- Research article
- Open Access
Identification of plant promoter constituents by analysis of local distribution of short sequences
BMC Genomics volume 8, Article number: 67 (2007)
Plant promoter architecture is important for understanding regulation and evolution of the promoters, but our current knowledge about plant promoter structure, especially with respect to the core promoter, is insufficient. Several promoter elements including TATA box, and several types of transcriptional regulatory elements have been found to show local distribution within promoters, and this feature has been successfully utilized for extraction of promoter constituents from human genome.
LDSS (Local Distribution of Short Sequences) profiles of short sequences along the plant promoter have been analyzed in silico, and hundreds of hexamer and octamer sequences have been identified as having localized distributions within promoters of Arabidopsis thaliana and rice. Based on their localization patterns, the identified sequences could be classified into three groups, pyrimidine patch (Y Patch), TATA box, and REG (Regulatory Element Group). Sequences of the TATA box group are consistent with the ones reported in previous studies. The REG group includes more than 200 sequences, and half of them correspond to known cis-elements. The other REG subgroups, together with about a hundred uncategorized sequences, are suggested to be novel cis-regulatory elements. Comparison of LDSS-positive sequences between Arabidopsis and rice has revealed moderate conservation of elements and common promoter architecture. In addition, a dimer motif named the YR Rule (C/T A/G) has been identified at the transcription start site (-1/+1). This rule also fits both Arabidopsis and rice promoters.
LDSS was successfully applied to plant genomes and hundreds of putative promoter elements have been extracted as LDSS-positive octamers. Identified promoter architecture of monocot and dicot are well conserved, but there are moderate variations in the utilized sequences.
The determination of complete genome sequences has allowed analysis by various statistical methods that have furthered understanding of the function of genomes. Analysis of promoter structure is one of the most important issues. Understanding of promoter structure allows predictions concerning promoter positions and expression profiles, and sheds light on hidden transcriptional networks.
Several functional elements have been identified as promoter constituents for precise and regulated transcriptional initiation: TATA box, Initiator (Inr) motif, Downstream Promoter Element (DPE, found from drosophila), TFIIB-Recognition Element (BRE), and so-called cis-regulatory elements [1–3]. In addition, some mammalian promoters are associated with CpG islands [4, 5], which is related to the Sp1 recognition site  and have some relationship with gene regulation by DNA-methylation [3, 7]. Human transcriptional regulatory elements are reported to make clusters (modules) at the promoter region as well as the 3' end of a gene . Transcription start sites (TSS) in plant promoters have a CG-compositional strand bias, or GC-skew, where C is more frequently observed in the (+) strand than G [9, 10]. Some of these features are well understood and some are not, but all these features are useful to understand individual promoters. Some of the above features have been utilized for promoter prediction [11–13]. Although these studies obtain certain success, our current knowledge of promoters is still insufficient .
Availability of microarray data on co-regulated gene expression on a genomic scale has enabled the prediction of novel cis-elements involved in gene regulation. Several approaches have been developed for this detection of consensus sequences in a co-regulated promoter set (Gibbs Motif Sampling [14, 15], MEME ), and detection of over-represented sequence in co-regulated promoters with a set of reference sequences [17, 18]. These approaches are also applicable to chromatin immunoprecipitation (ChIP) data [19, 20]. In addition, identification of conserved promoter sequences by comparative genomics supports the prediction of regulatory elements [21–24].
Studies on plant transcription factors and functional cis-regulatory elements have been summarized in several databases, and the collective information of cis-elements and/or transfactor-binding DNA sequences are utilized for interpretation of plant promoters (PLACE: , AGRIS: , AthaMap: [27, 28]). Basis of these databases are published articles reporting analyses of individual promoters or transfactors, rather than large scale genomic analyses. Therefore, lack of large scale functional analyses of transcription factors in plant science is reflected in these databases as well.
In contrast to the above fact-based approaches, in silico prediction of plant promoter elements by survey of the Arabidopsis genome is also reported. Molina and Grotewold applied the MEME and Gibbs sampling methods to Arabidopsis core promoter regions with genomic scale, and detected several motifs including a plant TATA motif and microsatellites .
Recent studies on mammalian promoter elements have revealed that some of them have localized appearance along the promoter region, exemplified by the TATA box , and binding sites for NRF-1, Sp1, CREB, ATF, and E2F . These studies evoke the idea that localized distribution is a signature of a functional element of the promoter. Recently, this feature was successfully utilized for extraction of functional sequences from human promoters . Large-scale deletion analysis of human promoters suggested that there is some relationship between presence of functional elements and distance from TSS .
In this report, we have detected hundreds of short sequences showing localized distribution in plant promoters by comprehensive analyses of short sequences. The extracted sequences are mentioned as "LDSS (Local Distribution of Short Sequence)-positive" in this work. These sequences includes TATA boxes, various regulatory sequences identified in previous studies, a novel sequence group that would be a general component of a core promoter, and also many novel sequences that share many characteristics with regulatory sequences. Our analyses have also revealed conservation of the promoter architecture between monocot and dicot plants.
Patterns of distribution of peaks
Typically, DNA elements recognized by a protein (complex) is within the range of 5 to 15 bp long . Within this range, we decided to analyze localization patterns of hexamer and octamer sequences. Our results suggest that sequences longer than 9 bps would not provide enough number of appearance to survive statistical analysis.
For each hexamer sequence, a distribution profile in relation to distance from the TSS was analyzed for Arabidopsis thaliana. Looking through all the distribution profiles, we noticed that there are quite a few patterns. Most sequences have a flat distribution profile with no special tendency (Fig. 1, GAAGAG). Sometimes the base line has a slight slope with a higher frequency toward the TSS. There are also groups with peaks, and they can be classified according to the peak position. We refer these sequences as LDSS-positive.
One example of a LDSS-positive sequence, (Fig. 1, CTCTTC) has a peak of appearance at the TSS. Its complementary sequence (Fig. 1, GAAGAG) has a distinct distribution profile, showing that its appearance is sensitive to the direction of transcription. Although hexamers with this type of distribution profile tend to have only C and T in the sequence (see later), there seems to be weak sequence preference, and not all the sequences filled with C and T show a peak-positive distribution (Fig. 1, CCTTTT is a peak-negative example).
