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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.

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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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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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