Glucocorticoid receptor-dependent gene regulatory networks.

Abstract

While the molecular mechanisms of glucocorticoid regulation of transcription have been studied in detail, the global networks regulated by the glucocorticoid receptor (GR) remain unknown. To address this question, we performed an orthogonal analysis to identify direct targets of the GR. First, we analyzed the expression profile of mouse livers in the presence or absence of exogenous glucocorticoid, resulting in over 1,300 differentially expressed genes. We then executed genome-wide location analysis on chromatin from the same livers, identifying more than 300 promoters that are bound by the GR. Intersecting the two lists yielded 53 genes whose expression is functionally dependent upon the ligand-bound GR. Further network and sequence analysis of the functional targets enabled us to suggest interactions between the GR and other transcription factors at specific target genes. Together, our results further our understanding of the GR and its targets, and provide the basis for more targeted glucocorticoid therapies.

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Glucocorticoid Receptor-Dependent Gene
Regulatory Networks
Phillip Phuc Le1, Joshua R. Friedman1,2, Jonathan Schug3, John E. Brestelli1, J. Brandon Parker1, Irina M. Bochkis1,
Klaus H. Kaestner1*
1 Department of Genetics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States of America, 2 Department of Pediatrics, University of
Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States of America, 3 Center for Bioinformatics, University of Pennsylvania School of Medicine,
Philadelphia, Pennsylvania, United States of America
While the molecular mechanisms of glucocorticoid regulation of transcription have been studied in detail, the global
networks regulated by the glucocorticoid receptor (GR) remain unknown. To address this question, we performed an
orthogonal analysis to identify direct targets of the GR. First, we analyzed the expression profile of mouse livers in the
presence or absence of exogenous glucocorticoid, resulting in over 1,300 differentially expressed genes. We then
executed genome-wide location analysis on chromatin from the same livers, identifying more than 300 promoters that
are bound by the GR. Intersecting the two lists yielded 53 genes whose expression is functionally dependent upon the
ligand-bound GR. Further network and sequence analysis of the functional targets enabled us to suggest interactions
between the GR and other transcription factors at specific target genes. Together, our results further our
understanding of the GR and its targets, and provide the basis for more targeted glucocorticoid therapies.
Citation: Le PP, Friedman JR, Schug J, Brestelli JE, Parker JB, et al. (2005) Glucocorticoid receptor-dependent gene regulatory networks. PLoS Genet 1(2): e16.
Introduction
Glucocorticoids are essential steroid hormones that are
secreted by the adrenal cortex and affect multiple organ
systems. Among these effects are the ability to depress the
immune system, repress inflammation, and help mobilize
glucose in the fasting state. Glucocorticoids and their
synthetic analogs are widely prescribed for adrenocortical
insufficiency and as an immune suppressant/anti-inflamma-
tory agent, but their systemic effects can often be debilitating.
An understanding of the genes regulated by the glucocorti-
coid signaling pathway may lead to more targeted therapies,
thereby preventing unwanted side effects.
Glucocorticoids act via a signaling pathway that involves
the glucocorticoid receptor (GR), a member of the nuclear
receptor superfamily of ligand-activated transcription factors
[1,2]. In the absence of glucocorticoids, the GR is sequestered
in the cytoplasm by a protein complex that includes heat
shock protein 70 (HSP70) and HSP90. When glucocorticoids
are present, they traverse the plasma membrane and bind to
the GR, allowing the GR to dissociate from its chaperone
proteins and translocate to the nucleus. Within the nucleus,
the ligand-bound GR can bind to DNA as a monomer or as a
dimer to palindromic glucocorticoid response elements
(GREs) and modulate transcription [3–7].
The mechanisms of action of the ligand-bound GR are
fairly complex, including the ability to both activate and
repress transcription, and to interact with other transcrip-
tional regulators such as activating protein-1 (AP-1) and
nuclear factor kappa B (NF-jB) (reviewed in McKay and
Cidlowski [5]). The net effect of glucocorticoid administra-
tion on a particular target gene is likely dependent upon the
other transcription factors present on the target gene’s
promoter or enhancer(s). Specifically, the integration of
multiple signaling pathways can occur at glucocorticoid
response units (GRUs), which consist of a combination of a
GRE and other transcription factor binding sites. Examples of
these include GRUs in the promoters of the phosphoenolpyr-
uvate carboxykinase (Pck) and carbamoylphosphate synthetase (Cps)
genes [8–10]. Thus, understanding the complete nature of
glucocorticoid action requires knowing not only the set of
genes bound and regulated by the GR, but also the tran-
scription factors that may interact with the GR, and the loci
where these interactions occur.
To better understand glucocorticoid signaling, RNA
expression profiling after glucocorticoid administration has
been performed by several groups [11–16]. However, it is
impossible to establish which differentially expressed genes
are direct targets of the GR and which are controlled by
downstream effectors. To address this limitation, Wang and
colleagues have developed a technique termed ‘‘ChIP scan-
ning,’’ which involves screening the promoter region of each
putative target gene individually using chromatin immuno-
precipitation (ChIP) and quantitative real-time PCR (QPCR)
[17]. Unfortunately, this technique is not high-throughput,
but instead involves designing multiple primer sets for each
potential target gene individually. Thus, this technique is not
suitable for global network analysis.
A modification of microarray technology utilizes spotted
promoter regions instead of cDNA sequences. After ChIP
Received February 23, 2005; Accepted June 16, 2005; Published August 5, 2005
DOI: 10.1371/journal.pgen.0010016
Copyright: ? 2005 Le et al. This is an open-access article distributed under the
terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author
and source are credited.
Abbreviations: AP-1, activating protein-1; ChIP, chromatin immunoprecipitation;
DEB, differentially expressed, GR bound; EASE, Expression Analysis Systematic
Explorer; GO, Gene Ontology Biological Function; GR, glucocorticoid receptor; GRE,
glucocorticoid response element; GRU, glucocorticoid response unit; HSP, heat
shock protein; kb, kilobase(s); NCBI, National Center for Biotechnology Information;
NF-jB, nuclear factor kappa B; OPG, osteoprotegerin; PWM, position-specific
weight matrix; QPCR, quantitative real-time PCR; RefSeq, NCBI reference sequence;
RT-QPCR, reverse-transcription quantitative real-time PCR; SACO, serial analysis of
chromatin occupancy
Editor: Greg Gibson, North Carolina State University, United States of America
*To whom correspondence should be addressed. E-mail: Kaestner@mail.med.
upenn.edu
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with an antiserum raised against a particular transcription
factor, the immunoprecipitated DNA is amplified, labeled,
and hybridized against these promoter microarrays. Spots
that are brighter in the immunoprecipitated channel than the
control represent promoter sequences to which the tran-
scription factor is bound. This technique, termed ‘‘genome-
wide location analysis,’’ or ‘‘ChIP-on-Chip’’ has been utilized
to determine binding sites for several transcription factors in
yeast [18,19], and higher organisms [20–25]. However, binding
data alone do not prove that the transcription factor of
interest is important in the regulation of a particular target
gene. This is especially true in higher eukaryotes, where many
genes are regulated by dozens of partly redundant tran-
scription factors [26]. Therefore, in order to identify direct
targets of a given transcription factor that are dependent on
the regulatory protein in question, a combination of location
analysis and expression profiling is required.
