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Nucleic Acids Research Jan 2019The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) is a premier public resource for literature-based, manually curated associations between chemicals,...
The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) is a premier public resource for literature-based, manually curated associations between chemicals, gene products, phenotypes, diseases, and environmental exposures. In this biennial update, we present our new chemical-phenotype module that codes chemical-induced effects on phenotypes, curated using controlled vocabularies for chemicals, phenotypes, taxa, and anatomical descriptors; this module provides unique opportunities to explore cellular and system-level phenotypes of the pre-disease state and allows users to construct predictive adverse outcome pathways (linking chemical-gene molecular initiating events with phenotypic key events, diseases, and population-level health outcomes). We also report a 46% increase in CTD manually curated content, which when integrated with other datasets yields more than 38 million toxicogenomic relationships. We describe new querying and display features for our enhanced chemical-exposure science module, providing greater scope of content and utility. As well, we discuss an updated MEDIC disease vocabulary with over 1700 new terms and accession identifiers. To accommodate these increases in data content and functionality, CTD has upgraded its computational infrastructure. These updates continue to improve CTD and help inform new testable hypotheses about the etiology and mechanisms underlying environmentally influenced diseases.
Topics: Databases, Pharmaceutical; Disease; Environmental Exposure; Humans; Phenotype; Toxicogenetics; Vocabulary, Controlled
PubMed: 30247620
DOI: 10.1093/nar/gky868 -
Biomarkers in Medicine 2014Drug-induced liver injury (DILI) is a frequent cause for the termination of drug development programs and a leading reason of drug withdrawal from the marketplace.... (Review)
Review
Drug-induced liver injury (DILI) is a frequent cause for the termination of drug development programs and a leading reason of drug withdrawal from the marketplace. Unfortunately, the current preclinical testing strategies, including the regulatory-required animal toxicity studies or simple in vitro tests, are insufficiently powered to predict DILI in patients reliably. Notably, the limited predictive power of such testing strategies is mostly attributed to the complex nature of DILI, a poor understanding of its mechanism, a scarcity of human hepatotoxicity data and inadequate bioinformatics capabilities. With the advent of high-content screening assays, toxicogenomics and bioinformatics, multiple end points can be studied simultaneously to improve prediction of clinically relevant DILIs. This review focuses on the current state of efforts in developing predictive models from diverse data sources for potential use in detecting human hepatotoxicity, and also aims to provide perspectives on how to further improve DILI prediction.
Topics: Animals; Biomarkers; Chemical and Drug Induced Liver Injury; Computational Biology; Drug-Related Side Effects and Adverse Reactions; Humans; Models, Biological; Pharmaceutical Preparations; Quantitative Structure-Activity Relationship; Toxicogenetics
PubMed: 24521015
DOI: 10.2217/bmm.13.146 -
Pharmacogenomics Apr 2010Adverse drug reactions (ADRs) are an important clinical issue and a serious public health risk. Understanding the underlying mechanisms is critical for clinical... (Review)
Review
Adverse drug reactions (ADRs) are an important clinical issue and a serious public health risk. Understanding the underlying mechanisms is critical for clinical diagnosis and management of different ADRs. Toxicogenomics can reveal impacts on biological pathways and processes that had not previously been considered to be involved in a drug response. Mechanistic hypotheses can be generated that can then be experimentally tested using the full arsenal of pharmacology, toxicology, molecular biology and genetics. Recent transcriptomic studies on drug-induced toxicity, which have provided valuable mechanistic insights into various ADRs, have been reviewed with a focus on nephrotoxicity and hepatotoxicity. Related issues have been discussed, including extrapolation of mechanistic findings from experimental model systems to humans using blood as a surrogate tissue for organ damage and comparative systems biology approaches.
Topics: Animals; Chemical and Drug Induced Liver Injury; Drug-Related Side Effects and Adverse Reactions; Gene Expression Profiling; Humans; Kidney Diseases; Toxicogenetics
PubMed: 20350139
DOI: 10.2217/pgs.10.37 -
Chemical Research in Toxicology Aug 2011Quantitative structure-activity relationship (QSAR) modeling and toxicogenomics are typically used independently as predictive tools in toxicology. In this study, we...
