Skip to main content

EDITORIAL article

Front. Immunol., 31 August 2022
Sec. Alloimmunity and Transplantation
This article is part of the Research Topic Future challenges and directions in determining allo-immunity in kidney transplantation View all 7 articles

Editorial: Future challenges and directions in determining allo-immunity in kidney transplantation

  • 1Department of Renal Medicine, Sir Charles Gairdner Hospital, Perth, WA, Australia
  • 2Medical School, University of Western Australia, Perth, WA, Australia
  • 3Department of Internal Medicine University of Manitoba, Winnipeg, MB, Canada
  • 4Department of Immunology, University of Manitoba, Winnipeg, MB, Canada
  • 5Transplant Manitoba Adult Kidney Program, Transplant Manitoba, Shared Health Manitoba, Winnipeg, MB, Canada
  • 6Department of Surgery, University of Cambridge and National Institute for Health Research Cambridge Biomedical Research Centre, Addenbrooke’s Hospital, Cambridge, United Kingdom
  • 7Blood and Transplant Research Unit in Organ Donation and Transplantation, National Institute for Health Research, University of Cambridge, Cambridge, United Kingdom
  • 8Centre for Outcomes Research and Evaluation, Research Institute of McGill University Health Centre, Montreal, QC, Canada
  • 9Division of Nephrology and Multi-Organ Transplant Program, Department of Medicine, McGill University, Montreal, QC, Canada

Improving long-term allograft survival remains one of the key contemporary challenges of transplantation medicine. Despite improvement in short-term kidney allograft outcomes, more than 1 in 2 kidney transplant recipients will lose their allograft within 15 years of transplantation (1). Returning to dialysis is associated with a substantial risk of death which is increased by almost 10-fold compared to patients with functioning kidney allografts (2). Maintaining a good functioning allograft over time is complex and multiple risk factors influence long-term allograft survival, ranging from organ procurement factors, post-transplant adverse events such as delayed graft function and rejection episodes, to the effects of chronic exposure to immunosuppression. To improve kidney allograft survival, both traditional and emerging potentially modifiable risk factors need to be identified.

Another equally important aspect of transplantation medicine is the assessment of sensitization status (3, 4). Pre-transplant immunological risk assessment typically involves the screening for anti-human leukocyte antigen (HLA) antibody, which can occur following prior allograft loss, infection, pregnancy and blood transfusion. Although the testing for non-HLA antibody may provide a more comprehensive profile of sensitization status pre-transplantation, the cost-benefit and cost-utility of this approach remains uncertain. Donor/recipient HLA incompatibility often increases the risk of allo-sensitization, resulting in the development of de novo donor specific anti-HLA antibody (dnDSA), which is strongly associated with acute rejection, premature allograft loss and reduced retransplant potential (5, 6).

The HLA system encompasses gene loci that determine tissue compatibility in organ transplantation and consequently, HLA-matching has been considered the standard triage test for immunological risk assessment for deceased donor kidney allocation worldwide allowing clinicians to modify immunosuppressive agents according to this risk (7). The HLA system is extremely polymorphic and functionally complex and in organ transplantation, this polymorphism has an important role in determining allo-immunity, including the development of acute rejection and dnDSA after transplantation. HLA-typing has evolved from serological (method based on testing the reactivity of specific anti-sera with antigens) to molecular typing involving all HLA Class I and II alleles in the last decade. The latter, combined with advances in structural HLA modelling, have provided opportunities for more accurate assessment of HLA compatibility at the molecular level and underpinned an interest into defining the structural determinants of HLA allorecognition, also known as HLA B-cell epitopes. These epitopes consist of configurations of polymorphic amino acid residues expressed on HLA molecules that are recognized by the host’s immune system, generating an immune response that leads to the production of anti-HLA antibody.