Given the importance of the glucocorticoid signaling path-
way in biology and medicine, we have undertaken a study to
determine functional GR targets. We performed parallel
mRNA expression profiling and location analysis on livers of
fasted mice injected with glucocorticoids and compared the
results with those of fed controls. Individually, the expression
analysis and the location analysis each produced lists of genes
that included many known or suspected GR targets. By
combining the expression and binding data, we were able to
identify 53 direct functional GR targets, many of which are
novel. In addition, network and sequence analysis of the GR
targets independently suggested functional interactions be-
tween the GR and several other transcription factors. Through
these experiments we have extended the understanding of the
complexity of the genetic networks modulated by glucocorti-
coids.
Results
Identification of GR Targets by Expression Profiling
The experimental paradigm we chose to use for parallel
expressionprofilingandlocationanalysisisoutlinedinFigure1.
Treated mice were fasted overnight and injected with
dexamethasone, a synthetic glucocorticoid. Control mice
were fed ad libitum and not injected with vehicle, as this
would generate a stress response. Treated and control mice
were sacrificed, and the left lobe of their livers was removed
for parallel expression and genome-wide location analysis.
Several alternative experimental designs were initially
considered and ultimately discarded. First, we opted to
evaluate the networks controlled by glucocorticoids in
hepatocytes in mice and not in hepatoma cell lines grown
in culture. While this approach was technically more
challenging, it was necessary because available cell lines do
not reflect the metabolic regulation of hepatic gene expres-
sion accurately [27,28]. Second, it was important to compare
mice in different feeding states due to the significant
differences in the levels of insulin, glucagon, and glucocorti-
coids that are present in each state. One alternative design
would have been to compare dexamethasone-injected fed
mice to fed controls. When we performed a preliminary
expression analysis using this design, the levels of many well-
known GR targets, such as Pck and insulin-like growth factor
Figure 1. Experimental Paradigm for Orthogonal Analysis of Glucocorti-
coid Receptor Targets
Treatment mice were fasted overnight, then given intraperitoneal
injections with dexamethasone. Treatment and control liver lobes were
splitinhalfandprocessedforbothRNAandchromatin.RNAwassubjected
to microarray analysis using the PancChip 5.0 cDNA microarray, which
contains over 13,000 transcripts. Location analysis was performed by
immunoprecipitating with antiserum raised against the GR. ChIP material
was amplified, fluorescently labeled, and hybridized against sheared
genomic DNA using the Mouse PromoterChip BCBC-3.0 promoter micro-
array, which contains approximately 7,000 genomic promoter elements.
DOI: 10.1371/journal.pgen.0010016.g001
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Glucocorticoid Receptor Networks
Synopsis
Glucocorticoids are essential steroid hormones, and synthetic
glucocorticoids are widely prescribed for a variety of medical
conditions. Understanding the mechanism by which glucocorticoids
act requires knowing the direct target genes whose expression
levels are modulated by the glucocorticoid signaling pathway. In
this publication, Le and colleagues have utilized two high-
throughput techniques to determine genes directly regulated in
vivo by the glucocorticoid receptor (GR). RNA and chromatin were
extracted from the livers of mice injected with the synthetic
glucocorticoid dexamethasone and compared to control littermates.
The analysis of RNA expression levels generated a list of genes
differentially expressed after addition of dexamethasone. The
analysis of the chromatin produced a list of gene promoter
sequences where the GR was bound to DNA. By intersecting the
two lists, the researchers obtained a list of genes that are directly
controlled by the GR, including several previously known targets.
This list of direct targets was then used as the basis for complex
pathways and sequence analyses, which suggested several inter-
actions between the GR and other transcription factors. This study
provides an evaluation of a medically important signaling pathway
and serves as a model for future analyses of transcriptional
regulation.

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binding protein 1 (Igfbp1), were unchanged (unpublished data).
These results are consistent with the well-described ability of
insulin signaling to inhibit glucocorticoid activation of many
targets, such as Pck, Igfbp1, glucose-6-phosphatase (G6pc), and 6-
phospho-2-fructokinase (Pfk2) (reviewed in [29]). In addition to
the presence of the inhibitory effects of insulin, an
experimental design in which both groups are fed risks a
significant loss of sensitivity due to a lack of fasting-induced
signals that act synergistically with glucocorticoids, such as
glucagon. Another alternative design would have been to
compare mice fasted and injected with dexamethasone to
fasted control mice. However, since endogenous glucocorti-
coids are released in the fasted state, the chromatin from the
control samples could not serve as negative controls for the
location analysis. Therefore, we elected to compare dexame-
thasone-injected fasted mice to fed controls.
Treated and control liver lobes were split in half. RNA was
extracted from one half, while chromatin was prepared from
the remaining half. Reverse-transcription QPCR (RT-QPCR)
was performed to measure relative expression levels of
several targets known to be induced by glucocorticoids in
fasted mice. As expected, the mRNA levels of these targets,
including Pck, tyrosine aminotransferase (Tat), and Igfbp1, were
induced between 6- and 60-fold relative to the levels in the
fed control mice (unpublished data).
The RNA was then used to perform a microarray hybrid-
ization using the PancChip 5.0 expression microarray [30]. Of
the 13,000 transcripts on the array, approximately 1,300
unique genes were differentially expressed between the two
conditions at a false discovery rate of 10% (complete dataset
available in Datasets S1 and S2). Of those, about 30% were
up-regulated in the dexamethasone-injected livers, while the
majority was repressed. Again, the list of differentially
expressed genes included many known GR targets, including
Pck, Igfbp1, and metallothionein 2 (Mt2) (unpublished data).
However, it is unclear which of these differentially expressed
genes are direct targets of the GR.
Global Analysis of GR Occupancy In Vivo
Chromatin immunoprecipitation, performed using an
antiserum raised against the GR, was compared to preim-
mune rabbit IgG to assess the specificity of the antibody at
two known GR targets, Mt2 and Tat (Figure 2A). This antibody
was then utilized in immunoprecipitations with chromatin
from both the dexamethasone-treated and the fed control
samples. GR occupancy on those same two targets, as
measured by QPCR, was increased by approximately 4-fold
and 13-fold, respectively, confirming the efficiency of the
ChIP (Figure 2B). Next, we amplified the immunoprecipitated
DNA by two rounds of ligation-mediated PCR, and confirmed
that the enrichment of the known GR targets was maintained
after amplification (unpublished data). Amplified samples
were then fluorescently labeled and hybridized against
sheared genomic DNA on the Mouse PromoterChip BCBC-
3.0 promoter microarray, which contains PCR amplicons of
two promoter regions for over 3,300 genes important in liver
function. The first PCR amplicon, called tile 1, is approx-
imately 1 kilobase (kb) in length and is located immediately
upstream of the putative transcriptional start site. The
second amplicon, tile 2, is approximately 2 kb in length and
is located immediately upstream of tile 1. In total, we spotted
onto the microarray approximately 3 kb of genomic
promoter sequence for each of the 3,300 genes.
