The Comparative Toxicogenomics Database: update 2013

Davis, Allan Peter; Murphy, Cynthia G.; Johnson, Robin J.; Lay, Jean M.; Lennon-Hopkins, Kelley; Saraceni-Richards, Cynthia A.; Sciaky, Daniela; King, Benjamin L.; Rosenstein, Michael C.; Wiegers, Thomas C.; Mattingly, Carolyn J.

doi:10.1093/nar/gks994

Cited by 391 publications

(290 citation statements)

References 30 publications

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“…The Library of Integrated NetworkBased Cellular Signatures (http://www.lincsproject.org/) measures pathway activities with more than 100 antibody-based assays, as well as gene expression data from more than 10,000 compounds and over 15 cellular models sharing data in standardized format [61]. The US Environmental Protection Agency aggregated computational toxicology resource (AcToR) database, PubChem and chEMBL databases as well as the Comparative Toxicogenomics Database connects chemicals with gene expression changes and disease or toxicity associations [50,52,56,59]. But the toxicology field would benefit from even further developed integrated tools such as the Mantra 2.0 or the cBio Cancer genomics portal that integrates gene expression information, genomic alterations, cancer survival analysis and antibody-based pathway assays from more than 20 different cancer types and 1000 cellular models [24,58].…”

Section: Surveying the Landscape Of Databases For Cancer Biology And mentioning

confidence: 99%

Cancer Biology, Toxicology and Alternative Methods Development Go Hand‐in‐Hand

Kohonen

Ceder

Smit

et al. 2014

Basic Clin Pharma Tox

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Toxicological research faces the challenge of integrating knowledge from diverse fields and novel technological developments generally in the biological and medical sciences. We discuss herein the fact that the multiple facets of cancer research, including discovery related to mechanisms, treatment and diagnosis, overlap many up and coming interest areas in toxicology, including the need for improved methods and analysis tools. Common to both disciplines, in vitro and in silico methods serve as alternative investigation routes to animal studies. Knowledge on cancer development helps in understanding the relevance of chemical toxicity studies in cell models, and many bioinformatics-based cancer biomarker discovery tools are also applicable to computational toxicology. Robotics-aided, cell-based, high-throughput screening, microscale immunostaining techniques and gene expression profiling analyses are common tools in cancer research, and when sequentially combined, form a tiered approach to structured safety evaluation of thousands of environmental agents, novel chemicals or engineered nanomaterials. Comprehensive tumour data collections in databases have been translated into clinically useful data, and this concept serves as template for computer-driven evaluation of toxicity data into meaningful results. Future 'cancer research-inspired knowledge management' of toxicological data will aid the translation of basic discovery results and chemicals-and materials-testing data to information relevant to human health and environmental safety.Assessing the intrinsic toxicological properties of environmental agents is central to defining hazard within the paradigm of human risk assessment used now for decades [1]. A constantly increasing number of chemicals and engineered nanomaterials (ENMs) emphasize the need of applying novel tools and screening technologies outside of the standard, both lengthy and expensive, rodent toxicological tests traditionally used in such work [1][2][3][4][5][6][7][8][9][10][11][12][13]. Especially considered for the future innovation of novel ENMs, safety evaluations should be integrated proactively and efficiently already in the material and product development phase [9,12]. Summarized under a concept termed 'Toxicity Testing in the 21st Century' or 'Tox21', biochemical and cell-based in vitro assays coupled with bioinformatics and modelling-driven in silico assays are now considered key to transforming toxicology from a previously animal-based testing practice into a computational science built on systems biology [2,4,6,[9][10][11]13]. The resulting novel research field is variably termed 'systems toxicology', 'toxicogenomics' or 'computational toxicology' [3,4,8,14,15]. Such effort typically relies on informatics-driven and modelling-based analyses of results from several experimental systems and data-rich technologies for measuring pools of biological molecules, such as mRNAs, the overall aim being to interdependently analyse toxicity data and profiling results for understanding...

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Section: Surveying the Landscape Of Databases For Cancer Biology And mentioning

confidence: 99%

Cancer Biology, Toxicology and Alternative Methods Development Go Hand‐in‐Hand

Kohonen

Ceder

Smit

et al. 2014

Basic Clin Pharma Tox

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“…When a set of chemicals of toxicological interest has been selected, statistical analyses are carried out using data from various sources. These sources can be (1) [18]; the database is updated on a monthly basis and contains both experimentally determined and inferred chemical-disease associations. The inferred chemicaldisease associations are based on data from various species, which can be considered either an advantage or a disadvantage depending on the aim of the study.…”

