1932

Abstract

This is an exciting time for immunology because the future promises to be replete with exciting new discoveries that can be translated to improve health and treat disease in novel ways. Immunologists are attempting to answer increasingly complex questions concerning phenomena that range from the genetic, molecular, and cellular scales to that of organs, whole animals or humans, and populations of humans and pathogens. An important goal is to understand how the many different components involved interact with each other within and across these scales for immune responses to emerge, and how aberrant regulation of these processes causes disease. To aid this quest, large amounts of data can be collected using high-throughput instrumentation. The nonlinear, cooperative, and stochastic character of the interactions between components of the immune system as well as the overwhelming amounts of data can make it difficult to intuit patterns in the data or a mechanistic understanding of the phenomena being studied. Computational models are increasingly important in confronting and overcoming these challenges. I first describe an iterative paradigm of research that integrates laboratory experiments, clinical data, computational inference, and mechanistic computational models. I then illustrate this paradigm with a few examples from the recent literature that make vivid the power of bringing together diverse types of computational models with experimental and clinical studies to fruitfully interrogate the immune system.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-immunol-041015-055325
2017-04-26
2024-06-19
Loading full text...

Full text loading...

/deliver/fulltext/immunol/35/1/annurev-immunol-041015-055325.html?itemId=/content/journals/10.1146/annurev-immunol-041015-055325&mimeType=html&fmt=ahah

Literature Cited

  1. Brenner S. 1.  2002. Nobel lecture: Nature's gift to Science. Biosci. Rep. 23:225–37 [Google Scholar]
  2. Germain RN, Meier-Schellersheim M, Nita-Lazar A, Fraser IDC. 2.  2011. Systems biology in immunology: a computational modeling perspective. Annu. Rev. Immunol. 29:1527–85 [Google Scholar]
  3. Chakraborty AK, Weiss A. 3.  2014. Insights into the initiation of TCR signaling. Nat. Immunol. 15:9798–807 [Google Scholar]
  4. Huang J, Brameshuber M, Zeng X, Xie J, Li Q-J. 4.  et al. 2013. A single peptide-major histocompatibility complex ligand triggers digital cytokine secretion in CD4+ T cells. Immunity 39:5846–57 [Google Scholar]
  5. Purbhoo MA, Irvine DJ, Huppa JB, Davis MM. 5.  2004. T cell killing does not require the formation of a stable mature immunological synapse. Nat. Immunol. 5:5524–30 [Google Scholar]
  6. Sykulev Y, Joo M, Vturina I, Tsomides TJ, Eisen HN. 6.  1996. Evidence that a single peptide-MHC complex on a target cell can elicit a cytolytic T cell response. Immunity 4:6565–71 [Google Scholar]
  7. Irvine DJ, Purbhoo MA, Krogsgaard M, Davis MM. 7.  2002. Direct observation of ligand recognition by T cells. Nature 419:6909845–49 [Google Scholar]
  8. Davis MM, Krogsgaard M, Huse M, Huppa J, Lillemeier BF, Li Q-J. 8.  2007. T cells as a self-referential, sensory organ. Annu. Rev. Immunol. 25:1681–95 [Google Scholar]
  9. Hoerter JAH, Brzostek J, Artyomov MN, Abel SM, Casas J. 9.  et al. 2013. Coreceptor affinity for MHC defines peptide specificity requirements for TCR interaction with coagonist peptide-MHC. J. Exp. Med. 210:91807–21 [Google Scholar]
  10. Krogsgaard M, Li Q-J, Sumen C, Huppa JB, Huse M, Davis MM. 10.  2005. Agonist/endogenous peptide-MHC heterodimers drive T cell activation and sensitivity. Nature 434:7030238–43 [Google Scholar]
  11. Li Q-J, Dinner AR, Qi S, Irvine DJ, Huppa JB. 11.  et al. 2004. CD4 enhances T cell sensitivity to antigen by coordinating Lck accumulation at the immunological synapse. Nat. Immunol. 5:8791–99 [Google Scholar]
  12. Daniels MA, Teixeiro E, Gill J, Hausmann B, Roubaty D. 12.  et al. 2006. Thymic selection threshold defined by compartmentalization of Ras/MAPK signalling. Nature 444:7120724–29 [Google Scholar]
  13. Gascoigne NR, Palmer E. 13.  2011. Signaling in thymic selection. Curr. Opin. Immunol. 23:2207–12 [Google Scholar]
  14. Starr TK, Jameson SC, Hogquist KA. 14.  2003. Positive and negative selection of T cells. Annu. Rev. Immunol. 21:1139–76 [Google Scholar]
  15. Govern CC, Paczosa MK, Chakraborty AK, Huseby ES. 15.  2010. Fast on-rates allow short dwell time ligands to activate T cells. PNAS 107:198724–29 [Google Scholar]
  16. Altan-Bonnet G, Germain RN. 16.  2005. Modeling T cell antigen discrimination based on feedback control of digital ERK responses. PLOS Biol 3:11e356 [Google Scholar]