The location analysis identified 318 promoter regions,
representing 302 distinct genes, enriched in the immunopre-
cipitations from dexamethasone-treated samples. This list of
GR targets (Table S1) contains many genes previously shown
to be regulated by GR, including Pck, Igfbp1, tumor necrosis
factor (Tnf), and hormone-sensitive lipase. The Gene Ontology
(GO) Biological Function categories of the GR-bound genes
are shown in Figure 3. The GR targets were also assessed for
statistical enrichment of GO Biological Function categories.
The ten most enriched GO Biological Function categories are
shown in Table 1. These categories are dominated by genes
Figure 2. ChIP Identifies Known GR Targets in Liver
(A) Agarose gel electrophoresis of PCR products for the known GREs in
Mt2 and Tat confirm the specificity of the anti-GR antibody (sc-1002,
Santa Cruz) compared to the control preimmune IgG. The PCR product
for the genomic locus encoding the 28S ribosomal RNA shows that equal
amounts of DNA were loaded into each reaction.
(B) QPCR was used to measure the enrichment of Mt2 and Tat in
chromatin immunoprecipitated with the anti-GR antiserum from five
dexamethasone-treated samples compared to five fed controls, as
described in Materials and Methods. The 28S PCR product was used to
normalize the samples.
DOI: 10.1371/journal.pgen.0010016.g002
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Glucocorticoid Receptor Networks

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important in metabolism, consistent with the function of
glucocorticoids in the liver. It is also important to note that
within the GO function hierarchy, some genes belong to
multiple categories.
We confirmed several of the targets identified in our
location analysis by measuring their enrichment in the
original immunoprecipitated DNA using QPCR. Two com-
putational programs, NUBIScan [31] and TESS (http://
www.cbil.upenn.edu/tess) were used to identify likely GR
binding sites within the spotted promoter regions. QPCR
was used to calculate the fold-enrichment of these genomic
loci in the unamplified, immunoprecipitated DNA of treated
samples compared to controls. The results are shown in
Figure 4. Of the 14 samples, 12 (85%) randomly chosen
promoters were enriched to the level of significance,
confirming the validity of our location analysis. Further-
more, it is possible that the remaining promoters have true
GR binding sites, but at loci that do not match the
consensus sequence well and thus were not assessed. We
also noted that the fold-enrichment measured by QPCR was
generally greater than that measured by the promoter
microarray. This ‘‘compression effect’’ has been previously
described in cDNA and oligonucleotide microarrays [32].
To further evaluate our list of GR-bound promoters, we
obtained a list of more than 50 previously published GR
targets [5], 12 of which contain one or more GRE consensus
sites within the sequences that are present on our promoter
array. If the location analysis had produced a random set of
302 genes (302/3300 [approximately 9%]), then of these 12
known targets, approximately one gene could be expected
to be on our list. However, of these 12, eight (67%) were
identified by our location analysis as occupied by the GR in
the liver in vivo (p , 2 3 10?6), confirming the usefulness of
our approach.
Integrating Expression and Binding Data Results in
Functional GR Targets
In order to determine the subset of genes that are direct
and meaningful targets (i.e., are changed in their expression
level) of the activated GR in the liver, we identified the
overlap of the GR-bound and GR-regulated genes (Figure 5).
There are approximately 2,500 genes common to both array
platforms used. Of these, 498 were differentially expressed
and 235 were bound by the GR. Intersecting the two sets
resulted in 53 genes that were both differentially expressed
and bound by the GR. These represent direct, functional
targets of the activated GR, which we hereafter refer to as
the differentially expressed, GR bound (DEB) set. All of
these genes are listed in Table 2, and include several
published GR targets, including alcohol dehydrogenase 1 (Adh1),
Pck, and Igfbp1, as well as likely GR target genes such as
catalase (Cat) [33]. A more thorough search of the literature
and use of expression data from a previously published
experiment in a different tissue [34], indicates that 22 of the
53 genes have been shown to be differentially expressed
after the addition of glucocorticoid. Thus, 31 of our target
genes appear to be novel GR targets.
Figure 3. Functional Categories of the Genes Generated by Location
Analysis
Location analysis was performed using antiserum against the GR. Three
treatedandfivecontrolsampleswereamplified,fluorescentlylabeled,and
hybridizedtotheMousePromoterChipBCBC-3.0promotermicroarrayina
common reference design. Standard statistical methods identified 302
promoter regions significantly enriched in the treated samples compared
to the fed controls (see Materials and Methods). The GO level 4 functional
categoryforeachgenewasretrievedandthetop20categoriesareshown.
Note that some genes belong to multiple GO categories.
DOI: 10.1371/journal.pgen.0010016.g003
Table 1. Enriched GO Biological Functional Categories
Category EASE Score
Aromatic compound metabolism
Amine catabolism
Amino acid catabolism
Glucan metabolism
Glycogen metabolism
Polysaccharide metabolism
Energy reserve metabolism
Steroid metabolism
Glucan biosynthesis
Polysaccharide biosynthesis
0.00682
0.01
0.0318
0.0318
0.0318
0.0318
0.0445
0.0215
0.0722
0.0722
The EASE analysis tool was used to determine enriched GO Biological Functional Categories within the set of 302 GR
targets. The top ten categories are shown, along with the EASE score, which represents a p-value corrected for
variations in category sizes.
DOI: 10.1371/journal.pgen.0010016.t001
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Network and Sequence Analysis Suggest Several Potential
GR-Protein Interactions and Sites
Pathway analysis can be a useful tool to help uncover
relationships among genes [35], such as the finding that
multiple members of the list may be regulated by the same
transcription factor. When seeded with the genes in the DEB
set plus the GR itself, pathway analysis produced two
networks of genes with functional relationships. These were
merged together and are shown in Figure 6. Our data suggest
a direct interaction between the GR (NR3C1, large red box;
Figure 6) with the DEB gene set (genes in boldface).
