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First published online 11 June 2008
doi: 10.1242/dev.019018
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Duke Institute for Genome Sciences and Policy, Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, NC 27710, USA.
* Author for correspondence (e-mail: nevin001{at}mc.duke.edu)
Accepted 12 May 2008
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SUMMARY |
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SUMMARY
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES |
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Key words: E2F transcription factor, Mammary gland, Microarray, Mouse
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INTRODUCTION |
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SUMMARY
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES |
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A number of groups have analyzed the transcriptional changes associated
with the various developmental phases of the mammary gland
(Blanchard et al., 2007
;
Clarkson et al., 2004;
Kouros-Mehr et al., 2006;
Kouros-Mehr and Werb, 2006;
Master et al., 2002;
McBryan et al., 2007;
Rudolph et al., 2007;
Stein et al., 2004). Three
reports are notable for their comprehensive coverage of the spectrum of
development from virgin through pregnancy, lactation and involution
(Clarkson et al., 2004;
Master et al., 2002;
Stein et al., 2004). In each
of these studies, self-organizing maps were used to identify groups of genes
that were elevated in the various stages of mammary development. Through
annotation of gene ontology, these reports noted a significant immune response
with the onset of involution. Further examination of individual genes led to
additional work in adaptive thermogenesis
(Master et al., 2002), on
annexin A8 in breast cancer (Stein et al.,
2005) and on the composition of connexin channels in the mammary
epithelium (Locke et al.,
2004).
In addition to these datasets that encompass pregnancy, lactation and
involution, several recent reports focused on pubertal ductal outgrowth
(Kouros-Mehr et al., 2006;
Kouros-Mehr and Werb, 2006;
McBryan et al., 2007). Using a
strategy to compare terminal end buds (TEBs) with ducts, it was noted that
GATA3 was highly expressed (Kouros-Mehr
and Werb, 2006) and was essential for mammary development
(Kouros-Mehr et al., 2006). In
addition, a recent report has examined transcription throughout puberty
(McBryan et al., 2007). Taken
together, there are now microarray datasets available that describe normal
mammary development through all major stages.
Although these previous studies identified interesting genes, an alternate
strategy is to look for higher-order structure in the expression data,
particularly evidence of discrete biological function associated with cell
signaling pathway activity. For example, Gene Set Enrichment Analysis (GSEA)
is a method that uses previously established gene sets defining pathways or
functions to interpret microarray expression data
(Subramanian et al., 2005).
Using gene sets defined through various methods including experimental
perturbations and literature-based studies, GSEA provides a pathway discovery
tool. A variation on GSEA, termed ASSESS, allows a measure of enrichment of
gene sets across multiple samples (Edelman
et al., 2006).
An alternative approach that we have previously described makes use of
supervised methods of analysis and data from the experimentally controlled
activation of specific oncogenic pathways to create signatures for each
oncogene (Bild et al., 2006).
These signatures can then be used to measure the probability of pathway
activation in a tissue or cell line sample. Here, we have applied various
pathway analysis techniques to the previously described mammary gland
development dataset (Stein et al.,
2004) and the pubertal dataset
(McBryan et al., 2007). These
analyses illustrate both the differential usage of various pathways in the
discrete phases of mammary gland development and the broad applicability of
these methods to a variety of developmental contexts.
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MATERIALS AND METHODS |
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SUMMARY
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
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), E2f2 (Murga et
al., 2001), E2f3
(Humbert et al., 2000) and
E2f4 (Rempel et al.,
2000) knockout mice were maintained in accordance with
institutional and federal guidelines. Lactating mammary glands were examined
on the fifth day of lactation. To examine involution of the mammary gland,
pups were removed from the lactating female on the fifth day of lactation.
Transplant experiments were conducted as previously described
(Deome et al., 1959). Briefly,
mammary glands were removed from the donor mice and small, 1-2 mm3
portions containing epithelium were isolated. Nu/nu mice were used as
recipients and their endogenous epithelium was removed by excising the entire
fat pad from the nipple to the inguinal lymph node. A small hole in the
remaining fat pad was made with fine forceps and the donor epithelium was
inserted. The mice were examined 25 days post-surgery for outgrowth
effects.
Histology
For wholemount analysis, the inguinal mammary gland was excised and stained
with Harris Modified Hematoxylin. To assess outgrowth of the mammary
epithelium, the distance from the nipple to the leading edge of the epithelium
was measured, as was the distance from the nipple to the midpoint to the
thoracic lymph node. The ratio of the distance of outgrowth and distance
between the lymph node and nipple was calculated for the control. This ratio
of outgrowth for the control was set to 100% and the various mutants were
compared with this standard.
