Mapping and analysis of chromatin state dynamics in nine human cell types

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Abstract

Chromatin profiling has emerged as a powerful means of genome annotation and detection of regulatory activity. The approach is especially well suited to the characterization of non-coding portions of the genome, which critically contribute to cellular phenotypes yet remain largely uncharted. Here we map nine chromatin marks across nine cell types to systematically characterize regulatory elements, their cell-type specificities and their functional interactions. Focusing on cell-type-specific patterns of promoters and enhancers, we define multicell activity profiles for chromatin state, gene expression, regulatory motif enrichment and regulator expression. We use correlations between these profiles to link enhancers to putative target genes, and predict the cell-type-specific activators and repressors that modulate them. The resulting annotations and regulatory predictions have implications for the interpretation of genome-wide association studies. Top-scoring disease single nucleotide polymorphisms are frequently positioned within enhancer elements specifically active in relevant cell types, and in some cases affect a motif instance for a predicted regulator, thus suggesting a mechanism for the association. Our study presents a general framework for deciphering cis-regulatory connections and their roles in disease.

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Figure 1: Chromatin state discovery and characterization.
Figure 2: Cell-type-specific promoter and enhancer states and associated functional enrichments.
Figure 3: Correlations in activity patterns link enhancers to gene targets and upstream regulators.
Figure 4: Validation of regulatory predictions by nucleosome depletions and enhancer activity.
Figure 5: Disease variants annotated by chromatin dynamics and regulatory predictions.

Accession codes

Primary accessions

Gene Expression Omnibus

Data deposits

Sequencing and expression data has been deposited into the Gene Expression Omnibus under accession number GSE26386.

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Acknowledgements

We thank members of the epigenomics community at the Broad Institute and the Bernstein and Kellis laboratories, and M. Daly, D. Altshuler and E. Lander for discussions and criticisms. We also thank M. Suva, E. Mendenhall and S. Gillespie for assistance with experiments, and L. Goff and A. Chess for critical reading of the manuscript. We acknowledge the Broad Institute Genome Sequencing Platform for their expertise and assistance with data production. This research was supported by the National Human Genome Research Institute under an ENCODE grant (U54 HG004570; B.E.B.), R01 HG004037 (M. Kellis), RC1 HG005334 (M. Kellis), the Howard Hughes Medical Institute (B.E.B.), the National Science Foundation (awards 0644282 (M. Kellis) and 0905968 (J.E.)) and the Sloan Foundation (M. Kellis).

Author information

J.E. conducted chromatin state analysis. J.E. and P.K. conducted regulatory motif analysis. J.E. and L.W. conducted GWAS SNP analysis. T.S.M., N.S. and T.D. implemented the ChIP-seq data processing pipeline. C.B.E., X.Z., L.W., R.I., M.C. and M. Ku developed the experimental pipeline and conducted experiments. M. Kellis designed and directed the computational analysis. B.E.B. designed the experimental approach and oversaw the work. J.E., M. Kellis and B.E.B. wrote the paper.

Correspondence to Manolis Kellis.

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The authors declare no competing financial interests.

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Supplementary Information

This file contains Supplementary Figures 1-19 with legends and additional references. (PDF 2664 kb)

Supplementary Data 1

This file contains the data for the Experimental Primers and Constructs. (XLS 35 kb)

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