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:

Denisovan, modern human and mouse TNFAIP3 alleles tune A20 phosphorylation and immunity

Nature Immunology volume 20, pages 1299–1310 (2019)Cite this article

Subjects

Abstract

Resisting and tolerating microbes are alternative strategies to survive infection, but little is known about the evolutionary mechanisms controlling this balance. Here genomic analyses of anatomically modern humans, extinct Denisovan hominins and mice revealed a TNFAIP3 allelic series with alterations in the encoded immune response inhibitor A20. Each TNFAIP3 allele encoded substitutions at non-catalytic residues of the ubiquitin protease OTU domain that diminished IκB kinase-dependent phosphorylation and activation of A20. Two TNFAIP3 alleles encoding A20 proteins with partial phosphorylation deficits seemed to be beneficial by increasing immunity without causing spontaneous inflammatory disease: A20 T108A;I207L, originating in Denisovans and introgressed in modern humans throughout Oceania, and A20 I325N, from an N-ethyl-N-nitrosourea (ENU)-mutagenized mouse strain. By contrast, a rare human TNFAIP3 allele encoding an A20 protein with 95% loss of phosphorylation, C243Y, caused spontaneous inflammatory disease in humans and mice. Analysis of the partial-phosphorylation A20 I325N allele in mice revealed diminished tolerance of bacterial lipopolysaccharide and poxvirus inoculation as tradeoffs for enhanced immunity.

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: Denisovan, modern human and mouse alleles substituting non-catalytic residues of the A20 OTU domain.
Fig. 2: The I325N OTU variant confers heightened immunity in mice.
Fig. 3: Subclinical inflammatory and metabolic consequences of the I325N allele.
Fig. 4: Alleles with OTU domain variants decrease A20 phosphorylation and NF-κB inhibition.
Fig. 5: Immunoblot and RT–PCR analysis of peripheral blood mononuclear cells from healthy individuals with the Denisovan T108A;I207L TNFAIP3 haplotype.
Fig. 6: C243Y causes spontaneous inflammation and early lethality in homozygous mice.
Fig. 7: Introduction of human T108A;I207L and C243Y alleles in mice tunes immunity.
Fig. 8: Shift to detrimental effects in response to environmental stressors and increased susceptibility to autoimmunity.

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request. PDB accession codes are 5V3P, 5V3B and 5DQ6.

References

  1. Caldwell, R. M., Schafer, J. F., Compton, L. E. & Patterson, F. L. Tolerance to cereal leaf rusts. Science 128, 714–715 (1958).

    Article  CAS  Google Scholar 

  2. Ayres, J. S. & Schneider, D. S. Tolerance of infections. Annu. Rev. Immunol. 30, 271–294 (2012).

    Article  CAS  Google Scholar 

  3. Soares, M. P., Teixeira, L. & Moita, L. F. Disease tolerance and immunity in host protection against infection. Nat. Rev. Immunol. 17, 83–96 (2017).

    Article  CAS  Google Scholar 

  4. Hooper, L. V., Littman, D. R. & Macpherson, A. J. Interactions between the microbiota and the immune system. Science 336, 1268–1273 (2012).

    Article  CAS  Google Scholar 

  5. Vereecke, L. et al. A20 controls intestinal homeostasis through cell-specific activities. Nat. Commun. 5, 5103 (2014).

    Article  CAS  Google Scholar 

  6. Turer, E. E. et al. Homeostatic MyD88-dependent signals cause lethal inflammation in the absence of A20. J. Exp. Med. 205, 451–464 (2008).

    Article  CAS  Google Scholar 

  7. Roy, B. A. & Kirchner, J. W. Evolutionary dynamics of pathogen resistance and tolerance. Evolution 54, 51–63 (2000).

    Article  CAS  Google Scholar 

  8. Fenner, F. The Florey lecture, 1983. Biological control, as exemplified by smallpox eradication and myxomatosis. Proc. R. Soc. Lond. B Biol. Sci. 218, 259–285 (1983).

    Article  CAS  Google Scholar 

  9. Boughton, C. R. Smallpox and Australia. Intern. Med. J. 32, 59–61 (2002).

    Article  CAS  Google Scholar 

  10. Bousfiha, A. et al. The 2017 IUIS phenotypic classification for primary immunodeficiencies. J. Clin. Immunol. 38, 129–143 (2018).

    Article  Google Scholar 

  11. Lee, E. G. et al. Failure to regulate TNF-induced NF-κB and cell death responses in A20-deficient mice. Science 289, 2350–2354 (2000).

    Article  CAS  Google Scholar 

  12. Zhou, Q. et al. Loss-of-function mutations in TNFAIP3 leading to A20 haploinsufficiency cause an early-onset autoinflammatory disease. Nat .Genet. 48, 67–73 (2016).

    Article  CAS  Google Scholar 

  13. Ma, A. & Malynn, B. A. A20: linking a complex regulator of ubiquitylation to immunity and human disease. Nat. Rev. Immunol. 12, 774–785 (2012).

    Article  CAS  Google Scholar 

  14. Wertz, I. E. et al. De-ubiquitination and ubiquitin ligase domains of A20 downregulate NF-κB signalling. Nature 430, 694–699 (2004).

    Article  CAS  Google Scholar 

  15. Lu, T. T. et al. Dimerization and ubiquitin mediated recruitment of A20, a complex deubiquitinating enzyme. Immunity 38, 896–905 (2013).

    Article  CAS  Google Scholar 

  16. Evans, P. C. et al. Zinc-finger protein A20, a regulator of inflammation and cell survival, has de-ubiquitinating activity. Biochem. J. 378, 727–734 (2004).

    Article  CAS  Google Scholar 

  17. Bosanac, I. et al. Ubiquitin binding to A20 ZnF4 is required for modulation of NF-κB signaling. Mol. Cell 40, 548–557 (2010).

    Article  CAS  Google Scholar 

  18. Wertz, I. E. et al. Phosphorylation and linear ubiquitin direct A20 inhibition of inflammation. Nature 528, 370–375 (2015).

    Article  CAS  Google Scholar 

  19. Hutti, J. E. et al. IκB kinase β phosphorylates the K63 deubiquitinase A20 to cause feedback inhibition of the NF-κB pathway. Mol. Cell Biol. 27, 7451–7461 (2007).

    Article  CAS  Google Scholar 

  20. De, A., Dainichi, T., Rathinam, C. V. & Ghosh, S. The deubiquitinase activity of A20 is dispensable for NF-κB signaling. EMBO Rep. 15, 775–783 (2014).

    Article  CAS  Google Scholar 

  21. Verhelst, K. et al. A20 inhibits LUBAC-mediated NF-κB activation by binding linear polyubiquitin chains via its zinc finger 7. EMBO J. 31, 3845–3855 (2012).

