TOXICOLOGICAL SCIENCES 92(2), 587–595 (2006)
doi:10.1093/toxsci/kfl008
Advance Access publication May 4, 2006
The Comparative Toxicogenomics Database: A Cross-Species
Resource for Building Chemical-Gene Interaction Networks
Carolyn J. Mattingly,*,1 Michael C. Rosenstein,* Allan Peter Davis,* Glenn T. Colby,*
John N. Forrest Jr,† and James L. Boyer†
*Department of Bioinformatics, Mount Desert Island Biological Laboratory, Salisbury Cove, Maine 04672; and
†Department of Medicine, Yale University School of Medicine, New Haven, Connecticut 06520
Received February 16, 2006; accepted April 29, 2006
Chemicals in the environment play a critical role in the etiology
of many human diseases. Despite their prevalence, the molecular
mechanisms of action and the effects of chemicals on susceptibility to disease are not well understood. To promote understanding
of these mechanisms, the Comparative Toxicogenomics Database
(CTD; http://ctd.mdibl.org/) presents scientifically reviewed and
curated information on chemicals, relevant genes and proteins,
and their interactions in vertebrates and invertebrates. CTD
integrates sequence, reference, species, microarray, and general
toxicology information to provide a unique centralized resource
for toxicogenomic research. The database also provides visualization capabilities that enable cross-species comparisons of gene and
protein sequences. These comparisons will facilitate understanding of structure-function correlations and the genetic basis of
susceptibility. Manual curation and integration of cross-species
chemical-gene and chemical-protein interactions from the literature are now underway. These data will provide information for
building complex interaction networks. New CTD features include (1) cross-species gene, rather than sequence, query and
visualization capabilities; (2) integrated cross-links to microarray
data from chemicals, genes, and sequences in CTD; (3) a reference
set related to chemical-gene and protein interactions identified by
an information retrieval system; and (4) a ‘‘Chemicals in the
News’’ initiative that provides links from CTD chemicals to
environmental health articles from the popular press. Here we
describe these new features and our novel cross-species curation of
chemical-gene and chemical-protein interactions.
Key Words: CTD; chemicals; environment; GO; toxicology;
pathways; interactions.
The etiology of most chronic diseases involves interactions
between environmental factors and genes that modulate important physiological processes (Olden and Wilson, 2000; Schwartz
et al., 2004). This assumption is supported by the many complex diseases caused by reversible behaviors or avoidable
1
To whom correspondence should be addressed at Department of Bioinformatics, Mount Desert Island Biological Laboratory, Old Bar Harbor Road,
Salisbury Cove, ME 04672. Fax: (207) 288-2130. E-mail: [email protected].
exposures and the relatively rare number of diseases attributed
to single gene mutations (Schwartz et al., 2004). For example,
environmental factors are implicated in common conditions
such as asthma, cancer, diabetes, hypertension, immune deficiency disorders, and Parkinson’s disease; however, the molecular mechanisms underlying these correlations are not well
understood (Toscano and Oehlke, 2004).
Diverse model organisms and their sequence data can
provide key insights into the molecular mechanisms of action
of environmental chemicals. Cross-species comparisons of toxicologically important genes present opportunities for associating sequence variation with functional differences (Hahn,
1998, 2002; Powell and Hahn, 2000). For example, lethal dose
studies show that the hamster is acutely sensitive to toxicity
induced by tetrachlorodibenzo-p-dioxin (TCDD), whereas the
guinea pig is nearly resistant. It is well established that TCDD
exerts its action by binding to the aryl hydrocarbon receptor
(AHR), and AHR protein sequence comparisons between hamsters and guinea pigs identified an expanded glutamine-rich
domain in the C-terminus that appears to correlate with reduced
toxicity (Korkalainen et al., 2001). Some differences in TCDD
toxicity in birds can be explained by differences in TCDDAHR–binding affinity (Hahn, 2002). Invertebrates and ancient
vertebrates exhibit poor AHR-binding affinity for TCDD and
are relatively insensitive to toxicity, whereas most vertebrate
AHR proteins have high binding affinities that correlate with
toxicity. Cross-species functional and sequence-based studies
like these are only beginning to be exploited and present important opportunities for understanding toxicity and differences
in responses among individuals and organisms to chemical
exposures.
