Bioinformatics Advance Access originally published online on May 12, 2005
Bioinformatics 2005 21(14):3074-3081; doi:10.1093/bioinformatics/bti490
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Cis-regulatory element based targeted gene finding: genome-wide identification of abscisic acid- and abiotic stress-responsive genes in Arabidopsis thaliana
1Department of Computer Science and Engineering, Washington University in Saint Louis Saint Louis, MO 63130, USA
2Department of Genetics, Washington University in Saint Louis Saint Louis, MO 63130, USA
3Department of Biology, Washington University in Saint Louis Saint Louis, MO 63130, USA
*To whom correspondence should be addressed.
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Abstract |
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 TOP Abstract 1 INTRODUCTION2 MATERIALS AND METHODS3 RESULTS4 DISCUSSIONREFERENCES |
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Motivation: A fundamental problem of computational genomics is identifying the genes that respond to certain endogenous cues and environmental stimuli. This problem can be referred to as targeted gene finding. Since gene regulation is mainly determined by the binding of transcription factors and cis-regulatory DNA sequences, most existing gene annotation methods, which exploit the conservation of open reading frames, are not effective in finding targetgenes.
Results: A viable approach to targeted gene finding is to exploit the cis-regulatory elements that are known to be responsible for the transcription of target genes. Given such cis-elements, putative target genes whose promoters contain the elements can be identified. As a case study, we apply the above approach to predict the genes in model plant Arabidopsis thaliana which are inducible by a phytohormone, abscisic acid (ABA), and abiotic stress, such as drought, cold and salinity. We first construct and analyze two ABA specific cis-elements, ABA-responsive element (ABRE) and its coupling element (CE), in A.thaliana, based on their conservation in rice and other cereal plants. We then use the ABRE–CE module to identify putative ABA-responsive genes in A.thaliana. Based on RT–PCR verification and the results from literature, this method has an accuracy rate of 67.5% for the top 40 predictions. The cis-element based targeted gene finding approach is expected to be widely applicable since a large number of cis-elements in many species are available.
Contact: zhang{at}cse.wustl.edu
Supplementary information: Supplementary data for this paper are available at Bioinformatics online.
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1 INTRODUCTION |
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TOPAbstract1 INTRODUCTION2 MATERIALS AND METHODS3 RESULTS4 DISCUSSIONREFERENCES |
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1.1 Targeted gene finding
It is fundamentally important, yet difficult, to identify the genes that respond to certain endogenous cues and/or environmental stimuli. For example, it is of great importance to find genes in plants that are responsive to abiotic stress to enhance the genomic makeup of plants to combat harsh stress, such as drought and low temperature. We call the genes of particular interest target genes and the problem of identifying target genes targeted gene finding.
One possible approach to targeted gene finding is to use the knowledge of experimentally verified target genes in closely related species and utilize gene conservation across different species to identify putative target genes [see Zhang (2002) for a review]. By focusing on the conservation of open reading frames (ORFs) of genes, this strategy has been used to annotate many genomes (The Arabidopsis Genome Initiative, 2000; Lander et al., 2001; Yu et al., 2002). Despite its great success, this method is able to discover only a small number of genes of particular functions, partly because the number of experimentally determined genes is limited. As a result, a large portion of the predicted genes of many species does not have any functional annotation at all. For example, about half of the genes of plant Arabidopsis thaliana currently do not have any definitive functional annotation.
Furthermore, an ORF-centric gene finding method may not be effective in discovering target genes that express under specific conditions. Although gene functions may be indicative of a gene's responsiveness to certain stimuli, there is no direct correlation between gene function and gene expression. Gene expression is controlled mainly at the transcription level, where the binding between transcription factors (TFs) and cis-regulatory DNA sequences (or cis-elements) in the upstream regions of genes plays an important role (Brivanlou and Darnell, 2002). In other words, a gene's responsiveness to certain conditions is ‘hard-wired’ by their cis-elements. Therefore, if some cis-elements are known to be directly involved in gene transcription regulation in responding to specific stimuli, we should be able to use the cis-elements to identify the genes of interest. When combined with experimental verification, this constitutes an effective approach to genome-wide targeted gene finding and function annotation. This approach is supported by the fact that a large number of TFs and their binding cis-elements have been identified over the years. For example, we know most of the TFs and their corresponding cis-binding elements in the yeast Saccharomyces cerevisiae (Harbison et al., 2004). Moreover, TRANSFAC, a database of experimentally verified and computationally predicted cis-elements in many species has been established and has been widely used for many years (Matys et al., 2003). There are also databases of plant-specific cis-elements, including PLACE (Higo et al., 1999) and PlantCARE (Lescot et al., 2002). Furthermore, there is abundant information on cis-elements in literature that has not been achieved as yet.
