nuggoo - unifi...explanation of bromobenzene-mediated hepatoxicity 0 50 100 150 200 250 cvc bb 262:...

the European Nutrigenomics Organisation

NuNuGOGONuNuGOGOUn Oslo

Un Munich

Un Florence

Un Balearic Illes

Un Cork

Trinity

Un. Ulster

Rowett

Un Newcastle

Un Reading

IFR DiFE

Un Krakow

Inserm Marseille

TNO

Un Wageningen

Un Maastricht

EBI

NuNuGOGO

Un Lund

RikiltRivm

Examples of pathway analysis in nutrigenomics and toxicogenomics studies

Rob Stierum, [email protected]


NuNuGOGONuNuGOGO

Opening up a few nutrition and

toxicology-related journals for pathway

content…..

Opening up a few nutrition and

toxicology-related journals for pathway

content…..


NuNuGOGONuNuGOGO Pathways in nutrition. Glycolysis


NuNuGOGONuNuGOGOBritish Journal of Nutrition, (1947) 1, 245-253

still… life seemed quite simple in the pre-genomics era…


NuNuGOGONuNuGOGO…mechanism of azo-dye induced liver tumours…

British Journal of Nutrition, (1947) 1, 245-253


NuNuGOGONuNuGOGO …worldwide…

Japanese Journal of Nutrition (1950-1952)


NuNuGOGONuNuGOGO ..more complex…Mannose metabolism: Am. J. Clin. Nut. (1971), 24, 488-498


NuNuGOGONuNuGOGO …modeling CYP gene expression

Toxicol. Appl. Pharmacol. (1998)


NuNuGOGONuNuGOGO Now, examples from the omics era

Establishing pathways in nutrition and toxicology from transcriptome, proteome and metabolomedatasets

Several approaches:•‘manual’ : use your brain; current knowledge•tools: pathway mapping and biological network tools•statistical networks biological inference


NuNuGOGONuNuGOGOCase I ‘use your brain’ + tool

Discovery of functional genomics-derivednew biomarkers to assess the hepatotoxiceffects at the mechanistic level ofcombined exposure to food additives.


NuNuGOGONuNuGOGO

thiabendazole


NuNuGOGONuNuGOGOAdministration of additives through the diet for 28 days, male Sprague-Dawley rats (n=6)

thiabendazole (TB)(102-5077 ppm)

control

Clinical signs, clinical chemistry and liver histopathology

Total RNA isolation from liver

Data analysis•Criteria for selecting genes:

•maximum of one missing value per dose response curve, •maximum CV of difference between the dye swap of 0.5 (dye swaps should behave in similar direction•at least 1.5 fold difference between any of the doses/compound

Confirmation of microarray data findings for selected genes at the biochemical level: •BHT and TB: RT PCR for CYP2B1 and CYP2B1/2; 7-ethoxyresorufin O-deethylase (CYP1A) and 7-

pentoxyresorufin O-depentylase (CYP2B) activity.•CC: palmitoyl-CoA oxidation as marker for peroxisome proliferation•TB: cellular tumor antigen p53 ELISA•All additives: western blotting for CYP1A2 and CYP2B1/2

cDNA array


NuNuGOGONuNuGOGO Gene expression profiles

3 x Control3 x Low

3 x Mid3 x High

3 x Control3 x Low

3 x Mid3 x High

5 x Control5 x Low

5 x Mid5 x High


NuNuGOGONuNuGOGO Applied bioinformatics versus manual

‘Manual’ network creation, concordance with a bioinformatics approach performed two years after

Network building around p53 protein, using MetacoreTM software from GeneGO, comparison with manually created network from array results from thiabendazoleexperiment


NuNuGOGONuNuGOGOSystems ReconstructionTM Technology

GeneGoGeneGo, Inc, Inc

Systems Biology for Drug Discovery

www.genego.com


NuNuGOGONuNuGOGO

Agilent Affymetrix Proteomic SAGE

Concurrent visualization of different data types and experiments


NuNuGOGONuNuGOGO Build new network from MetacoreTM

• Around p53 protein• Making us of biological dB• Filtered to reduce complexity:

