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CTD: a resource for predicting chemical-gene- disease networks Carolyn Mattingly North Carolina State University

CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University

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Page 1: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University

CTD: a resource for predicting chemical-gene-disease networks

Carolyn MattinglyNorth Carolina State University

Page 2: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University

Chemical landscape

• > 60,000

• ~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

Page 3: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University

Crofton et al. Congenital Anomalies 2012; 52, 140–146

Page 4: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University

Many disorders are on the rise

• Leukemia

• Cancers (Brain, breast, childhood)

• Asthma

• Fertility and full term pregnancies

• Birth defects

• Autism

Page 5: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University

How does the environment

affect our health?

Page 6: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University

Environment(chemicals)

Disease

Page 7: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University
Page 8: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University

ABC

Evans and Rzhetsky, Science, 329:399 (2010)

AB

+

BC

Page 9: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University

C-G interactions

C-D Relationships

G-D Relationships

Page 10: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University

Breast Cancer163 Genes

5,896 Genes 57 curated>1,000 inferred

Page 11: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University

http://ctdbase.org

Page 12: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University
Page 13: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University

+

Page 14: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University
Page 15: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University
Page 16: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University
Page 17: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University

PLoS One. 2012;7(11):e46524.

Page 18: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University

Chemical-disease pathways

Page 19: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University
Page 20: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University
Page 21: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University
Page 22: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University
Page 23: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University

CTD Data (June 2013) Status

Curated Chemicals 12,985

Curated Genes 32,464

Curated Diseases 6,354

Chemical-Gene Interactions 869,902

Unique Chemicals 10,150

Unique Genes 31,344

Unique Organisms 492

Curated Gene-Disease Relationship 27,397

Curated Chemical-Disease Relationships 186,986

>300 manuscripts using CTD data>30 public databases incorporating CTD data

Page 24: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University

Exposure data

•Grounding CTD data in “real-world” exposure data

•Centralizing exposure data

• Integrating exposure information with other biological data

Page 25: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University

Exposure StressorExposure Receptor

Interacts with

Exposure Event

via

Exposure Outcome

to result in

ExO

Page 26: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University

Mattingly CJ, McKone TE, Callahan MA, Blake JA, Hubal EA. Environ Sci Technol. 2012. 46(6):3046.

•Status •~50 data points captured

•~3,000 priority articles: 600 curated, 16,000 exposure statements, 300 unique chemicals, 100 diseases

Exposure data

Page 27: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University

Exposure data

Page 28: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University

Basics Exposure Studies

bisphenol A

Exposure Levels

The following are measured exposure levels of bisphenol A or its descendants curated from various Exposure Studies.Click on DETAILS to find more information about the measurement and conditions of the study.

displays the actual

measurements curated from

multiple papers, providing a view of the different

ranges

displays the actual

measurements curated from

multiple papers, providing a view of the different

ranges

data displayed if chemical or its descendant is in the

“Stressor” or “Biomarker” column

data displayed if chemical or its descendant is in the

“Stressor” or “Biomarker” column

Page 29: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University

Phenotype-disease curation

HypertensionHypotensionArrhythmias, Cardiac

Blood PressureHeart RateVasoconstriction

DiseasePhenotype

Page 30: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University

Phenotype-disease analysis

Page 31: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University

Nagiza Samatova. NCSU

Phenotype-disease analysis

Page 32: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University
Page 33: CTD: a resource for predicting chemical-gene-disease networks Carolyn Mattingly North Carolina State University

Scientific Curators

Allan Peter Davis

Cindy Murphy

Cynthia Richards

Kelley Lennon-Hopkins

Daniela Sciaky

Jean Lay

Scientific Software Engineers

Michael C. Rosenstein

Thomas Wiegers

Biostatistician

Benjamin King, MS

System Administrator

Roy McMorran

Funding

Acknowledgements