Effect of estrogenic binary mixtures in the yeast estrogen screen (YES)

Regulatory Toxicology and Pharmacology 70 (2014) 286–296

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Effect of estrogenic binary mixtures in the yeast estrogen screen (YES) Tzutzuy Ramirez a, Andreas Buechse b, Martina Dammann a, Stephanie Melching-Kollmuß c, Claudia Woitkowiak a, Bennard van Ravenzwaay a,⇑ a

BASF SE Experimental Toxicology and Ecology, Carl-Bosch Str. 38, 67056 Ludwigshafen am Rhein, Germany BASF SE Scientific Computing, Carl-Bosch Str. 38, 67056 Ludwigshafen am Rhein, Germany c BASF SE Product Safety, Carl-Bosch Str. 38, 67056 Ludwigshafen am Rhein, Germany b

a r t i c l e

i n f o

Article history: Received 3 March 2014 Available online 11 July 2014 Keywords: Endocrine active compounds Estrogenic mixtures Bisphenol A Genistein Trenbolone Yeast based assay Loewe additivity model

a b s t r a c t Endocrine disrupting compounds (EDCs) of natural or synthetic origin can interfere with the balance of the hormonal system, either by altering hormone production, secretion, transport, or their binding and consequently lead to an adverse outcome in intact animals. An important aspect is the prediction of effects of combined exposure to two or more EDCs at the same time. The yeast estrogen assay (YES) is a broadly used method to assess estrogenic potential of chemicals. Besides exhibiting good predictivity to identify compounds which interfere with the estrogen receptor, it is easy to handle, rapid and therefore allows screening of a large number of single compounds and varying mixtures. Herein, we applied the YES assay to determine the potential combination effects of binary mixtures of two estrogenic compounds, bisphenol A and genistein, as well as one classical androgen that in vitro also exhibits estrogenic activity, trenbolone. In addition to generating data from combined exposure, we fitted these to a fourparametric logistic dose–response model. As all compounds tested share the same mode of action dose additivity was expected. To assess this, the Loewe model was utilized. Deviations between the Loewe additivity model and the observed responses were always small and global tests based on the whole dose–response data set indicated in general a good fit of the Loewe additivity model. At low concentrations concentration additivity was observed, while at high concentrations, the observed effect was lower than additivity, most likely reflecting receptor saturation. In conclusion, our results suggest that binary combinations of genistein, bisphenol A and trenbolone in the YES assay do not deviate from expected additivity. Ó 2014 Elsevier Inc. All rights reserved.

1. Introduction Endocrine disruption has become an important topic of public concern. ‘‘An endocrine disrupter is an exogenous substance or mixture that alters the function(s) of the endocrine system and consequently causes adverse effects in an intact organism, or its progeny, or (sub) populations’’ (WHO/IPCS, 2002). Natural or man-made substances with endocrine activity occur in the environment and are able to interfere with the endocrine physiological responses in the organisms exposed to them. Endocrine active substances (EASs) are of broad physical and chemical variety; such as natural and synthetic hormones, plant constituents, pesticides, substances used in industry and in consumer products, and other industrial by-products and pollutants (Altenburger et al., 2012; Arcaro et al., 1998; Bermudez ⇑ Corresponding author. Address: BASF SE Experimental Toxicology and Ecology, Z470, Carl-Bosch Str. 38, 67056 Ludwigshafen am Rhein, Germany. E-mail address: [email protected] (B. van Ravenzwaay). http://dx.doi.org/10.1016/j.yrtph.2014.07.006 0273-2300/Ó 2014 Elsevier Inc. All rights reserved.

et al., 2010; Brian et al., 2007; Borgert et al., 2004; Brion et al., 2012; Charles et al., 2007; Evans et al., 2012; Kolle et al., 2012; Soto et al., 1997; Tinwell and Ashby, 2004; van Meeuwen et al., 2007). Among the most studied EASs are environmental estrogens that mimic the function of 17b-estradiol (Delclos et al., 2009; Giesy et al., 2002; Routledge et al., 2000; Rubin and Soto, 2009; Stoker et al., 2010; vom Saal et al., 2012). Natural or man-made endocrine active substances may occur in the environment at low concentrations and the chemical risk assessment of such compounds is predominantly performed on individual substances rather than mixtures. Major efforts have been made to understand the toxicology of chemical mixtures. A chemical mixture can be understood as ‘‘any set of multiple chemical substances that may or may not be identifiable, regardless of their sources that may jointly contribute to toxicity in the target population’’ (U.S. EPA, 2000). More than 50 years ago, two basic concepts were defined for mixtures: Bliss independence and Loewe additivity (Bliss, 1939; Loewe, 1953; Hewlett and Plackett, 1959; Plackett and Hewlett, 1948, 1952;

