Cellular and Molecular Life Sciences

, Volume 76, Issue 1, pp 67–87 | Cite as

Investigating the aetiology of adverse events following HPV vaccination with systems vaccinology

  • Joan Campbell-TofteEmail author
  • Aristidis Vrahatis
  • Knud Josefsen
  • Jesper Mehlsen
  • Kaj Winther


In contrast to the insidious and poorly immunogenic human papillomavirus (HPV) infections, vaccination with the HPV virus-like particles (vlps) is non-infectious and stimulates a strong neutralizing-antibody response that protects HPV-naïve vaccinees from viral infection and associated cancers. However, controversy about alleged adverse events following immunization (AEFI) with the vlps have led to extensive reductions in vaccine acceptance, with countries like Japan dropping it altogether. The AEFIs are grouped into chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME). In this review, we present a hypothesis that the AEFIs might arise from malfunctions within the immune system when confronted with the unusual antigen. In addition, we outline how the pathophysiology of the AEFIs can be cost-effectively investigated with the holistic principles of systems vaccinology in a two-step process. First, comprehensive immunological profiles of HPV vaccinees exhibiting the AEFIs are generated by integrating the data derived from serological profiling for prominent HPV antibodies and serum cytokines, with data from serum metabolomics, peripheral white blood cells transcriptomics and gut microbiome profiling. Next, the immunological profiles are compared with corresponding profiles generated for matched (a) HPV vaccinees without AEFIs; (b) non-HPV-vaccinated individuals with CFS/ME-like symptoms; and (c) non-HPV-vaccinated individuals without CFS/ME. In these comparisons, any causal links between HPV vaccine and the AEFIs, as well as the underlying molecular basis for the links will be revealed. Such a study should provide an objective basis for evaluating HPV vaccine safety and for identifying biomarkers for individuals at risk of developing AEFI with HPV vaccination.


‘Omics technologies Systems biology Chronic fatigue syndrome/myalgic encephalomyelitis Vaccine safety 



Human papillomavirus


Adverse events following immunization


Chronic fatigue syndrome/myalgic encephalomyelitis


Dendritic cells


Antigen-presenting cell


B cell receptor


T cell receptor


  1. 1.
    World Health Organization (WHO) (2018) Human papillomavirus (HPV) and cervical cancer at Accessed 8 Oct 2018
  2. 2.
    Mariani L, Venuti A (2010) HPV vaccine: an overview of immune response, clinical protection, and new approaches for the future. J Transl Med 8:105. CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Paul WE (2011) Bridging innate and adaptive immunity. Cell 147(6):1212–1215CrossRefPubMedGoogle Scholar
  4. 4.
    Larson HJ, Wilson R, Hanley S et al (2014) Tracking the global spread of vaccine sentiments: the global response to Japan’s suspension of its HPV vaccine recommendation. Hum Vaccines Immunother 10(9):2543–2550CrossRefGoogle Scholar
  5. 5.
    Brinth L, Pors K, Hoppe AAG et al (2015) Is chronic fatigue syndrome/myalgic encephalomyelitis a relevant diagnosis in patients with suspected side effects to human papilloma virus vaccine? Int J Vaccines Vaccin 1(1):00003. CrossRefGoogle Scholar
  6. 6.
    Global Advisory Committee on Vaccine Safety (2017) Safety update of HPV vaccines at Accessed 8 Oct 2018
  7. 7.
    Danish Health Authority, news (2016) Sharp fall in HPV vaccination rate at Accessed 8 Oct 2018
  8. 8.
    Brodin P, Jojic V, Gao T et al (2015) Variation in the human immune system is largely driven by non-heritable influences. Cell 160:37–47. CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Brodin P, Davis MM (2017) Human immune system variation. Nat Rev Immunol 17(1):21–29. CrossRefPubMedGoogle Scholar
  10. 10.
    Hagana T, Nakaya HI, Subramaniam S et al (2015) Systems vaccinology: enabling rational vaccine design with systems biological approaches. Vaccine 33(40):5294–5301. CrossRefGoogle Scholar
  11. 11.
    Querec TD, Akondy RS, Lee EK et al (2009) Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans. Nat Immunol 10(1):116–125. CrossRefPubMedGoogle Scholar
  12. 12.
    Avey S, Cheung F, Fermin D et al (2017) Multicohort analysis reveals baseline transcriptional predictors of influenza vaccination responses (HIPC-CHI Signatures Project Team and HIPC-I Consortium). Sci Immunol 2(14):eaal4656. CrossRefGoogle Scholar
  13. 13.
    Burd EM (2003) Human papillomavirus and cervical cancer. Clin Microbiol Rev 16(1):1–17CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Longworth MS, Laimins LA (2004) Pathogenesis of human papillomaviruses in differentiating epithelia. Microbiol Mol Biol Rev 68(2):362–372CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Fehrmann F, Laimins LA (2003) Human papillomaviruses: targeting differentiating epithelial cells for malignant transformation. Oncogene 22:5201–5207CrossRefPubMedGoogle Scholar
  16. 16.
    Smith JS, Lindsay L, Hoots B et al (2007) Human papillomavirus type distribution in invasive cervical cancer and high-grade cervical lesions: a meta-analysis update. Int J Cancer 121(3):621–632CrossRefPubMedGoogle Scholar
  17. 17.
    Ashrafi GH, Haghshenas MR, Marchetti B et al (2005) E5 protein of human papillomavirus type 16 selectively downregulates surface HLA. Int J Cancer 113:276–283CrossRefPubMedGoogle Scholar
  18. 18.
    Sapp M, Volpers C, Muller M, Streck RE (1995) Organization of the major and minor capsid proteins in human papillomavirus type 33 virus-like particles. J Gen Virol 76:2407–2412CrossRefPubMedGoogle Scholar
  19. 19.
    Buck CB, Cheng N, Thompson CD et al (2008) Arrangement of L2 within the papillomavirus capsid. J Virol 82:5190–5197CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Conway MJ, Meyers C (2009) Replication and assembly of human papillomaviruses. J Dent Res 88(4):307–317. CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Zheng Z-M, Baker CC (2006) Papillomavirus genome structure, expression and post-transcriptional regulation. Front Biosci 11:2286–2302CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Sterlinko GH, Weber M, Elston R et al (2004) Inhibition of E6-induced degradation of its cellular substrates by novel blocking peptides. J Mol Biol 335:971–985CrossRefGoogle Scholar
  23. 23.
    Yim EK, Park JS (2005) The role of HPV E6 and E7 oncoproteins in HPV-associated cervical carcinogenesis. Cancer Res Treat 37(6):319–324CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Hasan UA, Bates E, Takeshita F et al (2007) TLR9 expression and function is abolished by the cervical cancer-associated human papillomavirus type 16. J Immunol 178:3186–3197CrossRefPubMedGoogle Scholar
  25. 25.
    Kanodia S, Fahey LM, Kast WM (2007) Mechanisms used by human papillomaviruses to escape the host immune response. Curr Cancer Drug Targets 7:79–89CrossRefPubMedGoogle Scholar
  26. 26.
    Moscicki AB, Schiffman M, Kjaer S, Villa LL (2006) Chapter 5: updating the natural history of HPV and anogenital cancer. Vaccine 24(Suppl 3):S3-42–S3-51Google Scholar
  27. 27.
    Roden RBS, Lowy DR, Schiller JT (1997) Papillomavirus is resistant to desiccation. J Infect Dis 176:1076–1079CrossRefPubMedGoogle Scholar
  28. 28.
    zur Hausen H H, de Villiers EM (1994) Human papillomaviruses. Annu Rev Microbiol 48:427–447CrossRefPubMedGoogle Scholar
  29. 29.
    Walboomers JMM, Jacobs MV, Manos MM et al (1999) Human papillomavirus is a necessary cause of invasive cervical cancer worldwide. J Pathol 189:12–19CrossRefPubMedGoogle Scholar
  30. 30.
    Laimins LA (1993) The biology of human papillomaviruses: from warts to cancer. Infect Agents Dis 2(2):74–86PubMedGoogle Scholar
  31. 31.
    Vinokurova S, Wentzensen N, Kraus I et al (2008) Type-dependent integration frequency of human papillomavirus genomes in cervical lesions. Cancer Res 68(1):307–313CrossRefPubMedGoogle Scholar
  32. 32.
    Joura EA, Giuliano AR, Ole-Erik Iversen O et al (2015) A 9-valent HPV vaccine against infection and intraepithelial neoplasia in women. N Engl J Med 372:711–723. CrossRefPubMedGoogle Scholar
  33. 33.
    Mosser DM, Edwards JP (2008) Exploring the full spectrum of macrophage activation. Nat Rev Immunol 8(12):958–969. CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Epelman S, Kory J, Lavine KJ, Randolph GJ (2014) Origin and functions of tissue macrophages. Immunity 41(1):21–35. CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Chávez-Galán L, Olleros ML, Vesin D, Garcia I (2015) Much more than M1 and M2 macrophages, there are also CD169+ and TCR+ macrophages. Front Immunol 6:263. CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Kobayashi SD, DeLeo FR (2009) Towards a comprehensive understanding of the role of neutrophils in innate immunity: a systems biology-level approach. Wiley Interdiscip Rev Syst Biol Med 1(3):309–333. CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Tak T, Tesselaar K, Pillay J et al (2013) What’s your age again? Determination of human neutrophil half-lives revisited. J Leukoc Biol 94:595–601CrossRefPubMedGoogle Scholar
  38. 38.
    Lakschevitz FS, Hassanpour S, Rubin A et al (2016) Identification of neutrophil surface marker changes in health and inflammation using high-throughput screening flow cytometry. Exp Cell Res 342(2):200–209. CrossRefPubMedGoogle Scholar
  39. 39.
    Steinman RM (2006) Introduction to some of the issues and mysteries considered in this book on dendritic cells. In: Lutz MB, Romani N, Steinkasserer A (eds) Handbook of Dendritic Cells. Biology, Diseases, and Therapies, WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim, pp 3–11Google Scholar
  40. 40.
    Geissmann F, Manz MG, Jung S et al (2010) Development of monocytes, macrophages and dendritic cells. Science 327(5966):656–661CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Collin M, McGovern N, Haniffa M (2013) Human dendritic cell subsets. Immunology 140(1):22–30CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Helft J, Ginhoux F, Bogunovic M, Merad M (2010) Review Origin and functional heterogeneity of non-lymphoid tissue dendritic cells in mice. Immunol Rev 234(1):55–75CrossRefPubMedGoogle Scholar
  43. 43.
    Merad M, Sathe P, Helft J et al (2013) The dendritic cell lineage: ontogeny and function of dendritic cells and their subsets in the steady state and the inflamed setting. Annu Rev Immunol. CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Shamri R, Xenakis JJ, Spencer LA (2011) Eosinophils in innate immunity: an evolving story. Cell Tissue Res 343(1):57–83. CrossRefPubMedGoogle Scholar
  45. 45.
    Kobayashi T, Kouzaki H, Kita H (2010) Human eosinophils recognize endogenous danger signal crystalline uric acid and produce proinflammatory cytokines mediated by autocrine ATP. J Immunol 184(11):6350–6358CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Lee JJ, Jacobsen EA, Ochkur SI et al (2012) Human vs. mouse eosinophils: “That which we call an eosinophil, by any other name would stain as red”. J Allergy Clin Immunol 130(3):572–584. CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Sokol CL, Medzhitov R (2010) Role of basophils in the initiation of Th2 responses. Curr Opin Immunol 22:73–77CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Min B, Brown MA, LeGros G (2012) Understanding the roles of basophils: breaking dawn. Immunology 135(3):192–197. CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Dinarello CA (2009) Immunological and inflammatory functions of the interleukin-1 family. Annu Rev Immunol 27:519–550. CrossRefPubMedGoogle Scholar
  50. 50.
    Schroder K, Zhou R, Tschopp J (2010) The NLRP3 inflammasome: a sensor for metabolic danger? Science 327(5963):296–300. CrossRefPubMedGoogle Scholar
  51. 51.
    Caligiuri MA (2008) Human natural killer cells. Blood 112(3):461–910CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Orange JS (2013) Natural killer cell deficiency. J Allergy Clin Immunol 132(3):515–526. CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Ham H, Billadeau DD (2014) Human immunodeficiency syndromes affecting human natural killer cell cytolytic activity. Front Immunol 5:2. CrossRefPubMedPubMedCentralGoogle Scholar
  54. 54.
    Rothenberg EV (2014) Transcriptional control of early T and B cell developmental choices. Annu Rev Immunol 32:283–321. CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    LeBien TW, Tedder TF (2008) B lymphocytes: how they develop and function. Blood 112:1570–1580CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Zhu J, Paul WE (2008) CD4 T cells: fates, functions, and faults. Blood 112(5):1557–1569. CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Wan YY (2014) GATA3: a master of many trades in immune regulation. Trends Immunol 35(6):233–242. CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Bonilla FA, Oettgen HC (2010) Adaptive immunity. J Allergy Clin Immunol 125(2):S33–S40CrossRefPubMedGoogle Scholar
  59. 59.
    Artis D, Spits H (2015) The biology of innate lymphoid cells. Nature 517:293–301. CrossRefPubMedGoogle Scholar
  60. 60.
    Hepworth MR, Sonnenberg GF (2014) Regulation of the adaptive immune system by innate lymphoid cells. Curr Opin Immunol 27:75–82. CrossRefPubMedGoogle Scholar
  61. 61.
    Nestle FO, Di Meglio P, Qin JZ, Nickoloff BJ (2009) Skin immune sentinels in health and disease. Nat Rev Immunol 9:679–691CrossRefPubMedPubMedCentralGoogle Scholar
  62. 62.
    Franchi L, Warner N, Viani K, Nuñez G (2009) Function of Nod-like receptors in microbial recognition and host defence. Immunol Rev 227(1):106–128CrossRefPubMedPubMedCentralGoogle Scholar
  63. 63.
    Halverson R, Torres R, Pelanda R (2004) Receptor editing is the main mechanism of B cell tolerance toward membrane antigens. Nat Immunol 5(6):645–650. (PMID 15156139) CrossRefPubMedGoogle Scholar
  64. 64.
    Schwarz BA, Bhandoola A (2006) Trafficking from the bone marrow to the thymus: a prerequisite for thymopoiesis. Immunol Rev 209:47–57CrossRefPubMedGoogle Scholar
  65. 65.
    Takaba H, Morishita Y, Tomofuji Y et al (2015) Fezf2 orchestrates a thymic program of self-antigen expression for immune tolerance. Cell 163:975–987CrossRefPubMedGoogle Scholar
  66. 66.
    Sakaguchi S, Takahashi T, Nishizuka Y (1982) Study on cellular events in post-thymectomy autoimmune oophoritis in mice. I. Requirement of Lyt-1 effector cells for oocytes damage after adoptive transfer. J Exp Med 156:1565–1576CrossRefPubMedGoogle Scholar
  67. 67.
    Warrington R, Watson W, Kim HL, Antonetti FR (2011) An introduction to immunology and immunopathology. Allergy Asthma Clin Immunol 7(Suppl 1):S1CrossRefPubMedPubMedCentralGoogle Scholar
  68. 68.
    International Agency for Research on Cancer (IARC) (1995) Monographs on the evaluation of carcinogenic risks to humans, no. 64.1, human papillomavirus (HPV) infection. Accessed 8 Oct 2018
  69. 69.
    Carter JJ, Koutsky LA, Hughes JP et al (2000) Comparison of human papillomavirus types 16, 18, and 6 capsid antibody responses following incident infection. J Infect Dis 181(6):1911–1919CrossRefPubMedGoogle Scholar
  70. 70.
    Af Geijersstam V, Kibur M, Wang Z et al (1998) Stability over time of serum antibody levels to human papillomavirus type 16. J Infect Dis 177:1710–1714CrossRefPubMedGoogle Scholar
  71. 71.
    Lehtinen M, Pawlita M, Zumbach K et al (2003) Evaluation of antibody response to human papillomavirus early proteins in women in whom cervical cancer developed 1 to 20 years later. Am J Obstet Gynecol 188(1):49–55CrossRefPubMedGoogle Scholar
  72. 72.
    Leon S, Sánchez R, Patarroyo MA et al (2009) Prevalence of HPV-DNA and anti-HPV antibodies in women from Girardot, Colombia. Sex Transm Dis 36(5):290–296. CrossRefPubMedGoogle Scholar
  73. 73.
    de Jong A, van Poelgeest MI, van der Hulst JM et al (2004) Human papillomavirus type 16-positive cervical cancer is associated with impaired CD4+ T-cell immunity against early antigens E2 and E6. Cancer Res 64:5449–5455CrossRefPubMedGoogle Scholar
  74. 74.
    Woo YL, van den Hende M, Sterling JC et al (2010) A prospective study on the natural course of low-grade squamous intraepithelial lesions and the presence of HPV16 E2-, E6- and E7-specific T-cell responses. Int J Cancer 126:133–141CrossRefPubMedGoogle Scholar
  75. 75.
    Chan PKS, Liu SJ, Cheung TH et al (2010) T-cell response to human papillomavirus type 58 L1, E6, And E7 peptides in women with cleared infection, cervical intraepithelial neoplasia, or invasive cancer. Clin Vaccine Immunol 17(9):1315–1321. CrossRefPubMedPubMedCentralGoogle Scholar
  76. 76.
    Chan PKS, Liu SJ, Cheung JL et al (2011) T-cell response to human papillomavirus type 52 L1, E6, and E7 peptides in women with transient infection, cervical intraepithelial neoplasia, and invasive cancer. J Med Virol 83(6):1023–1030. CrossRefPubMedGoogle Scholar
  77. 77.
    van der Burg SH, Piersma SJ, de Jong A et al (2007) Association of cervical cancer with the presence of CD4+ regulatory T cells specific for human papillomavirus antigens. Proc Natl Acad Sci USA 104(29):12087–12092CrossRefPubMedGoogle Scholar
  78. 78.
    Molling JW, de Grujil TD, Glim J et al (2007) CD4(+) CD25hi regulatory T-cell frequency correlates with persistence of human papillomavirus type 16 and T helper cell responses in patients with cervical intraepithelial neoplasia. Int J Cancer 121(8):1749–1755CrossRefPubMedGoogle Scholar
  79. 79.
    Benton C, Shahidulhah H, Hunter JAA (1992) Human papillomaviruses in the immuno-suppressed; pp 23–26 in IARC Working Group on the evaluation of carcinogenic risk to humans. Human papillomaviruses. Lyon (FR): International Agency for Research on Cancer; 1995 (IARC monographs on the evaluation of carcinogenic risks to humans, no. 64.). Accessed 8 Oct 2018
  80. 80.
    Braun L (1994) Role of human immunodeficiency virus infection in the pathogenesis of human papillomavirus-associated cervical neoplasia. Am J Pathol 144:209–214PubMedPubMedCentralGoogle Scholar
  81. 81.
    Wheeler CM, Hunt WC, Schiffman M, Castle PE (2006) Human papillomavirus genotypes and the cumulative 2-year risk of cervical pre-cancer. J Infect Dis 194:1291–1299CrossRefPubMedGoogle Scholar
  82. 82.
