Molecular and Cellular Endocrinology 334 (2011) 83–90
Contents lists available at ScienceDirect
Molecular and Cellular Endocrinology
journal homepage: www.elsevier.com/locate/mce
Review
The gene regulatory networks controlled by estrogens
Hui Gao, Karin Dahlman-Wright ∗
Department of Biosciences and Nutrition, Karolinska Institutet, Novum, S141 83, Huddinge, Stockholm, Sweden
a r t i c l e
i n f o
Article history:
Received 30 November 2009
Received in revised form 7 August 2010
Accepted 6 September 2010
Keywords:
Estrogen receptor
Transcriptional regulation
Gene regulatory network
a b s t r a c t
Estrogen signaling occurs widely among vertebrates and in some invertebrates. Estrogen action is mediated by estrogen receptors through the regulation of target gene expression. Estrogen mediated control
of gene expression is a complex process including ligand–receptor interactions, receptor–DNA interactions and receptor–cofactor interactions. Recent technological advances allow global analysis of gene
expression and protein–DNA interactions facilitating a description of estrogen controlled gene regulatory networks. This paper reviews the current knowledge of estrogen regulation of gene expression and
subsequent gene regulatory networks with focus on studies using human cell lines and mouse models.
© 2010 Elsevier Ireland Ltd. All rights reserved.
Contents
1.
2.
3.
4.
5.
6.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Estrogen receptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Genome wide profiling of estrogen target gene networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.
Tissue specific effects of estrogen on gene regulatory networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.
Selectivity and interplay of estrogen receptors with regard to regulation of gene networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3.
MicroRNAs; new players in estrogen controlled gene networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The cis-regulatory code of estrogen controlled gene networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Co-factors for gene regulatory networks controlled by estrogen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. Introduction
Estrogens are a group of steroid compounds named for their
importance in the estrous cycle and were the first isolated steroid
hormones (Hertig, 1983). As the primary female sex hormone,
estrogens have traditionally been connected with female reproduction. The importance of these hormones for a wide range of
physiological processes, such as cardiovascular dynamics, cognition and energy metabolism has later been established (Chen et al.,
2009; Harvey, 2009).
Most of the known effects of estrogen are mediated via a direct
interaction of estrogen with estrogen receptors (ERs), ER␣ and ER␤,
which regulate the expression of specific sets of genes. Estrogen
Abbreviations: MAT, model-based analysis of tiling-arrays; TAS, tiling analysis
software; NA, not available.
∗ Corresponding author at: Department of Biosciences and Nutrition, Karolinska
Institutet, Novum, SE-14183 Huddinge, Sweden. Tel.: +46 8 6089215;
fax: +46 8 7745538.
E-mail address: [email protected] (K. Dahlman-Wright).
0303-7207/$ – see front matter © 2010 Elsevier Ireland Ltd. All rights reserved.
doi:10.1016/j.mce.2010.09.002
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signaling through ERs, which act as ligand-induced transcription
factors, was traditionally thought to be unique to vertebrates
and has been proposed to be an important component of the
complex differentiation and development in humans and other
vertebrates (Baker, 2003). There is however evidence for estrogen
signaling in invertebrates since ER orthologs have been identified
from mollusks and cephalochordates. Unlike the vertebrate ERs,
these invertebrate receptors are not generally activated by estrogen
(Thornton et al., 2003; Bridgham et al., 2008). However recently,
ERs isolated from annelids have been shown to specifically activate transcription in response to low estrogen concentrations and
to bind estrogen with high affinity (Keay and Thornton, 2009). This
finding indicates that estrogen signaling via ERs is as ancient as the
ancestral bilaterian animal and suggests that estrogen signaling is
widely distributed among organisms.
In human, estrogens play important roles in many physiological processes. It is thus not surprising that estrogen signaling has
been implicated in various clinical conditions including various
types of cancer (breast, ovarian, colorectal, prostate, endometrial),
osteoporosis, neurodegenerative diseases, cardiovascular disease,
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insulin resistance, lupus erythematosus, endometriosis, and obesity (Deroo and Korach, 2006). Currently, targeting estrogen
signaling at the level of estrogen production and ER function
are strategies for therapeutic intervention primarily for hormone
dependent breast cancer. Components of the gene regulatory networks controlled by estrogen might provide novel drug targets
and therapeutic opportunities where targeting estrogen production and/or ER function do not provide sufficient therapeutic effect.