    Article  CAS  Google Scholar 

  22. Mallick, S. et al. The Simons Genome Diversity Project: 300 genomes from 142 diverse populations. Nature 538, 201–206 (2016).

    Article  CAS  Google Scholar 

  23. Reich, D. et al. Genetic history of an archaic hominin group from Denisova Cave in Siberia. Nature 468, 1053–1060 (2010).

    Article  CAS  Google Scholar 

  24. Reich, D. et al. Denisova admixture and the first modern human dispersals into Southeast Asia and Oceania. Am. J. Hum. Genet. 89, 516–528 (2011).

    Article  CAS  Google Scholar 

  25. Sankararaman, S., Mallick, S., Patterson, N. & Reich, D. The combined landscape of Denisovan and Neanderthal ancestry in present-day humans. Curr. Biol. 26, 1241–1247 (2016).

    Article  CAS  Google Scholar 

  26. Vernot, B. et al. Excavating neandertal and denisovan DNA from the genomes of Melanesian individuals. Science 352, 235–239 (2016).

    Article  CAS  Google Scholar 

  27. Meyer, M. et al. A high-coverage genome sequence from an archaic Denisovan individual. Science 338, 222–226 (2012).

    Article  CAS  Google Scholar 

  28. Prufer, K. et al. The complete genome sequence of a Neanderthal from the Altai Mountains. Nature 505, 43–49 (2014).

    Article  Google Scholar 

  29. Gittelman, R. M. et al. Archaic hominin admixture facilitated adaptation to out-of-Africa environments. Curr. Biol. 26, 3375–3382 (2016).

    Article  CAS  Google Scholar 

  30. Hudjashov, G. et al. Complex patterns of admixture across the Indonesian archipelago. Mol. Biol. Evol. 34, 2439–2452 (2017).

    Article  CAS  Google Scholar 

  31. Wallace, A. R. On the physical geography of the Malay archipelago. J. R. Geog. Soc. London 33, 217–234 (1863).

    Google Scholar 

  32. Tang, D. et al. Reference genotype and exome data from an Australian Aboriginal population for health-based research. Sci. Data 3, 160023 (2016).

    Article  CAS  Google Scholar 

  33. Xu, S. et al. Genetic dating indicates that the Asian–Papuan admixture through Eastern Indonesia corresponds to the Austronesian expansion. Proc. Natl Acad. Sci. USA 109, 4574–4579 (2012).

    Article  CAS  Google Scholar 

  34. Skoglund, P. et al. Genomic insights into the peopling of the Southwest Pacific. Nature 538, 510–513 (2016).

    Article  Google Scholar 

  35. Kibrick, S. & Benirschke, K. Severe generalized disease (encephalohepatomyocarditis) occurring in the newborn period and due to infection with Coxsackie virus, group B: evidence of intrauterine infection with this agent. Pediatrics 22, 857–875 (1958).

    CAS  PubMed  Google Scholar 

  36. Hartig, P. C. & Webb, S. R. Heterogeneity of a human isolate of Coxsackie B4: biological differences. J. Infect. 6, 43–48 (1983).

    Article  CAS  Google Scholar 

  37. Dionne, M. S., Pham, L. N., Shirasu-Hiza, M. & Schneider, D. S. Akt and FOXO dysregulation contribute to infection-induced wasting in Drosophila. Curr. Biol. 16, 1977–1985 (2006).

    Article  CAS  Google Scholar 

  38. Malle, E. K. et al. Nuclear factor κB-inducing kinase activation as a mechanism of pancreatic beta cell failure in obesity. J. Exp. Med. 212, 1239–1254 (2015).

    Article  CAS  Google Scholar 

  39. Shigemura, T. et al. Novel heterozygous C243Y A20/TNFAIP3 gene mutation is responsible for chronic inflammation in autosomal-dominant Behçet’s disease. RMD Open 2, e000223 (2016).

    Article  Google Scholar 

  40. Komander, D. & Barford, D. Structure of the A20 OTU domain and mechanistic insights into deubiquitination. Biochem. J. 409, 77–85 (2008).

    Article  CAS  Google Scholar 

  41. Mevissen, T. E. et al. Molecular basis of Lys11-polyubiquitin specificity in the deubiquitinase Cezanne. Nature 538, 402–405 (2016).

    Article  CAS  Google Scholar 

  42. Appay, V., van Lier, R. A., Sallusto, F. & Roederer, M. Phenotype and function of human T lymphocyte subsets: consensus and issues. Cytometry A 73, 975–983 (2008).

    Article  Google Scholar 

  43. Appay, V. et al. Memory CD8+ T cells vary in differentiation phenotype in different persistent virus infections. Nat. Med. 8, 379–385 (2002).

    Article  CAS  Google Scholar 

  44. van den Berghe, G. et al. Intensive insulin therapy in critically ill patients. N. Engl. J. Med. 345, 1359–1367 (2001).

    Article  Google Scholar 

  45. Siggs, O. M. et al. Preponderance of CTLA4 variation associated with autosomal dominant immune dysregulation in the MYPPPY motif. Front. Immunol. 10, 1544 (2019).

    Article  Google Scholar 

  46. Jacobs, G. S. et al. Multiple deeply divergent Denisovan ancestries in Papuans. Am. J. Phys. Anthropol. 168, 112–112 (2019).

    Google Scholar 

  47. Malaspinas, A. S. et al. A genomic history of Aboriginal Australia. Nature 538, 207–214 (2016).

    Article  CAS  Google Scholar 

  48. Delaneau, O., Howie, B., Cox, A. J., Zagury, J. F. & Marchini, J. Haplotype estimation using sequencing reads. Am. J. Hum. Genet. 93, 687–696 (2013).

    Article  CAS  Google Scholar 

  49. International HapMap Consortium. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851–861 (2007).

    Article  Google Scholar 

  50. Barrett, J. C., Fry, B., Maller, J. & Daly, M. J. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21, 263–265 (2005).

    Article  CAS  Google Scholar 

  51. Ho, W. Y., Cooke, M. P., Goodnow, C. C. & Davis, M. M. Resting and anergic B cells are defective in CD28-dependent costimulation of naive CD4+ T cells. J. Exp. Med. 179, 1539–1549 (1994).

    Article  CAS  Google Scholar 

  52. Akkaraju, S. et al. A range of CD4 T cell tolerance: partial inactivation to organ-specific antigen allows nondestructive thyroiditis or insulitis. Immunity 7, 255–271 (1997).

    Article  CAS  Google Scholar 

  53. Tahiliani, V., Chaudhri, G., Eldi, P. & Karupiah, G. The orchestrated functions of innate leukocytes and T cell subsets contribute to humoral immunity, virus control, and recovery from secondary poxvirus challenge. J. Virol. 87, 3852–3861 (2013).

