Plant Physiology 132:1162-1176 (2003)
© 2003 American Society of Plant Biologists
BIOINFORMATICS
Computational Approaches to Identify Promoters and cis-Regulatory Elements in Plant Genomes1
Stephane Rombauts2,
Kobe Florquin2,
Magali Lescot,
Kathleen Marchal,
Pierre Rouzé* and
Yves Van de Peer
Department of Plant Systems Biology, Flanders Interuniversity Institute
for Biotechnology, Ghent University, B–9000 Gent, Belgium (S.R., K.F.,
Y.V.d.P.); Laboratoire de Génétique et Physiologie du
Développement, Equipe bioinformatique, Centre National de la Recherche
Scientifique, Parc Scientifique de Luminy, F-13288 Marseille Cedex 9, France
(M.L.); Department of Electrical Engineering (Electronics, Systems,
Automatisation and Technology-Signals, Identification, System Theory, and
Automation), Katholieke Universiteit Leuven, B–3001 Heverlee, Belgium
(K.M.); and Laboratoire Associé de l'Institut National de la Recherche
Agronomique (France), Ghent University, K.L. Ledeganckstraat 35, B–9000
Gent, Belgium (P.R.)
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ABSTRACT
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The identification of promoters and their regulatory elements is one of the
major challenges in bioinformatics and integrates comparative, structural, and
functional genomics. Many different approaches have been developed to detect
conserved motifs in a set of genes that are either coregulated or orthologous.
However, although recent approaches seem promising, in general, unambiguous
identification of regulatory elements is not straightforward. The delineation
of promoters is even harder, due to its complex nature, and in silico promoter
prediction is still in its infancy. Here, we review the different approaches
that have been developed for identifying promoters and their regulatory
elements. We discuss the detection of cis-acting regulatory elements using
word-counting or probabilistic methods (so-called "search by
signal" methods) and the delineation of promoters by considering both
sequence content and structural features ("search by content"
methods). As an example of search by content, we explored in greater detail
the association of promoters with CpG islands. However, due to differences in
sequence content, the parameters used to detect CpG islands in humans and
other vertebrates cannot be used for plants. Therefore, a preliminary attempt
was made to define parameters that could possibly define CpG and CpNpG islands
in Arabidopsis, by exploring the compositional landscape around the
transcriptional start site. To this end, a data set of more than 5,000 gene
sequences was built, including the promoter region, the 5'-untranslated
region, and the first introns and coding exons. Preliminary analysis shows
that promoter location based on the detection of potential CpG/CpNpG islands
in the Arabidopsis genome is not straightforward. Nevertheless, because the
landscape of CpG/CpNpG islands differs considerably between promoters and
introns on the one side and exons (whether coding or not) on the other, more
sophisticated approaches can probably be developed for the successful
detection of "putative" CpG and CpNpG islands in plants.
Arabidopsis, and probably most plants, encode an exceptionally large number
of DNA-binding proteins, potentially acting as transcription factors (TFs). In
fact, more than 3,000 genes have been anticipated to be involved in
transcription, more than one-half of which were expected to encode TFs
(Arabidopsis Genome Initiative,
2000 ), corresponding to more than 5% of the Arabidopsis genes, and
approximately twice the ratio observed for yeast and animal genomes
(Riechmann et al., 2000).
These TFs bind to the DNA on specific cis-acting regulatory elements (CAREs)
and orchestrate the initiation of transcription, which is one of the most
important control points in the regulation of gene expression. CAREs are short
conserved motifs of five up to 20 nucleotides usually found in the vicinity of
the 5' end of genes in what is called the promoter. The promoter
sequence is usually located upstream from the transcription start site (TSS),
but regulatory elements can also be located downstream, for example, in the
first intron of the gene itself (Zhang et
al., 1994; Gidekel et al.,
1996; de Boer et al.,
1999; Dorsett,
1999). The promoter can roughly be divided in two parts: a
proximal part, referred to as the core, and a distal part. The proximal part
is believed to be responsible for correctly assembling the RNA polymerase II
complex at the right position and for directing a basal level of transcription
(Nikolov et al., 1996;
Nikolov and Burley, 1997;
Berk, 1999). It is mediated by
elements, such as TATA and Initiator boxes through the binding of the TATA
box-binding protein, and other general TFs specific for the RNA polymerase II
(Featherstone, 2002). The
distal part of the promoter is believed to contain those elements that
regulate the spatio-temporal expression
(Tjian and Maniatis, 1994;
Fessele et al., 2002). How far
upstream (or downstream) such a distal part reaches is not defined. In
addition to the proximal and distal parts, somewhat isolated, regulatory
regions have also been described, mainly in animals, that contain enhancer
and/or repressors elements (Barton et al.,
1997; Bagga et al.,
2000). The latter elements can be found from a few kilobase pairs
upstream from the TSS, in the introns, or even at the 3' side of the
genes they regulate (Larkin et al.,
1993; Wasserman et al.,
2000). Lastly, eukaryotic genomes can be organized into domains of
transcriptional activity or transcriptional silencing, encompassing one or
more genes (Oki and Kamakaka,
2002).
A promoter region, as described above, presents a rather linear view of the
promoter. In reality, a supplementary layer of complexity is added by bringing
the TFs together on a promoter, by adopting a three-dimensional configuration,
enabling the interaction with other parts to activate the basal transcription
machinery (Fig. 1;
Buratowski; 1997;
Berk, 1999;
Struhl, 2001). The packaging
of DNA into chromatin (Kornberg and Lorch,
2002) limits the accessibility of the DNA template for the
transcriptional apparatus and inhibits transcriptional initiation. Therefore,
when compared with naked DNA, chromatin is able to repress transcription,
which is probably important for the tight regulation of gene activity in vivo
(Juo et al., 1996;
Marilley and Pasero, 1996;
Ioshikhes et al., 1999).
