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Traffic-related air pollution (TRAP) is a complex mixture of compounds that contributes to the pathogenesis of many diseases including several types of cancer, pulmonary, cardiovascular and neurodegenerative diseases, and more recently also diabetes mellitus. In search of an early diagnostic biomarker for improved environmental health risk assessment, recent human studies have shown that certain extracellular miRNAs are altered upon exposure to TRAP. Here, we present a global circulating miRNA analysis in a human population exposed to different levels of TRAP. The cross-over study, with sampling taking place during resting and physical activity in two different exposure scenarios, included for each subject personal exposure measurements of PM 10 ,PM 2.5 , NO, NO 2 , CO, CO 2 , BC and UFP. Next-generation sequencing technology was used to identify global circulating miRNA levels across all subjects. We identified 8 miRNAs to be associated with the mixture of TRAP and 27 miRNAs that were associated with the individual pollutants NO, NO 2 , CO, CO 2 , BC and UFP. We did not find significant associations between miRNA levels and PM 10 or PM 2.5 . Integrated network analysis revealed that these circulating miRNAs are potentially involved in processes that are implicated in the development of air pollution-induced diseases. Altogether, this study demonstrates that signatures consisting of circulating miRNAs present a potential novel biomarker to be used in health risk assessment., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Exposure to traffic-related air pollution (TRAP) has been associated with adverse health outcomes but underlying biological mechanisms remain poorly understood. Two randomized crossover trials were used here, the Oxford Street II (London) and the TAPAS II (Barcelona) studies, where volunteers were allocated to high or low air pollution exposures. The two locations represent different exposure scenarios, with Oxford Street characterized by diesel vehicles and Barcelona by normal mixed urban traffic. Levels of five and four pollutants were measured, respectively, using personal exposure monitoring devices. Serum samples were used for metabolomic profiling. The association between TRAP and levels of each metabolic feature was assessed. All pollutant levels were significantly higher at the high pollution sites. 29 and 77 metabolic features were associated with at least one pollutant in the Oxford Street II and TAPAS II studies, respectively, which related to 17 and 30 metabolic compounds. Little overlap was observed across pollutants for metabolic features, suggesting that different pollutants may affect levels of different metabolic features. After observing the annotated compounds, the main pathway suggested in Oxford Street II in association with NO 2 was the acyl-carnitine pathway, previously found to be associated with cardio-respiratory disease. No overlap was found between the metabolic features identified in the two studies., (Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The molecular mechanisms that promote pathologic alterations in human physiology mediated by short-term exposure to traffic pollutants remains not well understood. This work was to develop mechanistic networks to determine which specific pathways are activated by real-world exposures of traffic-related air pollution (TRAP) during rest and moderate physical activity (PA). A controlled crossover study to compare whole blood gene expression pre and post short-term exposure to high and low of TRAP was performed together with systems biology analysis. Twenty-eight healthy volunteers aged between 21 and 53 years were recruited. These subjects were exposed during 2 h to different pollution levels (high and low TRAP levels), while either cycling or resting. Global transcriptome profile of each condition was performed from human whole blood samples. Microarrays analysis was performed to obtain differential expressed genes (DEG) to be used as initial input for GeneMANIA software to obtain protein-protein (PPI) networks. Two networks were found reflecting high or low TRAP levels, which shared only 5.6 and 15.5% of its nodes, suggesting specific cell signaling pathways being activated in each environmental condition. However, gene ontology analysis of each PPI network suggests that each level of TRAP regulate common members of NF-κB signaling pathway. Our work provides the first approach describing mechanistic networks to understand TRAP effects on a system level.