Contaminants of Concern and Spatiotemporal Metabolomic Changes in Quagga Mussels (Dreissena bugensis rostriformis) from the Milwaukee Estuary (Wisconsin, USA)

Abstract

Environmental metabolomics has emerged as a promising technique in the field of biomonitoring and as an indicator of aquatic ecosystem health. In the Milwaukee Estuary (Wisconsin, USA), previous studies have used a non‐targeted metabolomic approach to distinguish between zebra mussels (Dreissena polymorpha) collected from sites of varying contamination. To further elucidate the potential effects of contaminants on bivalve health in the Milwaukee Estuary, the present study adopted a caging approach to study the metabolome of quagga mussels (Dreissena bugensis rostriformis) deployed in six sites of varying contamination for 2, 5 or 55 days. Caged mussels were co‐deployed with two types of passive sampler (POCIS and SPMDs) and data loggers. In conjunction, in‐situ quagga mussels were collected from the four sites studied previously and analyzed for residues of contaminants and metabolomics using a targeted approach. For the caging study, temporal differences in the metabolomic response were observed with few significant changes observed after 2 and 5 days, but larger differences (up to 97 significantly different metabolites) to the metabolome in all sites after 55 days. A suite of metabolic pathways were altered including biosynthesis and metabolism of amino acids, and up‐modulation of phospholipids at all sites, suggesting a potential biological influence such as gametogenesis. In the caging study, average temperatures appeared to have a greater effect on the metabolome than contaminants, despite a large concentration gradient in PAH residues measured in passive samplers and mussel tissue. Conversely, significant differences between the metabolome of mussels collected in situ from all three contaminated sites and the offshore reference site were observed. Overall, these findings highlight the importance of contextualizing the effects of environmental conditions and reproductive processes on the metabolome of model organisms to facilitate the wider use of this technique for biomonitoring and environmental health assessments.