Rapid analysis of the DNA-binding specificities of transcription factors with DNA microarrays

The jigsaw puzzle of sequence phenotype inference: Piecing together Shannon entropy, importance sampling, and Empirical Bayes

A nucleotide sequence 35 base pairs long can take 1,180,591,620,717,411,303,424 possible values. An example of systems biology datasets, protein binding microarrays, contain activity data from about 40,000 such sequences. The discrepancy between the number of possible configurations and the available activities is enormous. Thus, albeit that systems biology datasets are large in absolute terms, they oftentimes require methods developed for rare events due to the combinatorial increase in the number of possible configurations of biological systems. A plethora of techniques for handling large datasets, such as Empirical Bayes, or rare events, such as importance sampling, have been developed in the literature, but these cannot always be simultaneously utilized. Here we introduce a principled approach to Empirical Bayes based on importance sampling, information theory, and theoretical physics in the general context of sequence phenotype model induction. We present the analytical calculations that underlie our approach. We demonstrate the computational efficiency of the approach on concrete examples, and demonstrate its efficacy by applying the theory to publicly available protein binding microarray transcription factor datasets and to data on synthetic cAMP-regulated enhancer sequences. As further demonstrations, we find transcription factor binding motifs, predict the activity of new sequences and extract the locations of transcription factor binding sites. In summary, we present a novel method that is efficient (requiring minimal computational time and reasonable amounts of memory), has high predictive power that is comparable with that of models with hundreds of parameters, and has a limited number of optimized parameters, proportional to the sequence length.

Reliable scaling of position weight matrices for binding strength comparisons between transcription factors

Background

Scoring DNA sequences against Position Weight Matrices (PWMs) is a widely adopted method to identify putative transcription factor binding sites. While common bioinformatics tools produce scores that can reflect the binding strength between a specific transcription factor and the DNA, these scores are not directly comparable between different transcription factors. Other methods, including p-value associated approaches (Touzet H, Varré J-S. Efficient and accurate p-value computation for position weight matrices. Algorithms Mol Biol. 2007;2(1510.1186):1748–7188), provide more rigorous ways to identify potential binding sites, but their results are difficult to interpret in terms of binding energy, which is essential for the modeling of transcription factor binding dynamics and enhancer activities.

Results

Here, we provide two different ways to find the scaling parameter λ that allows us to infer binding energy from a PWM score. The first approach uses a PWM and background genomic sequence as input to estimate λ for a specific transcription factor, which we applied to show that λ distributions for different transcription factor families correspond with their DNA binding properties. Our second method can reliably convert λ between different PWMs of the same transcription factor, which allows us to directly compare PWMs that were generated by different approaches.

Conclusion

These two approaches provide computationally efficient ways to scale PWM scores and estimate the strength of transcription factor binding sites in quantitative studies of binding dynamics. Their results are consistent with each other and previous reports in most of cases.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0666-1) contains supplementary material, which is available to authorized users.

Nonconsensus Protein Binding to Repetitive DNA Sequence Elements Significantly Affects Eukaryotic Genomes

Recent genome-wide experiments in different eukaryotic genomes provide an unprecedented view of transcription factor (TF) binding locations and of nucleosome occupancy. These experiments revealed that a large fraction of TF binding events occur in regions where only a small number of specific TF binding sites (TFBSs) have been detected. Furthermore, in vitro protein-DNA binding measurements performed for hundreds of TFs indicate that TFs are bound with wide range of affinities to different DNA sequences that lack known consensus motifs. These observations have thus challenged the classical picture of specific protein-DNA binding and strongly suggest the existence of additional recognition mechanisms that affect protein-DNA binding preferences. We have previously demonstrated that repetitive DNA sequence elements characterized by certain symmetries statistically affect protein-DNA binding preferences. We call this binding mechanism nonconsensus protein-DNA binding in order to emphasize the point that specific consensus TFBSs do not contribute to this effect. In this paper, using the simple statistical mechanics model developed previously, we calculate the nonconsensus protein-DNA binding free energy for the entire C. elegans and D. melanogaster genomes. Using the available chromatin immunoprecipitation followed by sequencing (ChIP-seq) results on TF-DNA binding preferences for ~100 TFs, we show that DNA sequences characterized by low predicted free energy of nonconsensus binding have statistically higher experimental TF occupancy and lower nucleosome occupancy than sequences characterized by high free energy of nonconsensus binding. This is in agreement with our previous analysis performed for the yeast genome. We suggest therefore that nonconsensus protein-DNA binding assists the formation of nucleosome-free regions, as TFs outcompete nucleosomes at genomic locations with enhanced nonconsensus binding. In addition, here we perform a new, large-scale analysis using in vitro TF-DNA preferences obtained from the universal protein binding microarrays (PBM) for ~90 eukaryotic TFs belonging to 22 different DNA-binding domain types. As a result of this new analysis, we conclude that nonconsensus protein-DNA binding is a widespread phenomenon that significantly affects protein-DNA binding preferences and need not require the presence of consensus (specific) TFBSs in order to achieve genome-wide TF-DNA binding specificity.

Interactions between proteins and DNA trigger many important biological processes. Therefore, to fully understand how the information encoded on the DNA transcribes into RNA, which in turn translates into proteins in the cell, we need to unravel the molecular design principles of protein-DNA interactions. It is known that many interactions occur when a protein is attracted to a specific short segment on the DNA called a specific protein-DNA binding motif. Strikingly, recent experiments revealed that many regulatory proteins reproducibly bind to different regions on the DNA lacking such specific motifs. This suggests that fundamental molecular mechanisms responsible for protein-DNA recognition specificity are not fully understood. Here, using high-throughput protein-DNA binding data obtained by two entirely different methods for ~100 TFs in each case, we show that DNA regions possessing certain repetitive sequence elements exert the statistical attractive potential on DNA-binding proteins, and as a result, such DNA regions are enriched in bound proteins. This is in agreement with our previous analysis performed for the yeast genome. We use the term nonconsensus protein-DNA binding in order to describe protein-DNA interactions that occur in the absence of specific protein-DNA binding motifs. Here we demonstrate that the identified nonconsensus effect is highly significant for a variety of organismal genomes and it affects protein-DNA binding preferences and nucleosome occupancy at the genome-wide level.