Effects of estrogen on target gene expression are regulated by
a complex array of factors such as ER ligand-binding, receptor
species, the DNA sequence bound by ERs, ER interacting co-factors
and chromatin context. The final response at the level of gene
expression will depend on ligand identity and availability, the
cellular concentration and localization of ERs, levels of various coregulator proteins and other signal transduction components and
the chromatin state (Marino et al., 2006). Our understanding of
estrogen signaling in physiology and disease has been aided by the
identification of the molecular events that mediate estrogen signaling in different cells, different tissues, different physiological states,
different disease conditions and different species. This knowledge
includes the identification of ER target genes (Jakowlew et al., 1984;
Watanabe et al., 1998; Sabbah et al., 1999), functional estrogen
response elements (EREs) (Gruber et al., 2004) and ER interacting
cofactors (Shibata et al., 1997).
Recently, global analysis of gene expression profiles and global
identification of protein–DNA interactions have begun to reveal the
molecular architecture of ER binding to DNA and the subsequent
effects on gene regulatory networks.
This review aims to provide a summary of the current knowledge of gene regulatory networks controlled by estrogen with focus
on human cell lines and mouse models.
2. Estrogen receptors
The effects of estrogens are mediated via a direct interaction of
estrogens with ERs, which are ligand-regulated transcript factors
(Nilsson et al., 2001). The first ER was cloned in 1986 from a breast
cancer cell line (Green et al., 1986). This ER was regarded as the
only ER, until a novel ER was cloned from rat prostate (Kuiper et al.,
1996). The novel ER, called ER␤, is homologous to the first ER, now
called ER␣, particularly in the DNA binding domain (96% amino
acid identity) and in the ligand-binding domain (55% amino acid
identity) (Enmark and Gustafsson, 1999). The two ERs are encoded
by distinct genes located at different chromosomes in the human
and mouse genomes.
The ERs share structural characteristics with members of the
nuclear receptor (NR) super family of transcription factors including five distinguishable domains (Gronemeyer and Laudet, 1995).
These domains are involved in DNA-binding, dimerization, ligand
binding and transcriptional activation (Nilsson et al., 2001).
Most of the known actions of estrogens are so called genomic
effects, which are mediated by ERs through their interactions with
DNA, in different proposed models that activate or repress the
expression of specific sets of genes. Accumulating evidences also
suggests the existence of membrane ERs. Estrogen effects that
are mediated via membrane ERs are referred as the non-genomic
pathway as opposed to the genomic pathway that involves ER
interaction with DNA. Activation of these membrane ERs leads to
changes in signal transduction pathways and ultimately to regulation of gene expression (Driggers and Segars, 2002; Levin, 2009).
G protein-coupled receptor 30 (GPR30) is uniquely localized to the
endoplasmic reticulum, where it specifically binds estrogen and
active signal transduction. The functions of estrogen that are mediated specifically through GPR30 are currently under investigation
(Levin, 2009; Martensson et al., 2009). In this review, we focus on
gene regulatory networks controlled by estrogen via the genomic
pathway.
Estrogens can diffuse across the plasma and nuclear membranes
of cells and bind to ERs that exist in a complex with proteins,
including heat shock proteins within the cell nucleus (Landers and
Spelsberg, 1992; Pratt and Toft, 1997). Binding of ligand activates
ERs by a mechanism that involves dissociation of heat shock proteins followed by dimerization of receptor proteins. The activated
ERs bind as homodimers or heterodimers to EREs. The binding of
ERs to specific DNA sequences, estrogen response elements (EREs),
facilitates the assembly of basal transcription factors into a stable pre-initiation complex and increases the rate of target mRNA
synthesis (Nilsson et al., 2001).
ERs can also be activated by extracellular signals in the absence
of ligand. This model has primarily been shown for ER activation
by polypeptide growth factors such as EGF (Ignar-Trowbridge et al.,
1992; Curtis et al., 1996). The biological significance of this signaling
is not clear (Hall et al., 2001).
In addition to binding to the ERE, activated ERs can also regulate
transcription through functional contacts with other DNA-bound
transcription factors (TFs). Once tethered to the DNA-bound TFs,
for example, Ap1 or Sp1 (Kushner et al., 2000; Safe, 2001), ERs
can regulate target gene transcription without directly interacting
with DNA, a process referred to as the non-classical pathway for ER
signaling. Recent studies have revealed enrichment of FoxA1 DNAbinding motifs in ER binding regions in breast cancer cells (Carroll
et al., 2005, 2006; Laganiere et al., 2005; Carroll and Brown, 2006).
Additionally, FoxA1 was shown to be recruited to approximately
50% of the ER binding regions in these cells (Lupien et al., 2008).
Importantly, inhibition of FoxA1 expression suppressed ER binding
to chromatin and reduced estrogen-mediated transcription (Carroll
et al., 2005; Laganiere et al., 2005). It has been proposed that FoxA1
acts as a pioneer factor to increase access of ER to chromatin (Carroll
et al., 2005).
