of Human p53 Binding Sites:
Cell Cycle Versus Apoptosis
Monica M. Horvath, Xuting Wang, Michael A. Resnick, Douglas A. Bell*
Laboratory of Molecular Genetics, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, United States of America
The p53 tumor suppressor is a sequence-specific pleiotropic transcription factor that coordinates cellular responses to
DNA damage and stress, initiating cell-cycle arrest or triggering apoptosis. Although the human p53 binding site
sequence (or response element [RE]) is well characterized, some genes have consensus-poor REs that are nevertheless
both necessary and sufficient for transactivation by p53. Identification of new functional gene regulatory elements
under these conditions is problematic, and evolutionary conservation is often employed. We evaluated the
comparative genomics approach for assessing evolutionary conservation of putative binding sites by examining
conservation of 83 experimentally validated human p53 REs against mouse, rat, rabbit, and dog genomes and detected
pronounced conservation differences among p53 REs and p53-regulated pathways. Bona fide NRF2 (nuclear factor
[erythroid-derived 2]-like 2 nuclear factor) and NFjB (nuclear factor of kappa light chain gene enhancer in B cells)
binding sites, which direct oxidative stress and innate immunity responses, were used as controls, and both exhibited
high interspecific conservation. Surprisingly, the average p53 RE was not significantly more conserved than
background genomic sequence, and p53 REs in apoptosis genes as a group showed very little conservation. The
common bioinformatics practice of filtering RE predictions by 80% rodent sequence identity would not only give a
false positive rate of ;19%, but miss up to 57% of true p53 REs. Examination of interspecific DNA base substitutions as
a function of position in the p53 consensus sequence reveals an unexpected excess of diversity in apoptosis-regulating
REs versus cell-cycle controlling REs (rodent comparisons: p , 1.0 e?12). While some p53 REs show relatively high
levels of conservation, REs in many genes such as BAX, FAS, PCNA, CASP6, SIVA1, and P53AIP1 show little if any
homology to rodent sequences. This difference suggests that among mammalian species, evolutionary conservation
differs among p53 REs, with some having ancient ancestry and others of more recent origin. Overall our results reveal
divergent evolutionary pressure among the binding targets of p53 and emphasize that comparative genomics
methods must be used judiciously and tailored to the evolutionary history of the targeted functional regulatory
Citation: Horvath MM, Wang X, Resnick MA, Bell DA (2007) Divergent evolution of human p53 binding sites: Cell cycle versus apoptosis. PLoS Genet 3(7): e127. doi:10.1371/
Since the completion of the human genome, cataloging
transcription factor binding sites (TFBSs) has been critical for
understanding gene regulation. The use of comparative
genomics (evolutionary conservation across species) is often
championed as a method to separate the functional regu-
latory sequence ‘‘wheat’’ from the nonfunctional ‘‘chaff’’ .
As the number of mammalian full genome drafts increases,
the integration of TFBS predictions with lists of conserved
noncoding regions (CNCs) has emerged as a key step in the
TFBS identification process [2–4]. If TFBS predictions are
contiguous to DNA features that coincidentally have a critical
structural role such as maintenance of chromatin organiza-
tion, the appearance of conservation may be intensified even
Although these methods have greatly enhanced our knowl-
edge of the human genome’s regulatory repertoire, over-
reliance on conservation information can potentially exclude
genuine binding sites. Since TFBSs are typically small, they
can arise by chance in a gene’s promoter and therefore may
decrease selective pressures to maintain already existing sites
. Another wrinkle is that the evolutionary forces that
created the conservation blocks may no longer be function-
ally relevant to humans . Additionally, recent scans for
natural selection in human gene coding regions have revealed
that distinct biological pathways often are subject to widely
different evolutionary pressures [7,8], particularly since
mutation rates have been shown to vary across the genome
. Genes involved in oncogenesis and tumor suppression
have experienced recent selection for mutation in primate
lineages [7,8,10]. DNA binding sites of transcription factors
Editor: Takashi Gojobori, National Institute of Genetics, Japan
Received January 31, 2007; Accepted June 15, 2007; Published July 27, 2007
A previous version of this article appeared as an Early Online Release on June 15,
This is an open-access article distributed under the terms of the Creative Commons
Public Domain declaration, which stipulates that, once placed in the public
domain, this work may be freely reproduced, distributed, transmitted, modified,
built upon, or otherwise used by anyone for any lawful purpose.
Abbreviations: AUC, area under the curve; CNC, conserved noncoding region;
NFjB, nuclear factor of kappa light chain gene enhancer in B cells; NRF2; nuclear
factor (erythroid-derived 2)-like 2 nuclear factor; OR, odds ratio; RE, response
element; ROC, receiver operator characteristic; TFBS, transcription factor binding
* To whom correspondence should be addressed. E-mail: BELL1@niehs.nih.gov
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are also functional components of these pathways and are
likely under similar evolutionary pressures. Indeed, we have
focused recently on identifying human single nucleotide
polymorphisms that alter the function of transcription
factors [11,12]. As a result, we have investigated the
assumptions for using mammalian conservation as an
obligatory screening step for seeking TFBSs.
The p53 tumor suppressor gene, encoded by the TP53
master regulatory gene, is a transcription factor that
coordinates a network of cellular responses to environmental
insults. Over half of human cancers have a mutation in the
p53 protein or one of its partners . The p53 protein is
estimated to have several hundred transregulation target
genes that affect pathways including apoptosis, DNA damage
repair, and cell-growth arrest . As a result, p53 target
genes are highly sought-after drug targets for halting cancer
progression. According to in vitro experiments, the p53
protein binds specifically to a palindromic consensus
sequence, RRRCWWGYYY(N0?13)RRRCWWGYYY , with
nearly all REs containing at least one mismatch; in vivo results
have suggested that the spacer region may be much smaller
[14,15]. The sequence is typically located within 5,000 bases of
the target gene’s transcriptional start site, and p53 either
induces or represses expression upon p53 binding [16,17].
One feature of p53 that confounds the discovery of novel
transregulated genes is that while some binding sites match
the expected consensus sequence quite well, others can be
consensus poor and yet are both necessary, and sufficient, to
transactivate a gene . A recent study has suggested that
the ‘‘rules of engagement’’ for p53 REs may differ based on
the activated pathway, particularly in the apoptosis and cell-
cycle–related systems . Thus, we have used cross-species
conservation to examine if these groups of elements exhibit
distinct conservation profiles.
