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.

Hydrophobic CDR3 residues promote the development of self-reactive T cells

Nature Immunology volume 17, pages 946–955 (2016)Cite this article

Subjects

Abstract

Studies of individual T cell antigen receptors (TCRs) have shed some light on structural features that underlie self-reactivity. However, the general rules that can be used to predict whether TCRs are self-reactive have not been fully elucidated. Here we found that the interfacial hydrophobicity of amino acids at positions 6 and 7 of the complementarity-determining region CDR3β robustly promoted the development of self-reactive TCRs. This property was found irrespective of the member of the β-chain variable region (Vβ) family present in the TCR or the length of the CDR3β. An index based on these findings distinguished Vβ2+, Vβ6+ and Vβ8.2+ regulatory T cells from conventional T cells and also distinguished CD4+ T cells selected by the major histocompatibility complex (MHC) class II molecule I-Ag7 (associated with the development of type 1 diabetes in NOD mice) from those selected by a non–autoimmunity-promoting MHC class II molecule I-Ab. Our results provide a means for distinguishing normal T cell repertoires versus autoimmunity-prone T cell repertoires.

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

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

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

Figure 1: The YAe62β chain predisposes TCRs to interact with self peptide–H-2b MHC ligands.
Figure 2: Thymocytes expressing the YAe62β chain are biased to react to self peptides presented by various haplotypes of MHC.
Figure 3: B3K506β+ pre-selection thymocytes expressing the CDR3β P6-P7 doublet Phe-Trp show enhanced recognition of self peptide–MHC ligands.
Figure 4: Generation of a self-reactivity index on the basis of the identity of the amino acids at CDR3β P6 and P7.
Figure 5: Identical CDR3β P6-P7 doublets promote or limit the reaction of pre-selection DP thymocytes with various haplotypes and classes of MHC.
Figure 6: CDR3β P6-P7 doublets regulate the reactivity of DP thymocytes to self peptide–MHC regardless of TCR-Vβ-family use or CDR3β length.
Figure 7: Thymic selection biases the use of CDR3β P6-P7 doublets by mature Vβ2+, Vβ6+ and Vβ8.2+ T cells.
Figure 8: Mouse and human Treg cells and CD4+ T cells in NOD mice show enrichment for CDR3β P6-P7 doublets that promote self-reactivity.

Accession codes

Primary accessions

BioProject

Referenced accessions

Protein Data Bank

References

  1. Davis, M.M. & Bjorkman, P.J. T-cell antigen receptor genes and T-cell recognition. Nature 334, 395–402 (1988).

    Article  CAS  PubMed  Google Scholar 

  2. Sakaguchi, S., Yamaguchi, T., Nomura, T. & Ono, M. Regulatory T cells and immune tolerance. Cell 133, 775–787 (2008).

    CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Hogquist, K.A. & Jameson, S.C. The self-obsession of T cells: how TCR signaling thresholds affect fate 'decisions' and effector function. Nat. Immunol. 15, 815–823 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Vrisekoop, N., Monteiro, J.P., Mandl, J.N. & Germain, R.N. Revisiting thymic positive selection and the mature T cell repertoire for antigen. Immunity 41, 181–190 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Van Laethem, F. et al. Lck availability during thymic selection determines the recognition specificity of the T cell repertoire. Cell 154, 1326–1341 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Mandl, J.N., Monteiro, J.P., Vrisekoop, N. & Germain, R.N. T cell-positive selection uses self-ligand binding strength to optimize repertoire recognition of foreign antigens. Immunity 38, 263–274 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Persaud, S.P., Parker, C.R., Lo, W.L., Weber, K.S. & Allen, P.M. Intrinsic CD4+ T cell sensitivity and response to a pathogen are set and sustained by avidity for thymic and peripheral complexes of self peptide and MHC. Nat. Immunol. 15, 266–274 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Fulton, R.B. et al. The TCR's sensitivity to self peptide–MHC dictates the ability of naive CD8+ T cells to respond to foreign antigens. Nat. Immunol. 16, 107–117 (2015).

