Chromatin run-on and sequencing maps the transcriptional regulatory landscape of glioblastoma multiforme

Abstract

The human genome encodes a variety of poorly understood RNA species that remain challenging to identify using existing genomic tools. We developed chromatin run-on and sequencing (ChRO-seq) to map the location of RNA polymerase for almost any input sample, including samples with degraded RNA that are intractable to RNA sequencing. We used ChRO-seq to map nascent transcription in primary human glioblastoma (GBM) brain tumors. Enhancers identified in primary GBMs resemble open chromatin in the normal human brain. Rare enhancers that are activated in malignant tissue drive regulatory programs similar to the developing nervous system. We identified enhancers that regulate groups of genes that are characteristic of each known GBM subtype and transcription factors that drive them. Finally we discovered a core group of transcription factors that control the expression of genes associated with clinical outcomes. This study characterizes the transcriptional landscape of GBM and introduces ChRO-seq as a method to map regulatory programs that contribute to complex diseases.

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Fig. 1: ChRO-seq and leChRO-seq measure primary transcription in isolated chromatin.
Fig. 2: ChRO-seq detects transcription in primary human glioblastomas.
Fig. 3: Comparison of TREs in primary GBM or PDXs and reference DHSs.
Fig. 4: Tumor-associated TREs activate three regulatory programs.
Fig. 5: Transcription factors influencing transcriptional heterogeneity in GBM.
Fig. 6: Regulatory activities of transcription factors are controlled by transcription and post-transcriptional mechanisms in GBM.
Fig. 7: Transcription factors control survival-associated pathways in GBM.

Data availability

All ChRO-seq and leChRO-seq data can be downloaded from the database of Genotypes and Phenotypes (dbGaP) under accession phs001646.v1.p1. Data collected from Jurkat T cells are available in the Gene Expression Omnibus under accession GSE117832.

References

  1. 1.

    Cheng, J. et al. Transcriptional maps of 10 human chromosomes at 5-nucleotide resolution. Science 308, 1149–1154 (2005).

    CAS  Article  Google Scholar 

  2. 2.

    Kim, T.-K. et al. Widespread transcription at neuronal activity-regulated enhancers. Nature 465, 182–187 (2010).

    CAS  Article  Google Scholar 

  3. 3.

    Ulitsky, I. & Bartel, D. P. LincRNAs: genomics, evolution, and mechanisms. Cell 154, 26–46 (2013).

    CAS  Article  Google Scholar 

  4. 4.

    Quinodoz, S. & Guttman, M. Long noncoding RNAs: an emerging link between gene regulation and nuclear organization. Trends. Cell Biol. 24, 651–663 (2014).

    CAS  Article  Google Scholar 

  5. 5.

    De Santa, F. et al. A large fraction of extragenic RNA pol II transcription sites overlap enhancers. PloS Biol. 8, e1000384 (2010).

    Article  Google Scholar 

  6. 6.

    Preker, P. et al. RNA exosome depletion reveals transcription upstream of active human promoters. Science 322, 1851–1854 (2008).

    CAS  Article  Google Scholar 

  7. 7.

    Andersson, R. et al. Nuclear stability and transcriptional directionality separate functionally distinct RNA species. Nat. Commun. 5, 5336 (2014).

    CAS  Article  Google Scholar 

  8. 8.

    Core, L. J. et al. Defining the status of RNA polymerase at promoters. Cell Rep. 2, 1025–1035 (2012).

    CAS  Article  Google Scholar 

  9. 9.

    Boyle, A. P. et al. High-resolution mapping and characterization of open chromatin across the genome. Cell 132, 311–322 (2008).

    CAS  Article  Google Scholar 

  10. 10.

    Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

    CAS  Article  Google Scholar 

  11. 11.

    Core, L. J., Waterfall, J. J. & Lis, J. T. Nascent RNA sequencing reveals widespread pausing and divergent initiation at human promoters. Science 322, 1845–1848 (2008).

    CAS  Article  Google Scholar 

  12. 12.

    Churchman, L. S. & Weissman, J. S. Nascent transcript sequencing visualizes transcription at nucleotide resolution. Nature 469, 368–373 (2011).

