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Visualized Networking of Co-Regulated Lipids in Human Blood Based on High-Throughput Screening Data: Implications for Exposure Assessment

Author:
Gao, Shixiong, Wan, Yi, Li, Wenjuan, Huang, Chong
Source:
Environmental science & technology 2019 v.53 no.5 pp. 2862-2872
ISSN:
1520-5851
Subject:
automation, biochemical pathways, blood, blood sampling, epidemiological studies, exposure assessment, homeostasis, human resources, humans, metabolites, municipal solid waste, occupational exposure, pollutants, screening, sterols, waste incineration
Abstract:
Exposure to environmental chemicals could disturb lipidome homeostasis in biotas. Comprehensive identification and interpretation of lipid molecules in biological samples are of great importance to elucidate the potential changes in lipid homeostasis upon exposure to various environmental stimuli. In this study, a total of 156 human blood samples were collected including 108 general citizens (control group) and 48 employees in a municipal solid waste incineration (MSWI) plant (occupational exposure group). More than 1500 lipid molecules, belonging to five lipid classes, were screened in the blood samples by UPLC-QTOF–MS in the MSᴱ acquisition mode. All of the coupled compounds with correlation coefficients (R) of 0.7 or higher were selected for automated network correlation analysis. A global visual network was automatically produced from thousands of coregulated lipid species in the blood samples. In the automatically produced molecular network, the distributions of the major correlated lipids were in accordance with their metabolic pathways in the KEGG map. Different lipidomic profiles in the blood samples from the two groups of people were easily observed by this visualization technique. Among the intrinsic lipid classes, glycererides and sterol lipids might represent the most sensitively affected lipids upon exposure to various pollutants emitted from the MSWI plant. The visualized network of coregulated lipids identified in human blood presents a new approach for interpreting the metabolic relationships among the thousands of metabolites identified in toxicological and epidemiological studies.
Agid:
6322726