Reaction-Guided Metabolomics Accelerates High-Throughput Annotation of Xenobiotic Metabolites for Human Exposome

The human exposome features a highly expansive chemical space and substantial individual variability. Although screenings of xenobiotic compounds have revealed exposure landscapes of specific compounds, significant bottlenecks remain in profiling their biotransformed products for comprehensive exposome-wide analysis, including limitations to known metabolites, challenges in new metabolite annotation, and low throughput. In this study, we developed an untargeted metabolomics-based compound metabolite discovery network (CMDN) to facilitate high-throughput annotation of xenobiotic metabolites. CMDN integrates a triple-layered architecture comprising a differential expression metabolic space, a rule-based pseudo-MS1 candidature space and an MS2 spectrum similarity network. The utilities and advantages are demonstrated using pesticides as a representative example, given their widespread human exposure and well-documented toxicity. Coupled with enzymatic biotransformation assays involving 1,021 pesticides, CMDN nonredundantly annotated 2,886 biotransformed derivatives. Following multichannel validation, including standard verification, retention time prediction, murine studies, and time-course logistic modeling, identified metabolites were screened in a human cohort, revealing a novel, diverse, and extensive exposure spectrum. Collectively, this study establishes, for the first time, a scalable workflow for the annotation and screening of previously undercharacterized xenobiotic metabolites with unprecedented throughput, representing a significant advancement toward the characterization, interpretation, and prioritization of the human exposome.