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Selective deployment of transcription factor paralogs with submaximal strength facilitates gene regulation in the immune system

Nature Immunology volume 20, pages 1372–1380 (2019)Cite this article

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Abstract

In multicellular organisms, duplicated genes can diverge through tissue-specific gene expression patterns, as exemplified by highly regulated expression of RUNX transcription factor paralogs with apparent functional redundancy. Here we asked what cell-type-specific biologies might be supported by the selective expression of RUNX paralogs during Langerhans cell and inducible regulatory T cell differentiation. We uncovered functional nonequivalence between RUNX paralogs. Selective expression of native paralogs allowed integration of transcription factor activity with extrinsic signals, while non-native paralogs enforced differentiation even in the absence of exogenous inducers. DNA binding affinity was controlled by divergent amino acids within the otherwise highly conserved RUNT domain and evolutionary reconstruction suggested convergence of RUNT domain residues toward submaximal strength. Hence, the selective expression of gene duplicates in specialized cell types can synergize with the acquisition of functional differences to enable appropriate gene expression, lineage choice and differentiation in the mammalian immune system.

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Fig. 1: RUNX paralog evolution and RUNT domain conservation.
Fig. 2: Selective deployment of RUNX paralogs enables signal-responsive Langerhans cell differentiation.
Fig. 3: Selective deployment of RUNX paralogs enables signal-responsive induction of the Treg cell signature transcription factor FOXP3.
Fig. 4: Functional differences in Foxp3 regulation encoded by the RUNT domain.
Fig. 5: The RUNX1 and RUNX3 RUNT domains have different DNA binding affinities.
Fig. 6: Identification of residues that functionally distinguish paralogous RUNT domains.
Fig. 7: RUNT domain evolution towards submaximal strength.

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Source data are available online for Figs. 1–7 and Supplementary Figs. 2,5,6 and 8.

References

  1. Teichmann, S. & Babu, M. M. Gene regulatory network growth by duplication. Nat. Genet. 36, 492–496 (2004).

    Article  CAS  PubMed  Google Scholar 

  2. Innan, H. & Kondrashov, F. The evolution of gene duplications: Classifying and distinguishing between models. Nat. Rev. Genet. 11, 97–108 (2010).

    Article  CAS  PubMed  Google Scholar 

  3. Ohno, S. Evolution by Gene Duplication (Allen & Unwin; Springer-Verlag, 1970).

  4. Lynch, M. & Conery, J. S. The evolutionary fate and consequences of duplicate genes. Science 290, 1151–1155 (2000).

    Article  CAS  PubMed  Google Scholar 

  5. Lan, X. & Pritchard, J. K. Coregulation of tandem duplicate genes slows evolution of subfunctionalization in mammals. Science 352, 1009–1013 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Wapinski, I., Pfeffer, A., Friedman, N. & Regev, A. Natural history and evolutionary principles of gene duplication in fungi. Nature 449, 54–61 (2007).

    Article  CAS  PubMed  Google Scholar 

  7. Force, A. et al. Preservation of duplicate genes by complementary, degenerative mutations. Genetics 151, 1531–1545 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Wheeler, J. C., Shigesada, K., Gergen, J. P. & Ito, Y. Mechanisms of transcriptional regulation by Runt domain proteins. Semin. Cell Dev. Biol. 11, 369–375 (2000).

    Article  CAS  PubMed  Google Scholar 

  9. Levanon, D. Spatial and temporal expression pattern of Runx3 (Aml2) and Runx1 (Aml1) indicates non-redundant functions during mouse embryogenesis. Mech. Dev. 109, 413–417 (2001).

    Article  CAS  PubMed  Google Scholar 

  10. Goyama, S. et al. The transcriptionally active form of AML1 is required for hematopoietic rescue of the AML1-deficient embryonic para-aortic splanchnopleural (P-Sp) region. Blood 104, 3558–3564 (2004).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Berg, O. G. & von Hippel, P. H. Selection of DNA binding sites by regulatory proteins. Statistical-mechanical theory and application to operators and promoters. J. Mol. Biol. 193, 723–750 (1987).

    Article  CAS  PubMed  Google Scholar 

  13. Hentsch, B., Mouzaki, A., Pfeuffer, I., Rungger, D. & Serfling, E. The weak, fine-tuned binding of ubiquitous transcription factors to the Il-2 enhancer contributes to its T cell-restricted activity. Nucleic Acids Res. 20, 2657–2665 (1992).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Jiang, J. & Levine, M. Binding affinities and cooperative interactions with bHLH activators delimit threshold responses to the dorsal gradient morphogen. Cell 72, 741–752 (1993).

