Functional Transcription Factor Binding Sites Research Articles

Chromatin accessibility during human first-trimester neurodevelopment.

The human brain develops through a tightly organized cascade of patterning events, induced by transcription factor expression and changes in chromatin accessibility. Although gene expression across the developing brain has been described at single-cell resolution1, similar atlases of chromatin accessibility have been primarily focused on the forebrain2-4. Here we describe chromatin accessibility and paired gene expression across the entire developing human brain during the first trimester (6-13 weeks after conception). We defined 135 clusters and used multiomic measurements to link candidate cis-regulatory elements to gene expression. The number of accessible regions increased both with age and along neuronal differentiation. Using a convolutional neural network, we identified putative functional transcription factor-binding sites in enhancers characterizing neuronal subtypes. We applied this model to cis-regulatory elements linked to ESRRB to elucidate its activation mechanism in the Purkinje cell lineage. Finally, by linking disease-associated single nucleotide polymorphisms to cis-regulatory elements, we validated putative pathogenic mechanisms in several diseases and identified midbrain-derived GABAergic neurons as being the most vulnerable to major depressive disorder-related mutations. Our findings provide a more detailed view of key gene regulatory mechanisms underlying the emergence of brain cell types during the first trimester and a comprehensive reference for future studies related to human neurodevelopment.

TFBSPred: A functional transcription factor binding site prediction webtool for humans and mice

Transcription factors (TFs) play a major role in the regulation of gene expression. Discovering the TFs which bind to the regulatory regions of each gene has been long‑term focus of research. Since the experimental verification of TF binding sites (TFBSs) is a complex process, webtools that perform predictions have been developed. However, the majority of the tools do not provide a user‑friendly environment for data input and a number of these tools produce a large number of false‑positive results. The present study introduces TFBSPred, a TFBS prediction webtool that utilises hidden Markov model‑based TF flexible models (TFFM) for predicting binding sites while providing an automated and minimal input user interface. TFBSPred uses DNAse I hypersensitivity data from ENCODE to identify open chromatin regions and takes advantage of the conservation between <em>Homo sapiens</em> and <em>Mus musculus</em>, by using Ensembl Compara pairwise alignments, to increase the true positive rate of the prediction. The users input a gene name or genomic location of a human or mouse genome, select the cell types of interest and TFBSPred outputs the conserved open chromatin region of the selected regulatory sequences and cell types as a pairwise alignment and displays the predicted TFBSs. The present study benchmarked TFBSPred and several similar functioning webtools using experimentally verified TFBSs. TFBSPred exhibited the optimal trade‑off between sensitivity and specificity in the case of the well‑studied IFNB1 enhanceosome, while outperforming the other web tools in subsequent use‑cases. TFBSPred may thus prove to be a valuable tool for TFBS prediction and for the provision of hypotheses for experimental validation. TFBSPred also has the potential for further improvement with future updates of TFFM data. TFBSPred is freely available at: https://www.michalopoulos.net/tfbspred/.

Predicting the effects of SNPs on transcription factor binding affinity.

Genome-wide association studies have revealed that 88% of disease-associated single-nucleotide polymorphisms (SNPs) reside in noncoding regions. However, noncoding SNPs remain understudied, partly because they are challenging to prioritize for experimental validation. To address this deficiency, we developed the SNP effect matrix pipeline (SEMpl). SEMpl estimates transcription factor-binding affinity by observing differences in chromatin immunoprecipitation followed by deep sequencing signal intensity for SNPs within functional transcription factor-binding sites (TFBSs) genome-wide. By cataloging the effects of every possible mutation within the TFBS motif, SEMpl can predict the consequences of SNPs to transcription factor binding. This knowledge can be used to identify potential disease-causing regulatory loci. SEMpl is available from https://github.com/Boyle-Lab/SEM_CPP. Supplementary data are available at Bioinformatics online.

Inferring dynamic gene regulatory networks in cardiac differentiation through the integration of multi-dimensional data.

