The T cell receptor sequence influences the likelihood of T cell memory formation

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Learn more: PMC Disclaimer V(D)J recombination, somatic hypermutation and class switch recombination of immunoglobulins: mechanism and regulation . Immunology 160 , 233–247 (2020). [ PMC free article ] [ PubMed ] [ Google Scholar ]

2. Klein L., Kyewski B., Allen P. M. & Hogquist K. A. Positive and negative selection of the T cell repertoire: what thymocytes see (and don’t see) . Nat. Rev. Immunol. 14 , 377–391 (2014). [ PMC free article ] [ PubMed ] [ Google Scholar ]

3. Merkenschlager M. et al. How many thymocytes audition for selection? J. Exp. Med. 186 , 1149–1158 (1997). [ PMC free article ] [ PubMed ] [ Google Scholar ]

4. Yun T. J. & Bevan M. J. The Goldilocks conditions applied to T cell development . Nature immunology vol. 2 13–14 (2001). [ PubMed ] [ Google Scholar ]

5. Nakayama T. & Yamashita M. The TCR-mediated signaling pathways that control the direction of helper T cell differentiation . Semin. Immunol. 22 , 303–309 (2010). [ PubMed ] [ Google Scholar ]

6. 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). [ PMC free article ] [ PubMed ] [ Google Scholar ]

7. Kaech S. M. & Cui W. Transcriptional control of effector and memory CD8+ T cell differentiation . Nat. Rev. Immunol. 12 , 749–761 (2012). [ PMC free article ] [ PubMed ] [ Google Scholar ]

8. Pauken K. E. et al. TCR-sequencing in cancer and autoimmunity: barcodes and beyond . Trends Immunol . 43 , 180–194 (2022). [ PMC free article ] [ PubMed ] [ Google Scholar ]

9. Treiner E. et al. Selection of evolutionarily conserved mucosal-associated invariant T cells by MR1 . Nature 422 , 164–169 (2003). [ PubMed ] [ Google Scholar ]

10. Lagattuta K. A. et al. Repertoire analyses reveal T cell antigen receptor sequence features that influence T cell fate . Nat. Immunol. 23 , 446–457 (2022). [ PMC free article ] [ PubMed ] [ Google Scholar ]

11. DerSimonian H., Band H. & Brenner M. B. Increased frequency of T cell receptor V alpha 12.1 expression on CD8+ T cells: evidence that V alpha participates in shaping the peripheral T cell repertoire . J. Exp. Med. 174 , 1287 (1991). [ PMC free article ] [ PubMed ] [ Google Scholar ]

12. Stadinski B. D. et al. Hydrophobic CDR3 residues promote the development of self-reactive T cells . Nat. Immunol. 17 , 946–955 (2016). [ PMC free article ] [ PubMed ] [ Google Scholar ]

13. Li H. M. et al. TCRβ repertoire of CD4+ and CD8+ T cells is distinct in richness, distribution, and CDR3 amino acid composition . J. Leukoc. Biol. 99 , 505–513 (2016). [ PMC free article ] [ PubMed ] [ Google Scholar ]

14. Carter J. A. et al. Single T Cell Sequencing Demonstrates the Functional Role of αβ TCR Pairing in Cell Lineage and Antigen Specificity . Front. Immunol. 10 , 1516 (2019). [ PMC free article ] [ PubMed ] [ Google Scholar ]

15. Daley S. R. et al. Cysteine and hydrophobic residues in CDR3 serve as distinct T-cell self-reactivity indices . J. Allergy Clin. Immunol. 144 , 333–336 (2019). [ PMC free article ] [ PubMed ] [ Google Scholar ]

16. Kasatskaya S. A. et al. Functionally specialized human CD4+ T-cell subsets express physicochemically distinct TCRs . Elife 9 , (2020). [ PMC free article ] [ PubMed ] [ Google Scholar ]