T. Ramirez et al. / Regulatory Toxicology and Pharmacology 70 (2014) 286–296

Goldoni and Johansson, 2007; Howard and Webster, 2009; Webster, 2013). Loewe additivity (non-interaction) is applied when ‘‘compounds act without any interaction among them and the total effect does not differ from what can be expected from the dose–effect relations of the individual agents’’ (Goldoni and Johansson, 2007); in other words, the combined effect equals the expected ‘‘dose addition’’ (Howard and Webster, 2009). For instance, this could occur when different test substances in a mixture act on the same receptor ligand binding site, resulting in a dose additivity effect (Borgert et al., 2004; Boobis et al., 2008; EFSA, 2013). If an interaction occurs among the compounds, the combined effect could be lower than that expected from the sum of both doses, the effect is then called antagonism. In the case the effect is higher than the one expected from the sum of both doses, it is called synergism (Goldoni and Johansson, 2007; Howard and Webster, 2009). Whereas Loewe additivity assumes that the individual components of a mixture share the same mechanism/mode of action and only differ in their potencies (ECETOC, 2012; Wilkinson et al., 2000; Feron et al., 2002; Goldoni and Johansson, 2007), the Bliss independence model assumes that the toxicological effects of the individual compounds in a mixture are the results of separate mechanisms/mode of action (Goldoni and Johansson, 2007). While in real life exposure to chemical mixtures usually occurs, legislation is predominantly based on the effect of single compounds. There is therefore, increasing demand to address the potential toxicity of substances present in a mixture (Bjarnason, 2004; U.S. EPA, 2000). In the case of mixtures where individual substances are toxicologically more potent compared to the other components of the mixtures, these ‘‘risk drivers’’ often dominate the mixture’s toxicity (Kolle et al., 2011; Rider et al., 2008, 2009, 2010; Jacobsen et al., 2010; Howdeshell et al., 2008). The European Food Safety Authority (EFSA) recently proposed that the dose addition concept may be applied to predict the toxicological outcome of combined exposure toxicity involving several substances affecting a common target, ‘‘assuming that all of the substances to which exposure occurs might contribute to the common effect, depending on their individual potency and their individual concentration’’ (EFSA, 2013). Herein, we aim to identify the potential effects produced by a mixture of chemical compounds that alter estrogenic signaling; the binary mixtures of three different compounds showing in vitro estrogenic activity, bisphenol A (BPA), a known weak estrogenic compound (Tyl et al., 2008), genistein, a phytoestrogen with strong ER binding properties (Rozman et al., 2006) and trenbolone, an androgen that in vitro and vivo also present estrogenic activity (Kolle et al., 2012; and unpublished internal data) were analyzed for their estrogenic potential using the yeast estrogen screen (YES), which has been used for screening estrogenic effects (Sohoni and Sumpter, 1998; Kolle et al., 2010, 2011). A non-interactive type of mixture toxicity represented by dose additivity is assumed. 2. Material and methods 2.1. Test compounds Bisphenol A (BPA) was purchased from Aldrich (Cat No. 239658) with a purity of >99%; genistein and trenbolone were purchased from Sigma (Cat No. G6649 and T3925), presenting a purity of >98% and 98%, respectively.

287

University of Dresden. The cells are transgenic organisms that have the capacity to identify compounds that can interact with the human estrogen receptor alpha (hERa). For this purpose, the DNA sequence of the hERa was stably integrated into the main chromosome of the yeast. In addition, the yeast cells contain the expression plasmids carrying the reporter gene Lac-Z encoding the enzyme b-galactosidase (b-gal), which is used to measure the receptor activity and upstream the estrogen responsive element (YES). The hERa is functional and able to bind to the estrogen responsive sequence. Upon binding an active ligand, the estrogen-occupied receptor interacts with the estrogen responsive sequence and other transcriptional components to modulate gene transcription, and consequently induces the expression of the reporter gene Lac-Z. The enzyme produced, b-gal is secreted into the medium. Estrogenicity is measured by the quantity of chlorophenol red (CPR) produced from the chromogenic substrate, chlorophenol red-b-D-galactopyranoside (CPRG, yellow color) by b-gal activity. CPR is of red color and can be measured at an absorbance of 570 nm. The ability of a test substance to interact with the hERa is directly measured through the enzyme activity. The optical density of the culture measured at 690 nm was used as a measure of yeast growth and/or cell toxicity. The assay was performed as described by Sohoni and Sumpter (1998) with slight modifications as described by Kolle et al. (2010). Briefly, cells were seeded in 96well plates. The layout always contains a positive control, 17-b estradiol at increasing concentrations (1E-12 to 1E-06 M) this control is used to assess the validity of the experiment. Test substances were administered at increasing concentrations in DMSO horizontally for compound A or B and vertically for compound B or C. Four replicates corresponding to four plates were run in parallel per every binary mixture. Seven test substance concentrations were used in the range 3E-07 to 1E-04 M. All combinations of doses were tested. Additionally we tested one substance in all doses alone in the absence of the second compound. Data of two independent experiments were (each with four plates as described) were used. 2.3. Preprocessing the data The data obtained from the optical density (OD) at 570 were normalized first to the cell density (turbidity). Cytotoxicity of the test substance was checked by evaluating changes in the turbidity at 690 nm (i.e. if cytotoxic, a test substance would induce a decrease in the yeast growth, translated into a decrease in the OD 690 value). Raw data from every independent mixture experiment were first analyzed separately with a Linear Model using PROC MIXED of SAS 9.3 (Littell et al., 2006; SAS, 2011), to check for outliers. Five outliers were deleted before further processing (data not shown). The linear mixed model showed for all three mixtures a significant interaction between concentration–combination and experiment. Hence a combined analysis of both experiments was not justified and further analysis was performed separately for each mixture and each experiment. For the graphs and tables we aggregated the data by calculating the arithmetic mean per dose combination and experiment. This procedure yields 49 mean values per test compound combination and experiment. To allow plotting on a log-scale the zero doses were plotted at 0.0000001 M. Statistical tests for additivity or synergism were performed with the single values. 2.4. Statistical model

2.2. Human estrogen receptor mediated estrogenic and antiestrogenic activity in the YES assay The yeast cells (Saccharomyces cerevisiae) were obtained from the laboratory of Prof. Gunther Vollmer from the Technical

