* Toxicology and Environmental Research and Consulting, The Dow Chemical Company, 1803 Building, Midland, Michigan 48674;
Department of Biostatistics, Virginia Commonwealth University, Richmond, Virginia 23298; and
Department of Biochemistry and Molecular Biology and National Food Safety and Toxicology Center, Michigan State University, East Lansing, Michigan 48824
Received September 26, 2001; accepted April 8, 2002
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ABSTRACT |
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Key Words: mixture; endocrine; estrogen receptor; additivity; diethylstilbestrol; 17ß-estradiol; response surface; synergy.
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INTRODUCTION |
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One approach to the assessment of chemical mixtures is the toxic equivalency factor approach, which normalizes the dose of each component of the mixture against that of the most potent compound; the relative potencies are then summed to estimate the toxic potency of the mixture (Safe, 1998). Such an approach has also been suggested for use with endocrine active agents under an estrogen equivalency approach (Gaido et al., 1998
; Soto et al., 1997
).
Given the complexity of this issue, it is not surprising that research approaches in combination or mixtures toxicology have varied. These include the use of standard safety factor approaches and response surface analyses applied to multicomponent mixtures (Groten et al., 1997; Nesnow et al., 1998
), analyses of binary mixtures using the concepts of response and concentration addition (Kortenkamp and Altenburger, 1998
; Tully et al., 2000
), as well as integrated quantitative structureactivity relationships and physiologically based pharmacokinetic-pharmacodynamic modeling for complex mixtures (Verhaar et al., 1997
). Furthermore, because of the infinite number of potential chemical combinations, it would be impossible to test all of them using empirical approaches (Yang, 1992
). The majority of previous mixtures studies and their effects on endocrine end points have been restricted to binary mixtures (Arcaro et al., 1998
; Arnold et al., 1997
; Ramamoorthy et al., 1997
; Tully et al., 2000
). This contrasts with "real-world" mixtures that are much more complex. Furthermore, there have been few attempts to apply rigorous statistical methods to the assessment of the interactions of mixtures of endocrine-active chemicals.
As a step toward developing methods to assess complex mixtures (greater than binary), this report describes the development of an in vitro approach for assessing interactions within ternary mixtures of chemicals that act through the estrogen receptor (ER). This methodology involves the use of a chimeric receptor-reporter gene transactivation system (Zacharewski, 1997) utilizing a 43 factorial dosing design (64 dosing groups) (Fig. 1
). All possible combinations of four concentrations of three chemicals were modeled via a response surface methodology for the identification of interactions among the ternary chemical mixtures (Gennings et al., 2000
, in press
) so as to identify additivity (no interaction) or departures from additivity (less than additivity, greater than additivity) (Fig. 2
).
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MATERIALS AND METHODS |
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Cell culture.
MCF-7 cells (obtained from Dr. L. Murphy, University of Manitoba, Winnipeg, and originally acquired from Dr. C. M. McGrath, Meyer L. Prentis Cancer Center, Detroit, MI) were maintained in DMEM supplemented with 10% FBS, 2 mmol L-glutamine, 15 mmol HEPES augmented with 50 µg/ml gentamicin, penicillin/streptomycin (100 IU/ml/100 µg/ml), and amphotericin B (2.5 µg/ml). Cells were maintained at 3% CO2 and 95% humidity.
Transfection.
Cells were plated in triplicate in 96-well plates at a density of 68 x 103 cells/well in 5% FBS-DCC. After attachment and growth for 6 h, the cells were transfected using LipofectinTM (Life Technologies). To each well we added 50 ng of the ß-galactosidase (ß-gal) expression vector pCH110 (Pharmacia, Piscataway, NJ), 150 ng of 17m5-G-Luc, the Gal4-regulated luciferase reporter vector, and 5 ng of Gal4-HEG0, an ER expression vector (both provided by P. Chambon, INSERM, France). The plasmids were transfected in serum-free, antibiotic-free DMEM supplemented with 2 mmol L-glutamine. Cells were allowed to incubate overnight at 37°C in an humidified atmosphere of 3% CO2/air. Sixteen to 18 h after transfection, the medium was poured from the plate, the plate was inverted, and excess moisture was absorbed by placing the plate on sterile paper towels. The cells were then treated in triplicate with E2/DES/EE (mixture A) or E2/EGF/IGF-1 (mixture B) in 5% FBS-DCC. The pure antiestrogen ICI 182,780 (Wakeling et al., 1991) was used to verify that the reporter gene activity was strictly ER mediated because ICI 182,780 was able to completely inhibit responses to both individual and chemical combinations (data not shown). After treatment, wells were washed with phosphate-buffered saline, and 50 µl of lysis buffer (Promega, Madison, WI) was added to each well. Plates were then placed in a 70°C freezer for approximately 1 h. Thawing the plates after freezing at 70°C facilitated cell lysis. Aliquots from each well were divided into two 96-well plates for luciferase and ß-gal activity determination. The reference plasmid pCH110 was cotransfected as an internal control to correct for variations in transfection efficiency. The values presented are units of luciferase activity normalized to the ß-gal activity from individual wells and expressed as fold induction relative to control as the end point. Treatment regimens that resulted in observably reduced ß-gal activity relative to that of transfected cultures exposed to 10 nmol E2 under unaltered media conditions were considered cytotoxic and were not used for further analysis. In all experiments, the final concentration of the solvent (DMSO) did not exceed 0.3% in the culture medium; neither was the osmolality or pH of the dosing solutions changed by ± 6 mOsm/kg H2O or
0.06 units, respectively, relative to vehicle-treated controls.
Luciferase and ß-gal activity assays.
For transfected cells, 10 µl of lysate was combined with 100 µl of Luciferase Assay Reagent (Promega, Madison, WI), and luminescence was determined immediately using a Packard Topcount NXTTM luminescence counter (Packard Instrument, Meriden, CT). The ß-gal activity was measured using a chemiluminescent kit (Tropix, Bedford, MA). The ß-gal activity was initiated with 70 µl of galactosidase reaction buffer added to 10 µl of the cell lysate followed by a 30-min room temperature incubation. After the reaction was stopped by the addition of 100 µl of the Accelerator II stop buffer, the chemiluminescence was measured in the same manner as for luciferase.
Experimental Design
Reporter gene assay.
Test chemicals were evaluated in range-finding, reporter gene studies to establish individual chemical doseresponse data. These data were used to facilitate the selection of chemical concentrations for use in the interaction studies. For mixtures A and B in the in vitro ER reporter assay, assessment of interactions between chemicals in the mixture was facilitated by the use of a factorial design. This entailed the selection of 4 concentrations of all the chemicals in a given mixture, including zero (vehicle control), in all possible combinations to give a total of 64 dosing groups (see Fig. 1). For mixture A (E2/DES/EE) the concentrations used were E2/DES (0, 10-11, 10-10, and 10-9 M) and EE (0, 10-12, 10-11, and 10-10 M). For mixture B (E2/EGF/IGF-I) the concentrations were E2 (0, 10-11, 3 x 10-11, and 10-10 M) and EGF/IGF-I (0, 10-10, 3 x 10-10, and 10-9 M). Each experiment was repeated at least 3 times with independent passages of MCF-7 cells.
Uterotrophic assay.
Litters of postnatal day 1011 female CD-1 mice (obtained by cross-fostering and shipped with their foster dams) were purchased from Charles River Laboratories (Raleigh, NC). After arrival at the laboratory, the mice were acclimated for 1 week before testing. Litters with their foster dam were housed in separate polycarbonate cages with corncob bedding in rooms in which the relative humidity was maintained within a range of 4070%. The room temperature was maintained at 22 ± 3°C. A 12-h light-dark photocycle was maintained for all animal rooms with lights on at 6:00 A.M. and off at 6:00 P.M. Room air was exchanged at 1215 times/h. Mice were provided Purina Rodent Diet/Casein Base, 5K96, a low-phytoestrogen rodent diet in pellet form (a modification of the standard NIH-31 diet), with the soybean and alfalfa meal replaced with casein, and municipal drinking water provided ad libitum during the prestudy and study periods. Before dosing, the mice were weaned and randomized into dosing groups using a procedure designed to equalize groups based on body weights. Foster dams were euthanized by CO2 inhalation at the time of randomization.
An initial probe study was performed to gather individual chemical doseresponse data so as to facilitate the setting of dose levels for the mixtures to be used in the main study. Dosing suspensions were prepared by initially dissolving the test material in ethanol and further diluting (1:10) with corn oil (10% ethanol90% corn oil). Using E2, DES, and EE, 5 mice/group were dosed by oral gavage at the same time every morning for 3 consecutive days at 8 different doses: EE and DES at 0.03, 0.1, 0.3, 1, 3, 10, 30, and 100 µg/kg/day and E2 at 0.1, 0.3, 1, 3, 10, 30, 100, and 300 µg/kg/day. A single control group was treated with the vehicle. All mice were weighed on the day of randomization, on the 3 days of dosing, and on the day of necropsy (day 4). The mice were euthanized on day 4, and for each the uterus was excised and trimmed, the cervix removed, and wet weight determined. Each uterus was then nicked, blotted to remove luminal fluid, and reweighed as a measure of blotted weight.
For the evaluation of the mixture A, 27 groups of 6 mice were dosed in a full factorial study design in which the level of each test chemicals was 0, 0.25, or 2.5 µg/kg/day. These dose levels were chosen on the basis of the individual doseresponse curves obtained in probe studies.
Statistical analysis using a nonlinear mixed model.
Further details of the statistical methodologies used in evaluation of the mixtures data from these experiments are described in Gennings et al. (in press). In vitro experiments were performed on 3 separate days with independent passages of MCF-7 cells to satisfy the assumption of independent experiments. Luciferase activity was normalized to ß-gal, that is, (Luc)/(ß-gal). Fold induction was calculated as Luc/ß-gal divided by the average Luc/ß-gal in the DMSO group. Uterotrophic assay data were expressed as fold increase in response relative to the mean of vehicle controls.
The definition of additivity or additive responses used in this analysis is that of Berenbaum (1985) and is based on the classical isobolograms for the combination of two chemicals (Loewe and Muischnek, 1926
; Loewe, 1953
). That is, in a combination of c chemicals, let Ei represent the concentration/dose of the ith component alone that yields a fixed response, y0, and let xi represent the concentration/dose of the ith component in combination with the c agents that yields the same response. According to this definition of additivity, if the substances combine with zero interaction, then
![]() | ((1)) |
If the left-hand side of Equation 1 is less than 1, then a synergism can be claimed at the combination of interest. If the left-hand side of Equation 1
is greater than 1, then an antagonism can be claimed at the combination. Because Equation 1
is the equation of a plane in c dimensions, this definition of additivity implies that under additivity contours of constant response are planar.
For the mixture studies, a 4 x 4 x 4 factorial design was used for three chemicals. Ideally, the location of the concentration levels in such a design should support the "active" part of the response surface. Because this region may not be known a priori, a design based on arithmetic spacing that at least roughly evenly spans the experimental region was preferred instead of log spacing.
A nonlinear model was chosen for the analysis of the mixture data because a sigmoid-shaped relationship was expected. The form of the model was similar to model 2 based on a Gompertz model parameterized as follows:
![]() | ((2)) |
The model given in Equation 2 can be "linearized" by making the so-called complementary log-log transformation. For example, let xß = ß1x1 + ß2x2. Notice that
![]() | ((3)) |
Thus, is an intercept and ß1 and ß2 are slopes for x1 and x2 on the complementary log-log scale. Following the logic of Carter et al.(1988), with some algebra, Equation 3
can be put in the form of Equation 1
, the definition of additivity. Namely,
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Now, if xß = ß1x1 + ß2x2 + ß12x1x2, then
![]() | ((4)) |
Considering responses above background such that log(log(E(y)0/)) >
, the denominator of the right-hand side is positive. Using concentration values (i.e., not log concentration) that are also nonnegative, the algebraic sign of the numerator, and hence the entire term, is determined by the sign of ß12. If ß12 is positive, then the right-hand side of Equation 4
is less than 1, indicating a synergism at the combination of interest. If ß12 is negative, then the right-hand side of Equation 4
is greater than 1, indicating an antagonism at the combination.
It is important to note that the algebraic relationship between the model and the definition of additivity in Equation 1 does not hold if the concentration is modeled on the log scale. We follow the general rule that additivity is defined on the concentration scale unless there is reason to be interested in interactions on another scale. Clearly, if additivity holds on the arithmetic concentration scale, it generally will not hold on the log scale.
The model we used to fit the mixture data was given in Equation 2, in which
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A nonlinear Gompertz model was chosen for these data because a sigmoid-shaped relationship was expected, and this model can take such a shape without imposing a symmetry function. To account for the intra-experimental relationship of cell growth and responsiveness, observations within an experiment were allowed to be correlated. Data from different experiments were assumed to be independent.
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RESULTS |
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The dosing scheme used (see Fig. 1) allowed for the calculation of concentration and chemical interaction parameters based on our response surface modeling methodology. The data were presented as the mean of triplicate responses for each treatment group (i.e., average fold induction) and fit into the model to generate the resulting parameter estimates (see Table 1
) and response surface plots.
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However, because it appeared that the overall departure from additivity was due to an antagonistic interaction among the chemicals at higher concentrations, it was of interest to determine whether these 3 ER- agonists would be additive in the lower part of the concentrationresponse curve. To this end, these same data were reanalyzed in a 33 factorial analysis (27 treatment groups) in which the highest concentration of each chemical was excluded from the original data set. Figure 6AC
presents the new fitted response surfaces. The overall test for additivity among the three chemicals in combination was not rejected in this case (p = 0.759), confirming that the chemicals were additive in this region of the concentrationresponse curve. The linear nature of the contour plot (see Fig. 5B
, representative of response surface in Fig. 6B
) was more apparent compared with that of Figure 5A
(representative of response surface in Fig. 4B
). Also, the calculated 2-way interaction estimates shown in Table 1
(e.g., ß12 E2/EE, p = 0.375; ß23 EE/DES, p = 0.813, ß13 E2/DES, p = 0.763) were no longer significant, confirming that none of the chemicals antagonized each others responses at these concentrations.