The two ERs display similar, but not identical, ligand binding
properties (Kuiper et al., 1996). The term Selective Estrogen Receptor Modulator (SERM) was developed to provide a term to describe
compounds that regulate subsets of estrogen signaling pathways.
The concept involves the ability to selectively bind to a given receptor, promote selective interactions of ER␣ or ER␤, respectively, with
DNA and different proteins such as transcriptional co-activators or
co-repressors (Dutertre and Smith, 2000). The exploration of SERMs
to achieve improved therapeutic profiles is an area under intense
investigation. For example, tamoxifen and its derivatives are ER
agonists in bone and uterus, but ER antagonists in breast tissue,
and are therefore used for breast cancer treatment.
ERs are widely distributed in the body. ER␣ is mainly expressed
in uterus, prostate (stroma), ovary (theca cells), epididymis, bone,
breast, and various regions of the brain, liver and white adipose tissue. ER␤ is expressed in for example colon, prostate (epithelium),
ovary (granulosa cells), bone marrow, salivary gland, vascular
endothelium and certain regions of the brain. Furthermore, in some
tissues, both ERs are expressed albeit in different cell types. For
example, in human testis, ER␣ is exclusively present in spermatogonia and Sertoli cells, while both ERs are present in other cells,
such as Leydig cells and spermatocytes (Cavaco et al., 2009). The
different tissue distribution for the two ERs is likely to account for
some of the observed tissue specific effects of estrogen (Couse and
Korach, 1999; Muramatsu and Inoue, 2000).
Numerous mRNA splice variants have been identified for ERs in
different species and exhibit a species-specific pattern (Lu et al.,
1999; Zhao et al., 2005; Deroo and Korach, 2006). These splice variants are often co-expressed with their wild-type counterparts and
the function of these variants remain to be elucidated. Particularly,
the effects of ER variants on gene regulatory networks have not
been studied. However, we did not observe effects on gene expres-
H. Gao, K. Dahlman-Wright / Molecular and Cellular Endocrinology 334 (2011) 83–90
sion when the human ER␤ variant ER␤2, that is distinct from ER␤ at
C-terminal ligand-binding domain, was expressed in HEK293 cells
(unpublished data).
3. Genome wide profiling of estrogen target gene networks
Global gene expression profiling has been widely used to analyse effects of estrogen on gene regulatory networks in different
estrogen target tissues and various disease models in different
species. DNA microarrays and high throughput DNA sequencing
(HTS) technologies are currently available tools for global gene
expression profiling. Microarray technology has been widely used
for gene expression profiling for more than 10 years. However,
gene expression profiling using microarray technology is easily
biased towards protein coding genes, especially the 3 end exons,
with some of the current microarray designs. Furthermore, most
microarray designs cannot directly provide information about the
structure of transcripts and the expression of different splice variants. Over the last few years, HTS technologies have challenged
microarray technology for gene expression profiling by allowing for
additional analyses such as detection of low-expressed genes, alternative splice variants and novel transcripts (Cloonan et al., 2008;
Marioni et al., 2008; Mortazavi et al., 2008; Wilhelm et al., 2008).
Sequencing based method, such as Serial Analysis of Gene Expression (SAGE), has been used to identify estrogen target genes (Seth et
al., 2002). However, to our knowledge, no study has been published
that explores HTS to assay genome wide transcriptional regulation
by estrogen by directly assaying levels of estrogen response RNA
transcripts.
Recently, the profile of genome-wide RNA polymerase II (Pol
II) occupancy has been determined as a measure of transcriptional
regulation of target genes by estrogen, providing an alternative to
the direct determination of RNA levels (Carroll et al., 2005; Feng
et al., 2008; Fullwood et al., 2009; Kininis et al., 2009; Welboren
et al., 2009a,b). With the established central role of Pol II recruitment in ER mediated transcriptional regulation, this technology
has provided important information regarding the effects of ER on
target gene transcription. However, the overlap between estrogen
responsive genes identified by Pol II occupancy and the direct measurement of mRNA levels was limited (Welboren et al., 2009a,b).
Thus further studies are needed to clarify the relationship between
Pol II recruitment and steady state RNA levels for estrogen regulated gene networks. In line with this, one recent study showed that
the predominant determinant for estrogen regulated gene expression is the post-recruitment regulation of Pol II activity rather than
actual recruitment of Pol II to the promoter (Kininis et al., 2009).
A search of Medline-indexed papers, using the term “‘gene
expression profiling” microarray and Estradiol’ reveals 152 papers
published between 2000 and 2009. The well established role of
estrogen in the physiology and pathology in relation to female
reproduction is reflected in that half of these publications focus
on determining global gene expression profiles in systems related
to female reproductive tissues. However, global gene expression
profiles are also available for non-classical estrogen target tissues,
such as the brain, bone, and the liver.
