# Screening for PPAR Responsive Regulatory Modules in Cancer

Authors: Merja Heinäniemi; Carsten Carlberg
Journal: PPAR Research
 (2008)

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## Abstract

Peroxisome proliferator-activated receptors (PPARs) have via their large set of target genes a critical impact on numerous diseases including cancer. Cancer development involves numerous regulatory cascades that drive the progression of the malignancy of the cells. On a genomic level, these pathways converge on regulatory modules, some of which contain colocalizing PPAR binding sites (PPREs). We developed an in silico screening method that incorporates experiment- and informatics-derived evidence for a more reliable prediction of PPREs and PPAR target genes. This method is based on DNA-binding data of PPAR subtypes to a panel of DR1-type PPREs and tracking the enrichment of binding sites from multiple species. The ability of PPARγ to induce cellular differentiation and the existence of FDA-approved PPARγ agonists encourage the exploration of possibilities to activate or inactivate PPRE containing modules to arrest cancer progression. Recent advances in genomic techniques combined with computational analysis of binding modules are discussed in the review with the example of our recent screen for PPREs on human chromosome 19.


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## Body

## 1. Introduction

Cellular proliferation and differentiation are controlled
by transcriptional regulation of a large subset of the human genome. The
transcriptomes of normal and tumor cells as revealed by microarray analysis
show significant differences [1] suggesting that in cancer the precise transcriptional control got
lost due to overactive oncogenes and loss of function of tumor suppressor
genes, many of which are coding for transcription factors. For a molecular insight
into cancer, the transcriptional regulation of probably thousands of genes has
to be uncovered in detail by integrating expression array data with regulatory
site location data [2]. Although the understanding of the regulation of a couple of key
genes, like the cyclin-dependent kinase inhibitor p21WAFI/CIPI
[3], is already quite advanced, for the majority of the cancer-associated
genes such detailed analyses have not been performed. Even “big biology” projects, such
as ENCODE [4],
have focused only on 1% of the human genome sequence so far, while other
genome-wide scans, for example, for histone modifications [5–7] or transcription factor binding [8, 9], had to concentrate on only a subset of modifications and factors
under limited experimental conditions. Databases, such as oncomine [1] for gene expression data and the UCSC genome browser [10] for visualization of genome-wide chromatin
immunoprecipitation data and transcription factor binding site location data,
allow the combination of data from various projects. Together, these data
resources may provide sufficient insight to understand the regulation of an
individual gene in a complex disease state, such as cancer. In addition,
efforts to improve bioinformatics methods predicting the binding and
interaction of transcription factors together with more extensive experimental
datasets will fill important gaps [11].Each individual gene is under the control of a large set
of transcription factors that can bind upstream and downstream of its
transcription start site (TSS) [12]. These sites typically arrange into collections of
neighboring sites, the so-called modules or enhancers. Modules of transcription
factors that act on focused genomic regions have been shown to be far more
effective than individual factors on isolated locations and can act from large
distances up to hundreds of thousands of base pairs. In an ideal case such
transcription factor modules can be identified by parallel and comparative
analysis of their binding sites. Here, bioinformatics approaches can be of
great help, in case they can predict the actions of the transcription factors
precisely enough [13].PPARs are transcription factors that have the special
property to be ligand-inducible, which they share with most other members of
the nuclear receptor superfamily [14]. This property has attracted a lot of interest in the
nuclear receptor family as possible therapeutical targets in context of cancer.
PPARs were initially described as the nuclear receptors for compounds that
induce peroxisome proliferation in rodents [15], but now they are know to be important sensors of
cellular levels of fatty acids and fatty-acid derivatives that are mainly
derived from the lipoxygenase and cyclooxygenase pathways [16]. Polyunsaturated fatty acids activate the three PPAR
subtypes with relatively low affinity, whereas fatty acid derivatives show more
binding selectivity [17]. PPARs are
prominent players in the metabolic syndrome because of their role as important
regulators of lipid storage and catabolism [18],
but they also regulate cellular growth and differentiation and therefore have
an impact on hyperproliferative diseases, such as cancer [19].
Bioinformatic approaches to identify genomic targets of PPARs and important
cancer regulatory modules with colocalizing PPREs, as they will be described
below, should have a major impact on understanding the role and potential
therapeutic value of PPARs in cancer.


