In fact, discussions on clinical implementation and utilization of this approach in organ allocation algorithms are currently on-going. While the terms are often used synonymously, they are NOT equivalent. This short overview is meant to emphasize the differences between the terms epitope matching and eplet mismatching or mismatch load as well as to provide perspective on different approaches for interpretation of immune compatibility between the donor of an organ transplant and the recipient. It highlights some of the less explored qualities of HLA-epitopes, and stresses the need to understand the differences between donor and recipient in terms of immunogenicity and ability to initiate an immune response.
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In fact, discussions on clinical implementation and utilization of this approach in organ allocation algorithms are currently on-going. While the terms are often used synonymously, they are NOT equivalent. This short overview is meant to emphasize the differences between the terms epitope matching and eplet mismatching or mismatch load as well as to provide perspective on different approaches for interpretation of immune compatibility between the donor of an organ transplant and the recipient.
It highlights some of the less explored qualities of HLA-epitopes, and stresses the need to understand the differences between donor and recipient in terms of immunogenicity and ability to initiate an immune response. Further work is required before new approaches can be introduced into routine clinical practice and organ allocation schemes.
To avoid putting the cart in front of the horse, it is important to define the meaning, appreciate nuances in terminology, and understand the exact purpose of such approach. Is it simply differences in amino-acid sequences? Is that only semantics or does each term represent different entities with different effects on the immune response?
Importantly, can we assume that each amino-acid change affects immune reactivity to the same degree? How would we quantify a change between 2 amino-acid with similar properties e. If the former—what algorithm should be used? Are we to give the same weight to matching at class I and class II eplets? How would changes in algorithm affect organ allocation and equity for the different ethnic groups?
Will it inflate or deflate known differences? To address these questions one need to first gain understanding to the concept of epitopes in HLA. The past decade witnessed a burst in the use of the term HLA-epitopes in transplant related literature. Interestingly, there was a similar peak, albeit much shorter, around — This is an important observation as the concept of HLA antibodies recognizing a specific HLA epitope rather than the whole HLA antigen was already appreciated in the early days of histocompatibility testing 1 — 3.
A 4th serum can react positively with both A2 and A68, but not B57; another serum can react with A2 and B57, but not A68; and yet another serum can react with all 3 antigens. Thus, A2 and A68 share an epitope—a target for antibody recognition, different from the epitope shared by A2 and B57, and yet another epitope is shared by A2, A68, and B All these are in addition to the epitopes that are unique to the individual antigens.
The introduction of molecular typing technique and elucidation of the 3D structure of an HLA molecule helped in deciphering those intricate antibody recognition patterns. While the HLA system is the most polymorphic system known, it also has a very high degree of homology.
This description may sound like an oxymoron, but at the population level, it is required in order to support the physiologic role of the HLA system, namely, presentation of wide range of foreign proteins viruses, bacteria, etc.
Comparing AA sequences to antibody reactivity patterns revealed some correlation between the two, leading to the supposition that there is an association between AA sequence similarities or differences and antibody reactivity.
In fact, this is the basis of the HLA-matchmaker software developed by Rene Duquesnoy, using triplets and eplets to describe amino acids in linear and non-contiguous sequences. Polymorphism and homology in the HLA system. A The sequence of the first amino acids of HLA-A alleles is presented using the one letter naming convention. Allele identity is listed to the left.
The vast majority of the other alleles' sequences are homologous to the consensus sequence. Polymorphism is represented by a single letter designation of the different amino acid.
Even among polymorphic positions, there is identity with some of the other alleles. This is demonstrated by the red boxes—all of those alleles have Y in position 9 but other alleles are identical to the consensus sequence F and yet others have S. Other examples are illustrated by the green boxes. B Polymorphism can be distributed in different areas of the HLA molecule.
Three examples are illustrated by the yellow highlights in the sequence, and the corresponding sites of the molecule are shown in the 3 insets, listing the polymorphic amino acids that are highlighted in yellow. Those are located, from left to right: at the lower edge of the alpha helix, around center molecule; the alpha helix at the edge of the peptide binding groove; and at the bottom of the peptide binding groove—beta pleated sheets.
