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

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.