BackgroundDecoding the temporal control of gene expression patterns is key to the understanding of the complex mechanisms that govern developmental decisions during heart development. High-throughput methods have been employed to systematically study the dynamic and coordinated nature of cardiac differentiation at the global level with multiple dimensions. Therefore, there is a pressing need to develop a systems approach to integrate these data from individual studies and infer the dynamic regulatory networks in an unbiased fashion.ResultsWe developed a two-step strategy to integrate data from (1) temporal RNA-seq, (2) temporal histone modification ChIP-seq, (3) transcription factor (TF) ChIP-seq and (4) gene perturbation experiments to reconstruct the dynamic network during heart development. First, we trained a logistic regression model to predict the probability (LR score) of any base being bound by 543 TFs with known positional weight matrices. Second, four dimensions of data were combined using a time-varying dynamic Bayesian network model to infer the dynamic networks at four developmental stages in the mouse [mouse embryonic stem cells (ESCs), mesoderm (MES), cardiac progenitors (CP) and cardiomyocytes (CM)]. Our method not only infers the time-varying networks between different stages of heart development, but it also identifies the TF binding sites associated with promoter or enhancers of downstream genes.The LR scores of experimentally verified ESCs and heart enhancers were significantly higher than random regions (p <10−100), suggesting that a high LR score is a reliable indicator for functional TF binding sites. Our network inference model identified a region with an elevated LR score approximately −9400 bp upstream of the transcriptional start site of Nkx2-5, which overlapped with a previously reported enhancer region (−9435 to −8922 bp). TFs such as Tead1, Gata4, Msx2, and Tgif1 were predicted to bind to this region and participate in the regulation of Nkx2-5 gene expression. Our model also predicted the key regulatory networks for the ESC-MES, MES-CP and CP-CM transitions.ConclusionWe report a novel method to systematically integrate multi-dimensional -omics data and reconstruct the gene regulatory networks. This method will allow one to rapidly determine the cis-modules that regulate key genes during cardiac differentiation.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0460-0) contains supplementary material, which is available to authorized users.

Identifying Functional Transcription Factor Binding Sites in Yeast by Considering Their Positional Preference in the Promoters

Transcription factor binding site (TFBS) identification plays an important role in deciphering gene regulatory codes. With comprehensive knowledge of TFBSs, one can understand molecular mechanisms of gene regulation. In the recent decades, various computational approaches have been proposed to predict TFBSs in the genome. The TFBS dataset of a TF generated by each algorithm is a ranked list of predicted TFBSs of that TF, where top ranked TFBSs are statistically significant ones. However, whether these statistically significant TFBSs are functional (i.e. biologically relevant) is still unknown. Here we develop a post-processor, called the functional propensity calculator (FPC), to assign a functional propensity to each TFBS in the existing computationally predicted TFBS datasets. It is known that functional TFBSs reveal strong positional preference towards the transcriptional start site (TSS). This motivates us to take TFBS position relative to the TSS as the key idea in building our FPC. Based on our calculated functional propensities, the TFBSs of a TF in the original TFBS dataset could be reordered, where top ranked TFBSs are now the ones with high functional propensities. To validate the biological significance of our results, we perform three published statistical tests to assess the enrichment of Gene Ontology (GO) terms, the enrichment of physical protein-protein interactions, and the tendency of being co-expressed. The top ranked TFBSs in our reordered TFBS dataset outperform the top ranked TFBSs in the original TFBS dataset, justifying the effectiveness of our post-processor in extracting functional TFBSs from the original TFBS dataset. More importantly, assigning functional propensities to putative TFBSs enables biologists to easily identify which TFBSs in the promoter of interest are likely to be biologically relevant and are good candidates to do further detailed experimental investigation. The FPC is implemented as a web tool at http://santiago.ee.ncku.edu.tw/FPC/.

