Georgia Southern Receives NIH R21 grant to explore the role of interplay between environmental exposures, respiratory tract microbiome, and immune responses in children
The National Institute of Environmental Health Sciences (NIEHS), one of research institutes and centers that comprise the National Institutes of Health (NIH) has awarded a new Exploratory/Developmental Research Grant Award (R21) entitled, “Traffic-related Air Pollutants and Respiratory Tract Microbiome in Children.” Dr. Atin Adhikari, an Assistant Professor of Environmental Health Sciences at Jiann-Ping Hsu College of Public Health is one of the Principal Investigators (PIs) in this multi-PI grant. The interplay between environmental exposures, respiratory tract microbiome, and immune responses related to asthma and other respiratory diseases is not well understood. High levels of traffic-related air pollutants (TRAP) have been associated with children’s asthma. TRAP can increase adherence of microorganisms to the epithelial cells of the respiratory tract and damage the epithelial layers resulting in increased susceptibility to microbial growth. Many studies suggest a role for altered human microbiota in the etiology of asthma. Furthermore, circumstantial evidence indicates that bacterial infections in the respiratory tract may play a role in asthma development. The airway microbiota may interact with the innate and adaptive arms of the children’s developing mucosal immune system in the respiratory tract, which can be critically important in maintaining tolerance against allergic immune responses. This new research study will examine the influences of TRAP and indigenous bacteria of the respiratory tract on allergic immune responses and asthma. The researchers at University of Cincinnati and Dr. Adhikari will jointly explore how long-term exposure to TRAP could influence the respiratory tract microbiome of the children utilizing a unique cohort of the Cincinnati Childhood Allergy and Air Pollution Study as well as the previously collected TRAP exposure data incorporated in a land use regression (LUR) model.