Although studies of individual genes are valuable for
understanding function in a toxicological context, it is well
accepted that genes and their proteins do not function in
isolation, but rather as components of larger networks (Vidal,
2005). Similarly, chemicals affect larger networks and not just
individual genes or proteins. The richest sources of information
about chemical interactions are biomedical literature and highthroughput technologies such as microarrays. Traditionally,
The Author 2006. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved.
For Permissions, please email: [email protected]
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results described in the literature have been generated from
reductionist (one gene at a time) experimental approaches.
The emergence of high-throughput technologies holds considerable promise for toxicology. Microarrays provide transcriptional snapshots of many genes simultaneously in response to
exposures; however, they do not provide information about
the many significant nontranscriptional interactions between
chemicals, genes, and proteins such as binding or posttranslational modifications like phosphorylation. The Comparative
Toxicogenomics Database (CTD; http://ctd.mdibl.org/) is combining cross-species reductionist data from the literature with
data from high-throughput studies in an effort to improve
understanding about the molecular actions of chemicals
(Mattingly et al., 2004).
The abundance of sequence data has created a growing
need for computational tools and biological databases. Among
the existing public databases, only three, other than CTD,
exclusively address aspects of chemical-gene interactions: the
Pharmacogenetics and Pharmacogenomics Knowledge Base
(Abernethy et al., 2003) and two resources for toxicologically
relevant microarray data—the Environment, Drugs and Gene
Expression (EDGE2; http://edge.oncology.wisc.edu/edge.php)
database (Hayes et al., 2005) and the Chemical Effects in
Biological Systems (CEBS) Knowledge Base (Waters et al.,
2003). These resources address complementary, but nonoverlapping aspects of toxicogenomics. CTD is unique in its curation
and integration of chemical-gene interactions and genes of
toxicological significance in vertebrates and invertebrates. These
data may elucidate structure-function correlations, contribute to
building chemical-gene and protein interaction networks, and
provide insights into the basis of differential susceptibility.
METHODS AND RESULTS
Data Integration and Curation
Major types of data integrated in CTD include (1) nucleotide and protein
sequences; (2) published references; (3) curated genes and gene sets (sets of
curated genes and proteins); (4) a hierarchical vocabulary of organisms; (5)
a hierarchical vocabulary of chemicals; and (6) the Gene Ontology (GO;
hierarchical vocabulary of biological processes, cellular components, and
molecular functions) (Fig. 1). Through integrated cross-references, CTD also
centralizes access to additional molecular and toxicology data, including
FIG. 1. High-level view of the primary data types in CTD.
microarray data, and articles in the popular press about the effects of
environmental chemicals on health.
Sequences
CTD currently contains 1.2 million nucleotide and protein sequences for
vertebrates and invertebrates, which enable broad cross-species sequence
comparisons. Nucleotide sequences and annotations are acquired from GenBank
(Benson et al., 2005) at the National Center for Biotechnology Information
(NCBI; http://www.ncbi.nlm.nih.gov/). However, to minimize nucleotide sequence redundancy, we include only reference sequences (RefSeqs) for Homo
sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, and
Caenorhabditis elegans (Pruitt et al., 2005). Amino acid sequences and
annotations are acquired from the European Bioinformatics Institute’s UniProtKB Swiss-Prot and TrEMBL databases (Bairoch et al., 2005). Select
annotations included in sequence records, such as GO and protein domain
information, are also integrated in CTD. CTD data load and migration software
downloads, processes, and validates sequence data from NCBI and UniProt
before uploading to CTD. Sequences will be updated weekly in the future.
References and Information Retrieval
An information retrieval system was developed to identify relevant
references from PubMed (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db¼
PubMed) for inclusion in CTD (along with genes, proteins, and chemicals
associated with these references). The information retrieval system searches
abstracts and titles for co-occurrences of chemical and gene terms from CTD
vocabularies. These references replaced the original CTD data set, which
consisted only of references cited in sequence records. We are initially prioritizing the scope of literature to genes identified as environmentally responsive
by the National Institute of Environmental Health Sciences (NIEHS) Environmental Genome Project (Olden and Wilson, 2000). These genes provide
a guideline for prioritizing genes and are not intended to be a comprehensive set
of environmentally responsive genes. Our curation efforts will extend to
additional genes over time. Currently, the CTD gene vocabulary used for
information retrieval includes 1000 terms (names, symbols, and synonyms) for
over 120 genes. The chemical vocabulary for information retrieval derives from
a modified subset of the National Library of Medicine’s Medical Subject
Headings for indexing MEDLINE/PubMed (http://www.nlm.nih.gov/mesh/
meshhome.html; Nelson et al., 2001). The CTD chemical vocabulary includes
approximately 200,000 terms (names, symbols, and synonyms). By searching
references with multiple, large vocabularies, we are compiling a more comprehensive literature set that is relevant to chemical-gene and protein interactions
than would be possible through commonly used Web-based search options.