    Article  CAS  PubMed  Google Scholar 

  15. Gaudet, J. & Mango, S. E. Regulation of organogenesis by the Caenorhabditis elegans FoxA protein PHA-4. Science 295, 821–825 (2002).

    Article  CAS  PubMed  Google Scholar 

  16. Scardigli, R., Baumer, N., Gruss, P., Guillemot, F. & Le Roux, I. Direct and concentration-dependent regulation of the proneural gene Neurogenin2 by Pax6. Development 130, 3269–3281 (2003).

    Article  CAS  PubMed  Google Scholar 

  17. Tanay, A. Extensive low-affinity transcriptional interactions in the yeast genome. Genome Res. 16, 962–972 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Rowan, S. et al. Precise temporal control of the eye regulatory gene Pax6 via enhancer-binding site affinity. Genes Dev. 24, 980–985 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Crocker, J. et al. Low affinity binding site clusters confer Hox specificity and regulatory robustness. Cell 160, 191–203 (2015).

    Article  CAS  PubMed  Google Scholar 

  20. Farley, E. K. et al. Suboptimization of developmental enhancers. Science 350, 325–328 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Cannavò, E. Genetic variants regulating expression levels and isoform diversity during embryogenesis. Nature 541, 402–406 (2017).

    Article  PubMed  Google Scholar 

  22. Crocker, J., Noon, E. P. & Stern, D. L. The soft touch: low-affinity transcription factor binding sites in development and evolution. Curr. Top. Dev. Biol. 117, 455–469 (2016).

    Article  PubMed  Google Scholar 

  23. Nuzhdin, S. V., Rychkova, A. & Hahn, M. W. The strength of transcription-factor binding modulates co-variation in transcriptional networks. Trends Genet. 26, 51–53 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Bravo, J., Li, Z., Speck, N. A. & Warren, A. J. The leukemia-associated AML1 (Runx1)–CBFβ complex functions as a DNA-induced molecular clamp. Nat. Struct. Mol. Biol. 8, 371–378 (2001).

    Article  CAS  Google Scholar 

  25. Tahirov, T. H. et al. Structural analyses of DNA recognition by the AML1/Runx-1 Runt domain and its allosteric control by CBFβ. Cell 104, 755–767 (2001).

    Article  CAS  PubMed  Google Scholar 

  26. Komori, T. et al. Targeted disruption of Cbfa1 results in a complete lack of bone formation owing to maturational arrest of osteoblasts. Cell 89, 755–764 (1997).

    Article  CAS  PubMed  Google Scholar 

  27. Otto, F. et al. Cbfa1, a candidate gene for cleidocranial dysplasia syndrome, is essential for osteoblast differentiation and bone development. Cell 89, 765–771 (1997).

    Article  CAS  PubMed  Google Scholar 

  28. Levanon, D. et al. The Runx3 transcription factor regulates development and survival of TrkC dorsal root ganglia neurons. EMBO J. 21, 3454–3463 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Okuda, T., van Deursen, J., Hiebert, S. W., Grosveld, G. & Downing, J. R. AML1, the target of multiple chromosomal translocations in human leukemia, is essential for normal fetal liver hematopoiesis. Cell 84, 321–330 (1996).

    Article  CAS  PubMed  Google Scholar 

  30. Wang, Q. et al. Disruption of the Cbfa2 gene causes necrosis and hemorrhaging in the central nervous system and blocks definitive hematopoiesis. Proc. Natl Acad. Sci. USA 93, 3444–3449 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Collins, A., Littman, D. R. & Taniuchi, I. RUNX proteins in transcription factor networks that regulate T-cell lineage choice. Nat. Rev. Immunol. 9, 106–115 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Voon, D. C., Hor, Y. T. & Ito, Y. The RUNX complex: reaching beyond haematopoiesis into immunity. Front. Immunol. 146, 523–536 (2015).

    Article  CAS  Google Scholar 

  33. Bae, S. C. & Ito, Y. Regulation mechanisms for the heterodimeric transcription factor, PEBP2/CBF. Histol. Histopathol. 14, 1213–1221 (1999).