Derepression of transcription by partial unfolding of the chromatin structure
probably constitutes an important part of gene regulation, and several TFs and
transcriptional co-activators have been shown to disrupt or remodel the
chromatin structure (Beato and Eisfeld,
1997; Kass et al.,
1997; Travers and Drew,
1997; Langst and Becker,
2001; Brower-Toland et al.,
2002).
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Figure 1. Graphical, simplified view of the different elements involved in
transcription. The pre-initiation complex (PIC) situated at the
nucleosome-free TSS is shown containing RNA polymerase II (large gray hatched
oval), the TATA box-binding protein (gray sphere), and a number of general TFs
(white circles). Gene regulatory proteins upstream or downstream of the TSS
that stimulate gene-specific transcription and also contribute to the PIC
assembly are shown as small gray circles.
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The three-way connection between methylation, gene activity, and chromatin
structure has been known for almost two decades. DNA methylation has been
shown to repress transcription initiation by interfering directly with the
binding of transcriptional activators or indirectly by binding proteins with
affinity for methylated DNA (Weber et
al., 1990; Razin,
1998; Jones, 1999;
Kooter et al., 1999;
Ng and Bird, 1999;
Meyer, 2000). Proteins, which
bind to methylated DNA in a CpG density-controlled manner, have been detected
in both mammals and plants. Experiments have indicated that methylation is not
a consequence of the transcriptional state but apparently participates
actively in the regulation of gene expression
(Inamdar et al., 1991;
Finnegan et al., 1998b;
Pitto et al., 2000).
Furthermore, during transcription elongation, RNA polymerase and the DNA
template must rotate relative to each other inducing rotary constraints.
Scaffold or matrix attachment regions are involved at this level of gene
expression by stabilizing the formation of heterochromatin. These repetitive
regions enable the formation of Z-DNA dividing the DNA into topological
domains, which are delineated by torsionally locked boundaries
(Bentin and Nielsen,
2002).
Much attention has been paid to investigate the modular structure of
regulatory regions that control the transcription of eukaryotic genes
(Dynan, 1989;
Johnson and McKnight, 1989;
Struhl, 1999;
Klingenhoff et al., 2002). The
fuzziness of one binding site can be compensated by a higher fitness of the
adjacent binding site and enables the positioning of the additional TF thanks
to specific protein-protein interactions
(Rooney et al., 1995;
Struhl, 2001). Thus,
promoters can be described as the result of a modular hierarchy, in which the
individual CAREs constitute the lowest level; they are then grouped into
islands as composite elements, themselves organized in modules that confer the
specific expression of a gene. The consequence of this modularity is that each
promoter is unique and controls specifically the transcript level of its
downstream gene.
All of these different levels of complexity have great repercussions on the
in silico identification of binding sites and promoters. Here, we review
current approaches (summarized in Fig.
2) to identify promoters and their regulatory elements.
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Figure 2. Flow chart of the computational approaches to detect promoters and
cis-regulatory elements. 1, Promoter prediction through sequence context and
structural features, e.g. CpG islands; 2, CARE prediction through statistics
on overrepresentation, such as word counting; 3, CARE prediction through
comparative genomics (phylogenetic footprinting); 4, CARE prediction through
analysis of co-expressed gene clusters, for instance by Gibbs sampling (for
details, see text); 5, Promoter prediction through the identification of
CAREs; and 6, CARE motif prediction through comparative analysis of expression
profiles. These approaches are not described in the text.
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CONTENT-BASED FEATURES
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Promoter Prediction
Unlike gene prediction (Mathé
et al., 2002), prediction of promoters in silico is still in its
infancy. One of the main problems is that the promoter is defined functionally
and not structurally, which strongly limits the means to model it. Clear and
unequivocal descriptions of genomic segments that contain all elements
required to activate transcription would be useful but are still unavailable,
although the regulatory motifs of some specific genes have been investigated
in detail. Therefore, most in silico research on promoters is usually
restricted to the so-called intergenic regions of the genome, i.e. between the
coding regions of two neighboring genes. The most practical approach is to
limit the putative promoter region to an arbitrary number of base pairs
upstream of the translation start site of the gene of interest, the location
of the TSS being unknown most of the time. However, ideally, this number
should be chosen in function of the organism, because the length of intergenic
regions may differ considerably
(Arabidopsis Genome Initiative,
2000; International Human
Genome Sequencing Consortium, 2001;
Aparicio et al., 2002). In
multicellular organisms, regulatory elements may be found upstream or
downstream of the gene, as well as in introns, and may be spread over tens or
even hundreds of kilobases (Larkin et al.,
1993; Bagga et al.,
2000). In such cases, intergenic sequences will contain only a
part of the regulatory elements necessary to control transcription.
In 1997, Fickett and Hatzigeorgiou
(1997) thoroughly reviewed the
existing promoter prediction tools. The programs tested could not reliably
identify promoters in a genomic sequence, predicting too many false positives,
i.e. on average one false positive per 1 kb. One of the reasons why these
programs did not perform better was that they were focused on "search by
signal" using only one or two given features, such as the presence of a
TATA box or Initiator element, but disregarded structural and more general
sequence-based features characteristic for promoter elements
(Fickett and Hatzigeorgiou,
1997). Newer approaches do take into account more features; they
consider the higher order structure of a promoter DNA sequence important for
transcriptional regulation and are based on the concept that they share common
content features, although polymerase II promoters are quite different in
terms of individual organization.
On the one hand, sequence-based algorithms aim at identifying regulatory
regions and promoters based on their sequence composition compared with that
of non-promoters. Among others, Scherf et al.
(2000) and Bajic et al.
(2002) have used this approach,
in which the promoter context is described by oligonucleotides (see below;
Hutchinson, 1996;
Wolfertstetter et al., 1996).