In this paper, we investigate the targeted gene finding approach and demonstrate its validity and efficacy by identifying the genes inducible by abscisic acid (ABA) and abiotic stress in A.thaliana.
1.2 ABA in plants and abiotic stress
ABA is an important phytohormone that is prevalent in a plant's developmental stages. It plays many key roles in the synthesis of seed storage, the promotion of seed desiccation tolerance and dormancy, and the inhibition of the phase transitions from embryonic to germinative growth and from vegetative to reproductive growth (Finkelstein et al., 2002). Many genes have been identified as ABA-responsive; these include the genes encoding seed storage proteins, late embryogenesis abundant (LEA) proteins, and various other proteins and protein families. Examples of these genes are EM in wheat (Marcotte et al., 1989), EM and RAB16 in rice (Hattori et al., 1995; Mundy et al., 1990), HVA1, HVA22 and dehydrins in barley (Shen and Ho, 1997; Shen et al., 1996; Xu et al., 1996), and EM and RD29 in A.thaliana (Carles et al., 2002; Narusaka et al., 2003).
Most ABA-responsive genes have two eminent characteristics. First, they contain conserved ABA-responsive elements (ABREs) in their promoters (Hattori et al., 1995; Hattori et al., 2002; Marcotte et al., 1989; Mundy et al., 1990; Shen and Ho, 1997; Shen et al., 1996; Xu et al., 1996). ABREs are the binding sites of TFs, such as EmBP-1 (Guiltinan et al., 1990), TAF-1 (Oeda et al., 1991) and ABFs (Choi et al., 2000). Second, the ABREs need to be accompanied by some coupling elements (CEs) in order to be functional (Shen and Ho, 1997; Shen et al., 1996; Xu et al., 1996). The sequence specificity of CEs may be lower than that of ABREs. Moreover, other functional elements, such as ABREs themselves and dehydration-responsive element (DRE), can also function as CEs (Guiltinan et al., 1990; Hobo et al., 1999; Narusaka et al., 2003).
Importantly, ABA mediates many aspects of physiological responses to environmental stress, such as drought, cold and salinity. Many experiments have shown that abiotic stress also activate the processes underlying ABA (Finkelstein et al., 2002; Shinozaki and Yamaguchi-Shinozaki, 2000; Zhu, 2002). Specifically, a large number of genes that respond to abiotic stress are also inducible directly by ABA treatment (Seki et al., 2002a,b), providing direct evidences that ABA must be involved in the processes responding to these environmental stress.
In brief, we have gained a substantial amount of knowledge of the critical roles that ABA plays in plant development and stress response. It is clear that ABA is an essential element in gene transcription regulation in responding to abiotic stress.
In this paper, we study the cis-element based targeted gene finding method in the context of transcription regulation under ABA and abiotic stress in plants. We are particularly interested in the effectiveness of this method in finding, on a genomic scale, the genes that are inducible by ABA and abiotic stress in A.thaliana.
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2 MATERIALS AND METHODS |
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TOPAbstract1 INTRODUCTION2 MATERIALS AND METHODS3 RESULTS4 DISCUSSIONREFERENCES |
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2.1 Plant material, RNA preparation and RT–PCR
For plant material, A.thaliana ecotype Columbia seeds were grown at 24°C for 10 days with 16/8 h light/dark period on Murashige and Skoog modified basal medium with Gamborg vitamins (PhytoTechnology Laboratories, Prod. No. M404). About 20 seedlings were transferred to each MS medium plate with or without 100 µM ABA. After 24 h RNA was extracted from the two groups using TRIzolR reagent (Invitrogen, Cat. No. 15596-026) and further purified by using RNA clean-up columns from RNeasyR Plant Mini kit (Qiagen, Cat. No. 74904). The total RNA was then treated with DNase I (Invitrogen, Cat. No. 18068-015).