    CAS  PubMed  Google Scholar 

  34. Fainaru, O. et al. Runx3 regulates mouse TGF-β-mediated dendritic cell function and its absence results in airway inflammation. EMBO J. 23, 969–979 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Chopin, M. et al. Langerhans cells are generated by two distinct PU.1-dependent transcriptional networks. J. Exp. Med. 210, 2967–2980 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Borkowski, T. A., Letterio, J. J., Farr, A. G. & Udey, M. C. A role for endogenous transforming growth factor β 1 in Langerhans cell biology: the skin of transforming growth factor β 1 null mice is devoid of epidermal Langerhans cells. J. Exp. Med. 184, 2417–2422 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Bruno, L. et al. Runx proteins regulate Foxp3 expression. J. Exp. Med. 206, 2329–2337 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Kitoh, A. et al. Indispensable role of the Runx1–Cbfβ transcription complex for in vivo-suppressive function of FoxP3+ regulatory T cells. Immunity 31, 609–620 (2009).

    Article  CAS  PubMed  Google Scholar 

  39. Rudra, D. et al. Runx-CBFβ complexes control expression of the transcription factor Foxp3 in regulatory T cells. Nat. Immunol. 10, 1170–1177 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Josefowicz, S. Z., Lu, L. F. & Rudensky, A. Y. Regulatory T cells: mechanisms of differentiation and function. Ann. Rev. Immunol. 30, 531–564 (2012).

    Article  CAS  Google Scholar 

  41. Taniuchi, I. et al. Differential requirements for Runx proteins in CD4 repression and epigenetic silencing during T lymphocyte development. Cell 111, 621–633 (2002).

    Article  CAS  PubMed  Google Scholar 

  42. Telfer, J. C., Hedblom, E. E., Anderson, M. K., Laurent, M. N. & Rothenberg, E. V. Localization of the domains in Runx transcription factors required for the repression of CD4 in thymocytes. J. Immunol. 172, 4359–4370 (2004).

    Article  CAS  PubMed  Google Scholar 

  43. Berger, M. F. et al. Compact, universal DNA microarrays to comprehensively determine transcription-factor binding site specificities. Nat. Biotechnol. 24, 1429–1435 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Boucheron, N. et al. CD4+ T cell lineage integrity is controlled by the histone deacetylases HDAC1 and HDAC2. Nat. Immunol. 15, 439–448 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Aken, B. L. et al. Ensembl 2017. Nucleic Acids Res. 45, 635–642 (2017).

    Article  Google Scholar 

  46. Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).

    Article  CAS  PubMed  Google Scholar 

  47. Nah, G. S., Lim, Z. W., Tay, B. H., Osato, M. & Venkatesh, B. Runx family genes in a cartilaginous fish, the elephant shark (Callorhinchus milii). PLoS ONE. 9, e93816 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Takahashi, S. et al. GATA factor transgenes under GATA-1 locus control rescue germline GATA-1 mutant deficiencies. Blood 96, 910–916 (2000).

    CAS  PubMed  Google Scholar 

  49. Cruz-Guilloty, F. et al. Runx3 and T-box proteins cooperate to establish the transcriptional program of effector CTLs. J. Exp. Med. 206, 51–59 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Yan, J., Liu, Y., Lukasik, S. M., Speck, N. A. & Bushweller, J. H. CBFβ allosterically regulates the Runx1 Runt domain via a dynamic conformational equilibrium. Nat. Struct. Mol. Biol. 11, 901–906 (2004).

    Article  CAS  PubMed  Google Scholar 

  51. Caton, M. L., Smith-Raska, M. R. & Reizis, B. Notch–RBP-J signaling controls the homeostasis of CD8− dendritic cells in the spleen. J. Exp. Med. 204, 1653–1664 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Dakic, A. et al. PU.1 regulates the commitment of adult hematopoietic progenitors and restricts granulopoiesis. J. Exp. Med. 201, 1487–1502 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Growney, J. D. et al. Loss of Runx1 perturbs adult hematopoiesis and is associated with a myeloproliferative phenotype. Blood 106, 494–504 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Seibler, J. et al. Rapid generation of inducible mouse mutants. Nucleic Acids Res. 31, e12 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Horton, R. M. In vitro recombination and mutagenesis of DNA: SOEing together tailor-made genes. Methods Mol. Biol. 15, 251–261 (1993).

    CAS  PubMed  Google Scholar 

  56. Tenno., M. et al. Cbfβ2 deficiency preserves Langerhans cell precursors by lack of selective TGFβ receptor signalling. J. Exp. Med. 21, 2933–2946 (2017).

    Article  Google Scholar 

  57. Lin, H. W., Chang, Y. Y., Wong, M. L., Lin, J. W. & Chang, T. J. Functional analysis of virion host shutoff protein of pseudorabies virus. Virology 324, 412–418 (2004).

