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DNA Research 2005 12(4):247-256; doi:10.1093/dnares/dsi011
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© The Author 2006. Kazusa DNA Research Institute
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

RARTF: Database and Tools for Complete Sets of Arabidopsis Transcription Factors

Kei Iida1,2,4,*, Motoaki Seki1,3, Tetsuya Sakurai1,2 6, Masakazu Satou1,2 6, Kenji Akiyama1,2 6, Tetsuro Toyoda2, Akihiko Konagaya2 and Kazuo Shinozaki1,3,5 6

1Plant Mutation Exploration Team, Plant Functional Genomics Research Group, RIKEN Genomic Sciences Center (GSC), RIKEN Yokohama Institute 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
2Genomic Knowledge Base Research Team, Bioinformatics Group, RIKEN Genomic Sciences Center (GSC), RIKEN Yokohama Institute 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
3Laboratory of Plant Molecular Biology, RIKEN Tsukuba Institute 3-1-1 Koyadai, Tsukuba 305-0074, Japan
4Faculty of Bio-Science, Nagahama Institute of Bio-Science and Technology 1266 Tamura-cho, Nagahama 526-0829, Japan
5CREST, Japan Science and Technology Corporation (JST) Japan

Received 2 March 2005; revised 6 June 2005


   Abstract
Top
 Abstract
1. Introduction
 2. Materials and Methods
 3. Results and Discussion
 Acknowledgements
 References
 
More than 5% of all genes in the Arabidopsis thaliana genome have been assumed to code for transcription factors. However, it has been difficult to accurately identify them. To construct proper sets of transcription factors, we used PSI-BLAST and InterProScan, and also checked several families manually. Especially to determine major Arabidopsis transcription factors (MYB, AP2/EREBP, bHLH, NAC, MADS, bZIP, WRKY), we compared the PSI-BLAST search results with those in recent reports. Finally, we identified 1968 proteins as transcription factors (7.4% of all Arabidopsis genes). We established a database named RARTF (RIKEN Arabidopsis Transcription Factor database, http://rarge.gsc.riken.jp/rartf/) based on the identified transcription factors. In RARTF, we provide information on the functional motif of transcription factors, full-length cDNAs, alternative pre-mRNA splicing events and Ac/Ds transposon-tagged mutants. We also provide expression profiles of 400 transcription factor genes in six experiments. We will report expression profiles of all transcription factor genes in various plant tissues under various stress and hormone conditions in the near future.

Key words: Arabidopsis thaliana; transcription factor; database; full-length cDNA; expression profile


   1. Introduction
Top
 Abstract
 1. Introduction
2. Materials and Methods
 3. Results and Discussion
 Acknowledgements
 References
 
Arabidopsis thaliana is a model plant for studying developmental processes, responses to stress and signal transduction. In 2000, its whole genomic sequence consisting of ~26 000 genes was determined.1Go A genome-wide comparative analysis of transcription factors in Arabidopsis, Drosophila melanogaster, Caenorhabditis elegans and Saccharomyces cerevisiae by Riechmann et al.2Go revealed that the Arabidopsis genome codes for ~1500 transcription factors. This is ~6% of the total genes, which is a higher percentage than that in either D. melanogaster, C. elegans or S. cerevisiae. These transcription factors often have separate roles for different developmental stages, tissues or stress responses.3Go–5Go Identification of the transcription factors is essential for understanding the life system of Arabidopsis. Owing to the success of full-length cDNA projects,6Go–8Go the annotation of gene structures has been greatly improved, and the information of all transcription factors needs to be updated based on the new gene annotation. Furthermore, some novel transcription factor families have been identified after the report by Riechmann et al.9Go–18Go A new transcription factor database incorporating accumulated information on upstream regulatory sequences of genes, alternative pre-mRNA splicing events, transposon-tagged mutant lines and so on is needed. In this study, we first searched for the transcription factors with using PSI-BLAST.19Go Riechmann et al.2Go classified the transcription factors in Arabidopsis into ~30 families. Homology search tools or motif search tools such as BLAST19Go or InterProScan20Go are commonly used to search for protein family members by amino acid sequence similarities. Although these algorithms are very useful, none of them are perfect. PSI-BLAST is a powerful algorithm in the search for protein families based on amino acid sequence similarities. With PSI-BLAST, we can search for similar sequences from the database using the position-specific score matrix (PSSM). PSSM places more weight on highly conserved sites. Because transcription factors often have highly conserved DNA-binding domains, we expected to identify all the transcription factors with high sensitivity and specificity from all protein sequences of Arabidopsis with PSI-BLAST. To construct a proper set of Arabidopsis transcription factors, we also used InterProScan and checked for several transcription factor families manually. Finally, we obtained 1968 transcription factors. Based on the transcription factors identified, we constructed a new Arabidopsis transcription factor database named RARTF. We provide the list and the information on all Arabidopsis transcription factors from RARTF.


