Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

KLF4 is involved in the organization and regulation of pluripotency-associated three-dimensional enhancer networks

Nature Cell Biology volume 21, pages 1179–1190 (2019)Cite this article

Subjects

This article has been updated

Abstract

Cell fate transitions are accompanied by global transcriptional, epigenetic and topological changes driven by transcription factors, as is exemplified by reprogramming somatic cells to pluripotent stem cells through the expression of OCT4, KLF4, SOX2 and cMYC. How transcription factors orchestrate the complex molecular changes around their target gene loci remains incompletely understood. Here, using KLF4 as a paradigm, we provide a transcription-factor-centric view of chromatin reorganization and its association with three-dimensional enhancer rewiring and transcriptional changes during the reprogramming of mouse embryonic fibroblasts to pluripotent stem cells. Inducible depletion of KLF factors in PSCs caused a genome-wide decrease in enhancer connectivity, whereas disruption of individual KLF4 binding sites within pluripotent-stem-cell-specific enhancers was sufficient to impair enhancer–promoter contacts and reduce the expression of associated genes. Our study provides an integrative view of the complex activities of a lineage-specifying transcription factor and offers novel insights into the nature of the molecular events that follow transcription factor binding.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Dynamic KLF4 binding during reprogramming and association with chromatin accessibility and enhancer activity.
Fig. 2: Characterization of the 3D enhancer connectomes in MEFs and PSCs by H3K27ac HiChIP analysis.
Fig. 3: Highly connected enhancers are characterized by specific features.
Fig. 4: Coregulation of genes within highly interacting enhancer hubs.
Fig. 5: Chromatin reorganization around KLF4 binding sites during reprogramming associates with enhancer rewiring and requires additional cofactors.
Fig. 6: Inducible depletion of KLF proteins induces 3D enhancer reorganization and concordant transcriptional changes.
Fig. 7: Disruption of the KLF4 binding site within Tbx3 and Zfp42 enhancers induces looping abrogation and downregulation of linked genes in PSCs.

Similar content being viewed by others

Data availability

All genomics data (RNA-seq, ChIP-seq, ATAC-seq and HiChIP) have been deposited in the Gene Expression Omnibus (GEO) under the accession code GSE113431. RIME data have deposited to the ProteomeXchange Consortium via the PRIDE partner repository under the identifier PXD014631 (www.ebi.ac.uk/pride). The accession codes for all previously published datasets that were used in this study at listed in Supplementary Table 12. The source data for Figs. 4f, 6f,g, 7d,e,i,j and Supplementary Figs. 4i,j, 6b, 7d have been provided as Supplementary Table 11.

Code availability

The computational code for the processing of HiChIP/HiC is available under https://github.com/NYU-BFX/hic-bench. All other custom computational code are available from the corresponding author on request.

Change history

  • 07 October 2019

    In the HTML version of this article originally published, the contact information for co-corresponding author Aristotelis Tsirigos was not included. This should be linked in the author list, as well as in the correspondence section, which should read ‘Corresponding authors – Aristotelis Tsirigos or Effie Apostolou’. This has now been corrected.

References

  1. Young, R. A. Control of the embryonic stem cell state. Cell 144, 940–954 (2011).

    Article  CAS  Google Scholar 

  2. Natoli, G. Maintaining cell identity through global control of genomic organization. Immunity 33, 12–24 (2010).

    Article  CAS  Google Scholar 

  3. Graf, T. Historical origins of transdifferentiation and reprogramming. Cell Stem Cell 9, 504–516 (2011).

    Article  CAS  Google Scholar 

  4. Takahashi, K. & Yamanaka, S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126, 663–676 (2006).

    Article  CAS  Google Scholar 

  5. Apostolou, E. & Hochedlinger, K. Chromatin dynamics during cellular reprogramming. Nature 502, 462–471 (2013).

    Article  CAS  Google Scholar 

  6. Apostolou, E. & Stadtfeld, M. Cellular trajectories and molecular mechanisms of iPSC reprogramming. Curr. Opin. Genet. Dev. 52, 77–85 (2018).

