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Tcf1–CTCF cooperativity shapes genomic architecture to promote CD8+ T cell homeostasis

Nature Immunology volume 23, pages 1222–1235 (2022)Cite this article

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Abstract

CD8+ T cell homeostasis is maintained by the cytokines IL-7 and IL-15. Here we show that transcription factors Tcf1 and Lef1 were intrinsically required for homeostatic proliferation of CD8+ T cells. Multiomics analyses showed that Tcf1 recruited the genome organizer CTCF and that homeostatic cytokines induced Tcf1-dependent CTCF redistribution in the CD8+ T cell genome. Hi-C coupled with network analyses indicated that Tcf1 and CTCF acted cooperatively to promote chromatin interactions and form highly connected, dynamic interaction hubs in CD8+ T cells before and after cytokine stimulation. Ablating CTCF phenocopied the proliferative defects caused by Tcf1 and Lef1 deficiency. Tcf1 and CTCF controlled a similar set of genes that regulated cell cycle progression and promoted CD8+ T cell homeostatic proliferation in vivo. These findings identified CTCF as a Tcf1 cofactor and uncovered an intricate interplay between Tcf1 and CTCF that modulates the genomic architecture of CD8+ T cells to preserve homeostasis.

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Fig. 1: Tcf1 and Lef1 are required for homeostatic proliferation of CD8+ T cells.
Fig. 2: Tcf1 and Lef1 regulate homeostatic cytokine-induced changes in gene expression, chromatin accessibility and CTCF occupancy.
Fig. 3: Tcf1 and CTCF exhibit prevalent colocalization in naive CD8+ T cell genome.
Fig. 4: Tcf1 recruits CTCF to the CD8+ T cell genome via direct interaction.
Fig. 5: CTCF is mobilized by homeostatic cytokines.
Fig. 6: Tcf1 and Lef1 cooperate with CTCF to regulate chromatin interactions in CD8+ T cells.
Fig. 7: Tcf1 and Lef1 cooperate with CTCF to organize dynamic chromatin interaction hubs in homeostatic cytokine-stimulated CD8+ T cells.
Fig. 8: CTCF regulates homeostatic proliferation of CD8+ T cells by controlling a similar set of target genes as Tcf1 and Lef1.

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Data availability

The Hi-C, DNase-seq, RNA-seq and CTCF CUT&RUN data in IL-7 + IL-15-stimulated WT and dKO CD8+ T cells, along with CTCF ChIP-seq and CTCF CUT&RUN in naive WT and dKO CD8+ T cells have been deposited at the GEO under accession number under GSE179775. The Hi-C, DNase-seq and RNA-seq data in naive WT and dKO CD8+ T cells, Tcf1 ChIP-seq data in naive WT CD8+ T cells were previously deposited at the GEO under GSE164713. Source data are provided with this paper.

Code availability

The code for HiCHub is available at https://github.com/WeiqunPengLab/HiCHub

References

  1. Velardi, E., Tsai, J. J. & van den Brink, M. R. M. T cell regeneration after immunological injury. Nat. Rev. Immunol. 21, 277–291 (2021).

    Article  CAS  PubMed  Google Scholar 

  2. Tan, J. T. et al. IL-7 is critical for homeostatic proliferation and survival of naive T cells. Proc. Natl Acad. Sci. USA 98, 8732–8737 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Schluns, K. S., Kieper, W. C., Jameson, S. C. & Lefrancois, L. Interleukin-7 mediates the homeostasis of naive and memory CD8 T cells in vivo. Nat. Immunol. 1, 426–432 (2000).

    Article  CAS  PubMed  Google Scholar 

  4. Berard, M., Brandt, K., Bulfone-Paus, S. & Tough, D. F. IL-15 promotes the survival of naive and memory phenotype CD8+ T cells. J. Immunol. 170, 5018–5026 (2003).

    Article  CAS  PubMed  Google Scholar 

  5. Leonard, W. J., Lin, J. X. & O’Shea, J. J. The γc family of cytokines: basic biology to therapeutic ramifications. Immunity 50, 832–850 (2019).

    Article  CAS  PubMed  Google Scholar 

  6. Yao, Z. et al. Stat5a/b are essential for normal lymphoid development and differentiation. Proc. Natl Acad. Sci. USA 103, 1000–1005 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Zhao, X., Shan, Q. & Xue, H. H. TCF1 in T cell immunity: a broadened frontier. Nat. Rev. Immunol. https://doi.org/10.1038/s41577-021-00563-6 (2022).

  8. Gullicksrud, J. A., Shan, Q. & Xue, H. H. Tcf1 at the crossroads of CD4+ and CD8+ T cell identity. Front. Biol. 12, 83–93 (2017).

