Complex Linkage Disequilibrium Effects in HLA-DPB1 Expression and Molecular Mismatch Analyses of Transplantation Outcomes.
Journal
Transplantation
ISSN: 1534-6080
Titre abrégé: Transplantation
Pays: United States
ID NLM: 0132144
Informations de publication
Date de publication:
01 03 2021
01 03 2021
Historique:
pubmed:
18
4
2020
medline:
21
7
2021
entrez:
18
4
2020
Statut:
ppublish
Résumé
HLA molecular mismatch (MM) is a risk factor for de novo donor-specific antibody (dnDSA) development in solid organ transplantation. HLA expression differences have also been associated with adverse outcomes in hematopoietic cell transplantation. We sought to study both MM and expression in assessing dnDSA risk. One hundred three HLA-DP-mismatched solid organ transplantation pairs were retrospectively analyzed. MM was computed using amino acids (aa), eplets, and, supplementarily, Grantham/Epstein scores. DPB1 alleles were classified as rs9277534-A (low-expression) or rs9277534-G (high-expression) linked. To determine the associations between risk factors and dnDSA, logistic regression, linkage disequilibrium (LD), and population-based analyses were performed. A high-risk AA:GX (recipient:donor) expression combination (X = A or G) demonstrated strong association with HLA-DP dnDSA (P = 0.001). MM was also associated with HLA-DP dnDSA when evaluated by itself (eplet P = 0.007, aa P = 0.003, Grantham P = 0.005, Epstein P = 0.004). When attempting to determine the relative individual effects of the risk factors in multivariable analysis, only AA:GX expression status retained a strong association (relative risk = 18.6, P = 0.007 with eplet; relative risk = 15.8, P = 0.02 with aa), while MM was no longer significant (eplet P = 0.56, aa P = 0.51). Importantly, these risk factors are correlated, due to LD between the expression-tagging single-nucleotide polymorphism and polymorphisms along HLA-DPB1. The MM and expression risk factors each appear to be strong predictors of HLA-DP dnDSA and to possess clinical utility; however, these two risk factors are closely correlated. These metrics may represent distinct ways of characterizing a common overlapping dnDSA risk profile, but they are not independent. Further, we demonstrate the importance and detailed implications of LD effects in dnDSA risk assessment and possibly transplantation overall.
Sections du résumé
BACKGROUND
HLA molecular mismatch (MM) is a risk factor for de novo donor-specific antibody (dnDSA) development in solid organ transplantation. HLA expression differences have also been associated with adverse outcomes in hematopoietic cell transplantation. We sought to study both MM and expression in assessing dnDSA risk.
METHODS
One hundred three HLA-DP-mismatched solid organ transplantation pairs were retrospectively analyzed. MM was computed using amino acids (aa), eplets, and, supplementarily, Grantham/Epstein scores. DPB1 alleles were classified as rs9277534-A (low-expression) or rs9277534-G (high-expression) linked. To determine the associations between risk factors and dnDSA, logistic regression, linkage disequilibrium (LD), and population-based analyses were performed.
RESULTS
A high-risk AA:GX (recipient:donor) expression combination (X = A or G) demonstrated strong association with HLA-DP dnDSA (P = 0.001). MM was also associated with HLA-DP dnDSA when evaluated by itself (eplet P = 0.007, aa P = 0.003, Grantham P = 0.005, Epstein P = 0.004). When attempting to determine the relative individual effects of the risk factors in multivariable analysis, only AA:GX expression status retained a strong association (relative risk = 18.6, P = 0.007 with eplet; relative risk = 15.8, P = 0.02 with aa), while MM was no longer significant (eplet P = 0.56, aa P = 0.51). Importantly, these risk factors are correlated, due to LD between the expression-tagging single-nucleotide polymorphism and polymorphisms along HLA-DPB1.
CONCLUSIONS
The MM and expression risk factors each appear to be strong predictors of HLA-DP dnDSA and to possess clinical utility; however, these two risk factors are closely correlated. These metrics may represent distinct ways of characterizing a common overlapping dnDSA risk profile, but they are not independent. Further, we demonstrate the importance and detailed implications of LD effects in dnDSA risk assessment and possibly transplantation overall.
Identifiants
pubmed: 32301906
pii: 00007890-202103000-00027
doi: 10.1097/TP.0000000000003272
pmc: PMC8628253
mid: NIHMS1756507
doi:
Substances chimiques
HLA-DP beta-Chains
0
HLA-DPB1 antigen
0
Isoantibodies
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
637-647Subventions
Organisme : NHGRI NIH HHS
ID : K01 HG010498
Pays : United States
Informations de copyright
Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.
Déclaration de conflit d'intérêts
J.L.D., D.F., and D.S.M. receive royalties from Omixon Inc. The other authors declare no conflicts of interest.
Références
Wiebe C, Gibson IW, Blydt-Hansen TD, et al. Evolution and clinical pathologic correlations of de novo donor-specific HLA antibody post kidney transplant. Am J Transplant. 2012; 12:1157–1167.
Sellarés J, de Freitas DG, Mengel M, et al. Understanding the causes of kidney transplant failure: the dominant role of antibody-mediated rejection and nonadherence. Am J Transplant. 2012; 12:388–399.