    Article  CAS  Google Scholar 

  7. Di Giammartino, D. C. & Apostolou, E. The chromatin signature of pluripotency: establishment and maintenance. Curr. Stem Cell. Rep. 2, 255–262 (2016).

    Article  Google Scholar 

  8. Chronis, C. et al. Cooperative binding of transcription factors orchestrates reprogramming. Cell 168, 442–459 e420 (2017).

    Article  CAS  Google Scholar 

  9. Soufi, A., Donahue, G. & Zaret, K. S. Facilitators and impediments of the pluripotency reprogramming factors' initial engagement with the genome. Cell 151, 994–1004 (2012).

    Article  CAS  Google Scholar 

  10. Chen, J. et al. Hierarchical Oct4 binding in concert with primed epigenetic rearrangements during somatic cell reprogramming. Cell Rep. 14, 1540–1554 (2016).

    Article  CAS  Google Scholar 

  11. Sridharan, R. et al. Role of the murine reprogramming factors in the induction of pluripotency. Cell 136, 364–377 (2009).

    Article  CAS  Google Scholar 

  12. Knaupp, A. S. et al. Transient and permanent reconfiguration of chromatin and transcription factor occupancy drive reprogramming. Cell Stem Cell 21, 834–845 e836 (2017).

    Article  CAS  Google Scholar 

  13. Li, D. et al. Chromatin accessibility dynamics during iPSC reprogramming. Cell Stem Cell 21, 819–833 e816 (2017).

    Article  CAS  Google Scholar 

  14. Polo, J. M. et al. A molecular roadmap of reprogramming somatic cells into iPS cells. Cell 151, 1617–1632 (2012).

    Article  CAS  Google Scholar 

  15. Apostolou, E. et al. Genome-wide chromatin interactions of the Nanog locus in pluripotency, differentiation, and reprogramming. Cell Stem Cell 12, 699–712 (2013).

    Article  CAS  Google Scholar 

  16. de Wit, E. et al. The pluripotent genome in three dimensions is shaped around pluripotency factors. Nature 501, 227–231 (2013).

    Article  Google Scholar 

  17. Denholtz, M. et al. Long-range chromatin contacts in embryonic stem cells reveal a role for pluripotency factors and polycomb proteins in genome organization. Cell Stem Cell 13, 602–616 (2013).

    Article  CAS  Google Scholar 

  18. Wei, Z. et al. Klf4 organizes long-range chromosomal interactions with the Oct4 locus in reprogramming and pluripotency. Cell Stem Cell 13, 36–47 (2013).

    Article  CAS  Google Scholar 

  19. 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  Google Scholar 

  20. 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  Google Scholar 

  21. 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  Google Scholar 

  22. Rubin, A. J. et al. Lineage-specific dynamic and pre-established enhancer-promoter contacts cooperate in terminal differentiation. Nat. Genet. 49, 1522–1528 (2017).

    Article  CAS  Google Scholar 

  23. Drissen, R. et al. The active spatial organization of the β-globin locus requires the transcription factor EKLF. Genes Dev. 18, 2485–2490 (2004).

    Article  CAS  Google Scholar 

  24. Schoenfelder, S. et al. Preferential associations between co-regulated genes reveal a transcriptional interactome in erythroid cells. Nat. Genet. 42, 53–61 (2010).

    Article  CAS  Google Scholar 

  25. Stadtfeld, M., Maherali, N., Borkent, M. & Hochedlinger, K. A reprogrammable mouse strain from gene-targeted embryonic stem cells. Nat. Methods 7, 53–55 (2010).

    Article  CAS  Google Scholar 

  26. Stadtfeld, M.et al. Ascorbic acid prevents loss of Dlk1-Dio3 imprinting and facilitates generation of all-iPS cell mice from terminally differentiated B cells. Nat. Genet. 44, 398–405 (2012).

  27. Stadtfeld, M. et al. Aberrant silencing of imprinted genes on chromosome 12qF1 in mouse induced pluripotent stem cells. Nature 465, 175–181 (2010).

