Exposome-Scale Investigations Guided by Global Metabolomics, Pathway Analysis, and Cognitive Computing.

Area of Science:

• Environmental Health
• Metabolomics
• Toxicology

Background:

• Concurrent exposure to diverse xenobiotics and their combined toxic effects are significant in health and disease but remain understudied.
• Investigating the "exposome" (total exposures) and biological impacts is a new environmental health paradigm lacking user-friendly, high-throughput technology.

Purpose of the Study:

• To demonstrate a novel, high-throughput workflow for comprehensive exposome assessment.
• To elucidate the biological effects of environmental toxicants and their combined actions.
• To address methodological challenges in current exposomics research.

Main Methods:

• Utilized mass spectrometry-based global exposure metabolomics.
• Employed tailored database queries and cognitive computing for analysis.
• Expanded the METLIN Exposome database to over 950,000 small molecules.
• Integrated XCMS/METLIN platform with metabolomic pathway analysis and artificial intelligence.

Main Results:

• Successfully identified environmental toxicants, food contaminants, drugs, and their biotransformation products.
• Demonstrated the platform's capability to reveal biological effects through metabolomic pathway analysis.
• Showcased artificial intelligence's role in assessing potential toxicant impacts.

Conclusions:

• The presented workflow offers a user-friendly, high-throughput solution for exposomics research.
• This approach facilitates a deeper understanding of environmental exposures and combined toxic effects on human health.
• Advances the field of environmental health by enabling comprehensive exposure assessment and biological effect elucidation.