    Article  CAS  Google Scholar 

  54. Wang, G. G. et al. Quantitative production of macrophages or neutrophils ex vivo using conditional Hoxb8. Nat. Methods 3, 287–293 (2006).

    Article  CAS  Google Scholar 

  55. Grey, S. T., Lock, J., Bach, F. H. & Ferran, C. Adenovirus-mediated gene transfer of A20 in murine islets inhibits Fas-induced apoptosis. Transplant. Proc. 33, 577–578 (2001).

    Article  CAS  Google Scholar 

  56. Grey, S. T. et al. Genetic engineering of a suboptimal islet graft with A20 preserves beta cell mass and function. J. Immunol. 170, 6250–6256 (2003).

    Article  CAS  Google Scholar 

  57. Untergasser, A. et al. Primer3: new capabilities and interfaces. Nucleic Acids Res. 40, e115 (2012).

    Article  CAS  Google Scholar 

  58. Daley, S. R., Hu, D. Y. & Goodnow, C. C. Helios marks strongly autoreactive CD4+ T cells in two major waves of thymic deletion distinguished by induction of PD-1 or NF-κB. J. Exp. Med. 210, 269–285 (2013).

    Article  CAS  Google Scholar 

  59. Jeelall, Y. S. et al. Human lymphoma mutations reveal CARD11 as the switch between self-antigen-induced B cell death or proliferation and autoantibody production. J. Exp. Med. 209, 1907–1917 (2012).

    Article  CAS  Google Scholar 

  60. Bendall, S. C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011).

    Article  CAS  Google Scholar 

  61. Qiu, P. et al. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat. Biotechnol. 29, 886–891 (2011).

    Article  CAS  Google Scholar 

  62. Wertz, I. E. et al. Sensitivity to antitubulin chemotherapeutics is regulated by MCL1 and FBW7. Nature 471, 110–114 (2011).

    Article  CAS  Google Scholar 

  63. Tan, B. M. et al. Baculoviral inhibitors of apoptosis repeat containing (BIRC) proteins fine-tune TNF-induced nuclear factor κB and c-Jun N-terminal kinase signalling in mouse pancreatic beta cells. Diabetologia 56, 520–532 (2013).

    Article  CAS  Google Scholar 

  64. Langley, D. B. & Christ, D. Crystal structure of duck egg lysozyme isoform II (DEL-II). BMC Struct. Biol. 18, 10 (2018).

    Article  Google Scholar 

  65. McCoy, A. J. et al. Phaser crystallographic software. J. Appl. Crystallogr. 40, 658–674 (2007).

    Article  CAS  Google Scholar 

  66. Murshudov, G. N. et al. REFMAC5 for the refinement of macromolecular crystal structures. Acta Crystallogr. D Biol. Crystallogr. 67, 355–367 (2011).

    Article  CAS  Google Scholar 

  67. Adams, P. D. et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr. D Biol. Crystallogr. 66, 213–221 (2010).

    Article  CAS  Google Scholar 

  68. Terwilliger, T. C. et al. Iterative-build OMIT maps: map improvement by iterative model building and refinement without model bias. Acta Crystallogr. D Biol. Crystallogr. 64, 515–524 (2008).

    Article  CAS  Google Scholar 

  69. Chen, V. B. et al. MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr. D Biol. Crystallogr. 66, 12–21 (2010).

    Article  CAS  Google Scholar 

  70. Zoldak, G., Zubrik, A., Musatov, A., Stupak, M. & Sedlak, E. Irreversible thermal denaturation of glucose oxidase from Aspergillus niger is the transition to the denatured state with residual structure. J. Biol. Chem. 279, 47601–47609 (2004).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This manuscript is dedicated to the memory of Susan Watson, who led the isolation of the I325N mouse. We thank the National Computational Infrastructure, Xenon systems, Nvidia and the Multi-modal Australian ScienceS Imaging and Visualization Environment (MASSIVE; http://www.massive.org.au/) for computing resources. We thank O. Venn, I. Mathieson, B. Llamas, Y. Souilmi, R. Tobler and A. Cooper for stimulating discussions. C.N.J. thanks J. Cyster for mentorship. We thank K. Newton and V. Dixit for reagents (Genentech). We thank M. Flodström-Tullberg (Karolinska Institutet) for kindly providing coxsackievirus B4 strain E2. This study makes use of data generated by the Telethon Kids Institute. A full list of the investigators who contributed to the generation of the data is available from http://bioinformatics.childhealthresearch.org.au/AGHS. We thank CIRCA for collecting and coordinating human samples (http://www.garvan.org.au/research/collaborative-programs/circa/about-circa). Funding for the project was provided by the National Health and Medical Research Council of Australia (NHMRC) under award 634301. N.W.Z. was supported by an Australian postgraduate award and is supported by the International Pancreas and Islet Transplant Association Derek Gray Scholarship. A.M.B. was an NHMRC Senior Research Fellow (1022688). S.G.T. was a Principal Research Fellow (1042925) of the NHMRC. C.C.G. was supported by the Bill and Patricia Ritchie Chair and by an NHMRC Senior Principal Research Fellowship. S.T.G. was supported by an NHMRC Senior Research Fellowship. The research was supported by grants to C.C.G. from the NIH (AI52127, AI054523, AI100627) and NHMRC (1016953, 585490, 1081858) and to S.T.G. from the NIH (DK076169) and NHMRC (1130222, 1140691). CIRCA was supported by the Jeffrey Modell Foundation, the John Cook Brown Foundation and the Sydney Children’s Hospital Network.

Author information

Author notes

  1. Stephen R. Daley

    Present address: Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria, Australia

  2. Geeta Chaudhri

    Present address: Research School of Population Health, The Australian National University, Canberra, Australian Capital Territory, Australia

  3. Gunasegaran Karupiah

    Present address: School of Medicine, The University of Tasmania, Hobart, Tasmania, Australia

  4. Craig N. Jenne

    Present address: Snyder Institute for Chronic Diseases, University of Calgary, Calgary, Alberta, Canada

  5. Matthew H. Spitzer

    Present address: Departments of Otolaryngology and Microbiology & Immunology and the Parker Institute for Cancer Immunotherapy, University of California San Francisco, San Francisco, CA, USA

  6. Frank Schmitz

    Present address: Celgene, Seattle, WA, USA

  7. These authors contributed equally: Nathan W. Zammit, Owen M. Siggs, Christopher C. Goodnow, Shane T. Grey.

  8. Deceased: Susan R. Watson.

Authors and Affiliations

  1. Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia

    Nathan W. Zammit, Owen M. Siggs, David B. Langley, Stacey N. Walters, Claudia Loetsch, Joanna Warren, Jin Yan Yap, Daniele Cultrone, Amanda Russell, Elisabeth K. Malle, Jeanette E. Villanueva, Mark J. Cowley, Velimir Gayevskiy, Marcel E. Dinger, Robert Brink, David Zahra, Stuart G. Tangye, Cecile King, Daniel Christ, Christopher C. Goodnow & Shane T. Grey