Samples for histological analysis were fixed in 10% formalin and were
processed using standard procedures. Sections from involuting mammary glands
were also used to assess cell death in a TUNEL analysis using the In Situ Cell
Death Detection Kit POD (Roche Applied Science). Rabbit polyclonal anti-E2F3
(C18, Santa Cruz, 1:1000 dilution), rabbit polyclonal anti-E2F4 (A20, Santa
Cruz, 1:300), mouse monoclonal anti-PCNA (PC10, Santa Cruz Biotechnology,
1:300) and mouse monoclonal anti-
smooth muscle actin (clone 1A4, Sigma
Immunochemicals, 1:1000) were used in immunohistochemical experiments. For the
mouse monoclonal antibodies, the mouse-on-mouse staining procedures were
followed (Vector Laboratories). Secondary antibodies and blocking reagents
were from the Vectastain Elite Kits (Vector Laboratories). Signal detection
used DAB (Vector Laboratories) and a Hematoxylin counterstain.
Quantitative RT-PCR
Mammary glands were excised, snap frozen in liquid nitrogen and stored at
-80°C. Total RNA was isolated by guanidinium thiocyanate extraction and
CsCl gradient sedimentation (Chirgwin et
al., 1979). Quantitative RT-PCR was performed using a SYBR Green
One-Step RT-PCR Kit (Qiagen) with the following primers (5' to
3'): E2f1 forward, CGATTCTGACGTGCTGCTCT and reverse,
CAGCGAGGTACTGATGGTCA; E2f2 forward, GCGCATCTATGACATCACCA and reverse,
CGGGTGGGGTCTTCAAATAG; E2f3a forward, CCAGCAGCCTCTACACCAC and reverse,
GGTACTGATGGCCACTCTCG; E2f3b forward, CTTTCGGAAATGCCCTTACA and
reverse, GGTACTGATGGCCACTCTCG; E2f4 forward, CACTGAGGACGTCCAGAACA and
reverse, GATGGGCACCTCTAGACTGG; Gapdh forward, TCATGACCACAGTGGATGCC
and reverse, GGAGTTGCTGTTGAAGTCGC. Relative levels of product were calculated
using the 
Ct method.
E2F3-regulated genes
Fold expression differences from involution data for day 1 and day 2
between wild-type and E2f3 heterozygous samples were calculated using
Genespring
(www.chem.agileut.com/Scripts/PDS.asp?lPage=27811).
All target genes with a greater than 2-fold difference were used in GATHER to
identify genes with an E2F consensus binding site in their promoter. To
examine genes regulated by E2F3, mouse mammary HC11 cells were cultured under
growth conditions (RPMI 1640 medium with 0.3 g/l L-glutamine, 10% fetal bovine
serum, 20 mM HEPES, 10 µg/ml insulin and 10 ng/ml EGF) and apoptosis was
induced through serum reduction to 0.1%, insulin and growth factor withdrawl.
Chromatin immunoprecipitation was performed on growing and apoptotic cells as
described (Zhu et al., 2004).
Quantitative PCR using the CT method was conducted for targets
using the following primers (5' to 3'): ribonucleotide reductase
M2 forward, CGGAGAGCATGGCGAACGAGG and reverse, GCTCCTTAAAGGTCTTTGTGC; albumin
forward, GGACACAAGACTTCTGAAAGTCCTC and reverse, TTCCTACCCCATTACAAAATCATA;
Hexb forward, TCGGTCATCTGACTTGGTGA and reverse, ATGACTGCTCCGCGAGTCT;
Map3k4 forward, AGTCGAGTCACTCCCTCACC and reverse,
CTCTCATCCGTGCACCAAG; Mt1 forward, ACTATGCGTGGGCTGGAG and reverse,
GGTGACGCTTAGAGGACAGC; Grim19 (Ndufa13) forward,
GCGATCTGCGAGAAGAGTTC and reverse, TCGAGTTCATCGAAGTGTGC; Rhob forward,
CAATCAAGCTAAGGCGAACC and reverse, AGAGTCCGGCCACTTTCTTA; Trp53inp1
forward, CACGTGCGTTAGTGACAACC and reverse, GGGTCCTTTTGTTGTTGTGC.
