Pilot Project Recipients Year 1

Influences of environmental stressors on the epigenome

Yan Sun, Emory University

The epigenomic profile can be modified by environmental factors and regulate gene expression levels. Environmental stressors induce immune responses and lead to elevated levels of inflammation, a mechanism for chronic conditions such as cardiovascular disease, hypertension and cancer. DNA methylation (DNAm), a well-documented epigenetic mechanism, is associated with inflammatory markers. Through this pilot project, Dr. Sun plans to use a systems biology approach to study DNAm networks which are impacted by multiple environmental stressors (smoking, air pollution, and psychosocial stress).

Peripheral blood leukocytes (PBL) will be used since certain environmental stimuli leads to documented epigenetic change in genomic DNAm profiles. Dr. Sun’s current studies of 972 African Americans identified statistically significant cross-sectional relationships between genomic DNA methylation profiles and inter-individual differences in age and serum C-reactive protein (CRP) level (an inflammatory biomarker of chronic disease). Through this work, age-related and CRP-associated DNAm sites have been identified.

The goal of the pilot study is to identify clusters of DNAm association with environmental stressors, apply machine-learning methods to identify epigenetic predictors with interaction effects and construct epigenetic networks by integrating protein-protein interaction networks. These statistical and computational tools will allow for high-dimensional data analysis to gain a better understanding of the specific epigenomic profiles influenced by the environment and the networks/pathways linking environmental exposure and disease development.

Yan Sun, PhD is a professor in the Department of Epidemiology at the School of Public Health at Emory. His research focuses on two key areas: genomic epidemiology of cardiovascular disease and hypertension and computational and statistical modeling of complex diseases. The goal of his research centers around finding personalized and preventive health measures of cardiovascular diseases across ethnic groups to better understand the disease etiology and to improve the predictive modeling of the development, treatment and prevention of diseases.


Examining sources, mechanisms and response, associated with air pollution related acute pulmonary response in asthmatic children using a multi-tiered exposomics approach

Jeremy Sarnat, Emory University

Building on previous studies of air pollution around El Paso, TX, indoor and outdoor microenvironmental particulate and gaseous pollutant measurements used to indicate exposure, traffic-related VOCs and particulate matter were strongly predictive of pulmonary inflammation. Findings suggest exposure to traffic pollutants as a risk factor but traditional observation health effect studies are limited in their ability to identify specific endogenous and exogenous pollutant sources, causal agents and etiologies specifically related to asthma response.

Dr. Sarnat proposes a novel approach to more fully examine associations between exposures, including traffic, and acute respiratory response in a pediatric asthma cohort. The goal of this top-down approach is to measure untargeted chemicals and biomarkers in exhaled breath condensate and urine in order to allow for unbiased hypothesis generation about the etiology of acute pulmonary response in addition to the traditional bottom-up approach. The authors hypothesize that “increased short-term exposures to traffic-related VOCs are associated with acute pulmonary response consistent with an oxidative stress/inflammation-mediated response”. To assess this, metabolic profiles among individuals living near and away from the traffic source and longitudinal within-subject changes will be created.

Jeremy Sarnat, ScD is a professor in the Department of Environmental Health at the School of Public Health at Emory. His research focuses on characterizing human exposure to urban air pollution with a strong interest in environmental exposure science. The Atlanta Commuters’ Exposure Study is one such research project, testing whether commuters in Atlanta are exposed to high levels of in-vehicle particulate matter from traffic sources and if these exposures are associated with acute changes in cardiorespiratory response.


Minimally invasive collection of interstitial fluid from skin using a microneedle patch

Mark Prausnitz, Georgia Institute of Technology

In order to characterize the exposome, a large number of biological samples from many people will be needed. To facilitate the collection of these samples, Dr. Prausnitz proposes a minimally invasive microneedle patch be used to collect interstitial fluid from the skin. This would allow water-soluble compounds in the body to be detected. The patch would use arrays of microneedles to painlessly pierce the skin. The microneedles osmotically draw interstitial fluid from the skin into the patch backing over a few hours.

A microneedle patch for vaccine delivery has been previously developed. The primary goal of this project is to adapt that technology to extract interstitial fluid, develop a protocol for patch application and wearing on the skin along with compound collection from the patch backing for analysis. Another aim is to compare levels of a panel of compounds in both interstitial fluid and blood to determine how well they correlate. Such a technology would allow for the collection of interstitial fluid across large populations in a simple and cost-effective method.

Mark Prausnitz, PhD is a professor in the School of Chemical and Biomolecular Engineering and director of the Center for Drug Design, Development and Delivery at GeorgiaTech. His research focuses on the development of microneedle patches for self-administered influenza vaccinations, the microneedle patch as a tool for vaccination against polio, measles, and other diseases, the use of nanoneedle arrays on delivery of proteins and DNA into cells for gene-based therapies ex vivo along with many other research dimensions.