The most commonly used algorithm for determining HLA compatibility at the molecular level is HLAMatchmaker which assumes that each HLA incorporates multiple structural epitopes (15-22 polymorphic amino acid residues) that form part of the binding surface with alloantibody, with each structural epitope encompassing at least one, smaller, “functional epitope” (cluster of surface-exposed amino acid residues at least one of which is polymorphic) called eplet, which determines the specificity and binding strength of the alloantibody-HLA interaction. Eplets comprise of short sequences of amino acid residues within a 3 Angstrom radius that interact directly with the paratope of an anti-HLA antibody (8, 9). HLA immunogenicity defined at the eplet level, expressed as the total number of eplet mismatches present in a donor HLA molecule, has been shown to provide a more accurate metric of HLA incompatibility beyond conventional broad antigen HLA mismatch with incremental eplet mismatches associated with an excess risk of acute antibody mediated rejection (AMR), development of dnDSA, transplant glomerulopathy and premature allograft loss in pediatric and adult kidney transplant recipients (917). Therefore, immunological risk assessment focused on quantifying the total number of eplet mismatches, calculated using HLAMatchmaker (18), may provide a more precise determination of immunological compatibility and subsequent risk stratification (8, 19). There are increasing data suggesting that the relationship between the number of eplet mismatches and adverse allograft outcome is not linear, and the idea of considering a population data-driven defined threshold, ignoring the relative immunogenicity of individual eplet mismatches, to inform clinical risk prediction is conceivably flawed. Several groups have attempted to provide insights into the differential and potentially hierarchical effects of individual or clusters of eplet mismatches (and at specific loci such as HLA-DQ), identification of high-risk eplet mismatches, and definition of eplet mismatch at the single HLA molecule level using novel machine learning statistical techniques that may provide a more accurate assessment of immunological risk (2023).

Nevertheless, clinicians should be cognizant of the limitations of these studies and should cautiously interpret the study findings and applicability in clinical practice. Many of the studies reporting on the association between eplet mismatches and allograft outcome were confined to predominantly homogenous White populations, often undertaking imputation of the most likely 2-field molecular typing based on serological or low/intermediate resolution molecular typing using National Marrow Donor Program algorithm, the catalogue of Common and Well Documented (CWD) alleles integrated into the Allele Frequencies Net Database (AFND), Haplostats and the IMGT/HLA Database (2427); and therefore misclassification bias of the total number and specificity of eplet mismatches may occur (2832). The lack of large-scale data analysis from non-White populations, the inadequacies of imputation programs and presence of novel HLA alleles in Indigenous populations have further complicated the widespread acceptance of structural HLA compatibility in organ allocation and immunological risk assessment. There are several papers in this article collection that highlight the importance and limitations when considering HLA and non-HLA immunity in kidney transplantation. The paper by Larkins et al. highlights one of these issues and showed that a lack of high-resolution donor typing at the time of deceased donor kidney allocation can erroneously identify donor-specific anti-HLA antibody and eplet mismatches, which may have corresponding downstream effects on organ allocation and acceptance.

There is considerable debate regarding the optimal assessment of the immunogenicity of mismatched HLA alleles that could lead to the development of dnDSA and rejection. The methodology of defining clinically relevant immunogenic eplet mismatches and their reporting in the HLA-eplet registry is a subject of ongoing discussion (20, 3335). The papers by Bezstarosti et al. review the evaluation of antibody-verified eplets, highlighting the need for internationally accepted standardized eplet verification methods. Moreover, using recombinant human HLA-DQ-specific monoclonal antibodies generated by isolated allospecific memory B cells from immunized individuals, the authors also propose a new platform for future antibody-verification of eplets within HLA-DQ alleles. Undoubtedly, additional studies verifying the risks associated with mismatched eplets captured in the HLA Eplet Registry in other well characterized cohorts are urgently required to validate the HLAMatchmaker eplet concept and potentially inform clinical care. It is important to note additional strategies and available software to assess donor/recipient HLA compatibility considering quantification of amino acid sequence polymopshism, differences in donor-recipient HLA physicochemical properties (3-dimensional electrostatic mismatch), and donor-derived Predicted Indirectly Recognizable HLA Epitopes presented by recipient HLA class II (PIRCHE-II, assesses the HLA-mismatch derived T-cell epitopes by quantifying the number of polymorphic donor HLA-derived peptides that can be presented on recipient HLA class II molecules). In addition to HLAMatchmaker, these algorithms have also been shown to predict adverse allograft outcomes post-kidney and simultaneous pancreas-kidney transplantation (3645). Although these alternative algorithms independently predict the development of dnDSA beyond conventional broad antigen HLA-mismatches, there is little evidence to suggest that one algorithm is superior to another and a high degree of correlation between different outputs is often notable (46). Accordingly, the added advantage of a global integration of these measures in predicting HLA immunogenicity remains uncertain. Interestingly, a recent cohort study of 691 live-donor kidney transplant recipients suggested that eplet mismatches and PIRCHE score may be complementary in improving the discrimination of dnDSA risk, suggesting that a combined immunological risk prediction considering both B and T cell epitopes may be the way forward (43). Another European study showed that PIRCHE-II score may help to identify acceptable HLA mismatches that are associated with a lower risk of dnDSA independent of antigen mismatch and HLAMatchmaker epitopes, further suggesting the potential clinical applicability of this measure (40). With a growing body of evidence showing that mismatches at non-HLA variants may influence kidney transplant outcomes, a greater understanding of the clinical relevance of non-HLA genetic loci and their interaction with HLA-matching is required. The paper by Jethwani et al. provides a comprehensive review of the potential role and applicability of mismatches at non-HLA gene variants in predicting kidney allograft outcomes. The questions of the expected “value-added” of non-HLA variants in improving discrimination of adverse allograft outcomes above and beyond HLA incompatibility, as well as when and how to incorporate non-HLA genetic assessments in organ allocation and acceptance, require further study. Importantly, balancing between the cost-benefit of such approach must also be ensured. A group of younger kidney transplant candidates, who are more likely to require retransplantation in the context of suboptimal compatibility with their donors, may be the ideal group to benefit from a more precise assessment of HLA and non-HLA gene profiles.

