Model Of Transcription Factor Binding Research Articles

Modeling methyl-sensitive transcription factor motifs with an expanded epigenetic alphabet

BackgroundTranscription factors bind DNA in specific sequence contexts. In addition to distinguishing one nucleobase from another, some transcription factors can distinguish between unmodified and modified bases. Current models of transcription factor binding tend not to take DNA modifications into account, while the recent few that do often have limitations. This makes a comprehensive and accurate profiling of transcription factor affinities difficult.ResultsHere, we develop methods to identify transcription factor binding sites in modified DNA. Our models expand the standard A/C/G/T DNA alphabet to include cytosine modifications. We develop Cytomod to create modified genomic sequences and we also enhance the MEME Suite, adding the capacity to handle custom alphabets. We adapt the well-established position weight matrix (PWM) model of transcription factor binding affinity to this expanded DNA alphabet. Using these methods, we identify modification-sensitive transcription factor binding motifs. We confirm established binding preferences, such as the preference of ZFP57 and C/EBPβ for methylated motifs and the preference of c-Myc for unmethylated E-box motifs.ConclusionsUsing known binding preferences to tune model parameters, we discover novel modified motifs for a wide array of transcription factors. Finally, we validate our binding preference predictions for OCT4 using cleavage under targets and release using nuclease (CUT&RUN) experiments across conventional, methylation-, and hydroxymethylation-enriched sequences. Our approach readily extends to other DNA modifications. As more genome-wide single-base resolution modification data becomes available, we expect that our method will yield insights into altered transcription factor binding affinities across many different modifications.

Transcription Factor Binding Affinities and DNA Shape Readout.

SummaryAn essential event in gene regulation is the binding of a transcription factor (TF) to its target DNA. Models considering the interactions between the TF and the DNA geometry proved to be successful approaches to describe this binding event, while conserving data interpretability. However, a direct characterization of the DNA shape contribution to binding is still missing due to the lack of accurate and large-scale binding affinity data. Here, we use a binding assay we recently established to measure with high sensitivity the binding specificities of 13 Drosophila TFs, including dinucleotide dependencies to capture non-independent amino acid-base interactions. Correlating the binding affinities with all DNA shape features, we find that shape readout is widely used by these factors. A shape readout/TF-DNA complex structure analysis validates our approach while providing biological insights such as positively charged or highly polar amino acids often contact nucleotides that exhibit strong shape readout.

Contribution of Sequence Motif, Chromatin State, and DNA Structure Features to Predictive Models of Transcription Factor Binding in Yeast.

Transcription factor (TF) binding is determined by the presence of specific sequence motifs (SM) and chromatin accessibility, where the latter is influenced by both chromatin state (CS) and DNA structure (DS) properties. Although SM, CS, and DS have been used to predict TF binding sites, a predictive model that jointly considers CS and DS has not been developed to predict either TF-specific binding or general binding properties of TFs. Using budding yeast as model, we found that machine learning classifiers trained with either CS or DS features alone perform better in predicting TF-specific binding compared to SM-based classifiers. In addition, simultaneously considering CS and DS further improves the accuracy of the TF binding predictions, indicating the highly complementary nature of these two properties. The contributions of SM, CS, and DS features to binding site predictions differ greatly between TFs, allowing TF-specific predictions and potentially reflecting different TF binding mechanisms. In addition, a "TF-agnostic" predictive model based on three DNA “intrinsic properties” (in silico predicted nucleosome occupancy, major groove geometry, and dinucleotide free energy) that can be calculated from genomic sequences alone has performance that rivals the model incorporating experiment-derived data. This intrinsic property model allows prediction of binding regions not only across TFs, but also across DNA-binding domain families with distinct structural folds. Furthermore, these predicted binding regions can help identify TF binding sites that have a significant impact on target gene expression. Because the intrinsic property model allows prediction of binding regions across DNA-binding domain families, it is TF agnostic and likely describes general binding potential of TFs. Thus, our findings suggest that it is feasible to establish a TF agnostic model for identifying functional regulatory regions in potentially any sequenced genome.