A second example (Fig. 1, CTATAA) is a TATA box-related sequence. This has a peak around -35 bp, and the peak is very sharp. The complementary sequence showed a different pattern with no peak (Fig. 1, TTATAG).
A third example (Fig. 1, TGGGCC) has a relatively wide and low peak. Complementary sequence of this sequence shows the same peak (Fig. 1, GGCCCA). Peak position and direction-insensitivity suggest that sequences with this type of distribution profile are so-called cis-regulatory sequences involved in transcriptional regulation . In fact, TGGGCC in Figure 1 is reported to be necessary for meristematic expression in Arabidopsis, and mutation to TGAA CC abolished the expression (Element II of Arabidopsis PCNA-2, ). Interestingly, distribution of the mutated sequence does not have any peaks (Fig. 1, TGAACC), demonstrating a good correlation between functionality and peak distribution. In addition, one base substitution, TGA GCC, also caused the loss of the peak (Fig. 1). It is common that one base substitution drastically changes the distribution profile (data not shown).
As controls, a set of random genomic sequences of 1 kb length was used for the distribution analysis instead of the promoter database. When sequences with distribution patterns of peak-positive sequences were applied to this analysis, they were found to have no peaks in the random genome fragments (Fig. 1, CTCTTC/random genome, CTATAA/random genome, TGGGCC/random genome).
Beside LDSS-positive elements, there are many LDSS-negative sequences. Among them, frequently observed sequences beyond the theoretical occurrence rate (0.24 per a 1 kb region) are rich in AT and might promote promoter context, and rare sequences are rich in GC and they might disturb promoter function when located within the promoter region. Therefore, it might be possible to utilize these LDSS-negative sequences as well for evaluation of promoter context.
Parameters for peak evaluation
Figure 2A shows a close-up of a typical distribution profile of the regulator type. In order to detect peak-positive sequence, we calculated several parameters. Curve fitting with Gaussian did not give good results (data not shown), because the peak shape is not symmetrical, as seen in the figure. Through analysis of distribution profiles of all the hexamers, we noticed that all of the observed peaks were located downstream of -200 bp. This enabled a base line to be established (Base in the figure) as an average of occurrence between -1,000 and -500 bp. Then we calculated the Relative Peak Height (RPH), and Relative Peak Area (RPA) for evaluation of peak strength. Fluctuation around the base line between -1,000 and -500 bp was also evaluated (see figure legend).
Figure 2B shows the relationship between peak position and a parameter of peak strength. As shown, all the strong peaks locate downstream of -200 bp while weak peaks are scattered throughout the promoters. One important point of the figure is the continuous distribution of hexamers across the vertical axis. The continuous nature was also observed when RPH or RPA was represented in the graph on the vertical axis (data not shown). These results mean that there is no clear way to separate peaky and flat groups. In this study, we took a strategy to list sequences with strong peaks, leaving out a flat group and a group with ambiguous peaks.
Peak-positive hexamers can be classified according to their peak position
The LDSS-positive hexamers identified were then classified into three major groups as mentioned above. The first group, including CTCTTC, in Figure 1, localize from -100 to -13 bp. They typically have a peak at the most downstream region of the promoter (position -13, Table 1), but peak positions distribute from -13 to -60. Most of their sequences are composed of only C and T, we refer to this group as Y Patch (Y for pyrimidine). As shown in the table, Y Patch sequences are found in the majority of Arabidopsis promoters.
The second group contains TATA box-related sequences. An example is shown as CTATAA in Figure 1. The characteristics of this group are high peak height, narrow peak width, and stringent peak position (Table 1, TATA Box). Similar to Y Patch, the TATA box group sequences are also found in the majority of Arabidopsis promoters, although promoters with the TATA Box within the peak are is about 1,000 or less for each sequence.
The third group, including TGGGCC in Figure 1, is referred to as REG, for Regulatory Element Group, in this study. The peak positions of this group locate around -80 bp, and they have a wide peak width in comparison with that of the TATA box group (Table 2). Another feature of the group is high coverage of Peak Area against total area. This means high specificity of localization within a promoter. As shown in Relative Peak Area (RPA) of the table, around 50% to 30% of a REG sequence is found in the peak area. These ratios are much higher than those of the Y Patch (25 to 10%) or TATA box (11 to 5%) groups. Compared to these, the number of promoters containing a REG sequence is smaller, consistent with the idea that each REG is not a component of the general core promoter but a specific regulator of gene expression. In fact, Table 2 contains several known cis-regulatory elements, including Element II of Arabidopsis PCNA-2 (GGCCCA, TGGGCC, and AGCCCA)  and G-box/ABRE (CACGTG, CGTGGC, CCACGT, and GCCACG) .
In addition to these three groups, there is also small number of exceptional hexamers with peak positions in the core promoter (-13 to -60). They might constitute a minor type(s) within the core promoter (Table S1, "others" [see Additional file 1]. See also Table S2 and S3 for these elements). The complete list of the extracted sequences is shown in Table S1. The table shows 103 Y Patch, 39 TATA-related, 38 REG, and 22 unclassified hexamer sequences.
Directional preference relative to transcription
Subsequently, we examined if the orientation of the hexamers is critical. The identified hexamers were tested to determine if their complementary sequences were also included or not. If the complementary sequence was also found in this positive group, the original sequence is considered as direction-insensitive, and if not, direction-sensitive. As shown in Figure 3, the downstream region from -50, that is known to be the core promoter region  and includes the Y Patch and TATA box groups, is occupied with direction-sensitive sequences ("uniq" in the figure), while the upstream region, containing the REG group, is rich in direction-insensitive sequences ("comp" in the figure). These findings are consistent with the established idea that the core promoter determines position and direction of transcription, and cis-elements are direction insensitive. These findings further support the idea that the Y Patch and TATA box sequences are core promoter elements and REG sequences are the cis-elements .