## 2. The PPAR Subfamily

The three PPAR subtypesα (NR1C1), β/δ (NR1C2), and γ (NR1C3) are coexpressed in numerous cell types from either ectodermal, mesodermal, or
endodermal origin, although their concentration relative to each other varies
widely [20, 21]. Importantly, most tumor cells express at least one PPAR subtype at
higher levels suggesting that PPAR ligands may modulate the transcription of
many PPAR target genes in a beneficial way.PPARα is highly expressed in cells that have active fatty acid
oxidation capacity including hepatocytes, cardiomyocytes, enterocytes, and the
proximal tubule cells of the kidney [22]. This PPAR subtype is a central regulator of hepatic fatty acid
catabolism and glucose metabolism. Furthermore, it potently represses the
hepatic inflammatory response by downregulating the expression of numerous
genes, such as various acute-phase proteins. PPARα is the molecular target for
the hypolipidemic fibrates, a group of drugs that are prescribed for their
ability to lower plasma triacylglycerols and elevate plasma HDL (high-density
lipoprotein) levels.PPARβ/δ is expressed ubiquitously and often displays at higher expression levels than PPARα and γ. It
stimulates fatty acid oxidation in both adipose tissue and skeletal muscle,
regulates hepatic VLDL (very low-density lipoprotein) production and catabolism
and is involved in wound healing by governing keratinocyte differentiation [23].PPARγ is expressed predominantly in adipose tissue and the
immune system and exists as two distinct protein forms γ1 and γ2, which arise
by differential TSSs and alternative splicing [22]. PPARγ is the master regulator of adipogenesis and regulates cell-cycle
withdrawal, as well as induction of fat-specific target genes that are involved
in adipocyte metabolism [24]. PPARγ stimulates the
expression of numerous genes that are involved in lipogenesis, including those
for adipocyte fatty acid-binding protein, lipoprotein lipase, and fatty acid
translocase (CD36). The general role for PPARγ in the regulation of lipid
metabolism is underlined by the therapeutic utilization of the PPARγ ligands
thiazolidinediones in obesity-linked type II diabetes [25].


## 3. PPARs and the Transcriptional Machinery

An essential prerequisite for the direct modulation of
transcription by PPAR ligands is the location of at least one activated PPAR
protein close to the TSS of the respective primary PPAR target gene. This is
commonly achieved through the specific binding of PPARs to a DNA binding site,
the so-called PPRE, and DNA-looping towards the TSS [26]. In detail, the DNA-binding domain of PPARs contact the major groove of
a double-stranded hexameric DNA sequence with the optimal AGGTCA core binding
sequence. PPARs bind to DNA as heterodimers with the nuclear receptor
retinoid X-receptor (RXR) [27]. PPREs are therefore formed by two hexameric core binding motifs in a
direct repeat orientation with an optimal spacing of one nucleotide (DR1),
where PPAR occupies the 5′-motif [28]. However, characterization of PPREs from regulated gene promoters has
resulted in a large collection of PPREs that deviate significantly from this
consensus sequence. An extensive binding data collection for PPARs was recently
published [29], where more critical deviations and well-tolerated deviations from the
consensus were identified as will be further explained in the following
chapters.When a nuclear receptor, such as PPAR, is bound to PPREs
in the regulatory regions of its target genes, it recruits positive and negative coregulatory proteins,
referred to as coactivators [30]
and corepressors [31],
respectively. In consequence, the transcriptional output is dependent on cell-
and time-specific expression patterns of these coregulators and can produce
distinct modulations of transcription factors, such as PPARs, due to
differences in the relative corepressor and coactivator protein levels. This
aspect has diagnostic and therapeutic value and can be extracted from
expression level data in different types of cancer [32].
Most unliganded nuclear receptors preferentially interact with corepressors to mediate
repression, but PPARs have been found to show a reasonable level of
constitutive activity [33], that is, in the absence of
ligand coactivator proteins can compete for binding. Most coregulators are not
exclusive to PPARs and even not specific to nuclear receptors, but
are also used in a similar manner by other transcription factors [34].
One group of coregulators covalently modifies histone proteins, which are as
nucleosome constituents the main chromatin proteins. This
acetylation/deacetylation and methylation/demethylation follows a precise and
combinatorial code, the so-called histone code [35].
The second group of coregulators includes ATP-dependent chromatin remodeling
factors that modulate the accessibility of genomic regions to transcription
factors and to the basal transcriptional machinery [36].
Recently, their actions have been monitored on genome-wide level to reveal
common patterns of transcriptionally active regions and regulatory sites [5, 7, 9]. These snap-shots have
provided important insights to common regulatory code, whereas more detailed
studies have explored the dynamics of these processes as described below.Repression and activation are more likely achieved by a series of sequential events that are mediated by
multiple enzymatic activities that are promoter and cell-type specific.
Transcriptional regulation is a highly dynamic event of rapid association and
dissociation of proteins and their modification, including degradation and de
novo synthesis. A pattern of recruitment and release of cohorts of
coregulatory complexes was demonstrated on a single region of thetrefoil factor-1 promoter in breast
cancer cells [37]. This study revealed detailed
and coordinated patterns of coregulator recruitment and preferential
selectivity for factors that have similar enzymatic activities. Similar cycling
was also observed for the recruitment of PPARβ/δ to the TSS of the pyruvate dehydrogenase kinase 4 (Degenhardt et al., unpublished).
Understanding the events that lead to the disturbance of such coordinated
action of regulatory proteins in cancer progression could help finding means to
reinitiate the coordinated regulation. Partial restoration of regulation was
demonstrated on the trefoil factor-1 promoter by removal of methylation in an
unresponsive cell line [38].


