The main objective of this project is to develop a testing framework that will evaluate the toxicity of complex mixtures of per- and polyfluoroalkyl substances (PFAS) based on biological effects by connecting macromolecular and suborganismal responses to impacts on whole animals. The framework will use putative molecular initiating events identified computationally, toxicity pathways identified through transcriptomic signatures, and toxic effects integrated within bioenergetic models to predict whole organism responses that can be translatable to population risk. New approach methodologies will be incorporated into existing standardized tests so that mixture signatures can be detected and predicted within an organism in a natural setting and to determine what is the most efficient way to screen chemical mixtures so that they inform hazard estimations and remediation needs.
Standardized tests will be conducted on Daphnia and fathead minnows to evaluate lethality and sublethal impacts on growth and reproduction of a PFAS mixture found in Clark’s Marsh and also perfluorooctanesulfonic acid by itself. In addition to these standardized tests, toxicity will be evaluated using several new approaches, including transcriptomics and molecular docking. It is anticipated that these different lines of evidence will lead to different rankings of toxicity. In addition, novel computational methods will be used to connect new approach methods developed for transcriptomic and metabolomic data to established theory on metabolic dynamics of organisms to explain these differences. The carefully designed experiments will link transcriptomic signature responses to specific physiological modes of action (pMoA) relevant for dynamic energy budgets of organisms. Once this linkage is established, the project team will demonstrate how this approach can improve population predictions for ecological risk assessment, and test to see that approach is able to discern PFAS toxicity patterns from mixtures in the field using caged fathead minnows.
This research will compile and synthesize a large volume of data collected from several levels of biological organization in a thorough and systematic way to assess the toxicity of PFAS mixtures so that they can be interpreted for ecological risk assessment. The novel modeling framework aims to extrapolate effects across levels of biological organization from suborganismal level: molecular initiating events and transcriptomics to understand individual-level impacts (as identified through standardized toxicity tests) and that can then be used to predict impacts at the population-level. Suborganismal-level changes will be connected to impacts on individual-level bioenergetic processes (pMoA) as modeled in a Dynamic Energy Budget framework with the goal of developing a model that can predict the chronic impact of a mixture of contaminants on daphnid and fish populations using only suborganismal data and to distinguish mixture impacts from natural stressors. The advantage of developing this connection is that it could streamline future toxicity testing such that only short-term exposures with suborganismal data collection are necessary to predict and rank the toxicity of mixtures. This framework will also provide the opportunity for retrospective risk assessment and for a way to guide remediation efforts in impacted areas in natural settings.