17. Zhang Z., Xiong D., Wang X., Liu H. & Wang T. Mapping the functional landscape of T cell receptor repertoires by single-T cell transcriptomics . Nat. Methods 18 , 92–99 (2021). [ PMC free article ] [ PubMed ] [ Google Scholar ]

18. Schattgen S. A. et al. Integrating T cell receptor sequences and transcriptional profiles by clonotype neighbor graph analysis (CoNGA) . Nat. Biotechnol. 40 , 54–63 (2022). [ PMC free article ] [ PubMed ] [ Google Scholar ]

19. COvid-19 Multi-omics Blood ATlas (COMBAT) Consortium. Electronic address: julian.knight@well.ox.ac.uk & COvid-19 Multi-omics Blood ATlas (COMBAT) Consortium. A blood atlas of COVID-19 defines hallmarks of disease severity and specificity . Cell 185 , 916–938.e58 (2022). [ PMC free article ] [ PubMed ] [ Google Scholar ]

20. Ren X. et al. COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas . Cell 184 , 5838 (2021). [ PMC free article ] [ PubMed ] [ Google Scholar ]

21. Stephenson E. et al. Single-cell multi-omics analysis of the immune response in COVID-19 . Nat. Med. 27 , 904–916 (2021). [ PMC free article ] [ PubMed ] [ Google Scholar ]

22. Domínguez Conde C. et al. Cross-tissue immune cell analysis reveals tissue-specific features in humans . Science 376 , eabl5197 (2022). [ PMC free article ] [ PubMed ] [ Google Scholar ]

23. Boutet S. C. et al. Scalable and comprehensive characterization of antigen-specific CD8 T cells using multi-omics single cell analysis . The Journal of Immunology 202 , 131–134 (2019). [ Google Scholar ]

24. Suo C. et al. Mapping the developing human immune system across organs . Science 376 , eabo0510 (2022). [ PMC free article ] [ PubMed ] [ Google Scholar ]

25. Nathan A. et al. Single-cell eQTL models reveal dynamic T cell state dependence of disease loci . Nature 606 , 120–128 (2022). [ PMC free article ] [ PubMed ] [ Google Scholar ]

26. Venturi V., Price D. A., Douek D. C. & Davenport M. P. The molecular basis for public T-cell responses? Nat. Rev. Immunol. 8 , 231–238 (2008). [ PubMed ] [ Google Scholar ]

27. Lu T. et al. Deep learning-based prediction of the T cell receptor-antigen binding specificity . Nat Mach Intell 3 , 864–875 (2021). [ PMC free article ] [ PubMed ] [ Google Scholar ]

28. Atchley W. R., Zhao J., Fernandes A. D. & Drüke T. Solving the protein sequence metric problem . Proc. Natl. Acad. Sci. U. S. A. 102 , 6395–6400 (2005). [ PMC free article ] [ PubMed ] [ Google Scholar ]

29. Tilloy F. et al. An Invariant T Cell Receptor a Chain Defines a Novel TAP-independent Major Histocompatibility Complex Class Ib–restricted α/β T Cell Subpopulation in Mammals . J. Exp. Med. 189 , 1907–1921 (1999). [ PMC free article ] [ PubMed ] [ Google Scholar ]

30. Porcelli S., Morita C. T. & Brenner M. B. CDlb restricts the response of human CD4–8–T lymphocytes to a microbial antigen . Nature 360 , 593–597 (1992). [ PubMed ] [ Google Scholar ]

31. Miescher G. C., Howe R. C., Lees R. K. & MacDonald H. R. CD3-associated alpha/beta and gamma/delta heterodimeric receptors are expressed by distinct populations of CD4- CD8- thymocytes . J. Immunol. 140 , 1779–1782 (1988). [ PubMed ] [ Google Scholar ]

32. Lantz O. & Bendelac A. An invariant T cell receptor alpha chain is used by a unique subset of major histocompatibility complex class I-specific CD4+ and CD4-8- T cells in mice and humans . J. Exp. Med. 180 , 1097–1106 (1994). [ PMC free article ] [ PubMed ] [ Google Scholar ]