Comparison of Arabidopsis and rice promoters
Subsequently, we analyzed the distribution of octamer sequences. The average of octamer appearance rates is 15.7-fold less than the one of hexamers, consistent with a mathematical expectation of 16-fold difference (data not shown). Because rare sequences tend to show more fluctuations by chance, statistical evaluation was more critical for octamer analysis. We prepared random distribution populations and used them for statistical evaluation of each octamer (Figure S1 [see Additional file 2]). In this study, we have set a p value of 1 × 10-5 as a threshold. In addition, data of the complementary sequences was merged only for REG detection to increase total count of an octamer in the database. Through the octamer analyses, we have identified 350 and 418 LDSS-positive core elements (Table S2 [see Additional file 3] and S3 [see Additional file 4]), and 308 and 242 REG sequences from Arabidopsis and rice, respectively (Table S4 [see Additional file 5] and S5 [see Additional file 6]). Sum of the p values for all the extracted octamers of individual species were around 1 × 10-3 each, so false-positive sequences by pure random distribution are not likely to be included in the lists.
For comparison of Arabidopsis and rice elements, Relative Peak Height (RPH) values of all the positive octamers in either of the two promoter databases were represented (Fig. 4A). If a sequence has the same RPH value in the Arabidopsis and rice databases, a dot appears on the diagonal line. As shown in the figure, we found that RPH values are moderately conserved between Arabidopsis and rice (Fig. 4B). The figure also indicates that a considerable number of the sequences have a large difference in the parameter between Arabidopsis and rice. Figure 4B shows Venn diagram of the number of positive octamers in Arabidopsis and rice. As shown in the figure, approximately 30 to 50% of the octamers are conserved between Arabidopsis and rice for both core groups of Y Patch and TATA box, and the REG group. Presence of all the three categories in Arabidopsis and rice, and sequential conservation as shown in the figure indicate that promoter architecture of these plant species is essentially conserved. On the other hand, divergence of the positive sequences might reflect differentiation of the corresponding trans-factors between these species.
Classification of Arabidopsis LDSS-positive octamers by distribution profiles
All the LDSS-positive sequences from Arabidopsis were subjected to clustering analysis according to their distribution profiles. As expected from previous hexamer analyses, major clusters are REGs, TATA box, and Y Patch (Fig. 5). As shown in the figure, distribution profiles within each group (clusters) are quite similar, suggesting functional conservation within each group. The observed clear classification of the LDSS-positive sequences, represented in Figure 5, suggest that the local distribution is a quite useful feature in extraction of putative functional elements in the promoter.
Clustering of Arabidopsis REGs based on presence and absence in promoters
Subsequently, we did classification of 308 Arabidopsis REGs with the aid of the promoter database. For each promoter, number of appearance for each REG was scored, and two-dimensional REG-promoter clustering was performed. This REG-promoter association has revealed that 10,334 out of 12,951 Arabidopsis promoters have at least one REG at the region of -400 to -40 bp. This high coverage (80%) is due to the long list of REG sequences.
This 2D clustering puts co-localized REGs proximal, and promoters with similar REG compositions also come close. Two promoter clusters are shown in Figure 6A and 6B. One cluster of promoters (A) are rich in GCCCA-containing REGs, and another cluster (B) have ACGT-containing REGs. GCCCA-containing REGs is the same kind as TGGGCC (Figure 1) and known to show cell cycle-dependent expression and meristematic expression (Group 1, Table 3). Interestingly, this promoter group is rich in ribosomal proteins. As shown in Figure 6A, as high as 38% (6 out of 16) of the annotated promoters are for ribosomal proteins (Fig. 6A, blue). In contrast, ribosomal promoters are not included in the ACGT-containing promoter clusters (Fig. 6B). Instead, the latter cluster is rich in photosynthesis-related genes and stress-responsive genes, both of which would show environmental responses. In fact, as many as 34 out of 38 genes in this cluster with expression data are responsive to light (Fig. 6B, green) or abiotic stress including salt, drought, and cold (Fig. 6B, red and orange), according to public microarray data [37, 38]. Although this clustering is not so accurate as to distinguish between light and stress responses, it has been proved to classify genes with respect to gene expression with a certain range of accuracy. The results are reasonable because cis-elements for light response (G-box: CACGTG, ) and stress response (ABRE: ACGTGTC, ) are related sequences both of which belong to the ACGT motif for environmental responses (Group 2, Table 3). Therefore, clustering of promoters appears reasonable, although the accuracy may not be enough for pinpoint speculation of gene function.
Clustering of REGs turned out to be reliable as well, and thus useful for REG classification. According to this 2D method, overlapping REGs (e.g., CACGTGGA and ACGTGGA T, Fig. 6C) have a bias toward coexistence by chance. However, similar but mutually exclusive sequences (e.g., ACGTGGA T and ACGTGGA A, Fig. 6C) are also clustered into the same group, suggesting that REGs with the same role are clustered together. This is explained by existence of multiple copies of the same kind of a cis-element in a promoter as different octamer expression. Figure 7 shows the whole tree of Arabidopsis REGs. This figure demonstrates that REGs with related sequences are clustered together with high reliability. According to these results, 12 motifs have been extracted from Arabidopsis REGs (Fig. 7), and are summarized in Table 3.
One group has a GGCCCA core sequence that is known as Site IIa or Element II (Group 1, Table 3). Element II is necessary for cell cycle-related expression and for meristematic expression . Many sequences containing GGCCCA in the center of an octamer were found in REG group of both Arabidopsis and rice (Table 4). As seen in the table, this group is a good indicator of conservation.
Another group shown in the table has the bZIP protein-binding motif containing ACGT core sequence. This group mediates various environmental signals . Both species have this group in common, but Arabidopsis has wider variations than rice (Table 4).
Classification of Arabidopsis and rice REGs are shown in Table 3. The largest group is the Group 1, which includes Element II of the Arabidopsis PCNA-2 involved in cell cycle-related expression, as mentioned above. As shown in the table, this group is well conserved between Arabidopsis and rice and has many members for both species. There are several other REG groups, some of which are rich in only Arabidopsis and some are found from both (several examples in Table 4 and summarized in Table 3). Comparison between Arabidopsis and rice suggests both conserved and differentiated types of REGs.
The identified Arabidopsis REG sequences were referred to the PLACE database that is a collection of reported plant cis-regulatory elements . The comparison revealed that 155 out of 308 Arabidopsis REGs show 100 % match with at least one of the Arabidopsis PLACE entries, giving an estimation that 50 % of the REGs are of established cis-regulatory elements (Table S6 [see Additional file 7]). These results again provide strong evidence for biologically meaningful extraction of sequences by the LDSS method. From another point of view, 21 out of 48 Arabidopsis PLACE entries have been found in the REG list (Table 5). Comparison with another cis-element database, AGRIS , resulted in lower match than PLACE (27%) among Arabidopsis motif entries shorter than 9 bps (data not shown). These results suggest that not all of the cis-regulatory elements are detected by the LDSS strategy. One of the valuable finding of this analysis is the identification of a large number of novel REGs.