    Article  CAS  Google Scholar 

  9. Xing, S. et al. Tcf1 and Lef1 transcription factors establish CD8(+) T cell identity through intrinsic HDAC activity. Nat. Immunol. 17, 695–703 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Jeannet, G. et al. Essential role of the Wnt pathway effector Tcf-1 for the establishment of functional CD8 T cell memory. Proc. Natl Acad. Sci. USA 107, 9777–9782 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Zhou, X. et al. Differentiation and persistence of memory CD8(+) T cells depend on T cell factor 1. Immunity 33, 229–240 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Shan, Q. et al. Tcf1 preprograms the mobilization of glycolysis in central memory CD8(+) T cells during recall responses. Nat. Immunol. 23, 386–398 (2022).

    Article  CAS  PubMed  Google Scholar 

  13. Shan, Q. et al. Ectopic Tcf1 expression instills a stem-like program in exhausted CD8(+) T cells to enhance viral and tumor immunity. Cell. Mol. Immunol. 18, 1262–1277 (2021).

    Article  CAS  PubMed  Google Scholar 

  14. Im, S. J. et al. Defining CD8+ T cells that provide the proliferative burst after PD-1 therapy. Nature 537, 417–421 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Utzschneider, D. T. et al. T cell factor 1-expressing memory-like CD8(+) T cells sustain the immune response to chronic viral infections. Immunity 45, 415–427 (2016).

    Article  CAS  PubMed  Google Scholar 

  16. Leong, Y. A. et al. CXCR5(+) follicular cytotoxic T cells control viral infection in B cell follicles. Nat. Immunol. 17, 1187–1196 (2016).

    Article  CAS  PubMed  Google Scholar 

  17. Grosschedl, R., Giese, K. & Pagel, J. HMG domain proteins: architectural elements in the assembly of nucleoprotein structures. Trends Genet. 10, 94–100 (1994).

    Article  CAS  PubMed  Google Scholar 

  18. Giese, K., Kingsley, C., Kirshner, J. R. & Grosschedl, R. Assembly and function of a TCRα enhancer complex is dependent on LEF-1-induced DNA bending and multiple protein–protein interactions. Genes Dev. 9, 995–1008 (1995).

    Article  CAS  PubMed  Google Scholar 

  19. Love, J. J. et al. Structural basis for DNA bending by the architectural transcription factor LEF-1. Nature 376, 791–795 (1995).

    Article  CAS  PubMed  Google Scholar 

  20. Shan, Q. et al. Tcf1 and Lef1 provide constant supervision to mature CD8(+) T cell identity and function by organizing genomic architecture. Nat. Commun. 12, 5863 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Ohlsson, R., Renkawitz, R. & Lobanenkov, V. CTCF is a uniquely versatile transcription regulator linked to epigenetics and disease. Trends Genet. 17, 520–527 (2001).

    Article  CAS  PubMed  Google Scholar 

  22. Ong, C. T. & Corces, V. G. CTCF: an architectural protein bridging genome topology and function. Nat. Rev. Genet. 15, 234–246 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Li, F. et al. TFH cells depend on Tcf1-intrinsic HDAC activity to suppress CTLA4 and guard B-cell help function. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2014562118 (2021).

  24. Surh, C. D. & Sprent, J. Homeostasis of naive and memory T cells. Immunity 29, 848–862 (2008).

    Article  CAS  PubMed  Google Scholar 

  25. Schep, A. N., Wu, B., Buenrostro, J. D. & Greenleaf, W. J. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat. Methods 14, 975–978 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Johnson, J. L. et al. Lineage-determining transcription factor TCF-1 initiates the epigenetic identity of T cells. Immunity 48, 243–257 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Emmanuel, A. O. et al. TCF-1 and HEB cooperate to establish the epigenetic and transcription profiles of CD4(+)CD8(+) thymocytes. Nat. Immunol. 19, 1366–1378 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Bixel, G. et al. Mouse CD99 participates in T-cell recruitment into inflamed skin. Blood 104, 3205–3213 (2004).

    Article  CAS  PubMed  Google Scholar 

  29. Shi, H. et al. N4BP1 negatively regulates NF-κB by binding and inhibiting NEMO oligomerization. Nat. Commun. 12, 1379 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Skene, P. J. & Henikoff, S. An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites. eLife https://doi.org/10.7554/elife.21856 (2017).