A computational model for mechanistic investigation of negative feedback in interleukin-17 receptor signaling (CCR3P.201)

Abstract IL-17 is a pro-inflammatory cytokine that promotes autoimmunity and protects against certain pathogens. Here, we present a computational model of signaling downstream of the IL-17 receptor. To manage the complexity of the system we use a rule-based modeling approach, in which signaling proteins are modeled as structured objects and rules describe their biochemical interactions, allowing us to consider all possible complexes and phosphoforms generated from a set of basic molecules. This model encompasses major signaling components downstream of IL-17R, which activate NF-κB and in turn promote the production of pro-inflammatory cytokines. NF-κB also promotes production of A20, a deubiquitinating enzyme that inhibits NF-κB activation in multiple settings. We recently showed that A20 acts as a negative regulator of the IL-17 signaling pathway. By modeling A20 inhibition of TRAF6 and IKK, we are able to recapitulate experimentally-observed oscillations in NF-κB activity and A20 expression. Novel experimental data identifying the dynamics of signaling intermediates has further constrained model behavior and informed model development. Modeling the depletion of IκB, followed by its rebound above baseline levels, required the addition of a stochastic model of transcription factor binding, paired with a deterministic model of signaling events. We are currently using the hybrid model to identify which of several potential mechanisms A20 uses to attenuate IL-17 signaling.

MARZ: an algorithm to combinatorially analyze gapped n-mer models of transcription factor binding.

BackgroundA key challenge in understanding the molecular mechanisms that control gene regulation is the characterization of the specificity with which transcription factor proteins bind to specific DNA sequences. A number of computational approaches have been developed to examine these interactions, including simple mononucleotide and dinucleotide position weight matrix models.ResultsHere we develop a novel, unbiased computational algorithm, MARZ, that systematically analyzes all possible gapped matrices across a fixed number of nucleotides. In addition, to evaluate the ability of these matrix models to predict in vivo binding sites, we utilize a new scoring system and, in combination with established scoring methods and statistical analysis, test the performance of 32 different gapped matrices on the well characterized HUNCHBACK transcription factor in Drosophila.ConclusionsOur results indicate that in many cases gapped matrix models can outperform traditional models, but that the relative strength of the binding sites considered in the analysis can profoundly influence the predictive ability of specific models.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0446-3) contains supplementary material, which is available to authorized users.

Hybrid incompatibility despite pleiotropic constraint in a sequence-based bioenergetic model of transcription factor binding.

Hybrid incompatibility can result from gene misregulation produced by divergence in trans-acting regulatory factors and their cis-regulatory targets. However, change in trans-acting factors may be constrained by pleiotropy, which would in turn limit the evolution of incompatibility. We employed a mechanistically explicit bioenergetic model of gene expression wherein parameter combinations (number of transcription factor molecules, energetic properties of binding to the regulatory site, and genomic background size) determine the shape of the genotype-phenotype (G-P) map, and interacting allelic variants of mutable cis and trans sites determine the phenotype along that map. Misregulation occurs when the phenotype differs from its optimal value. We simulated a pleiotropic regulatory pathway involving a positively selected and a conserved trait regulated by a shared transcription factor (TF), with two populations evolving in parallel. Pleiotropic constraints shifted evolution in the positively selected trait to its cis-regulatory locus. We nevertheless found that the TF genotypes often evolved, accompanied by compensatory evolution in the conserved trait, and both traits contributed to hybrid misregulation. Compensatory evolution resulted in "developmental system drift," whereby the regulatory basis of the conserved phenotype changed although the phenotype itself did not. Pleiotropic constraints became stronger and in some cases prohibitive when the bioenergetic properties of the molecular interaction produced a G-P map that was too steep. Likewise, compensatory evolution slowed and hybrid misregulation was not evident when the G-P map was too shallow. A broad pleiotropic "sweet spot" nevertheless existed where evolutionary constraints were moderate to weak, permitting substantial hybrid misregulation in both traits. None of these pleiotropic constraints manifested when the TF contained nonrecombining domains independently regulating the respective traits.