RT–PCR analysis was done as follows. First-strand cDNA was synthesized from 1.5 µg of total RNA using ThermoScriptTM RNase H-reverse transcriptase (Invitrogen, Cat. No. 12236-014) with Oligo(dT)12–18 primer following the manufacturer's recommendation. Amplification of the cDNA was optimized using 0.5–2 µl of the cDNA in a total of 25 µl reaction volume and carried out at 94°C for 2 min, 30 cycles of 94°C for 1 min, 60°C for 1 min and 72°C for 1 min, and then 72°C for 5 min. Expression analysis of each gene was confirmed in at least three independent RT-reactions using forward and reverse primers, which are listed in Supplementary Table 1.
2.2 Genomic sequences
Predicting promoters is at least as difficult as predicting genes. The key is the identification of transcription start sites (TSSs). To predict TSSs, we combined an A.thaliana cDNA database and a software, TSSP (SoftBerry, http://www.softberry.com). As of January, 2004, there were 26 213 predicted A.thaliana genes (excluding pseudogenes and RNA genes) in GenBank, among which 12 359 (47%) had cDNA sequences (with annotated 5'-UTR >50 bp). For each gene, we retrieved a segment from the gene's start codon to 1500 bases upstream or its farthest 5' annotated gene boundary. We then applied the TSSP software to each upstream sequence to identify TSS. When multiple TSSs were predicted on a gene, the one closest to the ORF was chosen. The TSSs that were >50 bases downstream of the cDNA start point were considered false positive. Overall, TSSs of 18 343 (70%) genes were predicted and used in further analysis and prediction, whereas 3133 (12%) genes with cDNA and 4737 (18%) genes without cDNA did not have TSSs predicted. Given a TSS, we retrieved the sequence from 350 bp upstream to 50 bp downstream of the TSS as the proximal promoter. The intron and exon regions were retrieved from The Arabidopsis Information Resource (TAIR) at http://www.arabidopsis.org
2.3 Microarray gene expression data
Data on gene transcription profiling were from Hoth et al. (2002) Kreps et al. (2002) and Seki et al. (2002a,b). We took the datasets in Seki et al. (2002a,b) for motif analysis. Using the promoter prediction method mentioned above, we obtained 366 promoters of unique genes upregulated after ABA-treatment and/or under abiotic stress (drought, cold and salinity) for motif analysis.
2.4 Scoring
In order to search for instances of a known degenerate motif in promoters, we scored each short sequences of length w in a given sequence based on the motif and a background model. The degenerate motif W of length w is represented by a PWM 
W = (qi,b), where qi,b is the probability of finding base b at position i in the motif. The background model Bm is a m-th order Markov model, which can be estimated from background sequences. The probability that a wmer starting from the j-th position of a sequence is generated from the background model was calculated as 
, where bj is the j-th base of the sequence. The conditional probability P(bj+l–1|bj+l–2
bj+l–m–1) is the frequency of observing base bj+l–1 following a particular mmer bj+l–2bj+l–m–1 in background sequences. With a 0-th Markov model, the conditional probability is reduced to P(bj+l–1), which is the frequency of base bj+l–1 in the background sequences. The probability that a wmer was generated from the motif model W is 
, where ql,bj+l–1 is the probability of seeing base bj+l–1 at the l-th position of the motif. In our final model, we used a 0-th model since it has the highest accuracy for m-th models with m ranging from 0 to 5.
Based on these two probabilities, a log-ratio score Aj,W,Bm was assigned to each position j in the sequence, which is computed as
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To score a sequence S by a motif module consisting of two motifs M and N (e.g. ABRE and CE motifs), we considered all their possible positions i and j within S, as long as they did not overlap and were within a certain distance d. The combined score between positions i and j was computed as AS,M,N,Bm = maxi,j(Ai,M,Bm + Aj,N,Bm). The highest combined score among all positions was assigned to the sequence.
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3 RESULTS |
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TOPAbstract1 INTRODUCTION2 MATERIALS AND METHODS3 RESULTS4 DISCUSSIONREFERENCES |
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In our research, we began with an analysis of the cis-elements, i.e. ABREs and CEs, of the target genes in A.thaliana. We then predicted candidate genes, and verified them using RT–PCR experiments and published microarray profiling results.
