Identifying Unexpected Neurotoxicity Drivers with Acetylcholinesterase Inhibition by Virtual Effect-Directed Analysis in Nationwide Estuarine Waters

Neurotoxicity is frequently observed in the global aquatic environment, threatening aquatic ecosystems and human health. However, a very limited proportion of neurotoxic effects (~1%) has been explained by known chemicals of concern. Here, we integrated machine learning, nontargeted analysis, and in vitro biotesting to identify neurotoxic drivers of acetylcholinesterase (AChE) inhibition in estuarine waters along the coastline of China. Machine learning was used as a virtual fractionation tool to reduce the complexity of chemical mixtures, thus guiding nontargeted screening of AChE inhibitors. Ultimately, sixty chemicals with diverse known and presently unknown structures were identified, explaining 82.1% of the observed AChE inhibition in estuarine water samples. Polyunsaturated fatty acids were unexpectedly found to be neurotoxic drivers, accounting for 80.5% of the overall effect. This proof-of-concept study demonstrates that our approach enables rapid and comprehensive screening of causative organic pollutants associated with various in vitro endpoints for large-scale monitoring of water quality.