Gene-Environment Interactions Causing Differential Susceptibility to Chemical Exposure in High-Throughput Screening

ABSTRACT Exposure to environmental chemicals has been linked to increases in cancer incidence, birth defects, impaired cognitive development, and neurodegenerative disease. Unfortunately, the gap between the ever-expanding number of chemicals in the environment and data on their potential health hazards continues to widen. Although recent advancements that use in vitro, high-throughput screening (HTS) technologies may speed the pace of chemical testing, those platforms cannot detect adverse health effects diagnosable only at a systemic level, such as abnormal development or aberrant behavior. Additionally, an in vivo context is needed to quantify the contribution of interindividual genetic variation to susceptibility differences in developmental or behavioral consequences of exposure. There is strong evidence that gene-environment interactions (GxE) related to individual genetic variation play an important role in health outcomes, and that these interactions are likely a major source of the heterogeneity in response to chemical exposure. Thus, understanding the role of GxE in differential susceptibility to chemical exposure will be key to protecting public health. We propose development of a collaborative bioinformatic + experimental system to study health outcomes affected by gene-environment interactions (Y=GxE) that comprehensively describes (Y), refines characterization of (E), then investigates and probes (G). This system will leverage massive data generated by HTS of chemicals through morphological and behavioral assays in embryonic zebrafish during the critical period (the first 5 days immediately after fertilization) when developmental processes are most highly-conserved between this vertebrate model organism and humans. These data will be analyzed to quantify GxE that elicit differential health outcomes following chemical exposure. The lasting significance of this proposal will be a scalable, efficient system to rapidly address questions of differential genetic susceptibility to an expanding chemical universe.