Use of high-resolution metabolomics for the identification of metabolic signals associated with traffic-related air pollution

• Type of Material: Article
• Authors: Donghai Liang, Emory University; Jennifer L. Moutinho, Georgia Institute of Technology; Rachel Golan, Ben Gurion University of the Negev; Tianwei Yu, Emory University; Chandresh N. Ladva, Emory University; Megan Niedzwiecki, Icahn School of Medicine at Mount Sinai; Douglas Walker, Emory University; Stefanie Ebelt Sarnat, Emory University; Howard Chang, Emory University; Roby Greenwald, Georgia State University; Dean P Jones, Emory University; Armistead G. Russell, Georgia Institute of Technology; Jeremy Sarnat, Emory University
• Language: English
• Date: 2018-11-01
• Publisher: Elsevier: Creative Commons Licenses
• Publication Version: Final Published Version
• Copyright Statement: © 2018 Elsevier Ltd
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.envint.2018.07.044
• Title of Journal or Parent Work: Environment International
• ISSN: 0160-4120
• Volume: 120
• Start Page: 145
• End Page: 154
• Grant/Funding Information: We acknowledge the support from the HERCULES exposome research center, supported by the National Institute of Environmental Health Sciences of the National Institutes of Health (P30ES019776).; Dr. Golan gratefully acknowledges support by a post-doctoral fellowship from the Environment and Health Fund, Jerusalem, Israel.; We acknowledge NSF for providing a fellowship to JM (DGE-1650044), and Dr. Russell made use of funds provided by a generous gift from Howard T. Tellepson.; The study used on the instrumentation assembled for field studies conducted as part of the Southeastern Center for Air Pollution and Epidemiology (SCAPE), which was funded by a US Environmental Protection Agency STAR grant R834799.; upport for this project were provided through a contract with the Health Effects Institute (RFA #4942-RFA13–1/14–3).; Dr. Walker gratefully acknowledges the following grants supported by NIH (NIMH R01MH107205, NIEHS T32ES012870, and NIH S10OD018006).
• Keywords: High resolution metabolomics; Metabolic perturbations; Science & Technology; Environmental Sciences & Ecology; Inflammation; ENVIRONMENTAL METABOLOMICS; LUNG-FUNCTION; Traffic related air pollution; BREATH CONDENSATE; Environmental Sciences; Life Sciences & Biomedicine; Oxidative stress; Biomarkers; NITRIC-OXIDE; CYTOCHROMES P450; PARTICULATE MATTER; INFLAMMATION; LEUKOTRIENE B-4; OXIDATIVE STRESS; VITAMIN-E
• Research Categories: Environmental Sciences; Engineering, Environmental; Biology, Ecology

Abstract

Background: High-resolution metabolomics (HRM) is emerging as a sensitive tool for measuring environmental exposures and biological responses. The aim of this analysis is to assess the ability of high-resolution metabolomics (HRM) to reflect internal exposures to complex traffic-related air pollution mixtures. Methods: We used untargeted HRM profiling to characterize plasma and saliva collected from participants in the Dorm Room Inhalation to Vehicle Emission (DRIVE) study to identify metabolic pathways associated with traffic emission exposures. We measured a suite of traffic-related pollutants at multiple ambient and indoor sites at varying distances from a major highway artery for 12 weeks in 2014. In parallel, 54 students living in dormitories near (20 m) or far (1.4 km) from the highway contributed plasma and saliva samples. Untargeted HRM profiling was completed for both plasma and saliva samples; metabolite and metabolic pathway alternations were evaluated using a metabolome-wide association study (MWAS) framework with pathway analyses. Results: Weekly levels of traffic pollutants were significantly higher at the near dorm when compared to the far dorm (p < 0.05 for all pollutants). In total, 20,766 metabolic features were extracted from plasma samples and 29,013 from saliva samples. 45% of features were detected and shared in both plasma and saliva samples. 1291 unique metabolic features were significantly associated with at least one or more traffic indicator, including black carbon, carbon monoxide, nitrogen oxides and fine particulate matter (p < 0.05 for all significant features), after controlling for confounding and false discovery rate. Pathway analysis of metabolic features associated with traffic exposure indicated elicitation of inflammatory and oxidative stress related pathways, including leukotriene and vitamin E metabolism. We confirmed the chemical identities of 10 metabolites associated with traffic pollutants, including arginine, histidine, γ‑linolenic acid, and hypoxanthine. Conclusions: Using HRM, we identified and verified biological perturbations associated with primary traffic pollutant in panel-based setting with repeated measurement. Observed response was consistent with endogenous metabolic signaling related to oxidative stress, inflammation, and nucleic acid damage and repair. Collectively, the current findings provide support for the use of untargeted HRM in the development of metabolic biomarkers of traffic pollution exposure and response.