1. Carolyn J. Mattingly The Mount Desert Island Biological Laboratory Salisbury Cove, Maine The Comparative Toxicogenomics Database (CTD): Predicting mechanisms of toxicity

2. Chemicals in commerce > 80,000 ~2,000 added/year ~8,000 are carcinogens No toxicity data for ~40% of the 3,300 “high production volume” chemicals Full toxicity data for only 25% of chemicals in consumer products

3. Time, 1947

4. DISEASE What’s the relationship between chemicals and disease? Disease Distribution/ Metabolism Cell Death/ Differentiation DNA Repair Cell Cycle Control Genes/Proteins Chemical

5. Exploring environment-gene-disease connections What diseases are associated with Bisphenol A (BPA)? Which BPA-induced genes function during development? What biological functions are affected by BPA? Which molecular pathways are affected by exposure to BPA? Which other chemicals have interaction profiles similar to BPA? What are target genes that are common to BPA and arsenic?

6. Diseases Genes Chemicals Curated Data MeSH® (modified) Entrez-GeneMeSH/OMIM CTD interactions chemical-disease relationships chemical-disease relationships chemical-gene interactions chemical-gene interactions gene-disease relationships gene-disease relationships

7. EPA Superfund EPA ToxCast NTP Users/Collaborators Prioritizing Curation

8. Diseases Genes Chemicals chemical-disease relationships chemical-disease relationships 199,453 9,524 6,143 Curated Data chemical-gene interactions chemical-gene interactions gene-disease relationships gene-disease relationships

9. Diseases Genes Chemicals chemical-disease relationships chemical-disease relationships Integrated Data chemical-gene interactions chemical-gene interactions gene-disease relationships gene-disease relationships

10. Diseases Genes Chemicals chemical-disease relationships chemical-disease relationships Creating inferences chemical-gene interactions chemical-gene interactions gene-disease relationships gene-disease relationships

11. Diseases Genes Chemicals chemical-disease relationships chemical-disease relationships chemical-gene interactions chemical-gene interactions gene-disease relationships gene-disease relationships Creating inferences

18. BPA AGR2… Prostatic Neoplasms AGR2… Inferred chemical-disease relationships

19. Cancer and urologic diseases BPA-Prostate cancer genes Generated using Ingenuity Pathway Analysis

20. ~190,000 transitive inferences between chemicals and diseases Transitive Inference –If ‘A’ interacts with ‘B’ and ‘C’ interacts with ‘B’, then infer that ‘A’ interacts with ‘C’ How to assess which inferences are “good” or not? A B C Chemical-disease inferences

21. g2 g3 g22 … Lung Neoplasms 22 Genes 457 other genes or diseases 139 other Chemicals or genes Bisphenol A and Lung Neoplasms BPA g1 Geometric C vw = |N(v) N(w)| 2 |N(v)|. |N(w)|

22. Geometric C vw for “Real” C-D Inferences Geometric C vw for shuffled C-D Inferences

23. BPA IKBKB… AML IKBKB… Inferred chemical-pathway relationships

24. Tools

27. Tools: VennViewer Interacting Genes/Proteins 1271357118 Folic acid Arsenicals Pathways 47621 Folic acid Arsenicals

28. Tools

29. 0 10 100 64168920 Array dataCTD data MDIBL: Effects of arsenic on immune function

30. 0 10 100 Mattingly, C. J., T. Hampton, K. Brothers, N. E. Griffin and A. J. Planchart (2009). Perturbation of defense pathways by low-dose arsenic exposure in zebrafish embryos. Environ Health Perspect doi:10.1289/ehp.0900555. 64168920 Array dataCTD data MDIBL: Effects of arsenic on immune function

31. Gohlke, J., R. Thomas, Y. Zhang, M. D. Rosenstein, A. P. Davis, C. Murphy, C. J. Mattingly, K. G. Becker and C. J. Portier (2009). The Genetic And Environmental Pathways to Complex Diseases. BMC Syst Biol.May 5 3:46. NIEHS: Identifying chemical-gene-disease networks

32. 2096 Chemicals 213 Genes 213 Genes Autism EPA: Exploring the environmental etiology of autistic disorders Characterizing these chemicals –Structure –Regulatory features (e.g., High production, Carcinogen) –Function (e.g., Associated pathways) –Other associated diseases (e.g., Neurological) Mark Coralles, EPA

33. In Progress Tag Clouds Text mining Statistical analysis of data inferences Gene Ontology enrichment analysis

34. Coming Up Analysis tools and visualization capabilities Integration of additional data sets (SNPs, Chemical codes) Exposure data curation

35. Develop exposure ontology Define scope of data to be curated Test curation protocol Curate and integrate data in CTD Diseases Genes Chemicals chemical-disease relationships chemical-disease relationships chemical-gene interactions chemical-gene interactions gene-disease relationships gene-disease relationships Curating exposure data Exposure data

36. Acknowledgements Scientific Curators Allan Peter Davis, PhD Cindy Murphy, PhD Cynthia Saraceni-Richards, PhD Susan Mockus, PhD Scientific Software Engineers Michael C Rosenstein, JD Thomas Wiegers System Administrator Roy McMorran James L. Boyer, MD (Yale) http://ctd.mdibl.org/ Zebrafish work Antonio Planchart, PhD Thomas Hampton (Dartmouth) Funding NIEHS AND NLM (ES014065) NCRR (RR016463) Contact Us! cmattin@mdibl.org ctd@mdibl.org