– for ‘rat ortholog’– for ‘transcriptional regulation’– for ‘liver’


NuNuGOGONuNuGOGO


NuNuGOGONuNuGOGO

Filtering needed to reduce complexity


NuNuGOGONuNuGOGO


NuNuGOGONuNuGOGO Case II ‘use your brain’

• Transcriptome, proteome and metabolome changes in rats exposed to bromobenzene

• Based on work by Wilbert Heijne:– Wilbert H.M. Heijne, Rob H. Stierum, Monique Slijper, Peter J. van Bladeren and Ben van Ommen (2003)

Toxicogenomics of bromobenzene hepatotoxicity: a combined transcriptomics and proteomics approach. Biochemical Pharmacology, 2003 Mar 1;65(5):857-75.

– Heijne WH, Slitt AL, Van Bladeren PJ, Groten JP, Klaassen CD, Stierum RH, Van Ommen B. (2004) Bromobenzene-induced hepatotoxicity at the transcriptome level. Toxicol Sci. 2004 Jun;79(2):411-422.

– Wilbert H.M. Heijne et al. Metabolome changes in bromobenzene treated rat liver, accepted


NuNuGOGONuNuGOGOBr Br

O

BrOH

Br

O

Br

OH

Br

OH

BB BB 2,3 oxide2-Bromophenol

P450

P450

BB 3,4 oxide 3-Bromophenol

4-Bromophenol

P450

3,4 Dihydroxybromobenzene

+ GSSG

other bromophenolsand bromoquinones

Br

OHOH

Br

OHOH

GS

Br

OHOH

Br

OO

P450

4-Bromo-ortho-benzoquinone

GSH + GST

2 GSH

CYP450

CYP450

CYP450

CYP450 GST

Epoxide Hydrolase

Biotransformation of bromobenzene in rat liver


NuNuGOGONuNuGOGOBromobenzene Rat studies:

II. Oral dosing of rats n=3 • Low (0.5 mmol/kg) • Mid (2 mmol/kg) • High (5 mmol/kg)• Untreated and solvent controls• Liver isolation at 6, 24 and 48 hrs.• Transcriptomics and metabolomics

I. Intraperitoneal dosing of rats • High (5 mmol/kg), untreated and solvent controls• Liver isolation at 24 hrs.• Transcriptomics and proteomics


NuNuGOGONuNuGOGOBB upregulated (oral)

6 hours 24 hours 48 hoursDose (mmol/kg BW)

0.01.02.03.04.05.06.0

Electrophile response element (EpRe; ARE)

GST AGamma-GCSFerritinHeme oxygenasePeroxiredoxin1……


NuNuGOGONuNuGOGO4 4.5 5 5.5 6 6.5 7

94

67

43

30

20.1

14.4

pI

Mass

364: 4-hydroxyphenyl pyruvic

acid dioxygenase (N)

3436

112: ATP synthase β subunit (M)

157: HSP60 (M)

308: carbamoyl-phosphate synthase(N)

358: 4-hydroxyphenyl pyruvicacid

dioxygenase (M)and HMG-CoA synthase (N)

277 287

420422434

611

687: aldehydedehydrogenase 2, mitochondrial (M)

697: carbamoyl-phosphate synthase(N)

698

821

846883

925 926 934: D-Dopachrome tautomerase(M; N)

981: aryl sulfotransferase (M; N)

991

Proteomics results IP

Change in the expression of individual rat liver proteins upon exposure to bromobenzene--> additional explanation of bromobenzene-mediated hepatoxicity

0

50

100

150

200

250

C VC BB

262:glutathione synthase

NS

0100200300400500600700800900

100 0

UT CO BB

687:aldehyde dehydrogenase 2,

mitochondrialb***

112:ATP synthase beta subunit.

