Browse
Submit Data
Databases
API
Help

Dataset Information

35 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Chromatin information content landscapes inform transcription factor and DNA interactions.

ABSTRACT: Interactions between transcription factors and chromatin are fundamental to genome organization and regulation and, ultimately, cell state. Here, we use information theory to measure signatures of organized chromatin resulting from transcription factor-chromatin interactions encoded in the patterns of the accessible genome, which we term chromatin information enrichment (CIE). We calculate CIE for hundreds of transcription factor motifs across human samples and identify two classes: low and high CIE. The 10-20% of common and tissue-specific high CIE transcription factor motifs, associate with higher protein-DNA residence time, including different binding site subclasses of the same transcription factor, increased nucleosome phasing, specific protein domains, and the genetic control of both chromatin accessibility and gene expression. These results show that variations in the information encoded in chromatin architecture reflect functional biological variation, with implications for cell state dynamics and memory.

SUBMITTER: D'Oliveira Albanus R

PROVIDER: S-EPMC7910283 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

NeuroD1 reprograms chromatin and transcription factor landscapes to induce the neuronal program.

Project description:Cell fate specification relies on the action of critical transcription factors that become available at distinct stages of embryonic development. One such factor is NeuroD1, which is essential for eliciting the neuronal development program and possesses the ability to reprogram other cell types into neurons. Given this capacity, it is important to understand its targets and the mechanism underlying neuronal specification. Here, we show that NeuroD1 directly binds regulatory elements of neuronal genes that are developmentally silenced by epigenetic mechanisms. This targeting is sufficient to initiate events that confer transcriptional competence, including reprogramming of transcription factor landscape, conversion of heterochromatin to euchromatin, and increased chromatin accessibility, indicating potential pioneer factor ability of NeuroD1. The transcriptional induction of neuronal fate genes is maintained via epigenetic memory despite a transient NeuroD1 induction during neurogenesis. NeuroD1 also induces genes involved in the epithelial-to-mesenchymal transition, thereby promoting neuronal migration. Our study not only reveals the NeuroD1-dependent gene regulatory program driving neurogenesis but also increases our understanding of how cell fate specification during development involves a concerted action of transcription factors and epigenetic mechanisms.

| S-EPMC4718003 | biostudies-literature

Progesterone receptor mediates ovulatory transcription through RUNX transcription factor interactions and chromatin remodelling.

Project description:Progesterone receptor (PGR) plays diverse roles in reproductive tissues and thus coordinates mammalian fertility. In the ovary, rapid acute induction of PGR is the key determinant of ovulation through transcriptional control of a unique set of genes that culminates in follicle rupture. However, the molecular mechanisms for this specialized PGR function in ovulation is poorly understood. We have assembled a detailed genomic profile of PGR action through combined ATAC-seq, RNA-seq and ChIP-seq analysis in wildtype and isoform-specific PGR null mice. We demonstrate that stimulating ovulation rapidly reprograms chromatin accessibility in two-thirds of sites, correlating with altered gene expression. An ovary-specific PGR action involving interaction with RUNX transcription factors was observed with 70% of PGR-bound regions also bound by RUNX1. These transcriptional complexes direct PGR binding to proximal promoter regions. Additionally, direct PGR binding to the canonical NR3C motif enable chromatin accessibility. Together these PGR actions mediate induction of essential ovulatory genes. Our findings highlight a novel PGR transcriptional mechanism specific to ovulation, providing new targets for infertility treatments or new contraceptives that block ovulation.

| S-EPMC10325896 | biostudies-literature

NAUTICA: classifying transcription factor interactions by positional and protein-protein interaction information.

Project description:BackgroundInferring the mechanisms that drive transcriptional regulation is of great interest to biologists. Generally, methods that predict physical interactions between transcription factors (TFs) based on positional information of their binding sites (e.g. chromatin immunoprecipitation followed by sequencing (ChIP-Seq) experiments) cannot distinguish between different kinds of interaction at the same binding spots, such as co-operation and competition.ResultsIn this work, we present the Network-Augmented Transcriptional Interaction and Coregulation Analyser (NAUTICA), which employs information from protein-protein interaction (PPI) networks to assign TF-TF interaction candidates to one of three classes: competition, co-operation and non-interactions. NAUTICA filters available PPI network edges and fits a prediction model based on the number of shared partners in the PPI network between two candidate interactors.ConclusionsNAUTICA improves on existing positional information-based TF-TF interaction prediction results, demonstrating how PPI information can improve the quality of TF interaction prediction. NAUTICA predictions - both co-operations and competitions - are supported by literature investigation, providing evidence on its capability of providing novel interactions of both kinds.ReviewersThis article was reviewed by Zoltán Hegedüs and Endre Barta.

| S-EPMC7493360 | biostudies-literature

High-throughput chromatin information enables accurate tissue-specific prediction of transcription factor binding sites.

