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The NIEHS Environmental Genome Project: Enabling Studies of Gene-Environment Interaction Douglas A. Bell, Ph.D. Environmental Genomics Section National Institute of Environmental Health Sciences Professor, Dept of Epidemiology UNC School of Public Health NIEHS’s Environmental Genome Project Resequencing of ~500 Candidate Genes Potentially Involved in Environmental Disease Concept and rationale Examples of gene-environment interaction Resequencing studies, accomplishments, and accessing data. Modulation of Response to Exposure Exposure Early Effects Disease Genetic Susceptibility Genetic Modulation of Exposure, Damage, and Biological Response Exposure Target tissue Biological Response Disease Genetic Variation in: • Metabolism, or distribution, affects dose to the tissue • Detection and repair of damage • Differences in growth and recovery from damage Genetic Modulation of Exposure Risk No Exposure Resistant Genotype Sensitive Background Risk Level (low) Genotype Exposure Resistant Genotype 2-Fold Risk Sensitive Genotype 4-Fold Risk Benzo[a]pyrene Metabolism Glutathione HO GST HO + Glutathione Inactive CYP450 PAH-oxide DNA Reactive Benzo[a]pyrene Metabolism Glutathione HO GST HO + Glutathione Inactive GSTM1 Null CYP450 PAH-oxide DNA Reactive Bladder Cancer Risk Associated with Smoking and GSTM1 Null Genotype Nonsmokers >50 Packyears Smoking *P<0.001; Bell et al, JNCI 85:1559,1993 Exposure Risk 1- 50 Packyears Smoking GSTM1 (+) GSTM1 null 1 1.3 2.2* 4.3* 3.5* 5.9* Genetic Risk Examples of Gene-Environment Interaction (gene modifies environmental effect) Malaria and Sickle Cell gene. HIV infection and CCR5 receptor variant. LPS sensitivity and Toll Receptor (TLR4) Adverse drug response and CYP2D6 poor metabolism. Alcohol intolerance and aldehyde dehydrogenase. Smoking, GSTM1 null, NAT2 slow genotypes, and bladder cancer risk . Variation in Risk Estimates in Human Populations Phenotypic variation in response due to: Risk Physiology Metabolism Repair Growth Timing of Exposure Exposure Example: Metabolism Polymorphisms frequency Range of Enzyme Activity in Human Populations No Phenotypic Polymorphism Activity Distribution of Polymorphic Enzyme Activity in a Population frequency Low -/- High +/- +/+ Activity High Low -/- +/- Activity Examples: N-Acetyltransferase 2, GSTM1, CYP2D6 +/+ frequency How does frequency of a risk factor impact exposure induced (G x E) risk in the population? 5% 95% Activity Effects of Exposure in High and Low Risk Human Populations Risk frequency 95% 100 High Risk 10 5% Activity Average Low Risk 0 Exposure How will genetic data be used in public health risk assessment? Given detailed information on the relationship between genotype and phenotype, more accurate risk assessments may be possible. Risk Assessment Process Hazard/Risk Assessment Exposure Assessment Effects in Humans ? Animal toxicology Human Genetic Susceptibility R (dose/response) Risk Management More/Less Control S Risk Model (Extrapolation to humans) Replace default assumptions about variability Engineering design Incorporating Human Genetic Polymorphism Information Into Risk Assessment Cancer - Yes/No Chemical X Dose ? Extrapolate to Humans • Biochemistry Susceptible human subgroup? • Mechanism of toxicity • Genes, pathways • Human genetics Incorporating Genetics Into Risk Assessment: Issues A polymorphism may have different effects depending on the chemical, the target organ/ disease, and the population being considered. Thus, a protective allele for one chemical may convey risk for a different chemical. Similarly one organ system may be protected at the risk of another; e.g. immune system response could increase DNA damage or neurotoxicity. GST Theta 1 (GSTT1) - One gene with 2 effects Detoxication HO Ethylene oxide GSTT1 H2C Glutathione CH2 Inactive + Glutathione Activation Methylene chloride GSTT1 (Unstable) Glutathione Cl- CH2 + Glutathione (also Methyl chloride) + HCHO DNA DNA DNA Reactive Cl D.A.Bell NIEHS Activation vs. Detoxication Effects of polymorphism dependent on chemical and toxicity pathway: Activation - If the activation pathway is missing (null genotypes), some individuals may have zero risk even if they have exposure. Detoxication - Since this process will never be 100% efficient, both functional and low activity genotypes will exhibit risk associated with exposure. The Effect of GSTT1 Genotype on Metabolism of Methyl Chloride T1 Null No Metabolism Measure exhaled methyl chloride From Lof, A. et al, Pharmacogenetics 10:645, 2000. T1 + Metabolism to DNA reactive forms Smoking, GSTT1 Polymorphism, and Markers of Genotoxicity in Erythrocytes Background: Ethylene oxide –hemoglobin adducts are a good measure of smoking exposure in blood. Experiment: To test if GST genotypes modulated effects of smoking in erythrocytes, we measured ethylene oxide hemoglobin adducts in freshly collected human erythrocytes from nonsmokers and smokers. Results: Ethylene oxide adducts (HEV) were ~50% higher in GSTT1 null individuals. D.A.Bell NIEHS GSTT1 null genotypes have higher levels of smoking-induced hemoglobin adducts Effect of GSTT1 null Genotype: Ethylene Oxide-Hemoglobin Adducts Vs Cotinine 800 Series1 GST T1 Null HEVal Adducts (fmol) 700 GSTT1 null 600 GST T1 + Series2 500 Linear (Series1) 400 Linear (Series2) 300 GSTT1 + Study Design: 16 nonsmokers 32 smokers HEVal hemoglobin adducts measure by mass spectrometry P = 0.001 for difference in slopes; Nonparametric analysis similar. 