## 4. PPARs in Cancer

The rapid growth of tumor cells is highly dependent on the availability of macronutrients
and their metabolism. In their role as master regulators of lipid metabolism,
all three PPAR subtypes have at least an indirect function in controlling
cellular growth [26]. Moreover, the dominant
function of PPARγ in adipocyte differentiation and the suppression of apoptosis
in keratinocytes by PPARβ/δ suggest a direct role of PPARs in the control of
cellular growth and death [19]. As a consequence, a number
of prominent PPAR target genes, such as angiopoietin-like
4, lipoprotein lipase, LDL-receptor-related protein 1, and caveolin-1,
were described to be involved in the control of tumor cell growth [39–42].
Furthermore, there is a strong physiological link between chronic inflammation
and the onset of cancer [43]. In this way, the
anti-inflammatory actions of PPARs [44] provide an additional
argument for their control function on cellular proliferation, differentiation,
and apoptosis.However,
there is also evidence to state that PPARs may in some cases promote cancer
progression. PPARβ/δ has been implicated in colorectal carcinogenesis [45], its mRNA is often
upregulated in tumors and the deletion of the PPARβ/δ gene results in a profound loss of tumorigenicity in nude
mice [46]. Moreover, PPARβ/δ was found to have an
essential role in constraining tumor endothelial cell proliferation to allow
the formation of functional tumor microvessels, that is, the receptor is important for
angiogenesis [47].As a general argument, we can propose that the main role of PPARs, the control of
metabolism or inflammation, may also contribute to the regulation of cellular
growth. How that translates (via transcriptional regulation) into interference
in cancer progression or change to a more benign phenotype, may be highly
dependent on cancer type and state. In fact, the net effect of the activation
of some PPAR target genes may rather result in the stimulation of cellular
proliferation than in its inhibition, when examined alone. Data on gene
expression, on regulatory modules, on their accessibility, and on the binding
of PPARs to those modules need to be joined, in order to get a handle on the
pleiotropic effects of PPARs in cancer.


