Title: The MICA‐129 dimorphism affects NKG2D signaling and outcome of hematopoietic stem cell transplantation
Abstract: Research Article19 October 2015Open Access The MICA-129 dimorphism affects NKG2D signaling and outcome of hematopoietic stem cell transplantation Antje Isernhagen Antje Isernhagen Institute of Cellular and Molecular Immunology, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Dörthe Malzahn Dörthe Malzahn Institute of Genetic Epidemiology, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Elena Viktorova Elena Viktorova Institute of Genetic Epidemiology, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Leslie Elsner Leslie Elsner Institute of Cellular and Molecular Immunology, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Sebastian Monecke Sebastian Monecke Institute of Cellular and Molecular Immunology, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Frederike von Bonin Frederike von Bonin Department of Hematology and Medical Oncology, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Markus Kilisch Markus Kilisch Institute of Molecular Biology, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Janne Marieke Wermuth Janne Marieke Wermuth Department of Hematology and Medical Oncology, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Neele Walther Neele Walther Department of Hematology and Medical Oncology, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Yesilda Balavarca Yesilda Balavarca Institute of Genetic Epidemiology, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Christiane Stahl-Hennig Christiane Stahl-Hennig Unit of Infection Models, German Primate Center, Göttingen, Germany Search for more papers by this author Michael Engelke Michael Engelke Institute of Cellular and Molecular Immunology, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Lutz Walter Lutz Walter Primate Genetics Laboratory, German Primate Center, Göttingen, Germany Search for more papers by this author Heike Bickeböller Heike Bickeböller Institute of Genetic Epidemiology, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Dieter Kube Dieter Kube Department of Hematology and Medical Oncology, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Gerald Wulf Gerald Wulf Department of Hematology and Medical Oncology, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Ralf Dressel Corresponding Author Ralf Dressel Institute of Cellular and Molecular Immunology, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Antje Isernhagen Antje Isernhagen Institute of Cellular and Molecular Immunology, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Dörthe Malzahn Dörthe Malzahn Institute of Genetic Epidemiology, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Elena Viktorova Elena Viktorova Institute of Genetic Epidemiology, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Leslie Elsner Leslie Elsner Institute of Cellular and Molecular Immunology, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Sebastian Monecke Sebastian Monecke Institute of Cellular and Molecular Immunology, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Frederike von Bonin Frederike von Bonin Department of Hematology and Medical Oncology, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Markus Kilisch Markus Kilisch Institute of Molecular Biology, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Janne Marieke Wermuth Janne Marieke Wermuth Department of Hematology and Medical Oncology, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Neele Walther Neele Walther Department of Hematology and Medical Oncology, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Yesilda Balavarca Yesilda Balavarca Institute of Genetic Epidemiology, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Christiane Stahl-Hennig Christiane Stahl-Hennig Unit of Infection Models, German Primate Center, Göttingen, Germany Search for more papers by this author Michael Engelke Michael Engelke Institute of Cellular