3.1. Tissue specific effects of estrogen on gene regulatory
networks
Many studies have been published that determine gene expression profiles in breast cancer cell lines in response to estrogen
treatment (Kininis and Kraus, 2008). The use of different cell
lines, treatment times, platforms and analysis strategies makes
comparisons of published data difficult (Kininis and Kraus, 2008)
(Welboren et al., 2009a,b). However, a meta-analysis of published
85
data from MCF7 and T47D human breast cancer cell lines suggested
that these two ER␣ positive cell lines share common estrogen
responsive pathways (Lin et al., 2004). As estrogens are closely
related to breast cancer proliferation and progression, it is not
surprising that global gene expression changes in response to estrogen in breast cancer cells show a general up-regulation of genes
promoting proliferation and down-regulation of anti-proliferative
and proapoptotic genes (Frasor et al., 2003). Recently, a weighted
meta-analysis across 10 independent published datasets addressing the effect of 17␤-estradiol (E2) on MCF7 cells identified more
than 2000 genes as estrogen regulated genes (Ochsner et al., 2009).
However, only six genes were regulated more than two folds in
more than 5 datasets. These genes are adaptor-related protein complex 1, gamma 1 (AP1G1), carbonic anhydrase XII (CA12), v-myb
myeloblastosis viral oncogene homolog (MYBL1), PMA-induced
protein 1 (PMAIP1), ret proto-oncogene (RET) and growth regulation by estrogen in breast cancer 1 (GREB1).
Global gene expression profiling has been employed to approach
the molecular mechanism of the cell type specific effects of estrogen signaling. Comparison of estrogen regulated gene expression
profiles after 3 h treatment with E2 in MCF7 breast cancer cells and
in the osteoblast-like cell line U2OS revealed that less than 10%
of the E2 regulated genes are common to both cell lines (Krum et
al., 2008). In a comparison of different mouse female reproductive
organs, after 6 h E2 treatment, half of the estrogen up-regulated
genes in the uterus were also up-regulated in vagina. However,
less than 10% of the genes that were down-regulated in the uterus
were also down-regulated in vagina. Interestingly, no gene showed
altered expression in the mammary gland after 6 h estrogen treatment (Suzuki et al., 2007). There is evidence suggesting that the
mammary gland is controlled by progesterone and prolactin rather
than estrogen. This may account for the apparent lack of estrogen
regulated genes in this study.
Estrogen plays important roles in the regulation of bone growth
during puberty and in bone remolding in the adult (Manolagas et
al., 2002; Riggs et al., 2002). ERs are present in bone at a level about
10 fold lower than in reproductive tissues such as uterus. Estrogen seems to act directly on osteoblasts (Stossi et al., 2004). The
majority of genes regulated in human primary osteoblasts after 24 h
estrogen treatment belong to the group of DNA dependent transcriptional regulators and genes involved in signal transduction.
Many up-regulated genes are associated with cell adhesion, protein
phosphorylation, cell–cell signaling and intracellular signaling. In
contrast, many down-regulated genes are involved in inflammatory and immune responses, G protein coupled receptor pathways
and apoptosis. Interestingly, only one cell cycle controlling gene
was down-regulated by estrogen in human primary osteoblasts
(Denger et al., 2008). This observation is in line with the findings
that estrogen had no major effect on the proliferation rate of primary osteoblasts.
Evidence from studies in humans and rodents links estrogen
to the maintenance of glucose homeostasis (Louet et al., 2004).
Recently, studies of knockout mice that lack endogenous estrogen
synthesis (ARKO) or ERs (ERKO) have provided additional evidence
for the protective role of estrogen in maintaining glucose homeostasis and insulin sensitivity (Heine et al., 2000; Jones et al., 2001).
The absence of ER␣, but not ER␤, resulted in glucose intolerance and
insulin resistance in both female and male mice. This was shown to
be due to profound hepatic insulin resistance in ER␣KO mice. Gene
expression profiling revealed up-regulation of lipogenic genes and
down-regulation of genes involved in lipid transport in livers of
ER␣KO mice (Bryzgalova et al., 2006). Interestingly, the expression of lipogenic genes was decreased in diabetic Ob/Ob mice after
E2 treatment, supporting a connection between glucose tolerance
and the expression of lipogenic genes in the liver (Gao et al., 2006;
Lundholm et al., 2008a,b,c).