## 5. Methods for in Silico Screening of  Transcription Factor Binding Sites

The specificity of PPARs for their binding sites allows
constructing a model to describe the PPRE properties that can be used to
predict potential binding sites in genomic sequences. For this, the PPAR
binding preference, often expressed as position weight matrix (PWM), has to be described on the basis of experimental data,
such as series of gel shift assays with a large number of natural binding sites
[48–51]. However, PPAR-RXR heterodimers do not only recognize a pair of the consensus
binding motifs AGGTCA, but also a number of variations to it. Dependent of the
individual PWM description, this leads to a prediction of PPREs every 1000 to
10000 bp of genomic sequence. This probably contains many false positive
predictions, which is mainly due to scoring
methodology and the limitations that are imposed by the available experimental
data. For example, the quantitative characteristics of a transcription factor,
that is, its relative binding strength
to a number of different binding sites, is neglected in a position frequency
matrix, where simply the total number of observations of each nucleotide is
recorded for each position. Moreover, in the past there was a positional bias of transcription
factor binding sites upstream in close vicinity to the TSS. This
would be apparent from the collection of identified PPREs, but is in contrast with
a multigenome comparison of nuclear receptor binding site distribution [52] and other reports on wide-range
associations of distal regulatory sites [7].Internet-based software tools, such as TRANSFAC [53],
screen DNA sequences with databases of matrix
models. One approach used PWMs to describe the
binding preferences of PPARs using all published PPREs [54]. The accuracy of such methods can be
improved by taking the evolutionary conservation of the binding site and that
of the flanking genomic region into account. Moreover, cooperative interactions
between transcription factors, that is,
regulatory modules, can be taken into account by screening for binding
site clusters. The combination of phylogenetic footprinting and PWM searches
applied to orthologous human and mouse gene sequences reduces the rate of false
predictions by an order of magnitude, but leads to some reduction in
sensitivity [11]. Recent studies suggest that a surprisingly large
fraction of regulatory sites may not be conserved but yet are functional, which
suggests that sequence conservation revealed by alignments may not capture some
relevant regulatory regions [55].In effect, these approaches and tools are still
insufficient and there has to be a focus on the creation of bioinformatics
resources that include more directly the biochemical restrains to regulate gene
transcription. One important aspect is that
most putative transcription factor binding sites are covered by nucleosomes, so
that they are not accessible to the transcription factor. This repressive
environment is found in particular for those sequences that are either
contained within interspersed sequences, are located isolated from
transcription factor modules, or lie outside of insulator sequences marking the
border of chromatin loops [56]. This perspective strongly
discourages the idea that isolated, simple PPREs may be functional in vivo. In
turn, this idea implies that the more transcription factor binding sites a
given promoter region contains and the more of these transcription factors are
expressed, the higher is the chance that this area of the promoter becomes
locally decondensed.The PAZAR
information mall [57] is a tertiary database that
is build on the resource of a multitude of secondary databases and provides a
computing infrastructure for the creation, maintenance, and dissemination of
regulatory sequence annotation. The unambiguous identification of the
chromosome location for any given transcription factor binding site using
genomic coordinates allows to link the results from “big biology” projects,
such as ENCODE [4], and other whole genome scans
for histone modification and transcription factor association. Unfortunately,
so far only a few boutiques have been opened inside the PAZAR framework. In
order to benefit from binding site predictions, it is still necessary to
explore dedicated resources. For example, the well-known regulator of cell
cycle progression, the transcription factor p53, has an own dedicated database
(p53FamTaG) for integration of gene expression and binding site
data [58].The concept of cancer-specific regulatory modules has raised increasing attention recently.
Genome-wide prediction of enhancers based on analysis of transcription factor
binding affinity by a computational tool, called enhancer element locator [8],
was shown effective to dissect which types of cancer can be targeted by a given
transcription factor. Predictions validated in transgenic mouse embryos
revealed the presence of multiple tissue-specific enhancers in mouse c-myc and N-myc genes, which has implications for organ-specific growth
control and tumor-type specificity of oncogenes.