    Article  CAS  PubMed  Google Scholar 

  58. Berger, M. F. & Bulyk, M. L. Universal protein-binding microarrays for the comprehensive characterization of the DNA-binding specificities of transcription factors. Nat. Protoc. 4, 393–411 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Dudley, A. M., Aach, J., Steffen, M. A. & Church, G. M. Measuring absolute expression with microarrays with calibrated reference sample and an extended signal intensity range. Proc. Natl Acad. Sci. USA 99, 7554–7559 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Schreiber, F., Patricio, M., Muffato, M., Pignatelli, M. & Bateman, A. TreeFam v9: a new website, more species and orthology-on-the-fly. Nucleic Acids Res. 42, D922–D925 (2014).

    Article  CAS  PubMed  Google Scholar 

  61. Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Capella-Gutierrez, S., Silla-Martinez, J. M. & Gabaldon, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Junier, T. & Zdobnov, E. M. The Newick utilities: high-throughput phylogenetic tree processing in the UNIX shell. Bioinformatics 26, 1669–1670 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).

    Article  CAS  PubMed  Google Scholar 

  65. Anisimova, M. & Gascuel, O. Approximate likelihood-ratio test for branches: a fast, accurate, and powerful alternative. Syst. Biol. 55, 539–552 (2006).

    Article  PubMed  Google Scholar 

  66. Waterhouse, A. M., Procter, J. B., Martin, D. M., Clamp, M. & Barton, G. J. Jalview version 2—a multiple sequence alignment editor and analysis workbench. Bioinformatics 25, 1189–1191 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank A. Warren (University of Cambridge), F. Kondrashov (Institute of Science and Technology Austria), D. Odom (CRUK Cambridge), T. Warnecke, P. Sarkies, S. Santos and B. Lenhard for discussion and advice; N. Speck (University of Pennsylvania) for conditional Runx1 mutants; J. Elliott, B. Patel and T. Adejumo for cell sorting; P. Leung for assistance; D. Djeghloul for help with immunofluorescence; and L. Game and her team for sequencing. This work was funded by the Medical Research Council UK (M.M. and A.G.F.), by Wellcome (grant 099276/Z/12/Z to M.M.) and the NIH (R01A116829 to T.S.).

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  1. Romain A. Studer

    Present address: BenevolentAI, London, UK

Authors and Affiliations

  1. Lymphocyte Development Group, MRC London Institute for Medical Sciences, London, UK

    Ludovica Bruno, Stephan Sauer, Amanda G. Fisher & Matthias Merkenschlager

  2. Department of Biology, Boston University, Boston, MA, USA

    Vijendra Ramlall & Trevor Siggers

  3. European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK

    Romain A. Studer, David Bradley & Pedro Beltrao

  4. MRC London Institute for Medical Sciences, London, UK

    Gopuraja Dharmalingam & Thomas Carroll

  5. Molecular Virology, Department of Medicine, Imperial College London, London, UK

    Mohamed Ghoneim & David S. Rueda

  6. Single Molecule Imaging Group, MRC London Institute of Medical Sciences, London, UK

    Mohamed Ghoneim & David S. Rueda

  7. The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia

    Michaël Chopin & Stephen L. Nutt

  8. Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia

    Stephen L. Nutt

  9. Nuclear Dynamics Programme, The Babraham Institute, Cambridge, UK

    Sarah Elderkin

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Contributions

L.B. performed experiments, made figures and contributed to writing; V.R. performed experiments and made figures; R.A.S. analyzed data, made figures and contributed to writing; S.S. did experiments; D.B. analyzed data; G.D. analyzed data and made figures; T.C. analyzed data and made figures; M.G. performed experiments and made figures; M.C. provided materials and performed experiments; S.L.N. provided materials, conceptualized and supervised work; S.E. conceptualized and supervised work and performed experiments; D.S.R. provided materials and supervised work; T.S. conceptualized and supervised work, analyzed data and made figures; A.G.F. conceptualized work and contributed to writing; P.B. conceptualized and supervised work; and M.M. conceptualized and supervised work, analyzed data, made figures and contributed to writing.

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Correspondence to Matthias Merkenschlager.

