Missing Self-Induced Activation of NK Cells Combines with Non-Complement-Fixing Donor-Specific Antibodies to Accelerate Kidney Transplant Loss in Chronic Antibody-Mediated Rejection

Abstract

Background 

Binding of donor-specific antibodies (DSAs) to kidney allograft endothelial cells that does not activate the classic complement cascade can trigger the recruitment of innate immune effectors, including NK cells. Activated NK cells contribute to microvascular inflammation leading to chronic antibody-mediated rejection (AMR). Recipient NK cells can also trigger antibody-independent microvascular inflammation by sensing the absence of self HLA class I molecules (“missing self”) on allograft endothelial cells. This translational study investigated whether the condition of missing self amplifies DSA-dependent NK cell activation to worsen chronic AMR.

Methods and Results 

Among 1682 kidney transplant recipients who underwent an allograft biopsy at Lyon University Hospital between 2004 and 2017, 135 fulfilled the diagnostic criteria for AMR and were enrolled in the study. Patients with complement-fixing DSAs identified by a positive C3d binding assay (n=73, 54%) had a higher risk of transplant failure (P=0.002). Among the remaining patients with complement-independent chronic AMR (n=62, 46%), those in whom missing self was identified through donor and recipient genotyping exhibited worse allograft survival (P=0.02). In multivariable analysis, only proteinuria (HR: 7.24; P=0.01) and the presence of missing self (HR: 3.57; P=0.04) were independent predictors for transplant failure following diagnosis of chronic AMR. Cocultures of human NK cells and endothelial cells confirmed that addition of missing self to DSA-induced NK cell activation increased endothelial damage.

Conclusions 

The assessment of missing self at the time of diagnosis of chronic AMR identifies patients at higher risk for kidney transplant failure.

Visual Abstract

End-stage renal failure (ESRF), the incidence of which has rocketed worldwide over the last decades, has become a major public health problem (www.who.int). Kidney transplantation represents the best therapeutic option for patients with ESRF, providing both a better quality of life and a longer life expectancy,^1,2 for only a fraction of the costs of chronic hemodialysis.^3,4

The current major challenge faced by kidney transplantation is to prolong the duration of graft function.^5,6 Accumulating evidence has identified antibody-mediated rejection (AMR) as the main cause of kidney transplant loss.^7–10 The development of alloantibodies against mismatched donor HLA molecules (donor-specific antibody, DSA), despite maintenance immunosuppression, is due to: nonadherence to treatment,^7,8 CNI minimization strategies,^11,12 and the limited efficacy of available immunosuppressive drugs to completely abrogate the helper functions of recipients’ T follicular helper cells.^13,14

There is a significant interindividual heterogeneity in the natural evolution of AMR and in its response to therapy.^15–17 This suggests that the pathogenic mechanisms involved in the development of histologic lesions are not the same in all patients. Understanding the molecular mechanisms underlying this heterogeneity appears as a first mandatory step to improve personalized care of patients diagnosed with AMR.

The pathophysiologic sequence by which DSAs induce histologic damage to the graft has progressively been elucidated over the last two decades.¹⁸ DSAs, which have limited ability to diffuse outside a recipient’s circulation, bind to directly accessible allogeneic HLA molecules expressed by graft endothelium.¹⁹ Depending on the titer¹⁵ and on the physico-chemical properties of the Igs,^20,21 DSAs sometimes activate the classic complement pathway, thereby accelerating the rejection process.^15,22 Complement activation is, however, not mandatory for the development of chronic AMR,²³ as clearly demonstrated in experimental models.^24–26 Indeed, the binding of DSAs to graft endothelium also recruits Fcγ receptor–expressing innate immune effectors, which in turn promote damage to graft endothelial cells (ECs).^25,27,28

Among the innate immune cells participating in DSA-induced microvascular inflammation are natural killer (NK) cells,^29,30 which have been shown to be critical for the development of endothelial damage.^{25,27,28,31,32}