Figure 2 illustrates an overview of the information retrieval process. The
steps in the figure are described below and correspond to headings in the text.
Curation. Gene terms. Nomenclature inconsistencies for genes and
other concepts present a major challenge to searching toxicogenomics data
(Wakefield, 2003). Although nomenclature committees for several model organisms are establishing official gene symbols and names to promote consistency,
challenges persist because authors do not always use these terms, historical data
has not been amended to reflect new standards, and many organisms are without
nomenclature standards. Therefore, to perform a comprehensive search of
biomedical text for genes across diverse species, we compiled cross-species lists
of current and historical gene terms for genes of interest. Gene symbols and
names were compiled from the Human Genome Organization (HUGO; http://
www.gene.ucl.ac.uk/nomenclature), NCBI’s curated Gene database (Maglott
et al., 2005), and vertebrate and invertebrate sequence records in CTD, which
originate from the NCBI GenBank and Uniprot databases. All gene terms were
reviewed manually, and variation that may appear in biomedical text was added.
For example, the gene Cytochrome P450, family 1, subfamily A, polypeptide 1
has an official (HUGO) symbol of CYP1A1, but it may also appear in biomedical
text as ‘‘CYP 1A1,’’ ‘‘CYP1 A1,’’ ‘‘CYP1A-1,’’ ‘‘CYPIA1,’’ etc.
Negative gene terms. Many gene terms represent different concepts in
other contexts. For example, the human aryl hydrocarbon receptor gene is
CTD IS A CROSS-SPECIES RESOURCE FOR BUILDING CHEMICAL-GENE INTERACTION NETWORK
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FIG. 2. Overview of CTD information retrieval process. White objects represent manual or automated processes. Dark gray objects represent physical objects
(files or databases).
represented by the symbol AHR; however, AHR can also represent other
concepts such as acute hypoxic response, adjusted hazard ratio, airway
hyperresponsiveness, or anaerobic hybrid reactor. Such homonymous terms
often cause false-positive results when searching text. Therefore, we maintain
a list of negative gene terms for specific genes. References with titles or
abstracts containing both a gene term and one of its associated negative gene
terms were not retrieved for inclusion in CTD.
a type of 2,4-dichlorophenoxacetic acid, but in biomedical text, the term may
also represent a neurokinin, a tachykinin receptor, or a transcription factor.
Some chemical terms are also common English words (e.g., the heavy metal
lead vs. the verb lead, to guide). Therefore, we maintained a list of negative
chemical terms for specific chemicals. References with titles or abstracts
containing any of the chemical terms and any of the negative chemical terms
for the chemical were not retrieved for inclusion in CTD.
Negative chemical terms. Chemical terms, like gene terms, may also
represent different concepts in other contexts. For example, NK-2 represents
Excluded chemicals and synonyms. Many higher-level chemicals in our
hierarchical chemical vocabulary are simply too broad for identifying candidate
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references for curation (e.g., oils, solvents, chemicals, elements, gases, etc.).
Many chemical terms in CTD have synonyms (provided by NLM MeSH) that are
either common English words or are used to refer to other concepts (e.g., the
nitroimidazole drug metronidazole synonym Metric, a brand name, vs. the word
metric, a standard of measurement). We, therefore, maintained a list of excluded
chemicals and chemical synonyms that were not used in the searching process.
Retrieval. Query PubMed by gene terms. For each gene of interest, we
constructed an NCBI PubMed search to retrieve references that include any of
the gene terms. The ‘‘NOT’’ qualifier was used in conjunction with negative
gene terms. The ‘‘TIAB’’ qualifier was added to each term to limit the search to
titles and abstracts. A publication date range was specified using the Entrez
Programming Utilities (Wheeler et al., 2002).
