Clusters Of Transcription Factor Binding Sites Research Articles

In-silico identification and comparison of transcription factor binding sites cluster in anterior-posterior patterning genes in Drosophila melanogaster and Tribolium castaneum.

The cis-regulatory data that help in transcriptional regulation is arranged into modular pieces of a few hundred base pairs called CRMs (cis-regulatory modules) and numerous binding sites for multiple transcription factors are prominent characteristics of these cis-regulatory modules. The present study was designed to localize transcription factor binding site (TFBS) clusters on twelve Anterior-posterior (A-P) genes in Tribolium castaneum and compare them to their orthologous gene enhancers in Drosophila melanogaster. Out of the twelve A-P patterning genes, six were gap genes (Kruppel, Knirps, Tailless, Hunchback, Giant, and Caudal) and six were pair rule genes (Hairy, Runt, Even-skipped, Fushi-tarazu, Paired, and Odd-skipped). The genes along with 20 kb upstream and downstream regions were scanned for TFBS clusters using the Motif Cluster Alignment Search Tool (MCAST), a bioinformatics tool that looks for set of nucleotide sequences for statistically significant clusters of non-overlapping occurrence of a given set of motifs. The motifs used in the current study were Hunchback, Caudal, Giant, Kruppel, Knirps, and Even-skipped. The results of the MCAST analysis revealed the maximum number of TFBS for Hunchback, Knirps, Caudal, and Kruppel in both D. melanogaster and T. castaneum, while Bicoid TFBS clusters were found only in D. melanogaster. The size of all the predicted TFBS clusters was less than 1kb in both insect species. These sequences revealed more transversional sites (Tv) than transitional sites (Ti) and the average Ti/Tv ratio was 0.75.

Systematic analysis of low-affinity transcription factor binding site clusters in vitro and in vivo establishes their functional relevance

Binding to binding site clusters has yet to be characterized in depth, and the functional relevance of low-affinity clusters remains uncertain. We characterized transcription factor binding to low-affinity clusters in vitro and found that transcription factors can bind concurrently to overlapping sites, challenging the notion of binding exclusivity. Furthermore, small clusters with binding sites an order of magnitude lower in affinity give rise to high mean occupancies at physiologically-relevant transcription factor concentrations. To assess whether the observed in vitro occupancies translate to transcriptional activation in vivo, we tested low-affinity binding site clusters in a synthetic and native gene regulatory network in S. cerevisiae. In both systems, clusters of low-affinity binding sites generated transcriptional output comparable to single or even multiple consensus sites. This systematic characterization demonstrates that clusters of low-affinity binding sites achieve substantial occupancies, and that this occupancy can drive expression in eukaryotic promoters.

Common DNA methylation dynamics in endometriod adenocarcinoma and glioblastoma suggest universal epigenomic alterations in tumorigenesis

Trends in altered DNA methylation have been defined across human cancers, revealing global loss of methylation (hypomethylation) and focal gain of methylation (hypermethylation) as frequent cancer hallmarks. Although many cancers share these trends, little is known about the specific differences in DNA methylation changes across cancer types, particularly outside of promoters. Here, we present a comprehensive comparison of DNA methylation changes between two distinct cancers, endometrioid adenocarcinoma (EAC) and glioblastoma multiforme (GBM), to elucidate common rules of methylation dysregulation and changes unique to cancers derived from specific cells. Both cancers exhibit significant changes in methylation over regulatory elements. Notably, hypermethylated enhancers within EAC samples contain several transcription factor binding site clusters with enriched disease ontology terms highlighting uterine function, while hypermethylated enhancers in GBM are found to overlap active enhancer marks in adult brain. These findings suggest that loss of original cellular identity may be a shared step in tumorigenesis.

