Linking field‐based metabolomics and chemical analyses to prioritize contaminants of emerging concern in the Great Lakes basin

Abstract

The ability to focus on the most biologically relevant contaminants affecting aquatic ecosystems can be challenging because toxicity-assessment programs have not kept pace with the growing number of contaminants requiring testing. Because it has proven effective at assessing the biological impacts of potentially toxic contaminants, profiling of endogenous metabolites (metabolomics) may help screen out contaminants with a lower likelihood of eliciting biological impacts, thereby prioritizing the most biologically important contaminants. The authors present results from a study that utilized cage-deployed fathead minnows (Pimephales promelas) at 18 sites across the Great Lakes basin. They measured water temperature and contaminant concentrations in water samples (132 contaminants targeted, 86 detected) and used H-nuclear magnetic resonance spectroscopy to measure endogenous metabolites in polar extracts of livers. They used partial least-squares regression to compare relative abundances of endogenous metabolites with contaminant concentrations and temperature. The results indicated that profiles of endogenous polar metabolites covaried with at most 49 contaminants. The authors identified up to 52% of detected contaminants as not significantly covarying with changes in endogenous metabolites, suggesting they likely were not eliciting measurable impacts at these sites. This represents a first step in screening for the biological relevance of detected contaminants by shortening lists of contaminants potentially affecting these sites. Such information may allow risk assessors to prioritize contaminants and focus toxicity testing on the most biologically relevant contaminants. Environ Toxicol Chem 2016;35:2493-2502. Published 2016 Wiley Periodicals Inc. on behalf of SETAC. This article is a US Government work and, as such, is in the public domain in the United States of America.

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

“…One approach involves studying changes in specific metabolites or suites of metabolites to identify biological pathways affected by a given individual test chemical (e.g., Collette et al 2010; Ekman et al 2012; Davis et al 2017). The second approach employs changes in metabolite profiles to provide inferences about the nature of the complex chemical mixture associated with observed biological effects (e.g., Davis et al 2013, 2016; Collette et al 2019). As an illustration of this second type of application, Davis et al (2013) described a caged fish study in which comparative metabolite profiles enabled definition of the contribution of pulp and paper mill influent to the biological effects of a WWTP effluent by conducting measurements in fish exposed before, during, and after a scheduled shutdown of the mill.…”

“…The goal of the present study was to provide a practical demonstration of the application of pathway‐based targeted and nontargeted techniques for the identification of occurrence and potential effects of complex chemical mixtures in a Great Lakes AOC. Throughout the Great Lakes, human activities past and present have contributed to the presence of complex mixtures of legacy contaminants, current‐use pesticides, pharmaceuticals, flame retardants, plasticizers, and many other contaminants of emerging concern (Venier et al 2014; Baldwin et al 2016; Davis et al 2016; Elliott et al 2017). The Maumee River is the largest tributary of the Great Lakes by watershed area, significantly contributing to chemical and sediment loading to Lake Erie (Baker et al 2014).…”

“…We used partial least‐squares (PLS) regression (SIMCA‐13.0; Umetrics) to compare changes in endogenous metabolite profiles with concentrations of detected organic contaminants, and to screen out those contaminants whose concentrations did not significantly covary with metabolite changes. We have previously used this modeling approach to identify environmental stressors that have (or do not have) a significant biological impact on endogenous metabolites, and full modeling details can be found in these earlier studies (Davis et al 2016; Collette et al 2019). Briefly, for the upstream batch, we first constructed a “global” PLS model that included all 87 organic contaminants detected in at least one of the samples from the upstream sites.…”

“…Over the past several years, we have been involved in a large collaborative effort, supported through the Great Lakes Restoration Initiative (GLRI), to develop and demonstrate the use of pathway‐based tools for assessing complex mixtures of aquatic contaminants (Ekman et al 2013). As part of this effort, we have worked at several Great Lakes (USA/Canada) sites in the context of case studies/demonstration projects (e.g., Davis et al 2013, 2016; Kahl et al 2014; Cavallin et al 2016; Blackwell et al 2017, 2019; Li et al 2017; Perkins et al 2017; Mosley et al 2018; Corsi et al 2019). In the current study we present data and results from studies conducted in the Maumee River (OH, USA) demonstrating the practical application of several different pathway‐based approaches to collect and interpret analytical and biological data in the context of an integrated site assessment.…”

A Community Multi-Omics Approach towards the Assessment of Surface Water Quality in an Urban River System

“…Beale et al [27] used metabolomics with physico-chemical data to assess water pipeline infrastructure and water pipe biofilms, characterizing biofilms based on pipe material and the excreted metabolites that pass from the biofilm into the water stream. A similar study was used to investigate impacts of exposure to chemicals of emerging concern relative to other stressors in fathead minnows, which was used as a model species [28]. …”