To evaluate the utility of comparative genomics approaches
in the identification of potential p53 target REs, we gleaned
the literature for a high quality set of bona fide p53 REs to
estimate the degree of conservation between humans and
other mammals. To relate the TP53 system to other master
regulators, we compare its binding site conservation to those
of the transcription factors encoded by two other genes: NFjB
(nuclear factor of kappa light chain gene enhancer in B-cells),
central to inflammation responses, and NFE2L2, which
encodes NRF2 (nuclear factor [erythroid-derived 2]-like 2
nuclear factor), a regulator of oxidative stress. Their reper-
toire of interactions is expected to be highly preserved
throughout the mammalian lineage. The NFjB transcription
factor is a heavily studied biological switch of the inflamma-
tion, apoptosis, and immune responses [20,21]. It binds the
consensus sequence GGGRNNYYCC [22,23], and its signaling
system is highly conserved even when examined in inverte-
brates [21,24]. NRF2 binds antioxidant REs (consensus
sequence ¼ TMANNRTGAYNNNGCRWWWW ) that are
comparable in size to those of p53, show high levels of
conservation , and are found in the promoters of genes
that confer protection from oxidative stress and chemical
carcinogens . Mouse models of Nrf2-dependent response
to oxidative and electrophilic insults have been used to study
function [28,29]. Additionally, the Nrf2 pathway in zebrafish
operates similarly to humans and underscores the likelihood
of high conservation in regulatory binding sites . Because
the NFjB and NRF2 binding sites were determined to be
highly conserved, these two sets of TFBSs serve as positive
controls in estimates of conservation. Our comparative
genome analysis, which includes a coincident evaluation of
sampled promoter sequences and coding region sequence,
reveals that mammalian conservation does not apply to p53
target REs in general. However, among subgroups of target
genes we observe purifying selection acting on a number of
p53 binding sites, including many cell-cycle–related genes,
while rodent to human homology is lacking for p53 REs in
Conservation of TFBSs
The literature was scanned for validated p53 (83, Table 1),
NFjB (21, Table 2), and NRF2 (21, Table 3) binding sites
associated with human genes. Human genome coordinates
were located and then referenced within global multiple
alignments held at the University of California Santa Cruz
[UCSC] (California, United States) genome browser website
 to find their corresponding locations in eleven other
mammals. Using these global alignments, percent sequence
identity was calculated for each of the 125 binding sites across
the eleven mammals, with the calculation adapted to reflect
consensus sequence degeneracy, since every model RE had
positions where one or more of the four nucleotides could be
tolerated. Also, the p53 RE was unique in that the spacer
region between the two half sites could be any size or
sequence up to thirteen bases. We therefore calculated
sequence identity by omitting the p53 spacer region and
ignoring mismatches in the alignments that still fit the TFBS
consensus (CDKN1A and PCNA examples shown in Figure 1,
others in Figure S3).
Figure 2 plots the conservation distribution for each set of
human TFBSs across mouse, rabbit, rat, and dog. Although
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Divergence of p53 Response Elements
The p53 tumor suppressor is a transcription factor that coordinates
cellular responses to DNA damage and stress, initiating cell-cycle
arrest or triggering apoptosis. Evolutionary conservation is often
employed to separate the functional ‘‘wheat’’ from the nonfunc-
tional ‘‘chaff’’ when identifying binding sites of transcription factors
like p53. We evaluated evolutionary conservation of 83 experimen-
tally validated human p53 binding sites against mouse, rat, rabbit,
and dog genomes, and similarly examined binding sites for two
other transcription factors as controls, NRF2 (nuclear factor
[erythroid-derived 2]-like 2 nuclear factor) and NFjB (nuclear factor
of kappa light chain gene enhancer in B cells), which direct oxidative
stress and innate immunity responses, respectively. NRF2 and NFjB
binding sites both exhibited high interspecific conservation,
indicative of purifying natural selection, but surprisingly, human
p53 response elements on average displayed a lack of conservation.
Thus conservation is not useful in the prediction of p53 binding
sites. After grouping p53 REs by gene ontology, we observed that
binding sites in cell-cycle genes like CDKN1A displayed high
conservation, while p53 binding sites in apoptosis and DNA repair
genes showed an unexpected excess of diversity and very little
homology with rodent sequences. Overall these results reveal
divergent evolutionary pressure among the binding targets of p53
and suggest caution in generalizing about the similarity of
regulation of the p53 pathway between humans and rodents.
comparative data relating the chimpanzee and rhesus
monkey were also analyzed, we observed, as expected, that
these species were too evolutionary close to humans to be
informative (Figure S1). For example, nearly any human
sequence was in excess of 95% conserved in these two
primates. Also graphed in Figure 2 are the results for sets of
DNA sequence fragments randomly chosen from promoter
(gray) and protein-coding (blue) regions. This allows TFBS
conservation levels to be viewed in context of the evolu-
tionary pressures exerted on other genomic sequences. The
poorly conserved element shown in Figure 1 (PCNA) would
fall in a lower percentage bin (i.e., be on the left side of the
graph), as is the case for the randomly chosen promoter
sequences. The promoter fragments are representative of the
background genome sequence in which most TFBSs reside.
On the other hand, if TFBSs were very well conserved, then
the distribution would be right-shifted, as is the case for the
protein-coding region fragments (blue). To use conservation
as a metric to separate true binding sites from the rest of the
genome, their conservation should be significantly greater
than that of randomly chosen promoter regions. The spike of
TFBSs at 0%–9% identity in each panel represents species-
specific sites that are essentially not present in the other
For each of the human-to-mammal comparisons in Figure
2, NFjB and NRF2 sites produced identity distributions that
appear very similar to distributions from the coding region
fragment group (many sites with 90%–100% identity), which
was representative of genome sequence under high purifying
selection. This strongly suggests that NFjB and NRF2 sites
may be under purifying selection. The human-to-mammal
p53 site comparisons, on the other hand, produced con-
servation profiles in each species that have a high frequency
of sites at zero percent identity and fewer with 90%–100%
Table 1. Validated Human p53 TFBSs
Target GeneSequenceFunctionTarget GeneSequenceFunction
p53 consensus sequence¼RRRCWWGYYY(N0?13)RRRCWWGYYY. The number of spacer bases are shown in parentheses. R¼G or A; W¼A or T; Y¼C or T. ROS, Reactive oxygen species.
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Divergence of p53 Response Elements
identity. This distribution is similar to the distribution
obtained from randomly sampled promoter fragments (gray),
which we used to represent genome sequence under neutral
selection. In mouse the p53 RE identity distribution was
correlated with the promoter fragment identity distribution
while NFjB and NRF2 showed less correlation with the
promoter distribution (Table S3).
