Global view of enhancer–promoter interactome in human cells

Bing He

Changya Chen

Li Teng

Kai Tan

Supplementary Material

Supporting Information:

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^{Interdisciplinary Graduate Program in Genetics and}

^{Department of Internal Medicine, University of Iowa, Iowa City, IA, 52242}

^{To whom correspondence should be addressed. E-mail:}ude.awoiu@nat-iak.

Edited by Xiaole Shirley Liu, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, MA, and accepted by the Editorial Board April 17, 2014 (received for review October 28, 2013)

Author contributions: B.H. and K.T. designed research; B.H., C.C., and K.T. performed research; B.H., C.C., L.T., and K.T. contributed new reagents/analytic tools; B.H. and K.T. analyzed data; and B.H. and K.T. wrote the paper.

Edited by Xiaole Shirley Liu, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, MA, and accepted by the Editorial Board April 17, 2014 (received for review October 28, 2013)

Significance

In eukaryotes, gene expression is controlled by short regulatory DNA sequences called enhancers. Understanding how an enhancer selects its target promoter(s) is a major challenge in the field of gene regulation. Advances in genomic technologies have enabled rapid and comprehensive identification of active promoters and enhancers for many cell types. However, there is a lack of methods to link bona fide enhancers and their target promoters. Here, we develop and integrate multiple genomic features into a statistical predictor for enhancer–promoter interactions. Systematic analyses of the predicted interactions across 12 cell types reveals several global features of enhancer–promoter communication. Our approach presents a systematic and effective strategy to decipher the mechanisms underlying enhancer and promoter communication.

Keywords: chromatin interaction, genomics, bioinformatics, gene regulation, 3C

Significance

Abstract

Enhancer mapping has been greatly facilitated by various genomic marks associated with it. However, little is available in our toolbox to link enhancers with their target promoters, hampering mechanistic understanding of enhancer–promoter (EP) interaction. We develop and characterize multiple genomic features for distinguishing true EP pairs from noninteracting pairs. We integrate these features into a probabilistic predictor for EP interactions. Multiple validation experiments demonstrate a significant improvement over state-of-the-art approaches. Systematic analyses of EP interactions across 12 cell types reveal several global features of EP interactions: (i) a larger fraction of EP interactions are cell type specific than enhancers; (ii) promoters controlled by multiple enhancers have higher tissue specificity, but the regulating enhancers are less conserved; (iii) cohesin plays a role in mediating tissue-specific EP interactions via chromatin looping in a CTCF-independent manner. Our approach presents a systematic and effective strategy to decipher the mechanisms underlying EP communication.

Abstract

Transcriptional enhancers represent the primary basis for differential gene expression. These elements regulate cell type specificity, development, and metazoan evolution, with many human diseases resulting from altered enhancer action (1, 2).

A key gap in our knowledge is an understanding of how enhancers select specific promoters for activation. Linkage of enhancers and target promoters is challenged by enhancer properties. First, increasing evidence suggests that enhancers are not located adjacent to their target promoters. Instead, they are positioned tens of kilobases away and contact their targets via long-range interactions (3 –6). Second, enhancers are position independent, i.e., they may be located either upstream or downstream of the regulated promoter.

Experimental approaches to identifying enhancer targets have largely relied on chromosome conformation capture (3C) (7) and its variants such as circularized chromosome conformation capture (4C) and genome-wide chromosome conformation capture (Hi-C) (8), all of which determine the relative frequency of direct physical contact between linearly separated DNA sequences. Unlike 3C and 4C, Hi-C is a truly genome-wide technology, but its current resolution (1 Mbp) in general is not high enough to distinguish individual enhancer–promoter (EP) interactions (9). Newer methods such as ChIP-loop (10) and chromatin interaction analysis with paired-end tag sequencing (ChIA-PET) (11) combine the principles of 3C and ChIP to identify chromatin interactions mediated by protein factors. However, the assays are technically challenging and currently have a high false-negative rate (5, 12). Therefore, computational work, if successful, can complement experimental protocols and allow prioritization of future experiments much more effectively.

The most common computational approach is assigning the nearest promoter of an enhancer as its target. Improvements to this basic approach have been introduced by using insulator sites as an additional constraint (13), by correlating histone modification patterns at enhancers and their nearest promoters (14) or transcript levels of promoters within a given genomic domain (15), and by correlating Dnase I hypersensitivity signals at enhancers and promoters (16). The latter four approaches demonstrate that signals pertaining to EP interactions could be extracted from various types of genomic data to make predictions. However, current methods either still focus on the nearest promoter (13, 14) or only use limited types of genomic feature (15, 16). Furthermore, no rigorous characterization of the performance of these methods was reported.

Here, we introduce an integrated method for predicting enhancer targets (IM-PET). Leveraging abundant omics data, we develop multiple features and integrate them probabilistically to make robust predictions of EP pairs. The selected features are based on our current understanding of enhancer structure, function, and evolution. Using both computational and experimental validations, we show IM-PET significantly outperforms state-of-the-art methods. By analyzing global EP interactome across multiple cell types, we gain better insights into the mechanisms of enhancer and promoter communication.

