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A genome-wide study of the repressive effects of estrogen receptor beta on estrogen receptor alpha signaling in breast cancer cells


Transcriptional effects of estrogen result from its activation of two estrogen receptor (ER) isoforms; ERα that drives proliferation and ERβ that is antiproliferative. Expression of ERβ in xenograft tumors from the T47D breast cancer cell line reduces tumor growth and angiogenesis. If ERβ can halt tumor growth, its introduction into cancers may be a novel therapeutic approach to the treatment of estrogen-responsive cancers. To assess the complete impact of ERβ on transcription, we have made a full transcriptome analysis of ERα- and ERβ-mediated gene regulation in T47D cell line with Tet-Off regulated ERβ expression. Of the 35 000 genes and transcripts analysed, 4.1% (1434) were altered by ERα activation. Tet withdrawal and subsequent ERβ expression inhibited the ERα regulation of 998 genes and, in addition, altered expression of 152 non-ERα-regulated genes. ERα-induced and ERβ-repressed genes were involved in proliferation, steroid/xenobiotic metabolism and ion transport. The ERβ repressive effect was further confirmed by proliferation assays, where ERβ was shown to completely oppose the ERα–E2 induced proliferation. Additional analysis of ERβ with a mutated DNA-binding domain revealed that this mutant, at least for a quantity of genes, antagonizes ERα even more strongly than ERβ wt. From an examination of the genes regulated by ERα and ERβ, we suggest that introduction of ERβ may be an alternative therapeutic approach to the treatment of certain cancers.


Estrogen is involved in the regulation of the reproductive, immune, cardiovascular, musculo/skeletal and central nervous systems. The effects of estrogens are mediated via two estrogen receptor (ER) isoforms, that is, ER alpha (ERα) and ER beta (ERβ) (Hanstein et al., 2004). The second ER isoform, ERβ, was discovered some 10 years ago (Kuiper et al., 1996). ERα and ERβ are homologous, especially in their DNA-binding domains (97%), but they differ in their ligand-binding domains (59% identity) and in their transcriptional activating function-1 (AF-1) domains. Estrogen is non-selective for the two receptors and the binding of estrogen results in a receptor–ligand complex that binds with high affinity to estrogen responsive elements (EREs) on DNA. Transcriptional regulation by the ERs may occur through a direct interaction of ERs with EREs or through an interaction of ER with other transcription factors (Sp1, AP-1 and NF-κB; McDonnell and Norris, 2002). The two receptors can heterodimerize, and they may modulate each other's effects (Gustafsson, 2006). ERβ often behaves as an antagonist to ERα (Nilsson et al., 2001). The basis of this antagonism has been shown to be due to the reduction in ERα protein level and reduced recruitment of the activating protein-1 complex (Matthews et al., 2006).

Detection of ERα protein in a breast tumor helps to identify those breast cancer patients who may respond to hormonal intervention, and measurement of ERα has become standard in the clinical management of breast cancer. However, only about 50% of ERα-expressing tumors respond well to hormonal therapy. ERβ is expressed at high concentration in normal human mammary gland tissue and expression is lost or decreased in breast cancer (Palmieri et al., 2002; Esslimani-Sahla et al., 2005). When it is reintroduced into breast cancer cells, ERβ is antiproliferative (Paruthiyil et al., 2004; Strom et al., 2004; Murphy et al., 2005; Hartman et al., 2006).

Several investigators have used the breast cancer cell line MCF-7 to evaluate the transcriptional effect of ERα in response to estrogen (Frasor et al., 2003, 2004; Buterin et al., 2006), and shown that many genes associated with the control of cell cycle, proliferation and apoptosis are regulated by ERα. Few reports have focused on ERβ, in terms of global transcriptional effects, and no direct transcriptional targets of ERβ have been convincingly described. In an effort to elucidate the gene regulatory function of ERβ and the mechanisms behind its suggested anti-tumorigenic role, we performed a full transcriptome analysis of the cell line used in our recently reported experimental model of ERβ-dependent reduction of breast tumors (Strom et al., 2004; Hartman et al., 2006). We identify clear differences between gene regulation by ERα and ERβ, respectively, and report previously unknown targets of estrogen regulation. Our data provide further insight into the interplay between the two receptors and the antiproliferative actions of ERβ.