  2. Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia

    Nathan W. Zammit, David B. Langley, Stacey N. Walters, Joanna Warren, Daniele Cultrone, Mark J. Cowley, Robert Brink, David Zahra, Stuart G. Tangye, Cecile King, Daniel Christ, Christopher C. Goodnow & Shane T. Grey

  3. Department of Ophthalmology, Flinders University, Bedford Park, South Australia, Australia

    Owen M. Siggs

  4. Department of Immunology and Infectious Diseases, Sydney Children’s Hospital, Randwick, New South Wales, Australia

    Paul E. Gray & John B. Ziegler

  5. School of Women’s and Children’s Health, University of New South Wales, Sydney, New South Wales, Australia

    Paul E. Gray, John B. Ziegler & Maria E. Craig

  6. John Curtin School of Medical Research, The Australian National University, Canberra, Australian Capital Territory, Australia

    Keisuke Horikawa, Stephen R. Daley, Geeta Chaudhri, Gunasegaran Karupiah, Belinda Whittle, Carla Roots, Edward Bertram, Michiko Yamada, Yogesh Jeelall & Anselm Enders

  7. School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, New South Wales, Australia

    Marcel E. Dinger

  8. Research School of Chemistry, The Australian National University, Canberra, Australian Capital Territory, Australia

    Benjamin E. Clifton, Peter D. Mabbitt & Colin J. Jackson

  9. Department of Microbiology & Immunology and the Parker Institute for Cancer Immunotherapy, University of California San Francisco, San Francisco, CA, USA

    Susan R. Watson, Craig N. Jenne & Lewis L. Lanier

  10. Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA

    Tim Wiltshire

  11. Department of Microbiology & Immunology, Stanford University, Stanford, CA, USA

    Matthew H. Spitzer & Garry P. Nolan

  12. Seattle Biomedical Research Institute, Seattle, WA, USA

    Frank Schmitz & Alan Aderem

  13. Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria, Australia

    Benjamin T. Porebski & Ashley M. Buckle

  14. Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, OH, USA

    Derek W. Abbott

  15. Department of Haematology, The Children’s Hospital at Westmead, Westmead, New South Wales, Australia

    Maria E. Craig, Paul Benitez-Aguirre, Juliana Teo & Melanie Wong

  16. School of Fundamental Sciences, Massey University, Palmerston North, New Zealand

    Murray P. Cox

  17. Genentech Inc., South San Francisco, CA, USA

    Wilson Phung, Jia Tang, Wendy Sandoval & Ingrid E. Wertz

Authors
  1. Nathan W. Zammit
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Owen M. Siggs
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Paul E. Gray
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Keisuke Horikawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. David B. Langley
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Stacey N. Walters
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Stephen R. Daley
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Claudia Loetsch
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Joanna Warren
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jin Yan Yap
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Daniele Cultrone
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Amanda Russell
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Elisabeth K. Malle
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Jeanette E. Villanueva
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Mark J. Cowley
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Velimir Gayevskiy
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Marcel E. Dinger
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Robert Brink
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. David Zahra
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Geeta Chaudhri
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Gunasegaran Karupiah
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Belinda Whittle
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Carla Roots
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Edward Bertram
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Michiko Yamada
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Yogesh Jeelall
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Anselm Enders
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Benjamin E. Clifton
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Peter D. Mabbitt
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Colin J. Jackson
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. Susan R. Watson
    View author publications

    You can also search for this author in PubMed Google Scholar

  32. Craig N. Jenne
    View author publications

    You can also search for this author in PubMed Google Scholar

  33. Lewis L. Lanier
    View author publications

    You can also search for this author in PubMed Google Scholar

  34. Tim Wiltshire
    View author publications

    You can also search for this author in PubMed Google Scholar

  35. Matthew H. Spitzer
    View author publications

    You can also search for this author in PubMed Google Scholar

  36. Garry P. Nolan
    View author publications

    You can also search for this author in PubMed Google Scholar

  37. Frank Schmitz
    View author publications

    You can also search for this author in PubMed Google Scholar

  38. Alan Aderem
    View author publications

    You can also search for this author in PubMed Google Scholar

  39. Benjamin T. Porebski
    View author publications

    You can also search for this author in PubMed Google Scholar

  40. Ashley M. Buckle
    View author publications

    You can also search for this author in PubMed Google Scholar

  41. Derek W. Abbott
    View author publications

    You can also search for this author in PubMed Google Scholar

  42. John B. Ziegler
    View author publications

    You can also search for this author in PubMed Google Scholar

  43. Maria E. Craig
    View author publications

    You can also search for this author in PubMed Google Scholar

  44. Paul Benitez-Aguirre
    View author publications

    You can also search for this author in PubMed Google Scholar

  45. Juliana Teo
    View author publications

    You can also search for this author in PubMed Google Scholar

  46. Stuart G. Tangye
    View author publications

    You can also search for this author in PubMed Google Scholar

  47. Cecile King
    View author publications

    You can also search for this author in PubMed Google Scholar

  48. Melanie Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

  49. Murray P. Cox
    View author publications

    You can also search for this author in PubMed Google Scholar

  50. Wilson Phung
    View author publications

    You can also search for this author in PubMed Google Scholar

  51. Jia Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  52. Wendy Sandoval
    View author publications

    You can also search for this author in PubMed Google Scholar

  53. Ingrid E. Wertz
    View author publications

    You can also search for this author in PubMed Google Scholar

  54. Daniel Christ
    View author publications

    You can also search for this author in PubMed Google Scholar

  55. Christopher C. Goodnow
    View author publications

    You can also search for this author in PubMed Google Scholar

  56. Shane T. Grey
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Clinical recruitment and genome sequence analyses were conducted by P.G., O.M.S., A.R., J.B.Z., M.E.C., P.B.-A., J.T., M.W., M.J.C., V.G., M.E.D. and C.C.G. Tracing of Denisovan haplotypes in human populations was conducted by O.M.S., P.G., A.R., M.P.C. and C.C.G. Peripheral blood analysis was conducted by N.W.Z., A.R., J.Y.Y., S.G.T., C.C.G. and S.T.G. Identification of I325N mice was conducted by S.R.W., C.N.J., L.L.L., T.W., C.R., B.W. and C.C.G. Immune cell analysis was conducted by N.W.Z., K.H., S.R.D., Y.J., M.Y., E.B., A.E., M.H.S., G.P.N., F.S., A.A., I.E.W., S.N.W., S.T.G. and C.C.G. Coxsackievirus studies were carried out by N.W.Z., C.L., J.W., C.K. and S.T.G. Ectromelia studies were conducted by G.C. and G.K. Body weight, islet, LPS and inflammation studies were conducted by N.W.Z., S.N.W., E.K.M., J.E.V. and S.T.G. Protease activity and structural studies were conducted by N.W.Z., D.B.L., K.H., D.C., W.P., I.E.W., B.E.C., P.D.M., C.J.J., B.T.P., A.M.B., S.T.G. and C.C.G. C243Y and I207L mouse strain generation and analysis were conducted by N.W.Z., R.B., D.Z., S.N.W., D.C., J.W., C.C.G. and S.T.G. A20 phosphorylation and NF-κB luciferase analyses were performed by N.W.Z., D.C., D.W.A., W.P., W.S., J.T., I.E.W., C.C.G. and S.T.G.