Towards understanding the molecular basis of cockroach tergal gland morphogenesis. A transcriptomic approach

The tergal gland is a structure exclusive of adult male cockroaches that produces substances attractive to the female and facilitates mating. It is formed de novo in tergites 7 and 8 during the transition from the last nymphal instar to the adult. Thus, the tergal gland can afford a suitable case study to investigate the molecular basis of a morphogenetic process occurring during metamorphosis. Using Blattella germanica as model, we constructed transcriptomes from male tergites 7-8 in non-metamorphosing specimens, and from the same tergites in metamorphosing specimens. We performed a de novo assembly all available transcriptomes to construct a reference transcriptome and we identified transcripts by homology. Finally we mapped all reads into the reference transcriptome in order to perform analysis of differentially expressed genes and a GO-enrichment test. A total of 5622 contigs appeared to be overrepresented in the transcriptome of metamorphosing specimens with respect to those specimens that did not metamorphose. Among these genes, there were six GO-terms with a p-value lower than 0.05 and among them GO: 0003676 ("nucleic acid binding") was especially interesting since it included transcription factors (TFs). Examination of TF-Pfam-motifs revealed that the transcriptome of metamorphosing specimens contains the highest diversity of these motifs, with 29 different types (seven of them exclusively expressed in this stage) compared with that of non-metamorphosing specimens, which contained 24 motif types. Transcriptome comparisons suggest that TFs are important drivers of the process of tergal gland formation during metamorphosis.

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

Knowing the sequence specificities of DNA- and RNA-binding proteins is essential for developing models of the regulatory processes in biological systems and for identifying causal disease variants. Here we show that sequence specificities can be ascertained from experimental data with 'deep learning' techniques, which offer a scalable, flexible and unified computational approach for pattern discovery. Using a diverse array of experimental data and evaluation metrics, we find that deep learning outperforms other state-of-the-art methods, even when training on in vitro data and testing on in vivo data. We call this approach DeepBind and have built a stand-alone software tool that is fully automatic and handles millions of sequences per experiment. Specificities determined by DeepBind are readily visualized as a weighted ensemble of position weight matrices or as a 'mutation map' that indicates how variations affect binding within a specific sequence.

GABPα Binding to Overlapping ETS and CRE DNA Motifs Is Enhanced by CREB1: Custom DNA Microarrays

To achieve proper spatiotemporal control of gene expression, transcription factors cooperatively assemble onto specific DNA sequences. The ETS domain protein monomer of GABPα and the B-ZIP domain protein dimer of CREB1 cooperatively bind DNA only when the ETS (^C/_GCGGAAGT) and CRE (GTGACGTCAC) motifs overlap precisely, producing the ETS↔CRE motif (^C/_GCGGAAGTGACGTCAC). We designed a Protein Binding Microarray (PBM) with 60-bp DNAs containing four identical sectors, each with 177,440 features that explore the cooperative interactions between GABPα and CREB1 upon binding the ETS↔CRE motif. The DNA sequences include all 15-mers of the form ^C/_GCGGA—–CG—, the ETS↔CRE motif, and all single nucleotide polymorphisms (SNPs), and occurrences in the human and mouse genomes. CREB1 enhanced GABPα binding to the canonical ETS↔CRE motif CCGGAAGT two-fold, and up to 23-fold for several SNPs at the beginning and end of the ETS motif, which is suggestive of two separate and distinct allosteric mechanisms of cooperative binding. We show that the ETS-CRE array data can be used to identify regions likely cooperatively bound by GABPα and CREB1 in vivo, and demonstrate their ability to identify human genetic variants that might inhibit cooperative binding.

Context influences on TALE-DNA binding revealed by quantitative profiling

Transcription activator-like effector (TALE) proteins recognize DNA using a seemingly simple DNA-binding code, which makes them attractive for use in genome engineering technologies that require precise targeting. Although this code is used successfully to design TALEs to target specific sequences, off-target binding has been observed and is difficult to predict. Here we explore TALE-DNA interactions comprehensively by quantitatively assaying the DNA-binding specificities of 21 representative TALEs to ∼5,000-20,000 unique DNA sequences per protein using custom-designed protein-binding microarrays (PBMs). We find that protein context features exert significant influences on binding. Thus, the canonical recognition code does not fully capture the complexity of TALE-DNA binding. We used the PBM data to develop a computational model, Specificity Inference For TAL-Effector Design (SIFTED), to predict the DNA-binding specificity of any TALE. We provide SIFTED as a publicly available web tool that predicts potential genomic off-target sites for improved TALE design.

"Hit-and-run": Transcription factors get caught in the act

A key challenge for understanding transcriptional regulation is being able to measure transcription factor (TF)-DNA binding events with sufficient spatial and temporal resolution; that is, when and where TFs occupy their cognate sites. A recent study by Para et al. has highlighted the dynamics underlying the activation of gene expression by a master regulator TF. This study provides concrete evidence for a long-standing hypothesis in biology, the "hit-and-run" mechanism, which was first proposed decades ago. That is, gene expression is dynamically controlled by a TF that transiently binds and activates a target gene, which might stay in a transcriptionally active state after the initial binding event has ended. Importantly, the experimental procedure introduced, TARGET, provides a useful way for identifying multiple target genes transiently bound by their regulators, which can be used in conjunction with other well-established methods to improve our understanding of transcriptional regulatory dynamics.

A structure-based Multiple-Instance Learning approach to predicting in vitro transcription factor-DNA interaction

Background

Understanding the mechanism of transcriptional regulation remains an inspiring stage of molecular biology. Recently, in vitro protein-binding microarray experiments have greatly improved the understanding of transcription factor-DNA interaction. We present a method - MIL3D - which predicts in vitro transcription factor binding by multiple-instance learning with structural properties of DNA.

Results

Evaluation on in vitro data of twenty mouse transcription factors shows that our method outperforms a method based on simple-instance learning with DNA structural properties, and the widely used k-mer counting method, for nineteen out of twenty of the transcription factors. Our analysis showed that the MIL3D approach can utilize subtle structural similarities when a strong sequence consensus is not available.

Conclusion

Combining multiple-instance learning and structural properties of DNA has promising potential for studying biological regulatory networks.

The wrapping loop and Rap1 C-terminal (RCT) domain of yeast Rap1 modulate access to different DNA binding modes

Budding yeast Rap1 is a specific double-stranded DNA-binding protein involved in repression and activation of gene transcription and in the establishment of the nucleoprotein complex formed at telomeres. The DNA-binding domain (DBD) of Rap1 forms a high affinity complex with DNA where both Myb-like domains bind to the recognition site. However, we recently showed that the DBD can also access an alternative, lower affinity DNA-binding mode where a single Myb-like domain binds. This results in Rap1-DNA complexes with stoichiometry higher than previously anticipated. In this work, we show that the ability of the DBD to form higher stoichiometry complexes on DNA is maintained also in larger Rap1 constructs. This indicates that transition between at least two DNA-binding modes is a general property of the protein and not a specific feature of the DBD in isolation. The transition between binding modes is modulated by the C-terminal wrapping loop within the DBD, consistent with the proposed model in which the transient opening of this region allows a switch between binding modes. Finally, we provide evidence that the Rap1 C terminus interacts with the DNA-binding domain, suggesting a complex network of interactions that couples changes in conformation of the protein to the binding of its DNA recognition sequence.