    Article  CAS  Google Scholar 

  28. Zunder, E. R., Lujan, E., Goltsev, Y., Wernig, M. & Nolan, G. P. A continuous molecular roadmap to iPSC reprogramming through progression analysis of single-cell mass cytometry. Cell Stem Cell 16, 323–337 (2015).

    Article  CAS  Google Scholar 

  29. Stadtfeld, M., Maherali, N., Breault, D. T. & Hochedlinger, K. Defining molecular cornerstones during fibroblast to iPS cell reprogramming in mouse. Cell Stem Cell 2, 230–240 (2008).

    Article  CAS  Google Scholar 

  30. McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).

    Article  CAS  Google Scholar 

  31. Guo, L. et al. Resolving cell fate decisions during somatic cell reprogramming by single-cell RNA-Seq. Mol. Cell 73, 815–829 (2019).

    Article  CAS  Google Scholar 

  32. Allison, T. F. et al. Identification and single-cell functional characterization of an endodermally biased pluripotent substate in human embryonic stem cells. Stem Cell Rep. 10, 1895–1907 (2018).

    Article  CAS  Google Scholar 

  33. Schiebinger, G. et al. Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming. Cell 176, 928–943 (2019).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  36. Mumbach, M. R. et al. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat. Methods 13, 919–922 (2016).

    Article  CAS  Google Scholar 

  37. Phanstiel, D. H., Boyle, A. P., Heidari, N. & Snyder, M. P. Mango: a bias-correcting ChIA-PET analysis pipeline. Bioinformatics 31, 3092–3098 (2015).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  39. Mumbach, M. R. et al. Enhancer connectome in primary human cells identifies target genes of disease-associated DNA elements. Nat. Genet. 49, 1602–1612 (2017).

    Article  CAS  Google Scholar 

  40. Weintraub, A. S. et al. YY1 is a structural regulator of enhancer-promoter loops. Cell 171, 1573–1588 (2017).

    Article  CAS  Google Scholar 

  41. Beagan, J. A. et al. YY1 and CTCF orchestrate a 3D chromatin looping switch during early neural lineage commitment. Genome Res. 27, 1139–1152 (2017).

    Article  CAS  Google Scholar 

  42. Gomez-Diaz, E. & Corces, V. G. Architectural proteins: regulators of 3D genome organization in cell fate. Trends Cell Biol. 24, 703–711 (2014).

    Article  CAS  Google Scholar 

  43. Dowen, J. M. et al. Control of cell identity genes occurs in insulated neighborhoods in mammalian chromosomes. Cell 159, 374–387 (2014).

    Article  CAS  Google Scholar 

  44. Novo, C. L. et al. Long-range enhancer interactions are prevalent in mouse embryonic stem cells and are reorganized upon pluripotent state transition. Cell Rep. 22, 2615–2627 (2018).

    Article  CAS  Google Scholar 

  45. Kearns, N. A. et al. Functional annotation of native enhancers with a Cas9–histone demethylase fusion. Nat. Methods 12, 401–403 (2015).

    Article  CAS  Google Scholar 

  46. Larson, M. H. et al. CRISPR interference (CRISPRi) for sequence-specific control of gene expression. Nat. Protoc. 8, 2180–2196 (2013).

    Article  CAS  Google Scholar 

  47. Mas, G. & Di Croce, L. The role of Polycomb in stem cell genome architecture. Curr. Opin. Cell Biol. 43, 87–95 (2016).

    Article  CAS  Google Scholar 

  48. Mohammed, H. et al. Rapid immunoprecipitation mass spectrometry of endogenous proteins (RIME) for analysis of chromatin complexes. Nat. Protoc. 11, 316–326 (2016).

    Article  CAS  Google Scholar 

  49. Jiang, J. et al. A core Klf circuitry regulates self-renewal of embryonic stem cells. Nat. Cell Biol. 10, 353–360 (2008).

    Article  Google Scholar 

  50. Zhang, S. et al. Epigenetic regulation of REX1 expression and chromatin binding specificity by HMGNs. Nucleic Acids Res. 47, 4449–4461 (2019).