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Integrated supplementary information

Supplementary Figure 1 RUNX1 promotes LH cell differentiation.

a) Immature DC preferentially express RUNX3. 293 cells transfected with RUNX3 or RUNX1 are used as a control for antibody specificity. Lamin is the loading control (left). Comparison of endogenous end retrovirally encoded Runx3 in immature DC (right). b) For Langerhans (LH) cell cultures, bone marrow cells were transduced with the indicated vector and 500×103 transduced cells were plated per well with GM-CSF ± TGF-β. The number of cells recovered on the day of staining for Langerhans cell markers is shown (3 biological replicates per condition ± s.d.). c) MHC class II expression by Spi1-deficient CD11c+ bone marrow cells transduced with RUNX-IRES-GFP or IRES-GFP control vector and cultured with GM-CSF and TGF-β. Left: one representative experiment of 3. Right: mean ± s.d. of 3 independent experiments. * P<0.05, ** P<0.01 by two-tailed T test Runx1 versus control vector (blue) and Runx3 versus control vector (red). Below: the distribution of GFP expression by MHC class II+ CD11c+ cells. Representative of 3 independent experiments. d) Expression of FLAG-RUNX by GFP-low, intermediate and high immature DC following retroviral transduction with RUNX3, RUNX1 or control vector. Lamin is the loading control. Representative of 2 independent experiments. e) CD11c+ MHC class II+ BM wild-type BM cells were transduced with RUNX-IRES-GFP or IRES-GFP control vector and cultured with GM-CSF and TGF-β and analyzed for EPCAM and DEC205 expression by flow cytometry. Numbers indicate the percentage of CD11c+ cells that expressed MHC class II, DEC205 and Epcam (LH cells). Representative of 3 independent experiments. f) As in e), mean ± s.d. of 3 independent experiments. * P<0.05, ** P<0.01, *** P<0.001 by two-tailed T test between Runx1 and -3 (black), Runx1 and control vector (blue), Runx3 and control vector (red). g) As in e) but in the absence of TGF-β. Representative of 3 independent experiments. h) As in f) but in the absence of TGF-β. Statistics as in f.

Supplementary Figure 2 Naive CD4 T cells preferentially express RUNX1 and show increased Foxp3 induction in response to RUNX3, not RUNX1.

a) 293 cells were transfected with RUNX1, RUNX3 or control vector as controls for the specificity of antibodies to RUNX1 and -3. Lamin is a loading control (top). Levels of retrovirally encoded RUNX1 protein were comparable to endogenous RUNX1 protein in wild-type CD4 T cells (bottom). Representative of 2 independent experiments. b) The number of cells recovered on the day of staining for Foxp3 is shown (3 biological replicates per condition mean ± s.d. for full-length RUNX, mean of 2 biological replicates per condition for RUNT). c) Runx1-wild-type naive CD4 T cells were activated and transduced with RUNX1-IRES-GFP, RUNX3-IRES-GFP or control vector as in Fig. 3. Cells were cultured in the presence of TGF-β or with PI3K inhibitor LY294002 and mTOR inhibitor rapamycin. Foxp3 induction was evaluated by intracellular staining. The percentage of Foxp3+ cells is shown (mean ± s.d. of 3 independent experiments).

Source data

Supplementary Figure 3 RUNT domain constructs used in this study.

a) FLAG tag (orange), linker and cloning site (green), RUNT domain (black) and STOP codon (*) are shown. The putative nuclear localization sequence immediately downstream of the RUNT domain42 was not included and no exogenous nuclear localization sequences were added. All constructs were verified by sequencing. £ corresponds to full ‘Ancestral’ at positions 59, 123, 157 and 168, but contains D53, V102 and S121 and has equivalent functional activity to full ‘Ancestral’ with E53, L102 and T121 (Supplementary Fig. 8a).@ corresponds to the ancestor of Runx2 and -3 in Fig. 7c and ‘alternative ancestral’ in Fig. 7e, contains D53, V102 and S121. $ corresponds to ‘early vertebrate RUNX3’ in Fig. 7c, e, contains D53, V102 and S121. b) To map differential impact of Runx1 and -3 on LH cell differentiation, BM cells were transduced with RUNX1-IRES-GFP, RUNX3-IRES-GFP or with vectors encoding the isolated RUNT domains of Runx1 (RUNT1-IRES-GFP) or Runx3 (RUNT3-IRES-GFP). Numbers indicate percentages of CD11c+ MHC class II+ DEC205+ Epcam+ LH cells. Note that while full-length Runx1 promotes LH cell differentiation more effectively than full-length Runx3, the isolated RUNT domains of Runx1 (RUNT1) and Runx3 (RUNT3) have indistinguishable dominant negative activity in this assay. One experiment.