Süsal C, Wettstein D, Döhler B, et al.; Collaborative Transplant Study Report. Association of kidney graft loss with de novo produced donor-specific and non-donor-specific HLA antibodies detected by single antigen testing. Transplantation. 2015; 99:1976–1980.
Wiebe C, Kosmoliaptsis V, Pochinco D, et al. A comparison of HLA molecular mismatch methods to determine HLA immunogenicity. Transplantation. 2018; 102:1338–1343.
Wiebe C, Kosmoliaptsis V, Pochinco D, et al. HLA-DR/DQ molecular mismatch: a prognostic biomarker for primary alloimmunity. Am J Transplant. 2018; 19:1708–1719.
Petersdorf EW, Gooley TA, Malkki M, et al.; International Histocompatibility Working Group in Hematopoietic Cell Transplantation. HLA-C expression levels define permissible mismatches in hematopoietic cell transplantation. Blood. 2014; 124:3996–4003.
Petersdorf EW, Malkki M, O’hUigin C, et al. High HLA-DP expression and graft-versus-host disease. N Engl J Med. 2015; 373:599–609.
Thomas R, Thio CL, Apps R, et al. A novel variant marking HLA-DP expression levels predicts recovery from hepatitis B virus infection. J Virol. 2012; 86:6979–6985.
Nel M, Mulder N, Europa TA, et al. Using whole genome sequencing in an African subphenotype of myasthenia gravis to generate a pathogenetic hypothesis. Front Genet. 2019; 10:136.
Meurer T, Arrieta-Bolaños E, Metzing M, et al. Dissecting genetic control of HLA-DPB1 expression and its relation to structural mismatch models in hematopoietic stem cell transplantation. Front Immunol. 2018; 9:2236.
Kosmoliaptsis V, Sharples LD, Chaudhry AN, et al. Predicting HLA class II alloantigen immunogenicity from the number and physiochemical properties of amino acid polymorphisms. Transplantation. 2011; 91:183–190.
Hormozdiari F, Kostem E, Kang EY, et al. Identifying causal variants at loci with multiple signals of association. Genetics. 2014; 198:497–508.
Schöne B, Bergmann S, Lang K, et al. Predicting an HLA-DPB1 expression marker based on standard DPB1 genotyping: linkage analysis of over 32,000 samples. Hum Immunol. 2018; 79:20–27.
Balgansuren G, Regen L, Sprague M, et al. Identification of the rs9277534 HLA-DP expression marker by next generation sequencing for the selection of unrelated donors for hematopoietic cell transplantation. Hum Immunol. 2019; 80:828–833.
Morishima S, Shiina T, Suzuki S, et al.; Japan Marrow Donor Program. Evolutionary basis of HLA-DPB1 alleles affects acute GVHD in unrelated donor stem cell transplantation. Blood. 2018; 131:808–817.
Duquesnoy RJ. A structurally based approach to determine HLA compatibility at the humoral immune level. Hum Immunol. 2006; 67:847–862.
Tambur AR, Campbell P, Claas FH, et al. Sensitization in Transplantation: Assessment of Risk (STAR) 2017 Working Group Meeting Report. Am J Transplant. 2018; 18:1604–1614.
Duquesnoy RJ, Marrari M, Marroquim MS, et al. Second update of the international registry of HLA epitopes. I. The HLA-ABC epitope database. Hum Immunol. 2019; 80:103–106.
Robinson J, Halliwell JA, McWilliam H, et al. IPD–the immuno polymorphism database. Nucleic Acids Res. 2013; 41:D1234–D1240.
Grantham R. Amino acid difference formula to help explain protein evolution. Science. 1974; 185:862–864.
Epstein CJ. Non-randomness of ammo-acid changes in the evolution of homologous proteins. Nature. 1967; 215:355–359.
Kosmoliaptsis V, Chaudhry AN, Sharples LD, et al. Predicting HLA class I alloantigen immunogenicity from the number and physiochemical properties of amino acid polymorphisms. Transplantation. 2009; 88:791–798.
Ponomarenko J, Bui HH, Li W, et al. ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC Bioinform. 2008; 9:514.
Mallon DH, Kling C, Robb M, et al. Predicting humoral alloimmunity from differences in donor and recipient HLA surface electrostatic potential. J Immunol. 2018; 201:3780–3792.
Zhang J, Yu KF. What’s the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes. JAMA. 1998; 280:1690–1691.
Mantel N, Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst. 1959; 22:719–748.
Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014; 23:R89–R98.
Pingault J-B, O’Reilly PF, Schoeler T, et al. Using genetic data to strengthen causal inference in observational research. Nat Rev Genet. 2018; 19:566–580.
van Rossum G. Python tutorial CS-R9526, Centrum voor Wiskunde en informatica (CWI) technical report. Available at http://www.python.org/ . Accessed December 12, 2019.
R Core Team. R: A Language and Environment for Statistical Computing. 2018, Vienna, Austria: R Foundation for Statistical ComputingAvailable at https://www.R-project.org/ . Accessed December 14, 2019.
