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A clustering property of highly-degenerate transcription factor binding sites in the mammalian genome.

ABSTRACT: Transcription factor binding sites (TFBSs) are short DNA sequences interacting with transcription factors (TFs), which regulate gene expression. Due to the relatively short length of such binding sites, it is largely unclear how the specificity of protein-DNA interaction is achieved. Here, we have performed a genome-wide analysis of TFBS-like sequences for the transcriptional repressor, RE1 Silencing Transcription Factor (REST), as well as for several other representative mammalian TFs (c-myc, p53, HNF-1 and CREB). We find a nonrandom distribution of inexact sites for these TFs, referred to as highly-degenerate TFBSs, that are enriched around the cognate binding sites. Comparisons among human, mouse and rat orthologous promoters reveal that these highly-degenerate sites are conserved significantly more than expected by random chance, suggesting their positive selection during evolution. We propose that this arrangement provides a favorable genomic landscape for functional target site selection.

SUBMITTER: Zhang C

PROVIDER: S-EPMC1456330 | biostudies-literature | 2006

REPOSITORIES: biostudies-literature

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A clustering property of highly-degenerate transcription factor binding sites in the mammalian genome.

Zhang Chaolin C Xuan Zhenyu Z Otto Stefanie S Hover John R JR McCorkle Sean R SR Mandel Gail G Zhang Michael Q MQ

Nucleic acids research 20060502 8

Transcription factor binding sites (TFBSs) are short DNA sequences interacting with transcription factors (TFs), which regulate gene expression. Due to the relatively short length of such binding sites, it is largely unclear how the specificity of protein-DNA interaction is achieved. Here, we have performed a genome-wide analysis of TFBS-like sequences for the transcriptional repressor, RE1 Silencing Transcription Factor (REST), as well as for several other representative mammalian TFs (c-myc, p ...[more]

PMID: 16670430

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Project description:BackgroundTranscription factor (TF)-DNA binding loci are explored by analyzing massive datasets generated with application of Chromatin Immuno-Precipitation (ChIP)-based high-throughput sequencing technologies. These datasets suffer from a bias in the information about binding loci availability, sample incompleteness and diverse sources of technical and biological noises. Therefore adequate mathematical models of ChIP-based high-throughput assay(s) and statistical tools are required for a robust identification of specific and reliable TF binding sites (TFBS), a precise characterization of TFBS avidity distribution and a plausible estimation the total number of specific TFBS for a given TF in the genome for a given cell type.ResultsWe developed an exploratory mixture probabilistic model for a specific and non-specific transcription factor-DNA (TF-DNA) binding. Within ChiP-seq data sets, the statistics of specific and non-specific DNA-protein binding is defined by a mixture of sample size-dependent skewed functions described by Kolmogorov-Waring (K-W) function (Kuznetsov, 2003) and exponential function, respectively. Using available Chip-seq data for eleven TFs, essential for self-maintenance and differentiation of mouse embryonic stem cells (SC) (Nanog, Oct4, sox2, KLf4, STAT3, E2F1, Tcfcp211, ZFX, n-Myc, c-Myc and Essrb) reported in Chen et al (2008), we estimated (i) the specificity and the sensitivity of the ChiP-seq binding assays and (ii) the number of specific but not identified in the current experiments binding sites (BSs) in the genome of mouse embryonic stem cells. Motif finding analysis applied to the identified c-Myc TFBSs supports our results and allowed us to predict many novel c-Myc target genes.ConclusionWe provide a novel methodology of estimating the specificity and the sensitivity of TF-DNA binding in massively paralleled ChIP sequencing (ChIP-seq) binding assay. Goodness-of fit analysis of K-W functions suggests that a large fraction of low- and moderate- avidity TFBSs cannot be identified by the ChIP-based methods. Thus the task to identify the binding sensitivity of a TF cannot be technically resolved yet by current ChIP-seq, compared to former experimental techniques. Considering our improvement in measuring the sensitivity and the specificity of the TFs obtained from the ChIP-seq data, the models of transcriptional regulatory networks in embryonic cells and other cell types derived from the given ChIp-seq data should be carefully revised.

Project description:Despite almost 40 years of molecular genetics research in Escherichia coli a major fraction of its Transcription Start Sites (TSSs) are still unknown, limiting therefore our understanding of the regulatory circuits that control gene expression in this model organism. RegulonDB (http://regulondb.ccg.unam.mx/) is aimed at integrating the genetic regulatory network of E. coli K12 as an entirely bioinformatic project up till now. In this work, we extended its aims by generating experimental data at a genome scale on TSSs, promoters and regulatory regions. We implemented a modified 5' RACE protocol and an unbiased High Throughput Pyrosequencing Strategy (HTPS) that allowed us to map more than 1700 TSSs with high precision. From this collection, about 230 corresponded to previously reported TSSs, which helped us to benchmark both our methodologies and the accuracy of the previous mapping experiments. The other ca 1500 TSSs mapped belong to about 1000 different genes, many of them with no assigned function. We identified promoter sequences and type of sigma factors that control the expression of about 80% of these genes. As expected, the housekeeping sigma(70) was the most common type of promoter, followed by sigma(38). The majority of the putative TSSs were located between 20 to 40 nucleotides from the translational start site. Putative regulatory binding sites for transcription factors were detected upstream of many TSSs. For a few transcripts, riboswitches and small RNAs were found. Several genes also had additional TSSs within the coding region. Unexpectedly, the HTPS experiments revealed extensive antisense transcription, probably for regulatory functions. The new information in RegulonDB, now with more than 2400 experimentally determined TSSs, strengthens the accuracy of promoter prediction, operon structure, and regulatory networks and provides valuable new information that will facilitate the understanding from a global perspective the complex and intricate regulatory network that operates in E. coli.

Dataset Information

A clustering property of highly-degenerate transcription factor binding sites in the mammalian genome.

Publications

A clustering property of highly-degenerate transcription factor binding sites in the mammalian genome.

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