Since p53 sites as a group were observed to have as many
interspecific substitutions as the background genomic se-
quence, use of conservation level to predicting bona fide sites
would not be effective. However, this result could be due to
the fact that the set of 83 p53 TFBSs actually represents two
or more subsets of p53 REs with distinct conservation
profiles. A recent study hypothesized that the sequence
requirements of p53 REs may differ based on the activated
pathway such as apoptosis, DNA repair, cell-cycle check-
points, or cell-growth arrest . We therefore investigated if
low and high percent identity values would apportion with
p53 REs grouped by function, thereby detecting evolutionary
divergence between p53 pathways.
Among these 83 p53 REs, we carried out analysis of the two
largest subgroups (Table 1), apoptosis-related (n ¼ 29) and
cell-cycle/cell-growth–related (n ¼ 23), on the basis of
observations of Qian et. al . Average percent identity to
a consensus sequence was calculated for each of the tran-
scription factor groups, including the p53 subsets, and
compared via a two-tailed t-test assuming unequal variances
between the datasets. The results are displayed as odds ratios
(OR) in Table 4, and OR values represent the odds that one
type of human TFBS (columns) will be found as more
conserved than a second TFBS type (rows) in comparison
with other mammalian species. Ratios less than 1 (e.g., p53
apoptosis compared to all p53 sites) suggest lower conserva-
tion of the TFBS in the row. Among all species, the relative
conservation levels of NFjB compared to NRF2 sites were
similar and the p-values for difference were not significant.
NFjB sites were significantly more conserved than the entire
set of p53 sites in mouse and dog, while NRF2 sites were
significantly more conserved than all p53 sites in mouse and
rat. The magnitude and statistical significance of the differ-
ences between sequence motifs in Table 4 was greatest when
comparing either NRF2 or NFjB and the apoptotic p53 sites.
For example, in each species, the ORs for relative conserva-
tion between either NRF2 or NFjB and apoptosis genes were
all high (OR . 3.0) and at a significance of at least p , 0.019.
On the other hand, the mean conservation level of the cell-
cycle–regulating p53 REs were not statistically different from
the NRF2 or NFjB sites. These observations imply that the
Table 2. Validated Human NRF2 TFBSs
Target GeneSequence Target GeneSequence
NRF2 consensus sequence ¼ TMAnnRTGAYnnnGCRWWW.
Table 3. Validated Human NFjB TFBSs
Target GeneSequence Target GeneSequence
NFjB consensus sequence ¼ GGGRnnYYCC.
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Divergence of p53 Response Elements
mean p53 conservation level for all elements is really a
combination of the effect of the two ontological subgroups.
It has been proposed that an alternative method for
evaluating a DNA fragment’s conservation level is to ask
whether it sits within a block of conservation . All bona
fide TFBSs examined in this study were matched against the
‘‘most conserved’’ track of the UCSC genome browser to ask
whether a significant proportion fell within conserved
blocks. Only 19.2% of all p53 REs mapped to these regions,
while 52.8% of NRF2 and 57.1% of NFjB TFBSs could be
colocated (Table 5). We also assessed the conservation of the
randomly chosen promoter sequences according to this
block method and used a two-tailed binomial test to
calculate statistical significance. Intriguingly, all TFBS groups
mapped to more blocks than the random promoter
sequences except for the apoptosis subgroup of p53 REs.
These data mirrored the percent identity conservation
metric and again underscored that the apoptotic-regulating
p53 TFBSs may not have been under purifying selection
throughout mammalian evolution.
Inspection of the individual alignments between human
p53 REs and mouse reveal that 38% (11/29) apoptosis
elements and 9% (2/21) of cell-cycle element could not be
aligned with the multiz global alignment tool and thus
showed zero identity. Thus for human p53 REs in genes such
as BAX, FAS, PCNA, CASP6, SIVA1, and P53AIP1 we observed
little, if any, similarity with rodent sequences, and these
nonaligned sequences (zero identity) strongly impact the
calculations we have made.
Predictiveness of Mammalian Conservation for TFBSs
Although it was informative to know how well human p53
TFBSs are conserved relative to other regulatory motifs, an
aim of this study was to probe the utility of comparative
genomics for authenticating binding sites predicted by
computational methods. Receiver operator characteristic
(ROC) curves were employed to demonstrate the sensitivity
of TFBS prediction when qualified by conservation informa-
tion. ROC curves are traditionally used to measure the quality
of a binary classification algorithm, as a discrimination
threshold is varied. Area under a ROC curve provides a
visual representation of how well the conservation metric can
classify the sets of bona fide TFBSs as true positives. For
example, the area can be interpreted as the probability that
when both a bona fide TFBS and a random promoter
sequence of equal lengths are chosen at random, the decision
function (conservation in a species) assigns a higher value to
the bona fide TFBS. A perfect decision function would
generate a curve with an area of 1, meaning that 100%
sensitivity was obtained (i.e., all true positives were found),
and 100% specificity was reached (i.e., no false positives were
generated). If conservation predicted TFBS authenticity no
better than random chance, a line at 45 8 to the x-axis would
be generated that bisects the ROC space (area under the
curve [AUC] ¼ 0.5), because as the threshold is raised, equal
numbers of true and false positives compose the chosen set of
TFBSs. A ROC curve that fell below this diagonal would
indicate that conservation consistently predicted poorly,
meaning that one should employ the lack of conservation as
a decision classifier to authenticate TFBSs.
When TFBS conservation was evaluated as a TFBS
predictor in each of the four mammals, bona fide NRF2
and NFjB sites were consistently well predicted, whereas the
ROC curve describing all p53 sites approached the random
diagonal (Figure 3A–3D). The latter implies that conservation
analysis in these model organisms cannot enhance p53
binding site discovery, for the predictive capacity is only
slightly better than random. For example, if a cutoff of 80%
identity to mouse was employed as the rule for choosing p53
binding sites, only 43% of real p53 REs would be found, and
19% of the selections would be false positives (Figure 3A). We
were concerned that ascertainment bias (e.g., the presence of
spurious REs) in this large set of p53 sites might affect our
findings. However, the predictivity level for any of these
species does not change appreciably even when the p53 RE
list is restricted to only the 30 best-characterized sites (Figure
Figure 1. Examples of Calculated Interspecies Conservation Scores for
Multispecies alignments for p53 binding sites in the (A) CDKN1A and (B)
PCNA genes are shown. Mismatches (green) from human that do not
alter the p53 consensus motif, RRRCWWGYYY(N0?13)RRRCWWGYYY, do
not penalize the percent sequence identity, whereas consensus-altering
mismatches and insertion/deletion events do (red). For the p53 binding
sites, spacer elements are in gray and are not considered when
calculating percent identity. R ¼ G or A; W ¼ A or T; Y ¼ C or T.