Project description:In silico prediction of transcription factor binding sites (TFBSs) is central to the task of gene regulatory network elucidation. Genomic DNA sequence information provides a basis for these predictions, due to the sequence specificity of TF-binding events. However, DNA sequence alone is an impoverished source of information for the task of TFBS prediction in eukaryotes, as additional factors, such as chromatin structure regulate binding events. We show that incorporating high-throughput chromatin modification estimates can greatly improve the accuracy of in silico prediction of in vivo binding for a wide range of TFs in human and mouse. This improvement is superior to the improvement gained by equivalent use of either transcription start site proximity or phylogenetic conservation information. Importantly, predictions made with the use of chromatin structure information are tissue specific. This result supports the biological hypothesis that chromatin modulates TF binding to produce tissue-specific binding profiles in higher eukaryotes, and suggests that the use of chromatin modification information can lead to accurate tissue-specific transcriptional regulatory network elucidation.

| S-EPMC2662491 | biostudies-literature

TRACE: transcription factor footprinting using chromatin accessibility data and DNA sequence.

Project description:Transcription is tightly regulated by cis-regulatory DNA elements where transcription factors (TFs) can bind. Thus, identification of TF binding sites (TFBSs) is key to understanding gene expression and whole regulatory networks within a cell. The standard approaches used for TFBS prediction, such as position weight matrices (PWMs) and chromatin immunoprecipitation followed by sequencing (ChIP-seq), are widely used but have their drawbacks, including high false-positive rates and limited antibody availability, respectively. Several computational footprinting algorithms have been developed to detect TFBSs by investigating chromatin accessibility patterns; however, these also have limitations. We have developed a footprinting method to predict TF footprints in active chromatin elements (TRACE) to improve the prediction of TFBS footprints. TRACE incorporates DNase-seq data and PWMs within a multivariate hidden Markov model (HMM) to detect footprint-like regions with matching motifs. TRACE is an unsupervised method that accurately annotates binding sites for specific TFs automatically with no requirement for pregenerated candidate binding sites or ChIP-seq training data. Compared with published footprinting algorithms, TRACE has the best overall performance with the distinct advantage of targeting multiple motifs in a single model.

| S-EPMC7397869 | biostudies-literature

Chromatin sequesters pioneer transcription factor Sox2 from exerting force on DNA.

Project description:Biomolecular condensation constitutes an emerging mechanism for transcriptional regulation. Recent studies suggest that the co-condensation between transcription factors (TFs) and DNA can generate mechanical forces driving genome rearrangements. However, the reported forces generated by protein-DNA co-condensation are typically below one piconewton (pN), questioning its physiological significance. Moreover, the force-generating capacity of these condensates in the chromatin context remains unknown. Here, we show that Sox2, a nucleosome-binding pioneer TF, forms co-condensates with DNA and generates forces up to 7 pN, exerting considerable mechanical tension on DNA strands. We find that the disordered domains of Sox2 are required for maximum force generation but not for condensate formation. Furthermore, we show that nucleosomes dramatically attenuate the mechanical stress exerted by Sox2 by sequestering it from coalescing on bare DNA. Our findings reveal that TF-mediated DNA condensation can exert significant mechanical stress on the genome which can nonetheless be attenuated by the chromatin architecture.

| S-EPMC9271091 | biostudies-literature

Rapid RNA-ligand interaction analysis through high-information content conformational and stability landscapes.

Project description:The structure and biological properties of RNAs are a function of changing cellular conditions, but comprehensive, simultaneous investigation of the effect of multiple interacting environmental variables is not easily achieved. We have developed an efficient, high-throughput method to characterize RNA structure and thermodynamic stability as a function of multiplexed solution conditions using Förster resonance energy transfer (FRET). In a single FRET experiment using conventional quantitative PCR instrumentation, 19,400 conditions of MgCl2, ligand and temperature are analysed to generate detailed empirical conformational and stability landscapes of the cyclic diguanylate (c-di-GMP) riboswitch. The method allows rapid comparison of RNA structure modulation by cognate and non-cognate ligands. Landscape analysis reveals that kanamycin B stabilizes a non-native, idiosyncratic conformation of the riboswitch that inhibits c-di-GMP binding. This demonstrates that allosteric control of folding, rather than direct competition with cognate effectors, is a viable approach for pharmacologically targeting riboswitches and other structured RNA molecules.

| S-EPMC4686816 | biostudies-other

The Transcription Factor Runx3 Establishes Chromatin Accessibility of cis-Regulatory Landscapes that Drive Memory Cytotoxic T Lymphocyte Formation.