  17. Das J, Ho M, Zikherman J, Govern C, Yang M. 17.  2009. Digital signaling and hysteresis characterize Ras activation in lymphoid cells. Cell 136:2337–51 [Google Scholar]
  18. Huse M, Klein LO, Girvin AT, Faraj JM, Li Q-J. 18.  et al. 2007. Spatial and temporal dynamics of T cell receptor signaling with a photoactivatable agonist. Immunity 27:176–88 [Google Scholar]
  19. Germain RN. 19.  2010. Computational analysis of T cell receptor signaling and ligand discrimination—past, present, and future. FEBS Lett 584:244814–22 [Google Scholar]
  20. Chakraborty AK, Das J. 20.  2010. Pairing computation with experimentation: a powerful coupling for understanding T cell signalling. Nat. Rev. Immunol. 10:159–71 [Google Scholar]
  21. Lever M, Maini PK, van der Merwe PA, Dushek O. 21.  2014. Phenotypic models of T cell activation. Nat. Rev. Immunol. 14:9619–29 [Google Scholar]
  22. Coward J, Germain RN, Altan-Bonnet G. 22.  2010. Perspectives for computer modeling in the study of T cell activation. Cold Spring Harb. Perspect. Biol. 2:6a005538 [Google Scholar]
  23. Molina-Paris C, Lythe G. 23.  2011. Mathematical Models and Immune Cell Biology New York: Springer [Google Scholar]
  24. Gottschalk RA, Martins AJ, Sjoelund VH, Angermann BR, Lin B, Germain RN. 24.  2013. Recent progress using systems biology approaches to better understand molecular mechanisms of immunity. Syst. Immunol. 25:3201–8 [Google Scholar]
  25. Chylek LA, Harris LA, Tung C-S, Faeder JR, Lopez CF, Hlavacek W. 25.  2014. Rule-based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems. Wiley Interdisc. Rev. Sys. Biol. Med. 6:113–36 [Google Scholar]
  26. Lis M, Artyomov MN, Devadas S, Chakraborty AK. 26.  2009. Efficient stochastic simulation of reaction-diffusion processes via direct compilation. Bioinformatics 25:172289–91 [Google Scholar]
  27. Meier-Schellersheim M, Mack G. 27.  1999. SIMMUNE, a tool for simulating and analyzing immune system behavior. arXiv:cs/9903017
  28. Blinov ML, Faeder JR, Goldstein B, Hlavacek WS. 28.  2004. BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains. Bioinformatics 20:173289–91 [Google Scholar]
  29. Krogsgaard M, Juang J, Davis MM. 29.  2007. A role for “self” in T-cell activation. Syst. Immunol. 19:4236–44 [Google Scholar]
  30. Stepanek O, Prabhakar AS, Osswald C, King CG, Bulek A. 30.  et al. 2014. Coreceptor scanning by the T cell receptor provides a mechanism for T cell tolerance. Cell 159:2333–45 [Google Scholar]
  31. Paster W, Bruger AM, Katsch K, Gregoire C, Roncagalli R. 31.  et al. 2015. A THEMIS:SHP1 complex promotes T-cell survival. EMBO J 34:3393–409 [Google Scholar]
  32. Dittel BN, Štefanova I, Germain RN, Janeway CA Jr. 32.  1999. Cross-antagonism of a T cell clone expressing two distinct T cell receptors. Immunity 11:3289–98 [Google Scholar]
  33. Wylie DC, Das J, Chakraborty AK. 33.  2007. Sensitivity of T cells to antigen and antagonism emerges from differential regulation of the same molecular signaling module. PNAS 104:135533–38 [Google Scholar]
  34. Feinerman O, Germain RN, Altan-Bonnet G. 34.  2008. Quantitative challenges in understanding ligand discrimination by αβ T cells. Mol. Immunol. 45:3619–31 [Google Scholar]
  35. François P, Altan-Bonnet G. 35.  2016. The case for absolute ligand discrimination: modeling information processing and decision by immune T cells. J. Stat. Phys. 162:51130–52 [Google Scholar]
  36. Aleksic M, Dushek O, Zhang H, Shenderov E, Chen J-L. 36.  et al. 2010. Dependence of T cell antigen recognition on T cell receptor-peptide MHC confinement time. Immunity 32:2163–74 [Google Scholar]
  37. Huppa JB, Axmann M, Mortelmaier MA, Lillemeier BF, Newell EW. 37.  et al. 2010. TCR-peptide-MHC interactions in situ show accelerated kinetics and increased affinity. Nature 463:7283963–67 [Google Scholar]
  38. Huang J, Zarnitsyna VI, Liu B, Edwards LJ, Jiang N. 38.  et al. 2010. The kinetics of two-dimensional TCR and pMHC interactions determine T-cell responsiveness. Nature 464:7290932–36 [Google Scholar]
  39. Liu B, Chen W, Evavold BD, Zhu C. 39.  2014. Accumulation of dynamic catch bonds between TCR and agonist peptide-MHC triggers T cell signaling. Cell 157:2357–68 [Google Scholar]
  40. O'Donoghue GP, Pielak RM, Smoligovets AA, Lin JJ, Groves JT. 40.  2013. Direct single molecule measurement of TCR triggering by agonist pMHC in living primary T cells. eLife 2:681 [Google Scholar]
  41. Kim ST, Takeuchi K, Sun Z-YJ, Touma M, Castro CE. 41.  et al. 2009. The αβ T cell receptor is an anisotropic mechanosensor. J. Biol. Chem. 284:4531028–37 [Google Scholar]