On the other hand, promoter regions might be distinguished from non-promoter
regions on the basis of specific structural properties. These features are
either directly or indirectly correlated with the three-dimensional structure
a promoter region should adopt for gene expression in vivo
(Baldi et al., 1998; Pedersen
et al., 1998,
1999;
Zhang, 1998;
Fickett and Wasserman, 2000;
Hannenhalli and Levy, 2001;
Ohler and Niemann, 2001). The
three-dimensional structure can depend on characteristic physico-chemical
profiles of Z-DNA (Ho et al.,
1986) associated with scaffold and matrix attachment regions
(Bentin and Nielsen, 2002),
stability of duplex DNA (Breslauer et al.,
1986; Sugimoto et al.,
1996), DNA curvature (Bolshoy
et al., 1991), bending and curvature in B-DNA
(Goodsell and Dickerson,
1994), DNA bending/stiffness
(Sivolob and Khrapunov,
1995), bendability (Brukner et al.,
1995a,
1995b), propeller twist
(El Hassan and Calladine,
1996), B-DNA twist (Gorin et
al., 1995), and protein-induced deformability
(Crothers, 1998;
Olson et al., 1998). If
eukaryotic promoters have such general structural features independently of
the genes they control, looking for these should help in identifying promoters
in general. We will discuss two prediction tools each representing a different
approach to find promoters.
PromoterInspector (Scherf et al.,
2000) focuses on the sequence context of a promoter and is based
on libraries of IUPAC words. A promoter will be represented by a model that is
based on two groups of IUPAC words: one characteristic for promoter sequences
and one for non-promoter-related sequences. The IUPAC words that build the
model are directly computed from a set of training sequences. New promoter
sequences will be assigned to the promoter class when the ratio between the
numbers of observed promoter-specific and non-promoter-specific IUPAC words
exceeds a certain threshold. Instead of using only one model, the program
constructs three different models that differentiate promoters from exons,
from introns, and from 3'-untranslated regions (UTRs). A given sequence
will be assigned to the class of "promoters" only when all models
are in agreement with the decision. The specificity and significance of this
program is highly dependent on the given training sets that build up the
different models.
McPromoter (Ohler et al.,
1999,
2001;
Ohler, 2000) is a
content-based probabilistic promoter prediction program that uses an
integrative approach combining different structural features, such as
bendability (Brukner et al.,
1995a,
1995b), propeller twist
(El Hassan and Calladine,
1996), and CpG content
(Antequera and Bird, 1999;
Ioshikhes and Zhang, 2000).
Here, a promoter is represented as a sequence of consecutive segments
represented by joint likelihood of DNA sequence and profiles of physical
properties. A profile for a physical property consists of the corresponding
values from a chosen parameter, for example the bendability, set along the
given DNA sequence. These parameters usually refer to di- and trinucleotides
only, so the profiles are generally very noisy and are, therefore, smoothed
with a filter. The program tries to divide a given sequence into one region
upstream and one downstream from the TSS. A search by signal is used to
distinguish the core promoter from the other parts by looking for a TATA
and/or Initiator box separated by a spacer of approximately 15 bp.
Although the prediction tools hitherto developed can produce acceptable
results for certain species, none of them have been trained and adapted for
plants. For example, McPromoter is trained especially to analyze data of
fruitfly (Drosophila melanogaster) and has been used in the Genome
Annotation Assessment project (Reese et
al., 2000; Ohler,
2000; Ohler et al., 2002); however, when applied to plant genomes,
it is not as reliable nor as specific. Here, the same rule applies as with
gene prediction: Systems have to be trained and tailored for each species
separately (Mathé et al.,
2002). For the careful training of systems, large amounts of
reliable data are needed. Although the availability of large sets of
documented promoter sequences is still problematic, we expect this will
improve in the near future. An extensive overview of the available programs
for the prediction of promoters is given in
Table I.
Promoters and CpG Islands
A structural feature that has proven useful in the detection of promoters
in the human genome are the so-called CpG islands, i.e. regions that are rich
in CpGs, which are important because of their strong link with gene
regulation. In general, CpG-rich regions are methylated and are associated
with inactive DNA often linked to heterochromatin, gene silencing, and
pathogen control (Jeddeloh et al.,
1998; Kooter et al.,
1999; Wolffe and Matzke,
1999; Meyer,
2000; Bender,
2001; Vaucheret and Fagard,
2001; Richards and Elgin,
2002; Robertson,
2002). In vertebrate genomes, 60% to 90% of all CpGs are normally
methylated. Gene-associated CpG islands are mostly not methylated and are
usually linked to transcriptionally active DNA
(Panstruga et al., 1998;
Razin, 1998;
Antequera and Bird, 1999;
Jones, 1999;
Ng and Bird, 1999;
Ashikawa, 2001;
Li et al., 2001). Prediction
programs have been developed to search for the presence of CpG islands in the
5' region of genes (Ioshikhes and
Zhang, 2000; Ohler et al.,
2001; Davuluri et al.,
2001; Down and Hubbard,
2002; Ponger and Mouchiroud,
2002). However, so far, application of such prediction programs to
CpG islands in plants is very limited. A more detailed analysis on CpG and
CpNpG islands in Arabidopsis is given below.
Although the functional significance of methylation appears to be similar
in humans and plants (Hershkovitz et al.,
1990; Weber et al.,
1990; Inamdar et al.,
1991; Meyer et al.,
1994; Sorensen et al.,
1996; Rossi et al.,
1997; Meza et al.,
2002), in plants, DNA methylation is mainly found on the cytosine
of the di- and trinucleotide CpG and CpNpG and on nonsymmetrical
trinucleotides (Pradhan et al.,
1999; Cao et al.,
2000; Finnegan and Kovac,
2000; Lindroth et al.,
2001; Cao and Jacobsen,
2002). Many plant genomes contain methylated cytosine in
asymmetric sequence contexts (CpHpH with H = A, T, or C). Only symmetrical
methylation sites have been shown to be maintained through the propagation of
cells and methylation of a promoter CpG island has been proposed to play an
important role in gene silencing, genomic imprinting, heterochromatin
formation, chromatin modification, vernalization, and parent-dependent effects
(Finnegan et al., 1998a,
2000;
Jeddeloh et al., 1998;
Sturaro and Viotti, 2001).