For their activation, NK cells rely not only on their ability to collaborate with the humoral arm of the adaptive immune system, but also integrate signals provided by various surface receptors, including inhibitory killer-cell Ig-like receptors (KIRs). In steady state, the latter interact with self HLA I molecules of surrounding healthy cells and provide negative signals to NK cells, thereby preventing their activation.^33,34 Interestingly, our group has recently reported that in the setting of transplantation, the inability of graft ECs (which are from donor origin¹⁹) to provide HLA I–mediated inhibitory signals to recipients’ circulating NK cells triggers their activation.³⁵ This process, known as “missing self” (MS), in turn promotes microvascular inflammation and graft rejection independently from DSAs.³⁵

Whether DSAs and MS can synergize to enhance NK cell activation and worsen AMR outcome has not been evaluated so far. We therefore initiated the present translational study to evaluate in patients and experimental models whether MS could explain part of the heterogeneity of AMR outcome.

Methods

Study Population

The study was carried out in accordance with French legislation on biomedical research and the Declaration of Helsinki. All patients gave their informed consent for the utilization of clinical data (Données Informatiques Validées en Transplantation [DIVAT]) and biologic samples for research purposes (No. of biocollection: AC- 2011–1375 and AC-2016–2706).

The reports of all kidney allograft biopsies performed between September 1, 2004 and December 31, 2017 in either Edouard Herriot Hospital or Lyon Sud Hospital, the two university hospitals of Lyon (France), were screened (1682 patients) by means of the pathology department’s computer database (DIAMIC). Information from the DIAMIC database was crossed with one of the immunology department’s computer databases to identify patients with microvascular inflammation and concomitant DSAs (n=135), thus fulfilling the diagnostic criteria of AMR according to the Banff classification. The steps of selection are summarized in Figure 1A.

Figure 1.: Characteristics of the study population. Kidney allograft biopsy specimens of 1682 patients were screened for microvascular inflammation (MVI: g+ptc ≥2) lesions. Patients with MVI presenting circulating anti-HLA DSAs in synchronous serum were diagnosed with AMR and enrolled in the study (n=135). Depending on the ability of their DSA to activate the complement cascade in vitro, two groups of patients were defined: MVI⁺DSA⁺C3d⁺ (n=73; solid dark-gray line) and MVI⁺DSA⁺C3d⁻ (n=62; dashed purple line). The existence of a MS was assessed in MVI⁺DSA⁺C3d⁺ and MVI⁺DSA⁺C3d⁻ patients with available DNA for donor and recipient (respectively n=40 and n=44), allowing distribution of the patients into four subgroups: MVI⁺DSA⁺C3d⁺MS⁻ (n=17; solid light-gray line), MVI⁺DSA⁺C3d⁺MS⁺ (n=23; solid black line), MVI⁺DSA⁺C3d⁻MS⁻ (n=23; dashed blue line), and MVI⁺DSA⁺C3d⁻MS⁺ (n=21; dashed red line). (A) Flow chart showing the distribution of the patients in the different groups. (B) Renal graft survival of patients diagnosed with AMR (MVI⁺DSA⁺). Gray shading indicates 95% confidence interval. (C) Titer of immunodominant DSA was assessed in solid-phase assay. Distribution of this parameter is shown for the different groups. ****P<0.0001. One-way ANOVA.

All patients received an ABO-compatible transplant with negative historical and current complement-dependent cytotoxicity crossmatches. Clinical data were obtained from two independent national registries (Cristal [http://www.sipg.sante.fr/portail/] and Données Informatiques Validées en Transplantation [http://www.divat.fr/]) and crosschecked. The characteristics of the patients and their rejections are summarized in Table 1.