3.1 ABREs
To reiterate, abiotic stress, such as drought, cold and salinity, can trigger ABA. Many genes can be induced by ABA and/or one of these stress conditions. It is also known that ABA-responsive genes in rice, maize, barley and other cereals typically have ABREs and CEs as their determinant cis-elements. An ABRE for A.thaliana has also been identified (Zhu, 2002).
Our analysis showed that the ABREs are also significant cis-elements for genes responsive to abiotic stress in A.thaliana. In our analysis, we used the expression profiling results published in Hoth et al. (2002) Kreps et al. (2002) and Seki et al. (2002a,b). We first considered the promoters of the genes that are upregulated separately under ABA, cold, drought and salinity. MEME (Bailey and Elkan, 1994), which is one of the best motif finding algorithms (http://meme.sdsc.edu/meme/website/intro.html), was used to find statistically significant degenerate motifs from these four sets of promoters. They contain many common motifs. Specifically, ACGT-containing motifs, termed as G-box, are over-represented in all these sets of promoters. They are consistently ranked among the top motifs identified: they are the first for ABA-inducible genes, second for drought-responsive genes and third for both cold- and salinity-inducible genes. The ACGT-containing motifs from the three stress-induced genes are very similar to the ACGT-containing motif in ABA-induced genes. They were directly compared by a computer program CompareACE (downloadable at http://atlas.med.harvard.edu/download/). The similarity score used by CompareACE is the Pearson correlation coefficient between the nucleotide base frequencies of the alignment of two motifs. The scores vary between –1 and 1, where 1 means a perfect match. The three ABRE motifs from stress genes have similarity scores >0.99 as compared with those of the ABRE motif from ABA genes. The four ABRE motifs are shown in Supplementary Figure 1. Similar results were obtained using AlignACE motif finding algorithm(Hughes et al., 2000) (data not shown).
There are significant overlaps among the genes induced by ABA and one of the three stress conditions. We further analyzed the cis-elements in the set of genes exclusively induced by one of these stress, resulting in three sets of genes not overlapping with ABA-inducible genes. The analysis of the motifs from these three sets of genes led to similar conclusions, except that the ACGT-containing motifs were now slightly degenerate and ranked slightly lower than in the previous analysis. They ranked as the third, second and third for the genes unique to cold, drought and salinity, respectively. Nevertheless, their similarities to the ACGT-containing motif from ABA genes were still significant, i.e. >0.98. These ACGT-containing motifs are shown in online Supplementry Figure 2.
This specificity analysis indicated that ABREs are also determinant cis-elements for stress-related transcription regulations. This implies that the genes responsive to ABA and abiotic stress are difficult to separate from one another merely based on these cis-elements. It is possible that other cis-elements cooperate with ABREs to differentiate various stress regulations, a topic beyond the scope of this paper.
3.2 CEs
Cis-elements are generally degenerate. Even though the ACGT-core in ABREs is well conserved, the flanking sequences beyond the ACGT-core vary. For example, a rice ABRE is CGTACGTGTC (Hobo et al., 1999), whereas an ABRE for maize is GACGTG (Busk et al., 1997) and an ABRE for A.thaliana is CCACGTGG (Zhu, 2002).
When compared with ABREs, CEs are less conserved (Busk and Pages, 1997; Hobo et al., 1999; Shen and Ho, 1997). The EM gene of rice has a CE (CE3) of GACGCGTGTC (Hobo et al., 1999); maize RAB28 has CE3 of ACGCGCCTCCTC (Busk et al., 1997), and barley HVA1 has CE3 of ACGCGTGTCCTC and HVA22 has CE1 of TGCCACCGG (Shen and Ho, 1997). These CEs are more diverged than ABREs, whereas the CE3s have a relatively conserved CGCG core. To our knowledge, no experiment has been done to characterize the CEs of A.thaliana.
To obtain an accurate, as well as degenerate, pair of ABRE and CE for predicting ABA-inducible and stress-inducible genes in A.thaliana, we combined computational methods with the knowledge of known motifs from other plants to obtain the following three types of degenerate ABRE and CE motifs. The first type is based on the experimentally identified ABRE and CE motifs. We constructed PWMs (Stormo, 2000) for ABREs based on motifs from rice [OSEM (Hattori et al., 2002), RAB16A/B/D (Mundy et al., 1990; Ono et al., 1996)], maize [RAB28 (Busk and Pages, 1997)], barley [HVA1 (Shen and Ho, 1997)], and A.thaliana [RD29A (Zhu, 2002)]. The resulting ABRE is MGTACGTGKC. To obtain CEs, we considered the ones from monocots (as no CE from A.thaliana is known) and resulted in CEs of GMCGCGTGKC. The logos for these ABRE and CE are shown in Figure 1a. Since we used information from monocots, we refer to this type of motifs as Monocots-based motifs for convenience. Note that the number of experimentally verified genes is small; hence the accuracy of the motifs may be low.
