Tim Zacharewski Department of Biochemistry & Molecular Biology

1. Toxicogenomic Assessment of Estrogenic Endocrine Disruptors: Effects of Ethynyl Estradiol on Gene Expression Tim Zacharewski Department of Biochemistry & Molecular Biology Institute For Environmental Toxicology and The National Food Safety &Toxicology Center Michigan State University tel: (517) 355-1607 fax: (517) 353-9334 e-mail: tzachare@pilot.msu.edu http://www.bch.msu.edu/~zacharet Research Program Supported by: National Institutes of Health US Environmental Protection Agency American Chemistry Council

2. Endocrine Disruptor: Definition An endocrine disruptor is an exogenous substance that causes adverse health effects in an intact organism, or its progeny, secondary to changes in endocrine function.

3. Endocrine Disruption - Issues Human Concerns - increased incidence of hormone-dependent cancers - impaired cognitive abilities - compromised fertility - increased incidence of reproductive tract abnormalities Wildlife Concerns - intersex - abnormal reproductive behavior - increased incidence of developmental abnormalities - compromised reproductive fitness

4. - Safe Drinking Water Act (SDWA) U.S. LEGISLATION - Food Quality Protection Act (FQPA) Develop screening tests for chemicals that mimic estrogen, androgen and thyroid by August 1998 and implement program by August 1999 Includes effects on humans and wildlife

5. U.S. Environmental Protection Agency Endocrine Disruptor Screening Program http://www.epa.gov/oscpmont/oscpendo/index.htm Initial Sorting Priority Setting Tier 1 Screening Tier 2 Testing

6. Endocrine Disruptor Screening Program Initial Sorting - 87,000 chemicals - 900 pesticide active ingredients - 2,500 other pesticide formulate ingredients - 75,500 industrial chemicals - 8,000 cosmetics, food additives and nutritional supplements Priority Setting - based on: - production volume, environmental persistence, exposure - quantitative structure activity relationships (QSARs) - high throughput prescreening assays : competitive ligand binding : reporter gene induction

7. OH H3CO OCH3 CCl3 HO Diethylstilbestrol (DES) 4- t -Octylphenol Cl HO CH3 O Cl O CCl3 HO O 2 3 3` 2` Cly Clx 1` 4` 4 1 5 6 6` 5` o,p ' -DDT Structural Diversity of Estrogenic Endocrine Disruptors Pharmaceuticals Industrial Chemicals Ethynyl Estradiol OH C CH Methoxychlor H3C CH3 H3C HO CH3 HO Environmental Pollutants Phytoestrogens/Natural Products Zearalenone HO O Genistein Polychlorinated Biphenyl (PCB) HO O OH

8. ER* ER* L L Proposed Mechanism of Action of Estrogen Receptors L L ER hsp90 EREs hsp90 transcriptional effects protein level changes PLEIOTROPIC RESPONSE PLEIOTROPIC RESPONSE

9. Estrogenic Endocrine Disrupting Chemical Binding Globulin Extracellular Membrane Bound Estrogen Receptor Binding Globulin Receptor Intracellular Oxidative Metabolism Kinase New Ligands Transcription Factors Receptors Gene Expression Protein Cellular Effects Tissue Effects Other Possible Actions of Estrogenic Endocrine Disruptors

10. The Emerging Paradigm In order to fully assess the risk of chronic and subchronic exposure to synthetic chemicals and natural products, a more comprehensive understanding of the physiological, cellular and molecular effects is required within the context of the whole organism, its genome, transcriptome, proteome and metabonome.

11. Data Integration Across Biological Levels • Biological Networks • depicts interactions between • gene, protein and metabolite • levels • regulation distributed over all • levels • each level can influence the other • network provides molecular • basis for phenotype From Brazhnik et al, Trends Biotechnol, 2002

12. Translational Toxicogenomic Research Integration of global assessment technologies into environmental health and drug development. - Used to rank and prioritize drug candidates/chemicals for further development/testing • Earlier incorporation of toxicology in drug development pipeline • Identification of biomarkers for exposure and clinical trail monitoring

13. Toxicology Comprehensive Safety Assessment Strategy: correlate molecular changes to observed effects in order to enhance predictive accuracy Tissue Cell Metabolome Proteome Transcriptome Genome

14. Ethynyl Estradiol Induction of Global Gene Expression Study Design harvest tissue hrs 0 2 8 12 24 48 72 vehicle +/- estrogen • ovx immature C57BL/6 mice • 0.1 mg/kg EE or with vehicle (sesame oil) by gavage • uteri, liver, bone and mammary gland were harvested • uteri - Affymetrix Mu11KSubA GeneChips • liver, bone, mammary gland – cDNA microarray