    Article  Google Scholar 

  51. Soufi, A. et al. Pioneer transcription factors target partial DNA motifs on nucleosomes to initiate reprogramming. Cell 161, 555–568 (2015).

    Article  CAS  Google Scholar 

  52. Sardina, J. L. et al. Transcription factors drive Tet2-mediated enhancer demethylation to reprogram cell fate. Cell Stem Cell 23, 727–741 (2018).

    Article  CAS  Google Scholar 

  53. Schmitt, A. D. et al. A compendium of chromatin contact maps reveals spatially active regions in the human genome. Cell Rep. 17, 2042–2059 (2016).

    Article  CAS  Google Scholar 

  54. Huang, J. et al. Dissecting super-enhancer hierarchy based on chromatin interactions. Nat. Commun. 9, 943 (2018).

    Article  Google Scholar 

  55. Fukaya, T., Lim, B. & Levine, M. Enhancer control of transcriptional bursting. Cell 166, 358–368 (2016).

    Article  CAS  Google Scholar 

  56. Oudelaar, A. M. et al. Single-allele chromatin interactions identify regulatory hubs in dynamic compartmentalized domains. Nat. Genet. 50, 1744–1751 (2018).

    Article  CAS  Google Scholar 

  57. Zheng, M. et al. Multiplex chromatin interactions with single-molecule precision. Nature 566, 558–562 (2019).

    Article  CAS  Google Scholar 

  58. Allahyar, A. et al. Enhancer hubs and loop collisions identified from single-allele topologies. Nat. Genet. 50, 1151–1160 (2018).

    Article  CAS  Google Scholar 

  59. Olivares-Chauvet, P. et al. Capturing pairwise and multi-way chromosomal conformations using chromosomal walks. Nature 540, 296–300 (2016).

    Article  CAS  Google Scholar 

  60. Stadhouders, R., Filion, G. J. & Graf, T. Transcription factors and 3D genome conformation in cell-fate decisions. Nature 569, 345–354 (2019).

    Article  CAS  Google Scholar 

  61. Schoenfelder, S. et al. Polycomb repressive complex PRC1 spatially constrains the mouse embryonic stem cell genome. Nat. Genet. 47, 1179–1186 (2015).

    Article  CAS  Google Scholar 

  62. Jiang, T. et al. Identification of multi-loci hubs from 4C-seq demonstrates the functional importance of simultaneous interactions. Nucleic Acids Res. 44, 8714–8725 (2016).

    Article  CAS  Google Scholar 

  63. Boija, A. et al. Transcription factors activate genes through the phase-separation capacity of their activation domains. Cell 175, 1842–1855 (2018).

    Article  CAS  Google Scholar 

  64. Cho, W. K. et al. Mediator and RNA polymerase II clusters associate in transcription-dependent condensates. Science 361, 412–415 (2018).

    Article  CAS  Google Scholar 

  65. Liu, Y et al. Widespread mitotic bookmarking by histone marks and transcription factors in pluripotent stem cells. Cell Rep. 19, 1283–1293 (2017).

    Article  CAS  Google Scholar 

  66. Buenrostro, J. D., Wu, B., Chang, H. Y. & Greenleaf, W. J. ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr. Protoc. Mol. Biol. 109, 21.29.1–21.29.9 (2015).

  67. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  Google Scholar 

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

  69. Huang, D. W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).

  70. Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).

    Article  CAS  Google Scholar 

  71. Lazaris, C., Kelly, S., Ntziachristos, P., Aifantis, I. & Tsirigos, A. HiC-bench: comprehensive and reproducible Hi-C data analysis designed for parameter exploration and benchmarking. BMC Genom. 18, 22 (2017).

    Article  Google Scholar 

  72. Gong, Y. et al. Stratification of TAD boundaries reveals preferential insulation of super-enhancers by strong boundaries. Nat. Commun. 9, 542 (2018).

    Article  Google Scholar 

  73. Ramirez, F. et al. High-resolution TADs reveal DNA sequences underlying genome organization in flies. Nat. Commun. 9, 189 (2018).

    Article  Google Scholar 

  74. Sheffield, N. C. & Bock, C. LOLA: enrichment analysis for genomic region sets and regulatory elements in R and bioconductor. Bioinformatics 32, 587–589 (2016).