Since the individual components of the mixtures used in this study share the same mode of action, the Loewe additivity model (Loewe, 1953) was used for evaluating the potential effect of the mixture, either additive behavior, synergism or antagonism. Loewe

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additivity means ‘‘dose-additivity’’. The dose of one test compound can be replaced by a second test compound and both test compounds only differ in their relative potency R. With R = Dp,2/Dp,1 where Dp,1 is the dose of test compound1 to achieve a response of p when given alone and Dp,2 is the dose of test compound 2 to achieve the same response. That means all concentrations x1 and x2 satisfy

x1 x2 þ ¼1 Dp;1 Dp;2

ð1Þ

gðx1 ; 0Þ ¼ d



 expða1 þ b1 log x1 Þ þc 1 þ expða1 þ b1 log x1 Þ

ð4Þ

The parameters d and c describe the maximum and the minimum response and are expected to be similar for both test compounds. The minimum was estimated from the response at the control g(0,0). The parameter b is connected to the slope of the function. The higher b is the steeper the dose response function. The ratio between parameter a and b is connected with the ED50, the dose necessary to achieve 50% of the maximum effect:





For example if a dose of x1 = 8 or x2 = 4 both yield a relative effect of 50% when given alone we would expect an effect of 50% for the dose combinations (x1 = 4; x2 = 2) and also for the combination (x1 = 2; x2 = 3) if the system shows Loewe additivity. In the case of Loewe synergism lower doses are necessary to achieve the same effect as with the single test compound. Hence we have dose combinations x1, x2 which satisfy

With b = 1, the ED50 is simply exp(a). When fitting a response surface for the combined action of two test compounds we can use the relative potency factor R and obtain

x1 x2 þ <1 Dp;1 Dp;2

gðx1 ; x2 Þ ¼ d

ð2Þ

The opposite direction that a mixture needs higher doses than expected is called Loewe antagonism. To describe the amount of synergism or antagonism Hewlett (1969) introduced the additional parameter g.



x1 Dp;1

g

 þ

x2 Dp;2

g ¼1

ð3Þ

The case g = 1 represents Loewe additivity, g > 1 represents Loewe antagonism and g < 1 represents Loewe synergism. Graphically in the case of Loewe additivity all isoboles describing the concentration combinations x1, x2 of two test compounds 1 and 2 giving a constant response p lie on a straight line and synergism and antagonism form convex or concave curves (Fig. 1). To describe the dose–response relation of the data, the four-parameter logistic regression model was used. Different parameterizations of this model are possible. In this study the approach proposed by Whitehead and coworkers has been used (Whitehead et al., 2008). The effect of a single test compound 1 in absence of the second test compound is described by

Fig. 1. Schematic representation of isoboles, which ‘‘are contours of the concentration–response surface that connect points of equal response. Isoboles that are negatively sloped straight lines indicate concentration additivity, isoboles that curve inward indicate response greater than concentration additive’’ and isoboles that curve outward indicate response lower than concentration additive (figure modified from Webster (2013)).

ED50 ¼ exp

a

ð5Þ

b



 expða1 þ b1 logðx1 þ x2 =RÞÞ þc 1 þ expða1 þ b1 logðx1 þ x2 =RÞÞ

ð6Þ

The equation for the second test compound is achieved by replacing a1 and b1 with a2 and b2. In the case of synergism or antagonism between the two test compounds in a mixture the non-additivity factor g is added to Eq. (6), yielding:

( gðx1 ; x2 Þ ¼ d

expða1 þ b1 logððxg1 þ ðx2 =RÞg Þ

1=g

1 þ expða1 þ b1 logððxg1 þ ðx2 =RÞg Þ

)

ÞÞ

1=g

ÞÞ

þc

ð7Þ

Model (6) and (7) can only be used if the dose–response relationships of the two test compounds are parallel with b1 = b2 = b, what was not the case in our data. With non-parallel curves R depends on the value of p and there is no closed solution of R. To fit the models the method proposed by Whitehead et al. (2008) was used with an iterative fitting approach using a NewtonRaphson procedure. The Loewe additivity model and the synergism/antagonism model were fitted separately for each of the four plates of each of the two experiments per mixture. The residuals (the differences between the observations and the values predicted by the model) were stored and the sums of the squared residuals (SQres) were calculated per experiment. A difference in SQres between the Loewe additivity and non-additivity model is the variation that is additionally explained by the antagonism/synergism model compared to the Loewe additivity model. Since four plates were set up per experiment and one additional parameter g is estimated per plate, the antagonism/synergism model needs 4 degrees of freedom more than the additivity model. Both models were then compared using an F-test in an ordinary analysis of variance table. From fitting the Loewe additivity model and synergism/antagonism model separately to each of the four plates per experiment, four estimates of the model parameters a, b, d, c and g were obtained per experiment. For both experiments per mixture we calculated the mean, the standard deviation and the standard error of these parameter estimates. From the four plates within an experiment information about the repeatability was obtained and the two independent experiments yielded information about the reproducibility of the system. From the residuals of the fitted Loewe additivity model, the mean residual per plate for the test compounds combinations with both test compounds having concentration > 0 was calculated. If the mean residual of these test compounds combinations on a plate is positive this could indicate synergism since the observed responses are higher than the responses predicted from the model. If the mean residual is negative this indicates antagonism. This procedure yields four plate-specific mean values of residuals per

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experiment and mixture and again the mean and standard error per experiment and mixture were calculated. In summary, in this study three different approaches to estimate synergism or antagonism and to test its statistical significance have been used: (i) The comparison of the two models with and without the non-additivity parameter g using an F-test in an analysis of variance framework. (ii) A t-test for the non-additivity parameter g by comparing the ratio of the average g and its standard error with a tdistribution having 3 degrees of freedom (4 plates per experiment). (iii) A t-test for the average residuals for the combinations on a plate by comparing the ratio of the average and its standard error with a t-distribution having 3 degrees of freedom (4 plates per experiment). Additionally, the average residuals for specific dose combinations over the four plates of an experiment and mixture were analyzed. These position specific averages are illustrated as contour plots to gain deeper insights in the response surface and to detect possible regions of additivity, synergism or antagonism (see Fig. 4). To maximize the statistical power we did not apply a no correction for multiplicity.

