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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.

Bisphenols (BPs) have the potential to interfere with the androgen receptor (AR). However, in silico screening and substitute design were difficult because little was known about the mechanisms by ...

Lingling Li,1,2,* Qinchang Chen,1,* Qingui Chen,3 Ridong Wu,1 Shenming Wang,1 Chen Yao1 1Department of Vascular Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People’s Republic of China; 2Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, People’s Republic of China; 3Department of Medical Intensive Care Unit, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Shenming Wang; Chen YaoDepartment of Vascular Surgery, The First Affiliated Hospital, Sun Yat-sen University, No. 58 Zhongshan Road 2, Guangzhou, People’s Republic of ChinaEmail shenmingwang@hotmail.com; yaochen@mail.sysu.edu.cnPurpose: Theaimofthisstudywasto evaluate the association between blood glucose within 24 hours after intensive care unit (ICU) admission and prognosis.Patients and Methods: A retrospective cohort study was conducted using data from a large critical care database. Patients who had a length of ICU stay ≥ 24 hours and at least two blood glucose records within 24 hours after ICU admission were included and hospital mortality was chosen as the primary outcome. The average, minimum, and maximum blood glucose within 24 hours after ICU admission were a priori selected as exposures and associations between each exposure and outcomes were assessed after adjusted for potential confounders.Results: A totalof 14,237 patients were included finally with an average age of 62.9± 17.7 years and a mean SAPS II on admission of 34 (26– 44). Among the study population, 20.2% (2872/14,237) had uncomplicated diabetes, and 6.7% (953/14,237) had complicated diabetes. Lowest hospital mortality rate was observed in the stratum with an average blood glucose ranged 110– 140 mg/dL, a minimum blood glucose ranged 80– 110 mg/dL, and a maximum blood glucose ranged 110– 140 mg/dL. After adjusted for confounders including age, sex, disease severity scores and comorbidities, an average blood glucose ranged 110– 140 mg/dL, a minimum blood glucose ranged 80– 110 mg/dL, and a maximum blood glucose ranged 110– 140 mg/dL were associated with the lowest risk of hospital mortality. Consistent results were found among patients without diabetes in the subgroup analyses stratified by diabetes.Conclusion: A range of 110– 140 mg/dL for average and maximum blood glucose and a range of 80– 110 mg/dL for minimum blood glucose within 24 hours after ICU admission predicted better prognosis especially among patients without diabetes.Keywords: blood glucose, critical care, mortality, retrospective studies

Endocrine disrupting chemicals (EDCs) are of great concern given their potential influence on the endocrine system. In silico methods for the evaluation of EDCs have been widely recognized. However, subcellular molecular mechanisms of action, such as ligand-receptor interactions, receptor conformational switch and protein-protein interactions, are needed for the development of mechanism-based in silico models. Here, molecular mechanisms of action for steroid hormone receptors (SHRs), the important targets of EDCs, are systematically reviewed. Ligand binding and ligand-receptor interactions are required for SHR activation, and facilitate the nuclear translocation and the dimerization of SHRs. Coregulator recruitment results from conformational switch of SHR, which regulates the transcription and results in either an agonistic or an antagonistic effect. EDCs potentially interfere with SHRs by influencing ligand-receptor interactions, nuclear translocation, dimerization and coregulator recruitment. These new findings shed light on the development of mechanism-based computational models for the evaluation of EDCs.

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.