CpNpG and CpG islands can occur together in the same promoter region, but
their role might be different (Sorensen
et al., 1996).
The Landscape of CpG/CpNpG Islands around the TSS in the Arabidopsis
Genome
CpG islands are characterized by a locally increased GC percentage (GC%)
compared with local averages and by the presence of CpGs (and CpNpGs in
plants). The CpG dinucleotide, usually methylated at the fifth position on the
cytosine ring, is counter-selected and found much less frequently than
expected based on mononucleotide frequencies, for example, 5-fold lower in
genomes of vertebrates. This depletion is believed to result from accidental
mutations by deamination of 5-methylcytosine to thymine
(Sved and Bird, 1990;
Duret and Galtier, 2000). In
fact, CpG islands are considered evolutionary remnants, because some promoters
have somehow been kept free of methylation in the course of evolution, so the
deamination process is hampered. Another explanation could be that to function
as part of an expression pattern, a selection pressure has to be exerted and,
hence, CpG islands stand out in the surrounding regions.
The original pragmatic definition of a CpG island in human sequences
considers a GC% higher than 50 and a ratio between observed and expected (o/e)
occurrence of CG dinucleotides of 0.6 over a window of 200 bp
(Gardiner-Garden and Frommer,
1987). Recently, these parameters have been upscaled to a GC%
>55, an o/e CpG >0.65, and a window size of 500 bp, because the previous
parameters had been found to overestimate (50-fold) the number of potential
CpG islands (Takai and Jones,
2002). In animals, approximately 40% of genes are expected to be
associated with CpG islands
(Gardiner-Garden and Frommer,
1987; Antequera and Bird,
1999). Actually, this percentage might be too low because a total
of 29,000 CpG islands had been estimated after the completion of the human
genome sequence (Venter et al.,
2001). With the above-mentioned parameters, no CpG islands are
discovered in plants (Takai and Jones,
2002; our results). However, DNA methylation occurs in plants, and
DNA methylases are even more numerous and diverse in plants than in animals
(Finnegan and Kovac, 2000;
Cao and Jacobsen, 2002).
Therefore, we attempted to define the parameters that could possibly specify
CpG and CpNpG islands in Arabidopsis by exploring the compositional landscape
around the TSS. To this end, we built a data set of 5,025 gene sequences,
designated ARAPROM, by aligning the full-length cDNA sequences generated by
Seki et al. (2002) against
the genomic sequence (Arabidopsis Genome
Initiative, 2000). Generally, these sequences are 2.5 kb long, in
which 2 kb represent intergenic sequences upstream from the translation start
codon, and 500 bp are taken downstream. Nevertheless, when the upstream
neighbor gene lies closer than 2 kb, then only the intergenic sequence is
kept, up to the predicted coding boundary of the upstream gene. The genomic
sequences in the ARAPROM data set include the promoter region, the
5'-UTR, and the first introns and coding exons of each individual
gene.
A program in Perl was written that computes the GC content and the o/e
ratios of CpG and CpNpG compared with local characteristics over a certain
window size. By applying this program to the ARAPROM data set to extract
potential CpG/CpNpG islands, we tested the effect of setting the cut-off
values for the GC content and the o/e CpG/CpNpG ratios at different levels
(39% to 52% with a stepwise increase of 0.5% for the GC content; 0.6% to 2.0%
for the o/e CpG and CpNpG ratios with a stepwise increase of 0.1. The results
of this analysis with a window size of 200 bp are shown graphically for CpG
and CpNpG islands (Figs. 3 and
4, respectively). The first
observation is that no CpG island is detected with the cut-off parameters
tuned for humans, except for a few in coding exons. Both parameters, CG% and
o/e CpG, appear to influence strongly the number of CpG islands detected and,
depending on the position in the genome, to affect differently the number of
CpGs found. That number found in the "promoter" region sharply
increases while the GC% cut-off decreases
(Fig. 3). In contrast, for
coding exons, the landscape resembles more a plateau, with many CpG islands
found already at much higher GC% values. Only at the lowest GC% values, more
CpG islands are predicted in the "promoter" region than in the
coding exons. In UTR exons, which show a landscape similar to that of coding
exons, fewer CpG islands are found and introns, which show a landscape more
similar to that of the "promoter" region, show the lowest number
of CpG islands.
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Figure 3. CpG island landscape exploration of Arabidopsis gene sequences over a range
of CG content and CpG relative frequency. For the various gene elements, on
the z axis, the number of CpG islands found in the ARAPROM gene set
is plotted against the thresholds defined on the x and y
axes, being the CG percentage and the o/e CpG ratio, respectively. The window
size was 200 bp. Similar landscapes are obtained for other window sizes (100
and 400 bp) and are available at
http://www.psb.rug.ac.be/bioinformatics/.
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Figure 4. CpNpG island landscape exploration of Arabidopsis gene sequences over a
range of CG content and CpNpG relative frequency. For the various gene
elements, on the z axis, the number of CpNpG islands found in the
ARAPROM gene set is plotted against the thresholds defined on the x
and y axes, being the CG percentage and the o/e CpNpG ratio,
respectively. The window size was 200 bp. Similar landscapes are obtained for
other window sizes (100 and 400 bp) and are available at
http://www.psb.rug.ac.be/bioinformatics/.
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Regarding the CpNpG landscape (Fig.
4), the major observation is that the overall number of islands
lies well below that of the CpG islands. In addition, the same differences in
landscape hold, as observed for CpG islands between promoter (and introns), on
the one hand, and coding exons (and UTR exons), on the other hand.
Nevertheless, a striking difference is that for CpNpGs, the o/e CpNpG
threshold has to be very low for those islands to be detected. In terms of
number of genes associated with CpG/CpNpG islands, different parameter
settings lead to very different figures
(Table II).