Closer inspection revealed that many of the genes added to
the network encode transcription factors that are known to
physically interact with the GR on the promoters of
particular genes. Two well-studied examples include RelA
and Jun, which are members of the NF-jB and AP-1
transcriptional complexes, respectively. It has been suggested
that the activated GR modulates inflammation and the
immune response by physically interacting with NF-jB and
AP-1, thereby repressing the activation of many of their
targets [5,7]. In fact, recent work has shown that the LIM
protein TRIP6 is required for this interaction [36]. Other
transcription factors identified by the pathways analysis that
are known to interact with the GR to modulate particular
genes include Stat5 [37,38], FoxA family members [39,40],
HNF6 [41], PPARc [42], and Ets family members [43,44]. This
suggests that the other transcription factors in the network,
such as Myc and HNF4a, may also interact with the GR to co-
regulate target genes. Thus, by combining a database of
genetic relationships with a set of direct GR targets, we are
able to suggest interactions between the GR and other
transcription factors.
Next, we analyzed the sequences of the promoters in the
DEB set, searching for enriched transcription factor binding
sites. First, we scored the set of vertebrate transcription
factor position-specific weight matrices (PWMs) in the
TRANSFAC database [45] and in JASPAR [46] against the
entire set of tiles on the promoter array. We then determined
the ability of individual PWMs (representing single tran-
scription factor binding sites) to distinguish between the DEB
set and the background set. The GR PWM was enriched in the
DEB set compared to the entire set of promoters on the
microarray (p , 0.05), as well as compared to the promoters
of the 445 genes differentially expressed but not bound by the
GR (p , 0.01). For instance, at the particular score threshold,
66% of the DEB set contained a sequence surpassing the
threshold, compared to only 48% of the 445 genes differ-
entially expressed but not bound by GR.
Interestingly, the GR weight matrices (full-site and half-site)
were not the most significantly enriched matrices. Among the
remaining matrices representing vertebrate transcription
factors, the scores of the matrices for HNF4a and the GATA
family of transcription factors were more significant than the
GR matrices. It is likely that the significance of the half-site
GR consensus sequence, AGAACA [47], is hampered by the
high frequency with which it occurs in the background set of
DNA, while the full-site matrix scores probably suffer from
the fact that true GREs can vary significantly from the
published GRE consensus. For example, the functional GREs
in the promoter region of Pck scored poorly with the
consensus full-site matrix. As for the enrichment of other
matrices, it may be that in our DEB set the presence of
HNF4a binding sites is providing tissue specificity to the
glucocorticoid response.
Because it is known that the GR can interact with other
transcription factors to modulate gene expression, we
searched for significant combinations of binding sites for
either GR monomers or dimers and other nearby tran-
scription factors. This analysis resulted in several combina-
tions that were significantly enriched in the DEB set
compared to the set of all mouse promoter sequences on
the array (Table 3). Once again, the interaction between the
GR and the AP-1 transcriptional complex presented itself.
Figure 5. Intersection of Expression Data and Location Analysis
Of the genes common to both the cDNA microarray and the promoter
microarray, 498 were differentially expressed and 235 were bound by the
GR. Intersecting the two lists produced 53 genes in common. This list
represents direct, functional targets of the GR in hepatocytes.
DOI: 10.1371/journal.pgen.0010016.g005
Figure 4. Quantitative PCR Confirms GR Targets Identified by Location
Analysis
The enrichment of GR targets identified by location analysis was
measured in the original immunoprecipitated material by QPCR. The
graph shows the fold-enrichment of five dexamethasone-treated
samples compared to five fed controls. Of fourteen randomly selected
GR targets, 12 showed statistically significant enrichment. Fold-enrich-
ment and p-values were calculated as described in Materials and
Methods. *p , 0.05, **p , 0.01.
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Specifically, the combination of a GR monomer (not the
dimer) and an AP-1 site occurring within 34 base pairs (bp)
was highly enriched in the DEB set (p , 10?7). This is
consistent with published reports that the GR monomer, and
not the dimer, physically interacts with AP-1 to repress
certain AP-1 target genes [48]. Several other combinations
discovered using this analysis have previously been shown to
occur, such as the interaction between GR and CCAAT/
enhancer binding protein beta (C/EBPb) [39], GR and YY1
[38] , and GR and Oct-1 [49].
We were particularly interested in the potential interaction
between the GR and the C/EBP family of transcription
factors, since we had previously performed location analysis
for C/EBPb in mouse liver [21]. Of the promoters in the DEB
set that contained a high-scoring combination of GR and
nearby C/EBP binding site, 11 had been analyzed previously
for C/EBPb binding. Strikingly, five (45%) of these genes,
namely beta-2 microglobulin (b2m), protein-tyrosine sulfotransferase
2 (Tpst2), HSP 1 beta (Hspcb), ATP-binding cassette sub-family A
member 1 (Abca1), and hydroxysteroid (17-beta) dehydrogenase 12
(Hsd17b12), had shown enrichment in that analysis of C/EBPb
ChIP compared to null controls. In other words, of the 11
promoters that were bound and regulated by the GR, had a
computationally predicted C/EBP site near a GR site, and
were evaluated in an independent experiment, almost half
are true C/EBPb targets. The remaining promoters might be
bound by one of the other C/EBP family members expressed
in hepatocytes. In any case, this example validates our
approach to computationally identifying transcription fac-
tors binding to complex GRUs in vivo.
Discussion
Glucocorticoids are widely used in medical therapy for
immunosuppression and as potent anti-inflammatory agents.
However, their broad effects on different organ systems often
result in debilitating side effects such as bone loss and glucose
dysregulation. Knowing the direct, functional targets of the
GR increases our understanding of the different mechanisms
by which glucocorticoids act and aids the development of
directed therapies toward different ‘‘arms’’ of the glucocorti-
coid response. To approach that aim, we have utilized two
high-throughput techniques to obtain orthogonal data sets,
allowing us to determine the set of genes regulated by the GR
in the liver in vivo. We have found hundreds of genes whose
promoters are occupied by the ligand-bound GR and, of
those, dozens that are differentially expressed in hepatocytes
in the presence of exogenous glucocorticoid. These func-
tional GR targets span the known range of glucocorticoid
action, containing both induced and repressed genes, as well
as genes involved in metabolism, signal transduction, and the
immune response/inflammation. By applying pathway and
sequence analysis, we have also generated a list of tran-
scription factors that may interact with the GR to modulate
transcription of target genes.