Quantitative structure-activity relationship (QSAR) modeling and toxicogenomics are typically used independently as predictive tools in toxicology. In this study, we evaluated the power of several statistical models for predicting drug hepatotoxicity in rats using different descriptors of drug molecules, namely, their chemical descriptors and toxicogenomics profiles. The records were taken from the Toxicogenomics Project rat liver microarray database containing information on 127 drugs ( http://toxico.nibio.go.jp/datalist.html ). The model end point was hepatotoxicity in the rat following 28 days of continuous exposure, established by liver histopathology and serum chemistry. First, we developed multiple conventional QSAR classification models using a comprehensive set of chemical descriptors and several classification methods (k nearest neighbor, support vector machines, random forests, and distance weighted discrimination). With chemical descriptors alone, external predictivity (correct classification rate, CCR) from 5-fold external cross-validation was 61%. Next, the same classification methods were employed to build models using only toxicogenomics data (24 h after a single exposure) treated as biological descriptors. The optimized models used only 85 selected toxicogenomics descriptors and had CCR as high as 76%. Finally, hybrid models combining both chemical descriptors and transcripts were developed; their CCRs were between 68 and 77%. Although the accuracy of hybrid models did not exceed that of the models based on toxicogenomics data alone, the use of both chemical and biological descriptors enriched the interpretation of the models. In addition to finding 85 transcripts that were predictive and highly relevant to the mechanisms of drug-induced liver injury, chemical structural alerts for hepatotoxicity were identified. These results suggest that concurrent exploration of the chemical features and acute treatment-induced changes in transcript levels will both enrich the mechanistic understanding of subchronic liver injury and afford models capable of accurate prediction of hepatotoxicity from chemical structure and short-term assay results.
Topics: Animals; Chemical and Drug Induced Liver Injury; Databases, Factual; Hepatocyte Nuclear Factor 4; Male; Models, Statistical; Quantitative Structure-Activity Relationship; Rats; Rats, Sprague-Dawley; Small Molecule Libraries; Toxicogenetics
PubMed: 21699217
DOI: 10.1021/tx200148a -
World Journal of Clinical Oncology May 2014Head and neck cancer (HNC) is the sixth most common human malignancy worldwide. The main forms of treatment for HNC are surgery, radiotherapy (RT) and chemotherapy (CT).... (Review)
Review
Head and neck cancer (HNC) is the sixth most common human malignancy worldwide. The main forms of treatment for HNC are surgery, radiotherapy (RT) and chemotherapy (CT). However, the choice of therapy depends on the tumor staging and approaches, which are aimed at organ preservation. Because of systemic RT and CT genotoxicity, one of the important side effects is a secondary cancer that can result from the activity of radiation and antineoplastic drugs on healthy cells. Ionizing radiation can affect the DNA, causing single and double-strand breaks, DNA-protein crosslinks and oxidative damage. The severity of radiotoxicity can be directly associated with the radiation dosimetry and the dose-volume differences. Regarding CT, cisplatin is still the standard protocol for the treatment of squamous cell carcinoma, the most common cancer located in the oral cavity. However, simultaneous treatment with cisplatin, bleomycin and 5-fluorouracil or treatment with paclitaxel and cisplatin are also used. These drugs can interact with the DNA, causing DNA crosslinks, double and single-strand breaks and changes in gene expression. Currently, the late effects of therapy have become a recurring problem, mainly due to the increased survival of HNC patients. Herein, we present an update of the systemic activity of RT and CT for HNC, with a focus on their toxicogenetic and toxicogenomic effects.
PubMed: 24829856
DOI: 10.5306/wjco.v5.i2.93 -
Biomarkers in Medicine 2014Current testing models for predicting drug-induced liver injury are inadequate, as they basically under-report human health risks. We present here an approach towards... (Review)
Review
Current testing models for predicting drug-induced liver injury are inadequate, as they basically under-report human health risks. We present here an approach towards developing pathways based on hepatotoxicity-associated gene groups derived from two types of publicly accessible hepatotoxicity databases, in order to develop drug-induced liver injury biomarker profiles. One human liver 'omics-based and four text-mining-based databases were explored for hepatotoxicity-associated gene lists. Over-representation analysis of these gene lists with a hepatotoxicant-exposed primary human hepatocytes data set showed that human liver 'omics gene lists performed better than text-mining gene lists and the results of the latter differed strongly between databases. However, both types of databases contained gene lists demonstrating biomarker potential. Visualizing those in pathway format may aid in interpreting the biomolecular background. We conclude that exploiting existing and openly accessible databases in a dedicated manner seems promising in providing venues for translational research in toxicology and biomarker development.
