HTPSELEX--a database of high-throughput SELEX libraries for transcription factor binding sites.

HTPSELEX is a public database providing access to primary and derived data from high-throughput SELEX experiments aimed at characterizing the binding specificity of transcription factors. The resource is primarily intended to serve computational biologists interested in building models of transcription factor binding sites from large sets of binding sequences. The guiding principle is to make available all information that is relevant for this purpose. For each experiment, we try to provide accurate information about the protein material used, details of the wet lab protocol, an archive of sequencing trace files, assembled clone sequences (concatemers) and complete sets of in vitro selected protein-binding tags. In addition, we offer in-house derived binding sites models. HTPSELEX also offers reasonably large SELEX libraries obtained with conventional low-throughput protocols. The FTP site contains the trace archives and database flatfiles. The web server offers user-friendly interfaces for viewing individual entries and quality-controlled download of SELEX sequence libraries according to a user-defined sequencing quality threshold. HTPSELEX is available from ftp://ftp.isrec.isb-sib.ch/pub/databases/htpselex/ and http://www.isrec.isb-sib.ch/htpselex.

High-throughput SELEX SAGE method for quantitative modeling of transcription-factor binding sites.

The ability to determine the location and relative strength of all transcription-factor binding sites in a genome is important both for a comprehensive understanding of gene regulation and for effective promoter engineering in biotechnological applications. Here we present a bioinformatically driven experimental method to accurately define the DNA-binding sequence specificity of transcription factors. A generalized profile was used as a predictive quantitative model for binding sites, and its parameters were estimated from in vitro-selected ligands using standard hidden Markov model training algorithms. Computer simulations showed that several thousand low- to medium-affinity sequences are required to generate a profile of desired accuracy. To produce data on this scale, we applied high-throughput genomics methods to the biochemical problem addressed here. A method combining systematic evolution of ligands by exponential enrichment (SELEX) and serial analysis of gene expression (SAGE) protocols was coupled to an automated quality-controlled sequence extraction procedure based on Phred quality scores. This allowed the sequencing of a database of more than 10,000 potential DNA ligands for the CTF/NFI transcription factor. The resulting binding-site model defines the sequence specificity of this protein with a high degree of accuracy not achieved earlier and thereby makes it possible to identify previously unknown regulatory sequences in genomic DNA. A covariance analysis of the selected sites revealed non-independent base preferences at different nucleotide positions, providing insight into the binding mechanism.

Quantitative modelling of transcription factor binding sites

Abstract One of the most important issues in molecular biology is to understand regulatory mechanisms that control gene expression. Gene expression is often regulated by proteins, called transcription factors which bind to short (5 to 20 base pairs),degenerate segments of DNA. Experimental efforts towards understanding the sequence specificity of transcription factors is laborious and expensive, but can be substantially accelerated with the use of computational predictions. This thesis describes the use of algorithms and resources for transcriptionfactor binding site analysis in addressing quantitative modelling, where probabilitic models are built to represent binding properties of a transcription factor and can be used to find new functional binding sites in genomes. Initially, an open-access database(HTPSELEX) was created, holding high quality binding sequences for two eukaryotic families of transcription factors namely CTF/NF1 and LEFT/TCF. The binding sequences were elucidated using a recently described experimental procedure called HTP-SELEX, that allows generation of large number (> 1000) of binding sites using mass sequencing technology. For each HTP-SELEX experiments we also provide accurate primary experimental information about the protein material used, details of the wet lab protocol, an archive of sequencing trace files, and assembled clone sequences of binding sequences. The database also offers reasonably large SELEX libraries obtained with conventional low-throughput protocols.The database is available at http://wwwisrec.isb-sib.ch/htpselex/ and and ftp://ftp.isrec.isb-sib.ch/pub/databases/htpselex. The Expectation-Maximisation(EM) algorithm is one the frequently used methods to estimate probabilistic models to represent the sequence specificity of transcription factors. We present computer simulations in order to estimate the precision of EM estimated models as a function of data set parameters(like length of initial sequences, number of initial sequences, percentage of nonbinding sequences). We observed a remarkable robustness of the EM algorithm with regard to length of training sequences and the degree of contamination. The HTPSELEX database and the benchmarked results of the EM algorithm formed part of the foundation for the subsequent project, where a statistical framework called hidden Markov model has been developed to represent sequence specificity of the transcription factors CTF/NF1 and LEF1/TCF using the HTP-SELEX experiment data. The hidden Markov model framework is capable of both predicting and classifying CTF/NF1 and LEF1/TCF binding sites. A covariance analysis of the binding sites revealed non-independent base preferences at different nucleotide positions, providing insight into the binding mechanism. We next tested the LEF1/TCF model by computing binding scores for a set of LEF1/TCF binding sequences for which relative affinities were determined experimentally using non-linear regression. The predicted and experimentally determined binding affinities were in good correlation.