  42. Kim ST, Touma M, Takeuchi K, Sun Z-YJ, Dave VP. 42.  et al. 2010. Distinctive CD3 heterodimeric ectodomain topologies maximize antigen-triggered activation of αβ T cell receptors. J. Immunol. 185:52951–59 [Google Scholar]
  43. Yoon ST, Dianzani U, Bottomly K, Janeway CA. 43.  1994. Both high and low avidity antibodies to the T cell receptor can have agonist or antagonist activity. Immunity 1:7563–69 [Google Scholar]
  44. Lanier LL, Ruitenberg JJ, Allison JP, Weiss A. 44.  1986. Distinct epitopes on the T cell antigen receptor of HPB-ALL tumor cells identified by monoclonal antibodies. J. Immunol. 137:72286–92 [Google Scholar]
  45. Adams JJ, Narayanan S, Liu B, Birnbaum ME, Kruse AC. 45.  2011. T cell receptor signaling is limited by docking geometry to peptide-major histocompatibility complex. Immunity 35:5681–93 [Google Scholar]
  46. Kim ST, Shin Y, Brazin K, Mallis RJ, Sun Z-YJ. 46.  et al. 2012. TCR mechanobiology: torques and tunable structures linked to early T cell signaling. Front. Immunol. 3:76 [Google Scholar]
  47. Lee KH. 47.  2003. The immunological synapse balances T cell receptor signaling and degradation. Science 302:56481218–22 [Google Scholar]
  48. Dustin ML, Chakraborty AK, Shaw AS. 48.  2010. Understanding the structure and function of the immunological synapse. Cold Spring Harb. Perspect. Biol. 2:10a002311 [Google Scholar]
  49. Das J, Khakoo SI. 49.  2015. NK cells: tuned by peptide?. Immunol. Rev. 267:1214–27 [Google Scholar]
  50. Sjolin-Goodfellow H, Frushicheva MP, Ji Q, Cheng DA, Kadlecek TA. 50.  et al. 2015. The catalytic activity of the kinase ZAP-70 mediates basal signaling and negative feedback of the T cell receptor pathway. Sci. Signal. 8:377ra49 [Google Scholar]
  51. Houtman JCD, Yamaguchi H, Barda-Saad M, Braiman A, Bowden B. 51.  et al. 2006. Oligomerization of signaling complexes by the multipoint binding of GRB2 to both LAT and SOS1. Nat. Struct. Mol. Biol. 13:9798–805 [Google Scholar]
  52. Wilson BS, Pfeiffer JR, Surviladze Z, Gaudet EA, Oliver JM. 52.  2001. High resolution mapping of mast cell membranes reveals primary and secondary domains of FcεRI and LAT. J. Cell Biol. 154:3645–58 [Google Scholar]
  53. Nag A, Monine MI, Faeder JR, Goldstein B. 53.  2009. Aggregation of membrane proteins by cytosolic cross-linkers: theory and simulation of the LAT-Grb2-SOS1 system. Biophys. J. 96:72604–23 [Google Scholar]
  54. Su X, Ditlev JA, Hui E, Xing W, Banjade S. 54.  et al. 2016. Phase separation of signaling molecules promotes T cell receptor signal transduction. Science 352:6285590–95 [Google Scholar]
  55. Wang Z, Gerstein M, Snyder M. 55.  2009. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10:157–63 [Google Scholar]
  56. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA. 56.  et al. 2011. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 29:7644–52 [Google Scholar]
  57. Risso D, Schwartz K, Sherlock G, Dudoit S. 57.  2011. GC-content normalization for RNA-Seq data. BMC Bioinform 12:1480–17 [Google Scholar]
  58. Li B, Dewey CN. 58.  2011. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform 12:1323–16 [Google Scholar]
  59. Wagner GP, Kin K, Lynch VJ. 59.  2012. Measurement of mRNA abundance using RNA-Seq data: RPKM measure is inconsistent among samples. Theory Biosci 131:4281–85 [Google Scholar]
  60. Risso D, Ngai J, Speed TP, Dudoit S. 60.  2014. Normalization of RNA-Seq data using factor analysis of control genes or samples. Nat. Biotechnol. 32:9896–902 [Google Scholar]
  61. Friedman J, Hastie T, Tibshirani R. 61.  2001. The Elements of Statistical Learning 1 Berlin: Springer [Google Scholar]
  62. James G, Witten D, Hastie T, Tibshirani R. 62.  2013. An Introduction to Statistical Learning 6 New York: Springer [Google Scholar]
  63. Shalek AK, Satija R, Adiconis X, Gertner RS, Gaublomme JT. 63.  et al. 2013. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498:7453236–40 [Google Scholar]
  64. Shalek AK, Satija R, Shuga J, Trombetta JJ, Gennert D. 64.  et al. 2014. Single cell RNA-Seq reveals dynamic paracrine control of cellular variation. Nature 510:7505363–69 [Google Scholar]
  65. Korn T, Bettelli E, Oukka M, Kuchroo VK. 65.  2009. IL-17 and Th17 cells. Annu. Rev. Immunol. 27:1485–517 [Google Scholar]
  66. Littman DR, Rudensky AY. 66.  2010. Th17 and regulatory T cells in mediating and restraining inflammation. Cell 140:6845–58 [Google Scholar]
  67. Hernández-Santos N, Gaffen SL. 67.  2012. Th17 cells in immunity to Candida albicans. . Cell Host Microbe 11:5425–35 [Google Scholar]
  68. Lee Y, Awasthi A, Yosef N, Quintana FJ, Xiao S. 68.  et al. 2012. Induction and molecular signature of pathogenic TH17 cells. Nat. Immunol. 13:10991–99 [Google Scholar]
