Environmental Antidepressants Disrupt Metabolic Pathways in Spirostomum ambiguum and Daphnia magna: Insights from LC-MS-Based Metabolomics

Pharmaceuticals such as fluoxetine, paroxetine, sertraline, and mianserin occur in aquatic environments at low yet persistent concentrations due to their incomplete removal in wastewater treatment plants. Although frequently detected, these neuroactive compounds remain underrepresented in ecotoxicological assessments. Given their pharmacodynamic potency, environmentally relevant concentrations may induce sublethal effects in non-target organisms. In this study, we applied untargeted LC-MS-based metabolomics to investigate the sublethal effects of four widely used antidepressants-paroxetine, sertraline, fluoxetine (SSRIs), and mianserin (TeCA)-on two ecologically relevant freshwater invertebrates: <i>S. ambiguum</i> and <i>D. magna</i>. Organisms were individually exposed to each compound for 48 h at a concentration of 100 µg/L and 25 µg/L, respectively. Untargeted metabolomics captured the sublethal biochemical effects of these antidepressants, revealing both shared disruptions-e.g., in glycerophospholipid metabolism and cysteine and methionine metabolism-and species-specific responses. More pronounced pathway changes observed in <i>D. magna</i> suggest interspecies differences in metabolic capacity or xenobiotic processing mechanisms between taxa. Among the four antidepressants tested, sertraline in <i>D. magna</i> and fluoxetine in <i>S. ambiguum</i> exerted the most extensive metabolomic perturbations, as evidenced by the highest number and pathway impact scores. In <i>D. magna</i>, fluoxetine and mianserin produced similar metabolic profiles, largely overlapping with those of sertraline, whereas paroxetine affected only a single pathway, indicating minimal impact. In <i>S. ambiguum</i>, paroxetine and mianserin elicited comparable responses, also overlapping with those of fluoxetine, while sertraline triggered the fewest changes. These results suggest both compound-specific effects and a conserved metabolic response pattern among the antidepressants used. They also underscore the considerable potential of metabolomics as a powerful and sensitive tool for ecotoxicological risk assessments, particularly when applied across multiple model organisms to capture interspecies variations. However, further research is essential to identify which specific pathway disruptions are most predictive of adverse effects on organismal health.