0200400

600800

100 0

120 0140 0160 0

180 0

UT CO BB

b***

157:HSP60 protein

0

100

200

300

400

500

600

700

800

UT CO BB

b***

bromobenzene

Lipid peroxidation

4-hydroxy-2-nonenal

Detoxified by aldehydedehydrogenases

The mitochondrial, class 2,aldehyde dehydrogenase

has the highest affinity for aldehydes

Reactive intermediates, e.g. quinones


NuNuGOGONuNuGOGO

-0.2 -0.1 0 0.1 0.2PC1(22.67%)

-0.3

-0.2

-0.1

0

0.1

0.2

PC2(12.93%)

PCA Score plot PC1 vs PC2 urine 6 hours

bbua02abbua02bbbua02c

bbua04a

bbua04bbbua04c

bbua06a

bbua06bbbua06c

bbub20a

bbub20b

bbub20c

bbub22abbub22b

bbub22cbbub24a

bbub24b

bbub24c

bbuc38abbuc38bbbuc38c

bbuc40a

bbuc40bbbuc40c

bbuc42a

bbuc42bbbuc42c

bbud56a

bbud56b

bbud56c

bbud58abbud58b

bbud58c

bbud60abbud60b

bbud60c

bbue74abbue74b

bbue74c

bbue76abbue76bbbue76c

bbue78abbue78b

bbue78c

Winlin V1.11

Control

Vehicle

Low Dose

Medium Dose

High Dose

Metabolomics (oral)


NuNuGOGONuNuGOGO

: mRNA

: protein

: (Putative)metabolite


NuNuGOGONuNuGOGO Case 3 ‘mapping tool’

Time- and dose-dependent effects of curcumin on gene expression in human colon cancer cells.

Van Erk MJ, Teuling E, Staal YC, Huybers S, Van Bladeren PJ, Aarts JM, Van Ommen . Time- and dose-dependent effects of curcumin on gene expression in human colon cancer cells. J Carcinog. 2004 May 12;3(1):8.


NuNuGOGONuNuGOGOEffects of curcumin on gene expression in human colon cancer cells

• Curcumin incubation of colorectal cancer cell line HT29• Time course experiment: 3h, 6h, 12h, 24h, 48h• Two datasets: low dose and high dose• Select genes from expression data set present in at least 3 or

more time points• Use GenBank accession numbers to find up-to-date Unigene

Clusters• Final dataset: ~2600 genes for both low-dose and high-dose

dataset


NuNuGOGONuNuGOGOEffect of curcumin in colon cancer cells:effect on cell cycle parameters

Changes in cell cycle distribution in response to curcumin:G2/M phase arrest• decrease in % of cells in G1 phase, • increase in % of cells in G2/M phase

24h

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

sub-G1 G1 S G2/M

% o

f tot

al c

ell p

opul

atio

n

*

*

**

* *

3h

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

sub-G1 G1 S G2/M

% o

f tot

al c

ell p

opul

atio

n

*

*

*

*

controllower concentrationhigher concentration

6h

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

sub-G1 G1 S G2/M

% o

f tot

al c

ell p

opul

atio

n

*

**

*

Van Erk et al., 2004


NuNuGOGONuNuGOGOMicroarray data: Effect on cell cycle genes

p16-INK4 (CDKN2A)

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

3h 6h 12h 24h 48h

expr

essi

on ra

tio (2

log)

low high

PCNA

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

3h 6h 12h 24h 48h

expr

essi

on ra

tio (2

log)

low high

polo-like kinase

-2.00

-1.50

-1.00

-0.50

0.00

0.50

3h 6h 12h 24h 48h

expr

essi

on ra

tio (2

log)

low high

histone deacetylase 1

-0.80-0.70-0.60-0.50-0.40-0.30-0.20-0.100.000.100.20

3h 6h 12h 24h 48h

expr

essi

on ra

tio (2

log)

low high

Van Erk et al., 2004


NuNuGOGONuNuGOGO 24h high

early low

24h high

early high

early low+high12h+24h high

12h low

12h high

48h high

12h low

early low+high

24h high

24h high

12h low

early low

early low+high

12h high

12h high

24h high

early low

24h high

Visualize changes in ‘cell cycle genes’ in pathway using GenMAPP

Lipoprotein Metabolism

Atherosclerotic ChangesIn Mature Mice (25 wks)