    Article  CAS  PubMed  Google Scholar 

  10. Levine, A.G., Arvey, A., Jin, W. & Rudensky, A.Y. Continuous requirement for the TCR in regulatory T cell function. Nat. Immunol. 15, 1070–1078 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Vahl, J.C. et al. Continuous T cell receptor signals maintain a functional regulatory T cell pool. Immunity 41, 722–736 (2014).

    Article  CAS  PubMed  Google Scholar 

  12. Trowsdale, J. & Knight, J.C. Major histocompatibility complex genomics and human disease. Annu. Rev. Genomics Hum. Genet. 14, 301–323 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Beringer, D.X. et al. T cell receptor reversed polarity recognition of a self-antigen major histocompatibility complex. Nat. Immunol. 16, 1153–1161 (2015).

    Article  CAS  PubMed  Google Scholar 

  14. Yin, L., Scott-Browne, J., Kappler, J.W., Gapin, L. & Marrack, P. T cells and their eons-old obsession with MHC. Immunol. Rev. 250, 49–60 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Garcia, K.C. Reconciling views on T cell receptor germline bias for MHC. Trends Immunol. 33, 429–436 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Wucherpfennig, K.W., Call, M.J., Deng, L. & Mariuzza, R. Structural alterations in peptide-MHC recognition by self-reactive T cell receptors. Curr. Opin. Immunol. 21, 590–595 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Rossjohn, J. et al. T cell antigen receptor recognition of antigen-presenting molecules. Annu. Rev. Immunol. 33, 169–200 (2015).

    Article  CAS  PubMed  Google Scholar 

  18. Huseby, E.S. et al. How the T cell repertoire becomes peptide and MHC specific. Cell 122, 247–260 (2005).

    Article  CAS  PubMed  Google Scholar 

  19. Dai, S. et al. Crossreactive T Cells spotlight the germline rules for αβ T cell-receptor interactions with MHC molecules. Immunity 28, 324–334 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Huseby, E.S., Crawford, F., White, J., Marrack, P. & Kappler, J.W. Interface-disrupting amino acids establish specificity between T cell receptors and complexes of major histocompatibility complex and peptide. Nat. Immunol. 7, 1191–1199 (2006).

    Article  CAS  PubMed  Google Scholar 

  21. Kosmrlj, A., Jha, A.K., Huseby, E.S., Kardar, M. & Chakraborty, A.K. How the thymus designs antigen-specific and self-tolerant T cell receptor sequences. Proc. Natl. Acad. Sci. USA 105, 16671–16676 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Kosmrlj, A. et al. Effects of thymic selection of the T-cell repertoire on HLA class I-associated control of HIV infection. Nature 465, 350–354 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Moran, A.E. et al. T cell receptor signal strength in Treg and iNKT cell development demonstrated by a novel fluorescent reporter mouse. J. Exp. Med. 208, 1279–1289 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Garcia, K.C. et al. An αβ T cell receptor structure at 2.5 A and its orientation in the TCR-MHC complex. Science 274, 209–219 (1996).

    Article  CAS  PubMed  Google Scholar 

  25. White, S.H. & Wimley, W.C. Membrane protein folding and stability: physical principles. Annu. Rev. Biophys. Biomol. Struct. 28, 319–365 (1999).

    Article  CAS  PubMed  Google Scholar 

  26. Bouillet, P. et al. BH3-only Bcl-2 family member Bim is required for apoptosis of autoreactive thymocytes. Nature 415, 922–926 (2002).

    Article  CAS  PubMed  Google Scholar 

  27. Zerrahn, J., Held, W. & Raulet, D.H. The MHC reactivity of the T cell repertoire prior to positive and negative selection. Cell 88, 627–636 (1997).