Candidate references. Each search string was submitted to PubMed using
the NCBI EFetch tool (http://www.ncbi.nlm.nih.gov/entrez/query/static/
efetch_help.html), and a list of PubMed identifiers (IDs) was returned. Full
PubMed records of the references were then downloaded in MEDLINE format,
parsed using BioPython (http://www.biopython.org), and titles and abstracts
were extracted and combined as candidate references.
Tokenization. For candidate references, gene terms, and chemical terms,
we defined tokens as strings of alphanumeric characters separated by spaces or
punctuation. Our tokenization used the freely available MontyLingua package
(web.media.mit.edu/~hugo/montylingua) to add spaces before and after all
tokens to make punctuation consistent between the candidate references, gene
terms, and chemical terms. Hyphens were not considered to be punctuation
since doing so led to undesirable search results (e.g., deeming the hyphen in
‘‘AHR-like protein’’ to be punctuation would cause that term to be treated
erroneously as the gene AHR). Brackets were replaced by parentheses since
their usage is often inconsistent (e.g., ‘‘benzo[a]pyrene’’ vs. ‘‘benzo(a)pyrene’’).
All text was converted to uppercase and written to files for searching.
Searching. For each tokenized gene term, we searched for references that
contained the gene term. The output of the command was written to a genearticle file with each line containing the PubMed ID, official gene symbol,
matching gene term, and tokenized article.
For each tokenized chemical term, we searched for lines within the genearticle file that contained the tokenized chemical term. The output was parsed
using Python to write the following fields to a tab-delimited text file: PubMed
ID, official gene symbol, matching gene term, MeSH unique identifier, CTD
chemical name, and matching chemical term.
Processing. Remove embedded chemical terms. Some chemical terms
are embedded within other chemical terms (e.g., chloride within sodium
chloride). In such cases, our search strategy returned both chemical terms in the
results. Although it was possible that an article discussed both, our results
suggested that it was more likely that only the longer chemical term was
included and the shorter term matched simply because it was embedded within
the longer term. Therefore, prior to manual validation, we eliminated assignments to chemical terms that are embedded in other matching chemical terms.
Chemical-gene report. To make manual validation efficient, a chemicalgene report was created that included hypertext links for PubMed references
and MeSH terms, matching gene and chemical terms for each article, and other
related fields. The report was loaded into a spreadsheet application and
validated by CTD curators.
Curator review. During development of this system, scientific curators read
every abstract from a test set of retrieved references, evaluated the accuracy of
chemical and gene identifications, and recorded if true interactions between
these chemicals and genes were reported in the references. This review process
indicated that the information retrieval system achieved a 97–100% recall rate
(number of relevant documents identified compared to all relevant documents).
References and associated genes and chemicals in CTD are selectively
reviewed prior to inclusion in CTD. Validated chemical-gene interactions are
being manually curated from these references on an ongoing basis and will
begin to be integrated with CTD this year (see the ‘‘Discussion’’ and ‘‘Future
Directions’’ sections).
Remove higher-order chemicals. Some retrieved references discussed
a chemical that was simply a broader term for another chemical also discussed
in the article. In such cases, we identified the most specific chemical-gene
records in each article prior to loading the results into CTD and performing
subsequent manual curation of the references. For example, if a reference
mentioned dioxins as well as TCDD (a type of dioxin), we loaded only the
TCDD association into CTD. Since the CTD chemical vocabulary is
hierarchical, visitors may still search for dioxins to retrieve TCDD chemicalgene interactions.
Hierarchical Vocabularies
We integrate three controlled hierarchical vocabularies for organisms,
chemicals, and the GO (Harris et al., 2004) with data in CTD.
The organism vocabulary consists of the Eumetazoa portion (vertebrates and
invertebrates) of the NCBI Taxonomy vocabulary (Wheeler et al., 2002). This
vocabulary includes approximately 83,000 terms.
The chemical vocabulary derives from NLM’s MeSH vocabulary (Nelson
et al., 2001) and includes a modified subset of chemicals and drugs descriptors,
entry terms, and supplementary concepts. In MeSH, descriptors are the primary
terms used to represent concepts (e.g., arsenates). Entry terms are synonyms
and closely related terms. Supplementary concepts are a separate vocabulary
that consists predominantly of specific chemicals and drugs (e.g., arsenic acid).