Regulation of PLPP3 gene expression by NF-κB family transcription factors

Lipid phosphate phosphatase 3 (LPP3), encoded by the PLPP3 gene, is an integral membrane enzyme that dephosphorylates phosphate esters of glycero- and sphingophospholipids. Cell surface LPP3 can terminate the signaling actions of bioactive lysophosphatidic acid (LPA) and sphingosine 1 phosphate, which likely explains its role in developmental angiogenesis, vascular injury responses, and cell migration. Heritable variants in the final intron PLPP3 associate with interindividual variability in coronary artery disease risk that may result from disruption of enhancer sequences that normally act in cis to increase expression of the gene. However, the mechanisms regulating PLPP3 expression are not well understood. We show that the human PLPP3 promoter contains three functional NF-κB response elements. All of these are required for maximal induction of PLPP3 promoter activity in reporter assays. The identified sequences recruit RelA and RelB components of the NF-κB transcription complex to chromatin, and these transcription factors bind to the identified target sequences in two different cell types. LPA promotes binding of Rel family transcription factors to the PLPP3 promoter and increases PLPP3 gene expression through mechanisms that are attenuated by an NF-κB inhibitor, LPA receptor antagonists, and inhibitors of phosphoinositide 3 kinase. These findings indicate that up-regulation of PLPP3 during inflammation and atherosclerosis results from canonical activation of the NF-κB signaling cascade to increase PLPP3 expression through nuclear import and binding of RelA and RelB transcription factors to the PLPP3 promoter and suggest a mechanism by which the LPP3 substrate, LPA, can regulate PLPP3 expression.

Stable enhancers are active in development, and fragile enhancers are associated with evolutionary adaptation

BackgroundDespite continual progress in the identification and characterization of trait- and disease-associated variants that disrupt transcription factor (TF)-DNA binding, little is known about the distribution of TF binding deactivating mutations (deMs) in enhancer sequences. Here, we focus on elucidating the mechanism underlying the different densities of deMs in human enhancers.ResultsWe identify two classes of enhancers based on the density of nucleotides prone to deMs. Firstly, fragile enhancers with abundant deM nucleotides are associated with the immune system and regular cellular maintenance. Secondly, stable enhancers with only a few deM nucleotides are associated with the development and regulation of TFs and are evolutionarily conserved. These two classes of enhancers feature different regulatory programs: the binding sites of pioneer TFs of FOX family are specifically enriched in stable enhancers, while tissue-specific TFs are enriched in fragile enhancers. Moreover, stable enhancers are more tolerant of deMs due to their dominant employment of homotypic TF binding site (TFBS) clusters, as opposed to the larger-extent usage of heterotypic TFBS clusters in fragile enhancers. Notably, the sequence environment and chromatin context of the cognate motif, other than the motif itself, contribute more to the susceptibility to deMs of TF binding.ConclusionsThis dichotomy of enhancer activity is conserved across different tissues, has a specific footprint in epigenetic profiles, and argues for a bimodal evolution of gene regulatory programs in vertebrates. Specifically encoded stable enhancers are evolutionarily conserved and associated with development, while differently encoded fragile enhancers are associated with the adaptation of species.

Transcription factor binding site clusters identify target genes with similar tissue-wide expression and buffer against mutations.