Even though there are important caveats when considering the clinical applicability of utilizing structural HLA compatibility in immunological risk assessment and to inform allocation practices, there have been reports of successful integration of molecular compatibility in programs like the Eurotransplant Acceptable Mismatch program and in the context of deceased donor allocation to pediatric patients with kidney failure in Australia (47, 48). In a simulated model of implementing an alternative allocation strategy to avoid immunogenic “risk” molecular mismatches (i.e. mismatches associated with high risk of dnDSA) in lung transplantation, the avoidance of these high-risk mismatches could reduce the absolute rate of developing class II dnDSA by 30% (from 36% to 6%), with the trade-off that between 60% and 98% of donors would be excluded (22). On a practical level, by virtue of the population composition and more specifically the genetic profile of the donor pool and transplant candidates, different high risk molecular incompatibilities may be observed. Additionally, whether the added waiting time when striving to avoid high-risk mismatches at a population level might abolish the potential immunological gain from the avoidance of these high-risk molecular mismatches must be considered.

Despite the decreased incidence of acute rejection over the last few decades, this remains an important cause of allograft loss after kidney transplantation. Early identification and treatment of acute rejection is critical to avoid the deleterious effect on long-term allograft survival (49). Pre-transplant immunological risk stratification strategies can be used to personalize transplant immunosuppression and monitoring approaches and can be complemented by a careful assessment of the effect of immunosuppressive agents on the functionality, distribution and compartmentalization (peripheral blood, lymphoid organs, allograft) of alloreactive T and B cell subsets. This is critical to allow for ensuing strategies to personalized immunosuppression, balancing between the risk of “inadequate” immunosuppression (causing acute rejection) versus “excessive” immunosuppression (causing infection and cancer). While many centres administer induction immunosuppression based on patient’s sensitization history, the paper by Aschauer et al. reports that while reduced dose T-cell depleting induction therapy provided a greater early reduction of donor-reactive T cells compared to interleukin-2 inhibitor induction, there was no change on the overall T-cell receptor repertoire after immune reconstitution. The clinical implications of this observation and how the monitoring of circulating donor-specific T and B cells and corresponding T and B cell repertoires should inform modifications to immunosuppression regimens remain unknown and require evaluation in large prospective cohort studies and clinical trials.

The deleterious effect of post-transplant blood transfusion on risk of allosensitization (with development of dnDSA), acute rejection and allograft survival has been inconclusive, with the risk of allosensitization following blood transfusion being as high as 20% (5055). However, the paper in this article collection by Jouve et al. challenged this notion by showing that blood transfusion in the first 3 months post-kidney transplant was not associated with an excess risk of developing dnDSA, with over 80% of these recipients receiving T-cell depleting antibody as induction therapy. Given the short-term follow-up of this study, the longer-term risk of allo-sensitization cannot be determined with certainty.