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In human subcutaneous adipose tissue, gene expression profiling showed that genes involved in fatty acid synthesis were
down regulated by E2 in a subgroup of women (Lundholm et
al., 2008a,b,c). Gene expression analysis of estrogen effects on
white adipose tissue (WAT) and hypothalamus of mice revealed
marked changes in gene expression profiles for WAT and small
changes for hypothalamus (Lundholm et al., 2008a,b,c). This could
suggest a direct role of E2 in WAT rather than through the CNS
in regulation of obesity. Interestingly, lipogenic genes were not
regulated by estrogen in WAT in this study. As the adipose tissue weight was nevertheless decreased, this indicates that there
are additional mechanisms mediating effects of estrogen on fat
mass.
3.2. Selectivity and interplay of estrogen receptors with regard to
regulation of gene networks
Differential ligand activation, differential co-activator recruitment and differential tissue distribution indicate that ER␣ and ER␤
might regulate different gene networks. Models of action involving cooperation, as well as competition, between ERs have been
proposed (Matthews and Gustafsson, 2003; Zhao et al., 2008).
ER␤ appears to act as a dominant negative regulator of estrogen signaling and demonstrate repressive effects on ER␣ mediated
transcriptional activity when co-expressed with ER␣.
Cell lines that express endogenous ER␤, including in the presence of ER␣, have not been described. Therefore, the role of ER␤ in
regulating gene expression, including its effect on ER␣ gene regulatory networks, have been studied in model systems that were
engineered to express either or both ERs.
Because of the lack of endogenous expression of either ER␣ or
ER␤ in U2OS human osteosarcoma cells, ER␣ or ER␤ stably expressing derivatives of this cell line provides cell models permitting
investigation of ER-subtype specific actions on gene expression.
These studies have compared the gene regulatory activities of ER␣
and ER␤ in U2OS cells and showed that ER␣ and ER␤ share some
common target genes, although each receptor also appears to have
distinct sets of downstream target genes (Monroe et al., 2003, 2005;
Stossi et al., 2004).
The differential effect of ligands in regulating ER␣ and ER␤
gene activation has been studied using a similar system. It was
demonstrated that ER␣ requires ligand to regulate gene expression. In contrast, ER␤ mediated regulation of gene expression can
be divided into three classes dependent on its ligand requirement:
class I genes are regulated primarily by un-liganded ER␤. Class II
genes are regulated by ER␤ only in the presence of E2, whereas
class III genes are regulated by ER␤ in the presence and absence of
ligands. Differences in gene regulation by unliganded and liganded
ER␤ are mainly due to interactions with different transcriptional
co-regulators and not to differential binding of ER␤ to DNA (Vivar
et al., 2010).
To investigate the impact of ER␤ on gene networks controlled
by ER␣, a model system was created by introducing ER␤ into the
ER␣ positive human breast cancer cell line MCF7. Gene expression
profiling analysis revealed that un-liganded ER␤ could regulate the
expression of many genes that were normally regulated by estrogen through ER␣ suggesting that ER␤ has a significant impact on
ER␣ mediated gene expression. The effects of liganded ER␤ on ER␣
mediated gene regulation could not be studied in this system due
to the lack of ligands with appropriate ER␤ selectivity. In this model
system, the regulation of genes involved in the TGF␤ pathway, cell
cycle progression and apoptosis may contribute to the suppression
of cell proliferation observed with ER␤ expression (Chang et al.,
2006).
In in vivo models, gene expression profiling of bone and liver
tissues isolated from wild type mice and mice lacing ER␣ or ER␤,
supports repressive effects of ER␤ on ER␣ mediated gene expression (Lindberg et al., 2003).
3.3. MicroRNAs; new players in estrogen controlled gene
networks
MicroRNAs (miRNAs) are short (approximately 22 nucleotides)
naturally occurring non-coding RNAs. They usually act as endogenous repressors of target genes by either inhibiting translation or
causing mRNA degradation through base-pairing with 3 untranslated regions of target mRNAs. miRNAs play critical roles in various
cellular processes including development, differentiation and various diseases, including a wide spectrum of cancers (Friedman and
Jones, 2009).
Recent studies show that miRNAs can be regulated by estrogens
in human breast cancer cells, human endometrial stromal cells,
myometrial smooth muscle cells, rat mammary gland and mouse
uterus (Klinge, 2009). Through a genome wide-approach, miRNAs
encoded by primary transcripts pri-mir-17-92 and pri-mir-106a363 were found to be up-regulated after estrogen treatment in
the human breast cancer cell line MCF7 (Castellano et al., 2009).
Estrogen has been shown to down-regulate the expression of a
set of miRNAs in mice tissues and human cultured cells. Further investigation showed that estrogen-bound ER␣ could inhibit
the maturation of miRNAs by targeting the processing of primary
miRNA into pre-miRNA through estrogen-dependent association
with the Drosha complex (Yamagata et al., 2009). miRNAs as important components of estrogen regulated gene networks has been
high-lighted by the recent findings that miRNAs including miR107, miR-424, miR-570, miR-618 and miR-760 are regulated by E2.