This preliminary in silico analysis shows that prediction of promoter
location based on the detection of potential CpG/CpNpG islands in the
Arabidopsis genome is not straightforward. Nevertheless, because the landscape
of CpG/CpNpG islands differs considerably between promoters and introns on the
one side and exons (whether coding or not) on the other, there is some hope
that, based on such a classification, more sophisticated approaches can be
developed to detect CpG and CpNpG islands in plants.
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SIGNAL-BASED FEATURES
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Regulatory Elements
As stated in the introduction, CAREs are short, conserved motifs of
approximately 5 to 20 nucleotides. Detection of CAREs in the promoter is not
self-evident, because such short motifs are statistically expected to occur at
random every few hundred base pairs. Therefore, the main problem lies in
discriminating "true" from "false" regulatory elements
(Blanchette and Sinha, 2001).
It is important to distinguish whether unknown or known motifs are looked for.
Compared with the detection of unknown motifs, that of known motifs is fairly
straightforward and consists of the scanning of the DNA sequence with a given
motif, which can be found in specialized databases such as TRANSFAC
(Wingender et al., 1996) and
TFD (Ghosh, 2000) and in
plant-specific databases such as PLACE
(Higo et al., 1999) and
PlantCARE (Lescot et al.,
2002). An overview of the different databases and motif search
programs is given in Tables III
and IV, respectively. In
contrast, the detection of unknown CAREs in large regions of DNA requires the
development and use of novel approaches and algorithms. Specifically, local
multiple alignment algorithms that identify regulatory motifs have already
been developed, which are merely based on statistical properties. Such
algorithms search for DNA patterns that are more frequently present in a set
of "related" than "unrelated" sequences. Therefore,
the successful identification of regulatory DNA patterns depends on the size
of the promoter sequence and, to a great extent, on the quality of the set of
"related" sequences, i.e. genes that are co-expressed or
coregulated and are thus expected to share similar conserved regulatory
motifs. Such co-expressed genes are identified based on high-throughput gene
expression profiling experiments. Alternatively, instead of coregulated genes,
intergenic regions of orthologous sequences can also constitute a valuable
data set for motif detection (Duret and
Bucher, 1997). When selection pressure tends to conserve DNA
patterns in the intergenic regions of homologous genes in related species,
such DNA patterns can be expected to be of biological relevance and to reflect
a conserved ancestral mode of regulation.
Regulatory Elements in Coregulated Genes
Co-expressed genes can be identified through transcript profiling
techniques, such as microarrays (Brown and
Botstein, 1999; Lipshutz et
al., 1999; Southern,
2001) and cDNA-AFLP (Vos et
al., 1995; Breyne et al.,
2002). These high-throughput profiling techniques allow the
expression level of hundreds or thousands of genes to be monitored
simultaneously under the conditions tested. For each gene, an expression
profile is obtained that reflects its dynamic behavior during a time-course
experiment or its behavior under distinct conditions. Genes with similar
expression profiles are considered "co-expressed". To identify
sets of co-expressed genes from high-throughput expression data, clustering
techniques are required (Heyer et al.,
1999; Jensen and Knudsen,
2000). In addition to standard cluster algorithms, such as
hierarchical clustering, K-means, and self-organizing maps, more advanced
algorithms are also being developed, which are specifically fine-tuned for
biological applications (for a review, see
Moreau et al., 2002).
Because co-expressed genes tend to behave similarly, they are expected to
be coregulated. Under the simplifying assumption that this coregulation occurs
at the transcriptional level, co-expressed genes should contain similar
cis-regulatory elements in their promoter regions. As a consequence, these yet
unknown cis-regulatory elements will be statistically overrepresented in the
intergenic regions of the co-expressed genes in comparison with their frequent
occurrence in a set of unrelated sequences. This overrepresentation
constitutes the general principle on which motif detection algorithms is
based.
Regulatory Elements in Orthologs
Usually, genes are part of more extensive gene families that have
originated through both speciation and duplication events. Homologous genes in
distinct species are called orthologs, whereas paralogs refer to homologous
genes that are found in the same genome and have been created through gene
duplication (Mindell and Meyer,
2001). Regarding promoter analysis and study of regulatory
elements, it is important to discriminate between these two types of
homologous relationships. True orthologs have usually retained very similar
functions in distinct species, whereas this is not necessarily true for
paralogs. In many cases, paralogs have only been conserved if they have
acquired different or complementary functions. Hughes
(1994) and Force et al.
(1999) argued that when a gene
with multiple functions is duplicated, the duplicates are only redundant for
as long as each gene is capable of performing all ancestral roles. When one
mutated duplicate is prevented from carrying out one of these ancestral roles,
the other duplicate is no longer redundant. According to the
"duplication degeneration complementation" model of Force et al.
(1999), degenerative mutations
preserve rather than destroy duplicated genes, but also change their functions
or, at least, restrict them to become more specialized. Duplicated genes can
have different expression domains (i.e. the tissue in which both genes are
expressed might have changed as well as the time of expression) because of
changes in their regulatory elements in the promoter region
(Force et al., 1999;
Altschmied et al., 2002;
Prince and Pickett, 2002).
Therefore, promoter regions of true orthologs probably contain similar
regulatory motifs, which may no longer be true for paralogs.
Motif Prediction
To conceive a general method that can detect regulatory motifs is a great
challenge because of both the complexity and flexibility of the regulatory
mechanisms (see the introduction). An important distinction between the
different approaches used thus far to detect regulatory motifs lies in the
representation of the motif, i.e. the TF-binding site. The simplest
description for a motif is a string of characters (A, C, G, and T), extended
with the 11 IUPAC characters that represent partly unspecified or ambiguous
nucleotides, and is used in the string-based approaches, such as word
counting. A more sophisticated description is to represent a given motif by
describing it in a probabilistic manner in which a certain likelihood is
assessed for each nucleotide at a given position in the motif. An example of a
probabilistic representation is the position-weight matrix, where each column
corresponds to a position in the aligned binding sites and each row to a
nucleotide, as shown in Figure
5. The cells of these matrices contain a number indicating the
probability to find a given nucleotide at that particular position.