Table 1. - Baseline characteristics

Variable	MVI+DSA+ (n=135)	MVI+DSA+C3d+ (n=73)	MVI+DSA+C3d−MS− (£) (n=23)	MVI+DSA+C3d−MS+ ($) (n=21)	P Value £ versus $
Characteristics at the time of transplantation
Recipient
Age (yr)	39.7±14.7	36.2±12.8	50.0±14.3	43.3±15.6	0.1
Men, n (%)	82 (60.7)	44 (60.3)	14 (60.9)	15 (71.4)	0.5
Retransplantation, n (%)	29 (21.5)	18 (24.7)	3 (13.0)	5 (23.8)	0.4
Time since dialysis (mo)	45.9±58.3	45.0±61.5	55.3±62.4	40.7±55.6	0.1
Blood group, n (%)
Type A	66 (48.9)	36 (49.3)	12 (52.2)	10 (47.6)	1.0
Type B	14 (10.4)	6 (8.2)	3 (13.0)	3 (14.3)
Type O	50 (37.0)	27 (37.0)	7 (30.4)	8 (38.1)
Type AB	4 (3.0)	4 (5.45)	0 (0.0)	0 (0.0)
Donor
Deceased, n (%)	125 (92.6)	68 (93.2)	20 (87.0)	21 (100.0)	0.2
Age (yr)	39.6±17.5	36.8±17.5	48.0±16.2	42.8±18.8	0.4
Transplantation
Cold ischemia time (min)	916±365	913±375	891±325	853±266	0.07
No. of HLA A/B/DR mismatches	3.9±1.4	3.8±1.4	4.1±1.1	4.3±1.3	0.4
Combined transplantation, n (%) a	16 (11.9)	9 (12.3)	2 (8.7)	2 (9.5)	1.0
Delayed graft function, n (%)	25 (18.5)	14 (19.2)	5 (21.7)	5 (23.8)	1.0
Characteristics of AMR
Clinicobiologic characteristics
Time post-transplantation (mo)	66.2±68.7	76.4±68.3	28.6±35.0	36.7±37.6	0.5
Proteinuria (g/d)	1.0±2.0	1.3±2.6	0.8±1.0	0.7±0.9	0.6
Creatininemia (µmol/L)	221±185	249±232	195±116	179±73	0.9
eGFR (ml/min per 1.73 m2) b	39.0±20.4	36.9±20.2	41.4±23.4	41.7±18.4	0.7
Histologic characteristics (Banff scores c )
Microvascular inflammation d	3.4±1.2	3.5±1.2	3.1±1.2	3.5±1.3	0.2
Transplant glomerulopathy	1.0±1.2	1.1±1.2	0.4±0.7	0.7±0.9	0.2
Interstitial inflammation and tubulitis	2.4±2.1	2.7±2.2	1.9±2.3	2.1±2.0	0.7
Interstitial fibrosis and tubular atrophy	1.6±0.8	1.7±0.8	1.1±0.7	1.8±0.9	0.007
Arteriosclerosis	1.0±1.1	1.0±1.0	0.8±1.1	1.1±0.9	0.3
Endarteritis (vasculitis)	0.2±0.4	0.2±0.5	0.1±0.5	0.1±0.3	1.0
C4d deposition	1.7±1.2	2.0±1.1	1.5±1.2	1.4±1.3	0.7
Immunologic characteristics
Types of DSA, n (%)
Preformed DSA	12 (8.9)	5 (6.8)	2 (8.7)	2 (9.5)	0.3
De novo DSA	108 (80.0)	58 (79.5)	21 (91.3)	17 (81.0)
Preformed + de novo DSA	8 (5.9)	5 (6.8)	0 (0.0)	2 (9.5)
No. of DSAs	1.8±1.1	2.1±1.2	1.6±0.9	1.2±0.5	0.2
Classes of DSA, n (%)
Class I	17 (12.6)	6 (8.2)	4 (17.4)	4 (19.1)	0.7
Class II	99 (73.3)	55 (75.3)	15 (65.2)	15 (71.4)
Class I+II	19 (14.1)	12 (16.4)	4 (17.4)	2 (9.5)
MFI of immuno-dominant DSA e	8089±5775	11,802±4973	4322±2993	2607±1444	0.04
Treatment
Steroid pulses	100 (74.1)	54 (74.0)	17 (73.9)	16 (76.2)	1.0
Intravenous Igs	91 (67.4)	54 (74.0)	15 (65.2)	14 (66.7)	1.0
Rituximab	55 (40.7)	38 (52.1)	7 (30.4)	5 (23.8)	0.7
Bortezomib	16 (11.9)	11 (15.1)	1 (4.3)	3 (14.3)	0.3
Plasmapheresis	80 (59.3)	50 (68.5)	13 (56.5)	12 (57.1)	1.0

Data are displayed as mean±SD. MVI, microvascular inflammation.

^aSimultaneous pancreas and kidney transplantations.

^bCalculated with the Modification of Diet in Renal Disease formula.

^cBanff scores (0, no significant lesion; 1, mild; 2, moderate; 3, severe).

^dSum of the Banff scores for glomerulitis and capillaritis.

^eMFI of the standard IgG detection assay.

Allograft Pathology

Kidney graft biopsies were performed systematically as part of the routine follow-up procedure at 3 months and 1 year after transplantation, or when rejection was suspected at the other time points. Renal specimens were fixed in acetic acid–formol–absolute alcohol, and paraffin-embedded sections were stained by routine methods. C4d staining was performed by indirect immunofluorescence on frozen sections using an anti-human C4d complement–rabbit clonal antibody (clone A24-T, produced by DB Biotech, Kosice, Slovak Republic). Renal allograft lesions were graded according to the 2013 Banff classification.³⁶ Histologic criteria for AMR were defined by a sum of the scores for glomerulitis and peritubular capillaritis ≥2, with or without concurrent positive C4d staining. All of the biopsy specimens of the patients involved in the study were reviewed by a single renal pathologist (Maud Rabeyrin) who was blinded to clinical and immunologic data.

For computer-assisted analysis of graft inflammation (CAGI), double stainings with anti-CD34 (ECs) and respectively one antibody among anti-CD3 (T cells), anti-CD20 (B cells), anti-CD66b (granulocytes), anti-CD68 (macrophages), and anti-CD56 (NK cells) were performed by immunochemistry on paraffin-embedded sections using an anti-human CD34 (clone QBEnd10, 1/200; Dako, Les Ulis, France) and respectively an anti-human CD3 (clone SK7, 1/150; Becton Dickinson, Le Pont de Claix, France), an anti-human CD20 (Clone L26, 1/400; Dako), an anti-human CD66b (clone G10F5, 1/300; Becton Dickinson), an anti-human CD68 (clone PGM1, 1/100; Dako), and an anti-human CD56 (clone CD564, 1/10; produced by Novocastra and distributed by Leica Microsystemes SAS, Nanterre, France). Of note, CD56 staining, although extremely sensitive (94%), is not fully specific for NK cells. About three-quarters of CD56⁺ cells are NK cells (Nkp46⁺). The majority of CD56⁺ Nkp46⁻ cells are either NKT or CD8⁺ T cells. Computerized quantitative analyses were conducted to quantify the density of each immune cell type in the microcirculation and tubulointerstitial compartment of renal allograft as described.¹⁶

Transcriptomic Analysis of Renal Graft Biopsy Specimens

For each renal allograft biopsy specimen included in this study, at least half of a core was immediately stored on Allprotect Tissue Reagent (Qiagen Benelux BV, Venlo, The Netherlands), and after incubation at 4°C for at least 24 hours and maximum 72 hours, stored at −20°C. RNA extraction was performed using the Allprep DNA/RNA/miRNA Universal Kit (Qiagen Benelux BV, Venlo, The Netherlands) on a QIAcube instrument (Qiagen Benelux BV, Venlo, The Netherlands). The quantity (absorbance at 260 nm) and purity (ratio of the absorbance at 230, 260, and 280 nm) of the RNA isolated from the biopsy specimens were measured using the NanoDrop ND-1000 spectrophotometer (Thermo Scientific, Life Technologies Europe BV, Ghent, Belgium). RNA integrity was evaluated using the Eukaryote nano/pico RNA Kit (Agilent Technologies Belgium NV, Diegem, Belgium) on the Bioanalyzer 2100 instrument (Agilent Technologies). Samples were stored at −80°C until further analysis.

