Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

The transcription factor titration effect dictates level of gene expression.

ABSTRACT: Models of transcription are often built around a picture of RNA polymerase and transcription factors (TFs) acting on a single copy of a promoter. However, most TFs are shared between multiple genes with varying binding affinities. Beyond that, genes often exist at high copy number-in multiple identical copies on the chromosome or on plasmids or viral vectors with copy numbers in the hundreds. Using a thermodynamic model, we characterize the interplay between TF copy number and the demand for that TF. We demonstrate the parameter-free predictive power of this model as a function of the copy number of the TF and the number and affinities of the available specific binding sites; such predictive control is important for the understanding of transcription and the desire to quantitatively design the output of genetic circuits. Finally, we use these experiments to dynamically measure plasmid copy number through the cell cycle.

SUBMITTER: Brewster RC

PROVIDER: S-EPMC4080642 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

The transcription factor titration effect dictates level of gene expression.

Brewster Robert C RC Weinert Franz M FM Garcia Hernan G HG Song Dan D Rydenfelt Mattias M Phillips Rob R

Cell 20140306 6

Models of transcription are often built around a picture of RNA polymerase and transcription factors (TFs) acting on a single copy of a promoter. However, most TFs are shared between multiple genes with varying binding affinities. Beyond that, genes often exist at high copy number-in multiple identical copies on the chromosome or on plasmids or viral vectors with copy numbers in the hundreds. Using a thermodynamic model, we characterize the interplay between TF copy number and the demand for tha ...[more]

PMID: 24612990

Similar Datasets

Effect of transcription factor resource sharing on gene expression noise.

Project description:Gene expression is intrinsically a stochastic (noisy) process with important implications for cellular functions. Deciphering the underlying mechanisms of gene expression noise remains one of the key challenges of regulatory biology. Theoretical models of transcription often incorporate the kinetics of how transcription factors (TFs) interact with a single promoter to impact gene expression noise. However, inside single cells multiple identical gene copies as well as additional binding sites can compete for a limiting pool of TFs. Here we develop a simple kinetic model of transcription, which explicitly incorporates this interplay between TF copy number and its binding sites. We show that TF sharing enhances noise in mRNA distribution across an isogenic population of cells. Moreover, when a single gene copy shares it's TFs with multiple competitor sites, the mRNA variance as a function of the mean remains unaltered by their presence. Hence, all the data for variance as a function of mean expression collapse onto a single master curve independent of the strength and number of competitor sites. However, this result does not hold true when the competition stems from multiple copies of the same gene. Therefore, although previous studies showed that the mean expression follows a universal master curve, our findings suggest that different scenarios of competition bear distinct signatures at the level of variance. Intriguingly, the introduction of competitor sites can transform a unimodal mRNA distribution into a multimodal distribution. These results demonstrate the impact of limited availability of TF resource on the regulation of noise in gene expression.

| S-EPMC5411101 | biostudies-literature

Species-specific recruitment of transcription factors dictates toxin expression.

Project description:Tight and coordinate regulation of virulence determinants is essential for bacterial biology and involves dynamic shaping of transcriptional regulatory networks during evolution. The horizontally transferred two-partner secretion system ExlB-ExlA is instrumental in the virulence of different Pseudomonas species, ranging from soil- and plant-dwelling biocontrol agents to the major human pathogen Pseudomonas aeruginosa. Here, we identify a Cro/CI-like repressor, named ErfA, which together with Vfr, a CRP-like activator, controls exlBA expression in P. aeruginosa. The characterization of ErfA regulon across P. aeruginosa subfamilies revealed a second conserved target, the ergAB operon, with functions unrelated to virulence. To gain insights into this functional dichotomy, we defined the pan-regulon of ErfA in several Pseudomonas species and found ergAB as the sole conserved target of ErfA. The analysis of 446 exlBA promoter sequences from all exlBA+ genomes revealed a wide variety of regulatory sequences, as ErfA- and Vfr-binding sites were found to have evolved specifically in P. aeruginosa and nearly each species carries different regulatory sequences for this operon. We propose that the emergence of different regulatory cis-elements in the promoters of horizontally transferred genes is an example of plasticity of regulatory networks evolving to provide an adapted response in each individual niche.

| S-EPMC7049718 | biostudies-literature

A Transcription Factor Regulates Gene Expression in Chloroplasts.