Section: How To Perform a Network Analysis?mentioning

confidence: 99%

Applicability of Computational Systems Biology in Toxicology

Kongsbak

Hadrup

Audouze

et al. 2014

Basic Clin Pharma Tox

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Systems biology as a research field has emerged within the last few decades. Systems biology, often defined as the antithesis of the reductionist approach, integrates information about individual components of a biological system. In integrative systems biology, large data sets from various sources and databases are used to model and predict effects of chemicals on, for instance, human health. In toxicology, computational systems biology enables identification of important pathways and molecules from large data sets; tasks that can be extremely laborious when performed by a classical literature search. However, computational systems biology offers more advantages than providing a high-throughput literature search; it may form the basis for establishment of hypotheses on potential links between environmental chemicals and human diseases, which would be very difficult to establish experimentally. This is possible due to the existence of comprehensive databases containing information on networks of human protein-protein interactions and protein-disease associations. Experimentally determined targets of the specific chemical of interest can be fed into these networks to obtain additional information that can be used to establish hypotheses on links between the chemical and human diseases. Such information can also be applied for designing more intelligent animal/cell experiments that can test the established hypotheses. Here, we describe how and why to apply an integrative systems biology method in the hypothesis-generating phase of toxicological research. What is Computational Systems Biology?Systems biology is often defined as the antithesis of the reductionist approach. Although the reductionist approach has identified many important individual components of biological systems, it often fails to integrate the interconnections between the individual components. Systems biology, on the other hand, deals with the integration of information from a wealth of individual components, creating a holistic view of the biological system [1]. Each component is made up by larger or smaller data sets. For instance, analysis of microarray experiments gives quantitative estimates of changes in expression of a whole-organism genome. High-throughput screening and high-content screening are procedures giving biological activity data on a large number of chemicals (e.g. the U.S. Environmental Protection Agency's ToxCast programme [2]). Additionally, low-/medium-throughput single-target assays provide high-quality measures of the biological function of a single or more targets after chemical exposure. Combining such complementary data types may add value in creating realistic models of potential toxic or adverse effects of chemicals [3,4]. A key strategy to handle multiple data sources is data integration, the use of which has been demonstrated in several applications as reviewed by Mitra et al. [5], for example for the identification of new biomarkers for Alzheimer's disease [6].Computational toxicology integrates molecular...

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Section: Knowledge-driven Network Constructionmentioning

confidence: 99%

“…Hypotheses generated from this approach enables datadriven knowledge discovery. Next, the Comparative Toxicogenomics Database (CTD) [32] is used as an additional source of information Step-1 Upload a combined list of differentially identified metabolites and genes/proteins into STITCH; STEP-2 Retrieval of high-confidence human protein-chemical interactions (confidence score ≥ 0.9); STEP-3 Use the Comparative Toxicogenomics Database (CTD) as an additional source of information to annotate both metabolites and proteins that are molecular markers or therapeutic targets in the context of given phenotype or disease; STEP-4 Sub-set the interaction network by filtering out the gene-to-gene and metabolite-to-metabolite interactions; STEP-5 Re-construct the knowledge driven network based on direct interacting metabolites and genes/proteins; Step-6 Prepare the list of interacting genes/proteins and metabolites from sub-network constructed in the previous step;Step-7 Perform pathway enrichment analysis using CPDB (ConsensusPathDB) database;Step-8 Perform Fisher exact test (ORA Analysis) for both list of interacting metabolites and genes/proteins; Step-9 Scan for pathways populated with both metabolites and genes;Step-10 Investigate the list of overlapping biochemical pathways to assess the biological significance. This network view of interacting functional partners can provide new insights about their association with the phenotype of interest and a more granular understanding of interdependence and interconnectivity between underlying biochemical processes and pathways at a systems level.…”

mentioning

confidence: 99%

Integrative Analysis Workflow for Untargeted Metabolomics in Translational Research

Madhavan¹

2014

Metabolomics

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The ability to easily quantify changes in the biological system makes metabolomics an attractive translational research tool that can help identify biomarkers of disease through non-invasive measurements Abstract Background: Metabolomics is an emerging 'omics' science that has demonstrated its fast gaining importance as a powerful profiling tool for determining an individual's response to a foreign stimulus such as a drug, toxin, or environmental change; or as an indicator of disease progression. Such small molecule profiles can used as biological markers of disease, and provide an indicator of drug efficacy or toxicity.Several studies have demonstrated that the results of any single 'omics' analysis may not be sufficient to decode extremely complex biological mechanisms. Methods:We have developed a translational research workflow to enable researchers to perform cutting-edge integrative analysis of metabolomics data with transcriptomics (gene expression) data using knowledge-driven networks. This network based view of interconnected functional partners can provide valuable new insight about the underlying biochemical processes and pathways associated with the phenotype of interest. To enhance and simplify metabolomics annotation we built a fully cross-referenced database called MetPlus DB, which integrates data from the three most comprehensive metabolite databases tailored largely towards mammalian metabolomics: HMDB, HUAMNCYC, and LIPID MAPS.Cross-referencing information is provided for linking to several other mainstream cheminformatics/bioinformatics repositories including KEGG, METLIN, ChEBI, FooDB, Pubchem, and Chemspider to provide unambiguous knowledge on clinically and physiologically relevant metabolites. We have made the integrated database available freely to the research community (https://github.com/ICBI/MetPlus-DB). Results:To demonstrate the usefulness and strength of our methodology, we have tested it on a multi-omics profiling dataset from NCI-60 breast cancer cell lines to explore the biological dynamics of breast cancer. Conclusions:Our results demonstrate a streamlined approach for the comprehensive annotation of metabolites using MetPlus DB, and the subsequent integration of metabolomics and transcriptomics data to explore potentially relevant biological interactions and candidate biomarkers associated with disease phenotype. The use of a multi-omics, systems biology approach to study cellular metabolism is key to understanding disease etiology and outcome. Integration of omics data types can help identify biological pathways affected and provides a more complete picture disease biology and prognosis, and can even provide new drug targets. One recent study integrated the metabolomics profile of tissue from breast cancer patients with protein expression data for glycerol-3-phosphate acyltransferase (GPAM), an enzyme involved in lipid biosynthesis. This data was overlaid with gene expression data to show that GPAM expression in breast cancer is associated with changes in the cellular metabol...

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The Comparative Toxicogenomics Database: update 2013

Cited by 391 publications

References 30 publications

Cancer Biology, Toxicology and Alternative Methods Development Go Hand‐in‐Hand

Cancer Biology, Toxicology and Alternative Methods Development Go Hand‐in‐Hand

Applicability of Computational Systems Biology in Toxicology

Integrative Analysis Workflow for Untargeted Metabolomics in Translational Research

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