Project description:T cell receptor (TCR) stimulation of naive CD8+ T cells initiates reprogramming of cis-regulatory landscapes that specify effector and memory cytotoxic T lymphocyte (CTL) differentiation. We mapped regions of hyper-accessible chromatin in naive cells during TCR stimulation and discovered that the transcription factor (TF) Runx3 promoted accessibility to memory CTL-specific cis-regulatory regions before the first cell division and was essential for memory CTL differentiation. Runx3 was specifically required for accessibility to regions highly enriched with IRF, bZIP and Prdm1-like TF motifs, upregulation of TFs Irf4 and Blimp1, and activation of fundamental CTL attributes in early effector and memory precursor cells. Runx3 ensured that nascent CTLs differentiated into memory CTLs by preventing high expression of the TF T-bet, slowing effector cell proliferation, and repressing terminal CTL differentiation. Runx3 overexpression enhanced memory CTL differentiation during iterative infections. Thus, Runx3 governs chromatin accessibility during TCR stimulation and enforces the memory CTL developmental program.

| S-EPMC6750808 | biostudies-literature

Biophysical fitness landscapes for transcription factor binding sites.

Project description:Phenotypic states and evolutionary trajectories available to cell populations are ultimately dictated by complex interactions among DNA, RNA, proteins, and other molecular species. Here we study how evolution of gene regulation in a single-cell eukaryote S. cerevisiae is affected by interactions between transcription factors (TFs) and their cognate DNA sites. Our study is informed by a comprehensive collection of genomic binding sites and high-throughput in vitro measurements of TF-DNA binding interactions. Using an evolutionary model for monomorphic populations evolving on a fitness landscape, we infer fitness as a function of TF-DNA binding to show that the shape of the inferred fitness functions is in broad agreement with a simple functional form inspired by a thermodynamic model of two-state TF-DNA binding. However, the effective parameters of the model are not always consistent with physical values, indicating selection pressures beyond the biophysical constraints imposed by TF-DNA interactions. We find little statistical support for the fitness landscape in which each position in the binding site evolves independently, indicating that epistasis is common in the evolution of gene regulation. Finally, by correlating TF-DNA binding energies with biological properties of the sites or the genes they regulate, we are able to rule out several scenarios of site-specific selection, under which binding sites of the same TF would experience different selection pressures depending on their position in the genome. These findings support the existence of universal fitness landscapes which shape evolution of all sites for a given TF, and whose properties are determined in part by the physics of protein-DNA interactions.

| S-EPMC4091707 | biostudies-literature

Modulating DNA by polyamides to regulate transcription factor PU.1-DNA binding interactions.

Project description:Hairpin polyamides are synthetic small molecules that bind DNA minor groove sequence-selectively and, in many sequences, induce widening of the minor groove and compression of the major groove. The structural distortion of DNA caused by polyamides has enhanced our understanding of the regulation of DNA-binding proteins via polyamides. Polyamides have DNA binding affinities that are comparable to those proteins, therefore, can potentially be used as therapeutic agents to treat diseases caused by aberrant gene expression. In fact, many diseases are characterized by over- or under-expressed genes. PU.1 is a transcription factor that regulates many immune system genes. Aberrant expression of PU.1 has been associated with the development of acute myeloid leukemia (AML). We have, therefore, designed and synthesized ten hairpin polyamides to investigate their capacity in controlling the PU.1-DNA interaction. Our results showed that nine of the polyamides disrupt PU.1-DNA binding and the inhibition capacity strongly correlates with binding affinity. One molecule, FH1024, was observed forming a FH1024-PU.1-DNA ternary complex instead of inhibiting PU.1-DNA binding. This is the first report of a small molecule that is potentially a weak agonist that recruits PU.1 to DNA. This finding sheds light on the design of polyamides that exhibit novel regulatory mechanisms on protein-DNA binding.

| S-EPMC6883780 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data