Contribution of nucleosome binding preferences and co-occurring DNA sequences to transcription factor binding

BackgroundChromatin plays a critical role in regulating transcription factors (TFs) binding to their canonical transcription factor binding sites (TFBS). Recent studies in vertebrates show that many TFs preferentially bind to genomic regions that are well bound by nucleosomes in vitro. Co-occurring secondary motifs sometimes correlated with functional TFBS.ResultsWe used a logistic regression to evaluate how well the propensity for nucleosome binding and co-occurrence of a secondary motif identify which canonical motifs are bound in vivo. We used ChIP-seq data for three transcription factors binding to their canonical motifs: c-Jun binding the AP-1 motif (TGAC/GTCA), GR (glucocorticoid receptor) binding the GR motif (G-ACA---T/CGT-C), and Hoxa2 (homeobox a2) binding the Pbx (Pre-B-cell leukemia homeobox) motif (TGATTGAT). For all canonical TFBS in the mouse genome, we calculated intrinsic nucleosome occupancy scores (INOS) for its surrounding 150-bps DNA and examined the relationship with in vivo TF binding. In mouse mammary 3134 cells, c-Jun and GR proteins preferentially bound regions calculated to be well-bound by nucleosomes in vitro with the canonical AP-1 and GR motifs themselves contributing to the high INOS. Functional GR motifs are enriched for AP-1 motifs if they are within a nucleosome-sized 150-bps region. GR and Hoxa2 also bind motifs with low INOS, perhaps indicating a different mechanism of action.ConclusionOur analysis quantified the contribution of INOS and co-occurring sequence to the identification of functional canonical motifs in the genome. This analysis revealed an inherent competition between some TFs and nucleosomes for binding canonical TFBS. GR and c-Jun cooperate if they are within 150-bps. Binding of Hoxa2 and a fraction of GR to motifs with low INOS values suggesting they are not in competition with nucleosomes and may function using different mechanisms.

Epigenetic priors for identifying active transcription factor binding sites

Accurate knowledge of the genome-wide binding of transcription factors in a particular cell type or under a particular condition is necessary for understanding transcriptional regulation. Using epigenetic data such as histone modification and DNase I, accessibility data has been shown to improve motif-based in silico methods for predicting such binding, but this approach has not yet been fully explored. We describe a probabilistic method for combining one or more tracks of epigenetic data with a standard DNA sequence motif model to improve our ability to identify active transcription factor binding sites (TFBSs). We convert each data type into a position-specific probabilistic prior and combine these priors with a traditional probabilistic motif model to compute a log-posterior odds score. Our experiments, using histone modifications H3K4me1, H3K4me3, H3K9ac and H3K27ac, as well as DNase I sensitivity, show conclusively that the log-posterior odds score consistently outperforms a simple binary filter based on the same data. We also show that our approach performs competitively with a more complex method, CENTIPEDE, and suggest that the relative simplicity of the log-posterior odds scoring method makes it an appealing and very general method for identifying functional TFBSs on the basis of DNA and epigenetic evidence. FIMO, part of the MEME Suite software toolkit, now supports log-posterior odds scoring using position-specific priors for motif search. A web server and source code are available at http://meme.nbcr.net. Utilities for creating priors are at http://research.imb.uq.edu.au/t.bailey/SD/Cuellar2011. t.bailey@uq.edu.au Supplementary data are available at Bioinformatics online.

Computational Design and Application of Endogenous Promoters for Transcriptionally Targeted Gene Therapy for Rheumatoid Arthritis

The promoter regions of genes that are differentially regulated in the synovial membrane during the course of rheumatoid arthritis (RA) represent attractive candidates for application in transcriptionally targeted gene therapy. In this study, we applied an unbiased computational approach to define proximal-promoters from a gene expression profiling study of murine experimental arthritis. Synovium expression profiles from progressing stages of collagen-induced arthritis (CIA) were classified into six distinct groups using k-means clustering. Using an algorithm based on local over-representation and comparative genomics, we identified putatively functional transcription factor-binding sites (TFBS) in TATA-dependent proximal-promoters. Applying a filter based on spacing between TATA box and transcription start site (TSS) combined with the presence of over-represented nuclear factor kappaB (NFkappaB), AP-1, or CCAAT/enhancer-binding protein beta (C/EBPbeta) sites, 382 candidate murine and human promoters were reduced to 66, corresponding to 45 genes. In vitro, 9 out of 10 computationally defined promoter regions conferred cytokine-inducible expression in murine cells and human synovial fibroblasts. Under these conditions, the serum amyloid A3 (Saa3) promoter showed the strongest transcriptional induction and strength. We applied this promoter for driving therapeutically efficacious levels of the interleukin-1 receptor antagonist (Il1rn) in a disease-regulated fashion. These results demonstrate the value of bioinformatics for guiding the selection of endogenous promoters for transcriptionally targeted gene therapy.

