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Transcriptionally active HERV-H retrotransposons demarcate topologically associating domains in human pluripotent stem cells

Nature Genetics volume 51, pages 1380–1388 (2019)Cite this article

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Abstract

Chromatin architecture has been implicated in cell type-specific gene regulatory programs, yet how chromatin remodels during development remains to be fully elucidated. Here, by interrogating chromatin reorganization during human pluripotent stem cell (hPSC) differentiation, we discover a role for the primate-specific endogenous retrotransposon human endogenous retrovirus subfamily H (HERV-H) in creating topologically associating domains (TADs) in hPSCs. Deleting these HERV-H elements eliminates their corresponding TAD boundaries and reduces the transcription of upstream genes, while de novo insertion of HERV-H elements can introduce new TAD boundaries. The ability of HERV-H to create TAD boundaries depends on high transcription, as transcriptional repression of HERV-H elements prevents the formation of boundaries. This ability is not limited to hPSCs, as these actively transcribed HERV-H elements and their corresponding TAD boundaries also appear in pluripotent stem cells from other hominids but not in more distantly related species lacking HERV-H elements. Overall, our results provide direct evidence for retrotransposons in actively shaping cell type- and species-specific chromatin architecture.

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Fig. 1: Reorganization of TADs during human cardiomyocyte differentiation.
Fig. 2: Transcriptionally active HERV-H forms hESC-specific TAD boundaries.
Fig. 3: Deletion of two HERV-H sequences leads to the merging of TADs in hESCs.
Fig. 4: Silencing of HERV-H sequences weakens the TAD boundaries in hESCs.
Fig. 5: HERV-H insertion creates de novo TAD boundaries.
Fig. 6: HERV-H introduces new TAD boundaries during primate evolution.

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Data availability

All sequencing datasets have been deposited in the Gene Expression Omnibus repository with the accession number GSE116862.

Code availability

Scripts are available at https://github.com/shawnzhangyx/cvdc_scripts.

References

  1. Dekker, J. & Mirny, L. The 3D genome as moderator of chromosomal communication. Cell 164, 1110–1121 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Yu, M. & Ren, B. The three-dimensional organization of mammalian genomes. Annu. Rev. Cell Dev. Biol. 33, 265–289 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Meaburn, K. J. & Misteli, T. Cell biology: chromosome territories. Nature 445, 379–781 (2007).

    Article  CAS  PubMed  Google Scholar 

  4. 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 

  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. Sexton, T. et al. Three-dimensional folding and functional organization principles of the Drosophila genome. Cell 148, 458–472 (2012).

    Article  CAS  PubMed  Google Scholar 

  7. Nora, E. P. et al. Spatial partitioning of the regulatory landscape of the X-inactivation centre. Nature 485, 381–385 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Dixon, J. R. et al. Chromatin architecture reorganization during stem cell differentiation. Nature 518, 331–336 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Dixon, J. R., Gorkin, D. U. & Ren, B. Chromatin domains: the unit of chromosome organization. Mol. Cell 62, 668–680 (2016).

    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. 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 

  12. Lupianez, D. G. et al. Disruptions of topological chromatin domains cause pathogenic rewiring of gene–enhancer interactions. Cell 161, 1012–1025 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Lupianez, D. G., Spielmann, M. & Mundlos, S. Breaking TADs: how alterations of chromatin domains result in disease. Trends Genet. 32, 225–237 (2016).

    Article  CAS  PubMed  Google Scholar 

  14. Ibn-Salem, J. et al. Deletions of chromosomal regulatory boundaries are associated with congenital disease. Genome Biol. 15, 423 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Franke, M. et al. Formation of new chromatin domains determines pathogenicity of genomic duplications. Nature 538, 265–269 (2016).

    Article  CAS  PubMed  Google Scholar 

  16. Nasmyth, K. Disseminating the genome: joining, resolving, and separating sister chromatids during mitosis and meiosis. Annu Rev. Genet. 35, 673–745 (2001).

