Predicting the Activation of the Androgen Receptor by Mixtures of Ligands Using Generalized Concentration Addition

Reduced bioenergetics and mitochondrial fragmentation in human primary cytotrophoblasts induced by an EGFR-targeting chemical mixture

Exposures to complex environmental chemical mixtures during pregnancy reach and target the feto-placental unit. This study investigates the influence of environmental chemical mixtures on placental bioenergetics. Recognizing the essential role of the epidermal growth factor receptor (EGFR) in placental development and its role in stimulating glycolysis and mitochondrial respiration in trophoblast cells, we explored the effects of chemicals known to disrupt EGFR signaling on cellular energy production. Human primary cytotrophoblasts (hCTBs) and a first-trimester extravillous trophoblast cell line (HTR-8/SVneo) were exposed to a mixture of EGFR-interfering chemicals, including atrazine, bisphenol S, niclosamide, PCB-126, PCB-153, and trans-nonachlor. An RNA sequencing approach revealed that the mixture altered the transcriptional signature of genes involved in cellular energetics. Next, the impact of the mixture on cellular bioenergetics was evaluated using a combination of mitochondrial and glycolytic stress tests, ATP production, glucose consumption, lactate synthesis, and super-resolution imaging. The chemical mixture did not alter basal oxygen consumption but diminished the maximum respiratory capacity in a dose-dependent manner, indicating a disruption of mitochondrial function. The respiratory capacity and ATP production were increased by EGF, while the Chem-Mix reduced both EGF- and non-EGF-mediated oxygen consumption rate in hCTBs. A similar pattern was observed in the glycolytic medium acidification, with EGF increasing the acidification, and the Chem-Mix blocking EGF-induced glycolytic acidification. Furthermore, direct stochastic optical reconstruction microscopy (dSTORM) imaging demonstrated that the Chem-Mix led to a reduction of the mitochondrial network architecture, with findings supported by a decrease in the abundance of OPA1, a mitochondrial membrane GTPase involved in mitochondrial fusion. In conclusion, we demonstrated that a mixture of EGFR-disrupting chemicals alters mitochondrial remodeling, resulting in disturbed cellular bioenergetics, reducing the capacity of human cytotrophoblast cells to generate energy. Future studies should investigate the mechanism by which mitochondrial dynamics are disrupted and the pathological significance of these findings.

Chemical mixture that targets the epidermal growth factor pathway impairs human trophoblast cell functions

Pregnant women are exposed to complex chemical mixtures, many of which reach the placenta. Some of these chemicals interfere with epidermal growth factor receptor (EGFR) activation, a receptor tyrosine kinase that modulates several placenta cell functions. We hypothesized that a mixture of chemicals (Chem-Mix) known to reduce EGFR activation (polychlorinated biphenyl (PCB)-126, PCB-153, atrazine, trans-nonachlor, niclosamide, and bisphenol S) would interfere with EGFR-mediated trophoblast cell functions. To test this, we determined the chemicals' EGFR binding ability, EGFR and downstream effectors activation, and trophoblast functions (proliferation, invasion, and endovascular differentiation) known to be regulated by EGFR in extravillous trophoblasts (EVTs). The Chem-Mix competed with EGF for EGFR binding, however only PCB-153, niclosamide, trans-nonachlor, and BPS competed for binding as single chemicals. The effects of the Chem-Mix on EGFR phosphorylation were tested by exposing the placental EVT cell line, HTR-8/SVneo to control (0.1% DMSO), Chem-Mix (1, 10, or 100 ng/ml), EGF (30 ng/ml), or Chem-Mix + EGF. The Chem-Mix - but not the individual chemicals - reduced EGF-mediated EGFR phosphorylation in a dose dependent manner, while no effect was observed in its downstream effectors (AKT and STAT3). None of the individual chemicals affected EVT cell invasion, but the Chem-Mix reduced EVT cell invasion independent of EGF. In support of previous studies that have explored chemicals targeting a specific pathway (estrogen/androgen receptor), current findings indicate that exposure to a chemical mixture that targets the EGFR pathway can result in a greater impact compared to individual chemicals in the context of placental cell functions.

