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

Donghai Liang, Jennifer L. Moutinho, Rachel Golan, Tianwei Yu, Chandresh N. Ladva, Megan Niedzwiecki, Douglas I. Walker, Stefanie Ebelt Sarnat, Howard H. Chang, Roby Greenwald, Dean P. Jones, Armistead G. Russell, Jeremy A. Sarnat

Research output: Contribution to journalArticlepeer-review

114 Scopus citations

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.

Original languageEnglish
Pages (from-to)145-154
Number of pages10
JournalEnvironment international
Volume120
DOIs
StatePublished - 1 Nov 2018

Keywords

  • Biomarkers
  • High resolution metabolomics
  • Inflammation
  • Metabolic perturbations
  • Oxidative stress
  • Traffic related air pollution

ASJC Scopus subject areas

  • General Environmental Science

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