Integrating high-resolution chromatographic and machine learning-based virtual fractionation to identify aryl hydrocarbon receptor agonists in sediments

Complex organic chemical mixtures in aquatic ecosystems may cause adverse effects to aquatic and sediment-dwelling organisms. Identified chemicals typically explain less than 10% of the observed in vitro bioactivities of such complex mixtures extracted from sediments. In a proof-of-concept study, we combined high-resolution fractionation with virtual fractionation to identify aryl hydrocarbon receptor (AhR) agonists using an AhR reporter gene assay in sediment from the Elbe River, Germany. Only apolar fractions activated the AhR but without specificity among them, necessitating additional virtual fractionation after analysis by gas chromatography coupled with high-resolution mass spectrometry (GC-HRMS). Activity and potency predictions by machine learning models for deconvoluted GC-HRMS features from spectral reference library matching allowed the identification of 145 AhR-active HRMS features with 26 chemicals bioanalytically and chemically confirmed, most of which were polycyclic aromatic hydrocarbons (PAHs). With semi-quantified concentrations and estimated potency for the tentatively identified chemicals, the mixture effects of the identified agonists accounted for 14% to 47% of AhR activation in sediments, doubling the contribution of known US EPA priority PAHs. High-resolution fractionation combined with virtual fractionation is an effective tool for early screening of bioactive chemicals. This study provides a blueprint to identify causative chemicals for other environmentally relevant modes of toxic action.