  69. Gaublomme JT, Yosef N, Lee Y, Gertner RS, Yang LV. 69.  et al. 2015. Single-cell genomics unveils critical regulators of Th17 cell pathogenicity. Cell 163:61400–12 [Google Scholar]
  70. Wang C, Yosef N, Gaublomme J, Wu C, Lee Y. 70.  et al. 2015. CD5L/AIM regulates lipid biosynthesis and restrains Th17 cell pathogenicity. Cell 163:61413–27 [Google Scholar]
  71. Eisen HN, Siskind GW. 71.  1964. Variations in affinities of antibodies during the immune response. Biochemistry 3:7996–1008 [Google Scholar]
  72. Steiner LA, Eisen HN. 72.  1967. The relative affinity of antibodies synthesized in the secondary response. J. Exp. Med. 126:61185–205 [Google Scholar]
  73. Victora GD, Nussenzweig MC. 73.  2012. Germinal centers. Annu. Rev. Immunol. 30:1429–57 [Google Scholar]
  74. De Silva NS, Klein U. 74.  2015. Dynamics of B cells in germinal centres. Nat. Rev. Immunol. 15:3137–48 [Google Scholar]
  75. Tas JMJ, Mesin L, Pasqual G, Targ S, Jacobsen JT. 75.  et al. 2016. Visualizing antibody affinity maturation in germinal centers. Science 351:62771048–54 [Google Scholar]
  76. Perelson AS, Oster GF. 76.  1979. Theoretical studies of clonal selection: minimal antibody repertoire size and reliability of self-non-self discrimination. J. Theor. Biol. 81:4645–70 [Google Scholar]
  77. Kepler TB, Perelson AS. 77.  1993. Cyclic re-entry of germinal center B cells and the efficiency of affinity maturation. Immunol. Today 14:8412–15 [Google Scholar]
  78. Oprea M, Perelson AS. 78.  1997. Somatic mutation leads to efficient affinity maturation when centrocytes recycle back to centroblasts. J. Immunol. 158:115155–62 [Google Scholar]
  79. Allen CDC, Okada T, Tang HL, Cyster JG. 79.  2007. Imaging of germinal center selection events during affinity maturation. Science 315:5811528–31 [Google Scholar]
  80. Schwickert TA, Lindquist RL, Shakhar G, Livshits G, Skokos D. 80.  et al. 2007. In vivo imaging of germinal centres reveals a dynamic open structure. Nature 446:713183–87 [Google Scholar]
  81. Hauser AE, Junt T, Mempel TR, Sneddon MW, Kleinstein SH. 81.  et al. 2007. Definition of germinal-center B cell migration in vivo reveals predominant intrazonal circulation patterns. Immunity 26:5655–67 [Google Scholar]
  82. Meyer-Hermann M, Deutsch A, Or-Guil M. 82.  2001. Recycling probability and dynamical properties of germinal center reactions. J. Theor. Biol. 210:3265–85 [Google Scholar]
  83. IBER D, Maini PK. 83.  2002. A mathematical model for germinal centre kinetics and affinity maturation. J. Theor. Biol. 219:2153–75 [Google Scholar]
  84. Meyer-Hermann ME, Maini PK, Iber D. 84.  2006. An analysis of B cell selection mechanisms in germinal centers. Math. Med. Biol 233255–77 [Google Scholar]
  85. Zhang J, Shakhnovich EI. 85.  2010. Optimality of mutation and selection in germinal centers. PLOS Comput. Biol. 6:6e1000800 [Google Scholar]
  86. Meyer-Hermann M, Mohr E, Pelletier N, Zhang Y, Victora GD, Toellner K-M. 86.  2012. A theory of germinal center B cell selection, division, and exit. Cell Rep 2:1162–74 [Google Scholar]
  87. Takala SL, Coulibaly D, Thera MA, Batchelor AH, Cummings MP. 87.  et al. 2009. Extreme polymorphism in a vaccine antigen and risk of clinical malaria: implications for vaccine development. Sci. Trans. Med. 1:22ra5 [Google Scholar]
  88. Burton DR, Hangartner L. 88.  2016. Broadly neutralizing antibodies to HIV and their role in vaccine design. Annu. Rev. Immunol. 34:1635–59 [Google Scholar]
  89. Pappas L, Foglierini M, Piccoli L, Kallewaard NL, Turrini F. 89.  et al. 2014. Rapid development of broadly influenza neutralizing antibodies through redundant mutations. Nature 516:7531418–22 [Google Scholar]
  90. Julien JP, Cupo A, Sok D, Stanfield RL, Lyumkis D. 90.  et al. 2013. Crystal structure of a soluble cleaved HIV-1 envelope trimer. Science 342:61651477–83 [Google Scholar]
  91. Lyumkis D, Julien JP, de Val N, Cupo A, Potter CS. 91.  et al. 2013. Cryo-EM structure of a fully glycosylated soluble cleaved HIV-1 envelope trimer. Science 342:61651484–90 [Google Scholar]
  92. Liao H-X, Lynch R, Zhou T, Gao F, Alam SM. 92.  et al. 2013. Co-evolution of a broadly neutralizing HIV-1 antibody and founder virus. Nature 496:7446469–76 [Google Scholar]
  93. Doria-Rose NA, Schramm CA, Gorman J, Moore PL, Bhiman JN. 93.  et al. 2014. Developmental pathway for potent V1V2-directed HIV-neutralizing antibodies. Nature 509:749855–62 [Google Scholar]
  94. Wu X, Zhang Z, Schramm CA, Joyce MG, Do Kwon Y. 94.  et al. 2015. Maturation and diversity of the VRC01-antibody lineage over 15 years of chronic HIV-1 infection. Cell 161:3470–85 [Google Scholar]