Hybrid Incompatibility Arises in a Sequence-Based Bioenergetic Model of Transcription Factor Binding

Postzygotic isolation between incipient species results from the accumulation of incompatibilities that arise as a consequence of genetic divergence. When phenotypes are determined by regulatory interactions, hybrid incompatibility can evolve even as a consequence of parallel adaptation in parental populations because interacting genes can produce the same phenotype through incompatible allelic combinations. We explore the evolutionary conditions that promote and constrain hybrid incompatibility in regulatory networks using a bioenergetic model (combining thermodynamics and kinetics) of transcriptional regulation, considering the bioenergetic basis of molecular interactions between transcription factors (TFs) and their binding sites. The bioenergetic parameters consider the free energy of formation of the bond between the TF and its binding site and the availability of TFs in the intracellular environment. Together these determine fractional occupancy of the TF on the promoter site, the degree of subsequent gene expression and in diploids, and the degree of dominance among allelic interactions. This results in a sigmoid genotype-phenotype map and fitness landscape, with the details of the shape determining the degree of bioenergetic evolutionary constraint on hybrid incompatibility. Using individual-based simulations, we subjected two allopatric populations to parallel directional or stabilizing selection. Misregulation of hybrid gene expression occurred under either type of selection, although it evolved faster under directional selection. Under directional selection, the extent of hybrid incompatibility increased with the slope of the genotype-phenotype map near the derived parental expression level. Under stabilizing selection, hybrid incompatibility arose from compensatory mutations and was greater when the bioenergetic properties of the interaction caused the space of nearly neutral genotypes around the stable expression level to be wide. F2's showed higher hybrid incompatibility than F1's to the extent that the bioenergetic properties favored dominant regulatory interactions. The present model is a mechanistically explicit case of the Bateson-Dobzhansky-Muller model, connecting environmental selective pressure to hybrid incompatibility through the molecular mechanism of regulatory divergence. The bioenergetic parameters that determine expression represent measurable properties of transcriptional regulation, providing a predictive framework for empirical studies of how phenotypic evolution results in epistatic incompatibility at the molecular level in hybrids.

Understanding transcriptional regulation by integrative analysis of transcription factor binding data

Statistical models have been used to quantify the relationship between gene expression and transcription factor (TF) binding signals. Here we apply the models to the large-scale data generated by the ENCODE project to study transcriptional regulation by TFs. Our results reveal a notable difference in the prediction accuracy of expression levels of transcription start sites (TSSs) captured by different technologies and RNA extraction protocols. In general, the expression levels of TSSs with high CpG content are more predictable than those with low CpG content. For genes with alternative TSSs, the expression levels of downstream TSSs are more predictable than those of the upstream ones. Different TF categories and specific TFs vary substantially in their contributions to predicting expression. Between two cell lines, the differential expression of TSS can be precisely reflected by the difference of TF-binding signals in a quantitative manner, arguing against the conventional on-and-off model of TF binding. Finally, we explore the relationships between TF-binding signals and other chromatin features such as histone modifications and DNase hypersensitivity for determining expression. The models imply that these features regulate transcription in a highly coordinated manner.

Enhancer identification in mouse embryonic stem cells using integrative modeling of chromatin and genomic features