With the expansion in the knowledge of defining immunological risk and a greater availability and accessibility of high resolution molecular HLA typing techniques and Luminex technology to detect donor-specific anti-HLA and non-HLA antibodies, there continues to be a high degree of uncertainty as to the practical utility of eplet and other algorithms assessing structural HLA-compatibility, as well as the integration of this information into the complex process of organ allocation and decision making in clinical kidney transplantation. There is currently no single assay or measure that can capture all aspects of alloreactive cellular and humoral immune responses and the selection of one or multiple immunological risk prediction tools that may be complementary (or mutually exclusive) in risk stratification remain inadequately defined. Since the early seminal papers that have highlighted the potential clinical importance of HLA compatibility at the eplet or amino acid level, along with the prognostic significance of donor-specific anti-HLA and non-HLA antibodies, there has been an upsurge of studies that have attempted to validate these findings in different population cohorts and to identify other novel aspects of these associations or to report on diagnostic accuracy using big data and novel data science methodologies. However, there are important caveats the readers will need to consider when interpreting the study design, findings and conclusions of the growing number of publications addressing this issue, including highly selected and often homogenous patient populations and small sample sizes, differences in statistical techniques and adjustment of important confounders in multivariable models, lack of adherence to the Standards for Reporting of Diagnostic Accuracy Studies (STARD) statement (for diagnostic accuracy studies), inconsistent definitions and measurements of the predictors and outcomes, and the potential for inaccurate ascertainment of antibody profiles (incorrect or uncertain HLA imputation methods, lack of high resoluting typing across all HLA alleles and antibody verification) (56, 57). We hope this editorial, and selection of our article collection provides some insights into the challenges and limitations of the current landscape in the understanding of allo-immunity in kidney transplantation. We do envisage future prospective cohort studies and clinical trials focusing on a “global” evaluation of immunologic risk related to HLA and non-HLA-related injury and on immune monitoring using T cell receptor and other novel measures, such as HLA-specific B cells and donor-derived cell-free deoxyribonucleic acid, to provide much needed insight into this complex research field. Integration of the findings of such studies into clinical practice may inform future personalized immunosuppression strategies, reduce the risk of post-transplant allo-immune responses, and prolong allograft survival and clinical outcomes following kidney transplantation.

Author contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

1. Coemans M, Susal C, Dohler B, Anglicheau D, Giral M, Bestard O, et al. Analyses of the short- and long-term graft survival after kidney transplantation in Europe between 1986 and 2015. Kidney Int (2018) 94(5):964–73. doi: 10.1016/j.kint.2018.05.018

PubMed Abstract | CrossRef Full Text | Google Scholar

2. Ying T, Shi B, Kelly PJ, Pilmore H, Clayton PA, Chadban SJ. Death after kidney transplantation: An analysis by era and time post-transplant. J Am Soc Nephrol. (2020) 31(12):2887–99. doi: 10.1681/ASN.2020050566

PubMed Abstract | CrossRef Full Text | Google Scholar

3. Redfield RR, Scalea JR, Zens TJ, Mandelbrot DA, Leverson G, Kaufman DB, et al. The mode of sensitization and its influence on allograft outcomes in highly sensitized kidney transplant recipients. Nephrol Dial Transplant (2016) 31(10):1746–53. doi: 10.1093/ndt/gfw099

PubMed Abstract | CrossRef Full Text | Google Scholar

4. Tambur AR, Campbell P, Chong AS, Feng S, Ford ML, Gebel H, et al. Sensitization in transplantation: Assessment of risk (STAR) 2019 working group meeting report. Am J Transplant (2020) 20(10):2652–68. doi: 10.1111/ajt.15937

PubMed Abstract | CrossRef Full Text | Google Scholar

5. Lefaucheur C, Suberbielle-Boissel C, Hill GS, Nochy D, Andrade J, Antoine C, et al. Clinical relevance of preformed HLA donor-specific antibodies in kidney transplantation. Contrib. Nephrol. (2009) 162:1–12. doi: 10.1159/000170788

PubMed Abstract | CrossRef Full Text | Google Scholar

6. Kosmoliaptsis V, Gjorgjimajkoska O, Sharples LD, Chaudhry AN, Chatzizacharias N, Peacock S, et al. Impact of donor mismatches at individual HLA-a, -b, -c, -DR, and -DQ loci on the development of HLA-specific antibodies in patients listed for repeat renal transplantation. Kidney Int (2014) 86(5):1039–48. doi: 10.1038/ki.2014.106

PubMed Abstract | CrossRef Full Text | Google Scholar

7. Lim WH, Chadban SJ, Clayton P, Budgeon CA, Murray K, Campbell SB, et al. Human leukocyte antigen mismatches associated with increased risk of rejection, graft failure, and death independent of initial immunosuppression in renal transplant recipients. Clin Transplant (2012) 26(4):E428–37. doi: 10.1111/j.1399-0012.2012.01654.x

PubMed Abstract | CrossRef Full Text | Google Scholar

8. Duquesnoy RJ. A structurally based approach to determine HLA compatibility at the humoral immune level. Hum Immunol (2006) 67(11):847–62. doi: 10.1016/j.humimm.2006.08.001

PubMed Abstract | CrossRef Full Text | Google Scholar

9. Duquesnoy RJ. HLA epitope based matching for transplantation. Transpl. Immunol (2014) 31(1):1–6. doi: 10.1016/j.trim.2014.04.004

