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Transcription factor clusters regulate genes in eukaryotic cells.

ABSTRACT: Transcription is regulated through binding factors to gene promoters to activate or repress expression, however, the mechanisms by which factors find targets remain unclear. Using single-molecule fluorescence microscopy, we determined in vivo stoichiometry and spatiotemporal dynamics of a GFP tagged repressor, Mig1, from a paradigm signaling pathway of Saccharomyces cerevisiae. We find the repressor operates in clusters, which upon extracellular signal detection, translocate from the cytoplasm, bind to nuclear targets and turnover. Simulations of Mig1 configuration within a 3D yeast genome model combined with a promoter-specific, fluorescent translation reporter confirmed clusters are the functional unit of gene regulation. In vitro and structural analysis on reconstituted Mig1 suggests that clusters are stabilized by depletion forces between intrinsically disordered sequences. We observed similar clusters of a co-regulatory activator from a different pathway, supporting a generalized cluster model for transcription factors that reduces promoter search times through intersegment transfer while stabilizing gene expression.

SUBMITTER: Wollman AJ

PROVIDER: S-EPMC5602325 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Transcription factor clusters regulate genes in eukaryotic cells.

Wollman Adam Jm AJ Shashkova Sviatlana S Hedlund Erik G EG Friemann Rosmarie R Hohmann Stefan S Leake Mark C MC

eLife 20170825

Transcription is regulated through binding factors to gene promoters to activate or repress expression, however, the mechanisms by which factors find targets remain unclear. Using single-molecule fluorescence microscopy, we determined in vivo stoichiometry and spatiotemporal dynamics of a GFP tagged repressor, Mig1, from a paradigm signaling pathway of <i>Saccharomyces cerevisiae</i>. We find the repressor operates in clusters, which upon extracellular signal detection, translocate from the cyto ...[more]

PMID: 28841133

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Project description:Empirical studies have revealed that regulatory DNA sequences such as enhancers or promoters often harbor multiple binding sites for the same transcription factor. Such "homotypic site clustering" has been hypothesized as arising out of functional requirements of the sequences. Here, we propose an alternative explanation of this phenomenon that multisite enhancers are common because they are favored by evolutionary sampling of the genotype-phenotype landscape. To test this hypothesis, we developed a new computational framework specialized for population genetic simulations of enhancer evolution. It uses a thermodynamics-based model of enhancer function, integrating information from strong as well as weak binding sites, to determine the strength of selection. Using this framework, we found that even when simpler genotypes exist for a desired strength of regulation, relatively complex genotypes (enhancers with more sites) are more readily reached by the simulated evolutionary process. We show that there are more ways to "build" a fit genotype with many weak sites than with a few strong sites, and this is why evolution finds complex genotypes more often. Our claims are consistent with an empirical analysis of binding site content in enhancers characterized in Drosophila melanogaster and their orthologs in other Drosophila species. We also characterized a subtle but significant difference between genotypes likely to be sampled by evolution and equally fit genotypes one would obtain by uniform sampling of the fitness landscape, that is, an "evolutionary signature" in enhancer sequences. Finally, we investigated potential effects of other factors, such as rugged fitness landscapes, short local duplications, and noise characteristics of enhancers, on the emergence of homotypic site clustering. Homotypic site clustering is an important contributor to the complexity and function of cis-regulatory sequences. This work provides a simple null hypothesis for its origin, against which alternative adaptationist explanations may be evaluated, and cautions against "evolutionary mirages" present in common features of genomic sequence. The quantitative framework we develop here can be used more generally to understand how mechanisms of enhancer action influence their composition and evolution.