Cell type-specific enhancers and EP pairs are defined as those occurring in only one cell type. Cell type-specific promoters are defined as those with an expression specificity rank in the top 25%.

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Acknowledgments

We thank members of the K.T. Laboratory for helpful discussion. We thank David Eichmann, Lucas Van Tol, and the University of Iowa Institute for Clinical and Translational Science for providing computing support. This study was supported by National Institutes of Health Grant HG006130 (to K.T.).

Acknowledgments

Footnotes

The authors declare no conflict of interest.

This article is a PNAS Direct Submission. X.S.L. is a guest editor invited by the Editorial Board.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1320308111/-/DCSupplemental.

Footnotes

References

1. Visel A, Rubin EM, Pennacchio LAGenomic views of distant-acting enhancers. Nature. 2009;461(7261):199–205.[Google Scholar]
2. Williamson I, Hill RE, Bickmore WAEnhancers: From developmental genetics to the genetics of common human disease. Dev Cell. 2011;21(1):17–19.[PubMed][Google Scholar]
3. Gallo SM, et al REDfly v3.0: Toward a comprehensive database of transcriptional regulatory elements in Drosophila. Nucleic Acids Res. 2011;39(Database issue):D118–D123.[Google Scholar]
4. Sanyal A, Lajoie BR, Jain G, Dekker JThe long-range interaction landscape of gene promoters. Nature. 2012;489(7414):109–113.[Google Scholar]
5. Li G, et al Extensive promoter-centered chromatin interactions provide a topological basis for transcription regulation. Cell. 2012;148(1-2):84–98.[Google Scholar]
6. Chepelev I, Wei G, Wangsa D, Tang Q, Zhao KCharacterization of genome-wide enhancer-promoter interactions reveals co-expression of interacting genes and modes of higher order chromatin organization. Cell Res. 2012;22(3):490–503.[Google Scholar]
7. Dekker J, Rippe K, Dekker M, Kleckner NCapturing chromosome conformation. Science. 2002;295(5558):1306–1311.[PubMed][Google Scholar]
8. Simonis M, Kooren J, de Laat WAn evaluation of 3C-based methods to capture DNA interactions. Nat Methods. 2007;4(11):895–901.[PubMed][Google Scholar]
9. van Steensel B, Dekker JGenomics tools for unraveling chromosome architecture. Nat Biotechnol. 2010;28(10):1089–1095.[Google Scholar]
10. Horike S, Cai S, Miyano M, Cheng JF, Kohwi-Shigematsu TLoss of silent-chromatin looping and impaired imprinting of DLX5 in Rett syndrome. Nat Genet. 2005;37(1):31–40.[PubMed][Google Scholar]
11. Fullwood MJ, et al An oestrogen-receptor-alpha-bound human chromatin interactome. Nature. 2009;462(7269):58–64.[Google Scholar]
12. DeMare LE, et al The genomic landscape of cohesin-associated chromatin interactions. Genome Res. 2013;23(8):1224–1234.[Google Scholar]
13. Heintzman ND, et al Histone modifications at human enhancers reflect global cell-type-specific gene expression. Nature. 2009;459(7243):108–112.[Google Scholar]
14. Ernst J, et al Mapping and analysis of chromatin state dynamics in nine human cell types. Nature. 2011;473(7345):43–49.[Google Scholar]
15. Corradin O, et al Combinatorial effects of multiple enhancer variants in linkage disequilibrium dictate levels of gene expression to confer susceptibility to common traits. Genome Res. 2014;24(1):1–13.[Google Scholar]
16. Thurman RE, et al The accessible chromatin landscape of the human genome. Nature. 2012;489(7414):75–82.[Google Scholar]
17. Firpi HA, Ucar D, Tan KDiscover regulatory DNA elements using chromatin signatures and artificial neural network. Bioinformatics. 2010;26(13):1579–1586.[Google Scholar]
18. Borok MJ, Tran DA, Ho MC, Drewell RADissecting the regulatory switches of development: Lessons from enhancer evolution in Drosophila. Development. 2010;137(1):5–13.[Google Scholar]
19. Ahituv N, Prabhakar S, Poulin F, Rubin EM, Couronne OMapping cis-regulatory domains in the human genome using multi-species conservation of synteny. Hum Mol Genet. 2005;14(20):3057–3063.[PubMed][Google Scholar]
20. Kikuta H, et al Genomic regulatory blocks encompass multiple neighboring genes and maintain conserved synteny in vertebrates. Genome Res. 2007;17(5):545–555.[Google Scholar]
21. Breiman LRandom forests. Mach Learn. 2001;45(1):5–32.[PubMed][Google Scholar]