Involution array analysis
Mammary RNA from day 5 of lactation and days 1-4 of involution was isolated
(Chirgwin et al., 1979) from
three wild-type and three E2f3 heterozygous mice at each timepoint.
RNA was hybridized to Operon 3.0 mouse arrays
(www.operon.com/index.php?).
In order to examine a phenotypic signature, two datasets containing mouse
mammary involution data were downloaded
(Clarkson et al., 2004;
Stein et al., 2004).
Dataset analysis
Unsupervised clustering analysis was performed with Cluster 3.0
(http://bonsai.ims.u-tokyo.ac.jp/~mdehoon/software/cluster/).
Supervised clustering was performed with GeneCluster 2.0
(http://www.broad.mit.edu/cancer/software/genecluster2/gc2.html).
GSEA was performed using GSEA 1.0
(http://www.broad.mit.edu/gsea/)
(Subramanian et al., 2005).
ASSESS (Edelman et al., 2006)
was also employed.
TEB/duct E2F promoter analysis
The top 200 genes, as judged by fold change amongst genes expressed more
highly in the TEB than in the duct, were selected
(Kouros-Mehr et al., 2006;
Kouros-Mehr and Werb, 2006).
These genes were used in GATHER
[http://meddb01.duhs.duke.edu/gather/(Chang
and Nevins, 2006)] to predict transcription factor binding
sites.
Pathway signatures
Genomic pathway signatures were generated as previously described
(Bild et al., 2006). Gene sets
used in this analysis were described previously [GSE3151, GSE3158
(Bild et al., 2006)] or were
generated from existing datasets. Additional datasets for pathway signatures
were: STAT3 (Dauer et al.,
2005), TNF [GSE2638 and GSE2639
(Viemann et al., 2006)], RHOA
[GSE5913 (Berenjeno et al.,
2007)], TGFβ [GSE1724
(Renzoni et al., 2004)],
endotoxin [GSE3284 (Calvano et al.,
2005)] and immune (Galon et
al., 2006). The signatures were applied to several datasets
including the developmental dataset
[http://breast-cancer-research.com/content/supplementary/bcr753-s1.txt
(Stein et al., 2004)] and the
pubertal dataset [GSE6453 (McBryan et al.,
2007)]. Generation of the E2F4 signature was derived from a
comparison of wild-type and E2f4-null mouse embryonic fibroblasts
(MEFs). Three wild-type and five E2f4-null embryos were used to
generate multiple MEF plates for each cell line. RNA from each replicate was
prepared independently using the Qiagen RNeasy protocol and was used on the
Affymetrix Mouse Genome 430A 2.0 platform. The resulting data were used to
generate a signature (Bild et al.,
2006). However, the knockout samples were used in place of the
GFP-infected training samples and the wild-type samples were used in place of
the overexpression training samples to create a signature of E2F4 pathway
use.
Microarray data accession numbers
The accession number for the involution series microarray data is GSE11066.
The accession number for the E2F4 MEF microarray data is GSE11039.
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RESULTS |
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SUMMARY
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
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Examination of the individual clusters identified in the developmental time course (Fig. 2A) using various annotation sources, including GATHER, reveals distinct representation of functional groups in terms of gene ontology (Table 1). In particular, genes activated during the proliferative phases of pregnancy are enriched in cell cycle and proliferation-associated genes (cluster C). By contrast, genes activated during involution are enriched for various apoptotic-related activities (cluster A). Genes elevated during and associated with virgin development and early lactation are enriched for activities such as organ development and morphogenesis (cluster D). In addition, genes elevated during lactation are associated with activities such as localization and transport, presumably being involved with orientation of the secretory epithelium (cluster B).
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One limitation of GSEA is the inability to account for sample variability. To address this issue, we employed ASSESS, an application based on GSEA methods, to analyze the lactating and involuting subset of the developmental dataset. Several interesting gene sets for lactation and involution are shown in Fig. 4B. Although gene sets elevated in GSEA were present, the inclusion of variability both strengthened and expanded the data. For instance, GSEA identified Brentani immune function as a gene set enriched in the involuting samples, as did ASSESS. However, with the inclusion of variability, it is clearly shown that the highly elevated normalized enrichment scores for this gene set only began on the second day of involution, consistent with well-established histological knowledge. Further, one would expect the matrix metalloproteinases to be identified in the remodeling phase of involution. Whereas GSEA did not identify this gene set, ASSESS illustrated that the matrix metalloproteinases were involved in involution in the latter stages, particularly during day 3 and day 4 when mammary gland remodeling occurs (Fig. 4B). For the complete results, see Fig. S1 and Table S2 in the supplementary material. In addition, ASSESS differentiated between early and late puberty (Fig. 4C; for complete results see Fig. S2 and Table S3 in the supplementary material). This result was an interesting contrast to the lactation and involution analysis in that there was less variability between sample timepoints, and clustering of the results cleanly broke the pubertal dataset into two distinct clusters (see Fig. S1 versus Fig. S2 in the supplementary material). These results illustrate how annotated pathways can define the biology that underpins developmental changes.
