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In silico analysis of transcription factor repertoires and prediction of stress-responsive transcription factors from six major gramineae plants.

ABSTRACT: The interactions between transcription factors (TFs) and cis-regulatory DNA sequences control gene expression, constituting the essential functional linkages of gene regulatory networks. The aim of this study is to identify and integrate all putative TFs from six grass species: Brachypodium distachyon, maize, rice, sorghum, barley, and wheat with significant information into an integrative database (GramineaeTFDB) for comparative genomics and functional genomics. For each TF, sequence features, promoter regions, domain alignments, GO assignment, FL-cDNA information, if available, and cross-references to various public databases and genetic resources are provided. Additionally, GramineaeTFDB possesses a tool which aids the users to search for putative cis-elements located in the promoter regions of TFs and predict the functions of the TFs using cis-element-based functional prediction approach. We also supplied hyperlinks to expression profiles of those TF genes of maize, rice, and barley, for which data are available. Furthermore, information about the availability of FOX and Ds mutant lines for rice and maize TFs, respectively, are also accessible through hyperlinks. Our study provides an important user-friendly public resource for functional analyses and comparative genomics of grass TFs, and understanding of the architecture of transcriptional regulatory networks and evolution of the TFs in agriculturally important cereal crops.

SUBMITTER: Mochida K

PROVIDER: S-EPMC3190953 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature

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In silico analysis of transcription factor repertoires and prediction of stress-responsive transcription factors from six major gramineae plants.

Mochida Keiichi K Yoshida Takuhiro T Sakurai Tetsuya T Yamaguchi-Shinozaki Kazuko K Shinozaki Kazuo K Tran Lam-Son Phan LS

DNA research : an international journal for rapid publication of reports on genes and genomes 20110704 5

The interactions between transcription factors (TFs) and cis-regulatory DNA sequences control gene expression, constituting the essential functional linkages of gene regulatory networks. The aim of this study is to identify and integrate all putative TFs from six grass species: Brachypodium distachyon, maize, rice, sorghum, barley, and wheat with significant information into an integrative database (GramineaeTFDB) for comparative genomics and functional genomics. For each TF, sequence features, ...[more]

PMID: 21729923

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Project description:One of the goals of systems biology is the identification of regulatory mechanisms that govern an organism's response to external stimuli. Transcription factors have been hypothesized as a major contributor to an organism's response to various outside stimuli, and a great deal of work has been done to predict the set of transcription factors which regulate a given gene. Most of the current methods seek to identify possible binding sites from genomic sequence. Initial attempts at predicting transcription factors from genomic sequences suffered from the problem of false positives. Making the problem more difficult, it has also been shown that while predicted binding sites might be false positives, they can be shown to bind to their corresponding sequences in vitro. One method for rectifying this is through the use of phylogenetic analysis in which only regions which show high evolutionary conservation are analyzed. However such an approach may be too stringent because of the level of degeneracy shown in transcription factor binding site position weight matrices. Due to the degeneracy, there may be only a few bases that need to be conserved across species. Therefore, while a sequence may not show a high level of evolutionary conservation, these sequences may still show high affinity for the same transcription factor. In predicting transcription factor binding we explore the notion that "Co-expression implies co-regulation" [Allocco et al. BMC Bioinformatics 5:18, 2004]. With multiple genes requiring similar transcription factors binding sites, there exists a basis for eliminating false positives. This method allows for the selection of transcription factors binding sites that are active under a given experimental paradigm, thereby allowing us to indirectly incorporate the effects of chromosome and recognition site presentation upon transcription factor binding prediction. Rather than having to rationalize that a few transcription factors binding sites are over-represented in a cluster of genes, one can show that a few transcription factors are active in the cluster of genes that have been grouped together. Although the method focuses on predicting experiment-specific transcription factor binding sites, it is possible that if such a methodology were used in an iterative process where different experiments were analyzed, one could obtain a comprehensive set of transcription factors binding sites which regulate the various dynamic responses shown by biological systems under a variety of conditions hence building a more comprehensive model of transcriptional regulation.

Project description:Regulation of gene expression influences almost all biological processes in an organism; sequence-specific DNA-binding transcription factors are critical to this control. For most genomes, the repertoire of transcription factors is only partially known. Hitherto transcription factor identification has been largely based on genome annotation pipelines that use pairwise sequence comparisons, which detect only those factors similar to known genes, or on functional classification schemes that amalgamate many types of proteins into the category of 'transcription factor'. Using a novel transcription factor identification method, the DBD transcription factor database fills this void, providing genome-wide transcription factor predictions for organisms from across the tree of life. The prediction method behind DBD identifies sequence-specific DNA-binding transcription factors through homology using profile hidden Markov models (HMMs) of domains. Thus, it is limited to factors that are homologus to those HMMs. The collection of HMMs is taken from two existing databases (Pfam and SUPERFAMILY), and is limited to models that exclusively detect transcription factors that specifically recognize DNA sequences. It does not include basal transcription factors or chromatin-associated proteins, for instance. Based on comparison with experimentally verified annotation, the prediction procedure is between 95% and 99% accurate. Between one quarter and one-half of our genome-wide predicted transcription factors represent previously uncharacterized proteins. The DBD (www.transcriptionfactor.org) consists of predicted transcription factor repertoires for 150 completely sequenced genomes, their domain assignments and the hand curated list of DNA-binding domain HMMs. Users can browse, search or download the predictions by genome, domain family or sequence identifier, view families of transcription factors based on domain architecture and receive predictions for a protein sequence.

Project description:The plant hormones gibberellins (GAs) regulate plant growth and development and are closely related to the yield of cash crops. The GA oxidases (GAoxs), including the GA2ox, GA3ox, and GA20ox subfamilies, play pivotal roles in GAs' biosynthesis and metabolism, but their classification and evolutionary pattern in Gramineae crops remain unclear. We thus conducted a comparative genomic study of GAox genes in six Gramineae representative crops, namely, Setaria italica (Si), Zea mays (Zm), Sorghum bicolor (Sb), Hordeum vulgare (Hv), Brachypodium distachyon (Bd), and Oryza sativa (Os). A total of 105 GAox genes were identified in these six crop genomes, belonging to the C19-GA2ox, C20-GA2ox, GA3ox, and GA20ox subfamilies. Based on orthogroup (OG) analysis, GAox genes were divided into nine OGs and the number of GAox genes in each of the OGs was similar among all tested crops, which indicated that GAox genes may have completed their family differentiations before the species differentiations of the tested species. The motif composition of GAox proteins showed that motifs 1, 2, 4, and 5, forming the 2OG-FeII_Oxy domain, were conserved in all identified GAox protein sequences, while motifs 11, 14, and 15 existed specifically in the GA20ox, C19-GA2ox, and C20-GA2ox protein sequences. Subsequently, the results of gene duplication events suggested that GAox genes mainly expanded in the form of WGD/SD and underwent purification selection and that maize had more GAox genes than other species due to its recent duplication events. The cis-acting elements analysis indicated that GAox genes may respond to growth and development, stress, hormones, and light signals. Moreover, the expression profiles of rice and maize showed that GAox genes were predominantly expressed in the panicles of the above two plants and the expression of several GAox genes was significantly induced by salt or cold stresses. In conclusion, our results provided further insight into GAox genes' evolutionary differences among six representative Gramineae and highlighted GAox genes that may play a role in abiotic stress.

Dataset Information

In silico analysis of transcription factor repertoires and prediction of stress-responsive transcription factors from six major gramineae plants.

Publications

In silico analysis of transcription factor repertoires and prediction of stress-responsive transcription factors from six major gramineae plants.

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