2.5. Isobolographics In addition to the above-described method, isobolographics were estimated, where the estimation was not done simultaneously for all concentrations. The isobolographics are contours of the concentration–response surface that connect points of equal response. Those negatively sloped straight lines indicate concentration additivity, whereas isoboles that curve inward indicate a response greater than concentration additive, while those that curve outward indicate a response lower than concentration additive (Fig. 1). In this study, isoboles were created separately for each of the two experiments per mixture. As the dose–response curves have different maxima, DE,i instead of Dp,i was taken for the isobolographics. DE,i is the concentration of compound I, which results in a fixed absolute value (E) of b-gal. For each fixed concentration of compound 1 there are 7 concentrations (including 0) of compound 2. This dose–response relationship was used to estimate the concentration of compound 2 which produces a fixed effect of E using the four-parameter logistic model. The estimated combined concentrations producing a fixed effect E were plotted with the straight line which is expected to have Loewe additivity. This calculation was performed for E = 0.15 and E = 0.35. E = 0.15 was taken to gain an impression of the behavior at lower concentrations than the EC50, while E = 0.35 was taken because it lies close to the EC50 value where the variation of the estimation is the lowest. For each experiment this procedure results in at most 14 data points. The data points are fewer because the effect caused by the fixed concentration of one substance already exceeds E and this data cannot be used for the calculation.

3. Results 3.1. Dose response curves of individual test compounds First, the effect of single test compounds was investigated. The results of the individual experiments depicted a clear dose response relationship towards the activity of b-gal in relation to the concentrations tested in all cases. None of the tested concentrations induced cytotoxicity effects (data not shown). Low observed effect concentration (LOEC) values demonstrated that of the three compounds, genistein, showed the lowest LOEC of 3E07 M, followed by BPA and trenbolone, which curves more or less overlap and depict a LOEC of 3E-06 M (Table 1, Fig. 2). The EC50 values for genistein, BPA and trenbolone were estimated as 1.6E06 M, 1.2E-05 M and 2E-05 M, respectively. These data indicate that genistein possess a higher potency to activate estrogen receptor compared to BPA and trenbolone. 3.2. Dose response effects of binary mixtures In this part of the study we have assessed the estrogenicmediated transcriptional effects of binary mixtures containing estrogenic compounds. The effects of binary mixtures were analyzed using combinations at increasing concentrations from 3E-07 to 1E-04 M (Fig. 2). Initial assessment of the data was performed by plotting the data of each compound when present in a mixture as a response surface (Fig. 3A–C(i)) and as contour plots (Fig. 3A–C(ii–iii)). 3.3. Loewe additivity All test compounds had the capacity to bind agonistically to the estrogen receptor. Since they share a similar mode of action, the selected model for the analysis of potential interactions of the mixtures was Loewe additivity. For this purpose, the data were fitted to a non-linear regression model (Whitehead et al., 2008). In summary in the three mixtures tested, we found no evidence of biologically relevant synergism. The three-dimensional plot of the response surfaces and also the comparison between observed responses and the predictions from the additivity model showed that the Loewe additivity model is well suited to describe the behavior of the systems (Fig. 3). None of global F-tests showed a significantly better fit for the synergism/antagonism model compared to the Loewe additivity model (Table 2). The parameter estimates and mean residuals showed behavior slightly lower than additivity for the mixtures with trenbolone whereas the binary mixture of genistein and BPA showed behavior higher than additivity in one combination, 1E-06-3E-07 M (genistein–BPA) in experiment 2 (Table 3). This effect was not observed in experiment 1 and therefore, its biological relevance is questionable. 3.3.1. Genistein–BPA mixture In the genistein–BPA mixture experiments the average residuals at all positions were near to 0. The parameter log(eta) was estimated as 0.060 in experiment 1 and 0.153 in experiment 2 yielding g = 0.94 and 0.86, indicating a slightly positive deviation from additivity (Table 3). The contour plots of the

Table 1 Estimated LOEC, EC50 and potency values for the single test compounds calculated. Substance

