TOXICOLOGICAL SCIENCES 122(2), 383–394 (2011)
doi:10.1093/toxsci/kfr103
Advance Access publication May 10, 2011
Joint Effects of Heterogeneous Estrogenic Chemicals in the
E-Screen—Exploring the Applicability of Concentration Addition
Elisabete Silva,*,1 Nissanka Rajapakse,* Martin Scholze,* Thomas Backhaus,† Sibylle Ermler,* and Andreas Kortenkamp*
*Centre for Toxicology, School of Pharmacy, University of London, London WC1N 1AX, UK; and †Department of Plant and Environmental Sciences,
University of Gothenburg, 40530 Gothenburg, Sweden
1
To whom correspondence should be addressed at Centre for Toxicology, School of Pharmacy, University of London, 29-39 Brunswick Square, London WC1N
1AX, UK. Fax: þ44-207-753-5811. E-mail: [email protected].
Received February 2, 2011; accepted April 21, 2011
In the last few years, significant advances have been made
toward understanding the joint action of endocrine disrupting
chemicals (EDCs). A number of studies have demonstrated that
the combined effects of different types of EDCs (e.g., estrogenic,
antiandrogenic, or thyroid-disrupting agents) can be predicted by
the model of concentration addition (CA). However, there is still
limited information on the effects of mixtures of large numbers of
chemicals with varied structural features, which are more
representative of realistic human exposure scenarios. The work
presented here aims at filling this gap. Using a breast cancer cell
proliferation assay (E-Screen), we assessed the joint effects of five
mixtures, containing between 3 and 16 estrogenic agents, including
compounds as diverse as steroidal hormones (endogenous and
synthetic), pesticides, cosmetic additives, and phytoestrogens. CA
was employed to predict mixture effects, which were then compared
with experimental outcomes. The effects of two of the mixtures
tested were additive, being accurately predicted by CA. However,
for the three other mixtures, CA slightly overestimated the
experimental observations. In view of these results, we hypothesized
that the deviations were due to increased metabolism of steroidal
estrogens in the mixture setting. We investigated this by testing the
impact of two such mixtures on the activation and expression of
steroidal estrogen metabolizing enzymes, such as cytochrome P450
(CYP) 1A1, CYP 1B1, and CYP 3A4. Activation of CYP 1B1 and,
consequently, a reduction in the levels of steroidal estrogens in the
mixture could contribute to the shortfall from the additivity
prediction that we observed.
Key Words: endocrine disrupters; mixtures; xenoestrogens;
E-Screen; cytochrome P450.
Endocrine disrupting chemicals (EDCs) that exert their effects
through similar modes of action, such as estrogenic chemicals,
have been the subject of considerable research activities
(Kortenkamp, 2007). Combination effects were assessed in terms
of additivity, synergism, and antagonism, with additivity expectations derived from the concept of concentration addition (CA)
(Berenbaum, 1989). In studies with combinations of three and six
estrogenic agents, Charles et al. (2002a, 2007) observed mixture
effects in good agreement with CA using the rat uterotrophic
assay. Mixture experiments with estrogenic chemicals have been
conducted using in vitro assays for estrogenicity, and by far the
most widely used were reporter gene assays that capture estrogen
receptor (ER) activation in transfected mammalian cells or yeast
cell lines. Many studies with these assays revealed that mixtures
of estrogenic chemicals, whether natural, synthetic, or derived
from plants, acted in a concentration additive fashion (Le Page
et al., 2006; Payne et al., 2000; Rajapakse et al., 2002; Silva et al.,
2002; van Meeuwen et al., 2007). However, some deviations
from additivity were reported. Charles et al. (2002b) observed
antagonisms (i.e., less than concentration additive effects) with
a mixture of 17b-estradiol (E2), genistein, and 1-chloro-2[2,2,2-trichloro-1-(4-chlorophenyl)ethyl]benzene (o,p#-DDT) and
with combinations of six synthetic estrogenic chemicals in an
MCF-7 cell-based reporter gene assay (Charles et al., 2007).
Unlike reporter gene assays, cell proliferation assays like the
E-Screen offer the possibility to study processes that might
interfere with steroid receptor signaling through events beyond
ER binding, such as activation of growth factor signaling
cascades. Nevertheless, these processes are believed to converge
on the activation of the ER, which ultimately is responsible for
cell division and proliferation. The E-Screen measures the effects
of estrogens on cell proliferation in ER-competent human breast
cancer cells, MCF-7 BOS (Soto et al., 1995). Using this assay, in
the past, we have obtained varying results with estrogenic
mixtures. In an early study, we assessed the joint effects of four
organochlorine chemicals and obtained good agreement with CA
(Payne et al., 2001). We subsequently reported that the effects of
six-component mixtures composed of steroidal estrogens and
phenolic compounds fell short of the additivity expectations
derived from CA, indicating weak antagonisms (Rajapakse et al.,
2004). We concluded that interactions between mixture
components were at play. So far, little work was done to
investigate the causes of deviations from expected concentration
additivity. Information about the ways in which more than six
Ó The Author 2011. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved.
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384
SILVA ET AL.
estrogenic chemicals with a variety of structural features act
together in the E-Screen assay is missing altogether, but these
data are needed to come closer to assessments of the likely effects
of mixtures encountered in realistic exposure settings.
Our main interest was to fill this gap by conducting studies
with mixtures composed of 8, 10, 11, and 16 estrogenic
chemicals in the E-Screen assay. Our aims were to test the
agreement with expected concentration additivity and, if
deviations occurred, to investigate whether these could be
explained in terms of differential induction of steroidal estrogen
metabolizing enzyme systems (cytochrome P450 [CYP]).
Studies of this kind are of relevance not only for mixtures of
estrogenic chemicals but also for other combinations assumed to
act via similar mechanisms. For regulatory and risk assessment
purposes, it is important to establish whether CA provides
reasonable approximations of the combined effects of estrogenic agents that are presumed to act through similar modes of
action.
