Giving insight into how chemicals affect our health.
Comparative Toxicogenomics Database

Contents

1. Overview
2. Support
3. Data Categories
4. Footnotes

Top Overview

CTD advances understanding of the effects of environmental chemicals on human health.

The etiology of most chronic diseases involves interactions between environmental factors and genes that modulate important physiological processes.[1],[2] This assumption is supported by the many complex diseases caused by reversible behaviors or avoidable exposures, and by the relatively rare number of diseases attributed to single gene mutations.[2] Environmental factors are implicated in many common conditions such as asthma, cancer, diabetes, hypertension, immune deficiency disorders, and Parkinson’s disease. The molecular mechanisms underlying these correlations, however, are not well understood.[3]

CTD includes curated data describing cross-species chemical–gene/protein interactions and chemical– and gene–disease associations to illuminate molecular mechanisms underlying variable susceptibility and environmentally influenced diseases. These data will also provide insights into complex chemical–gene and protein interaction networks.

Top Support

CTD is a publicly available research resource. It is being developed at the North Carolina State University (NCSU). The development team comprises individuals located at NCSU and the Mount Desert Island Biological Laboratory (MDIBL), as well as several biocurators who work remotely. This program is supported by funds from the National Institute of Environmental Health Sciences (NIEHS) (ES014065, “Comparative Toxicogenomics Database”; R01 ES019604, “Generation of a centralized and integrated resource for exposure data”). Funding…

Top Data Categories

Chemicals

CTD integrates a chemical subset of the Medical Subject Headings (MeSH^®), the hierarchical vocabulary from the U.S. National Library of Medicine. You can view diverse information about chemicals, including chemical structures, curated interacting genes and proteins, curated and inferred disease relationships, and enriched pathways and functional annotations, using the Keyword search or formulating more complex queries.

Genes

The cross-species gene vocabulary (symbols, names, and synonyms) in CTD is derived from the Gene database at the National Center for Biotechnology Information (NCBI), a division of the U.S. National Library of Medicine. You can view diverse information about genes, including curated interacting chemicals, curated and inferred disease relationships, and associated pathways and functional annotations. Gene information can be accessed using the Keyword search or by formulating more complex queries.

Chemical–Gene/Protein Interactions

To improve understanding about the mechanisms of chemical actions, we manually curate chemical–gene and protein interactions in vertebrates and invertebrates from the published literature. These interactions are both direct (e.g., “chemical binds to protein”) and indirect (e.g., “chemical results in increased phosphorylation of a protein” via intermediate events).

Interactions are curated using a hierarchical interaction-type vocabulary that characterizes common physical, regulatory and biochemical interactions between chemicals and genes or proteins. This vocabulary comprises 70 terms including actions (e.g., “binds to”, “imports”), operators that describe the degree of a chemical's effect (e.g., “increases”), and qualifiers that specify the form of the gene or chemical involved in an interaction (e.g., “protein” or “chemical metabolite,” respectively).

You can search chemical–gene interactions directly on the chemical–gene interaction query form, or access them via a gene, chemical, or reference of interest.

Diseases

CTD's “MEDIC” disease vocabulary is a modified subset of descriptors from the “Diseases” category of the U.S. National Library of Medicine (NLM) Medical Subject Headings (MeSH^®), combined with genetic disorders from the Online Mendelian Inheritance in Man^® (OMIM^®) database. CTD curators mapped OMIM diseases to terms within the hierarchical MeSH disease vocabulary to expand our disease representation. This combined vocabulary is used to curate gene–disease and chemical–disease associations. You can browse diseases and use them to formulate gene and reference queries.

Gene–Disease Associations

CTD contains curated and inferred gene–disease associations. Curated gene–disease associations are extracted from the published literature by CTD curators, or are derived from the OMIM database using the mim2gene file from the NCBI Gene database. Inferred associations are established via CTD–curated chemical–gene interactions (e.g., gene A is associated with disease B because gene A has a curated interaction with chemical C, and chemical C has a curated association with disease B). Curated and inferred associations are identified, and help users develop hypotheses about mechanisms underlying environmental diseases.

Inference scores are calculated for all inferred relationships. The inference score reflects the degree of similarity between CTD chemical–gene–disease networks and a similar scale-free random network. The higher the score, the more likely the inference network has atypical connectivity. Many biological networks, such as disease and metabolic networks, have been shown to be scale-free random networks.[4] The inference score is calculated as the log-transformed product of two common-neighbor statistics used to assess the functional relationships between proteins in a protein–protein interaction network.[5] The first statistic takes into account the connectivity of the chemical and disease along with the number of genes used to make the inference. The second statistic takes into the account the connectivity of each of the genes used to make the inference.

Chemical–Disease Associations

CTD contains curated and inferred chemical–disease associations. Curated chemical–disease associations are extracted from the published literature by CTD curators. Inferred associations are established via CTD–curated chemical–gene interactions (e.g., chemical A is associated with disease B because chemical A has a curated interaction with gene C, and gene C has a curated association with disease B). Curated and inferred associations are identified, and help users develop hypotheses about mechanisms underlying environmental diseases.

References

CTD contains reference articles related to toxicologically significant vertebrate and invertebrate genes, diseases, and associated chemicals. References were identified by information retrieval methods and comprise a subset of MEDLINE ^®/PubMed^®, a database of the U.S. National Library of Medicine.

Organisms

CTD's hierarchical organism vocabulary consists of the Eumetazoa (vertebrates and invertebrates) branch of the Taxonomy Database from the National Center for Biotechnology Information (NCBI), a division of the U.S. National Library of Medicine. You can browse organisms and use them to formulate gene, interaction, and reference queries.

Gene Ontology

Gene Ontology (GO) annotations are integrated with gene data in CTD. In addition, GO terms that are statistically enriched among genes/proteins that interact with a chemical are displayed for each chemical. You can browse GO and use it to formulate gene and interaction queries.

Pathways

KEGG and REACTOME pathway data describe known molecular interaction and reaction networks. These data are integrated with chemicals, genes, and diseases in CTD to provide insights into molecular networks that may be affected by chemicals, and possible mechanisms underlying environmental diseases. You can browse pathways, and use KEGG or REACTOME pathway names or IDs to formulate gene and interaction queries. Pathway information is provided for chemical, gene, and disease detail pages. Pathways that are statistically enriched among genes/proteins that interact with a chemical are displayed for each chemical.

Exposures

We are working to enhance the capacity to identify environment–disease connections by developing an Exposure Ontology that will be used to curate and integrate exposure data into CTD. More…

Top Footnotes

[1]

Schwartz DA, Freedman JH, Linney EA. Environmental genomics: a key to understanding biology, pathophysiology and disease. Hum Mol Genet. 2004 Oct 1;13 Spec No 2:R217-24. [PMID:15358728]

[2]

Olden K, Wilson S. Environmental health and genomics: visions and implications. Nat Rev Genet. 2000 Nov;1(2):149-53. [PMID:11253655]

[3]

Toscano WA, Oehlke KP. Systems biology: new approaches to old environmental health problems. Int J Environ Res Public Health. 2005 Apr;2(1):4-9. [PMID:16705795]

[4]

Barabasi AL, Albert R. Emergence of scaling in random networks. Science. 1999 Oct 15;286(5439):509-12. [PMID:10521342]

[5]

Li H, Liang S. Local network topology in human protein interaction data predicts functional association. PLoS One. 2009 Jul 29;4(7):e6410. [PMID:19641626]