    Article  CAS  PubMed  Google Scholar 

  28. Merkenschlager, M. et al. How many thymocytes audition for selection? J. Exp. Med. 186, 1149–1158 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. van Meerwijk, J.P. et al. Quantitative impact of thymic clonal deletion on the T cell repertoire. J. Exp. Med. 185, 377–383 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Jerne, N.K. The somatic generation of immune recognition. Eur. J. Immunol. 1, 1–9 (1971).

    Article  CAS  PubMed  Google Scholar 

  31. Baker, B.M., Gagnon, S.J., Biddison, W.E. & Wiley, D.C. Conversion of a T cell antagonist into an agonist by repairing a defect in the TCR/peptide/MHC interface: implications for TCR signaling. Immunity 13, 475–484 (2000).

    Article  CAS  PubMed  Google Scholar 

  32. Chandler, D. Interfaces and the driving force of hydrophobic assembly. Nature 437, 640–647 (2005).

    Article  CAS  PubMed  Google Scholar 

  33. Bogan, A.A. & Thorn, K.S. Anatomy of hot spots in protein interfaces. J. Mol. Biol. 280, 1–9 (1998).

    Article  CAS  PubMed  Google Scholar 

  34. Liu, X. et al. T cell receptor CDR3 sequence but not recognition characteristics distinguish autoreactive effector and Foxp3+ regulatory T cells. Immunity 31, 909–920 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Ysern, X., Li, H. & Mariuzza, R.A. Imperfect interfaces. Nat. Struct. Biol. 5, 412–414 (1998).

    Article  CAS  PubMed  Google Scholar 

  36. Housset, D. & Malissen, B. What do TCR-pMHC crystal structures teach us about MHC restriction and alloreactivity? Trends Immunol. 24, 429–437 (2003).

    Article  CAS  PubMed  Google Scholar 

  37. Feng, D., Bond, C.J., Ely, L.K., Maynard, J. & Garcia, K.C. Structural evidence for a germline-encoded T cell receptor-major histocompatibility complex interaction 'codon'. Nat. Immunol. 8, 975–983 (2007).

    Article  CAS  PubMed  Google Scholar 

  38. Scott-Browne, J.P., White, J., Kappler, J.W., Gapin, L. & Marrack, P. Germline-encoded amino acids in the αβ T-cell receptor control thymic selection. Nature 458, 1043–1046 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Stadinski, B.D. et al. A role for differential variable gene pairing in creating T cell receptors specific for unique major histocompatibility ligands. Immunity 35, 694–704 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Kanagawa, O., Martin, S.M., Vaupel, B.A., Carrasco-Marin, E. & Unanue, E.R. Autoreactivity of T cells from nonobese diabetic mice: an I-Ag7-dependent reaction. Proc. Natl. Acad. Sci. USA 95, 1721–1724 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Ridgway, W.M., Ito, H., Fassò, M., Yu, C. & Fathman, C.G. Analysis of the role of variation of major histocompatibility complex class II expression on nonobese diabetic (NOD) peripheral T cell response. J. Exp. Med. 188, 2267–2275 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Kishimoto, H. & Sprent, J. A defect in central tolerance in NOD mice. Nat. Immunol. 2, 1025–1031 (2001).

    Article  CAS  PubMed  Google Scholar 

  43. Liston, A. et al. Generalized resistance to thymic deletion in the NOD mouse; a polygenic trait characterized by defective induction of Bim. Immunity 21, 817–830 (2004).

    CAS  PubMed  Google Scholar 

  44. Mingueneau, M., Jiang, W., Feuerer, M., Mathis, D. & Benoist, C. Thymic negative selection is functional in NOD mice. J. Exp. Med. 209, 623–637 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Suri, A., Levisetti, M.G. & Unanue, E.R. Do the peptide-binding properties of diabetogenic class II molecules explain autoreactivity? Curr. Opin. Immunol. 20, 105–110 (2008).