and Molecular Immunology, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Lutz Walter Lutz Walter Primate Genetics Laboratory, German Primate Center, Göttingen, Germany Search for more papers by this author Heike Bickeböller Heike Bickeböller Institute of Genetic Epidemiology, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Dieter Kube Dieter Kube Department of Hematology and Medical Oncology, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Gerald Wulf Gerald Wulf Department of Hematology and Medical Oncology, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Ralf Dressel Corresponding Author Ralf Dressel Institute of Cellular and Molecular Immunology, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Author Information Antje Isernhagen1, Dörthe Malzahn2, Elena Viktorova2, Leslie Elsner1, Sebastian Monecke1, Frederike Bonin3, Markus Kilisch4, Janne Marieke Wermuth3, Neele Walther3, Yesilda Balavarca2,7, Christiane Stahl-Hennig5, Michael Engelke1, Lutz Walter6, Heike Bickeböller2,‡, Dieter Kube3,‡, Gerald Wulf3,‡ and Ralf Dressel 1 1Institute of Cellular and Molecular Immunology, University Medical Center Göttingen, Göttingen, Germany 2Institute of Genetic Epidemiology, University Medical Center Göttingen, Göttingen, Germany 3Department of Hematology and Medical Oncology, University Medical Center Göttingen, Göttingen, Germany 4Institute of Molecular Biology, University Medical Center Göttingen, Göttingen, Germany 5Unit of Infection Models, German Primate Center, Göttingen, Germany 6Primate Genetics Laboratory, German Primate Center, Göttingen, Germany 7Present address: German Cancer Research Center, Division of Preventive Oncology, Heidelberg, Germany ‡HB, DK and GW share the second to last author position *Corresponding author. Tel: +49 551 395884; Fax: +49 551 395843; E-mail: [email protected] EMBO Mol Med (2015)7:1480-1502https://doi.org/10.15252/emmm.201505246 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract The MHC class I chain-related molecule A (MICA) is a highly polymorphic ligand for the activating natural killer (NK)-cell receptor NKG2D. A single nucleotide polymorphism causes a valine to methionine exchange at position 129. Presence of a MICA-129Met allele in patients (n = 452) undergoing hematopoietic stem cell transplantation (HSCT) increased the chance of overall survival (hazard ratio [HR] = 0.77, P = 0.0445) and reduced the risk to die due to acute graft-versus-host disease (aGVHD) (odds ratio [OR] = 0.57, P = 0.0400) although homozygous carriers had an increased risk to experience this complication (OR = 1.92, P = 0.0371). Overall survival of MICA-129Val/Val genotype carriers was improved when treated with anti-thymocyte globulin (HR = 0.54, P = 0.0166). Functionally, the MICA-129Met isoform was characterized by stronger NKG2D signaling, triggering more NK-cell cytotoxicity and interferon-γ release, and faster co-stimulation of CD8+ T cells. The MICA-129Met variant also induced a faster and stronger down-regulation of NKG2D on NK and CD8+ T cells than the MICA-129Val isoform. The reduced cell surface expression of NKG2D in response to engagement by MICA-129Met variants appeared to reduce the severity of aGVHD. Synopsis Allogeneic hematopoietic stem cell transplantation still has a high risk of post-transplant complications including graft versus host disease and relapse of malignancy. Detection of single nucleotide polymorphisms may help to provide a risk-adapted treatment. The MICA-129Met/Val dimorphism was associated with an increased survival and a reduced risk to die due to acute graft versus host disease in a cohort of 452 patients. The survival of MICA-129Val/Val genotype carriers was improved when treated with antithymocyte globulin (ATG). The MICA-129Met isoform triggered more NK-cell cytotoxicity and interferon-γ release and it co-stimulated cytotoxic T cells faster. The MICA-129Met isoform also induced a faster and stronger down-regulation of