Lipoprotein

ApoE3 Transgenic Mouse Model

• Over-expressed human mutant apolipoprotein gene• Apolipoprotein changes prevent binding to liver receptors• Clearance of plasma lipoproteins inhibited• Mice are pre-atherosclerotic at 9 weeks…

Case 4: statistical networks biologicalinference: Nutritional systems biology on ApoE3 Leiden Model

Methodology• Specimens: 10 male ApoE3 Leiden /10 male wild type controls

(WT)• Normal chow diets/environmentally similar• Sacrificed at 9 weeks• Samples analyzed:

CompleteNANAUrine

CompleteCompleteNAPlasma

CompleteCompleteCompleteLiver

MetabolomicAnalysis

ProteomicAnalysis

GenomicAnalysis

E+067.518

8:00 16:00 24:00 32:00 40:000

20

40

60

80

100

m/z:450>950

E+067.518

8:00 16:00 24:00 32:00 40:000

20

40

60

80

100

m/z:450>950

time (min)

Inte

nsity

• Mass Chromatograms of Plasma Lipids

Wildtype

APOE3

m/z:450>950

E+066.221

8:00 16:00 24:00 32:00 40:000

20

40

60

80

100 E+066.221

8:00 16:00 24:00 32:00 40:000

20

40

60

80

100

Inte

nsity

Metabolite Analysis- ApoE3 vs. WT: LC-MS Plasma Lipid Profiles

• Mouse Plasma• Metabolite (lipid)• LC-MS• IMPRESSTM algorithm

• Mouse Plasma• Metabolite (lipid)• LC-MS• IMPRESSTM algorithm

ApoE3

Wildtype

Lyso-PC’s

triglycerides

0 200 400 600 800 1000 1200 1400 1600

-1

-0.5

0

0.5

1

369.00

569.00

623.00

823.00847.00848.00

849.00850.00

851.00855.00

871.00872.00

873.00874.00

875.00876.00 877.00

878.00879.00

880.00900.00

901.00902.00

903.00904.00

905.00906.00

918.00919.00

926.00928.00

929.00931.00

932.00945.00946.00

948.00950.00

951.00952.00

953.00967.00972.00

975.00995.00

996.00

1040.001041.00

1062.001063.00

1064.001065.00 1086.00

1087.001088.00

1536.001537.00

1668.00

1706.001707.00 1711.00

1732.001733.00

1734.001735.00

1736.001737.00

1738.001739.00

m/z

Mor

eab

unda

ntIn

Apo

E3Le

ssab

unda

ntIn

Apo

E3

triglycerides

Lyso-phosphatidylcholine

Metabolite Analysis- ApoE3 vs. WT: Plasma Lipid

Difference Factor Spectrum

• Mouse Plasma• Metabolite (lipid)• LC-MS• IMPRESSTM algorithm• PARCTM pattern recognition

• Mouse Plasma• Metabolite (lipid)• LC-MS• IMPRESSTM algorithm• PARCTM pattern recognition

• Unexpected Metabolite Information

Relative Variance• Unsupervised clustering reveals segregation• Hundreds of components contribute to observed differences

can be used as powerful, selective and specific biomarkers

Rel

ativ

e Va

rianc

e

Proteomic Analysis- ApoE3 vs. WT: Principal Component and

Discriminant Analysis

• Mouse Plasma• Protein• ESI-Ion Trap• IMPRESSTM algorithm• PARCTM pattern recognition


