Metabolomics as a tool for in situ study of chronic metal exposure in estuarine invertebrates

Estuaries are subject to intense human use globally, with impacts from multiple stressors, such as metal contaminants. A key challenge is extending beyond traditional monitoring approaches to understand effects to biota and system function. To explore the metabolic effects of complex metal contaminants to sediment dwelling (benthic) fauna, we apply a multiple-lines-of-evidence approach, coupling environmental monitoring, benthic sampling, total metals analysis and targeted metabolomics. We characterise metabolic signatures of metal exposure in three benthic invertebrate taxa, which differed in distribution across sites and severity of metal exposure: sipunculid (very high), amphipod (high), maldanid polychaete (moderate). We observed sediment and tissue metal loads far exceeding sediment guidelines where toxicity-related adverse effects may be expected, for metals including, As, Cd, Pb, Zn and Hg. Change in site- and taxa-specific metabolite profiles was highly correlated with natural environmental drivers (sediment total organic carbon and water temperature). At the most metal influenced sites, metabolite variation was also correlated with sediment metal loads. Using supervised multivariate regression, taxa-specific metabolic signatures of increased exposure and possibility of toxic effects were characterised against multiple reference sites. Metabolic signatures varied according to each taxon and degree of metal exposure, but primarily indicated altered cysteine and methionine metabolism, metal-binding and elimination (lysosomal) activity, coupled to change in complex biosynthesis pathways, responses to oxidative stress, and cellular damage. This novel multiple-lines-of-evidence approach combining metabolomics with traditional environmental monitoring, enabled detection and characterisation of chronic metal exposure effects in situ in multiple invertebrate taxa. With capacity for application to rapid and effective monitoring of non-model species in complex environments, these approaches are critical for improved assessment and management of systems that are increasingly subject to anthropogenic drivers of change.