NKG2D on NK and cytotoxic T cells limiting the initially stronger functional effects. MICA-129Val/Val carriers might profit from a T-cell depleting treatment since this MICA variant has a lower ability to down-regulate NKG2D and to limit the activation of alloreactive T cells. Introduction Allogeneic hematopoietic stem cell transplantation (HSCT) offers an option to cure for several hematological diseases. Depending on the disease entity, 5-year survival rates vary, with limitations imposed by post-transplant complications, such as graft-versus-host disease (GVHD), relapse of malignancy, and infection (Dickinson, 2008). Human leukocyte antigen (HLA) matching is mandatory to reduce the risk of graft rejection and GVHD, but minor histocompatibility antigens also affect transplant outcome (Warren et al, 2012). Moreover, single nucleotide polymorphisms (SNPs) can influence the success of transplantations. SNPs in the tumor necrosis factor (TNF)-α or interleukin (IL)-6 genes, for example, have been associated with an increased risk of GVHD (Dickinson, 2008; Harris et al, 2013). A candidate gene that could affect the outcome of HSCT is the major histocompatibility complex (MHC) class I chain-related A (MICA). It is the most polymorphic non-classical MHC class I gene in humans (Bahram et al, 1994; Leelayuwat et al, 1994; Choy & Phipps, 2010), and currently, 100 alleles are known encoding for 79 protein variants (http://www.ebi.ac.uk/imgt/hla/, release 3.17.0). The structure of MICA is similar to classical MHC class I molecules, but it is not associated with β2-microglobulin and does not present peptides. It is constitutively expressed on a few cell types, such as gastrointestinal epithelium (Groh et al, 1996) but becomes induced by cellular and genotoxic stress (Groh et al, 1996; Gasser et al, 2005) in malignant or virus-infected cells (Champsaur & Lanier, 2010; Raulet et al, 2013). MICA functions as ligand for the activating NK receptor NKG2D (NK group 2, member D; Bauer et al, 1999), which is present on NK cells, CD8+ αβT cells, subsets of CD4+ αβT cells, γδT cells, and NKT cells (Champsaur & Lanier, 2010; Raulet et al, 2013). While NKG2D signaling triggers cytotoxicity (Billadeau et al, 2003) and cytokine secretion in NK cells (Andre et al, 2004) and Vγ9Vδ2T cells (Rincon-Orozco et al, 2005), it is a co-stimulator for activation of CD8+ αβT cells (Groh et al, 2001). NKG2D is important for elimination of malignant cells (Guerra et al, 2008), contributes to rejection of mouse bone marrow grafts (Ogasawara et al, 2005), and is critical in defense against some pathogens (Fang et al, 2008; Wesselkamper et al, 2008; Champsaur & Lanier, 2010; Choy & Phipps, 2010). MICA genotype matching and MICB genotype matching were associated with improved survival after HSCT (Kitcharoen et al, 2006), whereas mismatching was associated with increased incidence of acute GVHD (aGVHD; Parmar et al, 2009; Askar et al, 2014) although not in all studies (Anderson et al, 2009). The SNP at nucleotide position 454 (G/A) causing a valine (Val) to methionine (Met) exchange at amino acid position 129 in the α2 domain of the MICA protein was found to be associated with the incidence of chronic GVHD (cGVHD) and relapse (Boukouaci et al, 2009). This SNP has also been associated with the risks for nasopharyngeal carcinoma (Douik et al, 2009), hepatitis B virus-induced hepatocellular carcinoma (Tong et al, 2013), the severity of chronic Chagas heart disease (Ayo et al, 2015), and autoimmune diseases, including ankylosing spondylitis (Amroun et al, 2005), rheumatoid arthritis (Kirsten et al, 2009), inflammatory bowel disease (Lopez-Hernandez et al, 2010; Zhao et al, 2011), lupus erythematosus (Yoshida et al, 2011), type I diabetes (Raache et al, 2012), and psoriatic disease (Pollock et al, 2013). Moreover, the MICA-129 dimorphism is functionally relevant and the MICA alleles can be separated into two groups with respect to this