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Divergence of p53 Response Elements
S2). In contrast, for NFjB an 80% mouse conservation
threshold would allow discovery of 86% of real NFjB sites
with a 25% false positive rate (Figure 3A). As a result, human/
mouse multiple sequence alignments are highly useful for
identifying novel NFjB sites but not so for p53 motifs. This
conclusion is reiterated in all four model organisms by
statistical evaluation of the AUC calculations (Table 6). p53
curves had smaller AUCs compared to those for NFjB and
A recent study noted that although the spacer region
between half sites for p53 REs can be zero to13 bases, small
spacers were overwhelmingly preferred in a distribution of
spacer length derived from genome-wide chromatin immu-
noprecipitation experiments . This suggests that REs with
large spacers might not be valid, and we hypothesized that if
ROC curve analysis was restricted to only p53 REs with small
spacers (presumed higher quality), perhaps much greater
conservation would be observed. We examined the set of p53
REs having two or fewer spacer nucleotides between half sites
and observed no increase in conservation (Figure S2). Not
only was there no improvement in TFBS prediction sensi-
tivity for this subset, but the ROC areas were visibly greater
Figure 2. Distribution of Human-to-Mammal TFBS Conservation Scores
Graphed for each (human-to-mouse, -rabbit, -rat, and -dog) comparison in (A–D) are: the percentage of all TFBSs from each gene group (p53, 83 REs;
NRF2, 21 REs; or NFjB, 21 REs) that fall within the x-axis conservation bins (red bars); data for randomly sampled fragments from human coding regions
(blue bars); and gene promoters (gray bars). Coding regions (representing sequence under purifying selection) and randomly sampled promoter
regions (representing sequence under more neutral selection) were sampled 1,000 trials; error bars are narrower than the line art. Averages and
standard errors for the sets of known TFBSs are reported above each graph.
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Divergence of p53 Response Elements
for rat and rabbit comparisons in the inverse set of REs (i.e.,
3þ spacer bases). Thus, as judged by conservation, p53 RE
spacer region length could not be considered a measure of RE
We then examined p53 RE conservation in light of gene
ontology. When p53 REs were subdivided based on functional
class, the sensitivity of interspecific conservation to predict
cell-cycle/cell-growth sites improved considerably, approach-
ing that for the NFjB and NRF2 targeted genes (Figure 3).
The p53 apoptotic REs (Figure 3, dashed line), on the other
hand, showed a dramatically different conservation profile. In
the case of the mouse (AUC ¼ 0.616) and rat (AUC ¼ 0.568),
the ROC curve hovered just above the random line, which
indicates a lack of sensitivity. For two species (rabbit, AUC ¼
0.469 and dog, AUC ¼ 0.437), the sequence identity metric
had an apoptosis RE discovery rate worse than random
prediction. This suggests that the functional, apoptotic p53
binding sites are less conserved than randomly sampled
sequences in gene promoters. This phenomenon was also
observed when ROC curve analysis was carried out in other
distant mammalian species (tenrec, armadillo, elephant, and
opossum) (unpublished data).
One explanation for this provocative result could be that
apoptotic p53 sites might actually display a slightly different
consensus p53 binding site than that reported in the
literature. Perhaps a better-fitting consensus would improve
conservation. We aligned all p53 sites (83), apoptotic (29), and
cell-cycle (23) p53 sites (Figure S3) and generated sequence
logos  (Figure S3A–3C) to identify improved patterns, but
while there are small differences, none fit better than the
existing consensus of RRRCWWGYYYN0?13RRRCWWGYY.
Likewise, simply permitting any nucleotide at the least
compositionally biased positions in this p53 RE subset (bases
2, 8, 10, and 11 of the p53 consensus) did not improve the
area under the ROC curve (Figure S2). These data emphasize
not only that conservation analysis cannot improve identi-
fication of certain TFBSs like p53, but also that subclasses of
the same binding site may reflect distinct evolutionary
Sequence Diversity as a Function of Position in the
A second approach was used to detect if conservation
differed among nucleotide positions within the binding site.
That is, could we observe heightened human-to-mammal
interspecific substitutions or ‘‘sequence diversity’’ at partic-
ular locations within each TFBS consensus sequence? To
accomplish this, we aligned all TFBSs within each group (p53,
NRF2, or NFjB) and calculated the positional sequence
diversity, which was the percentage of aligned bases at each
position that varied from the accepted consensus sequence
(Figure 4). For example, in Figure 4A, the first position in the
p53 consensus sequence differed from a purine base (R) at the
equivalent position in the mouse in 35% of all p53 TFBSs,
while positional diversity for randomly sampled promoter
sequence was 63% and that for coding region sequence was
25%. Highly conserved sequence would be plotted lower on
Table 4. Comparison of Mean TFBS Conservation Values (Mouse to Human, Rabbit to Human, Rat to Human, and Dog to Human)
TP53 (Apoptosis)TP53 (Cell Cycle)NRF2NFKB
TP53 (cell cycle)
TP53 (cell cycle)
TP53 (cell cycle)
TP53 (cell cycle)
0.63 (0.218)1.74 (0.101)
Rabbit0.38 (0.006)* 1.60 (0.261)
Rat0.54 (0.105)2.21 (0.020)*
Dog0.44 (0.020)*1.33 (0.427)
aThe ORs are calculated as columns over rows. For example, in the human to mouse comparison, p53 cell cycle REs are 2.73 times more conserved than p53 apoptosis REs (p ¼ 0.023).
*Significant p-values for a two-tailed t-test between each sample’s mean conservation.
Table 5. Mapping of Human TFBSs to Hidden Markov Model-
Generated Conservation Blocks
TFBSNumber Conserved Binding
Sites out of Total (%)
p53, cell cycle
*In a set of randomly chosen promoter regions of the same length, two-tailed binomial
probability of finding an equivalent or greater fraction of conserved sites.
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Divergence of p53 Response Elements
the y-axis (less diversity) as displayed by the coding region
line (blue), while less conserved sequence would exhibit high
diversity and appear near the top of the graph (e.g., gray,
random promoter sequences). Small peaks observed in the
promoter and coding region plots reflect degeneracy of the
consensus sequence, with more degenerate positions exhibit-
ing less calculated diversity. Patterns of the promoter and
coding sequence lines are highly similar across species in
Figure 4 except for being shifted on the y-axis. This was an
expected feature of the data since these control curves were
plotted as the average result of 1,000 trials of sequence
fragment sampling across the human genome. When examin-
ing the population statistic of a large number of fragments,
the average coding or promoter region fragments will exhibit
similar transversion and transition mutation rates across
species, which are visualized in these patterns.
