Search For Transcription Factor Binding Sites Research Articles

The Effect of Glucocorticoids on TAF1 Gene Transcription in X-linked Dystonia Parkinsonism.

X-linked Dystonia Parkinsonism (XDP) is associated with a SINE-VNTR- Alu (SVA) retrotransposon insertion in an intron of the TAF1 gene that alters gene transcription and splicing. In this study, we determined if the SVA insertion introduces glucocorticoid (GC)-responsive cis-regulatory elements that may contribute to dysregulated TAF1 transcription and XDP disease progression. We performed in silico analysis to identify potential GC receptor (GR) binding sites within the XDP-SVA. We also conducted promoter-reporter assays on HeLa and HEK293T cells to assess the intrinsic promoter activity of three XDP-SVA variants representing different hexameric repeat lengths associated with differences in disease onset. We treated XDP fibroblast cell models with GR agonist (CORT) or antagonist (RU486), then subjected TAF1 and the XDP-associated aberrant transcript, TAF1-32i to gene expression analysis. A transcription factor binding site search revealed three binding sites for GR within the XDP-SVA-two within the SINE region and one in the Alu region. Promoter-reporter assays showed induction of XDP-SVA promoter activity upon CORT treatment that was dependent on the cell line and XDP-SVA hexamer repeat length. Gene expression analysis showed that baseline TAF1 levels differed between control and patient fibroblast cell lines, and treatment with CORT led to an increasing trend in the expression of the aberrant TAF1-32i transcript but did not reach statistical significance. Treatment with RU486 increased TAF1 mRNA expression only in the control cell lines. Using reporter assays, the XDP-SVA was shown to exhibit CORT-dependent transcriptional activation. Gene expression analysis also showed that GC signaling may influence TAF1 and TAF1-32i expression, possibly through interaction with the XDP-SVA. Our data provide a potential link between stress and XDP progression.

Transcriptome and Metabolome Analyses in Exogenous FABP4- and FABP5-Treated Adipose-Derived Stem Cells.

Adipose-derived stem cells (ADSC), which exist near adipocytes in adipose tissue, have been used as a potential tool of regenerative medicine. Lipid chaperones, fatty acid-binding protein 4 (FABP4) and 5 (FABP5), are abundantly expressed in adipocytes. FABP4 has recently been shown to be secreted from adipocytes during lipolysis in a non-classical pathway and may act as an adipokine. Here, we investigated the role of exogenous FABP4 and FABP5 in transcriptional and metabolic regulation in ADSC. FABP4 and FABP5 were little expressed in ADSC. However, both FABP4 and FABP5 were significantly induced after adipocyte differentiation of ADSC and were secreted from the differentiated adipocytes. Analysis of microarray data, including gene ontology enrichment analysis and cascade analysis of the protein-protein interaction network using a transcription factor binding site search, demonstrated that treatment of ADSC with FABP4 or FABP5 affected several kinds of genes related to inflammatory and metabolic responses and the process of cell differentiation. Notably, myogenic factors, including myocyte enhancer factors, myogenic differentiation 1 and myogenin, were modulated by treatment of ADSC with FABP4, indicating that exogenous FABP4 treatment is partially associated with myogenesis in ADSC. Metabolome analysis showed that treatment of ADSC with FABP4 and with FABP5 similarly, but differently in extent, promoted hydrolysis and/or uptake of lipids, consequentially together with enhancement of β oxidation, inhibition of downstream of the glycolysis pathway, accumulation of amino acids, reduction of nucleic acid components and increase in the ratio of reduced and oxidized nicotinamide adenine dinucleotide phosphates (NADPH/NADP+), an indicator of reducing power, and the ratio of adenosine triphosphate and adenosine monophosphate (ATP/AMP), an indicator of the energy state, in ADSC. In conclusion, secreted FABP4 and FABP5 from adipocytes as adipokines differentially affect transcriptional and metabolic regulation in ADSC near adipocytes. The adiposity condition in the host of regenerative medicine may affect characteristics of ADSC by exposure of the balance of FABP4 and FABP5.

Duplication and differentiation of common carp (Cyprinus carpio) myoglobin genes revealed by BAC analysis.