Characterization of transcription start site
We then analyzed sequence characteristics around the TSS. In this region, the Initiator motif (Inr: YYA N(T/A)YY, TSS is underlined) is known in some mammalian promoters , and it is also functional in plants . A survey of Arabidopsis TSS revealed that a limited number of promoters (less than 10%) have the Inr motif around the TSS. Thus, we looked for a more general rule. We surveyed which base is preferred at the -1/+1 position among Arabidopsis TSS. The most frequently observed sequence was CA (TSS is underlined), and TA was the second. As summarized in Figure 8A, there is a strong preference of a dimer sequence at the -1/+1 position. The graph clearly shows most of the TSS is A or G, and the -1 position is likely to be C or T. This "YR Rule" (YR, TSS underlined, Y: C or T, R: A or G) applies to as many as 77% of the Arabidopsis promoters that is a much higher frequency than expected random appearance (25%). Similar analysis for the -2/-1 and +1/+2 positions did not reveal clear extension of the rule. When the YR Rule was applied to the -6/-5 to +4/+5 positions, we found that the ratio of YR Rule-positive is highest at the -1/+1 position in the local region examined (Fig. 8B, Arabidopsis). The figure shows that this rule is also applicable to rice TSS (Fig. 8B, rice). These analyses have revealed that sequence preference at TSS is well conserved between Arabidopsis and rice.
An example of Arabidopsis promoter
Our simple LDSS analysis has successfully revealed three distinct groups consisting of hundreds of short sequences. Figure 9A illustrates the architecture of plant promoters based on these findings.
Tight positioning of the TATA boxes relative to the TSS fits with the general idea that the TATA boxes determine the position of the TSS. In addition, the YR Rule of Arabidopsis would be another important determinant as well. The Y Patches locate between the TATA boxes and the TSS, but they can be upstream of the TATA boxes, considering the wide distribution profiles (Figure 5). The role of the Y Patch is not known. The above three elements are orientation-sensitive, and constituents of a core promoter. REGs appear upstream of the TATA box, and they exist in an orientation-insensitive manner. Rice promoters share the above characteristics, showing architectural conservation between dicots and monocots.
An example of an Arabidopsis promoter that has the Y Patch and TATA box is shown in Figure 9B. Octamer analysis of the promoter revealed one cluster of Group 2 REGs (Table 3), one cluster of Y Patches, one cluster of TATA box, and YR Rule. An interesting feature of the figure is the multiple hits of a locus, detecting a longer element. This demonstrates that octamer analysis can detect long functional units as clusters of octamers.
Characteristics of LDSS analysis
In this study, we have identified hundreds of novel sequences solely based on local distribution in the promoter region of Arabidopsis and rice. Biological information, such as microarray data, was not used at all for sequence extraction, and it becomes useful only during interpretation of the extracted sequence. This method is equally sensitive in detection of major and minor motifs in a promoter population as demonstrated by simultaneous detection of major TATA elements and minor REG elements. This feature is an advantage of the LDSS method over other methods of detection of consensus sequences among promoter populations, such as Gibbs Sampling method. We successfully applied the LDSS method to Arabidopsis and rice promoters, and of course, it is applicable to bacterial and mammalian research as well.
The observed localized distribution is a direct result of the selection pressure. While the localization is an indication of a beneficial role for the organism, the relationship between local distribution of a sequence and its functionality is indirect. Therefore, the question arises if all regulatory elements can be picked up by the LDSS strategy.
When we compared REG sequences with established cis-elements in the PLACE database, it was found that 27 out of 48 Arabidopsis PLACE entries are absent in the extracted REGs (Table 5). These results indicate that not all of the functional elements are LDSS-positive, and thus some would not be detected by this method. There are two possibilities for the presence of cis-elements that do not show local distribution. One possibility is that these elements are relatively "new" so there has not been selection pressure for a long enough period. Another possibility is that there has not been any selection pressure because of functional differences from the LDSS-positive elements. The latter idea suggests localization-insensitive classes of regulatory elements that are distinct from REGs. So called long range-regulators [43, 44] might be one of the classes.
Generally, any functional sequences in the genome are recognized by trans-acting factors that are DNA-binding proteins. Promoter elements and their trans- factors have a relationship of co-evolution. Therefore, differentiation of REGs in the two species would reflect a different status of the corresponding trans-factors. Functional comparison of DNA-binding proteins of Arabidopsis and rice is expected to give some answers as to why these two species have differentiated REG sequences. As for the conserved REGs, it is reasonable that cell cycle-related elements (Group 1, Table 3) comprise the most conserved group, because the cell cycle is one of the most conserved activities in organisms.
REG sequences can be extracted form mammalian promoters as well. However, our preliminary analyses suggest that the LDSS method can detect much less REGs than of plants (YYY and JO, unpublished results). This may be reflected by different promoter architecture between plants and animals.
The discovery that the Y Patch is conserved in monocots and dicots is one of the major achievements of this study. A related motif is reported by Molina and Grotewold from Arabidopsis core promoter analysis using the Gibbs-sampling method (Motif 1 with a typical sequence, TTCTTCTTC, ). The biochemical role of Y Patch is not known, but its position, direction sensitivity, and its abundant nature strongly suggest that it is a general component of the core promoter. Our LDSS analyses suggest that human and mouse do not share this element with plants and thus this is a plant-specific core element (YYY and JO, unpublished results).
At the TSS, the Initiator (Inr) motif (Y Y A N T/A Y Y, TSS is underlined) is known as a recognition site by TFIID . Following their rules, the YR Rule can be considered as a less stringent form of Inr. According to this point of view, the YR Rule might be recognized by TFIID. The high coverage of the YR Rule is a useful feature for prediction of TSS. Recently, Carninci et al., have reported the same rule is applicable to mouse and human promoters as well , revealing conservation of YR Rule between plants and mammals.
