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Interaction of transcriptional regulators with specific nucleosomes across the Saccharomyces genome.

ABSTRACT: A canonical nucleosome architecture around promoters establishes the context in which proteins regulate gene expression. Whether gene regulatory proteins that interact with nucleosomes are selective for individual nucleosome positions across the genome is not known. Here, we examine on a genomic scale several protein-nucleosome interactions, including those that (1) bind histones (Bdf1/SWR1 and Srm1), (2) bind specific DNA sequences (Rap1 and Reb1), and (3) potentially collide with nucleosomes during transcription (RNA polymerase II). We find that the Bdf1/SWR1 complex forms a dinucleosome complex that is selective for the +1 and +2 nucleosomes of active genes. Rap1 selectively binds to its cognate site on the rotationally exposed first and second helical turn of nucleosomal DNA. We find that a transcribing RNA polymerase creates a delocalized state of resident nucleosomes. These findings suggest that nucleosomes around promoter regions have position-specific functions and that some gene regulators have position-specific nucleosomal interactions.

SUBMITTER: Koerber RT

PROVIDER: S-EPMC2760215 | biostudies-literature | 2009 Sep

REPOSITORIES: biostudies-literature

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Interaction of transcriptional regulators with specific nucleosomes across the Saccharomyces genome.

Koerber R Thomas RT Rhee Ho Sung HS Jiang Cizhong C Pugh B Franklin BF

Molecular cell 20090901 6

A canonical nucleosome architecture around promoters establishes the context in which proteins regulate gene expression. Whether gene regulatory proteins that interact with nucleosomes are selective for individual nucleosome positions across the genome is not known. Here, we examine on a genomic scale several protein-nucleosome interactions, including those that (1) bind histones (Bdf1/SWR1 and Srm1), (2) bind specific DNA sequences (Rap1 and Reb1), and (3) potentially collide with nucleosomes d ...[more]

PMID: 19782036

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Project description:Genome-wide libraries of yeast deletion strains have been used to screen for genes that drive phenotypes such as stress response. A surprising observation emerging from these studies is that the genes with the largest changes in mRNA expression during a state transition are not those that drive that transition. Here, we show that integrating gene expression data with context-independent protein interaction networks can help prioritize master regulators that drive biological phenotypes.Genes essential for survival had previously been shown to exhibit high centrality in protein interaction networks. However, the set of genes that drive growth in any specific condition is highly context-dependent. We inferred regulatory networks from gene expression data and transcription factor binding motifs in Saccharomyces cerevisiae, and found that high-degree nodes in regulatory networks are enriched for transcription factors that drive the corresponding phenotypes. We then found that using a metric combining protein interaction and transcriptional networks improved the enrichment for drivers in many of the contexts we examined. We applied this principle to a dataset of gene expression in normal human fibroblasts expressing a panel of viral oncogenes. We integrated regulatory interactions inferred from this data with a database of yeast two-hybrid protein interactions and ranked 571 human transcription factors by their combined network score. The ranked list was significantly enriched in known cancer genes that could not be found by standard differential expression or enrichment analyses.There has been increasing recognition that network-based approaches can provide insight into critical cellular elements that help define phenotypic state. Our analysis suggests that no one network, based on a single data type, captures the full spectrum of interactions. Greater insight can instead be gained by exploring multiple independent networks and by choosing an appropriate metric on each network. Moreover we can improve our ability to rank phenotypic drivers by combining the information from individual networks. We propose that such integrative network analysis could be used to combine clinical gene expression data with interaction databases to prioritize patient- and disease-specific therapeutic targets.

Dataset Information

Interaction of transcriptional regulators with specific nucleosomes across the Saccharomyces genome.

Publications

Interaction of transcriptional regulators with specific nucleosomes across the Saccharomyces genome.

Similar Datasets

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