Figure 4D and 4E demonstrate that the positional sequence
diversity of NRF2 and NFjB sites mirrored coding region
sequence diversity across all species, as expected from the
previous conservation analysis (Figure 3). Figure 4C shows a
similar effect, with p53 cell-cycle–related sites displaying low
sequence diversity. Intriguingly, the apoptotic-related p53
binding sites (Figure 4B) showed levels of sequence diversity
that often met or exceeded those of the background
promoter sequences. In rabbit and dog these apoptotic p53
binding sites have diverged so much that they may have lost
function or could be under positive selection for mutation.
The differences in positional sequence diversity between the
two p53 RE subgroups were all highly significant (two-tailed
paired t-test assuming unequal variances: dog ¼ 1.4e?13,
mouse ¼ 4.3e?12, rabbit ¼ 2.0e?17, and rat ¼ 2.0e?15). These
dramatic results indicate again that these different classes of
p53 binding sites may be subject to widely dissimilar sequence
The wide variety of genes transcriptionally regulated by p53
highlights the pleiotropic role of this master regulatory
protein in many different biological pathways. Here, we
addressed the conservation of human p53 binding sites across
several mammals commonly used as experimental models.
Examining global alignments of established human p53
binding sites, we found that common comparative genomics
methods do not generally enhance p53 binding site predic-
tion, although they can for NFjB, NRF2, and a subset of p53
target genes involved in cell-cycle regulation. This apparent
lack of conservation for many functional human p53 binding
sites suggests that regulation of the p53 response network may
Figure 3. ROC Curves for Four Mammalian Comparisons Indicate the Ability of Different Interspecific Comparisons to Identify Bona Fide Human TFBSs
(A–D) Using a dataset of experimentally verified human TFBSs and randomly chosen promoter sequences from human genes, the true positive and false
positive prediction rates were calculated given conservation thresholds that classify a TFBS as authentic or spurious. Each data point on a curve
represents a different conservation threshold for comparison in the indicated species. In (A) arrows indicate the 80% identity threshold. For NFjB 80%
identity with mouse is highly predictive for human. Prediction of apoptosis sites are similar to random classification (diagonal line).
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Divergence of p53 Response Elements
be fine tuned for the needs of each species. By comparing
sequence conservation between two p53 pathways, we have
detected differences in the evolution of their regulatory
elements. In particular, numerous functional human p53 REs
in apoptosis-regulating sites, as well as the surrounding local
sequence, show little homology to rodent sequences, suggest-
ing that this ontology may have been shaped by primate-
specific selection pressures that have resulted in turnover (loss
or gain) of binding sites. This is supported to some degree by
the very high mean sequence identity for all p53 REs between
human and chimpanzee or monkey (Figure S1). However,
turnover cannot be easily addressed by the global alignment
method. For example, a short species-specific rearrangement
such as an insertion of a repetitive DNA element (e.g., SINE,
LINE, etc.) that contains a RE would not globally align and
would show zero identity across species but might maintain
functional response across species.
There were also seven DNA repair-regulating p53 REs in
our dataset. Although there were not enough p53 RE sites to
perform a statistically significant analysis, the average percent
identity to the consensus sequence was similar to that of the
apoptosis-related subset for human-to-rodent comparisons.
These findings are significant considering the efforts to
functionally model human p53 responses in the mouse
(including cell cycle, apoptosis, and DNA repair) [33–35].
Complex molecular events (reviewed in ) regulate both
p53 levels and activity prior to the transregulation of cell-
cycle arrest and apoptosis genes. This results in large
increases in p53 availability for binding to REs. Presumably
the strength of p53 binding to a given target sequence has the
effect of tuning regulation of the components of the p53
network within a species. Our data suggest that regulation of
some p53 pathways, including apoptotic and DNA repair
genes, may differ between humans and other mammalian
species. Not only are REs in apoptotic and other genes
different from cell-cycle genes in rodents, but they appear to
differ from rabbits and dogs as well (Figure 4B). This
unexpected excess of sequence diversity for apoptotic
elements could be explained by recent positive selection in
all of these species. Support for this comes from an evolu-
tionary analysis of a functional, yet poor, consensus-matching
p53 RE in the apoptotic gene PIG3. This study revealed that
PIG3 became p53 responsive only recently, during primate
evolution  and is consequently only present and func-
tional in apes and humans.
A recent emergence of primate-specific apoptosis p53 RE
sites could explain the large number of interspecific differ-
ences identified following alignment to their orthologous
mouse, rat, rabbit, or dog sites (Figure 4). Dermitzakis and
Clark  observed a similar phenomena while surveying a
broad number of TFBSs and concluded that a large
percentage of apparently functional human sites were not
functional in rodents (and vice versa). The authors suggest
that loss and gain of TFBSs has been commonplace in both
rodents and humans. On the other hand, the p53 protein
itself has changed very little between species. Mouse and
human p53 proteins are 85% identical and show equivalent
transactivation of human apoptotic and cell-cycle REs in a
yeast-based system . The DNA binding domain of the p53
protein has near 100% homology across all mammalian
species indicating strong purifying selection to maintain
DNA binding function. Our data indicate that cell-cycle REs
are also being maintained by purifying selection, while the
evidence suggests that divergent positive selection has
occurred among REs of apoptosis genes. The evolution of
apoptosis-related p53 binding sites has strong biological
plausibility, as it seems likely that such modifications could
profoundly affect how a species responds to environmental
stress and cellular damage. With exposure to DNA-damaging
agents being a common environmental feature throughout
mammalian history, selection pressure and the evolution of
systems to maintain genome stability could be quite different
in rodents and primates. For example, it was recently shown
that the Spalax (mole rat), which lives its entire life under-
ground, has a p53 protein with a very limited ability to induce
several well-known human apoptotic genes in reporter assays.
It is, however, quite capable of transactivating cell-cycle
arrest genes . The adaptation of the Spalax p53 response
to a dramatically different environment underscores how
separate pathways jumpstarted by the same transcription
factor can have distinct evolutionary signatures.