This rule is not an artifact by the Cap-Trapper method that is the basis of TSS mapping of this study and mammalian studies mentioned above , because it is applicable to human TSS determined by another method (Oligo-Cap method, ) as well (YYY and JO, unpublished results).
A plant consensus around TSS (A/T n T/a C/t A/c a/t, TSS is underlined) is reported by Shahmuradov et al based on 217 dicot promoters (actual consensus is expressed by a matrix, ). This consensus also largely overlaps with YR Rule.
The TFIIB-Recognition Element (BRE) is another core promoter element of animal genes. It is located just upstream of the TATA box and has a GC-rich sequence, (G/C)(G/C)(G/C)CGCC [1, 48]. Our analysis did not detect the BRE as a LDSS-positive element, although CC is preferred at the neighboring sequence of the TATA box at the upstream side in both Arabidopsis and rice promoters (Table S2 [see Additional file 3] and S3 [see Additional file 4]).
LDSS analysis provides useful information toward precise promoter prediction
The hundreds of octamer sequences identified by the LDSS analysis can be used for promoter prediction. The presence of the TATA box is an important feature of a promoter, but there are many false-positives in the genome. For example, a TATA octamer sequence with the highest specific localization is found within the peak area 30% of times in the promoter region, meaning that 70% are found outside of the peak area. This is essentially consistent with a previous study, where more than 200,000 putative TBP-binding sites were detected from the Arabidopsis genome . Utilization of preferential sequence around the TATA box, and coexistence with the Y Patch and REG are expected to elevate accuracy of prediction. Although such a combinational approach is incorporated into several promoter prediction programs , motifs to be detected have been limited so far. Our long list of the LDSS-positive octamers is expected to serve as a thick dictionary for precise interpretation of plant genomes.
In this report, we showed that LDSS can be applied to plant genomes. We have successfully extracted hundreds of promoter elements as LDSS-positive octamers. All the observed behaviors of the isolated elements suggest functionality of these elements. Promoter architectures of monocot and dicot revealed in this study are well conserved, but there are moderate variations in the utilized sequences.
Preparation of promoter databases
Cap-Trapper  is one of the most reliable methods for identification of the 5' end of mRNA and thus suitable for determination of TSS. So-called full-length (fl) cDNAs of Arabidopsis and rice were made by the Cap-Trapper method, and around ten to twenty thousand of non-redundant fl-cDNA clones for each species have been completely sequenced [50, 51]. Therefore, we decided to use the information from the fl-cDNAs for positioning of promoters. Genome sequences of promoter regions from -1,000 to -1 bp were prepared with the aid of information of the 5' ends of fl-cDNAs of Arabidopsis [50, 52] and rice . The established Arabidopsis promoter database [50, 53] and a rice database with 11,370 promoters, prepared in this study, were utilized for our analysis.
Positions of rice fl-cDNA clones of rice  were mapped on to corresponding BAC clones according to description of "MappingData.txt" obtained from the KOME web site , and promoter regions from -1 kb to +200 bp relative to the TSS, that are 1.2 kbp long, were collected. BAC and fl-cDNA sequences were obtained from DDBJ. Special care was taken for 5' end of fl-cDNA sequences, and ones with less than 2 bp mismatch with the corresponding genomic sequences were used for the promoter mapping. Sequences of non-redundant 11,370 rice promoters have been prepared. For analyses of the TSS region, as shown in Figure 6, rice fl-cDNA sequences with no mismatch to the 5' end (6,209 promoters) were used. Establishment of the Arabidopsis promoter database is described elsewhere [50, 53]. Earlier analyses with Arabidopsis hexamers have been done using the distributed database containing 15,607 promoters. This database is based on distinct TSS and allows multiple promoters belonging to a single gene. A smaller set of 12,951 promoters was re-selected from the 15,607-version so as to pick-up one promoter from one gene, and used for octamer analyses. For preparation of random genomic fragments, non-overlapping Arabidopsis BAC clones were selected by consulting a TAIR web site , they were successively cut into 1 kb pieces and serial numbers were given to the fragments. Sequences corresponding to 3,000 randomly chosen numbers based on the Mersenne Twister method  were used as random genomic fragments of 1 kb length.
The programs used in this study will be freely provided upon request for non-profit purposes. A searchable web site to obtain results in this work will be released.
Generation of random distribution
Random distribution samples were generated with respect to Total Area, that is indication of total count in a promoter database. For each Total Area, 1,000 samples were prepared, and their RPA values were subjected to statistical analysis. Average and standard deviation are functions of Total Area (Figure S1 [Additional File 2]) and affected by a smoothing window. Model RPA populations of random distribution were calculated as the following equations:
REG detection (smoothing with a 21-bin (width of window), and Total Area < 2,000): log10(average) = -0.1861Ln(Total Area) – 0.5329, SD = 0.17 CORE detection (smoothing with a 3-bin, and Total Area < 10,000): log10(average) = -0.1784Ln(Total Area) – 0.8026, SD = 0.13
These models were utilized for estimation of p value for each octamer distribution.
Sequence analysis was achieved by a combination of home-made Perl and C++ programs and also Excel software (Microsoft Japan, Tokyo). The first step of the analysis was the preparation of index files for each promoter with all the possible 4,096 hexamer and 65,536 octamer sequences. Information of the index files was then rearranged for each hexamer and octamer sequence, and the occurrence of the short sequences was summarized according to the promoter position. Summarized distribution data of each hexamer was then subjected to smoothing with a bin of 15 bp. Generally, smoothing with a wide bin lowers the peak height of a sharp peak, and with a narrow bin capturing a wide and low peak is not always possible. Considering these tendencies, a bin of 21 bp was used for identification of octamer REGs, and a bin of 3 bp was used for octamer core elements. Octamer REGs were extracted after merging the distribution data of the complementary sequence to increase the count of occurrence. As for extraction of octamer Core elements that is orientation-sensitive, merging was avoided. Positions of octamers and hexamers were counted from the first base of the sequence. For example, the position of a hexamer sequence that locates from -6 to -1 is expressed as -6. Positions of average values for line smoothing are indicated at the centre of the region. Therefore, positions closest to TSS vary depending on the bin length as well.