Two distinct myoglobin (mb) transcripts have been reported in common carp, Cyprinus carpio, which is a hypoxia-tolerant fish living in habitats with greatly fluctuant dissolved oxygen levels. Recombinant protein analysis has shown functional specialization of the two mb transcripts. In this work, analysis for mb-containing bacterial artificial chromosome (BAC) clones indicated different genome loci for common carp myoglobin-1 (mb-1) and myoglobin-2 (mb-2) genes. Fluorescence in situ hybridization (FISH) revealed that mb-1 and mb-2 are located on separate chromosomes. In both of the mb-1 and mb-2 containing BAC clones, gene synteny was well conserved with the homologous region on zebrafish chromosome 1, supporting that the common carp specific mb-2 gene originated from the recent whole genome duplication event in cyprinid lineage. Transcription factor binding sites search indicated that both common carp mb genes lacked specificity Protein 1 (Sp1) and myocyte enhancer factor-2 (MEF2) binding sites, which mediated muscle-specific and calcium-dependent expression in the well-studied mb promoters. Potential hypoxia response elements (HREs) were predicted in the regulatory region of common carp mb genes. These characteristics of common carp mb gene regulatory region well interpreted the hypoxia-inducible, non-muscle expression pattern of mb-1. In the case of mb-2, a 10 bp insertion to the binding site of upstream stimulatory factor (USF), which was a co-factor of hypoxia inducible factor (HIF), might cause the non-response to hypoxia treatment of mb-2. The case of common carp mb gene duplication and subsequent differentiation in expression pattern and protein function provided an example for adaptive evolution toward aquatic hypoxia tolerance.

LASAGNA-Search 2.0: integrated transcription factor binding site search and visualization in a browser

LASAGNA-Search 2.0 is an integrated webtool for transcription factor (TF) binding site search and visualization. The tool is based on the LASAGNA (Length-Aware Site Alignment Guided by Nucleotide Association) algorithm. It eliminates manual TF model collection and promoter sequence retrieval. Search results can be visualized locally or in the University of California Santa Cruz Genome Browser. Gene regulatory network inference based on the search results offers another way of visualization. A list of TFs and target genes is all a user needs to start using the tool. LASAGNA-Search 2.0 currently offers 1792 TF models and supports 15 species for automatic promoter retrieval and visualization in the University of California Santa Cruz Genome Browser. It is a user-friendly tool designed for non-bioinformaticians and is suitable for research and teaching. We describe important changes made since the initial release. LASAGNA-Search 2.0 is freely available without registration at http://biogrid.engr.uconn.edu/lasagna_search/.

LASAGNA: a novel algorithm for transcription factor binding site alignment.

BackgroundScientists routinely scan DNA sequences for transcription factor (TF) binding sites (TFBSs). Most of the available tools rely on position-specific scoring matrices (PSSMs) constructed from aligned binding sites. Because of the resolutions of assays used to obtain TFBSs, databases such as TRANSFAC, ORegAnno and PAZAR store unaligned variable-length DNA segments containing binding sites of a TF. These DNA segments need to be aligned to build a PSSM. While the TRANSFAC database provides scoring matrices for TFs, nearly 78% of the TFs in the public release do not have matrices available. As work on TFBS alignment algorithms has been limited, it is highly desirable to have an alignment algorithm tailored to TFBSs.ResultsWe designed a novel algorithm named LASAGNA, which is aware of the lengths of input TFBSs and utilizes position dependence. Results on 189 TFs of 5 species in the TRANSFAC database showed that our method significantly outperformed ClustalW2 and MEME. We further compared a PSSM method dependent on LASAGNA to an alignment-free TFBS search method. Results on 89 TFs whose binding sites can be located in genomes showed that our method is significantly more precise at fixed recall rates. Finally, we described LASAGNA-ChIP, a more sophisticated version for ChIP (Chromatin immunoprecipitation) experiments. Under the one-per-sequence model, it showed comparable performance with MEME in discovering motifs in ChIP-seq peak sequences.ConclusionsWe conclude that the LASAGNA algorithm is simple and effective in aligning variable-length binding sites. It has been integrated into a user-friendly webtool for TFBS search and visualization called LASAGNA-Search. The tool currently stores precomputed PSSM models for 189 TFs and 133 TFs built from TFBSs in the TRANSFAC Public database (release 7.0) and the ORegAnno database (08Nov10 dump), respectively. The webtool is available at http://biogrid.engr.uconn.edu/lasagna_search/.