    Article  CAS  PubMed  Google Scholar 

  46. Candéias, S., Katz, J., Benoist, C., Mathis, D. & Haskins, K. Islet-specific T-cell clones from nonobese diabetic mice express heterogeneous T-cell receptors. Proc. Natl. Acad. Sci. USA 88, 6167–6170 (1991).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Lennon, G.P. et al. T cell islet accumulation in type 1 diabetes is a tightly regulated, cell-autonomous event. Immunity 31, 643–653 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Todd, J.A. Etiology of type 1 diabetes. Immunity 32, 457–467 (2010).

    Article  CAS  PubMed  Google Scholar 

  49. Marson, A., Housley, W.J. & Hafler, D.A. Genetic basis of autoimmunity. J. Clin. Invest. 125, 2234–2241 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Day, E.B. et al. Structural basis for enabling T-cell receptor diversity within biased virus-specific CD8+ T-cell responses. Proc. Natl. Acad. Sci. USA 108, 9536–9541 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Vanguri, V., Govern, C.C., Smith, R. & Huseby, E.S. Viral antigen density and confinement time regulate the reactivity pattern of CD4 T-cell responses to vaccinia virus infection. Proc. Natl. Acad. Sci. USA 110, 288–293 (2013).

    Article  PubMed  Google Scholar 

  52. Aronesty, E. ea-utils: “Command-line tools for processing biological sequencing data” https://code.google.com/archive/p/ea-utils/ 2011.

  53. Yang, X. et al. TCRklass: a new K-string-based algorithm for human and mouse TCR repertoire characterization. J. Immunol. 194, 446–454 (2015).

    Article  CAS  PubMed  Google Scholar 

  54. Kruschke, J. Doing Bayesian data analysis: A tutorial introduction with R. (Academic Press, 2010).

  55. Pham-Gia, T. Distributions of the ratios of independent beta variables and applications. Commun. Stat. Theory Methods 29, 2693–2715 (2000).

    Article  Google Scholar 

  56. Cover, T.M. & Thomas, J.A. Elements of Information Theory 2nd edn. (John Wiley & Sons, 2006).

Download references

Acknowledgements

Supported by the US National Institutes of Health (RO1-DK095077 and U19 AI109858 to E.S.H., and T32 AI 007349 to B.D.S.), the University of Massachusetts Diabetes and Endocrine Research Center (DK32520 for E.S.H.), Marie Curie Research (I.G.T.), Fundacion Barrie (I.G.T.), the National Institute for Health Research Biomedical Research Centre based at Guy's and St Thomas' National Health Service Foundation Trust and King's College London (M.P.) and the Ragon Institute of MGH, MIT and Harvard (A.K.C.).

Author information

Authors and Affiliations

  1. Department of Pathology, University of Massachusetts Medical School Worcester, Massachusetts, USA

    Brian D Stadinski, Jonathan Jung, Katsuhiro Sasaki & Eric S Huseby

  2. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

    Karthik Shekhar

  3. Department of Immunobiology, King's College London, London, UK

    Iria Gómez-Touriño & Mark Peakman

  4. Division of Infection and Immunity, Cardiff University School of Medicine, Cardiff, UK

    Andrew K Sewell

  5. Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, USA

    Arup K Chakraborty

  6. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Arup K Chakraborty

  7. Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Arup K Chakraborty

  8. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Arup K Chakraborty

  9. Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Arup K Chakraborty

  10. Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Arup K Chakraborty

Authors
  1. Brian D Stadinski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Karthik Shekhar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Iria Gómez-Touriño
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jonathan Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Katsuhiro Sasaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Andrew K Sewell
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mark Peakman
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Arup K Chakraborty
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Eric S Huseby
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

B.D.S., K.S., A.K.S., M.P., A.K.C. and E.S.H. conceived of various aspects of the project and designed and interpreted experiments; B.D.S. performed TCR-sequencing experiments; B.D.S. and K.S. performed statistical analyses; B.D.S., K.S., I.G.-T., J.J., K.S. and E.S.H. performed experiments; and B.D.S., K.S., A.K.C. and E.S.H. wrote the manuscript.

