» Articles » PMID: 39728452

Comprehensive Blood Metabolome and Exposome Analysis, Annotation, and Interpretation in E-Waste Workers

Overview
Journal Metabolites
Publisher MDPI
Date 2024 Dec 27
PMID 39728452
Authors
Affiliations
Soon will be listed here.
Abstract

Electronic and electrical waste (e-waste) production has emerged to be of global environmental public health concern. E-waste workers, who are frequently exposed to hazardous chemicals through occupational activities, face considerable health risks. To investigate the metabolic and exposomic changes in these workers, we analyzed whole blood samples from 100 male e-waste workers and 49 controls from the GEOHealth II project (2017-2018 in Accra, Ghana) using LC-MS/MS. A specialized computational workflow was established for exposomics data analysis, incorporating two curated reference libraries for metabolome and exposome profiling. Two feature detection algorithms, and , were applied. In comparison to , showed better sensitivity in detecting MS features, particularly at trace levels. Principal component analysis demonstrated distinct metabolic profiles between e-waste workers and controls, revealing significant disruptions in key metabolic pathways, including steroid hormone biosynthesis, drug metabolism, bile acid biosynthesis, vitamin metabolism, and prostaglandin biosynthesis. Correlation analyses linked metal exposures to alterations in hundreds to thousands of metabolic features. Functional enrichment analysis highlighted significant perturbations in pathways related to liver function, vitamin metabolism, linoleate metabolism, and dynorphin signaling, with the latter being observed for the first time in e-waste workers. This study provides new insights into the biological impact of prolonged metal exposure in e-waste workers.

References
1.
Kenar E, Franken H, Forcisi S, Wormann K, Haring H, Lehmann R . Automated label-free quantification of metabolites from liquid chromatography-mass spectrometry data. Mol Cell Proteomics. 2013; 13(1):348-59. PMC: 3879626. DOI: 10.1074/mcp.M113.031278. View

2.
Tsugawa H, cajka T, Kind T, Ma Y, Higgins B, Ikeda K . MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat Methods. 2015; 12(6):523-6. PMC: 4449330. DOI: 10.1038/nmeth.3393. View

3.
Tautenhahn R, Bottcher C, Neumann S . Highly sensitive feature detection for high resolution LC/MS. BMC Bioinformatics. 2008; 9:504. PMC: 2639432. DOI: 10.1186/1471-2105-9-504. View

4.
Wittsiepe J, Feldt T, Till H, Burchard G, Wilhelm M, Fobil J . Pilot study on the internal exposure to heavy metals of informal-level electronic waste workers in Agbogbloshie, Accra, Ghana. Environ Sci Pollut Res Int. 2016; 24(3):3097-3107. DOI: 10.1007/s11356-016-8002-5. View

5.
Lai Y, Koelmel J, Walker D, Price E, Papazian S, Manz K . High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage. Environ Sci Technol. 2024; 58(29):12784-12822. PMC: 11271014. DOI: 10.1021/acs.est.4c01156. View