Abstract

How evolutionary changes at enhancers affect the transcription of target genes remains an important open question. Previous comparative studies of gene expression have largely measured the abundance of messenger RNA, which is affected by post-transcriptional regulatory processes, hence limiting inferences about the mechanisms underlying expression differences. Here, we directly measured nascent transcription in primate species, allowing us to separate transcription from post-transcriptional regulation. We used precision run-on and sequencing to map RNA polymerases in resting and activated CD4+ T cells in multiple human, chimpanzee and rhesus macaque individuals, with rodents as outgroups. We observed general conservation in coding and non-coding transcription, punctuated by numerous differences between species, particularly at distal enhancers and non-coding RNAs. Genes regulated by larger numbers of enhancers are more frequently transcribed at evolutionarily stable levels, despite reduced conservation at individual enhancers. Adaptive nucleotide substitutions are associated with lineage-specific transcription and at one locus, SGPP2, we predict and experimentally validate that multiple substitutions contribute to human-specific transcription. Collectively, our findings suggest a pervasive role for evolutionary compensation across ensembles of enhancers that jointly regulate target genes.

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References

  1. 1.

    Jacob, F. & Monod, J. Genetic regulatory mechanisms in the synthesis of proteins. J. Mol. Biol. 3, 318–356 (1961).

  2. 2.

    Britten, R. J. & Davidson, E. H. Gene regulation for higher cells: a theory. Science 165, 349–357 (1969).

  3. 3.

    King, M. C. & Wilson, A. C. Evolution at two levels in humans and chimpanzees. Science 188, 107–116 (1975).

  4. 4.

    Rockman, M. V. et al. Ancient and recent positive selection transformed opioid cis-regulation in humans. PLoS. Biol. 3, e387 (2005).

  5. 5.

    Prabhakar, S. et al. Human-specific gain of function in a developmental enhancer. Science 321, 1346–1350 (2008).

  6. 6.

    Capra, J. A., Erwin, G. D., McKinsey, G., Rubenstein, J. L. R. & Pollard, K. S. Many human accelerated regions are developmental enhancers. Phil. Trans. R. Soc. B. 368, 20130025 (2013).

  7. 7.

    McLean, C. Y. et al. Human-specific loss of regulatory DNA and the evolution of human-specific traits. Nature 471, 216–219 (2011).

  8. 8.

    Arbiza, L. et al. Genome-wide inference of natural selection on human transcription factor binding sites. Nat. Genet. 45, 723–729 (2013).

  9. 9.

    Wilson, M. D. et al. Species-specific transcription in mice carrying human chromosome 21. Science 322, 434–438 (2008).

  10. 10.

    Haygood, R., Fedrigo, O., Hanson, B., Yokoyama, K.-D. & Wray, G. A. Promoter regions of many neural- and nutrition-related genes have experienced positive selection during human evolution. Nat. Genet. 39, 1140–1144 (2007).

  11. 11.

    Torgerson, D. G. et al. Evolutionary processes acting on candidate cis-regulatory regions in humans inferred from patterns of polymorphism and divergence. PLoS. Genet. 5, e1000592 (2009).

  12. 12.

    Cotney, J. et al. The evolution of lineage-specific regulatory activities in the human embryonic limb. Cell 154, 185–196 (2013).

  13. 13.

    Schmidt, D. et al. Five-vertebrate ChIP-Seq reveals the evolutionary dynamics of transcription factor binding. Science 328, 1036–1040 (2010).

  14. 14.

    Ballester, B. et al. Multi-species, multi-transcription factor binding highlights conserved control of tissue-specific biological pathways. eLife 3, e02626 (2014).

  15. 15.

    Vierstra, J. et al. Mouse regulatory DNA landscapes reveal global principles of cis-regulatory evolution. Science 346, 1007–1012 (2014).

  16. 16.

    Arnold, C. D. et al. Quantitative genome-wide enhancer activity maps for five Drosophila species show functional enhancer conservation and turnover during cis-regulatory evolution. Nat. Genet. 46, 685–692 (2014).

  17. 17.

    Doniger, S. W. & Fay, J. C. Frequent gain and loss of functional transcription factor binding sites. PLoS. Comput. Biol. 3, e99 (2007).

  18. 18.

    Zheng, W., Zhao, H., Mancera, E., Steinmetz, L. M. & Snyder, M. Genetic analysis of variation in transcription factor binding in yeast. Nature 464, 1187–1191 (2010).

  19. 19.

    Bradley, R. K. et al. Binding site turnover produces pervasive quantitative changes in transcription factor binding between closely related Drosophila species. PLoS. Biol. 8, e1000343 (2010).

  20. 20.

    Villar, D. et al. Enhancer evolution across 20 mammalian species. Cell 160, 554–566 (2015).

  21. 21.

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

  22. 22.

    Fuda, N. J., Ardehali, M. B. & Lis, J. T. Defining mechanisms that regulate RNA polymerase II transcription in vivo. Nature 461, 186–192 (2009).