Genomic regions harboring insecticide resistance-associated Cyp genes are enriched by transposable element fragments carrying putative transcription factor binding sites in two sibling Drosophila species

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Phylogenetic footprinting of transcription factor binding sites in proteobacterial genomes

Toward the goal of identifying complete sets of transcription factor (TF)-binding sites in the genomes of several gamma proteobacteria, and hence describing their transcription regulatory networks, we present a phylogenetic footprinting method for identifying these sites. Probable transcription regulatory sites upstream of Escherichia coli genes were identified by cross-species comparison using an extended Gibbs sampling algorithm. Close examination of a study set of 184 genes with documented transcription regulatory sites revealed that when orthologous data were available from at least two other gamma proteobacterial species, 81% of our predictions corresponded with the documented sites, and 67% corresponded when data from only one other species were available. That the remaining predictions included bona fide TF-binding sites was proven by affinity purification of a putative transcription factor (YijC) bound to such a site upstream of the fabA gene. Predicted regulatory sites for 2097 E.coli genes are available at http://www.wadsworth.org/resnres/bioinfo/.

Multiplex selection technique (MuST): an approach to clone transcription factor binding sites.

We have used a multiplex selection approach to construct a library of DNA-protein interaction sites recognized by many of the DNA-binding proteins present in a cell type. An estimated minimum of two-thirds of the binding sites present in a library prepared from activated Jurkat T cells represent authentic transcription factor binding sites. We used the library for isolation of "optimal" binding site probes that facilitated cloning of a factor and to identify binding activities induced within 2 hr of activation of Jurkat cells. Since a large fraction of the oligonucleotides obtained appear to represent "optimal" binding sites for sequence-specific DNA-binding proteins, it is feasible to construct a catalog of consensus binding sites for DNA-binding proteins in a given cell type. Qualitative and quantitative comparisons of the catalogs of binding site sequences from various cell types could provide valuable insights into the process of differentiation acting at the level of transcriptional control.

Assessing computational tools for the discovery of transcription factor binding sites

The prediction of regulatory elements is a problem where computational methods offer great hope. Over the past few years, numerous tools have become available for this task. The purpose of the current assessment is twofold: to provide some guidance to users regarding the accuracy of currently available tools in various settings, and to provide a benchmark of data sets for assessing future tools.

Experimental analysis and computer prediction of CTF/NFI transcription factor DNA binding sites.

Accurate prediction of transcription factor binding sites is needed to unravel the function and regulation of genes discovered in genome sequencing projects. To evaluate current computer prediction tools, we have begun a systematic study of the sequence-specific DNA-binding of a transcription factor belonging to the CTF/NFI family. Using a systematic collection of rationally designed oligonucleotides combined with an in vitro DNA binding assay, we found that the sequence specificity of this protein cannot be represented by a simple consensus sequence or weight matrix. For instance, CTF/NFI uses a flexible DNA binding mode that allows for variations of the binding site length. From the experimental data, we derived a novel prediction method using a generalised profile as a binding site predictor. Experimental evaluation of the generalised profile indicated that it accurately predicts the binding affinity of the transcription factor to natural or synthetic DNA sequences. Furthermore, the in vitro measured binding affinities of a subset of oligonucleotides were found to correlate with their transcriptional activities in transfected cells. The combined computational-experimental approach exemplified in this work thus resulted in an accurate prediction method for CTF/NFI binding sites potentially functioning as regulatory regions in vivo.