Chromatin mediation of a transcriptional memory effect in yeast

Previous studies have described a transcriptional "memory effect," whereby transcript levels of many Abf1-regulated genes in the budding yeast Saccharomyces cerevisiae are undiminished even after Abf1 has dissociated from its regulatory sites. Here we provide additional support for this effect and investigate its molecular basis. We show that the effect is observed in a distinct abf1 ts mutant from that used in earlier studies, demonstrating that it is robust, and use chromatin immunoprecipitation to show that Abf1 association is decreased similarly from memory effect and transcriptionally responsive genes at the restrictive temperature. We also demonstrate that the association of TATA-binding protein and Pol II decreases after the loss of Abf1 binding for transcriptionally responsive genes but not for memory effect genes. Examination of genome-wide nucleosome occupancy data reveals that although transcriptionally responsive genes exhibit increased nucleosome occupancy in abf1 ts yeast, the promoter regions of memory effect targets show no change in abf1 ts mutants, maintaining an open chromatin conformation even after Abf1 eviction. This contrasting behavior reflects different inherent propensity for nucleosome formation between the two classes, driven by the presence of A/T-rich sequences upstream of the Abf1 site in memory effect gene promoters. These sequence-based differences show conservation in closely related fungi and also correlate with different gene expression noise, suggesting a physiological basis for greater access to "memory effect" promoter regions. Thus, our results establish a conserved mechanism underlying a transcriptional memory effect whereby sequences surrounding Abf1 binding sequences affect local nucleosome occupancy following loss of Abf1 binding. Furthermore, these findings demonstrate that sequence-based differences in the propensity for nucleosome occupancy can influence the transcriptional response of genes to an altered regulatory signal.

Characterizing the DNA binding site specificity of NF-κB with protein-binding microarrays (PBMs)

NF-κB transcription factors control a wide array of important cellular and organismal processes in eukaryotes. All NF-κB transcription factors bind to DNA target sites as dimers. In vertebrates, there are five NF-κB subunits, p50, p52, RelA (p65), c-Rel, and RelB, that can form almost all combinations of homodimers and heterodimers, which recognize distinct, but overlapping, target sequences. In this chapter, we describe the use of protein-binding microarrays (PBMs), a high-throughput method to measure the binding of proteins to different DNA sequences. PBM datasets allow for sensitive comparisons of NF-κB dimer DNA-binding differences and can aid in the computational and experimental prediction of NF-κB target genes.

Using protein-binding microarrays to study transcription factor specificity: homologs, isoforms and complexes

Protein-DNA binding is central to specificity in gene regulation, and methods for characterizing transcription factor (TF)-DNA binding remain crucial to studies of regulatory specificity. High-throughput (HT) technologies have revolutionized our ability to characterize protein-DNA binding by significantly increasing the number of binding measurements that can be performed. Protein-binding microarrays (PBMs) are a robust and powerful HT platform for studying DNA-binding specificity of TFs. Analysis of PBM-determined DNA-binding profiles has provided new insight into the scope and mechanisms of TF binding diversity. In this review, we focus specifically on the PBM technique and discuss its application to the study of TF specificity, in particular, the binding diversity of TF homologs and multi-protein complexes.

Protein-DNA binding in the absence of specific base-pair recognition

Until now, it has been reasonably assumed that specific base-pair recognition is the only mechanism controlling the specificity of transcription factor (TF)-DNA binding. Contrary to this assumption, here we show that nonspecific DNA sequences possessing certain repeat symmetries, when present outside of specific TF binding sites (TFBSs), statistically control TF-DNA binding preferences. We used high-throughput protein-DNA binding assays to measure the binding levels and free energies of binding for several human TFs to tens of thousands of short DNA sequences with varying repeat symmetries. Based on statistical mechanics modeling, we identify a new protein-DNA binding mechanism induced by DNA sequence symmetry in the absence of specific base-pair recognition, and experimentally demonstrate that this mechanism indeed governs protein-DNA binding preferences.

A force-based, parallel assay for the quantification of protein-DNA interactions

Analysis of transcription factor binding to DNA sequences is of utmost importance to understand the intricate regulatory mechanisms that underlie gene expression. Several techniques exist that quantify DNA-protein affinity, but they are either very time-consuming or suffer from possible misinterpretation due to complicated algorithms or approximations like many high-throughput techniques. We present a more direct method to quantify DNA-protein interaction in a force-based assay. In contrast to single-molecule force spectroscopy, our technique, the Molecular Force Assay (MFA), parallelizes force measurements so that it can test one or multiple proteins against several DNA sequences in a single experiment. The interaction strength is quantified by comparison to the well-defined rupture stability of different DNA duplexes. As a proof-of-principle, we measured the interaction of the zinc finger construct Zif268/NRE against six different DNA constructs. We could show the specificity of our approach and quantify the strength of the protein-DNA interaction.

Regulation of gene expression: probing DNA–protein interactionsin vivoandin vitro

Tremendous efforts have been put together over the last several years to complete the entire sequencing of the human genome. As we enter the proteomic era, when the major aim is understanding which gene encodes which protein, the time has also come to identify their precise function inside the astonishing signaling network required to accomplish all cellular functions. Understanding when, why and how a gene is expressed has now become a necessity toward identifying all the regulatory pathways that mediate cellular processes such as differentiation, migration, replication, DNA repair and apoptosis. Regulation of gene transcription is a process that is primarily under the influence of nuclear-located transcription factors. Consequently, identifying which protein activates or represses a specific gene is a prerequisite for understanding cell fate and function. The current state of, and recent advances in, transcriptional regulation approaches are reviewed here, with special emphasis on new technologies required when probing for DNA-protein interactions. This review explores different strategies aimed at identifying both the regulatory sequences of any given gene and the trans-acting regulatory factors that recognize these elements as their target sites in the nucleus. Ongoing developments in the fields of nanotechnology, RNA silencing and protein modeling toward the investigation of DNA-protein interactions and their relevance in the battle against cancer are discussed.

Multimodal electrochemical sensing of transcription factor–operator complexes

Changes in diffusive movements, surface potential, and interfacial impedance of DNA monolayers are combined to analyze binding of unlabeled transcription factors.

