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The effect of exposure time and concentration of airborne PM2.5 on lung injury in mice: A transcriptome analysis.

ABSTRACT: The association between airborne fine particulate matter (PM2.5) concentration and the risk of respiratory diseases has been well documented by epidemiological studies. However, the mechanism underlying the harmful effect of PM2.5 has not been fully understood. In this study, we exposed the C57BL/6J mice to airborne PM2.5 for 3 months (mean daily concentration ~50 or ~110??g/m3, defined as PM2.5-3L or PM2.5-3H) or 6 months (mean daily concentration ~50??g/m3, defined as PM2.5-6L) through a whole-body exposure system. Histological and biochemical analysis revealed that PM2.5-3H exposure caused more severe lung injury than did PM2.5-3L, and the difference was greater than that of PM2.5-6L vs PM2.5-3L exposure. With RNA-sequencing technique, we found that the lungs exposed with different concentration of PM2.5 have distinct transcriptional profiles. PM2.5-3H exposure caused more differentially expressed genes (DEGs) in lungs than did PM2.5-3L or PM2.5-6L. The DEGs induced by PM2.5-3L or PM2.5-6L exposure were mainly enriched in immune pathways, including Hematopoietic cell lineage and Cytokine-cytokine receptor interaction, while the DEGs induced by PM2.5-3H exposure were mainly enriched in cardiovascular disease pathways, including Hypertrophic cardiomyopathy and Dilated cardiomyopathy. In addition, we found that upregulation of Cd5l and reduction of Hspa1 and peroxiredoxin-4 was associated with PM2.5-induced pulmonary inflammation and oxidative stress. These results may provide new insight into the cytotoxicity mechanism of PM2.5 and help to development of new strategies to attenuate air pollution associated respiratory disease.

SUBMITTER: Wang H

PROVIDER: S-EPMC6612658 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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The effect of exposure time and concentration of airborne PM2.5 on lung injury in mice: A transcriptome analysis.

Wang Hongyun H Shen Xiyue X Liu Jingli J Wu Chunyan C Gao Junling J Zhang Zezhong Z Zhang Fang F Ding Wenjun W Lu Zhongbing Z

Redox biology 20190702

The association between airborne fine particulate matter (PM2.5) concentration and the risk of respiratory diseases has been well documented by epidemiological studies. However, the mechanism underlying the harmful effect of PM2.5 has not been fully understood. In this study, we exposed the C57BL/6J mice to airborne PM2.5 for 3 months (mean daily concentration ~50 or ~110 μg/m3, defined as PM2.5-3L or PM2.5-3H) or 6 months (mean d ...[more]

PMID: 31279222

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Project description:BackgroundAirborne fine particulate matter with aerodynamic diameter ≤ 2.5 μm (PM2.5) pollution is associated with the prevalence of respiratory diseases, including asthma, bronchitis and chronic obstructive pulmonary disease. In patients with those diseases, circulating asymmetric dimethylarginine (ADMA) levels are increased, which contributes to airway nitric oxide deficiency, oxidative stress and inflammation. Overexpression of dimethylarginine dimethylaminohydrolase 1 (DDAH1), an enzyme degrading ADMA, exerts protective effects in animal models. However, the impact of DDAH1/ADMA on PM2.5-induced lung injury has not been investigated.MethodsDdah1-/- and DDAH1-transgenic mice, as well as their respective wild-type (WT) littermates, were exposed to either filtered air or airborne PM2.5 (mean daily concentration ~ 50 µg/m3) for 6 months through a whole-body exposure system. Mice were also acutely exposed to 10 mg/kg PM2.5 and/or exogenous ADMA (2 mg/kg) via intratracheal instillation every other day for 2 weeks. Inflammatory response, oxidative stress and related gene expressions in the lungs were examined. In addition, RAW264.7 cells were exposed to PM2.5 and/or ADMA and the changes in intracellular oxidative stress and inflammatory response were determined.ResultsDdah1-/- mice developed more severe lung injury than WT mice after long-term PM2.5 exposure, which was associated with greater induction of pulmonary oxidative stress and inflammation. In the lungs of PM2.5-exposed mice, Ddah1 deficiency increased protein expression of p-p65, iNOS and Bax, and decreased protein expression of Bcl-2, SOD1 and peroxiredoxin 4. Conversely, DDAH1 overexpression significantly alleviated lung injury, attenuated pulmonary oxidative stress and inflammation, and exerted opposite effects on those proteins in PM2.5-exposed mice. In addition, exogenous ADMA administration could mimic the effect of Ddah1 deficiency on PM2.5-induced lung injury, oxidative stress and inflammation. In PM2.5-exposed macrophages, ADMA aggravated the inflammatory response and oxidative stress in an iNOS-dependent manner.ConclusionOur data revealed that DDAH1 has a marked protective effect on long-term PM2.5 exposure-induced lung injury.