22. Rada-Iglesias A, et al A unique chromatin signature uncovers early developmental enhancers in humans. Nature. 2011;470(7333):279–283.[Google Scholar]
23. Creyghton MP, et al Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proc Natl Acad Sci USA. 2010;107(50):21931–21936.[Google Scholar]
24. Kim TK, et al Widespread transcription at neuronal activity-regulated enhancers. Nature. 2010;465(7295):182–187.[Google Scholar]
25. Visel A, et al ChIP-seq accurately predicts tissue-specific activity of enhancers. Nature. 2009;457(7231):854–858.[Google Scholar]
26. Harrow J, et al GENCODE: The reference human genome annotation for The ENCODE Project. Genome Res. 2012;22(9):1760–1774.[Google Scholar]
27. Jin F, et al A high-resolution map of the three-dimensional chromatin interactome in human cells. Nature. 2013;503(7475):290–294.[Google Scholar]
28. Li G, et al ChIA-PET tool for comprehensive chromatin interaction analysis with paired-end tag sequencing. Genome Biol. 2010;11(2):R22.[Google Scholar]
29. Hong JW, Hendrix DA, Levine MSShadow enhancers as a source of evolutionary novelty. Science. 2008;321(5894):1314.[Google Scholar]
30. Perry MW, Boettiger AN, Levine MMultiple enhancers ensure precision of gap gene-expression patterns in the Drosophila embryo. Proc Natl Acad Sci USA. 2011;108(33):13570–13575.[Google Scholar]
31. Li Q, Barkess G, Qian HChromatin looping and the probability of transcription. Trends Genet. 2006;22(4):197–202.[PubMed][Google Scholar]
32. Gibcus JH, Dekker JThe context of gene expression regulation. F1000 Biol Rep. 2012;4:8.[Google Scholar]
33. Handoko L, et al CTCF-mediated functional chromatin interactome in pluripotent cells. Nat Genet. 2011;43(7):630–638.[Google Scholar]
34. Dorsett DCohesin: Genomic insights into controlling gene transcription and development. Curr Opin Genet Dev. 2011;21(2):199–206.[Google Scholar]
35. Faure AJ, et al Cohesin regulates tissue-specific expression by stabilizing highly occupied cis-regulatory modules. Genome Res. 2012;22(11):2163–2175.[Google Scholar]
36. Schmidt D, et al A CTCF-independent role for cohesin in tissue-specific transcription. Genome Res. 2010;20(5):578–588.[Google Scholar]
37. Kagey MH, et al Mediator and cohesin connect gene expression and chromatin architecture. Nature. 2010;467(7314):430–435.[Google Scholar]
38. Nitzsche A, et al RAD21 cooperates with pluripotency transcription factors in the maintenance of embryonic stem cell identity. PLoS One. 2011;6(5):e19470.[Google Scholar]
39. van den Berg DL, et al An Oct4-centered protein interaction network in embryonic stem cells. Cell Stem Cell. 2010;6(4):369–381.[Google Scholar]
40. Smale STCore promoters: Active contributors to combinatorial gene regulation. Genes Dev. 2001;15(19):2503–2508.[PubMed][Google Scholar]
41. Butler JE, Kadonaga JTEnhancer-promoter specificity mediated by DPE or TATA core promoter motifs. Genes Dev. 2001;15(19):2515–2519.[Google Scholar]
42. Calhoun VC, Stathopoulos A, Levine MPromoter-proximal tethering elements regulate enhancer-promoter specificity in the Drosophila Antennapedia complex. Proc Natl Acad Sci USA. 2002;99(14):9243–9247.[Google Scholar]
43. Calo E, Wysocka JModification of enhancer chromatin: What, how, and why? Mol Cell. 2013;49(5):825–837.[Google Scholar]
44. Matys V, et al TRANSFAC and its module TRANSCompel: Transcriptional gene regulation in eukaryotes. Nucleic Acids Res. 2006;34(Database issue):D108–D110.[Google Scholar]
45. Bryne JC, et al JASPAR, the open access database of transcription factor-binding profiles: New content and tools in the 2008 update. Nucleic Acids Res. 2008;36(Database issue):D102–D106.[Google Scholar]
46. Newburger DE, Bulyk MLUniPROBE: An online database of protein binding microarray data on protein-DNA interactions. Nucleic Acids Res. 2009;37(Database issue):D77–D82.[Google Scholar]
47. Stormo GD, Fields DSSpecificity, free energy and information content in protein-DNA interactions. Trends Biochem Sci. 1998;23(3):109–113.[PubMed][Google Scholar]
48. Göke J, Schulz MH, Lasserre J, Vingron MEstimation of pairwise sequence similarity of mammalian enhancers with word neighbourhood counts. Bioinformatics. 2012;28(5):656–663.[Google Scholar]
49. Hagège H, et al Quantitative analysis of chromosome conformation capture assays (3C-qPCR) Nat Protoc. 2007;2(7):1722–1733.[PubMed][Google Scholar]