PubMed Abstract | CrossRef Full Text | Google Scholar

10. Duquesnoy RJ, Askar M. HLAMatchmaker: a molecularly based algorithm for histocompatibility determination. V. Eplet matching HLA-DR HLA-DQ HLA-DP. Hum Immunol (2007) 68(1):12–25. doi: 10.1016/j.humimm.2006.10.003

CrossRef Full Text | Google Scholar

11. Lim WH, Wong G, Heidt S, Claas FHJ. Novel aspects of epitope matching and practical application in kidney transplantation. Kidney Int (2018) 93(2):314–24. doi: 10.1016/j.kint.2017.08.008

PubMed Abstract | CrossRef Full Text | Google Scholar

12. Larkins NG, Wong G, Taverniti A, Lim WH. Epitope matching in kidney transplantation: recent advances and current limitations. Curr Opin Organ Transplant (2019) 24(4):370–7. doi: 10.1097/MOT.0000000000000657

PubMed Abstract | CrossRef Full Text | Google Scholar

13. Sapir-Pichhadze R, Tinckam K, Quach K, Logan AG, Laupacis A, John R, et al. HLA-DR and -DQ eplet mismatches and transplant glomerulopathy: a nested case-control study. Am J Transplant (2015) 15(1):137–48. doi: 10.1111/ajt.12968

PubMed Abstract | CrossRef Full Text | Google Scholar

14. Wiebe C, Pochinco D, Blydt-Hansen TD, Ho J, Birk PE, Karpinski M, et al. Class II HLA epitope matching-a strategy to minimize de novo donor-specific antibody development and improve outcomes. Am J Transplant (2013) 13(12):3114–22. doi: 10.1111/ajt.12478

PubMed Abstract | CrossRef Full Text | Google Scholar

15. Sharma A, Taverniti A, Graf N, Teixeira-Pinto A, Lewis JR, Lim WH, et al. The association between human leukocyte antigen eplet mismatches, de novo donor-specific antibodies, and the risk of acute rejection in pediatric kidney transplant recipients. Pediatr Nephrol. (2020) 35(6):1061–8. doi: 10.1007/s00467-020-04474-x

PubMed Abstract | CrossRef Full Text | Google Scholar

16. Snanoudj R, Kamar N, Cassuto E, Caillard S, Metzger M, Merville P, et al. Epitope load identifies kidney transplant recipients at risk of allosensitization following minimization of immunosuppression. Kidney Int (2019) 95(6):1471–85. doi: 10.1016/j.kint.2018.12.029

PubMed Abstract | CrossRef Full Text | Google Scholar

17. Do Nguyen HT, Wong G, Chapman JR, McDonald SP, Coates PT, Watson N, et al. The association between broad antigen HLA mismatches, eplet HLA mismatches and acute rejection after kidney transplantation. Transplant Direct. (2016) 2(12):e120. doi: 10.1097/TXD.0000000000000632

PubMed Abstract | CrossRef Full Text | Google Scholar

18. Duquesnoy RJ, Marrari M. HLAMatchmaker: a molecularly based algorithm for histocompatibility determination. II. Verification algorithm determination relat. immunogenicity Amino Acid triplet-defined epitopes. Hum Immunol (2002) 63(5):353–63. doi: 10.1016/s0198-8859(02)00381-6

CrossRef Full Text | Google Scholar

19. Tambur AR. HLA-epitope matching or eplet risk stratification: The devil is in the details. Front Immunol (2018) 9:2010. doi: 10.3389/fimmu.2018.02010

PubMed Abstract | CrossRef Full Text | Google Scholar

20. Senev A, Coemans M, Lerut E, Van Sandt V, Kerkhofs J, Daniëls L, et al. Eplet mismatch load and De novo occurrence of donor-specific anti-HLA antibodies, rejection, and graft failure after kidney transplantation: An observational cohort study. J Am Soc Nephrol. (2020) 31(9):2193–204. doi: 10.1681/ASN.2020010019

PubMed Abstract | CrossRef Full Text | Google Scholar

21. Wiebe C, Kosmoliaptsis V, Pochinco D, Gibson IW, Ho J, Birk PE, et al. HLA-DR/DQ molecular mismatch: A prognostic biomarker for primary alloimmunity. Am J Transplant (2019) 19(6):1708–19. doi: 10.1111/ajt.15177

PubMed Abstract | CrossRef Full Text | Google Scholar

22. McCaughan JA, Battle RK, Singh SKS, Tikkanen JM, Moayedi Y, Ross HJ, et al. Identification of risk epitope mismatches associated with de novo donor-specific HLA antibody development in cardiothoracic transplantation. Am J Transplant (2018) 18(12):2924–33. doi: 10.1111/ajt.14951