    Article  CAS  PubMed  Google Scholar 

  17. Alipour, E. & Marko, J. F. Self-organization of domain structures by DNA-loop-extruding enzymes. Nucleic Acids Res. 40, 11202–11212 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Rao, S. S. P. et al. Cohesin loss eliminates all loop domains. Cell 171, 305–320.e24 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Pombo, A. & Dillon, N. Three-dimensional genome architecture: players and mechanisms. Nat. Rev. Mol. Cell Biol. 16, 245–257 (2015).

    Article  CAS  PubMed  Google Scholar 

  20. Nora, E. P. et al. Targeted degradation of CTCF decouples local insulation of chromosome domains from genomic compartmentalization. Cell 169, 930–944.e22 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Schmidt, D. et al. Waves of retrotransposon expansion remodel genome organization and CTCF binding in multiple mammalian lineages. Cell 148, 335–348 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Bonev, B. et al. Multiscale 3D genome rewiring during mouse neural development. Cell 171, 557–572 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Stadhouders, R. et al. Transcription factors orchestrate dynamic interplay between genome topology and gene regulation during cell reprogramming. Nat. Genet. 50, 238–249 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Krijger, P. H. et al. Cell-of-origin-specific 3D genome structure acquired during somatic cell reprogramming. Cell Stem Cell 18, 597–610 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Beagan, J. A. et al. Local genome topology can exhibit an incompletely rewired 3D-folding state during somatic cell reprogramming. Cell Stem Cell 18, 611–624 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Veevers, J. et al. Cell-surface marker signature for enrichment of ventricular cardiomyocytes derived from human embryonic stem cells. Stem Cell Rep. 11, 828–841 (2018).

    Article  CAS  Google Scholar 

  28. Fields, P. A. et al. Dynamic reorganization of nuclear architecture during human cardiogenesis. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/222877v1 (2017).

  29. Crane, E. et al. Condensin-driven remodelling of X chromosome topology during dosage compensation. Nature 523, 240–244 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Grow, E. J. et al. Intrinsic retroviral reactivation in human preimplantation embryos and pluripotent cells. Nature 522, 221–225 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Goke, J. et al. Dynamic transcription of distinct classes of endogenous retroviral elements marks specific populations of early human embryonic cells. Cell Stem Cell 16, 135–141 (2015).

    Article  CAS  PubMed  Google Scholar 

  32. Lu, X. et al. The retrovirus HERVH is a long noncoding RNA required for human embryonic stem cell identity. Nat. Struct. Mol. Biol. 21, 423–425 (2014).

    Article  CAS  PubMed  Google Scholar 

  33. Romer, C., Singh, M., Hurst, L. D. & Izsvak, Z. How to tame an endogenous retrovirus: HERVH and the evolution of human pluripotency. Curr. Opin. Virol. 25, 49–58 (2017).

    Article  PubMed  Google Scholar 

  34. Santoni, F. A., Guerra, J. & Luban, J. HERV-H RNA is abundant in human embryonic stem cells and a precise marker for pluripotency. Retrovirology 9, 111 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Ohnuki, M. et al. Dynamic regulation of human endogenous retroviruses mediates factor-induced reprogramming and differentiation potential. Proc. Natl Acad. Sci. USA 111, 12426–12431 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Loewer, S. et al. Large intergenic non-coding RNA-RoR modulates reprogramming of human induced pluripotent stem cells. Nat. Genet. 42, 1113–1117 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Xie, W. et al. Epigenomic analysis of multilineage differentiation of human embryonic stem cells. Cell 153, 1134–1148 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Greenwald, W. W. et al. Subtle changes in chromatin loop contact propensity are associated with differential gene regulation and expression. Nat. Commun. 10, 1054 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Lengronne, A. et al. Cohesin relocation from sites of chromosomal loading to places of convergent transcription. Nature 430, 573–578 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Busslinger, G. A. et al. Cohesin is positioned in mammalian genomes by transcription, CTCF and Wapl. Nature 544, 503–507 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Jacques, P. E., Jeyakani, J. & Bourque, G. The majority of primate-specific regulatory sequences are derived from transposable elements. PLoS Genet. 9, e1003504 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Barakat, T. S. et al. Functional dissection of the enhancer repertoire in human embryonic stem cells. Cell Stem Cell 23, 276–288.e8 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Wang, J. et al. Primate-specific endogenous retrovirus-driven transcription defines naive-like stem cells. Nature 516, 405–409 (2014).