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The second type of degenerate ABRE and CE motif comes from a refinement to the first type through an iterative procedure that combined the experimentally verified motifs, i.e. the first-type motifs and the results from expression profiling. We applied a motif scan program, which we developed (see Section 2), to the 366 upregulated genes that were identified by gene expression profiling in Seki et al. (2002a,b). We explicitly took the first-type motifs as seeds to scan the promoters. Matched sequences were ranked by their scores (Section 2); the top 15 matched motifs were chosen to construct new PWMs. The new PWMs must be similar but not identical to the monocots-based PWMs. This step was repeated until the PWMs did not change or their specificity decreased. The refined motifs are SRTACGTGTC for ABRE and GACRCGTGKC for CE, respectively, whose sequence logos are shown in Figure 1b. Compared with the monocot-based motifs (Fig. 1a), there are substantial changes in the flanking sequences beyond the ACGT-core. Besides, A.thaliana seems to have a less conserved GCGT-core in its CE component, the first G in particular can be A about 30% of the time. As a result, the refined ABREs and CEs are similar to each other and become almost palindromic. We refer to these motifs as Arabidopsis-specific motifs.
A third type of ABRE and CE motifs was computationally inferred, as a reference, from the promoters of the 366 stress-responsive genes from microarray experiments under ABA treatment and abiotic stress (Seki et al., 2002a,b). Two motifs, the second and seventh, from the top 10 motifs produced by MEME, appear to be meaningful and the rest seem to be repeats. The motifs for ABREs and CEs are YKMCACGTGKC and MCGCGTCRNYYWCK, respectively, whose sequence logos are shown in Figure 1c. These two motifs are significantly different from the previous two types. For ABREs, the prefix before the ACGT-core is less conserved. The base immediately before the ACGT-core is a relatively conserved C, whereas it is T or A in the previous two types in Figure 1a and b. For CEs, the suffix sequence of the GCGT element does not at all match those in Figure 1a and b.
3.3 Motif modules as target gene indicators
The three types of motifs discussed above were constructed differently and have different degeneracies. One problem with the monocot-based motifs is that they may not be necessarily good indicators of ABA-inducible and stress-inducible genes in dicot A.thaliana. One problem with MEME-inferred motifs is that the ABREs and CEs were identified individually rather than as amodule.
The Arabidopsis-specific motif module seems to be a good indicator for ABA-responsive and stress-responsive genes. To quantify its prediction power, we compared it with the other two modules, measured by the number of predicted genes that were also identified by microarray experiments (Hoth et al., 2002; Kreps et al., 2002; Seki et al., 2002a,b). The results are shown in Figure 2. The monocot-based module has the worst prediction accuracy, and the Arabidopsis-specific module is superior to the MEME-derived module, except for the first eight predictions. This result partially supports the interactive approach we used to obtain refined ABRE and CE motifs.
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In the rest of this paper, we use the Arabidopsis-specific motif module (shown in Figure 1b) for our analysis and prediction.
3.4 ABRE and DRE as coupling elements
A close examination of the CE in the Arabidopsis-specific module shows that it is very often a G-box (ACGT-core) or has a GCGT-core. The GCGT-core has a strong conservation in the CEs for many monocots, as shown in Figure 1a. It is also known that ABREs can act as CEs, and so can dehydration responsive elements (DREs) (Guiltinan et al., 1990; Hobo et al., 1999; Narusaka et al., 2003).
We examined the utilities of ABREs, DREs and GCGT-containing motifs as CEs in ABRE–CE module. The results in Figure 3 show that the prediction accuracies using DREs and GCGT-containing motifs as CEs are significantly lower than those with ACGT-core as CE. The figure indicates that unlike most monocots, which very often have GCGT-core in their CEs, A.thaliana tends to have ABREs as CEs. The results also show that DREs are less effective as CEs than ABREs. One reason may be that the DREs have only six bases (RCCGAC), which may give rise to a relatively large number of false positive matches.