LOEC

EC50

95% Lower confidence limit

95% Upper confidence limit

Genistein BPA Trenbolone

3E-07 M 3E-06 M 3E-06 M

1.6E-06 M 1.2E-05 M 2.0E-05 M

1.4E-06 M 1.0E-05 M 1.9E-05 M

1.8E-06 M 1.4E-05 M 2.2E-05 M

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Fig. 2. Human estrogen receptor mediated estrogenic activity of genistein, BPA and trenbolone in the YES assay. Absorbance values were normalized to 1E-08 M E2 (PC). Mean values ± standard deviations of seven independent experiments are shown. (A) Relative estrogen receptor dependent enzyme activity of 1E-08 M E2 (PC) in the presence of 3E-07-1E-04 M genistein alone. Data were obtained from mixtures experiments with BPA and trenbolone run in two independent experiments in each case (Exp 1 and Exp 2).The curves represent genistein behavior obtained from the data of genistein alone generated in (a) experiment 1 of mixture genistein–BPA; (b) experiment 1 of mixture genistein–trenbolone, (c) experiment 2 of mixture genistein–BPA and (d) experiment 2 of mixture genistein–trenbolone. (B) Relative estrogen receptor dependent enzyme activity of 1E-08 M E2 (PC) in the presence of 3E-07-1E-04 M BPA alone. Data were obtained from mixtures experiments with genistein and trenbolone run in two independent experiments in each case (Exp 1 and Exp 2). The curves represent BPA behavior obtained from the data of BPA alone generated in (a) experiment 1 of mixture trenbolone–BPA; (b) experiment 1 of mixture genistein–BPA; (c) experiment 2 of mixture trenbolone–BPA and (d) experiment 2 of mixture genistein–BPA. (C) Relative estrogen receptor dependent enzyme activity of 1E-08 M E2 (PC) in the presence of 3E-07-1E-04 M trenbolone alone. Data were obtained from mixtures experiments with genistein and BPA run in two independent experiments in each case (Exp 1 and Exp 2). The curves represent trenbolone behavior obtained from the data of trenbolone alone generated in (a) experiment 1 of mixture trenbolone–BPA; (b) experiment 1 of mixture genistein–trenbolone; (c) experiment 2 of mixture trenbolone–BPA and (d) experiment 2 of mixture genistein–trenbolone.

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291

Fig. 3. Exemplary 3-dimensional plots and contour plots from experiment 1 in each mixture. Left contour plots show the observed responses, while the right contour plots show the responses predicted by the Loewe additivity model. Surface and contour plots of the estrogenic response surface of the Loewe interaction model. (A) The surface (i) and contour plot (ii, iii) visualize the concentration response of the binary mixtures of genistein and BPA. The contour lines indicate the relative estrogen receptor dependent enzyme activity of 1E-08 M E2 (PC) in the presence of increasing concentrations of genistein in the mixture with increasing concentrations of BPA, the observed (ii) and expected (iii) contour plots show similar behavior. (B) The surface (i) and contour plots (ii, iii) visualize the concentration response of the binary mixtures of genistein and trenbolone. The contour lines indicate the relative estrogen receptor dependent enzyme activity of 1E-08 M E2 (PC) in the presence of increasing concentrations of genistein in the mixture with increasing concentrations of trenbolone, the observed (ii) and expected (iii) contour plots show similar behavior. (C) The surface (i) and contour plots (ii, iii) illustrate the concentration response of the binary mixtures of BPA and trenbolone. The contour lines indicate the relative estrogen receptor dependent enzyme activity of 1E-08 M E2 (PC) in the presence of increasing concentrations of BPA in the mixture with increasing concentrations of trenbolone. The observed (ii) and expected (iii) contour plots show similar behavior. Lower values to additivity are shaded in blue, whereas higher values in red. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

residuals illustrate that the main region of over-additivity occurred at higher concentrations of 1E-05 to 3E-05 M, indicating a lack of fit due to decreasing responses with the highest concentration (Fig. 4A(i)). Additionally experiment 2, depicted a small ‘‘peak’’ of positive residuals at the dose combination 1E-06 M, 3E-07 M (Fig. 4A(ii)). The predicted response under the Loewe-additivity model at this position is between 0.292 and 0.297 in the four plates of this experiment. The observed responses are 0.39, 0.41, 0.33 and 0.29 giving residuals of 0.092, 0.115, 0.030 and 0.003, respectively. On average the residual is 0.06 at this position with a standard error of 0.026. This peak might indicate some slight synergism; however, this should be taken with caution since this finding

was not confirmed by experiment 1 and could be related to the biological variation of the system, rather than a biologically relevant effect. 3.3.2. Genistein–trenbolone mixture In the mixture genistein–trenbolone the residuals were negative on average, particularly in experiment 2, indicating some antagonism. The parameter log(eta) was estimated to be significantly positive in both experiments, 0.203, stderr = 0.104 and 0.271, stderr = 0.063 in experiment 1 and 2, respectively. In addition, the contour plots show a wide area of negative residuals in experiment 1 (Fig. 4B(i)) and valley of negative residuals in

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Table 2 ANOVA for Loewe additivity model per every binary mixture tested: genistein–BPA, genistein–trenbolone and trenbolone–BPA. Source

DF

Sum of squares

Mean squares

F value



= p < 0.001. P-value

Significance

Exp 1

Exp 2

Exp 1

Exp 2

Exp 1

Exp 2

Exp 1

Exp 2

Exp 1

Exp 2

Exp 1

Exp 2

20 4 227

20 4 228

136.991 0.00001 0.2734

179.605 0.0067 0.2868

0.6850 0.0000 0.0012

0.8980 0.0017 0.0013

571.21 0.002

713.91 1.33

0.000 1.000

0.000 0.259

⁄⁄⁄

⁄⁄⁄

n.s.

n.s.

Mixture genistein and trenbolone Loewe additivity model 20 Deviation from additivity 4 Error 228

20 4 225

21 0.0138 0.8984

18 0.0154 13.244

1 0.0034 0.0039

1 0.0038 0.0059

270.18 0.87

155.56 0.66

0.0000 0.481

0.0000 0.6201

⁄⁄⁄

⁄⁄⁄

n.s.

n.s.

Mixture trenbolone and BPA Loewe additivity model Deviation from additivity Error

20 4 227

17 0.0067 0.5275

20 0.0013 0.4462

0.8325 0.0017 0.0023

10.133 0.0003 0.0020

359.82 0.72

515.49 0.17

0.0000 0.5764

0.0000 0.9558

⁄⁄⁄

⁄⁄⁄

n.s.

n.s.

