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Toxicogenomics and Toxicogenetics

Toxicogenomics and Toxicogenetics. Maastricht University J. van Delft, D. van Leeuwen, H. Ketelslegers, R. Vlietinck, J. Kleinjans. General concept. toxicogenetics. toxicogenomics. Goals. Development, validation and application of:

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Toxicogenomics and Toxicogenetics

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  1. Toxicogenomics and Toxicogenetics MaastrichtUniversity J. van Delft, D. van Leeuwen, H. Ketelslegers, R. Vlietinck, J. Kleinjans

  2. General concept toxicogenetics toxicogenomics

  3. Goals Development, validation and application of: • biomarkers of effect as health indicator for exposure to carcinogenic compounds • biomarkers for genetic susceptibility related to those indicators Based on the newest genomic technologies: • Gene expression profiles as biomarker for effect, by Danitsja van Leeuwen • Multiplex genotyping as biomarker for genetic susceptibility, Hans Ketelslegers

  4. Phases Toxicogenomics Studies to select genes using DNA microarrays: • In vitro studies in human peripheral blood cells exposed to carcinogenic compounds • Small scale field study in monozygotic twins disconcordant in smoking Application in Environment & Health field study of “Luik III” on adults by quantitative RT-PCR

  5. Example of a DNA microarray

  6. Human 600 Toxarrays of Phase-1 Molecular Toxicology Gene Categories Types of Genes in Category Apoptosis Caspases, BAK, Bax, Fas, Cyclins, TNFs Cell Cycle Cyclins, DNA Binding Protein, Waf 1 Cell Proliferation Kinases, Transcription Factors, Growth Factors and Receptors, Connexins DNA Damage/ Repair DNA Repair Genes, ERCC’s, GADDs, Helicases, Topoisomerases Inflammation Serum Amyloids, Interleukins, Adhesion Molecules, Chemokines Metabolism P450s, Glucuronidation Enzymes, Glutathione Enzymes, Methyltransferases, Redox Enzymes Oxidative Stress O2 Response Genes, Superoxide Dimutase, Redox Enzymes Peroxisome Proliferators Peroxisomal Enzymes Transport Multi-drug Resistance Proteins, Organic Anion and Cation Transporters Cell-Environment Connexins, Integrins, Selectins, Cadherins

  7. In vitro study in human peripheral blood cells • Model carcinogenic compounds: • Cigarette smoke condensate • Benzo[a]pyrene • Tabaco specific nitrosamine (NNK) • 4-amino biphenyl • H2O2 • Possible biomarker genes: • Deregulated by all compounds • Correlating with DNA adducts

  8. Deregulated by CSC

  9. Deregulated by all compounds

  10. Small scale field study • Monozygotic twins discordant in smoking • Total peripheral blood cells • Analysis of: Gene expression DNA adducts (post labelling) Plasma cotinin levels • Data analyses of gene expression : • Smokers vs non-smokers • Correlations with DNA-adducts • Validation with RT-PCR

  11. Differentially expressed genes in smokers vs non-smokers

  12. Validation with RT-PCR

  13. Czech study • Another relevant field study, though not related to current program • Compared children from polluted versus clean area in Czech republic • Identified: • Differentially expressed genes • Genes correlating with micronuclei

  14. Deregulated and correlating genes

  15. Genes selected for field study

  16. CYP1B1 SOD2 ATF4 MAPK14 Based on smokers study with MZ twins CXCL1 PINK1 DGAT2 TIGD3 Based on Czech air pollution study 1) See NCBI at http://www.ncbi.nlm.nih.gov/Gene

  17. Field study on elderly people • aged 50-65 years, n = 398 • RNA from total peripheral blood cells • Quantitative RT-PCR of 8 genes vs 2 house keeping genes • Reference RNA sample: pool from 20 randomly selected individuals • Compared data with: • COMET • MN frequencies • 8-OH-dGin urine • Tumor markers in serum (p53, CEA, PSA)

  18. Effect of region Non-smokers All subjects

  19. Effect of season

  20. Comparison of regions

  21. Correlations with effect biomarkers

  22. Comparison with classical biomarkers • Majority of gene expressions differed significantly between 2 or more regions • Classical biomarkers did not always differ and if so, with lower significance • Magnitude of differences • gene expression: 1.2 (DGAT2) – 2.0 (ATF4) • classical biomarkers: 1.10 (COMET count) – 2.43 (COMET median) • Smoking significantly affected: • CYP1B1 and ATF4 • MN, CEA and p53 • Correlations with exposure markers not yet done