200 100 0 0 200 400 Plasma Cotinine (ng/ml) 600 Fennel et al CEBP 9:705,2000 Incorporating Genetics Into Risk Assessment Needs: Identify genes involved in toxicological response. Detailed population genetic information including: Determine functional relationship between genotype and phenotype Identify polymorphisms. Determine frequency in populations. Population-based risk estimates in large studies (n=2000). Biochemical In vitro, in vivo quantitative measurements of a cellular phenotype (tumors, adducts, mutation, cell death, gene expression). Consider role of multiple genes, multiple pathways, etc. Incorporate kinetic or other functional data into risk model. Environmental Genomics Discovery: Functional Analysis Phenotype-directed Genotype-directed Disease Risk Characterization CTTATGT A/C GGGTAT Genotype Phenotype Altered Binding Effects in Populations Polymorphism and Function Transcription Factors Coding region changes: aa subs, deletions, stops. Promoter Exon 1 Exon 2 3’ UTR Regulatory polymorphisms alter transcription factor binding and mRNA/protein level. Gene Deletions, Duplications e.g. GSTM1, CYP2D6 Effects of Polymorphism: Altered function Quantity of protein Phenotype—Directed Approach to Find SNPs That Alter Gene Expression Level C TGGGCCCCGCCCCCTTATGTAGGGTATAAAGCCC …. CCCGTCACC ATG SP1/Oct Liu, X. et al Sequence-Directed Approaches to Catalogue All Significant SNPs In The Human Population Resequencing Projects: Describing candidate gene polymorphisms in diverse populations. ~9 million SNPs in dbSNP now, by 2006, expect ~20 million human SNPs. A SNP every ~100 bases. Haplotype Map: Describing which SNPs occur together on chromosomes in populations (haplotypes). SNP Discovery Projects SNP Consortium – ~1 million SNPs across genome NIEHS – Environmental/toxicology genes NHLBI – Heart disease genes, inflammation NIGMS – Pharmacogenetic genes The SNP data is entered into the NCBI dbSNP database UCSC Hapmap U Wash EGP Website HapMap Website Characterize the large scale genetic structure across the genome. Genotyping SNPs at 1 kb interval across the genome in European, African, and Asian populations. Bioinformatic Tools Available For Picking Haplotype Tagging SNPs HapMap Website Seattle SNPs or EGP website Many other freely available programs NIEHS Environmental Genome Project Resequencing of candidate environmental disease genes Accomplishments: Total genes sequenced = 437 Total kilobases sequenced = 11,001 kb Total SNPs identified = 59,475 NIEHS’s Environmental Genome Project Summary: Gene-environment interaction affects disease risk. Effects of G x E interactions can be complex. Resequencing projects are providing many new candidate gene polymorphism. Determining the important functional SNPs that affect disease risk is a difficult challenge. Strategies For Incorporating SNPs Into Epidemiology Studies 1. Whole genome association studies Test 10,000-100,000 SNPs in case control studies. Identify candidate regions, genes, followup with candidate gene studies. 2. High resolution candidate gene studies. Test functional SNPs and additional haplotype tagging SNPs in case/control or other design. Bioinformatics to identify 1500 SNPs, 150 genes (10 SNPs/gene). Coding SNPs, regulatory SNPs, haplotype tag SNPs. Bioinformatic Identification of SNPs That Affect Gene Expression Application to p53 response elements Application to NRF2 response elements p53 inducible genes contain p53 Response Elements. Following UV exposure p53 binds RE of target gene. RNA Pol p53 p53 RRRCWWGYYY p53 SEI1 mRNA p53 RRRCWWAYYY RRRCWWGYYY ATG SEI1 gene Using bioinformatic methods, identify SNPs that disrupt p53 response elements. dbSNP Data Binding Site Consensus NCBI/Ensemble Genome Data Test SNPs Against p53 Response Element Consensus RRRCWWGYYYRRRCWWGYYY AAAGGACAAGTTGAAACTTGCACAAGCAGCCTCCATTCTG DNA ambiguity code R = A or G Y = C or T W = A or T Filter: Best Hits Access database Build Table of All Promoter SNPs Dan Tomso Mismatch with consensus CWWG motif Dan Tomso Do SNPs in putative p53 response elements affect p53 induced expression in Saos2 cells? Saos2 Osteosarcoma Cells (p53 null) RELATIVE INDUCTION 25 Strong Weak Strong 20 15 Weak 10 5 0 p21-5' ADARB1 DCC ARHGEF7 RRM1 TLR8 EOMES SEI-1 SCGB1D2 Mike Resnick, Alberto Inga, Daniel Menendez Environmental Genomics Section Douglas A. Bell Gary S. Pittman Merrill ‘Chip’ Miller, III Daniel J. Tomso Michelle R. Campbell Xuemei Liu Xuting Wang Monica Horvath Phylogenetic Footprinting of NRF2/ARE Genes ~4000 Human ARE containing genes 1000 human/mouse ~4000 Mouse ARE containing genes Human/ mouse/rat ~380 ~2100 Rat ARE containing genes Gene x Environment Interaction Pharmacogenetics: Environmental disease Adverse drug reactions (toxicity) Reduced efficacy Modification of exposure-induced toxicity Modification of exposure-induced disease Can we generalize about risk associated with a specific gene?