Hepatic P450 Enzyme Activity, Tissue Morphology and Histology of Mink (Mustela vison) Exposed to Polychlorinated Dibenzofurans

Dose- and time-dependent effects of environmentally relevant concentrations of 2,3,7,8-tetrachlorodibenzo-p-dioxin equivalents (TEQ) of 2,3,7,8-tetrachlorodibenzofuran (TCDF), 2,3,4,7,8-pentachlorodibenzofuran (PeCDF), or a mixture of these two congeners on hepatic P450 enzyme activity and tissue morphology, including jaw histology, of adult ranch mink were determined under controlled conditions. Adult female ranch mink were fed either TCDF (0.98, 3.8, or 20 ng TEQTCDF/kg bw/day) or PeCDF (0.62, 2.2, or 9.5 ng TEQPeCDF/kg bw/day), or a mixture of TCDF and PeCDF (4.1 ng TEQTCDF/kg bw/day and 2.8 ng TEQPeCDF/kg bw/day, respectively) for 180 days. Doses used in this study were approximately eight times greater than those reported in a parallel field study. Activities of the cytochrome P450 1A enzymes, ethoxyresorufin O-deethylase (EROD) and methoxyresorufin O-deethylase (MROD) were significantly greater in livers of mink exposed to TCDF, PeCDF, and a mixture of the two congeners; however, there were no significant histological or morphological effects observed. It was determined that EROD and MROD activity can be used as sensitive biomarkers of exposure to PeCDF and TCDF in adult female mink; however, under the conditions of this study, the response of EROD/MROD induction occurred at doses that were less than those required to cause histological or morphological changes

A Model for Aryl Hydrocarbon Receptor-Activated Gene Expression Shows Potency and Efficacy Changes and Predicts Squelching Due to Competition for Transcription Co-Activators

<div><p>A stochastic model of nuclear receptor-mediated transcription was developed based on activation of the aryl hydrocarbon receptor (AHR) by 2,3,7,8-tetrachlorodibenzodioxin (TCDD) and subsequent binding the activated AHR to xenobiotic response elements (XREs) on DNA. The model was based on effects observed in cells lines commonly used as <i>in vitro</i> experimental systems. Following ligand binding, the AHR moves into the cell nucleus and forms a heterodimer with the aryl hydrocarbon nuclear translocator (ARNT). In the model, a requirement for binding to DNA is that a generic coregulatory protein is subsequently bound to the AHR-ARNT dimer. Varying the amount of coregulator available within the nucleus altered both the potency and efficacy of TCDD for inducing for transcription of CYP1A1 mRNA, a commonly used marker for activation of the AHR. Lowering the amount of available cofactor slightly increased the EC50 for the transcriptional response without changing the efficacy or maximal response. Further reduction in the amount of cofactor reduced the efficacy and produced non-monotonic dose-response curves (NMDRCs) at higher ligand concentrations. The shapes of these NMDRCs were reminiscent of the phenomenon of squelching. Resource limitations for transcriptional machinery are becoming apparent in eukaryotic cells. Within single cells, nuclear receptor-mediated gene expression appears to be a stochastic process; however, intercellular communication and other aspects of tissue coordination may represent a compensatory process to maintain an organism’s ability to respond on a phenotypic level to various stimuli within an inconstant environment.</p></div

Modeled transcriptional dose-response plots at varying amounts of cofactor and non-AHR-ARNT cofactor binding sites.

<p>(<b>A</b>) Reduction in the amount of cofactor (CoF) at a constant concentration/amount of competing non-AHR binding proteins (1535 molecules). The modeled response and Hill equation fits are shown for cofactor amounts of 2000, 1500 and 1200 molecules. At 1000 molecules of cofactor and less, squelching was apparent, shown by a reduction in the responses at higher TCDD concentrations and the biphasic appearance of the DR curves. (<b>B</b>) Increasing the amount of competing non-AHR binding proteins (Other) also produced a squelching-like response at high ligand concentrations with squelching occurring at 7500 or more molecules of non-AHR cofactor binding proteins. The amount of cofactor was kept constant at 1500 molecules. The Hill equation fits are shown for competing non-AHR binding site (Other) amounts of 2500 or less.</p

Estimates of cancer potency of 2,3,7,8-tetrachlorodibenzo(p)dioxin using linear and nonlinear dose-response modeling and toxicokinetics

Linear and nonlinear toxicity criteria were derived for 2,3,7,8-tetrachlorodibenzo(p)dioxin (TCDD) using the recent National Toxicology Program rat cancer bioassay. Dose-response relationships were assessed for combined liver tumors based on lifetime average liver concentrations (LALCs) estimated with a toxicokinetic model. Rat LALC estimates at the 1% point of departure (POD) were obtained with benchmark dose (BMD) modeling to yield the BMD in terms of LALC. The same toxicokinetic model was used to backextrapolate the human-equivalent external dose (HED). A linear cancer slope factor (CSF) with a value of 1 × 10 per pg/kg/day was calculated as the ratio between the benchmark response rate and the HED at the lower confidence limit of the benchmark dose (BMDL). A nonlinear reference dose (RfD) with a value of 100 pg/kg/day was developed from the BMD value by applying uncertainty factors to rat internal and human external doses. The RfD was 100 times higher than the 10 risk-specific dose (RSD) based on the linear CSF. For comparison, BMD and BMDL values were developed for key events in the tumor promotion mode of action (MOA) of TCDD. This MOA involves dysregulation of the normal function of the aryl hydrocarbon receptor and its associated biological processes and results in pathologies that drive tumor promotion and progression. The BMD values for key events were consistent with the timing of the key events within the MOA and provided support for the choices of the 1% tumor rate as a POD and dichotomous Hill model for representing receptor-mediated carcinogenicity. Because a threshold toxicity criterion most accurately reflects the MOA, the RfD for TCDD with a value of 100 pg/kg/day is considered appropriate for regulatory purposes, consistent with a 2006 NRC panel's recommendation to develop a threshold-based cancer potency factor for TCDD and with the methodology in U.S. Environmental Protection Agency's Cancer Guidelines

Contour plots of the modeled transcriptional responses showing the relationship between number of cofactor molecules and the number of competing non-AHR (Other) binding proteins.

<p>The x-axes show the applied concentration of TCDD. The y-axes show the amount of other binding proteins available in the cell. The fold change in CYP1A1 mRNA is represented by the colors on the plots and the color bar to the right. The number at the upper left of each plot shows the number of molecules of cofactor.</p

Hill equation fits of modeled data at a range of cofactor amounts along with the fit to the transcriptional response in Fig 1 in Powis et al. (2011) [61].

<p>Fitted parameters are shown as the best-fit value ± standard error. The upper part of the table shows fits for a series of varying cofactor amounts. The lower part of the table shows fits for a series of varying competing non-AHR cofactor binding sites (Other). Fitting was conducted with Graphpad Prism. The rising portion of the curve was fit. It was not possible to obtain a Hill equation fit to the modeled results at 60 molecules of cofactor. The lower part of the table shows the effect of changing the number of competing binding sites for the cofactor.</p><p>¹ For 1000 cofactor molecules, points below 3 nM TCDD were fit, and for 800 molecules and lesser amounts, points below 1 nM TCDD were fit. It was not possible to obtain a Hill equation fit to the modeled results at 60 molecules of cofactor.</p><p>² Transitional dose values (TDVs) as a measure of threshold were estimated by projecting to the background response using the methods for the Hill model described in Simon et al., (2014). [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127952#pone.0127952.ref078" target="_blank">78</a>] The equations for estimating TDVs using background projection from Simon et al., 2014 are shown in Equation D in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127952#pone.0127952.s001" target="_blank">S1 File</a>.</p><p>Hill equation fits of modeled data at a range of cofactor amounts along with the fit to the transcriptional response in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127952#pone.0127952.g001" target="_blank">Fig 1</a> in Powis et al. (2011) [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127952#pone.0127952.ref061" target="_blank">61</a>].</p

Transcriptional dose-response using time-averaged species from the model results to demonstrate that squelching occurs at the cofactor-binding step.

<p>Each plot was fit to a Hill function (details in text) and the EC21 and transitional dose values are shown. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127952#pone.0127952.ref087" target="_blank">87</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127952#pone.0127952.ref088" target="_blank">88</a>] <b>(A)</b> Plot of CYP1A1 mRNA fold change vs. time-averaged ligand-bound AHR for responses without squelching. <b>(B)</b> Plot of time-averaged ligand-bound AHR for responses with squelching. <b>(C)</b> Plot of time-averaged cofactor bound to AHR-ARNT and thus contributing to CYP1A1 transcription.</p

Comparison of ChIP results for AHR and ARNT from Fig 2A of Powis et al. (2011) [64] with those of the model.

<p>ChIP results were estimated with Equation A and Equation B in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127952#pone.0127952.s001" target="_blank">S1 File</a>. <b>(A)</b> Percent recruitment of AHR to CYP1A1; <b>(B)</b> Percent recruitment of ARNT to CYP1A1.</p

Measured and modeled transcriptional dose-response to TCDD.

<p>The larger filled black circles show the transcriptional response of CYP1A1 at 6 hours in T47-D cells. These data were digitally extracted from Fig 1 in Powis et al. (2011). [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127952#pone.0127952.ref064" target="_blank">64</a>] The smaller symbols and dotted lines show the modeled transcriptional dose response at three different amounts of cofactor present. When 1500 molecules of cofactor were present, the modeled response is very similar to the observed response in T47-D cells by Powis et al. (2011). [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127952#pone.0127952.ref064" target="_blank">64</a>]</p

Estimates of cancer potency of 2,3,4,7,8-Pentachlorodibenzofuran using both nonlinear and linear approaches

Cancer potency estimates were derived for 2,3,4,7,8-pentachlorodibenzofuran (4-PeCDF) using data collected from the recently published National Toxicology Program bioassay in female Sprague-Dawley rats. By using a toxicokinetic model for 4-PeCDF, the dose-response relationship for combined liver tumors (hepatocellular adenomas and cholangiocarcinomas) in rats was assessed in terms of lifetime average liver concentration and lifetime average adipose concentration with data from both the lifetime and the stop-exposure components of the bioassay. Benchmark dose modeling was performed to estimate tissue concentrations at two points of departure (EC and EC and their 95% upper and lower confidence limits). The same toxicokinetic model with human input values was then used to back-extrapolate human equivalent doses that corresponded to the internal tissue concentration measures at the points of departure. Information regarding the cancer mode of action was used to support the development of several toxicity criterion values based on a nonlinear method, e.g., reference dose or tolerable daily intake. Nonlinear estimates of toxicity criteria based on observed noncancer toxic events as possible precursors to tumor formation were also derived and were similar in value to those based on combined liver tumors. For comparison purposes, linear estimates of cancer potency were also derived