These chemicals are ‘‘mapped to’’ descriptors in the main vocabulary (Nelson
et al., 2001). In CTD, supplementary concepts are integrated as children of
those descriptors to create a single hierarchical vocabulary.
The GO project is a collaborative effort to address the need for consistent
descriptions of gene products in different databases (Harris et al., 2004). GO
describes gene products in terms of their associated molecular functions,
biological processes, and cellular components in a species-independent
manner. Molecular functions describe activities such as catalytic or binding
activities. Biological processes describe events accomplished by one or more
ordered assemblies of molecular functions (e.g., signal transduction). A cellular
component is a component of a cell (e.g., nucleus) or a gene product group
(e.g., protein dimer). GO terms are organized in hierarchical structures called
directed acyclic graphs (DAGs). In a DAG, each term can have more than one
parent. In addition, each term can have one of two different types of
relationships with a parent: either ‘‘is a’’ or ‘‘part of’’ (e.g., immune response
is a defense response and is an organismal physiological process). The entire
GO vocabulary of approximately 19,000 terms is integrated in CTD.
Integration of organism, chemical, and GO vocabularies enables important
features in CTD. These vocabularies facilitate data integration within CTD and
allow visitors to make complex connections between diverse data using consistent
terminology. Individually, terms from each of these vocabularies can be used to
access reference, chemical, gene, and sequence data. However, when terms from
these vocabularies are used in combination, the complexity and specificity of
toxicogenomic questions that can be answered with CTD is significantly increased
(e.g., ‘‘what kinases [GO molecular function term] are affected by arsenic
[chemical term]?’’ or ‘‘which plasma membrane proteins [GO cellular component
term] are affected by hydrocarbons [chemical term] in teleosts [organism
term]?’’). The vocabularies include synonyms, which facilitate identification of
relevant references for CTD, and also allow visitors to retrieve the same data with
different but related terms (e.g., ‘‘TCDD’’ and ‘‘2,3,7,8-tetrachlorodibenzo-pdioxin’’ or ‘‘zebrafish’’ and ‘‘Danio rerio’’). The hierarchical structure of the
vocabularies gives visitors flexibility to retrieve information at broad and narrow
levels of specificity (e.g., ‘‘heavy metals’’ vs. ‘‘mercury’’ or ‘‘teleostei’’ vs.
‘‘zebrafish,’’ respectively). Finally, these vocabularies enable links to be made with
corresponding data in other resources. For example, detail pages for specific
chemical terms provide cross-references to related toxicology data in the
Environmental Protection Agency and NLM’s TOXNET databases and to
microarray data in EDGE2. These cross-references centralize access to what is
otherwise disconnected but complementary data in the public domain. The
organism, chemical, and GO vocabularies are used by other biological databases;
however, their combined implementation in CTD is unique because it facilitates
access to cross-species data directly relevant to toxicogenomics.
CTD IS A CROSS-SPECIES RESOURCE FOR BUILDING CHEMICAL-GENE INTERACTION NETWORK
Genes and Gene Sets
Cross-species genes are defined in CTD by their constituent nucleotide and
protein sequences from vertebrates and invertebrates. Curated genes direct
visitors to toxicologically relevant gene, protein, and cross-species sequence
data (DNA, mRNA, protein) that facilitate comparative sequence analyses.
Sequences for cross-species genes are curated using sequence analysis
methods in combination with literature review. Briefly, human nucleotide
and protein ‘‘reference sequences’’ are identified from the NCBI RefSeq and
Swiss-Prot, respectively. These ‘‘seed’’ sequences undergo reciprocal BLAST
analysis to identify ‘‘best hit’’ sequences. In some cases, our curation process
identifies genes that are highly similar but distinct in nonhuman species that are
included in CTD (e.g., avian CYP1A5). These genes are curated because they
often share toxicologically similar functions, and they augment CTD’s unique
comparative value. Whenever possible, genes are tagged with the human
gene name according to the HUGO Gene Nomenclature Committee (HGNC;
http://www.gene.ucl.ac.uk/nomenclature/) to promote nomenclature consistency. Names that appear in the sequence source records are collected and
made searchable as synonyms. These synonyms are critical for allowing
visitors to find gene data across species despite nomenclature differences.