   2. Materials and Methods
Top
 Abstract
 1. Introduction
 2. Materials and Methods
3. Results and Discussion
 Acknowledgements
 References
 
2.1. Datasets
We used amino acid sequences of all predicted Arabidopsis proteins released from MAtDB (MIPS A. thaliana Genome Database) of MIPS (Munich Information Centre for Protein Sequences, ftp://ftpmips.gsf.de/cress/, v110103).21Go In addition, we used amino acid sequences of a bZIP transcription factor, AREB1 (AB017160 [GenBank] ), not annotated by the MIPS protein entry code. We used 26 594 protein sequences in total.

2.2. Identification of transcription factors
We used the classification of transcription factor families reported by Riechmann et al.2Go We added another 12 families as novel transcription factor families of Arabidopsis to this classification.9Go–18Go To find novel transcription factor families, we used PubMed (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed).

We chose one representative sequence for each transcription factor family or subfamily. We used the sequence in table 3 of Riechmann et al.2Go when the accession number was found, and we selected a protein sequence which had the InterPro22Go motif of each transcription factor family when not. We used these representative sequences as query sequences for the PSI-BLAST search. We used the reported sequences for the 12 novel transcription factor families. We used the entire amino acid sequence of each representative transcription factor for PSI-BLAST search except for ‘GARP, ARR-B class’ family (TF10_2). For this family, we selected the DNA-binding region for PSI-BLAST query. We used PSI-BLAST version 2.2.5 to identify the transcription factors from all Arabidopsis proteins. In the first step, we constructed PSSMs19Go based on query sequences. The parameters we used were the maximum round of iteration (j) = 10, filter (F) = on, E-value threshold for iteration (h) = 0.0001, database (d) = amino acid sequences of all Arabidopsis proteins. In the next step, we searched for transcription factors with the constructed PSSMs. The parameters in the second step were E-value threshold (e) = 0.001, filter (F) = on. After the PSI-BLAST search, we used InterProScan for ‘C2H2-type zinc-finger’ family (TF_5_3) to complement PSI-BLAST search results. We performed a manual check for major Arabidopsis transcription factor families (MYB, AP2/EREBP, bHLH, NAC, MADS, bZIP, WRKY) and ‘PcG; Esc class’ transcriptional regulator family (TF_60). For check processes of major transcription factor families, we referred to the most recent papers.23Go–30Go