The projected effects on T cell receptor and the bound peptide are likely to change based on the location of the polymorphism. Earlier reports using HLAMatchmaker provided insight into how one can interpret antibody reactivity patterns. For example—providing rationale to common observations of patients, exposed to a particular donor HLA-mismatch, developing antibodies not only against this donor but also against additional non-donor HLA antigens 5 — 7 ; explanation for development of antibodies only to a subset of alleles within a single antigen at the serological level but not to other alleles within that antigen family of alleles 8 , 9 ; etc.
The excitement that followed Duquesnoy's innovative software was quickly translated into a search for clinical applications. The vast majority of the published work utilized HLAMatchmaker in a retrospective fashion, to document that recipients of high eplet mismatch load organs had worse outcome. Wiebe et al. Sapir-Pichhadze et al. Sullivan et al.
Unfortunately, since HLAMatchmaker requires high resolution typing information, and the data in the SRTR is only at the serologic level, associations were found only between recipients with 2—4 class I mismatches and graft loss. Their cohort of patients was previously analyzed for the effect of medication non-adherence, using electric monitors in medication vial caps. The above studies strongly support the conclusion that, at the population level, patients with higher eplet MM load are more likely to have worse graft outcome.
Moreover, these studies suggest that eplet MM load can provide guidance for risk stratification post transplantation, when considering immunosuppressive drug minimization strategies.
However, at the individual patient level, only one group attempted to use eplet MM load strategies to inform allocation. Kausman et al. Nineteen patients were transplanted during the 1-year study period. Of those, 8 were chosen for the modified deceased-donor exclusion criteria; 8 received a living-donor transplant, with no exclusion; and 3 received deceased-donor transplant with no exclusion. A similar approach was utilized by this group to transplant 7 pediatric patients through the Australian Kidney Exchange AKX program 17 as well as in their adult Kidney Paired Exchange program While potentially promising, this single center study includes very small cohorts, which precludes attaining conclusive results.
This selection is based on identification of all mismatched eplets that are present in the antigens targeted by the patients' serum but absent from their own HLA antigens. The universe of these mismatched eplets is then compared against the rest of the HLA specificities and any HLA antigen that carries one of those mismatched eplets is removed as well.
Thus, two categories of HLA antigens are removed from the potential acceptable mismatched antigens: those that the recipient has proven antibodies to and those that share AA sequences with the first category, but no antibodies were reported.
After excluding all of these antigens, the patient is left with a cohort of potentially acceptable antigens that includes his own HLA antigens and the antigens left after the exclusion Of note, patients in the Acceptable MM program are offered donors from all geographic areas covered by Eurotransplant, as long as they fulfill one of two criteria. A recent report summarizing the Acceptable MM program with 10 years follow-up showed that these patients have significantly superior year graft survival compared to highly sensitized patients transplanted on the basis of avoidance of unacceptable mismatches The concept of the acceptable MM program has been in use by Eurotransplant for about 25 years.
While very successful, it is important to appreciate that it tackles a different aspect of matching, namely, extending the universe of unacceptable mismatches beyond those that the patients exhibit antibodies to. The immunological rationale driving this approach is that the patient is more likely to develop antibodies [or harbor memory 21 ] to these additional antigens, and less likely to develop antibodies against HLA antigens that do not share mismatches with current antibody targets.
This rationale is supported by multiple studies using the HLAMatchmaker software to explain the generation of 3rd party antibodies together with the generation of DSA 5 — 9. The 3-dimentional structure of an HLA molecule has evolved during evolution to fit its role in presentation of foreign molecules to the cellular arm of the immune system. Specifically, the two most distal domains of an HLA molecule form a peptide-binding groove in which the foreign peptide is to be nestled Accordingly, the polymorphic amino acids of an HLA molecule are concentrated in these two domains, mostly in areas that participate in forming the peptide-binding site and T cell receptor recognition site Thus, it is important to appreciate that not every polymorphism will have the same impact on immune recognition.
The first polymorphism—amino acid AA positions 62,65,66,67 highlighted in yellow is located within the alpha helix, around the center of the molecule; the second—AA positions 76,79,80,81,82,83 is also located within the alpha helix but closer to the area that frames the edges of the peptide binding group; both of these polymorphic sites are likely to be part of both the peptide binding site as well as the TCR recognition site; lastly, the third polymorphism—AA positions 95,97,99 are actually buried below the peptide, within the beta pleated sheets, not directly accessible to TCR recognition, but definitely affecting the peptide that can be bound by the HLA molecule.
