Dataset Information

Integration of in silico methods and computational systems biology to explore endocrine-disrupting chemical binding with nuclear hormone receptors.

ABSTRACT: Thousands of potential endocrine-disrupting chemicals present difficult regulatory challenges. Endocrine-disrupting chemicals can interfere with several nuclear hormone receptors associated with a variety of adverse health effects. The U.S. Environmental Protection Agency (U.S. EPA) has released its reviews of Tier 1 screening assay results for a set of pesticides in the Endocrine Disruptor Screening Program (EDSP), and recently, the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) data. In this study, the predictive ability of QSAR and docking approaches is evaluated using these data sets. This study also presents a computational systems biology approach using carbaryl (1-naphthyl methylcarbamate) as a case study. For estrogen receptor and androgen receptor binding predictions, two commercial and two open source QSAR tools were used, as was the publicly available docking tool Endocrine Disruptome. For estrogen receptor binding predictions, the ADMET Predictor, VEGA, and OCHEM models (specificity: 0.88, 0.88, and 0.86, and accuracy: 0.81, 0.84, and 0.88, respectively) were each more reliable than the MetaDrug™ model (specificity 0.81 and accuracy 0.77). For androgen receptor binding predictions, the Endocrine Disruptome and ADMET Predictor models (specificity: 0.94 and 0.8, and accuracy: 0.78 and 0.71, respectively) were more reliable than the MetaDrug™ model (specificity 0.33 and accuracy 0.4). A consensus approach is proposed that reaches general agreement among the models (specificity 0.94 and accuracy 0.89). This study integrates QSAR, docking, and systems biology approaches as a virtual screening tool for use in risk assessment. As such, this systems biology pathways and network analysis approach provides a means to more critically assess the potential effects of endocrine-disrupting chemicals.

SUBMITTER: Ruiz P

PROVIDER: S-EPMC8265162 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Glucocorticoid signaling plays a critical role in regulating energy metabolism. Emerging data implicate environmental endocrine-disrupting chemicals as contributors to the obesity and diabetes epidemics. Previous studies have shown that the phenylsulfamide fungicide tolylfluanid (TF) augments glucocorticoid receptor (GR)-dependent luciferase expression in 3T3-L1 preadipocytes while modulating insulin action in primary murine and human adipocytes. Studies were performed to interrogate glucocorticoid signaling in primary adipocytes exposed to TF. TF mimicked the gene transcription profile of the murine glucocorticoid corticosterone (Cort). Cellular fractionation assays demonstrated that TF treatment promoted the activating serine phosphorylation of GR, augmenting its cytoplasmic-to-nuclear translocation as well as its enrichment at glucocorticoid response elements on the glucocorticoid-induced leucine zipper gene promoter. After acute treatment, Cort or TF promoted insulin receptor substrate-1 (IRS-1) gene and protein expression. Either treatment also enriched GR binding at an identified glucocorticoid response element in the IRS-1 gene. TF or Cort each increased insulin-stimulated lipogenesis, an effect resulting from increased lipogenic gene expression and enhanced insulin-stimulated dephosphorylation of acetyl-coenzyme A carboxylase. The augmentation of insulin-stimulated lipogenesis was mediated through a specific enhancement of Akt phosphorylation at T308. These findings support modulation of IRS-1 levels as a mechanism for glucocorticoid-mediated changes in insulin action in primary adipocytes. Albeit with less affinity than Cort, in silico analysis suggests that TF can interact with the ligand binding pocket of GR. Collectively, these studies identify TF as a structurally unique environmental glucocorticoid. Glucocorticoid signaling may thus represent a novel pathway by which environmental toxicants promote the development of metabolic diseases.

Dataset Information

Integration of in silico methods and computational systems biology to explore endocrine-disrupting chemical binding with nuclear hormone receptors.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets