1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Immunology
  4. Volume 32, 2014
  5. Article

Annual Review of Immunology

Volume 32, 2014

Systems-Level Analysis of Innate Immunity

Abstract

Systems-level analysis of biological processes strives to comprehensively and quantitatively evaluate the interactions between the relevant molecular components over time, thereby enabling development of models that can be employed to ultimately predict behavior. Rapid development in measurement technologies (omics), when combined with the accessible nature of the cellular constituents themselves, is allowing the field of innate immunity to take significant strides toward this lofty goal. In this review, we survey exciting results derived from systems biology analyses of the immune system, ranging from gene regulatory networks to influenza pathogenesis and systems vaccinology.

Keyword(s): influenza, lipidomics, proteomics, systems biology, systems vaccinology, transcriptomics
Loading

Article metrics loading...

/content/journals/10.1146/annurev-immunol-032713-120254
2014-03-21
2024-04-26
Loading full text...

Full text loading...

/deliver/fulltext/immunol/32/1/annurev-immunol-032713-120254.html?itemId=/content/journals/10.1146/annurev-immunol-032713-120254&mimeType=html&fmt=ahah

Literature Cited

  1. Ramsey SA, Klemm SL, Zak DE, Kennedy KA, Thorsson V. 1.  et al. 2008. Uncovering a macrophage transcriptional program by integrating evidence from motif scanning and expression dynamics. PLoS Comput. Biol. 4:e1000021 [Google Scholar]
  2. Germain RN, Meier-Schellersheim M, Nita-Lazar A, Fraser ID. 2.  2011. Systems biology in immunology: a computational modeling perspective. Annu. Rev. Immunol. 29:527–85 [Google Scholar]
  3. Poultney CS, Greenfield A, Bonneau R. 3.  2012. Integrated inference and analysis of regulatory networks from multi-level measurements. Methods Cell Biol. 110:19–56 [Google Scholar]
  4. Ghosh S, Matsuoka Y, Asai Y, Hsin KY, Kitano H. 4.  2011. Software for systems biology: from tools to integrated platforms. Nat. Rev. Genet. 12:821–32 [Google Scholar]
  5. De Smet R, Marchal K. 5.  2010. Advantages and limitations of current network inference methods. Nat. Rev. Microbiol. 8:717–29 [Google Scholar]
  6. Ciofani M, Madar A, Galan C, Sellars M, Mace K. 6.  et al. 2012. A validated regulatory network for Th17 cell specification. Cell 151:289–303 [Google Scholar]
  7. Newell EW, Sigal N, Bendall SC, Nolan GP, Davis MM. 7.  2012. Cytometry by time-of-flight shows combinatorial cytokine expression and virus-specific cell niches within a continuum of CD8+ T cell phenotypes. Immunity 36:142–52 [Google Scholar]
  8. Zhang JA, Mortazavi A, Williams BA, Wold BJ, Rothenberg EV. 8.  2012. Dynamic transformations of genome-wide epigenetic marking and transcriptional control establish T cell identity. Cell 149:467–82 [Google Scholar]
  9. O'Neill LA, Bowie AG. 9.  2007. The family of five: TIR-domain-containing adaptors in Toll-like receptor signalling. Nat. Rev. Immunol. 7:353–64 [Google Scholar]
  10. Ishii KJ, Koyama S, Nakagawa A, Coban C, Akira S. 10.  2008. Host innate immune receptors and beyond: making sense of microbial infections. Cell Host Microbe 3:352–63 [Google Scholar]
  11. Paludan SR, Bowie AG. 11.  2013. Immune sensing of DNA. Immunity 38:870–80 [Google Scholar]
  12. Underhill DM, Ozinsky A. 12.  2002. Phagocytosis of microbes: complexity in action. Annu. Rev. Immunol. 20:825–52 [Google Scholar]
  13. Trinchieri G, Sher A. 13.  2007. Cooperation of Toll-like receptor signals in innate immune defence. Nat. Rev. Immunol. 7:179–90 [Google Scholar]
  14. Goodridge HS, Underhill DM. 14.  2008. Fungal recognition by TLR2 and Dectin-1. Handb. Exp. Pharmacol. 183:87–109 [Google Scholar]
  15. Aderem AA, Cohen DS, Wright SD, Cohn ZA. 15.  1986. Bacterial lipopolysaccharides prime macrophages for enhanced release of arachidonic acid metabolites. J. Exp. Med. 164:165–79 [Google Scholar]
  16. Blander JM, Medzhitov R. 16.  2006. Toll-dependent selection of microbial antigens for presentation by dendritic cells. Nature 440:808–12 [Google Scholar]
  17. Mohr S, Bakal C, Perrimon N. 17.  2010. Genomic screening with RNAi: results and challenges. Annu. Rev. Biochem. 79:37–64 [Google Scholar]
  18. Beutler B, Moresco EM. 18.  2008. The forward genetic dissection of afferent innate immunity. Curr. Top. Microbiol. Immunol. 321:3–26 [Google Scholar]
  19. Cook MC, Vinuesa CG, Goodnow CC. 19.  2006. ENU-mutagenesis: insight into immune function and pathology. Curr. Opin. Immunol. 18:627–33 [Google Scholar]
  20. Bolouri H, Davidson EH. 20.  2003. Transcriptional regulatory cascades in development: initial rates, not steady state, determine network kinetics. Proc. Natl. Acad. Sci. USA 100:9371–76 [Google Scholar]
  21. Smith J, Theodoris C, Davidson EH. 21.  2007. A gene regulatory network subcircuit drives a dynamic pattern of gene expression. Science 318:794–97 [Google Scholar]
  22. Gilchrist M, Thorsson V, Li B, Rust AG, Korb M. 22.  et al. 2006. Systems biology approaches identify ATF3 as a negative regulator of Toll-like receptor 4. Nature 441:173–78 [Google Scholar]
  23. Litvak V, Ramsey SA, Rust AG, Zak DE, Kennedy KA. 23.  et al. 2009. Function of C/EBPδ in a regulatory circuit that discriminates between transient and persistent TLR4-induced signals. Nat. Immunol. 10:437–43 [Google Scholar]
  24. Alon U. 24.  2007. Network motifs: theory and experimental approaches. Nat. Rev. Genet. 8:450–61 [Google Scholar]
  25. Litvak V, Ratushny AV, Lampano AE, Schmitz F, Huang AC. 25.  et al. 2012. A FOXO3-IRF7 gene regulatory circuit limits inflammatory sequelae of antiviral responses. Nature 490:421–25 [Google Scholar]
  26. Seymour RE, Hasham MG, Cox GA, Shultz LD, Hogenesch H. 26.  et al. 2007. Spontaneous mutations in the mouse Sharpin gene result in multiorgan inflammation, immune system dysregulation and dermatitis. Genes Immun. 8:416–21 [Google Scholar]
  27. Siggs OM, Berger M, Krebs P, Arnold CN, Eidenschenk C. 27.  et al. 2010. A mutation of Ikbkg causes immune deficiency without impairing degradation of IκBα. Proc. Natl. Acad. Sci. USA 107:3046–51 [Google Scholar]
  28. Zak DE, Schmitz F, Gold ES, Diercks AH, Peschon JJ. 28.  et al. 2011. Systems analysis identifies an essential role for SHANK-associated RH domain-interacting protein (SHARPIN) in macrophage Toll-like receptor 2 (TLR2) responses. Proc. Natl. Acad. Sci. USA 108:11536–41 [Google Scholar]
  29. Amit I, Garber M, Chevrier N, Leite AP, Donner Y. 29.  et al. 2009. Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses. Science 326:257–63 [Google Scholar]
  30. Kulkarni MM. 30.  2011. Digital multiplexed gene expression analysis using the NanoString nCounter system. Curr. Protoc. Mol. Biol. 94:25B.10 [Google Scholar]
  31. Garber M, Yosef N, Goren A, Raychowdhury R, Thielke A. 31.  et al. 2012. A high-throughput chromatin immunoprecipitation approach reveals principles of dynamic gene regulation in mammals. Mol. Cell 47:810–22 [Google Scholar]
  32. Stender JD, Glass CK. 32.  2013. Epigenomic control of the innate immune response. Curr. Opin. Pharmacol. 13:582–87 [Google Scholar]
  33. Kaikkonen MU, Spann NJ, Heinz S, Romanoski CE, Allison KA. 33.  et al. 2013. Remodeling of the enhancer landscape during macrophage activation is coupled to enhancer transcription. Mol. Cell 51:310–25 [Google Scholar]
  34. Chevrier N, Mertins P, Artyomov MN, Shalek AK, Iannacone M. 34.  et al. 2011. Systematic discovery of TLR signaling components delineates viral-sensing circuits. Cell 147:853–67 [Google Scholar]
  35. de Veer MJ, Holko M, Frevel M, Walker E, Der S. 35.  et al. 2001. Functional classification of interferon-stimulated genes identified using microarrays. J. Leukoc. Biol. 69:912–20 [Google Scholar]
  36. Schoggins JW, Wilson SJ, Panis M, Murphy MY, Jones CT. 36.  et al. 2011. A diverse range of gene products are effectors of the type I interferon antiviral response. Nature 472:481–85 [Google Scholar]
  37. Versteeg GA, Rajsbaum R, Sánchez-Aparicio MT, Maestre AM, Valdiviezo J. 37.  et al. 2013. The E3-ligase TRIM family of proteins regulates signaling pathways triggered by innate immune pattern-recognition receptors. Immunity 38:384–98 [Google Scholar]
  38. McNab FW, Rajsbaum R, Stoye JP, O'Garra A. 38.  2011. Tripartite-motif proteins and innate immune regulation. Curr. Opin. Immunol. 23:46–56 [Google Scholar]
  39. Ozato K, Shin DM, Chang TH, Morse HC III. 39.  2008. TRIM family proteins and their emerging roles in innate immunity. Nat. Rev. Immunol. 8:849–60 [Google Scholar]
  40. Li S, Wang L, Berman M, Kong YY, Dorf ME. 40.  2011. Mapping a dynamic innate immunity protein interaction network regulating type I interferon production. Immunity 35:426–40 [Google Scholar]
  41. Lee MN, Roy M, Ong SE, Mertins P, Villani AC. 41.  et al. 2013. Identification of regulators of the innate immune response to cytosolic DNA and retroviral infection by an integrative approach. Nat. Immunol. 14:179–85 [Google Scholar]
  42. Elkon R, Linhart C, Halperin Y, Shiloh Y, Shamir R. 42.  2007. Functional genomic delineation of TLR-induced transcriptional networks. BMC Genomics 8:394 [Google Scholar]
  43. Ramsey SA, Knijnenburg TA, Kennedy KA, Zak DE, Gilchrist M. 43.  et al. 2010. Genome-wide histone acetylation data improve prediction of mammalian transcription factor binding sites. Bioinformatics 26:2071–75 [Google Scholar]
  44. Li F, Thiele I, Jamshidi N, Palsson BO. 44.  2009. Identification of potential pathway mediation targets in Toll-like receptor signaling. PLoS Comput. Biol. 5:e1000292 [Google Scholar]
  45. Bordbar A, Mo ML, Nakayasu ES, Schrimpe-Rutledge AC, Kim YM. 45.  et al. 2012. Model-driven multi-omic data analysis elucidates metabolic immunomodulators of macrophage activation. Mol. Syst. Biol. 8:558 [Google Scholar]
  46. Bordbar A, Lewis NE, Schellenberger J, Palsson BO, Jamshidi N. 46.  2010. Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions. Mol. Syst. Biol. 6:422 [Google Scholar]
  47. Kalisky T, Blainey P, Quake SR. 47.  2011. Genomic analysis at the single-cell level. Annu. Rev. Genet. 45:431–45 [Google Scholar]
  48. Kalisky T, Quake SR. 48.  2011. Single-cell genomics. Nat. Methods 8:311–14 [Google Scholar]
  49. Bandura DR, Baranov VI, Ornatsky OI, Antonov A, Kinach R. 49.  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. 81:6813–22 [Google Scholar]
  50. Bendall SC, Nolan GP. 50.  2012. From single cells to deep phenotypes in cancer. Nat. Biotechnol. 30:639–47 [Google Scholar]
  51. Diercks AH, Ozinsky A, Hansen CL, Spotts JM, Rodriguez DJ, Aderem A. 51.  2009. A microfluidic device for multiplexed protein detection in nano-liter volumes. Anal. Biochem. 386:30–35 [Google Scholar]
  52. Gottschalk RA, Martins AJ, Sjoelund VH, Angermann BR, Lin B, Germain RN. 52.  2012. Recent progress using systems biology approaches to better understand molecular mechanisms of immunity. Semin. Immunol. 25:201–8 [Google Scholar]
  53. Shalek AK, Satija R, Adiconis X, Gertner RS, Gaublomme JT. 53.  et al. 2013. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498:236–40 [Google Scholar]
  54. Ma C, Fan R, Ahmad H, Shi Q, Comin-Anduix B. 54.  et al. 2011. A clinical microchip for evaluation of single immune cells reveals high functional heterogeneity in phenotypically similar T cells. Nat. Med. 17:738–43 [Google Scholar]
  55. Varadarajan N, Julg B, Yamanaka YJ, Chen H, Ogunniyi AO. 55.  et al. 2011. A high-throughput single-cell analysis of human CD8+ T cell functions reveals discordance for cytokine secretion and cytolysis. J. Clin. Investig. 121:4322–31 [Google Scholar]
  56. Bodenmiller B, Zunder ER, Finck R, Chen TJ, Savig ES. 56.  et al. 2012. Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators. Nat. Biotechnol. 30:858–67 [Google Scholar]
  57. Berry MP, Graham CM, McNab FW, Xu Z, Bloch SA. 57.  et al. 2010. An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis. Nature 466:973–77 [Google Scholar]
  58. Weiner J, Maertzdorf J, Kaufmann SH. 58.  2013. The dual role of biomarkers for understanding basic principles and devising novel intervention strategies in tuberculosis. Ann. N.Y. Acad. Sci. 1283:22–29 [Google Scholar]
  59. Palermo RE, Fuller DH. 59.  2013. ‘Omics investigations of HIV and SIV pathogenesis and innate immunity. Curr. Top. Microbiol. Immunol. 363:87–116 [Google Scholar]
  60. Bosinger SE, Jacquelin B, Benecke A, Silvestri G, Muller-Trutwin M. 60.  2012. Systems biology of natural simian immunodeficiency virus infections. Curr. Opin. HIV AIDS 7:71–78 [Google Scholar]
  61. Peretz Y, Cameron C, Sekaly RP. 61.  2012. Dissecting the HIV-specific immune response: a systems biology approach. Curr. Opin. HIV AIDS 7:17–23 [Google Scholar]
  62. Walters KA, Olsufka R, Kuestner RE, Cho JH, Li H. 62.  et al. 2013. Francisella tularensis subsp. tularensis induces a unique pulmonary inflammatory response: role of bacterial gene expression in temporal regulation of host defense responses. PLoS ONE 8:e62412 [Google Scholar]
  63. Thompson LJ, Dunstan SJ, Dolecek C, Perkins T, House D. 63.  et al. 2009. Transcriptional response in the peripheral blood of patients infected with Salmonella enterica serovar Typhi. Proc. Natl. Acad. Sci. USA 106:22433–38 [Google Scholar]
  64. Ravi LI, Li L, Sutejo R, Chen H, Wong PS. 64.  et al. 2013. A systems-based approach to analyse the host response in murine lung macrophages challenged with respiratory syncytial virus. BMC Genomics 14:190 [Google Scholar]
  65. Jagger BW, Wise HM, Kash JC, Walters KA, Wills NM. 65.  et al. 2012. An overlapping protein-coding region in influenza A virus segment 3 modulates the host response. Science 337:199–204 [Google Scholar]
  66. Kash JC, Walters KA, Davis AS, Sandouk A, Schwartzman LM. 66.  et al. 2011. Lethal synergism of 2009 pandemic H1N1 influenza virus and Streptococcus pneumoniae coinfection is associated with loss of murine lung repair responses. mBio 2:e00172–11 [Google Scholar]
  67. Tisoncik JR, Korth MJ, Simmons CP, Farrar J, Martin TR, Katze MG. 67.  2012. Into the eye of the cytokine storm. Microbiol. Mol. Biol. Rev. 76:16–32 [Google Scholar]
  68. Imai M, Watanabe T, Hatta M, Das SC, Ozawa M. 68.  et al. 2012. Experimental adaptation of an influenza H5 haemagglutinin (HA) confers respiratory droplet transmission to a reassortant H5 HA/H1N1 virus in ferrets. Nature 486:420–28 [Google Scholar]
  69. Herfst S, Schrauwen EJA, Linster M, Chutinimitkul S, de Wit E. 69.  et al. 2012. Airborne transmission of influenza A/H5N1 virus between ferrets. Science 336:1534–41 [Google Scholar]
  70. Kash JC, Tumpey TM, Proll SC, Carter V, Perwitasari O. 70.  et al. 2006. Genomic analysis of increased host immune and cell death responses induced by 1918 influenza virus. Nature 443:578–81 [Google Scholar]
  71. Cillóniz C, Shinya K, Peng X, Korth MJ, Proll SC. 71.  et al. 2009. Lethal influenza virus infection in macaques is associated with early dysregulation of inflammatory related genes. PLoS Pathog. 5:e1000604 [Google Scholar]
  72. Askovich PS, Sanders CJ, Rosenberger CM, Diercks AH, Dash P. 72.  et al. 2013. Differential host response, rather than early viral replication efficiency, correlates with pathogenicity caused by influenza viruses. PLoS ONE 8:e74863 [Google Scholar]
  73. Brandes M, Klauschen F, Kuchen S, Germain RN. 73.  2013. A systems analysis identifies a feedforward inflammatory circuit leading to lethal influenza infection. Cell 154:197–212 [Google Scholar]
  74. Li Y, Chan EY, Li J, Ni C, Peng X. 74.  et al. 2010. MicroRNA expression and virulence in pandemic influenza virus-infected mice. J. Virol. 84:3023–32 [Google Scholar]
  75. Peng X, Gralinski L, Ferris MT, Frieman MB, Thomas MJ. 75.  et al. 2011. Integrative deep sequencing of the mouse lung transcriptome reveals differential expression of diverse classes of small RNAs in response to respiratory virus infection. mBio 2:e00198–11 [Google Scholar]
  76. Rosenberger CM, Podyminogin RL, Navarro G, Zhao GW, Askovich PS. 76.  et al. 2012. miR-451 regulates dendritic cell cytokine responses to influenza infection. J. Immunol. 189:5965–75 [Google Scholar]
  77. Baas T, Baskin CR, Diamond DL, García-Sastre A, Bielefeldt-Ohmann H. 77.  et al. 2006. Integrated molecular signature of disease: analysis of influenza virus-infected macaques through functional genomics and proteomics. J. Virol. 80:10813–28 [Google Scholar]
  78. Brown JN, Palermo RE, Baskin CR, Gritsenko M, Sabourin PJ. 78.  et al. 2010. Macaque proteome response to highly pathogenic avian influenza and 1918 reassortant influenza virus infections. J. Virol. 84:12058–68 [Google Scholar]
  79. Kroeker AL, Ezzati P, Halayko AJ, Coombs KM. 79.  2012. Response of primary human airway epithelial cells to influenza infection: a quantitative proteomic study. J. Proteome Res. 11:4132–46 [Google Scholar]
  80. Lietzén N, Öhman T, Rintahaka J, Julkunen I, Aittokallio T. 80.  et al. 2011. Quantitative subcellular proteome and secretome profiling of influenza A virus-infected human primary macrophages. PLoS Pathog. 7:e1001340 [Google Scholar]
  81. Cheung CY, Chan EY, Krasnoselsky A, Purdy D, Navare AT. 81.  et al. 2012. H5N1 virus causes significant perturbations in host proteome very early in influenza virus-infected primary human monocyte-derived macrophages. J. Infect. Dis. 206:640–45 [Google Scholar]
  82. Shapira SD, Gat-Viks I, Shum BOV, Dricot A, de Grace MM. 82.  et al. 2009. A physical and regulatory map of host-influenza interactions reveals pathways in H1N1 infection. Cell 139:1255–67 [Google Scholar]
  83. Tam VC, Quehenberger O, Oshansky CM, Suen R, Armando AM. 83.  et al. 2013. Lipidomic profiling of influenza infection identifies mediators that induce and resolve inflammation. Cell 154:213–27 [Google Scholar]
  84. Morita M, Kuba K, Ichikawa A, Nakayama M, Katahira J. 84.  et al. 2013. The lipid mediator protectin D1 inhibits influenza virus replication and improves severe influenza. Cell 153:112–25 [Google Scholar]
  85. Lawrence T, Willoughby DA, Gilroy DW. 85.  2002. Anti-inflammatory lipid mediators and insights into the resolution of inflammation. Nat. Rev. Immunol. 2:787–95 [Google Scholar]
  86. Quehenberger O, Dennis EA. 86.  2011. The human plasma lipidome. N. Engl. J. Med. 365:1812–23 [Google Scholar]
  87. Serhan CN, Krishnamoorthy S, Recchiuti A, Chiang N. 87.  2011. Novel anti-inflammatory–pro-resolving mediators and their receptors. Curr. Top. Med. Chem. 11:629–47 [Google Scholar]
  88. Serhan CN, Chiang N, Van Dyke TE. 88.  2008. Resolving inflammation: dual anti-inflammatory and pro-resolution lipid mediators. Nat. Rev. Immunol. 8:349–61 [Google Scholar]
  89. Buczynski M, Dumlao D, Dennis E. 89.  2009. An integrated omics analysis of eicosanoid biology. J. Lipid Res. 6:1015–38 [Google Scholar]
  90. Norris PC, Dennis EA. 90.  2012. Omega-3 fatty acids cause dramatic changes in TLR4 and purinergic eicosanoid signaling. Proc. Natl. Acad. Sci. USA 109:8517–22 [Google Scholar]
  91. Yang R, Chiang N, Oh SF, Serhan CN. 91.  2001. Metabolomics-Lipidomics of Eicosanoids and Docosanoids Generated by Phagocytes Hoboken, NJ: Wiley
  92. Karlas A, Machuy N, Shin Y, Pleissner K-P, Artarini A. 92.  et al. 2010. Genome-wide RNAi screen identifies human host factors crucial for influenza virus replication. Nature 463:818–22 [Google Scholar]
  93. Munier S, Rolland T, Diot C, Jacob Y, Naffakh N. 93.  2013. Exploration of binary virus-host interactions using an infectious protein complementation assay. Mol. Cell. Proteomics 12:2845–55 [Google Scholar]
  94. Zak DE, Aderem A. 94.  2012. Overcoming limitations in the systems vaccinology approach: a pathway for accelerated HIV vaccine development. Curr. Opin. HIV AIDS 7:58–63 [Google Scholar]
  95. Pulendran B, Li S, Nakaya HI. 95.  2010. Systems vaccinology. Immunity 33:4516–29 [Google Scholar]
  96. Andersen-Nissen E, Heit A, McElrath MJ. 96.  2012. Profiling immunity to HIV vaccines with systems biology. Curr. Opin. HIV AIDS 7:32–37 [Google Scholar]
  97. Rappuoli R, Aderem A. 97.  2011. A 2020 vision for vaccines against HIV, tuberculosis and malaria. Nature 473:463–69 [Google Scholar]
  98. Koff WC, Burton DR, Johnson PR, Walker BD, King CR. 98.  et al. 2013. Accelerating next-generation vaccine development for global disease prevention. Science 340:61361232910 [Google Scholar]
  99. Mooney M, McWeeney S, Sekaly RP. 99.  2013. Systems immunogenetics of vaccines. Semin. Immunol. 25:2124–29 [Google Scholar]
  100. Plotkin SA. 100.  2008. Vaccines: correlates of vaccine-induced immunity. Clin. Infect. Dis. 47:3401–9 [Google Scholar]
  101. Querec T, Bennouna S, Alkan S, Laouar Y, Gorden K. 101.  et al. 2006. Yellow fever vaccine YF-17D activates multiple dendritic cell subsets via TLR2, 7, 8, and 9 to stimulate polyvalent immunity. J. Exp. Med. 203:413–24 [Google Scholar]
  102. Lindsay RW, Darrah PA, Quinn KM, Wille-Reece U, Mattei LM. 102.  et al. 2010. CD8+ T cell responses following replication-defective adenovirus serotype 5 immunization are dependent on CD11c+ dendritic cells but show redundancy in their requirement of TLR and nucleotide-binding oligomerization domain-like receptor signaling. J. Immunol. 185:1513–21 [Google Scholar]
  103. Delaloye J, Roger T, Steiner-Tardivel QG, Le Roy D, Knaup Reymond M. 103.  et al. 2009. Innate immune sensing of modified vaccinia virus Ankara (MVA) is mediated by TLR2-TLR6, MDA-5 and the NALP3 inflammasome. PLoS Pathog. 5:e1000480 [Google Scholar]
  104. Baum LL. 104.  2010. Role of humoral immunity in host defense against HIV. Curr. HIV/AIDS Rep. 7:11–18 [Google Scholar]
  105. Seder RA, Darrah PA, Roederer M. 105.  2008. T-cell quality in memory and protection: implications for vaccine design. Nat. Rev. Immunol. 8:247–58 [Google Scholar]
  106. Flatz L, Roychoudhuri R, Honda M, Filali-Mouhim A, Goulet JP. 106.  et al. 2011. Single-cell gene-expression profiling reveals qualitatively distinct CD8 T cells elicited by different gene-based vaccines. Proc. Natl. Acad. Sci. USA 108:5724–29 [Google Scholar]
  107. Baum PD, Venturi V, Price DA. 107.  2012. Wrestling with the repertoire: the promise and perils of next generation sequencing for antigen receptors. Eur. J. Immunol. 42:2834–39 [Google Scholar]
  108. Autran B, Descours B, Avettand-Fenoel V, Rouzioux C. 108.  2011. Elite controllers as a model of functional cure. Curr. Opin. HIV AIDS 6:181–87 [Google Scholar]
  109. Walker BD. 109.  2007. Elite control of HIV infection: implications for vaccines and treatment. Top. HIV Med. 15:134–36 [Google Scholar]
  110. Thomas PG, Doherty PC. 110.  2009. Rules to ‘prime’ by. Nat. Immunol. 10:14–16 [Google Scholar]
  111. Brooks JPL, Lee EK. 111.  2008. Analysis of the consistency of a mixed integer programming-based multi-category constrained discriminant model. Ann. Oper. Res. 174:147–68 [Google Scholar]
  112. Querec TD, Akondy RS, Lee EK, Cao W, Nakaya HI. 112.  et al. 2009. Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans. Nat. Immunol. 10:116–25 [Google Scholar]
  113. Nakaya HI, Wrammert J, Lee EK, Racioppi L, Marie-Kunze S. 113.  et al. 2011. Systems biology of vaccination for seasonal influenza in humans. Nat. Immunol. 12:786–95 [Google Scholar]
  114. Zou H, Hastie T. 114.  2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67:301–20 [Google Scholar]
  115. Seok J, Warren HS, Cuenca AG, Mindrinos MN, Baker HV. 115.  et al. 2013. Genomic responses in mouse models poorly mimic human inflammatory diseases. Proc. Natl. Acad. Sci. USA 110:93507–12 [Google Scholar]
  116. Shultz LD, Brehm MA, Garcia-Martinez JV, Greiner DL. 116.  2012. Humanized mice for immune system investigation: progress, promise and challenges. Nat. Rev. Immunol. 12:786–98 [Google Scholar]
  117. Chaussabel D, Quinn C, Shen J, Patel P, Glaser C. 117.  et al. 2008. A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus. Immunity 29:150–64 [Google Scholar]
  118. Shen-Orr SS, Tibshirani R, Khatri P, Bodian DL, Staedtler F. 118.  et al. 2010. Cell type–specific gene expression differences in complex tissues. Nat. Methods 7:287–89 [Google Scholar]
  119. Novershtern N, Subramanian A, Lawton LN, Mak RH, Haining WN. 119.  et al. 2011. Densely interconnected transcriptional circuits control cell states in human hematopoiesis. Cell 144:2296–309 [Google Scholar]
  120. Shay T, Kang J. 120.  2013. Immunological Genome Project and systems immunology. Trends Immunol. 34:12602–9 [Google Scholar]
  121. Gaucher D, Therrien R, Kettaf N, Angermann BR, Boucher G. 121.  et al. 2008. Yellow fever vaccine induces integrated multilineage and polyfunctional immune responses. J. Exp. Med. 205:3119–31 [Google Scholar]
  122. Zak DE, Andersen-Nissen E, Peterson ER, Sato A, Hamilton MK. 122.  et al. 2012. Merck Ad5/HIV induces broad innate immune activation that predicts CD8+ T-cell responses but is attenuated by preexisting Ad5 immunity. Proc. Natl. Acad. Sci. USA 109:E3503–12 [Google Scholar]