Results and discussions

We have recently reported on the use of the breast cancer cell line T47D with a tetracyclin (Tet) responsive element regulating ERβ expression to study the role of ERβ in tumors formed from T47D cells. In the absence of Tet, ERβ was induced in these tumors resulting in the reduction of tumor size and inhibition of angiogenesis (Hartman et al., 2006). To investigate the mechanisms by which ERβ opposes the growth of ERα-positive tumors, we analysed the genome-wide transcriptional effects of ERβ induction in T47D breast cancer cells. We examined the response of the cells to estrogen in the presence of ERα alone or ERα and ERβ together, and the effects of induction of ERβ. In the presence of Tet, ERα is the predominant receptor expressed and following Tet withdrawal, ERβ is induced to ERα at a ratio of approximately 4:1 (Strom et al., 2004). A mock T47D Tet-Off PBI control was analysed to define non-ERβ-related gene expression inherent in the Tet-Off model used. A 240-fold increase in ERβ transcript levels was observed after Tet withdrawal in 17β-estradiol (E2) treated cultures; the relative ERβ mRNA levels as analysed by real-time PCR are shown in Figure 1a. Observation of ERβ protein co-expressed with green fluorescent protein (GFP) confirms that there was a corresponding increase from undetectable to clearly visible levels of induced protein in the cells (Figure 1b).

Figure 1

Estrogen receptor beta (ERβ) expression and microarray analysis of the T47D ERβ Tet-Off cell line. (a) Real-time PCR analysis of ERβ expression under four different culture conditions; Tet+ and ICI, Tet− and ICI, Tet+ and E2, Tet− and E2. Relative expression is compared to expression in Tet+ and ICI culture. (b) Picture of cells (upper panel) and green fluorescent protein (GFP) detection (lower panel) of Tet+ and Tet− cultured cells treated with E2. (c) Design of microarray experiment of the four conditions analysed. Direct comparisons were performed in a loop-wise design. Comparison (I) measures ERα−E2 gene regulation; (II) measures regulation due to the induced expression of ERβ in active state (E2); (III) measures regulation due to the induced expression of ‘inactive’ ERβ (ICI); (IV) measures ERα/ERβ–E2 regulation. (d) Correlation of M-values (2 log of fold change) between replicates of corresponding analysis. Gray arrow (in I) indicates the most prominent ERα-induced gene pS2 (TFF1). Black arrows (in Comparisons II and III) indicate the probes corresponding to induced ERβ (3–4 probes).

To estimate ERα-induced gene expression, E2-treated cells without the expression of ERβ were compared to an equivalent cell line treated with the antiestrogen ICI 182780 (ICI) (Comparison I: E2 T47D Tet+ compared to ICI T47D Tet+). To obtain ERβ-induced alterations, three separate studies were performed comparing genes altered upon the introduction of ‘active’ ERβ (Comparison II: E2 T47D Tet− compared to E2 T47D Tet+); genes altered upon the introduction of ‘inactive’ ERβ (Comparison III: ICI T47D Tet− compared to ICI T47D Tet+); and genes altered upon E2 treatment in cultures expressing both ERα and ERβ (Comparison IV: E2 T47D Tet− compared to ICI T47D Tet−). The design of the microarray experiment is shown in Figure 1c. Agreement with respect to fold change (FC) and P-values was observed in several cases where independent probes represented different regions of the same gene, validating the accuracy of the method. Differential expression of regulated genes was confirmed with real-time PCR of 37 randomly selected genes. A selection of the results is presented in Tables 1, 2, 3 and 4, and Figures 1, 2 and 3; complete results are available as Supplementary Infomation and deposited in ArrayExpress (E-MEXP-969).

Table 1 The 35 genes most regulated by E2 activation of ERα
Table 2 The 35 genes most regulated by induction of ERβ
Table 3 The genes regulated only by the induction of ERβ
Table 4 The most regulated biological gene groups
Figure 2

Comparisons of estrogen receptor (ER) profiles. (a) Our study. Overlap between ‘ERα profile’ (differentially expressed genes from Comparison I) and ‘ERα/ERβ profile’ (differentially expressed genes from Comparison IV) after 24 h E2 treatment. (b) Overlap between ‘ERα profile’ and ‘ERβ profile’ (differentially expressed genes from Comparison II) after 24 h E2 treatment. Overlapping 92 genes are differently regulated, and bar below shows the type of regulation by ERα and ERβ (up- or downregulated). (c) Comparison between ERα profiles of our T47D data set and previously published of MCF-7 cells (Frasor et al., 2003), both 24 h E2 treatment, upregulated genes (left) and downregulated genes (right), respectively.

Figure 3

Real-time PCR analysis confirms the microarray data. (a) Expression analysis of 10 genes with upregulated expression after 24 h E2 treatment, when estrogen receptor alpha (ERα) is the predominant ER expressed (Tet+ culture). Relative expression of each gene is set to 1 when cells are cultured in ICI. (b) Left: Expression analysis of two genes (APOD and LRRC15) found to be exclusively regulated by ERβ (Tet− culture) and not affected by ERα (Tet+ culture), and corresponding expression in mock cell line (Tet+ and Tet− culture, striped bars). Relative expression of each gene is set to 1 when cells are cultured in Tet+ and ICI. Right: Their respective mRNA induction followed over time, 1–48 h after E2 addition in separate experiment.