Background: The distribution and composition of cis-regulatory modules composed of transcription factor (TF) binding site (TFBS) clusters in promoters substantially determine gene expression patterns and TF targets. TF knockdown experiments have revealed that TF binding profiles and gene expression levels are correlated. We use TFBS features within accessible promoter intervals to predict genes with similar tissue-wide expression patterns and TF targets using Machine Learning (ML). Methods: Bray-Curtis Similarity was used to identify genes with correlated expression patterns across 53 tissues. TF targets from knockdown experiments were also analyzed by this approach to set up the ML framework. TFBSs were selected within DNase I-accessible intervals of corresponding promoter sequences using information theory-based position weight matrices (iPWMs) for each TF. Features from information-dense clusters of TFBSs were input to ML classifiers which predict these gene targets along with their accuracy, specificity and sensitivity. Mutations in TFBSs were analyzed in silico to examine their impact on TFBS clustering and predict changes in gene regulation. Results:The glucocorticoid receptor gene ( NR3C1), whose regulation has been extensively studied, was selected to test this approach. SLC25A32 and TANK exhibited the most similar expression patterns to NR3C1. A Decision Tree classifier exhibited the best performance in detecting such genes, based on Area Under the Receiver Operating Characteristic curve (ROC). TF target gene prediction was confirmed using siRNA knockdown, which was more accurate than CRISPR/CAS9 inactivation. TFBS mutation analyses revealed that accurate target gene prediction required at least 1 information-dense TFBS cluster. Conclusions: ML based on TFBS information density, organization, and chromatin accessibility accurately identifies gene targets with comparable tissue-wide expression patterns. Multiple information-dense TFBS clusters in promoters appear to protect promoters from effects of deleterious binding site mutations in a single TFBS that would otherwise alter regulation of these genes.

Transcription factor binding site clusters identify target genes with similar tissue-wide expression and buffer against mutations

Background: The distribution and composition of cis-regulatory modules composed of transcription factor (TF) binding site (TFBS) clusters in promoters substantially determine gene expression patterns and TF targets. TF knockdown experiments have revealed that TF binding profiles and gene expression levels are correlated. We use TFBS features within accessible promoter intervals to predict genes with similar tissue-wide expression patterns and TF targets using Machine Learning (ML). Methods: Bray-Curtis Similarity was used to identify genes with correlated expression patterns across 53 tissues. TF targets from knockdown experiments were also analyzed by this approach to set up the ML framework. TFBSs were selected within DNase I-accessible intervals of corresponding promoter sequences using information theory-based position weight matrices (iPWMs) for each TF. Features from information-dense clusters of TFBSs were input to ML classifiers which predict these gene targets along with their accuracy, specificity and sensitivity. Mutations in TFBSs were analyzed in silico to examine their impact on TFBS clustering and predict changes in gene regulation. Results: The glucocorticoid receptor gene ( NR3C1), whose regulation has been extensively studied, was selected to test this approach. SLC25A32 and TANK exhibited the most similar expression patterns to NR3C1. A Decision Tree classifier exhibited the best performance in detecting such genes, based on Area Under the Receiver Operating Characteristic curve (ROC). TF target gene prediction was confirmed using siRNA knockdown, which was more accurate than CRISPR/CAS9 inactivation. TFBS mutation analyses revealed that accurate target gene prediction required at least 1 information-dense TFBS cluster. Conclusions: ML based on TFBS information density, organization, and chromatin accessibility accurately identifies gene targets with comparable tissue-wide expression patterns. Multiple information-dense TFBS clusters in promoters appear to protect promoters from effects of deleterious binding site mutations in a single TFBS that would otherwise alter regulation of these genes.

Transcription factor binding site clusters identify target genes with similar tissue-wide expression and buffer against mutations