While the exact impact of these polymorphisms has not been elucidated, it is expected that they will have different effects on immune reactivity. In fact, some evidence to this statement was already demonstrated by Paul Terasaki's group analyzing HLA antibody profiles of allosera and verification by absorption elution studies, leading to the Ter-Ep nomenclature Similarly, it is important to appreciate that only a few the eplets are considered verified in the epitope registry Beyond the exact location of the mismatch, one should also consider the unique properties of the polymorphic amino acids e.
The polarity and charge of the amino acid can have a profound effect not only at the specific area of polymorphism but it can also affect neighboring AAs and thus impacting the structure and biological activity of the HLA molecule at large.
Assuming the recipient has an HLA-DQ with Glycine at position 13 and his donor has Alanine at that position, the substitution involves two AAs with similar characteristics.
Moreover, position 13 is located at the bottom of the peptide-binding grove. The overall effect on the properties of the HLA molecule are therefore not likely to be significant, thus having low impact on initiating a significant alloimmune response.
On the other hand, when the AA mismatch involves a substitution of a small nonpolar Glycine with a bulky and polar Tyrosine position 26 , the expected effect is significantly higher. This substitution is located within the beta pleated sheets, just below the alpha helix framing the peptide-binding site. The mismatch between the recipient's Glycine at position 45 with a donor's Glutamic acid will introduce an acidic and bulky AA instead of the small nonpolar original component.
This substitution is located further from the peptide binding site. Lastly, a similar scenario is illustrated by a mismatch between Valine and Aspartic acid at position In this example, the substitution is located within the alpha helix.
In other words, these substitutions are likely to carry over to affect the location of neighboring AA, as they are bound to displace them even if no additional substitutions are present. One may expect that this change will be associated with a significant effect on allorecognition.
Replacing Glycine with the much bulkier and polar Tyrosine is likely to displace not only the amino acids in position 25 and 27 that are neighboring in the 2D sequence, but also other amino acids that are adjacent to it in the 3D structure, including most likely AA 70— Thus, a change of one AA can drive a significant change of the dimensions HLA molecule as well as the peptides it can bind and its interactions with TCR and antibodies.
A Amino Acid substitutions can come in different flavors. Amino acids can be classified based on several characteristics: Polarity, Electrostatic charge, Aliphatic, Aromatic, Size, etc. The nature of the substitution and potentially its immunological magnitude is likely to be influenced by how similar or different the mismatched amino acid is. The panels on the right highlight the area of the molecule where the substitution takes place, emphasizing the nature of the substitution based on the parameters listed above.
B Ripple effect of an amino acid substitution on overall 3D structure. The single amino acid substitution in this example is at position 26, demonstrating an eplet mismatch between Glycine and Tyrosine. Tyrosine is polar and significantly bulkier than the small, non-polar Glycine illustrated as small inserts at top right.
Replacing Glycine with Tyrosine is likely to displace not only the amino acids adjacent to it in the sequence 2D; positions 25 and 27 but also all other neighboring amino acids at the 3D structure—shown in pink arrows. In fact, there are numerous examples in which the donor and recipient differ only by a single amino-acid difference and yet the recipient developed de-novo DSA.
Of note, additional approaches and tools are currently used to explore the concepts of immunogenicity and antigenicity, and whether we can apply those for better donor-recipient matching. Vasilis Kosmaliaptsis and his group are studying physiochemical disparities between donor and recipient 26 ,
HLA-Epitope Matching or Eplet Risk Stratification: The Devil Is in the Details
To save this word, you'll need to log in. Send us feedback. See more words from the same year Dictionary Entries near epitope epitomizer Epitoniidae Epitonium epitope epitoxoid epitrachelion epitrite. Accessed 4 Jun. Keep scrolling for more More Definitions for epitope epitope.
Difference Between a Paratope and an Epitope
A paratope , also called an antigen-binding site , is a part of an antibody which recognizes and binds to an antigen. It is a small region of 5 to 10 amino acids [ citation needed ] of the antibody's Fab region , part of the fragment antigen-binding Fab region , and contains parts of the antibody's heavy and light chains. The part of the antigen to which the paratope binds is called an epitope. This can be mimicked by a mimotope. The figure given on the right hand side depicts the antibody commonly found on a B leukocyte.