  23. 23.

    Cain, C. E., Blekhman, R., Marioni, J. C. & Gilad, Y. Gene expression differences among primates are associated with changes in a histone epigenetic modification. Genetics 187, 1225–1234 (2011).

  24. 24.

    Xiao, S. et al. Comparative epigenomic annotation of regulatory DNA. Cell 149, 1381–1392 (2012).

  25. 25.

    Zhou, X. et al. Epigenetic modifications are associated with inter-species gene expression variation in primates. Genome Biol. 15, 547 (2014).

  26. 26.

    Paris, M. et al. Extensive divergence of transcription factor binding in Drosophila embryos with highly conserved gene expression. PLoS. Genet. 9, e1003748 (2013).

  27. 27.

    Cusanovich, D. A., Pavlovic, B., Pritchard, J. K. & Gilad, Y. The functional consequences of variation in transcription factor binding. PLoS. Genet. 10, e1004226 (2014).

  28. 28.

    Wong, E. S. et al. Decoupling of evolutionary changes in transcription factor binding and gene expression in mammals. Genome Res. 25, 167–178 (2015).

  29. 29.

    Hah, N., Murakami, S., Nagari, A., Danko, C. G. & Kraus, W. L. Enhancer transcripts mark active estrogen receptor binding sites. Genome Res. 23, 1210–1223 (2013).

  30. 30.

    Domené, S. et al. Enhancer turnover and conserved regulatory function in vertebrate evolution. Phil. Trans. R. Soc. B 368, 20130027 (2013).

  31. 31.

    Wunderlich, Z. et al. Krüppel expression levels are maintained through compensatory evolution of shadow enhancers. Cell. Rep. 12, 1740–1747 (2015).

  32. 32.

    Ludwig, M. Z., Bergman, C., Patel, N. H. & Kreitman, M. Evidence for stabilizing selection in a eukaryotic enhancer element. Nature 403, 564–567 (2000).

  33. 33.

    Cannavò, E. et al. Shadow enhancers are pervasive features of developmental regulatory networks. Curr. Biol. 26, 38–51 (2016).

  34. 34.

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

  35. 35.

    Vietri Rudan, M. et al. Comparative Hi-C reveals that CTCF underlies evolution of chromosomal domain architecture. Cell. Rep. 10, 1297–1309 (2015).

  36. 36.

    Khan, Z. et al. Primate transcript and protein expression levels evolve under compensatory selection pressures. Science 342, 1100–1104 (2013).

  37. 37.

    Battle, A. et al. Genomic variation. Impact of regulatory variation from RNA to protein. Science 347, 664–667 (2015).

  38. 38.

    Bauernfeind, A. L. et al. Evolutionary divergence of gene and protein expression in the brains of humans and chimpanzees. Genome Biol. Evol. 7, 2276–2288 (2015).

  39. 39.

    Pai, A. A. et al. The contribution of RNA decay quantitative trait loci to inter-individual variation in steady-state gene expression levels. PLoS. Genet. 8, e1003000 (2012).

  40. 40.

    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).

  41. 41.

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

  42. 42.

    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).

  43. 43.

    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).

  44. 44.

    Prescott, S. L. et al. Enhancer divergence and cis-regulatory evolution in the human and chimp neural cres. Cell 163, 68–83 (2015).

  45. 45.

    Zentner, G. E., Tesar, P. J. & Scacheri, P. C. Epigenetic signatures distinguish multiple classes of enhancers with distinct cellular functions. Genome Res. 21, 1273–1283 (2011).

  46. 46.

    Gronau, I., Arbiza, L., Mohammed, J. & Siepel, A. Inference of natural selection from interspersed genomic elements based on polymorphism and divergence. Mol. Biol. Evol. 30, 1159–1171 (2013).

  47. 47.

    Berthelot, C., Villar, D., Horvath, J. E., Odom, D. T. & Flicek, P. Complexity and conservation of regulatory landscapes underlie evolutionary resilience of mammalian gene expression. Nat. Ecol. Evol. 2, 152–163 (2017).

  48. 48.

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

  49. 49.

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

  50. 50.

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

  51. 51.

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

  52. 52.

    Zhao, H. et al. CrossMap: a versatile tool for coordinate conversion between genome assemblies. Bioinformatics 30, 1006–1007 (2014).

  53. 53.

    Kent, W. J., Baertsch, R., Hinrichs, A., Miller, W. & Haussler, D. Evolution’s cauldron: duplication, deletion, and rearrangement in the mouse and human genomes. Proc. Natl. Acad. Sci. USA 100, 11484–11489 (2003).

  54. 54.

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

  55. 55.

    Chepelev, I., Wei, G., Wangsa, D., Tang, Q. & Zhao, K. Characterization of genome-wide enhancer–promoter interactions reveals co-expression of interacting genes and modes of higher order chromatin organization. Cell. Res. 22, 490–503 (2012).

  56. 56.

