Heeding the Hormonal Call

Zebrafish (Danio rerio) as a model organism for investigating endocrine disruption

Endocrine-disrupting compounds (EDCs) are widespread in the aquatic environment and can cause alterations in development, physiological homeostasis and health of vertebrates. Zebrafish, Danio rerio, has been suggested as a model species to identify targets as well as modes of EDC action. In fact, zebrafish has been found useful in EDC screening, in EDC effects assessment and in studying targets and mechanisms of EDC action. Since many of the environmental EDCs interfere with the sex steroid system of vertebrates, most EDC studies with zebrafish addressed disruption of sexual differentiation and reproduction. However, other targets of EDCs action must not be overlooked. For using a species as a toxicological model, a good knowledge of the biological traits of this species is a pre-requisite for the rational design of test protocols and endpoints as well as for the interpretation and extrapolation of the toxicological findings. Due to the genomic resources available for zebrafish and the long experience with zebrafish in toxicity testing, it is easily possible to establish molecular endpoints for EDC effects assessment. Additionally, the zebrafish model offers a number of technical advantages including ease and cost of maintenance, rapid development, high fecundity, optical transparency of embryos supporting phenotypic screening, existence of many mutant strains, or amenability for both forward and reverse genetics. To date, the zebrafish has been mainly used to identify molecular targets of EDC action and to determine effect thresholds, while the potential of this model species to study immediate and delayed physiological consequences of molecular interactions has been instrumentalized only partly. One factor that may limit the exploitation of this potential is the still rather fragmentary knowledge of basic biological and endocrine traits of zebrafish. Information on species-specific features in endocrine processes and biological properties, however, need to be considered in establishing EDC test protocols using zebrafish, in extrapolating findings from zebrafish to other vertebrate species, and in understanding how EDC-induced gene expression changes translate into disease.

Androgen receptor binding sites identified by a GREF_GATA model

Changes in transcriptional regulation can be permissive for tumor progression by allowing for selective growth advantage of tumor cells. Tumor suppressors can effectively inhibit this process. The PMEPA1 gene, a potent inhibitor of prostate cancer cell growth is an androgen-regulated gene. We addressed the question of whether or not androgen receptor can directly bind to specific PMEPA1 promoter upstream sequences. To test this hypothesis we extended in silico prediction of androgen receptor binding sites by a modeling approach and verified the actual binding by in vivo chromatin immunoprecipitation assay. Promoter upstream sequences of highly androgen-inducible genes were examined from microarray data of prostate cancer cells for transcription factor binding sites (TFBSs). Results were analyzed to formulate a model for the description of specific androgen receptor binding site context in these sequences. In silico analysis and subsequent experimental verification of the selected sequences suggested that a model that combined a GREF and a GATA TFBS was sufficient for predicting a class of functional androgen receptor binding sites. The GREF matrix family represents androgen receptor, glucocorticoid receptor and progesterone receptor binding sites and the GATA matrix family represents GATA binding protein 1-6 binding sites. We assessed the regulatory sequences of the PMEPA1 gene by comparing our model-based GREF_GATA predictions to weight matrix-based predictions. Androgen receptor binding to predicted promoter upstream sequences of the PMEPA1 gene was confirmed by chromatin immunoprecipitation assay. Our results suggested that androgen receptor binding to cognate elements was consistent with the GREF_GATA model. In contrast, using only single GREF weight matrices resulted in additional matches, apparently false positives. Our findings indicate that complex models based on datasets selected by biological function can be superior predictors as they recognize TFBSs in their functional context.