    Article  CAS  PubMed  Google Scholar 

  44. Ng, S. Y., Johnson, R. & Stanton, L. W. Human long non-coding RNAs promote pluripotency and neuronal differentiation by association with chromatin modifiers and transcription factors. EMBO J. 31, 522–533 (2012).

    Article  CAS  PubMed  Google Scholar 

  45. Liu, J., Li, Y., Lin, B., Sheng, Y. & Yang, L. HBL1 is a human long noncoding RNA that modulates cardiomyocyte development from pluripotent stem cells by counteracting MIR1. Dev. Cell 42, 333–348.e5 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Koyanagi-Aoi, M. et al. Differentiation-defective phenotypes revealed by large-scale analyses of human pluripotent stem cells. Proc. Natl Acad. Sci. USA 110, 20569–20574 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Goodchild, N. L., Wilkinson, D. A. & Mager, D. L. Recent evolutionary expansion of a subfamily of RTVL-H human endogenous retrovirus-like elements. Virology 196, 778–788 (1993).

    Article  CAS  PubMed  Google Scholar 

  48. Mager, D. L. & Freeman, J. D. HERV-H endogenous retroviruses: presence in the New World branch but amplification in the Old World primate lineage. Virology 213, 395–404 (1995).

    Article  CAS  PubMed  Google Scholar 

  49. Kijima, T. E. & Innan, H. On the estimation of the insertion time of LTR retrotransposable elements. Mol. Biol. Evol. 27, 896–904 (2010).

    Article  CAS  PubMed  Google Scholar 

  50. Britten, R. J. & Davidson, E. H. Repetitive and non-repetitive DNA sequences and a speculation on the origins of evolutionary novelty. Q. Rev. Biol. 46, 111–138 (1971).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Wang, Y. et al. Endogenous miRNA sponge lincRNA-RoR regulates Oct4, Nanog, and Sox2 in human embryonic stem cell self-renewal. Dev. Cell 25, 69–80 (2013).

    Article  CAS  PubMed  Google Scholar 

  54. De Cecco, M. et al. L1 drives IFN in senescent cells and promotes age-associated inflammation. Nature 566, 73–78 (2019).

    Article  CAS  PubMed  Google Scholar 

  55. Kong, Y. et al. Transposable element expression in tumors is associated with immune infiltration and increased antigenicity. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/388215v1 (2018).

  56. Reilly, M. T., Faulkner, G. J., Dubnau, J., Ponomarev, I. & Gage, F. H. The role of transposable elements in health and diseases of the central nervous system. J. Neurosci. 33, 17577–17586 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Lian, X. et al. Directed cardiomyocyte differentiation from human pluripotent stem cells by modulating Wnt/β-catenin signaling under fully defined conditions. Nat. Protoc. 8, 162–175 (2013).

    Article  CAS  PubMed  Google Scholar 

  58. Hashem, S. I. et al. Brief Report: oxidative stress mediates cardiomyocyte apoptosis in a human model of Danon disease and heart failure. Stem Cells 33, 2343–2350 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Shen, Y. et al. A map of the cis-regulatory sequences in the mouse genome. Nature 488, 116–120 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Gorkin, D. et al. Systematic mapping of chromatin state landscapes during mouse development. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/166652v1 (2017).