Mixture genistein and BPA Loewe additivity model Deviation from additivity Error

20 4 228

Table 3 Parameter estimates for Loewe additivity model per every binary mixture tested: genistein–BPA, genistein–trenbolone and trenbolone–BPA. Loewe additivity

Loewe synergism/antagonism

Experiment 1

Genistein–BPA alpha1 (Genistein) alpha2 (BPA) beta1 (Genistein) beta2 (BPA) gamma delta Log(eta) Mean residual Conclusion



Experiment 2

Experiment 1

Experiment 2

Mean⁄

stderr⁄

Mean⁄

stderr⁄

Mean⁄

stderr⁄

Mean⁄

stderr⁄

18.402 10.835 1.362 0.978 0.024 0.647

(0.227) (0.503) (0.016) (0.052) (0.003) (0.022)

17.121 13.619 1.283 1.222 0.031 0.726

(0.250) (0.191) (0.016) (0.016) (0.003) (0.009)

0.00146

(0.00116)

0.00043

(0.00024)

18.959 10.660 1.408 0.961 0.025 0.646 0.060 0.0020

(0.269) (0.635) (0.024) (0.063) (0.003) (0.023) (0.082) (0.00076)

17.208 13.483 1.294 1.215 0.032 0.725 0.153 0.0021

(0.251) (0.187) (0.016) (0.015) (0.003) (0.009) (0.013) (0.00008)

Additivity

Additivity

Additivity

Genistein–Trenbolone alpha1 (Genistein) alpha2 (Trenbolone) beta1 (Genistein) beta2 (Trenbolone) gamma delta Log(eta) Mean residual

9.848 14.350 0.759 1.289 0.028 0.868

0.705 0.828 0.052 0.071 0.003 0.034

4.318 11.162 0.380 1.062 0.020 1.059

0.272 0.427 0.015 0.041 0.008 0.046

0.0042

(0.0039)

0.0104

(0.0016)

Conclusion

Additivity

Antagonism

9.911 14.470 0.762 1.296 0.027 0.867 0.203 0.0019

Log(eta) indicates synergism

0.711 0.737 0.052 0.064 0.003 0.034 0.104 (0.0029)

Antagonism

Trenbolone–BPA alpha1 (Trenbolone) alpha2 (BPA) beta1 (Trenbolone) beta2 (BPA) gamma delta Log(eta) mean Residual

9.082 6.247 0.832 0.597 0.004 0.972

0.523 0.593 0.035 0.042 0.001 0.060

9.077 6.713 0.856 0.661 0.003 1.086

0.375 0.514 0.027 0.040 0.003 0.071

0.0061

(0.0013)

0.0047

(0.0014)

Conclusion

Antagonism

Antagonism

9.042 6.249 0.826 0.596 0.004 0.975 0.171 0.0038 Antagonism

4.470 11.407 0.389 1.079 0.020 1.044 0.271 0.0077

0.289 0.450 0.016 0.042 0.008 0.044 0.063 (0.0015)

Antagonism

0.544 0.593 0.036 0.042 0.000 0.062 0.051 (0.0009)

9.067 6.713 0.855 0.661 0.003 1.087 0.054 0.0038

0.364 0.511 0.026 0.040 0.003 0.071 0.028 (0.0011)

Antagonism

= calculated from four plates per experiment

experiment 2, divided by a residual mountain at genistein 3E-06 M and trenbolone 1E-06 M (Fig. 4B(ii)). 3.3.3. BPA–trenbolone mixture In the experiments with the trenbolone–BPA mixture, the residuals were negative on average indicating an effect lower than additivity. The estimate for log(eta) was positive at 0.171 and 0.054, also indicating behavior less than additivity. The contour plots of the residuals show that the region of antagonism mainly occurred at a high trenbolone dose of 3E10-05 M and at dose combinations of levels below 3E-06 M. Similar to the genistein–BPA experiments

the ‘‘mountains’’ and ‘‘valleys’’ of the residuals show more a lack of fit than a real biological antagonism. Nevertheless despite all artifacts when fitting a dose–response function to biological data, we might conclude that there is no positive deviation from the Loewe additivity at low doses in a mixture of trenbolone–BPA (Fig. 4C). 3.4. Isobolographics The isobologram is a graphical method introduced by Loewe for assessing interactions when using pairs of compounds that produce overtly similar effects (Tallarida, 2006) In this study, isoboles

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Fig. 4. Two-dimensional contour plots of the residuals of the Loewe additivity model. (A) and (B) the two-dimensional contour plots show the residuals of the binary mixtures of genistein and BPA in experiment 1 (i) and 2 (ii), respectively. (C) and (D) The contour plots show the residuals of the binary mixtures of genistein and trenbolone in experiment 1 (i) and 2 (ii), respectively. (E) and (F) The contour plots show the residuals of the binary mixtures of BPA and trenbolone in experiment 1 (i) and 2 (ii), respectively. Higher values represent synergismus (red), while lower values represent antagonism (blue). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

for E 0.15 and 0.35 (15 and 35 are the fixed absolute values of bgal.) were calculated. Representative isoboles (experiment 1 in all cases) for both fixed effects are displayed in Fig. 5 and show that for all binary combinations of genistein, BPA and trenbolone for ß-gal at 15 and 35, the effect depicts typical additivity (straight lines), suggesting no interaction between the compounds present in each binary mixture.