LASAGNA-Search: an Integrated Web Tool for Transcription Factor Binding Site Search and Visualization

The release of ChIP-seq data from the ENCyclopedia Of DNA Elements (ENCODE) and Model Organism ENCyclopedia Of DNA Elements (modENCODE) projects has significantly increased the amount of transcription factor (TF) binding affinity information available to researchers. However, scientists still routinely use TF binding site (TFBS) search tools to scan unannotated sequences for TFBSs, particularly when searching for lesser-known TFs or TFs in organisms for which ChIP-seq data are unavailable. The sequence analysis often involves multiple steps such as TF model collection, promoter sequence retrieval, and visualization; thus, several different tools are required. We have developed a novel integrated web tool named LASAGNA-Search that allows users to perform TFBS searches without leaving the web site. LASAGNA-Search uses the LASAGNA (Length-Aware Site Alignment Guided by Nucleotide Association) algorithm for TFBS alignment. Important features of LASAGNA-Search include (i) acceptance of unaligned variable-length TFBSs, (ii) a collection of 1726 TF models, (iii) automatic promoter sequence retrieval, (iv) visualization in the UCSC Genome Browser, and (v) gene regulatory network inference and visualization based on binding specificities. LASAGNA-Search is freely available at http://biogrid.engr.uconn.edu/lasagna_search/.

BioSpotlight

BioTechniquesVol. 54, No. 3 BioSpotlightOpen AccessBioSpotlightKristie Nybo & Patrick C.H. LoKristie NyboSearch for more papers by this author & Patrick C.H. LoSearch for more papers by this authorPublished Online:3 Apr 2018https://doi.org/10.2144/000113994AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInRedditEmail LASAGNA: A better taste for transcription factor analysisTranscription factors (TF) play a critical role in cell homeostasis, signaling, and response to stimuli by binding gene regulatory regions in a sequence-specific manner and subsequently alterating gene expression. Functional studies of individual TFs can provide specific details on the interactions between a TF and its binding site (TFBS), but such functional analyses are specific, expensive, and time-consuming. As an alternative, researchers frequently take advantage of computational tools to scan genomic sequences and make predictions regarding putative TFBSs. In addition to basic TF search functions, these currently available tools can also search variable length binding sites, retrieve promoter sequences, and predict binding specificities, among other functions. Although several useful programs are currently available to facilitate such computational studies of TFBS, none of these can complete all the possible analysis steps just described, requiring interested users to go between several programs for their searches. To improve the efficiency of TF analysis, Chih Lee and Chun-His Huang at the University of Connecticut describe the development of a web-based program they call LASAGNA-Search in the current issue of BioTechniques. LASAGNA-Search allows for complete TFBS search and visualization, including the ability to search variable-length TFBS, incorporation of five pre-computed models and 1726 TF models collected from user-curated TF databases, use of position dependence data for modeling, automatic promoter sequence retrieval, visualization of search results in the UCSC Genome Browser, and construction of gene regulatory networks, all without leaving the webpage. LASAGNA-Search currently supports promoter searches for seven species, and the authors extensively compared and contrasted their new pipeline with other available programs, demonstrating that LASAGNA-Search consistently outperforms MAPPER2, the most closely related program. LASAGNA-Search promises a more efficient and streamlined method for TFBS searches, with the added promise of expanded content and improved features in the future.User workflow for the LASAGNA-Search TF binding site analysis platform.See “LASAGNA-Search: an integrated web tool for transcription factor binding site search and visualization”Optimal miRNA RT-qPCRMicroRNAs (miRNAs) are key regulators of various biological processes, including development, cell proliferation, and apoptosis. Dysregulation of miRNA expression has been implicated in several disease states, driving recent interest in the suitability of miRNAs as biomarkers. In basic research and for clinical applications, it is essential to assure the accuracy and reproducibility of the techniques utilized for miRNA quantitation. Of these techniques, RT-qPCR is considered the gold standard based on superior sensitivity and specificity. In this issue, Redshaw et al., at LGC Ltd. (Middlesex, UK), examine several key issues potentially affecting the accuracy and repeatability of RT-qPCR measurements of miRNA. Firstly, although there are several widely used, commercially available RT-qPCR platforms for miRNA quantitation employing different approaches, these have not been extensively compared experimentally in the literature for measurement accuracy, precision, and linearity. Redshaw et al. compared two of the most popular commercial RT-qPCR platforms, which use fundamentally different technologies: the Taqman miRNA assay and the miRCURY LNA Universal RT microRNA PCR assay. A second issue is that upstream procedures, such as RNA sample preparation, have not been carefully examined for their effects on downstream miRNA quantification either. Here, RNA samples are typically enriched for the short RNA fraction containing miRNAs, but the effect of this enrichment on miRNA quantitation by RT-qPCR has not been fully analyzed. Finally, the authors also compare external synthetic miRNA spikes to endogenous miRNAs for use in sample normalization to control for experimental variability during miRNA RT-qPCR. Overall, the results demonstrate that: i) although the two RT-qPCR platforms showed similar efficiencies, there was a difference in variability which could impact miRNA measurements; ii) the performance of synthetic Arabidopsis miRNAs spiked into human samples as controls for experimental variability was comparable to that of the endogenous human miRNAs they examined; and iii) short RNA enrichment of samples can result in significant reductions in miRNA signal, indicating that miRNA quantitation using such pre-enriched samples can be highly problematic. The findings of Redshaw et al. should enable miRNA researchers to more properly design and interpret their miRNA RT-qPCR reactions to obtain the most accurate and reproducible measurements.See “A comparison of miRNA isolation and RT-qPCR technologies and their effects on quantification accuracy and repeatability”FiguresReferencesRelatedDetailsCited ByUse of MicroRNAs to Screen for Colon Cancer1 January 2017 | Insights in Biology and Medicine, Vol. 1, No. 1 Vol. 54, No. 3 Follow us on social media for the latest updates Metrics Downloaded 170 times History Published online 3 April 2018 Published in print March 2013 Information© 2013 Author(s)PDF download