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

    Article  CAS  PubMed  Google Scholar 

  62. Yan, J. et al. Transcription factor binding in human cells occurs in dense clusters formed around cohesin anchor sites. Cell 154, 801–813 (2013).

    Article  CAS  PubMed  Google Scholar 

  63. Liu, Z. et al. Efficient CRISPR/Cas9-mediated versatile, predictable, and donor-free gene knockout in human pluripotent stem cells. Stem Cell Rep. 7, 496–507 (2016).

    Article  CAS  Google Scholar 

  64. Haeussler, M. et al. Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR. Genome Biol. 17, 148 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. Kim, S., Kim, D., Cho, S. W., Kim, J. & Kim, J. S. Highly efficient RNA-guided genome editing in human cells via delivery of purified Cas9 ribonucleoproteins. Genome Res. 24, 1012–1019 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Zuris, J. A. et al. Cationic lipid-mediated delivery of proteins enables efficient protein-based genome editing in vitro and in vivo. Nat. Biotechnol. 33, 73–80 (2015).

    Article  CAS  PubMed  Google Scholar 

  67. Santos, D. P., Kiskinis, E., Eggan, K. & Merkle, F. T. Comprehensive protocols for CRISPR/Cas9-based gene editing in human pluripotent stem cells. Curr. Protoc. Stem Cell Biol. 38, 5B.6.1–5B.6.60 (2016).

    Article  Google Scholar 

  68. Radzisheuskaya, A., Shlyueva, D., Muller, I. & Helin, K. Optimizing sgRNA position markedly improves the efficiency of CRISPR/dCas9-mediated transcriptional repression. Nucleic Acids Res. 44, e141 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. Konermann, S. et al. Genome-scale transcriptional activation by an engineered CRISPR–Cas9 complex. Nature 517, 583–588 (2015).

    Article  CAS  PubMed  Google Scholar 

  70. Xie, S., Duan, J., Li, B., Zhou, P. & Hon, G. C. Multiplexed engineering and analysis of combinatorial enhancer activity in single cells. Mol. Cell 66, 285–299.e5 (2017).

    Article  CAS  PubMed  Google Scholar 

  71. Ding, S. et al. Efficient transposition of the piggyBac (PB) transposon in mammalian cells and mice. Cell 122, 473–483 (2005).

    Article  CAS  PubMed  Google Scholar 

  72. Meir, Y. J. et al. Genome-wide target profiling of piggyBac and Tol2 in HEK 293: pros and cons for gene discovery and gene therapy. BMC Biotechnol. 11, 28 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  75. 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 

  76. Ramirez, F., Dundar, F., Diehl, S., Gruning, B. A. & Manke, T. deepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 42, W187–W191 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Dobin, A. et al. STAR: ultrafast universal RNA-Seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  78. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    Article  CAS  PubMed  Google Scholar 

  79. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  80. Durand, N. C. et al. Juicebox provides a visualization system for Hi-C contact maps with unlimited zoom. Cell Syst. 3, 99–101 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  83. Casper, J. et al. The UCSC Genome Browser database: 2018 update. Nucleic Acids Res. 46, D762–D769 (2018).