33. Teh H. S. et al. Thymic major histocompatibility complex antigens and the alpha beta T-cell receptor determine the CD4/CD8 phenotype of T cells . Nature 335 , 229–233 (1988). [ PubMed ] [ Google Scholar ]

34. Li J. et al. KIR+CD8+ T cells suppress pathogenic T cells and are active in autoimmune diseases and COVID-19 . Science 376 , eabi9591 (2022). [ PMC free article ] [ PubMed ] [ Google Scholar ]

35. Elhanati Y., Sethna Z., Callan C. G. Jr, Mora T. & Walczak A. M. Predicting the spectrum of TCR repertoire sharing with a data-driven model of recombination . Immunol. Rev. 284 , 167–179 (2018). [ PMC free article ] [ PubMed ] [ Google Scholar ]

36. Rubelt F. et al. Individual heritable differences result in unique cell lymphocyte receptor repertoires of naïve and antigen-experienced cells . Nat. Commun. 7 , 11112 (2016). [ PMC free article ] [ PubMed ] [ Google Scholar ]

37. Stephens M. False discovery rates: a new deal . Biostatistics 18 , 275–294 (2017). [ PMC free article ] [ PubMed ] [ Google Scholar ]

38. Zeldovich K. B., Berezovsky I. N. & Shakhnovich E. I. Protein and DNA sequence determinants of thermophilic adaptation . PLoS Comput. Biol. 3 , e5 (2007). [ PMC free article ] [ PubMed ] [ Google Scholar ]

39. Egorov E. S. et al. The Changing Landscape of Naive T Cell Receptor Repertoire With Human Aging . Front. Immunol. 9 , 1618 (2018). [ PMC free article ] [ PubMed ] [ Google Scholar ]

40. Janeway C. A. Jr, Travers P. & Walport M. The rearrangement of antigen-receptor gene segments controls lymphocyte development . : The Immune System … (2001). [ Google Scholar ]

41. Manfras B. J., Terjung D. & Boehm B. O. Non-productive human TCR β chain genes represent VDJ diversity before selection upon function: insight into biased usage of TCRBD and TCRBJ genes and diversity of CDR3 region length . Hum. Immunol. 60 , 1090–1100 (1999). [ PubMed ] [ Google Scholar ]

42. Zhang W. et al. A framework for highly multiplexed dextramer mapping and prediction of T cell receptor sequences to antigen specificity . Sci Adv 7 , (2021). [ PMC free article ] [ PubMed ] [ Google Scholar ]

43. Chiang D. et al. Single-cell profiling of peanut-responsive T cells in patients with peanut allergy reveals heterogeneous effector TH2 subsets . J. Allergy Clin. Immunol. 141 , 2107–2120 (2018). [ PMC free article ] [ PubMed ] [ Google Scholar ]

44. Rajasekaran K. et al. Tetramer-aided sorting and single-cell RNA sequencing facilitate transcriptional profiling of antigen-specific CD8+ T cells . Transl. Oncol. 27 , 101559 (2023). [ PMC free article ] [ PubMed ] [ Google Scholar ]

45. Solouki S. et al. TCR Signal Strength and Antigen Affinity Regulate CD8+ Memory T Cells . J. Immunol. 205 , 1217–1227 (2020). [ PMC free article ] [ PubMed ] [ Google Scholar ]

46. Rock K. L., Reits E. & Neefjes J. Present Yourself! By MHC Class I and MHC Class II Molecules . Trends Immunol . 37 , 724–737 (2016). [ PMC free article ] [ PubMed ] [ Google Scholar ]

47. Van Rhijn I., Godfrey D. I., Rossjohn J. & Moody D. B. Lipid and small-molecule display by CD1 and MR1 . Nat. Rev. Immunol. 15 , 643–654 (2015). [ PMC free article ] [ PubMed ] [ Google Scholar ]