Topics: Biomarkers; Chemical and Drug Induced Liver Injury; Databases, Factual; Gene Expression Profiling; Humans; Liver; Toxicogenetics; Transcriptome
PubMed: 24521014
DOI: 10.2217/bmm.13.154 -
Infectious Agents and Cancer Aug 2023Esophageal squamous cell carcinoma (ESCC) has a poor prognosis and is one of the deadliest gastrointestinal malignancies. Despite numerous transcriptomics studies to...
BACKGROUND
Esophageal squamous cell carcinoma (ESCC) has a poor prognosis and is one of the deadliest gastrointestinal malignancies. Despite numerous transcriptomics studies to understand its molecular basis, the impact of population-specific differences on this disease remains unexplored.
AIMS
This study aimed to investigate the population-specific differences in gene expression patterns among ESCC samples obtained from six distinct global populations, identify differentially expressed genes (DEGs) and their associated pathways, and identify potential biomarkers for ESCC diagnosis and prognosis. In addition, this study deciphers population specific microbial and chemical risk factors in ESCC.
METHODS
We compared the gene expression patterns of ESCC samples from six different global populations by analyzing microarray datasets. To identify DEGs, we conducted stringent quality control and employed linear modeling. We cross-compared the resulting DEG lists of each populations along with ESCC ATLAS to identify known and novel DEGs. We performed a survival analysis using The Cancer Genome Atlas Program (TCGA) data to identify potential biomarkers for ESCC diagnosis and prognosis among the novel DEGs. Finally, we performed comparative functional enrichment and toxicogenomic analysis.
RESULTS
Here we report 19 genes with distinct expression patterns among populations, indicating population-specific variations in ESCC. Additionally, we discovered 166 novel DEGs, such as ENDOU, SLCO1B3, KCNS3, IFI35, among others. The survival analysis identified three novel genes (CHRM3, CREG2, H2AC6) critical for ESCC survival. Notably, our findings showed that ECM-related gene ontology terms and pathways were significantly enriched among the DEGs in ESCC. We also found population-specific variations in immune response and microbial infection-related pathways which included genes enriched for HPV, Ameobiosis, Leishmaniosis, and Human Cytomegaloviruses. Our toxicogenomic analysis identified tobacco smoking as the primary risk factor and cisplatin as the main drug chemical interacting with the maximum number of DEGs across populations.
CONCLUSION
This study provides new insights into population-specific differences in gene expression patterns and their associated pathways in ESCC. Our findings suggest that changes in extracellular matrix (ECM) organization may be crucial to the development and progression of this cancer, and that environmental and genetic factors play important roles in the disease. The novel DEGs identified may serve as potential biomarkers for diagnosis, prognosis and treatment.
PubMed: 37641095
DOI: 10.1186/s13027-023-00525-8 -
International Journal of Molecular... Sep 2010A systems-level understanding of molecular perturbations is crucial for evaluating chemical-induced toxicity risks appropriately, and for this purpose comprehensive gene... (Review)
Review
A systems-level understanding of molecular perturbations is crucial for evaluating chemical-induced toxicity risks appropriately, and for this purpose comprehensive gene expression analysis or toxicogenomics investigation is highly advantageous. The recent accumulation of toxicity-associated gene sets (toxicogenomic biomarkers), enrichment in public or commercial large-scale microarray database and availability of open-source software resources facilitate our utilization of the toxicogenomic data. However, toxicologists, who are usually not experts in computational sciences, tend to be overwhelmed by the gigantic amount of data. In this paper we present practical applications of toxicogenomics by utilizing biomarker gene sets and a simple scoring method by which overall gene set-level expression changes can be evaluated efficiently. Results from the gene set-level analysis are not only an easy interpretation of toxicological significance compared with individual gene-level profiling, but also are thought to be suitable for cross-platform or cross-institutional toxicogenomics data analysis. Enrichment in toxicogenomics databases, refinements of biomarker gene sets and scoring algorithms and the development of user-friendly integrative software will lead to better evaluation of toxicant-elicited biological perturbations.