    Article  CAS  PubMed  Google Scholar 

  84. Xu, C. & Corces, V. G. Nascent DNA methylome mapping reveals inheritance of hemimethylation at CTCF/cohesin sites. Science 359, 1166–1170 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Selvaraj, S., R Dixon, J., Bansal, V. & Ren, B. Whole-genome haplotype reconstruction using proximity-ligation and shotgun sequencing. Nat. Biotechnol. 31, 1111–1118 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Edge, P., Bafna, V. & Bansal, V. HapCUT2: robust and accurate haplotype assembly for diverse sequencing technologies. Genome Res. 27, 801–812 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Greenwald, W. W. et al. Pgltools: a genomic arithmetic tool suite for manipulation of Hi-C peak and other chromatin interaction data. BMC Bioinformatics 18, 207 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  89. Hu, T. T. et al. The Arabidopsis lyrata genome sequence and the basis of rapid genome size change. Nat. Genet. 43, 476–481 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  90. Slotte, T. et al. The Capsella rubella genome and the genomic consequences of rapid mating system evolution. Nat. Genet. 45, 831–835 (2013).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank S. Kuan and B. Li for sequencing and bioinformatic support. We thank E. Nostrand for RNA extraction. We thank M. Daadi (University of Texas Health Science Center at San Antonio) for providing marmoset iPSCs. We thank F. Gage (Salk Institute) for providing chimpanzee and bonobo iPSCs. This project is supported by funding from the Ludwig Institute for Cancer Research (to B.R.) and NIH (1UM1HL128773 to S.M.E., N.C.C., E.D. and B.R., and U54 DK107977 to B.R.). J.W. is the Virginia Murchison Linthicum Scholar in Medical Research. L.Y. was supported by a Hamon Center for Regenerative Science and Medicine fellowship from UT Southwestern Medical Center. S.P. was supported by a postdoctoral fellowship from the Deutsche Forschungsgemeinschaft (PR 1668/1-1). E.N.F. was supported by an NIH pre-doctoral training grant (5T32HL007444-35). M.L.A. was supported by an NIH training grant (T32GM008806-18). J.C.I.B. was supported by the Moxie Foundation.

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Author notes

  1. These authors contributed equally: Yanxiao Zhang, Ting Li, Sebastian Preissl.

Authors and Affiliations

  1. Ludwig Institute for Cancer Research, La Jolla, CA, USA

    Yanxiao Zhang, Sebastian Preissl, Yunjiang Qiu, Rong Hu, Ah Young Lee, Sora Chee, Kaiyue Ma, Zhen Ye, Quan Zhu, Hui Huang, Rongxin Fang & Bing Ren

  2. Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA

    Ting Li, Jonathan D. Grinstein, Elie N. Farah, Eugin Destici, Sylvia M. Evans & Neil C. Chi

  3. Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA

    Sebastian Preissl & Bing Ren

  4. Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA, USA

    Maria Luisa Amaral, Yunjiang Qiu & Rongxin Fang

  5. Biomedical Sciences Graduate Program, University of California San Diego, La Jolla, CA, USA

    Elie N. Farah & Hui Huang

  6. Department of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Leqian Yu & Jun Wu

  7. Hamon Center for Regenerative Science and Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Leqian Yu & Jun Wu

  8. Salk Institute for Biological Studies, San Diego, CA, USA

    Juan Carlos Izpisua Belmonte

  9. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA

    Sylvia M. Evans

  10. Institute of Genomic Medicine, School of Medicine, University of California San Diego, La Jolla, CA, USA

    Neil C. Chi & Bing Ren

  11. Moores Cancer Center, School of Medicine, University of California San Diego, La Jolla, CA, USA

    Bing Ren

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  1. Yanxiao Zhang
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Contributions

N.C.C. and B.R. designed and supervised the experiments, analysis and data interpretation. Y.Z. implemented the analysis pipeline, analyzed all of the sequencing datasets, interpreted the results and designed the experiments for the HERV-H functional studies. T.L. generated the CRISPR–Cas9-edited cell lines for the HERV-H functional studies, and performed differentiation and quantitative PCR of the corresponding cell lines. S.P. performed the Hi-C experiments for all stages of the cardiomyocyte differentiation, and helped with interpretation of the results. M.L.A. analyzed the HERV-H knock-in data with help from Y.Q. regarding allelic analysis. J.D.G. and E.N.F. performed the cell culture and differentiation, and collected the cells for the Hi-C, ChIP-Seq and RNA-Seq assays. E.D. contributed to analysis and interpretation of the ChIP-Seq data. R.H. performed the Hi-C experiments for the HERV-H knockout, CRISPRi, HERV-H knock-in and primate iPSC cell lines. The ChIP-Seq experiments were performed by A.Y.L. (H3K27ac), S.C. (CTCF), and Q.Z. and H.H. (SMC3). Y.Q. and R.F. helped with the analysis of the Hi-C datasets. K.M. helped with the genome editing experiments. L.Y., J.C.I.B. and J.W. cultured and prepared the non-human primate iPSCs for sequencing and interpreted the data. Z.Y. performed the RNA-Seq experiments. S.M.E. helped with interpretation of the results. Y.Z., T.L., S.P., N.C.C. and B.R. wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Neil C. Chi or Bing Ren.

