metabolòmicadiposit.ub.edu/.../2445/96405/1/seminari3cascante.pdf · 2016-03-11 · metabolòmica...

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

RNA

Protein

Biochemicals (Metabolites)

Genomics

Transcriptomics

Metabolomics

Proteomics Proteins

SYSTEMS BIOLOGY

Cytomics

Genomics

Proteomics

Metabolomics

Information

Highest capacity to predict phenotype

Fluxomics

FROM MOLECULAR BIOLOGY TO SYSTEMS BIOLOGY

Metabolomics and fluxomics in the

systems Biology approach

-These networks are not independent but form “network of networks“

-Metabolic network crosstalk with other networks must be considered

in a 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

Metabolites

Metabolites are not only the “end point” also the “driving force”?

Metabolomics and fluxomics in the systems

Biology approach

Gene

mRNA

Proteines

-Biological processes

regulation is a complex

phenomena more

“democratic” than

“hierarchical”

Central dogma of molecular biology:

• 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



biological sample.





• The study of the total metabolite pools (metabolome)

in a cell-organism at one particular point in time.







biological sample.













biological sample.













biological sample.











• 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.

Complementary approaches:

• 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.

Challenges : all metabolic activity has

to be stopped in the moment of

sampling

Metabolomics experimental approaches:

-Large differences in: Physicochemical properties (polarity, hydrophobicity),

structure, concentration range…… Combination of techniques is necessary

• Enzymatic assays, HPLC, Capillary electrophoresis-mass spectrometry

(CE-MS/MS)

• Liquid chromatography-mass spectrometry (LC-MS/MS)

• Gas chromatography-mass spectrometry (GC-MS)

• NMR

Currently most used methods

The metabolome does not consist of a limited number of

building blocks…. we are far away to have a “microarray”!

Metabolome analysis:

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

- 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

What is needed?

Van der Greef et al. “The Role of Metabolomics in Systems

Biology” In: Metabolic Profiling, Kluwer (2003).

Tissue or biofluid sample

Bioanalytical tools 1. Mass spectrometry

2. 1H NMR spectroscopy

Measure the

metabolite profile

e.g. by NMR

Treat profile as ‘fingerprint’ for diagnostic purposes

Statistical bioinformatic tools

Explore profile to determine mechanism and potential

biomarkers

Metabolomics: diagnostic, mechanism, biomarkers....

E.g. plasma samples randomly selected from 12 students….

10 of the profiles are very similar (“normal”)

2 abnormal profiles

- too much alcohol?

- diseased?

Use computerized pattern recognition

methods

alcoholic

diseased

normal

citrate

histidine

Insight into mechanism of disease/toxicant

E.g. plasma samples randomly selected from 12 students….

Analysis of human samples

Blood – serum or plasma

Urine

Tumour samples

Biopsies

Biomarker Discovery

Cell based model studies

Choice of cell line

Cell content or

secreted metabolites

Systems Biology Approach: - Drug target discovery

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).

• 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

Fluxomics

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.

• 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?

Metabolomics and fluxomics in Cancer

Systems Biology

(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 PROTEOME

Signaling pathways, transcription

factors…

Changes in GENOME

Oncogenes and tumor supressor

genes…

Limitless replicative potential

DNA damage and DNA replication stress

Mitotic stress

Genomic instability

Metabolic stress

Evading immune surveillance

Sustained angiogenesis

Tissue invasion and metastasis Evading cell death and senescence

Activated growth signalling

TUMOR METABOLOME

Is metabolic network reorganization a consequence or a cause of tumor progression?

Could metabolism be used as therapeutic target against tumor progression?

Alterations in METABOLISM

Changes in PROTEOME

Signaling pathways, transcription

factors…

Changes in GENOME

Oncogenes and tumor supressor

genes…

CANCER

Satisfy energetic tumor requirements

Creation of acidic environment

Insensibility to O2

Decrease of pyruvate oxidation in the mitochondria

General increase of glycolytic intermediates

High glucose consumption and lactate production. Warburg effect

Activation of biosynthetic pathways

Expression of isoforms, changes in enzymatic activities and affinities

- M2-Pyruvate kinase (M2-PK)

- Transketolase-like 1 (TKTL1)

- Hexokinase I and II (HK)