Project description:Previous studies have shown that long-term exposure to fine particulate matter (PM2.5) increases the morbidity and mortality of pulmonary diseases such as asthma, chronic obstructive pulmonary disease and pulmonary emphysema. Oxidative stress and inflammation play key roles in pulmonary damage caused by PM2.5. Nuclear factor erythroid 2-related factor 2 (Nrf2) could regulate the expression of antioxidant and anti-inflammatory genes and is pivotal for protection against PM2.5-induced oxidative stress. In this study, a real-ambient exposure system was constructed with the outdoor ambient air in north China. Wild-type (WT) and Nrf2-/- (KO) mice were exposed to the real-ambient system for six weeks. After PM2.5 exposure, our data showed that the levels of inflammatory factors and malondialdehyde were significantly increased in WT and KO mice. Moreover, the lung function and pathological phenotype of the WT mice were altered but there was no obvious change in the Nrf2-/- mice. To further explore the potential molecular mechanisms, we performed RNA-sequencing. The RNA-sequence analysis results showed that the CYP450 pathway in the first ten pathways of KEGG was related to the metabolism of PM2.5. In WT and KO mice, the expression of CYP2E1 in the CYP450 pathway showed opposite trends after PM2.5 exposure. The data showed that the expression of the CYP2E1 gene in WT-PM mice increased while it decreased in KO-PM; the expression of the CYP2E1 protein showed a similar trend. CYP2E1 is primarily distributed in the endoplasmic reticulum (ER) where it could metabolize various exogenous substances attached to PM2.5 and produce highly toxic oxidation products closely related to ER stress. Consistently, the expression level of GRP94, a biomarker of ER stress, was increased in WT mice and reduced in KO mice under PM2.5 exposure. Persistent ER stress is a mechanism that causes lung damage under PM2.5 exposure. Nrf2 facilitates lung injury during PM2.5 exposure and CYP2E1 metabolism is involved in this process.

Project description:It is well recognized that exposure to fine particulate matter (PM2.5) affects health adversely, yet few studies from South America have documented such associations due to the sparsity of PM2.5 measurements. Lima's topography and aging vehicular fleet results in severe air pollution with limited amounts of monitors to effectively quantify PM2.5 levels for epidemiologic studies. We developed an advanced machine learning model to estimate daily PM2.5 concentrations at a 1 km2 spatial resolution in Lima, Peru from 2010 to 2016. We combined aerosol optical depth (AOD), meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF), parameters from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), and land use variables to fit a random forest model against ground measurements from 16 monitoring stations. Overall cross-validation R2 (and root mean square prediction error, RMSE) for the random forest model was 0.70 (5.97 μg/m3). Mean PM2.5 for ground measurements was 24.7 μg/m3 while mean estimated PM2.5 was 24.9 μg/m3 in the cross-validation dataset. The mean difference between ground and predicted measurements was -0.09 μg/m3 (Std.Dev. = 5.97 μg/m3), with 94.5% of observations falling within 2 standard deviations of the difference indicating good agreement between ground measurements and predicted estimates. Surface downwards solar radiation, temperature, relative humidity, and AOD were the most important predictors, while percent urbanization, albedo, and cloud fraction were the least important predictors. Comparison of monthly mean measurements between ground and predicted PM2.5 shows good precision and accuracy from our model. Furthermore, mean annual maps of PM2.5 show consistent lower concentrations in the coast and higher concentrations in the mountains, resulting from prevailing coastal winds blown from the Pacific Ocean in the west. Our model allows for construction of long-term historical daily PM2.5 measurements at 1 km2 spatial resolution to support future epidemiological studies.

Dataset Information

The effect of exposure time and concentration of airborne PM2.5 on lung injury in mice: A transcriptome analysis.

Publications

The effect of exposure time and concentration of airborne PM<sub>2.5</sub> on lung injury in mice: A transcriptome analysis.

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