They are also used to increase the effectiveness of information retrieval in
identifying relevant references from PubMed for inclusion in CTD. Determining definitive orthology of sequences remains a major challenge, particularly among sequences from large families or that have undergone
significant evolutionary divergence. Curated genes in CTD are intended to
cluster highly similar sequences and associated data to facilitate subsequent
visitor analysis.
Gene set is a novel feature of CTD that groups closely related curated genes
according to relationships defined in the literature such as species-specific gene
duplications (e.g., CYP1A4 and CYP1A5) or family members (e.g., ATPbinding cassette family). They provide visitors with broader perspective about
their gene of interest. For example, the CYP1A gene set, which includes the
curated genes CYP1A1, CYP1A2, CYP1A3, CYP1A4, and CYP1A5, combines information about mammalian-, teleostei-, and avian-specific genes.
Where applicable, the HGNC Gene Grouping/Family Nomenclature is used to
name gene sets to promote nomenclature consistency.
Cross-References to Other Databases
CTD centralizes access to interdisciplinary information essential to
toxicogenomics by linking data in CTD to 26 other biological databases. A
compendium is also provided to point visitors to over 40 databases with
information related to sequences, protein structures, literature, species, and
traditional toxicology.
We recently introduced cross-references from sequence, gene, and chemical
data in CTD to microarray data sets. Microarrays allow simultaneous
measurement of transcriptional changes for thousands of genes under different
exposure conditions, and these data will substantially augment the scope of
chemical-gene transcriptional interactions in CTD. We targeted four public
databases as sources for microarray data: (1) EDGE2 (Hayes et al., 2005); (2)
CEBS Knowledge Base (Waters et al., 2003); (3) ArrayExpress (Parkinson
et al., 2005); and (4) Gene Expression Omnibus (GEO; Barrett et al., 2005).
Currently, CTD is only cross-linked to EDGE2 because of the structure,
consistent relevance, and availability of data in this database. Establishment of
cross-references to CEBS is planned and pending required data from that group.
ArrayExpress and GEO do not have an exclusive toxicological focus.
Adaptation of our reference information retrieval system may assist with
identification of relevant data sets from these databases in the future.
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associated chemical detail pages. Currently, 139 chemicals in CTD have one or
more links to such articles.
Data Searching and Visualization
The CTD Web interface is designed to provide access to chemical, gene,
sequence, and reference data from a range of perspectives. Where possible, data
are presented in a unique, cross-species context. From the CTD home page,
visitors can find information using the new gene query form, reference query
form, or by using the chemical vocabulary browser.
Gene Query and Results
Major modifications were made to CTD to introduce querying and
visualization of cross-species sequence and chemical data from a gene
perspective. Now, visitors may retrieve information about genes using the
gene query form by gene, gene set, chemical, GO term, organism, and sequence
accession ID. Vocabulary browsers for chemicals, GO terms, and organisms are
provided to help visitors construct queries. Complex queries using combinations of these fields are possible. For example, a query for genes with receptor
activity (GO term) that are affected by 2,3,7,8-tetrachlorodibenzo-p-dioxin
(chemical term) retrieves a list of 22 unique genes. Each retrieved gene may be
selected to access supplementary information on a gene detail page. Results
are retrieved based on relevance of associated references, sequences, and sequence annotations in CTD. Gene results may also be restricted to those with
associated microarray data.
Detail pages for individual genes display numerous categories of data
including basic information about the gene, references and cited chemicals, GO
annotations, sequences, and microarray data. The basic information section
provides the name of the gene, cross-species synonyms, and a link to a gene set,
where curated. A set of references and chemicals cited in the references are
displayed to provide a preliminary chemical-gene association. A GO annotation
summary lists all GO terms annotated to member sequences. Additional details
about GO annotations, including the source sequence and organism for each
term and corresponding evidence codes, are accessible through term-related
links or linked full reports. Member vertebrate and invertebrate sequences for
the gene are provided and organized by organism and type (DNA, mRNA, or
protein). Visitors may view or download selected FASTA-formatted sequences.
A microarray data section provides links to microarray experiments in EDGE
containing information about member sequences. All references, chemicals,
GO terms, and sequences on a gene page are linked to corresponding detail
pages.