    CAS  Article  Google Scholar 

  13. 13.

    Kwak, H., Fuda, N. J., Core, L. J. & Lis, J. T. Precise maps of RNA polymerase reveal how promoters direct initiation and pausing. Science 339, 950–953 (2013).

    CAS  Article  Google Scholar 

  14. 14.

    Mayer, A. et al. Native elongating transcript sequencing reveals human transcriptional activity at nucleotide resolution. Cell 161, 541–554 (2015).

    CAS  Article  Google Scholar 

  15. 15.

    Nojima, T. et al. Mammalian NET-Seq reveals genome-wide nascent transcription coupled to RNA processing. Cell 161, 526–540 (2015).

    CAS  Article  Google Scholar 

  16. 16.

    Schwalb, B. et al. TT-seq maps the human transient transcriptome. Science 352, 1225–1228 (2016).

    CAS  Article  Google Scholar 

  17. 17.

    Core, L. J. et al. Analysis of nascent RNA identifies a unified architecture of initiation regions at mammalian promoters and enhancers. Nat. Genet. 46, 1311–1320 (2014).

    CAS  Article  Google Scholar 

  18. 18.

    Scruggs, B. S. et al. Bidirectional transcription arises from two distinct hubs of transcription factor binding and active chromatin. Mol. Cell 58, 1101–1112 (2015).

    CAS  Article  Google Scholar 

  19. 19.

    Danko, C. G. et al. Identification of active transcriptional regulatory elements from GRO-seq data. Nat. Methods 12, 433–438 (2015).

    CAS  Article  Google Scholar 

  20. 20.

    Azofeifa, J. G. & Dowell, R. D. A generative model for the behavior of RNA polymerase. Bioinformatics 33, 227–234 (2016).

    Article  Google Scholar 

  21. 21.

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

    CAS  Article  Google Scholar 

  22. 22.

    Bradner, J. E., Hnisz, D. & Young, R. A. Transcriptional addiction in cancer. Cell 168, 629–643 (2017).

    CAS  Article  Google Scholar 

  23. 23.

    Parsons, D. W. et al. An integrated genomic analysis of human glioblastoma multiforme. Science 321, 1807–1812 (2008).

    CAS  Article  Google Scholar 

  24. 24.

    Brennan, C. W. et al. The somatic genomic landscape of glioblastoma. Cell 155, 462–477 (2013).

    CAS  Article  Google Scholar 

  25. 25.

    Mohan, M., Lin, C., Guest, E. & Shilatifard, A. Licensed to elongate: a molecular mechanism for MLL-based leukaemogenesis. Nat. Rev. Cancer 10, 721–728 (2010).

    CAS  Article  Google Scholar 

  26. 26.

    Wuarin, J. & Schibler, U. Physical isolation of nascent RNA chains transcribed by RNA polymerase II: evidence for cotranscriptional splicing. Mol. Cell. Biol. 14, 7219–7225 (1994).

    CAS  Article  Google Scholar 

  27. 27.

    Mahat, D. B. et al. Base-pair-resolution genome-wide mapping of active RNA polymerases using precision nuclear run-on (PRO-seq). Nat. Protoc. 11, 1455–1476 (2016).

    Article  Google Scholar 

  28. 28.

    Khodor, Y. L. et al. Nascent-seq indicates widespread cotranscriptional pre-mRNA splicing in Drosophila. Genes Dev. 25, 2502–2512 (2011).

    CAS  Article  Google Scholar 

  29. 29.

    Menet, J. S., Rodriguez, J., Abruzzi, K. C. & Rosbash, M. Nascent-Seq reveals novel features of mouse circadian transcriptional regulation. eLife 1, e00011 (2012).

    Article  Google Scholar 

  30. 30.

    Cai, H. & Luse, D. S. Transcription initiation by RNA polymerase II in vitro. Properties of preinitiation, initiation, and elongation complexes. J. Biol. Chem. 262, 298–304 (1987).

    CAS  PubMed  Google Scholar 

  31. 31.

    Choder, M. & Aloni, Y. RNA polymerase II allows unwinding and rewinding of the DNA and thus maintains a constant length of the transcription bubble. J. Biol. Chem. 263, 12994–13002 (1988).