    Stanley M (2010) HPV—immune response to infection and vaccination. Infect Agents Cancer 5:19CrossRefPubMedPubMedCentralGoogle Scholar
  83. 83.
    Giannini SL, Hanon E, Moris P et al (2006) Enhanced humoral and memory B cellular immunity using HPV16/18 L1 VLP vaccine formulated with the MPL/aluminium salt combination (AS04) compared to aluminium salt only. Vaccine 24(33–34):5937–5949CrossRefPubMedGoogle Scholar
  84. 84.
    De Carvalho N, Teixeira J, Roteli-Martins CM et al (2010) Sustained efficacy and immunogenicity of the HPV-16/18 AS04-adjuvanted vaccine up to 7.3 years in young adult women. Vaccine 28:6247–6255CrossRefPubMedGoogle Scholar
  85. 85.
    Scherpenisse M, Schepp RM, Mollers M et al (2013) Characteristics of HPV-specific antibody responses induced by infection and vaccination: cross-reactivity, neutralizing activity, avidity and IgG subclasses. PLoS One 8(9):e74797. CrossRefPubMedPubMedCentralGoogle Scholar
  86. 86.
    Handisurya A, Schellenbacher C, Haitel A et al (2016) Human papillomavirus vaccination induces neutralising antibodies in oral mucosal fluids. Br J Cancer 114:409–416. CrossRefPubMedPubMedCentralGoogle Scholar
  87. 87.
    Ferris DG, Samakoses R, Block SL et al (2017) 4-Valent human papillomavirus (4vHPV) vaccine in preadolescents and adolescents after 10 years. Pediatrics 140(6):e20163947CrossRefPubMedGoogle Scholar
  88. 88.
    Narayan S, Choyce A, Linedale R et al (2009) Epithelial expression of human papillomavirus type 16 E7 protein results in peripheral CD8 T-cell suppression mediated by CD4+ CD25+ T cells. Eur J Immunol 39:481–490CrossRefPubMedGoogle Scholar
  89. 89.
    Varricchio F, Iskander J, Destefano F et al (2004) Understanding vaccine safety information from the vaccine adverse event reporting system. Pediatr Infect Dis J 23(4):287–294CrossRefPubMedGoogle Scholar
  90. 90.
    Xie J, Zhao L, Zhou S, He Y (2016) Statistical and ontological analysis of adverse events associated with monovalent and combination vaccines against hepatitis A and B diseases. Sci Rep 6:34318. CrossRefPubMedPubMedCentralGoogle Scholar
  91. 91.
    Committee on the Diagnostic Criteria for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome; Board on the Health of Select Populations; Institute of Medicine (2015) Beyond myalgic encephalomyelitis/chronic fatigue syndrome: redefining an illness. National Academies Press (US), Washington (DC). Accessed 8 Oct 2018
  92. 92.
    Dantoft TM, Ebstrup JF, Linneberg A et al (2017) Cohort description: the Danish study of functional disorders. Clin Epidemiol 9:127–139CrossRefPubMedPubMedCentralGoogle Scholar
  93. 93.
    Wong R, Lopaschuk G, Zhu G et al (1992) Skeletal muscle metabolism in the chronic fatigue syndrome. In vivo assessment by 31P nuclear magnetic resonance spectroscopy. Chest 102(6):1716–1722CrossRefPubMedGoogle Scholar
  94. 94.
    Filler K, Lyon D, Bennett J et al (2014) Association of mitochondrial dysfunction and fatigue: a review of the literature. BBA Clin 1:12–23CrossRefPubMedPubMedCentralGoogle Scholar
  95. 95.
    Rutherford G, Manning P, Newton JL (2016) Understanding muscle dysfunction in chronic fatigue syndrome. J Aging Res 2016:2497348. CrossRefPubMedPubMedCentralGoogle Scholar
  96. 96.
    Lacourt TE, Vichaya EG, Chiu GS et al (2018) The high costs of low-grade inflammation: persistent fatigue as a consequence of reduced cellular-energy availability and non-adaptive energy expenditure. Front Behav Neurosci 12:78. CrossRefPubMedPubMedCentralGoogle Scholar
  97. 97.
    Blomberg J, Gottfries CG, Elfaitouri A et al (2018) Infection elicited autoimmunity and myalgic encephalomyelitis/chronic fatigue syndrome: an explanatory model. Front Immunol (Hypothesis Theory) 9:229. CrossRefGoogle Scholar
  98. 98.
    Montoya JG, Holmes TH, Anderson JN et al (2017) Cytokine signature associated with disease severity in chronic fatigue syndrome patients. Proc Natl Acad Sci USA 114:E7150–E7158. CrossRefPubMedGoogle Scholar
  99. 99.
    Brenu EW, van Driel ML, Staines DR et al (2011) Immunological abnormalities as potential biomarkers in chronic fatigue syndrome/myalgic encephalomyelitis. J Transl Med 9:81CrossRefPubMedPubMedCentralGoogle Scholar
  100. 100.
    Hardcastle SL, Brenu EW, Johnston S et al (2015) Longitudinal analysis of immune abnormalities in varying severities of chronic fatigue syndrome/myalgic encephalomyelitis patients. J Transl Med 13:299. CrossRefPubMedPubMedCentralGoogle Scholar
  101. 101.
    Nacul L, O’Donovan DG, Eliana M et al (2014) Considerations in establishing a post-mortem brain and tissue bank for the study of myalgic encephalomyelitis/chronic fatigue syndrome: a proposed protocol. BMC Res Notes 7:370CrossRefPubMedPubMedCentralGoogle Scholar
  102. 102.
    Schondorf R, Benoit J, Wein T, Phaneuf D (1999) Orthostatic intolerance in the chronic fatigue syndrome. J Auton Nerv Syst 75(2–3):192–201CrossRefPubMedGoogle Scholar
  103. 103.
    Kanduc D (2009) Quantifying the possible cross-reactivity risk of an HPV16 vaccine. J Exp Ther Oncol 8(1):65–76PubMedGoogle Scholar
  104. 104.
    Kanduc D, Shoenfeld Y (2016) From HBV to HPV: designing vaccines for extensive and intensive vaccination campaigns worldwide. Autoimmun Rev 15(11):1054–1061. CrossRefPubMedGoogle Scholar
  105. 105.
    Lu C, Diehl SA, Noubade R et al (2010) Endothelial histamine H1 receptor signaling reduces blood-brain barrier permeability and susceptibility to autoimmune encephalomyelitis. Proc Natl Acad Sci USA 107(44):18967–18972CrossRefPubMedGoogle Scholar
  106. 106.
    Shanas U, Bhasin R, Sutherland AK et al (1998) Brain mast cells lack the c-kit receptor: immunocytochemical evidence. J Neuroimmunol 90(2):207–211CrossRefPubMedGoogle Scholar
  107. 107.
    Letourneau R, Rozniecki JJ, Dimitriadou V, Theoharides TC (2003) Ultrastructural evidence of brain mast cell activation without degranulation in monkey experimental allergic encephalomyelitis. J Neuroimmunol 145:18–26CrossRefPubMedGoogle Scholar
  108. 108.
    Twisk FNM (2014) The status of and future research into myalgic encephalomyelitis and chronic fatigue syndrome: the need of accurate diagnosis, objective assessment, and acknowledging biological and clinical subgroups. Front Physiol 5:109. CrossRefPubMedPubMedCentralGoogle Scholar
  109. 109.
    Balagopalan L, Sherman E, Barr VA, Samelson LE (2011) Imaging techniques for assaying lymphocyte activation in action. Nat Rev Immunol 11(1):21–33CrossRefPubMedPubMedCentralGoogle Scholar
  110. 110.
    Schietinger A, Delrow JJ, Basom RS et al (2012) Rescued tolerant CD8 T cells are preprogrammed to reestablish the tolerant state. Science 335:723–727CrossRefPubMedPubMedCentralGoogle Scholar
  111. 111.
    Schietinger A, Greenberg PD (2014) Tolerance and exhaustion: defining mechanisms of T cell dysfunction. Trends Immunol 35(2):51–60. CrossRefPubMedGoogle Scholar
  112. 112.
    Aderem A (2005) Systems biology: its practice and challenges. Cell 20:511–513CrossRefGoogle Scholar
  113. 113.
    Pulendran B, Ahmed R (2006) Translating innate immunity into immunological memory: implications for vaccine development. Cell 124:849–863CrossRefPubMedGoogle Scholar
  114. 114.
    Daniel E, Zak DE, Aderem A (2009) Systems biology of innate immunity. Immunol Rev 227(1):264–282. CrossRefGoogle Scholar
  115. 115.
    Querec TD, Akondy RS, Lee EK et al (2009) Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans. Nat Immunol 10:116–125CrossRefPubMedGoogle Scholar
  116. 116.
    Pulendran B, Li S, Nakaya HI (2010) Systems vaccinology. Immunity 33(4):516–529. CrossRefPubMedPubMedCentralGoogle Scholar
  117. 117.
    Nakaya HI, Li S, Pulendran B (2012) Systems vaccinology: learning to compute the behavior of vaccine induced immunity. Wiley Interdiscip Rev Syst Biol Med 4:193–205CrossRefPubMedGoogle Scholar
  118. 118.
    Jangi S, Gandhi R, Cox LM, Li N et al (2016) Alterations of the human gut microbiome in multiple sclerosis. Nat Commun 7:12015. CrossRefPubMedPubMedCentralGoogle Scholar
  119. 119.
    Nyamundanda G, Gormley IC, Fan Y et al (2013) MetSizeR: selecting the optimal sample size for metabolomic studies using an analysis based approach. BMC Bioinform 14:338CrossRefGoogle Scholar
  120. 120.