  31. Tian, B., Yang, J. & Brasier, A. R. Two-step cross-linking for analysis of protein-chromatin interactions. Methods Mol. Biol. 809, 105–120 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Canela, A. et al. Genome organization drives chromosome fragility. Cell 170, 507–521 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Tang, Z. et al. CTCF-mediated human 3D genome architecture reveals chromatin topology for transcription. Cell 163, 1611–1627 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Hu, Y. et al. Superenhancer reprogramming drives a B-cell-epithelial transition and high-risk leukemia. Genes Dev. 30, 1971–1990 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Qi, Q. et al. Dynamic CTCF binding directly mediates interactions among cis-regulatory elements essential for hematopoiesis. Blood 137, 1327–1339 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Ren, G. et al. CTCF-mediated enhancer-promoter interaction is a critical regulator of cell-to-cell variation of gene expression. Mol. Cell 67, 1049–1058 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Fang, D. et al. Bcl11b, a novel GATA3-interacting protein, suppresses TH1 while limiting TH2 cell differentiation. J. Exp. Med. 215, 1449–1462 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Yue, F. et al. A comparative encyclopedia of DNA elements in the mouse genome. Nature 515, 355–364 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Lai, J. S. & Herr, W. Ethidium bromide provides a simple tool for identifying genuine DNA-independent protein associations. Proc. Natl Acad. Sci. USA 89, 6958–6962 (1992).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Piazza, R. et al. SETBP1 induces transcription of a network of development genes by acting as an epigenetic hub. Nat. Commun. 9, 2192 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Nishimura, H. et al. A novel role of CD30/CD30 ligand signaling in the generation of long-lived memory CD8+ T cells. J. Immunol. 175, 4627–4634 (2005).

    Article  CAS  PubMed  Google Scholar 

  44. Madsen, J. G. S. et al. Highly interconnected enhancer communities control lineage-determining genes in human mesenchymal stem cells. Nat. Genet. 52, 1227–1238 (2020).

    Article  CAS  PubMed  Google Scholar 

  45. Li, X., Yuan, S., Zhu, S., Xue, H.-H. & Peng, W. HiCHub: a network-based approach to identify domains of differential interactions from 3D genome data. Preprint at bioRxiv https://doi.org/10.1101/2022.04.16.488566 (2022).

  46. Xue, H. H. & Zhao, D. M. Regulation of mature T cell responses by the Wnt signaling pathway. Ann. NY Acad. Sci. 1247, 16–33 (2012).

    Article  CAS  PubMed  Google Scholar 

  47. Zhao, X. et al. β-Catenin and γ-catenin are dispensable for T lymphocytes and AML leukemic stem cells. eLife https://doi.org/10.7554/elife.55360 (2020).

  48. Pongubala, J. M. R. & Murre, C. Spatial organization of chromatin: transcriptional control of adaptive immune cell development. Front. Immunol. 12, 633825 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Chisolm, D. A. et al. CCCTC-binding factor translates interleukin 2- and α-ketoglutarate-sensitive metabolic changes in T cells into context-dependent gene programs. Immunity 47, 251–267 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Hu, G. et al. Transformation of accessible chromatin and 3D nucleome underlies lineage commitment of early T cells. Immunity 48, 227–242 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Steinke, F. C. et al. TCF-1 and LEF-1 act upstream of Th-POK to promote the CD4(+) T cell fate and interact with Runx3 to silence Cd4 in CD8(+) T cells. Nat. Immunol. 15, 646–656 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Yu, S. et al. The TCF-1 and LEF-1 Transcription factors have cooperative and opposing roles in T cell development and malignancy. Immunity 37, 813–826 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Heath, H. et al. CTCF regulates cell cycle progression of αβ T cells in the thymus. EMBO J. 27, 2839–2850 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Shan, Q. et al. The transcription factor Runx3 guards cytotoxic CD8(+) effector T cells against deviation towards follicular helper T cell lineage. Nat. Immunol. 18, 931–939 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Jin, W. et al. Genome-wide detection of DNase I hypersensitive sites in single cells and FFPE tissue samples. Nature 528, 142–146 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. 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 

  62. Zang, C. et al. A clustering approach for identification of enriched domains from histone modification ChIP-seq data. Bioinformatics 25, 1952–1958 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Durand, N. C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst. 3, 95–98 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Fang, C. et al. Cancer-specific CTCF binding facilitates oncogenic transcriptional dysregulation. Genome Biol. 21, 247 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. 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 

  66. Blondel, V., Guillaume, J., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008, 10008 (2008).

    Article  Google Scholar 

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Acknowledgements

We thank the University of Iowa Flow Cytometry Core facility (J. Fishbaugh, H. Vignes and G. Rasmussen) and the HMH-CDI Flow Cytometry Core facility (M. Poulus and W. Tsao) for cell sorting and S. Xing for his contribution during the early phase of this study. We thank N. Galjart (Erasmus Medical Center, Netherlands) for permission to use the Ctcf-floxed mouse strain and A.M. Melnick and M.A. Rivas (Weill Cornell Medical College) for providing the mice. This study is supported in part by grants from the National Institutes of Health (AI112579 to H.-H.X., AI121080 and AI139874 to H.-H.X. and W.P.) and the Veteran Affairs (BX005771 to H.-H.X.).