As we curate and integrate gene sets in CTD, new gene set detail pages
become available. These pages include descriptions of member genes,
synonyms for each member gene, and all vertebrate and invertebrate sequences
for member genes organized by gene and organism. Visitors may also view or
download files with FASTA-formatted sequences for the gene set and
corresponding multiple alignments and phylogenetic trees built using these
sequences. Details about analysis parameters are also provided.
Reference Query and Results
The CTD reference query form allows visitors to search for references by
chemical, gene, organism, author, or citation information. For each resulting
reference, citations and cited chemicals and genes are presented. Cited
chemicals and genes are identified from MeSH annotation of references and
by the CTD information retrieval method, which searches titles and abstracts
using our chemical and gene vocabularies. This presentation allows visitors to
efficiently identify references of greatest interest to them.
Chemicals in the News
Links are now provided from chemical data in CTD to environmental health
articles from the popular press. Curators review and assign articles identified
through the Environmental Health News service (http://www.Environmental
HealthNews.org) to chemicals in CTD on a weekly basis. Visitors may access
these links via a ‘‘Chemicals in the News’’ section on the CTD home page or
Chemical Query and Results
Visitors may also query data in CTD from a chemical perspective using the
chemical browser. The browser consists of an orientation page, a query
mechanism, and individual detail pages for terms. Detail pages for each
chemical provide a valuable and unique synthesis of molecular and traditional
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FIG. 3. Sample CTD chemical detail page. The CTD chemical detail page provides a description, synonyms and Chemical Abstracts Service (CAS) name,
a chemical structure drawing, links to supplemental data in other databases such as microarray data, and access to associated genes and references in CTD.
toxicology information about a chemical term (Fig. 3). Pages include names
and synonyms, a description, a structural drawing, hyperlinks to associated
genes and references in CTD and related data in other chemical and microarray
databases, and the ability to navigate to data associated with more general or
specific related chemicals or classes of chemicals.
DISCUSSION
In CTD, we present associations between chemicals, genes,
and proteins based on the published literature, provide a filtered
set of relevant references, integrate high-throughput experimental data, curate toxicologically important genes and their
proteins, and provide visualization capabilities that facilitate
cross-species sequence comparisons of genes and proteins.
These comparisons will enhance understanding about the
function of these genes and proteins and the molecular basis
of differential susceptibility. CTD centralizes data that are core
to toxicogenomics by integration and manual review of diverse
molecular, reference, and chemical data.
Although many chemical-gene interactions have yet to be
described in the literature, it still remains the most valuable
source for detailed functional data. High-throughput techniques like microarrays are beginning to provide insights into the
range of genes that may be affected by chemical exposures, but
CTD IS A CROSS-SPECIES RESOURCE FOR BUILDING CHEMICAL-GENE INTERACTION NETWORK
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FIG. 4. Chemical-gene interaction network schematic. Cross-species interactions between chemicals and genes and proteins are curated manually from the
literature for CTD. These data will be important for building complex interaction networks. This high-level schematic represents specific interactions that have
been curated between LPS and 286 genes and proteins, as well as integration with other relevant data such as associated diseases. A list of these genes and proteins
are provided as supplementary data. Curated interaction data will be integrated with the CTD Web interface in future releases. Square, unique genes and proteins
(not a 1:1 ratio); circle, chemical; rounded rectangle, CTD interactions; shadowed rectangle, associated data (e.g., diseases associated with LPS); arrowed lines,
activation; bulleted lines, inhibition (e.g., LPS activates expression of 152 genes and inhibits activity of four proteins).
these techniques lack specifics about nontranscriptional mechanisms underlying chemical effects. CTD integrates data from
both sources to coordinate this complementary information and
enhance understanding of chemical actions.
Genes and proteins function together in complex networks
rather than in isolation (Vidal, 2005). Understanding the mechanisms of chemical actions requires knowledge of these networks and constituent chemical-gene and protein interactions,
which may be direct (e.g., ‘‘chemical binds to protein’’) or
indirect (e.g., ‘‘chemical results in activated transcription of
a gene’’ via intermediate events). CTD currently presents
associations between groups of chemicals and genes based on
information retrieval of reference titles and abstracts as well as
MeSH annotations. Although this approach is valuable for
creating preliminary correlations, it is limited in that these
associations are only inferred, and the types of interactions are
not specified. To address these limitations, we have begun
manually curating specific chemical-gene and protein interactions in diverse species from the published literature. Identifying
interactions across species offers advantages to developing
network hypotheses, including validating conserved mechanisms of action, identifying species-specific differences in
chemical actions, and testing functionality of networks and
network components in experimentally tractable systems.
