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Reconstruction of enhancer–target networks in 935 samples of human primary cells, tissues and cell lines

Nature Genetics volume 49, pages 1428–1436 (2017)Cite this article

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Abstract

We propose a new method for determining the target genes of transcriptional enhancers in specific cells and tissues. It combines global trends across many samples and sample-specific information, and considers the joint effect of multiple enhancers. Our method outperforms existing methods when predicting the target genes of enhancers in unseen samples, as evaluated by independent experimental data. Requiring few types of input data, we are able to apply our method to reconstruct the enhancer–target networks in 935 samples of human primary cells, tissues and cell lines, which constitute by far the largest set of enhancer–target networks. The similarity of these networks from different samples closely follows their cell and tissue lineages. We discover three major co-regulation modes of enhancers and find defense-related genes often simultaneously regulated by multiple enhancers bound by different transcription factors. We also identify differentially methylated enhancers in hepatocellular carcinoma (HCC) and experimentally confirm their altered regulation of HCC-related genes.

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Figure 1: Quantitative relationships between FANTOM5 enhancer and target activities.
Figure 2: The JEME method.
Figure 3: Reliability of the inferred enhancer networks.
Figure 4: Context specificity of the inferred enhancer networks.
Figure 5: Enhancer co-regulation modes.
Figure 6: HCC cancer genes identified by the liver-related enhancer–target networks.

References

  1. Shlyueva, D., Stampfel, G. & Stark, A. Transcriptional enhancers: from properties to genome-wide predictions. Nat. Rev. Genet. 15, 272–286 (2014).

    Article  CAS  PubMed  Google Scholar 

  2. Visel, A., Rubin, E.M. & Pennacchio, L.A. Genomic views of distant-acting enhancers. Nature 461, 199–205 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. 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 

  5. Dixon, J.R. et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376–380 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Heidari, N. et al. Genome-wide map of regulatory interactions in the human genome. Genome Res. 24, 1905–1917 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Jin, F. et al. A high-resolution map of the three-dimensional chromatin interactome in human cells. Nature 503, 290–294 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Kalhor, R., Tjong, H., Jayathilaka, N., Alber, F. & Chen, L. Genome architectures revealed by tethered chromosome conformation capture and population-based modeling. Nat. Biotechnol. 30, 90–98 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Li, G. et al. Extensive promoter-centered chromatin interactions provide a topological basis for transcription regulation. Cell 148, 84–98 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Rao, S.S. 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 

  11. Tang, Z. et al. CTCF-mediated human 3D genome architecture reveals chromatin topology for transcription. Cell 163, 1611–1627 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. 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. 24, 1–13 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 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 

  14. He, B., Chen, C., Teng, L. & Tan, K. HeB. Global view of enhancer–promoter interactome in human cells. Proc. Natl. Acad. Sci. USA 111, E2191–E2199 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Roy, S. et al. A predictive modeling approach for cell line–specific long-range regulatory interactions. Nucleic Acids Res. 43, 8694–8712 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Whalen, S., Truty, R.M. & Pollard, K.S. Enhancer–promoter interactions are encoded by complex genomic signatures on looping chromatin. Nat. Genet. 48, 488–496 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Zhu, Y. et al. Constructing 3D interaction maps from 1D epigenomes. Nat. Commun. 7, 10812 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 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 

  20. Cheng, C. et al. Understanding transcriptional regulation by integrative analysis of transcription factor binding data. Genome Res. 22, 1658–1667 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Dong, X. et al. Modeling gene expression using chromatin features in various cellular contexts. Genome Biol. 13, R53 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Lou, S. et al. Whole-genome bisulfite sequencing of multiple individuals reveals complementary roles of promoter and gene body methylation in transcriptional regulation. Genome Biol. 15, 408 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Ernst, J. & Kellis, M. Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues. Nat. Biotechnol. 33, 364–376 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. 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 

  27. Hnisz, D. et al. Super-enhancers in the control of cell identity and disease. Cell 155, 934–947 (2013).

    Article  CAS  PubMed  Google Scholar 

  28. Whyte, W.A. et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell 153, 307–319 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  30. Heyn, H. et al. Epigenomic analysis detects aberrant super-enhancer DNA methylation in human cancer. Genome Biol. 17, 11 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Yao, L., Shen, H., Laird, P.W., Farnham, P.J. & Berman, B.P. Inferring regulatory element landscapes and transcription factor networks from cancer methylomes. Genome Biol. 16, 105 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Bojesen, S.E. et al. Multiple independent variants at the TERT locus are associated with telomere length and risks of breast and ovarian cancer. Nat. Genet. 45, 371–384 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Horn, S. et al. TERT promoter mutations in familial and sporadic melanoma. Science 339, 959–961 (2013).

    Article  CAS  PubMed  Google Scholar 

  34. Huang, F.W. et al. Highly recurrent TERT promoter mutations in human melanoma. Science 339, 957–959 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Schulze, K. et al. Exome sequencing of hepatocellular carcinomas identifies new mutational signatures and potential therapeutic targets. Nat. Genet. 47, 505–511 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Shay, J.W., Zou, Y., Hiyama, E. & Wright, W.E. Telomerase and cancer. Hum. Mol. Genet. 10, 677–685 (2001).

    Article  CAS  PubMed  Google Scholar 

  37. Lo, P.K. et al. Identification of a novel mouse p53 target gene DDA3. Oncogene 18, 7765–7774 (1999).

    Article  CAS  PubMed  Google Scholar 

  38. Hsieh, P.-C. et al. p53 downstream target DDA3 is a novel microtubule-associated protein that interacts with end-binding protein EB3 and activates β-catenin pathway. Oncogene 26, 4928–4940 (2007).

    Article  CAS  PubMed  Google Scholar 

  39. Yang, J.D. et al. Genes associated with recurrence of hepatocellular carcinoma: integrated analysis by gene expression and methylation profiling. J. Korean Med. Sci. 26, 1428–1438 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Jiang, Y. et al. Rbm24, an RNA-binding protein and a target of p53, regulates p21 expression via mRNA stability. J. Biol. Chem. 289, 3164–3175 (2014).

    Article  CAS  PubMed  Google Scholar 

  41. Javierre, B.M. et al. Lineage-specific genome architecture links enhancers and non-coding dsease variants to target gene promoters. Cell 167, 1369–1384 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Pellacani, D. et al. Analysis of normal human mammary epigenomes reveals cell-specific active enhancer states and associated transcription factor networks. Cell Rep. 17, 2060–2074 (2016).

    Article  CAS  PubMed  Google Scholar 

  43. Korkmaz, G. et al. Functional genetic screens for enhancer elements in the human genome using CRISPR–Cas9. Nat. Biotechnol. 34, 192–198 (2016).

    Article  CAS  PubMed  Google Scholar 

  44. Rajagopal, N. et al. High-throughput mapping of regulatory DNA. Nat. Biotechnol. 34, 167–174 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Killela, P.J. et al. TERT promoter mutations occur frequently in gliomas and a subset of tumors derived from cells with low rates of self-renewal. Proc. Natl. Acad. Sci. USA 110, 6021–6026 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Landa, I. et al. Frequent somatic TERT promoter mutations in thyroid cancer: higher prevalence in advanced forms of the disease. J. Clin. Endocrinol. Metab. 98, E1562–E1566 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Liu, X. et al. Highly prevalent TERT promoter mutations in aggressive thyroid cancers. Endocr. Relat. Cancer 20, 603–610 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Rachakonda, P.S. et al. TERT promoter mutations in bladder cancer affect patient survival and disease recurrence through modification by a common polymorphism. Proc. Natl. Acad. Sci. USA 110, 17426–17431 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Vinagre, J. et al. Frequency of TERT promoter mutations in human cancers. Nat. Commun. 4, 2185 (2013).

    Article  PubMed  Google Scholar 

  50. Thakore, P.I. et al. Highly specific epigenome editing by CRISPR–Cas9 repressors for silencing of distal regulatory elements. Nat. Methods 12, 1143–1149 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Kent, W.J. et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Khan, A. & Zhang, X. dbSUPER: a database of super-enhancers in mouse and human genome. Nucleic Acids Res. 44, D164–D171 (2016).

    Article  CAS  PubMed  Google Scholar 

  53. 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 

  54. Kulakovskiy, I.V. et al. HOCOMOCO: a comprehensive collection of human transcription factor binding sites models. Nucleic Acids Res. 41, D195–D202 (2013).

    Article  CAS  PubMed  Google Scholar 

  55. Grant, C.E., Bailey, T.L. & Noble, W.S. FIMO: scanning for occurrences of a given motif. Bioinformatics 27, 1017–1018 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Siepel, A. et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 15, 1034–1050 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Love, M.I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We would like to thank Y. Ruan and Z. Tang for providing the list of CCDs in the GM12878 cell line and W.-L. Chan, J. Chen, M. Gu, S. Hu, X. Hu, X. Ma and B. Zou for helpful discussions. The data for patients with HCC were generated by the TCGA Research Network (see URLs). This project is supported by HKSAR RGC TRS T12-401/13-R, T12-402/13-N and T12C-714/14-R, CRF C4017-14G, GRF 14145916, and grants 3132964 and 3132821 from the Research Committee of CUHK.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

    Qin Cao, Christine Anyansi, Xihao Hu & Kevin Y Yip

  2. Department of Computer Science, Vrije Universiteit, Amsterdam, The Netherlands

    Christine Anyansi

  3. School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

    Liangliang Xu, Wenshu Tang, Myth T S Mok & Alfred S L Cheng

  4. Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

    Lei Xiong

  5. Department of Biomedical Data Sciences, Dartmouth College, Hanover, New Hampshire, USA

    Chao Cheng

  6. Department of Statistics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

    Xiaodan Fan

  7. Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA

    Mark Gerstein

  8. Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, USA

    Mark Gerstein

  9. Department of Computer Science, Yale University, New Haven, Connecticut, USA

    Mark Gerstein

  10. Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

    Kevin Y Yip

  11. CUHK-BGI Innovation Institute of Trans-omics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

    Kevin Y Yip

  12. Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

    Kevin Y Yip

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  1. Qin Cao
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Contributions

K.Y.Y. conceived the study. Q.C. and K.Y.Y. developed the JEME method. Q.C., C.A., X.H., M.T.S.M., C.C., X.F., M.G., A.S.L.C. and K.Y.Y. analyzed the data. L. Xu, L. Xiong, W.T. and M.T.S.M. performed the molecular experiments. Q.C., C.A., M.T.S.M. and K.Y.Y. prepared the manuscript.

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Cao, Q., Anyansi, C., Hu, X. et al. Reconstruction of enhancer–target networks in 935 samples of human primary cells, tissues and cell lines. Nat Genet 49, 1428–1436 (2017). https://doi.org/10.1038/ng.3950

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