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Early enhancer establishment and regulatory locus complexity shape transcriptional programs in hematopoietic differentiation

Nature Genetics volume 47, pages 1249–1259 (2015)Cite this article

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Abstract

We carried out an integrative analysis of enhancer landscape and gene expression dynamics during hematopoietic differentiation using DNase-seq, histone mark ChIP-seq and RNA sequencing to model how the early establishment of enhancers and regulatory locus complexity govern gene expression changes at cell state transitions. We found that high-complexity genes—those with a large total number of DNase-mapped enhancers across the lineage—differ architecturally and functionally from low-complexity genes, achieve larger expression changes and are enriched for both cell type–specific and transition enhancers, which are established in hematopoietic stem and progenitor cells and maintained in one differentiated cell fate but lost in others. We then developed a quantitative model to accurately predict gene expression changes from the DNA sequence content and lineage history of active enhancers. Our method suggests a new mechanistic role for PU.1 at transition peaks during B cell specification and can be used to correct assignments of enhancers to genes.

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Figure 1: The DHS atlas defines high- and low-complexity genes during hematopoietic differentiation.
Figure 2: High-complexity genes contain enhancers with distinct dynamics.
Figure 3: Gain of active enhancers in cell state transitions correlates with increased expression.
Figure 4: SeqGL identifies multiple transcription factor sequence signals in B cell DNase peaks.
Figure 5: Regression model suggests a role for PU.1 in the early establishment of B cell enhancers.
Figure 6: Regression model proposes reassignment of enhancers to genes.

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Acknowledgements

We thank A. Kundaje for extensive advice on the processing of Roadmap Epigenomics data sets, and we thank A. Arvey for helpful discussions at early stages in the project. This work was supported by US National Institutes of Health grants R01-HG006798, U01-HG007893 and U01-HG007033.

Author information

Author notes

  1. Alvaro J González and Manu Setty: These authors contributed equally to this work.

Authors and Affiliations

  1. Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA

    Alvaro J González, Manu Setty & Christina S Leslie

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  1. Alvaro J González
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Contributions

A.J.G. performed computational analyses to construct the DHS atlas, characterize gene complexity classes, describe histone modifications at enhancer classes, and quantify gain and loss of active DHSs with gene expression changes and contributed to writing the manuscript. M.S. developed the DNase peak calling pipeline and the SeqGL tool, performed the regression analysis and iterative reassignment of enhancers, and contributed to writing the manuscript. C.S.L. conceived the project, advised on the analysis and algorithm development, supervised the research and wrote the manuscript.

Corresponding author

Correspondence to Christina S Leslie.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–43. (PDF 24850 kb)

Supplementary Table 1

Data sets used in this study and their accession numbers. (XLSX 39 kb)

Supplementary Table 2

Number of DHSs in each cell type in promoters, introns and intergenic regions. (XLSX 38 kb)

Supplementary Table 3

GO analysis of high-complexity, highly expressed genes in different cell types. (XLSX 49 kb)

Supplementary Table 4

Sharing of peaks between monocytes and B cells and between T cells and NK cells. (XLSX 34 kb)

Supplementary Table 5

Transcription factor SeqGL scores learned in different cell types. (XLSX 43 kb)

Supplementary Table 6

Performance of regression model for predicting changes in gene expression in cell state transitions. (XLSX 28 kb)

Supplementary Table 7

Gene reassignments for all the cell types. (XLSX 62 kb)

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González, A., Setty, M. & Leslie, C. Early enhancer establishment and regulatory locus complexity shape transcriptional programs in hematopoietic differentiation. Nat Genet 47, 1249–1259 (2015). https://doi.org/10.1038/ng.3402

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