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Author notes

  1. These authors contributed equally: Qiang Shan, Shaoqi Zhu.

Authors and Affiliations

  1. Center for Discovery and Innovation, Hackensack University Medical Center, Nutley, NJ, USA

    Qiang Shan, Xia Chen, Jia Liu & Hai-Hui Xue

  2. Department of Physics, The George Washington University, Washington, DC, USA

    Shaoqi Zhu, Shuang Yuan, Xiang Li & Weiqun Peng

  3. New Jersey Veterans Affairs Health Care System, East Orange, NJ, USA

    Hai-Hui Xue

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Contributions

Q.S. performed the experiments and analyzed the data, with assistance from X.C. and J.L. S.Z. analyzed the high-throughput sequencing data, with assistance from S.Y. and X.L. W.P. and H.H.X. conceived the project, supervised the study and wrote the paper.

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Correspondence to Weiqun Peng or Hai-Hui Xue.

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Nature Immunology thanks Axel Kallies and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Ioana Visan was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Tcf1+Lef1-deficient CD8+ T cells remain in naïve state and are not prone to apoptosis.

a. Detection of Tcf1 and Lef1 expression in splenic naïve CD8+ T cells from WT, dKO and Ctcf−/− mice by intranuclear staining. Values denote geometric mean fluorescence intensity (gMFI). Data are representative from ≥2 experiments. b. Detection of CD44loCD62L+ naïve TCRβ+GFP+CD8+ T cells in splenocytes from mice of indicated genotypes, with cumulative data (right) as means ± s.d. from 3-4 independent experiments. ***, p < 0.001; ns, not statistically significant by one-way ANOVA coupled with Tukey’s correction. c. Detection of activation markers including CD25, CD69, PD1 and ICOS in the naïve cells from WT and dKO mice. Data are representative from 2 experiments. d. Detection of cell apoptosis in splenic TCRβ+GFP+CD8+ T cells in 22-27 weeks old WT and dKO mice by staining for Annexin V and 7-AAD positivity. Representative contour plots (left) are from two independent experiments, and cumulative data on frequency of Annexin V+ cells are means ± s.d. ns, not statistically significant by two-tailed Student’s t-test. e. Gating strategy for detecting CTV-labelled GFP+CD8+ T cells that underwent homeostatic proliferation in vivo after transfer into lymphopenic or replete hosts, or ex vivo after IL-7+IL-15 or TCR stimulation. This strategy was applied to Fig. 1b–g, 8a–c, Extended Data Fig. 2a,b, 10a,b, and to cell sorting for all multiomics analyses in this work.

Extended Data Fig. 2 Tcf1 and Lef1 deficiency does not affect T cell proliferation and signalling in general.

a. Cell division of CTV-labelled naïve CD45.2+GFP+CD8+ T cells at 72 (top) or 96 hrs (bottom) after ex vivo stimulation with plate-bound anti-CD3 in the presence of soluble anti-CD28 and IL-2, with frequency of cells showing ≥1 division summarized (right). Representative histographs are from 3 experiments (left), and cumulative data (right) are means ± s.d. *, p < 0.05; ***, p < 0.001; ns, not statistically significant by one-way ANOVA coupled with Tukey’s correction. b. Detection of indicated cytokine receptor expression on GFP+CD8+ T cells. Representative half-stacked histographs are from 3 experiments (top), with values denoting gMFI. Cumulative data on gMFI (bottom) are means ± s.d, with no statistically significant differences observed and thus unmarked. c. Detection of Stat5a and Akt phosphorylation in WT and dKO GFP+CD8+ T cells in response to IL-7 and IL-15 stimulation for 0-180 minutes by immunoblotting with indicated antibodies. Gel images are representative from two independent experiments. The signal strength of pY694-Stat5a and pS473-Akt was normalized to respective total protein, and their time-dependent changes were plotted in the right panels. Note that the pY694-Stat5a antibody also detects Tyr699-phosphorylated Stat5b.

