Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, USA.
School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, USA.
Environ Int. 2018 Nov;120:145-154. doi: 10.1016/j.envint.2018.07.044. Epub 2018 Aug 7.
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.
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.
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.
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.
高分辨率代谢组学（HRM）作为一种测量环境暴露和生物反应的敏感工具正在兴起。本分析旨在评估高分辨率代谢组学（HRM）反映复杂交通相关空气污染物混合物内部暴露的能力。
我们使用非靶向 HRM 分析来描述 2014 年在一条主要公路沿线的多个环境和室内地点，在 12 周内以不同的距离测量一系列与交通相关的污染物，以确定与交通排放暴露相关的代谢途径。同时，2014 年有 54 名居住在公路附近（20 米）或远处（1.4 公里）宿舍的学生提供了血浆和唾液样本。对血浆和唾液样本进行了非靶向 HRM 分析；使用代谢组关联研究（MWAS）框架和途径分析评估代谢物和代谢途径的改变。
与远宿舍相比，近宿舍每周的交通污染物水平明显更高（所有污染物的 p 值均<0.05）。总共从血浆样本中提取了 20766 个代谢特征，从唾液样本中提取了 29013 个代谢特征。45%的特征在血浆和唾液样本中都被检测到并共享。1291 个独特的代谢特征与至少一种或多种交通指标显著相关，包括黑碳、一氧化碳、氮氧化物和细颗粒物（所有显著特征的 p 值均<0.05），在控制混杂因素和假发现率后。与交通暴露相关的代谢特征的途径分析表明，白细胞三烯和维生素 E 代谢等炎症和氧化应激相关途径被激发。我们确认了 10 种与交通污染物相关的代谢物的化学身份，包括精氨酸、组氨酸、γ-亚麻酸和次黄嘌呤。
使用 HRM，我们在具有重复测量的基于面板的设置中，确定并验证了与原发性交通污染物相关的生物扰动。观察到的反应与氧化应激、炎症和核酸损伤与修复相关的内源性代谢信号一致。总的来说，目前的研究结果为非靶向 HRM 在开发交通污染暴露和反应的代谢生物标志物方面提供了支持。