BackgroundEpigenetic modifications, transcription factor (TF) availability and differences in chromatin folding influence how the genome is interpreted by the transcriptional machinery responsible for gene expression. Enhancers buried in non-coding regions are found to be associated with significant differences in histone marks between different cell types. In contrast, gene promoters show more uniform modifications across cell types. Here we used histone modification and chromatin-associated protein ChIP-Seq data sets in mouse embryonic stem (ES) cells as well as genomic features to identify functional enhancer regions. Using co-bound sites of OCT4, SOX2 and NANOG (co-OSN, validated enhancers) and co-bound sites of MYC and MYCN (limited enhancer activity) as enhancer positive and negative training sets, we performed multinomial logistic regression with LASSO regularization to identify key features.ResultsCross validations reveal that a combination of p300, H3K4me1, MED12 and NIPBL features to be top signatures of co-OSN regions. Using a model from 10 signatures, 83% of top 1277 putative 1 kb enhancer regions (probability greater than or equal to 0.8) overlapped with at least one TF peak from 7 mouse ES cell ChIP-Seq data sets. These putative enhancers are associated with increased gene expression of neighbouring genes and significantly enriched in multiple TF bound loci in agreement with combinatorial models of TF binding. Furthermore, we identified several motifs of known TFs significantly enriched in putative enhancer regions compared to random promoter regions and background. Comparison with an active H3K27ac mark in various cell types confirmed cell type-specificity of these enhancers.ConclusionsThe top enhancer signatures we identified (p300, H3K4me1, MED12 and NIPBL) will allow for the identification of cell type-specific enhancer regions in diverse cell types.

Quantitative models of the mechanisms that control genome-wide patterns of transcription factor binding during early Drosophila development.

Transcription factors that drive complex patterns of gene expression during animal development bind to thousands of genomic regions, with quantitative differences in binding across bound regions mediating their activity. While we now have tools to characterize the DNA affinities of these proteins and to precisely measure their genome-wide distribution in vivo, our understanding of the forces that determine where, when, and to what extent they bind remains primitive. Here we use a thermodynamic model of transcription factor binding to evaluate the contribution of different biophysical forces to the binding of five regulators of early embryonic anterior-posterior patterning in Drosophila melanogaster. Predictions based on DNA sequence and in vitro protein-DNA affinities alone achieve a correlation of ∼0.4 with experimental measurements of in vivo binding. Incorporating cooperativity and competition among the five factors, and accounting for spatial patterning by modeling binding in every nucleus independently, had little effect on prediction accuracy. A major source of error was the prediction of binding events that do not occur in vivo, which we hypothesized reflected reduced accessibility of chromatin. To test this, we incorporated experimental measurements of genome-wide DNA accessibility into our model, effectively restricting predicted binding to regions of open chromatin. This dramatically improved our predictions to a correlation of 0.6–0.9 for various factors across known target genes. Finally, we used our model to quantify the roles of DNA sequence, accessibility, and binding competition and cooperativity. Our results show that, in regions of open chromatin, binding can be predicted almost exclusively by the sequence specificity of individual factors, with a minimal role for protein interactions. We suggest that a combination of experimentally determined chromatin accessibility data and simple computational models of transcription factor binding may be used to predict the binding landscape of any animal transcription factor with significant precision.

A Bayesian Search for Transcriptional Motifs

Identifying transcription factor (TF) binding sites (TFBSs) is an important step towards understanding transcriptional regulation. A common approach is to use gaplessly aligned, experimentally supported TFBSs for a particular TF, and algorithmically search for more occurrences of the same TFBSs. The largest publicly available databases of TF binding specificities contain models which are represented as position weight matrices (PWM). There are other methods using more sophisticated representations, but these have more limited databases, or aren't publicly available. Therefore, this paper focuses on methods that search using one PWM per TF. An algorithm, MATCHTM, for identifying TFBSs corresponding to a particular PWM is available, but is not based on a rigorous statistical model of TF binding, making it difficult to interpret or adjust the parameters and output of the algorithm. Furthermore, there is no public description of the algorithm sufficient to exactly reproduce it. Another algorithm, MAST, computes a p-value for the presence of a TFBS using true probabilities of finding each base at each offset from that position. We developed a statistical model, BaSeTraM, for the binding of TFs to TFBSs, taking into account random variation in the base present at each position within a TFBS. Treating the counts in the matrices and the sequences of sites as random variables, we combine this TFBS composition model with a background model to obtain a Bayesian classifier. We implemented our classifier in a package (SBaSeTraM). We tested SBaSeTraM against a MATCHTM implementation by searching all probes used in an experimental Saccharomyces cerevisiae TF binding dataset, and comparing our predictions to the data. We found no statistically significant differences in sensitivity between the algorithms (at fixed selectivity), indicating that SBaSeTraM's performance is at least comparable to the leading currently available algorithm. Our software is freely available at: http://wiki.github.com/A1kmm/sbasetram/building-the-tools.

