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Proteomic profiling of yeast heterochromatin connects direct physical and genetic interactions.

ABSTRACT: Heterochromatin domains are stably repressed chromatin structures composed of a core assembly of silencing proteins that condense adjacent nucleosomes. The minimal heterochromatin structure can serve as a platform for recruitment of complementary regulatory factors. We find that a reconstituted budding yeast heterochromatin domain can act as a platform to recruit multiple factors that play a role in regulating heterochromatin function. We uncover the direct interaction between the SIR heterochromatin complex and a chromosomal boundary protein that restricts the spread of heterochromatin. We find that the SIR complex relieves a mechanism of auto-inhibition within the boundary protein Yta7, allowing the Yta7 bromodomain to engage chromatin. Our results suggest that budding yeast shares with other eukaryotes the ability to establish complex heterochromatin domains that coordinate multiple mechanisms of silencing regulation through physical interactions.

SUBMITTER: Zukowski A

PROVIDER: S-EPMC6421107 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Proteomic profiling of yeast heterochromatin connects direct physical and genetic interactions.

Zukowski Alexis A Phillips Juliana J Park Soyeon S Wu Ronghu R Gygi Steven P SP Johnson Aaron M AM

Current genetics 20181012 2

Heterochromatin domains are stably repressed chromatin structures composed of a core assembly of silencing proteins that condense adjacent nucleosomes. The minimal heterochromatin structure can serve as a platform for recruitment of complementary regulatory factors. We find that a reconstituted budding yeast heterochromatin domain can act as a platform to recruit multiple factors that play a role in regulating heterochromatin function. We uncover the direct interaction between the SIR heterochro ...[more]

PMID: 30310994

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Project description:BackgroundGenes might have different gene interactions in different cell conditions, which might be mapped into different networks. Differential analysis of gene networks allows spotting condition-specific interactions that, for instance, form disease networks if the conditions are a disease, such as cancer, and normal. This could potentially allow developing better and subtly targeted drugs to cure cancer. Differential network analysis with direct physical gene interactions needs to be explored in this endeavour.ResultsC3NET is a recently introduced information theory based gene network inference algorithm that infers direct physical gene interactions from expression data, which was shown to give consistently higher inference performances over various networks than its competitors. In this paper, we present, DC3net, an approach to employ C3NET in inferring disease networks. We apply DC3net on a synthetic and real prostate cancer datasets, which show promising results. With loose cutoffs, we predicted 18583 interactions from tumor and normal samples in total. Although there are no reference interactions databases for the specific conditions of our samples in the literature, we found verifications for 54 of our predicted direct physical interactions from only four of the biological interaction databases. As an example, we predicted that RAD50 with TRF2 have prostate cancer specific interaction that turned out to be having validation from the literature. It is known that RAD50 complex associates with TRF2 in the S phase of cell cycle, which suggests that this predicted interaction may promote telomere maintenance in tumor cells in order to allow tumor cells to divide indefinitely. Our enrichment analysis suggests that the identified tumor specific gene interactions may be potentially important in driving the growth in prostate cancer. Additionally, we found that the highest connected subnetwork of our predicted tumor specific network is enriched for all proliferation genes, which further suggests that the genes in this network may serve in the process of oncogenesis.ConclusionsOur approach reveals disease specific interactions. It may help to make experimental follow-up studies more cost and time efficient by prioritizing disease relevant parts of the global gene network.

Dataset Information

Proteomic profiling of yeast heterochromatin connects direct physical and genetic interactions.

Publications

Proteomic profiling of yeast heterochromatin connects direct physical and genetic interactions.

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