## 6. The Classifier Method for PPREs

Approaches for PPRE predictions have been based
on a collection of disparate binding data. To combine evidence from several publications
for an efficient binding model has challenges thus creating a demand for a
coherent binding dataset. The recently published classifier method [29] used the in vitro binding preferences of the
three PPAR subtypes on a panel of 39 systematic single nucleotide variations of
the consensus DR1-type PPRE (AGGTCAAAGGTCA) [59] as an experimental dataset. The single
nucleotide variants were sorted into three classes, where in class I the PPAR
subtypes are able to bind the sequence with a strength of 75 ± 15% of that of
the consensus PPRE, in class II with 45 ± 15%, and in class III with 15 ± 15%. Although
the overall binding pattern of the three PPAR subtypes showed no
major differences, some variations gave rise to a PPAR subtype-specific
classification. Additional 130 DR1-type PPREs were sorted on the basis of
counting increasing number of variations from the consensus and taking into
account the single nucleotide variant binding strength. Those variants that
alone decrease the binding only modestly (class I) could be combined with even
three deviations from consensus still resulting in more than 20% binding
relative to consensus. Other combinations resulted in faster loss of binding
detailed in 11 categories, where such combinations still resulted in more than
1% relative binding.The in silico binding
strength predictions of PPAR-RXR heterodimers were confirmed by gel shift
assays for the six PPREs of theuncoupling
protein 3 (UCP3) gene and showed
a deviation of less than 15% (Figure 1). Moreover, from 23 investigated genomic
regions that were selected from eight genes, 17 regions display significant
inducibility in the presence of PPAR ligands and in living cells. PPARα
and RXRα associated with 16 of these regions. For the UCP3 gene, for which previously no regulatory regions had been
described to account for the effect of PPAR ligands on its mRNA transcription,
three functional areas were identified [29].Figure 1
Comparison of in silico and experimental analysis of PPAR target genes. Overview of the genomic
organization of theUCP3 gene; 10 kB upstream and downstream
of its TSS are shown (horizontal black line). Putative PPREs were identified
using the classifier method performing in silico screening of the genomic
sequences. For each predicted PPRE, the calculated binding strength of PPARγ is represented
by column height. The average in vitro DNA binding strength of PPARγ-RXR
heterodimers was also determined by gel shift experiments.The main advantage, when comparing the
classifier to PWM methods, is a clear separation between weak PPREs and those
of medium and strong strength [29]. For the discovery of potential binding sites,
this is extra information that could be especially of interest in processes
considered context dependent, for example, for PPREs that reside in genomic
context of transcription factor modules. Predicting the strength of PPAR
binding can be a predictor of how prominent effect this receptor can have on a
target gene. For example, if binding is easily competed by other transcription
factors, the effect may not manifest in most tissues or it may manifest only in
tissues expressing all transcription factors of a module containing the PPRE.
As an example of the latter case, the insulin-like
growth factor binding protein 1 gene has a weak PPRE located inside a
well-conserved area (suggesting presence of other transcription factor binding
sites) and was only in liver responsive to PPAR ligands [59]. In contrast, genes with strong PPREs, such as carnitine
palmitoyltransferase1A and angiopoietin-like 4, are PPAR
responsive in many tissues (Heinäniemi et al., unpublished data).


## 7. Clustering of Known PPAR Target Genes

The data added by binding strength analysis and by covering
a larger regulatory region (±10 kB) was examined with all 38 human
genes that are known to be primary PPAR targets together with their mouse
ortholog. The clustering by predicted binding
strength and evolutionary conservation of their PPREs resulted in four
groups [29]. In general, clusters I to II contain genes that are well conserved
between human and mouse. Cluster I contains genes that carry multiple conserved
PPREs, while genes in cluster II have only one or two strong or medium
conserved PPRE in human, which are found in comparable strength and location in
the mouse. Cluster III contains genes that have strong or medium PPREs in one
species that are conserved only as weak PPREs in the other species. Finally,
cluster IV contains more than 25% of all tested genes, which have the common
property that they carry one or more PPREs, but none of them is conserved.
These examples suggest that regulation of target gene can survive turnover of binding
sites and might even benefit from it as indicated in Figure 2.Figure 2
Possible evolutionary changes to PPRE location, strength, and conservation. Hypothetical
genes from two different species (e.g., human and mouse) were compared for
their PPREs (black bars, their height indicates relative strength). When the
PPRE pattern is preserved, the genes will be sorted into cluster I, when
extended in cluster II, when replaced in cluster III and when not at all
conserved (e.g., when turned-over) in cluster IV.The clustering analysis indicated some useful features for whole genome PPRE
screens. Either the presence of at least one strong PPRE or more than two
medium PPREs within the 20 kB surrounding the annotated TSS of a gene is a strong indication for a PPAR target gene. In this way,
28 out of the 38 the human genes would have been identified as PPAR targets.
Similarly, for 29 of these 38 genes the analysis of their murine ortholog would
have come to the same conclusion. A combination of these two criteria (passing
the threshold in either the human or mouse ortholog) would have identified 37
of the 38 genes as PPAR targets.


