Dataset Information

Toxicogenomics to Evaluate Endocrine Disrupting Effects of Environmental Chemicals Using the Zebrafish Model.

ABSTRACT: The extent of our knowledge on the number of chemical compounds related to anthropogenic activities that can cause damage to the environment and to organisms is increasing. Endocrine disrupting chemicals (EDCs) are one group of potentially hazardous substances that include natural and synthetic chemicals and have the ability to mimic endogenous hormones, interfering with their biosynthesis, metabolism, and normal functions. Adverse effects associated with EDC exposure have been documented in aquatic biota and there is widespread interest in the characterization and understanding of their modes of action. Fish are considered one of the primary risk organisms for EDCs. Zebrafish (Danio rerio) are increasingly used as an animal model to study the effects of endocrine disruptors, due to their advantages compared to other model organisms. One approach to assess the toxicity of a compound is to identify those patterns of gene expression found in a tissue or organ exposed to particular classes of chemicals, through new technologies in genomics (toxicogenomics), such as microarrays or whole-genome sequencing. Application of these technologies permit the quantitative analysis of thousands of gene expression changes simultaneously in a single experiment and offer the opportunity to use transcript profiling as a tool to predict toxic outcomes of exposure to particular compounds. The application of toxicogenomic tools for identification of chemicals with endocrine disrupting capacity using the zebrafish model system is reviewed.

SUBMITTER: Caballero-Gallardo K

PROVIDER: S-EPMC5282603 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Toxicogenomics to Evaluate Endocrine Disrupting Effects of Environmental Chemicals Using the Zebrafish Model.

Caballero-Gallardo Karina K Olivero-Verbel Jesus J Freeman Jennifer L JL

Current genomics 20161201 6

The extent of our knowledge on the number of chemical compounds related to anthropogenic activities that can cause damage to the environment and to organisms is increasing. Endocrine disrupting chemicals (EDCs) are one group of potentially hazardous substances that include natural and synthetic chemicals and have the ability to mimic endogenous hormones, interfering with their biosynthesis, metabolism, and normal functions. Adverse effects associated with EDC exposure have been documented in aqu ...[more]

PMID: 28217008

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Project description:BackgroundDevelopment and application of transcriptomics-based gene classifiers for ecotoxicological applications lag far behind those of biomedical sciences. Many such classifiers discovered thus far lack vigorous statistical and experimental validations. A combination of genetic algorithm/support vector machines and genetic algorithm/K nearest neighbors was used in this study to search for classifiers of endocrine-disrupting chemicals (EDCs) in zebrafish. Searches were conducted on both tissue-specific and tissue-combined datasets, either across the entire transcriptome or within individual transcription factor (TF) networks previously linked to EDC effects. Candidate classifiers were evaluated by gene set enrichment analysis (GSEA) on both the original training data and a dedicated validation dataset.ResultsMulti-tissue dataset yielded no classifiers. Among the 19 chemical-tissue conditions evaluated, the transcriptome-wide searches yielded classifiers for six of them, each having approximately 20 to 30 gene features unique to a condition. Searches within individual TF networks produced classifiers for 15 chemical-tissue conditions, each containing 100 or fewer top-ranked gene features pooled from those of multiple TF networks and also unique to each condition. For the training dataset, 10 out of 11 classifiers successfully identified the gene expression profiles (GEPs) of their targeted chemical-tissue conditions by GSEA. For the validation dataset, classifiers for prochloraz-ovary and flutamide-ovary also correctly identified the GEPs of corresponding conditions while no classifier could predict the GEP from prochloraz-brain.ConclusionsThe discrepancies in the performance of these classifiers were attributed in part to varying data complexity among the conditions, as measured to some degree by Fisher's discriminant ratio statistic. This variation in data complexity could likely be compensated by adjusting sample size for individual chemical-tissue conditions, thus suggesting a need for a preliminary survey of transcriptomic responses before launching a full scale classifier discovery effort. Classifier discovery based on individual TF networks could yield more mechanistically-oriented biomarkers. GSEA proved to be a flexible and effective tool for application of gene classifiers but a similar and more refined algorithm, connectivity mapping, should also be explored. The distribution characteristics of classifiers across tissues, chemicals, and TF networks suggested a differential biological impact among the EDCs on zebrafish transcriptome involving some basic cellular functions.

Project description:BackgroundMiscarriage is a prevalent public health issue and many events occur before women are aware of their pregnancy, complicating research design. Thus, risk factors for miscarriage are critically understudied. Our goal was to identify environmental chemicals with a high number of interactions with miscarriage genes, based on known toxicogenomic responses.MethodsWe used miscarriage (MeSH: D000022) and chemical gene lists from the Comparative Toxicogenomics Database in human, mouse, and rat. We assessed enrichment for gene ontology biological processes among the miscarriage genes. We prioritized chemicals (n = 25) found at Superfund sites or in the blood or urine pregnant women. For chemical-disease gene sets of sufficient size (n = 13 chemicals, n = 20 comparisons), chi-squared enrichment tests and proportional reporting ratios (PRR) were calculated. We cross-validated enrichment results.ResultsMiscarriage was annotated with 121 genes and overrepresented in inflammatory response (q = 0.001), collagen metabolic process (q = 1 × 10-13), cell death (q = 0.02), and vasculature development (q = 0.005) pathways. The number of unique genes annotated to a chemical ranged from 2 (bromacil) to 5607 (atrazine). In humans, all chemicals tested were highly enriched for miscarriage gene overlap (all p < 0.001; parathion PRR = 7, cadmium PRR = 6.5, lead PRR = 3.9, arsenic PRR = 3.5, atrazine PRR = 2.8). In mice, highest enrichment (p < 0.001) was observed for naphthalene (PRR = 16.1), cadmium (PRR = 12.8), arsenic (PRR = 11.6), and carbon tetrachloride (PRR = 7.7). In rats, we observed highest enrichment (p < 0.001) for cadmium (PRR = 8.7), carbon tetrachloride (PRR = 8.3), and dieldrin (PRR = 5.3). Our findings were robust to 1000 permutations each of variable gene set sizes.ConclusionWe observed chemical gene sets (parathion, cadmium, naphthalene, carbon tetrachloride, arsenic, lead, dieldrin, and atrazine) were highly enriched for miscarriage genes. Exposures to chemicals linked to miscarriage, and thus linked to decreased probability of live birth, may limit the inclusion of fetuses susceptible to adverse birth outcomes in epidemiology studies. Our findings have critical public health implications for successful pregnancies and the interpretation of adverse impacts of environmental chemical exposures on pregnancy.

Dataset Information

Toxicogenomics to Evaluate Endocrine Disrupting Effects of Environmental Chemicals Using the Zebrafish Model.

Publications

Toxicogenomics to Evaluate Endocrine Disrupting Effects of Environmental Chemicals Using the Zebrafish Model.

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