    Javierre, B. M. et al. Lineage-specific genome architecture links enhancers and non-coding disease variants to target gene promoters. Cell 167, 1369–1384.e19 (2016).

  57. 57.

    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).

  58. 58.

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

  59. 59.

    Weirauch, M. T. et al. Determination and inference of eukaryotic transcription factor sequence specificity. Cell 158, 1431–1443 (2014).

  60. 60.

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

  61. 61.

    Sherwood, R. I. et al. Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape. Nat. Biotechnol. 32, 171–178 (2014).

  62. 62.

    Pollard, K. S., Hubisz, M. J., Rosenbloom, K. R. & Siepel, A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 20, 110–121 (2010).

  63. 63.

    Tehranchi, A. K. et al. Pooled ChIP-Seq links variation in transcription factor binding to complex disease risk. Cell 165, 730–741 (2016).

  64. 64.

    Hah, N. et al. A rapid, extensive, and transient transcriptional response to estrogen signaling in breast cancer cells. Cell 145, 622–634 (2011).

  65. 65.

    Chae, M., Danko, C. G. & Kraus, W. L. groHMM: a computational tool for identifying unannotated and cell type-specific transcription units from global run-on sequencing data. BMC Bioinforma. 16, 222 (2015).

  66. 66.

    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 Genom. 15, 155 (2014).

  67. 67.

    Kozomara, A. & Griffiths-Jones, S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 42, D68–D73 (2014).

  68. 68.

    Dukler, N. et al. Nascent RNA sequencing reveals a dynamic global transcriptional response at genes and enhancers to the natural medicinal compound celastrol. Genome Res. 27, 1816–1829, (2017).

  69. 69.

    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).

  70. 70.

    Neph, S. et al. BEDOPS: high-performance genomic feature operations. Bioinformatics 28, 1919–1920 (2012).

  71. 71.

    Franco, H. L., Nagari, A. & Kraus, W. L. TNFα signaling exposes latent estrogen receptor binding sites to alter the breast cancer cell transcriptome. Mol. Cell. 58, 21–34 (2015).

  72. 72.

    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).

  73. 73.

    Pham, H., Kearns, N. A. & Maehr, R. Transcriptional regulation with CRISPR/Cas9 effectors in mammalian cells. Methods Mol. Biol. 1358, 43–57 (2016).

  74. 74

    Green, R. E. et al. A draft sequence of the Neandertal genome. Science 328, 710–722 (2010).

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Acknowledgements

We thank M. Jin for assistance in establishing the magnetic separation of CD4+ T cells, J. Rogers for help establishing contacts with primate centres, L. Core, H. Kwak, N. Fuda and I. Jonkers for assistance troubleshooting the PRO-Seq library prep, and A. Wetterau for preparing nuclei for mouse and rat CD4+ T cells. This work was supported by generous seed grants from the Cornell University Center for Vertebrate Genomics, Center for Comparative and Population Genetics, National Human Genome Research Institute grant HG009309 to C.G.D., National Heart, Lung, and Blood Institute grant UHL129958A to C.G.D. and J.T.L., National Institute of General Medical Sciences grant GM102192 to A.S., National Human Genome Research Institute grant HG0070707 to A.S. and J.T.L., National Institute of Diabetes and Digestive and Kidney Diseases DK058110 to W.L.K. and Cancer Prevention and Research Institute of Texas RP160319 to W.L.K. The content is solely the responsibility of the authors and does not necessarily represent the official views of the US National Institutes of Health. Finally, we would like to thank the anonymous human and non-human primate donors who gave blood in support of this study.

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Affiliations

  1. Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA

    • Charles G. Danko
    • , Lauren A. Choate
    • , Brooke A. Marks
    • , Edward J. Rice
    • , Zhong Wang
    • , Tinyi Chu
    • , Andre L. Martins
    • , Scott A. Coonrod
    •  & Elia D. Tait Wojno
  2. Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA

    • Charles G. Danko
    •  & Scott A. Coonrod
  3. Graduate Field of Computational Biology, Cornell University, Ithaca, NY, USA

    • Tinyi Chu
    •  & Andre L. Martins
  4. Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA

    • Noah Dukler
    •  & Adam Siepel
  5. Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, USA

    • Noah Dukler
  6. Department of Microbiology and Immunology, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA

    • Elia D. Tait Wojno
  7. Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA

    • John T. Lis
  8. Laboratory of Signaling and Gene Regulation, Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA

    • W. Lee Kraus
  9. Division of Basic Research, Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA

    • W. Lee Kraus

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Contributions

L.A.C., B.A.M., C.G.D., E.J.R. and E.D.T.W. performed the CD4+ T cell extraction, validation and PRO-Seq experiments. C.G.D., Z.W., T.C., A.L.M., L.A.C. and N.D. analysed the data. C.G.D., A.S., J.T.L., W.L.K. and S.A.C. supervised the data collection and analysis. C.G.D. and A.S. wrote the paper with input from the other authors.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Charles G. Danko or Adam Siepel.

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https://doi.org/10.1038/s41559-017-0447-5

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