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Using transcriptomic tools to evaluate biological effects across effluent gradients at a diverse set of study sites in Minnesota, USA
Citation:
Berninger, J., D. Martinovic-Weigelt, N. Garcia-Reyero, E. Perkins, G. Ankley, AND Dan Villeneuve. Using transcriptomic tools to evaluate biological effects across effluent gradients at a diverse set of study sites in Minnesota, USA. ENVIRONMENTAL SCIENCE AND TECHNOLOGY. John Wiley & Sons, Ltd., Indianapolis, IN, 48(4):2404-2412, (2014).
Impact/Purpose:
The ability to assess exposure based on chemical measurements alone is becoming unrealistic as the number and variety of anthropogenic chemicals known to be present in the environment increases. Consequently, there is growing recognition of the utility of biological response monitoring as a complement to chemical detection. While apical toxicity endpoints (e.g., lethality, growth, reproduction) have been used to complement chemical testing, they are inadequate for detecting the full range of effects of concern including a variety of sublethal, long-term or multi-generational effects, effects specific to sensitive subpopulations or life stages, etc. Consequently, there is a need for pathway-based effects monitoring approaches that employ a variety of mechanistically-oriented endpoints (e.g., targeted molecular and biochemical measurements anchored to specific toxicity/adverse outcome pathways; ‘omics) in effects-based exposure assessments. Regulatory bodies (county, state, and federal) will increasingly need to make use of these tools for environmental monitoring. The current paper provides a case study that demonstrates the application of an effects-based monitoring approach utilizing “omics” endpoints in an environmental surveillance and monitoring context. In particular, the current example demonstrates how analysis of biological responses along a spatial exposure gradient can aid in identifying the relative contribution of different stressors and sources to the overall biological response profile observed at a site. This work supports Agency needs for effective effects-based monitoring approaches for detecting and characterizing hazards associated with exposure to complex mixtures of chemical in the environment.
Description:
The aim of this overall project was to explore the utility of ?‘omics’ approaches in monitoring aquatic environments where complex, often unknown, stressors make chemical-specific risk assessment untenable. This specific component of the effort examined changes in the fathead minnow (Pimephales promelas) ovarian transcriptome following 4 day in situ exposures conducted at three sites in MN. Within each site, fish were exposed to water from three locations along a spatial gradient relative to a waste water treatment plant (WWTP) discharge. After exposure, ovarian transcripts were analyzed using a custom 15000 feature microarray. Microarray data were normalized among all the sites and differentially expressed genes (DEGs) at each site were identified. Principal components analysis (PCA; with median centering) was used to examine the relationship among DEGs at each location within a site. To evaluate the biological implications of gene response profiles from a systems prospective DAVID functional enrichment analyses, specifically KEGG pathways, were conducted. Additionally GSEA was used to identify enrichments of genes within predetermined gene sets associated with the HPG-axis in female fathead minnows. Using an intra-gradient point of comparison, biological responses specifically associated with the WWTP effluent were able to be identified. Fish from locations downstream of the effluent discharges, exhibited many biological responses in common with those exposed to the effluent, indicating that effect of the discharge do not fully dissipate downstream. Both DAVID analysis and GSEA showed biological responses that could be putatively linked to potential adverse reproductive outcomes, resulting from effluent exposure at all three study sites. The use intra-gradient comparisons for the analysis of transcriptomic data provides a reasonable and reproducible study design, which has demonstrated the ability to discern effluent-associated biological responses across multiple aquatic environments with differing anthropogenic and environmental influences.