RNA extracted from the biopsy samples was first amplified and biotinylated to complementary RNA (cRNA) using the GeneChip 3′ IVT PLUS Reagent Kit (Affymetrix Inc., High Wycombe, UK) and subsequently hybridized onto Affymetrix GeneChip Human Genome U133 Plus 2.0 Arrays (Affymetrix Inc.), which cover over 54,000 transcripts, according to the manufacturer’s instructions. The arrays were scanned using the GeneChip Scanner 3000 7G System (Affymetrix Inc.), and image files were generated using the GeneChip Command Console Software (AGCC). Finally, Robust Multichip Average (RMA) background correction and normalization was performed using the Affymetrix Expression Console Software, and expression values were log₂ scaled. Fifty-four biopsy specimens survived prehybridization quality control checks, and were analyzed.

CIBERSORT Algorithm

CIBERSORT (Cell type Identification By Estimating Relative Subsets Of known RNA Transcripts) is a recently introduced computational method allowing the exploration of the heterogeneity of infiltrating immune cells in complex solid tissues.³⁷ This deconvolution algorithm calculates the relative fraction of different phenotypes of human hematopoietic cells in tissue samples, on the basis of the expression of 547 genes. To obtain an estimation of absolute intragraft NK-cell count, we adjusted the CIBERSORT data for the expression of pan-leukocyte marker CD45/PTPRC.

Detection and Characterization of Anti-HLA Antibodies

Serum samples banked at the time of biopsy were analyzed using Single Antigen Flow Beads assays (LSA class I and class II, Immucor, Norcross, GA). The MFI was measured on a LABscan IS 200, and all of the specificities with an MFI>500 and AD-BCR>5 were considered positive (AD-BCR is MFI adjusted to the quantity of coated antigen per bead).

Serum samples were analyzed in a blinded fashion for the presence of C3d-binding anti-HLA DSA (Valérie Dubois at the Etablissement Français du Sang, Lyon, France) with the use of single-antigen flow bead assays according to the manufacturer’s protocol (C3d-binding antibody assay; Immucor).

HLA and KIR Genotyping

Donor and recipient HLA typing were performed by PCR-SSO reverse (One Lambda, Canoga Park, CA). HLA-C1 and -C2 groups were determined for the donors and recipients considering the HLA C typing obtained by PCR-SSO reverse. The presence or not of Bw4 motif was determined for the donors and recipients considering the HLA A and B typing obtained by PCR-SSO reverse (Supplemental Figure 1).

Genotyping of inhibitory KIR genes (2DL1, 2DL2, 2DL3, 3DL1, 3DL2) was realized for the recipients by PCR-SSO reverse (KIR SSO Genotyping Test; One Lambda and Lifecodes KIR Genotyping, Immucor) according to the manufacturers’ instructions (Supplemental Figure 1).

Genetic Prediction of MS

2DL1, 2DL2, 2DL3, 3DL1, and 3DL2 inhibitory KIRs educated NK cells only when the recipient expressed their respective HLA class I ligand: KIR2DL1/C2; KIR2DL2/C1; KIR2DL3/C1; KIR3DL1/Bw4; and KIR3DL2/A*03, *11.

Genetic prediction of MS was defined as the lack of expression by the graft of the type of HLA class I molecule able to bind to an educating inhibitory KIR of the recipient (Supplemental Figure 1).

Cell Preparation and Cultures

Primary human arterial ECs were isolated from organ donors (agreement PFS08–017 from the Agence de la Biomédecine, https://www.agence-biomedecine.fr) and prospectively stored in the DIVAT biobank (No. of biocollection #02G55). They were cultured in EC growth medium 2 (Promocell, Heidelberg, Germany) in flasks coated with fibronectin (Promocell) or gelatin 1% (Sigma, Saint Quentin-Fallavier, France) and used between passages 2 and 7.

Peripheral blood mononuclear cells (PBMCs) were isolated from the blood of healthy volunteers by Ficoll gradient centrifugation (Eurobio, Courtaboeuf, France). PBMCs were cultured overnight at 37°C in 5% CO₂ in RPMI-1640 (ThermoFisher Scientific, Courtaboeuf, France) supplemented with FBS 10%, l-glutamine 2 mM (ThermoFisher Scientific), penicillin 100 U/ml, streptomycin 100 µM, HEPES 25 mM (ThermoFisher Scientific), and 60 UI of recombinant human IL-2 (R&D Systems, Minneapolis, MN). NK cells were purified (>90%) from PBMCs by negative selection with magnetic enrichment kits (Stemcell, Grenoble, France).

Evaluation of CD16⁺inhKIR⁺ NK Cell Population

One-hundred-microliter whole-blood samples were incubated with anti-CD3 (clone SK7, 1/20; Biolegend), -CD14 (clone M5E2, 1/20; BD Biosciences), -CD19 (clone HIB19, 1/20; BD Biosciences), -CD56 (clone NCAM16.2, 1/40; BD Biosciences), -CD16 (clone B73.1, 1/20; BD Biosciences), -KIR3DL1 (clone DX9, 1/10; BD Biosciences), -KIR2DL1/S1 (clone HP3E4, 1/10; BD Biosciences), and -KIR2DL2–3/S2 (clone GL183, 1/10; Beckman Coulter, Villepinte, France) antibodies for 30 minutes at 4°C. The samples were then incubated with a lysing solution (BD Biosciences) to eliminate the red blood cells for 3 minutes at room temperature. After washing, cells were fixed with paraformaldehyde 2% (ThermoFisher Scientific) and the samples were stored at 4°C until analysis. Sample acquisition was done on a LSR FORTESSA (BD Biosciences) and analyses were performed with FlowJo software version 10.0.8r1 (Tree Star Inc., Ashland, OR). Briefly, the following gating strategy was applied to assess the proportion of CD16⁺inhKIR⁺ NK cells among PBMCs. After a morphologic gate on lymphocytes followed by a gating on single cells, CD56⁺CD3⁻CD14⁻CD19⁻ cells (i.e., NK cells) were selected. Then, CD16⁺inhKIR⁺ NK cells were easily selected by using anti-KIR2DL1, -2DL2–3, and -3DL1 antibodies coupled to the same fluorochrome. NKG2A expression by NK cells was not assessed in this experiment.

Analysis of EC Viability

In each culture well, 10,000 human primary ECs expressing HLA A2 and the inhibitory KIR ligands (C1⁺, C2⁺, Bw4⁺) were seeded. When indicated, human primary ECs were preincubated with a serum containing an anti-A2 antibody (pure or diluted at 1:2 or 1:5) for 30 minutes at 37°C before seeding. After 24 hours, bulk purified NK cells from donors all expressing the following functional inhibitory KIRs: KIR2DL1⁺/C2⁺, KIR2DL2–3⁺/C1⁺, and KIR3DL1⁺/Bw4⁺ were added to the culture (the number of NK cells was standardized in order to have the same number of CD16⁺inhKIR⁺ NK cells, i.e., 35,000 for all of the donors). When indicated, anti-KIR2DL1 (clone HP3E4; BD Biosciences), -KIR2L2–3 (clone GL183; Beckman Coulter), and -KIR3LD1 (clone DX9; BD Biosciences) blocking monoclonal antibodies or isotype controls were added to the culture at a final concentration of 1 µg/ml for each antibody. In absence of an existing anti-KIR3DL2 blocking mAb, KIR3DL2/A3, A11 MS was not tested in this experiment.

EC viability was monitored every 5 minutes for 10 hours by electrical impedance measurement with an xCELLigence RTCA SP instrument (ACEA Biosciences, San Diego, CA). The cell indexes were normalized to the reference value (measured just before adding NK cells to the culture). EC viability in the experimental well was normalized over the control well.

Statistical Analyses

Categoric variables were expressed as percentages and compared with the chi-squared test. Continuous variables were expressed as mean±SD and compared using the unpaired t test.