Solberg OD, Mack SJ, Lancaster AK, et al. Balancing selection and heterogeneity across the classical human leukocyte antigen loci: a meta-analytic review of 497 population studies. Hum Immunol. 2008; 69:443–464.
Marsh SGE, Albert ED, Bodmer WF, et al. Nomenclature for factors of the HLA system, 2010. Tissue Antigens. 2010; 75:291–455.
VanLiere JM, Rosenberg NA. Mathematical properties of the measure of linkage disequilibrium. Theor Popul Biol. 2008; 74:130–137.
Duke JL, Lind C, Mackiewicz K, et al. Determining performance characteristics of an NGS-based HLA typing method for clinical applications. HLA. 2016; 87:141–152.
Wehmeier C, Hönger G, Schaub S. Caveats of HLA antibody detection by solid-phase assays. Transpl Int. 2020; 33:18–29.
Klasberg S, Lang K, Günther M, et al. Patterns of non-ARD variation in more than 300 full-length HLA-DPB1 alleles. Hum Immunol. 2019; 80:44–52.
Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Ser B Stat Methodol. 2005; 67:301–320.
Hill JL. Bayesian nonparametric modeling for causal inference. J Comput Graph Stat. 20111):217–240.
Athey S, Tibshirani J, Wager S. Generalized random forests. Ann Stat. 2019; 47:1148–1178.
Mancuso N, Freund MK, Johnson R, et al. Probabilistic fine-mapping of transcriptome-wide association studies. Nat Genet. 2019; 51:675–682.
Hormozdiari F, van de Bunt M, Segrè AV, et al. Colocalization of GWAS and eQTL signals detects target genes. Am J Hum Genet. 2016; 99:1245–1260.
Giambartolomei C, Vukcevic D, Schadt EE, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. Plos Genet. 2014; 10:e1004383.
Fleischhauer K, Shaw BE, Gooley T, et al.; International Histocompatibility Working Group in Hematopoietic Cell Transplantation. Effect of T-cell-epitope matching at HLA-DPB1 in recipients of unrelated-donor haemopoietic-cell transplantation: a retrospective study. Lancet Oncol. 2012; 13:366–374.
Geneugelijk K, Spierings E. Matching donor and recipient based on predicted indirectly recognizable human leucocyte antigen epitopes. Int J Immunogenet. 2018; 45:41–53.
Geneugelijk K, Thus KA, van Deutekom HWM, et al. Exploratory study of predicted indirectly recognizable HLA epitopes in mismatched hematopoietic cell transplantations. Front Immunol. 2019; 10:880.
Thus KA, Ruizendaal MT, de Hoop TA, et al. Refinement of the definition of permissible HLA-DPB1 mismatches with predicted indirectly recognizable HLA-DPB1 epitopes. Biol Blood Marrow Transplant. 2014; 20:1705–1710.
Shieh M, Chitnis N, Clark P, et al. Computational assessment of miRNA binding to low and high expression HLA-DPB1 allelic sequences. Hum Immunol. 2019; 80:53–61.
Mizuno A, Okada Y. Biological characterization of expression quantitative trait loci (eQTLs) showing tissue-specific opposite directional effects. Eur J Hum Genet. 2019; 27:1745–1756.
Dimas AS, Nica AC, Montgomery SB, et al.; MuTHER Consortium. Sex-biased genetic effects on gene regulation in humans. Genome Res. 2012; 22:2368–2375.
Handunnetthi L, Ramagopalan SV, Ebers GC, et al. Regulation of major histocompatibility complex class II gene expression, genetic variation and disease. Genes Immun. 2010; 11:99–112.
Papaz T, Allen U, Blydt-Hansen T, et al. Pediatric outcomes in transplant: PersOnaliSing Immunosuppression To ImproVe Efficacy (POSITIVE Study): the collaboration and design of a national transplant precision medicine program. Transplant Direct. 2018; 4:e410.
Loupy A, Lefaucheur C. Antibody-mediated rejection of solid-organ allografts. N Engl J Med. 2018; 379:1150–1160.
Philogene MC, Amin A, Zhou S, et al. Eplet mismatch analysis and allograft outcome across racially diverse groups in a pediatric transplant cohort: a single-center analysis. Pediatr Nephrol. 2020; 35:83–94.
Chowell D, Morris LGT, Grigg CM, et al. Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy. Science. 2018; 359:582–587.
Mallon DH, Bradley JA, Taylor CJ, et al. Structural and electrostatic analysis of HLA B-cell epitopes: inference on immunogenicity and prediction of humoral alloresponses. Curr Opin Organ Transplant. 2014; 19:420–427.
Monos DS, Tekolf WA, Shaw S, et al. Comparison of structural and functional variation in class I HLA molecules: the role of charged amino acid substitutions. J Immunol. 1984; 132:1379–1385.
Lachmann N, Niemann M, Reinke P, 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; 17:3076–3086.
Otten HG, Calis JJ, Keşmir C, et al. Predicted indirectly recognizable HLA epitopes presented by HLA-DR correlate with the de novo development of donor-specific HLA IgG antibodies after kidney transplantation. Hum Immunol. 2013; 74:290–296.