Identification of Putative Ortholog Gene Blocks Involved in Gestant and Lactating Mammary Gland Development: A Rodent Cross-Species Microarray Transcriptomics Approach

The mammary gland (MG) undergoes functional and metabolic changes during the transition from pregnancy to lactation, possibly by regulation of conserved genes. The objective was to elucidate orthologous genes, chromosome clusters and putative conserved transcriptional modules during MG development. We analyzed expression of 22,000 transcripts using murine microarrays and RNA samples of MG from virgin, pregnant, and lactating rats by cross-species hybridization. We identified 521 transcripts differentially expressed; upregulated in early (78%) and midpregnancy (89%) and early lactation (64%), but downregulated in mid-lactation (61%). Putative orthologous genes were identified. We mapped the altered genes to orthologous chromosomal locations in human and mouse. Eighteen sets of conserved genes associated with key cellular functions were revealed and conserved transcription factor binding site search entailed possible coregulation among all eight block sets of genes. This study demonstrates that the use of heterologous array hybridization for screening of orthologous gene expression from rat revealed sets of conserved genes arranged in chromosomal order implicated in signaling pathways and functional ontology. Results demonstrate the utilization power of comparative genomics and prove the feasibility of using rodent microarrays to identification of putative coexpressed orthologous genes involved in the control of human mammary gland development.

SiTaR: a novel tool for transcription factor binding site prediction

Prediction of transcription factor binding sites (TFBSs) is crucial for promoter modeling and network inference. Quality of the predictions is spoiled by numerous false positives, which persist as the main problem for all presently available TFBS search methods. We suggest a novel approach, which is alternative to widely used position weight matrices (PWMs) and Hidden Markov Models. Each motif of the input set is used as a search template to scan a query sequence. Found motifs are assigned scores depending on the non-randomness of the motif's occurrence, the number of matching searching motifs and the number of mismatches. The non-randomness is estimated by comparison of observed numbers of matching motifs with those predicted to occur by chance. The latter can be calculated given the base compositions of the motif and the query sequence. The method does not require preliminary alignment of the input motifs, hence avoiding uncertainties introduced by the alignment procedure. In comparison with PWM-based tools, our method demonstrates higher precision by the same sensitivity and specificity. It also tends to outperform methods combining pattern and PWM search. Most important, it allows reducing the number of false positive predictions significantly. The method is implemented in a tool called SiTaR (Site Tracking and Recognition) and is available at http://sbi.hki-jena.de/sitar/index.php. ekaterina.shelest@hki-jena.de Supplementary data are available at Bioinformatics online.

