Abstract
Asthma, a chronic inflammatory disorder of the airway, has features of both heritability as well as environmental influences which can be introduced in utero exposures and modified through aging, and the features may attribute to epigenetic regulation. Epigenetic regulation explains the association between early prenatal maternal smoking and later asthma-related outcomes. Epigenetic marks (DNA methylation, modifications of histone tails or noncoding RNAs) work with other components of the cellular regulatory machinery to control the levels of expressed genes, and several allergy- and asthma-related genes have been found to be susceptible to epigenetic regulation, including genes important to T-effector pathways (IFN-γ, interleukin [IL] 4, IL-13, IL-17) and T-regulatory pathways (FoxP3). Therefore, the mechanism by which epigenetic regulation contributes to allergic diseases is a critical issue. In the past most published experimental work, with few exceptions, has only comprised small observational studies and models in cell systems and animals. However, very recently exciting and elegant experimental studies and novel translational research works were published with new and advanced technologies investigating epigenetic mark on a genomic scale and comprehensive approaches to data analysis. Interestingly, a potential link between exposure to environmental pollutants and the occurrence of allergic diseases is revealed recently, particular in developed and industrialized countries, and endocrine disrupting chemicals (EDCs) as environmental hormone may play a key role. This review addresses the important question of how EDCs (nonylphenol, 4 octylphenol, and phthalates) influences on asthma-related gene expression via epigenetic regulation in immune cells, and how anti-asthmatic agents prohibit expression of inflammatory genes via epigenetic modification. The discovery and validation of epigenetic biomarkers linking exposure to allergic diseases might lead to better epigenotyping of risk, prognosis, treatment prediction, and development of novel therapies.