Project description:The chloroplast is a semi-autonomous organelle with its own genome. The expression of chloroplast genes depends on both chloroplasts and the nucleus. Although many nucleus-encoded proteins have been shown to localize in chloroplasts and are essential for chloroplast gene expression, it is not clear whether transcription factors can regulate gene expression in chloroplasts. Here we report that the transcription factor NAC102 localizes in both chloroplasts and nucleus in Arabidopsis. Specifically, NAC102 localizes in chloroplast nucleoids. Yeast two-hybrid assay and co-immunoprecipitation assay suggested that NAC102 interacts with chloroplast RNA polymerases. Furthermore, overexpression of NAC102 in chloroplasts leads to reduced chloroplast gene expression and chlorophyll content, indicating that NAC102 functions as a repressor in chloroplasts. Our study not only revealed that transcription factors are new regulators of chloroplast gene expression, but also discovered that transcription factors can function in chloroplasts in addition to the canonical organelle nucleus.

| S-EPMC8268430 | biostudies-literature

A quantitative model of transcription factor-activated gene expression.

Project description:A challenge facing biology is to develop quantitative, predictive models of gene regulation. Eukaryotic promoters contain transcription factor binding sites of differing affinity and accessibility, but we understand little about how these variables combine to generate a fine-tuned, quantitative transcriptional response. Here we used the PHO5 promoter in budding yeast to quantify the relationship between transcription factor input and gene expression output, termed the gene-regulation function (GRF). A model that captures variable interactions between transcription factors, nucleosomes and the promoter faithfully reproduced the observed quantitative changes in the GRF that occur upon altering the affinity of transcription factor binding sites, and implicates nucleosome-modulated accessibility of transcription factor binding sites in increasing the diversity of gene expression profiles. This work establishes a quantitative framework that can be applied to predict GRFs of other eukaryotic genes.

| S-EPMC2696132 | biostudies-literature

SLC7A11 expression level dictates differential responses to oxidative stress in cancer cells.

Project description:The cystine transporter solute carrier family 7 member 11 (SLC7A11; also called xCT) protects cancer cells from oxidative stress and is overexpressed in many cancers. Here we report a surprising finding that, whereas moderate overexpression of SLC7A11 is beneficial for cancer cells treated with H2O2, a common oxidative stress inducer, its high overexpression dramatically increases H2O2-induced cell death. Mechanistically, high cystine uptake in cancer cells with high overexpression of SLC7A11 in combination with H2O2 treatment results in toxic buildup of intracellular cystine and other disulfide molecules, NADPH depletion, redox system collapse, and rapid cell death (likely disulfidptosis). We further show that high overexpression of SLC7A11 promotes tumor growth but suppresses tumor metastasis, likely because metastasizing cancer cells with high expression of SLC7A11 are particularly susceptible to oxidative stress. Our findings reveal that SLC7A11 expression level dictates cancer cells' sensitivity to oxidative stress and suggests a context-dependent role for SLC7A11 in tumor biology.

| S-EPMC10281978 | biostudies-literature

Gene Expression Profiling of FFPE Samples: A Titration Test.

Project description:The gene expression analysis of formalin-fixed paraffin-embedded (FFPE) tissues is often hampered by poor RNA quality, which results from the oxidation, cross-linking and other chemical modifications induced by the inclusion in paraffin. Yet, FFPE samples are a valuable source for molecular studies and can provide great insights into disease progression and prognosis. With the advancement of genomic technologies, new methods have been established that offer reliable and accurate gene expression workflows on samples of poor quality. NanoString is a probe-based technology that allows the direct counting of the mRNA transcripts and can be applied to degraded samples. Here, we have tested 2 RNA extraction methods for FFPE samples, and we have performed a titration experiment to evaluate the impact of RNA degradation and RNA input on the gene expression profiles assessed using the NanoString IO360 panel. We have selected FFPE samples of different DV200 values and assessed them on the nCounter platform with 2 different amounts of input RNA. This study concludes that the nCounter is a robust and reliable platform to assess the gene expression of RNA samples with DV200 > 30%; its robustness and ease of use could be of particular benefit to clinical settings.

| S-EPMC9706083 | biostudies-literature

Signal-dependent dynamics of transcription factor translocation controls gene expression.