  123. Buchbinder SP, Mehrotra DV, Duerr A, Fitzgerald DW, Mogg R. 123.  et al. 2008. Efficacy assessment of a cell-mediated immunity HIV-1 vaccine (the Step Study): a double-blind, randomised, placebo-controlled, test-of-concept trial. Lancet 372:1881–93 [Google Scholar]
  124. Gray GE, Allen M, Moodie Z, Churchyard G, Bekker LG. 124.  et al. 2011. Safety and efficacy of the HVTN 503/Phambili study of a clade-B-based HIV-1 vaccine in South Africa: a double-blind, randomised, placebo-controlled test-of-concept phase 2b study. Lancet Infect. Dis. 11:507–15 [Google Scholar]
  125. McElrath MJ, De Rosa SC, Moodie Z, Dubey S, Kierstead L. 125.  et al. 2008. HIV-1 vaccine-induced immunity in the test-of-concept Step Study: a case-cohort analysis. Lancet 372:1894–905 [Google Scholar]
  126. Rolland M, Tovanabutra S, deCamp AC, Frahm N, Gilbert PB. 126.  et al. 2011. Genetic impact of vaccination on breakthrough HIV-1 sequences from the STEP trial. Nat. Med. 17:366–71 [Google Scholar]
  127. Vahey MT, Wang Z, Kester KE, Cummings J, Heppner DG Jr. 127.  et al. 2010. Expression of genes associated with immunoproteasome processing of major histocompatibility complex peptides is indicative of protection with adjuvanted RTS,S malaria vaccine. J. Infect. Dis. 201:580–89 [Google Scholar]
  128. Li S, Rouphael N, Duraisingham S, Romero-Steiner S, Presnell S. 128.  et al. 2014. Molecular signatures of antibody responses derived from a systems biology study of five human vaccines. Nat. Immunol. 15:195–204 [Google Scholar]
  129. Obermoser G, Presnell S, Domico K, Xu H, Wang Y. 129.  et al. 2013. Systems scale interactive exploration reveals quantitative and qualitative differences in response to influenza and pneumococcal vaccines. Immunity 38:4831–44 [Google Scholar]
  130. Reif DM, Motsinger-Reif AA, McKinney BA, Rock MT, Crowe JE Jr, Moore JH. 130.  2009. Integrated analysis of genetic and proteomic data identifies biomarkers associated with adverse events following smallpox vaccination. Genes Immun. 10:2112–19 [Google Scholar]
  131. Araki K, Turner AP, Shaffer VO, Gangappa S, Keller SA. 131.  et al. 2009. mTOR regulates memory CD8 T-cell differentiation. Nature 460:108–12 [Google Scholar]
  132. Tan X, Sande JL, Pufnock JS, Blattman JN, Greenberg PD. 132.  2011. Retinoic acid as a vaccine adjuvant enhances CD8+ T cell response and mucosal protection from viral challenge. J. Virol. 85:8316–27 [Google Scholar]
  133. Wang N, Calpe S, Westcott J, Castro W, Ma C. 133.  et al. 2010. Cutting edge: The adapters EAT-2A and -2B are positive regulators of CD244- and CD84-dependent NK cell functions in the C57BL/6 mouse. J. Immunol. 185:5683–87 [Google Scholar]
  134. Aldhamen YA, Appledorn DM, Seregin SS, Liu CJ, Schuldt NJ. 134.  et al. 2011. Expression of the SLAM family of receptors adapter EAT-2 as a novel strategy for enhancing beneficial immune responses to vaccine antigens. J. Immunol. 186:722–32 [Google Scholar]
  135. Olotu A, Fegan G, Wambua J, Nyangweso G, Awuondo KO. 135.  et al. 2013. Four-year efficacy of RTS,S/AS01E and its interaction with malaria exposure. N. Engl. J. Med. 368:121111–20 [Google Scholar]
  136. Seder RA, Chang LJ, Enama ME, Zephir KL, Sarwar UN. 136.  et al. 2013. Protection against malaria by intravenous immunization with a nonreplicating sporozoite vaccine. Science 341:1359–65 [Google Scholar]
  137. Tameris MD, Hatherill M, Landry BS, Scriba TJ, Snowden MA. 137.  et al. 2013. Safety and efficacy of MVA85A, a new tuberculosis vaccine, in infants previously vaccinated with BCG: a randomised, placebo-controlled phase 2b trial. Lancet 381:98711021–28 [Google Scholar]
  138. Rerks-Ngarm S, Pitisuttithum P, Nitayaphan S, Kaewkungwal J, Chiu J. 138.  et al. 2009. Vaccination with ALVAC and AIDSVAX to prevent HIV-1 infection in Thailand. N. Engl. J. Med. 361:2209–20 [Google Scholar]
  139. Zak DE, Aderem A. 139.  2009. Systems biology of innate immunity. Immunol. Rev. 227:264–82 [Google Scholar]