Discrimination between thermodynamic models of cis-regulation using transcription factor occupancy data

Many studies have identified binding preferences for transcription factors (TFs), but few have yielded predictive models of how combinations of transcription factor binding sites generate specific levels of gene expression. Synthetic promoters have emerged as powerful tools for generating quantitative data to parameterize models of combinatorial cis-regulation. We sought to improve the accuracy of such models by quantifying the occupancy of TFs on synthetic promoters in vivo and incorporating these data into statistical thermodynamic models of cis-regulation. Using chromatin immunoprecipitation-seq, we measured the occupancy of Gcn4 and Cbf1 in synthetic promoter libraries composed of binding sites for Gcn4, Cbf1, Met31/Met32 and Nrg1. We measured the occupancy of these two TFs and the expression levels of all promoters in two growth conditions. Models parameterized using only expression data predicted expression but failed to identify several interactions between TFs. In contrast, models parameterized with occupancy and expression data predicted expression data, and also revealed Gcn4 self-cooperativity and a negative interaction between Gcn4 and Nrg1. Occupancy data also allowed us to distinguish between competing regulatory mechanisms for the factor Gcn4. Our framework for combining occupancy and expression data produces predictive models that better reflect the mechanisms underlying combinatorial cis-regulation of gene expression.

Protein-DNA binding: complexities and multi-protein codes

Binding of proteins to particular DNA sites across the genome is a primary determinant of specificity in genome maintenance and gene regulation. DNA-binding specificity is encoded at multiple levels, from the detailed biophysical interactions between proteins and DNA, to the assembly of multi-protein complexes. At each level, variation in the mechanisms used to achieve specificity has led to difficulties in constructing and applying simple models of DNA binding. We review the complexities in protein–DNA binding found at multiple levels and discuss how they confound the idea of simple recognition codes. We discuss the impact of new high-throughput technologies for the characterization of protein–DNA binding, and how these technologies are uncovering new complexities in protein–DNA recognition. Finally, we review the concept of multi-protein recognition codes in which new DNA-binding specificities are achieved by the assembly of multi-protein complexes.

Predicting tissue specific transcription factor binding sites

BACKGROUND

Studies of gene regulation often utilize genome-wide predictions of transcription factor (TF) binding sites. Most existing prediction methods are based on sequence information alone, ignoring biological contexts such as developmental stages and tissue types. Experimental methods to study in vivo binding, including ChIP-chip and ChIP-seq, can only study one transcription factor in a single cell type and under a specific condition in each experiment, and therefore cannot scale to determine the full set of regulatory interactions in mammalian transcriptional regulatory networks.

RESULTS

We developed a new computational approach, PIPES, for predicting tissue-specific TF binding. PIPES integrates in vitro protein binding microarrays (PBMs), sequence conservation and tissue-specific epigenetic (DNase I hypersensitivity) information. We demonstrate that PIPES improves over existing methods on distinguishing between in vivo bound and unbound sequences using ChIP-seq data for 11 mouse TFs. In addition, our predictions are in good agreement with current knowledge of tissue-specific TF regulation.

CONCLUSIONS

We provide a systematic map of computationally predicted tissue-specific binding targets for 284 mouse TFs across 55 tissue/cell types. Such comprehensive resource is useful for researchers studying gene regulation.

An improved SELEX-Seq strategy for characterizing DNA-binding specificity of transcription factor: NF-κB as an example

SELEX-Seq is now the optimal high-throughput technique for characterizing DNA-binding specificities of transcription factors. In this study, we introduced an improved EMSA-based SELEX-Seq strategy with several advantages. The improvements of this strategy included: (1) using a FAM-labeled probe to track protein-DNA complex in polyacrylamide gel for rapidly recovering the protein-bound dsDNA without relying on traditional radioactive labeling or ethidium bromide staining; (2) monitoring the specificity of SELEX selection by detecting a positive and negative sequence doped into the input DNAs used in each round with PCR amplification; (3) using nested PCR to ensure the specificity of PCR amplification of the selected DNAs after each round; (4) using the nucleotides added at the 5′ end of the nested PCR primers as the split barcode to code DNAs from various rounds for multiplexing sequencing samples. The split barcode minimized selection times and thus greatly simplified the current SELEX-Seq procedure. The reliability of the strategy was demonstrated by performing a successful SELEX-Seq of a well-known transcription factor, NF-κB. Therefore, this study provided a useful SELEX-Seq strategy for characterizing DNA-binding specificities of transcription factors.

De novo prediction of DNA-binding specificities for Cys2His2 zinc finger proteins

Proteins with sequence-specific DNA binding function are important for a wide range of biological activities. De novo prediction of their DNA-binding specificities from sequence alone would be a great aid in inferring cellular networks. Here we introduce a method for predicting DNA-binding specificities for Cys₂His₂ zinc fingers (C2H2-ZFs), the largest family of DNA-binding proteins in metazoans. We develop a general approach, based on empirical calculations of pairwise amino acid–nucleotide interaction energies, for predicting position weight matrices (PWMs) representing DNA-binding specificities for C2H2-ZF proteins. We predict DNA-binding specificities on a per-finger basis and merge predictions for C2H2-ZF domains that are arrayed within sequences. We test our approach on a diverse set of natural C2H2-ZF proteins with known binding specificities and demonstrate that for >85% of the proteins, their predicted PWMs are accurate in 50% of their nucleotide positions. For proteins with several zinc finger isoforms, we show via case studies that this level of accuracy enables us to match isoforms with their known DNA-binding specificities. A web server for predicting a PWM given a protein containing C2H2-ZF domains is available online at http://zf.princeton.edu and can be used to aid in protein engineering applications and in genome-wide searches for transcription factor targets.