15. Ethynyl Estradiol Induction of Uterine Global Gene Expression • Uterus • Affymetrix Mu11KSubA GeneChip

16. Data Analysis Approach 6523 Probe sets on Mu11KSubA GeneChip Screen 1: Nonparametric empirical Bayes 881 Significant time and/or treatment effect Screen 2: ANOVA 392 Significant treatment or treatment*time effect k-means clustering; Annotation: UniGene 268 Genes (not unknown ESTs) Annotation: Gene Ontology 263 Physiological/Toxicological interpretation Used gene ontology and RefSeq annotation to link transcriptional and physiologic changes

17. Affymetrix GeneChip

18. Affymetrix GeneChips:Photolithographic Synthesis of GeneChips Lamp GeneChip Mask http://www.affymetrix.com

19. GeneChip Expression Tiling Array Design 5´ 3´ Gene Sequence Multiple oligo probes Perfect Match Mismatch Perfect match A-C-T-G-T-T-T-A-C-G-C-T-C-A-G-T-C-G-G-G-T-C-A-A-T Mismatch A-C-T-G-T-T-T-A-C-G-C-T-A-A-G-T-C-G-G-G-T-C-A-A-T

20. GeneChip Expression AnalysisHybridization and Staining Array Hybridized Array cRNA Target Streptravidin-phycoerythrin conjugate

21. Microarray Data Management, Analysis, and Storage

22. Modular Architecture of dbZach(http://dbZach.fst.msu.edu) • Under Development: • Promoter Subsystem • Pathway Subsystem • Toxicology Subsystem • Real-Time PCR • Subsystem • Sample Annotation • Subsystem • Gene Annotation Tool • Correlation Tool • QA/QC monitoring • Feature Inspection Tool

23. Verification by QRT-PCR • 26 known genes selected based on p1z value • Pearson correlations were calculated • profiles for 23/26 genes exhibited strong correlation • profiles for 2/26 exhibited marginal correlation • profile for 1/26 genes did not correlate

24. K-Means Clustering 7 K-means clusters General response Confirm previously reported estrogen-regulated gene responses 1. 2. 3. 4. 5. Describe new estrogen-regulated gene responses and hypothesize about specific mechanisms 6. 7.

25. Cell cycle G1/S S G2/M DNA synthesis (d)NTPs dNDPs dNTP recycling RNA/protein synthesis RNAPol; polyA-BP; tRNA-synth.; eIFs Immune +/- migration and cytokine signaling suppressors complement components - protectin (inhibits complement-mediated lysis) Summary: Temporal Trends 0 4 8 12 16 20 24 . . . 3x24 hr Increased dry mass, hyperplasia Water imbibition Energy ATP ATP transport Solute/ water transport Cl- transport

26. Sat 3x24 hr Abp1 3x24 hr Mxi1 8-24hr Odc 8-24 hr Arg1 3x24 hr Rars 8-12 hr Pdi2 3x24 hr Nos3 2-8 hr Arginine/Ornithine Utilization polyamine depletion tissue proliferation polyamines Myc/Max -neg Oaz2 2-3x24 hr Oazi N.D. -neg -neg ornithine -neg proteins -neg arginine citrulline vasodilation, immune stimulation, inhibition of smooth muscle cell growth nitric oxide

27. Sat 3x24 hr Abp1 3x24 hr Mxi1 8-24hr Odc 8-24 hr Arg1 3x24 hr Rars 8-12 hr Pdi2 3x24 hr Nos3 2-8 hr 2-24 hr Following EE Exposure polyamine depletion tissue proliferation polyamines Myc/Max -neg Oaz2 2-3x24 hr Oazi N.D. -neg -neg ornithine -neg proteins -neg arginine citrulline vasodilation, immune stimulation, inhibition of smooth muscle cell growth nitric oxide

28. Sat 3x24 hr Abp1 3x24 hr Mxi1 8-24hr Odc 8-24 hr Oaz2 2-3x24 hr Arg1 3x24 hr Rars 8-12 hr Pdi2 3x24 hr Nos3 2-8 hr 3x24 hr Following EE Exposure polyamine depletion tissue proliferation polyamines Myc/Max -neg Oazi N.D. -neg -neg ornithine -neg proteins -neg arginine citrulline vasodilation, immune stimulation, inhibition of smooth muscle cell growth nitric oxide

