Some hydroxylated polybrominated diphenyl ethers (HO-PBDEs), that have been widely detected in the environment and tissues of humans and wildlife, bind to thyroid hormone (TH) receptor (TR) and can disrupt functioning of systems modulated by the TR. However, mechanisms of TH disrupting effects are still equivocal. Here, disruption of functions of TH modulated pathways by HO-PBDEs was evaluated by assays of competitive binding, coactivator recruitment, and proliferation of GH3 cells. In silico simulations considering effects of coregulators were carried out to investigate molecular mechanisms and to predict potencies for disrupting functions of the TH. Some HO-PBDEs were able to bind to TR with moderate affinities but were not agonists. In GH3 proliferation assays, 13 out of 16 HO-PBDEs were antagonists for the TH. In silico simulations of molecular dynamics revealed that coregulators were essential for identification of TH disruptors. Among HO-PBDEs, binding of passive antagonists induced repositioning of H12, blocking AF-2 (transactivation function 2) and preventing recruitment of the coactivator. Binding of active antagonists exposed the coregulator binding site, which tended to bind to the corepressor rather than the coactivator. By considering both passive and active antagonisms, anti-TH potencies of HO-PBDEs could be predicted from free energy of binding.

A tiered screening strategy based on extensive virtual fractionation and elucidation was developed to simplify identification of toxicants in complex environments. In tier1-virtual fractionation, multivariate analysis (MVA) was set up as an alternative of physical fractionation. In tier2-virtual structure elucidation, in-house quantitative structure-retention relationship (QSRR) models and toxicity simulation methods were developed to simplify nontarget identification. The efficiency of the tiered virtual strategy was tentatively verified by soil samples from a chemical park contaminated by antiandrogenic substances. Eight out of 18 sites were detected as antiandrogenic, while none of them exhibited androgenic agonist potencies. Sixty-seven peaks were selected for further identification by MVA, among which over 90% were verified in androgenic fractions in traditional effect-directed analysis (EDA). With 579 tentative structures generated by in silico fragmentation, 74% were elucidated by QSRR and 65% were elucidated by in silico toxicity prediction. All prior peaks were identified at different confidence levels with over 40% of the identified peaks above confidence level 2b, which has been increased over 40% with less than half of the time spent compared to traditional EDA. Such a combination of tiered virtual screening methods provides more efficient and rapid identifications of key toxicants at contaminated sites.

Endocrine-disrupting chemicals (EDCs) can inadvertently interact with 12 classic nuclear receptors (NRs) that disrupt the endocrine system and cause adverse effects. There is no widely accepted understanding about what structural features make thousands of EDCs able to activate different NRs as well as how these structural features exert their functions and induce different outcomes at the cellular level. This paper applies the hierarchical characteristic fragment methodology and high-throughput screening molecular docking to comprehensively explore the structural and functional features of EDCs for the 12 NRs based on more than 7000 chemicals from curated datasets. EDCs share three levels of key fragments. The primary and secondary fragments are associated with the binding of EDCs to four groups of receptors: steroidal nuclear receptors (SNRs, including androgen, estrogen, glucocorticoid, mineralocorticoid, and progesterone), retinoic acid receptors, thyroid hormone receptors, and vitamin D receptors. The tertiary fragments determine the activity type by interacting with two key locations in the ligand-binding domains of NRs (N–H5–H3–C and N–H7–H11–C for SNRs and N–H5–H5′–H2′–H3–C and N–H6′–H11–C for non-SNRs). The resulting compiled structural fragments of EDCs together with elucidated compound NR binding modes provide a framework for understanding the interactions between EDCs and NRs, facilitating faster and more accurate screening of EDCs for multiple NRs in the future.