Gene expression changes caused by ERα and ERβ

We found that the activation of ERα alone by E2 (Comparison I, referred to as the ‘ERα profile’) resulted in the differential expression of 1 434 out of the analysed 35 000 transcripts. Correlations of M-values (2 log of FC) between the replicated arrays are shown in Figure 1d, where the arrow in Comparison (I) indicates the maximally ERα-regulated gene TFF1/pS2, FC 34.8 (confirmed with real-time PCR as upregulated 301 times). Of the 50 strongest regulated genes, 40 (80%) were upregulated. Overall the proportion of upregulated genes was 63% (897 out of 1 434 genes). When the cells expressed both ERα and ERβ (Tet− culture), E2 treatment affected 588 genes (Comparison IV). The pS2 gene was now upregulated only 4.4-fold (confirmed with real-time PCR as upregulated 53 times). The ERβ-mediated negative effect on this gene is in line with previously published data (Matthews et al., 2006). Venn diagram in Figure 2a further shows that Tet withdrawal and the subsequent ERβ expression more or less inhibited ERα regulation of 998 genes and, in addition, altered expression of 152 genes not regulated when ERα was the only ER expressed. The remaining 436 ERα-regulated genes were still differentially expressed when ERβ was present at high levels. Selective induction of ERβ (Comparison II), excluding gene regulation possibly inherent to the Tet-Off system as determined by control analysis, strongly affected the expression of 196 genes as a result of ERβ expression. Among the 50 most affected genes, 37 (74%) were downregulated following ERβ expression. Thus, ERα induces and ERβ negatively modulates a majority of their regulated genes. Tables 1 and 2 show the top regulated genes of the ‘ERα profile’ and ‘ERβ profile’, respectively.

We used the Gene Ontology classification and the EASE package for a two-step functional analysis to identify biological themes that were overrepresented among the differentially expressed genes (a Gene Ontology annotation was assigned to approximately 50% of the regulated genes). The most overrepresented gene group of the ‘ERα profile’ was the upregulated genes within the ‘cell cycle’ (EASE score 4.2 e–034). Here, 101 genes were significantly upregulated upon E2 stimulation; of these 53 belonged to the subgroup ‘regulation of cell cycle’ and 11 to the subgroup of ‘cell cycle checkpoint’ (BRCA2, TP53, BUB1, BUB1B, BUB3, CCNA2, CHEK1, MAD2L1, RBBP8, TTK and GTSE1). Only one ‘cell cycle checkpoint’ gene (CCNG2) was downregulated by ERα. Other overrepresented upregulated gene groups were ‘cytokinesis’ and ‘sterol biosynthesis’. Among the genes downregulated by ERα, the groups of ‘morphogenesis’, ‘cell communication’, ‘protein amino acid dephosphorylation’, ‘cell adhesion’, ‘oncogenesis’ and ‘apoptosis’ were overrepresented. For the ERβ-regulated genes, the ‘regulation of cell proliferation’ gene group was the most downregulated one (EASE score 0.006) followed by ‘transition metal ion homeostasis’ and ‘xenobiotic metabolism’. Overrepresented gene groups that were upregulated following the ERβ induction belonged to ‘negative regulation of cell growth’, ‘energy pathways’, ‘lipid metabolism’, ‘ion homeostasis’ and ‘apoptosis’. Details of overrepresented groups are shown in Table 4.

ERβ compared to ERα gene regulation

Comparison I in relation to Comparison IV (Venn diagram in Figure 2a) indicated that 998 less genes were estrogen regulated when Tet was withdrawn and ERβ and ERα were co-expressed, compared to when ERα was expressed alone. However, a full transcriptome comparison has a relatively large degree of uncertainty and, although it clearly indicates that ERβ opposes ERα regulation of many genes, it is not sufficient for a complete comparative evaluation. To investigate the action of ERβ in relation to that of ERα in detail, we have studied how the different receptor isoforms regulate one and the same gene. Of the genes in the ERβ profile, 92 out of the 196 genes (Comparison II) were also regulated by ERα (Figure 2b). Of these, 76% were upregulated by ERα and reduced upon the induction of ERβ. Overrepresentation analysis of this group shows that the biological context where the opposing effect of ERβ was predominant was in ‘cell cycle’, ‘xenobiotic metabolism’ and ‘ion transport’. The reverse pattern, where genes strongly reduced by ERα (upon E2 stimulation) were considerably increased as a consequence of ERβ expression, was seen in a smaller set of 12 transcripts (for example, TP53INP1, NSE2, RIN2, CPEB4, PSAP, LIPH, PDCD6IP and SEP1). ERβ was synergized with ERα in the regulation of transcription of only 10 genes; ERα was one such gene. All genes are listed in Supplementary Information.