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Competing interests

B.R. is a co-founder of Arima Genomics.

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Integrated supplementary information

Supplementary Figure 1 Imaging and Flow cytometry data for cardiomyocyte differentiation.

(a) Immunostaining for MYL2 (white) shows exclusive expression in MYL2-H2B-GFP + hESC-derived cardiomyocytes after 80 days of cardiac differentiation. MYL2-H2B-GFP + cells were positive for cardiac troponin T (cTnT) (red), but not all cTnT+ cells were MYL2+ cardiomyocytes. DNA was stained with DAPI (blue). Images are representative of a minimum of three independent experiments. (b) Flow cytometric quantification of distinct markers for D2-D15 time points: T (D2), KDR/PDGFRα (D5), cTnT (D15). H2B-GFP + ventricular cardiomyocytes were sorted at day 80. Numbers represent the respective percentage of cells. The experiments were routinely carried out in Chi lab with similar results and had been repeated two times in this study.

Supplementary Figure 2 Quality metrics and reproducibility of sequencing datasets.

(a) Smoothed scatterplot of the Hi-C contacts between biological replicates for each stage. bin size = 100 kb. Spearman correlation coefficients are indicated on top of each sample pair. (b) Hierarchical clustering of Hi-C contact matrices based on 1-PCC (Spearman correlation coefficients) of the contacts between all samples, bin size = 100 kb. (c) Hierarchical clustering of the compartment A/B scores (PC1 values) between samples. (d) Hierarchical clustering of the insulation scores between samples. (e) Boxplot showing the number of reads for each sample. (f) Boxplot showing the percentage of alignments to human genome for each sample. (g) Boxplot showing the percentage of potential duplicated reads for each sample. (h) Boxplot showing the number of peaks called for each ChIP-seq sample. (e-h) For each boxplot the sample size N=12, the elements of the boxplot are: center line, median; box limit, upper and lower quartiles; whiskers, 1.5x interquartile range. (i) Barplots showing the relative expression levels (by RNA-seq) of representative genes during cardiomyocyte differentiation.

Supplementary Figure 3 Global changes in chromatin organization during cardiomyocyte differentiation.

(a) Frequency distribution of Hi-C contacts over genomic distances (log2) at different stages of cardiomyocyte differentiation (colored lines). (b) Barplot showing the percentages of compartment switches for each stage transition. (c) A histogram showing the distribution of the Pearson correlation coefficients (PCC) between gene expression levels and PC1 (indicating compartment A or B) value derived from the Hi-C contact matrix. (d) Snapshots of genome browser view of SOX2, HAND2 and RYR2 loci, showing PC1 values (Blue/red), RNA-seq (Black) and H3K27ac (Blue) signals.

Supplementary Figure 4 Dynamics of TADs during cardiomyocyte differentiation.

(a) Number of TADs in each stage of differentiation as defined by a domain caller algorithm first reported by Dixon et al. 20125. (b) Number of TADs (or loop domains) found by Arrowhead algorithm10. (c) Number of TADs found by Insulation score29. (d) Fraction of TAD boundaries that contain CTCF ChIP-seq peaks at each stage of cardiomyocyte differentiation. ‘stable’ group stands for TADs that are present at all stages. (e) Boxplot showing the dynamics of H3K27ac signal at ESC(+) TADs (N=198). (f) Boxplot showing the dynamics of RNA-seq signal at ESC(+) TADs (N=198). The elements of the boxplot are: center line, median; box limit, upper and lower quartiles; whiskers, 1.5x interquartile range.

