Transcription Factor Binding Properties Research Articles

Deciphering the Gene Regulatory Networks during Embryonic Stem Cell Differentiation

With the development of sequencing technologies, genomics, and the advent of the era of big data, bioinformatics has become more and more important. One very important research area is the regulation of gene expression, which has become one of the hot research fields in bioinformatics. Transcription factors are important transcriptional regulators. In the process of gene expression, A key step is binding of transcription factors in gene expression by combining with a specific DNA sequence to regulate gene expression and inhibit or enhance its role. The difference between these specific DNA sequences is important for understanding gene regulation. With the rapid development of high-throughput sequencing technology, ChIP-seq, which combines chromatin immunoprecipitation technology and next-generation sequencing, provides massive data for transcription factor binding site across the genome. HiChIP, as a protein-centric chromatin conformation analysis method, is concerned because of its sensitive and efficient characteristics. In this project, we are working on the early demobilization of embryonic stem cells, in which ZIC3, Otx2, etc. play an important regulatory role. We are starting from analyzing HiChIP data on ZIC3 but mainly on calling peaks on HiChIP data, and ground tools to analysis HiChIP data. To integrate the chromatin binding properties of transcription factors with other chromatin markers. This report is based on the work done on HiChIP data during the early differentiation of stem cells, and has compared the methods required to reach the peak of this data. In addition, this project verified the output by using ground truth from histone mark ChIP-seq data (H3K27ac) and did the motif analysis from some of the output peaks.

Entropy-driven electrochemiluminescence ultra-sensitive detection strategy of NF-κB p50 as the regulator of cytokine storm

2019 novel coronavirus (2019-nCoV) with strong contagion in the crowd, has ravaged worldwide and severely impacts the human health and epidemic prevention system, by producing a series of significant stress reactions in the body to induce further cytokine storm. Transcription factors (TFs) served as essential DNA binding proteins play an integral role in regulating cytokine storm, and the detection of it in the human coronavirus environment provides especially valuable approaches to diagnosis and treatment of 2019-nCoV and development of antiviral drugs. In this work, an entropy-driven electrochemiluminescence (ECL) biosensor was constructed for ultra-sensitive bioassay of NF-κB p50. The strategy primarily capitalizing the splendid double-stranded DNA (dsDNA) binding properties of transcription factors, employing GOAu-Ru composite material as ECL emitter, utilizing entropy-driven reactions for signal amplification method, offered a repeatable proposal for TFs detection. In the absence of TFs, the released DNA1 further went in the entropy-driven reaction, contributing to an “ECL off” state. However, in the presence of TFs, the dsDNA avoided being digested, which blocked DNA1 for participating in the entropy-driven reaction, and the system exhibited an “ECL on” state. Most importantly, the ECL bioanalytical method denoted broad application prospects for NF-κB p50 detection with a lower detection limit (9.1 pM).

Abstract MP171: Transcription Start Site Profiling Defines Promoter and Enhancer Regions for Cardiomyopathy Genes

Background: Mutations in more than 100 genes lead to dilated, hypertrophic and other forms of cardiomyopathy. Autosomal dominant mutations in the MYH7 and LMNA genes cause autosomal dominant hypertrophic and dilated cardiomyopathy, respectively. Individual mutations display a range of clinical expression from severe early onset disease to minimal or no symptoms. Genetic variation in noncoding gene regulatory regions including enhancers is expected to modify expression of cardiomyopathy genes and disease expressivity. In addition, heart failure is associated a fetal gene re-expression program, mediated by genetic regulatory regions. The contribution of noncoding genetic variation to cardiomyopathy and heart failure has been hampered by limited genome wide descriptions of human cardiac regulatory regions. Methods and Results: We used C ap A nalysis of G ene E xpression by sequencing (CAGE-seq) to profile the transcriptional start sites in healthy and failed human hearts. CAGE-seq detects the unidirectional signals of gene promoters and the bidirectional signal of transcribed enhancer regions. We identified ~17,000 transcriptional start sites associated with gene promoters and ~1,500 putative enhancer regions active in cardiac tissue. These CAGE-defined regulatory regions carried histone modifications and transcription factor binding properties characteristic of enhancers or promoters. We specifically identified promoter switching and differential enhancer usage between healthy and failed hearts. We intersected CAGE-defined enhancers with additional epigenomic datasets to identify regulatory regions for MYH7 and LMNA genes. We identified 13 putative enhancer regions and validated the functionality of a subset of these regulatory regions using reporter assays and gene editing. Conclusions: This CAGE-seq dataset defines the regulatory environment for heart failure. These promoter and enhancer regions could be used to target heart-failure associated gene expression changes. Additionally, this data can be used to identify enhancer regions regulating cardiomyopathy genes.