These miRNAs can target a significant number of transcripts belonging to one or more estrogen-responsive gene clusters in breast
cancer cells (Cicatiello et al., 2010).
4. The cis-regulatory code of estrogen controlled gene
networks
Genome wide expression analysis will reveal genes that are regulated by estrogen. Combining this analysis with a global analysis
of ER DNA-binding regions will aid in the discrimination of direct
versus indirect estrogen target genes.
Chromatin Immunoprecipitation (ChIP) has emerged as a
powerful technique to detect the binding of transcription
factors/co-factors to DNA in intact chromatin in vivo. Combined with DNA microarray technology (ChIP-on-chip) or DNA
sequencing technology (ChIP-seq), ChIP provides a powerful highthroughput method for genome-wide mapping of protein–DNA
interactions in vivo. In general, ChIP-seq offers some potential
advantages over ChIP-on-chip, including potential for lower cost,
minimal hands-on processing and less input material. Overall,
the ChIP-seq data have a high degree of similarity to the results
obtained by ChIP-on-chip for the same type of experiment. However, usually a larger number of binding regions were mapped
in ChIP-seq experiments (Mardis, 2007). It has been shown that
regions that overlapped between platforms had significantly higher
array signal or number of sequence reads than regions that were
unique to either platform (Robertson et al., 2007).
Recently, ChIP-on-chip and ChIP-seq have been employed to
detect ER binding sites in intact chromatin. Currently, whole
genome wide investigations were primarily focused on ER␣ and
the widely used model is MCF7 cells (Table 1) (Ross-Innes et al.,
2010; Carroll et al., 2006; Lin et al., 2007; Gao et al., 2008; Krum
et al., 2008; Fullwood et al., 2009; Welboren et al., 2009b). Comparison of one ChIP-seq dataset (Welboren et al., 2009b) with one
ChIP-on-chip dataset (Lupien et al., 2008) shows a substantial over-
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Table 1
Summary of published whole genome wide analysis of ER␣ binding sites.
Reference
Model system
Technique
Number of ER binding regions
Distribution of ER binding regions
Carroll et al. (2006)
Lin et al. (2007)
MCF7 cells
MCF7 cells
ChIP-on-chip
ChIP–PETs
3665 (MAT and U test)
1234
Krum et al. (2008)
Gao et al. (2008)
U2OS-ER␣ cells
Mouse liver tissue
ChIP-on-chip (chr1 and chr6)
ChIP-on-chip
1137
5568 (TAS)
Welboren et al. (2009b)
Ross-Innes et al. (2010)
Fullwood et al. (2009)
MCF7 cells
MCF7 cells
MCF7 cells
ChIP-seq
ChIP-seq
ChIA–PET
10205
14505
14468
4% within 1 kb upstream of TSS
5% within 5 kb upstream of TSS. 38%
overlap genes
NA
6% within 1 kb upstream of TSS. 50%
overlap Refseq genes
7% in the promoter. 41% in the intron
NA
NA
lap where around 60% of the sites identified by ChIP-on-chip were
also identified by ChIP-seq (Welboren et al., 2009a,b). A comparison of two recently published ChIP-seq datasets (Ross-Innes et al.,
2010; Fullwood et al., 2009), each dataset including around 14,000
identified ER␣ binding regions in MCF7 cells after 45 min or 1 h
E2 treatment, shows an overlap of around 60%. Due to a variety
of factors such as different technology for mapping enriched DNA
sequences, the use of different antibodies for ChIP and the expected
biology variation (including differences in cell culture conditions
and tissue origin), there are discrepancies in the number of binding
regions identified in the different studies. However overall, these
studies have revealed global features of functional cis-regulatory
elements that mediate estrogen action in the context of chromatin.
Particularly, the distribution of ER␣ binding regions at locations far
away from identified transcription start sites (TSSs) was observed
in both breast cancer cell lines and mouse liver (Carroll et al., 2006;
Gao et al., 2008; Welboren et al., 2009a,b). The majority of ER binding regions is located in intergenic and intronic regions (70–80%)
and only a small percentage (7%) is located in the 5 kb upstream
region proximal to the TSS. The distribution of ER binding regions
presents a major challenge to assign ER binding events to target gene regulation. However, a correlation between ER binding
regions and gene regulation has been reported for binding regions
within 50 kb of TSSs. Interestingly, the recruitment of ER was biased
towards estrogen up-regulated genes (89%) compared with estrogen down-regulated genes (47%) (Biddie et al., 2010; Carroll et al.,
2006; Welboren et al., 2009a,b). A similar distributed location of
binding regions has also been reported for other nuclear receptors
and it might be a common character of most transcription factors
(Biddie et al., 2010).
