general principles of toxicogenomics

Healthy Environments and Consumer Safety Branch

General Principle of Toxicogenomics

Carole YaukEnvironmental Health Sciences and Research BureauEnvironmental Health Sciences and Research Bureau

Health Canada


OUTLINE

1. General genomics2. What is toxicogenomics?g3. Overview of microarray technologies4. Data handling and data analysis5 Experimental Design5. Experimental Design6. An example from our lab7. Conclusions and needs


Genome = all an individual organism's genes

Genomics = the study of all of the genes of a cell or tissues at the DNA, RNA and protein level, p

Genome and EpigenomeDNA sequence

DNA methylation

Histones and histone modification

Credit: Moving AHEAD with an international human epigenome project. Nature 454, 711-715

Healthy Environments and Consumer Safety BranchGene Expression (mRNA Transcriptome)

Messenger RNA

Source: http://www.news-medical.net/health/What-is-Gene-Expression.aspx

MicroRNAs: the newest piece of the puzzle

Helping the people of Canada maintain and improve their health

Aider les Canadiens et les Canadiennes à mainteniret à améliorer leur santé

Controls mRNA translation by ymRNA degradation or translational repression

Source: microRNAs join the p53 network — another piece in the tumour-suppression puzzleLin He, Xingyue He, Scott W. Lowe & Gregory J. HannonNature Reviews Cancer 7, 819-822 (November 2007)

Healthy Environments and Consumer Safety BranchA single microRNAs controls many mRNA products

miRNA

mRNAmRNA

mRNA RNA RNA

mRNA

mRNA

mRNA

mRNA mRNA


ToxicogenomicsToxicogenomicsTreatment

G DNAGenome DNA

Response Transcriptome RNA

Disease Proteome Protein


Gene expression PRECEDES protein changes and toxicity

FOCUS: mRNA (gene expression)p p g y

Changes in gene expression are measurable at low doses

Gene Expression

Healthy Environments and Consumer Safety BranchChemicals perturb gene expressionExample: aryl hydrocarbon receptor agonists

Source: Miller and Ramos, Drug Metabolism Reviews, 2001

Healthy Environments and Consumer Safety BranchGenes are part of pathways that carry out cellular functions

Source: www.rndsystems.com/mini_review_detail_objectname_MR03_DNADamageResponse.aspx

Healthy Environments and Consumer Safety BranchIdentify perturbed genes and their pathways/functions

Elevated and Prolonged Lead Exposure in Fisher 344 Rats Leads to Marked Hepatic Differentially Expressed Genes. Gato and Means, 2010.

Healthy Environments and Consumer Safety BranchAssociate genes with biological pathways and processes

Perturbations in specific pathways lead to disease

Source: Kyoto Encyclopedia for Genes and Genomes


Applications

• Deciphering mechanism of action (pathway analysis) of toxicant• Response at low doses• Revealing potentially novel health effects• Revealing potentially novel health effects• Identification of perturbed pathways – targeted follow-up• Biomarker discovery• Investigating assumptions in toxicology• Predictive toxicogenomics


Gene ExpressionAnalytical methods to study mRNA transcription

• Gene by gene analysis: Northern Blotting, RT-PCR, qRT-PCR

• PUBLICATION OF GENOMES

DNA i• DNA microarrays• Real-time PCR arrays


Cells of interestMicrorray Technology

Cells of interest

mRNA (“target”)isolation and labelling

Laser Scanning

Microscope slideHybridize & wash

Healthy Environments and Consumer Safety BranchTwo colour experiment

Slide 2Slide 1

G AGene A

Gene B

Healthy Environments and Consumer Safety BranchTwo colour reference designUniversal Mouse

fBiological Sample

External control RNA

RNA

Reference RNAg p

control RNA

Cy5 labelled cRNA

Cy3 labelled cRNAcRNA cRNA

Array Hybridization

FluorescenceFluorescence detection and

image analysis

Healthy Environments and Consumer Safety BranchExpression profiling using Affymetrix GeneChips