  23. Conclusions • Gene expression profiling as possible biomarker has been developed and applied • More in-dept analyses are required in order to establish relevance: • Exposure markers • Effect markers • Susceptibility markers • Confounding factors  Gene expression profiling is promising for molecular epidemiology on the risks of environmental exposures for humans

  24. General concept toxicogenetics toxicogenomics

  25. Phases Toxicogenetics • Select genes and polymorphism to be included • Develop and validate methods for multiplex genotyping • Apply in Environment & Health field studies of “Luik III” on newborns, adolescents, elderly

  26. Selection criteria of genes and polymorphisms • Genes must be relevant for endpoints / biomarkers in filed studies: • Asthma and allergy • Cancer • Polymorphisms must be relevant: • Highly frequent (>5%) • Cause a phenotypic effect (proven or highly likely)

  27. SNP Database: Database: 66 SNPs in 41 genes • Biotransformation (Set 1&7) • E.g. CYP1A1, -1A2, -1B1, GSTs, NATs, mEH etc. • DNA repair (Set 2) • E.g. XRCC, XPD, BRCA2, OGG1 etc. • Oxidative stress related (Set 3) • E.g. CAT, SOD, NQO, GPX etc. • Inflammation (Set 4&5) • E.g. Interleukins, TNFα, PAFAH etc. • Apoptosis & Cell Cycle control (Set 6) • E.g. p53, p21, Cylin D, CDKs etc.

  28. Examples

  29. Examples for genotyping by Single Base Extension

  30. Validation of genotyping method (1)

  31. Validation of genotyping method(2)

  32. Adolescent study • Population: +/- 450 adolescents (age: 16 years old) • Biomarkers: • Effect: Comet Analysis (DNA damage) • Exposure: 1-OHP (PAHs), PCBs, DDE, Cd, Pb • Genotyping: Biotransformation, DNA repair and oxidative stress related

  33. NAT2*6: p=0,006 Statistical approaches Univariate analyses: e.g. Mann Whitney or Kruskall Wallis

  34. Statistical approaches Multivariate analyses: e.g. Multiple Linear Regression, Discriminant Analyses or Binary Logistic Regression

  35. Exposure Marker - (Confounding) Effect of Smoking? + Remove Smokers from Analysis Total Population In Non-Smokers Relationship Exposure with Effect Marker? Dose-Response 2 groups based on Regression Line: 0 & 1 Most important predictors? Logistic Regression Group as Dependent; Sex, Cig/Day*, Smoking Y/N*, SNPs as Independents

  36. Linear Relationships-Adolescents

  37. Ethylbenzene * CometLinear Regression

  38. Ethylbenzene * CometLogistic Regression Catalase (p=0.027) GSTT1 (p=0.035) P=0.201 P=0.131

  39. Adult study • Population: +/- 400 adolescents (age: 65 years old) • Biomarkers: • Effect: Comet Analysis (DNA damage), 8-OHdG, PSA, CEA, p53 • Exposure: 1-OHP (PAHs), PCBs, DDE, Cd, Pb • Genotyping: Biotransformation, DNA repair and oxidative stress related

  40. Linear Relationships-Adults

  41. Cadmium (Urine) * 8-OHdG Linear Regression non-smokers

  42. Cadmium (Urine) * 8-OHdGLogistic Regression GSTT1 (p=0.041) P=0.224

  43. 1-OH-pyrene (Urine) * 8-OHdGLinear Regression non-smokers

  44. Adults: 1-OH-pyrene (Urine) * 8-OHdGLogistic Regression Gender CYP1A1*m4 mEH*3 (p=0.001) (p=0.05) (p=0.023) P=0.003 P=0.035 P=0.098

  45. Comparison with classical biomarkers • Majority of gene expressions differed significantly between 2 or more regions • Classical biomarkers did not always differ and if so, with lower significance • Magnitude of differences • gene expression: 1.2 (DGAT2) – 2.0 (ATF4) • classical biomarkers: 1.10 (COMET count) – 2.43 (COMET median) • Smoking significantly affected: • CYP1B1 and ATF4 • MN, CEA and p53 • Correlations with exposure markers not yet done

  46. Conclusions • Genetic polymorphisms affect susceptibility for effect biomarkers related to exposure • Sensitive populations can be genotyping for relevant polymorphisms • More in-dept analyses are required on order to establish relevance: • Interactions between genotypes • Univariate analyses • Effect of / interaction with smoking • Relations with gene expression  Genotyping enables to identify sensitive populations for specific exposure – effect relations

  47. Demonstrated the value for molecular epidemiology toxicogenetics toxicogenomics

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