Thresholds for distribution of peaks are as follows:
Hexamer: (peak height/Base Line > 3) & (peak height/SD > 5) & (Peak Area/basal fluctuation) > 5),
Octamer Core: (p value < 10-4) & (peak height/Base Line > 5) & (peak height/SD > 10) & (Peak Area/basal fluctuation > 6) & (peak position > -51),
Octamer REG: (p values < 10-4) & (peak height/Base Line > 3) & (Peak Area/total area > 0.1) & (peak height/SD > 5) & (Peak Area/basal fluctuation > 6) & (peak position <-50).
Fitting the distribution data with the Gaussian curve was achieved using Igor Pro (Hulinks, Tokyo). All the LDSS-positive octamers together with above parameters can be viewed at our web site ().
Clustering analyses were achieved with Cluster  and visualized with TreeView . For clustering of LDSS-positive elements based on distribution profiles, peak value of each profile was adjusted to 5.0. For REG-promoter clustering, number of each REG appeared at a region between -400 to -40 bp was scored for each promoter and a REG-promoter table was prepared. Among the Cluster options, the hierarchical clustering method (centroid linkage) gave the most natural results over the k-means and SOM methods.
Among the PLACE database , 48 entries with definition sequences of 8 bases or less and also with description containing "Arabidopsis" were subjected to REG survey.
Local Distribution of Short Sequences
transcription start site
Carey M, Smale ST: Concepts and strategies: I. promoter and the general transcription machinery. Transcriptional regulation in eukaryotes. 2001, New York , Cold Spring Harbor Laboratory Press
Butler JE, Kadonaga JT: The RNA polymerase II core promoter: a key component in the regulation of gene expression. Genes Dev. 2002, 16 (20): 2583-2592. 10.1101/gad.1026202.
Smale ST, Kadonaga JT: The RNA polymerase II core promoter. Annu Rev Biochem. 2003, 72: 449-479. 10.1146/annurev.biochem.72.121801.161520.
Antequera F, Bird A: Number of CpG islands and genes in human and mouse. Proc Natl Acad Sci USA. 1993, 90 (24): 11995-11999. 10.1073/pnas.90.24.11995.
Ioshikhes IP, Zhang MQ: Large-scale human promoter mapping using CpG islands. Nat Genet. 2000, 26 (1): 61-63. 10.1038/79189.
Kriwacki RW, Schultz SC, Steitz TA, Caradonna JP: Sequence-specific recognition of DNA by zinc-finger peptides derived from the transcription factor Sp1. Proc Natl Acad Sci USA. 1992, 89 (20): 9759-9763. 10.1073/pnas.89.20.9759.
Bird A: DNA methylation patterns and epigenetic memory. Genes Dev. 2002, 16 (1): 6-21. 10.1101/gad.947102.
Blanchette M, Bataille AR, Chen X, Poitras C, Laganiere J, Lefebvre C, Deblois G, Giguere V, Ferretti V, Bergeron D, Coulombe B, Robert F: Genome-wide computational prediction of transcriptional regulatory modules reveals new insights into human gene expression. Genome Res. 2006, 16 (5): 656-668. 10.1101/gr.4866006.
Tatarinova T, Brover V, Troukhan M, Alexandrov N: Skew in CG content near the transcription start site in Arabidopsis thaliana. Bioinformatics. 2003, 19 Suppl 1: i313-4. 10.1093/bioinformatics/btg1043.
Fujimori S, Washio T, Tomita M: GC-compositional strand bias around transcription start sites in plants and fungi. BMC Genomics. 2005, 6 (1): 26-10.1186/1471-2164-6-26.
Bajic VB, Brent MR, Brown RH, Frankish A, Harrow J, Ohler U, Solovyev VV, Tan SL: Performance assessment of promoter predictions on ENCODE regions in the EGASP experiment. Genome Biol. 2006, 7 Suppl 1: S3 1-13.
Sonnenburg S, Zien A, Ratsch G: ARTS: accurate recognition of transcription starts in human. Bioinformatics. 2006, 22 (14): e472-80. 10.1093/bioinformatics/btl250.
Bajic VB, Tan SL, Suzuki Y, Sugano S: Promoter prediction analysis on the whole human genome. Nat Biotechnol. 2004, 22: 1467-1473. 10.1038/nbt1032.
Lawrence CE, Altschul SF, Boguski MS, Liu JS, Neuwald AF, Wootton JC: Detecting subtle sequence signals: a Gibbs sampling strategy for multiple alignment. Science. 1993, 262 (5131): 208-214. 10.1126/science.8211139.
Roth FP, Hughes JD, Estep PW, Church GM: Finding DNA regulatory motifs within unaligned noncoding sequences clustered by whole-genome mRNA quantitation. Nat Biotechnol. 1998, 16 (10): 939-945. 10.1038/nbt1098-939.
Bailey TL, Elkan C: The value of prior knowledge in discovering motifs with MEME. Proc Int Conf Intell Syst Mol Biol. 1995, 3: 21-29.
van Helden J, Andre B, Collado-Vides J: Extracting regulatory sites from the upstream region of yeast genes by computational analysis of oligonucleotide frequencies. J Mol Biol. 1998, 281 (5): 827-842. 10.1006/jmbi.1998.1947.
Hughes JD, Estep PW, Tavazoie S, Church GM: Computational identification of cis-regulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae. J Mol Biol. 2000, 296 (5): 1205-1214. 10.1006/jmbi.2000.3519.
Ren B, Robert F, Wyrick JJ, Aparicio O, Jennings EG, Simon I, Zeitlinger J, Schreiber J, Hannett N, Kanin E, Volkert TL, Wilson CJ, Bell SP, Young RA: Genome-wide location and function of DNA binding proteins. Science. 2000, 290 (5500): 2306-2309. 10.1126/science.290.5500.2306.
Lieb JD, Liu X, Botstein D, Brown PO: Promoter-specific binding of Rap1 revealed by genome-wide maps of protein-DNA association. Nat Genet. 2001, 28 (4): 327-334. 10.1038/ng569.
Manson McGuire A, Church GM: Predicting regulons and their cis-regulatory motifs by comparative genomics. Nucleic Acids Res. 2000, 28 (22): 4523-4530. 10.1093/nar/28.22.4523.