Our expression analysis resulted in approximately 1,300
differentially expressed genes. Of these genes, many are
Table 2. DEB Set
RefSeqDescription RefSeqDescription
NM_008256
NM_007380
NM_007408
NM_009627
NM_007409
NM_013474
NM_020577
NM_018794
NM_013454
NM_009735
NM_010238
NM_009804
NM_007744
NM_016904
NM_023143
NM_028850
NM_007840
NM_007853
NM_007830
NM_019427
NM_008033
NM_010321
NM_008086
HMG-coenzyme A synthase 2(Hmgcs2)
Abl-interactor 1(Abi1)
Adipose diff related protein (adfp)
Adrenomedullin (Adm)
Alcohol dehydrogenase 1 (class I) (Adh1)
Apolipoprotein A-II (Apoa2)
Arsenic methyltransferase (As3mt)
ATPase, Hþtransporting (Atp6ip1)
ATP-binding cassette, subfamily member A member 1 (Abca1)
Beta-2 microglobulin (b2m)
Bromodomain containing 2 (Brd2)
Catalase (Cat)
Catechol-O-methyltransferase (Comt)
CDC28 protein kinase 1 (Cks1b)
Complement component 1 (C1r)
Cysteine-rich hydrophobic domain 2 (Chic2)
DEAD box polypeptide 5 (Ddx5)
Degenerative spermatocyte homolog (Degs)
Diazepam binding inhibitor (Dbi)
Erythrocyte protein band 4.1-like 4b (Epb4.1l4b)
Farnesyltransferase, CAAX box, alpha (Fnta)
Glycine N-methyltransferase (Gnmt)
Growth arrest specific 1 (Gas1)
NM_008294
NM_010499
NM_008341
NM_010497
NM_008280
NM_021462
NM_008590
NM_019758
NM_016978
NM_011044
NM_011968
NM_008974
NM_009419
NM_011231
NM_017382
NM_016698
NM_013876
NM_013759
NM_009243
NM_019719
NM_011660
NM_012032
NM_009536
Hydroxysteroid dehydrogenase-4, delta,5.-3-beta (Hsd3b4)
Immediate early response 2 (Ier2)
Insulin-like growth factor binding protein 1 (Igfbp1)
Isocitrate dehydrogenase 1 (NADPþ), soluble (Idh1)
Lipase, hepatic (Lipc)
MAP kinase-interacting serine/threonine kinase 2 (Mknk2)
Mesoderm specific transcript (Mest)
Mitochondrial carrier homolog 2 (C. elegans) (Mtch2)
Ornithine aminotransferase (Oat)
Phosphoenolpyruvate carboxykinase 1, cytosolic (Pck)
Proteasome (prosome, macropain) (Psma6)
Protein tyrosine phosphatase 4a2 (Ptp4a2)
Protein-tyrosine sulfotransferase 2 (Tpst2)
RAB geranylgeranyl transferase, b subunit (Rabggtb)
RAB11a, member RAS oncogene family
Ring finger protein 10 (Rnf10)
Ring finger protein 11 (Rnf11)
Selenoprotein X 1 (Sepx1)
Serpina1a
STIP1 homology and U-Box containing protein 1 (Stub1)
Thioredoxin 1 (Txn1)
Tumor differentially expressed 1 (Tde1)
Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase
activation protein, epsilon polypeptide (Ywhae)
UDP-glucose ceramide glucosyltransferase (Ugcg)
Urate oxidase (Uox)
Wingless-related MMTV integration site 5A (Wnt5a)
NM_008302
NM_022331
NM_013547
NM_019657
Heat shock protein 1 beta (Hsp1b)
Herpud1
Homogentisate 1, 2-dioxygenase (Hgd)
Hydroxysteroid (17-beta) dehydrogenase 12 (Hsd17b12)
NM_011673
NM_009474
NM_009524
The set of genes differentially expressed between treatment and control mice was intersected with the set of genes generated by the location analysis, resulting in this list of 53 genes. These genes represent direct, functional targets of the GR
in mouse hepatocytes.
DOI: 10.1371/journal.pgen.0010016.t002
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Figure 6. A Regulatory Network for the GR
Pathway analysis was seeded with the 53 differentially expressed and GR-bound genes, plus the GR itself, as described in Materials and Methods. Genes
in colored, bold text were in the seed set, while all others were brought into the network by the pathway analysis program based on their known
relationships to the genes in the seed set. Color indicates induction (red) or repression (green) of expression.
DOI: 10.1371/journal.pgen.0010016.g006
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differentially expressed solely due to the change in the
feeding state of the animal. However, the alternative
experimental designs were unacceptable. In our preliminary
comparison of RNA from livers of mice that were fed versus
fed and dexamethasone-injected, the lack of differential
expression of known GR targets such as Pck and Igfbp1
suggested that an orthogonal analysis with this data set would
miss many potential targets. Since the goal of the study was
not to produce a list of genes differentially expressed after
glucocorticoid administration, which has been previously
published by others, but rather to perform an orthogonal
analysis utilizing both expression and location data from the
same tissue, we deemed it acceptable to trade-off specificity
for sensitivity. Moreover, by intersecting the expression and
location analysis data, it is likely that false positives within the
expression data were removed.
The list of 302 GR-bound promoters contains many genes
that are known or suspected GR targets. One important
category of target genes are transcriptional regulators,
including the homeobox genes Hoxa13, Hoxb4, Hoxc5, Hoxc9,
and Hesx1; transcription factor 15 (Tcf15), transcription factor 21
(Tcf21), and transcription factor AP-4 (Tfap4); Kru ¨ppel-like factor 3
(Klf3) and Klf15; as well as Trp53, Creb1, and Fos. In addition,
many metabolic enzymes are present, consistent with the
known role of glucocorticoids in regulating glucose metab-
olism.
One well-known effect of glucocorticoid administration is
decreased bone density [50]. Bone density is controlled by a
delicate balance between osteoclasts, which resorb bone, and
osteoblasts, which mineralize bone. Our location analysis
identified many GR targets that modulate bone remodeling,
including TNFa and TGFb signaling pathway members,
adrenomedullin (Adm), calumenin (Calu), and annexin 2 (Anxa2)
[51–56]. Of particular interest is the TNF receptor super-
family member osteoprotegerin (OPG), encoded by Tnfrsf11b.
OPG is a secreted glycoprotein that acts as a decoy receptor
for receptor activator of NF-jB ligand, impairing its
interaction with receptor activator of NF-jB and thereby
inhibiting osteoclast differentiation [57]. Thus, increased
levels of OPG promote increased bone density via decreased
osteoclast differentiation, while mice that are null for OPG
have decreased total bone density, severe bone porosity, and
high incidence of fractures [58]. In humans, it has been shown
that a deletion of the OPG gene can cause juvenile Paget’s
disease, an autosomal recessive osteopathy characterized by
debilitating fractures and deformities resulting from in-
creased bone turnover [59]. While it has previously been
shown that OPG is expressed in liver [60] and that
glucocorticoid administration lowers OPG mRNA levels
[61], our location analysis provides the first evidence that
glucocorticoids repress OPG expression by directly binding
to the OPG promoter. Interestingly, the protein encoded by
core binding factor beta (cbfb), another GR target identified by
our analysis, also plays an important role in bone develop-
ment, possibly via its interaction with core binding factor
alpha, a known regulator of OPG [62,63]. Overall, we have
demonstrated that our location analysis identified many
known and suspected GR targets that directly affect bone
remodeling, one of the major side effects associated with
prolonged glucocorticoid administration.