Genes uniquely regulated by ERβ

Totally 104 genes or transcript variants were scored as being regulated by ERβ only (Figure 2b), and after eliminating border-line ERα-regulated genes (genes possibly regulated by ERα but below cut-off), 49 genes listed in Table 3 remained as exclusively ERβ-regulated. Seventy-one percent of the genes within this subgroup (35/49 genes) were upregulated as a consequence of ERβ expression. Here, we especially note the genes involved in ‘energy pathways’ of cholesterol biosynthesis and lipid metabolism (LDHC, HMGCL and CYB5R3) as upregulated and the cell–cell signaling genes (ADM and EDN1) as downregulated. Two of the strongest ERβ specifically upregulated genes were apolipoprotein D (APOD) and leucine-rich repeat-containing 15 (LRRC15), confirmed by real-time PCR as 14- and 70-fold increased, respectively (Figure 3b). These genes were further analysed in Tet+ and Tet− cultures at several time intervals after E2 treatment, where these genes were shown to be strongly induced 3 h after E2 treatment, with a maximum response at 18–48 h (Figure 3b, right).

Comparisons with literature

Several previously known ERα-regulated genes were detected in this study, for example, TFF1/pS2 (FC 34.8), GREB1 (FC 8 for three transcript variants), SIAH 2 (FC 5.3), SDF1/CXCL12 (FC 5.6), IGFBP-4 (FC 5.5), CCNA2 (FC 4.2), STC2 (FC 2.3), PR (FC 1.9), RBBP-8 (FC 1.7), NRIP1/RIP140 (FC 1.7), CTSD (FC 1.6) and NR5A2/LRH1 (FC 1.7), all confirming the data previously published (Roberts et al., 1988; Inoue et al., 2002; Hall and Korach, 2003; Annicotte et al., 2005; Frasor et al., 2005). Also downregulated genes such as ERα itself (FC 0.6), EFNB2 (FC 0.4), CYPIA1 (FC 0.3), IL1R1 (FC 0.4), ERBB2 (FC 0.5) and CCNG2 (FC 0.4) have previously been shown or indicated to be downregulated by estrogen in breast or other tissues (Nikolova et al., 1998; Ricci et al., 1999; Inoue et al., 2002; Frasor et al., 2004; Schaefer et al., 2005; Matthews et al., 2006; Stossi et al., 2006). Furthermore, the estrogen stimulated the increase of IGFBP4 and LIG1 by ERα and decrease of S100A6/calcyclin by ERβ correlates to the earlier findings of in vivo gene expression in wt and ER knockout mouse bone tissue (Lindberg et al., 2003).

Since other array analyses of ERα action have been published previously, we compared our results with another study (Frasor et al., 2003). It should be noted that the experiments compared were performed using different cell lines (MCF-7 versus our T47D, both breast cancer cell lines predominantly expressing ERα but with large differences in chromosomal abnormalities and mutations). In our study, E2 treatment was compared to treatment with ICI, which was different from the protocol employed by Frasor et al. (2003), and different technology platforms were utilized (Affymetrix GeneChip versus Operon's oligomer spotted array). Comparisons can only be made between annotated genes present on both arrays and with comparable annotations (7597 genes). Venn diagrams of the comparisons are shown in Figures 2c and d. Of 45 genes upregulated by ERα (at 24 h) in (Frasor et al., 2003), we confirm that 19 genes are significantly upregulated also in T47D (24 h E2 treatment), and for most of the remaining 25 genes we still observe an upregulation, but below our cut-off. Furthermore, we describe an additional 585 upregulated genes, including known genes (for example, pS2, SIAH2, GREB1 and BCL2) as well as a large number of previously unreported genes. For only one gene we show a contradictory downregulation in our study (CCBP2). Of 93 downregulated genes in MCF-7 cells, 19 are also downregulated in our T47D cells; again we see indications for many of the remaining genes to be downregulated but below the cut-off. There are two genes where our results differ (IER3/IEX1, which we have confirmed by real-time PCR, and LDLR) and we show 324 more genes to be downregulated.

Taken together, correlation with literature and confirmation by real-time PCR support the high consistency of our data, and we present a large number of novel ERα-regulated genes.