MATERIALS AND METHODS
Chemicals. 17b-Estradiol (E2, 99% purity), 17a-ethinylestradiol (EE2,
98%), estrone (E1, 99%), estriol (98%), dienestrol (98%), hexestrol (98%),
aldrin (98.6%), dieldrin (99.8%), endosulfan a (I, 99.5%), endosulfan b (II,
99.2%), methoxychlor (99.5%), kepone (99%), o,p#-DDT (97.5%), 1-chloro-4[2,2-dichloro-1-(4-chlorophenyl)ethyl]benzene (o,p#-DDD) (99%), 1,1’-(2,2,2trichloroethylidene) bis [4-chlorobenzene] (p,p#-DDT, 99.1%), 1-chloro-4-[2,2dichloro-1-(4-chlorophenyl)ethenyl]benzene (p,p#-DDE, 99.5%), b-hexachlorocyclohexane (b-HCH, 98.1%), coumestrol (98%), zearalenone (98%),
mestranol, diethylstilbestrol (DES, 97%), butyl paraben, propyl paraben, and
bisphenol A (> 99%) were purchased from Sigma-Aldrich Company (Dorset,
UK). 3-(4-Methylbenzylidene)camphor (4-MBC, Eusolex 6300, > 99.7%) and
octyl-methoxycinnamate (OMC, Eusolex 2292, > 98%) were from VWR
international (Poole, UK). 3-Benzylidene camphor (3-BC, Unisol-22, > 97%)
was from Induchem (Volketswil, Switzerland). Genistein was obtained from
Alfa Aesar (Lancashire, UK). All chemicals were used as supplied, and stock
solutions (1–10mM) were prepared in HPLC-grade ethanol (VWR international). Stock solutions and subsequent dilutions were stored at 20°C. All
remaining chemicals were purchased from Sigma-Aldrich, unless stated
otherwise.
Routine cell culture. MCF-7 BOS breast cancer cells were kindly
provided by Ana Soto, Tufts University, Boston, and routinely maintained in
75-cm2 canted-neck tissue culture flasks (Greiner, Gloucestershire, UK) in
Dulbecco’s modified Eagle’s medium (DMEM; Invitrogen Corporations, UK)
supplemented with 5% fetal bovine serum (Invitrogen) and 1% (vol/vol) MEM
nonessential amino acids (Invitrogen) in a humidified incubator, at 37°C, with
5% CO2. Cells were subcultured at approximately 70% confluence over
a maximum of 10 passages and regularly tested negative for Mycoplasma.
E-Screen assay procedure. The protocol described previously (Rajapakse
et al., 2004), carried out in 12-well microtiter plates, was used. In addition, we
adapted the procedure to a miniaturized format using 96-well plates, exactly as
described in Silva et al. (2007). The two formats were used in parallel to test
single substances and mixtures in order to compare data variability. Comparison
of normalized concentration-response data obtained by these two protocols
revealed excellent agreement, with overlapping confidence belts of the resulting
concentration response curves (data not shown). A detailed description of the
data normalization procedure that we employed can be found in Rajapakse et al.
(2004). All compounds were tested in at least four independent experiments, run
on up to three plates.
The mixtures analyzed in experimental studies are given in Table 1: the 96well format of the assay was used with mixtures composed of 17b-estradiol,
estriol, and estrone (reference mixtures 1a and 1b) and with mixtures composed
of 10 and 8 compounds (mixtures 2 and 3). The remaining mixtures of 11 and
16 compounds (mixtures 4 and 5) were studied by employing the 12-well
format.
Statistical analysis and regression modeling. Statistical dose-response
regression analyses were carried out by applying a best-fit approach (Scholze
et al., 2001). Various nonlinear regression models (logit, probit, Weibull,
generalized logit I and II), which all describe monotonic sigmoidal doseresponse relationships, were fitted independently to the same data set, and the
best fitting model was selected on the basis of a statistical goodness-of-fit
criterion, the information criterion of Schwarz.
Data analysis was always performed on pooled data from all the repeat
studies, and all data were used. However, if toxicity was observed with the
highest concentration tested, this was excluded from regression analysis but
kept in the figure for information purposes. To account for the intra- and
interstudy variability associated with this nested data scenario, the generalized
nonlinear mixed modeling approach was used, in which both fixed and random
effects are permitted to have a nonlinear relationship with the effect end point
(Vonesh and Chinchilli, 1996). As potential sources for random effects, two
cases were identified for the normalized end point: dose-response data from
different studies varied in their maximal effect plateaus, which were dealt with
by including an additional random effect to the model parameter describing the
maximal effect asymptote, and slight shifts of the whole curves based on the
log10-transformed concentration scale were observed, which was accounted for
by including an additional shift parameter as random effect in the nonlinear
regression model. The random effects were assumed to follow a Gaussian
distribution with an expectation of zero.
The effect concentrations shown in Table 2 were selected for three medium
to low response levels (50%, 10%, and 1% normalized cell proliferation) and
were calculated from the functional inverse of the best fitting model. Statistical
uncertainties for the estimated effect doses were expressed as 95% confidence
belts and approximately determined by applying the bootstrap method (Efron
and Tibshirani, 1993). All statistical analyses were conducted using SAS
version 9.2 (Cary).
Calculation of mixture effect predictions using CA. The mathematical
and statistical procedures used for calculating expected mixture effects by using
the concept of CA were exactly as described in Rajapakse et al. (2004). The
composition of the mixtures analyzed in experimental studies is given in Table 1.
Reverse transcriptase real-time PCR. MCF-7 BOS cells were seeded in
25-cm2 flasks at a density of 250,000 cells per flask and allowed to attach at
37°C for 24 h in DMEM. Cells were then washed with 5 ml phenol red-free
DMEM and changed into dosing medium (phenol red-free DMEM supplemented with 5% charcoal-dextran treated fetal bovine serum containing the test
compounds or mixtures).
Appropriate dilutions of E2, mixtures 2 and 5, and reference mixture 1a
were prepared and added to the dosing medium. The cells were treated with
final concentrations of 10 pM and 10nM E2, 20lM mixture 2, 20lM mixture 5,
and 5nM reference mixture 1a. Control cells were treated with medium
supplemented with 0.5% absolute ethanol (solvent control). The final ethanol
concentration did not exceed 0.5%. Cells were harvested by trypsinization after
treatment for 24 h. Total RNA was isolated from the obtained cell pellets using
a Nucleospin RNAII kit (Macherey-Nagel, Abgene, Epson, UK) following the
manufacturer’s instructions. RNA was solubilized in RNAse/DNAse-free
water, and RNA concentration and purity were determined by measuring the
absorbance (Abs) at 260 nm (Nanodrop, 2000 spectrophotometer, Thermo
scientific) and calculating the Abs ratio 260/280, which usually exceeded 1.9.
One microgram of total RNA was reverse transcribed into complementary
DNA (cDNA) using M-MLV reverse transcriptase (Promega) according to the
manufacturer’s instructions. The cDNA was stored at 80°C until further use.