Background: The distribution and composition of cis-regulatory modules composed of transcription factor (TF) binding site (TFBS) clusters in promoters substantially determine gene expression patterns and TF targets. TF knockdown experiments have revealed that TF binding profiles and gene expression levels are correlated. We use TFBS features within accessible promoter intervals to predict genes with similar tissue-wide expression patterns and TF targets. Methods: Genes with correlated expression patterns across 53 tissues and TF targets were respectively identified from Bray-Curtis Similarity and TF knockdown experiments. Corresponding promoter sequences were reduced to DNase I-accessible intervals; TFBSs were then identified within these intervals using information theory-based position weight matrices for each TF (iPWMs) and clustered. Features from information-dense TFBS clusters predicted these genes with machine learning classifiers, which were evaluated for accuracy, specificity and sensitivity. Mutations in TFBSs were analyzed to in silico examine their impact on cluster densities and the regulatory states of target genes. Results: We initially chose the glucocorticoid receptor gene ( NR3C1), whose regulation has been extensively studied, to test this approach. SLC25A32 and TANK were found to exhibit the most similar expression patterns to NR3C1. A Decision Tree classifier exhibited the largest area under the Receiver Operating Characteristic (ROC) curve in detecting such genes. Target gene prediction was confirmed using siRNA knockdown of TFs, which was found to be more accurate than those predicted after CRISPR/CAS9 inactivation. In-silico mutation analyses of TFBSs also revealed that one or more information-dense TFBS clusters in promoters are required for accurate target gene prediction. Conclusions: Machine learning based on TFBS information density, organization, and chromatin accessibility accurately identifies gene targets with comparable tissue-wide expression patterns. Multiple information-dense TFBS clusters in promoters appear to protect promoters from effects of deleterious binding site mutations in a single TFBS that would otherwise alter regulation of these genes.

Information-dense transcription factor binding site clusters identify target genes with similar tissue-wide expression profiles and serve as a buffer against mutations - Additional files

Information-dense transcription factor binding site clusters identify target genes with similar tissue-wide expression profiles and serve as a buffer against mutations - Additional files

Information-dense transcription factor binding site clusters identify target genes with similar tissue-wide expression profiles and serve as a buffer against mutations - Source datasets, sample results and compiled software

Information-dense transcription factor binding site clusters identify target genes with similar tissue-wide expression profiles and serve as a buffer against mutations - Source datasets, sample results and compiled software

Information-dense transcription factor binding site clusters identify target genes with similar tissue-wide expression profiles and serve as a buffer against mutations

Background: The distribution and composition of cis-regulatory modules (e.g. transcription factor binding site (TFBS) clusters) in promoters substantially determine gene expression patterns and TF targets, whose expression levels are significantly regulated by TF binding. TF knockdown experiments have revealed correlations between TF binding profiles and gene expression levels. We present a general framework capable of predicting genes with similar tissue-wide expression patterns from activated or repressed TF targets using machine learning to combine TF binding and epigenetic features. Methods: Genes with correlated expression patterns across 53 tissues were identified according to their Bray-Curtis similarity. DNase I-accessible promoter intervals of direct TF target genes were scanned with previously derived information theory-based position weight matrices (iPWMs) of 82 TFs. Features from information density-based TFBS clusters were used to predict target genes with machine learning classifiers. The accuracy, specificity and sensitivity of the classifiers were determined for different feature sets. Mutations in TFBSs were also introduced to in silico examine their impact on cluster densities and the regulatory states of target genes. Results: We initially chose the glucocorticoid receptor gene (NR3C1), whose regulation has been extensively studied, to test this approach. SLC25A32 and TANK were found to exhibit the most similar expression patterns to this gene across 53 tissues. A Decision Tree classifier exhibited the largest area (0.9987 and 0.9956 respectively before and after eliminating inaccessible promoter intervals based on DNase I HyperSensitive Regions (DHSs)) under the Receiver Operating Characteristic (ROC) curve in detecting such coordinately regulated genes. Target gene prediction was confirmed using siRNA knockdown data of TFs, which was more accurate than CRISPR inactivation. In-silico mutation analyses of TFBSs also revealed that one or more information-dense TFBS clusters in promoters are required for accurate target gene prediction. Conclusions: Machine learning based on TFBS information density, organization, and chromatin accessibility accurately identifies gene targets with comparable tissue-wide expression patterns. Multiple, information-dense TFBS clusters in promoters appear to protect promoters from the effects of deleterious binding site mutations in a single TFBS that would effectively alter the expression state of these genes.