Kellis M, Patterson N, Endrizzi M, Birren B, Lander ES: Sequencing and comparison of yeast species to identify genes and regulatory elements. Nature. 2003, 423 (6937): 241-254. 10.1038/nature01644.
Harbison CT, Gordon DB, Lee TI, Rinaldi NJ, Macisaac KD, Danford TW, Hannett NM, Tagne JB, Reynolds DB, Yoo J, Jennings EG, Zeitlinger J, Pokholok DK, Kellis M, Rolfe PA, Takusagawa KT, Lander ES, Gifford DK, Fraenkel E, Young RA: Transcriptional regulatory code of a eukaryotic genome. Nature. 2004, 431 (7004): 99-104. 10.1038/nature02800.
Prakash A, Tompa M: Discovery of regulatory elements in vertebrates through comparative genomics. Nat Biotechnol. 2005, 23 (10): 1249-1256. 10.1038/nbt1140.
Higo K, Ugawa Y, Iwamoto M, Korenaga T: Plant cis-acting regulatory DNA elements (PLACE) database: 1999. Nucleic Acids Res. 1999, 27 (1): 297-300. 10.1093/nar/27.1.297.
Davuluri RV, Sun H, Palaniswamy SK, Matthews N, Molina C, Kurtz M, Grotewold E: AGRIS: Arabidopsis gene regulatory information server, an information resource of Arabidopsis cis-regulatory elements and transcription factors. BMC Bioinformatics. 2003, 4: 25-10.1186/1471-2105-4-25.
Steffens NO, Galuschka C, Schindler M, Bulow L, Hehl R: AthaMap: an online resource for in silico transcription factor binding sites in the Arabidopsis thaliana genome. Nucleic Acids Res. 2004, 32 (Database issue): D368-72. 10.1093/nar/gkh017.
Bülow L, Steffens NO, Galuschka C, Shindler M, Hehl R: AthaMap: from in silico data to real transcription factor binding sites. In Silico Biol. 2006, 6: 23-
Molina C, Grotewold E: Genome wide analysis of Arabidopsis core promoters. BMC Genomics. 2005, 6 (1): 25-10.1186/1471-2164-6-25.
Ohler U, Liao GC, Niemann H, Rubin GM: Computational analysis of core promoters in the Drosophila genome. Genome Biol. 2002, 3 (12): RESEARCH0087-10.1186/gb-2002-3-12-research0087.
Elkon R, Linhart C, Sharan R, Shamir R, Shiloh Y: Genome-wide in silico identification of transcriptional regulators controlling the cell cycle in human cells. Genome Res. 2003, 13 (5): 773-780. 10.1101/gr.947203.
FitzGerald PC, Shlyakhtenko A, Mir AA, Vinson C: Clustering of DNA sequences in human promoters. Genome Res. 2004, 14 (8): 1562-1574. 10.1101/gr.1953904.
Cooper SJ, Trinklein ND, Anton ED, Nguyen L, Myers RM: Comprehensive analysis of transcriptional promoter structure and function in 1% of the human genome. Genome Res. 2006, 16 (1): 1-10. 10.1101/gr.4222606.
Fickett JW, Hatzigeorgiou AG: Eukaryotic promoter recognition. Genome Res. 1997, 7 (9): 861-878.
Trémousaygue D, Garnier L, Bardet C, Dabos P, Hervé C, Lescure B: Internal telomeric repeats and 'TCP domain' protein-binding sites co-operate to regulate gene expression in Arabidopsis thaliana cycling cells. Plant J. 2003, 33 (6): 957-966. 10.1046/j.1365-313X.2003.01682.x.
Foster R, Izawa T, Chua NH: Plant bZIP proteins gather at ACGT elements. FASEB J. 1994, 8 (2): 192-200.
Bevan M, Walsh S: The Arabidopsis genome: a foundation for plant research. Genome Res. 2005, 15 (12): 1632-1642. 10.1101/gr.3723405.
Yamaguchi-Shinozaki K, Shinozaki K: Organization of cis-acting regulatory elements in osmotic- and cold-stress-responsive promoters. Trends Plant Sci. 2005, 10 (2): 88-94. 10.1016/j.tplants.2004.12.012.
Nakamura M, Tsunoda T, Obokata J: Photosynthesis nuclear genes generally lack TATA-boxes: a tobacco photosystem I gene responds to light through an initiator. Plant J. 2002, 29 (1): 1-10. 10.1046/j.0960-7412.2001.01188.x.
Carter D, Chakalova L, Osborne CS, Dai YF, Fraser P: Long-range chromatin regulatory interactions in vivo. Nat Genet. 2002, 32 (4): 623-626. 10.1038/ng1051.
Lettice LA, Horikoshi T, Heaney SJ, van Baren MJ, van der Linde HC, Breedveld GJ, Joosse M, Akarsu N, Oostra BA, Endo N, Shibata M, Suzuki M, Takahashi E, Shinka T, Nakahori Y, Ayusawa D, Nakabayashi K, Scherer SW, Heutink P, Hill RE, Noji S: Disruption of a long-range cis-acting regulator for Shh causes preaxial polydactyly. Proc Natl Acad Sci USA. 2002, 99 (11): 7548-7553. 10.1073/pnas.112212199.
Carninci P, Sandelin A, Lenhard B, Katayama S, Shimokawa K, Ponjavic J, Semple CA, Taylor MS, Engstrom PG, Frith MC, Forrest AR, Alkema WB, Tan SL, Plessy C, Kodzius R, Ravasi T, Kasukawa T, Fukuda S, Kanamori-Katayama M, Kitazume Y, Kawaji H, Kai C, Nakamura M, Konno H, Nakano K, Mottagui-Tabar S, Arner P, Chesi A, Gustincich S, Persichetti F, Suzuki H, Grimmond SM, Wells CA, Orlando V, Wahlestedt C, Liu ET, Harbers M, Kawai J, Bajic VB, Hume DA, Hayashizaki Y: Genome-wide analysis of mammalian promoter architecture and evolution. Nat Genet. 2006, 38 (6): 626-635. 10.1038/ng1789.
Yamashita R, Suzuki Y, Wakaguri H, Tsuritani K, Nakai K, Sugano S: DBTSS: DataBase of Human Transcription Start Sites, progress report 2006. Nucleic Acids Res. 2006, 34 (Database issue): D86-9. 10.1093/nar/gkj129.