    Article  CAS  Google Scholar 

  75. 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  Google Scholar 

  76. Raviram, R. et al. 4C-ker: a method to reproducibly identify genome-wide interactions captured by 4C-seq experiments. PLoS Comput. Biol. 12, e1004780 (2016).

    Article  Google Scholar 

Download references

Acknowledgements

We are grateful to A. Melnick and the members of the Apostolou, Tsirigos and Stadtfeld laboratories for critical reading of the manuscript. WWe applied an unpaired, one-sidede also want to thank Z. Chen and the Biostatistics and Epidemiology Consulting Service for the advice and final evaluation on the statistical tools and analyses and L. Dow for sharing the CRISPR–Cas9 vectors. D.C.G. was supported by the New York Stem Cell Foundation and the Family-Friendly Postdoctoral Initiative at Weill Cornell Medicine. A.A. is supported by a Medical Scientist Training Program grant from the National Institute of General Medical Sciences of the National Institutes of Health (NIH) under award number T32GM007739 to the Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program. A.T. is supported by the American Cancer Society (grant no. RSG-15-189-01-RMC), the Leukemia and Lymphoma Society and the St. Baldrick’s Foundation. E.A. is supported by the NIH Director’s New Innovator Award (grant no. DP2DA043813) and the Tri-Institutional Stem Cell Initiative by the Starr Foundation.

Author information

Author notes

  1. These authors contributed equally: Dafne Campigli Di Giammartino, Andreas Kloetgen, Alexander Polyzos, Yiyuan Liu.

Authors and Affiliations

  1. Sanford I Weill Department of Medicine, Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA

    Dafne Campigli Di Giammartino, Alexander Polyzos, Yiyuan Liu, Daleum Kim, Dylan Murphy, Abderhman Abuhashem, Boaz Aronson, Veevek Shah, Matthias Stadtfeld & Effie Apostolou

  2. Department of Pathology, NYU School of Medicine, New York, NY, USA

    Andreas Kloetgen & Aristotelis Tsirigos

  3. Developmental Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Abderhman Abuhashem

  4. Weill-Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD program, New York, NY, USA

    Abderhman Abuhashem

  5. Department of Biochemistry, Sandra and Edward Meyer Cancer Center, Weill Cornell Medical College, New York, NY, USA

    Paola Cavaliere & Noah Dephoure

  6. Skirball Institute of Biomolecular Medicine, Department of Cell Biology and Helen L. and Martin S. Kimmel Center for Biology and Medicine, NYU School of Medicine, New York, NY, USA

    Matthias Stadtfeld

  7. Laura and Isaac Perlmutter Cancer Center and Helen L. and Martin S. Kimmel Center for Stem Cell Biology, NYU School of Medicine, New York, NY, USA

    Aristotelis Tsirigos

  8. Applied Bioinformatics Laboratories, NYU School of Medicine, New York, NY, USA

    Aristotelis Tsirigos

Authors
  1. Dafne Campigli Di Giammartino
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andreas Kloetgen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alexander Polyzos
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yiyuan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Daleum Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Dylan Murphy
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Abderhman Abuhashem
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Paola Cavaliere
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Boaz Aronson
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Veevek Shah
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Noah Dephoure
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Matthias Stadtfeld
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Aristotelis Tsirigos
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Effie Apostolou
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

E.A. conceived, designed and supervised the study, and wrote the manuscript together with D.C.D.G., with help from all of the authors. D.C.D.G. performed all of the experiments with help from D.K. and V.S. A.K. and A.P. performed all HiChIP, HiC and integrative computational analyses under the guidance of A.T. Y.L. performed the initial ChIP-seq, RNA-seq and ATAC-seq analyses. D.M. performed the HiC and CRISPRi experiments using a stable dCas9–KRAB ESC line generated by B.A. A.A. performed the RIME experiments and iPSC ChIP-seq. P.C. and N.D. ran and analysed the RIME results. M.S. provided the reprogrammable cells and guidance on the reprogramming experiments.

