DNA G-quadruplexes are transcription factor binding hot spots in human chromatin
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE145090 | GEO | 2021/03/24
REPOSITORIES: GEO
Similar Datasets
Project description:This model is from the article:
A regulatory role for repeated decoy transcription factor binding sites in target gene expression.
Lee TH, Maheshri N. Mol Syst Biol.
2012 Mar 27;8:576. 22453733
,
Abstract:
Tandem repeats of DNA that contain transcription factor (TF) binding sites could serve as decoys, competitively binding to TFs and affecting target gene expression. Using a synthetic system in budding yeast, we demonstrate that repeated decoy sites inhibit gene expression by sequestering a transcriptional activator and converting the graded dose-response of target promoters to a sharper, sigmoidal-like response. On the basis of both modeling and chromatin immunoprecipitation measurements, we attribute the altered response to TF binding decoy sites more tightly than promoter binding sites. Tight TF binding to arrays of contiguous repeated decoy sites only occurs when the arrays are mostly unoccupied. Finally, we show that the altered sigmoidal-like response can convert the graded response of a transcriptional positive-feedback loop to a bimodal response. Together, these results show how changing numbers of repeated TF binding sites lead to qualitative changes in behavior and raise new questions about the stability of TF/promoter binding.
Note:
This model corresponds to the basic model for gene expression in the presence of decoys, described in the paper.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication
for more information.
In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.
2005-01-01 | MODEL1202270000 | BioModels
Project description:This model is from the article:
A regulatory role for repeated decoy transcription factor binding sites in target gene expression.
Lee TH, Maheshri N. Mol Syst Biol.
2012 Mar 27;8:576. 22453733
,
Abstract:
Tandem repeats of DNA that contain transcription factor (TF) binding sites could serve as decoys, competitively binding to TFs and affecting target gene expression. Using a synthetic system in budding yeast, we demonstrate that repeated decoy sites inhibit gene expression by sequestering a transcriptional activator and converting the graded dose-response of target promoters to a sharper, sigmoidal-like response. On the basis of both modeling and chromatin immunoprecipitation measurements, we attribute the altered response to TF binding decoy sites more tightly than promoter binding sites. Tight TF binding to arrays of contiguous repeated decoy sites only occurs when the arrays are mostly unoccupied. Finally, we show that the altered sigmoidal-like response can convert the graded response of a transcriptional positive-feedback loop to a bimodal response. Together, these results show how changing numbers of repeated TF binding sites lead to qualitative changes in behavior and raise new questions about the stability of TF/promoter binding.
Note:
This model corresponds to the comprehensive model encompassing the basic model (MODEL1202270000) as well as the tTA/dox (tet-transcriptional activator/doxycycline) interaction, described in the paper.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication
for more information.
In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.
2005-01-01 | MODEL1202270001 | BioModels
Project description:Activation domains (ADs) within transcription factors (TFs) induce gene expression by recruiting coactivators to specific regulatory regions. Within the prevailing model, TF-coactivator recruitment is independent of DNA binding, which is consistent with direct AD-coactivator interactions seen outside cells. However, this independence was not yet tested within the genomic context. Here, we targeted two Med15-interacting ADs to hundreds of budding yeast promoters through fusions with multiple DNA binding domains (DBDs), gradually controlling their abundances using libraries of synthetic promoters. Genomic profiling revealed that AD identity influences DNA binding locations and that transcription induction and Med15 recruitment are restricted to a subset of DBD-bound promoters displaying flexible expression, multiple-TFs binding, and fuzzy nucleosome architecture. Further, when fused to a DBD, Med15 redirected binding towards promoters of fuzzy nucleosomes, overcoming DBD-based preferences. Our results demonstrate that ADs and their recruited coactivators posses an inherent preference for genomic localization and, therefore, define the subset of induced promoters.
2023-12-19 | GSE234431 | GEO
Project description:Patterns of transcriptional activity are encoded in our genome through regulatory elements such as promoters or enhancers that, paradoxically, often contain similar assortments of sequence-specific transcription factor (TF) binding sites. Knowledge of how these sequence motifs encode multiple, often overlapping gene expression programs is central to understanding gene regulation and how mutations in non-coding DNA manifest in disease. Exploiting natural genetic variation, perturbation of endogenous TF protein levels, and analysis of synthetic regulatory elements using a novel transcription start site (TSS) capturing massively parallel reporter assay approach, here we show that the outcome of TF binding on transcription initiation is position dependent. Analyzing TF motif occurrences relative to the TSS, we identified several motifs with highly preferential and superhelical positioning. We show that these patterns are a combination of a TF's distinct functional profiles: many TFs, including canonical activators like NRF1, NFY, Sp1 or YY1, activate or repress transcription initiation depending on their precise position relative to the TSS. As such, TFs can collectively guide the site and frequency of transcription initiation. More broadly, these findings reveal how similar assortments of TFs, but with different spatial relationships, can result in distinct gene regulatory outcomes and underscore a critical role for TSS data in decoding the regulatory information of our genome.
2024-04-26 | GSE199431 | GEO
Project description:Transcription factors (TFs) orchestrate the gene expression programs that define each cell’s identity, and the canonical TF accomplishes this with two functional domains1–7. The DNA-binding domain interacts with specific genomic sequences, and the effector domain binds coactivators or co-repressors that contribute to transcriptional regulation. We report here that TFs also frequently contain an RNA-binding domain and that this also contributes to gene regulation. Nearly half of TFs in human cells show evidence of RNA-binding and their affinities for RNA are similar to those of well-studied RNA-binding proteins. The RNA-binding sites of TFs have sequence and functional features analogous to the arginine-rich motif of the HIV transcriptional activator Tat8,9. RNA-binding contributes to TF function by promoting the dynamic association of TFs with chromatin. We conclude that the canonical definition of transcription factors is incomplete, and that the ability of many TFs to bind DNA, RNA and protein is fundamental to gene regulation.