ABS: a database of Annotated regulatory Binding Sites from orthologous promoters

Information about the genomic coordinates and the sequence of experimentally identified transcription factor binding sites is found scattered under a variety of diverse formats. The availability of standard collections of such high-quality data is important to design, evaluate and improve novel computational approaches to identify binding motifs on promoter sequences from related genes. ABS (http://genome.imim.es/datasets/abs2005/index.html) is a public database of known binding sites identified in promoters of orthologous vertebrate genes that have been manually curated from bibliography. We have annotated 650 experimental binding sites from 68 transcription factors and 100 orthologous target genes in human, mouse, rat or chicken genome sequences. Computational predictions and promoter alignment information are also provided for each entry. A simple and easy-to-use web interface facilitates data retrieval allowing different views of the information. In addition, the release 1.0 of ABS includes a customizable generator of artificial datasets based on the known sites contained in the collection and an evaluation tool to aid during the training and the assessment of motif-finding programs.

Regulatable and modulable background expression control in prokaryotic synthetic circuits by auxiliary repressor binding sites.

Expression control in synthetic genetic circuitry, for example, for construction of sensitive biosensors, is hampered by the lack of DNA parts that maintain ultralow background yet achieve high output upon signal integration by the cells. Here, we demonstrate how placement of auxiliary transcription factor binding sites within a regulatable promoter context can yield an important gain in signal-to-noise output ratios from prokaryotic biosensor circuits. As a proof of principle, we use the arsenite-responsive ArsR repressor protein from Escherichia coli and its cognate operator. Additional ArsR operators placed downstream of its target promoter can act as a transcription roadblock in a distance-dependent manner and reduce background expression of downstream-placed reporter genes. We show that the transcription roadblock functions both in cognate and heterologous promoter contexts. Secondary ArsR operators placed upstream of their promoter can also improve signal-to-noise output while maintaining effector dependency. Importantly, background control can be released through the addition of micromolar concentrations of arsenite. The ArsR-operator system thus provides a flexible system for additional gene expression control, which, given the extreme sensitivity to micrograms per liter effector concentrations, could be applicable in more general contexts.

Identification of novel functional TBP-binding sites and general factor repertoires.

Our current knowledge of the general factor requirement in transcription by the three mammalian RNA polymerases is based on a small number of model promoters. Here, we present a comprehensive chromatin immunoprecipitation (ChIP)-on-chip analysis for 28 transcription factors on a large set of known and novel TATA-binding protein (TBP)-binding sites experimentally identified via ChIP cloning. A large fraction of identified TBP-binding sites is located in introns or lacks a gene/mRNA annotation and is found to direct transcription. Integrated analysis of the ChIP-on-chip data and functional studies revealed that TAF12 hitherto regarded as RNA polymerase II (RNAP II)-specific was found to be also involved in RNAP I transcription. Distinct profiles for general transcription factors and TAF-containing complexes were uncovered for RNAP II promoters located in CpG and non-CpG islands suggesting distinct transcription initiation pathways. Our study broadens the spectrum of general transcription factor function and uncovers a plethora of novel, functional TBP-binding sites in the human genome.

Interactions of decay-accelerating factor (DAF) with haemagglutinating human enteroviruses: utilizing variation in primate DAF to map virus binding sites

A cellular receptor for the haemagglutinating enteroviruses (HEV), and the protein that mediates haemagglutination, is the membrane complement regulatory protein decay accelerating factor (DAF; CD55). Although primate DAF is highly conserved, significant differences exist to enable cell lines derived from primates to be utilized for the characterization of the DAF binding phenotype of human enteroviruses. Thus, several distinct DAF-binding phenotypes of a selection of HEVs (viz. coxsackievirus A21 and echoviruses 6, 7, 11-13, 29) were identified from binding and infection assays using a panel of primate cells derived from human, orang-utan, African Green monkey and baboon tissues. These studies complement our recent determination of the crystal structure of SCR(34) of human DAF [Williams, P., Chaudhry, Y., Goodfellow, I. G., Billington, J., Powell, R., Spiller, O. B., Evans, D. J. & Lea, S. (2003). J Biol Chem 278, 10691-10696] and have enabled us to better map the regions of DAF with which enteroviruses interact and, in certain cases, predict specific virus-receptor contacts.