    CAS  PubMed  Google Scholar 

  32. 32.

    Verhaak, R. G. W. et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell. 17, 98–110 (2010).

    CAS  Article  Google Scholar 

  33. 33.

    Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).

    CAS  Article  Google Scholar 

  34. 34.

    Wang, Q. et al. Tumor evolution of glioma-intrinsic gene expression subtypes associates with immunological changes in the microenvironment. Cancer Cell 32, 42–56.e6 (2017).

    Article  Google Scholar 

  35. 35.

    Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061–1068 (2008).

  36. 36.

    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  Google Scholar 

  37. 37.

    Liu, S. J. et al. CRISPRi-based genome-scale identification of functional long noncoding RNA loci in human cells. Science 355, eaah7111 (2016).

    Article  Google Scholar 

  38. 38.

    Xi, Z. et al. Overexpression of miR-29a reduces the oncogenic properties of glioblastoma stem cells by downregulating Quaking gene isoform 6. Oncotarget 8, 24949–24963 (2017).

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Ma, Y. et al. PVT1 affects growth of glioma microvascular endothelial cells by negatively regulating miR-186. Tumour Biol. 39, 1010428317694326 (2017).

    PubMed  Google Scholar 

  40. 40.

    Zhao, D. et al. Heat shock protein 47 regulated by miR-29a to enhance glioma tumor growth and invasion. J. Neurooncol. 118, 39–47 (2014).

    CAS  Article  Google Scholar 

  41. 41.

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

    Article  Google Scholar 

  42. 42.

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

    Article  Google Scholar 

  43. 43.

    Tentler, J. J. et al. Patient-derived tumour xenografts as models for oncology drug development. Nat. Rev. Clin. Oncol. 9, 338–350 (2012).

    CAS  Article  Google Scholar 

  44. 44.

    Suvà, M. L. et al. Reconstructing and reprogramming the tumor-propagating potential of glioblastoma stem-like cells. Cell 157, 580–594 (2014).

    Article  Google Scholar 

  45. 45.

    Ricci-Vitiani, L. et al. Tumour vascularization via endothelial differentiation of glioblastoma stem-like cells. Nature 468, 824–828 (2010).

    CAS  Article  Google Scholar 

  46. 46.

    Ricci-Vitiani, L. et al. Mesenchymal differentiation of glioblastoma stem cells. Cell Death Differ. 15, 1491–1498 (2008).

    CAS  Article  Google Scholar 

  47. 47.

    Wang, Z., Martins, A. L. & Danko, C. G. RTFBSDB: an integrated framework for transcription factor binding site analysis. Bioinformatics 32, 3024–3026 (2016).

    CAS  Article  Google Scholar 

  48. 48.

    Bhat, K. P. L. et al. Mesenchymal differentiation mediated by NF-κB promotes radiation resistance in glioblastoma. Cancer Cell 24, 331–346 (2013).

    CAS  Article  Google Scholar 

  49. 49.

    Carro, M. S. et al. The transcriptional network for mesenchymal transformation of brain tumours. Nature 463, 318–325 (2010).

    CAS  Article  Google Scholar 

  50. 50.

    Danko, C. G. et al. Dynamic evolution of regulatory element ensembles in primate CD4+ T cells. Nat. Ecol. Evol. 2, 537–548 (2018).

    Article  Google Scholar 

  51. 51.

    Luo, X., Chae, M., Krishnakumar, R., Danko, C. G. & Kraus, W. L. Dynamic reorganization of the AC16 cardiomyocyte transcriptome in response to TNFα signaling revealed by integrated genomic analyses. BMC Genomics 15, 155 (2014).

    Article  Google Scholar 

  52. 52.

    Chuong, E. B., Elde, N. C. & Feschotte, C. Regulatory evolution of innate immunity through co-option of endogenous retroviruses. Science 351, 1083–1087 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Wang, M., Zhao, Y. & Zhang, B. Efficient test and visualization of multi-set intersections. Sci. Rep. 5, 16923 (2015).

    CAS  Article  Google Scholar 

  54. 54.

