Daphnia magna metabolic profiling as a promising water quality parameter for the biological early warning system

Abstract

The inclusion of omics data into water quality monitoring programs is being considered to help alleviate the growing threat to water resources and ecosystem services. Despite the increasing need, the biological early warning system (BEWS), the widely used real-time water quality monitoring system, does not currently incorporate omics information, despite that metabolomics is a highly sensitive indicator of organism health and stress. We examined Daphnia magna metabolomics, which is the analysis of small molecules in living D. magna, as a potential water quality parameter for incorporation in the BEWS. The concentrations of 24 metabolites were measured with changes in water quality and variation of metabolite abundances was compared within and between conditions. Age-dependent monitoring revealed that matured individuals older than 8 days are appropriate model organisms for monitoring based on their low metabolomic variation as compared to younger daphnids. Hourly monitoring of metabolic variability and regulation under ambient and starved conditions demonstrated the rapid and sensitive detection of nutritional changes. Moreover, the metabolomic dysregulation due to exposure to the pollutant propranolol was also observed. By integrating all the observations, we found that the D. magna metabolome is a sensitive and useful parameter for detecting water quality changes and how these alter the function of keystone organisms. As such, this metabolomics-based framework is applicable to BEWS and highlights the beneficial advantages of integrating biomolecular and apical endpoint observations for enhanced performance in biomonitoring programs.