To ensure consistency of literature curation, we developed an
‘‘interaction’’-controlled vocabulary that characterizes com-
mon 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., ‘‘increase’’), and ‘‘qualifiers’’ that specify the
form of the gene or chemical involved in an interaction (e.g.,
‘‘protein’’ or ‘‘chemical metabolite,’’ respectively). Curators
use this vocabulary to construct detailed annotations of
chemical-gene and protein interactions from the literature.
The interaction vocabulary was initiated in collaboration with
Dr Andrey Rzhetsky (Columbia University), and it continues to
be refined (Rzhetsky et al., 2004).
To date, we have manually curated over 22,000 interactions
involving 2000 chemicals, 2300 genes and proteins, and 75
different species from more than 3000 references. The CTD
curation process efficiently adds valuable information about
chemical-gene and protein relationships by combining an
information retrieval strategy to identify relevant references
and manual curation to extract and validate interactions. The
importance of using manual curation to validate results from
automated information retrieval methods was demonstrated by
a comparison of CTD-curated interactions with automated
‘‘chemical compound relationships’’ displayed by GeneCards,
a database of human genes (Safran et al., 2002). For example,
a GeneCards query for ATP-binding cassette, subfamily B
(MDR/TAP), member 11 (ABCB11) on 10 February, 2006,
594
MATTINGLY ET AL.
listed eight associated chemicals for this gene. However,
manual review of the supporting references confirmed relationships with only four of these chemicals. Three of the remaining
chemicals were invalid and based solely on co-occurrence of
terms in the title or abstract of the references. One term
presented as a chemical is an acronym for a disease associated
with ABCB11 mutations. In addition to the four valid
chemicals in GeneCards, CTD identified an additional 88
chemicals with valid ABCB11 interactions.
Manually curated interactions will be integrated with public
CTD data and identified in future releases. This integration will
allow visitors to ask mechanistic questions about relationships
between chemicals and genes or proteins by querying with
numerous parameters (e.g., ‘‘tetrachlorodibenzodioxin [chemical term] results in increased transcription [CTD interaction] of
which genes?’’; ‘‘what gene promoters are demethylated [CTD
interaction] in response to the green tea extract epigallocatechin
gallate [chemical term]?’’; ‘‘what protein kinases [GO molecular function term] play a role in chemical resistance [CTD
interaction term] to tamoxifen [chemical term]?’’; ‘‘what
proteins undergo phosphorylation [CTD interaction term] in
response to the adamantine class of compounds [chemical
term]?’’). Ultimately, these interactions will make substantial
contributions to predicting and visualizing complex chemical
interaction networks from either a molecular (gene or protein) or
chemical perspective. Figure 4 is a high-level schematic
illustrating the currently curated 322 interactions between
lipopolysaccharides (LPS) and 286 unique genes and proteins.
LPS exposure results in activated expression of 152 genes,
decreased expression of 143 genes, decreased activity of four
proteins, increased phosphorylation of six proteins, increased
secretion of two proteins, and localization effects on four
proteins. A table listing all LPS interactions with genes and
proteins curated to date is provided as supplementary data.
Extensive integration of additional data with chemicals and
genes in CTD will enable visitors to evaluate these interactions
in important contexts such as diseases (e.g., can these interactions with specific genes and proteins help explain the
correlation between LPS and the onset or severity of asthma?).
Access to and understanding of complex chemical-gene and
protein networks will directly support hypothesis-driven research about the mechanisms of chemical actions and have
important implications for predicting both past exposures and
chemically induced toxicity or disease.
SUPPLEMENTARY DATA
Supplementary data are available online at http://toxsci.
oxfordjournals.org/.
ACKNOWLEDGMENTS
The development of CTD is supported by R33 ES011267 from the National
Institute of Environmental Health Sciences (NIEHS) and P20 RR-016463 from
the National Center for Research Resources (NCRR), components of the
National Institutes of Health (NIH), and MDIBL. Its contents are solely the
responsibiltiy of the authors and do not necessarily represent the official views
of NCRR or NIH.
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