The next generation of transcription factor binding site prediction

Finding where transcription factors (TFs) bind to the DNA is of key importance to decipher gene regulation at a transcriptional level. Classically, computational prediction of TF binding sites (TFBSs) is based on basic position weight matrices (PWMs) which quantitatively score binding motifs based on the observed nucleotide patterns in a set of TFBSs for the corresponding TF. Such models make the strong assumption that each nucleotide participates independently in the corresponding DNA-protein interaction and do not account for flexible length motifs. We introduce transcription factor flexible models (TFFMs) to represent TF binding properties. Based on hidden Markov models, TFFMs are flexible, and can model both position interdependence within TFBSs and variable length motifs within a single dedicated framework. The availability of thousands of experimentally validated DNA-TF interaction sequences from ChIP-seq allows for the generation of models that perform as well as PWMs for stereotypical TFs and can improve performance for TFs with flexible binding characteristics. We present a new graphical representation of the motifs that convey properties of position interdependence. TFFMs have been assessed on ChIP-seq data sets coming from the ENCODE project, revealing that they can perform better than both PWMs and the dinucleotide weight matrix extension in discriminating ChIP-seq from background sequences. Under the assumption that ChIP-seq signal values are correlated with the affinity of the TF-DNA binding, we find that TFFM scores correlate with ChIP-seq peak signals. Moreover, using available TF-DNA affinity measurements for the Max TF, we demonstrate that TFFMs constructed from ChIP-seq data correlate with published experimentally measured DNA-binding affinities. Finally, TFFMs allow for the straightforward computation of an integrated TF occupancy score across a sequence. These results demonstrate the capacity of TFFMs to accurately model DNA-protein interactions, while providing a single unified framework suitable for the next generation of TFBS prediction.

Navigating the transcriptional roadmap regulating plant secondary cell wall deposition

The current status of lignocellulosic biomass as an invaluable resource in industry, agriculture, and health has spurred increased interest in understanding the transcriptional regulation of secondary cell wall (SCW) biosynthesis. The last decade of research has revealed an extensive network of NAC, MYB and other families of transcription factors regulating Arabidopsis SCW biosynthesis, and numerous studies have explored SCW-related transcription factors in other dicots and monocots. Whilst the general structure of the Arabidopsis network has been a topic of several reviews, they have not comprehensively represented the detailed protein-DNA and protein-protein interactions described in the literature, and an understanding of network dynamics and functionality has not yet been achieved for SCW formation. Furthermore the methodologies employed in studies of SCW transcriptional regulation have not received much attention, especially in the case of non-model organisms. In this review, we have reconstructed the most exhaustive literature-based network representations to date of SCW transcriptional regulation in Arabidopsis. We include a manipulable Cytoscape representation of the Arabidopsis SCW transcriptional network to aid in future studies, along with a list of supporting literature for each documented interaction. Amongst other topics, we discuss the various components of the network, its evolutionary conservation in plants, putative modules and dynamic mechanisms that may influence network function, and the approaches that have been employed in network inference. Future research should aim to better understand network function and its response to dynamic perturbations, whilst the development and application of genome-wide approaches such as ChIP-seq and systems genetics are in progress for the study of SCW transcriptional regulation in non-model organisms.

Optimized position weight matrices in prediction of novel putative binding sites for transcription factors in the Drosophila melanogaster genome

Position weight matrices (PWMs) have become a tool of choice for the identification of transcription factor binding sites in DNA sequences. DNA-binding proteins often show degeneracy in their binding requirement and thus the overall binding specificity of many proteins is unknown and remains an active area of research. Although existing PWMs are more reliable predictors than consensus string matching, they generally result in a high number of false positive hits. Our previous study introduced a promising approach to PWM refinement in which known motifs are used to computationally mine putative binding sites directly from aligned promoter regions using composition of similar sites. In the present study, we extended this technique originally tested on single examples of transcription factors (TFs) and showed its capability to optimize PWM performance to predict new binding sites in the fruit fly genome. We propose refined PWMs in mono- and dinucleotide versions similarly computed for a large variety of transcription factors of Drosophila melanogaster. Along with the addition of many auxiliary sites the optimization includes variation of the PWM motif length, the binding sites location on the promoters and the PWM score threshold. To assess the predictive performance of the refined PWMs we compared them to conventional TRANSFAC and JASPAR sources. The results have been verified using performed tests and literature review. Overall, the refined PWMs containing putative sites derived from real promoter content processed using optimized parameters had better general accuracy than conventional PWMs.

Rap1 relocalization contributes to the chromatin-mediated gene expression profile and pace of cell senescence

Cellular senescence is accompanied by dramatic changes in chromatin structure and gene expression. Using Saccharomyces cerevisiae mutants lacking telomerase (tlc1Δ) to model senescence, we found that with critical telomere shortening, the telomere-binding protein Rap1 (repressor activator protein 1) relocalizes to the upstream promoter regions of hundreds of new target genes. The set of new Rap1 targets at senescence (NRTS) is preferentially activated at senescence, and experimental manipulations of Rap1 levels indicate that it contributes directly to NRTS activation. A notable subset of NRTS includes the core histone-encoding genes; we found that Rap1 contributes to their repression and that histone protein levels decline at senescence. Rap1 and histones also display a target site-specific antagonism that leads to diminished nucleosome occupancy at the promoters of up-regulated NRTS. This antagonism apparently impacts the rate of senescence because underexpression of Rap1 or overexpression of the core histones delays senescence. Rap1 relocalization is not a simple consequence of lost telomere-binding sites, but rather depends on the Mec1 checkpoint kinase. Rap1 relocalization is thus a novel mechanism connecting DNA damage responses (DDRs) at telomeres to global changes in chromatin and gene expression while driving the pace of senescence.

A primer on thermodynamic-based models for deciphering transcriptional regulatory logic

A rigorous analysis of transcriptional regulation at the DNA level is crucial to the understanding of many biological systems. Mathematical modeling has offered researchers a new approach to understanding this central process. In particular, thermodynamic-based modeling represents the most biophysically informed approach aimed at connecting DNA level regulatory sequences to the expression of specific genes. The goal of this review is to give biologists a thorough description of the steps involved in building, analyzing, and implementing a thermodynamic-based model of transcriptional regulation. The data requirements for this modeling approach are described, the derivation for a specific regulatory region is shown, and the challenges and future directions for the quantitative modeling of gene regulation are discussed.

STAT1:DNA sequence-dependent binding modulation by phosphorylation, protein:protein interactions and small-molecule inhibition

The DNA-binding specificity and affinity of the dimeric human transcription factor (TF) STAT1, were assessed by total internal reflectance fluorescence protein-binding microarrays (TIRF-PBM) to evaluate the effects of protein phosphorylation, higher-order polymerization and small-molecule inhibition. Active, phosphorylated STAT1 showed binding preferences consistent with prior characterization, whereas unphosphorylated STAT1 showed a weak-binding preference for one-half of the GAS consensus site, consistent with recent models of STAT1 structure and function in response to phosphorylation. This altered-binding preference was further tested by use of the inhibitor LLL3, which we show to disrupt STAT1 binding in a sequence-dependent fashion. To determine if this sequence-dependence is specific to STAT1 and not a general feature of human TF biology, the TF Myc/Max was analysed and tested with the inhibitor Mycro3. Myc/Max inhibition by Mycro3 is sequence independent, suggesting that the sequence-dependent inhibition of STAT1 may be specific to this system and a useful target for future inhibitor design.