Corresponding authors

Correspondence to Christopher C. Goodnow or Shane T. Grey.

Ethics declarations

Competing interests

W.P., J.T., W.S. and I.E.W. were employees of Genentech. All other authors declare no competing interests.

Additional information

Peer review information Laurie Dempsey was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Integrated supplementary information

Supplementary Figure 1 Evolutionary conservation of A20 OTU alleles amongst jawed vertebrates and global distribution of the T108A;I207L TNFAIP3 haplotype.

(a) Aligned amino acid sequence and secondary structure elements of the A20 OTU domain, from40. T108, I207, C243 and I325 residues are boxed in red (b) gnomAD r2.0.2 population allele frequencies for the T108A and I207L missense alleles in TNFAIP3, with allele counts above each bar (total alleles as denominator). Populations were defined by principal component clustering of samples with individuals of known ancestry: those which do not cluster fall into the OTH group. AFR, African; AMR, Latino; ASJ, Ashkenazi Jew; EAS, East Asian; FIN, Finnish European; NFE, non-Finnish European; OTH, other; SAS, South Asian. The OTH population includes one homozygote, while all other alleles are heterozygous. (c) Frequency of the T108A;I207L TNFAIP3 haplotype within 279 individual genomes from the Simons Genome Diversity Project22.

Supplementary Figure 2 Population genetics of the Denisovan TNFAIP3 haplotype.

(a) Geographic distribution of 514 samples used for population genetic analyses. Wallace Line (red)31, Lydekker Line (yellow). Eastern Oceania was defined here as islands east of the Bismarck Archipelago (green line). (b) Principal component analysis (PCA) of the samples presented in (a), with the proportion of variance explained by each PC indicated on each axis (c) Genome-wide FST values between indigenous populations east and west of the Wallace Line. (d) Maximum FST values spanning the TNFAIP3 locus on chromosome 6. Shaded regions correspond to peak FST values between populations east and west of the Wallace Line (grey, 6:137881500-138448062), a previously described haplotype of elevated Denisovan ancestry in Oceanians (blue, 6:137800000-138300000,25), and a putatively adaptively introgressed haplotype in Papuans (red, 6:138160925-138246514,29). (e) Phred-scaled CADD scores of all PASS variants with gnomAD allele frequency <0.01 across the extended Denisovan haplotype27. Shading as per panel d. (f) Relative abundance of Denisovan T108A;I207L and reference TNFAIP3 mRNA from heterozygous leukocytes. Peripheral blood mononuclear cells from five healthy T108A;I207L heterozygous donors were cultured with (+) or without (-) TNF for 2 hours, mRNA isolated and converted to cDNA and amplified with primers in exon 3 and 4 on either side of the I207 codon. The products were deep sequenced on an Illumina MiSeq. Shown are the fraction and percentage (above bars) of reads derived from the Denisovan T108A;I207L allele. (g) RT-PCR analysis of TNFIAP3 gene expression in peripheral blood mononuclear cells from individuals heterozygous (green circles; n=5) or homozygous (purple circle; n=1) for the Denisovan TNFAIP3 haplotype (hap) and 15 healthy control donors (HCs; blue squares), comparing induction kinetics following stimulation with recombinant human TNF for 0, 2 or 4 h. Fold change was determined by comparing stimulated values against non-stimulated PBMC preparations within the same group. Significance between groups was determined by area under the curve analysis (AUC) and data shown as mean ± s.d. P values represent two-tailed Student’s t-test.

Supplementary Figure 3 Reduced IκBα protein in immune cell subpopulations with prolonged NF-κB signaling and cell autonomous activation in T cells extracted from Tnfaip3I325N C57BL/6 mice.

(a) Mass cytometric analysis of intracellular IκBα in immune cells. Unstimulated spleen cells from Tnfaip3+/+ or Tnfaip3I325N/I325N mice (n=4 per genotype from two independent experiments) were individually labeled with mass-barcodes, mixed, permeabilized and stained with mass-labeled antibodies to a panel of cell surface markers and intracellular proteins including IκBα, and analysed by mass cytometry66,67. Spanning-tree Progression Analysis of Density-normalized Events (SPADE;68) analysis was used to resolve leukocyte lineages and subsets, and the relative intensity of IκBα in each subset was compared between Tnfaip3I325N/I325N and wild-type cell counterparts. Shown by color and numbers is the mean hyperbolic arcsine (arcsinh) ratio in minor and major leukocyte subsets. Note that IκBα levels are reduced in B cell subsets indicating increased NF-κB activation. Significant differences indicated by two-tailed Student’s t-test comparison of major subsets are marked: *P < 0.05; **P < 0.01; ***P < 0.001. (b) Bone marrow macrophages derived from conditionally Hoxb8-immortalized bone marrow progenitor cells were derived from two separate pairs of wild-type and I325N homozygous mice, stimulated with LPS (10ng/ml) for the indicated times, and Cxcl1 and Cxcl11 mRNA determined by qPCR relative to Ef1a expression. Data shown is representative of three independent experiments, each with two different mouse donors of each genotype. (c) Representative immunoblot analysis (IB; n=3 independent experiments) of canonical NF-κB components following immunoprecipitation (IP) of TNFR1 from lysates of Tnfaip3+/+ or Tnfaip3I325N/I325N thymocytes treated with 200 U/ml hTNF for the indicated times. Following TNF stimulation RIPK1 was recruited to the TNFR1 complex, with increased high molecular weight polyubiquitinated forms of RIPK1 (UbRIP1) and auto-phosphorylated transforming growth factor β activated kinase-1 (TAK1) in Tnfaip3I325N/I325N thymocytes. Mutant thymocytes exhibited a modest increase in IκBα degradation relative to wild-type, and increased JNK phosphorylation (lysate blots). A similar pattern of enhanced TAK1 activation and JNK phosphorylation was seen in murine embryonic fibroblasts and bone marrow derived macrophages expressing catalytically dead A20 OTU or ZnF4 mutants18. (d-f) Two B6.CD45.1+ wild-type mice were transplanted with a congenic bone marrow mixture from Tnfaip3+/+ CD45.1+ donors, to provide wild-type lymphocytes as an internal control, or CD45.2+ donors of Tnfaip3I325N/I325N, Tnfaip3C103A/C103A or Tnfaip3+/+ genotypes. (d) Pairwise comparison of the percentage of HELIOS+ cells among the indicated CD45.2+ and CD45.1+ subsets of CD4+ CD8- CCR7+ CD24+ FOXP3- thymocytes from the same chimera, pooled from two independent experiments. Analysis by paired Student’s t-test; ****P < 0.0001. Compared to the C103A mutation, the I325N mutation exaggerated thymic formation of FOXP3+ CD4+ cells and their Helios+ FOXP3- precursors, which depend on TCR signaling through CARD11 to NF-κB. (e, f) Representative profiles gated on CD45.2+ cells of the indicated Tnfaip3 genotypes. (e) Representative, analysis of CD4+ CD8- CCR7+ CD24+ FOXP3- thymocytes, showing the percentage of immature medullary CD4 T cells induced by strong self-reactivity to express high levels of HELIOS and BIM. (f) Percentage of FOXP3+ CD44+ cells among CD4+ splenic T cells. (d-f) were performed two independent times.