Alternatives to describe motifs in a probabilistic manner are the hidden
Markov models (Jarmer et al.,
2001) or neural networks
(Workman and Stormo, 2000).
Software tools for motif prediction are listed in
Table V.
View larger version (13K):
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Figure 5. Schematic representation of a set of intergenic sequences upstream of the
ATG translation initiation site, with a common motif shown as black boxes. On
the basis of such a data set, "words" can be counted and
statistically evaluated for their overrepresentation. On the other hand, the
"putative" motifs can be aligned and frequencies of occurrence of
each nucleotide can be calculated for each column within the generated
alignment, producing a position weight matrix. See text for details.
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String-Based Motif Prediction
Counting all of the possible words that may occur across the different
promoter sequences is one of the simplest approaches to find CAREs in a set of
promoters. Among word-counting methods, enumerative and suffix-tree approaches
can be distinguished, the latter being an optimization of the former. Both
methods are string based: The DNA sequence is considered as text in which
oligonucleotides are represented as words or strings. For a given set of
promoter sequences, the frequency of each possible word of a defined length is
computed (Hutchinson, 1996;
Wolfertstetter et al., 1996;
Br zma et al., 1998;
van Helden et al., 1998;
Vanet et al., 1999;
Bussemaker et al., 2000a,
2000b;
Sinha and Tompa, 2000;
Hampson et al., 2002). The
difference between the two representations is that the enumerative approach
will search for each word in the sequence and calculate its frequency, whereas
the suffix-tree approach will only look for a certain subset. The suffix tree
is used to represent each word together with all of its subwords (or suffixes)
so that each word can be reconstructed by going down the tree. For example,
when a certain suffix (for instance, ACCT) is not found in the data set, none
of the words containing ACCT will be counted anymore
(Sagot and Myers, 1998;
Marsan and Sagot, 2000). As a
consequence, the computational time needed to count words can be highly
reduced, which allows the analysis of larger data sets.
Once the frequencies of different words are calculated, the words that are
likely to be a "true" regulatory motif have to be differentiated
from those that are not. Therefore, in each of these word-counting methods,
the number of occurrences of a word needs to be compared with the expected
frequency in a set of non-related sequences, represented by a background
model, which is used to obtain an expected probability. The simplest way to
build a background model is by creating a set of randomly generated sequences,
based on the single nucleotide composition of the submitted sequence. More
sophisticated ways to generate a background model are based on Markov chain
statistics (Schbath et al.,
1995; Schbath,
1997,
2000;
van Helden et al., 2000a;
Thijs et al., 2001), a
lexicon (Bussemaker et al.,
2000a,
2000b), or by simulations in
which words are randomly reassembled to rebuild a set of sequences
(Coward, 1999;
Marsan and Sagot, 2000). The
choice of the background model can be critical. In our experience,
representations closest to real biological sequences or a set of well-chosen
biological sequences appear to be the most reliable. The statistical methods
to evaluate the significance of an observed versus expected frequency and to
conclude whether a word is overrepresented or not are, for example, binomial
probability (van Helden et al.,
1998), composed Poisson law
(Robin and Schbath, 2001),
z-score (Kleffe and Borodovsky,
1992; van Helden et al.,
2000a), and  2 test
(Vanet et al., 1999).
Although the latter is a very simple statistical method to evaluate unknown
motifs, its merits in previous studies has been proven in looking for
regulatory elements in the yeast genome
(van Helden et al., 1998;
Sinha and Tompa, 2002).
Probabilistic Motif Detection
Probabilistic motif detection aims at constructing a multiple alignment by
locally aligning small conserved regions in a set of unaligned sequences.
Here, we will focus on the matrix-based approaches to illustrate probabilistic
motif detection procedures. All methods start from a random motif model,
represented as a weight matrix and altered through a series of iterations by
machine-learning algorithms that are aimed at finding the optimal score. The
process of optimizing the score for a local alignment already tends to
converge toward conserved motifs that occur frequently in the data set. The
more advanced algorithms incorporate a background model to compensate for
given motifs occurring at high frequencies because of compositions similar to
those of the non-conserved parts of the sequence (the
"background"). A motif in which the average nucleotide composition
differs strongly from the background will be assigned a higher score.
Implementations differ from each other in the way the background is
represented, in how the score is calculated, and in how the optimization is
performed. For motif detection algorithms that describe the motif by a weight
matrix, expectation maximization and its stochastic variant, Gibbs sampling,
are often used for optimization strategies.
The program CONSENSUS was one of the first algorithms that represented a
motif by a weight matrix (Hertz et al.,
1990; Hertz and Stormo,
1996,
1999). The algorithm starts
with a first sequence from the submitted data set and creates a weight matrix
for each possible word of user-specified length. Subsequently, it aligns each
possible word from the next sequence with each weight matrix. The obtained
alignments are scored for their information content, and those with the
highest score are retained for the next iteration. This process is reiterated
until all sequences have contributed to the alignment and weight matrix.
CONSENSUS was used for example in the identification of CAREs involved in the
heat shock response in Caenorhabditis elegans
(GuhaThakurta and Stormo,
2001).
The expectation-maximization (EM) method (Stormo,
1988,
1990;
Stormo and Hartzell, 1989;
Lawrence and Reilly, 1990;
Cardon and Stormo, 1992;
Bailey and Elkan, 1995) is a
two-step iterative procedure that aims at obtaining, for each possible motif
position, the likelihood that the motif located at that position corresponds
to the current motif model (weight matrix). In the maximization step, the
parameters that optimize the likelihood are estimated. Once the motif
positions are known, the observed frequencies of the nucleotides at each motif
position correspond to the maximum-likelihood estimates of the parameters of
the motif model. On the basis of the updated probabilities of all motif
positions of the previous step, the model parameters are re-estimated. For
motif finding, EM simultaneously computes the alignment positions, the motif
weight matrix, and the background model that maximize the likelihood of the
sequence. In the original implementation
(Lawrence and Reilly, 1990),
the "exactly one occurrence" of the motif in each sequence was
assumed. This assumption is a problem because in a cluster of co-expressed
genes, sequences might be present without a (or the) motif. Because EM-based
motif detection algorithms are deterministic, results for particular queries
with similar parameter settings and initializations will be identical. A
drawback of the method is that results depend strongly on the initial
conditions and often converge into local optima. One of the most widely used
EM applications is the program MEME (Bailey
and Elkan, 1995).