48. Scott-Browne J. P., White J., Kappler J. W., Gapin L. & Marrack P. Germline-encoded amino acids in the alphabeta T-cell receptor control thymic selection . Nature 458 , 1043–1046 (2009). [ PMC free article ] [ PubMed ] [ Google Scholar ]

49. Deibel M. R. Jr et al. Expression of terminal deoxynucleotidyl transferase in human thymus during ontogeny and development . J. Immunol. 131 , 195–200 (1983). [ PubMed ] [ Google Scholar ]

50. Marcou Q., Mora T. & Walczak A. M. High-throughput immune repertoire analysis with IGoR . Nat. Commun. 9 , 561 (2018). [ PMC free article ] [ PubMed ] [ Google Scholar ]

51. Berzins S. P., Cochrane A. D., Pellicci D. G., Smyth M. J. & Godfrey D. I. Limited correlation between human thymus and blood NKT cell content revealed by an ontogeny study of paired tissue samples . Eur. J. Immunol. 35 , 1399–1407 (2005). [ PubMed ] [ Google Scholar ]

52. Martin E. et al. Stepwise development of MAIT cells in mouse and human . PLoS Biol . 7 , e54 (2009). [ PMC free article ] [ PubMed ] [ Google Scholar ]

53. Leeansyah E., Loh L., Nixon D. F. & Sandberg J. K. Acquisition of innate-like microbial reactivity in mucosal tissues during human fetal MAIT-cell development . Nat. Commun. 5 , 3143 (2014). [ PMC free article ] [ PubMed ] [ Google Scholar ]

54. López-Sagaseta J. et al. The molecular basis for Mucosal-Associated Invariant T cell recognition of MR1 proteins . Proc. Natl. Acad. Sci. U. S. A. 110 , E1771–8 (2013). [ PMC free article ] [ PubMed ] [ Google Scholar ]

55. Patel O. et al. Recognition of vitamin B metabolites by mucosal-associated invariant T cells . Nat. Commun. 4 , 2142 (2013). [ PubMed ] [ Google Scholar ]

56. Zareie P. et al. Canonical T cell receptor docking on peptide–MHC is essential for T cell signaling . Science 372 , eabe9124 (2021). [ PubMed ] [ Google Scholar ]

57. Ernst B., Lee D. S., Chang J. M., Sprent J. & Surh C. D. The peptide ligands mediating positive selection in the thymus control T cell survival and homeostatic proliferation in the periphery . Immunity 11 , 173–181 (1999). [ PubMed ] [ Google Scholar ]

58. 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). [ PMC free article ] [ PubMed ] [ Google Scholar ]

59. 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 (2015). [ PMC free article ] [ PubMed ] [ Google Scholar ]

60. Rocha B. & von Boehmer H. Peripheral selection of the T cell repertoire . Science 251 , 1225–1228 (1991). [ PubMed ] [ Google Scholar ]

61. Parker M. E. & Ciofani M. Regulation of γδ T Cell Effector Diversification in the Thymus . Front. Immunol. 11 , 42 (2020). [ PMC free article ] [ PubMed ] [ Google Scholar ]

62. Korsunsky I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony . Nat. Methods 16 , 1289–1296 (2019). [ PMC free article ] [ PubMed ] [ Google Scholar ]

63. Kang J. B. et al. Efficient and precise single-cell reference atlas mapping with Symphony . Nat. Commun. 12 , 5890 (2021). [ PMC free article ] [ PubMed ] [ Google Scholar ]

64. Nathan A. et al. Multimodally profiling memory T cells from a tuberculosis cohort identifies cell state associations with demographics, environment and disease . Nat. Immunol. 22 , 781–793 (2021). [ PMC free article ] [ PubMed ] [ Google Scholar ]

65. Amari S., Murata N., Muller K. R., Finke M. & Yang H. H. Asymptotic statistical theory of overtraining and cross-validation . IEEE Trans. Neural Netw. 8 , 985–996 (1997). [ PubMed ] [ Google Scholar ]

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