Supplementary Figure 4 B cell lymphocytosis and cell autonomous exaggeration of B cell activation by Tnfaip3I325N more than C103A mutations.

(a, b) Representative flow cytometry from four independent experimetns. Plots from mice of the indicated genotypes showing frequencies and absolute numbers of (a) splenic and (b) peritoneal B cells, based upon their expression of CD23 and CD21, IgD+, and IgM+ (a) or CD23, CD11b, IgM, and CD5 (b). The number of splenocyte B cell subsets in (a) is increased in Tnfaip3I325N/I325N mice (Fig. 2c), as is the number of B cells in the peritoneum of individual Tnfaip3 mice (c). (d) Wild-type (+/+) and I325N homozygous (I325N/I325N) Tnfaip3 splenic B lymphocytes were stimulated for the indicated times with anti-IgM, or lipopolysaccharide (LPS) and lysates analysed by immunoblotting for A20, IκBα and β-actin (loading control). Data represent three independent experiments. (e-g) Two B6.CD45.1+ wild-type mice were transplanted with a congenic bone marrow mixture from each Tnfaip3+/+ CD45.1+ donors, to provide wild-type lymphocytes as an internal control, or CD45.2+ donors of Tnfaip3I325N/I325N, Tnfaip3C103A/C103A or Tnfaip3+/+ genotypes. Chimeric sets were set up two independent times. (e) Representative flow cytometric histograms of CD25 and CD44 expression on B cells from chimeric mice cultured for 1 day and CFSE dilution after 3 days with 0.1 ug/ml LPS or anti-IgM. Black or colored open histograms, CD45.2+ B cells of the indicated Tnfaip3 genotype; grey line open histograms, CD45.1+ control B cells in the same stimulated culture; filled histograms, B cells in a parallel unstimulated culture. (f, g) Data from independent (n=2) mixed chimeric mice (n=4) showing relative mean fluorescence intensity (MFI) of CD25 or CD44 on CD45.2+ B cells of the indicated genotypes (red, I325N; blue, C103A; black, WT) compared to the co-cultured CD45.1+ wild-type B cells. Statistical analysis by ANOVA: *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

Supplementary Figure 5 Increased resistance to Coxsackievirus in Tnfaip3I325N mice.

Infection of C57BL/6 female mice of the indicated Tnfaip3 genotypes with 20 plaque forming units (PFU) of Coxsackievirus by intraperitoneal injection. (a) Kaplan-Meier survival curve showing a mean survival time of 6.5 and 12.5 days for Tnfaip3+/+ and Tnfaip3+/I325N mice, respectively. No Tnfaip3I325N/I325N mice succumbed to infection. Tnfaip3+/+ (n=15), Tnfaip3I325N/+ (n=14) or Tnfaip3I325N/I325N (n=10). Significance determined by Log-rank test. (b) Percent change in body weight after Coxsackievirus infection and (c) Blood glucose levels of mice with indicated Tnfaip3 genotype, compared to wild-type by area under the curve analysis. Tnfaip3+/+ (n=29), Tnfaip3I325N/+ (n=34) or Tnfaip3I325N/I325N (n=20). Cumulative data from five independent experiments. (d) Coxsackievirus mRNA (CVB4) abundance on indicated days (D0-3) post infection. (e) Hematoxylin & eosin stained sections of pancreas from Tnfaip3+/+ and Tnfaip3I325N/I325N mice at post-infection day 9. Note better preserved pancreatic architecture in Tnfaip3I325N/I325N mice. Scale bar = 200 µm. (f, g) RTPCR analysis for inflammatory genes Il1b (f) or Ifnb1 (G) at post-infection day 0, 1 and 3. Representative of two independent experiments (n=2). Columns represent mean ± s.e.m and two-tailed Student t-test used for significance analysis unless otherwise stated, *P < 0.05; ***P < 0.001; ****P < 0.0001.

Supplementary Figure 6 Histological analysis of tissues from Tnfaip3I325N mice.

(a) Colon length (cm) and (b) hematoxylin and eosin (H&E) stained sections from Tnfaip3I325N mice colon (20x magnification; scale bar = 200 µm) and (c) power-mosaic image of whole colon cross section of indicated genotypes (R=rectum; C=Caecum; m=muscularis; mu=mucosa; pp=Peyer’s patches; scale = 1 mm). (d) H&E sections of kidney (4× top panel, scale bar = 500 µm; 10× bottom panel, scale bar = 200 µm) and (e) liver (4× top panel, scale bar = 500 µm; 10× bottom panel, scale bar = 200 µm). (f) H&E stained sections of pancreas (10x images, scale bar = 200 µm) or, (g) insulin stained sections (20x images, scale bar = 100 µm). (h) Weight of spleens in grams (g). (i) Gross appearance of spleen and salivary glands from Tnfaip3+/+ and Tnfaip3I325N/I325N mice. Images are representative of n=4 Tnfaip3+/+ and n=4 Tnfaip3I325N/I325N 16 week-old mice from three different litters. Columns represent mean ± s.e.m, **P < 0.01.

Supplementary Figure 7 Glucose homeostasis of Tnfaip3I325N mice and isolated islets.