A Microfluidics-Based Platform For Identification and Detailed Characterization of Transcription Factor Binding Sites

Transcriptional regulation of gene expression is mediated by transcription factors that bind DNA sequence-specifically within gene promoters to activate or repress transcription. However, biochemical data linking transcription factors to their consensus binding sites has traditionally been difficult to obtain, complicating reconstruction of cellular pathways contributing to a transcriptional response.We have developed a versatile and sensitive microfluidics-based technique for de novo identification and subsequent detailed characterization of transcription factor consensus motifs and binding energy landscapes. Our technique offers several advantages over current methods. First, our technique mechanically traps all complexes at equilibrium prior to measurement, allowing detection of weak or transient interactions and providing direct, quantitative measurements of reaction parameters. Second, our technique requires extremely small amounts of reagents, permitting protein production via cell-free transcription/translation of PCR-generated templates and eliminating laborious and time-consuming cloning steps. Finally, our technique allows high-throughput screening of transcription factor binding to all possible DNA 8mers in a single experiment.To evaluate the performance of our new technique, we probed DNA binding patterns for 30 yeast transcription factors from various families and used a statistical-mechanical model of transcription factor binding to determine preferred consensus motifs. In all cases, the core consensus obtained agreed with previous literature results, validating the utility of our technique for de novo identification of transcription factor binding sites. This quantitative data set provides critical information that can be used to revise and refine current models of transcription factor binding interactions.

A role for Fra1 in the control of transcriptional network reorganization following ras transformation

RAS proteins act as molecular switches transmitting signals from the cell surface to the nucleus, thereby affecting several downstream signaling cascades. Among these cascades, the mitogenic MEK/ERK pathway affects a network of transcription factors such as SRF, ELK and AP1 components, largely known to play an important role in regulating cellular proliferation. While the targets of individual transcription factors have been identified, the structure of the transcription factor network down-stream of MEK/ERK signaling mediating transformation is not well understood. In addition, both gene activation and repression are necessary for tumor formation and it is unclear how certain MEK/ERK stimulated transcription factors participate in both processes. We performed genome-wide gene expression analysis to identify transcription factors differentially regulated via MEK/ERK between immortalized and HRAS-transformed cells. Individual transcription factors such as Fra1, over-expressed in RAS-transformed cells and human tumors derived from lung or bone, were knocked-down using siRNA. A second gene expression profiling was used to determine the target genes of these transcription factors. TRAP (TRanscription factor Affinity Prediction), a biophysical model of transcription factor binding and gene set enrichment analysis (GSEA) was used to screen for genes with conserved binding motifs and for functional gene sets exhibiting similar regulation. These approaches revealed novel insights into the role of Fra1 upon activation in RAS-transformed cells. We could define a previously unknown involvement of the MEK/ERK-dependent Fra1 transcription factor in governing the alteration of the transcriptional network in tumor cells: Fra1 seems to play a role in chromatin remodeling and in circadian functions. In addition, we observed a Fra1-dependent suppression of interferon target genes, which are known to be regulated via DNA methylation. These data suggest a key role for the AP1 complex and the Fra1 transcription factor in the reorganization of chromatin and the transcriptional network following oncogenic transformation.

Models of transcription factor binding: Sensitivity of activation functions to model assumptions

We present three models of how transcription factors (TFs) bind to their specific binding sites on the DNA: a model based on statistical physics, a Markov-chain model and a computational simulation. Comparison of these models suggests that the effect of non-specific binding can be significant. We also investigate possible mechanisms for cooperativity. The simulation model suggests that direct interactions between TFs are unlikely to be the main source of cooperativity between specific binding sites, because such interactions tend to lead to the formation of clusters on the DNA with undesirable side-effects.