29. Ethynyl Estradiol Induction of Hepatic Global Gene Expression • Liver • cDNA/EST microarray

30. Construction and Use of cDNA Arrays cDNA clones in bacteria plasmid DNA PCR tissue/cells agarose gel analysis purified PCR product total RNA array production probe generation by RT labeling informatics db probe hybridization signal detection data analysis

31. Gene Expression Analysis Using cDNA Microarrays Control Treated RNA Isolation Cy5 Cy3 Reverse Transcription Mix cDNAs and Apply to µArray Hybridize Under Coverslip Scan cDNA µArray

32. Current and Future cDNA/EST Arrays Includes ESTs with >70% similarity Mus musculus: dbZach NIA 15K Affy subset Lion Biosciences UniGene build: 113 Homo sapiens: VAI 40K dbZach UniGene build: 154 3636 10656 6432 1162 2625 3472 Rattus norvegicus: Lion Biosciences UniGene build: 106 5801 STRATEGY: Orthologs represented on each array in order to examine in vitro and in vivo extrapolation between species

33. Ethynyl Estradiol Induction of Hepatic Global Gene Expression Summary • growth and proliferation • cytoskeleton and extracellular matrix • monoxygenases, antioxidants • glutathoine transferases • lipid metabolism and transport

34. Hepatic vs. Uterine Global Gene Expression Uterus (Affymetric Mu11KSubA GeneChip) Liver (cDNA/EST microarray) 2,258 unique genes 5,543 unique genes 5,543 genes with LocusLink 2,150 genes with LocusLink 1,318 genes with common LocusLink 979 genes exhibit significant change in expression in at least one tissue • 264 genes only • expressed in liver • 429 genes only • expressed in uterus 693 genes exhibited significant change in only one tissue 286 genes exhibited significant change in both tissues

35. Other Global Gene Expression Comparisons Uterus vs. Liver vs. Mammary Gland vs. Bone • no common ethynyl estradiol elicited gene expression profile • significant differences in gene expression profile kinetics that can not be explained by metabolism Mouse Hep1c1c7 cells vs. Mouse Liver - only 10% overlap in ethynyl estradiol elicited gene expression profiles

36. Summary Ethynyl estradiol gene expression profile overlap between tissues and in vitro vs. in vivo models is minimal Examination of pathways and elucidation of mechanisms will identify biomarkers with greater predictive value e.g. Arginase 1 indicates attenuation of proliferation Several anomalous and absent responses were observed suggesting estrogen receptor-independent mechanisms and post-transcriptional activities Predictive ability of in vitro screens to identify endocrine disruptors with in vivo activity is questionable

37. Future Directions Complementary “omic” technologies e.g. proteomics, metabonomics Phenotypic anchoring e.g. in situ hybridization, immunohistochemistry, histology, clinical chemistry, toxicology Mechanisms/Pathway Discovery e.g. ChIP on Chip, reverse engineering, support vector machines Computational Biology e.g. PBPK, network elucidation, computational modeling Risk Assessment

38. Systems Toxicology The iterative development of computational models that integrate disparate biological (DNA, RNA, protein, protein interactions, biomodules, cells, tissues, etc.), chemical, and toxicological data which can be used to further elucidate the mechanisms of toxicity of a substance as well as support risk assessment.

39. Acknowledgements Chris Gennings Virginia Commonwealth University Jennette Eckel Mayo Clinic

40. Molecular & Genomic Toxicology Lab

41. TOXICOGENOMICS Research Associate, Post Doctoral Fellow and Graduate Student Positions Available • Positions available to investigate: • Gene expression profiles for estrogenic and dioxin-like • chemicals and mixtures using human, rat and mouse • in vitro and in vivo models • Develop bioinformatic and computational resources • (e.g. relational database, analysis tools, modeling) in • support of toxicology studies Further information regarding research activities in the laboratory is available at www.bch.msu.edu/~zacharet

42. Toxicogenomic Positions cont’d These are multifaceted position that will require a highly motivated and well organized individual with excellent writing and verbal communication skills. Knowledge of molecular biology and/or biochemistry is essential. Experience with animal handling, statistical analysis, genomics, bioinformatics, computer programming and database management is highly desirable. Competitive salary, including benefits, will be based on training and experience. Interested individuals are requested to submit a cover letter outlining their research experience, career aspirations, a curriculum vitae and copies of relevant reprints to: Tim Zacharewski, PhD, Michigan State University Department of Biochemistry & Molecular Biology 223 Biochemistry Building, Wilson Road East Lansing, Michigan 48824-1319 USA Tel: (517) 355-1607 Fax: (517) 353-9334 E-mail: tzachare@pilot.msu.edu