  140. Ramsey SA, Gold ES, Aderem A. 140.  2010. A systems biology approach to understanding atherosclerosis. EMBO Mol. Med. 2:79–89 [Google Scholar]
  141. Kozhenkov S, Dubinina Y, Sedova M, Gupta A, Ponomarenko J, Baitaluk M. 141.  2010. BiologicalNetworks 2.0—an integrative view of genome biology data. BMC Bioinforma. 11:610 [Google Scholar]
  142. Breitkreutz BJ, Stark C, Tyers M. 142.  2003. Osprey: a network visualization system. Genome Biol. 4:R22 [Google Scholar]
  143. Breuer K, Foroushani AK, Laird MR, Chen C, Sribnaia A. 143.  et al. 2013. InnateDB: systems biology of innate immunity and beyond—recent updates and continuing curation. Nucleic Acids Res. 41:D1228–33 [Google Scholar]
  144. Barsky A, Gardy JL, Hancock RE, Munzner T. 144.  2007. Cerebral: a Cytoscape plugin for layout of and interaction with biological networks using subcellular localization annotation. Bioinformatics 23:1040–42 [Google Scholar]
  145. Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T. 145.  2011. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27:431–32 [Google Scholar]
  146. Hu Z, Chang YC, Wang Y, Huang CL, Liu Y. 146.  et al. 2013. VisANT 4.0: Integrative network platform to connect genes, drugs, diseases and therapies. Nucleic Acids Res. 41:W225–31 [Google Scholar]
  147. Croft D, O'Kelly G, Wu G, Haw R, Gillespie M. 147.  et al. 2011. Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res. 39:D691–97 [Google Scholar]
  148. Nishimura D. 148.  2001. BioCarta. Biotech Softw. Internet Rep. 2:117–20 [Google Scholar]
  149. Latendresse M, Paley S, Karp PD. 149.  2012. Browsing metabolic and regulatory networks with BioCyc. Methods Mol. Biol. 804:197–216 [Google Scholar]
  150. Kanehisa M. 150.  2013. Molecular network analysis of diseases and drugs in KEGG. Methods Mol. Biol. 939:263–75 [Google Scholar]
  151. Mi H, Muruganujan A, Thomas PD. 151.  2013. PANTHER in 2013: modeling the evolution of gene function, and other gene attributes, in the context of phylogenetic trees. Nucleic Acids Res. 41:D377–86 [Google Scholar]
  152. Kelder T, van Iersel MP, Hanspers K, Kutmon M, Conklin BR. 152.  et al. 2012. WikiPathways: building research communities on biological pathways. Nucleic Acids Res. 40:D1301–7 [Google Scholar]
  153. Fahy E, Sud M, Cotter D, Subramaniam S. 153.  2007. LIPID MAPS online tools for lipid research. Nucleic Acids Res. 35:W606–12 [Google Scholar]
  154. Cerami EG, Gross BE, Demir E, Rodchenkov I, Babur O. 154.  et al. 2011. Pathway Commons, a web resource for biological pathway data. Nucleic Acids Res. 39:D685–90 [Google Scholar]
  155. Kerrien S, Aranda B, Breuza L, Bridge A, Broackes-Carter F. 155.  et al. 2012. The IntAct molecular interaction database in 2012. Nucleic Acids Res. 40:D841–46 [Google Scholar]
  156. Keshava Prasad TS, Goel R, Kandasamy K, Keerthikumar S, Kumar S. 156.  et al. 2009. Human Protein Reference Database—2009 update. Nucleic Acids Res. 37:D767–72 [Google Scholar]
  157. Licata L, Briganti L, Peluso D, Perfetto L, Iannuccelli M. 157.  et al. 2012. MINT, the molecular interaction database: 2012 update. Nucleic Acids Res. 40:D857–61 [Google Scholar]
  158. Chatr-Aryamontri A, Breitkreutz BJ, Heinicke S, Boucher L, Winter A. 158.  et al. 2013. The BioGRID interaction database: 2013 update. Nucleic Acids Res. 41:D816–23 [Google Scholar]
  159. Barrett T, Edgar R. 159.  2006. Gene expression omnibus: microarray data storage, submission, retrieval, and analysis. Methods Enzymol. 411:352–69 [Google Scholar]
  160. Rustici G, Kolesnikov N, Brandizi M, Burdett T, Dylag M. 160.  et al. 2013. ArrayExpress update—trends in database growth and links to data analysis tools. Nucleic Acids Res. 41:D987–90 [Google Scholar]
/content/journals/10.1146/annurev-immunol-032713-120254
Loading
Systems-Level Analysis of Innate Immunity
Annual Review of Immunology 32, 547 (2014); https://doi.org/10.1146/annurev-immunol-032713-120254
/content/journals/10.1146/annurev-immunol-032713-120254
/content/journals/10.1146/annurev-immunol-032713-120254
Loading

Data & Media loading...

  • Article Type: Review Article

Most Cited Most Cited RSS feed

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