C-REDUCE: Incorporating sequence conservation to detect motifs that correlate with expression

BackgroundComputational methods for characterizing novel transcription factor binding sites search for sequence patterns or "motifs" that appear repeatedly in genomic regions of interest. Correlation-based motif finding strategies are used to identify motifs that correlate with expression data and do not rely on promoter sequences from a pre-determined set of genes.ResultsIn this work, we describe a method for predicting motifs that combines the correlation-based strategy with phylogenetic footprinting, where motifs are identified by evaluating orthologous sequence regions from multiple species. Our method, c-REDUCE, can account for variability at a motif position inferred from evolutionary information. c-REDUCE has been tested on ChIP-chip data for yeast transcription factors and on gene expression data in Drosophila.ConclusionOur results indicate that utilizing sequence conservation information in addition to correlation-based methods improves the identification of known motifs.

In silico approach to study and functionally analyze interferon regulated genes

Finding genes which have biologically meaningful ISRE (interferon-stimulated response element) is important for better understanding of the Jak-STAT activated cellular IFN response. We used transcription factor binding site (TFBS) search with gene orthology filtering to find putative ISREs in the promoters of protein-coding genes of Rattus norvegicus, and used Gene Ontology (GO) analysis to check the validity of ISRE search results in terms of biological meaning. A total of 23286 promoters of rat genes were analyzed. ISRE search with 80 % threshold produced 5214 sites in 4571 promoters. 850 ISREs in 768 promoters passed orthology filtering. Distribution of ISREs along the promoter in 768-gene set reveals 3 regions of ISRE localization: 0 to –250, –250 to –550, and above –550 relative to TSS (transcription start site). It is not yet known whether ISRE localization has any functional implications. Using BayGO, a total of 84 GO terms were found to be enriched at P < 0.05 in the 768-gene set. Among these categories some are directly related to known IFN actions (positive regulation of B cell differentiation, humoral immune response, response to virus, cell differentiation etc.). 768 gene set was compared to the 4571 gene set using GO Tree Machine. Such categories as cell differentiation, cell cycle, regulation of cell cycle, viral life cycle and some others were found to be enriched, and belong to the well-known domains of interferon actions. Their relative enrichment is an indirect indication that the applied orthology filtering does increase the quality of results. Gene orthology-based filtering of the initial TFBS search results was shown to produce viable and expected results. Genes identified in this research as containing ISRE in promoters will be used to seed the construction of the IFN-a-induced gene regulatory network.

DPattern: transcription factor binding site (TFBS) discovery in human genome using a discriminative pattern analysis

Transcription factor binding sites (TFBSs) are typically short in length, thus search with a profile model from known TFBSs produces many false positives. When combined with additional information, gene expression data in this article, sensitivity and specificity of TFBS search can be improved significantly. By modifying our previous REFINEMENT approach, we developed dPattern that searches for occurrences of TFBSs in the promotor regions of up/down regulated or random genes.

PREDICTION OF TRANSCRIPTION FACTOR BINDING SITES USING GENETICAL GENOMICS METHODS

In this paper, we wanted to test whether it is possible to use genetical genomics information such as expression quantitative trait loci (eQTL) mapping results as input to a transcription factor binding site (TFBS) prediction algorithm. Furthermore, this new approach was compared to the more traditional cluster based TFBS prediction. The results of eQTL mapping are used as input to one of the top ranking TFBS prediction algorithms. Genes with observed expression profiles showing the same eQTL region are collected into eQTL groups. The promoter sequences of all the genes within the same eQTL group are used as input in the transcription factor binding site search. This approach is tested with a real data set of a recombinant inbred line population of Arabidopsis thaliana. The predicted motifs are compared to results obtained from the conventional approach of first clustering the gene expression values and then using the promoter sequences of the genes within the same cluster as input for the transcription factor binding site prediction. Our eQTL based approach produced different motifs compared to the cluster based method. Furthermore the score of the eQTL based motifs was higher than the score of the cluster based motifs. In a comparison to already predicted motifs from the AtcisDB database, the eQTL based and the cluster based method produced about the same number of hits with binding sites from AtcisDB. In conclusion, the results of this study clearly demonstrate the usefulness of eQTL to predict transcription factor binding sites.