A multi-parametric flow cytometric assay to analyze DNA-protein interactions

Interactions between DNA and transcription factors (TFs) guide cellular function and development, yet the complexities of gene regulation are still far from being understood. Such understanding is limited by a paucity of techniques with which to probe DNA–protein interactions. We have devised magnetic protein immobilization on enhancer DNA (MagPIE), a simple, rapid, multi-parametric assay using flow cytometric immunofluorescence to reveal interactions among TFs, chromatin structure and DNA. In MagPIE, synthesized DNA is bound to magnetic beads, which are then incubated with nuclear lysate, permitting sequence-specific binding by TFs, histones and methylation by native lysate factors that can be optionally inhibited with small molecules. Lysate protein–DNA binding is monitored by flow cytometric immunofluorescence, which allows for accurate comparative measurement of TF-DNA affinity. Combinatorial fluorescent staining allows simultaneous analysis of sequence-specific TF-DNA interaction and chromatin modification. MagPIE provides a simple and robust method to analyze complex epigenetic interactions in vitro.

TATA binding proteins can recognize nontraditional DNA sequences

We demonstrate an accurate, quantitative, and label-free optical technology for high-throughput studies of receptor-ligand interactions, and apply it to TATA binding protein (TBP) interactions with oligonucleotides. We present a simple method to prepare single-stranded and double-stranded DNA microarrays with comparable surface density, ensuring an accurate comparison of TBP activity with both types of DNA. In particular, we find that TBP binds tightly to single-stranded DNA, especially to stretches of polythymine (poly-T), as well as to the traditional TATA box. We further investigate the correlation of TBP activity with various lengths of DNA and find that the number of TBPs bound to DNA increases >7-fold as the oligomer length increases from 9 to 40. Finally, we perform a full human genome analysis and discover that 35.5% of human promoters have poly-T stretches. In summary, we report, for the first time to our knowledge, the activity of TBP with poly-T stretches by presenting an elegant stepwise analysis of multiple techniques: discovery by a novel quantitative detection of microarrays, confirmation by a traditional gel electrophoresis, and a full genome prediction with computational analyses.

An expansive human regulatory lexicon encoded in transcription factor footprints

Regulatory factor binding to genomic DNA protects the underlying sequence from cleavage by DNase I, leaving nucleotide-resolution footprints. Using genomic DNase I footprinting across 41 diverse cell and tissue types, we detected 45 million transcription factor occupancy events within regulatory regions, representing differential binding to 8.4 million distinct short sequence elements. Here we show that this small genomic sequence compartment, roughly twice the size of the exome, encodes an expansive repertoire of conserved recognition sequences for DNA-binding proteins that nearly doubles the size of the human cis-regulatory lexicon. We find that genetic variants affecting allelic chromatin states are concentrated in footprints, and that these elements are preferentially sheltered from DNA methylation. High-resolution DNase I cleavage patterns mirror nucleotide-level evolutionary conservation and track the crystallographic topography of protein-DNA interfaces, indicating that transcription factor structure has been evolutionarily imprinted on the human genome sequence. We identify a stereotyped 50-base-pair footprint that precisely defines the site of transcript origination within thousands of human promoters. Finally, we describe a large collection of novel regulatory factor recognition motifs that are highly conserved in both sequence and function, and exhibit cell-selective occupancy patterns that closely parallel major regulators of development, differentiation and pluripotency.

A genome-wide regulator-DNA interaction network in the human pathogen Mycobacterium tuberculosis H37Rv

Transcription regulation translates static genome information to dynamic cell behaviors, making it central to understand how cells interact with and adapt to their environment. However, only a limited number of transcription regulators and their target genes have been identified in the pathogen Mycobacterium tuberculosis , which has greatly impeded our understanding of its pathogenesis and virulence. In this study, we constructed a genome-wide transcription regulatory network of M. tuberculosis H37Rv using a high-throughput bacterial one-hybrid technique. A transcription factor skeleton network was derived on the basis of the identification of more than 5400 protein-DNA interactions. Our findings further highlight the regulatory mechanism of the mammalian cell entry 1 (mce1) module, which includes mce1R and the mce1 operon. Mce1R was linked to global negative regulation of cell growth, but was found to be positively regulated by the dormancy response regulator DevR. Expression of the mce1 operon was shown to be negatively regulated by the virulence regulator PhoP. These findings provide important new insights into the molecular mechanisms of several mce1 module-related hypervirulence phenotypes of the pathogen. Furthermore, a model of mce1 module-centered signal circuit for dormancy regulation in M. tuberculosis is proposed and discussed.

[DNA-binding profiles of mammalian transcription factors]

The differential gene expression is the molecular base of development and responses to stimuli of organisms. Transcription factors (TFs) play important regulatory roles in this kind of differential gene expression. Therefore, to elucidate how these TFs regulate the complex differential gene expression, it is necessary to identify all target genes of them and construct the gene transcription regulatory network controlled by them. DNA binding is a key step for TFs regulating gene transcription. Therefore, in order to identify their target genes, it is indispensable to identify all possible DNA sequences that can be recognized and bound by TFs at the molecular level of their interactions with DNA, i.e., construction of the DNA-binding profiles of TFs. In recent years, along with the development of DNA microarray and high-throughput DNA sequencing techniques, there appeared some revolutionary new techniques for constructing DNA-binding profiles of TFs, which greatly promotes studies in this field. These techniques include ChIP-chip and ChIP-Seq for constructing in vivo DNA-binding profiles of TFs, dsDNA microarray, SELEX-SAGE, Bind-n-Seq, MMP-SELEX, EMSA-Seq, and HiTS-FLIP for constructing in vitro DNA-binding profiles of TFs. This paper reviewed these techniques.

One Hand Clapping: detection of condition-specific transcription factor interactions from genome-wide gene activity data

We present One Hand Clapping (OHC), a method for the detection of condition-specific interactions between transcription factors (TFs) from genome-wide gene activity measurements. OHC is based on a mapping between transcription factors and their target genes. Given a single case–control experiment, it uses a linear regression model to assess whether the common targets of two arbitrary TFs behave differently than expected from the genes targeted by only one of the TFs. When applied to osmotic stress data in S. cerevisiae, OHC produces consistent results across three types of expression measurements: gene expression microarray data, RNA Polymerase II ChIP-chip binding data and messenger RNA synthesis rates. Among the eight novel, condition-specific TF pairs, we validate the interaction between Gcn4p and Arr1p experimentally. We apply OHC to a large gene activity dataset in S. cerevisiae and provide a compendium of condition-specific TF interactions.

