx101+ 2H+p
xxxx01xx ⇆xxxx10xx
xxx0xx01 ⇆xxx1xx00
vf31 = kf31 ·Cxx01
vr31 =kr31 ·Cxx10
vf32 = kf32 ·Cx0xx01
vr32 =kr32 ·Cx1xx00
vf30= kf30 ·Cxxxxx011
vr30 =k r30 ·Cxxxxx101·Hp2
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
Glycolysis
NAD
Glc
Clinical Data connection
ADP
O2uptake
Mitochondria
Exhalates
ATP
NADH Pyr
NAD
TCA cycle Cit
AcCoA
NAD
OAA
NADH Succ
ADP
O2
Lac
ATP
RESPIRATION
Clinical Data connection
Lac Omics
in
Blood
ROS
”OMICS” in biopsies
O2 transport
antioxidant system
cell damage
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