PubMed Abstract | CrossRef Full Text | Google Scholar

23. Mohammadhassanzadeh H, Oualkacha K, Zhang W, Klement W, Bourdiec A, Lamsatfi J, et al. On path to informing hierarchy of eplet mismatches as determinants of kidney transplant loss. Kidney Int Rep (2021) 6(6):1567–79. doi: 10.1016/j.ekir.2021.03.877

PubMed Abstract | CrossRef Full Text | Google Scholar

24. Robinson J, Halliwell JA, Hayhurst JD, Flicek P, Parham P, Marsh SG. The IPD and IMGT/HLA database: allele variant databases. Nucleic Acids Res (2015) 43(Database issue):D423–31. doi: 10.1093/nar/gku1161

PubMed Abstract | CrossRef Full Text | Google Scholar

25. Mack SJ, Cano P, Hollenbach JA, He J, Hurley CK, Middleton D, et al. Common and well-documented HLA alleles: 2012 update to the CWD catalogue. Tissue Antigens (2013) 81(4):194–203. doi: 10.1111/tan.12093

PubMed Abstract | CrossRef Full Text | Google Scholar

26. Gonzalez-Galarza FF, Takeshita LY, Santos EJ, Kempson F, Maia MHT, Soares da Silva AL, et al. Allele frequency net 2015 update: new features for HLA epitopes, KIR and disease and HLA adverse drug reaction associations. Nucleic Acids Res (2015) 43(Database issue):D784–8. doi: 10.1093/nar/gku1166

PubMed Abstract | CrossRef Full Text | Google Scholar

27. Santos E, McCabe A, Gonzalez-Galarza FF, Jones AR, Middleton D. Allele frequencies net database: Improvements for storage of individual genotypes and analysis of existing data. Hum Immunol (2016) 77(3):238–48. doi: 10.1016/j.humimm.2015.11.013

PubMed Abstract | CrossRef Full Text | Google Scholar

28. Fidler S, D'Orsogna L, Irish AB, Lewis JR, Wong G, Lim WH. Correlation and agreement between eplet mismatches calculated using serological, low-intermediate and high resolution molecular human leukocyte antigen typing methods. Oncotarget (2018) 9(17):13116–24. doi: 10.18632/oncotarget.24349

PubMed Abstract | CrossRef Full Text | Google Scholar

29. Senev A, Emonds MP, Van Sandt V, Lerut E, Coemans M, Sprangers B, et al. Clinical importance of extended second field high-resolution HLA genotyping for kidney transplantation. Am J Transplant (2020) 20(12):3367–78. doi: 10.1111/ajt.15938

PubMed Abstract | CrossRef Full Text | Google Scholar

30. Engen RM, Jedraszko AM, Conciatori MA, Tambur AR. Substituting imputation of HLA antigens for high-resolution HLA typing: Evaluation of a multiethnic population and implications for clinical decision making in transplantation. Am J Transplant (2021) 21(1):344–52. doi: 10.1111/ajt.16070

PubMed Abstract | CrossRef Full Text | Google Scholar

31. D'Souza Y, Ferradji A, Saw C, Oualkacha K, Richard L, Popradi G, et al. Inaccuracies in epitope repertoire estimations when using multi-locus allele-level hla genotype imputation tools. HLA (2018) 92:33–9. doi: 10.1111/tan.13307

PubMed Abstract | CrossRef Full Text | Google Scholar

32. Ferradji A, D'Souza Y, Saw CL, Oualkacha K, Richard L, Sapir-Pichhadze R. Performance of an allele-level multi-locus HLA genotype imputation tool in hematopoietic stem cell donors from Quebec. Immun Inflamm Dis (2017) 5(4):551–9. doi: 10.1002/iid3.185

PubMed Abstract | CrossRef Full Text | Google Scholar

33. Sapir-Pichhadze R, Zhang X, Ferradji A, Madbouly A, Tinckam KJ, Gebel HM, et al. Epitopes as characterized by antibody-verified eplet mismatches determine risk of kidney transplant loss. Kidney Int (2020) 97(4):778–85. doi: 10.1016/j.kint.2019.10.028

PubMed Abstract | CrossRef Full Text | Google Scholar

34. Kramer CSM, Franke-van Dijk MEI, Bakker KH, Uyar-Mercankaya M, Karahan GE, Roelen DL, et al. Generation and reactivity analysis of human recombinant monoclonal antibodies directed against epitopes on HLA-DR. Am J Transplant (2020) 20(12):3341–53. doi: 10.1111/ajt.15950