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3.5 Where ABRE–CE module locate
Before applying the Arabidopsis-specific ABRE–CE module, we need to assess if this module is indeed unique to ABA-inducible and stress-inducible genes. For this purpose, we analyzed the distributions of high-quality matches of the module in various regions of A.thaliana genome. We considered the promoters, the intron regions and the second exon regions of all genes, and the promoters of the upregulated and downregulated genes under ABA and stress conditions (Hoth et al., 2002; Kreps et al., 2002; Seki et al., 2002a,b). We also included randomly constructed sequences using the frequencies of nucleotides in all promoters, to evaluate the possibility that the module appears by chance. The results in Figure 4 show that the ABRE–CE module occurs more often in the promoters of the target genes than in the coding and other non-codingregions.
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3.6 Prediction and experimental verification
Using the Arabidopsis-specific ABRE–CE module, we detected a large number of putative ABA-responsive and stress-responsive genes. We closely examined the highest scored 40 predictions, listed in Table 1. We tested 27 genes using RT–PCR on 10-day-old seedlings (Section 2). Among these 27 genes, 17 (63.0%) are verified as upregulated, 3 (11.1%) have no transcripts detected and 7 (25.9%) have no significant expression change. Some of the RT–PCR results are shown in Supplementary Figure 3. In combination with the results from previously published microarray results (Hoth et al., 2002; Kreps et al., 2002; Seki et al., 2002a,b), we found that 27 of the top 40 genes were confirmed as ABA and/or abiotic-stress responsive, giving a prediction accuracyof 67.5%.
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Three genes (At3g33131, At5g52290 and At1g65200) from the set of 27 genes were not detected by RT–PCR. Based on the GenBank annotation as of May 2004, two of these three genes (At3g33131 and At5g52290) were annotated as hypothetical proteins. In addition, seven genes (At1g79040, At1g77450, At1g28530, At1g52230, At2g18700, At4g21270 and At4g21280) did not show significant expression changes in the RT–PCR experiment. It is possible that these genes express in different developmental stages or tissues other than the conditions of the RT–PCR experiments. Indeed, one of these genes (At1g77450) was verified as upregulated in Hoth et al. (2002). One of the differences between the experimental conditions used in Hoth et al. (2002) and our RT–PCR is the age of the seedlings [4-week-old seedlings in Hoth et al. (2002)] versus 10-day-old seedlings in our experiments). Combining these observations, the 67.5% prediction accuracy is, apparently, a lower bound on theprediction accuracy.
Although we used the degenerate ABRE–CE module, which allows an ACGT component in the CE motif, only one gene among the top 40 predictions actually has the GCGT-core in its CE. In other words, most of the top candidates match to ABRE–ABRE module well, in agreement with our analysis of CEs in Section 3.4.
3.7 Where ABREs and CEs locate
It is worthwhile to know the locations of ABRE and CE within promoters. We examined two position statistics. The first is the gap between the ABRE and CE in a module. Figure 5a shows the results in the promoters of all genes and ABA-inducible and stress-inducible genes identified in (Hoth et al., 2002; Kreps et al., 2002; Seki et al., 2002a,b) whose promoters contain these ABRE–CE module. The gaps are typically <150 bases in these genes, although a few gaps beyond 150 bases exist. The most possible gap between these two elements are 
40–50 bases.
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The second statistic is the start position of an ABRE–CE module from the transcription start site (TSS) of a gene. The results on all genes and ABA-inducible and stress-inducible genes are shown in Figure 5b. ABRE–CE modules are usually within 200 bases from TSSs; the majority of them are <120 bases from TSSs. Note that a few ABRE–CE modules start within 5'-UTRs.
3.8 Functional categories
The function of ABA-responsive genes are diverse, reflected by the large number of functional categories these genes may be involved in. We carried out a functional analysis on two sets of genes, the ABA-inducible and stress-inducible genes reported in Hoth et al. (2002), Kreps et al. (2002) and Seki et al. (2002a, b), and the top 150 putative genes predicted in our study. This was done using the MIPS functional category classification from http://mips.gsf.de/projects/plants
Among the 1825 stress-inducible genes from the microarray experiments, 1530 (83.8%) can be assigned to at least one functional category. Among our top 150 predicted genes, 126 (84.0%) have a functional category. Moreover, these two sets of genes have similar distributions across a wide range of functional categories, as depicted in Supplementary Figures 4 and 5. Except the unclassified proteins, the three largest categories are transcription, metabalism and binding proteins. This result suggests that there may be a lot of gene regulation activities after ABA treatment andstress.