polymorphism. MICA isoforms containing a methionine at position 129 have been characterized to bind NKG2D with high avidity, whereas those with a valine bind NKG2D with low avidity (Steinle et al, 2001). However, the finding that high-avidity MICA-129Met isoforms were associated with an increased incidence of relapse whereas the low-avidity isoforms were associated with an increased incidence of cGVHD (Boukouaci et al, 2009) appeared to be counterintuitive in view of the known functions of NKG2D. Therefore, we analyzed the MICA-129 dimorphism in an independent cohort of HSCT patients and investigated its functional effects beyond NKG2D binding to understand the mechanism how the SNP might impact the outcome of HSCT. Results Association of the MICA-129Val/Met dimorphism with the outcome of HSCT The characteristics of 452 consecutive patients (P), who underwent allogeneic HSCT in the Department of Hematology and Medical Oncology of the University Medical Center Göttingen (UMG) between October 2002 and July 2013, and their donors (D) are shown in Table 1. Recipients and donors were typed at high resolution for HLA-loci A, B, C, DR, and DQ, and a match of at least 7/8 loci at HLA-A, B, DRB1, and DQB1 was considered eligible for transplantation. We analyzed these 452 P/D pairs for the SNP rs1051792, responsible for the MICA-129Val/Met dimorphism. Most P/D pairs (90.7%) had the same MICA-129 genotype. About 54.4% of the patients experienced aGVHD (any grade) and 30.5% cGVHD (any grade), and in 19.0%, a relapse occurred. The overall mortality was 39.4%, and the treatment-related mortality (TRM) amounted to 24.1%. One reason for TRM was aGVHD, and 11.5% of the patients succumbed to aGVHD complications (Table 1). Table 1. HSCT pairs, diseases, transplantation characteristics, and outcome Characteristics Values Recipients (n = 452) Median age, years (y) 52 Younger than 20 y, n (%) 7 (1.5) 20 to 40 y, n (%) 72 (15.9) Older than 40 y, n (%) 373 (82.5) Male, n (%) 275 (60.8) MICA-129 genotype frequencies Val/Val, n (%) 232 (51.3) Met/Val, n (%) 161 (35.6) Met/Met, n (%) 59 (13.1) MICA-129 allele frequencies Val, n (%) 625 (69.1) Met, n (%) 279 (30.9) Underlying diagnosis Acute leukemia, n (%) 180 (39.8) Hodgkin lymphoma, non-Hodgkin lymphoma, n (%) 165 (36.5) Multiple myeloma, n (%) 49 (10.8) Myelodysplastic syndrome, n (%) 28 (6.2) Myeloproliferative diseases, chronic myeloid leukemia, n (%) 15 (3.3) Other diagnoses, n (%) 15 (3.3) Disease status for malignant disorders Early, n (%) 94 (20.8) Intermediate, n (%) 97 (21.5) Advanced, n (%) 120 (26.5) NDa, n (%) 141 (31.2) Donors (n = 452) Median age, y 40 Younger than 20 y, n (%) 2 (0.4) 20 to 40 y, n (%) 209 (46.2) Older than 40 y, n (%) 189 (41.8) ND, n (%) 52 (11.5) Male, n (%) 283 (62.6) Female, n (%) 134 (29.6) ND, n (%) 35 (7.7) Female donor to male recipient, n (%) 77 (17.0) HLA-matched unrelated donor, n (%) 307 (67.9) Less than 8/8-matched unrelated donor, n (%) 68 (15.0) Matched-related donor, n (%) 143 (31.6) MICA-129 genotype frequencies Val/Val, n (%) 224 (49.6) Met/Val, n (%) 156 (34.5) Met/Met, n (%) 63 (13.9) ND, n (%) 9 (2.0) MICA-129 allele frequencies Val, n (%) 604 (68.2) Met, n (%) 282 (31.8) Transplantation Source of stem cells Peripheral blood, n (%) 441 (97.6) Bone marrow, n (%) 11 (2.4) Busulfan-based conditioning, n (%) 402 (88.9) Total body irradiation-based conditioning, n (%) 47 (10.4) Reduced intensity conditioning, n (%) 170 (37.6) T-cell depletion, n (%) 252 (55.8) Outcome Occurrence of acute GVHD, n (%) 246 (54.4) Grade I to II, n (%) 159 (35.2) Grade III to IV, n (%) 87 (19.2) Occurrence of chronic GVHD, n (%) 138 (30.5) Occurrence of relapse, n (%) 86 (19.0) Mortality, n (%) 178 (39.4) Treatment-related mortality (TRM), n (%) 109 (24.1) Mortality due to acute GVHD, n (%) 52 (11.5) Infection and other TRM, n (%) 57 (12.6) Mortality due to relapse, n (%) 57 (12.6) Unknown reason of mortality, n (%) 12 (2.7) a ND, missing data or not