    Muller P, Parmigiani G, Robert C, Rousseau J (2004) Optimal sample size for multiple testing. J Am Stat Assoc 99(468):990–1000CrossRefGoogle Scholar
  121. 121.
    Tibshirani R (2006) A simple method for assessing sample sizes in microarray experiments. BMC Bioinform 7:106CrossRefGoogle Scholar
  122. 122.
    Lin WJ, Hsueh HM, Chen JJ (2010) Power and sample size estimation in microarray studies. BMC Bioinform 11:48CrossRefGoogle Scholar
  123. 123.
    Gonzalez E, van Liempd S, Conde-Vancells J, Gutierrez-de Juan V, Perez-Cormenzana M, Mayo R, Berisa A, Alonso C, Marquez CA, Barr J, Lu SC, Mato JM, Falcon-Perez JM (2012) Serum UPLC-MS/MS metabolic profiling in an experimental model for acute-liver injury reveals potential biomarkers for hepatotoxicity. Metabolomics 8(6):997–1011. CrossRefPubMedGoogle Scholar
  124. 124.
    Timmons JA, Knudsen S, Rankinen T et al (2010) Using molecular classification to predict gains in maximal aerobic capacity following endurance exercise training in humans. J Appl Physiol 108:1487–1496CrossRefPubMedPubMedCentralGoogle Scholar
  125. 125.
    Gallagher IJ, Scheele C, Keller P et al (2010) Integration of microRNA changes in vivo identifies novel molecular features of muscle insulin resistance in type 2 diabetes. Genome Med 2:9CrossRefPubMedPubMedCentralGoogle Scholar
  126. 126.
    Scherpenisse M, Schepp RM, Mollers M et al (2013) Comparison of different assays to assess human papillomavirus (HPV) type 16- and 18-specific antibodies after HPV infection and vaccination. Clin Vaccine Immunol 20(8):1329–1332. CrossRefPubMedPubMedCentralGoogle Scholar
  127. 127.
    Pritchard CC, Cheng HH, Tewari M (2015) MicroRNA profiling: approaches and considerations. Nat Rev Genet 13(5):358–369. CrossRefGoogle Scholar
  128. 128.
    Bizuayehu TT, Lanes CF, Furmanek T et al (2012) Differential expression patterns of conserved miRNAs and isomiRs during Atlantic halibut development. BMC Genom 13:11. CrossRefGoogle Scholar
  129. 129.
    Baker M (2011) Metabolomics:from small molecules to big ideas. Nat Methods 8(2):117–121CrossRefGoogle Scholar
  130. 130.
    Duportet X, Aggio RB, Carneiro S, Villas-Bôas S (2012) The biological interpretation of metabolomics data can be misled by the extraction method used. Metabolomics 8(3):410–421CrossRefGoogle Scholar
  131. 131.
    Martinaz-Arranz I, Mayo R, Perez-Cormenzana M et al (2015) Data in support of enhancing metabolomics research through data mining. Data Br 3:155–164CrossRefGoogle Scholar
  132. 132.
    Guo L, Milburn MV, Ryals JA et al (2015) Plasma metabolomics profiles enhance precision medicine for volunteers of normal health. Proc Natl Acad Sci. CrossRefPubMedGoogle Scholar
  133. 133.
    Nagana Gowda GA, Zhang S, Gu H et al (2008) Metabolomics-based methods for early disease diagnostics: a review. Exp Rev Mol Diagn 8(5):617–633. CrossRefGoogle Scholar
  134. 134.
    Wishart DS, Tzur D, Knox C et al (2007) HMDB: the human metabolome database. Nucleic Acids Res 35:D521–D526. CrossRefPubMedPubMedCentralGoogle Scholar
  135. 135.
    Wen L, Ley RE, Volchkov PV et al (2008) Innate immunity and intestinal microbiota in the development of type 1 diabetes. Nature 455(7216):1109–1113CrossRefPubMedPubMedCentralGoogle Scholar
  136. 136.
    Xavier RJ, Podolsky DK (2007) Unravelling the pathogenesis of inflammatory bowel disease. Nature 5:427–434. CrossRefGoogle Scholar
  137. 137.
    Arthur JC, Perez-Chanona E, Mühlbauer M et al (2012) Intestinal inflammation targets cancer-inducing activity of the microbiota. Science 5:120–123CrossRefGoogle Scholar
  138. 138.
    Delzenne NM, Neyrinck AM, Backhed F, Cani PD (2011) Targeting gut microbiota in obesity: effects of prebiotics and probiotics. Nat Rev Endocrinol 5:639–646CrossRefGoogle Scholar
  139. 139.
    Foster JA, McVey Neufeld KA (2013) Gut-brain axis: how the microbiome influences anxiety and depression. Trends Neurosci 36(5):305–312CrossRefPubMedGoogle Scholar
  140. 140.