Lactate

Glucose G6P

F1,6BP

DHAP

Pyr

Acetyl-CoA

CO2

Citrate

Acetyl-CoA Malonyl-CoA

Fatty acids

PEP

Pyr

1,3BPG

3PG

2PG

F6P

6PGT Ru5P

E4P

6PGL

S7P

GAP

X5P R5P

Nucleotide

synthesis

Lactate

NADPH NADPH

Palmitate

Citrate

TKTL1

M2-PK

HK II

TUMOR METABOLOME

See as a review: Robust metabolic adaptation underlying tumor progression

Vizan P, Mazurek S and Cascante M, Metabolomics (2008) 4:1–12

Cancer cells are perfect systems

to invade and parasite other tissues

Robust metabolic profile

FRAGILITY

Exploitable Target

for

CANCER THERAPY?

unexpected

perturbations

CANCER AS A METABOLIC ALTERATION

MULTIPLE HIT CANCER THERAPY AT METABOLIC LEVEL

Multiple hit strategies can avoid bypass of single inhibitions

A

B

C

D

E F

1

3

2

Parallel reactions

In series reactions

Tumor metabolism robustness counteracts single hits

Synergy

Addition

Antagonism

Tumor metabolism response to multiple inhibition is

unpredictable

Rational design of new therapeutical combinations is necessary

MULTIPLE HIT CANCER THERAPY AT METABOLIC LEVEL

Multiple hit strategies can avoid bypass of single inhibitions

A

B

C

D

E F

1

3

2

Tumor metabolism robustness counteracts single hits

Synergy

Addition

Antagonism

Tumor metabolism response to multiple inhibition is

unpredictable

Rational design of new therapeutical combinations is necessary

KNOWLEDGE

OF THE

METABOLIC NETWORK

[1,2-13C]-glucose

[2,3-13C]-pyr

[1,2-13C]-acetylCoA

Fatty acid synthesis

[5,6-13C]-citrate

[4,5-13C]--

ketoglutarate Glutamate

[2,3-13C]-OAA

[2,3-13C]--

ketoglutarate Glutamate

Pyruvate dehydrogenase Pyruvate

carboxylase

FLUXOMICS FOR THE ANALYSIS OF TUMOR METABOLOME

Metabolomics and Fluxomics are necessary for rational design of new

therapeutical combinations

TRACER-BASED METABOLOMICS

Metabolic Pathways

Metabolome

FLUXOME

AN ALGORITHM FOR DYNAMICS ANALYSIS OF THE

ISOTOPE TRACER DISTRIBUTION IN METABOLITES

Experimental tools

for Tracer-based

metabolomics permit

to obtain dynamic

data that need to be

analyzed and can be

used for fluxes

estimation

They should permit

to evaluate

metabolic fluxes

under non-steady

state in situ

conditions and to

provide insight to the

kinetic mechanisms

which govern the

metabolic networks

in vivo

EXPERIMENTAL TOOLS COMPUTATIONAL TOOLS

Able to analyze:

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

ALGORITHM

Useful to:

Analyze and understand the metabolic adaptations supporting cell functions

Design metabolic interventions in drug development

Obtain dynamic data Evaluate metabolic

fluxes

METABOLIC

FLUX MAP TRACER-BASED

METABOLOMICS

Selivanov et al, 2005 Bioinformatics Selivanov et al, 2004 Bioinformatics

Selivanov et al, 2006 Bioinformatics

Glucose

G6P

F6P

GAP

Pyruvate Lactate

AcetylCoA

ribose

Pentose-phosphate

pathways enhanced Purine

Pyrimidine

oxidative

non oxidative

Exploiting tumoral metabolic adaptation of adenocarcinoma cancer cells

for new antitumoral therapies

Phase Plane Analysis

non oxidative

oxidative

DEVELOPING DRUGS FOR NEW THERAPEUTICAL STRATEGIES

AIMING TO DISRUPT METABOLIC ROBUSTNESS OF CANCER CELLS

ROBUSTNESS

FRAGILITY

?

1-Control

MTX nM

MTX + 20 mM DHEA

MTX + 2 mM OT +

20 mM DHEA

0

0 10 20 30 40 50 60 70

MTX ( nM ) MTX ( nM )

0

0 10 20 30 40 50 60 70

MTX ( nM ) MTX (nM)

20

40

60

80

100

120

Via

bil

idad

V

iab

ilid

ad

20

40

60

80

100

120

Via

bil

idad

V

iab

ilit

y

1

2

3

4

2-MTX

3-DHEA+MTX

4-OT+DHEA+MTX

0,05 0,1 0,15

non oxidative

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

0

ox

ida

tiv

e

4 3

2

1

4 3 2

Pyrimidine

Biosynthesis

R5P PRPP

dUMP dTMP

Dihydrofolate

NADPH + H+

NADP+ Tetrahydrofolate

Serine

Glicine

Timidilate

sintase

DHFR

dTTP

DNA UMP

Purine

Biosynthesis RNA

G6P

G6PDH

TKT

N6,N10-methylene

tetrahydrofolate OT

DHEA

MTX

MTX

Oxidative/non-oxidative balance is essential to cancer cells and is a possible new target within the

cancer metabolic network for novel therapies.