Cross-species analysis of p53-regulated genes in relation to
biological function is largely absent. Thus it is unknown
whether any preservation of functionality in apoptosis-
related p53 binding sites exists, or if divergence and positive
selection have created uniquely primate response character-
istics. We are currently evaluating how p53 RE variation
across species affects binding and transactivation in a
functional model system and have observed that some weak
binding REs show high conservation (D. A. Bell, unpublished
data). Other aspects of p53 pathways may evolve, such as the
proteins that regulate the availability or quantity of active
p53 protein, so that the sequence and binding affinity of
affiliated binding sites could be coevolving with such changes.
Table 6. Efficacy of Mammalian Conservation to Predict Human TFBSs
TFBSArea under ROC Curvea
p53, cell cycle
aAn AUC of 1 would represent a conservation metric that perfectly identified genuine TFBSs. Standard errors for the curve areas are shown.
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Divergence of p53 Response Elements
This study makes several suggestions for computational
analysis of p53 REs and regulatory sequence in general. Since
these binding sites seem to be experiencing much short-term
evolution and turnover, comparative genome analysis in a
panel of old and new world monkeys, ‘‘phylogenetic shadow-
ing,’’ may be a promising direction to enhance prediction
accuracy . Secondly, among those p53 REs exhibiting high
conservation, mutations or polymorphisms that alter such
sequences may be significant . A key practical point is that
if comparative genomics methods are used to identify
putative functional regulatory regions, one should ensure
that the choice of comparative species data is relevant to the
selection pressure on the motifs of interest. For the p53
pathway, predictions based on mouse- or rat-to-human will
not only generate a large excess of false positives, but many
bona fide REs will be missed.
Overall, conservation analysis may be a convenient
measuring stick for regulatory element function, but we have
shown that it must be used with caution and may depend on
the TFBS category being analyzed. A reduction in evolu-
tionary conservation in p53 regulatory elements is likely due
to species-specific selective pressures acting on the distinct
biological differences among p53-regulatory pathways.
Materials and Methods
TFBSs with experimental support were located in the literature
(see Table S1). Genome coordinates (National Center for Biotechnol-
ogy [NCBI] 35.1, May 2004 release) were located using BLAT 
Figure 4. Positional Diversity of Human TFBSs Compared to Four Species
(A–E) Sequence diversity is calculated as the percentage of aligned bases at a certain position that vary from the accepted transcription factor
consensus sequence, which is listed along the x-axis. The diversity levels of randomly chosen promoter sequences (gray) and randomly chosen coding
region (blue) sequences (1,000 trials, error bars are narrower than the data points) are plotted so that level of genuine TFBS diversity (red, known sites)
can be viewed in a genomic context. The shape of each plot reflects the level of degeneracy of the consensus at each nucleotide position (e.g., gray line,
the p53 consensus has C and G at fixed positions creating peaks in the samples from the promoter)
PLoS Genetics | www.plosgenetics.org July 2007 | Volume 3 | Issue 7 | e1271293
Divergence of p53 Response Elements
searches against the human genome within the UCSC genome
browser (Table S2) [31,40]. If a TFBS could not be found in the
genome, it was removed from analysis, which left 83, 21, and 21
binding sites for p53, NRF2, and NFjB, respectively. The UCSC
‘‘multiz17way’’ conservation track [31,40,41] provided a 17-way
multiple sequence alignment between current releases of the Homo
sapiens genome and eleven additional mammals: Pan troglodytes
(chimpanzee, November 2003), Canis familiaris (dog, May 2005), Mus
musculus (mouse, May 2004), Rattus norvegicus (rat, June 2003), Macaca
mulatta (rhesus monkey, January 2006), Monodelphis domestica (opossum,
June 2005), Bos taurus (cow, March 2005), Echinops telfairi (tenrec, July
2005), Loxodonta africana (elephant, May 2005), Oryctolagus cuniculus
(rabbit, May 2005), and Dasypus novemcinctus (armadillo, May 2005).
This alignment set was used to find the corresponding locations of
each TFBS within each genome. The accuracy of these alignment
regions were manually inspected and verified by both confirming
similar local gene organization as well as referencing independently
generated paired human–mammal alignments (UCSC tables netMm7,
netMonDom1, netBosTau2, netRn4, netCanFam2, netRheMac2, and
netPanTro1) [42,43]. Similarly, a random list of promoter and cDNA
sequences were obtained from Ensembl (http://www.ensembl.org) by
referencing a genome coordinate list of all known human protein-
encoding genes (version 35.1) with an Ensembl gene identifier .
For each gene, a coordinate range of length equal to the TFBS of
interest was randomly picked in the (a) 3,500 bases 59 to the gene start
site and (b) within protein coding DNA sequence. Sites from these
two lists of coordinates were randomly chosen to form a set of
genome regions with the same number of members as each TFBS
category: NRF2 (21), NFjB (21), p53 (83), p53 apoptosis (29), and p53
cell cycle/cell growth (23). This process was repeated 1,000 times with
replacement to capture the variance in the data. As with the known
TFBSs, the mammalian multiple alignment data from multiz17way
were retrieved for each of these promoter and cDNA sites. The
placement of target genes into p53 subcategories was based on the
grouping used in Qian et al. and an annotated literature search
Pair-wise percent identities relative to the each TFBS consensus
were calculated as the percentage of RE bases that were either (a)
identical between the human and second genome or (b) mismatched
but do not deviate from the consensus sequence (Figure 1). For p53
REs, the variable spacer region was not considered. The distribution
of conservation for each TFBS set is shown in Figure 2. The
conservation of the randomly chosen sets of coding region and
promoter sequences, which represent the high and low extremes
respectively of human genome conservation for TFBS comparisons,
was calculated in the same fashion, and average results per x-axis bin
for 1,000 trials are shown in Figure 2. p-Values describing the
statistical difference between percent sequence identity means for
each TFBS set (Table 4) were calculated using an unpaired two-tailed
t-test with the assumption of unequal variances. The ORs in Table 4
were calculated as OR ¼ ad/bc, where a ¼ mean conservation of the
column element, b¼mean conservation of the row element, c¼100?
a, and d ¼ 100 ? b.
A list of CNC blocks, which represent the 5% most conserved
portions of the human genome, were downloaded from the ‘‘most
conserved’’ track of the UCSC genome browser. These were generated
by constructing a phylogenetic two-state Hidden Markov Model 
from the 17-way multiple alignment, which includes the human 35.1
genome release [31,41,44]. To generate the data in Table 5, the
coordinates of each TFBS set were intersected with the CNC blocks.