Conserved sections of the transcription regulatory modules in Drosophila early genes, including homotypic transcription factor-binding sites, are arranged with an 84-nt period, which corresponds to the superhelical turn length of nucleosomal DNA

The periodicity of nucleotide arrangement was studied for conserved regions of the regulatory modules of 16 early development genes in two Drosophila species. The relative disposition of the conserved segments was found to be a multiple of the length of one coil of the nucleosomal DNA superhelix. Homotypic and heterotypic clusters of transcription-factor binding sites were observed in the conserved regions. The sites are usually at a distance that corresponds to the coil length of superhelical DNA of the nucleosome. The transcription-factor binding sites were assumed to play a role in regulating DNA condensation according to the nucleosome type.

Transcriptional regulatory networks in Arabidopsis thaliana during single and combined stresses.

Differentially evolved responses to various stress conditions in plants are controlled by complex regulatory circuits of transcriptional activators, and repressors, such as transcription factors (TFs). To understand the general and condition-specific activities of the TFs and their regulatory relationships with the target genes (TGs), we have used a homogeneous stress gene expression dataset generated on ten natural ecotypes of the model plant Arabidopsis thaliana, during five single and six combined stress conditions. Knowledge-based profiles of binding sites for 25 stress-responsive TF families (187 TFs) were generated and tested for their enrichment in the regulatory regions of the associated TGs. Condition-dependent regulatory sub-networks have shed light on the differential utilization of the underlying network topology, by stress-specific regulators and multifunctional regulators. The multifunctional regulators maintain the core stress response processes while the transient regulators confer the specificity to certain conditions. Clustering patterns of transcription factor binding sites (TFBS) have reflected the combinatorial nature of transcriptional regulation, and suggested the putative role of the homotypic clusters of TFBS towards maintaining transcriptional robustness against cis-regulatory mutations to facilitate the preservation of stress response processes. The Gene Ontology enrichment analysis of the TGs reflected sequential regulation of stress response mechanisms in plants.

Identification and characterization of putative methylation targets in the MAOA locus using bioinformatic approaches

Monoamine oxidase A (MAO A) is an enzyme that catalyzes the oxidation of neurotransmitter amines. A functional polymorphism in the human MAOA gene (high- and low- MAOA) has been associated with distinct behavioral phenotypes. To investigate directly the biological mechanism whereby this polymorphism influences brain function, we recently measured the activity of the MAO A enzyme in healthy volunteers. When found no relationship between the individual’s brain MAO A level and the MAOA genotype, we postulated that there are additional regulatory mechanisms that control the MAOA expression. Given that DNA methylation is linked to the regulation of gene expression, we hypothesized that epigenetic mechanisms factor into the MAOA expression. Our underplaying assumption was that the differences in an individual’s genotype play a key role in the epigenetic potential of the MAOA locus and, consequently, determine the individual’s level of MAO A activity in the brain. As a first step towards experimental validation of the hypothesis, we performed a comprehensive bioinformatic analysis aiming to interrogate genomic features and attributes of the MAOA locus that might modulate its epigenetic sensitivity. Major findings of our analysis are the following: (1) the extended MAOA regulatory region contains two CpG islands (CGIs), one of which overlaps with the canonical MAOA promoter and the other is located further upstream; both CGIs exhibit sensitivity to differential methylation. (2) The uVNTR’s effect on the MAOA’s transcriptional activity might have epigenetic nature: this polymorphic region resides within the MAOA’s CGI and itself contains CpGs, thus, the number of repeating increments effectively changes the number of methylatable cytosines in the MAOA promoter. An array of in silico analyses (the nucleosome positioning, the physical properties of the local DNA, the clustering of transcription-factor binding sites) together with experimental data on histone modifications and Pol 2 sites and data from the RefSeq mRNA library suggest that the MAOA gene might have an alternative promoter. Based on our findings, we propose a regulatory mechanism for the human MAOA according to which the MAOA expression in vivo is executed by the generation of tissue-specific transcripts initiated from the alternative promoters (both CGI-associated) where transcriptional activation of a particular promoter is under epigenetic control.

