Dataset Information

Quantitative Multiplexed ChIP Reveals Global Alterations that Shape Promoter Bivalency in Ground State Embryonic Stem Cells.

ABSTRACT: To understand the epigenomic foundation of naive pluripotency, we implement a quantitative multiplexed chromatin immunoprecipitation sequencing (ChIP-seq) method comparing mouse embryonic stem cells (ESCs) grown in 2i versus 2i/serum and serum conditions. MINUTE-ChIP has a large linear dynamic range for accurately quantifying relative differences in genome-wide histone modification patterns across multiple pooled samples. We find compelling evidence for a broad H3 lysine 27 trimethylation (H3K27me3) hypermethylation of the genome, while bivalent promoters stably retain high H3K27me3 levels in 2i. We show that DNA hypomethylation, as observed in 2i, is a contributor to genome-wide gain of H3K27me3, while active demethylation by JMJD3/UTX counteracts further accumulation of H3K27me3. In parallel, we find hypomethylation of H3 lysine 4 trimethylation (H3K4me3), particularly at bivalent promoters, to be a characteristic of the 2i ground state. Serum stimulates H3K4me3 independent of GSK-3b and ERK signaling, suggesting that low H3K4me3 and high H3K27me3 levels at bivalent promoters are a product of two independent mechanisms that safeguard naive pluripotency.

SUBMITTER: Kumar B

PROVIDER: S-EPMC6859498 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Publications

Quantitative Multiplexed ChIP Reveals Global Alterations that Shape Promoter Bivalency in Ground State Embryonic Stem Cells.

Kumar Banushree B Elsässer Simon J SJ

Cell reports 20190901 12

To understand the epigenomic foundation of naive pluripotency, we implement a quantitative multiplexed chromatin immunoprecipitation sequencing (ChIP-seq) method comparing mouse embryonic stem cells (ESCs) grown in 2i versus 2i/serum and serum conditions. MINUTE-ChIP has a large linear dynamic range for accurately quantifying relative differences in genome-wide histone modification patterns across multiple pooled samples. We find compelling evidence for a broad H3 lysine 27 trimethylation (H3K27 ...[more]

PMID: 31533047

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Project description:BACKGROUND:Cell-specific gene expression is controlled by epigenetic modifications and transcription factor binding. While genome-wide maps for these protein-DNA interactions have become widely available, quantitative comparison of the resulting ChIP-Seq data sets remains challenging. Current approaches to detect differentially bound or modified regions are mainly borrowed from RNA-Seq data analysis, thus focusing on total counts of fragments mapped to a region, ignoring any information encoded in the shape of the peaks. RESULTS:Here, we present MMDiff, a robust, broadly applicable method for detecting differences between sequence count data sets. Based on quantifying shape changes in signal profiles, it overcomes challenges imposed by the highly structured nature of the data and the paucity of replicates.We first use a simulated data set to compare the performance of MMDiff with results obtained by four alternative methods. We demonstrate that MMDiff excels when peak profiles change between samples. We next use MMDiff to re-analyse a recent data set of the histone modification H3K4me3 elucidating the establishment of this prominent epigenomic marker. Our empirical analysis shows that the method yields reproducible results across experiments, and is able to detect functional important changes in histone modifications. To further explore the broader applicability of MMDiff, we apply it to two ENCODE data sets: one investigating the histone modification H3K27ac and one measuring the genome-wide binding of the transcription factor CTCF. In both cases, MMDiff proves to be complementary to count-based methods. In addition, we can show that MMDiff is capable of directly detecting changes of homotypic binding events at neighbouring binding sites. MMDiff is readily available as a Bioconductor package. CONCLUSIONS:Our results demonstrate that higher order features of ChIP-Seq peaks carry relevant and often complementary information to total counts, and hence are important in assessing differential histone modifications and transcription factor binding. We have developed a new computational method, MMDiff, that is capable of exploring these features and therefore closes an existing gap in the analysis of ChIP-Seq data sets.

Dataset Information

Quantitative Multiplexed ChIP Reveals Global Alterations that Shape Promoter Bivalency in Ground State Embryonic Stem Cells.

Publications

Quantitative Multiplexed ChIP Reveals Global Alterations that Shape Promoter Bivalency in Ground State Embryonic Stem Cells.

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