(a) Random blood glucose levels of Tnfaip3+/+, Tnfaip3I325N/+ or Tnfaip3I325N/I325N female (♀) or male (♂) mice at 8 or 12 weeks of age. (b, c) Blood glucose levels (mM) were monitored following an intraperitoneal injection of glucose (2 g/kg) in 12 week-old mice. (d, e) Pancreatic islets were isolated from individual mice of the indicated A20 genotypes, incubated overnight, and treated with 200 U/ml TNFα for the indicated times. (d) Representative immunoblot for phosphorylated JNK (pJNK) and total JNK (TJNK, loading control). Cumulative densitometry from 4 independent experiments is shown below. Columns represent mean ± s.e.m. Two-tailed Student t-test used for significance analysis, *P < 0.05. (e) Immunoblot for non-canonical NF-κB components NIK, p100/p52 and RelB; representative of 2 independent experiments. Tnfaip3I325N/I325N islets exhibited increased activation of the non-canonical NF-κB pathway that can alter beta cell transcriptional programs to favor reduced insulin output38.

Supplementary Figure 8 Tnfaip3I325N islet grafts exhibit a reduced first-phase insulin secretory response compared to wild-type Tnfaip3 islet grafts independent to beta cell area.

(a) Islets isolated from B6 mice of the indicated genotypes were transplanted under the kidney capsule of Tnfaip3+/+ B6 recipients that had been rendered diabetic with streptozotocin. Mean and s.e.m blood glucose on the indicated days relative to islet transplantation is shown. (b-c) At post-operative day 14 when euglycemia was established, mice were challenged with an (b) intravenous injection of glucose (1 g/kg), and blood glucose monitored over time (min). (c) Blood insulin levels (ng/ml) were also measured at the same time points via an enzyme-linked immunosorbent assay from samples in (b). AUC = area under the curve. (d) Insulin immunostaining of wild-type (Tnfaip3+/+) or A20 I325N homozygous mutant (Tnfaip3I325N/I325N) islet grafts transplanted under the kidney capsule of diabetic C57BL/6 mice for 30 days (Tnfaip3+/+ donor grafts into Tnfaip3+/+ recipients, n = 5; Tnfaip3I325N/I325N donor grafts into Tnfaip3+/+ recipients, n=5). (e) Islet graft beta cell area was determined by insulin-positive area quantification in continuous serial graft sections. Tnfaip3I325/I325N grafts exhibited equivalent insulin-positive graft area, confirming that loss of glucose tolerance (b, c) was due to a defect in insulin secretion (Fig. 3h). (f) Tnfaip3+/+ or Tnfaip3I325N/I325N islet grafts from an independent cohort of recipients were isolated on post-operative day 10 and analysed for the indicated mRNAs by RT-qPCR 86. The difference in gene expression of indicated islet grafts is shown. Fold change and mean calculated using average wild-type ΔCt value. Columns represent mean ±s.e.m and ANOVA used for significance, *P < 0.05.

Supplementary Figure 9 Analysis of allele effects on A20 phosphorylation and control of NF-κB.

(a) A20 accumulation in βTC3 mouse insulinoma cells transfected with wild-type (WT) A20, C103A A20, T108A;I207L A20, I325N A20, C243Y A20, S381A A20 or S184N A20 was determined by densitometry analysis of independent experimental immunoblots. Fast migratory (A20) 98 kDa A20 was corrected to loading control and compared to WT A20 of the same immunoblot [(FM A20/β-actin)/A20]. (b) Results of mass spectrometry analysis of full length WT A20, C103A mutant A20, or I325N mutant A20 expressed in and purified from HEK293 cells and analysed by mass spectrometry for phosphorylation of the indicated residues as per references18,19. Data representative to two independent experiments. (c) βTC3 cells co-transfected with an NF-κB.luciferase reporter and a CMV.βgal expression construct alone or with PCDNA3.1 encoding human wild-type A20 (hA20 WT; blue), A20 T108A/I207L (purple), A20 C243Y (orange), A20 S184N (grey), A20 I325N (red) or A20 S381A (white). Cells were stimulated with (+) 200 U/ml hTNF for 8 h or left untreated (-). Data presented as relative luciferase units. Each column represents 3-5 independently transfected aliquots of cells within one experiment marked by circle symbols (representative of 3 independent experiments) and columns are arithmetic means ± s.e.m. Statistical comparison by two-tailed Student’s t-test against WT hA20 (blue). (d) βTC3 cells co-transfected with a NF-κB.luciferase reporter and a CMV.βgal expression construct alone or with PCDNA3.1 encoding human wild-type A20 (hA20 WT; blue), or A20 I325N (I325N; red), or A20 with serine 381 substituted to non-phosphorylatable alanine (S381A or I325N S381A), or to phosphomimetic glutamate (S381E or I325N;S381E). Cells were stimulated with (+) 200 U/ml hTNF for 8 h or left untreated (-). Data presented as relative luciferase units. Each column represents mean of 3-6 independently transfected aliquots of cells within one experiment marked by circle symbols (representative of 3 independent experiments) and columns are arithmetic means ± s.e.m. Statistical comparison by two-tailed Student’s t-test against stimulated (*) or unstimulated (^) WT hA20 (blue). *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

Supplementary Figure 10 A20I325N subtly impacts the OTU structure.

(a) Structures of human I325N (orange) and wild-type (WT; grey) A20 OTU domains superposed (each with 6 molecules per asymmetric unit). The dimer partner of one of these is shown in pink. Loop 317 and 280 are indicated on the periphery. (b) The I325N substitution does not alter the fold of the putative catalytic triad (orange cartoon and sticks) compared to previously published wild-type structures (cyan cartoon and sticks; PDB entries 3DKB, 2VFJ and 3ZJD). (c) The fold is additionally unperturbed by acetamidylation of C103 (orange mesh; 2mFo-DFc composite omit map contoured at 1.2 sigma). (d-f) The I325N substituted side-chain sits at the base of the β7-β8 loop containing residue 317, in a pocket also lined by the β6-β9 loop containing residue 280 (d). Adjacent hydrophobic residues include W85, V276, F283, I312, V314 (e). The I325N substitution results in the base of the β7-β8 loop splaying apart (f, arrow), increasing β7-β8 loop disorder. (g) Structures of human I325N (orange) and wild-type (WT; grey) A20 OTU domains superposed on the wild-type OTU-ubiquitin structure 5LRX44. Conserved residues on the posterior surface are coloured orange. The β3-β4 loop containing the C243Y substitution and the structured base of the β7-β8 loop containing the I325N allele marked.

Supplementary Figure 11 Molecular characterization of A20 I325N and A20 C103A.