  95. Bhiman JN, Anthony C, Doria-Rose NA, Karimanzira O, Schramm CA. 95.  et al. 2015. Viral variants that initiate and drive maturation of V1V2-directed HIV-1 broadly neutralizing antibodies. Nat. Med. 21:111332–36 [Google Scholar]
  96. Chaudhury S, Reifman J, Wallqvist A. 96.  2014. Simulation of B cell affinity maturation explains enhanced antibody cross-reactivity induced by the polyvalent malaria vaccine AMA1. J. Immunol. 193:52073–86 [Google Scholar]
  97. Wang S, Mata-Fink J, Kriegsman B, Hanson M, Irvine DJ. 97.  et al. 2015. Manipulating the selection forces during affinity maturation to generate cross-reactive HIV antibodies. Cell 160:4785–97 [Google Scholar]
  98. Luo S, Perelson AS. 98.  2015. Competitive exclusion by autologous antibodies can prevent broad HIV-1 antibodies from arising. PNAS 112:3711654–59 [Google Scholar]
  99. Childs LM, Baskerville EB, Cobey S. 99.  2015. Trade-offs in antibody repertoires to complex antigens. Phil. Trans. R. Soc. B 370:167620140245 [Google Scholar]
  100. Jardine J, Julien JP, Menis S, Ota T, Kalyuzhniy O. 100.  et al. 2013. Rational HIV immunogen design to target specific germline B cell receptors. Science 340:6133711–16 [Google Scholar]
  101. Jardine JG, Ota T, Sok D, Pauthner M, Kulp DW. 101.  et al. 2015. Priming a broadly neutralizing antibody response to HIV-1 using a germline-targeting immunogen. Science 349:6244156–61 [Google Scholar]
  102. Jardine JG, Kulp DW, Havenar-Daughton C, Sarkar A, Briney B. 102.  et al. 2016. HIV-1 broadly neutralizing antibody precursor B cells revealed by germline-targeting immunogen. Science 351:62801458–63 [Google Scholar]
  103. Briney B, Sok D, Jardine JG, Kulp DW, Skog P. 103.  et al. 2016. Tailored immunogens direct affinity maturation toward HIV neutralizing antibodies. Cell 166:61459–70.e11 [Google Scholar]
  104. Escolano A, Steichen JM, Dosenovic P, Kulp DW, Golijanin J. 104.  et al. 2016. Sequential immunization elicits broadly neutralizing anti-HIV-1 antibodies in Ig knockin mice. Cell 166:61445–58.12 [Google Scholar]
  105. Dutta S, Dlugosz LS, Drew DR, Ge X, Ababacar D. 105.  et al. 2013. Overcoming antigenic diversity by enhancing the immunogenicity of conserved epitopes on the malaria vaccine candidate apical membrane antigen-1. PLOS Pathog 9:12e1003840 [Google Scholar]
  106. Deem MW, Lee HY. 106.  2003. Sequence space localization in the immune system response to vaccination and disease. Phys. Rev. Lett. 91:6068101–4 [Google Scholar]
  107. Heo M, Zeldovich KB, Shakhnovich EI. 107.  2011. Diversity against adversity: how adaptive immune system evolves potent antibodies. J. Stat. Phys. 144:2241–67 [Google Scholar]
  108. Sanders RW, Derking R, Cupo A, Julien J-P, Yasmeen A. 108.  et al. 2013. A next-generation cleaved, soluble HIV-1 Env trimer, BG505 SOSIP.664 gp140, expresses multiple epitopes for broadly neutralizing but not non-neutralizing antibodies. PLOS Pathog 9:9e1003618 [Google Scholar]
  109. de Taeye SW, Ozorowski G, Torrents de la Peña A, Guttman M, Julien J-P. 109.  et al. 2015. Immunogenicity of stabilized HIV-1 envelope trimers with reduced exposure of non-neutralizing epitopes. Cell 163:71702–15 [Google Scholar]
  110. Murphy K, Weaver C. 110.  2016. Janeway's Immunobiology New York: Garland Sci, 9th ed.. [Google Scholar]
  111. Hogquist KA, Jameson SC. 111.  2014. The self-obsession of T cells: How TCR signaling thresholds affect fate “decisions” and effector function. Nat. Immunol. 15:9815–23 [Google Scholar]
  112. Klein L, Kyewski B, Allen PM, Hogquist KA. 112.  2014. Positive and negative selection of the T cell repertoire: What thymocytes see (and don't see). Nat. Rev. Immunol. 14:6377–91 [Google Scholar]
  113. Vrisekoop N, Monteiro JP, Mandl JN, Germain RN. 113.  2014. Revisiting thymic positive selection and the mature T cell repertoire for antigen. Immunity 41:2181–90 [Google Scholar]
  114. Jenkins MK, Chu HH, McLachlan JB, Moon JJ. 114.  2010. On the composition of the preimmune repertoire of T cells specific for peptide-major histocompatibility complex ligands. Annu. Rev. Immunol. 28:1275–94 [Google Scholar]
  115. Eisen HN, Chakraborty AK. 115.  2010. Evolving concepts of specificity in immune reactions. PNAS 107:5222373–80 [Google Scholar]
  116. Huseby ES, White J, Crawford F, Vass T, Becker D. 116.  et al. 2005. How the T cell repertoire becomes peptide and MHC specific. Cell 122:2247–60 [Google Scholar]
  117. Huseby ES, Crawford F, White J, Marrack P, Kappler JW. 117.  2006. Interface-disrupting amino acids establish specificity between T cell receptors and complexes of major histocompatibility complex and peptide. Nat. Immunol. 7:111191–99 [Google Scholar]
