Danger model explained

The danger model of the immune system proposes that it differentiates between components that are capable of causing damage, rather that distinguishing between self and non-self.

History of immunologic models

The first major immunologic model was the Self/Non-self Model proposed by Macfarlane Burnet and Frank Fenner in 1949 with later refinement by Burnet.[1] [2] It theorizes that the immune system distinguishes between self, which is tolerated, and non-self, which is attacked and destroyed. According to this theory, the chief cell of the immune system is the B cell, activated by recognizing non-self structures. Later research showed that B cell activation is reliant on CD4+ T helper cells and a co-stimulatory signal from an antigen-presenting cell (APC). Because APCs are not antigen-specific, capable of processing self structures, Charles Janeway proposed the Infectious Non-self Model in 1989.[3] Janeway's theory involved APCs being activated by pattern recognition receptors (PRRs) that recognize evolutionarily conserved pathogen-associated molecular patterns (PAMPs) as infectious non-self, whereas PRRs are not activated by non-infectious self. However, neither of these models are sufficient to explain non-cytopathic viral infections, graft rejection, or anti-tumor immunity.[4]

Danger model

In 1994, Polly Matzinger formulated the danger model, theorizing that the immune system identifies threats to initiate an immune response based on the presence of pathogens and/or alarm signals from cells under stress.[5] [6] When injured or stressed, tissues typically undergo non-silent types of cell death, such as necrosis or pyroptosis, releasing danger signals like DNA, RNA, heat shock proteins (Hsps), hyaluronic acid, serum amyloid A protein, ATP, uric acid, and cytokines like interferon-α, interleukin-1β, and CD40L for detection by dendritic cells.[7] In comparison, neoplastic tumors do not induce significant immune responses because controlled apoptosis degrades most danger signals, preventing the detection and destruction of malignant cells.[8]

Matzinger's work emphasizes that bodily tissues are the drivers of immunity, providing alarm signals on the location and extent of damage to minimize collateral damage.[9] [10] The adaptive immune system relies on the innate immune system using its antigen-presenting cells to activate B and T lymphocytes for specific antibodies, exemplified by low dendritic cell counts resulting in common variable immunodeficiency (CVID).[11] For example, gut cells secrete transforming growth factor beta (TGF-β) during bacterial invasions to stimulate B cell production of Immunoglobulin A (IgA).[12] Similarly, 30-40% of the liver's T cells are Type I Natural Killer T (NTK) cells, providing Interleukin 4 (IL-4) for an organ-specific response of driving naïve CD4+ T cells to become Type 2 Helper T cells, as opposed to Type 1.[13] [14]

Damage-associated molecular pattern (DAMP) model

See also: Damage-associated molecular pattern.

Whereas the danger model proposes non-silent cell death releasing intracellular contents and/or expressing unique signalling proteins to stimulate an immune response, the damage-associated molecular pattern (DAMP) model theorizes that the immune system responds to exposed hydrophobic regions of biological molecules. In 2004, Seung-Yong Seong and Matzinger argued that as cellular damage causes denaturing and protein misfolding, exposed hydrophobic regions aggregate into clumps for improved binding to immune receptors.[15]

Pattern Recognition Receptors (PRRs)

Pattern Recognition Receptors (PRRs) are a family of surface receptors on antigen-presenting cells that includes toll-like receptors (TLRs), nucleotide oligomerization domain (NOD)-like receptors,[16] retinoic acid inducible gene-I (RIG-I)-like receptors[17] and C-type lectin-like receptors (CLRs).[18] They recognize alarmins, a category that includes both DAMPs and PAMPs, to process their antigenic regions for presentation to T helper cells.