Previously, few, if any, studies of the transcriptional profile of ERβ in breast cancer cells have been published, but recently Chang et al. (2006) published a first analysis of transcriptional changes of genes induced by ERβ in breast cancer cell line MCF-7. This study is similar to ours, the major differences being the use of different cell lines (MCF-7 versus T47D), the mode of induction of ERβ expression (adenoviral gene delivery versus Tet-Off construct), the ratio of ERβ to ERα (approximately 1:3 in Chang et al. (2006) versus our ratio of 4:1) as well as the use of ICI (not used by Chang et al., 2006) and the type of technology (Affymetrix GeneChip versus Operon's oligomer spotted array). Despite these differences, several major conclusions are identical. In both studies ERβ was found to oppose ERα in the transcription of many genes, especially those active in ‘cell proliferation’ and ‘ion homeostasis’. Similar to our findings, these authors observed that the induction of ERβ itself, without E2 treatment, elicited changes in gene expression. In the study by Chang et al. (2006) no complete lists of regulated genes were published, but for several of the genes described in the article we confirm the regulation by ERβ (for example, THBS1 and CLDND1). We also observed a similar downregulation of other genes (for example, CXCL12/SDF1), but where the regulation could not be differentiated from the Tet-Off system itself and, consequently, these genes were excluded from our ERβ profile. For other genes, we could not confirm the results of Chang et al. (2006) (for example, BIRC3, IL17RB, CSRP2, BMP7, ERBB2, CDC25A, FOXM1, E2F1, CXCL1, 2, 10 and 20). Other differences between the two studies are as follows: (1) expression of 16 genes involved in the TGFβ pathway was affected by E2 treatment but we confirmed the downregulation of only two of these genes (TGFβ3 and SMAD6) by ERα and an opposing regulation of THBS1. Furthermore, in our study TGFβ2 was upregulated by ERα; (2) semaphorins and two of their receptors were regulated in the Chang et al. (2006) study, but no semaphorins detected in our study (sema3B, 3C, 3F, 4F, 4G and 6A1) were changed by ERβ (sema3C was upregulated by ERα); (3) Chang et al. (2006) found 44 genes to be specifically upregulated by ERβ and we detect 49 genes; however, there is no similarity in this gene set in the two studies. Some of the differences may be related to the cell lines, mode of ERβ delivery, levels of ERβ, effects induced by ICI and to the differences in the presence of genes on the array.

Antiproliferative and antitumorigenic actions of ERβ

The majority of the genes whose expression we found to be increased following the introduction of ERβ oppose proliferation or invasiveness of breast cancer. Examples are the three strongly ERα upregulated genes, which are all breast cancer related: pS2, BCL2 and GREB1 (Real et al., 2002; Kang et al., 2005; Rae et al., 2005). The strong ERβ negative influence on the ERα upregulation of these genes would most likely help in the reduction of tumor proliferation. In addition, the cell cycle–related genes MYC, NME2, SAP30, MATK, NOL1 and NOLC1 are upregulated by ERα and opposed by ERβ. MYC is a direct target of ERα transcription (Park et al., 2005) and NME2 is a downstream target of MYC. Decreasing MYC protein levels in MCF-7 cells significantly inhibits tumor growth (Wang et al., 2005). Two other oncogenes (AURUKA and PTTG1) were found to be increased by ERα and opposed by ERβ. AURUKA has been associated with breast cancer development and genomic instability (Lo et al., 2005) and PTTG1 is overexpressed in breast cancer, induces aneuploidy and is associated with tumor metastasis (Vlotides et al., 2007).

Further evidence of the proliferative actions of ERα is that the most strongly ERα-downregulated gene was the cell cycle repressor TP53INP1, a downregulation that was opposed by ERβ. Increase of TP53INP1 leads to cycle arrest in G1 and enhanced p53-mediated apoptosis (Tomasini et al., 2005). In addition, several antiproliferative genes (QSCN6, NDRG3, SEPT9, KCTD11 and STK3) are affected by ERβ alone, strongly supporting the notion that ERβ is antiproliferative and capable of inhibiting or reducing the growth of tumors. Subsequently, several cyclins are changed by the ERs; CCNA2, CCNB1, CCNB2, CCND1 and CCNF are all upregulated by ERα and opposed by Tet withdrawal induced ERβ expression.

The effect of ERβ expression on the proliferation was further demonstrated by proliferation assay. Here, T47D breast cancer cells, Tet-Off ERβ expressing clone as well as Tet-Off PBI expressing mock control were treated with ICI and E2 and proliferation measured after 5 days. E2 addition in both Tet+ cultures increases the proliferation significantly compared to ICI and non-treated cultures. Also E2 addition to Tet− PBI culture increases the proliferation, whereas E2 addition to Tet− ERβ-expressing culture induces no proliferation (Figure 4). This was replicated twice, in culture conditions with either 5 or 2% dextran-coated charcoal-treated FBS (DCC). Thus, the expression of ERβ at the levels used here appears to oppose ERα–E2 induced proliferation completely.

Figure 4

Proliferation assay confirms estrogen receptor beta (ERβ) reduces proliferation. ERβ Tet-Off T47D and mock PBI Tet-Off T47D cells were synchronized and cultured in serum-free media with and without Tet. After 5 days of incubation with respective treatment (ICI, E2 or no treatment) the cell viability was assayed. Cells treated with ICI or no treatment did not increase in proliferation, E2 induced viability with approximately 50%, except when ERβ was expressed and, apparently, abolished E2-induced proliferation.