Corresponding authors

Correspondence to Aristotelis Tsirigos or Effie Apostolou.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

Supplementary Figure 1 Isolation and molecular characterization of reprogramming intermediates (related to Fig. 1).

a, FACS analysis plots showing expression of SSEA1 (early pluripotency marker) and Thy1 (somatic marker) at different stages of reprogramming, before and after SSEA1 enrichment by MACS isolation. Representative plots from 2 biological replicates. b, Pie charts of functional classification of KLF4 Early, Mid, Late and Transient peaks (based on chromatin states8) (piPSC = partial iPSCs). c, PCA analysis of ATAC-seq peaks (108373 accessible sites) in MEF, PSC and different stages of reprogramming. d, Average line plot showing the methylated CG to non-methylated CG ratio from MEF data12 centred (+/-2.5Kb) around different clusters of KLF4 binding sites (Early (n = 6275), Mid (n = 3712), Late (n = 9287) and Transient (n = 17891) KLF4 targets, Fig. 2b). e, Motif enrichment for Early, Mid, Late and Transient KLF4 binding sites. Selected factors are shown and their significance is expressed as Z-score of –log10(pvalue) (hypegeometric test) (left) or z-score of motif frequency (right). f, PCA analysis of H3K27ac ChIP-seq peaks (106751 H3K27ac peaks) called in MEF, PSC and different stages of reprogramming g, PCA of RNA-seq (22 K genes) in MEF, PSC and different stages of reprogramming. h, Line plots of the median expression (red line) of genes closest to Early, Mid, Late and Transient peaks, expressed as TPM (transcripts per million) with their corresponding confidence intervals (CI) calculated as described in the Methods.

Supplementary Figure 2 HiChIP and HiC pipelines and comparisons (related to Fig. 2).

a, Schematic work-flow for HiChIP and HiC analysis. b, Percentages of PSC-specific, constant or MEF-specific H3K27ac HiChIP loops that were detected in HiC experiments (either generated in-house or published ultra-resolution HiC in PSC38). c, Normalized HiChiP (top) and HiC (bottom) signals in MEF and PSC are illustrated in a virtual 4 C format around the indicated viewpoint (Tbx3 promoter). Signal is shown as average CPM across 2 biological replicates. Representative H3K27ac ChIP-seq tracks are shown in MEF and PSC from 2 biological replicates d, Violin plot representing log2 fold change of distance-normalized HiC signal in PSCs versus MEFs of MEF-specific, constant and PSC-specific loops as called by H3K27ac HiChIP. Only contacts detected as significant in HiC data were considered. Numbers of considered loops per category are shown in parenthesis n = 9747 for MEF-specific, n = 5681 for constant and n = 8975 for PSC-specific. [Unpaired two-tailed t-test was used to determine the p-value. The minimum, maximum values and q25%, q50% and q75% are shown in each violin.

Supplementary Figure 3 HiChIP and HiC connectivity (related to Fig. 3).

a, Histogram of anchor connectivity based on H3K27ac MEF and PSC HiChIP called loops. The numbers of contacts per anchor are grouped as shown in the bottom and the actual number of anchors is depicted on top of each bar. b, Connectivity of MEF or PSC anchors based on HiC-called loops represented as number of high-confidence contacts around each 10 kb anchor. Statistics were calculated by Wilcoxon rank sum test. (min = 1, max = 27, q25% = 1, q50% = 3, q75% = 6; n = 115900 unique MEF anchors : min = 1, max = 36, q25% = 2, q50% = 5, q75% = 8; n = 111213 unique PSC anchors). c, Scatter plot showing the correlation (r = 0.289) of H3K27ac ChIP-seq strength (sum of H3K27ac ChIP/input of all peaks within the anchor) with the number of H3K27ac HiChIP contacts per anchor in PSCs.