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4 DISCUSSION |
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TOPAbstract1 INTRODUCTION2 MATERIALS AND METHODS3 RESULTS4 DISCUSSIONREFERENCES |
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We advocated and investigated a cis-element based method for finding genes that are responsive to certain conditions, which can be referred to as targeted gene finding. This method is orthogonal and complementary to conventional approaches to gene finding and function annotation that are based on the conservation of ORFs. The cis-element based targeted gene finding method explicitly utilizes the information of transcription regulations. By exploiting experimentally verified cis-elements in a genome-wide screening, it naturally combines the fidelity of gene functions elucidated in experimental analyses with computational efficiency of a genome-scalesearch.
In this study, we focused on ABA-responsive and abiotic stress-responsive genes in A.thaliana and their cis-elements, i.e. ABREs and CEs. By employing the experimentally identified cis-elements, we are able to leverage genome-wide targeted gene finding with a huge amount of previous experimental efforts devoted to identifying ABA-responsive and stress-responsive genes and elucidating their regulatory mechanisms.
The idea of using cis-elements to identify genes responsive to certain stimuli is intuitive, and was pursued in at least two previous studies. The first study was reported in Markstein et al. (2002) on a genome-wide analysis of the binding sites for Dorsal, one of the best-characterized sequence-specific TFs in Drosophila. It was known that many Dorsal targeted genes contain a cluster of multiple Dorsal binding sites in a small vicinity of their promoter regions. Using the known Dorsal binding motifs, fifteen promoters that contains clusters of Dorsal binding motifs were identified from Drosophila genome. Among the fifteen genes, three are known Dorsal target genes. Using in situ localization assays, two other genes were shown to be upregulated in the presumptive mesoderm of early embryos, leading to a total prediction accuracy of 34% (5 positive of 15 putative ones). The second study was on interneurons called AIY in Caenorhabditis elegans (Wenick and Hobert, 2004). Using the newly sequenced Caenorhabditis briggsae genome, another nematode diverged from C.elegans 70–100 million years ago. Wenick and Hobert found eight genes in AIY in C.elegans that are also conserved in C.briggsae. Using a standard promoter–dissection approach, they discovered cis-elements that are necessary and sufficient for AIY transcriptome. Moreover, they carried out a genome-wide screening using the discovered cis-elements to predict genes in C.elegans that may express in AIY. They experimentally tested 15 of the top 26 predictions and confirmed 14 of them expressed in AIY, giving a prediction accuracy of 14/15 or 94%. Overall, the verified AIY hit rate was 41 of 57 or 72%. As a comparison, we achieved a comparable prediction accuracy of 67.5% for the top 40 predictions in ourstudy.
Our approach in this paper and the approach taken in Markstein et al. (2002) and Wenick and Hobert (2004) are similar and complement one another. These studies used similar genome-wide search strategies to predict genes that may have certain expression profiles. They did not make strong assumptions about where within promoters the motif matches should be. However, these approaches differ in the way that cis-elements were derived. Markstein seemed to use exact known Dorsal binding motifs in the analysis. Wenick et al. relied on the conservation of genes in C.elegans and C.briggsae, to infer cis-elements that are characteristic to the target genes. We obtained cis-elements based on previous experimental analyses.
In summary, based on the previous studies and the results in this paper, it is evident that the cis-element based targeted gene finding approach is effective and general; it has a high prediction accuracy and is applicable to different organisms and different type of genes. With more information of TFs and their DNA binding information becoming freely available, including those in TRANSFAC database, we expect this cost-effective and accurate approach to be widely applied to various targeted gene finding problems in the future.
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Acknowledgments |
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This research was supported in part by NSF grant EIA-0113618 and a grant from Monsanto Corporation to W.Z. and in part by a grant from Monsanto Corporation to R.S.Q. We thank the other members of W.Z. and R.S.Q.'s groups for the helpful discussions.
Received on January 20, 2005; revised on May 5, 2005; accepted on May 6, 2005
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