Supplementary Figure 5 HERV-H elements are enriched at ESC(+) TAD boundaries.

(a) Aggregated RNA-seq expression profile (RPKM normalized) at ESC(+) TAD boundaries that overlap each of the eight repeat elements (HERVH-int and LTR7 are combined as they both belong to HERV-H). (b) Heatmap of DI scores within 70 kb of the top 50 most highly-expressed HERV-Hs. (c,d,e) heatmaps displaying Hi-C contacts for three ESC(+) TAD boundaries harboring HERV-H/LTR7 sequences are shown together with the DI, CTCF ChIP-seq, histone modification H3K27ac and RNA-seq profiles at the corresponding regions.

Supplementary Figure 6 Further characterization of the relationship between transcription levels at HERV-H elements and regular genes and formation of TAD boundaries.

(a) Aggregated DI score profiles of the TAD-boundary associated with HERV-Hs in multiple H1 ESC derived lineages and iPSCs. ESC: embryonic stem cell; MES: mesendoderm. MSC: mesenchymal stem cell; NPC: neural progenitor cell; TRO: trophoblast-like cell; iPSC: induced pluripotent stem cell. Interestingly, in human mesendoderm cells (an early human embryonic state that gives rise to mesoderm and endoderm cells), both the expression of these HERV-Hs and their corresponding TAD boundary strength were approximately half the levels compared to those observed in hESCs. There might be some un-differentiated cells in this population. (b) Heatmaps showing the aggregated DI score profile centered on the TSSs of genes (ranked by expression levels from high to low; every 1000 genes were segregated into bins, and separated by whether or not having a CTCF peak within 20kb of TSS). The DI score profile of top 50 HERV-H is shown at the bottom and the distribution of the HERV-H’s rank is shown on the right.

Supplementary Figure 7 Transcription of a solo LTR7 is correlated with appearance of TAD boundary in primate PSCs but not in mouse ESC.

(a) Hi-C interaction matrices of a solo LTR7 loci located at ESC(+) TAD boundaries at D0, D2 and D5 (top) are shown as heatmaps along with genome browser tracks of DI score, POLR2A, SMC3, CTCF, H3K27ac ChIP-seq and RNA-seq data of the expanded genomic region containing the TAD boundary (arrow). (b) Hi-C interaction matrices of the syntenic regions in bonobo iPSC, chimp iPSC and mouse ESC. (marmoset data not shown because the syntenic region is in an unassembled contig).

Supplementary Figure 8 Enrichment of TF or histone ChIP-seq signals at TAD associated HERV-H loci.

Barplot shows the ChIP-seq signal fold enrichment of the top 50 HERV-Hs comparing to HERV-Hs ranking 51-300. The red dashed line (value = 1) shows no fold enrichment.

Supplementary Figure 9 HERV-H knockout leads to alterations of gene expression programs in hESCs.

(a) Boxplots showing expression levels (RPKMs) of genes whose TSSs are located within TADs immediately 5’ (N=43) or 3’ (N=28) to boundary-associated HERV-Hs. P-values are from two-sided paired t-test on the log-transformed expression levels. The elements of the boxplot are: center line, median; box limit, upper and lower quartiles; whiskers, 1.5x interquartile range. (b) MA-plot (log ratio vs mean) showing average gene expression levels and fold changes of each gene in HERV-H1-KO and wild-type (WT). (c) Same as (b) but for HERV-H2-KO. (d) Scatterplot shows the changes in gene expression in HERV-H1-KO and HERV-H2-KO cells over WT cells. The red dots mark genes that with significantly changed gene expression in both mutant cell lines. The numbers of significantly changed genes in each Quadrant are indicated at the corner of each quadrant. Pearson correlation coefficient (PCC) and p-value are indicated (total number of genes N= 15623). (e) Barplot showing the number of significantly changed genes located within 20 kb of the HERV-H sequences. Genes down-regulated in both HERV-H knockouts were more likely to be within 20 kb of HERV-H sequences. P-value is from two-sided Fisher’s exact test (N=76). (f,g) RNA-seq profile of wild-type (WT) and HERV-H1-KO and HERV-H2-KO lines at the SCGB3A2 and LINC00458/HBL1 gene loci. The experiments were repeated twice independently with similar results.

