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Molecular Basis of Immune Diversity and Emerging Clinical Applications

Introduction

A wide variety of immune mechanisms have evolved in living organisms that serve to protect them from many types of disease-causing agents. These mechanisms differ in various ways including in how the immunity develops and how the agents or pathogens are processed and destroyed by the organism’s immune cells. These different mechanisms of defense constitute the immune diversity of species and are important for survival. A better understanding of the molecular basis of this diversity can be used to develop clinical tools to predict disease and treatment outcomes, as well as develop new immunotherapies.

Overview of Sources of Immune Diversity

Two processes or mechanisms that alter DNA appear to lead to vertebrate immune diversity, V(D)J recombination, and somatic hypermutation/class switch recombination. V(D)J recombination is the rearrangement of antigen receptor genes. Class switch recombination involves changes in B cell production of immunoglobulins (antibodies) from one type to another (such as switching from expressing IgM immunoglobin type to the IgG type). Although these changes allow the interaction with different effector molecules, the affinity for the same antigens remains.

Pathogens also contribute to immune diversity. Mayer et al. used mathematical models to understand the role of long-term adaptation to pathogen attributes in immune diversity3. They found that the generational presence of pathogens and how fast they can disappear from the environment affect the diversity of immune systems in species.

Genes that code for major histocompatibility (MHC) proteins are another source of immune diversity4. Degraded antigen proteins bind to MHC proteins located on the surface of antigen-presenting cells. These MHC-peptide complexes are recognized by T-cell receptors. While the individual MHC is limited, the diversity at the population level is large and predates vertebrate evolution. If a pathogen escapes MHC presentation in one person, it may not in another due to this population diversity. However, the diversity seen with MHC proteins is not due to a specific dedicated mechanism, and their mutation rate is similar to that of other genes5,6.

Characterizing the Basis of Immune Diversity

Studies in recombination and somatic mutation have provided advances in the understanding of immune diversity. Somatic or V(D)J recombination is a form of genetic reassembly or rearrangement occurring in B and T receptor genes during the early stages of T and B cell maturation. Somatic mutation allows the immune system to adapt to new antigens or pathogens encountered. These processes provide a means for the immune system to confront a wide variety of pathogens.  

The ability to study the impressive repertoire of B and T cell receptors is possible with the application of high-throughput sequencing technologies (HTS). High throughput sequencing allows the uncovering of information that illustrates the important aspects of adaptive immunity. B cell and T cell receptors have been identified as desirable targets for HTS given the number of sequences needed to study an appreciable degree of existing diversity in the lymphocyte repertoire. Various considerations in choosing HTS platforms are necessary including achievable read lengths and depth. This ensures lower error rates and the capture of information from complex repertoires. Once data is generated, however, appropriate bioinformatics tools are needed to process and interpret the findings. A number of tools exist for this purpose including IMGT/V-QUEST, IgBLAST, iHMMune-align, and more7.

Clinical Applications of Immune Diversity Assessment

From a personalized medicine perspective, the look at a person’s lymphocyte receptor repertoire may provide a profile of the immune status that can be used to determine disease risk, therapeutic options, and treatment success. Greiff et al. developed a bioinformatics approach to profile lymphocyte repertoires in order to characterize immune repertoire diversity and distribution8.  The machine-learning approaches used correlated a patients’ immunological status with their B- and T-cell repertoire data. The researchers were able to accurately predict various immunological statuses as healthy, indicative of a transplantation recipient, and related to the presence of lymphoid cancer.

Weinberger et al. compared B and T cell receptor repertoire data (generated by next-generation sequencing (NGS) data) of lymphocytes found in kidney and blood samples of patients with various renal diseases. They found that 94% of the B and T cells with clonal expansion in the kidney can also be found in the blood. These findings indicate that it may be possible to more easily monitor the progression or response to treatment of disease via examination of biomarkers in blood samples9.