Patterns of cell signaling pathway activity
As an alternative approach, we have described the use of expression
signatures developed to reflect various aspects of cell function, including
cell signaling pathway activation. The value in this approach is the ability
to bring a higher level of understanding to the transitions of mammary
development, going beyond simple gene lists to representations of defined
biology. We have made use of these signatures to predict the probability of
activation of these pathways in the samples representing distinct stages of
mammary development.
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)
(Fig. 5A). The pathway
prediction illustrates the elevated probability of p63 pathway activation
during lactation, consistent with the original descriptions of p63 in the
mammary gland.
Given the STAT3 and p63 results, we then examined the activation state for
a number of additional cell signaling pathways
(Bild et al., 2006;
Black et al., 2003;
Huang et al., 2003). This
analysis revealed a number of interesting trends that were associated with
defined stages of mammary development (Fig.
5B). Specifically, the E2F1-3 pathway signatures were all elevated
during pregnancy and were decreased during involution. This coincided with the
retinoblastoma (Rb; Rb1)-knockout signature, consistent with
the control of E2F as a major Rb function and with the annotations that
indicated an enrichment for proliferation activities in these samples
(Fig. 2A, cluster B and
Table 1). These results also
indicate that although E2F1 and E2F3 are active during proliferation
associated with pregnancy, they are not active during the lactation process. A
signature for E2F4 was also developed by comparing E2f4-null with
wild-type mouse embryonic fibroblasts (MEFs). The resulting E2F4 signature is
roughly the inverse of the E2F1-3 patterns, consistent with the fact that E2F4
has been shown to function as a transcriptional repressor, whereas E2F1-3 are
transcriptional activators. As such, this result suggests that not only is
there a period of E2F-specific activation followed by loss of the activators,
but that this is accompanied by active repression of the target genes through
the action of E2F4. In sharp contrast to the Rb/E2F1-3 profiles, signatures of
RAS (HRAS1), SRC, β-catenin, and MYC were low in the pregnancy samples
and then dramatically elevated upon lactation and continuing through early
stages of involution (Fig.
5B).
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In a similar manner, datasets that defined phenotypic states were used to generate predictions in the mammary dataset (Fig. 5C). Here we have shown that a predictor built on breast cancer basal/luminal cell types identifies the lactating mammary gland as being highly luminal in nature. Further predictions with both an endotoxin signature and an immune signature suggest that there is a strong immune response in lactation as well as the expected elevated signature in involution.
The same sets of pathway predictors were used on the pubertal dataset to assess the importance of the various pathways (Fig. 5D). Although the probabilities of pathway activation were not as strong as for the developmental dataset, the changes associated with mammary gland development were reflected in the puberty pathway probabilities. For instance, the E2F1 and E2F3 pathway probabilities were elevated in the latter stages of puberty, as were MYC, TGFβ and β-catenin. Taken together, the signatures examined in puberty and the developmental time courses illustrate the utility of examining pathways in interpreting the underlying biology.
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Based on a wholemount examination of mammary epithelium transplants, the proliferation and differentiation defects appeared to be a cell-autonomous phenotype for the various E2F-deficient mice (n=3 for each genotype) (Fig. 6C). In contrast to the controls, E2f1-knockout epithelium transplanted into a nu/nu recipient exhibited reduced outgrowth and branching. More strikingly, E2f3-knockout and E2f4-knockout transplants lagged far behind the controls. Quantitation revealed that there were significant differences in E2f1-knockout, E2f3 heterozygous, E2f3-knockout and E2f4-knockout transplants (Fig. 6D). These results clearly indicate that the epithelial effects noted in the various mutant mice are cell-autonomous and are in agreement with the proliferative role for the E2Fs identified in the pathway predictions.