  118. Ignatowicz L, Kappler J, Marrack P. 118.  1996. The repertoire of T cells shaped by a single MHC/peptide ligand. Cell 84:4521–29 [Google Scholar]
  119. Kosmrlj A, Jha AK, Huseby ES, Kardar M, Chakraborty AK. 119.  2008. How the thymus designs antigen-specific and self-tolerant T cell receptor sequences. PNAS 105:4316671–76 [Google Scholar]
  120. Chao DL, Davenport MP, Forrest S, Perelson AS. 120.  2005. The effects of thymic selection on the range of T cell cross-reactivity. Eur. J. Immunol. 35:123452–59 [Google Scholar]
  121. 121.  Deleted in proof
  122. Košmrlj A, Chakraborty AK, Kardar M, Shakhnovich EI. 122.  2009. Thymic selection of T-cell receptors as an extreme value problem. Phys. Rev. Lett. 103:6068103 [Google Scholar]
  123. Chakraborty AK, Kosmrlj A. 123.  2010. Statistical mechanical concepts in immunology. Annu. Rev. Phys. Chem. 61:1283–303 [Google Scholar]
  124. Stadinski BD, Shekhar K, Gomez-Tourino I, Jung J, Sasaki K. 124.  et al. 2016. Hydrophobic CDR3 residues promote the development of self-reactive T cells. Nat. Immunol. 17:8946–55 [Google Scholar]
  125. Suri A, Levisetti MG, Unanue ER. 125.  2008. Do the peptide-binding properties of diabetogenic class II molecules explain autoreactivity?. Curr. Opin. Immunol. 20:1105–10 [Google Scholar]
  126. Kosmrlj A, Read EL, Qi Y, Allen TM, Altfeld M. 126.  et al. 2010. Effects of thymic selection of the T-cell repertoire on HLA classI-associated control of HIV infection. Nature 465:7296350–54 [Google Scholar]
  127. Peters B, Sidney J, Bourne P, Bui H-H, Buus S. 127.  et al. 2005. The Immune Epitope Database and Analysis Resource: from vision to blueprint. PLOS Biol 3:3e91–e93 [Google Scholar]
  128. Rao X, Fontaine Costa AICA, van Baarle D, Kesmir C. 128.  2009. A comparative study of HLA binding affinity and ligand diversity: implications for generating immunodominant CD8+ T cell responses. J. Immunol. 182:31526–32 [Google Scholar]
  129. Kim AY, Kuntzen T, Timm J, Nolan BE, Baca MA. 129.  et al. 2011. Spontaneous control of HCV is associated with expression of HLA-B*57 and preservation of targeted epitopes. Gastroenterology 140:2686–96.e1 [Google Scholar]
  130. Murugan A, Mora T, Walczak AM, Callan CG. 130.  2012. Statistical inference of the generation probability of T-cell receptors from sequence repertoires. PNAS 109:4016161–66 [Google Scholar]
  131. Elhanati Y, Murugan A, Callan CG Jr, Mora T, Walczak AM. 131.  2014. Quantifying selection in immune receptor repertoires. PNAS 111:279875–80 [Google Scholar]
  132. Brodin P, Jojic V, Gao T, Bhattacharya S, Angel CJL. 132.  et al. 2015. Variation in the human immune system is largely driven by non-heritable influences. Cell 160:1–237–47 [Google Scholar]
  133. Carr EJ, Dooley J, Garcia-Perez JE, Lagou V, Lee JC. 133.  et al. 2016. The cellular composition of the human immune system is shaped by age and cohabitation. Nat. Immunol. 17:4461–68 [Google Scholar]
  134. Tsang JS, Schwartzberg PL, Kotliarov Y, Biancotto A, Xie Z. 134.  et al. 2014. Global analyses of human immune variation reveal baseline predictors of postvaccination responses. Cell 157:2499–513 [Google Scholar]
  135. Roederer M, Quaye L, Mangino M, Beddall MH, Mahnke Y. 135.  et al. 2015. The genetic architecture of the human immune system: a bioresource for autoimmunity and disease pathogenesis. Cell 161:2387–403 [Google Scholar]
  136. Orrù V, Steri M, Sole G, Sidore C, Virdis F. 136.  et al. 2013. Genetic variants regulating immune cell levels in health and disease. Cell 155:1242–56 [Google Scholar]
  137. Maecker HT, McCoy JP, Nussenblatt R. 137.  2012. Standardizing immunophenotyping for the Human Immunology Project. Nat. Rev. Immunol. 12:3191–200 [Google Scholar]
  138. Nakaya HI, Wrammert J, Lee EK, Racioppi L, Marie-Kunze S. 138.  et al. 2011. Systems biology of vaccination for seasonal influenza in humans. Nat. Immunol. 12:8786–95 [Google Scholar]
  139. Chattopadhyay PK, Roederer M. 139.  2015. A mine is a terrible thing to waste: high content, single cell technologies for comprehensive immune analysis. Am. J. Transplant. 15:51155–61 [Google Scholar]
  140. Bandura DR, Baranov VI, Ornatsky OI, Antonov A, Kinach R. 140.  et al. 2009. Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. Anal. Chem. 81:166813–22 [Google Scholar]
  141. Spitzer MH, Nolan GP. 141.  2016. Mass cytometry: single cells, many features. Cell 165:4780–91 [Google Scholar]
  142. Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K. 142.  et al. 2015. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161:51202–14 [Google Scholar]
  143. Qiu P, Simonds EF, Bendall SC, Gibbs KD, Bruggner RV. 143.  et al. 2011. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat. Biotech. 29:10886–91 [Google Scholar]