Gibbs sampling-based strategies have originally been developed to detect
protein motifs but have been adapted later on to handle DNA sequences
(Neuwald et al., 1995;
Roth et al., 1998;
Hughes et al., 2000;
Liu et al., 2001;
Thijs et al., 2002a). Gibbs
sampling is a stochastic variant of EM
(Lawrence et al., 1993;
Neuwald et al., 1995).
Because of the stochastic nature of the Gibbs sampling approach, an initially
detected motif can be replaced by another one that has a higher score, thus
allowing escape from local optima. This feature is the reason why the output
of a stochastic motif detection algorithm results in different outputs, even
with the same input and parameter settings. However, the more pronounced the
optimal solution is in a given data set, the more a motif is overrepresented,
and the stronger its conservation, the more frequently it will be retrieved
over different runs. Statistics on the outcome of multiple runs of a
stochastic implementation can facilitate interpretation of the results.
Adaptative quality-based clustering (De
Smet et al., 2002) combined with Motif Sampler based on Gibbs
sampling (Thijs et al.,
2002a) was applied to the data published by Reymond et al.
(2000) in which gene
expression was studied in response to mechanical wounding in Arabidopsis
leaves. After clustering, the four most populated clusters (>3 genes) of
co-expressed genes were selected, and the upstream sequences were analyzed
with the Motif Sampler to discover common regulatory elements. To avoid the
problem of local optima, each data set was submitted 10 times to the Motif
Sampler with the same parameters. The output of these 10 runs was compiled
taking into account the individual scores of each motif and the order in which
they were found. Subsequently, the consensus of the motifs found were compared
with regulatory sites described in the PlantCARE database
(Lescot et al., 2002). From
all of the high-ranking motifs returned by the Motif Sampler, several were
similar to known cis-regulatory elements involved in plant defense (methyl
jasmonate-, abscisic acid-, or elicitor-responsive elements) or in light
responsiveness. Among these elements, a 12-bp motif was found composed of two
sites involved in methyl jasmonate responsiveness. These motifs have been
described previously in the upstream sequence of the lipoxygenase isoenzyme 1
gene of barley, where they were separated by 15 bp
(Thijs et al., 2002a).
Motif Prediction by Phylogenetic Footprinting
The procedure that identifies regulatory elements based on a set of
orthologous sequences is named phylogenetic footprinting
(Koop, 1995;
Duret and Bucher, 1997;
Wasserman et al., 2000).
Phylogenetic footprinting has proven its usefulness to detect CAREs in the
human genome, based on the pairwise comparison between human and mouse
(Hardison, 2000;
Wasserman et al., 2000;
Krivan and Wasserman, 2001;
Dermitzakis and Clark, 2002;
Jegga et al., 2002). However,
producing a reliable data set for phylogenetic footprinting is not
self-evident. When the overall degree of conservation in intergenic sequences
between two homologs is too high, conserved motifs will not be detected. At
the other extreme, when homologs are compared from species that are too
distantly related, the intergenic regions may no longer show any similarity
(Tompa, 2001). The ideal
composition of a data set can only be derived in retrospect, implying that an
algorithm suited for phylogenetic footprinting should ideally identify and
discard (or counter-weigh) sequences that are too similar and cope with the
presence of sequences that do not contain the conserved motif. Furthermore,
the phylogenetic distance between organisms should be taken into account in
the weighting schemes of the algorithm. As stated before, closely related
sequences are less useful for identifying a motif because of their high
overall conservation, complicating the search for functionally conserved
regions. Alignment algorithms, such as ClustalW
(Thompson et al., 1994) and
Bayes-Block Aligner (Zhu et al.,
1998), have proven useful for phylogenetic footprinting, but the
length of the conserved motif is often too small compared with the length of
the non-conserved part of the sequence; therefore, multiple sequence alignment
will fail.
A promising novel algorithm has recently been published that identifies the
most conserved motifs among the input sequences as measured by a parsimony
score on the underlying phylogenetic tree
(Blanchette et al., 2002;
Blanchette and Tompa, 2002).
In general, the algorithm selects motifs that are characterized by a minimal
number of mismatches and are conserved over long evolutionary distances.
Furthermore, the motifs should not have undergone independent losses in
multiple branches. In other words, the motif should be present in the
sequences of subsequent taxa along a branch. The algorithm, based on dynamic
programming, proceeds from the leaves of the phylogenetic tree to its root and
seeks for motifs of a user-defined length with a minimum number of mismatches.
Moreover, the algorithm allows a higher number of mismatches for those
sequences that span a greater evolutionary distance. Motifs that are lost
along a branch of the tree are assigned an additional cost because it is
assumed that multiple independent losses are unlikely in evolution. To
compensate for spurious hits, statistical significance is calculated based on
a random set of sequences in which no motifs occur. Phylogenetic footprinting
for the detection of CAREs is steadily gaining importance (Koch et al.,
2001,
2002;
Quiros et al., 2001;
Colinas et al., 2002) and will
continue to do so when more plant genomic sequences will become available. To
give just one example, using phylogenetic footprinting, Tompa
(2001) was able to predict
several new binding sites in the 5'-UTR of plant genes coding for the
small subunit of ribulose-1,5-bisphosphate carboxylase.