While our microarray-based location analysis was highly
accurate, with 85% of the targets confirmed by RT-QPCR, it
appears somewhat surprising that only about a quarter of the
GR-bound genes (53 of 235 for which we have expression
data) were differentially expressed. A small fraction of these
targets may be enriched due to nonspecific binding of the
ligand-bound GR to DNA, or false positives introduced by the
ligation-mediated PCR method used to amplify the material
prior to hybridization. Another possibility is that the
expression level of the gene may not have been high enough
to detect in our expression analysis, and that the use of a
more sensitive assay, such as RT-QPCR, would show more GR-
bound genes to be differentially expressed. We believe,
however, that the majority of these GR targets are likely to
be functional, but in a different context, such as another
tissue. The specific combination of other transcriptional
regulators present, as well as higher-order chromatin
structure, likely play a role in determining which glucocorti-
coid target genes are expressed and regulated. The complex
regulatory mechanism present in the GRU of the Pck gene is a
well-studied example [8]. Insulin signaling represses Pck
transcription, and this effect is dominant over the transcrip-
tional activation that is promoted by both glucocorticoid and
glucagon signaling pathways. Furthermore, Pck expression is
activated by glucocorticoids in the liver, but repressed by
glucocorticoids in adipose tissue [64]. Similar mechanisms
may be at work on many of the GR-occupied targets that did
not show expression level changes in our particular experi-
ment. It may be possible to expand our list of functional GR
targets by incorporating other expression data.
The use of weight matrices to predict transcription factor
binding sites without any prior knowledge is highly non-
specific, due to the degenerate nature of the binding sites and
the complexity of the eukaryotic genome [65]. Some attempts
to refine these predictions have utilized evolutionary con-
servation [66], while others have used functional information
such as coexpression [67]. Our analysis results demonstrate
that performing complex sequence analysis on a functional
set of promoters can result in more specific predictions
regarding transcription factor binding sites and can even
allow the prediction of binding sites for other transcription
factors that cooperatively regulate target genes.
An alternative to location analysis using a promoter
microarray is a technique developed by Impey and colleagues
Table 3. Significant Combinations of GR Monomer/Dimer and
Other Transcription Factors
Transcription
Factor
Companion
Consensus
p-ValueDistance
(Basepairs)
AP-1
ERR
Gata-1
YY1
CEBPb
E47
RORa
Oct-1
TGA[CG]TCAKC
T[GC]AAGGTCA
[TA]GATA[AG]
GCCATnTT
KnTTGCnYAAY
TCTGGMWT
WAWnnAGGTCA
ATGCAAATnA
1 3 10?7
2 3 10?5
3 3 10?5
2 3 10?5
2 3 10?6
4 3 10?6
1 3 10?6
5 3 10?7
34
84
44
124
34
224
144
114
The promoter sequences from the DEB set (Table 2) were searched for enriched combinations of the GR monomer
or dimer binding site and another transcription factor binding site within a short distance, as described in Materials
and Methods. Shown is the companion transcription factor, its consensus weight matrix (taken from TRANSFAC), the
distance from the GR site, and the calculated p-value (uncorrected for multiple testing).
DOI: 10.1371/journal.pgen.0010016.t003
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called serial analysis of chromatin occupancy (SACO) [68]. It
involves ChIP followed by long serial analysis of gene
expression. The difference between the two techniques is
significant. Promoter microarrays can only detect ChIP
enrichment in the region immediately within and around
the sequences spotted on the array. In contrast, SACO is
unbiased in the binding sites that it can detect, thus allowing
investigators to interrogate the whole genome, including
introns and intergenic sequences. However, while the devel-
opment of the promoter microarray has a large initial
expense, all future experiments are relatively inexpensive.
This contrasts with SACO, in which the sequencing cost must
repeatedly be borne for each additional experiment. Thus, to
perform SACO on groups of treated and untreated animals,
as we have done here, is prohibitively expensive and time-
consuming.
Although this work has expanded the set of genes known to
be regulated by the GR, the list remains incomplete. Since
glucocorticoids exert different effects on the various organ
systems of the body, it is likely that the functional targets of
the GR are different in each tissue. This would suggest that
repeating this experiment on other glucocorticoid-respon-
sive organs would yield new targets. Alternatively, the target
list might remain the same, but the intersection between
differentially expressed genes and GR-bound promoters
might be different. In the future, it would be extremely
useful to determine the sets of genes regulated by individual
transcription factors versus those regulated by combinations
of factors. It may be that those genes controlled by a more
complex regulatory network are key genes in the processes in
which they participate. This determination could be achieved
by performing location analysis with antisera against differ-
ent transcription factors and then intersecting the lists
generated by each analysis, or by performing a single location
analysis after sequential immunoprecipitations with different
antisera [69].
In summary, we have performed parallel expression
analysis and location analysis on the livers of mice treated
with dexamethasone in order to determine direct, functional
targets of the GR. Our analysis identified many genes
previously shown to be GR targets, many genes that were
suspected GR targets, and some novel GR target genes. Some
of these genes may be critical for particular aspects of the
glucocorticoid response, and would therefore make attractive
candidates for targeted therapies. Other target genes may be
control points where the integration of multiple signaling
pathways occurs via GRUs, such as on the Pck promoter. We
believe that these targets provide many opportunities for
future research, and that this work has moved us one step
closer to understanding the complete genetic network
modulated by the GR and the set of transcriptional regulators
with which it interacts. In addition, this work establishes a
paradigm for similar orthogonal analyses, and demonstrates
that pathway and sequence analyses can be used to suggest
functional interactions between transcription factors on the
promoters of particular genes.
Materials and Methods
The complete expression and location analysis data sets are
available as Datasets S1 and S2, respectively. The microarray feature
location annotation for the two platforms (Mouse PancChip5.0 and
MousePromoterChipBCBC-3.0)aredownloadablefromtheEpconDB
website (http://www.cbil.upenn.edu/EPConDB/Downloads.shtml).
Mouse treatment. Ten adult male CD1 mice were randomly split
into two groups. The control mice were fed ad libitum, while the
treatment group was fasted overnight and then given intraperitoneal
injections of 0.1 mg/g body weight dexamethasone (Sigma-Aldrich, St.
Louis, Missouri, United States) diluted in PBS 3 h prior to sacrifice.
Control mice were not injected with vehicle (PBS), as this would have
caused the release of endogenous glucocorticoid. Control and treat-
ment animals were housed similarly with standard day/night cycles.
Expression analysis. RNA was extracted from one half of each liver
using TRIZOL (Invitrogen, Carlsbad, California, United States) and
lithium chloride precipitation and then analyzed on an Agilent
Bioanalyzer 2100 for quality and quantity. All samples showed intact
ribosomal bands with a minimum 28S to 18S ratio of 2.0.