Source: Affymetrix.com

Healthy Environments and Consumer Safety BranchDNA microarrays: poor reproducibility in the early days led to a bad rap

Publication Platforms Probe ID Validation Authors’ Conclusion

Kane et al., 2000 Operon 50mer, cDNA Sequence similarity None Agreement

early days led to a bad rap

Hughes et al., 2001 Agilent oligo, cDNA Sequence similarity None Agreement

Yuen et al., 2002 Affymetrix, custom cDNA Sequence similarity QRT-PCR Agreement

Kuo et al., 2002 cDNA versus Affymetrix Sequence similarity None

h ll l ff l hKothapalli et al., 2002 Incyte cDNA Affymetrix Sequence similarity Northern

Li et al., 2002 Affymetrix, Incyte cDNA Unigene or Genbank QRT-PCR

Barczak et al., 2003 Affymetrix, Operon 70mer Unigene ID None Agreement

Carter et al., 2003 Agilent 60mer, cDNA Sequence matched QRT-PCR Agreement

W l 2003 C l d DN l RT PCRWang et al., 2003 Custom oligo and cDNA Sequence similarity RT-PCR Agreement

Rogojina et al., 2003 Affymetrix, Clontech cDNA Genbank ID QRT-PCR and Q-immunoblot

Tan et al., 2003 Agilent cDNA, Affy, Amersham 30mer

Genbank ID None

Meecham et al.,2004 Agilent cDNA, Affymetrix Sequence matched None Agreement

Mah et al., 2004*Two different labs

cDNA array, Affymetrix Unigene (sequence verified)

QRT-PCR

Järvinen et al., 2004 Affymetrix, Agilent cDNA,Custom-cDNA

Unigene ID None


EARLY PROBLEMS

N ifi ( i t) b

I t t ti

Non-specific (or incorrect) probes.

Incorrect annotation.

Poor printing technology.

Sub-optimal protocols.


Problems with statistical analysis and experimental designProblems with statistical analysis and experimental design

Leniant filtering methods for poor or low intensity spotsLeniant filtering methods for poor or low intensity spots.

Incorrect probe matching across platforms.

Improper data handling (i.e. Normalization).

Incorrect statistical analysis.

Biological replication.

Healthy Environments and Consumer Safety BranchPublication Platforms Probe ID Validation Authors’ Conclusion

Yauk et al., 2004 Codelink, Agilent cDNA, Agilent Oligo, NIA cDNA, Mergen, Affymetrix

Unigene ID None Dependant on platform – good platforms correlate

Improved reproducibility after 2004

M rg n, ffym tr c rr at

Shippy et al., 2004 Affymetrix, Amersham Unigene ID Real Time RT-PCR Agreement after noise adjusted

Irizarry et al., 2005 Lab-lab comparison(10 labs)

Affymetrix (5 labs), cDNA (3 labs), 2 colour Oligo (2 labs)

Unigene, LocusLink, RefSeq

Real Time RT-PCR Agreement among best performing labs

Larkin et al., 2005 Affymetrix, TIGR cDNA Sequence mapped TIGR

Real Time RT-PCR AgreementTIGR

TRC Group, 2005*Lab-lab comparison7 labs, 12 platforms

5 custom cDNA, Amersham, Compugen, Agilent, Affy, Operon, 2 custom Oligo

Transcripts matched using NIA mouse index

None Moderate Agreement (standardized protocols and data analysis required)

Pylatuik et al., 2005 Genomic Amplicon Arrays,Operon Oligo, Affymetrix

Locus ID Northern blot Moderate agreement (signal intensity-dependant)

Shi et al 2005 Tan et al 2003 dataset Genbank Acc No N/A Alternate analysis had 10X Shi et al., 2005 Tan et al., 2003 dataset Genbank Acc. No. N/A Alternate analysis had 10X +concordance.

