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Enhancer–promoter interactions are encoded by complex genomic signatures on looping chromatin

Abstract

Discriminating the gene target of a distal regulatory element from other nearby transcribed genes is a challenging problem with the potential to illuminate the causal underpinnings of complex diseases. We present TargetFinder, a computational method that reconstructs regulatory landscapes from diverse features along the genome. The resulting models accurately predict individual enhancer–promoter interactions across multiple cell lines with a false discovery rate up to 15 times smaller than that obtained using the closest gene. By evaluating the genomic features driving this accuracy, we uncover interactions between structural proteins, transcription factors, epigenetic modifications, and transcription that together distinguish interacting from non-interacting enhancer–promoter pairs. Most of this signature is not proximal to the enhancers and promoters but instead decorates the looping DNA. We conclude that complex but consistent combinations of marks on the one-dimensional genome encode the three-dimensional structure of fine-scale regulatory interactions.

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Figure 1: Predictive power of promoter-proximal genomic features.
Figure 2: Ratio of the CTCF and RAD21 ChIP-seq signals occurring within interacting enhancers and non-interacting enhancers, anchored at peaks for CTCF, RAD21, and the transcription factors CUX1 and HCFC1 for the K562 cell line.
Figure 3: Predicting a chromatin loop that skips over multiple active promoters in K562 cells.
Figure 4: The TargetFinder pipeline.
Figure 5: TargetFinder performance by cell line, model type, and number of features.
Figure 6: Predictive importance of genomic features across cell lines and regions.
Figure 7: Influence of features by region.
Figure 8: Identification of complex interactions between DNA-binding proteins and epigenetic marks.

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References

  1. 1

    Schaub, M.A., Boyle, A.P., Kundaje, A., Batzoglou, S. & Snyder, M. Linking disease associations with regulatory information in the human genome. Genome Res. 22, 1748–1759 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. 2

    Lomelin, D., Jorgenson, E. & Risch, N. Human genetic variation recognizes functional elements in noncoding sequence. Genome Res. 20, 311–319 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. 3

    Alexandrov, N.N. et al. Features of Arabidopsis genes and genome discovered using full-length cDNAs. Plant Mol. Biol. 60, 69–85 (2006).

    Article  CAS  Google Scholar 

  4. 4

    Hillier, L.W. et al. Whole-genome sequencing and variant discovery in C. elegans. Nat. Methods 5, 183–188 (2008).

    Article  CAS  Google Scholar 

  5. 5

    Massouras, A. et al. Genomic variation and its impact on gene expression in Drosophila melanogaster. PLoS Genet. 8, e1003055 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. 6

    Tang, R. et al. Candidate genes and functional noncoding variants identified in a canine model of obsessive-compulsive disorder. Genome Biol. 15, R25 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. 7

    Manolio, T.A., Brooks, L.D. & Collins, F.S. A HapMap harvest of insights into the genetics of common disease. J. Clin. Invest. 118, 1590–1605 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. 8

    Gusev, A. et al. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am. J. Hum. Genet. 95, 535–552 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. 9

    Frazer, K.A., Murray, S.S., Schork, N.J. & Topol, E.J. Human genetic variation and its contribution to complex traits. Nat. Rev. Genet. 10, 241–251 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. 10

    Lindblad-Toh, K. et al. A high-resolution map of human evolutionary constraint using 29 mammals. Nature 478, 476–482 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. 11

    ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

  12. 12

    Celniker, S.E. et al. Unlocking the secrets of the genome. Nature 459, 927–930 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 13

    Bernstein, B.E. et al. The NIH Roadmap Epigenomics Mapping Consortium. Nat. Biotechnol. 28, 1045–1048 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. 14

    Ernst, J. et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473, 43–49 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. 15

    Boyle, A.P. et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 22, 1790–1797 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. 16

    Ward, L.D. & Kellis, M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 40, D930–D934 (2012).

    CAS  Article  Google Scholar 

  17. 17

    Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. 18

    Gulko, B., Hubisz, M.J., Gronau, I. & Siepel, A. A method for calculating probabilities of fitness consequences for point mutations across the human genome. Nat. Genet. 47, 276–283 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 19

    Lettice, L.A. et al. A long-range Shh enhancer regulates expression in the developing limb and fin and is associated with preaxial polydactyly. Hum. Mol. Genet. 12, 1725–1735 (2003).