determined parameters. Patients carrying a MICA-129Met allele had an increased probability of overall survival (hazard ratio [HR] = 0.77 per allele, P = 0.0445), and MICA-129Met homozygote patients had a trend toward a lower TRM (odds ratio [OR] = 0.51, P = 0.0907; Table 2). Specifically, the mortality due to aGVHD was reduced (OR = 0.57 per allele, P = 0.0400) despite an increased risk for MICA-129Met homozygous carriers to experience this complication (OR = 1.92, P = 0.0371). These homozygous carriers showed also a trend toward a lower severity of aGVHD (OR = 0.55, P = 0.1570), which might explain this finding. Notably, having a < 8/8 HLA-matched unrelated donor (n = 68, 15.0%) did not significantly affect these outcomes, in contrast to the MICA genotype. Table 2. Association of the MICA-129 genotype with the outcome of HSCT Cohort n = 452 Effect of MICA-129Meta Adjusted covariatesc HR/OR 95%-CI P-value Risk modelb Overall survival 0.77 [0.60, 0.99] 0.0445 additive MUD, TCD, diagnosis Treatment-related mortality 0.51 [0.22, 1.07] 0.0907 recessive MUD, TCD, diagnosis Mortality due to aGVHD 0.57 [0.32, 0.95] 0.0400 additive MUD, TCD, diagnosis Mortality due to relapse 1.64 [0.71, 4.01] 0.2607 additive MUD, TCD, FtoM, diagnosis Occurrence of aGVHD 1.92 [1.05, 3.63] 0.0371 recessive MUD, TCD, diagnosis Severity of aGVHDd 0.55 [0.23, 1.21] 0.1570 recessive MUD, TCD, diagnosis Occurrence of cGVHD 1.30 [0.96, 1.77] 0.0884 additive MUD, TCD, TBI, diagnosis Occurrence of relapse 0.87 [0.61, 1.23] 0.4313 additive MUD, TBI, diagnosis w/o T-cell depletion n = 197e Effect of MICA-129Meta Effect of no T-cell depletionf HR/OR 95%-CI P-value Risk modelb HR/OR P-value Overall survival 0.67 [0.46, 0.98] 0.0382 additive 1.59 0.0165 Treatment-related mortality 0.35 [0.08,1.17] 0.1220 recessive 1.60 0.0991 Mortality due to aGVHD 0.44 [0.18, 0.92] 0.0420 additive 2.16 0.0559 Mortality due to relapse 3.43 [0.82, 26.1] 0.1450 additive 4.92 0.0285 Occurrence of aGVHD 1.54 [0.59, 4.53] 0.4037 recessive 2.16 0.0011 Severity of aGVHDd 0.30 [0.06, 1.02] 0.0768 recessive 1.57 0.1720 Occurrence of cGVHD 1.18 [0.76, 1.81] 0.4600 additive 2.59 0.0001 Occurrence of relapse 0.65 [0.37, 1.11] 0.1257 additive 1.01 0.9670 T-cell depletion n = 252e Effect of MICA-129Meta Effect of T-cell depletionf HR/OR 95%-CI P-value Risk modelb HR/OR P-value Overall survival 0.88 [0.62, 1.25] 0.4821 additive 0.63 0.0165 Treatment-related mortality 0.65 [0.22, 1.61] 0.3799 recessive 0.62 0.0991 Mortality due to aGVHD 0.71 [0.31, 1.50] 0.3941 additive 0.46 0.0559 Mortality due to relapse 1.68 [0.45, 7.60] 0.4565 additive 0.20 0.0285 Occurrence of aGVHD 2.46 [1.13, 5.66] 0.0271 recessive 0.46 0.0011 Severity of aGVHDd 0.89 [0.29, 2.52] 0.8365 recessive 0.64 0.1720 Occurrence of cGVHD 1.44 [0.92, 2.25] 0.1039 additive 0.39 0.0001 Occurrence of relapse 1.14 [0.71, 1.81] 0.5720 additive 0.99 0.9670 a Significant effects (HR/OR) with P-values ≤ 0.05 are highlighted in bold. b For each outcome, statistics are given for the most powerful genetic model. A recessive MICA-129 effect was quantified for the Met/Met genotype compared to the pooled Val/Met and Val/Val genotypes. An additive MICA-129 effect was quantified per Met allele. c In addition, all analyses were adjusted for a binary indicator distinguishing whether patient and donor had the same MICA-129 genotype or not. The following abbreviations for covariates are used: FtoM, female donor for male recipient; MUD, HLA-matched unrelated donor; TBI, total body irradiation; and TCD, T-cell-depleting treatment. Analyses for the strata not receiving (w/o T-cell depletion) and receiving ATG (T-cell depletion) were adjusted for the same covariates except for TCD. d To analyze severity of aGVHD, grades I and II were compared versus grades III and IV. e Three patients were omitted in the stratified analyses because of missing information on T-cell depletion. f Effects of applying or not applying a T-cell-depleting