Notes and References

  1. Book: Burnet FM, Fenner F . The Production of Antibodies . 2nd . Macmillan . Melbourne . 1949.
  2. Book: Burnet FM . Cellular Immunology: Self and Notself . Cambridge University Press . Cambridge . 1969.
  3. Janeway CA . Approaching the asymptote? Evolution and revolution in immunology . Cold Spring Harbor Symposia on Quantitative Biology . 54 Pt 1 . 1 . 1–13 . 1989-01-01 . 2700931 . 10.1101/sqb.1989.054.01.003 .
  4. Matzinger P . The danger model: a renewed sense of self . Science . 296 . 5566 . 301–305 . April 2002 . 11951032 . 10.1126/science.1071059 . 13615808 . 10.1.1.127.558 . 2002Sci...296..301M .
  5. Matzinger P . Tolerance, danger, and the extended family . Annual Review of Immunology . 12 . 1 . 991–1045 . 1994 . 8011301 . 10.1146/annurev.iy.12.040194.005015 .
  6. Hallenbeck J, Del Zoppo G, Jacobs T, Hakim A, Goldman S, Utz U, Hasan A . Immunomodulation strategies for preventing vascular disease of the brain and heart: workshop summary . Stroke . 37 . 12 . 3035–3042 . December 2006 . 17082471 . 1853372 . 10.1161/01.STR.0000248836.82538.ee .
  7. Jounai N, Kobiyama K, Takeshita F, Ishii KJ . Recognition of damage-associated molecular patterns related to nucleic acids during inflammation and vaccination . Frontiers in Cellular and Infection Microbiology . 2 . 168 . 2012 . 23316484 . 3539075 . 10.3389/fcimb.2012.00168 . free .
  8. Pradeu T, Cooper EL . The danger theory: 20 years later . Frontiers in Immunology . 3 . 287 . 2012-01-01 . 23060876 . 3443751 . 10.3389/fimmu.2012.00287 . free .
  9. Matzinger P . Friendly and dangerous signals: is the tissue in control? . Nature Immunology . 8 . 1 . 11–13 . January 2007 . 17179963 . 10.1038/ni0107-11 . 6448542 .
  10. Matzinger P, Kamala T . Tissue-based class control: the other side of tolerance . Nature Reviews. Immunology . 11 . 3 . 221–230 . March 2011 . 21350581 . 10.1038/nri2940 . 10809131 .
  11. Bayry J, Lacroix-Desmazes S, Kazatchkine MD, Galicier L, Lepelletier Y, Webster D, Lévy Y, Eibl MM, Oksenhendler E, Hermine O, Kaveri SV . 6 . Common variable immunodeficiency is associated with defective functions of dendritic cells . Blood . 104 . 8 . 2441–2443 . October 2004 . 15226176 . 10.1182/blood-2004-04-1325 . free .
  12. Bauché D, Marie JC . Transforming growth factor β: a master regulator of the gut microbiota and immune cell interactions . Clinical & Translational Immunology . 6 . 4 . e136 . April 2017 . 28523126 . 5418590 . 10.1038/cti.2017.9 .
  13. Gao B, Jeong WI, Tian Z . Liver: An organ with predominant innate immunity . Hepatology . 47 . 2 . 729–736 . February 2008 . 18167066 . 10.1002/hep.22034 . 5441697 . free .
  14. Yoshimoto T . The Hunt for the Source of Primary Interleukin-4: How We Discovered That Natural Killer T Cells and Basophils Determine T Helper Type 2 Cell Differentiation In Vivo . Frontiers in Immunology . 9 . 716 . 2018 . 29740428 . 10.3389/fimmu.2018.00716 . 5924770 . free .
  15. Seong S, Matzinger P . Hydrophobicity: an ancient damage-associated molecular pattern that initiates innate immune responses. Nature Reviews Immunology . 4 . 6. 469–478. 2004 . 15173835 . 10.1038/nri1372. 13336660.
  16. Tanti JF, Ceppo F, Jager J, Berthou F . 2012 . Implication of inflammatory signaling pathways in obesity-induced insulin resistance . Front Endocrinol (Lausanne) . 3 . 181 . 10.3389/fendo.2012.00181 . 3539134 . 23316186 . free.
  17. Beckham SA, Brouwer J, Roth A, Wang D, etal . 2012 . Conformational rearrangements og RIG-I receptor on formation of a multiprotein: dsRNA assembly . Nucleic Acids Res. . 41 . 5 . 3436–45 . 10.1093/nar/gks1477 . 3597671 . 23325848.
  18. Kuroki K, Furukawa A, Maenaka K . 2012 . Molecular recognition of paired receptors in the immune system . Front Microbiol . 3 . 429 . 10.3389/fmicb.2012.00429 . 3533184 . 23293633 . free.

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