Further evidence for a role of E2F activities in the proliferative phase of mammary development was indicated by an analysis of gene expression patterns developed to profile TEB formation. Comparing RNA expression in dissected TEB structures with that in ductal structures revealed a pattern of expression unique to the TEBs. Analysis of this group of genes for various sources of annotation revealed a very significant enrichment for genes containing E2F binding sites in their promoters (Table 2 and see Fig. S4 in the supplementary material).
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Finally, we also made use of a signature generated to reflect the involution process (see Fig. S5 in the supplementary material) and then used the signature to predict the probability of this phenotype in the samples from wild-type and E2f3+/- mice. As shown in Fig. 7D, there was a marked reduction in the involution signature in the E2f3+/- mice, corresponding to the phenotypic delay noted in the histology results.
In order to examine the involution microarray data for targets that could be potentially regulated directly by E2F3, the wild-type and E2f3+/- data from involution day 1 and day 2 were compared for fold differences. Genes with greater than a 2-fold difference were used to identify differentially expressed genes with E2F consensus sites in their promoters (see Table S4 in the supplementary material). The expression pattern of these genes was examined and genes with strong E2F binding sites and variable expression patterns were chosen for further analysis in a chromatin immunoprecipitation (ChIP) assay. HC11 mouse mammary epithelial cells that were either proliferating or undergoing apoptosis were examined by ChIP. Compared with the ribonucleotide reductase M2 positive control, there were indeed genes with differential expression, the promoters of which were bound by E2F3 (see Fig. S6 and Table S4 in the supplementary material).
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DISCUSSION |
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SUMMARY
INTRODUCTION
MATERIALS AND METHODS
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DISCUSSION
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Although genome-scale measures of gene expression provide a global picture of the expression changes associated with mammary development, and clearly have tremendous power to reveal subtle phenotypes not otherwise detected, it is also a significant challenge to extract and understand the underlying biology highlighted by these profiles. The work we describe here has made use of methods of expression analysis that relate profiles (signatures) to defined function, and then evaluate the extent to which this signature is represented in a given biological sample. Importantly, these signatures can be assessed quantitatively in a given sample to determine the extent to which that signature is present and thus the extent to which the biological phenotype represented by the signature is present.
The power of the expression signature is twofold. First, it is portable in the sense that it can be assayed in different contexts. That is, an expression signature developed in a cell culture context can be measured in a tumor or developmental stage, providing the capacity to link otherwise heterologous systems. A cell culture phenotype, such as pathway activation, is difficult to represent in a diverse sample, such as a tumor. By contrast, the expression profile that represents pathway activation in cell culture can then be used to interrogate the expression data from a tumor. Second, it is quantitative and can be assessed in the context of other signatures to identify patterns of signatures. This latter characteristic is perhaps of greatest significance because it provides the opportunity to not only go beyond single genes, but to also go beyond single pathways to begin to describe more complex interactions that ultimately define networks.
The analyses we report here suggest that although cell signaling pathways such as Rb/E2F, RAS, MYC, SRC and others are all generally associated with cell proliferation, there is a sharp distinction in the activity of these pathways during the course of mammary gland development. In particular, E2F activity was seen as prominent during early stages prior to lactation, whereas other pathways, including MYC, β-catenin, RAS and SRC, were generally silent during this phase and then abruptly activated upon lactation. These latter pathways remained active through involution and then returned to baseline levels as the mammary gland returned to the pre-lactation state.
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). Conversely, Src transgenics have deficiencies in
lactation (Webster et al.,
1995) and Src-knockouts have deficiencies in mammary
outgrowth (Kim et al., 2005),
corresponding to an elevated pathway signature in the puberty dataset, but to
date experiments have not directly assessed the role of SRC in lactation.
Other work has demonstrated that TNF expression is increased during pregnancy
and is decreased during lactation (Varela
and Ip, 1996), correlating with the pathway probability for TNF.
The TGFβ signature was elevated in early pregnancy but was eliminated
during lactation, consistent with the lactational failure of WAP-TGFβ,
designed to express TGFβ during pregnancy and lactation
(Jhappan et al., 1993).
