Grantee Research Project Results
Estrogen Receptor Agonists in Swine Waste: Using a Concentration Addition Model to Predict Mixture EffectsEPA Grant Number: FP917151
Title: Estrogen Receptor Agonists in Swine Waste: Using a Concentration Addition Model to Predict Mixture Effects
Investigators: Yost, Erin Elizabeth
Institution: North Carolina State University
EPA Project Officer: Cobbs-Green, Gladys M.
Project Period: August 16, 2010 through August 15, 2013
Project Amount: $111,000
RFA: STAR Graduate Fellowships (2010)
Research Category: Fellowship - Pesticides and Toxic Substances , Academic Fellowships
Swine waste is known to contain numerous known and suspected endocrine-disrupting compounds, including animal hormones and bioavailable nitrogen. Although the precise mechanism for endocrine disruption by nitrogen compounds remains unclear, it has recently been observed that the nitrite anion may be able to directly activate the estrogen receptor (ER). I intend to test the hypothesis that a mixture of nitrite and hormonal estrogens, such as that which may be found in runoff or leachate from an industrial hog farm, will activate the ER in a manner that is predictable using a concentration addition model.
Waste from industrial hog farms has been shown to contrain known endocrine distruptors, including animal hormones and bioavabilable nitrogen. Although the precise mechanism for endocrine disruption by nitrogen remains unclear, it has recently been observed that the nitrite anion (NO2-) may be able to activate the estrogen receptor (ER). This project examines the ability of NO2- to activate the ER, and explores the use of a mathematical model to predict toxicty of an estrogen-nitrite mixture.Approach:
As part of a concurrent study of the fate and transport of estrogens in a swine feeding operation, I currently am determining the concentrations of estrogenic hormones present in swine waste lagoon slurry. In this proposed project, I will establish concentration-response curves for ER activation by these estrogens (e.g., estradiol, estrone, estriol) and nitrite as individual compounds. This will be done in vitro using a mammalian cell-based reporter assay, as well as in vivo using the male Japanese medaka as a model organism. Parameters from these concentration-response curves will then be plugged into a concentration addition model, and used to predict the effects of an estrogen-nitrite mixture. Validity of the model predictions will then be tested both in vitro and in vivo.Expected Results:
This project will quantify the ability of the nitrite anion to activate the ER in both in vitro and in vivo models, and will examine the utility of a concentration addition model in predicting the effects of an estrogen-nitrite mixture. If the concentration addition model fails, then the applicability of other models (e.g., an integrated addition and interaction model) will be examined. This project will also help determine whether activation of the ER is a relevant mechanism for nitrogen-mediated endocrine disruption in vivo. Additionally, it will examine the efficacy of using an in vitro bioassay to predict concentration additivity of a mixture in vivo.
Potential to Further Environmental/Human Health Protection:
This study will advance our understanding of the links between nitrogen-polluted waters and endocrine disruption, and will help us understand the potential contribution of nitrite in an estrogenic milieu. Hormones and nitrogen are both ubiquitous contaminants of concern in aquatic environments, and it is realistic to assume that these two contaminants will often occur in tandem—not only downstream from industrial animal farms, but in many other sites as a result of human development. The U.S. EPA has acknowledged the importance of evaluating the health effects of chemical mixtures, and biological studies such as this one are needed to help us accurately predict the risk that these mixtures will pose in the environment.Supplemental Keywords:
swine, CAFO, estrogen, nitrogen, nitrite, chemical mixture, endocrine disruption, medaka, concentration addition model,