    Article  CAS  Google Scholar 

  20. 20

    Sanyal, A., Lajoie, B.R., Jain, G. & Dekker, J. The long-range interaction landscape of gene promoters. Nature 489, 109–113 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. 21

    Kvon, E.Z. et al. Genome-scale functional characterization of Drosophila developmental enhancers in vivo. Nature 512, 91–95 (2014).

    Article  CAS  PubMed  Google Scholar 

  22. 22

    Wang, D., Rendon, A. & Wernisch, L. Transcription factor and chromatin features predict genes associated with eQTLs. Nucleic Acids Res. 41, 1450–1463 (2013).

    Article  CAS  PubMed  Google Scholar 

  23. 23

    Yip, K.Y. et al. Classification of human genomic regions based on experimentally determined binding sites of more than 100 transcription-related factors. Genome Biol. 13, R48 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. 24

    Aran, D., Sabato, S. & Hellman, A. DNA methylation of distal regulatory sites characterizes dysregulation of cancer genes. Genome Biol. 14, R21 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. 25

    Rödelsperger, C. et al. Integrative analysis of genomic, functional and protein interaction data predicts long-range enhancer–target gene interactions. Nucleic Acids Res. 39, 2492–2502 (2011).

    Article  CAS  PubMed  Google Scholar 

  26. 26

    Thurman, R.E. et al. The accessible chromatin landscape of the human genome. Nature 489, 75–82 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. 27

    Wilczynski, B., Liu, Y.-H., Yeo, Z.X. & Furlong, E.E.M. Predicting spatial and temporal gene expression using an integrative model of transcription factor occupancy and chromatin state. PLoS Comput. Biol. 8, e1002798 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. 28

    Fullwood, M.J. et al. An oestrogen-receptor-α-bound human chromatin interactome. Nature 462, 58–64 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. 29

    Dekker, J., Rippe, K., Dekker, M. & Kleckner, N. Capturing chromosome conformation. Science 295, 1306–1311 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. 30

    Dostie, J. et al. Chromosome Conformation Capture Carbon Copy (5C): a massively parallel solution for mapping interactions between genomic elements. Genome Res. 16, 1299–1309 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. 31

    de Wit, E. & de Laat, W. A decade of 3C technologies: insights into nuclear organization. Genes Dev. 26, 11–24 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. 32

    Rao, S.S.P. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. 33

    Dixon, J.R. et al. Chromatin architecture reorganization during stem cell differentiation. Nature 518, 331–336 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. 34

    Schoenfelder, S. et al. The pluripotent regulatory circuitry connecting promoters to their long-range interacting elements. Genome Res. 25, 582–597 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. 35

    Mifsud, B. et al. Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C. Nat. Genet. 47, 598–606 (2015).

    Article  CAS  Google Scholar 

  36. 36

    Maston, G.A., Evans, S.K. & Green, M.R. Transcriptional regulatory elements in the human genome. Annu. Rev. Genomics Hum. Genet. 7, 29–59 (2006).

    Article  CAS  Google Scholar 

  37. 37

    Moore, B.L., Aitken, S. & Semple, C.A. Integrative modeling reveals the principles of multi-scale chromatin boundary formation in human nuclear organization. Genome Biol. 16, 110 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. 38

    Zhang, Y. et al. Chromatin connectivity maps reveal dynamic promoter-enhancer long-range associations. Nature 504, 306–310 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. 39

    Corradin, O. & Scacheri, P.C. Enhancer variants: evaluating functions in common disease. Genome Med. 6, 85 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  40. 40

    Shaulian, E. & Karin, M. AP-1 as a regulator of cell life and death. Nat. Cell Biol. 4, E131–E136 (2002).