treatment with ATG were analyzed irrespective of the MICA genotype with adjustment for the relevant covariates to confirm the expected effects of applying or omitting ATG treatment on outcome in the cohort. The HR/OR in these strata are reciprocal values having the same P-value. Kaplan–Meyer curves stratified for the MICA-129 genotypes are shown in Fig 1A for the complete cohort. An improved overall survival was observed similarly in the subgroup transplanted with MICA-129-matched grafts (HR = 0.73 per allele, P = 0.0226), showing that the effects were associated with the MICA-129 variants itself and not caused by a MICA-129 mismatch (Fig 1B). The beneficial effect of the MICA-129Met allele on overall survival was present in patients experiencing aGVHD (HR = 0.61 per allele, P = 0.0116; Fig 1C) but not in patients without aGVHD (HR 1.37, P = 0.4634; Fig 1D), suggesting that the SNP modulates the risk of fatal aGVHD. The beneficial effect was also present in patients who did not receive a T-cell-depleting treatment (HR = 0.67 per allele, P = 0.0382; Fig 1E), in contrast to those receiving anti-thymocyte globulin (ATG; HR = 0.88, P = 0.4821; Fig 1F), suggesting an effect on T-cell function. Particularly patients carrying a MICA-129Val/Val genotype appeared to profit from T-cell depletion when overall survival was compared with MICA-129Val/Val carriers not treated with ATG (HR = 0.54, P = 0.0166; Fig 1G). No advantage of treatment with ATG was seen in patients carrying one or two MICA-129Met alleles (HR = 1.26, P = 0.3960; Fig 1H). Figure 1. Cumulative survival according to MICA-129 genotype Kaplan–Meier survival curves stratified by the patient MICA-129 genotype for all patients (n = 446). The survival curve is displayed for the first 66 months; 7.6% of the patients were followed longer. Effects on overall survival were determined by Cox regression with covariate adjustment as indicated in Table 2. The HR indicates the risk per MICA-129Met allele carried by the patients (additive risk model). The numbers of patients carrying the three genotypes and the number of events (in brackets) are indicated. Kaplan–Meier survival curves for patients receiving a graft matched for the MICA-129 genotype (n = 404). Kaplan–Meier survival curves for patients who experienced aGVDH (any grade, n = 244). Kaplan–Meier survival curves for patients not experiencing aGVHD (n = 189). The HR indicates the risk of patients carrying two MICA-129Met alleles (recessive risk model). Kaplan–Meier survival curves for patients who did not receive a T-cell-depleting treatment with ATG (n = 193). Kaplan–Meier survival curves for patients treated with ATG (n = 250). Kaplan–Meier survival curves for patients with the MICA-129Val/Val genotype (n = 229) stratified by treatment with ATG. Kaplan–Meier survival curves for patients carrying one or two MICA-129Met alleles (n = 214) stratified by treatment with ATG. Download figure Download PowerPoint In patients not treated with ATG, the beneficial effect of carrying a MICA-129Met allele on overall survival (HR = 0.67 per allele, P = 0.0382) and mortality due to aGVHD (OR = 0.44 per allele, P = 0.0420) even canceled out the unfavorable effects of not giving ATG on overall survival (HR = 1.59, P = 0.0165; Table 2). In this patient subgroup, an even clearer trend toward a lower severity of aGVHD (OR = 0.30, P = 0.0768) was observed than in all patients. On the other side, MICA-129Met alleles might increase the risk of death due to relapse although not at a significant level in this patient subgroup (OR = 3.43 per allele, P = 0.1450). In patients receiving ATG, the association of the MICA-129Met/Met genotype with an increased risk of aGVHD (OR = 2.46, P = 0.0271) was more prominent than in all patients despite an overall lower risk of occurrence of aGVHD due to T-cell depletion (OR = 0.46, P = 0.0011; Table 2). In summary, MICA-129Met alleles appeared to confer a higher risk of aGVHD albeit