A novel ab initio identification system of transcriptional regulation motifs in genome DNA sequences based on direct comparison scheme of signal/noise distributions

A novel ab initio parameter-tuning-free system to identify transcriptional factor (TF) binding motifs (TFBMs) in genome DNA sequences was developed. It is based on the comparison of two types of frequency distributions with respect to the TFBM candidates in the target DNA sequences and the non-candidates in the background sequence, with the latter generated by utilizing the intergenic sequences. For benchmark tests, we used DNA sequence datasets extracted by ChIP-on-chip and ChIP-seq techniques and identified 65 yeast and four mammalian TFBMs, with the latter including gaps. The accuracy of our system was compared with those of other available programs (i.e. MEME, Weeder, BioProspector, MDscan and DME) and was the best among them, even without tuning of the parameter set for each TFBM and pre-treatment/editing of the target DNA sequences. Moreover, with respect to some TFs for which the identified motifs are inconsistent with those in the references, our results were revealed to be correct, by comparing them with other existing experimental data. Thus, our identification system does not need any other biological information except for gene positions, and is also expected to be applicable to genome DNA sequences to identify unknown TFBMs as well as known ones.

Improved models for transcription factor binding site identification using nonindependent interactions

Identifying transcription factor (TF) binding sites is essential for understanding regulatory networks. The specificity of most TFs is currently modeled using position weight matrices (PWMs) that assume the positions within a binding site contribute independently to binding affinity for any site. Extensive, high-throughput quantitative binding assays let us examine, for the first time, the independence assumption for many TFs. We find that the specificity of most TFs is well fit with the simple PWM model, but in some cases more complex models are required. We introduce a binding energy model (BEM) that can include energy parameters for nonindependent contributions to binding affinity. We show that in most cases where a PWM is not sufficient, a BEM that includes energy parameters for adjacent dinucleotide contributions models the specificity very well. Having more accurate models of specificity greatly improves the interpretation of in vivo TF localization data, such as from chromatin immunoprecipitation followed by sequencing (ChIP-seq) experiments.

3DTF: a web server for predicting transcription factor PWMs using 3D structure-based energy calculations

We present the webserver 3D transcription factor (3DTF) to compute position-specific weight matrices (PWMs) of transcription factors using a knowledge-based statistical potential derived from crystallographic data on protein–DNA complexes. Analysis of available structures that can be used to construct PWMs shows that there are hundreds of 3D structures from which PWMs could be derived, as well as thousands of proteins homologous to these. Therefore, we created 3DTF, which delivers binding matrices given the experimental or modeled protein–DNA complex. The webserver can be used by biologists to derive novel PWMs for transcription factors lacking known binding sites and is freely accessible at http://www.gene-regulation.com/pub/programs/3dtf/.

Chip-based protein-protein interaction studied by atomic force microscopy

In this article, a technique for accurate direct measurement of protein-to-protein interactions before and after the introduction of a drug candidate is developed using atomic force microscopy (AFM). The method is applied to known immunosuppressant drug candidate Echinacea purpurea derived cynarin. T-cell/CD28 is on-chip immobilized and B-cell/CD80 is immobilized on an AFM tip. The difference in unbinding force between these two proteins before and after the introduction of cynarin is measured. The method is described in detail including determination of the loading rates, maximum probability of bindings, and average unbinding forces. At an AFM loading rate of 1.44 × 10(4) pN/s, binding events were largely reduced from 61 ± 5% to 47 ± 6% after cynarin introduction. Similarly, maximum probability of bindings reduced from 70% to 35% with a blocking effect of about 35% for a fixed contact time of 0.5 s or greater. Furthermore, average unbinding forces were reduced from 61.4 to 38.9 pN with a blocking effect of ≈ 37% as compared with ≈ 9% by SPR. AFM, which can provide accurate quantitative measures, is shown to be a good method for drug screening. The method could be applied to a wider variety of drug candidates with advances in bio-chip technology and a more comprehensive AFM database of protein-to-protein interactions.

DNA binding site analysis of Burkholderia thailandensis response regulators

Bacterial response regulators (RR) that function as transcription factors in two component signaling pathways are crucial for ensuring tight regulation and coordinated expression of the genome. Currently, consensus DNA binding sites in the promoter for very few bacterial RRs have been identified. A systematic method to characterize these DNA binding sites for RRs would enable prediction of specific gene expression patterns in response to extracellular stimuli. To identify RR DNA binding sites, we functionally activated RRs using beryllofluoride and applied them to a protein-binding microarray (PBM) to discover DNA binding motifs for RRs expressed in Burkholderia, a Gram-negative bacterial genus. We identified DNA binding motifs for conserved RRs in Burkholderia thailandensis, including KdpE, RisA, and NarL, as well as for a previously uncharacterized RR at locus BTH_II2335 and its ortholog in the human pathogen Burkholderia pseudomallei at locus BPSS2315. We further demonstrate RR binding of predicted genomic targets for the two orthologs using gel shift assays and reveal a pattern of RR regulation of expression of self and other two component systems. Our studies illustrate the use of PBMs to identify DNA binding specificities for bacterial RRs and enable prediction of gene regulatory networks in response to two component signaling.

Genome-wide protein-DNA binding dynamics suggest a molecular clutch for transcription factor function

Dynamic access to genetic information is central to organismal development and environmental response. Consequently, genomic processes must be regulated by mechanisms that alter genome function relatively rapidly. Conventional chromatin immunoprecipitation (ChIP) experiments measure transcription factor occupancy, but give no indication of kinetics and are poor predictors of transcription factor function at a given locus. To measure transcription-factor-binding dynamics across the genome, we performed competition ChIP (refs 6, 7) with a sequence-specific Saccharomyces cerevisiae transcription factor, Rap1 (ref. 8). Rap1-binding dynamics and Rap1 occupancy were only weakly correlated (R(2) = 0.14), but binding dynamics were more strongly linked to function than occupancy. Long Rap1 residence was coupled to transcriptional activation, whereas fast binding turnover, which we refer to as 'treadmilling', was linked to low transcriptional output. Thus, DNA-binding events that seem identical by conventional ChIP may have different underlying modes of interaction that lead to opposing functional outcomes. We propose that transcription factor binding turnover is a major point of regulation in determining the functional consequences of transcription factor binding, and is mediated mainly by control of competition between transcription factors and nucleosomes. Our model predicts a clutch-like mechanism that rapidly engages a treadmilling transcription factor into a stable binding state, or vice versa, to modulate transcription factor function.

