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The Comparative Toxicogenomics Database (CTD):

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

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

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

7 Prioritizing Curation
EPA Superfund EPA ToxCast NTP Users/Collaborators

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

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

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

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

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

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

20 Chemical-disease inferences
~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

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

22 Geometric Cvw for “Real” C-D Inferences
Geometric Cvw for shuffled C-D Inferences

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

24 Tools

25 Tools

26 Tools

27 Tools: VennViewer 127 1357 118 4 76 21 Interacting Genes/Proteins
Folic acid Arsenicals Pathways 4 76 21 Folic acid Arsenicals

28 Tools

29 MDIBL: Effects of arsenic on immune function
64 1689 20 Array data CTD data

30 MDIBL: Effects of arsenic on immune function
64 1689 20 Array data CTD data 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: /ehp

31 NIEHS: Identifying chemical-gene-disease networks
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.

32 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) 2096 Chemicals 213 Genes 213 Genes Autism 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 Curating exposure data
Develop exposure ontology Define scope of data to be curated Test curation protocol Curate and integrate data in CTD Exposure data Diseases Genes Chemicals chemical-disease relationships chemical-gene interactions gene-disease

36 Acknowledgements Allan Peter Davis, PhD Cindy Murphy, PhD
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) Zebrafish work Antonio Planchart, PhD Thomas Hampton (Dartmouth) Funding NIEHS AND NLM (ES014065) NCRR (RR016463) Contact Us!


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