Prioritization of Contaminants of Emerging Concern in Wastewater Treatment Plant Discharges Using Chemical:Gene Interactions in Caged Fish

Abstract

We examined whether contaminants present in surface waters could be prioritized for further assessment by linking the presence of specific chemicals to gene expression changes in exposed fish. Fathead minnows were deployed in cages for 2, 4, or 8 days at three locations near two different wastewater treatment plant discharge sites in the Saint Louis Bay, Duluth, MN and one upstream reference site. The biological impact of 51 chemicals detected in the surface water of 133 targeted chemicals was determined using biochemical endpoints, exposure activity ratios for biological and estrogenic responses, known chemical:gene interactions from biological pathways and knowledge bases, and analysis of the covariance of ovary gene expression with surface water chemistry. Thirty-two chemicals were significantly linked by covariance with expressed genes. No estrogenic impact on biochemical endpoints was observed in male or female minnows. However, bisphenol A (BPA) was identified by chemical:gene covariation as the most impactful estrogenic chemical across all exposure sites. This was consistent with identification of estrogenic effects on gene expression, high BPA exposure activity ratios across all test sites, and historical analysis of the study area. Gene expression analysis also indicated the presence of nontargeted chemicals including chemotherapeutics consistent with a local hospital waste stream. Overall impacts on gene expression appeared to be related to changes in treatment plant function during rain events. This approach appears useful in examining the impacts of complex mixtures on fish and offers a potential route in linking chemical exposure to adverse outcomes that may reduce population sustainability.

Pathway‐Based Approaches for Assessing Biological Hazards of Complex Mixtures of Contaminants: A Case Study in the Maumee River

“…There is an ever‐increasing amount of alternative data that could contribute to complex mixture assessment. For example, we recently described a chemical–gene association network modeling approach that employs linked chemical and gene/protein expression information from extensive, open‐source databases, to develop hypotheses as to possible perturbation of biological pathways by complex mixtures of chemicals detected in surface waters or effluent (Martinovic‐Weigelt et al 2014; Cavallin et al 2016; Li et al 2017; Perkins et al 2017; Schroeder et al 2017; Berninger et al 2019). One challenge associated with this chemical–gene network modeling approach is that it is based on the presence or absence of chemicals rather than concentrations in samples, so the ability to quantitatively consider exposure as part of the complex mixture analysis is limited (Schroeder et al 2016, 2017; Blackwell et al 2017).…”

Towards Sustainable Environmental Quality: Priority Research Questions for the Australasian Region of Oceania

“…With increasing urbanization, multiple land uses are interfacing in peri-urban watersheds, which inherently increases the likelihood of diverse contaminants from urban, agricultural, and industrial activities that co-occur in complex mixture scenarios. Unprecedented opportunities are emerging with use of high-throughput in vitro, transgenic fish lines, and in situ toxicogenomic platforms when coupled with targeted and nontargeted chemical analyses (Bradley et al 2017) in the field Bradley et al 2017;Perkins et al 2017). For example, changes in benthic community distributions have been reported at concentrations below individual metal guideline values .…”

“…In recent years, bioassay tools with increasing mechanistic specificity have become important for diagnostic applications (Escher et al 2014) beyond the traditional morphometric aquatic toxicity responses introduced above that are employed in TIEs (USEPA 1991). Unprecedented opportunities are emerging with use of high-throughput in vitro, transgenic fish lines, and in situ toxicogenomic platforms when coupled with targeted and nontargeted chemical analyses (Bradley et al 2017) in the field Bradley et al 2017;Perkins et al 2017). However, metabolic transformation of contaminants and other basic scientific limitations remain when extrapolating in vitro to in vivo effects and even comparing responses among the 2 most common fish models (Corrales et al 2016;Steele et al 2018).…”

Review of and Recommendations for Monitoring Contaminants and their Effects in the San Francisco Bay−Delta

“…The 'Omics movement is now beginning to probe the epigenome (Vandegehuchte and Janssen 2011;Head et al 2012;Brander et al 2017) and the whole genome through re-sequencing methods (Bentley 2006), enabling investigators to better understand multigenerational effects and sensitivity differences between populations, which are described below in more detail. As with all 'Omics techniques, the costs of these assessments are rapidly decreasing, and such approaches are highly valuable both to generate detailed mechanism-based, adverseoutcome pathways, and to develop biomarkers of effect, for use in contaminant monitoring and risk assessments (Monsinjon and Knigge 2007;Martyniuk and Simmons 2016) in laboratory or field settings (Perkins et al 2017).…”