Real-time PCR was performed on an iCycler iQ real-time PCR detection
system (Bio-Rad), using iQ SYBR Green Supermix (Bio-Rad), as previously
385
MIXTURES OF ESTROGENS IN THE E-SCREEN
TABLE 1
Test Mixtures
Relative proportions (%)
Reference mixture
Components i
Bisphenol A
Butylparaben
Coumestrol
o,p#-DDT
DES
Dienestrol
Endosulfan a (I)
Endosulfan b (II)
17b-estradiol
Estriol
Estrone
17a-Ethinylestradiol
Genistein
b-HCH
Hexestrol
Kepone
Mestranol
Methoxychlor
Propyl paraben
Zearalenone
1a
1b
—
—
—
—
—
—
—
—
2.69
25.96
71.35
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
0.55
26.54
72.91
—
—
—
—
—
—
—
—
—
Mixture 2
(10 components)
—
9.03
—
2.72
—
—
12.24
14.44
2.27 3 107
—
—
3.44 3 107
—
9.24
—
5.58
—
32.70
14.05
—
Mixture 3
(8 components)
Mixture 4
(11 components)
Mixture 5
(16 components)
—
—
31.32
9.27 3 104
4.90
—
—
51.42
—
9.14 3 1010
—
5.47 3 107
2.23 3 108
—
—
6.16 3 1011
12.26
3.13 3 108
—
—
4.06 3 105
1.32
29.51
0.10
4.51
4.61 3 107
3.26 3 106
52.74
—
4.74 3 109
8.43 3 109
1.02 3 106
5.20 3 108
0.23
—
6.08 3 109
11.58
1.22 3 106
—
—
4.18 3 105
—
2.72
—
—
12.24
14.44
—
—
—
9.03
—
9.24
—
5.58
—
32.70
14.05
—
Note. Rounded values given for relative proportions. DES, diethylstilbestrol.
described (Silva et al., 2010). Primers were designed with the Beacon designer 5.1
software (Premier Biosoft International, Palo Alto) and purchased as high-quality
purified OliGold primers from Eurogentech Ltd (Hampshire, UK). GenBank
accession numbers for the target messenger RNA (mRNA), primer sequences, and
concentrations were: glyceraldehyde 3-phosphate dehydrogenase (GAPDH),
NM_002046, forward: 5#-TCTCTGCTCCTCCTGTTC-3#, reverse: 5#GCCCAATACGACCAAATCC-3#, 900nM; CYP1B1: NM_000104, forward:
5#-TTGTGCCTGTCACTATTCC-3#-3#, reverse: 5#- CCACTTGACTGGGTCATG-3#, 200nM; CYP 3A4: NM_017460, forward 5#-ATCATTGCTGTCTCCAACCTTCAC-3#, reverse: 5#-TGCTTCCCGCCTCAGATTTCTC-3#, 200nM.
GAPDH was used as the reference gene.
PCR data analysis was performed according to the method developed by
Pfaffl (2001) to determine the relative expression ratio of the target genes
(CYP1B1 and CYP3A4) versus the control, as previously described (Silva
et al., 2010). The gene expression was normalized to the reference gene
GAPDH. The expression ratios for CYP1B1 were tested for statistical
significance using the Pair Wise Fixed Reallocation Randomization Test
(REST, Relative Expression Software Tool) (Pfaffl et al., 2002). Statistical
uncertainties for the relative expression ratios were expressed as 95%
confidence belts by applying the bootstrap method (Efron and Tibshirani,
1993).
Ethoxyresorufin-O-deethylase. Ethoxyresorufin-O-deethylase
(EROD)
assay was carried out as described by Donato et al. (1993) with slight
modifications. Briefly, MCF-7 BOS cells were seeded in 96-well plates at
a density of 20,000 cells per well and allowed to attach for 24 h. The cells were
then washed with 200ll phenol red-free DMEM and incubated with
experimental medium containing the test compounds at the desired concentrations, ensuring that the concentration of solvent (ethanol) did not exceed
0.5%. Following 24-h incubation, the medium was removed and 200ll fresh
phenol red-free DMEM containing 2.2lM EROD and 10lM dicumarol was
added. Fluorescence was determined at 37°C at an excitation wavelength of
530nm and an emission wavelength of 590nm, every 5 min until an optimum
was reached. EROD activity was linear for approximately 25 min. Finally, to
correct for cell number, the medium was removed from each well, and the cells
fixed with trichloroacetic acid and stained with sulfur rhodamine B, as
described previously (Silva et al., 2007). The fluorescence measured was
compared with a resorufin standard curve (data not shown) to assess resorufin
formation. Data are presented as fold increase relative to controls.
RESULTS
To produce the data that are needed for making predictions of
mixture effects across a large range of effects, we conducted
extensive concentration-response analyses with all the individual
chemicals that were included in any of the investigated mixtures.
We used only single chemical concentration-response data from
one and the same experimental protocol for the prediction of
mixture effects and did not substitute data from the 12-well
format with those from the miniaturized version of the assay.
The model parameters and effect concentrations from the 12well format are shown in Table 2; corresponding data from the
96-well format system can be found in Silva et al. (2007). In the
interest of providing unbiased response curves for the calculation
of expected mixture responses, we used a variety of regression
models and selected the best-fitting model for each chemical
(Scholze et al., 2001).