2.3. Validation of sensitivities and specificities
We validated pre-fixed PSI-BLAST search results on seven major transcription factor families: MYB, AP2/EREBP, bHLH, NAC, MADS, bZIP, WRKY. We calculated the sensitivities and specificities using the new corrected list we made for each transcription factor family based on the most recent papers,23Go–30Go which we refer to as the ‘correct list’ hereafter. Sensitivity is calculated as [number of transcription factors identified in this study in the correct list]/[number of transcription factors in the correct list] = [True positive]/([true positive] + [false negative]). Specificity is calculated as [number of transcription factors identified in this study in the correct list]/[number of all transcription factors identified in this study] = [True positive]/([true positive] + [false positive]). For comparison, we searched transcription factors by BLAST19Go and InterProScan.20Go In the BLAST search, we used a parameter for E-value threshold (e) = 0.01 which revealed the best sensitivities and specificities within E-value = 0.1, 0.01, 0.001, 0.0001, 0.00001. In InterProScan search, we used IPR001005 motif (PS50090, PF00249, SM0071) for MYB family, IPR001471 motif (PF00847, PR00367, SM00380, PD001423) for AP2/EREBP family, IPR001092 motif (PS50888, SM00353, PF00010, SSF47459) for bHLH family, IPR003441 motif (PF02365) for NAC family, IPR002100 motif (PS50066, SM00432, PF00319, PR00404, SSF55455, PS00350) for MADS family, IPR004827 motif (PF00170, PS50217, PS00036, SM00338) for bZIP family, IPR003657 motif (PF03106, PS50811) for WRKY family. We used InterProScan version 3.3 and dataset version 7.2 with default parameters. We listed proteins that had motifs with a true decision or matched sequence pattern.

2.4. Database construction
We classified the transcription factors into families and subfamilies according to Riechmann et al.2Go In each family or subfamily, we included transcription factors which were identified by PSI-BLAST search with one query sequence. As a result of PSI-BLAST search, we found that some transcription factors belonged to more than two families. To re-organize these transcription factors, we made two lists: the Phase 1 list includes all transcription factors obtained by the search using each query sequence, and the Phase 2 list gives the proteins detected with the smallest E-value. In the Phase 1 list, some transcription factors were annotated to more than two families. In the Phase 2 list, the transcription factor belonged to one family. In the Phase 2 list, there is no redundancy. Based on these two lists, we constructed the transcription factor database: RARTF, in which we related various contents based on AGI gene codes21Go (Fig. 1).


Figure 1
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  		Data flow of identification of transcription factors and database construction in RARTF. In the first step, we constructed PSSMs for each transcription factor family. In the next step, we identified all transcription factors from all Arabidopsis proteins with the PSSMs. We constructed RARTF, based on the identified transcription factors. RARTF provides a variety of information on transcription factors.

 

   3. Results and Discussion
Top
 Abstract
 1. Introduction
 2. Materials and Methods
 3. Results and Discussion
Acknowledgements
 References
 
3.1. Identification of transcription factors
We analyzed 54 transcription factor families and 7 transcriptional regulator families according to Riechmann et al.2Go We also analyzed 12 additional transcription factor families9Go–18Go reported after 2000 (Table 1). Seventeen families had some subfamilies. We used 121 representative sequences of each family or subfamily. We chose representative sequences from Riechmann et al.2Go or other reported novel transcription factors9Go–18Go (see Materials and Methods). We used the entire amino acid sequence of representative proteins for PSI-BLAST search except for the ‘GARP, ARR-B class’ family (TF10_2). For ‘GARP, ARR-B class’ family, we chose the DNA-binding region for PSI-BLAST search to obtain much superior search results. Using these sequences as a query, we performed a PSI-BLAST19Go search for all transcription factors in Arabidopsis (Fig. 1). We performed the PSI-BLAST search using as a parameter the maximum round of iteration set 10. With this parameter, converged results were obtained in 116 out of 121 cases. In two cases (TF_32 and TF_46), there was no significant hit on the first search round. Unconverged results were obtained in another three cases (TF_1_4, TF_17_2 and TF_22_1), and we manually checked them.


View this table:
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  		Transcription factor families in Arabidopsis thaliana.