### 7.1. A Look at PPREs in their Genomic Context: Putative Target Genes and Binding Modules

In the paper described above, the gene-dense human
chromosome 19 (63.8 MB, 1445 known genes) and its syntenic mouse regions
(956 genes have known orthologs) were selected for an in silico screening based
on the above explained criteria; that is, both species were investigated for
medium and strong PPREs (based on a PPARγ prediction) [29]. Interestingly,
20% of genes of chromosome 19 contain a colocalizing strong PPRE and additional 4% have more
than two medium PPREs or a proximal medium PPRE. These numbers suggest a total
of 4000 to 5000 targets for PPARs in the human genome, if no false positives
are assumed. Certainly, not all sites will be accessible and the human genome
also contains weak binding sites that could gain function via interaction with
other transcription factors. The latter can also be screened with the acquired
knowledge on PPAR binding preferences down to 1% relative to the consensus
PPRE. Experimentally, a complete evaluation of the selectivity of any such
screen is complicated by the restricted expression profiles of the predicted
genes, which prevents simple readouts from individual target tissues. When
requiring the detection in human and mouse, 12.1% of genes from chromosome 19
were predicted as PPAR targets. In this approach, full alignment was not
required, just preservation of what could be called PPAR binding potential. The
more strong PPREs a gene has accumulated, the smaller the chances are that
given all 250 human tissues none of these sites would get accessible or be
built into a regulatory module with other transcription factor binding sites. Of relevance to cancer several cell cycle regulating genes
were found by the screen, some of which have been reported as PPAR targets by
others, such as G1/S-specific cyclin E [60],
p19INK4d [61], prostate tumor overexpressed
gene, serine protease hepsin [62] and the serine/threonine
kinases associated with cell cycle regulation p21-activated kinase 4
(PAK4), and homeodomain-interacting protein kinase 4.
In addition, the prostate tumor marker kallikrein-3 [63]
and several other kallikrein gene family members were detected. From
novel targets, the regulatory regions of a ceramide synthesis regulator, LASS1,
were experimentally confirmed [29]. Overexpression of this protein
has been shown to restore normal ceramide levels and inhibit the growth of head and neck squamous
carcinomas [64].The complete list of putative PPAR
target genes in chromosome 19 [29] offers
interesting candidates representing physiological functions connected to PPARs.
It will gain more power, when it can be integrated with other genomic screens,
both experimental and bioinformatics, as has been outlined in the previous
discussion. A vision for future of targeting cancer regulatory modules with
colocalizing PPREs is depicted in Figure 3. A PPRE
track (for simplicity binding strength was not indicated) provided by bioinformatics approaches can
be compared against evidence of other regulatory modules provided by
conservation analysis and screens for other transcription factors. Experimental
data comparing regulation in a specific cancer type versus normal cells can be
visualized in the same context to detect overlap in functional binding sites.
Given the high interest of the scientific community to better characterize
binding profiles of different transcription factors and the improved
experimental techniques to detect genome-wide binding events, such additional
tracks combined with a PPRE binding track could be available in near future.Figure 3
A gene module map compiled from bioinformatics data and experimental datasets. The superimposition of the PPRE track (in green on top) on
other genome-wide datasets can reveal promising PPRE-containing binding modules
for targeted therapy via PPAR activation. In this imaginary setting,
transcription factor 1 (in blue) is known to be one main regulator of the
hypothetical gene X and this regulation is altered in cancer. Transcription
factor 2 (in yellow) synergistically activates gene X, but is lost in cancer
cells. Chromatin immunoprecipitation data comparing normal and cancer binding
profiles for this transcription factor reveal two main regulatory modules under
normal conditions and a weaker binding in cancer samples due to loss of
transcription factor 2. A colocalizing PPRE in module 2 could enable PPARs to
replace transcription factor 2 in this module and to restore strong activation
of this gene.