Corresponding author

Correspondence to Eric S Huseby.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 A β-strand positions consistently places the residues at CDR3β P6 and P7 within human and mouse TCR-peptide-MHC interfaces.

(a) E8 TCR containing Vβ 13*06 carrying with an 11mer CDR3β loop bound to HLA-DR1 + pTPI, PDB code 2IAM (b) HA1.7 TCR containing Vβ 3.1 carrying with a 14mer CDR3β loop bound to HLA-DR1 + pIAV, PDB code 1FYT (c) KB5-C20 TCR containing Vβ 1*01 carrying with an 18mer CDR3β loop bound to H2-Kb + self-peptide, PDB code 1KJ2. CDR3β loop residues are space filled and shown in orange. A 90o rotation shows the location of the internal disulfide bond that forms between Vβ residues Cys21 to Cys89 that connects the Vβ B and F strands, which fixes the positions the β strand.

Supplementary Figure 2 The ability of residues at CDR3β P6 or P7 to regulate reactivity to self peptide–MHC correlates with hydrophobicity, measured by octanol-water partitioning, but not with the weight or surface area of the amino acids, and correlates weakly with hydropathy.

Correlation plots of P6 or P7 amino acid amino acid fold change in self-reactive TCRs and (a) hydrophobicity based on octanol/water partitioning, amino acid molecular weight (b), solvent assessable surface area42 (c), or hydrophobicity as indexed by Kyte and Doolittle43 (d). These data are derived from the natural log of the average fold increase of the twenty amino acids at the CDR3β residues 6 or 7 in Vβ2+, Vβ6+ and Vβ8.2+ thymocyte TCRs recognizing the MHC alleles: I-Ab, I-Ag7, I-Ad, H2-Kb, H2-Db and H2-Kd. Analysis is nonparametric 2 tailed Spearman correlation (r) computed in Graphpad Prism v6.04.

Supplementary Figure 3 A self-reactivity enrichment- and/or depletion-factor threshold of 0.4 balances the accuracy and completeness of the self-reactivity index.

(a) Scatter plot of CDR3β P6/7-doublets expressed on pre-selection CD4+CD8+ Vβ8.2+ thymocytes derived from C57BL/6 MHC-deficient mice, and pre-selection Vβ8.2+ thymocytes that express CD69 and Nur77-gfp following incubation with β2M-/- I-Ab-expressing cells. Shown are data derived from TCRs carrying CDR3β length of 13 amino acids. CDR3β P6/7-doublets that are significantly enriched (red), unchanged (gray) or depleted (blue) in the I-Ab + self-peptide reactive repertoire are shown. (b) Clustering of empirically-derived self-reactivity enrichment factors for CDR3β 6/7-doublets expressed on Vβ 8.2+ T cells recognizing three alleles of murine MHC class I: H2-Kb, H2-Db, H2-Kd, and three alleles of MHC class II: I-Ab, I-Ag7, IAd. Enrichment factors (rows) were clustered using the k-means algorithm for different values of k and an optimal value of k= 4 was determined based on the “gap-statistic” method44. Self-reactivity indexes were generated based on multiplying the enrichment factors of single amino acids at CDR3β P6 and P7 using threshold cutoffs of ln of 0, 0.2/-0.175, 0.4/-0.35, 0.6/-0.525, 0.8/-0.7, 1/-0.875. The indexes were tested for the ability to identify CDR3β P6/7-doublets that are enriched or deplete on self-reactive thymocytes. (c) Average accuracy in correctly identifying significantly enriched or depleted doublets as red or blue. in Vβ2+, Vβ6+, Vβ8+ TCRs. Accuracy is defined as: the number of doublets correctly identified / number of doublets correctly identified + number of false positive doublets. (d) Average percent of identifying significantly enriched/depleted doublets in Vβ2+, Vβ6+, Vβ8+ TCRs. Completeness is defined as: the number of doublets correctly identified / total number of significantly enriched or depleted doublets.