Identification of muscle-specific regulatory modules inCaenorhabditis elegans

Transcriptional regulation is the major regulatory mechanism that controls the spatial and temporal expression of genes during development. This is carried out by transcription factors (TFs), which recognize and bind to their cognate binding sites. Recent studies suggest a modular organization of TF-binding sites, in which clusters of transcription-factor binding sites cooperate in the regulation of downstream gene expression. In this study, we report our computational identification and experimental verification of muscle-specific cis-regulatory modules in Caenorhabditis elegans. We first identified a set of motifs that are correlated with muscle-specific gene expression. We then predicted muscle-specific regulatory modules based on clusters of those motifs with characteristics similar to a collection of well-studied modules in other species. The method correctly identifies 88% of the experimentally characterized modules with a positive predictive value of at least 65%. The prediction accuracy of muscle-specific expression on an independent test set is highly significant (P<0.0001). We performed in vivo experimental tests of 12 predicted modules, and 10 of those drive muscle-specific gene expression. These results suggest that our method is highly accurate in identifying functional sequences important for muscle-specific gene expression and is a valuable tool for guiding experimental designs.

GenomeTrafac: a whole genome resource for the detection of transcription factor binding site clusters associated with conventional and microRNA encoding genes conserved between mouse and human gene orthologs

Transcriptional cis-regulatory control regions frequently are found within non-coding DNA segments conserved across multi-species gene orthologs. Adopting a systematic gene-centric pipeline approach, we report here the development of a web-accessible database resource—GenomeTraFac ()—that allows genome-wide detection and characterization of compositionally similar cis-clusters that occur in gene orthologs between any two genomes for both microRNA genes as well as conventional RNA-encoding genes. Each ortholog gene pair can be scanned to visualize overall conserved sequence regions, and within these, the relative density of conserved cis-element motif clusters form graph peak structures. The results of these analyses can be mined en masse to identify most frequently represented cis-motifs in a list of genes. The system also provides a method for rapid evaluation and visualization of gene model-consistency between orthologs, and facilitates consideration of the potential impact of sequence variation in conserved non-coding regions to impact complex cis-element structures. Using the mouse and human genomes via the NCBI Reference Sequence database and the Sanger Institute miRBase, the system demonstrated the ability to identify validated transcription factor targets within promoter and distal genomic regulatory regions of both conventional and microRNA genes.

TFBScluster web server for the identification of mammalian composite regulatory elements

Identification of transcriptional regulatory elements represents a critical step in our ability to reconstruct transcriptional regulatory networks from gene expression profiling datasets. To facilitate computational identification of candidate gene regulatory elements from whole genome sequences, we have developed the TFBScluster web server that integrates several tools for the genome-wide identification and subsequent characterization of transcription factor binding site clusters that are conserved in multiple mammalian species. Either the human or mouse genomes can be used as the reference sequence with direct links from the search results to the ENSEMBL and UCSC genome browsers. Moreover, TFBScluster provides seamless integration of transcription factor binding site searches with genome annotation and gene expression profiling data, to allow prioritising computational predictions for subsequent experimental validation. TFBScluster is publicly available at .

TFBScluster: a resource for the characterization of transcriptional regulatory networks

One major challenge of the post-sequencing era of the human genome project will be the functional annotation of the non-coding portion of the genome, in particular gene regulatory sequences. We have developed a new web-based tool, TFBScluster, which performs genome-wide identification of transcription factor binding site clusters that are conserved in multiple mammalian genomes. Clusters representing candidate gene regulatory elements can be filtered further, based on the presence or absence of additional user-defined DNA sequence motifs or by constraining the orientation or order of binding sites. Comprehensive results files, returned by email, are designed to facilitate experimental validation of computationally identified candidate gene regulatory sequences. TFBScluster, therefore, has the potential to contribute to deciphering transcriptional networks that regulate a wide range of mammalian developmental processes.