Project description:Background: The distribution and composition of cis-regulatory modules composed of transcription factor (TF) binding site (TFBS) clusters in promoters substantially determine gene expression patterns and TF targets. TF knockdown experiments have revealed that TF binding profiles and gene expression levels are correlated. We use TFBS features within accessible promoter intervals to predict genes with similar tissue-wide expression patterns and TF targets using Machine Learning (ML). Methods: Bray-Curtis Similarity was used to identify genes with correlated expression patterns across 53 tissues. TF targets from knockdown experiments were also analyzed by this approach to set up the ML framework. TFBSs were selected within DNase I-accessible intervals of corresponding promoter sequences using information theory-based position weight matrices (iPWMs) for each TF. Features from information-dense clusters of TFBSs were input to ML classifiers which predict these gene targets along with their accuracy, specificity and sensitivity. Mutations in TFBSs were analyzed in silico to examine their impact on TFBS clustering and predict changes in gene regulation. Results: The glucocorticoid receptor gene ( NR3C1), whose regulation has been extensively studied, was selected to test this approach. SLC25A32 and TANK exhibited the most similar expression patterns to NR3C1. A Decision Tree classifier exhibited the best performance in detecting such genes, based on Area Under the Receiver Operating Characteristic curve (ROC). TF target gene prediction was confirmed using siRNA knockdown, which was more accurate than CRISPR/CAS9 inactivation. TFBS mutation analyses revealed that accurate target gene prediction required at least 1 information-dense TFBS cluster. Conclusions: ML based on TFBS information density, organization, and chromatin accessibility accurately identifies gene targets with comparable tissue-wide expression patterns. Multiple information-dense TFBS clusters in promoters appear to protect promoters from effects of deleterious binding site mutations in a single TFBS that would otherwise alter regulation of these genes.

Project description:BackgroundAxonal myelination is critical for the functioning of vertebrate nervous system. Myelin sheath malformation or degeneration can cause a variety of neurological diseases. Our previous study identified multiple potential myelination-related transcriptional factors (TFs), including expressed sequence tag (ETS) variant transcription factor 1 (Etv1)/Er81, via gene microarray analysis of Schwann cells (SCs) at various myelination stages. Etv1 is known to be involved in the regulation of neuronal specialization, muscle spindle differentiation, and sensorimotor connectivity. However, to our knowledge, to date, there are no relevant studies that Etv1 regulates SC myelination.MethodsTo investigate the roles of Etv1 in SC re-myelination, an in vivo mouse myelination model was used, in which the sciatic nerve is crushed. Etv1 in nerves was knocked down via in situ injection of cholesterol-modified Etv1-small interfering (si)RNA. The expression of myelin-associated glycoprotein (MAG) was evaluated by Western blotting (WB) and immunohistochemistry (IHC). Myelination was assessed by transmission electron microscopy (TEM). The effects of Etv1 on SC proliferation, migration, and differentiation were assessed in vitro using the EdU cell proliferation kit, a culture-insert scratch assay, a SC aggregate sphere migration assay on the axons of dorsal root ganglions (DRGs), and a SC differentiation model. Chromatin immunoprecipitation (ChIP) united with quantitative real-time PCR (qPCR), known as ChIP-qPCR, and luciferase activity reporter assays were performed to explore the possible mechanisms by which Etv1 controls SC differentiation and myelination.ResultsThe results demonstrated that Etv1 promoted myelination by facilitating SC proliferation, migration, and differentiation. Etv1 expression in SCs was upregulated during re-myelination, and knocking down Etv1 expression dramatically abrogated SC re-myelination in the crushed sciatic nerves. Moreover, silencing of Etv1 by siRNA in SCs in vitro inhibited its migration, proliferation, and differentiation. The results of ChIP-qPCR and luciferase reporter assay showed that Etv1 may regulate SC differentiation and myelination by binding to the promoters of myelination-related genes, such as MAG and Runx2, to initiate their transcription.ConclusionsTaken together, these findings demonstrated a previously unknown role of Etv1 in SC differentiation and myelination, providing a candidate molecular target for clinical interventions in demyelinating diseases.

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Transcription factor clusters regulate genes in eukaryotic cells.

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Transcription factor clusters regulate genes in eukaryotic cells.

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