386
TABLE 2
Effect Data of the Tested Xenoestrogens, 17b-Estradiol, and Their Mixtures
Model
Substance
ĥ1
ĥ2
Logit
3.33 1.76
G.Logit
2.22 2.65
Weibull
1.62 1.19
Logit
3.49 1.99
G.Logit
0.74 4.19
BC-Logit
0.85 0.79
BC-Logit
1.14 0.95
Logit
0.91 1.68
Logit
1.75 2.22
BC-Logit
0.17 1.67
Logit
3.16 2.09
Logit
5.21 2.40
G.Logit
11.54 3.94
Logit
8.31 3.03
Logit
9.34 3.09
Logit
9.59 2.53
Logit
14.83 4.36
ĥ3
—
0.300
—
—
0.243
0.126
0.127
—
—
0.346
—
—
0.475
—
—
—
—
ĥmin ĥmax
0*
0*
0*
0*
0*
0*
0*
0*
0*
0*
0*
0*
0*
0*
0*
0*
0*
0.99
0.90
0.96
1.07
0.96
0.86
0.90
1.01
0.92
0.78
0.92
0.98
0.81
0.81
0.64
1.02
0.49
EC01,nmol/l (CI)
3.3
3.3
6.2
8.2
2.2
4.0
1.9
5.2
1.5
3.8
2.2
1.8
3.9
2.0
4.9
9.3
3.3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
105 (1.6 3 105 to 6.2
107 (1.1 3 107 to 1.3
106 (1.9 3 106 to 1.6
105 (4.3 3 105 to 1.5
105 (6.4 3 106 to 1.2
107 (1.6 3 109 to 5.4
105 (5.3 3 107 to 1.9
104 (1.7 3 104 to 1.5
103 (8.3 3 104 to 2.9
104 (3.8 3 106 to 3.7
101 (1.6 3 101 to 3.1
100 (1.0 3 100 to 3.1 3
100 (2.0 3 100 to 8.7 3
101 (1.4 3 101 to 2.8 3
101 (3.2 3 101 to 8.1 3
101 (5.6 3 101 to 1.6 3
102 (2.2 3 102 to 4.7 3
EC10,nmol/l (CI)
3 105)
3 106)
3 105)
3 104)
3 104)
3 106)
3 103)
3 103)
3 103)
3 101)
3 101)
100)
100)
101)
101)
102)
102)
7.6
2.6
6.0
1.3
4.0
7.7
1.2
1.4
1.8
2.0
3.2
1.8
6.6
1.3
3.0
8.3
1.2
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
104 (5.6 3 104 to 1.1
104 (1.6 3 104 to 4.0
104 (3.7 3 104 to 9.1
103 (9.8 3 104 to 1.7
103 (2.3 3 103 to 7.0
103 (4.9 3 103 to 1.3
102 (4.0 3 103 to 3.4
102 (8.4 3 103 to 2.3
102 (1.4 3 102 to 2.4
101 (9.9 3 102 to 4.3
100 (2.8 3 100 to 3.6 3
101 (1.5 3 101 to 2.3 3
101 (5.2 3 101 to 8.3 3
102 (1.1 3 102 to 1.4 3
102 (2.6 3 102 to 3.7 3
102 (7.0 3 102 to 1.0 3
103 (1.1 3 103 to 1.4 3
EC50,nmol/l (CI)
3 103)
3 104)
3 104)
3 103)
3 103)
3 102)
3 102)
3 102)
3 102)
3 101)
100)
101)
101)
102)
102)
103)
103)
1.3
3.1
2.4
1.5
1.6
5.1
3.6
2.8
1.9
1.3
3.9
1.5
6.1
8.0
2.7
6.1
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
102 (1.2 3 102 to 1.6 3 102)
102 (1.9 3 102 to 5.0 3 102)
102 (1.9 3 102 to 3.0 3 102)
102 (1.2 3 102 to 1.9 3 102)
101 (1.1 3 101 to 2.4 3 101)
101 (3.2 3 101 to 8.1 3 101)
101 (1.8 3 101 to 1.0 3 100)
101 (2.0 3 101 to 4.0 3 101)
101 (1.5 3 101 to 2.5 3 101)
100 (7.2 3 101 to 2.7 3 100)
101 (3.6 3 101 to 4.3 3 101)
102 (1.3 3 102 to 1.9 3 102)
102 (5.1 3 102 to 7.6 3 102)
102 (7.0 3 102 to 9.3 3 102)
103 (2.1 3 103 to 3.4 3 103)
103 (5.3 3 103 to 6.9 3 103)
—
Reference mixture: 3 components (composition and ratio as defined in Table 1)
Ia
Logit
1.80 2.17
—
0* 1.01 1.1 3 103 (7.0 3 104 to 1.8 3 103) 1.4 3 102 (1.2 3 102 to 1.8 3 102) 1.5 3 101 (1.2 3 101 to 1.9 3 101)
Ib
Logit
1.80 2.47
—
0* 0.93 2.8 3 103 (1.4 3 103 to 5.7 3 103) 2.6 3 102 (1.9 3 102 to 3.6 3 102) 2.1 3 101 (1.6 3 101 to 3.0 3 101)
Mixture 2: 10 components (composition and ratio as defined in Table 1)
G.Logit
22.19 6.15 0.350 0* 0.69 4.4 3 101 (2.5 3 101 to 7.5 3 101)
5.2 3 102 (4.1 3 102 to 6.3 3 102)
3.5 3 103 (2.9 3 103 to 4.0 3 103)
Mixture 3: 8 components (composition and ratio as defined in Table 1)
Weibull
10.65 3.18
—
0* 0.73 1.1 3 102 (5.7 3 101 to 1.9 3 102)
6.1 3 102 (4.2 3 102 to 8.2 3 102)
2.6 3 103 (1.8 3 103 to 3.9 3 103)
Mixture 4: 11 components (composition and ratio as defined in Table 1)
Logit
12.52 3.64
—
0* 0.61 2.1 3 102 (1.2 3 102 to 3.6 3 102)
9.9 3 102 (8.1 3 102 to 1.2 3 103)
7.3 3 103 (5.4 3 103 to 1.5 3 104)
Mixture 5: 16 components (composition and ratio as defined in Table 1)
Logit
14.29 4.34
—
0* 0.62 2.2 3 102 (1.4 3 102 to 3.4 3 102)
8.2 3 102 (6.9 3 102 to 9.8 3 102)
4.2 3 103 (3.2 3 103 to 6.6 3 103)
Note. EC50, EC10, and EC01: concentrations associated with 50%, 10%, and 1% effect, respectively. Values in brackets denote the upper and lower limits of the approximate 95% confidence interval
based on bootstrap replicates; the column ‘‘RM’’ indicates the mathematical regression function, used for describing the concentration response relationships (see Scholze et al., 2001, for more details).
ĥ1; ĥ2; ĥ3; ĥ; min and ĥmax estimated model parameters, if marked by *, then held fixed, i.e., not estimated. ** from 96-well format system, all other single compounds from 12-well format system (remaining
data from 96-well format system can be found in Silva et al., 2007).
SILVA ET AL.
17b-estradiol**
Hexestrol
17b-estradiol
17a-Ethinylestradiol
DES
Estriol
Mestranol
Estrone
Dienestrol
Zearalenone
Coumestrol
Genistein
Bisphenol A
DDT
Kepone
Butylparaben
Endosulfan a(I)
RM
MIXTURES OF ESTROGENS IN THE E-SCREEN
In view of our previous findings of weak antagonisms with
mixtures of phenolic chemicals in the E-Screen (Rajapakse
et al., 2004), we became interested in exploring whether it
would be possible to construct a reference mixture, which acts
in accordance with CA in the E-Screen. We reasoned that
combinations of structurally similar steroidal estrogens would
most likely meet the pharmacological assumptions of CA in the
E-Screen and selected E2, estrone, and estriol.
These three estrogens were combined in two different mixture
compositions; one with a ratio in proportion to the EC50 of the
three agents (mixture 1a, Table 1) and the other with levels of E2
reduced by a factor of 5 (mixture 1b, Table 1). Mixture 1b was
tested in order to reduce the disproportionately high contribution
of E2 to the CA model at low effects caused by a very flat doseresponse curve at low concentrations (Silva et al., 2007). In both
cases, the agreement between predicted and observed combination effects was excellent over the whole range of effects (Fig. 1).
Having established a reference case for concentration
additivity, we explored the predictability of combination effects
of a 10-component mixture composed of two steroidal estrogens,
E2 and EE2, and a variety of synthetic estrogenic chemicals,
including pesticides and antioxidants (mixture 2, see Table 1).
The mixture ratio of the constituents corresponded to the ratio of
their individual EC50 values (i.e., the concentrations that
provoke 50% of the maximal effect of E2). CA predicted
proliferative responses higher than those actually observed
(Fig. 2A). At low responses, the effect concentrations predicted
by CA were up to fivefold lower than those experimentally
observed (Table 3), and at higher effect levels, the predicted
effect concentrations approached the observed ones (Fig. 2A).
These differences in effect concentrations were equivalent to
387
a shortfall in effect levels by approximately 50% at the lower
concentrations. The antagonism was more pronounced at lower
concentrations.
Next, we analyzed the expected contribution of each
component to the overall mixture effect by constructing plots
that depict the effects produced by each component at the levels
present in the mixture (Fig. 2B, gray and black lines). The
analysis showed that the expected effects attributable to EE2
exceeded the observed responses for total concentration of the
mixture below EC20. At effect levels below 5%, the anticipated
contribution of E2 was also higher than that of any of the other
components (black lines in Fig. 2B). This suggested that the two
steroidal estrogens, although present in the mixture, produced
much lower effects than expected, or none at all. To test this idea,
we recalculated the expected effects of the mixture, but this time
omitting EE2 and E2. As shown in Figure 2C, the effects
predicted by assuming that the steroidal estrogens were ineffective agreed excellently with the observed effects of the 10component mixture. We confirmed this by conducting a mixture
experiment with the same combination of estrogenic chemicals
but without EE2 or E2. The predicted and observed effects of this
mixture agreed very well (mixture 3, Fig. 3).
These findings suggest that EE2 and E2 were less effective
than anticipated when present in combination with the other
xenostrogens of the 10-component mixture. It is conceivable
that increased metabolism of the steroidal estrogens, possibly
induced by some of the other mixture components, may have
diminished their effective concentrations. Such a phenomenon
is not accounted for when calculating expected mixture effects
according to CA, as the concept assumes that the compounds
do not interact pharmacokinetically.
FIG. 1. (A) Predicted and observed effects of equipotent reference mixture (1a) of E2, estrone, and estriol. (B) The same mixture was tested also with a lower
impact of E2 (1b). Observed mixture effects (circles) are from at least two independent mixture experiments. Predicted effects were calculated using the model of
CA (solid red lines), with dotted red lines indicating the corresponding approximate 95% confidence belts.
388
SILVA ET AL.
FIG. 2. Predicted and observed mixture effects of an equipotent mixture
with 10 components (mixture 2). Observed effects (circles), best-fitting
regression model (blue line, with corresponding 95% confidence belt as dotted
blue lines) and predicted mixture effects (solid red lines, with dotted red lines
indicating the corresponding approximate 95% confidence belt) are shown in
(A). The individual regression lines of the compounds at concentrations present
in the mixture are shown in (B, gray lines), together with the regression fit of
the observed mixture effects (blue line). E2 and EE2 are highlighted (black
line). Under the assumption that E2 and EE2 have not contributed to the overall
mixture effect, predictions were recalculated (red line) and compared with
observed effects (circles) (C).
Further mixtures were investigated, this time by combining
11 estrogenic chemicals with differing potencies and varying
structural features, including steroidal estrogens (E2, EE2,
estrone, hexestrol, and mestranol), synthetic estrogens (butyl
paraben, o,p#-DDT, kepone), phytoestrogens (coumestrol), as
well as zearalenone (mixture 4, see Table 1). The mixture
ratio was chosen such that all chemicals were present at
fractions of concentrations that induced approximately the
same proliferative effect in the E-Screen (around 50% of the
maximal effect induced by E2, see Table 1). On the basis of
the single chemical regression models, we calculated the
predicted effects of this mixture, assuming CA, together with
a 95% confidence belt (Fig. 4A, solid and dotted red lines).
The experimentally observed effects of this mixture fell short
of the CA prediction. The best-fitting regression model for the
observed combination effects (Fig. 4A, solid and dotted blue
lines) showed a slope similar to that of the prediction curve,
but was displaced to the right, toward higher concentrations.
The observed effect concentrations for the mixture were
approximately twofold higher than those predicted using CA
(Table 3). At no point did the confidence belt for the
prediction overlap with that of the regression model for the
experimentally observed effects.
We added further five estrogenic agents (diethylstilbestrol,
dienestrol, estriol, genistein, and bisphenol A) to this
combination to produce a 16-component mixture with a mixture
ratio in proportion to effect concentrations of each individual
component that induced 2.5% cell proliferation (mixture 5,
Table 1). Again, the observed combination effects fell somewhat
short of the predicted concentration additive responses, with
predicted effect concentrations that were approximately twofold
lower (Table 3). However, at concentrations corresponding to
higher proliferative effects, the expected effect concentrations
approached those that were observed (Fig. 4B). Recalculation of
joint effects and disregarding the contribution of E2 and EE2 did
not shift the CA prediction sufficiently to coincide with the
observed responses, as seen with mixture 3. To achieve
agreement between prediction and observation, it was necessary
to speculate that a larger number of steroidal estrogens present in
the mixture were inactivated (data not shown).
Cytochrome P450 isoforms 1A1 and 1B1 are responsible for
reactions that metabolize steroidal estrogens. These isoforms
catalyze hydroxylation reactions of E2 (and EE2 and E1) to
produce 4-hydroxy- and 2-hydroxy-steroids, respectively, which,
ultimately, undergo conjugation reactions. An upregulation of
CYP 1A1 and 1B1 in the presence of other estrogenic agents
may therefore lead to conjugation (and thus diminished action) of
steroidal estrogens in the mixture, and it is conceivable that such
reactions can at least partly explain the weak antagonisms that
occurred with mixtures 2, 4, and 5. We investigated this possibility
by monitoring CYP 1A1 expression through measuring EROD
activity and CYP 1B1 transcription by reverse transcriptase
real-time PCR.
389
MIXTURES OF ESTROGENS IN THE E-SCREEN
TABLE 3
Statistical Uncertainty of Predicted and Observed Effect Concentrations for Mixtures
Effect concentration ECxmix in nmol/l
Predicted by CA
Effect level x
Reference mixture 1a: 3 componentsa
10%
30%
50%
Reference mixture 1b: 3 componentsa
10%
30%
50%
Mixture 2: 10 componentsa
10%
30%
50%
Mixture 3: 8 componentsa
10%
30%
50%
Mixture 4: 11 componentsa
10%
30%
50%
Mixture 5: 16 componentsa
10%
30%
50%
Observed
Mean
95% CI
Mean
95% CI
0.014
0.069
0.178
0.011–0.018
0.063–0.077
0.164–0.203
0.014
0.060
0.146
0.012–0.018
0.051–0.071
0.116–0.186
0.023
0.102
0.244
0.019–0.029
0.093–0.116
0.222–0.281
0.026
0.093
0.213
0.019–0.036
0.073–0.119
0.163–0.297
115
1072
2942
96–152
1014–1184
2494–3283
519
1742
3523
405–631
1512–1945
2942–3994
518
1508
3460
461–593
1388–1643
2852–3901
607
1513
2629
423–817
1106–2049
1793–3917
465
1502
n.d.b
418–507
1373–1614
n.d.
988
2719
7295
812–1221
2251–3321
5445–15193
342
1233
n.d.b
304–368
1146–1309
n.d.
817
1897
4237
690–982
1571–2388
3167–6600
Note. CI, confidence interval; n.d., not determined.
a
Mixture ratio as defined in Table 1.
b
Prediction could not be determined as the maximal effect of Endosulfan a(I) was < 50% (Table 2).
Varying concentrations of the 10-component mixture
(mixture 2), as well as its individual constituents were tested
for EROD activity in MCF-7 BOS cells after 24, 48, and 120 h
of exposure. PCB 126 was chosen as a positive control to
monitor proper functioning of the assay. As shown in Figure 5A
for exposure times of 24 h, PCB 126 induced very significant
increases in EROD activity at concentrations between 10nM
and 10lM. In contrast, no changes in EROD activity were seen
with mixture 2 over a wide range of concentrations. Similar
results were obtained with the other mixtures and their
individual mixture components (data not shown).
The 3-component reference mixture of steroidal estrogens
(mixture 1a), the 10-component mixture (mixture 2), and the
16-component mixture (mixture 5) were selected for studies of
modulation of CYP 1B1 expression, by using real time PCR, as
it has been shown that changes in CYP 1B1 mRNA levels
result in altered enzyme activity (Husbeck and Powis, 2002).
To ensure comparability of results, we chose approximately
equieffective mixture concentrations associated with cell
proliferative responses of 0.7–0.9 on the scale of normalized
values (see Figs. 1–4). CYP 1B1 expression levels after
exposure to the mixtures were compared with those seen after
treatment with E2 alone as well as those seen in untreated
controls. Two different concentrations of E2 were selected, one
associated with near maximal cell proliferative responses
(10nM, similar to the responses of the mixtures), the other
1000-fold lower (similar to the E2 levels present in the
mixtures).
In general, CYP 1B1 mRNA levels were quite low in MCF-7
BOS cells, and this has introduced a high degree of variation to
our data, reflected by the size of the error bars in Figure 5B. In
cell cultures exposed to 10 pM E2, CYP 1B1 expression levels
very similar to solvent controls were observed. With the 16component mixture (mixture 5), CYP 1B1 transcription was
significantly higher than in solvent-exposed controls. Reference
mixture 1a and the 10-component mixture (mixture 2) induced
small elevations of CYP 1B1 expression, but these did not reach
statistical significance. In cells exposed to the higher concentration of 10nM E2, CYP 1B1 expression was significantly higher
than in controls (Fig. 5B). We also analyzed the transcription of
CYP 3A4, but consistent differences between solvent controls
and cells treated with the mixtures or with E2 could not be
390
SILVA ET AL.
FIG. 3. Predicted and observed mixture effects of equipotent mixtures with
eight components (mixture 3). Observed mixture effects (circles) are from at
least two independent mixture experiments, predicted effects were calculated
using the model of CA (solid red lines), with dotted red lines indicating the
corresponding approximate 95% confidence belt.
observed, mainly because CYP 3A4 mRNA levels in MCF-7
BOS cells were very low (data not shown).
DISCUSSION
Of the six mixtures investigated in the E-Screen assay, three
showed combination effects that matched well with the
prediction according to concentration additivity (reference
mixtures 1a and 1b, mixture 3), while the remainder produced
joint effects somewhat lower than additivity, indicative of weak
antagonisms (mixtures 2, 4, and 5). Although the deviations
from additivity seen with mixtures 2, 4, and 5 were never larger
than fivefold in terms of predicted effect concentrations, they
were statistically significant.
Even though the experiments for mixtures 4 and 5 were
conducted using a 12-well microtiter plate format, while for the
remainder, a miniaturized E-Screen version with 96-well plates
was employed, the resulting concentration-response curves for
individual mixture components matched very well. Despite
this good agreement, we were careful to use single chemical
concentration-response data from one and the same experimental protocol for the prediction of mixture effects and did
not substitute data from, e.g., the 12-well format with those
from the miniaturized version of the assay.
All compounds were repeatedly tested throughout the
study but not simultaneously with the mixtures. Due to the
large number of mixture components, the parallel testing of
all single agents and mixtures was not possible, as this was
beyond our testing capacities. Thus, we cannot rule out
a priori that one or more compounds did change their
potency at the time of the mixture testing, although we judge
this as unlikely because the dilutions made from stock
solutions kept at 20°C yielded reproducible results over
FIG. 4. Predicted and observed mixture effects of equipotent mixtures with
11 components (A, mixture 4) and 16 components (B, mixture 5). Observed
mixture effects (circles) are from at least two independent mixture experiments.
The best-fitting regression models are shown as blue lines with the corresponding
95% confidence belt for the mean effect as dotted blue lines. Predicted effects
were calculated using the model of CA (solid red lines), with dotted red lines
indicating the corresponding approximate 95% confidence belt.
time. If this had occurred, the prediction would have been
based on the wrong information and, therefore, would be
inappropriate for the comparative assessment of the tested
mixtures. However, a CA prediction becomes less and less
vulnerable to such systematic errors, the more components
are included in a mixture, provided none of the affected
constituents contributes disproportionately to the overall
mixture effect and not all compounds are influenced in the
same way by the causative factor. The reason for this
robustness lies in the fact that, mathematically, the predicted
effect concentrations correspond to the weighted harmonic
mean of all individual effect concentrations (with weights
corresponding to the fractions of the individual compounds in
the mixture). With multicomponent mixtures, a systematic error
in one direction by an individual component is more likely to
be ‘‘cancelled out’’ by an error in the other direction by another
compound. Viewed from this perspective, false-positive
MIXTURES OF ESTROGENS IN THE E-SCREEN
391
FIG. 5. (A) Effects of mixtures in the EROD assay. Gray circles and black line represent the effects of PCB 126 (positive control). Black and red data points show
the effects of mixtures 2 and 5, respectively. White point is the negative control (0.5% ethanol). Data are the mean ± SD (n ¼ 3). (B) Regulation of CYP1B1 gene
expression. Bars represent treatment with E2 or mixtures at the following concentrations: 10 pM E2, 10nM E2, 5lM reference mixture 1a (Mix1a), 20lM mixture 2
(Mix2), 20lM mixture 5 (Mix5). The horizontal dashed line corresponds to the negative controls, set to 1. Data are from at least three independent experiments, and
error bars show the 95% confidence belt. *, significant difference with a p value around 5%; **, significant differences with a p value < 5%.
deviations from expected additivity would have been more
likely with the 3-component reference mixtures 1a and 1b, but
not with the 10-component (mixture 2), the 11-component
(mixture 4), or the 16-component mixture (mixture 5), as was
observed. Instead, good agreement with CA was seen with the
3-component reference mixtures—comparatively less likely,
considering the opportunities for systematic experimental
errors. Furthermore, we use EE2 as our standard internal
assay control and results from routine testing after the mixture
studies provided no indications for a systematic error (data
not shown). All this strongly suggests that the deviations from
additivity observed with mixtures 2, 4, and 5 are not the result
of a biased prediction.
We also reflected whether cytotoxicity of individual
chemicals might have had an impact on the observed
antagonisms. We considered this an unlikely proposition
because cytotoxicity would have produced more pronounced
antagonisms at effect levels associated with higher total
mixture concentrations. However, the extent of antagonism
either did not change much with concentration or was more
marked in the low concentration range, as observed with
mixture 2 (Fig. 2). Some chemical reactions between
components could conceivably have resulted in antagonisms,
but we are not aware of such reactions between the chosen
mixture components.
Another point for consideration is whether our statistical
decision criterion for declaring deviations from predicted
additivity is not too strict. To conclude agreement with the
additivity expectation, we demand that the confidence belt for
the best estimate of the prediction curve overlaps with that of
the regression model for the observed effects. Especially when
the mixture is composed of many compounds, and each
compound is tested repeatedly with many observations, the
statistical confidence belt for the prediction curve becomes very
tight. This allows the detection of very small deviations
between the prediction curve and the regression line with
a high degree of statistical certainty. In an extreme situation,
the statistically significant deviation might be deemed as too
small to be biologically relevant. Although we cannot specify
this benchmark in terms of biological significance, we can
address it by comparing the prediction curve with observed
individual values (instead of their mean). For instance, despite
statistically significant deviation of the mean from the
prediction curve, the prediction curve can still overlap with
an important number of the observed individual values.
However, this was not the case with any of the deviations
investigated here. This suggests that the deviations from
additivity are not only judged as significant by statistics but
should be considered also as relevant from a toxicological
point of view.
Finally, another relevant issue that should be taken into
account is the applicability of the CA model. In order for CA to
accurately predict the effects of a mixture, it relies on the
assumption that the mixture constituents share a common
mechanism or site of action, i.e., they have similar effects. Due
to the features of the E-Screen, which might respond to events
independent of direct ER binding, it is plausible that multiple
mechanisms might be at play in inducing the observed cell
proliferation. It could then be argued that this would violate our
definition of similarity and void the use of CA. However, based
on previous publications, and our own observations (Silva
et al., 2010), it seems clear that in MCF-7 cells, in spite of the
interaction with ER-independent signaling cascades, the effects
of estrogens culminate in ER activation (be it ligand dependent
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SILVA ET AL.
or independent), which then results in cell proliferation. For
this reason, to our knowledge, the estrogens tested in this work
all have a common site of action—ER activation—and therefore,
are similarly acting.
Also important to this argument are the observations
presented in Figure 2B. Here, the effects seen with E2 are
higher than the effects of the mixture at low effect concentrations. This phenomenon cannot be explained by any of the
existing additivity models, which is a clear indication of
unexpected interactions. We therefore conclude that some of
the constituents may have affected the activity of others when
in combination and that has produced the observed shortfall
from predicted concentration additivity.
Explanations for these shortfalls will have to be sought in the
composition of the various mixtures that we have investigated. It is
striking that weak antagonisms occurred only with combinations
where steroidal estrogens acted together with other estrogenic
agents, either synthetic or plant-derived (mixtures 2–4) but not
with mixtures where only structurally similar steroidal estrogens
(reference mixtures 1a and 1b) or synthetic estrogenic agents were
present (mixture 5). One possible explanation would be that the
presence of synthetic estrogenic agents has induced processes that
diminish the effectiveness of steroidal estrogens. Alternatively,
steroidal estrogens might have provoked events that reduced the
potency of synthetic estrogenic chemicals.
Both E2 and E1 undergo hydroxylation reactions to yield 4hydroxy and 2-hydroxy metabolites. Transfer of hydroxyl
groups to carbon atoms 5 and 15 is also possible (Coumoul
et al., 2001). In mammary tissue and in MCF-7 cells, the
production of these metabolites depends on the activity of
cytochrome P450 isoforms CYP 1A1 and 1B1. Hydroxylations
at carbon 2 (and to a lesser extent carbons 5 and 15) are due to
CYP 1A1 activity, while CYP 1B1 catalyzes hydroxylations at
carbon 4. With ratios of 4-/2-hydroxylation products of around
3 in MCF-7 cells, the CYP 1B1-mediated hydroxylation
pathway is the dominant route (van Duursen et al., 2003).
Some of these hydroxylated estrogen metabolites still have
estrogenic effects; the 4-hydroxylated product shows similar
potency to E2 in terms of MCF-7 proliferation and receptor
affinity (Lippert et al., 2003; Seeger et al., 2006). Therefore, it
could be argued that, even if the metabolism of the steroidal
estrogens was enhanced by the presence of synthetic
compounds in the mixture, the metabolites would still
contribute to the overall mixture effect, in a similar way to
the parent compound. However, hydroxylated estrogens are
substrates for catechol-O-methyltransferase (COMT), which is
responsible for transferring methyl groups to the hydroxyl
groups, in preparation for conjugation reactions. High levels of
COMT have been found in MCF-7 cells, allowing the rapid
conversion of hydroxylated products into methoxylated ones,
which have been shown to induce significant antiproliferative
effects, both in human tumors and MCF-7 cells (especially
2-methoxyestradiol) (Fotsis et al., 1994; Lippert et al., 2003).
The formation of these methoxylated metabolites could,
therefore, be responsible for the deviations from additive
effects that we observed.
Based on this hypothesis, we proceeded to evaluate the
impact of the tested mixtures on CYP 1A1 and CYP 1B1
activation and expression. In MCF-7 cells, the expression of
CYP 1A1, and to a lesser degree of 1B1, depends on activation
of the aryl-hydrocarbon receptor (AhR) and can be strongly
induced by 2,3,7,8-Tetrachlorodibenzo-p-dioxin and other
coplanar halogenated hydrocarbons (Pang et al., 1999; Spink
et al., 1990). On its own, E2 weakly suppresses the
transcription of CYP 1A1 (Coumoul et al., 2001). There were
no known AhR agonists in any of the investigated mixtures,
and, therefore, strong CYP 1A1 induction is not a likely
prospect. The results we obtained by monitoring CYP 1A1
expression in MCF-7 BOS cells (EROD activity) confirm this
expectation. Significant upregulation of CYP 1A1 was not seen
with any of the mixtures, nor with any of the individual
components, which leads us to conclude that differential
expression of CYP 1A1 can be ruled out as an explanation for
the observed deviations from expected activity. In any case, the
CYP 1A1-mediated hydroxylation pathway is not the dominant
process by which steroidal estrogens are removed in MCF-7
cells.
CYP 1B1 transcription, however, can be directly stimulated
by E2, due to the presence of an estrogen response element in
the gene’s promoter region (Tsuchiya et al., 2004). In
agreement with Tsuchiya et al. (2004), we saw significant
upregulations of CYP 1B1 in cells exposed to 10nM E2, when
compared with solvent-exposed controls. In contrast, the lower
concentration of 10 pM E2 did not provoke measurable
changes in the expression of the CYP 1B1 gene, suggesting
that removal of E2 by hydroxylation reactions was more
effective at higher concentrations of the hormone. This should
have led to a more pronounced antagonism at high mixture
concentrations, but this was not observed. It is striking that
mixture 2 produced antagonisms that were more marked at low
concentrations; we are unable to offer a mechanistic explanation for this observation. In a way similar to E2, the
16-component mixture (mixture 5) induced CYP 1B1 expression (Fig. 5B) indicating that hydroxylation reactions might
have led to diminished effective concentrations of some
steroids, despite the fact that some hydroxylation products
themselves are estrogenic. The available evidence suggests that
COMT activity in MCF7 cells is sufficiently high to bring
about efficient removal of these metabolites (Fotsis et al., 1994;
Lippert et al., 2003). However, the contribution of E2, EE2,
and E1 to the proliferative effects of mixtures 4 and 5 is too
small to explain the shortfall from additivity by assuming their
being rendered inactive through CYP 1B1 enzyme action. To
fully account for the observed antagonism, it is necessary to
invoke hydroxylation of other mixture components by CYP
1B1. The expected impact of these differences is likely to be
small but so were the observed deviations from expected
additivity. Our mixture effect modeling with mixture 2, where
393
MIXTURES OF ESTROGENS IN THE E-SCREEN
we achieved agreement with additivity by disregarding the
contribution of E2 and EE2 (Fig. 3), together with the empirical
confirmation of these expectations with mixture 4 (Fig. 3), is
suggestive and would support this reasoning.
Even so, we have doubts as to whether these ideas provide the
full explanations for the weak antagonisms that we observed
with mixtures 2, 4, and 5. The CYP 1B1 expression levels seen
with the 10-component mixture (mixture 2) were quite similar to
those of reference mixture 1a (Fig. 5B), but the reference
mixture produced combination effects in good agreement with
CA. For this reason, we considered the alternative possibility,
i.e., that some mixture components may have induced CYP 3A4,
which can also remove steroidal estrogens by hydroxylations.
CYP 3A4 is under the control of the PXR receptor and can be
induced by agents that are PXR agonists (Mikamo et al., 2003).
Of the chemicals present in the tested mixtures, only
methoxychlor has been described as a PXR agonist. Others
including bisphenol A, coumestrol, diethylstilbestrol, and
genistein were found to be inactive (Mikamo et al., 2003). We
were unable to detect differences in CYP 3A4 expression
between mixtures 2, 5, reference mixture 1a and E2, and the
expression levels of this CYP isoform in MCF-7 BOS cells were
very low (data not shown). We conclude that it is difficult to
ascribe the weak antagonisms to differential CYP 3A4
expression in MCF-7 BOS cells. It is conceivable that other
factors, including, e.g., induction of efflux pumps, which are
also under the control of nuclear receptors such as PXR or CAR,
may have contributed to the deviations from additivity, but we
were unable to investigate this possibility.
In summary, we identified estrogen mixtures that followed
CA, but statistically significant deviations from additivity
occurred with mixtures that contained both steroidal estrogens
and synthetic estrogenic chemicals. There are some indications
that the observed weak antagonisms are related to differential
expression of CYP 1B1, although it is likely that additional
factors are also at play. In any case, the relevance of the
observed small deviations from predicted additivity for
regulatory purposes can be questioned. Such antagonistic
deviations may occur, but they do not seem to be the rule. This
phenomenon should not invalidate the observation that CA
provides reasonable approximations for combination effects of
estrogenic chemicals with the end point of cell proliferation.
FUNDING
European Commission (contract numbers: EVK1-200100091 and QLRT-CT2002-00603).
ACKNOWLEDGMENTS
The MCF-7 BOS cells were a kind gift from Ana Soto (Tufts
University School of Medicine), and Janine Calabro (Tufts)
provided invaluable technical assistance.
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