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Background
Particulate matter (PM) is a complex mixture of small particles and liquid droplets that contains a number of components, including acids, organic chemicals, metals, and soil or dust particles. PM exposure is known to increase overall mortality and morbidity, mainly due to its effect on the cardiorespiratory system (Alfaro-Moreno et al. 2007; Pope et al. 2004). Exposure to PM may disturb normal physiological pathways that maintain homeostasis and this may activate cellular processes that mediate the adverse effects of PM (Kleensang et al. 2014). Gene expression changes play an important role in the activation of pathways of toxicity and gene signatures have the potential to serve as biomarkers of exposure (van Leeuwen et al. 2008; van Breda et al. 2015) and recent reports demonstrate their potential use as biomarkers of effect (La Rocca et al. 2014; Fink et al. 2014). As it has been shown previously that transcriptomic responses to diverse environmental stimuli (i.e., chemical exposure, smoking) can be significantly different between men and women (De Coster et al. 2013; Paul and Amundson 2014), we have opted to perform a sex-specific analysis.
Several studies have suggested that elevated oxidative stress may mediate toxic effects of air pollutants (Donaldson et al. 2005; Nel et al. 2001). The systemic inflammatory response following acute inhalation exposure to PM can induce leukocytosis and monocyte release from the bone marrow (Fujii et al. 2002). Controlled exposure studies of recent diesel exhaust exposure (Pettit et al. 2012) and recent exposure to ultra-fine particles (Huang et al. 2010) have reported evidence of altered gene expression in leukocytes but, to our knowledge, associations between patterns of gene expression and long-term particulate air pollution have not been studied in general populations.
Materials and Methods
Study Design
As our goal was to identify transcriptomic biomarkers of exposure and effect in a healthy adult population, we started by applying microarray analysis in a discovery cohort of 98 adults for which we modelled particulate matter exposure. On the resulting dataset containing significantly modulated genes and pathways, we applied a literature and bio-informatics approach to identify potential exposure effect biomarkers. Subsequently, these were validated using qPCR analysis in an independent cohort with similar characteristics as the discovery population (Figure 1). Study protocols for the discovery and validation...