Furthermore, E2 regulates many genes involved in cell adhesion, which plays a significant role for the capability of tumors to metastasize. Our results show that ERα appears to reduce while ERβ increases cell adhesion. ERα downregulates adhesion by decreasing the levels of 17 transcripts (NCAM2, ALCAM, L1CAM, LAMB2, SCARB2, THBS3, COL5A1, CYR61, ITGB4, MLLT4, PTPRF, CCL2, ZYX, LRRN1, NTN4, ANTXR1 and CLDN1). CLDN1 is one of the few genes downregulated by both ERα and ERβ, as was also discussed by Chang et al. (2006). Loss of expression of this gene has been suggested to play a role in invasion and metastasis of breast cancer (Tokes et al., 2005). ERβ upregulates two adhesion genes (COL6A1 and ANNEXIN A9). Furthermore, ERα upregulates adhesion genes linked to metastasis or oncogenic potential (CD44, RET, CXCL12/SDF-1 and THBS1) and an additional five genes involved in cell adhesion (ITGA6, MICB, BYSL, TROAP and TPBG) of which ERβ opposes THBS1. In addition, PDCD6IP reported to have roles in regulating adhesion was increased by ERβ and reduced by ERα. Possibly, ERβ may change adhesion properties, as well as metastasis potential, of breast cancer cells.

In addition, the ERα upregulated IL-20, shown to promote angiogenesis in endothelial cells (Hsieh et al., 2006), was not upregulated when ERβ was expressed. IL-20 was one of the strongest ERα-induced genes detected in this study (Figure 3a), and has not earlier been described as estrogen induced or breast cancer related, but its promoter has been determined as a target of ERα in MCF-7 breast cancer cells via genome-wide immunoprecipitation (Laganiere et al., 2005). IL-20 is a proinflammatory cytokine and a key player in the pathology of rheumatoid arthritis (Hsu et al., 2006), atherosclerosis (Chen et al., 2006) and psoriasis (Wang et al., 2006).

The mechanism by which ERβ opposes ERα regulation

We note a strong opposing effect on many ERα-regulated genes upon the introduction of ERβ. This effect could be mediated in several ways; ERβ downregulation of ERα at the mRNA level, ERβ may oppose ERα at the protein level, for example, via heteroduplex formation, ERβ may compete with ERα for binding to DNA sites (for example, EREs) or oppose ERα by occupying necessary cofactors. The basis for ERβ's antagonism against ERα has previously been shown to be due to the reduction in ERα protein level and reduced recruitment of the activating protein-1 complex upon ERβ expression (Matthews et al., 2006). We confirm that ERβ downregulates ERα at the mRNA level and, thus, some of the opposing effect on ERα signaling by ERβ may be directly related to less expression of ERα. Also, ERα downregulates its own transcript upon E2 treatment and in addition, in our material, ERα strongly upregulates a splice variant of ERα lacking exon 7. This variant is commonly increased in breast cancer tissue and acts in a dominant-negative way (Poola and Speirs, 2001; Marshburn et al., 2004). This implies a double negative feedback loop for ERα at both the transcript (downregulation of wt variant) and protein level (upregulation of dominant-negative variant), whereas ERβ opposes ERα by downregulating its wt variant but does not induce the dominant negative ERα variant. In addition, we observe that LRH-1 (NR5A2), which controls the expression of aromatase, is upregulated by ERα and opposed by ERβ, thus affecting local estrogen production, which for breast tumors in vivo is thought to be the most important estrogen source (Russo and Russo, 2006).

In an effort to elucidate the mechanism behind the ERβ-mediated opposing effect on ERα signaling, we expressed an ERβ protein with a mutated DNA-binding domain (DBDm) in T47D cells using lentivirus delivery, to compare the effect to that with ERα and ERβ wt. Here, we see that genes strongly upregulated by ERα and opposed by ERβ wt expression (such as pS2 with known ERE-binding and SPINK4 with several half-site EREs within the 5 kb promoter area) are even more opposed by ERβ DBDm (Figure 5). Also for genes strongly downregulated by ERα (TP53INP1), this effect is modulated by ERβ wt but completely inhibited by ERβ DBDm (Figure 5).

Figure 5

Effect of estrogen receptor beta (ERβ) wt and ERβ DNA-binding domain (DBDm) in T47D breast cancer cells via lentivirus delivered expression. Genes detected as oppositely regulated in the Tet-Off ERβ T47D system were further analysed in T47D parental cells infected with ERβ wt, ERβ DBDm and mock, respectively. Opposing effects by ERβ wt are clear, both for the ERα upregulated genes pS2 and SPINK4 and for the ERα downregulated gene TP53INP1. When ERβ DBDm is introduced, the ERα regulation is even further antagonized.

From this experiment, we conclude that the opposing effect of ERβ can be accentuated when ERβ lacks the ability to bind to DNA. The ERβ DBDm may act as a dominant negative on ERα, possibly by heterodimerizing with ERα and preventing the heterodimer from binding to EREs. When ERβ wt, on the other hand, forms a heterodimer with ERα the heterodimer can still bind to EREs and regulate the genes, although not with the same efficiency as the ERα homodimer (explaining why ERβ wt negatively modulates, but does not inhibit ERα transcription). The lower efficiency may depend on reduced transactivating functions, in line with the results by Gougelet et al. (2007), where the transactivation function-1 is implied as the keystone of ERβ-mediated transcriptional repression of ERα. For other genes, however, other mechanisms may be important, for example, for MYC (upregulated by ERα) where we saw a similar reduction by both ERβ wt and ERβ DBDm and for PR (upregulated by ERα) where we did not see any strong opposing effect by ERβ (neither in the Tet-Off expression model nor using lentivirus delivery) nor by ERβ DBDm at this time-point (24 h).


