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Abstract: The studies presented in this manuscript focus on characterization of transcriptomic responses to anti-androgens in zebrafish (Danio rerio). Research on the effects of anti-androgens in fish has been characterized by a heavy reliance on apical endpoints, and molecular mechanisms of action (MOA) of anti-androgens remain poorly elucidated. In the present study, we examined effects of a short term exposure (24–96h) to the androgen receptor antagonists flutamide (FLU) and vinclozolin (VZ) on gene expression in gonads of sexually mature zebrafish, using commercially available zebrafish oligonucleotide microarrays (4×44K platform). We found that VZ and FLU potentially impact reproductive processes via multiple pathways related to steroidogenesis, spermatogenesis, and fertilization. Observed changes in gene expression often were shared by VZ and FLU, as demonstrated by overlap in differentially-expressed genes and enrichment of several common key pathways including: (1) integrin and actin signaling, (2) nuclear receptor 5A1 signaling, (3) fibroblast growth factor receptor signaling, (4) polyamine synthesis, and (5) androgen synthesis. This information should prove useful to elucidating specific mechanisms of reproductive effects of anti-androgens in fish, as well as developing biomarkers for this important class of endocrine-active chemicals. [ABSTRACT FROM AUTHOR]

Abstract: This study sought to construct a transcriptomics-based framework of signal transduction pathways, transcriptional regulatory networks, and the hypothalamic-pituitary gonadal (HPG) axis in zebrafish (Danio rerio) to facilitate formulation of specific, testable hypotheses regarding the mechanisms of endocrine disruption in fish. For the analyses involved, we used data from a total of more than 300 microarrays representing 58 conditions, which encompassed 4 tissue types from zebrafish of both genders exposed for 1 of 3 durations to 10 different test chemicals (17α-ethynyl estradiol, fadrozole, 17β-trenbolone, fipronil, prochloraz, flutamide, muscimol, ketoconazole, trilostane, and vinclozolin). Differentially expressed genes were identified by one class t-tests for each condition, and those with false discovery rates of less than 40% and treatment/control ratios ≥1.3-fold were mapped to orthologous human, mouse, and rat pathways by Ingenuity Pathway Analysis to look for overrepresentation of known biological pathways. To complement the analysis of known biological pathways, the genes regulated by approximately 1800 transcription factors were inferred using the ARACNE mutual information-based algorithm. The resulting gene sets for all transcriptional factors, along with a group of compiled HPG-axis genes and approximately 130 publicly available biological pathways, were analyzed for their responses to the 58 treatment conditions by Gene Set Enrichment Analysis (GSEA) and its variant, Extended-GSEA. The biological pathways and transcription factors associated with multiple distinct treatments showed substantial interactions among the HPG-axis, TGF-beta, p53, and several of their cross-talking partners. These candidate networks/pathways have a variety of profound impacts on such cellular functions as stress response, cell cycle, and apoptosis. [Copyright &y& Elsevier]

• Events of adverse outcome pathways annotated into computable logical definitions. • Individual adverse outcome pathways characterized semantically. • Multiple logical definitions of an adverse outcome pathway as a phenotypic profile. • Phenotypic profiles semantically mapped to those of genes, pathways, and diseases. • Ontology-base semantic analysis has utilities in adverse outcome pathway research. This study was undertaken to systematically assess the utilities and performance of ontology-based semantic analysis in adverse outcome pathway (AOP) research. With an increasing number of AOPs developed by scientific domain experts to organize toxicity information and facilitate chemical risk assessment, there is a pressing need for objective approaches to evaluate the biological coherence and quality of these AOPs. Powered by ontologies covering a wide range of biological domains, abundant phenotypic data annotated ontologically, and some sophisticated knowledge computing tools, semantic analysis has great potential in this area of application. With the events in the AOP-Wiki first annotated into logical definitions and then grouped into phenotypic profiles by individual AOPs, the coherence and quality of AOPs were assessed at several levels: paired key event relationships (KER), all possible event pair combinations within AOPs, and the phenotypic profiles of AOPs, genes, biological pathways, human diseases, and selected chemicals. The semantic similarities were assessed at all these levels based on a unified cross-species vertebrate phenotype ontology encompassing the logical definitions of AOP events as well as many other domain ontologies. A substantial number of KERs and AOPs in the AOP-Wiki were found to be semantically coherent. These same coherent AOPs also mapped to many more genes, pathways, and diseases biologically aligned with the intended chain of events therein leading to their respective adverse outcomes. Significantly, these findings imply that semantic analysis should also have utilities in developing future AOPs by selecting candidate events from either the existing AOP-Wiki events or a broader collection of ontology terms semantically similar to the molecular initiating events or adverse outcomes of interest. In addition, semantic analysis enabled AOP networks to be constructed at the level of phenotypic profiles based on similarities, complementing those based on event sharing by bringing genes, pathways, diseases, and chemicals into the networks too—thus greatly expanding the biological scope and our understanding of AOPs. [ABSTRACT FROM AUTHOR]

Abstract: Knowledge of possible toxic mechanisms (or modes) of action (MOA) of chemicals can provide valuable insights as to appropriate methods for assessing exposure and effects, thereby reducing uncertainties related to extrapolation across species, endpoints and chemical structure. However, MOA-based testing seldom has been used for assessing the ecological risk of chemicals. This is in part because past regulatory mandates have focused more on adverse effects of chemicals (reductions in survival, growth or reproduction) than the pathways through which these effects are elicited. A recent departure from this involves endocrine-disrupting chemicals (EDCs), where there is a need to understand both MOA and adverse outcomes. To achieve this understanding, advances in predictive approaches are required whereby mechanistic changes caused by chemicals at the molecular level can be translated into apical responses meaningful to ecological risk assessment. In this paper we provide an overview and illustrative results from a large, integrated project that assesses the effects of EDCs on two small fish models, the fathead minnow (Pimephales promelas) and zebrafish (Danio rerio). For this work a systems-based approach is being used to delineate toxicity pathways for 12 model EDCs with different known or hypothesized toxic MOA. The studies employ a combination of state-of-the-art genomic (transcriptomic, proteomic, metabolomic), bioinformatic and modeling approaches, in conjunction with whole animal testing, to develop response linkages across biological levels of organization. This understanding forms the basis for predictive approaches for species, endpoint and chemical extrapolation. Although our project is focused specifically on EDCs in fish, we believe that the basic conceptual approach has utility for systematically assessing exposure and effects of chemicals with other MOA across a variety of biological systems. [Copyright &y& Elsevier]