Mathematical Modeling of Avidity Distribution and Estimating General Binding Properties of Transcription Factors from Genome-Wide Binding Profiles.

The shape of the experimental frequency distributions (EFD) of diverse molecular interaction events quantifying genome-wide binding is often skewed to the rare but abundant quantities. Such distributions are systematically deviated from standard power-law functions proposed by scale-free network models suggesting that more explanatory and predictive probabilistic model(s) are needed. Identification of the mechanism-based data-driven statistical distributions that provide an estimation and prediction of binding properties of transcription factors from genome-wide binding profiles is the goal of this analytical survey. Here, we review and develop an analytical framework for modeling, analysis, and prediction of transcription factor (TF) DNA binding properties detected at the genome scale. We introduce a mixture probabilistic model of binding avidity function that includes nonspecific and specific binding events. A method for decomposition of specific and nonspecific TF-DNA binding events is proposed. We show that the Kolmogorov-Waring (KW) probability function (PF), modeling the steady state TF binding-dissociation stochastic process, fits well with the EFD for diverse TF-DNA binding datasets. Furthermore, this distribution predicts total number of TF-DNA binding sites (BSs), estimating specificity and sensitivity as well as other basic statistical features of DNA-TF binding when the experimental datasets are noise-rich and essentially incomplete. The KW distribution fits equally well to TF-DNA binding activity for different TFs including ERE, CREB, STAT1, Nanog, and Oct4. Our analysis reveals that the KW distribution and its generalized form provides the family of power-law-like distributions given in terms of hypergeometric series functions, including standard and generalized Pareto and Waring distributions, providing flexible and common skewed forms of the transcription factor binding site (TFBS) avidity distribution function. We suggest that the skewed binding events may be due to a wide range of evolutionary processes of creating weak avidity TFBS associated with random mutations, while the rare high-avidity binding sites (i.e., high-avidity evolutionarily conserved canonical e-boxes) rarely occurred. These, however, may be positively selected in microevolution.

Allelic mutations in noncoding genomic sequences construct novel transcription factor binding sites that promote gene overexpression.

The growth and survival factor hepatocyte growth factor (HGF) is expressed at high levels in multiple myeloma (MM) cells. We report here that elevated HGF transcription in MM was traced to DNA mutations in the promoter alleles of HGF. Sequence analysis revealed a previously undiscovered single-nucleotide polymorphism (SNP) and crucial single-nucleotide variants (SNVs) in the promoters of myeloma cells that produce large amounts of HGF. The allele-specific mutations functionally reassembled wild-type sequences into the motifs that affiliate with endogenous transcription factors NFKB (nuclear factor kappa-B), MZF1 (myeloid zinc finger 1), and NRF-2 (nuclear factor erythroid 2-related factor 2). In vitro, a mutant allele that gained novel NFKB-binding sites directly responded to transcriptional signaling induced by tumor necrosis factor alpha (TNFα) to promote high levels of luciferase reporter. Given the recent discovery by genome-wide sequencing (GWS) of numerous non-coding mutations in myeloma genomes, our data provide evidence that heterogeneous SNVs in the gene regulatory regions may frequently transform wild-type alleles into novel transcription factor binding properties to aberrantly interact with dysregulated transcriptional signals in MM and other cancer cells.

Methylation of the Corticotropin Releasing Hormone Gene Promoter in BeWo Cells: Relationship to Gene Activity.

Corticotropin releasing hormone (CRH) production by the human placenta increases exponentially as pregnancy advances, and the rate of increase predicts gestational length. CRH gene expression is regulated by cAMP in trophoblasts through a cyclic AMP-response element (CRE), which changes its transcription factor binding properties upon methylation. Here we determined whether methylation of the CRH proximal promoter controls basal and cAMP-stimulated CRH expression in BeWo cells, a well-characterized trophoblastic cell line. We treated the cells with 8-Br-cAMP and the DNA methyltransferase inhibitor 5-aza-2′ deoxycytidine (5-AZA-dC) and determined the effects on CRH mRNA level and promoter methylation. Clonal bisulfite sequencing showed partial and allele independent methylation of CpGs in the CRH promoter. CRH mRNA expression and the methylation of a subset of CpGs (including CpG2 in the CRE) increased spontaneously during culture. 8-Br-cAMP stimulated CRH expression without affecting the increase in methylation. 5-AZA-dC decreased methylation and augmented 8-Br-cAMP-stimulated CRH expression, but it blocked the spontaneous increase of CRH mRNA level. We conclude that the CRH promoter is a dynamically and intermediately methylated genomic region in BeWo cells. Promoter methylation did not inhibit CRH gene expression under the conditions employed; rather it determined the contribution of alternative cAMP-independent pathways and cAMP-independent mechanisms to CRH expression control.