For RT-QPCR, 3 lg of total RNA was reverse transcribed using
Superscript II (Invitrogen) primed with an oligo-dT primer and then
diluted to 300 lL. Each reaction contained 1 lL of diluted cDNA. RT-
QPCR was performed in triplicate using a Stratagene MX4000 QPCR
machine and Stratagene Brilliant SYBR mix, as per the manufacturer’s
instructions (Strategene, La Jolla, California, United States). Cycling
conditions were 95 8C for 10 min, then 40–45 cycles of 30 s at 95 8C, 1
min at 60 8C, and 30 s at 72 8C, followed by a dissociation curve
analysis. Fold enrichments and p-values were calculated as using the
Relative Expression Software Tool (REST) [70] with 2,000 random-
izations and using the expression of TATA box binding protein (Tbp)
as the normalizing gene. Primer sequences are provided in Table S2.
Microarray hybridizations were performed as previously described
[30]. A common reference design was utilized, where the common
reference was a mixture of all ten samples. This resulted in ten
microarray hybridizations. For each sample, 20 lg of total RNA was
reverse transcribed using Superscript III (Invitrogen), oligo-dT
primers, and amino-allyl dUTP. The RNA was then degraded and
the cDNA coupled to fluorescent Cy3 or Cy5. The samples were
hybridized to the PancChip 5.0 cDNA microarray, which contains
over 13,000 transcripts (http://www.epcondb.org). Slides were scanned
on an Agilent (Palo Alto, California, United States) scanner and
analyzed with GenePix 5.0 software. Data were normalized using the
SMA add-on [71] in the ‘‘R’’ software package [72] and differentially
expressed genes were identified using the SAM software package at
10% false discovery rate [73].
Chromatin immunoprecipitation. Chromatin immunoprecipita-
tions were performed as previously described [21]. Half of each
mouse liver sample was minced in cold PBS, pushed through a 21-
gauge needle, and then crosslinked using 1% formaldehyde for 10
min. The crosslinking was quenched by adding glycine to a final
concentration of 0.125 M. The samples were then washed in PBS and
Dounce homogenized in ChIP cell lysis buffer (5 mM Pipes [pH 8.0],
85 mM KCl, 0.5% Nonidet P-40, 10 lM leupeptin, and 1 mM PMSF).
Nuclei were sedimented and separated from the cellular debris, then
placed into nuclear lysis buffer (50 mM Tris HCl [pH 8.1], 10 mM
EDTA, 1% SDS, 10 lM aprotinin, 10 lM leupeptin, and 1 mM PMSF).
After 10 min on ice, the lysate was sonicated on ice (Sonic
Dismembrator Model 100; Fisher, Pittsburgh, Pennsylvania, United
States) using three pulses for 20 s at 4–6 W. Insoluble debris was
removed by centrifugation, and the supernatant was collected and
flash frozen in liquid nitrogen. An input fraction was generated by
taking a portion of nonimmunoprecipitated chromatin material and
reversing the crosslinking. When visualized on an agarose gel, the
DNA produced a smear of approximately 200–600 bp in length.
For each immunoprecipitation, 2 lg of chromatin was used. The
chromatin was precleared by incubating with protein-G agarose at 4
8C for 1 h. Antiserum raised against the GR (sc-1002 Santa Cruz,
utilized in ChIP assay by [74]) or pre-immune IgG were added (2 lg
each) and the samples rotated at 4 8C overnight. Immunoprecipitates
were isolated by incubating with blocked protein-G agarose and
washed extensively. Chromatin was eluted from the antibody by
incubating for 10 min at room temperature with elution buffer (0.1 M
NaHCO3and 1% SDS). The crosslinking was reversed by adding NaCl
to 0.2 M and incubating at 65 8C for at least 4 h. Samples were then
digested with 40 ng of proteinase K, and the DNA was isolated via
phenol/chloroform extraction followed by ethanol precipitation.
DNA concentrations were calculated by measuring A260 on a
NanoDrop ND-1000 spectrophotometer (NanoDrop, Wilmington,
Delaware, United States). Sheared genomic DNA was generated by
sonicating genomic DNA purified from CD1 mouse liver.
QPCR to determine enrichment of genomic loci was performed as
for expression analysis, except that the reaction was performed on
immunoprecipitated DNA. All occupancy calculations are the
average of five samples in both the treated and fed control groups,
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with all QPCR reactions performed in triplicate. The fold-enrich-
ment and p-value calculations were performed with the REST
software package, with 2,000 randomizations. As our normalizing
sequence, we used the loci encoding the 28S ribosomal RNA, a
sequence of DNA present in multiple copies in the mouse genome. If
no product was detected in a control sample in two of three triplicate
wells after 45 cycles of PCR, 45 was used as the threshold cycle
crossing point. Primer sequences are provided in Table S2.
Location analysis. For location analysis, a common reference
design was utilized, where the common reference was sheared
genomic DNA. The three immunoprecipitated treatment samples
showing the largest enrichment for the known GR targets in the
promoters/enhancers of the Tat and Mt2 genes were used for location
analysis and were compared to immunoprecipitations from five fed
control samples. This resulted in a total of eight microarray
hybridizations. Approximately one half of the material from an
immunoprecipitation was amplified using an LM-PCR protocol
modified from that developed by Oberley and colleagues [75]. The
DNA was blunt-ended in a 50 ll reaction using 5 U of T4 DNA
Polymerase (Promega, Madison, Wisconsin, United States), 13 T4
DNA polymerase buffer, and 400 lM dNTPs. The samples were
incubated at 11 8C for 15 min, then purified using MinElute columns
(Qiagen, Valencia, California, United States) per the manufacturer’s
protocol. The oligonucleotides OJW102 and OJW103 were annealed
to produce a directional linker, which was blunt-ligated to the blunt-
ended ChIP DNA in a 50 ll reaction containing 1 ll of high-
concentration T4 DNA ligase (New England Biolabs, Beverly,
Massachusetts, United States), 13 T4 Ligase buffer, and 0.9 lM
annealed OJW102/OJW103. The reaction was incubated at 16 8C
overnight and then purified using QiaQuick columns (Qiagen). The
samples were PCR amplified in a 50 ll PCR reaction containing 5 U
Taq DNA Polymerase (Promega), 13 Taq Polymerase buffer, 2 mM
MgCl2, 400 lM dNTPs, and 1 lM OJW102 at the following cycling
parameters: 1 cycle at 55 8C for 2 min and 72 8C for 5 min; then 15
cycles of 95 8C for 30 s, 55 8C for 30 s, and 72 8C for 1 min; then a final
extension at 72 8C for 5 min. This first round of LM-PCR was purified
using QiaQuick columns and then quantified using spectrophotom-
etry. The second round of LM-PCR was performed with 100 ng of the
first round product and identical PCR settings except that only ten
amplification cycles were performed. This produced 3–4 lg of DNA.
Amplified material (1 lg) was labeled and hybridized against 1 lg
of sheared genomic DNA. Samples were labeled using Ready-To-Go
DNA labeling beads (Amersham, Little Chalfont, United Kingdom)
per manufacturer’s instructions. Briefly, water was added to the
sample to a volume of 45 ll. The DNA was denatured at 95 8C for 3
min, then cooled on ice. Next, 5 ll of dCTP coupled to Cy3 or Cy5 dye
(Amersham) was added, along with the Ready-To-Go labeling bead.