Immune repertoire sequencing could be used to predict which patients will have improved survival in response to a treatment. Cha et al. used NGS to assess the impact of CTLA-4 blockade treatment on changes to the T cell repertoire in patients with prostate cancer and metastatic melanoma10. Their results showed that patients with longer survival had lower decreases in the frequency of T cell clones after treatment compared to patients with shorter survival.

B and T cell receptor profiles can provide biomarkers that can be used as a basis to develop new immunotherapies. As is true for biomarkers in general, lymphocyte profile data has the potential to provide targets for the development of treatment strategies to treat infectious disease, cancer, and more. High-throughput sequencing platforms such as immunoSEQ and pairSEQ are being used by pharmaceutical companies to characterize immune responses to disease in order to select drugs currently in development11,12.

Conclusions

The use of HTS such as NGS platforms has permitted the ability to gather a considerable amount of valuable data relating patient health status with their immunological profiles.  This information is being harnessed to identify biomarkers that can be used at various points in the clinical management of diseases. Lymphocyte repertoire profiles are analyzed to determine their use as biomarkers of disease for early detection, monitoring of disease progression, determining prognoses, and applied in the drug discovery arena.  Although still in the developmental stages, the potential to realize these objectives is positive and may be available at the point-of-care level in the near future.

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References

  1. Litman GW, Rast JP, Fugmann SD. The origins of vertebrate adaptive immunity. Nature reviews Immunology. 2010;10(8):543-553.
  2. Cato L, Stanlie A, Begum NA, Kobayashi M, Aida M, Honjo T. An evolutionary view of the mechanism for immune and genome diversity. J Immunol. 2012 Apr 15;188(8):3559-66.
  1. Mayer A, Mora T, Rivoire O, Walczak AM. Diversity of immune strategies explained by adaptation to pathogen statistics. Proc Natl Acad Sci U S A. 2016 Aug 2;113(31):8630-5.
  2. Borghans, J. A. 2000: Diversity in the Immune System. PhD Dissertation. Universiteit Utrecht,
  3. Parham, P., R. J. Benjamin, B. P. Chen, C. Clayberger, P. D. Ennis,
  4. M. Krensky, D. A. Lawlor, D. R. Littman, A. M. Norment, H. T. Orr,
  5. D. Salter, and J. Zemmour. \Diversity of Class I HLA Molecules: Functional and Evolutionary Interactions with T Cells.” Cold Spring Harbor Symp. Quant. Biol. 54 (1989): 529-543.
  6. Satta, Y., C. O’huigin, N. Takahata, and J. Klein. \The Synonymous Substitution Rate of the Major Histocompatibility Complex Loci in Primates.” Proc. Natl. Acad. Sci. USA 90 (1993): 7480-7484.
  7. Calis JJ, Rosenberg BR. Characterizing immune repertoires by high throughput sequencing: strategies and applications. Trends Immunol. 2014 Oct 8;35(12):581-590.
  8. Greiff V, Bhat P, Cook SC, Menzel U, Kang W, Reddy ST. A bioinformatic framework for immune repertoire diversity profiling enables detection of immunological status. Genome Med. 2015 May 28;7(1):49.
  9. Weinberger J, Jimenez-Heredia R, Schaller S, et al. Immune Repertoire Profiling Reveals that Clonally Expanded B and T Cells Infiltrating Diseased Human Kidneys Can Also Be Tracked in Blood. Lythe G, ed. PLoS ONE. 2015;10(11):e0143125.
  10. Cha E, Klinger M, Hou Y, et al. Improved Survival with T Cell Clonotype Stability After Anti–CTLA-4 Treatment in Cancer Patients. Science translational medicine. 2014;6(238):238ra70.
  11. Recent Pharma Dx Agreements 2014. Available from: http://www.clpmag.com/reference/recent-pharma-dx-agreements.
  12. Eisenstein M. Immune profiling players shift gear to guide cancer drug development. Nat Biotech. 2016;34(3):215-6.

Written by Macrogen Corp.

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