Prediction and Experimental Validation of Novel STAT3 Target Genes in Human Cancer Cells

The comprehensive identification of functional transcription factor binding sites (TFBSs) is an important step in understanding complex transcriptional regulatory networks. This study presents a motif-based comparative approach, STAT-Finder, for identifying functional DNA binding sites of STAT3 transcription factor. STAT-Finder combines STAT-Scanner, which was designed to predict functional STAT TFBSs with improved sensitivity, and a motif-based alignment to minimize false positive prediction rates. Using two reference sets containing promoter sequences of known STAT3 target genes, STAT-Finder identified functional STAT3 TFBSs with enhanced prediction efficiency and sensitivity relative to other conventional TFBS prediction tools. In addition, STAT-Finder identified novel STAT3 target genes among a group of genes that are over-expressed in human cancer cells. The binding of STAT3 to the predicted TFBSs was also experimentally confirmed through chromatin immunoprecipitation. Our proposed method provides a systematic approach to the prediction of functional TFBSs that can be applied to other TFs.

Correction: Frequent Gain and Loss of Functional Transcription Factor Binding Sites

An incorrect version of Table S1 was published. The correct Table S1 file can be found here: Click here for additional data file.(38K, xls)

Genomic Promoter Analysis Predicts Functional Transcription Factor Binding

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.

Genetic algorithm and optimized weight matrix application for peroxisome proliferator response elements recognition: Prerequisites of accuracy growth for wide genome research

Development of reliable transcription factor binding site (TFBS) recognition methods is an important step in the large-scale genome analysis. The most of currently applied methods to predict functional TFBSs are hampered by the high false-positive rates that occur when too few functionally characterised sequences are available and only sequence conservation within a site core is considered. We propose two methods to search for binding sites (BSs) of peroxisome proliferator-activated receptor (PPAR) (peroxisome proliferator response elements, PPREs). The first method is the optimized dinucleotide position weight matrix (PWM) model, the second method represented by SiteGA model that used genetic algorithm with a discriminant function of locally positioned dinucleotides to infer the most important positions and dinucleotides. We used in our analysis two PPRE datasets, consisting of 37 and 98 BSs, correspondingly. We showed that dataset extension improved the accuracy of SiteGA, but not PWM model. Finally we combined both models (PWM and SiteGA) to the dataset of annotated human promoters (EPD). We demonstrated that the larger dataset and the longer window length supported notable growth of accuracies for PWM and SiteGA models. Consequently, a combined PWM and SiteGA application may better restrict the number of potential targets in the EPD promoter dataset.

Frequent Gain and Loss of Functional Transcription Factor Binding Sites

Cis-regulatory sequences are not always conserved across species. Divergence within cis-regulatory sequences may result from the evolution of species-specific patterns of gene expression or the flexible nature of the cis-regulatory code. The identification of functional divergence in cis-regulatory sequences is therefore important for both understanding the role of gene regulation in evolution and annotating regulatory elements. We have developed an evolutionary model to detect the loss of constraint on individual transcription factor binding sites (TFBSs). We find that a significant fraction of functionally constrained binding sites have been lost in a lineage-specific manner among three closely related yeast species. Binding site loss has previously been explained by turnover, where the concurrent gain and loss of a binding site maintains gene regulation. We estimate that nearly half of all loss events cannot be explained by binding site turnover. Recreating the mutations that led to binding site loss confirms that these sequence changes affect gene expression in some cases. We also estimate that there is a high rate of binding site gain, as more than half of experimentally identified S. cerevisiae binding sites are not conserved across species. The frequent gain and loss of TFBSs implies that cis-regulatory sequences are labile and, in the absence of turnover, may contribute to species-specific patterns of gene expression.

Cooperation between MEF2 and PPARγ in human intestinal β,β-carotene 15,15'-monooxygenase gene expression

BackgroundVitamin A and its derivatives, the retinoids, are essential for normal embryonic development and maintenance of cell differentiation. β, β-carotene 15,15'-monooxygenase 1 (BCMO1) catalyzes the central cleavage of β-carotene to all-trans retinal and is the key enzyme in the intestinal metabolism of carotenes to vitamin A. However, human and various rodent species show markedly different efficiencies in intestinal BCMO1-mediated carotene to retinoid conversion. The aim of this study is to identify potentially human-specific regulatory control mechanisms of BCMO1 gene expression.ResultsWe identified and functionally characterized the human BCMO1 promoter sequence and determined the transcriptional regulation of the BCMO1 gene in a BCMO1 expressing human intestinal cell line, TC-7. Several functional transcription factor-binding sites were identified in the human promoter that are absent in the mouse BCMO1 promoter. We demonstrate that the proximal promoter sequence, nt -190 to +35, confers basal transcriptional activity of the human BCMO1 gene. Site-directed mutagenesis of the myocyte enhancer factor 2 (MEF2) and peroxisome proliferator-activated receptor (PPAR) binding elements resulted in decreased basal promoter activity. Mutation of both promoter elements abrogated the expression of intestinal cell BCMO1. Electrophoretic mobility shift and supershift assays and transcription factor co-expression in TC-7 cells showed MEF2C and PPARγ bind to their respective DNA elements and synergistically transactivate BCMO1 expression.ConclusionWe demonstrate that human intestinal cell BCMO1 expression is dependent on the functional cooperation between PPARγ and MEF2 isoforms. The findings suggest that the interaction between MEF2 and PPAR factors may provide a molecular basis for interspecies differences in the transcriptional regulation of the BCMO1 gene.

Endogenous retrovirus long terminal repeats as ready-to-use mobile promoters: The case of primate β3GAL-T5

Throughout the course of vertebrate evolution, germline retroviral infections have resulted in heritable provirus insertions into host DNA. These endogenous retroviruses (ERVs) contain long terminal repeat (LTR) promoters that can be adopted for use by nearby host genes. It is not known whether the transcription factor (TF) binding sites and tissue-specificities of modern LTR gene promoters have been retained since the time of ERV insertion, or if these features evolved later as the LTR became involved in host gene regulation. To address this issue, we have conducted a case study of the ERV-L LTR promoter of human β1,3-galactosyltransferase 5 ( β3GAL-T5). We have previously shown that the human β3GAL-T5 LTR promoter is responsible for the majority of gene transcripts in the colon. The murine β3gal-t5 gene is also expressed primarily in the colon, despite the absence of an orthologous ERV-L LTR in the mouse genome. We therefore hypothesized that both the ERV-L LTR and the non-retroviral ancestral β3GAL-T5 promoter were active in the colon at the time of ERV insertion. In support of this hypothesis, we have shown that the orthologous LTRs of four non-human primates are also active in a human colorectal cell line, and that the baboon LTR is active in primary baboon colon tissue. We also present evidence that the functional TF binding sites of the human β3GAL-T5 LTR promoter were present in the original consensus sequence for this class of LTRs. Upon similar analysis of other ERV sequences, we have concluded that this evolutionary history is shared by certain other LTR gene promoters, and may be a general phenomenon.

