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Chemical-gene interaction networks and causal reasoning for biological effects prediction and prioritization of contaminants for environmental monitoring and surveillance (poster)
Citation:
Schroeder, A., D. Martinovic-Weigelt, G. Ankley, K. Lee, N. Garcia-Reyero, E. Perkins, H. Schoenfuss, AND Dan Villeneuve. Chemical-gene interaction networks and causal reasoning for biological effects prediction and prioritization of contaminants for environmental monitoring and surveillance (poster). SETAC North America, Minneapolis, MN, November 12 - 16, 2017.
Impact/Purpose:
Evaluation of the potential effects of complex mixtures of chemicals in the environment is challenged by the lack of extensive toxicity data for many chemicals. However, there are growing sources of online information that curate and compile literature reports of chemical interactions with various genes, proteins, and biological pathways. The present work demonstrates how this information can be used to generate testable hypotheses regarding potential effects of chemicals detected in the environment and how this can help focus monitoring. This research helps to address identified needs by state and regional partners to better connect the occurrence of contaminants in the environment with an understanding of potential hazards. This presentation was invited by representatives from the Minnesota Department of Health who has expressed interest in these methods.
Description:
Product Description:Evaluation of the potential effects of complex mixtures of chemicals in the environment is challenged by the lack of extensive toxicity data for many chemicals. However, there are growing sources of online information that curate and compile literature reports of chemical interactions with various genes, proteins, and biological pathways. We show how this information can be leveraged to generate testable hypotheses regarding potential effects of chemicals detected in the environment and how this can help focus monitoring. Evaluating the potential human health and ecological risks associated with exposures to complex chemical mixtures in the environment is one of the main challenges of chemical safety assessment and environmental protection. There is a need for the development of approaches to integrate chemical monitoring and biological effects data to evaluate risks associated with chemicals present in the environment. Here, we used prior knowledge about chemical-gene interactions to develop a knowledge assembly model (KAM) for detected chemicals at five locations near the North Branch and Chisago wastewater treatment plants (WWTP) in the St. Croix River Basin, MN and WI. Site-specific KAMs were developed to generate hypotheses about the potential biological impacts of the chemicals at each location. Additionally, empirical gene expression data were also mapped to the assembly models to evaluate the likelihood of a chemical contributing to the observed biological responses using richness and concordance statistics. The integration of the gene expression data with the site-specific KAMs allowed for the prioritization of potential chemical contributors at each location. Atrazine was identified as a potential contributor to the observed gene expression responses at a location upstream of the North Branch WTTP. Four chemicals were identified as contributors to the observed biological responses at the effluent and downstream of the North Branch WWTP, with carbamazepine being a significant contributor at both locations. Four chemicals were identified as the greatest contributors to the observed biological responses in fish exposed to the effluent at the Chisago WWTP. Five chemicals were identified as contributors to the observed biological responses in fish exposed downstream of the Chisago WWTP, with 17-estradiol and estrone being two of the significant chemicals. Knowledge assembly models have strong potential for associating chemical occurrence with potential biological effects and providing a foundation for hypothesis generation to guide research and/or monitoring efforts related to the effects of contaminants in the environment.