High-throughput AR dimerization assay identifies androgen disrupting chemicals and metabolites

Introduction: Analysis of streamlined computational models used to predict androgen disrupting chemicals revealed that assays measuring androgen receptor (AR) cofactor recruitment/dimerization were particularly indispensable to high predictivity, especially for AR antagonists. As the original dimerization assays used to develop the minimal assay models are no longer available, new assays must be established and evaluated as suitable alternatives to assess chemicals beyond the original 1,800+ supported by the current data. Here we present the AR2 assay, which is a stable, cell-based method that uses an enzyme complementation approach.

Methods: Bipartite domains of the NanoLuc luciferase enzyme were fused to the human AR to quantitatively measure ligand-dependent AR homodimerization. 128 chemicals with known endocrine activity profiles including 43 AR reference chemicals were screened in agonist and antagonist modes and compared to the legacy assays. Test chemicals were rescreened in both modes using a retrofit method to incorporate robust cytochrome P450 (CYP) metabolism to assess CYP-mediated shifts in bioactivity.

Results: The AR2 assay is amenable to high-throughput screening with excellent robust Z’-factors (rZ’) for both agonist (0.94) and antagonist (0.85) modes. The AR2 assay successfully classified known agonists (balanced accuracy = 0.92) and antagonists (balanced accuracy = 0.79–0.88) as well as or better than the legacy assays with equal or higher estimated potencies. The subsequent reevaluation of the 128 chemicals tested in the presence of individual human CYP enzymes changed the activity calls for five compounds and shifted the estimated potencies for several others.

Discussion: This study shows the AR2 assay is well suited to replace the previous AR dimerization assays in a revised computational model to predict AR bioactivity for parent chemicals and their metabolites.

Predicting the effects of per- and polyfluoroalkyl substance mixtures on peroxisome proliferator-activated receptor alpha activity in vitro

Human exposure to per- and polyfluoroalkyl substances (PFAS) is ubiquitous, with mixtures of PFAS detected in drinking water, food, household dust, and other exposure sources. Animal toxicity studies and human epidemiology indicate that PFAS may act through shared mechanisms including activation of peroxisome proliferator activated receptor α (PPARα). However, the effect of PFAS mixtures on human relevant molecular initiating events remains an important data gap in the PFAS literature. Here, we tested the ability of modeling approaches to predict the effect of diverse PPARα ligands on receptor activity using Cos7 cells transiently transfected with a full length human PPARα (hPPARα) expression construct and a peroxisome proliferator response element-driven luciferase reporter. Cells were treated for 24 h with two full hPPARα agonists (pemafibrate and GW7647), a full and a partial hPPARα agonist (pemafibrate and mono(2-ethylhexyl) phthalate), or a full hPPARα agonist and a competitive antagonist (pemafibrate and GW6471). Receptor activity was modeled with three additive approaches: effect summation, relative potency factors (RPF), and generalized concentration addition (GCA). While RPF and GCA accurately predicted activity for mixtures of full hPPARα agonists, only GCA predicted activity for full and partial hPPARα agonists and a full agonist and antagonist. We then generated concentration response curves for seven PFAS, which were well-fit with three-parameter Hill functions. The four perfluorinated carboxylic acids (PFCA) tended to act as full hPPARα agonists while the three perfluorinated sulfonic acids (PFSA) tended to act as partial agonists that varied in efficacy between 28-67 % of the full agonist, positive control level. GCA and RPF performed equally well at predicting the effects of mixtures with three PFCAs, but only GCA predicted experimental activity with mixtures of PFSAs and a mixture of PFCAs and PFSAs at ratios found in the general population. We conclude that of the three approaches, GCA most accurately models the effect of PFAS mixtures on hPPARα activity in vitro. Understanding the differences in efficacy with which PFAS activate hPPARα is essential for accurately predicting the effects of PFAS mixtures. As PFAS can activate multiple nuclear receptors, future analyses should examine mixtures effects in intact cells where multiple molecular initiating events contribute to proximate effects and functional changes.

