Study on non-invasive and time-course metabolomics-based biological early warning systems (BEWS) using Daphnia magna...

The application of metabolomics to the water quality monitoring system, biological early warning system (BEWS), has been proposed; however, its development has not been attempted due to challenges such as high inter-individual variability and invasive sampling requirements in metabolomics applications. In this study, we employed an extracellular metabolomics (exo-metabolomics) approach using Daphnia magna to overcome these limitations and evaluate its utility in field river water conditions. From BEWS flow-through chambers, we collected exo-metabolites under ambient, copper exposure (0-80 μg/L), and post-exposure conditions. A total of 1228 metabolic features were detected, of which 736 were significantly elevated in the presence of D. magna that are considered exo-metabolite features. Among these, 86 showed significant differences across copper exposures at five different concentrations (adjusted p-value < 0.05), and hierarchical clustering revealed copper concentration-dependent patterns of metabolomic dysregulation. Several metabolic features responded within 24 h of sublethal copper exposure at 5 μg/L, indicating high sensitivity and early warning potential. Moreover, specific exo-metabolites showed either temporary or consistent responses, providing insights into different molecular responses. The exo-metabolic indicator candidates discussed in this study meet a range of environmental biomarker criteria. These findings demonstrate the promise of exo-metabolomic profiling as a real-time, non-destructive, and sensitive tool for the early detection of water pollution.