Shahmuradov IA, Gammerman AJ, Hancock JM, Bramley PM, Solovyev VV: PlantProm: a database of plant promoter sequences. Nucleic Acids Res. 2003, 31 (1): 114-117. 10.1093/nar/gkg041.
Lagrange T, Kapanidis AN, Tang H, Reinberg D, Ebright RH: New core promoter element in RNA polymerase II-dependent transcription: sequence-specific DNA binding by transcription factor TFIIB. Genes Dev. 1998, 12: 34-44.
Carninci P, Kvam C, Kitamura A, Ohsumi T, Okazaki Y, Itoh M, Kamiya M, Shibata K, Sasaki N, Izawa M, Muramatsu M, Hayashizaki Y, Schneider C: High-efficiency full-length cDNA cloning by biotinylated CAP trapper. Genomics. 1996, 37 (3): 327-336. 10.1006/geno.1996.0567.
Seki M, Narusaka M, Kamiya A, Ishida J, Satou M, Sakurai T, Nakajima M, Enju A, Akiyama K, Oono Y, Muramatsu M, Hayashizaki Y, Kawai J, Carninci P, Itoh M, Ishii Y, Arakawa T, Shibata K, Shinagawa A, Shinozaki K: Functional annotation of a full-length Arabidopsis cDNA collection. Science. 2002, 296 (5565): 141-145. 10.1126/science.1071006.
Kikuchi S, Satoh K, Nagata T, Kawagashira N, Doi K, Kishimoto N, Yazaki J, Ishikawa M, Yamada H, Ooka H, Hotta I, Kojima K, Namiki T, Ohneda E, Yahagi W, Suzuki K, Li CJ, Ohtsuki K, Shishiki T, Otomo Y, Murakami K, Iida Y, Sugano S, Fujimura T, Suzuki Y, Tsunoda Y, Kurosaki T, Kodama T, Masuda H, Kobayashi M, Xie Q, Lu M, Narikawa R, Sugiyama A, Mizuno K, Yokomizo S, Niikura J, Ikeda R, Ishibiki J, Kawamata M, Yoshimura A, Miura J, Kusumegi T, Oka M, Ryu R, Ueda M, Matsubara K, Kawai J, Carninci P, Adachi J, Aizawa K, Arakawa T, Fukuda S, Hara A, Hashizume W, Hayatsu N, Imotani K, Ishii Y, Itoh M, Kagawa I, Kondo S, Konno H, Miyazaki A, Osato N, Ota Y, Saito R, Sasaki D, Sato K, Shibata K, Shinagawa A, Shiraki T, Yoshino M, Hayashizaki Y, Yasunishi A: Collection, mapping, and annotation of over 28,000 cDNA clones from japonica rice. Science. 2003, 301 (5631): 376-379. 10.1126/science.1081288.
Yamada K, Lim J, Dale JM, Chen H, Shinn P, Palm CJ, Southwick AM, Wu HC, Kim C, Nguyen M, Pham P, Cheuk R, Karlin-Newmann G, Liu SX, Lam B, Sakano H, Wu T, Yu G, Miranda M, Quach HL, Tripp M, Chang CH, Lee JM, Toriumi M, Chan MM, Tang CC, Onodera CS, Deng JM, Akiyama K, Ansari Y, Arakawa T, Banh J, Banno F, Bowser L, Brooks S, Carninci P, Chao Q, Choy N, Enju A, Goldsmith AD, Gurjal M, Hansen NF, Hayashizaki Y, Johnson-Hopson C, Hsuan VW, Iida K, Karnes M, Khan S, Koesema E, Ishida J, Jiang PX, Jones T, Kawai J, Kamiya A, Meyers C, Nakajima M, Narusaka M, Seki M, Sakurai T, Satou M, Tamse R, Vaysberg M, Wallender EK, Wong C, Yamamura Y, Yuan S, Shinozaki K, Davis RW, Theologis A, Ecker JR: Empirical analysis of transcriptional activity in the Arabidopsis genome. Science. 2003, 302 (5646): 842-846. 10.1126/science.1088305.
Sakurai T, Satou M, Akiyama K, Iida K, Seki M, Kuromori T, Ito T, Konagaya A, Toyoda T, Shinozaki K: RARGE: a large-scale database of RIKEN Arabidopsis resources ranging from transcriptome to phenome. Nucleic Acids Res. 2005, 33 (Database issue): D647-50. 10.1093/nar/gki014.
Matsumoto M, Nishimura T: Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Transactions on Modeling and Computer Simuation. 1998, 8: 3-30. 10.1145/272991.272995.
Eisen MB, Spellman PT, Brown PO, Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA. 1998, 95 (25): 14863-14868. 10.1073/pnas.95.25.14863.
Kosugi S, Ohashi Y: PCF1 and PCF2 specifically bind to cis elements in the rice proliferating cell nuclear antigen gene. Plant Cell. 1997, 9 (9): 1607-1619. 10.1105/tpc.9.9.1607.
Martinez-Garcia JF, Huq E, Quail PH: Direct targeting of light signals to a promoter element-bound transcription factor [see comments]. Science. 2000, 288 (5467): 859-863. 10.1126/science.288.5467.859.
Yang T, Poovaiah BW: Calcium/calmodulin-mediated signal network in plants. Trends Plant Sci. 2003, 8 (10): 505-512. 10.1016/j.tplants.2003.09.004.
This work was supported in part by KAKENHI (Grant-in-Aid for Scientific Research) on Priority Areas "Comparative Genomics" from the Ministry of Education, Culture, Sports, Science and Technology of Japan (to Y.Y.Y. and J.O.).
YYY designed and performed the analyses including writing Perl programs. HI and TA prepared rice promoter database and wrote C++ programs. MM, TS, MSatou, MSeki, and KS prepared Arabidopsis promoter database. JO contributed in identification of YR Rule. All authors read and approved the final manuscript.
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Yamamoto, Y.Y., Ichida, H., Matsui, M. et al. Identification of plant promoter constituents by analysis of local distribution of short sequences. BMC Genomics 8, 67 (2007) doi:10.1186/1471-2164-8-67
- Transcription Start Site
- Core Promoter
- Relative Peak Area
- Plant Promoter
- Relative Peak Height