  144. Bendall SC, Simonds EF, Qiu P, Amir EAD, Krutzik PO. 144.  et al. 2011. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332:6030687–96 [Google Scholar]
  145. Van Der Maaten L, Postma E, Van den Herik J. 145.  2009. Dimensionality reduction: A comparative. J. Mach. Learn. Res. 10:66–71 [Google Scholar]
  146. Jolliffe I. 146.  2002. Principal Component Analysis New York: Springer [Google Scholar]
  147. Newell EW, Sigal N, Bendall SC, Nolan GP, Davis MM. 147.  2012. Cytometry by time-of-flight shows combinatorial cytokine expression and virus-specific cell niches within a continuum of CD8+ T cell phenotypes. Immunity 36:1142–52 [Google Scholar]
  148. Shekhar K, Brodin P, Davis MM, Chakraborty AK. 148.  2014. Automatic classification of cellular expression by nonlinear stochastic embedding (ACCENSE). PNAS 111:1202–7 [Google Scholar]
  149. Amir E-AD, Davis KL, Tadmor MD, Simonds EF, Levine JH. 149.  et al. 2013. ViSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat. Biotech. 31:6545–52 [Google Scholar]
  150. Maaten LVD, Hinton G. 150.  2008. Visualizing data using t-SNE. J. Mach. Learn. Res. 9:Nov2579–605 [Google Scholar]
  151. Evans DM, Zhu G, Duffy DL, Frazer IH, Montgomery GW, Martin NG. 151.  2004. A major quantitative trait locus for CD4-CD8 ratio is located on chromosome 11. Genes Immun 5:7548–52 [Google Scholar]
  152. de Craen AJM, Posthuma D, Remarque EJ, van den Biggelaar AHJ, Westendorp RGJ, Boomsma DI. 152.  2005. Heritability estimates of innate immunity: An extended twin study. Genes Immun 6:2167–70 [Google Scholar]
  153. Shaw AC, Goldstein DR, Montgomery RR. 153.  2013. Age-dependent dysregulation of innate immunity. Nat. Rev. Immunol. 13:12875–87 [Google Scholar]
  154. Giefing-Kröll C, Berger P, Lepperdinger G, Grubeck-Loebenstein B. 154.  2015. How sex and age affect immune responses, susceptibility to infections, and response to vaccination. Aging Cell 14:3309–21 [Google Scholar]
  155. Sylwester AW, Mitchell BL, Edgar JB, Taormina C, Pelte C. 155.  et al. 2005. Broadly targeted human cytomegalovirus-specific CD4+ and CD8+ T cells dominate the memory compartments of exposed subjects. J. Exp. Med. 202:5673–85 [Google Scholar]
  156. Chidrawar S, Khan N, Wei W, McLarnon A, Smith N. 156.  et al. 2009. Cytomegalovirus-seropositivity has a profound influence on the magnitude of major lymphoid subsets within healthy individuals. Clin. Exp. Immunol. 155:3423–32 [Google Scholar]
  157. Hooper LV, Littman DR, Macpherson AJ. 157.  2012. Interactions between the microbiota and the immune system. Science 336:60861268–73 [Google Scholar]
  158. Furman D, Jojic V, Kidd B, Shen-Orr S, Price J. 158.  et al. 2013. Apoptosis and other immune biomarkers predict influenza vaccine responsiveness. Mol. Syst. Biol. 9:1659–59 [Google Scholar]
  159. De Gregorio E, Rappuoli R. 159.  2014. From empiricism to rational design: A personal perspective of the evolution of vaccine development. Nat. Rev. Immunol. 14:7505–14 [Google Scholar]
  160. Ho DD, Neumann AU, Perelson AS, Chen W, Leonard JM, Markowitz M. 160.  1995. Rapid turnover of plasma virions and CD4 lymphocytes in HIV-1 infection. Nature 373:6510123–26 [Google Scholar]
  161. Wei X, Ghosh SK, Taylor ME, Johnson VA, Emini EA. 161.  et al. 1995. Viral dynamics in human immunodeficiency virus type 1 infection. Nature 373:6510117–22 [Google Scholar]
  162. Perelson AS, Neumann AU, Markowitz M, Leonard JM, Ho DD. 162.  1996. HIV-1 dynamics in vivo: virion clearance rate, infected cell life-span, and viral generation time. Science 271:52551582–86 [Google Scholar]
  163. Perelson AS, Nelson PW. 163.  1999. Mathematical analysis of HIV-I: dynamics in vivo. SIAM Rev 41:3–44 [Google Scholar]
  164. Kelleher AD, Long C, Holmes EC, Allen RL, Wilson J. 164.  et al. 2001. Clustered mutations in HIV-1 Gag are consistently required for escape from Hla-B27-restricted cytotoxic T lymphocyte responses. J. Exp. Med. 193:3375–86 [Google Scholar]
  165. Martinez-Picado J, Prado JG, Fry EE, Pfafferott K, Leslie A. 165.  et al. 2006. Fitness cost of escape mutations in p24 Gag in association with control of human immunodeficiency virus type 1. J. Virol. 80:73617–23 [Google Scholar]
  166. Brockman MA, Schneidewind A, Lahaie M, Schmidt A, Miura T. 166.  et al. 2007. Escape and compensation from early HLA-B57-mediated cytotoxic T-lymphocyte pressure on human immunodeficiency virus type 1 Gag alter capsid interactions with cyclophilin A. J. Virol. 81:2212608–18 [Google Scholar]
  167. Hinkley T, Martins J, Chappey C, Haddad M, Stawiski E. 167.  et al. 2011. A systems analysis of mutational effects in HIV-1 protease and reverse transcriptase. Nat. Genet. 43:5487–89 [Google Scholar]
