Prior knowledge-based approach for associating contaminants with biological effects: A case study in the St. Croix River basin, MN, WI, USA

Anthony L. Schroeder, Dalma Martinović-Weigelt, Gerald T. Ankley, Kathy E. Lee, Natalia Garcia-Reyero, Edward J. Perkins, Heiko L. Schoenfuss, Daniel L. Villeneuve

Evaluating potential adverse effects of complex chemical mixtures in the environment is challenging. One way to address that challenge is through more integrated analysis of chemical monitoring and biological effects data. In the present study, water samples from five locations near two municipal wastewater treatment plants in the St. Croix River basin, on the border of MN and WI, USA, were analyzed for 127 organic contaminants. Known chemical-gene interactions were used to develop site-specific knowledge assembly models (KAMs) and formulate hypotheses concerning possible biological effects associated with chemicals detected in water samples from each location. Additionally, hepatic gene expression data were collected for fathead minnows (Pimephales promelas) exposed in situ, for 12 d, at each location. Expression data from oligonucleotide microarrays were analyzed to identify functional annotation terms enriched among the differentially-expressed probes. The general nature of many of the terms made hypothesis formulation on the basis of the transcriptome-level response alone difficult. However, integrated analysis of the transcriptome data in the context of the site-specific KAMs allowed for evaluation of the likelihood of specific chemicals contributing to observed biological responses. Thirteen chemicals (atrazine, carbamazepine, metformin, thiabendazole, diazepam, cholesterol, p-cresol, phenytoin, omeprazole, ethyromycin, 17β-estradiol, cimetidine, and estrone), for which there was statistically significant concordance between occurrence at a site and expected biological response as represented in the KAM, were identified. While not definitive, the approach provides a line of evidence for evaluating potential cause-effect relationships between components of a complex mixture of contaminants and biological effects data, which can inform subsequent monitoring and investigation.