 
In a preliminary analysis, we found it difficult to identify members belonging to the ‘C2H2-type zinc-finger’ family (TF_5) because there were a few conserved sites in this family and there were many gaps within their zinc-finger sequences. By a PSI-BLAST search of the proteins detected by InterProScan,20Go we listed 171 proteins into TF_5_3. The preliminary analysis revealed another family in which many proteins were clearly not transcription factors, i.e. ‘PcG; Esc class’ transcriptional regulator family (TF_60). We selected three proteins from the 200 listed proteins in this group, by eye.

The Arabidopsis genome has several large transcription factor families that have 100–200 members. These families have been well studied and are known to have important functions. Especially for these major transcription factor families, we compared the PSI-BLAST search results with recent publication. We referred to a review by Stracke et al.23Go for the MYB family, Gutterson and Reuber24Go in 2004 and Sakuma et al.25Go in 2002 for the AP2/EREBP family, Bailey et al. in 200326Go for the bHLH family, Ooka et al.27Go in 2003 for the NAC family, Parenicova et al.28Go for the MADS family, Jakoby et al.29Go for the bZIP family and Ulker and Somssich in 200430Go for the WRKY family. We found 15 false negatives and 38 false positives (Table 2). In each case, we checked their transcription factor lists and redescribed them with AGI codes of our dataset. We corrected our PSI-BLAST search results according to this check.


View this table:
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  		Sensitivities and specificities of PSI-BLAST and other programs.

 
We added AREB1 (accession number: AB017160 [GenBank] )31Go to ‘ABSCISIC ACID-INSENSITIVE5’ subfamily of bZIP family. AREB1 is mapped onto chromosome 1 of Arabidopsis, but the locus and entry of AREB1 do not exist in the MIPS dataset. We checked the proteins not identified as transcription factors and added some proteins to the ‘TF_other’ list, if one protein was annotated as transcription factor. We listed 17 proteins as general transcription factors, GTF. In total, we obtained 1968 transcription factors and transcriptional regulators, which account for 7.4% of all Arabidopsis proteins (Table 1).

The number of identified transcription factor genes, 1968, was larger than the 1533 reported by Riechmann et al.2Go This difference was caused by the added transcription factor families and additional members of each family identified by PSI-BLAST. Recently, Jiao et al.32Go reported 1864 transcription factors in Arabidopsis, which were similar to those in our list (data not shown).

3.2. Comparison of sensitivity and specificity
We used PSI-BLAST, InterProScan and checked their results manually to obtain a proper transcription factor set. In this process, we referred several papers about their transcription factor families. But this identification strategy is not used in all cases, because not all transcription factor families are well studied and reported in reviews. It is important to recognize the sensitivity and specificity of the PSI-BLAST search results without manual check. We checked the sensitivity and specificity of pre-fixed PSI-BLAST search results on major transcription factor families (MYB, AP2/EREBP, bHLH, NAC, MADS, bZIP, WRKY). We also compared the results with those obtained using BLAST19Go and InterProScan.20Go

For the BLAST search, we used the best parameters provided as a result of some parameter tests (see Materials and Methods). For the InterProScan, we selected motifs that gave the best results. For BLAST and PSI-BLAST, we listed non-redundant proteins as search results for comparison because some families (MYB, AP2/EREBP, bHLH, MADS, bZIP) had several query sequences each providing search results. For PSI-BLAST, the numbers of proteins listed in each family are described on the column named ‘genes in family’ of Table 1.

Results of PSI-BLAST or BLAST change when we choose another query sequence. We selected query sequences according to the list written by Riechmann et al.2Go We thought their sequences were enough to find all proteins in each family with PSI-BLAST, because we got good consistency between PSI-BLAST search results and description of reviews23Go–30Go (Table 2). We used the same query sequences for BLAST search for this comparison.