Source data

Extended Data Fig. 3 WT and Tcf1+Lef1-deficient CD8+ T cells show dynamic and largely concordant changes in transcriptomic and chromatin accessibility in response to IL-7+IL-15 stimulation.

a. Principal component analysis (PCA) of RNA-seq libraries from WT or dKO GFP+CD8+ T cells before and after 72-hr ex vivo stimulation with IL-7 and IL-15. b. Diagram showing key pairwise comparisons to define the dynamic transcriptomic and chromatin accessibility changes in this work. c–d. Gene ontology analysis of IL-7+IL-15-repressed genes in ExpC6 (c) and ExpC7 (d), as determined with the DAVID Bioinformatics Resources. Dot size denotes gene counts, and dot colour denotes statistical significance. Selected IL-7+IL-15-repressed genes are shown in heat maps (right panels). e. PCA of DNase-seq libraries from WT or dKO GFP+CD8+ T cells before and after 72-hr ex vivo stimulation with IL-7 and IL-15. f. Differential ChrAcc clusters as determined with DNase-seq, based on the key comparisons in b. Values in parentheses denote site numbers in each cluster. g. Correlation between transcriptomic and ChrAcc changes. Genes in expression clusters (defined in Fig. 2a) were stratified against genes linked to Diff. ChrAcc clusters (f), and the number of overlapping genes was counted. The value of each element in the correlation matrix is the ratio of the observed over expected overlapping gene counts, and all elements are colour-coded based on the enrichment values. Colour scale in c and d denotes relative gene expression, and that in f denotes relative strength of ChrAcc signals.

Extended Data Fig. 4 Dynamic CTCF binding shows concordant changes with gene expression in IL-7+IL-15-stimulated CD8+ T cells.

a. PCA of CTCF CUT&RUN libraries from WT or dKO GFP+CD8+ T cells before and after 72-hr ex vivo stimulation with IL-7 and IL-15. b. Top motifs of CTCF binding peaks in naïve WT CD8+ T cells as determined with HOMER, with motif logos and statistical significance listed. Note that similar results were obtained for CTCF binding peaks in naïve dKO CD8+ T cells, IL-7 + IL-15-stimulated WT and dKO CD8+ T cells (not shown). c–d. Correlation of CTCF dynamics with gene expression changes. Genes in expression clusters (defined in Fig. 2a) were stratified against genes linked to differential CTCF clusters (defined in Fig. 2f), and the number of overlapping genes was counted (c). The ratio of the observed over expected overlapping gene counts was determined as a measurement of relative enrichment and shown in the correlation matrix (d). All elements in both matrices are colour-coded based on gene numbers (c) and enrichment values (d). e–f. Performance test of CTCF antibodies in ChIP-seq assays. WT and dKO naïve CD8+ T cells were sequentially fixed with disuccinimidyl glutarate and formaldehyde, and the resulting chromatin was immunoprecipitated with anti-CTCF antibodies from Santa Cruz Biotechnology (clone B-5), Millipore, or Cell Signaling Technology (CST). CTCF peaks were called on merged replicates under the stringent criteria by requiring ≥4 fold enrichment over IgG control and FDR < 0.05. Signal-to-noise ratio was determined as read count on CTCF peaks divided by read count on non-peak regions (e). Venn diagram in f shows the overlap of CTCF peaks determined with each ChIP-seq antibody in WT CD8+ T cells.

Extended Data Fig. 5 Tcf1–CTCF cooperativity is prevalent and specific in naïve CD8+ T cells.