with beneficial effects on survival after HSCT. Specifically, the risk to die due to aGVHD was reduced in patients carrying a MICA-129Met allele, whereas patients carrying a MICA-129Val/Val genotype profited particularly from ATG treatment. To address the immunological mechanisms involved in these partially counterintuitive outcomes, we investigated whether the MICA-129 variants differ in their ability to trigger NKG2D signals after binding. Experimental tools used to study functional effects of the MICA-129Val/Met dimorphism We generated two sets of tools to analyze the functional effects of the MICA-129Val/Met dimorphism. First, we stably transfected L cells, which like all mouse cells do not possess a MICA gene, with expression constructs encoding a MICA-129Met or MICA-129Val variant. The MICA-129Met variant was the MICA*00701 allele, which has a methionine at amino acid position 129. In the MICA-129Val construct, the amino acid position 129 was changed to valine. The resulting L-MICA-129Met and L-MICA-129Val cells, in contrast to vector-only transfected L-con cells, expressed MICA and bound a human NKG2D-Fc protein (Appendix Fig S1A). A broad range of MICA expression intensities was observed on different clones, but on average, these intensities were similar for both variants (Appendix Fig S1B). Analysis of the ratio of MICA expression and NKG2D binding revealed clearly a higher avidity of the MICA-129Met than MICA-129Val variant for NKG2D (Appendix Fig S1C and D) in accord with previous results (Steinle et al, 2001). Notably, binding of NKG2D to the MICA-129Met isoform was more dependent on the intensity of MICA expression on individual clones (coefficient of determination R2 = 0.62) than to the MICA-129Val isoform (R2 = 0.39). The slope (regression coefficient) of NKG2D binding with increasing MICA expression intensity was steeper for the MICA-129Met (0.23; Fig 2A, left panel) than for the MICA-129Val variant (0.08; Fig 2A, right panel). Figure 2. NKG2D binding to the MICA-129Met and MICA-129Val isoform and triggering of phosphorylation of SRC family kinases The linear regression of MICA expression intensity and binding of a recombinant NKG2D-Fc fusion protein both determined as MFI by flow cytometry is displayed for L-MICA-129Met (n = 79, left panel) and L-MICA-129Val clones (n = 81, right panel). The coefficients of determination (R2), the regression coefficients (reg. coeff.), and the P-values for Pearson correlation are indicated. Purified IL-2-stimulated (100 U/ml for 4 days) NK cells (106) were stimulated with immobilized MICA-129Met-mIgG2a-Fc or MICA-129Val-mIgG2a-Fc or OVA-mIgG2a-Fc fusion proteins (10 μg/ml) for 3, 10, or 30 min. The protein lysates of these cells were separated by SDS–PAGE, and the blot was probed subsequently with an anti-phospho-Tyr mAb, an anti-phospho-SRC family (Tyr419) kinases Ab, and an anti-β-actin mAb as a loading control. The arrow points toward phosphorylated SRC family kinases. Blots obtained from three independent experiments were analyzed by densitometry, and the means plus SD of the ratio between phospho-SRC family kinase and β-actin signals is displayed. The difference between NK cells stimulated for 10 min by MICA-129Met-Fc or MICA-129Val-Fc proteins was assessed by t-test. Purified IL-2-stimulated NK cells (100 U/ml for 4 days, 106) were incubated with the SRC kinase inhibitor PP2 (25 μM), the vehicle DMSO, or medium only (Ø) for 30 min before being added to immobilized MICA-129Met-Fc, MICA-129Val-Fc, or OVA-Fc fusion proteins (10 μg/ml) for 10 min. The protein lysates of these cells were separated by SDS–PAGE, and the blot was probed subsequently with an anti-phospho-Tyr mAb and an anti-β-actin mAb as a loading control. The blot is representative for two independent experiments. In parallel, degranulation of the NK cells was measured by anti-CD107a staining in flow cytometry. The difference between DMSO- and PP2-treated cells with respect to C