(a, b) Thermal stability of mouse and human wild-type (WT), C103A, and I325N variants were examined using circular dichroism, where mean residue ellipticity (MRE) was measured at 220 nm as a function of temperature as solutions were heated at a rate of 1° C/min. Human (a) or mouse (b) A20 OTU residues 1-370; WT (blue squares), C103A (red triangles), I325N (black circles). Curves were fit by linear regression. (c) Unfolding transitions temperature (Ttrs) of human A20 OTU variants (residues 1-370) and mouse variants (residues 1-360), derived from the fit of the curves in (a) and (b). Data represents three independent experiments. (d) HEK293T cells were transfected with bicistronic retrovirus vector pMXs-IRES-GFP, expressing FLAG-tagged mouse full-length TNFAIP3 wild-type or I325N. Each group of transfected cells was replated 24 h after transfection and incubated with 10 µM of cycloheximide (CHX) for the indicated times before harvesting. Lysates were probed with anti-FLAG and anti-GFP antibodies. (e) Reactions containing K48-linked tetra-ubiquitin (Ub4) and E. coli-expressed and purified WT or I325N A20 OTU domain, or the catalytically deactivated A20 OTU domain, C103A, were incubated for the indicated times. Depolymerization of polyubiquitin was detected by immunoblotting for ubiquitin and A20; representative of two independent experiments. (f) Reactions containing mixed Ub2-Ub6 K48-linked polyubiquitin chains and E. coli-expressed and purified WT or I325N A20 OTU domain ± iodoacetamide (IA) treatment were incubated at 37°C for 60 or 150 minutes. Depolymerization of polyubiquitin was detected by immunoblotting for ubiquitin and the N-terminus of A20; representative of 4 independent experiments. (g, h) Flag-tagged full length human A20 proteins (WT or I325N mutant) were expressed in HEK-293T cells and purified as previously described14. Purified A20 proteins were tested for K63-linked tetra-ubiquitin depolymerization (g) and in vitro ubiquitination assay (h). Immunoblots are representative of three (g) or two (h) independent experiments, respectively.

Supplementary Figure 12 Histological analysis of tissues from Tnfaip3I207L and Tnfaip3C243Y mice.

(a) Body weights of 8 week-old Tnfaip3I207L and Tnfaip3C243Y mice of the indicated genotype and sex. Bars represent mean ± s.e.m. Statistical analysis by one-way ANOVA: ****P < 0.0001. Hematoxylin and eosin (H&E) stained sections from Tnfaip3I207L mice of the indicated genotypes, including colon (b) (20× magnification; scale bar = 200 µm; see Fig. 6d for C243Y genotype), kidney (c) (4× top panel, scale bar = 500 µm; 10× bottom panel, scale bar = 200 µm), liver (d) (4× top panel, scale bar = 500 µm; 10× bottom panel, scale bar = 200 µm), and pancreas (e) (10× images, scale bar = 200 µm). Images are representative from 16 week old mice of the indicated genotype (n=4).

Supplementary Figure 13 Flow cytometry analysis of T cells from mice with human A20 OTU domain alleles.

(a) Cumulative data showing CD4+FOXP3+ regulatory T cells from spleens of T108A;I207L (I207L), I325N or C243Y mice. Each point indicates an individual mouse collected form 3 independent experiments (I207L and C243Y) or 4 independent experiments (I325N). Statistical analysis by 2-tailed Student’s t-test, data shown as mean ±s.e.m, **P < 0.01, ****P < 0.0001. (b, c) Representative flow cytometric analysis of splenocytes from mice of the indicated genotypes, showing percentages of CD4+ or CD8+ T cells, and the percentage of CD44hi activated/memory cells within these subsets, and the percentage of CD25+CD44int regulatory T cells or CD25- CD44hi effector/memory T cells among CD4+ cells. Data is representative of the same 3 independent experiments conducted in (a).

Supplementary Figure 14 Flow cytometry analysis of human T108A;I207L peripheral blood mononuclear cells.

(a) Gating strategy used to analyse human peripheral blood mononuclear cell data collected by flow cytometry. Arrows used to illustrate the step wise analysis of cell populations identified on the basis of physical properties and surface marker staining. Heading titles above each plot indicates gated cell population analysed in that plot. (b-d) Peripheral blood mononuclear cells from individuals heterozygous (green circles) or homozygous (purple circle) for the Denisovan TNFAIP3 haplotype (hap) and 6 age-matched healthy control donors (HCs; blue circles), were assessed by flow cytometry for percentages of CD4+CD25hiCD127lo regulatory T cells (b), different subsets of non-regulatory CD4+CD25loCD127hi/lo T cells (c), and CD8+ T cell subsets (d). (CM=central memory; EM=effector memory; EMRA = terminally differentiated effector memory cells). Bars show mean ± s.d. Two-tailed Student’s t-test was used to test for significance, only significant differences are indicated, *P < 0.05.

Supplementary Figure 15 Diminished tolerance to self-antigen caused by the I325N A20 allele.

InsHEL-transgenic mice or non-transgenic controls, both with wild-type A20 in pancreatic islets, were transplanted with a congenic bone marrow mixture from Tnfaip3+/+ CD45.1+ TCR3A9-transgenic donors, to serve as an internal control, and CD45.2 TCR3A9-transgenic donors with either Tnfaip3I325N/I325N (black circles) or Tnfaip3+/+ (grey circles) genotype. (a) Frequency and mean of TCR3A9 positive CD4+ T cells among CD45.2+ Tnfaip3+/+ (grey circles) or Tnfaip3I325N/I325N (black circles) lymphocytes in pancreatic lymph nodes. Test recipients expressed the insHEL antigen in pancreatic islets (insHEL-transgenic), while parallel non-transgenic recipients lacked the autoantigen. (b) Percentage and mean of CD45.2+ Tnfaip3+/+ (grey circles) or Tnfaip3I325N/I325N (black circles) cells among TCR3A9+ CD4+ T cells in spleen. (c, d) Analysis of insHEL transgenic recipients. Relative mean fluorescence intensity (MFI) of staining for cell surface MHC class II or CD86 on CD45.2+ Tnfaip3+/+ (grey circles) or Tnfaip3I325N/I325N (black circles) B cells (c) or CD11c+ dendritic cells (d), compared to CD45.1+ Tnfaip3+/+ B cells or dendritic cells in the same sample. (e) Frequency of germinal center (GC) B cells among CD45.2+ B cells (black circles) compared to the frequency among CD45.1+ Tnfaip3+/+ B cells in the same animal (linked by lines). Left panel, CD45.2 Tnfaip3+/+ marrow donor; right panel, CD45.2 Tnfaip3I325N/I325N marrow donor. In (c-e), each symbol, or pair of symbols joined by a line, represents one chimeric mouse, all analyzed in a single experiment. Statistical analysis by 2-tailed Student’s t-tests: *P < 0.05; **P < 0.01; ***P < 0.001.

Supplementary information

Supplementary information

Supplementary Figs. 1–15, Supplementary Information Fig. 1 and Supplementary Tables 1–7.

Reporting Summary

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zammit, N.W., Siggs, O.M., Gray, P.E. et al. Denisovan, modern human and mouse TNFAIP3 alleles tune A20 phosphorylation and immunity. Nat Immunol 20, 1299–1310 (2019). https://doi.org/10.1038/s41590-019-0492-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41590-019-0492-0

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