Moreover, the early pregnancy signature associated with TGFβ is in
accordance with the theory that TGFβ acts in pregnancy to prevent
premature milk synthesis and the associated apoptosis, but is downregulated in
lactation to allow milk synthesis and secretion
(Robinson et al., 1993). In
contrast to these pregnancy-associated signatures, the pathway probability for
Rho showed a high level of activation during lactation, consistent with the
role of the Rho family in mediating signals from integrins and hormones in
milk synthesis (Akhtar and Streuli,
2006). Interestingly, when both an endotoxin infection and an
immune signature were applied there was activation of these states in the
involuting samples, consistent with prior studies
(Clarkson et al., 2004;
Master et al., 2002;
Stein et al., 2004). Further,
these immune signatures were also present in the lactating sample, an
observation not made in the previous microarray studies. The presence of an
immune signature in the lactating mammary gland is consistent with the
presence of host defense proteins secreted into milk
(Smolenski et al., 2007) and
the use of pathways such as NF-
B and Stat in both lactation and
inflammation (Vorbach et al.,
2006).
The observation that E2F1 and E2F3 loss-of-function affect proliferation is
consistent with the majority of in vitro work that details the importance of
transcriptional activator E2F proteins in mediating proliferation and was
predicted by the pathway signatures. Functional overlap and compensation by
other family members is likely to mute some of the effects of mutating
individual E2Fs. However, given the distinct role identified for E2F3 in
mediating DNA replication and mitotic activity in vitro
(Kong et al., 2007), it is not
unexpected that the E2F3 phenotype is the most striking amongst those of the
transcriptional activators. The defect in outgrowth of the E2f4-null
mammary epithelium was striking and displayed an even more severe phenotype
than that of E2f3-null mice. Although previous work has suggested a
role for E2F4 as the predominant E2F transcriptional repressor in the
regulation of cell fate and differentiation, recent studies have shown that
loss of E2F4 function can also result in a reduction in proliferation as well
as altered cell fate decisions (Kinross et
al., 2006). Further evidence for a role of E2F activities in the
mammary gland proliferative process came from the observation of enrichment of
E2F elements in the promoters of genes induced in the TEB. Future work might
reveal the precise mechanism by which select E2Fs can regulate these branching
and outgrowth processes and why certain E2Fs are essential for this
process.
In addition, although previous work has established E2F1 as a trigger for
p53 (TRP53)-mediated apoptosis in fibroblast cultures
(DeGregori et al., 1995;
Hallstrom and Nevins, 2003;
Kowalik et al., 1998;
Kowalik et al., 1995), the
pathway analysis suggested that there might be an E2F3-specific role during
involution, based on the changes in involution at days 3 and 4 in the E2F3
signature. Although it is possible that E2F1-mediated apoptosis, like that
mediated by p53, might also be affected by background, our results suggest
that E2F1 is not required for involution
(Jerry et al., 1998;
Li et al., 1996;
Matthews and Clarke, 2005). By
contrast, and consistent with the pathway analysis, we demonstrated that
perturbations in E2F3 levels resulted in a delay in involution-mediated
apoptosis. Although previous studies have illustrated a role for E2F3 in
mediating apoptosis, they have done so in an Rb-null background
(Tsai et al., 1998;
Ziebold et al., 2001) and
proposed that apoptosis is induced once E2F3 expression exceeds a certain
threshold. By contrast, our results show that E2F3 expression is lowered in
involution as compared with lactation and then mediates apoptosis. Given the
switch of E2F3 to E2F4 in DNA binding at the lactation/involution switch
(Gadd et al., 2001), our
results suggest that the balance between E2F-mediated transcriptional
activation and repression is critical for the induction of mammary apoptosis.
To test whether genes involved in involution are bound by E2F3, we performed a
ChIP analysis and identified a number of promoters bound by E2F3.
Interestingly, several of these targets (Trp53inp1, Map3k4 and
Hexb) have previously been observed to be involved in mediating
apoptosis (Huang et al., 1997;
Takekawa and Saito, 1998;
Tomasini et al., 2005).
Unfortunately, because the E2f4-null mice fail to generate
significant numbers of secretory alveoli (data not shown), we were unable to
accurately examine involution to test the pathway analysis prediction.
However, the results observed in the E2F-knockout mice served to validate the
pathway activation predictions that indicated that the E2Fs have a role in
mammary gland development.
Taken together, we have shown that using a computational model in conjunction with genome-scale measurements of gene expression enables the prediction of a role for various genetic pathways in a developmental context. Importantly, the utility of these predictive models was tested in vivo, revealing important insights into the differential regulation of mammary development by the E2F family of transcription factors. Given the nature of this method, it will immediately lend itself to examination of pathways in other contexts, whether in the development of a tissue or in the aberrant development of a tumor.
Supplementary material
Supplementary material for this article is available at
http://dev.biologists.org/cgi/content/full/135/14/2403/DC1
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ACKNOWLEDGMENTS |
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SUMMARY
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
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