Thermodynamic state ensemble models of cis-regulation

A major goal in computational biology is to develop models that accurately predict a gene's expression from its surrounding regulatory DNA. Here we present one class of such models, thermodynamic state ensemble models. We describe the biochemical derivation of the thermodynamic framework in simple terms, and lay out the mathematical components that comprise each model. These components include (1) the possible states of a promoter, where a state is defined as a particular arrangement of transcription factors bound to a DNA promoter, (2) the binding constants that describe the affinity of the protein-protein and protein-DNA interactions that occur in each state, and (3) whether each state is capable of transcribing. Using these components, we demonstrate how to compute a cis-regulatory function that encodes the probability of a promoter being active. Our intention is to provide enough detail so that readers with little background in thermodynamics can compose their own cis-regulatory functions. To facilitate this goal, we also describe a matrix form of the model that can be easily coded in any programming language. This formalism has great flexibility, which we show by illustrating how phenomena such as competition between transcription factors and cooperativity are readily incorporated into these models. Using this framework, we also demonstrate that Michaelis-like functions, another class of cis-regulatory models, are a subset of the thermodynamic framework with specific assumptions. By recasting Michaelis-like functions as thermodynamic functions, we emphasize the relationship between these models and delineate the specific circumstances representable by each approach. Application of thermodynamic state ensemble models is likely to be an important tool in unraveling the physical basis of combinatorial cis-regulation and in generating formalisms that accurately predict gene expression from DNA sequence.

De novo motif discovery facilitates identification of interactions between transcription factors in Saccharomyces cerevisiae

MOTIVATION

Gene regulation involves complicated mechanisms such as cooperativity between a set of transcription factors (TFs). Previous studies have used target genes shared by two TFs as a clue to infer TF-TF interactions. However, this task remains challenging because the target genes with low binding affinity are frequently omitted by experimental data, especially when a single strict threshold is employed. This article aims at improving the accuracy of inferring TF-TF interactions by incorporating motif discovery as a fundamental step when detecting overlapping targets of TFs based on ChIP-chip data.

RESULTS

The proposed method, simTFBS, outperforms three naïve methods that adopt fixed thresholds when inferring TF-TF interactions based on ChIP-chip data. In addition, simTFBS is compared with two advanced methods and demonstrates its advantages in predicting TF-TF interactions. By comparing simTFBS with predictions based on the set of available annotated yeast TF binding motifs, we demonstrate that the good performance of simTFBS is indeed coming from the additional motifs found by the proposed procedures.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Intrinsic disorder within and flanking the DNA-binding domains of human transcription factors

While the term 'protein structure' is commonplace, it is increasingly appreciated that proteins may not possess a single, well-defined structure: some regions of proteins are intrinsically disordered. The role these intrinsically disordered regions (IDRs) play in protein function is an area of significant interest. In particular, because proteins containing IDRs are largely involved in processes related to molecular recognition, the question arises whether IDRs are important in these recognition events. It has been observed that IDRs are enriched in transcription factors (TFs) in comparison with other proteins, and we sought to explore this enrichment more precisely, with an eye toward functional dissection of the prevalence and locations of IDRs in different classes of TFs. Specifically, we considered the occurrences of 76 classes of DNA-binding domains (DBDs) within a comprehensive set of 1,747 human, sequence-specific TFs. For each DBD class, we analyzed whether a significant level of disorder was present within the DBD itself, the N-terminal or C-terminal sequence flanking the DBD, or both flanking sequences. We found that although the DBDs themselves exhibit significant order, the regions flanking the DBDs exhibit significant disorder, which suggests a functional role for such IDRs in TF DNA binding. These results may have important implications for studies of TFs not just in human but across all eukaryotes, and suggest future studies focused on testing the roles of N- and C-terminal flanking regions in determining or modulating the DNA binding affinity and/or specificity of the associated TFs.

MUSI: an integrated system for identifying multiple specificity from very large peptide or nucleic acid data sets

Peptide recognition domains and transcription factors play crucial roles in cellular signaling. They bind linear stretches of amino acids or nucleotides, respectively, with high specificity. Experimental techniques that assess the binding specificity of these domains, such as microarrays or phage display, can retrieve thousands of distinct ligands, providing detailed insight into binding specificity. In particular, the advent of next-generation sequencing has recently increased the throughput of such methods by several orders of magnitude. These advances have helped reveal the presence of distinct binding specificity classes that co-exist within a set of ligands interacting with the same target. Here, we introduce a software system called MUSI that can rapidly analyze very large data sets of binding sequences to determine the relevant binding specificity patterns. Our pipeline provides two major advances. First, it can detect previously unrecognized multiple specificity patterns in any data set. Second, it offers integrated processing of very large data sets from next-generation sequencing machines. The results are visualized as multiple sequence logos describing the different binding preferences of the protein under investigation. We demonstrate the performance of MUSI by analyzing recent phage display data for human SH3 domains as well as microarray data for mouse transcription factors.

Curated collection of yeast transcription factor DNA binding specificity data reveals novel structural and gene regulatory insights

Background

Transcription factors (TFs) play a central role in regulating gene expression by interacting with cis-regulatory DNA elements associated with their target genes. Recent surveys have examined the DNA binding specificities of most Saccharomyces cerevisiae TFs, but a comprehensive evaluation of their data has been lacking.

Results

We analyzed in vitro and in vivo TF-DNA binding data reported in previous large-scale studies to generate a comprehensive, curated resource of DNA binding specificity data for all characterized S. cerevisiae TFs. Our collection comprises DNA binding site motifs and comprehensive in vitro DNA binding specificity data for all possible 8-bp sequences. Investigation of the DNA binding specificities within the basic leucine zipper (bZIP) and VHT1 regulator (VHR) TF families revealed unexpected plasticity in TF-DNA recognition: intriguingly, the VHR TFs, newly characterized by protein binding microarrays in this study, recognize bZIP-like DNA motifs, while the bZIP TF Hac1 recognizes a motif highly similar to the canonical E-box motif of basic helix-loop-helix (bHLH) TFs. We identified several TFs with distinct primary and secondary motifs, which might be associated with different regulatory functions. Finally, integrated analysis of in vivo TF binding data with protein binding microarray data lends further support for indirect DNA binding in vivo by sequence-specific TFs.

Conclusions

The comprehensive data in this curated collection allow for more accurate analyses of regulatory TF-DNA interactions, in-depth structural studies of TF-DNA specificity determinants, and future experimental investigations of the TFs' predicted target genes and regulatory roles.