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Genomic promoter analysis predicts functional transcription factor binding.

ABSTRACT: Background. The computational identification of functional transcription factor binding sites (TFBSs) remains a major challenge of computational biology. Results. We have analyzed the conserved promoter sequences for the complete set of human RefSeq genes using our conserved transcription factor binding site (CONFAC) software. CONFAC identified 16296 human-mouse ortholog gene pairs, and of those pairs, 9107 genes contained conserved TFBS in the 3 kb proximal promoter and first intron. To attempt to predict in vivo occupancy of transcription factor binding sites, we developed a novel marginal effect isolator algorithm that builds upon Bayesian methods for multigroup TFBS filtering and predicted the in vivo occupancy of two transcription factors with an overall accuracy of 84%. Conclusion. Our analyses show that integration of chromatin immunoprecipitation data with conserved TFBS analysis can be used to generate accurate predictions of functional TFBS. They also show that TFBS cooccurrence can be used to predict transcription factor binding to promoters in vivo.

SUBMITTER: Rao JS

PROVIDER: S-EPMC2768302 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

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Genomic promoter analysis predicts functional transcription factor binding.

Rao J Sunil JS Karanam Suresh S McCabe Colleen D CD Moreno Carlos S CS

Advances in bioinformatics 20081030

Background. The computational identification of functional transcription factor binding sites (TFBSs) remains a major challenge of computational biology. Results. We have analyzed the conserved promoter sequences for the complete set of human RefSeq genes using our conserved transcription factor binding site (CONFAC) software. CONFAC identified 16296 human-mouse ortholog gene pairs, and of those pairs, 9107 genes contained conserved TFBS in the 3 kb proximal promoter and first intron. To attempt ...[more]

PMID: 19865592

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Project description:MotivationThe identification of transcription factor (TF) binding sites and the regulatory circuitry that they define is currently an area of intense research. Data from whole-genome chromatin immunoprecipitation (ChIP-chip), whole-genome expression microarrays, and sequencing of multiple closely related genomes have all proven useful. By and large, existing methods treat the interpretation of functional data as a classification problem (between bound and unbound DNA), and the analysis of comparative data as a problem of local alignment (to recover phylogenetic footprints of presumably functional elements). Both of these approaches suffer from the inability to model and detect low-affinity binding sites, which have recently been shown to be abundant and functional.ResultsWe have developed a method that discovers functional regulatory targets of TFs by predicting the total affinity of each promoter for those factors and then comparing that affinity across orthologous promoters in closely related species. At each promoter, we consider the minimum affinity among orthologs to be the fraction of the affinity that is functional. Because we calculate the affinity of the entire promoter, our method is independent of local alignment. By comparing with functional annotation information and gene expression data in Saccharomyces cerevisiae, we have validated that this biophysically motivated use of evolutionary conservation gives rise to dramatic improvement in prediction of regulatory connectivity and factor-factor interactions compared to the use of a single genome. We propose novel biological functions for several yeast TFs, including the factors Snt2 and Stb4, for which no function has been reported. Our affinity-based approach towards comparative genomics may allow a more quantitative analysis of the principles governing the evolution of non-coding DNA.AvailabilityThe MatrixREDUCE software package is available from http://www.bussemakerlab.org/software/MatrixREDUCE.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:BackgroundThe use of global gene expression profiling is a well established approach to understand biological processes. One of the major goals of these investigations is to identify sets of genes with similar expression patterns. Such gene signatures may be very informative and reveal new aspects of particular biological processes. A logical and systematic next step is to reduce the identified gene signatures to the regulatory components that induce the relevant gene expression changes. A central issue in this context is to identify transcription factors, or transcription factor binding sites (TFBS), likely to be of importance for the expression of the gene signatures.ResultsWe develop a strategy that efficiently produces TFBS/promoter databases based on user-defined criteria. The resulting databases constitute all genes in the Santa Cruz database and the positions for all TFBS provided by the user as position weight matrices. These databases are then used for two purposes, to identify significant TFBS in the promoters in sets of genes and to identify clusters of co-occurring TFBS. We use two criteria for significance, significantly enriched TFBS in terms of total number of binding sites for the promoters, and significantly present TFBS in terms of the fraction of promoters with binding sites. Significant TFBS are identified by a re-sampling procedure in which the query gene set is compared with typically 10(5) gene lists of similar size randomly drawn from the TFBS/promoter database. We apply this strategy to a large number of published ChIP-Chip data sets and show that the proposed approach faithfully reproduces ChIP-Chip results. The strategy also identifies relevant TFBS when analyzing gene signatures obtained from the MSigDB database. In addition, we show that several TFBS are highly correlated and that co-occurring TFBS define functionally related sets of genes.ConclusionsThe presented approach of promoter analysis faithfully reproduces the results from several ChIP-Chip and MigDB derived gene sets and hence may prove to be an important method in the analysis of gene signatures obtained through ChIP-Chip or global gene expression experiments. We show that TFBS are organized in clusters of co-occurring TFBS that together define highly coherent sets of genes.

Project description:BAG-1 is an anti-apoptotic protein that interacts with a variety of cellular molecules to inhibit apoptosis. The mechanisms by which BAG-1 interacts with other proteins to inhibit apoptosis have been extensively explored. However, it is currently unknown how BAG-1 expression is regulated at the molecular level, especially in cancer cells. Here we reported to clone a novel down-regulated BAG-1 expression gene named FLJ20420 using hBAG-1 promoter as a probe to screen Human Hela 5' cDNA library by Southernwestern blot. The FLJ20420 gene encodes a ?26-kDa protein that is localized in both the cytoplasm and nucleus. We proved that FLJ20420 protein can specially bind hBAG-1 promoter region by EMSA in vivo and ChIP assay in vivo. Northern blot analysis revealed a low level of FLJ20420 transcriptional expression in normal human tissues (i.e., brain, placenta, lung, liver, kidney, pancreas and cervix), except for heart and skeletal muscles, which showed higher levels. Furthermore, enhanced FLJ20420 expression was observed in tumor cell lines (i.e., MDA468, BT-20, MCF-7, C33A, HeLa and Caski). Knockdown of endogenous FLJ20420 expression significantly increased BAG-1 expression in A549 and L9981 cells, and also significantly enhanced their sensitivity to cisplatin-induced apoptosis. A microarray assay of the FLJ20420 siRNA -transfectants showed altered expression of 505 known genes, including 272 upregulated and 233 downregulated genes. Finally, our gene array studies in lung cancer tissue samples revealed a significant increase in FLJ20420 expression in primary lung cancer relative to the paired normal lung tissue controls (p?=?0.0006). The increased expression of FLJ20420 corresponded to a significant decrease in BAG-1 protein expression in the primary lung cancers, relative to the paired normal lung tissue controls (p?=?0.0001). Taken together, our experiments suggest that FLJ20420 functions as a down-regulator of BAG-1 expression. Its abnormal expression may be involved in the oncogenesis of human malignancies such as lung cancer.

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Genomic promoter analysis predicts functional transcription factor binding.

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Genomic promoter analysis predicts functional transcription factor binding.

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