Likewise, the average number of randomly chosen promoter regions
(1,000 trials) found within CNCs was determined. The probability of
finding a greater fraction of CNCs in randomly chosen promoter
For the ROC curves in Figure 3, the sensitivity (true positive rate)
and 1-specificity (false positive rate) of TFBS prediction were
calculated at each of 11 conservation thresholds. The true positive
rate was the fraction of bona fide TFBSs with a consensus sequence
percent identity above a given level. The false positive rate was
calculated as the average number of sites (1,000 trials) that fell above
a conservation threshold (consensus sequence percent identity) in an
equivalently sized set of random promoter sequences. For example,
83 TFBSs composed the p53 ROC curve. Therefore, sets of 83 random
promoter sites, where each site was 20 bases in length, were used to
estimate the false positive rate. ROC AUCs (Table 6) and standard
errors were calculated directly from the graphs using the trapezoid
rule as described by Hanley and McNeil .
Positional sequence diversity for a TFBS, which is related to the
inverse of sequence identity, was calculated as the percentage of
human nucleotides (nt) at each position that had a nonconsensus
mismatch or insertion/deletion event when compared to one of the
four mammals (Figure 4). Each TFBS of a given class (i.e., TP53) and
its alignment to a mammal (i.e., mouse) was pulled from the multiz
multiple alignments to produce a set of paired sequence alignments.
For each RE member of this pool, we counted the number of times
where the first position of the mammalian sequence in the alignment
differed from the human nt due to either (a) a nonconsensus
mismatch or (b) an insertion/deletion event. This count was divided
by the total number of TFBSs in a group (i.e., 83 for all p53 REs or 21
for all NFjB REs) to get the percentage of two-way alignments that
differed at that position. This value is taken as the sequence diversity
at that first position. This calculation was then performed for the
remaining nts in the TFBS. Mismatches from human that did not
alter the consensus motif did not increase the percent sequence
diversity and were ignored.
Figure S1. Distribution of Human-to-Primate TFBS Conservation
Found at doi:10.1371/journal.pgen.0030127.sg001 (295 KB DOC).
Figure S2. ROC Curves for Four Human-to-Mammal Comparisons
Indicate the Ability of Different Interspecific Comparisons to
Identify Bona Fide TFBSs
Found at doi:10.1371/journal.pgen.0030127.sg002 (332 KB DOC).
Figure S3. Alignments for 52 p53 REs across Seven Mammalian
Species Obtained from UCSC Multi17way Alignment Tool
Found at doi:10.1371/journal.pgen.0030127.sg003 (91 KB DOC).
Figure S4. Sequence Logos for Human p53 REs Used for Position
Weight Matrix Model Construction
Found at doi:10.1371/journal.pgen.0030127.sg004 (152 KB DOC).
Table S1. Validated TFBSs Used in This Study and Supplementary
Found at doi:10.1371/journal.pgen.0030127.st001 (454 KB DOC).
Table S2. Coordinates of Validated TFBSs
Found at doi:10.1371/journal.pgen.0030127.st002 (152 KB DOC).
Table S3. Correlation between RE Identity Distributions and
Distributions Obtained from Sampling of Promoter Fragments
Found at doi:10.1371/journal.pgen.0030127.st003 (39 KB DOC).
The authors would like to thank Daniel Tomso, Maher Noureddine,
Michelle Campbell, and Gary Pittman of the National Institute of
Environmental Health Sciences (NIEHS) for helpful discussions.
Krystal Finney (NIEHS) catalogued p53 RE information. Ben van
Houten and Kellen Meadows of NIEHS and anonymous PloS
reviewers provided helpful suggestions on the manuscript.
Author contributions. MMH, MAR, and DAB conceived and
designed the experiments and wrote the paper. MMH performed
the experiments. MMH, XW, and DAB analyzed the data. MMH and
MAR contributed reagents/materials/analysis tools.
Funding. This research was entirely funded by the Intramural
Research Program of the National Institutes of Health and NIEHS.
Competing interests. The authors have declared that no competing
1. Boffelli D, McAuliffe J, Ovcharenko D, Lewis KD, Ovcharenko I, et al. (2003)
Phylogenetic shadowing of primate sequences to find functional regions of
the human genome. Science 299: 1391–1394.
2.Jones NC, Pevzner PA (2006) Comparative genomics reveals unusually long
motifs in mammalian genomes. Bioinformatics 22: e236–e242.
Dickmeis T, Muller F (2005) The identification and functional character-
isation of conserved regulatory elements in developmental genes. Brief
Funct Genomic Proteomic 3: 332–350.
PLoS Genetics | www.plosgenetics.orgJuly 2007 | Volume 3 | Issue 7 | e1271294
Divergence of p53 Response Elements
4. Zhang Z, Gerstein M (2003) Of mice and men: Phylogenetic footprinting
aids the discovery of regulatory elements. J Biol 2: 11.
Berg J, Willmann S, Lassig M (2004) Adaptive evolution of transcription
factor binding sites. BMC Evol Biol 4: 42.
Drake JA, Bird C, Nemesh J, Thomas DJ, Newton-Cheh C, et al. (2006)
Conserved noncoding sequences are selectively constrained and not
mutation cold spots. Nat Genet 38: 223–227.
Nielsen R, Bustamante C, Clark AG, Glanowski S, Sackton TB, et al. (2005) A
scan for positively selected genes in the genomes of humans and
chimpanzees. PLoS Biol 3: e170. doi:10.1371/journal.pbio.0030170
Clark AG, Glanowski S, Nielsen R, Thomas PD, Kejariwal A, et al. (2003)
Inferring nonneutral evolution from human-chimp-mouse orthologous
gene trios. Science 302: 1960–1963.
Wolfe KH, Sharp PM, Li WH (1989) Mutation rates differ among regions of
the mammalian genome. Nature 337: 283–285.
10. Contente A, Zischler H, Einspanier A, Dobbelstein M (2003) A promoter
that acquired p53 responsiveness during primate evolution. Cancer Res 63:
11. Wang X, Tomso DJ, Liu X, Bell DA (2005) Single nucleotide polymorphism
in transcriptional regulatory regions and expression of environmentally
responsive genes. Toxicol Appl Pharmacol 207: 84–90.
12. Tomso DJ, Inga A, Menendez D, Pittman GS, Campbell MR, et al. (2005)
Functionally distinct polymorphic sequences in the human genome that
are targets for p53 transactivation. Proc Natl Acad Sci U S A 102: 6431–
13. Hollstein M, Shomer B, Greenblatt M, Soussi T, Hovig E, et al. (1996)
Somatic point mutations in the p53 gene of human tumors and cell lines:
Updated compilation. Nucleic Acids Res 24: 141–146.