Supplementary Figure 4 Supporting analyses and experiments for the nature and function of 3D enhancer hubs (Related to Fig. 4).

a, Venn diagram showing overlap between previously assigned target genes for superenhancers (SE), newly identified SE target genes based on H3K27ac HiChIP contacts in PSCs, and genes connected to PSC-specific enhancer hubs, which represent enhancers contacting more than one gene according H3K27ac HiChIP (see also Fig. 4a). b, Comparison of the RNA levels of hub genes, non-hub genes or genes connected to SE in PSC samples as measured by RNA-seq and expressed as transcripts per million (TPM). All genes that are not connected to enhancer hubs, but are still detected within PSC-specific HiChIP loops were considered. Expression of all protein coding genes expressed in PSC ( > 1TPM) is shown as reference. Statistics were calculated by Wilcoxon rank sum test. n = 713 non-hub genes, n = 715 genes within hubs, n = 646 genes connected to SE and n = 22,000 total number of genes considered. c, RNA-seq signal (TPM) of Med13l -which is not part of the Tbx3 enhancer hub (see Fig. 4b)- during reprogramming. 2 biological replicates. d, Genotyping strategy and results confirming the homozygous deletion of the distal (left) or the proximal (right) Tbx3 enhancers. 2 KO PSC clones and 1 WT PSC clone. e, Example of an enhancer hub in PSCs. Normalized HiChIP signal around the viewpoint is illustrated as a virtual 4 C plot, showing average CPM across 2 biological replicates. Statistics were calculated with the R-package edgeR (see Methods for more details) f, H3K27ac ChIP-seq IGV tracks during reprogramming. g, Mean RNA-seq signal from 2 biological replicates of genes within the hub (Zic2 and Zic5), or nearby genes (Clybl and Pcca), are shown for each reprogramming stage to highlight concordance with H3K27ac HiChIP data and coordinated upregulation of genes within the hub. h, Schematic illustration of the CRSIPRi (dCas9-KRAB) targeting strategy for inactivation of the Zic2/Zic5 enhancer hub. i, RT-qPCR showing percentage expression of the enhancer RNA in dCas9-KRAB-targeted ESCs (n = 3 biological replicates) relative to matched WT samples (n = 3 biological replicates), normalized to an unaffected enhancer RNA (IG-DMR). P-values were calculated using paired one-tailed t-test. Error bar represent standard deviation and the measure of center is 9.63. j, RT-qPCR showing expression changes of genes within the hub (Zic2 and Zic5) and nearby genes (Clybl and Pcca) in dCas9-KRAB-targeted ESCs (n = 3 biological replicates), calculated as percentage relative to matched WT (n = 3 biological replicates) from three independent experiments after normalization to hprt expression. P-values were calculated using paired one-tailed t-test. Error bars represent standard deviation. For source data see Supplementary Table 11.

Supplementary Figure 5 QC for KLF4 HiChIP and supporting evidence for the distinct categories of KLF4 HiChIP contacts (Related to Fig. 5).

a, PCA analysis of loops called as significant by H3K27ac and KLF4 HiChIP in different samples. b, Left: Chromatin loops that were detected by both KLF4 and H3k27ac HiChIP in PSCs were clustered based on the timing of KLF4 binding and looping during reprogramming. Right: Line plot showing expression changes of genes that belong to each of the indicated loop categories during reprogramming (median values are plotted relative to PSC). c, Pie chart showing the percentage of KLF4 PSC loops that were also detected by H3K27ac HiChIP in PSCs (H3K27ac-dependent) or not (H3K27ac-independent). d, Boxplot showing the significant difference (two-sided Wilcoxon rank sum test) between the expression of genes within all anchors of KLF4-mediated loops that are either H3K27ac-dependent. Boxplots are showing the minimum, maximum and q25%, q50% and q75% values. Number of genes considered for the statistical test is n = 5824 genes within H3K27ac-dependent and n = 2472 genes within H3K27ac-independent KLF4 HiChIP loops. e, Gene ontology for genes within anchors of H3K27ac-dependent (n = 5824) or -independent (n = 2472) KLF4 loops. Enrichment and significance of GO terms was calculated with DAVID knowledgebase and two-sided Fisher’s exact test. f, Proposed model for different categories of chromatin loops mediated by KLF4 and cofactors. Example genes are reported for each category.