Barnes et al., 2005 Affymetrix, Illumina BeadArrays Sequence matched using BLAST

None Agreement

Carter et al., 2005 Affymetrix, Stanford cDNA sequence matching None Agreement (overlapping probes)

hl l ff h l l D l PSchlingemann et al., 2005 Affymetrix, In-house long Oligo Unigene ID Real Time RT-PCR Agreement

Warnat et al., 2005 6 different cDNA and oligo array studies previously published

Unigene ID N/A Agreement (more platforms better for predictive anal.)

Ali-Seyed et al., 2006 AffymetrixApplied Biosystems

Promoter Analysis Real Time RT-PCR AB more sensitive/ correlated RT-PCR.

Severgnini et al., 2006 Affymetrix, Codelink LocusLink ID Real Time RT-PCR Disagreementg y g

De Reyniès et al., 2006 Affymetrix, GE Healthcare (Amersham), Agilent Sequence mapped Real Time RT-PCR Moderate agreement (1 colour better than 2 colour)

Wang et al., 2006 Applied Biosystems, Agilent Sequence matched (BLAST)

Real Time RT-PCR Agreement (1375 genes confirmed with RT-PCR)

Kuo et al., 2006*Lab-lab comparison

Affymetrix, AmershamMergen ABI Custom cDNA MGH MWG Agilent

Probes sequence matched within 1 exon

Real Rime RT-PCR Agreement (commercial better than in-house 1-colour better than 2)Lab lab comparison

added*Mergen, ABI, Custom cDNA, MGH, MWG, Agilent, Compugen, Operon

matched within 1 exon (Unigene, LocusLink, RefSeq, Refseq exon)

house, 1 colour better than 2)

Green = correlation between platformsyellow = moderate correlation between platformsred = poor correlation between platforms

See Yauk et al. Nucleic Acids Research, 2004Yauk and Berndt, Environ Mol Mutagen 2007


1. Quality Control

Obtaining useful information from a microarray experiment

y

2. Remove probes in background.

3. Adjust (normalize) the measurements to facilitate comparisons.

4. Select genes that are differentially expressed between lsamples.

5. Identify the biological processes and molecular functions that are alteredare altered.

6. Place data in the context of a health outcome.

Healthy Environments and Consumer Safety Branch1. Quality Measures: Garbage in, Garbage out

A. Sample and RNA Quality

B. Array (slide) quality• Percentage of spots with no signal• Number of saturated spots• Intensity Distribution• Summary Measures of the negative• Summary Measures of the negative

control spots• Median Signal to Noise Ratio

M di B i ht• Median Brightness• External Controls

Healthy Environments and Consumer Safety BranchExample: Agilent Quality Reports


2 B k d i f l iti2. Background noise: false positives

• Estimate the background?gLocalNegative Control Spotsg pShould we background subtract?

• Limits of Detection, Presence/Absence CallsFlagging spots in the backgroundgg g p g

Healthy Environments and Consumer Safety Branch3. Normalization: Cross-slide comparisons and removing biasnt

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ties

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Raw

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rm

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Array number

No

Array number

No


4 Identify genes that are affected by the treatment4. Identify genes that are affected by the treatment

• Fold change is not a statistical test• 50,000 comparisons on one chip – adjust for multiple comparisons• Levels of filtering to identify changing genes

1. Fold Change

2 T t t /ANOVA2. T-tests/ANOVA

3. Permutation testa) MAANOVAb) Significance Analysis of Microarrays (SAM)b) Significance Analysis of Microarrays (SAM)


5. Identify biological processes/molecular functions/pathways that are altered and link to a potential health outcome

BIOINFORMATICSGene Ontology: a controlled vocabulary of terms for describing gene product characteristics and gene product annotation data

Includes: cellular compartmentbiological functionbiological functionmolecular process

Pathway: collection of manually drawn pathway maps representing knowledge on th l l i t ti d ti t kthe molecular interaction and reaction networks

Looking for over-representation of changing genes within these groups.

