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Environmental Factor

February 2011

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NIEHS-supported database joins TOXNET

By Eddy Ball
February 2011

NIEHS grantee Carolyn Mattingly, Ph.D.

Mattingly said of her work with CTD, "This effort is significant, because there are a tremendous number of chemicals in commerce..., [and] we really don't understand their mechanisms of action or their consequences on our health." (Photo courtesy of Carolyn Mattingly)

A new report from a team of scientists led by NIEHS grantee Carolyn Mattingly, Ph.D., presents an update on a massive public database supported in part by NIEHS that is now a part of TOXNET (TOXicology Data NETwork)( Exit NIEHS, the U.S. National Library of Medicine's portal for toxicology data.

According to Mattingly, the new resource, known as the Comparative Toxicogenomics Database (CTD)( Exit NIEHS, is a powerful tool that will contribute substantively to the emerging field of predictive toxicology by providing curated data from the literature to enhance understanding about the connections between chemicals, genes and proteins, and diseases.

Funded in part by an NIEHS grant( awarded in 2005, CTD was developed by scientists at the Mount Desert Island Biological Laboratory (MDIBL)( Exit NIEHS in Maine, where Mattingly is an associate professor. The project receives additional funding from NIH's National Center for Research Resources (NCRR) and through a collaborative research project with the pharmaceutical giant Pfizer to investigate 1,500 chemical compounds that may be useful in drug development.

As Mattingly said in a Nov. 11, 2010 interview with the Bar Harbor (Maine) Times, CTD is an effort to compile and organize increasingly copious amounts of formal research on the relationship between chemicals, genes, and disease (see text box). The database is an attempt to map the complex ways in which chemicals are related to diseases. CTD is part of a larger effort to track the approximately 85,000 chemicals used in American industry - the overwhelming majority of which have never been subject to rigorous testing or regulation.

In the study( Exit NIEHS, which appeared in the January issue of Nucleic Acids Research, Mattingly explained why the CTD is unique among the other resources in the TOXNET suite of integrated databases. "CTD provides detailed information about chemical-gene interactions, chemical-disease relationships, and gene-disease relationships," the CTD team wrote. "By integrating these core data with other datasets, CTD helps turn knowledge into discoveries by identifying novel connections between chemicals, genes, diseases, pathways, and GO [gene ontology] annotations that might not otherwise be apparent using other biological resources."

TOXNET is a cluster of databases covering information on toxicology, hazardous chemicals, environmental health, and other related areas. The network is managed by the Toxicology and Environmental Health Information Program( Exit NIEHS in the Division of Specialized Information Services( Exit NIEHS of the U.S. National Library of Medicine( Exit NIEHS. CTD is the twelfth database in this growing publicly accessible family of databases.

Mattingly's grant is overseen by NIEHS Program Administrator David Balshaw, Ph.D.

Citation: Davis AP, King BL, Mockus S, Murphy CG, Saraceni-Richards C, Rosenstein M, Wiegers T, Mattingly CJ.( Exit NIEHS 2011. The Comparative Toxicogenomics Database: update 2011. Nucleic Acids Res 39(Database issue):D1067-D1072.

Harnessing massive amounts of information with an integrated database

In her Bar Harbor Times interview, Mattingly offered an example of how querying the nearly 300,000 direct and indirect chemical-disease interactions charted in the database could help researchers better understand potential links between a chemical and diseases. Entering "bisphenol A" in the database, for example, will yield connections with many potentially associated diseases, including schizophrenia, leukemia, and melanoma. Researchers can then explore the data more closely as they formulate hypotheses about chemical-disease connections and underlying mechanisms.

Several analysis tools are integrated with the database to facilitate access to and interpretation of data in CTD. For example inferred chemical-disease associations can be ranked statistically and Venn diagrams can be generated, to either compare researchers' data to CTD data or to disease associations or mechanisms of action among chemicals.

According to Mattingly, the integrated data about chemical-gene/protein interactions and chemical- and gene-disease relationships enable scientists to develop novel hypotheses about the origins of environmentally influenced diseases.

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