Supplementary Figure 4 A CDR3β P6 × P7 self-reactivity index with a threshold value of 0.4 accurately predicts whether a CDR3β P6-P7 doublet promotes or limits reactivity to self peptide–MHC.

Fold change of differentially expressed of CDR3β P6-P7 doublets that promote (red), are neutral (white) or limit self-pMHC reactivity (blue) expressed by self-reactive Vβ2+, Vβ6+ or Vβ8.2+ thymocytes as compared to the preselection. Analyses is separated by self-pMHC allele being recognized, (a) I-Ab, (b) I-Ag7, (c) I-Ad, (d) H2-Kb, (e) H2-Db and (f) H2-Kd. The analyses are for Vβ2+, Vβ6+ or Vβ8.2+ TCRs carrying CDR3β loops of 13, 14 and 15 amino acids in length. Self-reactivity enrichment thresholds used are, red > ln(0.4), and blue, < ln (-0.35). P values are from a hypergeometric test of the distribution in red or blue doublets against all significantly different doublets.

Supplementary Figure 5 CDR3β P6-P7 doublets that promote reactivity to self peptide–MHC are greater in frequency among CD4+ Treg cells than among CD4+ Tconv cells and lead to increased levels of CD5 in CD4+ Tconv cells.

(a) Relative fold change of differentially expressed of CDR3β P6-P7 doublets that promote (red), are neutral (white) or limit self-pMHC reactivity (blue) expressed by Vβ2+, Vβ6+ or Vβ8.2+ mouse Tregs versus CD4+ Tconv in NOD.H2b, C57BL/6.H2g7, and NOD mice. P-values are derived from a hypergeometric test. (b-d) CD5 expression level on CD4+ Tconv cells (b), on CD8+ T cells (c), and CD4+ Tregs (d) from splenocytes in C57BL/6 plus TCRβ chain transgenic YAe62 FW, B3K506 SS, or retrogenic Vβ8.2 SW, Vβ8.2 AW, Vβ8.2 EG, Vβ8.2 EQ mixed bone-marrow chimeric mice. CD5 ratios are calculated from the TCRβ transgenic containing cells compared to the same non-transgenic C57BL/6 derived cell populations within the same mouse. Splenocyte populations are pre-gated for TCRβ+ B220- and CD4 or CD8 and Foxp3 expression. Data are derived from YAe62β FW (n=5), Vβ8.2 SW (n=8), Vβ8.2 AW (n=6), B3K506 SS (n=4), Vβ8.2 EG (n=5), Vβ8.2 EQ (n=3) mice. Error bars indicate standard deviation. Statistical significance was assessed by one-way ANOVA: ****p < 0.0001.

Supplementary Figure 6 The H-2g7 haplotype is the main contributing factor to the repertoire-wide shifts toward self-reactivity in the CD4+ Tconv cell repertoire of NOD mice.

Relative fold change of differentially expressed of CDR3β P6-P7 doublets that promote (red), are neutral (white) or limit self-pMHC reactivity (blue) expressed by CD4+ Tconv (a) or CD8+ Tconv (b) in NOD v. B6, B6.H-2g7 v. B6, NOD v. NOD.H-2b, NOD.H-2b v. B6 and NOD v. B6.H-2g7. Fold change in doublet distribution classified by the self-reactivity index is reported as natural log (ln). p values are derived from a hypergeometric test of the distribution in red or blue doublets against all enriched doublets.

Supplementary Figure 7 Estimating Neff using CV versus f relationship.