4. Discussion The investigation of potential risk to human health as a result of exposure to EDCs and mixtures thereof is a major topic for consumer safety (Bjarnason, 2004; Rudel, 1997; Sarigiannis and Hansen, 2012; SCHER/SCENIHR/SCCS, 2011; U.S. EPA, 2000; Vandenberg et al., 2012). It has been reported that combinations of estrogenic chemicals can act together at dosage levels at which the single test compounds do not cause the observed effects (Kortenkamp, 2007; Yang and Dennison, 2007). Combined exposure to chemical mixtures with estrogenic effects have been investigated in several models (Andersen et al., 1999; Brion et al.,

2012; Delclos et al., 2009; Kolle et al., 2011). In the present study, we investigated the estrogenic effects of binary mixtures of estrogenic compounds by applying the YES assay. Three chemicals with estrogenic activity were selected: a natural isoflavonoid, genistein and two man-made chemicals, BPA, a worldwide industrial chemical, and trenbolone, a potent anabolic androgenic steroid with in vitro and in vivo estrogenic activity (Xing et al., 2010; Kolle et al., 2010; Yarrow et al., 2010; Lee et al., 2012, internal unpublished data). The YES assay is a relevant in vitro test system that can provide rapid and reliable identification of compounds that interact with the human estrogen receptor (Lee et al., 2012; Kolle et al., 2010; Frische et al., 2009; Silva et al., 2002). Moreover, it has been most recently used to understand the interactions among estrogenic mixtures with the estrogen receptor (Kortenkamp et al., 2009; Frische et al., 2009; Kang et al., 2002; Fent et al., 2006). Prior to this study, Tinwell and Ashby reported on the in vivo combined effect of BPA and genistein (Tinwell and Ashby, 2004), but to the best of our knowledge no data have been reported in the literature about the potential interactive effects of BPA, genistein and trenbolone on assays that report the receptor binding capacity of mixtures of these compounds. In this study,

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Fig. 5. Isoboles of additivity for binary mixtures of genistein, BPA and trenbolone. (A) are isoboles of mixture genistein–BPA at E = 0.15 (red crosses) and E = 0.35 (blue circles), (B) are isoboles of mixture genistein–trenbolone at E = 0.15 and E = 0.35, respectively; E and F are isoboles of mixture BPA–trenbolone at E = 0.15 and E = 0.35. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

we first observed the effects of the single compounds of the mixtures at non-cytotoxic concentrations. All compounds presented estrogenic activity but displayed differential potency among themselves. Our analysis revealed that genistein exhibited greater estrogenic activity starting at lower concentrations 1E-06 M, followed by BPA and trenbolone, which both induced 40% activity at 1E-05 M and increasing to above 60% at 1E-04 M. These

data are in accordance with previous reports in which the potency of genistein and BPA to bind to the estrogen receptor have been evaluated, indicating that genistein presents a higher potency than BPA to bind to this receptor (Leffers et al., 2001; Mueller et al., 2003). Results from in vitro and in vivo mixture studies, provide considerable evidence that combined toxicity of two or more compounds is mostly non-interactive (Kortenkamp et al., 2009; Boobis et al., 2008; ECETOC, 2012; SCHER/SCENIHR/SCCS, 2011). The combined toxicological effects of two or more compounds can consequently take one of two forms, either independence (response additivity) or dose addition. Dose additivity can be rationalized, for instance, by several compounds competing independently for the same receptor (Wilkinson et al., 2000; Feron et al., 2002). In 2011, a task force from the European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC) performed an extensive evaluation of mixture studies where combinations of substances were tested at doses which are at or below the NO(A)ELs (No Observed (Adverse) Effect Level) or NOECs (No Observed Effect Concentration) for each component in the mixture. Those studies found that independent action of chemicals is the predominant principle of mixture toxicity and based on this evaluation, ‘‘there is no evidence that exposure to complex mixtures of components, each well regulated according to established risk assessment approaches, would pose a health risk to humans’’ (ECETOC, 2012). Furthermore, the studies reviewed did not show evidence for a different pattern of combination effects according to the type of toxicity examined, for example acute toxicity, organ toxicity, developmental toxicity, endocrine toxicity or carcinogenicity (ECETOC, 2012). In the present work the estrogenic compounds investigated exert their estrogenic effects via similar mechanisms (i.e. binding to estrogen receptor), therefore, the effects of the binary mixtures were expected to be dose additive, as previously observed in compounds sharing the same target (Evans et al., 2012; Feron et al., 2002; Kortenkamp et al., 2009). In our study we have shown that mixture effects of compounds with an endocrine mode of action at low concentrations are mainly additive (no interaction) while at high concentrations (P1E-05 M of each compound), the effects are to some extend less than additive due to most likely saturation effects. Our data do not provide evidence of other effect apart from dose addition, and the compounds in the present study do not mutually enforce each other when administered in combination (Figs. 4 and 5), a fact based on balance of receptor saturation/ligand binding site availability as well as competition for it (Borgert et al., 2004). In the case of the binary mixtures combined with trenbolone, the response at high concentrations seems to be elicited by trenbolone. However, at the concentration 1E-04 M (of each compound) the effects are considerably reduced to the expected effect in the case of positive addition. The effect is likely a result of either potential receptor-saturation effects or strong competition among the test compounds for the binding site of the receptors. Our study demonstrates that the dose additivity of estrogenic mixtures of genistein, BPA and/or trenbolone is based on the receptor occupancy since they share the same mode of action. The results of this study are also in line with those reported for binary mixtures of those compounds interacting with the androgen receptor (Kolle et al., 2011). Some reports support the idea that chemical mixtures could have significant effects when used at doses below their individual NOEL (Kortenkamp et al., 2007). Our data did not show differences from additivity, suggesting that in vitro no major significant differences were produced by the mixtures in comparison to the effects induced by the individual substances. This type of information could support the risk assessment process when dealing with exposure to chemical mixtures.