PubMed Abstract | CrossRef Full Text | Google Scholar

35. Kramer CSM, Israeli M, Mulder A, Doxiadis I, Haasnoot GW, Heidt S, et al. The long and winding road towards epitope matching in clinical transplantation. Transpl. Int (2019) 32(1):16–24. doi: 10.1111/tri.13362

PubMed Abstract | CrossRef Full Text | Google Scholar

36. Geneugelijk K, Niemann M, Drylewicz J, van Zuilen A, Joosten I, Allebes WA, et al. PIRCHE-II is related to graft failure after kidney transplantation. Front Immunol (2018) 9:321. doi: 10.3389/fimmu.2018.00321

PubMed Abstract | CrossRef Full Text | Google Scholar

37. Kramer CSM, Koster J, Haasnoot GW, Roelen DL, Claas FHJ, Heidt S. HLA-EMMA: A user-friendly tool to analyse HLA class I and class II compatibility on the amino acid level. HLA (2020) 96(1):43–51. doi: 10.1111/tan.13883

PubMed Abstract | CrossRef Full Text | Google Scholar

38. Kosmoliaptsis V, Chaudhry AN, Sharples LD, Halsall DJ, Dafforn TR, Bradley JA, et al. Predicting HLA class I alloantigen immunogenicity from the number and physiochemical properties of amino acid polymorphisms. Transplantation (2009) 88(6):791–8. doi: 10.1097/TP.0b013e3181b4a9ff

PubMed Abstract | CrossRef Full Text | Google Scholar

39. Kosmoliaptsis V, Mallon DH, Chen Y, Bolton EM, Bradley JA, Taylor CJ. Alloantibody responses after renal transplant failure can be better predicted by donor-recipient HLA amino acid sequence and physicochemical disparities than conventional HLA matching. Am J Transplant (2016) 16(7):2139–47. doi: 10.1111/ajt.13707

PubMed Abstract | CrossRef Full Text | Google Scholar

40. Lachmann N, Niemann M, Reinke P, Budde K, Schmidt D, Halleck F, et al. Donor-recipient matching based on predicted indirectly recognizable HLA epitopes independently predicts the incidence of De novo donor-specific HLA antibodies following renal transplantation. Am J Transplant (2017) 17(12):3076–86. doi: 10.1111/ajt.14393

PubMed Abstract | CrossRef Full Text | Google Scholar

41. Geneugelijk K, Honger G, van Deutekom HW, Thus KA, Kesmir C, Hösli I, et al. Predicted indirectly recognizable HLA epitopes presented by HLA-DRB1 are related to HLA antibody formation during pregnancy. Am J Transplant (2015) 15(12):3112–22. doi: 10.1111/ajt.13508

PubMed Abstract | CrossRef Full Text | Google Scholar

42. Ladowski JM, Mullins H, Romine M, Kloda D, Young C, Hauptfeld-Dolejsek V, et al. Eplet mismatch scores and de novo donor-specific antibody development in simultaneous pancreas-kidney transplantation. Hum Immunol (2021) 82(3):139–46. doi: 10.1016/j.humimm.2020.12.009

PubMed Abstract | CrossRef Full Text | Google Scholar

43. Sakamoto S, Iwasaki K, Tomosugi T, Niemann M, Spierings E, Miwa Y, et al. Analysis of T and b cell epitopes to predict the risk of de novo donor-specific antibody (DSA) production after kidney transplantation: A two-center retrospective cohort study. Front Immunol (2020) 11:2000. doi: 10.3389/fimmu.2020.02000

PubMed Abstract | CrossRef Full Text | Google Scholar

44. Mallon DH, Kling C, Robb M, Ellinghaus E, Bradley JA, Taylor CJ, et al. Predicting humoral alloimmunity from differences in donor and recipient HLA surface electrostatic potential. J Immunol (2018) 201(12):3780–92. doi: 10.4049/jimmunol.1800683

PubMed Abstract | CrossRef Full Text | Google Scholar

45. Kosmoliaptsis V, Sharples LD, Chaudhry AN, Halsall DJ, Bradley JA, Taylor CJ. Predicting HLA class II alloantigen immunogenicity from the number and physiochemical properties of amino acid polymorphisms. Transplantation (2011) 91(2):183–90. doi: 10.1097/TP.0b013e3181ffff99

PubMed Abstract | CrossRef Full Text | Google Scholar

46. Wiebe C, Kosmoliaptsis V, Pochinco D, Taylor CJ, Nickerson P. A comparison of HLA molecular mismatch methods to determine HLA immunogenicity. Transplantation (2018) 102(8):1338–43. doi: 10.1097/TP.0000000000002117