2023-07-02 | PXD035484 | Pride
Project description:Purpose: 253 GSM Samples from GSE32970 and GSE29692 was reanalyzed to find the highly occupied target (HOT) regions of 154 cell lines. Methods: 1. We assigned the binding sites of 542 TFs in 154 cell lines as GSE53962 (Our last submission). 2.We performed a Gaussian kernel density estimation across the genome with a bandwidth of 300 bp, using the centers of each of the TF binding peaks as points. Then, we scanned this density for peaks, and denoted each peak a TF region(Candidate region to find HOT regions).To determine the complexity of the TF region, we summed the Gaussian kernalized distance from the peak to each TF that contributed at least 0.1 to its strength. The TF region around eat peak was derived by finding the maximum distance (in bp) from the peak to a contributing TF, and then adding 150 bp (one half of the bandwidth). Each TF region is centered on the peak, and have a TF complexity value. 3.To define HOT region according to the TF complexity, we required a complexity cutoff for each cell line.To geometrically define the cutoff we first scaled the TF complexity such that the x and y axis were from 0-1. We then found the x axis point for which a line with a slope of 1 was tangent to the curve. We define this point as the cutoff value,TF region whose complexity above this point to be HOT region, and TF region complexity below that point to be lowly occupied target (LOT) regions. Result: Using the binding sites of 542 TFs in 145 cell lines, we assigned a TF complexity score to each TF region corresponding to the number of distinct TFs bound, resulting in HOT regions of 145 cell lines.
2015-01-14 | GSE54296 | GEO
Project description:We measured transcription factor binding and expression of designed syththetic promoter libraries using Calling Cards Reporter Arrays (CCRAs). In this study, we showed that CCRAs is able to make quantatitive measurements for many TFs in yeast. We then demonstrate the quantitative analysis of cooperative interactions by measuring Cbf1p binding at synthetic promoters with multiple sites. Finally, we characterize the binding and expression of a group of TFs, Tye7p, Gcr1p, and Gcr2p, that act together as a “TF collective”, an important but poorly characterized model of TF cooperativity. We demonstrate that Tye7p often binds promoters without its recognition site because it is recruited by other collective members, whereas these other members require their recognition sites, suggesting a hierarchy where these factors recruit Tye7p but not vice versa. Our experiments establish CCRA as a useful tool for quantitative investigations into TF binding and function.
2020-01-30 | GSE144437 | GEO
Project description:High-throughput (HT) in vitro methods for measuring protein-DNA binding have become invaluable for characterizing transcription factor (TF) complexes and modeling gene regulation. However, current methods do not utilize endogenous proteins and, therefore, do not quantify the impact of cell-specific post-translational modifications (PTMs) and cooperative cofactors. We introduce the HT nextPBM (nuclear extract protein-binding microarray) approach to study DNA binding of native cellular TFs that accounts for PTMs and cell-specific cofactors. We integrate immune-depletion and phosphatase-treatment steps into our nextPBM pipeline to characterize the impact of cofactors and phosphorylation on TF binding. We analyze binding of PU.1/SPI1 and IRF8 from human monocytes, delineate DNA-sequence determinants for their cooperativity, and show how PU.1 affinity correlates with enhancer status and the presence of cooperative and collaborative cofactors. We describe how nextPBMs, and our accompanying computational framework, can be used to discover cell-specific cofactors, screen for synthetic cooperative DNA elements, and characterize TF cooperativity at single-nucleotide resolution.
2018-12-18 | GSE123946 | GEO
Project description:During cell division, transcription factors (TFs) are removed from chromatin twice, during DNA synthesis, and during condensation of chromosomes. How TFs can efficiently find their sites following these stages has been unclear. Here, we have analyzed the binding pattern of expressed TFs in human colorectal cancer cells. We find that binding of TFs is highly clustered, and that the clusters are enriched in binding motifs for several major TF classes. Strikingly, almost all clusters are formed around cohesin, and loss of cohesin decreases both DNA accessibility and binding of TFs to clusters. We show that cohesin remains bound in S phase, holding the nascent sister chromatids together at the TF cluster sites. Furthermore, cohesin remains bound to the cluster sites when TFs are evicted in early M-phase. These results suggest that cohesin binding functions as a cellular memory that promotes re- stablishment of TF clusters after DNA replication and chromatin condensation. Examination of TF binding by ChIP-seq in a CRC cell-line.
2013-09-25 | E-GEOD-51142 | biostudies-arrayexpress
Project description:During cell division, transcription factors (TFs) are removed from chromatin twice, during DNA synthesis, and during condensation of chromosomes. How TFs can efficiently find their sites following these stages has been unclear. Here, we have analyzed the binding pattern of expressed TFs in human colorectal cancer cells. We find that binding of TFs is highly clustered, and that the clusters are enriched in binding motifs for several major TF classes. Strikingly, almost all clusters are formed around cohesin, and loss of cohesin decreases both DNA accessibility and binding of TFs to clusters. We show that cohesin remains bound in S phase, holding the nascent sister chromatids together at the TF cluster sites. Furthermore, cohesin remains bound to the cluster sites when TFs are evicted in early M-phase. These results suggest that cohesin binding functions as a cellular memory that promotes re- stablishment of TF clusters after DNA replication and chromatin condensation. Examination of TF binding by ChIP-seq in LoVo CRC cell-lines.
2013-08-15 | E-GEOD-49402 | biostudies-arrayexpress