Topics: Biomarkers; Gene Expression Profiling; Gene Regulatory Networks; Oligonucleotide Array Sequence Analysis; Toxicogenetics; Transcriptome
PubMed: 20957103
DOI: 10.3390/ijms11093397 -
GigaScience Jun 2019Chemicals induce compound-specific changes in the transcriptome of an organism (toxicogenomic fingerprints). This provides potential insights about the cellular or...
BACKGROUND
Chemicals induce compound-specific changes in the transcriptome of an organism (toxicogenomic fingerprints). This provides potential insights about the cellular or physiological responses to chemical exposure and adverse effects, which is needed in assessment of chemical-related hazards or environmental health. In this regard, comparison or connection of different experiments becomes important when interpreting toxicogenomic experiments. Owing to lack of capturing response dynamics, comparability is often limited. In this study, we aim to overcome these constraints.
RESULTS
We developed an experimental design and bioinformatic analysis strategy to infer time- and concentration-resolved toxicogenomic fingerprints. We projected the fingerprints to a universal coordinate system (toxicogenomic universe) based on a self-organizing map of toxicogenomic data retrieved from public databases. Genes clustering together in regions of the map indicate functional relation due to co-expression under chemical exposure. To allow for quantitative description and extrapolation of the gene expression responses we developed a time- and concentration-dependent regression model. We applied the analysis strategy in a microarray case study exposing zebrafish embryos to 3 selected model compounds including 2 cyclooxygenase inhibitors. After identification of key responses in the transcriptome we could compare and characterize their association to developmental, toxicokinetic, and toxicodynamic processes using the parameter estimates for affected gene clusters. Furthermore, we discuss an association of toxicogenomic effects with measured internal concentrations.
CONCLUSIONS
The design and analysis pipeline described here could serve as a blueprint for creating comparable toxicogenomic fingerprints of chemicals. It integrates, aggregates, and models time- and concentration-resolved toxicogenomic data.
Topics: Animals; Computational Biology; Drug-Related Side Effects and Adverse Reactions; Models, Biological; Risk Assessment; Toxicogenetics; Transcriptome; Zebrafish
PubMed: 31140561
DOI: 10.1093/gigascience/giz057 -
Mutation Research Dec 2010Gene-environment interactions contribute to complex disease development. The environmental contribution, in particular low-level and prevalent environmental exposures,... (Review)
Review
Gene-environment interactions contribute to complex disease development. The environmental contribution, in particular low-level and prevalent environmental exposures, may constitute much of the risk and contribute substantially to disease. Systematic risk evaluation of the majority of human chemical exposures, has not been conducted and is a goal of regulatory agencies in the U.S. and worldwide. With the recent recognition that toxicological approaches more predictive of effects in humans are required for risk assessment, in vitro human cell line data as well as animal data are being used to identify toxicity mechanisms that can be translated into biomarkers relevant to human exposure studies. In this review, we discuss how data from toxicogenomic studies of exposed human populations can inform risk assessment, by generating biomarkers of exposure, early effect, and/or susceptibility, elucidating mechanisms of action underlying exposure-related disease, and detecting response at low doses. Good experimental design incorporating precise, individual exposure measurements, phenotypic anchors (pre-disease or traditional toxicological markers), and a range of relevant exposure levels, is necessary. Further, toxicogenomic studies need to be designed with sufficient power to detect true effects of the exposure. As more studies are performed and incorporated into databases such as the Comparative Toxicogenomics Database (CTD) and Chemical Effects in Biological Systems (CEBS), data can be mined for classification of newly tested chemicals (hazard identification), and, for investigating the dose-response, and inter-relationship among genes, environment and disease in a systems biology approach (risk characterization).
Topics: Animals; Arsenic; Benzene; Environmental Exposure; Epigenesis, Genetic; Female; Gene Expression Profiling; Genomics; Humans; Male; Proteomics; Risk Assessment; Systems Biology; Toxicogenetics; Toxicology
PubMed: 20382258
DOI: 10.1016/j.mrrev.2010.04.001