This was gently mixed and incubated at 37 8C for 30 min. The Cy3 and
Cy5 samples were combined and purified using MinElute columns
(Qiagen). After purification, 500 ng of Cot1 Mouse DNA was added to
each sample and denatured at 95 8C for 5 min. The samples were then
cooled to 42 8C and an equal volume of 23hybridization buffer (50%
formamide, 103 SSC, and 0.2% SDS) was added, mixed, and applied
to the Mouse PromoterChip BCBC-3.0 microarray slide.
Microarray slides were hybridized overnight, then washed and
scanned with Agilent G2565BA Microarray Scanner. Images were
analyzed with GenePix 5.0 software (Axon Instruments). Median
background subtracted intensities were obtained for each spot and
imported into the mathematical software package ‘‘R.’’ M and A
values were calculated as log2(sample/control) and (log2[sample] þ
log2[control])/2, respectively. M values were Lowess-normalized with
the SMA package developed by Speed and colleagues [71], and M
values for the duplicate spots present on the array were averaged
whenever both spots were present.
We used two criteria to determine whether the binding data indicated
that a particular promoter region was enriched in the GR-immunopre-
cipitated material from the treatment samples. We first used SAM to
determine the promoter regions differentially enriched between the
treatment and control samples. SAM produced a list of differentially
bound promoters, the majority of which were enriched in the treatment
samples. Then we required that the dexamethasone-injected samples had
to show enrichment compared to the sheared, unenriched genomic DNA
by utilizing an average M value cutoff of M . 0.
Statistical significance. The probability of obtaining eight or more
GR targets from the list of 12 known targets present on the promoter
array was calculated using the hypergeometric distribution. The
family of cyclin-dependent kinases and cytochrome P450 enzymes
were not considered, because the source did not list individual family
members.
EASE functional category analysis. The Expression Analysis
Systematic Explorer (EASE) annotation tool provided by the National
Center for Biotechnology Information (NCBI; http://apps1.niaid.nih.
gov/david/) was utilized to determine GO Biological Function
categories that are enriched in the set of 302 GR target genes
compared to the entire set of genes on the promoter microarray. Also
shown is a metric known as the ‘‘EASE score.’’ This is calculated as the
upper bound of the distribution of jackknife Fisher exact proba-
bilities. This score is a conservative adjustment of p-values generated
by the Fisher exact test that penalizes the significance of categories
supported by few genes and negligibly penalizes categories supported
by many genes.
Pathways analysis. The network was generated using Ingenuity
Pathways Analysis (Ingenuity Systems, http://www.ingenuity.com). The
NCBI reference sequence (RefSeq) identifier for the 53 DEB genes,
plus the GR itself, was utilized to seed the network generation
algorithm. The algorithm produced only two networks with more
than four genes, which were then merged into a single network.
Sequence analysis. The set of vertebrate transcription factor PWMs
in TRANSFAC v7.3 and JASPAR was scored against the promoters in
the DEB set and a background set of 1,120 other promoters randomly
selected from those present on the Mouse PromoterChip BCBC-3.0
promoter microarray. The background set of promoters contained
the same proportion of 1-kb tiles (tile 1) and 2-kb tiles (tile 2) as the
DEB set. The results are the average of three runs with different
background sets. The ability of each PWM, or combination of PWMs,
to differentiate between the DEB and the background sets was
calculated by measuring the area under the curve in a plot of
sensitivity versus 1 ? specificity (false positive rate) at different score
thresholds. In the case of a combination of PWMs, the threshold
scores of both PWMs, as well as the distance between the two, were
varied to generate the curve. The optimal spacing and scoring were
determined by choosing the parameter values that yielded the largest
positive difference between the sensitivity and the false positive rate.
The spacing reported is the distance between the outer ends of the
binding sites. Values of p were calculated using the one-sided two-
sample proportion test (‘‘prop.text’’ function in the software package
‘‘R’’) to determine the significance of the difference between the true
positive and false positive rates. For the analysis of all combinations
of GR and other matrices, we corrected for multiple testing by using a
Bonferroni correction, setting the threshold for significance to p , 4
3 10?5(0.05/1,145 combinations).
Supporting Information
Table S1. Location Analysis Results
Immunoprecipitations with antiserum against GR were performed on
chromatin from livers of dexamethasone-treated mice and fed
controls. The samples were amplified using LM-PCR and then
hybridized to the Mu7K promoter microarray, using sheared genomic
DNA as common control. Location analysis resulted in 318 promoters
representing 302 genes that were differentially bound by the activated
GR. We have listed the RefSeq identifier, a short description of the
gene, and the increase in occupancy as measured by the microarray.
Some genes are listed more than once, due to both tile 1 and tile 2
showing enrichment. This indicates either multiple GR binding sites
or a single GR binding site near the overlap of the two tiles.
Found at: DOI: 10.1371/journal.pgen.0010016.st001 (119 KB DOC).
Table S2. Primer Sequences
Primers were designed to amplify putative GREs within the promoter
regions enriched in the location analysis. These were utilized in
QPCR reactions as described in Materials and Methods.
Found at: DOI: 10.1371/journal.pgen.0010016.st002 (40 KB DOC).
Dataset S1. Complete Expression Data
This table contains the annotated expression data for the five
treatment (D6–D10) and control (F6–F10) samples. Hybridizations
were carried out with a common reference design, where the
common reference was a mixture of all samples. Values are expressed
as Log (red/green), where the common reference sample was labeled
in red and the test samples were labeled in green. Also included are
fold-change calculations and a column indicating whether SAM
software indicated that the spot was differentially expressed.
Found at: DOI: 10.1371/journal.pgen.0010016.sd001 (10.5 MB XLS).
Dataset S2. Complete Location Analysis Data
This table contains the annotated location analysis data for the three
treatment (D8–D10) and control (F6–F10) samples used in our
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calculations. Hybridizations were carried out with a common reference
design, where the common reference was sheared genomic DNA. Values
are expressed as Log (red/green), where the common reference sample
was labeled in red and the test samples were labeled in green.
Found at: DOI: 10.1371/journal.pgen.0010016.sd002 (2.3 MB XLS).
Acknowledgments
The authors wish to thank Rana Gupta for critical reading of the
manuscript and James Fulmer for care of the animals. This work was
supported by grants DK49210 and DK56947 to KHK from the
National Institute of Diabetes and Digestive and Kidney Diseases.
Competing interests. The authors have declared that no competing
interests exist.
Author contributions. PPL, JRF, and KHK conceived and designed
the experiments. PPL, JRF, JEB, JBP, and IMB performed the
experiments. PPL, JS, JEB, and IMB analyzed the data. JS contributed
reagents/materials/analysis tools. PPL, JRF, and KHK wrote the
paper.
&
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