a. Most CTCF binding sites are co-occupied by cohesin. Rad21 ChIP-seq data in total T cells were download from GEO (Ref. 32, GSM2635601 under SuperSeries GSE99197). Rad21 binding peaks were then called, and their overlap with CTCF binding peaks detected by ChIP-seq and/or CUT&RUN methods (this study) was summarized in the Venn diagrams. Values in parentheses denote percentages of CTCF peaks co-bound by Rad21 in each group. b. CTCF mapping with ChIP-seq and CUT&RUN in human H1 ESCs. CTCF ChIP-seq peaks were retrieved from the ENCODE project (accession number ENCFF023LAA), and CTCF CUT&RUN peaks from the 4D Nucleome Data Portal (accession number 4DNFI6OF4ZMC). The overlapping and unique peaks are summarized in a Venn diagram. c. CTCF binding sites are associated with chromatin interactions in human ESCs. CTCF ChIA-PET data in human ESCs were retrieved from the ENCODE project (accession number ENCFF401IWZ). Each group of CTCF binding sites defined in b was analyzed for association with long-range chromatin interactions (by requiring ≥5 paired-end tags (PET)/site). Randomly selected 10,000 genomic locations were used as a negative control. P values were determined with one-sided binomial test. d. Assessing transcription factor (TF)-CTCF cooperativity in blood cell lines. The peaks of 107 and 383 TF ChIP-seq in GM12878 lymphoblastoid cells and K562 myelogenous leukaemia cells, respectively, were retrieved from the ENCODE project. For each TF, its overlap rate with CTCF ChIP-seq peaks in each cell type was determined. Top 50 TFs with the highest TF-CTCF overlapping rates are plotted, where red dotted lines denote Tcf1–CTCF overlapping rate in naïve CD8+ T cells (based on CUN&RUN-detected CTCF peaks). e. Assessing TF-CTCF cooperativity in primary B lymphocytes. EBF1 and CTCF ChIP-seq in primary mouse B cells were retrieved from the public domain and their overlap was determined. EBF1 ChIP-seq peaks were obtained from Cistrome Data Browser under ID 71163. CTCF ChIP-seq data in total B cells were downloaded from Ref. 32 (GSM2635594 under SuperSeries GSE99197), and peak calling was processed in-house. f–g. Tcf1 is more frequently associated with CTCF peaks specifically detected in T but not B cells. For a fair comparison, we used CTCF ChIP-seq data in total T and B cells from the same published study (Ref. 32, GSM2635596 and GSM2635594 under SuperSeries GSE99197, respectively). CTCF binding strength was then compared to identify 5,003 T cell-specific and 4,512 B cell-specific CTCF binding sites (fold changes ≥4), as shown in blue and red on the scatter-plot, respectively (f). T- and B-specific CTCF binding sites that overlapped with Tcf1 ChIP-seq peaks were then enumerated, with percentages in parentheses denoting the overlapping rate (g). h. Tcf1 and CTCF co-occupied sites exhibit higher CTCF binding strength at T cell-specific than that at B cell-specific CTCF binding sites. CTCF binding strength (in reads per million per kb) was assessed in T and B cells for comparison for the following groups: 1,075 T and 152 B cell-specific CTCF binding sites that are co-occupied by Tcf1 (left panel), and 5,065 non-differential CTCF binding sites between T and B cells (right panel). Statistical significance is determined with one-sided MWU test. The box centre lines denote the median, box edge denotes interquartile range (IQR), and whiskers denote the most extreme data points that are no more than 1.5 × IQR from the edge. i. T cell- and B cell-specific CTCF binding at select gene loci. Shown are sequencing tracks on the UCSC Genome Browser at the Myb and Pax5 gene loci. T cell-specific CTCF binding was found in a cluster of CTCF peaks upstream of Myb TSS (blue box), which colocalized with Tcf1 binding peaks, was also detectable with CTCF CUT&RUN in nave CD8+ T cells, showing strong dependence on Tcf1 and Lef1 (left panel). B cell-specific CTCF binding was found in 3 clusters of CTCF peaks at the Pax5 locus (red boxes), which were devoid of Tcf1 peaks or CTCF binding in T cells as determined with either ChIP-seq or CUT&RUN method. Arrows mark non-differential CTCF binding sites in T and B cells.

Extended Data Fig. 6 CTCF CUT&RUN and ChIP-seq methods both capture similar characteristics of dynamic and constitutive CTCF binding events in CD8+ T cells.

a-b. Distribution of Tcf1+CTCF+ and Tcf1–CTCF+ sites at the promoters (TSS + /–1 kb) and distal regulatory regions, as determined with CTCF CUT&RUN (a) or CTCF ChIP-seq (b). Values in bars denote the actual numbers of Tcf1 peaks in Tcf1+CTCF+ sites and CTCF peaks in Tcf1–CTCF+ sites. c–d. Top motifs of Tcf1+CTCF+ sites at gene promoters as determined with HOMER. The analysis was based on CTCF CUT&RUN (c) or CTCF ChIP-seq data (d). e–f. Top motifs of Tcf1+CTCF+ sites in distal regulatory regions as determined with HOMER. The analysis was based on CTCF CUT&RUN (e) or CTCF ChIP-seq data (f). g. CTCF motif distribution in Tcf1+CTCF+ and Tcf1–CTCF+ sites (determined with CTCF ChIP-seq using the motifmatchr package in R), with values in bars denoting actual numbers of sites with or without the motif. h. The positional distribution of Tcf1–CTCF+ (left) and Tcf1+CTCF+ (right) sites within TADs based on CTCF ChIP-seq data. i. Profiles of insulation index around the four types of CTCF binding sites (determined with CTCF ChIP-seq), where the index values are indicative of insulation effects.