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It was also of interest to determine whether these chemical interactions that had been observed and studied in our in vitro system related to interactions in vivo. The rodent uterotrophic assay was chosen as the basic model for response assessment using mixture A. Individual doseresponse data for each chemical were obtained via gavage studies in immature CD-1 mice, as illustrated in Figure 8. This figure presents the effect on both wet and blotted weight for the individual chemicals. Doses of 0, 0.25, and 2.5 µg/kg/day were chosen for evaluation in the ternary mixture using a 33 factorial design (27 dosing groups). These doses were based on the individual chemical doseresponse data (see Fig. 8
) with the goal of having individual responses in the low, linear region of the doseresponse curves, which, based on the in vitro analyses, would allow for the most readily observable departures from strictly additive responses.
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DISCUSSION |
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The results with mixture A indicated that the ER agonists interacted in an additive manner when the chemicals were present at levels ranging from just above their individual response thresholds through most of their linear response range. However, a less-than-additive or antagonistic interaction was observed at higher concentrations (generally in the upper half of the individual chemical doseresponse range). It can be speculated that this was the consequence of receptor saturation at high agonist concentrations.
Because of the greater complexity of whole animal models, there is assumed to be a far greater range of possible chemical interactions. Hence, the mixture of E2/DES/EE was also evaluated in an in vivo uterotrophic assay system to assess the correlation with the in vitro data. The analysis of the 3 ER agonists in vivo resulted in an additive interaction between the mixture components similar to that found in vitro. Furthermore, this interaction held regardless of whether absolute or relative uterine wet or blotted weights were used in the analysis.
EGF and IGF-I have previously been shown to interact synergistically with E2 in binary mixtures (Aronica and Katzenellenbogen, 1993; Dupont et al., 2000
; Ignar-Trowbridge et al., 1996
). These interactions are thought to be due to cross talk mediated through integrated signaling pathways involving cyclic adenosine monophosphate, protein kinases, and receptor phosphorylation (Apostolakis et al., 2000
; Kahlert et al., 2000
; Nelson et al., 1991
). The resulting analysis of the data from our model confirmed a greater-than-additive interaction in the overall mixture, which was mostly a consequence, in this case, of the interaction between E2 and EGF.
However, the assay system used in these studies consisted of a chimeric Gal4 receptor-reporter system lacking a functional AF-1 domain (Zacharewski et al., 1995). It has been demonstrated that the transcriptional activity of ER-
can be mediated by two activation functions, AF-1 and AF-2, located at the amino and carboxyl termini, respectively (Tzukerman et al., 1994
), and furthermore, the ER-dependent transcription by IGF-I is predominantly mediated through AF-I (Ignar-Trowbridge et al., 1996
). It should be noted that both EGF and IGF-I produced apparent proliferation in the assay as evidenced by 2- to 3-fold increases in basal ß-gal activity relative to control cultures (data not shown), indicating that they were bioactive in the assay system. The lack of interaction between IGF-I and E2 may have been due to the lower concentration of IGF-I relative to that used previously in the characterization of synergistic interactions (Aronica and Katzenellenbogen, 1993
). Thus, the possibility cannot be excluded that a different promoter or receptor construct than the one used here would have been necessary to detect an interaction between IGF-I and E2. This chimeric system has been shown to exhibit differential responsiveness in cell lines other than MCF-7 (Connor et al., 1996
). MCF-7 cells were used in this study because of their previously characterized responsiveness to this system (Balaguer et al., 1996
), and the chimeric construct does not exhibit different binding affinities or ligand specificities relative to the native ER (Kumar et al., 1987
) and proven to be less sensitive to serum-borne estrogen in media (Zacharewski et al., 1995
).
It should be stressed that the test materials and test concentrations used in this study were chosen to demonstrate the ability of the experimental system and associated statistical methods to detect additivity and less-than additivity (antagonism) and greater-than-additive interactions. All three types of responses were, in fact, generated by the experimental model and were detected statistically.
The results of the current investigation also indicated that there can be multiple types of interaction among the components of a given mixture and that the nature of the interaction depends on the region of the response curve from which doses were derived. Hence, interactions should not be expected to be globally uniform across the doseresponse spectrum. Therefore, studies addressing environmentally relevant mixtures need to be tested at environmentally relevant concentrations because high-dose interactions are likely to differ from interactions at low (particularly subthreshold) exposure levels. Therefore, proper design and interpretation of component mixture interactions requires knowledge of the individual chemical dose responses and appropriate selection of dose levels.
In conclusion, the data demonstrated the ability of an ER- reporter gene system, coupled with response surface statistical methodology, to detect additivity and less-than-additive (antagonistic) and greater-than-additive interactions in ternary mixtures. Such methods are now available for the assessment of environmentally relevant mixtures.
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ACKNOWLEDGMENTS |
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NOTES |
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