Abstract
BackgroundExposure to air particulate matter is known to elevate blood biomarkers of inflammation and to increase cardiopulmonary morbidity and mortality. Major components of airborne particulate matter typically include black carbon from traffic and sulfates from coal-burning power plants. DNA methylation is thought to be sensitive to these environmental toxins and possibly mediate environmental effects on clinical outcomes via regulation of gene networks. The underlying mechanisms may include epigenetic modulation of major inflammatory pathways, yet the details remain unclear.MethodsWe sought to elucidate how short-term exposure to air pollution components, singly and/or in combination, alter blood DNA methylation in certain inflammation-associated gene networks, MAPK and NF-κB, which may transmit the environmental signal(s) and influence the inflammatory pathway in vivo. To this end, we utilized a custom-integrated workflow—molecular processing, pollution surveillance, biostatical analysis, and bioinformatic visualization—to map novel human (epi)gene pathway-environment interactions.ResultsSpecifically, out of 84 MAPK pathway genes considered, we identified 11 whose DNA methylation status was highly associated with black carbon exposure, after adjusting for potential confounders—age, sulfate exposure, smoking, blood cell composition, and blood pressure. Moreover, after adjusting for these confounders, multi-pollutant analysis of synergistic DNA methylations significantly associated with sulfate and BC exposures yielded 14 MAPK genes. No associations were found with the NF-κB pathway.ConclusionExposure to short-term air pollution components thus resulted in quantifiable epigenetic changes in the promoter areas of MAPK pathway genes. Bioinformatic mapping of single- vs. multi-exposure-associated epigenetic changes suggests that these alterations might affect biological pathways in nuanced ways that are not simply additive or fully predictable via individual-level exposure assessments.Electronic supplementary materialThe online version of this article (doi:10.1186/1476-069X-13-94) contains supplementary material, which is available to authorized users.
Highlights
Exposure to air particulate matter is known to elevate blood biomarkers of inflammation and to increase cardiopulmonary morbidity and mortality
Black carbon (BC), a component of particulate matter (PM) commonly used as a marker of traffic particles, has been reported to be more strongly associated with increased blood pressure in the elderly compared to other pollutants [5]
We focused on a key molecular pathway and its downstream target—Mitogen-activated protein kinase (MAPK) and Nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), respectively—which have been shown to be activated by PM exposure and may operate as biological mediators of pathophysiological responses to PM [23,24,25,26,27,28]
Summary
Exposure to air particulate matter is known to elevate blood biomarkers of inflammation and to increase cardiopulmonary morbidity and mortality. Major components of airborne particulate matter typically include black carbon from traffic and sulfates from coal-burning power plants. Exposure to air particulate matter (PM) is well known to augment oxidative stress in exposed individuals, and it has been consistently linked to reduced lung function as well as hospitalization and mortality for various cardiopulmonary diseases [1]. In highly PM-exposed individuals, blood leukocyte gene expression profiling has revealed responses related to worsened systemic oxidative stress and inflammation, which exacerbate aggravation of the airways and disease outcomes [4], yet the underlying mechanisms remain unclear. Black carbon (BC), a component of PM commonly used as a marker of traffic particles, has been reported to be more strongly associated with increased blood pressure in the elderly compared to other pollutants [5]. Questions remain about how best to model these two pollutants, individually and/or in combination, in order to understand their effects on human populations [10]
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