Analysis

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

Received:
Accepted:
Published online:

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.

  • Subscribe to Nature Genetics for full access:

    $59

    Subscribe

Additional access options:

Already a subscriber?  Log in  now or  Register  for online access.

References

  1. 1.

    , & Transcriptional enhancers: from properties to genome-wide predictions. Nat. Rev. Genet. 15, 272–286 (2014).

  2. 2.

    , & Genomic views of distant-acting enhancers. Nature 461, 199–205 (2009).

  3. 3.

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

  4. 4.

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

  5. 5.

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

  6. 6.

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

  7. 7.

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

  8. 8.

    , , , & Genome architectures revealed by tethered chromosome conformation capture and population-based modeling. Nat. Biotechnol. 30, 90–98 (2011).

  9. 9.

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

  10. 10.

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

  11. 11.

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

  12. 12.

    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).

  13. 13.

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

  14. 14.

    , , & HeB. Global view of enhancer–promoter interactome in human cells. Proc. Natl. Acad. Sci. USA 111, E2191–E2199 (2014).

  15. 15.

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

  16. 16.

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

  17. 17.

    , & Enhancer–promoter interactions are encoded by complex genomic signatures on looping chromatin. Nat. Genet. 48, 488–496 (2016).

  18. 18.

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

  19. 19.

    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).

  20. 20.

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

  21. 21.

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

  22. 22.

    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).

  23. 23.

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

  24. 24.

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

  25. 25.

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

  26. 26.

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

  27. 27.

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

  28. 28.

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

  29. 29.

    , & DNA methylation of distal regulatory sites characterizes dysregulation of cancer genes. Genome Biol. 14, R21 (2013).

  30. 30.

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

  31. 31.

    , , , & Inferring regulatory element landscapes and transcription factor networks from cancer methylomes. Genome Biol. 16, 105 (2015).

  32. 32.

    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).

  33. 33.

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

  34. 34.

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

  35. 35.

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

  36. 36.

    , , & Telomerase and cancer. Hum. Mol. Genet. 10, 677–685 (2001).

  37. 37.

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

  38. 38.

    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).

  39. 39.

    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).

  40. 40.

    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).

  41. 41.

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

  42. 42.

    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).

  43. 43.

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

  44. 44.

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

  45. 45.

    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).

  46. 46.

    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).

  47. 47.

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

  48. 48.

    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).

  49. 49.

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

  50. 50.

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

  51. 51.

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

  52. 52.

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

  53. 53.

    & BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

  54. 54.

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

  55. 55.

    , & FIMO: scanning for occurrences of a given motif. Bioinformatics 27, 1017–1018 (2011).

  56. 56.

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

  57. 57.

    , & Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

Download references

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.

Author information

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

Authors

  1. Search for Qin Cao in:

  2. Search for Christine Anyansi in:

  3. Search for Xihao Hu in:

  4. Search for Liangliang Xu in:

  5. Search for Lei Xiong in:

  6. Search for Wenshu Tang in:

  7. Search for Myth T S Mok in:

  8. Search for Chao Cheng in:

  9. Search for Xiaodan Fan in:

  10. Search for Mark Gerstein in:

  11. Search for Alfred S L Cheng in:

  12. Search for Kevin Y Yip in:

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.

Competing interests

The authors declare no competing financial interests.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–17, Supplementary Tables 1–18 and Supplementary Note.

  2. 2.

    Life Sciences Reporting Summary