RESUMO
Accessible chromatin is important for RNA polymerase II recruitment and transcription initiation at eukaryotic promoters. We investigated the mechanistic links between promoter DNA sequence, nucleosome positioning, and transcription. Our results indicate that positioning of the transcription start site-associated +1 nucleosome in yeast is critical for efficient TBP binding and is driven by two key factors, the essential chromatin remodeler RSC and a small set of ubiquitous general regulatory factors (GRFs). Our findings indicate that the strength and directionality of RSC action on promoter nucleosomes depends on the arrangement and proximity of two specific DNA motifs. This, together with the effect on nucleosome position observed in double depletion experiments, suggests that, despite their widespread co-localization, RSC and GRFs predominantly act through independent signals to generate accessible chromatin. Our results provide mechanistic insight into how the promoter DNA sequence instructs trans-acting factors to control nucleosome architecture and stimulate transcription initiation.
Assuntos
Montagem e Desmontagem da Cromatina , Nucleossomos/metabolismo , RNA Polimerase II/metabolismo , Proteínas de Saccharomyces cerevisiae/metabolismo , Saccharomyces cerevisiae/metabolismo , Transcrição Gênica , Nucleossomos/genética , RNA Polimerase II/genética , Saccharomyces cerevisiae/genética , Proteínas de Saccharomyces cerevisiae/genéticaRESUMO
Many transcription factors (TFs) localize in nuclear clusters of locally increased concentrations, but how TF clustering is regulated and how it influences gene expression is not well understood. Here, we use quantitative microscopy in living cells to study the regulation and function of clustering of the budding yeast TF Gal4 in its endogenous context. Our results show that Gal4 forms clusters that overlap with the GAL loci. Cluster number, density and size are regulated in different growth conditions by the Gal4-inhibitor Gal80 and Gal4 concentration. Gal4 truncation mutants reveal that Gal4 clustering is facilitated by, but does not completely depend on DNA binding and intrinsically disordered regions. Moreover, we discover that clustering acts as a double-edged sword: self-interactions aid TF recruitment to target genes, but recruited Gal4 molecules that are not DNA-bound do not contribute to, and may even inhibit, transcription activation. We propose that cells need to balance the different effects of TF clustering on target search and transcription activation to facilitate proper gene expression.
Assuntos
Proteínas de Saccharomyces cerevisiae , Fatores de Transcrição , Fatores de Transcrição/genética , Fatores de Transcrição/metabolismo , Ativação Transcricional , Proteínas Repressoras/metabolismo , Saccharomyces cerevisiae/genética , Saccharomyces cerevisiae/metabolismo , Proteínas de Saccharomyces cerevisiae/genética , Proteínas de Saccharomyces cerevisiae/metabolismoRESUMO
Transcription, the process of copying genetic information from DNA to messenger RNA, is regulated by sequence-specific DNA-binding proteins known as transcription factors (TFs). Recent advances in single-molecule tracking (SMT) technologies have enabled visualization of individual TF molecules as they diffuse and interact with the DNA in the context of living cells. These SMT studies have uncovered multiple populations of DNA-binding events characterized by their distinctive DNA residence times. In this perspective, we review recent insights into how these residence times relate to specific and non-specific DNA binding, as well as the contribution of TF domains on the DNA-binding dynamics. We discuss different models that aim to link transient DNA binding by TFs to bursts of transcription and present an outlook for how future advances in microscopy development may broaden our understanding of the dynamics of the molecular steps that underlie transcription activation.
Assuntos
Proteínas de Ligação a DNA , Fatores de Transcrição , Sítios de Ligação , DNA/química , Proteínas de Ligação a DNA/metabolismo , Ligação Proteica , Imagem Individual de Molécula , Fatores de Transcrição/metabolismoRESUMO
An important distinction is frequently made between constitutively expressed housekeeping genes versus regulated genes. Although generally characterized by different DNA elements, chromatin architecture and cofactors, it is not known to what degree promoter classes strictly follow regulatability rules and which molecular mechanisms dictate such differences. We show that SAGA-dominated/TATA-box promoters are more responsive to changes in the amount of activator, even compared to TFIID/TATA-like promoters that depend on the same activator Hsf1. Regulatability is therefore an inherent property of promoter class. Further analyses show that SAGA/TATA-box promoters are more dynamic because TATA-binding protein recruitment through SAGA is susceptible to removal by Mot1. In addition, the nucleosome configuration upon activator depletion shifts on SAGA/TATA-box promoters and seems less amenable to preinitiation complex formation. The results explain the fundamental difference between housekeeping and regulatable genes, revealing an additional facet of combinatorial control: an activator can elicit a different response dependent on core promoter class.
Assuntos
Regulação Fúngica da Expressão Gênica , Genes Essenciais , Regiões Promotoras Genéticas , Saccharomyces cerevisiae/genética , Fator de Transcrição TFIID/genética , Ativação Transcricional , Adenosina Trifosfatases/metabolismo , Proteínas de Saccharomyces cerevisiae/metabolismo , Fatores Associados à Proteína de Ligação a TATA/metabolismo , Proteína de Ligação a TATA-Box/metabolismoRESUMO
Protein-DNA interactions are dynamic, and these dynamics are an important aspect of chromatin-associated processes such as transcription or replication. Due to a lack of methods to study on- and off-rates across entire genomes, protein-DNA interaction dynamics have not been studied extensively. Here, we determine in vivo off-rates for the Saccharomyces cerevisiae chromatin organizing factor Abf1, at 191 sites simultaneously across the yeast genome. Average Abf1 residence times span a wide range, varying between 4.2 and 33 min. Sites with different off-rates are associated with different functional characteristics. This includes their transcriptional dependency on Abf1, nucleosome positioning and the size of the nucleosome-free region, as well as the ability to roadblock RNA polymerase II for termination. The results show how off-rates contribute to transcription factor function and that DIVORSEQ (Determining In Vivo Off-Rates by SEQuencing) is a meaningful way of investigating protein-DNA binding dynamics genome-wide.
Assuntos
Sequenciamento de Cromatina por Imunoprecipitação/métodos , Cromatina/metabolismo , Proteínas de Ligação a DNA/metabolismo , Nucleossomos/metabolismo , Proteínas de Saccharomyces cerevisiae/metabolismo , Saccharomyces cerevisiae/metabolismo , Fatores de Transcrição/metabolismo , Transcrição Gênica , Sítios de Ligação , Cromatina/genética , Montagem e Desmontagem da Cromatina/genética , Proteínas de Ligação a DNA/genética , Regulação Fúngica da Expressão Gênica/genética , Genoma Fúngico , Nuclease do Micrococo/metabolismo , Nucleossomos/genética , Regiões Promotoras Genéticas , Ligação Proteica , RNA Polimerase II/genética , RNA Polimerase II/metabolismo , Saccharomyces cerevisiae/genética , Proteínas de Saccharomyces cerevisiae/genética , Fatores de Transcrição/genética , Transcrição Gênica/genéticaRESUMO
Transcription factors are important regulators of cell fate and function. Knowledge about where transcription factors are bound in the genome is crucial for understanding their function. A common method to study protein-DNA interactions is chromatin immunoprecipitation (ChIP). Here, we present a revised ChIP protocol to determine protein-DNA interactions for the yeast Saccharomyces cerevisiae. We optimized several aspects of the procedure, including cross-linking and quenching, cell lysis, and immunoprecipitation steps. This protocol facilitates sensitive and reproducible quantitation of protein-DNA interactions. For complete details on the use and execution of this protocol, please refer to (de Jonge et al., 2019).