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LINKAGE OF EXPOSURE AND EFFECTS USING GENOMICS, PROTEOMICS, AND METABONOMICS IN SMALL FISH MODELS
Impact/Purpose:
We propose to use a combination of whole organism endpoints, genomic, proteomic, and metabonomic approaches, and computational modeling to (a) identify new molecular biomarkers of exposure to endocrine disrupting compounds (EDCs) representing several modes/mechanisms of action (MOA) and (b) link those biomarkers to effects that are relevant for both diagnostic and predictive risk assessments using small fish models. These goals will be achieved through a three-phase approach that incorporates expertise across EPA/ORD and capitalizes on partnerships with other federal and academic laboratories. During Phase 1, effects of a candidate list of nine compounds having different MOA within the hypothalamic-pituitary-gonadal (HPG) axis will be characterized using the fathead minnow (Pimephales promelas) Phase 2 will take advantage of the well characterized zebrafish (Danio rerio) genome, to identify transcriptome and proteome level changes in addition to metabolite changes, associated with zebrafish exposure to the same suite of EDCs. Phase 2 data will be used to identify relevant molecular changes that could (a) serve as diagnostic markers for various types of EDC exposure and (b) begin to inform a systems-level characterization of the responses to those exposures. In Phase 3, candidate genes/diagnostic markers identified in zebrafish (Phase 2) will be validated in fathead minnows through focused toxicological testing aimed at examination of changes in specific gene expression and protein levels. In this way, changes at the genomic, proteomic, and metabonomic level will be linked to one another, linked across multiple teleost species, and ultimately linked to adverse effects at the individual- and, through modeling, population-level (i.e. linkage back to Phase 1). This three phase effort will identify new, potentially cost-effective, diagnostic exposure markers for EDCs, and developing source-to-outcome linkages critical for effective use of biomarkers for risk assessments. This is one of three objectives of the EPA/ORD Computational Toxicology program (Kavlock et al. 2004).
Description:
Over the past decade there has been a focused international effort to identify possible adverse effects of EDCs on humans and wildlife. Effects on reproduction and development mediated through alterations in the HPG axis have been of particular concern (USEPA 1998). There is convincing evidence that fish in the environment are being affected by EDCs both at the individual and population levels (World Health Organization 2002). Unfortunately, because feral fish populations are simultaneously exposed to multiple stressors, it is difficult, if not impossible, to accurately assess the role of EDCs in producing adverse impacts (World Health Organization 2002). As a result, many protocols using fish have been developed and validated, both nationally and internationally, for regulatory programs for EDCs (Ankley and Johnson 2004). In the US, a 1996 congressional mandate directed the US Environmental Protection Agency (EPA) to develop a formal screening and testing program for EDCs. Among the Tier 1 tests recommended (USEPA 1998) to detect endocrine disruption of the HPG axis is a 21?d reproduction assay with adult fathead minnows (Ankley et al. 2001). This approach is also being applied to EDC testing with two other small fish models (i.e., medaka, zebrafish) in other countries via activities coordinated by the Organization for Economic Cooperation and Development (OECD; Ankley and Johnson 2004). While of great utility, an important limitation of these tests is that they require significant investment in time and resources. Furthermore, many of the effects endpoints measured in the assays are not diagnostic of specific endocrine-related MOA. An ideal screening assay for EDCs would quickly identify diagnostic endpoints directly indicative of exposure in adult organisms. Detection of anomalies at the genomic level would enable screening methods of shorter duration to identify effects at the molecular level, soon after exposure, before they are manifested at the population level. This research also directly addresses the Computational Toxicology objective of providing approaches for prioritizing chemicals for testing.