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.