Deep Learning Bridged Bioactivity, Structure, and GC-HRMS-Readable Evidence to Decipher Nontarget Toxicants in Sediments.

Identifying causative toxicants in mixtures is critical, but this task is challenging when mixtures contain multiple chemical classes. Effect-based methods are used to complement chemical analyses to identify toxicants, yet conventional bioassays typically rely on an apical and/or single endpoint, providing limited diagnostic potential to guide chemical prioritization. We proposed an event-driven taxonomy framework for mixture risk assessment that relied on high-throughput screening bioassays and toxicant identification integrated by deep learning. In this work, the framework was evaluated using chemical mixtures in sediments eliciting aryl-hydrocarbon receptor activation and oxidative stress response. Mixture prediction using target analysis explained <10% of observed sediment bioactivity. To identify additional contaminants, two deep learning models were developed to predict fingerprints of a pool of bioactive substances (event driver fingerprint, EDFP) and convert these candidates to MS-readable information (event driver ion, EDION) for nontarget analysis. Two libraries with 121 and 118 fingerprints were established, and 247 bioactive compounds were identified at confidence level 2 or 3 in sediment extract using GC-qToF-MS. Among them, 12 toxicants were analytically confirmed using reference standards. Collectively, we present a "bioactivity-signature-toxicant" strategy to deconvolute mixtures and to connect patchy data sets and guide nontarget analysis for diverse chemicals that elicit the same bioactivity.

A multi-scenario risk assessment strategy applied to mixtures of chemicals of emerging concern in the River Aconcagua basin in Central Chile.

Environmental risk assessments strategies that account for the complexity of exposures are needed in order to evaluate the toxic pressure of emerging chemicals, which also provide suggestions for risk mitigation and management, if necessary. Currently, most studies on the co-occurrence and environmental impacts of chemicals of emerging concern (CECs) are conducted in countries of the Global North, leaving massive knowledge gaps in countries of the Global South. In this study, we implement a multi-scenario risk assessment strategy to improve the assessment of both the exposure and hazard components in the chemical risk assessment process. Our strategy incorporates a systematic consideration and weighting of CECs that were not detected, as well as an evaluation of the uncertainties associated with Quantitative Structure-Activity Relationships (QSARs) predictions for chronic ecotoxicity. Furthermore, we present a novel approach to identifying mixture risk drivers. To expand our knowledge beyond well-studied aquatic ecosystems, we applied this multi-scenario strategy to the River Aconcagua basin of Central Chile. The analysis revealed that the concentrations of CECs exceeded acceptable risk thresholds for selected organism groups and the most vulnerable taxonomic groups. Streams flowing through agricultural areas and sites near the river mouth exhibited the highest risks. Notably, the eight risk drivers among the 153 co-occurring chemicals accounted for 66-92 % of the observed risks in the river basin. Six of them are pesticides and pharmaceuticals, chemical classes known for their high biological activity in specific target organisms.

Association between mixed exposure to per- and polyfluoroalkyl substances and metabolic syndrome in Korean adults: Data from the Korean National environmental health survey cycle 4.

AIM: To explore the effect of mixed exposure to per- and polyfluoroalkyl substances (PFAS) on metabolic syndrome (MetS). METHODS: This cross-sectional study used data from the Korean National Environmental Health Survey Cycle 4 (2018-2020). The serum concentrations of five PFAS (perfluorooctanoic acid [PFOA], perfluorooctanesulfonic acid [PFOS], perfluorohexanesulfonic acid, perfluorononanoic acid [PFNA], and perfluorodecanoic acid [PFDeA]) were measured, and the relative potency factor approach was employed for the mixture of PFAS (Cmix) assessment. MetS was diagnosed if the patient satisfied three of five criteria: central obesity, elevated triglycerides, reduced high-density lipoprotein cholesterol, elevated blood pressure (BP), and elevated glycated hemoglobin (HbA1c). Age, sex, smoking, drinking, and exercise status were considered as covariates. The risk of MetS for single and mixed exposure to PFAS was analyzed using binomial regression and Bayesian kernel machine regression (BKMR). RESULTS: A total of 2984 (male:female = 1:1.3; age range, 19-80 years) adults were enrolled. The prevalence of MetS was 45.6%. Each PFAS and Cmix levels were higher in participants with MetS than in those without MetS. Cmix increased the risk of elevated BP and HbA1c, and eventually MetS (odds ratio [OR] = 2.00, 95% confidence interval [CI] 1.11-3.60 per log10Cmix; OR = 1.57, 95% CI 1.07-2.31 in the highest quartile of Cmix [Q4] vs. the lowest [Q1]). Sex-specific analyses revealed that the impact of Cmix was valid in females but not in males (Cmix Q4 vs. Q1: OR = 1.01, 95% CI 0.57-1.8 in males; OR = 2.30, 95% CI 1.38-3.84 in females). In the BKMR analysis, mixed exposure to PFAS dose-dependently increased the risk of MetS, particularly in females. Among single exposures, PFNA contributed significantly to the cumulative effect. CONCLUSION: Mixed exposure to PFAS was associated with a higher risk of MetS in females. Further studies on potential health concerns associated with PFAS mixtures are warranted.