DEVELOPING DRUGS FOR NEW THERAPEUTICAL STRATEGIES

AIMING TO DISRUPT METABOLIC ROBUSTNESS OF CANCER CELLS

Ramos-Montoya et al., 2006. Int J Cancer; 119(12):2733-41

Multiple hit target strategy to disrupt this balance

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.

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

Vizan et al., 2009. Int J Cancer; 124(12):2789-96

G6PDH activity SD

G1 rich population 457,41 14,22

S-G2 rich population 554,96 14,81

TKT activity SD

29,79 1,10

35,03 1,24

G1 rich population

S-G2 rich population

G1 rich

population

S-G2 rich population

%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

Increase in ribose enrichment (Smn)/hour

0

0,01

0,02

0,03

0-5h 5-10h 10-15h 15-20h

0

20

40

60

80

Ct OT+DH Ct OT+DH Ct OT+DH Ct OT+DH

0h 10h 15h 20h %

%G1 %S %G2 Cell Cycle (IC50 inhibitors)

.

Exploiting angiogenesis metabolic adaptation of HUVEC cells for new

antiangiogenic therapies

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.

Characterization of metabolic adaptation underlying growth factor

angiogenic activation: Identification of potential therapeutic targets

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.

Vizan et al., 2009. Carcinogenesis; 30(6):946-52

3. The Direct inhibition of key enzymes of glycogen

metabolism and pentose phosphate pathways reduced

HUVEC cell viability and migration.

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.

• TKT, and its isoenzyme TKTL1, play a key role for tumor cell metabolism.

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

• 46 men + 17 women (69

12 years) with colorectal cancer in different

stages were included to confirm TKTL1 as a biomarker for tumor progression.

TKTL1 AS BIOMARKER FOR TUMOR PROGRESSION IN

COLORECTAL CANCER

• TKTL1 Immunohistochemical staining of 2 mm thick sections of tumors was performed.

Stage No. Samples

I 9

II 21

III 16

IV 17

(collaboration with Dr. Antoni Castells, Hospital Clinic)

0

5

10

15

20

25

30

35

40

45

50

N 0 N 1-2

** P = 0.0014

TK

TL

1 e

xp

ressio

n

(Rela

tive v

alu

e x

1000)

TKTL1 increase correlates significantly

(p=0,0014) with regional lymph node affection degree (N).

0

5

10

15

20

25

30

35

40

M0 M1

*** P = 0.0004

TK

TL

1 e

xp

ressio

n

(Rela

tive v

alu

e x

1000)

TKTL1 levels decrease

significantly (p=0,0004) when distant metastasis appears (M).

RESULTS

0

5

10

15

20

25

30

35

40

T 1-2 T 3-4

* P = 0.029

TK

TL

1 e

xp

ressio

n

(Rela

tive v

alu

e x

1000)

There is a slightly correlation

between TKTL1 levels and local growth (p=0,029).

Stage III tumors present the highest levels

of TKTL1 expression (p=0,000008).

Stage Mean SD

I 13,3 7,9

II 20,8 9,9

III 32,9 11,5

IV 13,7 8,7

Stage I Stage II Stage III Stage IV

0

10

20

30

40

50*** P = 0.000008

TK

TL

1 e

xp

ressio

n

(Re

lative

va

lue

x 1

00

0)


0

10

20

30

40

50*** P = 0.000008


0

10

20

30

40

50


0

10

20

30

40

50*** P = 0.000008

TK

TL

1 e

xp

ressio

n

(Re

lative

va

lue

x 1

00

0)

Diaz-Moralli et al. Plos One 2011; 11;6(9) e25323.

METABOLIC CHANGES ACCOMPANYING TUMOR CELL

DIFFERENTATION

N

NHOH

O O

TSA

Butyrate (NaB)

ONa

O

Histone deacetylase enzymes downregulate genes that cause or induce cell differentiation.