Project description:Information about environmental stimuli is often transmitted using common signaling molecules, but the mechanisms that ensure signaling specificity are not entirely known. Here we show that the identities and intensities of different stresses are transmitted by modulation of the amplitude, duration or frequency of nuclear translocation of the Saccharomyces cerevisiae general stress response transcription factor Msn2. Through artificial control of the dynamics of Msn2 translocation, we reveal how distinct dynamical schemes differentially affect reporter gene expression. Using a simple model, we predict stress-induced reporter gene expression from single-cell translocation dynamics. We then demonstrate that the response of natural target genes to dynamical modulation of Msn2 translocation is influenced by differences in the kinetics of promoter transitions and transcription factor binding properties. Thus, multiple environmental signals can trigger qualitatively different dynamics of a single transcription factor and influence gene expression patterns.

| S-EPMC3936303 | biostudies-literature

Controlling for gene expression changes in transcription factor protein networks.

Project description:The development of affinity purification technologies combined with mass spectrometric analysis of purified protein mixtures has been used both to identify new protein-protein interactions and to define the subunit composition of protein complexes. Transcription factor protein interactions, however, have not been systematically analyzed using these approaches. Here, we investigated whether ectopic expression of an affinity tagged transcription factor as bait in affinity purification mass spectrometry experiments perturbs gene expression in cells, resulting in the false positive identification of bait-associated proteins when typical experimental controls are used. Using quantitative proteomics and RNA sequencing, we determined that the increase in the abundance of a set of proteins caused by overexpression of the transcription factor RelA is not sufficient for these proteins to then co-purify non-specifically and be misidentified as bait-associated proteins. Therefore, typical controls should be sufficient, and a number of different baits can be compared with a common set of controls. This is of practical interest when identifying bait interactors from a large number of different baits. As expected, we found several known RelA interactors enriched in our RelA purifications (NF?B1, NF?B2, Rel, RelB, I?B?, I?B?, and I?B?). We also found several proteins not previously described in association with RelA, including the small mitochondrial chaperone Tim13. Using a variety of biochemical approaches, we further investigated the nature of the association between Tim13 and NF?B family transcription factors. This work therefore provides a conceptual and experimental framework for analyzing transcription factor protein interactions.

| S-EPMC4047470 | biostudies-literature

Prediction of single-cell gene expression for transcription factor analysis.

Project description:Single-cell RNA sequencing is a powerful technology to discover new cell types and study biological processes in complex biological samples. A current challenge is to predict transcription factor (TF) regulation from single-cell RNA data. Here, we propose a novel approach for predicting gene expression at the single-cell level using cis-regulatory motifs, as well as epigenetic features. We designed a tree-guided multi-task learning framework that considers each cell as a task. Through this framework we were able to explain the single-cell gene expression values using either TF binding affinities or TF ChIP-seq data measured at specific genomic regions. TFs identified using these models could be validated by the literature. Our proposed method allows us to identify distinct TFs that show cell type-specific regulation. This approach is not limited to TFs but can use any type of data that can potentially be used in explaining gene expression at the single-cell level to study factors that drive differentiation or show abnormal regulation in disease. The implementation of our workflow can be accessed under an MIT license via https://github.com/SchulzLab/Triangulate.

| S-EPMC7596801 | biostudies-literature

Transcription Factor Sp1 Promotes the Expression of Porcine ROCK1 Gene.

Project description:Rho-associated, coiled-coil containing protein kinase 1 (ROCK1) gene plays a crucial role in maintaining genomic stability, tumorigenesis and myogenesis. However, little is known about the regulatory elements governing the transcription of porcine ROCK1 gene. In the current study, the transcription start site (TSS) was identified by 5'-RACE, and was found to differ from the predicted one. The region in ROCK1 promoter which is critical for promoter activity was investigated via progressive deletions. Site-directed mutagenesis indicated that the region from -604 to -554 bp contains responsive elements for Sp1. Subsequent experiments showed that ROCK1 promoter activity is enhanced by Sp1 in a dose-dependent manner, whereas treatment with specific siRNA repressed ROCK1 promoter activity. Electrophoretic mobility shift assay (EMSA), DNA pull down and chromatin immunoprecipitation (ChIP) assays revealed Sp1 can bind to this region. qRT-PCR and Western blotting research followed by overexpression or inhibition of Sp1 indicate that Sp1 can affect endogenous ROCK1 expression at both mRNA and protein levels. Overexpression of Sp1 can promote the expression of myogenic differentiation 1(MyoD), myogenin (MyoG), myosin heavy chain (MyHC). Taken together, we conclude that Sp1 positively regulates ROCK1 transcription by directly binding to the ROCK1 promoter region (from -604 to -532 bp) and may affect the process of myogenesis.

| S-EPMC4730353 | 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