Citation:

Leung, M., J. Phuong, N. Baker, N. Sipes, G. Klinefelter, M. Martin, K. McLaurin, Woodrow Setzer, S. Darney, R. Judson, AND T. Knudsen. TOXICOLOGY OF MALE REPRODUCTIVE DEVELOPMENT: PROFILING 774 CHEMICALS FOR MOLECULAR TARGETS AND ADVERSE OUTCOMES (SOT). Presented at Society of Toxicology, San Diego, CA, March 22 - 26, 2015. https://doi.org/10.23645/epacomptox.5082793

Impact/Purpose:

Although estrogenic and anti-androgenic activities have been extensively studied in testicular dysgenesis syndrome, the present study showed these receptor targets to be only a subset of the potential landscape of molecular targets

Description:

Adverse trends in male reproductive health have been reported for increased rates of testicular germ cell tumor, low semen quality, cryptorchidism, and hypospadias. An association with prenatal environmental exposure has been inferred from human and animal studies underlying male reproductive developmental defects. The present study established the links between environmental chemicals, molecular targets, and adverse outcomes using U.S. EPA animal study (ToxRefDB) and high-throughput screening (HTS; ToxCast) databases. This systems-based approach revealed a phenotypic hierarchy of testicular atrophy, sperm effects, tumors, and malformations across 63 of 774 (8%) chemicals in ToxRefDB, resembling what might be expected in testicular dysgenesis syndrome (TDS) in humans; 48 of these 63 chemicals had ToxCast data, interacting with 126 of 286 (44%) molecular targets in HTS. Although estrogenic and anti-androgenic activities have been extensively studied in TDS, the present study showed these receptor targets to be only a subset of the potential landscape of molecular targets. A variety of chemical (e.g. phthalates, conazoles, carbamates, and phenol compounds) and bioactivity (e.g. nuclear receptors, vascular remodeling proteins, and cytochrome-P450 reductases) clusters further suggested multiple pathways leading to the adverse outcomes. This points to the need for multi-scale systems models to predict whether the occurrence of one adverse outcome may increase the risk of another.

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