Healthy Environments and Consumer Safety BranchExample: Kegg pathway for P53 signalling


Designing an experiment to study mechanism of actionDesigning an experiment to study mechanism of action

1. Adequate sample size!q p2. Appropriate selection of time points (e.g., early, downstream,

transformation, disease effects)3. Appropriate selection of treatment conditions (non-toxic)4. Appropriate tissue/cells sampled5. Sample collection – randomization (time effects)6. HIGH QUALITY RNA!!! 7 R d i ti d i i t l d i7. Randomization and microarray experimental design8. Implementation of QA/QC9. Appropriate normalization and filtering

10. VALIDATION WITH ALTERNATIVE TECHNOLOGIES


An example from our lab

Healthy Environments and Consumer Safety BranchToxicological Profiles of Cigarette Smoke Toxicological Profiles of Cigarette Smoke C d tC d tCondensateCondensate

Use of high-density DNA microarrays toUse of high-density DNA microarrays toInvestigate pathways induced by CSC exposure

C l i i h h i i d iCorrelation with other toxicity endpoints

5 cigarette brands:1. Export A full flavour2. 3. Gauloises Blonde3 Gau o ses o de3. Player’s Light King Size

Carole Yauk and Paul White collaboration


Cigarette smoke condensate collection and characterization

Brand # of cigarettes Smoked

Total TPM Yield(mg)

TPM/cig

1 – Export A 60 1625.5 27.09

2 – Gauloises Blondes 108 1826.0 16.91

3 – Player’s Light King Size 117 1659.0 14.18

Healthy Environments and Consumer Safety BranchAnalyte Export A Gauloises

BlondePlayer’s

LightCarcinogenicity

g

Tar (mg/cig) 15.6 12.9 12.4 NA

Nicotine (mg/cig) 1.3 1.1 1.1 NA

CO (mg/cig) 14.0 13.6 12.7 NA( g/ g)

Benzo[a]pyrene (ng/cig) 9 8 10 1

4-aminobiphenyl (ng/cig) 2 2 2 1

NNN (ng/cig) 37 178 25 1NNN (ng/cig) 37 178 25 1

NNK (ng/cig) 75 63 52 1

Cadmium (ng/cig) 90 47 90 1

Lead (ng/cig) NQ 19 NQ 2BLead (ng/cig) NQ 19 NQ 2B

Formaldehyde (μg/cig) 82 54 44 2A

Acetaldehyde (μg/cig) 698 680 587 2B

1 3 butadiene (μg/cig) 52 44 48 2A1,3-butadiene (μg/cig) 52 44 48 2A

Isoprene (μg/cig) 276 376 301 2B

Acrylonitrile (μg/cig) 11 12 10 2B

Benzene (μg/cig) 49 43 49 1Benzene (μg/cig) 49 43 49 1

Styrene (μg/cig) 14 10 10 2B

Healthy Environments and Consumer Safety BranchToxicity/genotoxicityPhenotypic anchoring and dose selection

Toxicity – Cloning Efficiency in Muta™Mouse Lung Epithelial Cellsy g y g pMutagenicity – Mutations Salmonella typhimuriumMutagenicity – Mutations in Muta™Mouse Lung Epithelial CellsCl t i it Mi l i i M t ™M L E ith li l C llClastogenicity – Micronuclei in Muta™Mouse Lung Epithelial Cells

Essential to select meaningful concentrationsfor microarray experimentsfor microarray experiments

The Muta™MouseThe Muta™Mouse


Toxicity Profiling Via Cloning Efficiency(LD50 Values Determined Using Probit Link

Function)

100

120

60

80

100

ml m

edia

)

20

40

60

LD 50

(µg/

0

20

Brand 1 Brand 2 Brand 3 Brand 4 Brand 5Export A Player’s Special Gauloises Player’s Plain Player’s LightE p y p y y L g

Yauk et al., manuscript in preparation

Healthy Environments and Consumer Safety BranchMutagenicity in the Ames Assay

1 2 Export A

1.0

1.2

TPM

)

Export AGauloisesPlayer's King

0.6

0.8

Pot.