  168. Kouyos RD, Wyl von V, Hinkley T, Petropoulos CJ, Haddad M. 168.  et al. 2011. Assessing predicted HIV-1 replicative capacity in a clinical setting. PLOS Pathog 7:11e1002321–26 [Google Scholar]
  169. Ferguson AL, Mann JK, Omarjee S, Ndung'u T, Walker BD, Chakraborty AK. 169.  2013. Translating HIV sequences into quantitative fitness landscapes predicts viral vulnerabilities for rational immunogen design. Immunity 38:3606–17 [Google Scholar]
  170. Mann JK, Barton JP, Ferguson AL, Omarjee S, Walker BD. 170.  et al. 2014. The fitness landscape of HIV-1 Gag: advanced modeling approaches and validation of model predictions by in vitro testing. PLOS Comput. Biol. 10:8e1003776–11 [Google Scholar]
  171. Łuksza M, Lässig M. 171.  2014. A predictive fitness model for influenza. Nature 507:749057–61 [Google Scholar]
  172. Neher RA, Russell CA, Shraiman BI. 172.  2014. Predicting evolution from the shape of genealogical trees. eLife 3:e01914–28 [Google Scholar]
  173. Neher RA, Bedford T, Daniels RS, Russell CA, Shraiman BI. 173.  2016. Prediction, dynamics, and visualization of antigenic phenotypes of seasonal influenza viruses. PNAS 113:12E1701–9 [Google Scholar]
  174. Barton JP, De Leonardis E, Coucke A, Cocco S. 174.  2016. ACE: adaptive cluster expansion for maximum entropy graphical model inference. Bioinformatics 32:3089–97 [Google Scholar]
  175. Barton JP, Goonetilleke N, Butler TC, Walker BD, McMichael AJ, Chakraborty AK. 175.  2016. Relative rate and location of intra-host HIV evolution to evade cellular immunity are predictable. Nat. Commun. 7:11660–27 [Google Scholar]
  176. Sella G, Hirsh AE. 176.  2005. The application of statistical physics to evolutionary biology. PNAS 102:279541–46 [Google Scholar]
  177. Friedrich TC, Dodds EJ, Yant LJ, Vojnov L, Rudersdorf R. 177.  et al. 2004. Reversion of CTL escape-variant immunodeficiency viruses in vivo. Nat. Med. 10:3275–81 [Google Scholar]
  178. Korber B. 178.  2001. Evolutionary and immunological implications of contemporary HIV-1 variation. Br. Med. Bull 58119–42 [Google Scholar]
  179. Korber B, Gaschen B, Yusim K, Thakallapally R, Kesmir C, Detours V. 179.  2001. Evolutionary and immunological implications of contemporary HIV-1 variation. Br. Med. Bull 58119–42 [Google Scholar]
  180. Moore CB. 180.  2002. Evidence of HIV-1 adaptation to HLA-restricted immune responses at a population level. Science 296:55721439–43 [Google Scholar]
  181. Zanini F, Brodin J, Thebo L, Lanz C, Bratt G. 181.  et al. 2015. Population genomics of intrapatient HIV-1 evolution. eLife 4:13239–39 [Google Scholar]
  182. Moradigaravand D, Kouyos R, Hinkley T, Haddad M, Petropoulos CJ. 182.  et al. 2014. Recombination accelerates adaptation on a large-scale empirical fitness landscape in HIV-1. PLOS Genet 10:e1004439 [Google Scholar]
  183. Yang W-L, Kouyos RD, Böni J, Yerly S, Klimkait T. 183.  et al. 2015. Persistence of transmitted HIV-1 drug resistance mutations associated with fitness costs and viral genetic backgrounds. PLOS Pathog 11:3e1004722–29 [Google Scholar]
  184. Butler TC, Barton JP, Kardar M, Chakraborty AK. 184.  2016. Identification of drug resistance mutations in HIV from constraints on natural evolution. Phys. Rev. E 93:2022412–15 [Google Scholar]
  185. Deeks SG, Walker BD. 185.  2007. Human immunodeficiency virus controllers: mechanisms of durable virus control in the absence of antiretroviral therapy. Immunity 27:3406–16 [Google Scholar]
  186. Dahirel V, Shekhar K, Pereyra F, Miura T, Artyomov M. 186.  et al. 2011. Coordinate linkage of HIV evolution reveals regions of immunological vulnerability. PNAS 108:2811530–35 [Google Scholar]
  187. Liu MKP, Hawkins N, Ritchie AJ, Ganusov VV, Whale V. 187.  et al. 2012. Vertical T cell immunodominance and epitope entropy determine HIV-1 escape. J. Clin. Investig. 123:1–18 [Google Scholar]
  188. Cocco S, Monasson R. 188.  2011. Adaptive cluster expansion for inferring Boltzmann machines with noisy data. Phys. Rev. Lett. 106:9090601–13 [Google Scholar]
  189. Pandit A, de Boer RJ. 189.  2016. Reliable reconstruction of HIV-1 whole genome haplotypes reveals clonal interference and genetic hitchhiking among immune escape variants. Retrovirology 11:156–38 [Google Scholar]
  190. Borthwick N, Ahmed T, Ondondo B, Hayes P, Rose A. 190.  et al. 2013. Vaccine-elicited human T cells recognizing conserved protein regions inhibit HIV-1. Mol. Ther. 22:2464–75 [Google Scholar]
/content/journals/10.1146/annurev-immunol-041015-055325
Loading
/content/journals/10.1146/annurev-immunol-041015-055325
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error