Identification of functional transcription factor binding sites using closely related Saccharomyces species

Comparative genomics provides a rapid means of identifying functional DNA elements by their sequence conservation between species. Transcription factor binding sites (TFBSs) may constitute a significant fraction of these conserved sequences, but the annotation of specific TFBSs is complicated by the fact that these short, degenerate sequences may frequently be conserved by chance rather than functional constraint. To identify intergenic sequences that function as TFBSs, we calculated the probability of binding site conservation between Saccharomyces cerevisiae and its two closest relatives under a neutral model of evolution. We found that this probability is <5% for 134 of 163 transcription factor binding motifs, implying that we can reliably annotate binding sites for the majority of these transcription factors by conservation alone. Although our annotation relies on a number of assumptions, mutations in five of five conserved Ume6 binding sites and three of four conserved Ndt80 binding sites show Ume6- and Ndt80-dependent effects on gene expression. We also found that three of five unconserved Ndt80 binding sites show Ndt80-dependent effects on gene expression. Together these data imply that although sequence conservation can be reliably used to predict functional TFBSs, unconserved sequences might also make a significant contribution to a species' biology.

Large-scale turnover of functional transcription factor binding sites in Drosophila

The gain and loss of functional transcription-factor binding sites has been proposed as a major source of evolutionary change in cis-regulatory DNA and gene expression. We have developed an evolutionary model to study binding site turnover that uses multiple sequence alignments to assess the evolutionary constraint on individual binding sites, and to map gain and loss events along a phylogenetic tree. We apply this model to study the evolutionary dynamics of binding sites of the Drosophila melanogaster transcription factor Zeste, using genome-wide in vivo (ChIP-chip) binding data to identify functional Zeste binding sites, and the genome sequences of D. melanogaster, D. simulans, D. erecta and D. yakuba to study their evolution. We estimate that more than 5 percent of functional Zeste binding sites in D. melanogaster were gained along the D. melanogaster lineage or lost along one of the other lineages. We find that Zeste bound regions have a reduced rate of binding site loss and an increased rate of binding site gain relative to flanking sequences. Finally, we show that binding site gains and losses are asymmetrically distributed with respect to D. melanogaster, consistent with lineage-specific acquisition and loss of Zeste-responsive regulatory elements.

RVista for Comparative Sequence-Based Discovery of Functional Transcription Factor Binding Sites

rVista for Comparative Sequence-Based Discovery of Functional Transcription Factor Binding Sites

Characterization of Nucleophosmin (B23) as a Myc Target by Scanning Chromatin Immunoprecipitation

The genetic program through which a specific transcription factor regulates a biological response is fundamental to our understanding how instructions in the genome are implemented. The emergence of DNA microarray technology for gene expression analysis has generated vast numbers of target genes resulting from specific transcription factor activity. We use the oncogenic transcription factor c-Myc as proof-of-principle that human genome sequence analysis and scanning of a specific gene by chromatin immunoprecipitation can be coupled to identify target transcription factor binding sequences. We focused on nucleophosmin, also known as B23, which was identified as a candidate Myc-responsive gene from a subtractive hybridization screen, and we found that sequences in intron 1, and not 5' sequences in the proximal promoter, are bound by c-Myc in vivo. Hence, a scanning chromatin immunoprecipitation (SChIP) strategy is useful in analyzing functional transcription factor-binding sites.

Experimental data of a single promoter can be used for in silico detection of genes with related regulation in the absence of sequence similarity

Gene expression is presently a major focus in genome analysis, and the experimental data on regulatory mechanisms and functional transcription factor binding sites are steadily growing. However, the annotation of transcriptional regulation of sequences cannot keep pace with the exponential growth of sequence databases. Employing detailed experimental data of a single promoter or enhancer to predict genes with similar regulation would provide a powerful method to link the literature about transcriptional regulation and sequence databases. To this end, we used information on individual functional transcription factor binding sites to compose in silico promoter and enhancer models of muscle-specific genes and to analyze the rodents section of EMBL with these models. Exhaustive evaluation of all hits revealed every second to third match to be a muscle-associated gene. Moreover, functionally related regulatory regions were detected by our model-based approach even in the absence of sequence similarity. We believe that this new approach is a substanial extension to database analysis by BLAST or FASTA, which are restricted to sequence similarity.