Recently, using a newly developed approach, chromatin interaction analysis by paired-end tag sequencing (ChIA–PET), a
comprehensive map of the chromatin interaction network around
genome bound by ER␣ has been described. In this study, most
high confidence distal ER␣ binding sites were anchored at gene
promoters through long-range chromatin interactions, suggesting
that ER␣ functions by extensive chromatin looping to bring genes
together for coordinated transcriptional regulation (Fullwood et al.,
2009).
Although cross-species conservation has been successfully used
to identify functional regulatory sequences in the genome, only a
minority of the identified ER␣ binding regions appear to be conserved at the sequence level between species (Bourque et al., 2008).
Notably, an association of ER binding regions with genomic repeats
has been found for ER␣ binding regions (Bourque et al., 2008).
Motif-finding algorithms identified the ERE, and variants thereof,
together with binding sites for Ap1, basic-helix-loop-helix proteins, and forkhead proteins as the most common motifs present in
identified ER␣-binding regions for both the human and the mouse
system. The enrichment of binding sites for other transcription
factors in ER binding regions highlights the role of interactions
between ER and other signaling pathways for the ultimate gene
regulatory networks controlled by estrogen. A detailed analysis
of the DNA sequence of ER␣-bound DNA regions revealed that
approximately 50% of ER␣-bound regions do not include a discernable ERE and might represent ER binding to atypical EREs or
sites of ER tethering to DNA via other transcription factors. Furthermore, the majority of ER-bound EREs are not perfect consensus
EREs and 75% of those identified ERE sequences have a 10–20%
nucleotide divergence (3–4 mismatch residues) from the 15 bp consensus ERE sequence (AGGTCAnnnTGACCT). In addition, the 3-bp
spacer between the inverted ERE half-sites, rather than including
random nucleotides, is C(A/T)G-enriched. Furthermore, about onethird of the ER␣-bound ERE sequences reside within repetitive DNA
sequences, most commonly belonging to the AluS family (Mason et
al., 2010). The repeat-associated binding sites have been shown
to possess significant regulatory potential throughout the mammalian species and are likely to be ancestral to mammals (Bourque
et al., 2008).
In general, estrogen regulated gene expression shows a tissue specific pattern in line with the function of the tissue and
the tissue specific-effects of estrogen. Mechanisms of tissue specificity for estrogen signaling may include differences in ER␣ and
ER␤ levels, differential co-activator recruitment and/or cell typespecific metabolism of estrogens (Simpson et al., 2001; Shang
and Brown, 2002; Monroe et al., 2005). Recently, a genome wide
scan of ER␣ binding regions using ChIP-on-chip in two different cell lines demonstrated that the vast majority of ER binding
regions are cell type specific and correlate both in position and
number with cell type specific regulation of gene expression. This
finding suggests that the recruitment of specific ERs to the regulatory regions of target genes is an important mediator of the cell
type specific action of estrogen on gene expression (Krum et al.,
2008).
Analysis of epigenetic modifications has further contributed
to our understanding of how ER␣ distinguishes between binding
regions in two cell types (Krum et al., 2008). Before E2 treatment,
active chromatin marks (dimethylation at histone 3, lysine 4) or
heterochromatin marks (dimethylation at histone 3, lysine 9) of
enhancers correlate with ER␣ binding to DNA.
An important role of FoxA1 as a pioneering factor in facilitating
ER␣ binding to chromatin in MCF7 cells has been demonstrated
(Lupien et al., 2008). In contrast, in U2OS cells, FoxA1 is not
expressed and the forkhead motif was not enriched at ER-binding
regions (Krum et al., 2008). This might imply an important role for
cell-specific transcription factors in mediating cell-specific binding
of ERs to chromatin.
The binding sites for the two ERs, ER␣ and ER␤ are partly overlapping in MCF7 cells that are engineered to express ER␤ to a similar
level as the endogenous ER␣ (Liu et al., 2008). However, the binding
regions for the two different receptors showed distinct properties
in terms of genome landscape, sequence features, and conservation.
For example, TA-rich motifs are overrepresented in the ER␣ binding
sites and the GC-rich motifs are enriched in ER␤ binding sites. Differences in the properties of ER bound regions might explain some
of the differences in gene expression programs and physiological
88
H. Gao, K. Dahlman-Wright / Molecular and Cellular Endocrinology 334 (2011) 83–90
effects shown by the respective ERs (Liu et al., 2008). Examination of
the effects of ligand occupied and unoccupied ER␣ and ER␤ on chromatin binding highlight the dynamic interplay between the two ERs
in their selection of binding sites (Charn et al., 2010). When present
alone, there was substantial overlap in binding regions between
both ERs, but the number of overlapping regions decreased when
both ERs were present. The presence of ER␤ had a limited effect on
ER␣ binding to chromatin. However, ER␣ had a profound effect on
ER␤ binding to chromatin such that, ER␤ now occupies many novel
sites. This dynamic interplay provides new insight for our understanding of the mechanisms by which ERs modulate each other in
target cells.