Extended Data Fig. 7 Correlation of Tcf1 and CTCF binding strength in CD8+ T cells.

a. The frequency of Tcf+Lef motif occurrence (top) and CTCF motif occurrence (bottom) at Tcf1+CTCF+ sites (detected by both CUT&RUN and ChIP-seq methods). Note that Motif+CTCF sites accounted for < 30% of distal Tcf1+CTCF+ sites (Fig. 3h), and Tcf + Lef motif occurred at ~20% of all Tcf1 ChIP-seq peaks (determined in Ref. 20). As a result, the motif occurrence frequency is relatively low in absolute values but remains a useful indicator for comparison between different groups. b–c. Correlation of Tcf1 and CTCF binding strength in Motif– (left) and Motif+ (right) subsets of Tcf1+CTCF+ sites as determined with CTCF CUT&RUN (b) or CTCF ChIP-seq (c). Tcf1 binding peaks were distributed into four groups based on their binding scores, with peak numbers in each group denoted in parentheses, and CTCF binding scores in each Tcf1 peak group was then determined. The binding score is defined as –log10(q-value) from MACS2, and p value was determined with one-sided MWU test. The box centre lines denote the median, box edge denotes interquartile range (IQR), and whiskers denote the most extreme data points that are no more than 1.5 × IQR from the edge. Colour scale denotes relative strength of each molecular feature. d. Overlap between CTCF peaks with ChrAcc sites as determined with DNase-seq in B cells (top) and Th2 cells (bottom). CTCF ChIP-seq peaks in B cells were defined as in Extended Data Fig. 5f, and DNase-seq peaks in B cells were obtained from Cistrome Data Browser under ID 45090. CTCF ChIP-seq and DNase-seq peaks in Th2 cells were obtained from Cistrome Data Browser under ID 88187 and ID 92251, respectively. The overlap rates were based on CTCF peak numbers in each cell type. e. Overlap between CTCF peaks (determined with CUT&RUN and/or ChIP-seq methods) with ChrAcc sites (determined with DNase-seq) in naïve CD8+ T cells. f. Heat maps showing local chromatin characteristics of distal Tcf1–CTCF+ sites (detected by both CUT&RUN and ChIP-seq methods), in Motif– and Motif+ subsets. Shown are aggregated profiles (top) and heat maps (bottom) for each molecular feature in separate subsets. Colour scale denotes relative strength of each molecular feature.

Extended Data Fig. 8 Tcf1 and Lef1 mediate CTCF recruitment in naïve CD8+ T cells.

a–b. Volcano plots showing differential CTCF binding strength between WT and dKO CD8+ T cells as determined with CUT&RUN (a) or ChIP-seq (b). Note that due to differences in signal-to-noise ratios between CUT&RUN and ChIP-seq, the criteria were adjusted for defining differential CTCF binding between WT and dKO cells, as detailed in Methods. c. Tcf1 + Lef1-independent CTCF binding sites are associated with weak ChrAcc or H3K27ac signals in WT and dKO CD8+ T cells with no apparent differences. 2,000 non-differential CTCF binding peaks between WT and dKO CD8+ T cells were randomly selected as a negative control for Tcf1 + Lef1-dependent CTCF binding sites in Fig. 4e, and the indicated molecular features were displayed as aggregated profiles (top) and heat maps (bottom). Colour scale denotes relative strength of each molecular feature. d. Distal Tcf1+CTCF+ sites show concordant decrease in CTCF binding strength and ChrAcc in naive dKO compared to naïve WT CD8+ T cells, as displayed at the Ccne1, Tox, and Irf4 loci. Shown are Tcf1 ChIP-seq (top), CTCF CUT&RUN and CTCF ChIP-seq tracks in WT and dKO CD8+ T cells (middle), and DNase-seq and H3K27ac ChIP-seq tracks in WT and dKO CD8+ T cells (bottom). Whole or partial gene structure, transcription orientation, and genomic scale are displayed on top of each panel. Yellow bars denote Tcf1 + Lef1-dependent Tcf1+CTCF+ site(s) detected with both CUT&RUN and CTCF ChIP-seq methods, while open bars with cyan borders denote those determined with one method only.