Estimating binding properties of transcription factors from genome-wide binding profiles.

The binding of transcription factors (TFs) is essential for gene expression. One important characteristic is the actual occupancy of a putative binding site in the genome. In this study, we propose an analytical model to predict genomic occupancy that incorporates the preferred target sequence of a TF in the form of a position weight matrix (PWM), DNA accessibility data (in the case of eukaryotes), the number of TF molecules expected to be bound specifically to the DNA and a parameter that modulates the specificity of the TF. Given actual occupancy data in the form of ChIP-seq profiles, we backwards inferred copy number and specificity for five Drosophila TFs during early embryonic development: Bicoid, Caudal, Giant, Hunchback and Kruppel. Our results suggest that these TFs display thousands of molecules that are specifically bound to the DNA and that whilst Bicoid and Caudal display a higher specificity, the other three TFs (Giant, Hunchback and Kruppel) display lower specificity in their binding (despite having PWMs with higher information content). This study gives further weight to earlier investigations into TF copy numbers that suggest a significant proportion of molecules are not bound specifically to the DNA.

Signal-dependent dynamics of transcription factor translocation controls gene expression

Information about environmental stimuli is often transmitted using common signaling molecules, but the mechanisms that ensure signaling specificity are not entirely known. Here we show that the identities and intensities of different stresses are transmitted by modulation of the amplitude, duration or frequency of nuclear translocation of the Saccharomyces cerevisiae general stress response transcription factor Msn2. Through artificial control of the dynamics of Msn2 translocation, we reveal how distinct dynamical schemes differentially affect reporter gene expression. Using a simple model, we predict stress-induced reporter gene expression from single-cell translocation dynamics. We then demonstrate that the response of natural target genes to dynamical modulation of Msn2 translocation is influenced by differences in the kinetics of promoter transitions and transcription factor binding properties. Thus, multiple environmental signals can trigger qualitatively different dynamics of a single transcription factor and influence gene expression patterns.

Creating PWMs of transcription factors using 3D structure-based computation of protein-DNA free binding energies

BackgroundKnowledge of transcription factor-DNA binding patterns is crucial for understanding gene transcription. Numerous DNA-binding proteins are annotated as transcription factors in the literature, however, for many of them the corresponding DNA-binding motifs remain uncharacterized.ResultsThe position weight matrices (PWMs) of transcription factors from different structural classes have been determined using a knowledge-based statistical potential. The scoring function calibrated against crystallographic data on protein-DNA contacts recovered PWMs of various members of widely studied transcription factor families such as p53 and NF-κB. Where it was possible, extensive comparison to experimental binding affinity data and other physical models was made. Although the p50p50, p50RelB, and p50p65 dimers belong to the same family, particular differences in their PWMs were detected, thereby suggesting possibly different in vivo binding modes. The PWMs of p63 and p73 were computed on the basis of homology modeling and their performance was studied using upstream sequences of 85 p53/p73-regulated human genes. Interestingly, about half of the p63 and p73 hits reported by the Match algorithm in the altogether 126 promoters lay more than 2 kb upstream of the corresponding transcription start sites, which deviates from the common assumption that most regulatory sites are located more proximal to the TSS. The fact that in most of the cases the binding sites of p63 and p73 did not overlap with the p53 sites suggests that p63 and p73 could influence the p53 transcriptional activity cooperatively. The newly computed p50p50 PWM recovered 5 more experimental binding sites than the corresponding TRANSFAC matrix, while both PWMs showed comparable receiver operator characteristics.ConclusionsA novel algorithm was developed to calculate position weight matrices from protein-DNA complex structures. The proposed algorithm was extensively validated against experimental data. The method was further combined with Homology Modeling to obtain PWMs of factors for which crystallographic complexes with DNA are not yet available. The performance of PWMs obtained in this work in comparison to traditionally constructed matrices demonstrates that the structure-based approach presents a promising alternative to experimental determination of transcription factor binding properties.

