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Anal Chem. 2017 Nov 7;89(21):11505-11513. doi: 10.1021/acs.analchem.7b02759. Epub 2017 Oct 9.

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

Author information

1
Department of Food Chemistry and Toxicology, Faculty of Chemistry, University of Vienna , Währingerstraße 38, 1090 Vienna, Austria.
2
IBM Almaden Research Lab , 650 Harry Road, San Jose, California 95120, United States.
3
School of Civil and Environmental Engineering, Nanyang Technological University , 639798 Singapore.
4
Department of Environmental Health Sciences, Yale School of Public Health, Yale University , 60 College Street, New Haven, Connecticut 06520, United States.
5
Department of Chemistry and Biochemistry, University of South Carolina , Columbia, South Carolina 29208, United States.
6
National Center for Computational Toxicology, U.S. Environmental Protection Agency , 109 T.W. Alexander Drive, Research Triangle Park, North Carolina 27711, United States.

Abstract

Concurrent exposure to a wide variety of xenobiotics and their combined toxic effects can play a pivotal role in health and disease, yet are largely unexplored. Investigating the totality of these exposures, i.e., the "exposome", and their specific biological effects constitutes a new paradigm for environmental health but still lacks high-throughput, user-friendly technology. We demonstrate the utility of mass spectrometry-based global exposure metabolomics combined with tailored database queries and cognitive computing for comprehensive exposure assessment and the straightforward elucidation of biological effects. The METLIN Exposome database has been redesigned to help identify environmental toxicants, food contaminants and supplements, drugs, and antibiotics as well as their biotransformation products, through its expansion with over 700 000 chemical structures to now include more than 950 000 unique small molecules. More importantly, we demonstrate how the XCMS/METLIN platform now allows for the readout of the biological effect of a toxicant through metabolomic-derived pathway analysis, and further, artificial intelligence provides a means of assessing the role of a potential toxicant. The presented workflow addresses many of the methodological challenges current exposomics research is facing and will serve to gain a deeper understanding of the impact of environmental exposures and combinatory toxic effects on human health.

PMID:
28945073
DOI:
10.1021/acs.analchem.7b02759
[Indexed for MEDLINE]

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