(rev

/µg

0.4

Mut

agen

ic P

0.0

0.2

G G

M

TA98 YG1041 YG5161


Healthy Environments and Consumer Safety BranchCigarette smoke condensate does not induce DNA

sequence mutations in the FE1 cell linesequence mutations in the FE1 cell line

Pilot Brand Pre-incubation

S9 Dose CSC ug/ml

Summaryincubation ug/ml

#1 Gauloises No 0 0, 20,40,60,80

High Sp MF, No response

#2 Gauloises No 0 5% 0 High Sp MF No #2 Gauloises No 0.5% 0, 20,40,60,80

High Sp MF, No response

#3 Export A Full Flavor

60min 0.5% 0, 60 Good Sp MF, No response

#4 Export Full Flavor

15, 30, 60min

0.5% 0,40,60,80,100

Good Sp MF, No response

#5 Players Light 60 min 0.5% 0-150 No dose responseresponse

#6 Gauloise 60 min 0.5,1,2,4% 100 No response

#7 Players Special

No 0,1,2,4 100,150,200 No dose responseSpecial response

#8 Players Plain No No 20-120 No response


Cytokinesis-Block Micronucleus Assay

Healthy Environments and Consumer Safety BranchCBMN Assay in Muta™Mouse FE1 Cells Results for Player’s Light

35.0

) )Total MN/500 cells***

Results for Player s Light

20 0

25.0

30.0

500

cells

)

0.80

Freq

. (%Binucleate Freq

******

10.0

15.0

20.0

Freq

. (pe

r

0.40

ucle

ate

F

0.0

5.0

0 60 90 120

MN

F

0.00

Bin

u

0 60 90 0Cigarette Smoke Condensate (µg/mL)

*p<0.05, **p<0.01, ***p<0.001 Fisher’s Exact Test


Healthy Environments and Consumer Safety BranchCigarette smoke condensate is clastogenic in mouse pulmonary epithelial cells in vitro

40

45DMSO

***

p y p

30

35

40

1000

90 μg/mL120 μg/mL150 μg/mL ** ** *

**

*

20

25

with

MN

per

10

15

Cel

ls w

0

5

Export Gauloises Player's



TOXICOGENOMICS

Healthy Environments and Consumer Safety BranchFinal Decision for Design of Microarray Study

2 time points (early response/late response)

Early = after 6hr exposure

Late = after 4hr recovery (10hr total)

3 doses (control, low, high)

Low 45μg TPM/mL High 90μg TPM/mLLow = 45μg TPM/mL, High= 90μg TPM/mL

5 replicates/dose (required to obtain statistical significance)5 replicates/dose (required to obtain statistical significance)

Agilent 22k toxicology arraysg gy y

Healthy Environments and Consumer Safety BranchMicroarray Analyses Microarray Analyses –– Main ExperimentMain Experiment

Generated 1,365,000 data points

MAANOVA to Identify Significant ChangesMAANOVA to Identify Significant Changes

Clustering and pathway analyses

Affected genes, biomarkers and brand-specific signatures

Healthy Environments and Consumer Safety BranchSummary of gene expression findings

296 known genes were up- or down-regulated relative to solvent

54 down-regulatedg6 hours 115 genes

61 up-regulated

172 down-regulated10 hours 254 genes

82 up-regulated


Healthy Environments and Consumer Safety BranchSummary of gene expression findings