Genome adaptation to chemical stress: clues from comparative transcriptomics in Saccharomyces cerevisiae and Candida glabrata

Comparative transcriptomics of Saccharomyces cerevisiae and Candida glabrata revealed a remarkable conservation of response to drug-induced stress, despite underlying differences in the regulatory networks.

Exploring Transcription Factor Binding Properties of Several Non-coding DNA Sequence Elements in the Human NF-IL6 Gene

We examined several DNA segments upstream of the transcription start site of the human NF-IL6 gene to evaluate the predictions of two computational models developed to identify potential regulatory elements in the non-coding regions of genes. One model, comparative genomics, is based on the hypothesis that functional regulatory sequences can be localized in alignments of genomic DNA from several species. The other model is based on the hypothesis that protein-binding sites in genomic DNA may include sequence elements that occur frequently in proximal promoters of genes. The segments selected for DNA binding and functional evaluations included: (1) two conserved regions identified in multi-species sequence alignments; (2) a region containing several localized hits with 9-mers that ranked highly in studies of proximal promoters of human genes; and (3) two regions that were either GC-rich and/or contained tracts of G. The assays were done under nearly identical experimental conditions, using a cell line (U937) representing human monocytes/macrophages. The experiments also aimed at evaluating what effect, if any, cellular stimulation could have on the interactions of nuclear proteins with naturally occurring GC-rich elements in a human genomic DNA. In DNA binding assays, several complexes were formed with the conserved regions identified in multi-species sequence alignment. Furthermore, these regions were active in functional assays. The region containing several matches with 9-mers derived from proximal promoters of human genes was not conserved but formed several complexes with nuclear proteins including Sp1, Egr-1, and an unidentified protein. In addition, this region was active in functional assays and responded to cellular stimulations. Overall, the results of the assays suggest an important role for the sequence context of genomic DNA in protein binding and selection.

Integrated analysis of yeast regulatory sequences for biologically linked clusters of genes.

Dramatic progress in deciphering the regulatory controls in Saccharomyces cerevisiae has been enabled by the fusion of high-throughput genomics technologies with advanced sequence analysis algorithms. Sets of genes likely to function together and with similar expression profiles have been identified in diverse studies. By fusing an advanced pattern recognition algorithm for identification of transcription factor binding sites with a new method for the quantitative comparison of binding properties of transcription factors, we provide an integrated means to move from expression data to biological insights. The Yeast Regulatory Sequence Analysis system, YRSA, combines standard functions with a novel pattern characterization procedure in an intuitive interface designed for use by a broad range of scientists. The features of the system include automated retrieval of user-defined promoter sequences, binding site discovery by pattern recognition, graphical displays of the observed pattern and positions of similar sequences in the specified genes, and comparison of the new pattern against a collection of binding patterns for characterized transcription factors. The comprehensive YRSA system was used to study the regulatory mechanisms of yeast regulons. Analysis of the regulatory controls of a battery of genes induced by DNA damaging agents supports a putative mediating role for the cell-cycle checkpoint regulatory element MCB. YRSA is available at http://yrsa.cgb.ki.se. [YRSA: ancient Scandinavian name meaning old she-bear (Latin Ursus arctos = brown bear/grizzly).]

The Role of Enhancer A in the Locus-Specific Transactivation of Classical and Nonclassical HLA Class I Genes by Nuclear Factor κB

AbstractHLA class I expression is tightly controlled at the transcriptional level by several conserved regulatory elements in the proximal promoter region. In this study, the two putative κB motifs of enhancer A (κB1 and κB2) of the classical and nonclassical HLA class I genes were investigated for their binding properties of transcription factors and tested for their contribution to the NF-κB-induced route of transactivation. It was shown that NF-κB-induced transactivation through enhancer A is most important for the HLA-A locus, which contains two NF-κB binding sites. Although the enhancer A of HLA-B contains only one NF-κB binding site (κB1), there was still a moderate transactivation by NF-κB. Since HLA-F, which also possesses one NF-κB binding site but lacks protein binding to its κB2 site, was not transactivated by NF-κB, the NF-κB-mediated transactivation through the κB1 motif in HLA-B is most probably facilitated by binding of the transcription factor Sp1 to the upstream κB2 site. Thus, transcriptional regulation of HLA class I genes by NF-κB is restricted to the HLA-A and HLA-B loci.