Supplementary Figure 10 Analysis of chromatin contacts between actively transcribed HERV-Hs.

(a) Heatmap showing the contact matrix between the HERV-H1 locus and LINC00458/HBL1 locus. There is no visible chromatin contact between the two loci. (b) Heatmap shows the averaged and normalized (scaled and centered to zero) contact frequencies among each pair of HERV-H loci with RPKM greater than 1 (N=122). X-axis and y-axis show the genomic regions ±40kb surrounding HERV-H.

Supplementary Figure 11 Characterization of de novo HERV-H insertions in two engineered human ESC clones.

(a) Chromosomal view of de novo HERV-H insertions in the HERV-H-ins.clone1 transgenic line. The y-axis shows the counts of Hi-C read pairs with one end mapped to the HERV-H2 sequence. Based on proximity ligation principle, loci with high pileup should harbor HERV-H2 insertion. (b) Same as (a) but for the HERV-H-ins.clone2 transgenic line. (c) Genome browser view showing the Directionality Index (DI) score and transcription levels (RPKM) of the parental and HERV-H inserted cell lines. Note that in HERV-H-ins.clone1, the predicted HERV-H insertion creates a chimeric transcript with PIWIL1, which is not expressed in the other two cell lines. The experiments were repeated twice independently with similar results.

Supplementary Figure 12 Analysis of HERV-H related chromatin architecture and LTR7s in different primate PSC lines and in the mouse ESC.

(a) Heatmaps of DI scores within 70 kb of the syntenic regions of top 50 most highly-expressed human HERV-Hs in other indicated species. (b) Bar graph shows the percentage of HERV-Hs flanked by various types of LTRs. HERV-Hs are ranked by their expression levels in the hESCs, and grouped by bins of 50. (c) Violin plot shows the length of the flanking LTRs. HERV-Hs ranked and binned (N=50) as described in (b). The violin box displays kernel density of the LTR length distribution. (d) Boxplot shows the sequence divergence of the 5′ LTR and 3′ LTR for each bin of HERV-Hs. HERV-Hs ranked and binned (N=50) same as (b). The elements of the boxplot are: center line, median; box limit, upper and lower quartiles; whiskers, 1.5x interquartile range.

Supplementary Figure 13 A working model for HERV-H mediated TAD boundary formation.

When HERV-H is highly transcribed, the RNA polymerase complex moves directionally from the 5′LTR towards 3′LTR. Accumulation of the RNA polymerase complex presents a significant physical barrier to the movement of the cohesin complex (shown as a ring), causing it to accumulate at the 3′ end of HERV-H sequences, leading to creation of the TAD boundary. A previously characterized enhancer42 located at the 5′ LTR may regulate genes on its 5′ TAD, while the enhancer function is blocked by the TAD boundary formed downstream of the 3′ LTR.

Supplementary information

Supplementary Information

Supplementary Figs. 1–13

Reporting Summary

Supplementary Table 1

Summary statistics for the Hi-C data.

Supplementary Table 2

List of stage-specific TAD boundaries.

Supplementary Table 3

List of differentially expressed genes in HERV-H1-KO and HERV-H2-KO.

Supplementary Table 4

List of HERV-H insertions.

Supplementary Table 5

List of the primers and cell lines used in this study.

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Zhang, Y., Li, T., Preissl, S. et al. Transcriptionally active HERV-H retrotransposons demarcate topologically associating domains in human pluripotent stem cells. Nat Genet 51, 1380–1388 (2019). https://doi.org/10.1038/s41588-019-0479-7

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