6 hours 10 hours

45 ug/ml 90 ug/ml 45 ug/ml 90 ug/ml

↑ ↓ ↑ ↓ ↑ ↓ ↑ ↓↑ ↓ ↑ ↓ ↑ ↓ ↑ ↓Export A 5 3 22 24 24 5 66 171

Gauloise 2 0 54 30 13 11 54 46

Player’sLight

4 12 37 42 8 7 82 103


Healthy Environments and Consumer Safety BranchLarge overlap among the brands

e.g., 10 hours, 90 μg/ml

E t A Pl ’ Li ht

93 28

Export A Player’s Light

8749 3

2

Gauloises


Healthy Environments and Consumer Safety BranchUp-regulated in Exposed (Higher in Dose 90)

guanine nucleotide bi di t i b t 4

Down-regulated in Exposed (Lower in Dose 90)

aurora kinase Abinding protein, beta 4

sulfiredoxin 1 homolog

tetraspanin 33

cell division cycle 20 homolog

cell division cycle 2 homolog A

ll di i i l i t d 5DNA-damage inducible transcript 3

serine peptidase inhibitor, clade E,

cell division cycle associated 5

DEP domain containing 1B

F-box only protein 5, ,

member 1

glutathione synthetase

zinc finger protein 330

histone 1, H1b

inner centromere protein

karyopherin (importin) alpha 2

cytochrome P450, family 1, subfamily b, polypeptide 1

y p ( p ) p

polo-like kinase 1

protein regulator of cytokinesis 1

Control

Export A

Gauloises Blonde

Control

90 μg/ml

45 μg/ml

6 hours

10 hours

Gauloises Blonde

Player’s light king size

90 μg/ml



B j i i B j i i

10 hour Gene Ontology Analysis

TermBenjamini p-value Term

Benjamini p-value

cell division 0.000 p53 signaling 0.015

metabolic mitosis 0.000

etabo cprocesses 0.017

regulation of cell cycle 0.000

cell cycle

gcell death 0.019

DNA damage yprocess 0.000

cell division 0 000

gresponse 0.027

regulation of apoptosis 0.047cell division 0.000

mitosis 0.000

apoptosis 0.047


Helping the people of Canada maintain and improve their health

Aider les Canadiens et les Canadiennes à mainteniret à améliorer leur santé

Healthy Environments and Consumer Safety BranchDose Trends between 6 and 10 hrs

77 genes decreasingdecreasing

in expression with dose

tensi

ty

6 hrs 10 hrszed Int

66 genes increasing

in expression ith dN

orm

ali

with doseN

6 hrs 10 hrs


1 year 2 year

Screening in genetic toxicology

yea yea

CancerCost:$2M/cmpd2 year rodent cancer bioassay

Mutation

Cost:$2M/cmpd Time: 3 years

Cost: $60K/cmpdDominant Lethal Test

In vitro mammalian mutation }Cost: $60K/cmpd Time: 6-12 months

In vivo mutation

Salmonella bacteria assays} Cost: $60K/cmpd

Time: 3 months}Cost: $10K/cmpd

Genomics

Gene expression analysis Time: 1 month

HIGH CONTENT!

Healthy Environments and Consumer Safety BranchPredictive toxicogenomics:

medium throughput MOA analysis

Ellinger-Ziegelbauer H, et al., Toxicology Letters 186 (2009) 36-44.

Healthy Environments and Consumer Safety BranchIncreasing number of papers analyzing gene expression to support

observed endpoints:Focussed quantitative real-time PCR arraysy


Concluding remarks on gene expression technologies

• Technologies have come a long way over the past decade• Appropriate experimental design in combination with correct data

handling generate reproducible and reliable data• Improved annotation and bioinformatics tools are leading to a better

ability to interpret findings

• Expression technologies are highly useful for:• The identification of mechanisms of action• Biomarker discovery• Exploring potentially novel health effects• Chemical categorization


Needs for application to identify MOA

• Identification and validation of adverse outcome genes/pathways (differentiating adaptive versus adverse effects)

• Increasing the database of chemicals analyzedc eas g t e database o c e ca s a a y ed• Identification of low-dose effects• Improved bioinformatics tools for data interpretation• Guidelines for use of expression data in regulatory assessments

general principles of toxicogenomics

Documents

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