## 7.1. A Look at PPREs in their Genomic Context: Putative Target Genes and Binding Modules

In the paper described above, the gene-dense human
chromosome 19 (63.8 MB, 1445 known genes) and its syntenic mouse regions
(956 genes have known orthologs) were selected for an in silico screening based
on the above explained criteria; that is, both species were investigated for
medium and strong PPREs (based on a PPARγ prediction) [29]. Interestingly,
20% of genes of chromosome 19 contain a colocalizing strong PPRE and additional 4% have more
than two medium PPREs or a proximal medium PPRE. These numbers suggest a total
of 4000 to 5000 targets for PPARs in the human genome, if no false positives
are assumed. Certainly, not all sites will be accessible and the human genome
also contains weak binding sites that could gain function via interaction with
other transcription factors. The latter can also be screened with the acquired
knowledge on PPAR binding preferences down to 1% relative to the consensus
PPRE. Experimentally, a complete evaluation of the selectivity of any such
screen is complicated by the restricted expression profiles of the predicted
genes, which prevents simple readouts from individual target tissues. When
requiring the detection in human and mouse, 12.1% of genes from chromosome 19
were predicted as PPAR targets. In this approach, full alignment was not
required, just preservation of what could be called PPAR binding potential. The
more strong PPREs a gene has accumulated, the smaller the chances are that
given all 250 human tissues none of these sites would get accessible or be
built into a regulatory module with other transcription factor binding sites. Of relevance to cancer several cell cycle regulating genes
were found by the screen, some of which have been reported as PPAR targets by
others, such as G1/S-specific cyclin E [60],
p19INK4d [61], prostate tumor overexpressed
gene, serine protease hepsin [62] and the serine/threonine
kinases associated with cell cycle regulation p21-activated kinase 4
(PAK4), and homeodomain-interacting protein kinase 4.
In addition, the prostate tumor marker kallikrein-3 [63]
and several other kallikrein gene family members were detected. From
novel targets, the regulatory regions of a ceramide synthesis regulator, LASS1,
were experimentally confirmed [29]. Overexpression of this protein
has been shown to restore normal ceramide levels and inhibit the growth of head and neck squamous
carcinomas [64].The complete list of putative PPAR
target genes in chromosome 19 [29] offers
interesting candidates representing physiological functions connected to PPARs.
It will gain more power, when it can be integrated with other genomic screens,
both experimental and bioinformatics, as has been outlined in the previous
discussion. A vision for future of targeting cancer regulatory modules with
colocalizing PPREs is depicted in Figure 3. A PPRE
track (for simplicity binding strength was not indicated) provided by bioinformatics approaches can
be compared against evidence of other regulatory modules provided by
conservation analysis and screens for other transcription factors. Experimental
data comparing regulation in a specific cancer type versus normal cells can be
visualized in the same context to detect overlap in functional binding sites.
Given the high interest of the scientific community to better characterize
binding profiles of different transcription factors and the improved
experimental techniques to detect genome-wide binding events, such additional
tracks combined with a PPRE binding track could be available in near future.Figure 3
A gene module map compiled from bioinformatics data and experimental datasets. The superimposition of the PPRE track (in green on top) on
other genome-wide datasets can reveal promising PPRE-containing binding modules
for targeted therapy via PPAR activation. In this imaginary setting,
transcription factor 1 (in blue) is known to be one main regulator of the
hypothetical gene X and this regulation is altered in cancer. Transcription
factor 2 (in yellow) synergistically activates gene X, but is lost in cancer
cells. Chromatin immunoprecipitation data comparing normal and cancer binding
profiles for this transcription factor reveal two main regulatory modules under
normal conditions and a weaker binding in cancer samples due to loss of
transcription factor 2. A colocalizing PPRE in module 2 could enable PPARs to
replace transcription factor 2 in this module and to restore strong activation
of this gene.


## 8. Conclusion

The identification of genes showing a
primary response to PPARs and their ligands, the so-called PPAR regulome, can
be used as a prediction of their therapeutic potential as well as their
possible side effects. Methods incorporating both experimental- and
informatics-derived evidence to arrive at a more reliable prediction of PPAR
targets and binding modules can bring all available data together with the aim
to predict outcome in specific context. Taking the chromosome 19 in silico screening trial as an example and
extrapolating the results to the whole human genome, we suggest that
approximately 10% of all human genes (an estimate of 2000 to 2500 genes) have
the potential to be directly regulated by PPARs by their PPRE content within 10
kB distance to their TSS. Translated to regulatory modules that colocalize with
PPREs, an even larger number of genomic regions could be targeted by PPARs. In
conclusion, in this review we have addressed the identification of direct
targets using genomic sequences and binding data. In parallel, we have
discussed the potential of looking for PPREs inside regulatory modules
foreseeing that in future, very likely the emphasis will shift from target
genes to target regulatory modules to alter a physiological response and from
individual genes to whole genome response.



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*Source: 749073-2008-06-05.xml*