Extended Data Fig. 9 Global analysis of chromatin interaction scores reveals Tcf1–CTCF cooperativity in organizing genomic architecture in CD8+ T cells.

a. Scatter-plots showing reproducibility of two biological replicates of Hi-C libraries from WT or dKO CD8+ T cells that were stimulated with IL-7 and IL-15 for 72 hrs ex vivo. The x- and y-axis values for each data point (marked with a dot) represent the interaction scores of an anchor in replicate 1 (Rep1) and replicate 2 (Rep 2), respectively. The R values denote Pearson correlation coefficient. b–c. Visualization of chromatin interaction hubs and connectivity with CTCF binding sites in naïve CD8+ T cells. Comparing chromatin interactions in naïve WT and dKO CD8+ T cells using HiCHub identified cell type-specific interaction hubs. b, WT-specific hub containing the Myb locus, where the nodes represent 10-kb bins and the lines represent chromatin interactions decreased in dKO CD8+ T cells. c, dKO-specific hub containing multiple Ccl genes, where the lines represent chromatin interactions increased in dKO CD8+ T cells. Circles denote bins containing gene promoters (with select gene symbols marked), and diamonds denote the presence of Tcf1 binding peaks. Triangles filled with blue and red denote statistically significant decrease and increase in CTCF binding strength in naïve dKO compared to naïve WT CD8+ T cells, respectively. d–e. IL-7 and IL-15 induce concordant changes in CTCF binding and chromatin interactions in CD8+ T cells. Diamond graphs in d show distance-normalized chromatin interactions in naïve WT (left) and IL-7+IL-15-stimulated (right panel) WT CD8+ T cells at the Setbp1 gene locus. Blue boxes denote chromatin interaction ‘patches’ showing dynamic changes by IL-7+IL-15 stimulation. Shown in the lower panels are gene structure, Tcf1 ChIP-seq tracks in naïve WT, CTCF CUT&RUN tracks in naïve (left) and IL-7+IL-15-stimulated (right panel) WT CD8+ T cells, along with genomic scale. Yellow bars mark dynamic CTCF binding sites, while green bars mark constitutive CTCF binding sites. Both dynamic and constitutive CTCF sites are numbered as chromatin interaction anchors in naïve cells (left), and the interaction ‘patches’ between the numbered anchor regions are annotated in IL-7+IL-15-stimulated cells (right). Colour scale denotes chromatin interaction strength. e. Chromatin interaction strength for each pair of 10-kb anchors within each annotated interaction ‘patch’ at the Setbp1 gene locus was determined in naïve and IL-7+IL-15-stimulated WT CD8+ T cells, with p value calculated using one-sided paired Wilcoxon test. The box centre lines denote the median, box edge denotes interquartile range (IQR), and whiskers denote the most extreme data points that are no more than 1.5 × IQR from the edge. f. Projected model of homeostatic cytokine-induced chromatin reorganization that involves both dynamic and constitutive CTCF binding and brings distal regulatory regions into contact with the Setbp1 gene promoter.

Extended Data Fig. 10 Characterization of CTCF-deficient CD8+ T cells.

a. Detection of CD44loCD62L+ naïve TCRβ+GFP+CD8+ T cells in splenocytes from WT and Ctcf−/− mice, and further analysis of activation markers including CD25, CD69, PD1, and ICOS of naïve cells. b. Cell division of CTV-labelled naïve WT or Ctcf–/– CD45.2+GFP+CD8+ T cells at 72 (top) or 96 hrs (bottom) after ex vivo stimulation with plate-bound anti-CD3 in the presence of soluble anti-CD28 and IL-2, with frequency of cells showing ≥1 division summarized (right). Representative contour plots (a) and histographs (b) are from 2 experiments, and cumulative data are means ± s.d. *, p < 0.05; ***, p < 0.001; ns, not statistically significant by two-tailed Student’s t-test. c. PCA of RNA-seq libraries from CTV-labelled WT, dKO and Ctcf–/– GFP+CD8+ T cells sorted at 72 hrs after transfer into Rag1–/– recipients. d–e. Heat maps showing the top 100 genes in the leading edge from GSEA comparing WT and Ctcf–/– (d) or that comparing WT and dKO CD8+ T cell transcriptomes (e), as in Fig. 8d. Note that among the 616 ExpC1 genes, 337 and 392 genes were in the leading edge of WT vs. Ctcf–/– and WT vs. Tcf1/Lef1 dKO comparisons, respectively. f. Heat maps of select genes in Clusters C and D as defined in Fig. 8f. Note that dKO but not Ctcf–/– CD8+ T cells showed aberrantly induced expression of TREG cell-associated genes (Foxp3, Nrp1 and Ikzf4) and effector CD8+T cell-associated genes (Prdm1, Fasl and Prf1), consistent with a specific requirement for Tcf1 and Lef1, but not CTCF in providing constant supervision to mature CD8+ T cell identity.

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Shan, Q., Zhu, S., Chen, X. et al. Tcf1–CTCF cooperativity shapes genomic architecture to promote CD8+ T cell homeostasis. Nat Immunol 23, 1222–1235 (2022). https://doi.org/10.1038/s41590-022-01263-6

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