Uncovering the contribution of ERβ in estrogenic regulation of breast tumor growth is important for understanding and treatment of this disease. In the present study, we describe the effect at the transcriptome level of introducing ERβ into ERα-expressing breast cancer cells, using a model shown earlier to reduce xenograft tumor growth (Hartman et al., 2006). We show that the induced ERβ expression opposes the ERα driven upregulation of cell proliferation genes, which we functionally confirm by proliferation assays. ERβ also affects several other processes and genes; not all of these actions of ERβ oppose ERα and some transcripts appear to be uniquely regulated by ERβ. In addition, we present many novel genes not previously reported as regulated by ERα. For ERβ regulation there are very few published reports, thus most data presented here are novel. Furthermore, results from the expression of a DBD-mutated ERβ indicates that the ERα opposing action of ERβ can be accentuated if ERβ cannot bind to DNA. It should be noted that this report describes changes related to 24 h of E2 activation. It is possible that some immediate regulatory effects, by this time, have disappeared.

In conclusion, ERβ opposes ERα at the transcriptome level and in so doing exhibits antiproliferative actions as well as suppression of potent proinflammatory cytokines. Since ERβ is expressed in small amounts along with ERα in approximately 70% of breast cancers (Kurebayashi et al., 2000), breast cancer therapy involving specific ERβ agonists may prove to be an interesting complement in future therapies.

Materials and methods

Cell culture

Transfections of T47D cell line and cell culture have previously been described by Strom et al. (2004) and Hartman et al. (2006). Cells were cultured in DMEM/F12 mixed (1:1) medium supplemented with 5% FBS. For synchronization the medium was changed to phenol red-free DMEM/F12 mixed (1:1) medium supplemented with 5% DCC for 24 h; the serum was then reduced to 0.5% DCC and 10 nM ICI 182780 was added. Tet was withdrawn in half of the plates 12 h before the start of treatment. Tet+ and Tet− cultures were treated with either 10 nM of the pure ER antagonist ICI or 10 nM of ER ligand E2 (17β-estradiol) for 24 h after which all cells were collected simultaneously for RNA extraction. RNA was extracted using TRIzol precipitation. This was subsequently repeated with a mock-control T47D stably transfected with an empty Tet-Off PBI construct.

Lentivirus vectors and infection of T47D cells

Follow-up experiments to study the effect of DBD-mutated ERβ were performed using lentivirus vectors and infection of T47D cells. The plasmid pcDNA3-FLAG ERβ was used as a template for TOPO cloning into pLenti6/V5-D-TOPO according to the instructions (Invitrogen, Carlsbad, CA, USA). The pLenti6/V5-D-FLAG ERβ DBDm was constructed from pLenti6/V5-D-FLAG ERβ using Quick-Change (Stratagene, La Jolla, CA, USA) according to the manufacturer's instructions. Forward oligo IndexTermATCACTATGGAG TCTGGTCGTTG CA GCATGTAAGGCCTTTTTTAAAA GA changing E167 and G168 to A.

Lentivirus was produced with the ViraPower Lentivirus Expression system. The titer of the virus was estimated according to the instructions (Invitrogen). T47D cells were spread on six-well plates at a density of 200 000 cells/well. The next day lentivirus at 2 MOI was added in 1 ml of growth media supplemented with 6ìg of polybrene. After 24 h at 37°C the cells were washed and 2 ml of normal growth media was added. Blasticidine to 5 μg/ml was added after another 24 h and the cells were then incubated for another 5 days at 37°C. Non-infected dead cells were washed off and the remaining cells were trypsinized and spread onto 100 mm plates. One well/100 mm plate in 10 ml of phenol red-free media was supplemented with 5% DCC, after 24 h the media was changed to 0.5% DCC cells incubated at 37°C for 24 h after which 10 nM 17β-estradiol was added to half of the plates and the cells were incubated at 37°C for 24 h. A parallel mock infection was performed, where lentivirus without ERβ construct was used. Cells were harvested in 1 ml TRIzol/100 mm plate; RNA was extracted with chloroform and cleaned using Qiagen RNeasy spin columns with on-column DNase I digestion. Infection procedure (mock, ERβ wt and ERβ DBDm) was repeated twice on different occasions, once with subsequent ICI versus E2 treatment and once with non-treatment control versus E2.