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In addition, it has been suggested that EDCs could induce nonmonotonic dose responses (NMDRs, Vandenberg et al., 2012). This type of effect was not observed in this study, in all cases a monotonic dose–response was observed. This could be explained either as a limitation of the in vitro system to determine such effects or because the test substances used produce monotonic effects. In general, it can be concluded that under the test conditions of this study, binary combinations of genistein, BPA and trenbolone did not show any interaction among each other, nor was there any evidence of biologically relevant synergism/antagonism. Therefore, the combinatorial effect observed is in accordance with dose additivity, an effect that could be justifiable, since all test compound possess ‘‘similar’’ mechanism. Conflict of interest The authors declare no conflict of interest. Acknowledgments Prof. J. P. Sumpter from Brunel University, United Kingdom, Prof. G. Vollmer and Dr. Oliver Zierau from the Technical University Dresden, Germany are acknowledged for giving permission to use and providing the recombinant yeast strains. Likewise we thank Dr Hans Martin Kaufmann for reviewing the manuscript and Hans-Albrecht Huener for his technical support. References Altenburger, R., Scholz, S., Schmitt-Jansen, M., Busch, W., Escher, B.I., 2012. Mixture toxicity revisited from a toxicogenomic perspective. Environ. Sci. Technol. 46 (5), 2508–2522. Andersen, H.R., Andersson, A.M., Arnold, S.F., Autrup, H., Barfoed, M., Beresford, N.A., Bjerregaard, P., Christiansen, L.B., Gissel, B., Hummel, R., Jørgensen, E.B., Korsgaard, B., Le Guevel, R., Leffers, H., McLachlan, J., Møller, A., Nielsen, J.B., Olea, N., Oles-Karasko, A., Pakdel, F., Pedersen, K.L., Perez, P., Skakkeboek, N.E., Sonnenschein, C., Soto, A.M., et al., 1999. Comparison of short-term estrogenicity tests for identification of hormone-disrupting chemicals. Environ. Health Perspect. 107 (Suppl 1), 89–108. Arcaro, K.F., Vakharia, D.D., Yang, Y., Gierthy, J.F., 1998. Lack of synergy by mixtures of weakly estrogenic hydroxylated polychlorinated biphenyls and pesticides. Environ. Health Perspect. 106 (Suppl 4), 1041–1046. Bermudez, D.S., Gray Jr., L.E., Wilson, V.S., 2010. Modeling the interaction of binary and ternary mixtures of estradiol with bisphenol A and bisphenol AF in an in vitro estrogen-mediated transcriptional activation assay (T47D-KBluc). Toxicol. Sci. 116 (2), 477–487. Bjarnason, S.G., 2004. Chapter 5 – Toxicology of mixtures: A review of mixtures assessment. In: NATO HFM-057/RTG-009 (Protection against Adverse Effects of Toxic Hazards). Bliss, C.I., 1939. The toxicity of poisons applied jointly. Ann. Appl. Biol. 26, 585–615. Boobis, A.R., Ossendorp, B.C., Banasiak, U., Hamey, P.Y., Sebestyen, I., Moretto, A., 2008. Cumulative risk assessment of pesticide residues in food. Toxicol. Lett. 180 (2), 137–150. Borgert, C.J., Quill, T.F., McCarty, L.S., Mason, A.M., 2004. Can mode of action predict mixture toxicity for risk assessment? Toxicol. Appl. Pharmacol. 201 (2), 85–96. Brian, J.V., Harris, C.A., Scholze, M., Kortenkamp, A., Booy, P., Lamoree, M., Pojana, G., Jonkers, N., Marcomini, A., Sumpter, J.P., 2007. Evidence of estrogenic mixture effects on the reproductive performance of fish. Environ. Sci. Technol. 41 (1), 337–344. Brion, F., Le Page, Y., Piccini, B., Cardoso, O., Tong, S.K., Chung, B.C., Kah, O., 2012. Screening estrogenic activities of chemicals or mixtures in vivo using transgenic (cyp19a1b-GFP) zebrafish embryos. PLoS One 7 (5), e36069. Charles, G.D., Gennings, C., Tornesi, B., Kan, H.L., Zacharewski, T.R., Bhaskar Gollapudi, B., Carney, E.W., 2007. Analysis of the interaction of phytoestrogens and synthetic chemicals: an in vitro/in vivo comparison. Toxicol. Appl. Pharmacol. 218 (3), 280–288. Delclos, K.B., Weis, C.C., Bucci, T.J., Olson, G., Mellick, P., Sadovova, N., Latendresse, J.R., Thorn, B., Newbold, R.R., 2009. Overlapping but distinct effects of genistein and ethinylestradiol (EE(2)) in female Sprague–Dawley rats in multigenerational reproductive and chronic toxicity studies. Reprod. Toxicol. 27 (2), 117–132. ECETOC, 2012. Technical Report No.115: Effect of Chemicals Co-exposures at Doses Relevant for Human Safety Assessment. Brussels, July 2012. EFSA, 2013. EFSA Scientific Committee; Scientific Opinion on the hazard assessment of endocrine disruptors: scientific criteria for identification of endocrine disruptors and appropriateness of existing test methods for assessing effects

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