Improvements and Fine Tuning of Motif Detection Algorithms
The most obvious reason why motif detection algorithms fail is because of
their sensitivity to noise. All parts of a sequence that do not contain the
motif constitute noise in the context of motif detection. Moreover, because
sets of related sequences are usually based on other predictive tools, for
instance clustering, they are expected to contain sequences without any shared
motif. A decreasing signal-to-noise ratio exacerbates the identification of
statistically overrepresented motifs and increases the chance of finding false
positives. Probabilistic motif detection methods have been improved
considerably to cope with a large noise level. Current implementations, such
as AlignACE (Hughes et al.,
2000) and MEME (Bailey and
Elkan, 1995), usually take into account that some sequences lack a
shared motif and allow the influence of such sequences to be discarded by
estimating the motif model parameters. The more advanced implementations
derive the optimal number of motif occurrences in each sequence from the data.
Modeling the background with a more complex sequence model contributes also
considerably to the robustness of the algorithm in the presence of noise (Liu
et al., 2001,
2002; Thijs et al.,
2001,
2002a). Besides making more
robust algorithms that facilitate discrimination between true and false
positives, advanced scoring schemes are being developed that assign a
statistical significance to the motifs detected, i.e. that describe the
probability of observing a motif with a similar score in a set of unrelated
sequences.
Because regulatory motifs, in particular in higher eukaryotes, are
concentrated in modules, current research is focusing toward adapting motif
detection algorithms to retrieve dyads, i.e. motifs spaced by a fixed or
variable gap. Within the enumerative statistical methods, Sinha and Tompa
(2000) created an algorithm
that searches for motifs with a gap of variable size between them. The
algorithm developed by van Helden et al. (2000) enables a search for dyads
with a fixed number of base pairs between 3 and 20.
Vanet et al. (2000) and
Marsan and Sagot (2000)
developed approaches that look for two motifs separated by a fixed number of
nucleotides by using the suffix-tree method. Cardon and Stormo
(1992) adapted their EM-based
algorithm to detect dyads with variable gap size. In their Gibbs
sampling-based implementation, Liu et al.
(2001) have included an
extension that allows searching for dyads, whereas the program Co-bind of
GuhaThakurta and Stormo (2001)
was specifically created to identify two regulatory sites of gap-separated
cooperative TFs.
The need for extensive parameter fine tuning complicates nonexpert use of
most of the motif detection approaches described above. Novel implementations
of motif detection algorithms tackle this problem by estimating the optimal
parameter settings themselves, hence, minimizing the number of user-defined
parameters. An example of such a user-defined parameter is the motif length.
Because the motif length is generally unknown in advance, it is not obvious to
choose the parameter setting that results in the true motif. Some algorithms
compute the optimal motif length; for instance, Pattern assembly
(van Helden et al., 2000b)
groups overlapping motifs to build a longer motif consensus. The
implementation of AlignAce determines the optimal motif length from the data
(Roth et al., 1998;
Hughes et al., 2000). Manually
generating a suitable data set (see above) that can be used readily for motif
detection can be a tedious job. Therefore, some on-line implementations have
been developed, such as the INCLUSIve
(Thijs et al., 2002b;
Engelen et al., 2003) Web site
that offers a pipeline to combine microarray preprocessing, the adaptive
quality-based clustering, automatic sequence retrieval, and motif detection
based on Gibbs sampling (Motif Sampler). A tool similar to INCLUSIve, called
expression profiler (Brzma et al.,
1998), is provided by the European Bioinformatics Institute.
"Regulatory Sequence Analysis tools"
(van Helden et al., 1998)
proposes a word counting-based set of tools to analyze a set of intergenic
sequences.
|
CONCLUSIONS
|
|---|
Promoters are very complex structures, defined by many different structural
features. The actual regulatory elements are usually very short, which highly
complicates their unambiguous identification. As a consequence, the in silico
prediction of promoters and regulatory motifs is not straightforward. In
addition, our knowledge of transcription regulation in general and
organism-specific expression regulation in particular, is still very limited.
Especially for plants, solid "intrinsic" genomic data are still
needed that can be integrated into existing prediction tools. In this respect,
we have started with the analysis of CpG and CpNpG islands, known to be often
associated with promoters. Although several implementations for the detection
of such "islands" in vertebrates have been described
(Ioshikhes and Zhang, 2000),
parameter settings used to detect these islands in animals cannot be used to
find similar islands in the Arabidopsis genome. Software and parameters have
to be adapted to the species under investigation. Moreover, even if a reliable
tool were available for the detection of CpG and CpNpG islands associated with
plant promoters, it remains to be proven whether these islands would be
biologically functional and relevant. In addition to a lack of
"intrinsic" genomic data, experimental data on promoters are also
scarce, because in general, thorough analysis of even one single promoter is
very time consuming. Furthermore, the technology is still missing for
exhaustive knowledge of gene expression or for understanding the mechanisms
behind it. Therefore, for now and despite its many shortcomings, in silico
analysis seems to be a privileged alternative to analyze simultaneously a
great number of regulatory elements or promoter regions. Experimental testing
of these in silico predictions may be a manner to increase knowledge on
promoters more quickly and at a lower cost, especially for plants.
|
ACKNOWLEDGMENTS
|
|---|
We thank two anonymous reviewers for helpful suggestions.
Received November 14, 2002;
returned for revision January 10, 2003;
accepted March 17, 2003.
|
FOOTNOTES
|
|---|
Article, publication date, and citation information can be found at
www.plantphysiol.org/cgi/doi/10.1104/pp.102.017715.
1 This work was supported by the Vlaams Instituut voor de Bevordering van het
Wetenschappelijk-Technologisch Onderzoek (grant no. STWW–980396). K.F.
is indebted to the Instituut voor de aanmoediging van Innovatie door
Wetenschap en Technologie in Vlaanderen for a predoctoral fellowship, K.M. is
Research Fellow of the Fund for Scientific Research (Flanders), and P.R. is a
Research Director of the Institut National de la Recherche Agronomique
(France). 
2 These authors contributed equally to the paper.
*
Corresponding author; e-mail
pierre.rouze{at}gengenp.rug.ac.be;
fax 32–9–264–5349.
|
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ABSTRACT
CONTENT-BASED FEATURES