5. Co-factors for gene regulatory networks controlled by
estrogen
Recruitment of co-regulatory proteins to ERs is required for
ER-mediated transcriptional activities and subsequent biological
effects (Hall and McDonnell, 2005). These co-factors, such as histone acetyltransferase p300 and steroid receptor co-activators
(SRCs), enable the ERs to communicate with the general transcription apparatus, regulate chromatin modifications and ultimately
regulate the expression of specific genes (Hall and McDonnell,
2005; Kininis and Kraus, 2008). Co-factors have been proposed as
key players in mediating ligand- and tissue-selective activities of
the ERs. However, the genome wide recruitment of co-regulators in
the context of estrogen signaling has only recently been explored.
In a recent study, a strong correlation of ER␣ binding and SRC
recruitment has been shown across the genome. Nearly all of the
SRC binding sites were occupied by ER␣ in MCF7 cells. Interestingly, E2-dependent recruitment of ER␣ and SRC are only found
at promoters of E2-stimulated genes, while SRC recruitment is not
observed at E2-repressed genes (Kininis et al., 2007; Kininis and
Kraus, 2008). The effects of co-factors on estrogen controlled gene
networks remain to be explored.
The retinoic acid receptor ␣ (RAR␣), another member of the
nuclear receptor protein family, has been shown to be an ERassociated protein required for maintaining cofactor interactions
but not for ER recruitment to DNA (Ross-Innes et al., 2010).
It was shown that RAR␣ is required for efficient ER-mediated
transcription and cell proliferation. RAR␣ acted as part of the
ER transcriptional complex to maintain cofactor interactions. ER
recruitment to chromatin was not affected by inhibiting the expression of RAR␣.
Traditional models of ER transcriptional regulation tend to
involve a static, direct or indirect, association of ligand activated ERs
with DNA that will serve as a platform for assembly of co-regulatory
protein complexes that facilitate the initiation of transcription
(Fowler and Alarid, 2004; Hinojos et al., 2005). However, the traditional model has been challenged by the observation that ER␣
and co-factors bind endogenous target genes in a cyclical manner,
showing a periodicity of 40–60 min (Shang et al., 2000; Metivier
et al., 2003; Reid et al., 2003; Liu and Bagchi, 2004). These studies introduce a kinetic model for transcriptional activation by ER␣.
Through a careful analysis of the coordinated recruitment of 46 coregulators to the estrogen-responsive pS2 gene promoter in MCF7
cells, a comprehensive picture of events resulting in transcriptional
activation by estrogen has been provided (Metivier et al., 2003).
This report shows the multiple ER␣ complexes can be identified for
one promoter and that the cofactors comprising those complexes
can change with time. Furthermore, three different protein recruitment cycles have been defined for the pS2 promoter in the presence
of estrogen. An initial unproductive cycle prepares the promoter for
subsequent transcription followed by two different transcriptionally productive cycles (Metivier et al., 2003, 2006). New insights
that arise from viewing ER transcriptional regulation as a dynamic
process provide new opportunities in understanding and developing new strategies that modulate the activity of the ER (Johnsen et
al., 2006).
6. Conclusions
Estrogen signaling controls gene regulatory networks in a
complex process that can be regulated at many levels including
ligand-binding, DNA-binding and co-factor recruitment.
We have just begun to describe the gene regulatory networks,
including global DNA-binding and global gene expression by ERs,
in specific model systems under a limited number of conditions.
Future studies will need to explore time courses of estrogen regulated genes thus facilitating the identification of primary, secondary
and higher order estrogen regulated genes, finally revealing the
gene regulatory networks. Defining gene regulatory networks in
different tissues will reveal the molecular basis for tissue-specific
effects of estrogen. Furthermore, comparative data between different species will contribute to our understanding of species selective
estrogen signaling and ultimately for species selective effects of
estrogen. Recent developments in high throughput sequencing
technologies will allow assaying the complete repertoire of estrogen regulated transcripts and thus add a level of complexity to most
so far published studies that focus on annotated mRNAs. These
technological developments will need to develop in association
with developments in analysis strategies that will be compatible
with the vast amount and the complexity of the generated data.
The identification of gene regulatory networks controlled by estrogen will increase our understanding of the molecular mechanism of
estrogen signaling in different tissues and is also likely to have diagnostic and therapeutic implications. We can envision applications
in personalized medicine and identification of novel therapeutic
targets.
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