Supplementary Figure 4 Estimation of RUNT domain DNA-binding affinities.

a) Replicate titration EMSAs for RUNT1 and RUNT3 complexed with CBFB1 (Fig. 5d), and titration EMSAs for the two chimeric proteins RUNT3 V123A and RUNT3 V168I complexed with CBFB1 binding to a probe derived from the Foxp3 promoter (inset). CBFB1 protein is 270 nM in binding reactions. b) EMSA probe sequence and dissociation constant (KD) estimates are shown. Titration EMSAs were performed in duplicate.

Supplementary Figure 5 Impact of the RUNT3 V168I substitution on Foxp3 induction.

Runx1-wild-type naive CD4 T cells were activated and transduced with IRES-GFP constructs encoding the isolated RUNT domains of RUNX1, RUNX3, RUNX3 V123A, RUNX3 V168I or control vector. Cells were cultured with TGF-β or PI3K inhibitor LY294002 and mTOR inhibitor rapamycin. Foxp3 induction was evaluated by intracellular staining (mean ± s.d. of 3 independent experiments).

Source data

Supplementary Figure 6 Functional differences between paralogous RUNT domains are partially offset by compensatory amino acid substitutions.

The impact of RUNT domain residue substitutions was assessed by dominant negative activity on Foxp3 induction in naive CD4 T cells. The X axis shows dominant negative activity as in Fig. 7c (n = 4 to 20, mean ± s.d. of 3 to 10 independent experiments. Two-tailed Student’s T test p<0.005 for RUNT3 versus all other constructs).

Source data

Supplementary Figure 7 Reconstruction of the ancestral RUNT domain sequence.

a) Phylogenetic tree of the Runx family, with topology extracted from the EnsemblCompara tree. b) The EnsemblCompara tree was built using the TreeBeST algorithm (see methods). After removal of spurious sequences, the dataset for RUNX contains 188 sequences from the three paralogs (RUNX1, -2 and -3) and from organisms basal to the vertebrates (ascidians, nematode and fruit fly). The tree was rooted using the protostomian species (nematodes and fruit fly). Branch supports were estimated by an approximate likelihood ratio test (see methods). The names at the tips correspond to the Ensembl Protein ID. c) Sequence and posterior probability (in brackets) of the ancestral RUNT domain of Runx1, -2 and -3. S77 p(S) = 0.56, p(T) = 0.39; V87 p(V) = 0.72, p(I) = 0.26; L102 p(L) = 0.86, p(V) = 0.13; T121 p(T) = 0.89, p(S) = 0.11; V123 p(V) = 0.99; I168 p(I) = 0.74, p(V) = 0.26. d) Posterior probabilities for other RUNT domains described in this paper.

Supplementary Figure 8 RUNT domain residues E/D53, L/V102 and T/S121 confer equivalent dominant negative activity.

a) Dominant negative activity of the reconstructed ancestral RUNT domain featuring P59, V123, P157 and I168 and E53, L102 and T121 compared with a RUNT domain that features the Runx3 aa D53, V102 and S121 alongside the putative ancestral P59, V123, P157 and I168. The X axis shows dominant negative activity as in Fig. 7c. See Supplementary Fig. 3a for sequences. Mean ± s.d. of 6 independent experiments, P = 0.175 by two-tailed Student’s T test. b) Functional evolution of RUNT domain residues starting from an ancestral sequence with Ile in position 168 (alternat. ancestr.). Blue letters and arrows indicate amino acid substitutions that reduce RUNT domain activity (n = 6 to 20, mean ± s.d. of 3 to 10 independent experiments. The X axis shows dominant negative activity as in panel c). Constructs to test the activity of the alternative ancestral RUNT domain and early vertebrate Runx3 contained P59, L102 and T121, which in the context of the ancestral RUNT domain were functionally equivalent to E53, L102, and T121 (Supplementary Fig. 8a). See Supplementary Fig. 3a for sequences. Two-tailed Student’s T test RUNT3 versus early vertebrate Runx3: P = 9.52 x 10-5, RUNT3 versus alternative ancestral RUNT: P = 4.85 x 10-6, RUNT3 versus RUNT1 P = 4.89 x 10-17.

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Bruno, L., Ramlall, V., Studer, R.A. et al. Selective deployment of transcription factor paralogs with submaximal strength facilitates gene regulation in the immune system. Nat Immunol 20, 1372–1380 (2019). https://doi.org/10.1038/s41590-019-0471-5

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