Dataset Information

Assessment of clusters of transcription factor binding sites in relationship to human promoter, CpG islands and gene expression.

ABSTRACT: BACKGROUND: Gene expression is regulated mainly by transcription factors (TFs) that interact with regulatory cis-elements on DNA sequences. To identify functional regulatory elements, computer searching can predict TF binding sites (TFBS) using position weight matrices (PWMs) that represent positional base frequencies of collected experimentally determined TFBS. A disadvantage of this approach is the large output of results for genomic DNA. One strategy to identify genuine TFBS is to utilize local concentrations of predicted TFBS. It is unclear whether there is a general tendency for TFBS to cluster at promoter regions, although this is the case for certain TFBS. Also unclear is the identification of TFs that have TFBS concentrated in promoters and to what level this occurs. This study hopes to answer some of these questions. RESULTS: We developed the cluster score measure to evaluate the correlation between predicted TFBS clusters and promoter sequences for each PWM. Non-promoter sequences were used as a control. Using the cluster score, we identified a PWM group called PWM-PCP, in which TFBS clusters positively correlate with promoters, and another PWM group called PWM-NCP, in which TFBS clusters negatively correlate with promoters. The PWM-PCP group comprises 47% of the 199 vertebrate PWMs, while the PWM-NCP group occupied 11 percent. After reducing the effect of CpG islands (CGI) against the clusters using partial correlation coefficients among three properties (promoter, CGI and predicted TFBS cluster), we identified two PWM groups including those strongly correlated with CGI and those not correlated with CGI. CONCLUSION: Not all PWMs predict TFBS correlated with human promoter sequences. Two main PWM groups were identified: (1) those that show TFBS clustered in promoters associated with CGI, and (2) those that show TFBS clustered in promoters independent of CGI. Assessment of PWM matches will allow more positive interpretation of TFBS in regulatory regions.

SUBMITTER: Murakami K

PROVIDER: S-EPMC375527 | biostudies-literature | 2004 Feb

REPOSITORIES: biostudies-literature

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Assessment of clusters of transcription factor binding sites in relationship to human promoter, CpG islands and gene expression.

Murakami Katsuhiko K Kojima Toshio T Sakaki Yoshiyuki Y

BMC genomics 20040223 1

<h4>Background</h4>Gene expression is regulated mainly by transcription factors (TFs) that interact with regulatory cis-elements on DNA sequences. To identify functional regulatory elements, computer searching can predict TF binding sites (TFBS) using position weight matrices (PWMs) that represent positional base frequencies of collected experimentally determined TFBS. A disadvantage of this approach is the large output of results for genomic DNA. One strategy to identify genuine TFBS is to util ...[more]

PMID: 15053842

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Project description:This study was designed to test the idea that the regulatory regions of human genes have evolved to be resistant to the effects of mutations in their primary function, the control of gene expression. It is proposed that the transcription factor/transcription factor binding site (TF/TFBS) pair having the greatest effect on control of a gene is the one with the highest abundance among the regulatory elements. Other pairs would have the same effect on gene expression and would predominate in the event of a mutation in the most abundant pair. It is expected that the overall regulatory design proposed here will be highly resistant to mutagenic change that would otherwise affect expression of the gene. The idea was tested beginning with a database of 42 human genes highly specific for expression in brain. For each gene, the five most abundant TF/TFBS pairs were identified and compared in their TFBS occupancy as measured by their ChIP-seq signal. A similar signal was observed and interpreted as evidence that the TF/TFBS pairs can substitute for one another. TF/TFBS pairs were also compared in their ability to substitute for one another in their effect on the level of gene expression. The study of brain specific genes was complemented with the same analysis performed with 31 human liver specific genes. Like the study of brain genes, the liver results supported the view that TF/TFBS pairs in liver specific genes can substitute for one another in the event of mutagenic damage. Finally, the TFBSs in the brain specific and liver specific gene populations were compared with each other with the goal of identifying any brain selective or liver selective TFBSs. Of the 89 TFBSs in the pooled population, 58 were found only in brain specific but not liver specific genes, 8 only in liver specific but not brain specific genes and 23 were found in both brain and liver specific genes. The results were interpreted to emphasize the large number of TFBS in brain specific genes.

Project description:Empirical studies have revealed that regulatory DNA sequences such as enhancers or promoters often harbor multiple binding sites for the same transcription factor. Such "homotypic site clustering" has been hypothesized as arising out of functional requirements of the sequences. Here, we propose an alternative explanation of this phenomenon that multisite enhancers are common because they are favored by evolutionary sampling of the genotype-phenotype landscape. To test this hypothesis, we developed a new computational framework specialized for population genetic simulations of enhancer evolution. It uses a thermodynamics-based model of enhancer function, integrating information from strong as well as weak binding sites, to determine the strength of selection. Using this framework, we found that even when simpler genotypes exist for a desired strength of regulation, relatively complex genotypes (enhancers with more sites) are more readily reached by the simulated evolutionary process. We show that there are more ways to "build" a fit genotype with many weak sites than with a few strong sites, and this is why evolution finds complex genotypes more often. Our claims are consistent with an empirical analysis of binding site content in enhancers characterized in Drosophila melanogaster and their orthologs in other Drosophila species. We also characterized a subtle but significant difference between genotypes likely to be sampled by evolution and equally fit genotypes one would obtain by uniform sampling of the fitness landscape, that is, an "evolutionary signature" in enhancer sequences. Finally, we investigated potential effects of other factors, such as rugged fitness landscapes, short local duplications, and noise characteristics of enhancers, on the emergence of homotypic site clustering. Homotypic site clustering is an important contributor to the complexity and function of cis-regulatory sequences. This work provides a simple null hypothesis for its origin, against which alternative adaptationist explanations may be evaluated, and cautions against "evolutionary mirages" present in common features of genomic sequence. The quantitative framework we develop here can be used more generally to understand how mechanisms of enhancer action influence their composition and evolution.

Dataset Information

Assessment of clusters of transcription factor binding sites in relationship to human promoter, CpG islands and gene expression.

Publications

Assessment of clusters of transcription factor binding sites in relationship to human promoter, CpG islands and gene expression.

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