(a) Neff calculation illustrated for pre-selection Vβ8 TCRs (top), B6-CD4 Vβ6 TCRs (middle) and Bim-CD4 Vβ2 TCRs (bottom). Within each row, the three panels correspond to the three different CDR3 lengths. Neff is calculated independently for each case. Each panel plots the CV vs. f relationship on a log-log scale. The amino acids are distributed across 12-20 frequency bins such that each bin had at least 8 doublets. The black dots correspond to the average CV within each bin and the dashed black line depicts the best fit according to Eq. 3. The colored lines indicate theoretical CV vs. f curves for various population sizes (N=103,104,105,106) calculated using Eq. 3. The inferred Neff is indicated on each panel, together with the number of raw read counts (Nreads).

(b) Neff estimation on simulated data – We used simulations to explore how Neff compares with the true population size (when known). We simulated doublet counts by sampling a multinomial distribution based the frequency distribution seen in the B6-CD4 data. This for done for various sample sizes (N (true)) ranging from 500 to 100,000, and for each case we simulated scenarios involving varying number of replicates (n(rep) = 3, 5, 10, 100). For each case, Neff was calculated (y-axis) based on the CV vs. f relationship described by Eq. (3) and illustrated in fig. S9. These simulations show that at n=3 replicates (red line), the computed Neff underestimates the true population size.

Supplementary Figure 8 Bayesian statistical and cooperativity analysis for the identification of differential expression in CDR3β P6-P7 doublet frequencies.

(a) The posterior probability distributions (Eqs. (7) and (9)) for doublet frequencies are illustrated for three doublets (FW, AW, GK) across three TCR populations. In each panel, we plot the replicate-specific distributions (red, blue, magenta) and the average distribution (black). (b) The probability distribution of the enrichment factor (Eq. (12)) for three doublets (FW, QK and GM) for three representative comparisons (Left: IAb-activated vs. pre-selection, Vβ2, 15-mer, Middle: Bim-/- CD8+ T cells vs. B6 CD8+ T cells, Vβ6, 13-mer, Right: B6 CD4+ T cells vs. NOD CD4+ T cells, Vβ6, 13-mer) illustrating significant enrichment, depletion and no change, respectively. The black dashed line corresponds to an enrichment factor of 1.

(c) Enrichment factor distributions for doublet FW: Comparison of FW CDR3β doublet enrichment in H-2 Db activated versus preselection TCRs for the 9 different cases, (rows: Vβ8, Vβ6, Vβ2, and columns: CDR3 lengths of 13, 14, 15 amino acids). 6 out of 9 cases show a significant enrichment, while 3 cases, owing to poor statistical power, cannot be called based on our threshold (P(Enrichment) > 0.95). It is noteworthy that in the three non-significant cases, the distribution shows a clear bias to the right.

(d) Cooperativity between amino acid distributions at CDR3β P6 and P7: Bar graphs show the Mutual information between the frequency distributions at P6 and P7, MI(P6, P7) expressed as the percentage of the Shannon entropy at P6 (Panel d) or P7 (Panel e). Within each panel, the calculation was performed on the pre-selection TCRs and the MHC-activated TCRs separately (left and right set of bars).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8 (PDF 1259 kb)

Supplementary Table 1

Number of pMHC contacts created by CDR3β residues - atoms with 4Å distance (XLSX 18 kb)

Supplementary Table 2

PCR primers and bar codes for mouse and human TCR sequencing (XLSX 13 kb)

Supplementary Table 3

Genotype & Phenotype of murine T cells subsets and number of sorted T cells and Sequences analyzed (XLSX 14 kb)

Supplementary Table 4

Genotype of donors and number of sorted T cells (XLSX 10 kb)

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Stadinski, B., Shekhar, K., Gómez-Touriño, I. et al. Hydrophobic CDR3 residues promote the development of self-reactive T cells. Nat Immunol 17, 946–955 (2016). https://doi.org/10.1038/ni.3491

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ni.3491

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