    Belkaid Y, Hand T (2014) Role of the microbiota in immunity and inflammation. Cell 157(1):121–141CrossRefPubMedPubMedCentralGoogle Scholar
  141. 141.
    Turovskiy Y, Sutyak NK, Chikindas ML (2011) The aetiology of bacterial vaginosis. J Appl Microbiol 110:1105–1128CrossRefPubMedPubMedCentralGoogle Scholar
  142. 142.
    Iwase T, Uehara Y, Shinji H et al (2010) Staphylococcus epidermidis Esp inhibits Staphylococcus aureus biofilm formation and nasal colonization. Nature 465:346–349CrossRefPubMedGoogle Scholar
  143. 143.
    Kamada N, Chen GY, Inohara N, Nunez G (2013) Control of pathogens and pathobionts by the gut microbiota. Nat Immunol 14:685–690CrossRefPubMedPubMedCentralGoogle Scholar
  144. 144.
    Pacheco AR, Curtis MM, Ritchie JM et al (2012) Fucose sensing regulates bacterial intestinal colonization. Nature 492:113–117CrossRefPubMedPubMedCentralGoogle Scholar
  145. 145.
    Gantois I, Ducatelle R, Pasmans F et al (2006) Butyrate specifically down-regulates salmonella pathogenicity island 1 gene expression. Appl Environ Microbiol 72:946–949 [PubMed: 16391141] CrossRefPubMedPubMedCentralGoogle Scholar
  146. 146.
    Molloy MJ, Grainger JR, Bouladoux N et al (2013) Intraluminal containment of commensal outgrowth in the gut during infection-induced dysbiosis. Cell Host Microbe 14:318–328CrossRefPubMedPubMedCentralGoogle Scholar
  147. 147.
    Sansonetti PJ, Di Santo JP (2007) Debugging how bacteria manipulate the immune response. Immunity 26:149–161CrossRefPubMedGoogle Scholar
  148. 148.
    Manzel A, Muller DN, Hafler DA et al (2014) Role of “Western Diet” in inflammatory autoimmune diseases. Curr Allergy Asthma Rep 14(1):404. CrossRefPubMedPubMedCentralGoogle Scholar
  149. 149.
    Wu HJ, Ivanov II, Darce J et al (2010) Gut residing segmented filamentous bacteria drive autoimmune arthritis via T helper 17 cells. Immunity 32:815–827 (PubMed: 20620945) CrossRefPubMedPubMedCentralGoogle Scholar
  150. 150.
    Richter M, Rosselló-Móra R (2009) Shifting the genomic gold standard for the prokaryotic species definition. Proc Natl Acad Sci 106:19126–19131CrossRefPubMedGoogle Scholar
  151. 151.
    Kearse M, Moir R, Wilson A et al (2012) Geneious basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28:1647–1649CrossRefPubMedPubMedCentralGoogle Scholar
  152. 152.
    Khatri P, Sirota M, Butte AJ (2012) Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol 8:e1002375CrossRefPubMedPubMedCentralGoogle Scholar
  153. 153.
    Vrahatis AG, Dimitrakopoulou K, Balomenos P et al (2015) CHRONOS: a time-varying method for microRNA-mediated sub-pathway enrichment analysis. Bioinformatics 32:884–892CrossRefPubMedGoogle Scholar
  154. 154.
    Vrahatis AG, Balomenos P, Tsakalidis AK, Bezerianos A (2016) DEsubs: an R package for flexible identification of differentially expressed subpathways using RNA-seq experiments. Bioinformatics 32:3844–3846CrossRefPubMedGoogle Scholar
  155. 155.
    Kanehisa FM, Tanabe M, Sato Y, Morishima K (2017) KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 45:D353–D361CrossRefPubMedGoogle Scholar
  156. 156.
    Judeh T, Johnson C, Kumar A, Zhu D (2013) TEAK: topology enrichment analysis framework for detecting activated biological subpathways. Nucleic Acids Res 41:1425–1437CrossRefPubMedGoogle Scholar
  157. 157.
    Li C, Han J, Yao Q et al (2013) Subpathway-GM: identification of metabolic subpathways via joint power of interesting genes and metabolites and their topologies within pathways. Nucleic Acids Res 41:e101CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Joan Campbell-Tofte
    • 1
    Email author
  • Aristidis Vrahatis
    • 2
  • Knud Josefsen
    • 3
  • Jesper Mehlsen
    • 4
  • Kaj Winther
    • 5
  1. 1.Gaudeo-IKGentofteDenmark
  2. 2.Department of Medical PhysicsUniversity of PatrasPatrasGreece
  3. 3.Bartholin InstituteRigshospitaletCopenhagen ØDenmark
  4. 4.Coordinating Research CentreBispebjerg and Frederiksberg HospitalFrederiksbergDenmark
  5. 5.Department of Nutrition, Exercise and SportsUniversity of CopenhagenCopenhagen NDenmark

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