    Stergachis, A. B. et al. Developmental fate and cellular maturity encoded in human regulatory DNA landscapes. Cell 154, 888–903 (2013).

    CAS  Article  Google Scholar 

  55. 55.

    Azofeifa, J. G. et al. Enhancer RNA profiling predicts transcription factor activity. Genome Res. 28, 334–344 (2018).

    CAS  Article  Google Scholar 

  56. 56.

    Canute, G. W. et al. Hydroxyurea accelerates the loss of epidermal growth factor receptor genes amplified as double-minute chromosomes in human glioblastoma multiforme. Neurosurgery 39, 976–983 (1996).

    CAS  PubMed  Google Scholar 

  57. 57.

    Eller, J. L., Longo, S. L., Hicklin, D. J. & Canute, G. W. Activity of anti-epidermal growth factor receptor monoclonal antibody C225 against glioblastoma multiforme. Neurosurgery 51, 1005–1013 (2002). discussion 1013–1014.

    PubMed  Google Scholar 

  58. 58.

    Schmieder, R. & Edwards, R. Quality control and preprocessing of metagenomic datasets. Bioinformatics 27, 863–864 (2011).

    CAS  Article  Google Scholar 

  59. 59.

    Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011).

    Article  Google Scholar 

  60. 60.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS  Article  Google Scholar 

  61. 61.

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

    CAS  Article  Google Scholar 

  62. 62.

    Kuhn, R. M., Haussler, D. & Kent, W. J. The UCSC genome browser and associated tools. Brief. Bioinform. 14, 144–161 (2013).

    CAS  Article  Google Scholar 

  63. 63.

    R Development Core Team R: a language and environment for statistical computing (R Foundation for Statistical Computing, 2010).

  64. 64.

    Wang, Z., Chu, T., Choate, L. A. & Danko, C. G. Rgtsvm: support vector machines on a GPU in R. Preprint at arXiv [stat.ML] (2017). https://arxiv.org/abs/1706.05544.

  65. 65.

    Danko, C. G. et al. Signaling pathways differentially affect RNA polymerase II initiation, pausing, and elongation rate in cells. Mol. Cell 50, 212–222 (2013).

    CAS  Article  Google Scholar 

  66. 66.

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

    Article  Google Scholar 

  67. 67.

    Hastie, T., Mazumder, R., Lee, J. & Zadeh, R. Matrix completion and low-rank SVD via fast alternating least squares. Preprint at arXiv [stat.ME] (2014). https://arxiv.org/abs/1410.2596.

  68. 68.

    Jolma, A. et al. DNA-binding specificities of human transcription factors. Cell 152, 327–339 (2013).

    CAS  Article  Google Scholar 

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Acknowledgements

We thank M. Viapiano and P. Sethupathy for valuable comments on the manuscript and other members of the Danko, Kwak and Lis laboratories for valuable discussions. This research was supported in part by US National Institutes of Health (NIH) grants HG009309 (to C.G.D. and H.K.) and GM25232 (to J.T.L.), and by the Walbridge Foundation for Brain Cancer Research. T.C. thanks the Croucher Foundation for the Croucher Scholarships for Doctoral Study (2013). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or the Walbridge Foundation.

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T.C., Z.W. and C.G.D. analyzed the data. E.J.R., G.B. and H.K. performed molecular experiments. H.K. conceived the chromatin run-on technique, with input from L.J.C. and J.T.L. H.H.S. selected tumors for analysis from the GBM tissue bank. R.J.C. and H.H.S. completed the pathologic analysis. L.S.C. and H.H.S. dissected GBM-15-90 brain tissue. S.L.L. runs the GBM tissue bank and performed the murine xenograft experiments. Data collection and analysis was supervised by C.G.D. The manuscript was written by C.G.D. and T.C., with input from the other authors.

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Correspondence to Hojoong Kwak or Charles G. Danko.

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Chu, T., Rice, E.J., Booth, G.T. et al. Chromatin run-on and sequencing maps the transcriptional regulatory landscape of glioblastoma multiforme. Nat Genet 50, 1553–1564 (2018). https://doi.org/10.1038/s41588-018-0244-3

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