14. Wei CL, Wu Q, Vega VB, Chiu KP, Ng P, et al. (2006) A global map of p53
transcription-factor binding sites in the human genome. Cell 124: 207–219.
15. el-Deiry WS, Kern SE, Pietenpol JA, Kinzler KW, Vogelstein B (1992)
Definition of a consensus binding site for p53. Nat Genet 1: 45–49.
16. Harms K, Nozell S, Chen X (2004) The common and distinct target genes of
the p53 family transcription factors. Cell Mol Life Sci 61: 822–842.
17. Wong J, Li PX, Klamut HJ (2002) A novel p53 transcriptional repressor
element (p53TRE) and the asymmetrical contribution of two p53 binding
sites modulate the response of the placental transforming growth factor-
beta promoter to p53. J Biol Chem 277: 26699–26707.
18. Contente A, Dittmer A, Koch MC, Roth J, Dobbelstein M (2002) A
polymorphic microsatellite that mediates induction of PIG3 by p53. Nat
Genet 30: 315–320.
19. Qian H, Wang T, Naumovski L, Lopez CD, Brachmann RK (2002) Groups of
p53 target genes involved in specific p53 downstream effects cluster into
different classes of DNA binding sites. Oncogene 21: 7901–7911.
20. Dixit V, Mak TW (2002) NF-kappaB signaling. Many roads lead to madrid.
Cell 111: 615–619.
21. Ghosh S, May MJ, Kopp EB (1998) NF-kappa B and Rel proteins:
Evolutionarily conserved mediators of immune responses. Annu Rev
Immunol 16: 225–260.
22. Pierce JW, Lenardo M, Baltimore D (1988) Oligonucleotide that binds
nuclear factor NF-kappa B acts as a lymphoid-specific and inducible
enhancer element. Proc Natl Acad Sci U S A 85: 1482–1486.
23. Martone R, Euskirchen G, Bertone P, Hartman S, Royce TE, et al. (2003)
Distribution of NF-kappaB-binding sites across human chromosome 22.
Proc Natl Acad Sci U S A 100: 12247–12252.
24. Wang XW, Tan NS, Ho B, Ding JL (2006) Evidence for the ancient origin of
the NF-kappaB/IkappaB cascade: Its archaic role in pathogen infection and
immunity. Proc Natl Acad Sci U S A 103: 4204–4209.
25. Wasserman WW, Fahl WE (1997) Functional antioxidant responsive
elements. Proc Natl Acad Sci U S A 94: 5361–5366.
26. Wang X, Tomso DJ, Chorley BN, Cho HY, Cheung VG, et al. (2007)
Identification of polymorphic antioxidant response elements in the human
genome. Hum Mol Genet 16: 1188–1200.
27. Cho HY, Reddy SP, Kleeberger SR (2006) Nrf2 defends the lung from
oxidative stress. Antioxid Redox Signal 8: 76–87.
28. Nioi P, McMahon M, Itoh K, Yamamoto M, Hayes JD (2003) Identification
of a novel Nrf2-regulated antioxidant response element (ARE) in the mouse
NAD(P)H:quinone oxidoreductase 1 gene: Reassessment of the ARE
consensus sequence. Biochem J 374: 337–348.
29. Ramos-Gomez M, Kwak MK, Dolan PM, Itoh K, Yamamoto M, et al. (2001)
Sensitivity to carcinogenesis is increased and chemoprotective efficacy of
enzyme inducers is lost in nrf2 transcription factor-deficient mice. Proc
Natl Acad Sci U S A 98: 3410–3415.
30. Kobayashi M, Itoh K, Suzuki T, Osanai H, Nishikawa K, et al. (2002)
Identification of the interactive interface and phylogenic conservation of
the Nrf2-Keap1 system. Genes Cells 7: 807–820.
31. Hinrichs AS, Karolchik D, Baertsch R, Barber GP, Bejerano G, et al. (2006)
The UCSC genome browser database: Update 2006. Nucleic Acids Res 34:
32. Schneider TD, Stephens RM (1990) Sequence logos: A new way to display
consensus sequences. Nucleic Acids Res 18: 6097–6100.
33. Hasty P, Campisi J, Hoeijmakers J, van Steeg H, Vijg J (2003) Aging and
genome maintenance: Lessons from the mouse? Science 299: 1355–1359.
34. Hoeijmakers JH (2001) Genome maintenance mechanisms for preventing
cancer. Nature 411: 366–374.
35. Toledo F, Wahl GM (2006) Regulating the p53 pathway: In vitro hypotheses,
in vivo veritas. Nat Rev Cancer 6: 909–923.
36. Dermitzakis ET, Clark AG (2002) Evolution of transcription factor binding
sites in mammalian gene regulatory regions: Conservation and turnover.
Mol Biol Evol 19: 1114–1121.
37. Resnick MA, Inga A (2003) Functional mutants of the sequence-specific
transcription factor p53 and implications for master genes of diversity.
Proc Natl Acad Sci U S A 100: 9934–9939.
38. Ashur-Fabian O, Avivi A, Trakhtenbrot L, Adamsky K, Cohen M, et al.
(2004) Evolution of p53 in hypoxia-stressed Spalax mimics human tumor
mutation. Proc Natl Acad Sci U S A 101: 12236–12241.
39. Kent WJ (2002) BLAT–the BLAST-like alignment tool. Genome Res 12:
40. Karolchik D, Baertsch R, Diekhans M, Furey TS, Hinrichs A, et al. (2003)
The UCSC genome browser database. Nucleic Acids Res 31: 51–54.
41. Blanchette M, Kent WJ, Riemer C, Elnitski L, Smit AF, et al. (2004) Aligning
multiple genomic sequences with the threaded blockset aligner. Genome
Res 14: 708–715.
42. Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, et al. (2003) Human-
mouse alignments with BLASTZ. Genome Res 13: 103–107.
43. Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D (2003) Evolution’s
cauldron: Duplication, deletion, and rearrangement in the mouse and
human genomes. Proc Natl Acad Sci U S A 100: 11484–11489.
44. Hubbard T, Andrews D, Caccamo M, Cameron G, Chen Y, et al. (2005)
Ensembl 2005. Nucleic Acids Res 33: D447–D453.
45. Siepel A, Bejerano G, Pedersen JS, Hinrichs AS, Hou M, et al. (2005)
Evolutionarily conserved elements in vertebrate, insect, worm, and yeast
genomes. Genome Res 15: 1034–1050.
46. Hanley JA, McNeil BJ (1982) The meaning and use of the area under a
receiver operating characteristic (ROC) curve. Radiology 143: 29–36.
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