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.