    Article  CAS  PubMed  Google Scholar 

  41. 41

    Bailey, S.D. et al. ZNF143 provides sequence specificity to secure chromatin interactions at gene promoters. Nat. Commun. 2, 6186 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. 42

    Michaud, J. et al. HCFC1 is a common component of active human CpG-island promoters and coincides with ZNF143, THAP11, YY1, and GABP transcription factor occupancy. Genome Res. 23, 907–916 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. 43

    Adelman, K. & Lis, J.T. Promoter-proximal pausing of RNA polymerase II: emerging roles in metazoans. Nat. Rev. Genet. 13, 720–731 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. 44

    Margueron, R. & Reinberg, D. The Polycomb complex PRC2 and its mark in life. Nature 469, 343–349 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. 45

    Benveniste, D., Sonntag, H.-J., Sanguinetti, G. & Sproul, D. Transcription factor binding predicts histone modifications in human cell lines. Proc. Natl. Acad. Sci. USA 111, 13367–13372 (2014).

    Article  CAS  PubMed  Google Scholar 

  46. 46

    Visel, A. et al. ChIP-seq accurately predicts tissue-specific activity of enhancers. Nature 457, 854–858 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. 47

    Schwartz, C. et al. Recruitment of p300 by C/EBPβ triggers phosphorylation of p300 and modulates coactivator activity. EMBO J. 22, 882–892 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. 48

    Wang, H. et al. Role of histone H2A ubiquitination in Polycomb silencing. Nature 431, 873–878 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. 49

    Niskanen, E.A. et al. Global SUMOylation on active chromatin is an acute heat stress response restricting transcription. Genome Biol. 16, 153 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. 50

    Hay, R.T. SUMO: a history of modification. Mol. Cell 18, 1–12 (2005).

    Article  CAS  PubMed  Google Scholar 

  51. 51

    MacPherson, M.J., Beatty, L.G., Zhou, W., Du, M. & Sadowski, P.D. The CTCF insulator protein is posttranslationally modified by SUMO. Mol. Cell. Biol. 29, 714–725 (2009).

    Article  CAS  PubMed  Google Scholar 

  52. 52

    Fujioka, S. et al. NF-κB and AP-1 connection: mechanism of NF-κB-dependent regulation of AP-1 activity. Mol. Cell. Biol. 24, 7806–7819 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. 53

    Hanlon, M. & Sealy, L. Ras regulates the association of serum response factor and CCAAT/enhancer-binding protein β. J. Biol. Chem. 274, 14224–14228 (1999).

    Article  CAS  PubMed  Google Scholar 

  54. 54

    Jozwik, K.M. & Carroll, J.S. Pioneer factors in hormone-dependent cancers. Nat. Rev. Cancer 12, 381–385 (2012).

    Article  CAS  PubMed  Google Scholar 

  55. 55

    Sharma, M. et al. hZimp10 is an androgen receptor co-activator and forms a complex with SUMO-1 at replication foci. EMBO J. 22, 6101–6114 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. 56

    Upadhyay, G., Chowdhury, A.H., Vaidyanathan, B., Kim, D. & Saleque, S. Antagonistic actions of Rcor proteins regulate LSD1 activity and cellular differentiation. Proc. Natl. Acad. Sci. USA 111, 8071–8076 (2014).

    Article  CAS  PubMed  Google Scholar 

  57. 57

    Nolis, I.K. et al. Transcription factors mediate long-range enhancer-promoter interactions. Proc. Natl. Acad. Sci. USA 106, 20222–20227 (2009).

    Article  PubMed  Google Scholar 

  58. 58

    Deshane, J. et al. Sp1 regulates chromatin looping between an intronic enhancer and distal promoter of the human heme oxygenase-1 gene in renal cells. J. Biol. Chem. 285, 16476–16486 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. 59

    Listman, J.A. et al. Conserved ETS domain arginines mediate DNA binding, nuclear localization, and a novel mode of bZIP interaction. J. Biol. Chem. 280, 41421–41428 (2005).

    Article  CAS  PubMed  Google Scholar 

  60. 60

    van Riel, B. & Rosenbauer, F. Epigenetic control of hematopoiesis: the PU.1 chromatin connection. Biol. Chem. 395, 1265–1274 (2014).

    Article  CAS  PubMed  Google Scholar 

  61. 61

    Liu, Z., Scannell, D.R., Eisen, M.B. & Tjian, R. Control of embryonic stem cell lineage commitment by core promoter factor, TAF3. Cell 146, 720–731 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. 62

    Bertolino, E. & Singh, H. POU/TBP cooperativity: a mechanism for enhancer action from a distance. Mol. Cell 10, 397–407 (2002).