Supplementary Figure 6 Supporting results for the inducible TKO PSC line and connectivity analyses (Related to Fig. 6).

a, Western blot analysis showing KLF4 protein levels before (0) and after (48 hr) dox induction in 2 biological replicates ESC clones that harbour dox-inducible CRISPR–Cas9 and gRNAs that target the Klf4 gene (KLF4 KO1 and KLF4 KO2). Relative position and size (kDa) of protein marker is shown on the right. (See also Supplementary Fig. 8). b, RT-qPCR showing elevated levels of Klf2 and Klf5 genes in dox-induced KLF4 KO ESCs. Paired one-tailed Student’s t-test was used to determine significance. n = 2 biological replicates c, Representative Western blot analysis from n = 2 independent experiments showing levels of indicated proteins in a clonal population of ESCs containing an inducible CRISPR–Cas9 construct and gRNAs that target the Klf2, Klf5 and Klf4 genes. Cells were either untreated (0, wild type or WT cells) or treated with dox for 24 hours (triple knock-out or TKO). Relative position and size (kDa) of protein marker is shown on the right. (See also Supplementary Fig. 8). d, Boxplot showing the sum connectivity of all H3K27ac HiChIP anchors in WT or TKO ESCs (WT/TKO) within each enhancer category (n = 348 for hubs, n = 231 for SE and n = 8563 for TE). Significance was calculated using two-sided Wilcoxon rank sum test. The minimum, maximum and q25%, q50% and q75% values are shown.

Supplementary Figure 7 Supporting results for the genetic targeting of KLF4 binding site within the Tbx3 hub (Related to Figure 7).

a, IGV tracks of H3K27ac and KLF4 ChIP-seq in PSCs showing the whole Tbx3 distal enhancer (top), the region that was deleted by CRISPR/Cas9 (Dist-KO, bottom, see Fig. 4f) and the location of the gRNA used to mutate a specific KLF4 binding motif (Dis-KLF4mut gRNA). b, Genotyping strategy of the surveyor assay used to detect mutation/indel at the target KLF4 binding site within the distal Tbx3 enhancer (Dis-KLF4mut). The results for 4 homozygous mutant clones (mut1-4) are shown (See also Supplementary Fig. 8). c, Sequencing results of the four mutant (mut) clones compared to the wild type (WT). d, ChIP-qPCR showing the relative levels of KLF4 binding to Tbx3 distal enhancer in n = 2 WT clones and n = 4 mut clones (left panel). Values show ChIP signal over input. As control, binding of KLF4 to an unaffected region (Fbxo15 promoter) was tested (right panel). Unpaired on-tailed t-test was performed to compare WT vs MUT and the specific p-values are shown in the graph.

Supplementary Figure 8

Unprocessed images of all gels and blots.

Supplementary information

Supplementary Information

Supplementary Figures 1–8, Supplementary Tables titles/legends

Reporting Summary

Supplementary Table 1

KLF4 ChIP-seq peaks during reprogramming.

Supplementary Table 2

H3K27ac ChIP-seq peaks during reprogramming.

Supplementary Table 3

TPM values for RNA-seq in MEF, PSC and different stages of reprogramming.

Supplementary Table 4

Coordinates of H3K27ac HiChIP MEF-specific loops, PSC-specific loops and constant.

Supplementary Table 5

Coordinates of enhancer hubs.

Supplementary Table 6

Coordinates of KLF4 HiChIP gained loops and lost loops.

Supplementary Table 7

List of proteins found to interact with KLF4 by RIME.

Supplementary Table 8

Coordinates of H3K27ac HiChIP in WT and TKO cells.

Supplementary Table 9

Primers and gRNAs used in this study.

Supplementary Table 10

Antibody information.

Supplementary Table 11

Statistics source data.

Supplementary Table 12

Accession codes of published datasets used in this study.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Di Giammartino, D.C., Kloetgen, A., Polyzos, A. et al. KLF4 is involved in the organization and regulation of pluripotency-associated three-dimensional enhancer networks. Nat Cell Biol 21, 1179–1190 (2019). https://doi.org/10.1038/s41556-019-0390-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41556-019-0390-6

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing