-
The Journal of Biological Chemistry Jun 2022Formation of transcription factor (TF)-coregulator complexes is a key step in transcriptional regulation, with coregulators having essential functions as hub nodes in...
Formation of transcription factor (TF)-coregulator complexes is a key step in transcriptional regulation, with coregulators having essential functions as hub nodes in molecular networks. How specificity and selectivity are maintained in these nodes remain open questions. In this work, we addressed specificity in transcriptional networks using complexes formed between TFs and αα-hubs, which are defined by a common αα-hairpin secondary structure motif, as a model. Using NMR spectroscopy and binding thermodynamics, we analyzed the structure, dynamics, stability, and ligand-binding properties of the Arabidopsis thaliana RST domains from TAF4 and known binding partner RCD1, and the TAFH domain from human TAF4, allowing comparison across species, functions, and architectural contexts. While these αα-hubs shared the αα-hairpin motif, they differed in length and orientation of accessory helices as well as in their thermodynamic profiles of ligand binding. Whereas biologically relevant RCD1-ligand pairs displayed high affinity driven by enthalpy, TAF4-ligand interactions were entropy driven and exhibited less binding-induced structuring. We in addition identified a thermal unfolding state with a structured core for all three domains, although the temperature sensitivity differed. Thermal stability studies suggested that initial unfolding of the RCD1-RST domain localized around helix 1, lending this region structural malleability, while effects in TAF4-RST were more stochastic, suggesting variability in structural adaptability upon binding. Collectively, our results support a model in which hub structure, flexibility, and binding thermodynamics contribute to αα-hub-TF binding specificity, a finding of general relevance to the understanding of coregulator-ligand interactions and interactome sizes.
Topics: Arabidopsis; Arabidopsis Proteins; Humans; Ligands; Nuclear Proteins; Protein Binding; TATA-Binding Protein Associated Factors; Transcription Factor TFIID; Transcription Factors; Transcription Factors, TFII
PubMed: 35452682
DOI: 10.1016/j.jbc.2022.101963 -
G3 (Bethesda, Md.) Jul 2022The ability to predict which genes will respond to the perturbation of a transcription factor serves as a benchmark for our systems-level understanding of...
The ability to predict which genes will respond to the perturbation of a transcription factor serves as a benchmark for our systems-level understanding of transcriptional regulatory networks. In previous work, machine learning models have been trained to predict static gene expression levels in a biological sample by using data from the same or similar samples, including data on their transcription factor binding locations, histone marks, or DNA sequence. We report on a different challenge-training machine learning models to predict which genes will respond to the perturbation of a transcription factor without using any data from the perturbed cells. We find that existing transcription factor location data (ChIP-seq) from human cells have very little detectable utility for predicting which genes will respond to perturbation of a transcription factor. Features of genes, including their preperturbation expression level and expression variation, are very useful for predicting responses to perturbation of any transcription factor. This shows that some genes are poised to respond to transcription factor perturbations and others are resistant, shedding light on why it has been so difficult to predict responses from binding locations. Certain histone marks, including H3K4me1 and H3K4me3, have some predictive power when located downstream of the transcription start site. However, the predictive power of histone marks is much less than that of gene expression level and expression variation. Sequence-based or epigenetic properties of genes strongly influence their tendency to respond to direct transcription factor perturbations, partially explaining the oft-noted difficulty of predicting responsiveness from transcription factor binding location data. These molecular features are largely reflected in and summarized by the gene's expression level and expression variation. Code is available at https://github.com/BrentLab/TFPertRespExplainer.
Topics: Gene Expression Regulation; Gene Regulatory Networks; Humans; Protein Binding; Transcription Factors; Transcription Initiation Site
PubMed: 35666184
DOI: 10.1093/g3journal/jkac144 -
The Journal of Biological Chemistry May 2023The conversion of signal transducer and activator of transcription (STAT) proteins from latent to active transcription factors is central to cytokine signaling....
The conversion of signal transducer and activator of transcription (STAT) proteins from latent to active transcription factors is central to cytokine signaling. Triggered by their signal-induced tyrosine phosphorylation, it is the assembly of a range of cytokine-specific STAT homo- and heterodimers that marks a key step in the transition of hitherto latent proteins to transcription activators. In contrast, the constitutive self-assembly of latent STATs and how it relates to the functioning of activated STATs is understood less well. To provide a more complete picture, we developed a co-localization-based assay and tested all 28 possible combinations of the seven unphosphorylated STAT (U-STAT) proteins in living cells. We identified five U-STAT homodimers-STAT1, STAT3, STAT4, STAT5A, and STAT5B-and two heterodimers-STAT1:STAT2 and STAT5A:STAT5B-and performed semi-quantitative assessments of the forces and characterizations of binding interfaces that support them. One STAT protein-STAT6-was found to be monomeric. This comprehensive analysis of latent STAT self-assembly lays bare considerable structural and functional diversity in the ways that link STAT dimerization before and after activation.
Topics: Cytokines; Gene Expression Regulation; Phosphorylation; STAT1 Transcription Factor; STAT2 Transcription Factor; STAT3 Transcription Factor; STAT4 Transcription Factor; STAT5 Transcription Factor; Trans-Activators; STAT Transcription Factors; Protein Multimerization
PubMed: 37059181
DOI: 10.1016/j.jbc.2023.104703 -
International Journal of Molecular... Jan 2021As sessile organisms, plants have evolved unique patterns of growth and development, elaborate metabolism and special perception and signaling mechanisms to... (Review)
Review
As sessile organisms, plants have evolved unique patterns of growth and development, elaborate metabolism and special perception and signaling mechanisms to environmental cues. Likewise, plants have complex and highly special programs for transcriptional control of gene expression. A case study for the special transcription control in plants is the expansion of general transcription factors, particularly the family of Transcription Factor IIB (TFIIB)-like factors with 15 members in Arabidopsis. For more than a decade, molecular and genetic analysis has revealed important functions of these TFIIB-like factors in specific biological processes including gametogenesis, pollen tube growth guidance, embryogenesis, endosperm development, and plant-microbe interactions. The redundant, specialized, and diversified roles of these TFIIB-like factors challenge the traditional definition of general transcription factors established in other eukaryotes. In this review, we discuss general transcription factors in plants with a focus on the expansion and functional analysis of plant TFIIB-like proteins to highlight unique aspects of plant transcription programs that can be highly valuable for understanding the molecular basis of plant growth, development and responses to stress conditions.
Topics: Arabidopsis Proteins; Archaeal Proteins; Bacterial Proteins; Eukaryota; Gene Expression Regulation, Plant; Plant Proteins; Transcription Factor TFIIB; Transcription Factors
PubMed: 33498602
DOI: 10.3390/ijms22031078 -
Cell Reports Mar 2024Whole-body regeneration requires the ability to produce the full repertoire of adult cell types. The planarian Schmidtea mediterranea contains over 125 cell types, which...
Whole-body regeneration requires the ability to produce the full repertoire of adult cell types. The planarian Schmidtea mediterranea contains over 125 cell types, which can be regenerated from a stem cell population called neoblasts. Neoblast fate choice can be regulated by the expression of fate-specific transcription factors (FSTFs). How fate choices are made and distributed across neoblasts versus their post-mitotic progeny remains unclear. We used single-cell RNA sequencing to systematically map fate choices made in S/G/M neoblasts and, separately, in their post-mitotic progeny that serve as progenitors for all adult cell types. We defined transcription factor expression signatures associated with all detected fates, identifying numerous new progenitor classes and FSTFs that regulate them. Our work generates an atlas of stem cell fates with associated transcription factor signatures for most cell types in a complete adult organism.
Topics: Animals; Transcription Factors; Planarians; Stem Cells; Cell Differentiation; Gene Expression Regulation
PubMed: 38401119
DOI: 10.1016/j.celrep.2024.113843 -
PloS One 2021Genes from the Grainyhead-like (GRHL) family code for transcription factors necessary for the development and maintenance of various epithelia. These genes are also very...
Genes from the Grainyhead-like (GRHL) family code for transcription factors necessary for the development and maintenance of various epithelia. These genes are also very important in the development of many types of cancer. However, little is known about the regulation of expression of GRHL genes. Previously, there were no systematic analyses of the promoters of GRHL genes or transcription factors that bind to these promoters. Here we report that the Krüppel-like factor 4 (KLF4) and the paired box 5 factor (PAX5) bind to the regulatory regions of the GRHL genes and regulate their expression. Ectopic expression of KLF4 or PAX5 alters the expression of GRHL genes. In KLF4-overexpressing HEK293 cells, the expression of GRHL1 and GRHL3 genes was upregulated by 32% and 60%, respectively, whereas the mRNA level of GRHL2 gene was lowered by 28% when compared to the respective controls. The levels of GRHL1 and GRHL3 expression were decreased by 30% or 33% in PAX5-overexpressing HEK293 cells. The presence of minor frequency allele of single nucleotide polymorphism rs115898376 in the promoter of the GRHL1 gene affected the binding of KLF4 to this site. The evidence presented here suggests an important role of KLF4 and PAX5 in the regulation of expression of GRHL1-3 genes.
Topics: Animals; Chromatin Immunoprecipitation; Computer Simulation; DNA-Binding Proteins; Electrophoretic Mobility Shift Assay; Gene Expression Regulation; Gene Frequency; HEK293 Cells; Humans; Kruppel-Like Factor 4; Kruppel-Like Transcription Factors; Mice; PAX5 Transcription Factor; Polymorphism, Single Nucleotide; Real-Time Polymerase Chain Reaction; Repressor Proteins; Transcription Factors
PubMed: 34570823
DOI: 10.1371/journal.pone.0257977 -
Nature Structural & Molecular Biology Jan 2024Gene expression in Escherichia coli is controlled by well-established mechanisms that activate or repress transcription. Here, we identify CedA as an unconventional...
Gene expression in Escherichia coli is controlled by well-established mechanisms that activate or repress transcription. Here, we identify CedA as an unconventional transcription factor specifically associated with the RNA polymerase (RNAP) σ holoenzyme. Structural and biochemical analysis of CedA bound to RNAP reveal that it bridges distant domains of β and σ subunits to stabilize an open-promoter complex. CedA does so without contacting DNA. We further show that cedA is strongly induced in response to amino acid starvation, oxidative stress and aminoglycosides. CedA provides a basal level of tolerance to these clinically relevant antibiotics, as well as to rifampicin and peroxide. Finally, we show that CedA modulates transcription of hundreds of bacterial genes, which explains its pleotropic effect on cell physiology and pathogenesis.
Topics: Escherichia coli; Sigma Factor; Transcription Factors; Escherichia coli Proteins; DNA-Directed RNA Polymerases; Transcription Factors, General; Transcription, Genetic; Bacterial Proteins
PubMed: 38177674
DOI: 10.1038/s41594-023-01154-w -
The Biochemical Journal Jul 1996This review focuses on the regulation of transcription factors, many of which are DNA-binding proteins that recognize cis-regulatory elements of target genes and are the... (Review)
Review
This review focuses on the regulation of transcription factors, many of which are DNA-binding proteins that recognize cis-regulatory elements of target genes and are the most direct regulators of gene transcription. Transcription factors serve as integration centres of the different signal-transduction pathways affecting a given gene. It is obvious that the regulation of these regulators themselves is of crucial importance for differential gene expression during development and in terminally differentiated cells. Transcription factors can be regulated at two, principally different, levels, namely concentration and activity, each of which can be modulated in a variety of ways. The concentrations of transcription factors, as of intracellular proteins in general, may be regulated at any of the steps leading from DNA to protein, including transcription, RNA processing, mRNA degradation and translation. The activity of a transcription factor is often regulated by (de) phosphorylation, which may affect different functions, e.g. nuclear localization DNA binding and trans-activation. Ligand binding is another mode of transcription-factor activation. It is typical for the large super-family of nuclear hormone receptors. Heterodimerization between transcription factors adds another dimension to the regulatory diversity and signal integration. Finally, non-DNA-binding (accessory) factors may mediate a diverse range of functions, e.g. serving as a bridge between the transcription factor and the basal transcription machinery, stabilizing the DNA-binding complex or changing the specificity of the target sequence recognition. The present review presents an overview of different modes of transcription-factor regulation, each illustrated by typical examples.
Topics: Animals; DNA-Binding Proteins; Gene Expression Regulation; Models, Genetic; Protein Binding; Protein Processing, Post-Translational; Transcription Factors; Transcription, Genetic
PubMed: 8713055
DOI: 10.1042/bj3170329 -
Experimental Cell Research Jan 2023DDIT3 is a tightly regulated basic leucine zipper (bZIP) transcription factor and key regulator in cellular stress responses. It is involved in a variety of pathological...
DDIT3 is a tightly regulated basic leucine zipper (bZIP) transcription factor and key regulator in cellular stress responses. It is involved in a variety of pathological conditions and may cause cell cycle block and apoptosis. It is also implicated in differentiation of some specialized cell types and as an oncogene in several types of cancer. DDIT3 was originally believed to act as a dominant-negative inhibitor by forming heterodimers with other bZIP transcription factors, preventing their DNA binding and transactivating functions. DDIT3 has, however, been reported to bind DNA and regulate target genes. Here, we employed ChIP sequencing combined with microarray-based expression analysis to identify direct binding motifs and target genes of DDIT3. The results reveal DDIT3 binding to motifs similar to other bZIP transcription factors, known to form heterodimers with DDIT3. Binding to a class III satellite DNA repeat sequence was also detected. DDIT3 acted as a DNA-binding transcription factor and bound mainly to the promotor region of regulated genes. ChIP sequencing analysis of histone H3K27 methylation and acetylation showed a strong overlap between H3K27-acetylated marks and DDIT3 binding. These results support a role for DDIT3 as a transcriptional regulator of H3K27ac-marked genes in transcriptionally active chromatin.
Topics: Binding Sites; Transcription Factors; Genomics; Basic-Leucine Zipper Transcription Factors; DNA
PubMed: 36402425
DOI: 10.1016/j.yexcr.2022.113418 -
Transcription factors and their genes in higher plants functional domains, evolution and regulation.European Journal of Biochemistry Jun 1999A typical plant transcription factor contains, with few exceptions, a DNA-binding region, an oligomerization site, a transcription-regulation domain, and a nuclear... (Review)
Review
A typical plant transcription factor contains, with few exceptions, a DNA-binding region, an oligomerization site, a transcription-regulation domain, and a nuclear localization signal. Most transcription factors exhibit only one type of DNA-binding and oligomerization domain, occasionally in multiple copies, but some contain two distinct types. DNA-binding regions are normally adjacent to or overlap with oligomerization sites, and their combined tertiary structure determines critical aspects of transcription factor activity. Pairs of nuclear localization signals exist in several transcription factors, and basic amino acid residues play essential roles in their function, a property also true for DNA-binding domains. Multigene families encode transcription factors, with members either dispersed in the genome or clustered on the same chromosome. Distribution and sequence analyses suggest that transcription factor families evolved via gene duplication, exon capture, translocation, and mutation. The expression of transcription factor genes in plants is regulated at transcriptional and post-transcriptional levels, while the activity of their protein products is modulated post-translationally. The purpose of this review is to describe the domain structure of plant transcription factors, and to relate this information to processes that control the synthesis and action of these proteins.
Topics: Amino Acid Sequence; Biological Evolution; Gene Expression Regulation, Plant; Molecular Sequence Data; Transcription Factors
PubMed: 10336605
DOI: 10.1046/j.1432-1327.1999.00349.x