    Article  CAS  PubMed  Google Scholar 

  63. 63

    Nimura, K. et al. A histone H3 lysine 36 trimethyltransferase links Nkx2-5 to Wolf-Hirschhorn syndrome. Nature 460, 287–291 (2009).

    Article  CAS  PubMed  Google Scholar 

  64. 64

    Blackwood, E.M. & Kadonaga, J.T. Going the distance: a current view of enhancer action. Science 281, 60–63 (1998).

    Article  CAS  PubMed  Google Scholar 

  65. 65

    Islam, A.B., Richter, W.F., Lopez-Bigas, N. & Benevolenskaya, E.V. Selective targeting of histone methylation. Cell Cycle 10, 413–424 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. 66

    Dorsett, D. & Kassis, J.A. Checks and balances between cohesin and polycomb in gene silencing and transcription. Curr. Biol. 24, R535–R539 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. 67

    Levine, S.S. et al. The core of the polycomb repressive complex is compositionally and functionally conserved in flies and humans. Mol. Cell. Biol. 22, 6070–6078 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. 68

    Vernimmen, D. et al. Polycomb eviction as a new distant enhancer function. Genes Dev. 25, 1583–1588 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. 69

    Fabre, P.J. et al. Nanoscale spatial organization of the HoxD gene cluster in distinct transcriptional states. Proc. Natl. Acad. Sci. USA 112, 13964–13969 (2015).

    Article  CAS  PubMed  Google Scholar 

  70. 70

    Ing-Simmons, E. et al. Spatial enhancer clustering and regulation of enhancer-proximal genes by cohesin. Genome Res. 25, 504–513 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. 71

    Hoffman, M.M. et al. Unsupervised pattern discovery in human chromatin structure through genomic segmentation. Nat. Methods 9, 473–476 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. 72

    Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215–216 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. 73

    Ramsköld, D., Wang, E.T., Burge, C.B. & Sandberg, R. An abundance of ubiquitously expressed genes revealed by tissue transcriptome sequence data. PLoS Comput. Biol. 5, e1000598 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. 74

    Li, Q., Brown, J.B., Huang, H. & Bickel, P.J. Measuring reproducibility of high-throughput experiments. Ann. Appl. Stat. 5, 1752–1779 (2011).

    Article  Google Scholar 

  75. 75

    Harrow, J. et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. 76

    Langmead, B. & Salzberg, S.L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. 77

    Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. 78

    Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Google Scholar 

  79. 79

    McKinney, W. Python for Data Analysis (O'Reilly, 2012).

  80. 80

    Quinlan, A.R. & Hall, I.M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. 81

    Burges, C.J.C. A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2, 121–167 (1998).

    Article  Google Scholar 

  82. 82

    Kingsford, C. & Salzberg, S.L. What are decision trees? Nat. Biotechnol. 26, 1011–1013 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. 83

    Friedman, J.H. Stochastic gradient boosting. Comput. Stat. Data Anal. 38, 367–378 (2002).

    Article  Google Scholar 

  84. 84

    Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning (Springer, 2009).

  85. 85

    Guyon, I., Weston, J., Barnhill, S. & Vapnik, V. Gene selection for cancer classification using support vector machines. Mach. Learn. 46, 389–422 (2002).

    Article  Google Scholar 

  86. 86

    Ambroise, C. & McLachlan, G.J. Selection bias in gene extraction on the basis of microarray gene-expression data. Proc. Natl. Acad. Sci. USA 99, 6562–6566 (2002).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This project was supported by the Bench to Bassinet Program of the NHLBI (U01HL098179 and UM1HL098179), the NIH/NHLBI (HL089707), the San Simeon Fund, and the Gladstone Institutes.

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S.W., R.M.T., and K.S.P. designed the experiments and wrote the manuscript. S.W. and R.M.T. implemented the experiments.

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Correspondence to Sean Whalen or Katherine S Pollard.

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The authors declare no competing financial interests.

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Whalen, S., Truty, R. & Pollard, K. Enhancer–promoter interactions are encoded by complex genomic signatures on looping chromatin. Nat Genet 48, 488–496 (2016). https://doi.org/10.1038/ng.3539

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