The sensitivity of PSI-BLAST, InterProScan and BLAST was 0.981, 0.980 and 0.924, respectively (Table 2; Fig. 2), there being little difference between PSI-BLAST and InterProScan. These two methods gave a higher sensitivity than BLAST. We evaluated the specificity in six families, because we could not calculate the number of false-positive proteins in the MYB family. The specificity of PSI-BLAST, InterProScan and BLAST was 0.944, 0.981 and 0.962, respectively (Table 2; Fig. 2). Although there was little difference among the three methods, InterProScan showed the highest specificity and PSI-BLAST the lowest. These results showed that the InterProScan is the best method for identifying transcription factors with high sensitivity and specificity. However, PSI-BLAST may be the best tool for detecting all the transcription factors since PSI-BLAST showed the best sensitivity. PSI-BLAST can detect sequence similarities of proteins even if the proteins have partial sequences. By this analysis, we found At2g25820 in a PSI-BLAST search result of the AP2/EREBP family. Indeed, At2g25820 has a partial AP2-type DNA-binding domain (Fig. 3), but if we include the upstream genomic region together, it has the entire DNA-binding domain (Fig. 3). No full-length cDNA or EST supported the translation of the upstream region of the annotated first methionine codon of At2g25820, but the amino acid sequence translated from 5'-UTR has a highly conserved AP2-type DNA-binding domain. Thus, there is a high enough probability that At2g25820 with the entire AP2 domain is translated. A motif search based on the Hidden Markov Model algorithm cannot detect this partial DNA-binding domain. This example showed that PSI-BLAST can detect sequence similarities even from partial protein sequences. Genome sequencing projects on Poplar or Lotus japonicus are in progress.33Go,34Go In many species, there are issues about correct detection of exons or ORFs or CDSs just after sequencing of their genomes.8Go PSI-BLAST is expected to be useful for detecting transcription factors more correctly from these genomes. When a novel transcription factor family member is found, we can search for other members of the family quickly based on one query sequence. All of these points indicate the usefulness of PSI-BLAST for identifying transcription factors efficiently from accumulating genome information of plants.


Figure 2
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  		Comparison of three methods. (A) PSI-BLAST and InterProScan showed a higher sensitivity than BLAST. (B) PSI-BLAST showed a slightly worse specificity than either InterProScan or BLAST.

 

Figure 3
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  		Putative 5' translated region of At2g25820. Initial codon of this gene in public database is shown with an arrow. At4g32800 has the amino acid sequence most similar to At2g25820. Bar under alignment shows an AP2-type DNA-binding region. At2g25820 has an intact and highly conserved DNA-binding region.

 
3.3. RARTF: RIKEN Arabidopsis transcription factor database
We constructed a transcription factor database named RARTF (RIKEN Arabidopsis transcription factor database, http://rarge.gsc.riken.jp/rartf/) based on detected transcription factors (Figs 1 and 4). RARTF provides information on transcription factor families and each transcription factor, and search tools such as PSI-BLAST and RPS-BLAST based on the PSSMs. We made two lists. In the Phase 1 list, there is redundancy of listed proteins and the Phase 2 list is non-redundant (see Materials and Methods). Multiple alignments of amino acid sequences and a phylogenic tree are available for each family or subfamily (Fig. 4). For each transcription factor, we provide the PSI-BLAST search results, functional motif information of InterProScan. We also made some links for other Arabidopsis databases such as MAtDB of MIPS,21Go the Arabidopsis Information Resource (TAIR)35Go and the Institute for Genomic Research (TIGR) DB.8Go For some proteins, the amino acid sequences in these databases are different. In these cases, we recommend checking all the databases. RARTF provides a RPS-BLAST (Reverse PSI-BLAST) search36Go tool. By using RPS-BLAST, it is possible to check whether the sequence of the amino acid or DNA or mRNA is one of a transcription factor or not. With the PSI-BLAST search tool on RARTF, we provided PSSMs and datasets of latest Arabidopsis proteins and proteins of O. sativa. The latest lists of transcription factors of A. thaliana and O. sativa37Go are available as results of PSI-BLAST searches.


Figure 4
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  		Sample views of RIKEN Arabidopsis transcription factor database: RARTF. (A) A list of members of a transcription factor family. From this page, we provide links for information of each member (B), phylogenic tree (C), and amino acid sequences and multiple-alignments.

 
The contents of RARTF linked with RARGE38Go (RIKEN Arabidopsis genome encyclopedia) provide more detailed information about transcription factors (Figs 1 and 4). We collected 1072 RIKEN Arabidopsis full-length (RAFL) cDNAs coding for transcription factors.6Go,7Go From the full-length cDNAs, we can identify the exon–intron structures accurately. We can identify the upstream sequences of genes by mapping the full-length cDNAs to the genome. From RARTF, the cis-element database of RARGE38Go is available, and cis-elements located in the upstream region of genes that code for transcription factors can be identified. It is important to study the regulatory region of transcription factor genes to understand the transcription network of Arabidopsis. With RARTF, users can search the database of RARGE for alternative splicing events. Out of 1968 transcription factors, 110 genes which code for transcription factors undergo alternative splicing. Alternative splicing events have been reported in the tissue-specific expression of transcription factors in mice.39Go This suggested that some transcription factors have another isoform and function in each expressed tissue. In Arabidopsis, strong relationships between alternative splicing events and environmental stress conditions and expressed tissues have been reported.40Go It is important to check whether a transcription factor has alternative splicing isoforms or not. RARGE is the largest database on alternative splicing events in Arabidopsis. It might provide useful information on alternative splicing events of transcription factors.

RARTF provides information on Ac/Ds transposon mutants.41Go There are 836 (~42%) transcription factor genes tagged by Ac/Ds transposons. Seeds of most of these mutant lines are available from RIKEN Bioresource Center (http://www.brc.riken.jp/en/). We made links to genome maps on RARGE with which users can check the locations of the genes and sites tagged by transposons. Expression profiles of transcription factors are also important. Transcription factors often have critical roles for control expression of other genes under various stress conditions or developmental stages.3Go–5Go,42Go For example, we showed expression profiles of DREB family transcription factors under drought stress in Table 3. The DREB family is a key transcription factor family that responds to drought stress. Indeed, there are transcription factors with various expression profiles in the DREB family (Table 3). This indicates that transcription factors in one family have a distinct expression manner. We provide the expression profiling results of the transcription factor genes using the 7K RIKEN Arabidopsis full-length (RAFL) cDNA microarray from RARTF. The expression profiling data of 379 transcription factor genes in treatments with cold,42Go drought,42Go high-salinity,42Go abscisic acid,43Go high light-stress44Go and rehydration45Go are available from RARTF. In the near future, we will provide the expression profiles of all 1968 transcription factors in Arabidopsis in various tissues at various developmental stages and under various stress conditions from RARTF.


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  		Expression profiles of DREB family transcription factors under drought stress.

 


   Acknowledgements
Top
 Abstract
 1. Introduction
 2. Materials and Methods
 3. Results and Discussion
 Acknowledgements
References
 
We thank Asako Kamiya, Maiko Nakajima, Junko Ishida, Akiko Enju and Mari Narusaka for their excellent technical assistance. We also thank Dr Yoshihide Hayashizaki, Takashi Kuromori, Takuya Ito, Prof. Joseph R. Ecker, Athanasios Theologis and Ronald W. Davis for their collaboration. This work was supported in part by a grant for CREST, Genome Research from RIKEN, BRAIN to K.S. The work was also supported in part by a Grant-in-Aid for Scientific Research on Priority Areas ‘Genome Science’ from MECSST to M.S.


   Footnotes
 
*To whom correspondence should be addressed. Tel. +81-749-64-8100, Fax. +81-749-64-8126, E-mail: k_iida{at}nagahama-i-bio-ac.jp

6Present address: RIKEN Plant Science Center (PSC), RIKEN Yokohama Institute, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan Back

Communicated by Satoshi Tabata


   References
Top
 Abstract
 1. Introduction
 2. Materials and Methods
 3. Results and Discussion
 Acknowledgements
 References
 

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