500 1000 1500 2000

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

Regression

Factor Spectrum

465.00485.00

535.00

622.00652.00

662.00

750.00

811.00

911.00926.00

992.00

1041.001050.00

1208.001216.00

1217.001243.00

1267.001268.00

1269.001298.00

1299.00

1340.001342.00

1583.00

m/z

Fraction 2

HigherIn

ApoE3

Higherin

WT



Proteomic Analysis- ApoE3 vs. WT: Difference Factor Spectrum

Peptides Exhibiting Significant Differences

1366.00

Identifyusing

MS-MS

Beyond Nutrition 40

Mor

eab

unda

ntIn

Apo

E3Le

ssab

unda

ntIn

Apo

E3

• Mouse Plasma and Liver• Protein + Metabolite + mRNA• IMPRESSTM algorithm• PARCTM/WinlinTM pattern recognition

• Mouse Plasma and Liver• Protein + Metabolite + mRNA• IMPRESSTM algorithm• PARCTM/WinlinTM pattern recognition

peptidesmetabolites

genes

Each bar represents a measured componentand up/down-regulation

Proteomic, Genomic, Metabolomic Data: Identifying / Mining Differences- ApoE3 vs. WT: Difference Spectrum

M-7: Diacylglycerol (DAG)

G-693:Fatty Acid Binding Gene

P-1059: Fatty Acid Binding Protein

G-8147: Apolipoprotein A1

M-9: LysoPC

BioSystematics™- Mapping Known Relations onto Non-Linear PCA Correlations

Down-regulatedUp-regulated

Non-Linear Kernal PCAFeature Correlation (ρ > 0.8)

Protein Kinase Cepsilon

Cell Signaling

PCA Correlation

Text / PubMed MiningRegulatory Factors

Fatty Acid Metabolism

Apoptosis Inhibitory 6 Gene

Apoptosis

RNP H1 GeneTranscription Splicing

Ubiquitin Conj E3A GeneProteosome

Translation Init FactorProtein Synthesis

PLA2 VII

PPARα

Phosphatidylcholine Lipid Metabolism Pathway Model Suggested by Results

• New insights in targets/biomarkers in the ApoE3 system

transmembraneprotein

small molecule


NuNuGOGONuNuGOGO Acknowledgements• TNO, NL:

– Dr. Marjan van Erk: curcumin in vitro studies– Ing. Marinus Dansen: array quality control– Prof. Dr. Louis Havekes and Prof. Dr. Jan van de Greef + colleagues from Beyond

Genomics Medicine, Waltham, MA, USA: APOeE3 systems biology– Prof. Dr. John Groten: food additives project– Dr. Wilbert Heijne: bromobenzene studies – Dr. Robert Jan Lamers and Dr. Joop van Nesselrooij: metabolomics of bromobenzene– Dr. Ben van Ommen: coordinator of The European Nutrigenomics Organisation, group

leader Genomics

• BIBRA INTERNATIONAL LTD, UK– Prof. Dr. Brian Lake, Dr. Clive Meredith + colleagues thiabendazole project

• EUROPEAN BIOINFORMATICS INSTITUTE, HINXTON, UK– Dr. Susanna Assunta Sansone and Dr. Phillipe Rocca-Serra: support with

ArrayExpress

• UNIVERSITY OF UTRECHT, NL– Dr. Frank Holstege group: microarrays fabrication– Prof Dr. Albert Heck group: BB proteomics mass spectrometry

• FOOD STANDARDS AGENCY, UK financial support (project number T01021)


NuNuGOGONuNuGOGOPathways and networks assisted me in getting here!

NederlandseSpoorwegen(Static, delay!)

Meridiana Airlines(Dynamic)

Taxi in the ancient city of Florence(Conserved)

nuggoo - unifi...explanation of bromobenzene-mediated hepatoxicity 0 50 100 150 200 250 cvc bb 262:...

Documents