Predicting the effects of per- and polyfluoroalkyl substance mixtures on peroxisome proliferator-activated receptor alpha activity in vitro

Human exposure to per- and polyfluoroalkyl substances (PFAS) is ubiquitous, with mixtures of PFAS detected in drinking water, food, household dust, and other exposure sources. Animal toxicity studies and human epidemiology indicate that PFAS may act through shared mechanisms including activation of peroxisome proliferator activated receptor α (PPARα). However, the effect of PFAS mixtures on human relevant molecular initiating events remains an important data gap in the PFAS literature. Here, we tested the ability of modeling approaches to predict the effect of diverse PPARα ligands on receptor activity using Cos7 cells transiently transfected with a full length human PPARα (hPPARα) expression construct and a peroxisome proliferator response element-driven luciferase reporter. Cells were treated for 24 h with two full hPPARα agonists (pemafibrate and GW7647), a full and a partial hPPARα agonist (pemafibrate and mono(2-ethylhexyl) phthalate), or a full hPPARα agonist and a competitive antagonist (pemafibrate and GW6471). Receptor activity was modeled with three additive approaches: effect summation, relative potency factors (RPF), and generalized concentration addition (GCA). While RPF and GCA accurately predicted activity for mixtures of full hPPARα agonists, only GCA predicted activity for full and partial hPPARα agonists and a full agonist and antagonist. We then generated concentration response curves for seven PFAS, which were well-fit with three-parameter Hill functions. The four perfluorinated carboxylic acids (PFCA) tended to act as full hPPARα agonists while the three perfluorinated sulfonic acids (PFSA) tended to act as partial agonists that varied in efficacy between 28-67 % of the full agonist, positive control level. GCA and RPF performed equally well at predicting the effects of mixtures with three PFCAs, but only GCA predicted experimental activity with mixtures of PFSAs and a mixture of PFCAs and PFSAs at ratios found in the general population. We conclude that of the three approaches, GCA most accurately models the effect of PFAS mixtures on hPPARα activity in vitro. Understanding the differences in efficacy with which PFAS activate hPPARα is essential for accurately predicting the effects of PFAS mixtures. As PFAS can activate multiple nuclear receptors, future analyses should examine mixtures effects in intact cells where multiple molecular initiating events contribute to proximate effects and functional changes.

Application of generalized concentration addition to predict mixture effects of glucocorticoid receptor ligands

Environmental exposures often occur in complex mixtures and at low concentrations. Generalized concentration addition (GCA) is a method used to estimate the joint effect of receptor ligands that vary in efficacy. GCA models have been successfully applied to mixtures of aryl hydrocarbon receptor (AhR) and peroxisome proliferator-activated receptor gamma (PPARγ) ligands, each of which can be modeled as a receptor with a single binding site. Here, we evaluated whether GCA could be applied to homodimer nuclear receptors, which have two binding sites, to predict the combined effect of full glucocorticoid receptor (GR) agonists with partial agonists. We measured transcriptional activation of GR using a cell-based bioassay. Individual concentration-response curves for dexamethasone (full agonist), prednisolone (full agonist), and medroxyprogesterone 17-acetate (partial agonist) were generated and applied in three additivity models, GCA, effect summation (ES), and relative potency factor (RPF), to generate response surfaces. GCA and RPF yielded adequate predictions of the experimental data for two full agonists. However, GCA fit experimental data significantly better than ES and RPF for all other binary mixtures. This work extends the application of GCA to homodimer nuclear receptors and improves prediction accuracy of mixture effects of GR agonists.