E2F4-RB and E2F4-p107 complexes suppress gene expression by transforming growth factor β through E2F binding sites

Transforming growth factor β (TGF-β) causes growth arrest in most cell types. TGF-β induces hypophosphorylation of retinoblastoma susceptibility gene 1 product (RB), which sequesters E2F factors needed for progression into S phase of the cell cycle, thereby leading to cell cycle arrest at G1. It is possible, however, that the E2F-RB complex induced by TGF-β may bind to E2F sites and suppress expression of specific genes whose promoters contain E2F binding sites. We show here that TGF-β treatment of HaCaT cells induced the formation of E2F4-RB and E2F4-p107 complexes, which are capable of binding to E2F sites. Disruption of their binding to DNA with mutation in the E2F sites did not change the expression from promoters of E2F1, B-myb, or HsORC1 genes in cycling HaCaT cells. However, the same mutation stimulated 5- to 6-fold higher expression from all three promoters in cells treated with TGF-β. These results suggest that E2F binding sites play an essential role in the transcription repression of these genes under TGF-β treatment. Consistent with their repression of TGF-β-induced gene expression, introduction of E2F sites into the promoter of cyclin-dependent kinase inhibitor p15INK4B gene effectively inhibited its induction by TGF-β. Experiments utilizing Gal4-RB and Gal4-p107 chimeric constructs demonstrated that either RB or p107 could directly repress TGF-β induction of p15INK4B gene when tethered to p15INK4B promoter through Gal4 DNA binding sites. Therefore, E2F functions to bring RB and p107 to E2F sites and represses gene expression by TGF-β. These results define a specific function for E2F4-RB and E2F4-p107 complexes in gene repression under TGF-β treatment, which may constitute an integral part of the TGF-β-induced growth arrest program.

ADP-Ribosylation Factor 1 Transiently Activates High-Affinity Adaptor Protein Complex AP-1 Binding Sites On Golgi Membranes

Association of the Golgi-specific adaptor protein complex 1 (AP-1) with the membrane is a prerequisite for clathrin coat assembly on the trans-Golgi network (TGN). The AP-1 adaptor is efficiently recruited from cytosol onto the TGN by myristoylated ADP-ribosylation factor 1 (ARF1) in the presence of the poorly hydrolyzable GTP analog guanosine 5′-O-(3-thiotriphosphate) (GTPγS). Substituting GTP for GTPγS, however, results in only poor AP-1 binding. Here we show that both AP-1 and clathrin can be recruited efficiently onto the TGN in the presence of GTP when cytosol is supplemented with ARF1. Optimal recruitment occurs at 4 μM ARF1 and with 1 mM GTP. The AP-1 recruited by ARF1·GTP is released from the Golgi membrane by treatment with 1 M Tris-HCl (pH 7) or upon reincubation at 37°C, whereas AP-1 recruited with GTPγS or by a constitutively active point mutant, ARF1(Q71L), remains membrane bound after either treatment. An incubation performed with added ARF1, GTP, and AlFn, used to block ARF GTPase-activating protein activity, results in membrane-associated AP-1, which is largely insensitive to Tris extraction. Thus, ARF1·GTP hydrolysis results in lower-affinity binding of AP-1 to the TGN. Using two-stage assays in which ARF1·GTP first primes the Golgi membrane at 37°C, followed by AP-1 binding on ice, we find that the high-affinity nucleating sites generated in the priming stage are rapidly lost. In addition, the AP-1 bound to primed Golgi membranes during a second-stage incubation on ice is fully sensitive to Tris extraction, indicating that the priming stage has passed the ARF1·GTP hydrolysis point. Thus, hydrolysis of ARF1·GTP at the priming sites can occur even before AP-1 binding. Our finding that purified clathrin-coated vesicles contain little ARF1 supports the concept that ARF1 functions in the coat assembly process rather than during the vesicle-uncoating step. We conclude that ARF1 is a limiting factor in the GTP-stimulated recruitment of AP-1 in vitro and that it appears to function in a stoichiometric manner to generate high-affinity AP-1 binding sites that have a relatively short half-life.