PubMed Abstract | CrossRef Full Text | Google Scholar

47. Kausman JY, Walker AM, Cantwell LS, Quinlan C, Sypek MP, Ierino FL. Application of an epitope-based allocation system in pediatric kidney transplantation. Pediatr Transplant (2016) 20(7):931–8. doi: 10.1111/petr.12815

PubMed Abstract | CrossRef Full Text | Google Scholar

48. Heidt S, Witvliet MD, Haasnoot GW, Claas FH. The 25th anniversary of the eurotransplant acceptable mismatch program for highly sensitized patients. Transpl. Immunol (2015) 33(2):51–7. doi: 10.1016/j.trim.2015.08.006

PubMed Abstract | CrossRef Full Text | Google Scholar

49. Clayton PA, McDonald SP, Russ GR, Chadban SJ. Long-term outcomes after acute rejection in kidney transplant recipients: An ANZDATA analysis. J Am Soc Nephrol. (2019) 30(9):1697–707. doi: 10.1681/ASN.2018111101

PubMed Abstract | CrossRef Full Text | Google Scholar

50. Leffell MS, Kim D, Vega RM, Zachary AA, Petersen J, Hart JM, et al. Red blood cell transfusions and the risk of allosensitization in patients awaiting primary kidney transplantation. Transplantation (2014) 97(5):525–33. doi: 10.1097/01.tp.0000437435.19980.8f

PubMed Abstract | CrossRef Full Text | Google Scholar

51. Fidler S, Swaminathan R, Lim W, Ferrari P, Witt C, Christiansen FT, et al. Peri-operative third party red blood cell transfusion in renal transplantation and the risk of antibody-mediated rejection and graft loss. Transpl. Immunol (2013) 29(1-4):22–7. doi: 10.1016/j.trim.2013.09.008

PubMed Abstract | CrossRef Full Text | Google Scholar

52. Yabu JM, Anderson MW, Kim D, Bradbury BD, Lou CD, Petersen J, et al. Sensitization from transfusion in patients awaiting primary kidney transplant. Nephrol Dial Transplant (2013) 28(11):2908–18. doi: 10.1093/ndt/gft362

PubMed Abstract | CrossRef Full Text | Google Scholar

53. Gaiffe E, Vernerey D, Bardiaux L, Leroux F, Meurisse A, Bamoulid J, et al. Early post-transplant red blood cell transfusion is associated with an increased risk of transplant failure: A nationwide French study. Front Immunol (2022) 13:854850. doi: 10.3389/fimmu.2022.854850

PubMed Abstract | CrossRef Full Text | Google Scholar

54. Massicotte-Azarniouch D, Sood MM, Fergusson DA, Chasse M, Tinmouth A, Knoll GA. Blood transfusion and the risk for infections in kidney transplant patients. PloS One (2021) 16(11):e0259270. doi: 10.1371/journal.pone.0259270

PubMed Abstract | CrossRef Full Text | Google Scholar

55. Daloul R, Braga JR, Diez A, Logan A, Pesavento T. Early posttransplant blood transfusion and risk for worse graft outcomes. Kidney Int Rep (2021) 6(4):986–94. doi: 10.1016/j.ekir.2020.12.038

PubMed Abstract | CrossRef Full Text | Google Scholar

56. Bossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwig LM, et al. Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. BMJ (2003) 326(7379):41–4. doi: 10.1136/bmj.326.7379.41

PubMed Abstract | CrossRef Full Text | Google Scholar

57. Rodriguez-Ramirez S, Kim SJ. How to ask the right question and find the right answer: Clinical research for transplant nephrologists. Front Immunol (2022) 13:879200. doi: 10.3389/fimmu.2022.879200

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: allo-immune response, kidney transplant, HLA, epitope, donor specific antibodies, sensitization, HLA compatibility, immunological risk assessment

Citation: Lim WH, Ho J, Kosmoliaptsis V and Sapir-Pichhadze R (2022) Editorial: Future challenges and directions in determining allo-immunity in kidney transplantation. Front. Immunol. 13:1013711. doi: 10.3389/fimmu.2022.1013711

Received: 07 August 2022; Accepted: 16 August 2022;
Published: 31 August 2022.

Edited and Reviewed by:

Antoine Toubert, Université Paris Diderot, France

Copyright © 2022 Lim, Ho, Kosmoliaptsis and Sapir-Pichhadze. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Wai H. Lim, d2FpLmxpbUBoZWFsdGgud2EuZ292LmF1

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.