Similar Datasets
Project description:Protein-DNA interactions are dynamic, and these dynamics are an important aspect of chromatin-associated processes such as transcription or replication. Due to a lack of methods to study on- and off-rates across entire genomes, protein-DNA interaction dynamics have not been studied extensively. Here, we determine in vivo off-rates for the Saccharomyces cerevisiae chromatin organizing factor Abf1, at 191 sites simultaneously across the yeast genome. Average Abf1 residence times span a wide range, varying between 4.2 and 33 min. Sites with different off-rates are associated with different functional characteristics. This includes their transcriptional dependency on Abf1, nucleosome positioning and the size of the nucleosome-free region, as well as the ability to roadblock RNA polymerase II for termination. The results show how off-rates contribute to transcription factor function and that DIVORSEQ (Determining In Vivo Off-Rates by SEQuencing) is a meaningful way of investigating protein-DNA binding dynamics genome-wide.
| S-EPMC7586999 | biostudies-literature
Project description:Protein-DNA interactions are dynamic and these dynamics are an important aspect of chromatin-associated processes such as transcription or replication. Due to a lack of methods to study on- and off-rates across entire genomes, protein-DNA interaction dynamics have not been studied extensively. Here we determine in vivo off-rates for the Saccharomyces cerevisiae chromatin organizing factor Abf1, at 191 sites simultaneously across the yeast genome. Average Abf1 residence times span a wide-range, varying between 4.5 and 37 minutes. Sites with different off-rates are associated with different functional characteristics. This includes their transcriptional dependency on Abf1, nucleosome positioning and the size of the nucleosome-free region, as well as the ability to roadblock RNA polymerase II for termination. The results show how off-rates contribute to transcription factor function and that DIVORSEQ (Determining In Vivo Off-Rates by SEQuencing) is a meaningful way of investigating protein-DNA binding dynamics genome-wide.
2020-06-04 | GSE151468 | GEO
Project description:Protein-DNA interactions are dynamic and these dynamics are an important aspect of chromatin-associated processes such as transcription or replication. Due to a lack of methods to study on- and off-rates across entire genomes, protein-DNA interaction dynamics have not been studied extensively. Here we determine in vivo off-rates for the Saccharomyces cerevisiae chromatin organizing factor Abf1, at 191 sites simultaneously across the yeast genome. Average Abf1 residence times span a wide-range, varying between 4.5 and 37 minutes. Sites with different off-rates are associated with different functional characteristics. This includes their transcriptional dependency on Abf1, nucleosome positioning and the size of the nucleosome-free region, as well as the ability to roadblock RNA polymerase II for termination. The results show how off-rates contribute to transcription factor function and that DIVORSEQ (Determining In Vivo Off-Rates by SEQuencing) is a meaningful way of investigating protein-DNA binding dynamics genome-wide.
2020-06-04 | GSE151692 | GEO
Project description:Genome-wide off-rates reveal how DNA binding dynamics shape transcription factor function
| PRJNA636969 | ENA
Project description:Genome-wide off-rates reveal how DNA binding dynamics shape transcription factor function [ChIP-Seq]
| PRJNA636974 | ENA
Project description:Genome-wide off-rates reveal how DNA binding dynamics shape transcription factor function [RNA-Seq]
| PRJNA635918 | ENA
Project description: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.
| S-EPMC3341663 | biostudies-literature
Project description:An 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.
| S-EPMC7607496 | biostudies-literature
Project description:DNA sequences with high affinity for transcription factors occur more frequently in the genome than instances of genes bound or regulated by these factors. It is not clear what factors determine the genome-wide pattern of binding or regulation for a given transcription factor. We used an integrated approach to study how trans influences shape the binding and regulatory landscape of Pho4, a budding yeast transcription factor activated in response to phosphate limitation. We find that nucleosomes significantly restrict Pho4 binding. At nucleosome-depleted sites, competition from another transcription factor, Cbf1, determines Pho4 occupancy, raising the threshold for transcriptional activation in phosphate replete conditions and preventing Pho4 activation of genes outside the phosphate regulon during phosphate starvation. Pho4 binding is not sufficient for transcriptional activation-a cooperative interaction between Pho2 and Pho4 specifies genes that are activated. Combining these experimental observations, we are able to globally predict Pho4 binding and its functionality.
| S-EPMC3127084 | biostudies-literature
Project description:DNA binding specificities of transcription factors (TFs) are a key component of gene regulatory processes. Underlying mechanisms that explain the highly specific binding of TFs to their genomic target sites are poorly understood. A better understanding of TF-DNA binding requires the ability to quantitatively model TF binding to accessible DNA as its basic step, before additional in vivo components can be considered. Traditionally, these models were built based on nucleotide sequence. Here, we integrated 3D DNA shape information derived with a high-throughput approach into the modeling of TF binding specificities. Using support vector regression, we trained quantitative models of TF binding specificity based on protein binding microarray (PBM) data for 68 mammalian TFs. The evaluation of our models included cross-validation on specific PBM array designs, testing across different PBM array designs, and using PBM-trained models to predict relative binding affinities derived from in vitro selection combined with deep sequencing (SELEX-seq). Our results showed that shape-augmented models compared favorably to sequence-based models. Although both k-mer and DNA shape features can encode interdependencies between nucleotide positions of the binding site, using DNA shape features reduced the dimensionality of the feature space. In addition, analyzing the feature weights of DNA shape-augmented models uncovered TF family-specific structural readout mechanisms that were not revealed by the DNA sequence. As such, this work combines knowledge from structural biology and genomics, and suggests a new path toward understanding TF binding and genome function.
| S-EPMC4403198 | biostudies-literature