general principles of toxicogenomics
DESCRIPTION
Presentation on Toxicogenomics given by Carole Yauk of the Environmental Health Sciences and Research Bureau of Health Canada in March 2011TRANSCRIPT
Healthy Environments and Consumer Safety Branch
General Principle of Toxicogenomics
Carole YaukEnvironmental Health Sciences and Research BureauEnvironmental Health Sciences and Research Bureau
Health Canada
Healthy Environments and Consumer Safety Branch
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
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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
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ToxicogenomicsToxicogenomicsTreatment
G DNAGenome DNA
Response Transcriptome RNA
Disease Proteome Protein
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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
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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
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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
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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
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EARLY PROBLEMS
N ifi ( i t) b
I t t ti
Non-specific (or incorrect) probes.
Incorrect annotation.
Poor printing technology.
Sub-optimal protocols.
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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
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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
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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
ensi
ties
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ensi
ties
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Raw
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Array number
No
Array number
No
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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)
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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
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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
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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
Healthy Environments and Consumer Safety Branch
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
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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
Yauk et al., manuscript in preparation
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
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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
Yauk et al., manuscript in preparation
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
Yauk et al., manuscript in preparation
Healthy Environments and Consumer Safety Branch
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
Yauk et al., manuscript in preparation
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
Yauk et al., manuscript in preparation
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
Yauk et al., manuscript in preparation
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
Yauk et al., manuscript in preparation
Healthy Environments and Consumer Safety Branch
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
Yauk et al., manuscript in preparation
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
Healthy Environments and Consumer Safety Branch
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
Healthy Environments and Consumer Safety Branch
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
Healthy Environments and Consumer Safety Branch
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