Deacetylated chromatin

no gene expression

Acetylated chromatin

HDAC HAT HDI

Gene expresion

HDI

HT29 Differentiation

HT29 TSA

Butyrate (NaB)

Butirato o TSA

HDAC

Butyrate or TSA

HDAC

HT29

NADPH

F6P

GAP

PYR LACTATE

OAA Ac-CoA

GLUTAMATE

RIBOSE

GLUCOSE

G6P G6PDH

PDH

- cetoglutarate - - -

Transformation

Differentiation

NADPH

F6P

GAP

PYR LACTATE

OAA Ac-CoA

GLUTAMATE

RIBOSE

GLUCOSE

G6P


HT29

METABOLIC CHANGES ACCOMPANYING TUMOR CELL

DIFFERENTATION

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

Jurkat cells + low dosis edelfosine

(apoptosis < 5%)

NADPH

F6P

GAP

PYR LACTATE

OAA Ac-CoA

RIBOSE

GLUCOSE

G6P G6PDH

PDH


EARLY METABOLIC CHANGES PRECEED EDELFOSINE (ET-18-OCH3 )

INDUCED APOPTOSIS

Jurkat cells without edelfosine

NADPH

F6P

GAP

PYR LACTATE

OAA Ac-CoA

RIBOSE

GLUCOSE

G6P G6PDH

PDH


•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

.

0: Q-Q-bh-b

l-c

1-FeS-Q-Q

xxxxx011 ⇆xxxxx101+ 2H+

p

vf30

= kf30

·Cxxxxx011

vr30

=k r30

·Cxxxxx101

·Hp

2

Fe3++ QH2 ⇆

Fe2++ Q-+ 2H+

p

0: Q-Q-bh-b

l-c

1-FeS-Q-Q


p

vf30

= kf30

·Cxxxxx011

vr30

=k r30

·Cxxxxx101

·Hp

2

0: Q-Q-bh-b

l-c

1-FeS-Q-Q


p

vf30

= kf30

·Cxxxxx011

vr30

=k r30

·Cxxxxx101

·Hp

2

0: Q-Q-bh-b

l-c

1-FeS-Q-Q


p

vf30

= kf30

·Cxxxxx011

vr30

=k r30

·Cxxxxx101

·Hp

2

Fe3++ QH2 ⇆

Fe2++ Q-+ 2H+

p

1: Q-Q-bh-b

l-c

1-FeS-Q-Q

Fe2++ c1

ox ⇆Fe3++ c1

red

xxxx01xx⇆xxxx10xx

vf31

= kf31

·Cxx01

vr31

=kr31

·Cxx10

1: Q-Q-bh-b

l-c

1-FeS-Q-Q

Fe2++ c1

ox ⇆Fe3++ c1

red

xxxx01xx⇆xxxx10xx

vf31

= kf31

·Cxx01

vr31

=kr31

·Cxx10

1: Q-Q-bh-b

l-c

1-FeS-Q-Q

Fe2++ c1

ox ⇆Fe3++ c1

red

xxxx01xx⇆xxxx10xx

vf31

= kf31

·Cxx01

vr31

=kr31

·Cxx10

2: Q-Q-bh-b

l-c

1-FeS-Q-Q

Q-+ blox⇆b

lred+ Q

xxx0xx01 ⇆xxx1xx00

vf32

= kf32

·Cx0xx01

vr32

=kr32

·Cx1xx00

2: Q-Q-bh-b

l-c

1-FeS-Q-Q

Q-+ blox⇆b

lred+ Q


vf32

= kf32

·Cx0xx01

vr32

=kr32

·Cx1xx00

2: Q-Q-bh-b

l-c

1-FeS-Q-Q

Q-+ blox⇆b

lred+ Q


vf32

= kf32

·Cx0xx01

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.

•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

Selivanov et al., 2009. PLOS Comput. Biol. and Selivanov et al 2011, PLOS Comput. Biol.

CONCLUSIONS FROM ROS MODELLING

Clinical Data connection

signalling

cell damage

G l y c o l y s i s

L a c

T C A c y c l e

antioxidant system

G l c

P y r

L a c N A D

N A D H

N A D

M i t o c h o n d r i a

A c C o A

O A A

C i t

S u c c

N A D

N A D H

A D P

A T P

O 2 A D P

A T P

O 2 uptake

Exhalates

ROS

Omics

in

Blood

O 2 transport

Clinical Data connection ”OMICS” in biopsies

RESPIRATION

Developing a modelling environment, which links clinical characteristics

with the redox status of cell

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)

Dr. Silvia Marín

Dr. Gema Alcarraz-Vizán

Dr. Vitaly Selivanov

Susana Sánchez

Dr. Pedro de Atauri

ACKNOWLEDGEMENTS

Group of Integrative Biochemistry

Department of Biochemistry and Molecular Biology, University of Barcelona

Dr. Josep Centelles

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

Collaborators

Santiago Díaz-Moralli

Igor Marín

Miriam Zanuy

Roldán Cortés

Adrián Benito

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