Control of myelination in Schwann cells: a Krox20 cis‐regulatory element integrates Oct6, Brn2 and Sox10 activities

Myelination in Schwann cells is governed by several transcription factors, including the POU proteins Oct6 and Brn2, the high mobility group protein Sox10 and the zinc-finger protein Krox20. How the function of these factors is integrated in the control of myelination has not been established. Previously, we identified an enhancer element controlling Krox20 expression throughout myelination in Schwann cells. In this paper, cell culture experiments were combined with transgenesis to identify transcription factors acting directly upstream of Krox20. The results show that during the promyelin-myelin transition, Krox20 expression is directly activated by Oct6 and Brn2 acting on this enhancer. In addition, the enhancer-dependent synergism between these POU proteins and Sox10 suggests that Krox20 expression requires this combination of factors. These results resolve previous controversy concerning the mechanism of action of Oct6 and Brn2 during myelination and provide an explanation for myelin deficiencies in Waardenberg-Hirschsprung disease patients whereby Sox10 mutations could lead to a loss of Krox20 expression.

Virtual Footprint and PRODORIC: an integrative framework for regulon prediction in prokaryotes

A new online framework for the accurate and integrative prediction of transcription factor binding sites (TFBSs) in prokaryotes was developed. The system consists of three interconnected modules: (1) The PRODORIC database as a comprehensive data source and extensive collection of TFBSs with corresponding position weight matrices. (2) The pattern matching tool Virtual Footprint for the prediction of genome based regulons and for the analysis of individual promoter regions. (3) The interactive genome browser GBPro for the visualization of TFBS search results in their genomic context and links to gene and regulator-specific information in PRODORIC. The aim of this service is to provide researchers a free and easy to use collection of interconnected tools in the field of molecular microbiology, infection and systems biology. http://www.prodoric.de/vfp.

P-Match: transcription factor binding site search by combining patterns and weight matrices

P-Match is a new tool for identifying transcription factor (TF) binding sites in DNA sequences. It combines pattern matching and weight matrix approaches thus providing higher accuracy of recognition than each of the methods alone. P-Match is closely interconnected with the TRANSFAC® database. In particular, P-Match uses the matrix library as well as sets of aligned known TF-binding sites collected in TRANSFAC® and therefore provides the possibility to search for a large variety of different TF binding sites. Using results of extensive tests of recognition accuracy, we selected three sets of optimized cut-off values that minimize either false negatives or false positives, or the sum of both errors. Comparison with the weight matrix approaches such as Match™ tool shows that P-Match generally provides superior recognition accuracy in the area of low false negative errors (high sensitivity). As familiar to the user of Match™, P-Match also allows to save user-specific profiles that include selected subsets of matrices with corresponding TF-binding sites or user-defined cut-off values. Furthermore, a number of tissue-specific profiles are provided that were compiled by the TRANSFAC® team. A public version of the P-Match tool is available at .

HBx and SP1 upregulate DKK1 expression.

Numerous evidences suggested that the hepatitis B virus (HBV) was recognized as an important factor in the development of hepatocellular carcinoma (HCC). Dickkopf-1 (DKK1) recently was reported to be involved in the progress of HCC. HBV may regulate DKK1 expression in hematoma carcinogenesis. Here, we demonstrated that HBV could regulate DKK1 promoter activity which resulted in upregulation of its mRNA and protein expression in several HBV existing cell lines, and HBx played a prominent role in this process. Transcription factor binding site search result showed that there is a SP1 site in DKK1 promoter region. Luciferase assay showed that overexpression of SP1 could increase DKK1 promoter activity in a dose dependent manner. Accordingly, siRNA inhibition of SP1 expression reduced DKK1 promoter activity and decreased the expression of DKK1 protein.