Cell proliferation assay

Cells were plated at 2500 cells/well in 96-well plates and cultured in 2% DCC and 1 μg/ml Tet. The next day cells were synchronized using 10 nM ICI 182780 for 24 h, the following day cells were washed once with PBS and the medium was changed to include respective treatments (Tet 1 μg/ml, E2 10 nM and ICI 10 nM). After 5 days of incubation the cell viability was assayed using MTS kit (Promega, Madison, WI, USA) (‘CellTiter 96 Aqueous Non-Radioactive Cell Proliferation Assay’). Assay was replicated with cells cultured in 5% DCC. The absorbance was measured at 490 nm. Results are presented in Figure 4 as percent of control (control is Tet+, ICI).

Microarray experiment and analysis

We used two-color comparative microarray technique, covering the fully known transcriptome of 35 000 genes and variants using Operon's long-oligonucleotide spotted array. In addition, the array was complemented with 133 oligos specifically synthesized for a detailed and robust analysis of nuclear receptors, splice variants and co-regulators. Each comparison was replicated and dye-swapped. In addition, we performed a duplicated dye-swap comparison and real-time PCR controls of the mock control (Tet-Off PBI T47D) for comparison and for identification of differential expression caused by the system by itself.

Microarray analysis was performed essentially as described by Richter et al. (2006). For each cDNA synthesis, 10 μg of total RNA was used. Samples to be co-hybridized on a slide were pooled and direct comparisons were performed using replicated samples, cDNA synthesis and hybridizations with the Cy5/Cy3 dye assignments reversed. Eight hybridizations (each comparison replicated) were performed (and an additional two for the mock control); design of the experiment is shown in Figure 1C. Operon's human 35 K arrays were used (printed by the microarray facility at KTH, Royal Institute of Technology, Sweden), hybridized for 35–40 h at 42°C, washed and scanned at 10-μm resolution using the G2565BA DNA microarray scanner (Agilent Technologies, Palo Alto, CA, USA) photo multiplier tube set to 100. The obtained Tiff images were analysed using the GenePix Pro 6.0 software (Axon Instruments, MDS Analytical Technologies, Sunnyvale, CA, USA). All data analysis steps were performed in the R environment for statistical computing and programming essentially as previously described (Richter et al., 2006). Differentially expressed genes were identified using an empirical Bayes moderated t-test (Smyth, 2004) ranking the genes according to significance; cut-off for differential expression was set to B-value >0.0 (an FC of at least 1.4, and P-value <0.005), and confirmed by real-time PCR analysis of genes close to this cut-off. In addition, genes regulated by the Tet-Off system, as determined from the mock control, were removed from the ERβ profile. The raw data and detailed protocols are available from the ArrayExpress data repository using the accession number A-MEXP-571 and E-MEXP-969.

Classification into Gene Ontology functional groups (Harris et al., 2004) and analysis of overrepresented themes were performed using the Expression Analysis Systematic Explorer (EASE) package (Hosack et al., 2003). The complete human transcriptome was used for calculation of the expected frequencies in the overrepresentation analysis, and a Gene Ontology theme was considered overrepresented if the calculated EASE score was below 0.3.

Gene expression changes caused by the Tet-Off system (T47D transfected with a Tet-Off PBI empty vector) were characterized under the same conditions as above, but to exclude all possibly regulated genes, the cut-off was lowered to B<−4. All these genes were excluded from the comparisons and analyses, unless real-time PCR proved them to be significantly changed only in the ERβ-expressing cell line.

Real-time PCR

RNA was isolated as described above and DNA degraded using DNase I, 1 μg of the total RNA was used for each cDNA synthesis. First strand cDNA was synthesized using Superscript III and random hexamers (Invitrogen). Linear phase of logarithmic amplification was used for quantification, and cycle number was compared between triplicate samples using cDNA template corresponding to 5 ng RNA per sample, 1 pmol of each forward and reverse primer and SYBR green PCR master mix in a total volume of 10 μl. Runs were performed using ABI 7500 instrument (Applied Biosystems, Foster City, CA, USA) according to the manufacturer's instructions. The expression of genes was normalized to the expression of 18S, ARHGDIA and GAPDH. Relative expression and standard deviation were calculated using ΔΔCt formula. Primer pairs for 37 genes are detailed in Supplementary Data 2.

Comparisons between studies using different microarray formats

To compare our results to published studies where Affymetrix GeneChips were utilized, we used the EASE software to match features across platforms. By providing Affy-ID to GeneID, a direct comparison was possible to perform. Only features with annotation present in GeneID database and present on both arrays are directly comparable (7597 genes).

Accession codes




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We thank Dr Chunyan Zhao and Associate Professor Karin Dahlman-Wright for valuable discussions. This study was supported by grants from the Swedish Cancer Fund and from KaroBio AB.

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Correspondence to C Williams.

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Williams, C., Edvardsson, K., Lewandowski, S. et al. A genome-wide study of the repressive effects of estrogen receptor beta on estrogen receptor alpha signaling in breast cancer cells. Oncogene 27, 1019–1032 (2008).

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  • estrogen receptor
  • microarray
  • breast cancer
  • gene regulation

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