Graft survival was calculated from the date of rejection diagnosis until the beginning of hemodialysis. Survival curves were constructed with the Kaplan–Meier method and compared with the log-rank test.

The Cox proportional hazards regression model was used in both univariable and multivariable models. All significant variables in the univariate analysis with a level set at <0.05 were incorporated into multivariate models. All tests were two sided, and P values <0.05 were considered to represent statistically significant differences. Statistical analyses were done using R software version 3.5.1 (R Foundation for Statistical Computing, Vienna, Austria, 2018, https://www.R-project.org/) and GraphPad Prism 6.0a for Mac OS X.

Results

Characteristics of the Study Population

Of 1682 kidney transplant recipients followed in our institutions over the study period for whom a graft biopsy was performed, 135 (8%) fulfilled the diagnostic criteria for AMR (i.e., presence of circulating anti-HLA DSAs and sum of the Banff scores for glomerulitis and peritubular capillaritis ≥2) and were enrolled in the study (Figure 1A). The clinical characteristics of these patients are detailed in Table 1.

In accordance with the literature,^15,22 kidney allograft survival after AMR diagnosis was highly heterogeneous (77%, 50%, and 30% at 1, 4, and 7 years, respectively) (Figure 1B).

Activation of Complement Cascade by DSAs Worsens AMR Outcome

On the basis of abundant literature demonstrating the role of complement in antibody-mediated accelerated graft destruction,^15,22 the ability of DSAs to activate the classic complement pathway was evaluated at the time of diagnosis with the C3d binding assay. Of the 135 patients, 73 (54%) had C3d-binding DSAs (MVI⁺DSA⁺C3d⁺ group) and 62 (46%) had non–C3d-binding DSAs (MVI⁺DSA⁺C3d⁻ group) (Figure 1A). As expected, the intensity of C4d deposits in allograft biopsy specimens was higher in the MVI⁺DSA⁺C3d⁺ group (Banff score 2.0±1.1 versus 1.3±1.2 for MVI⁺DSA⁺C3d⁻ group; P=0.006). It is well known that the ability of DSAs to recruit C1q highly depends on DSA quantity.³⁸ MVI⁺DSA⁺C3d⁺ patients had a more diverse DSA repertoire (number of DSA specificities: 2.1±1.2 versus 1.5±0.7 for MVI⁺DSA⁺C3d⁻; P=0.002) and higher DSA titers (MFI of immuno-dominant DSA obtained with the standard IgG detection assay: 11,802±4973 versus 3719±2862 for MVI⁺DSA⁺C3d⁻; P<0.0001). In line with previous publications,^15,39 the presence of circulating C3d-binding DSAs at time of rejection strongly correlated with a higher risk for renal graft failure (P=0.002, log-rank test; Figure 2A). We confirmed that this correlation was the consequence of complement activation and not of the higher titer of immuno-dominant DSA in the MVI⁺DSA⁺C3d⁺ group by conducting the same analysis on patients with comparable MFI within MVI⁺DSA⁺C3d⁻ and MVI⁺DSA⁺C3d⁺ groups (P=0.02, log-rank test; Supplemental Figure 2).

Figure 2.: C3d binding capacity of DSA and MS affect allograft survival. (A) Renal graft survival of MVI⁺DSA⁺C3d⁻ (dashed purple line) and of MVI⁺DSA⁺C3d⁺ (solid dark-gray line) were compared. (B) Renal graft survival of MVI⁺DSA⁺C3d⁻MS⁻ (dashed blue line) and of MVI⁺DSA⁺C3d⁻MS⁺ (dashed red line) were compared. (C) Renal graft survival of MVI⁺DSA⁺C3d⁺MS⁻ (solid light-gray line) and of MVI⁺DSA⁺C3d⁺MS⁺ (solid black line) were compared. *P<0.05, **P<0.01. Log-rank test.

MS Effect on Allograft Survival of Patients with AMR

Low-titer DSAs, which cannot activate the complement cascade, trigger chronic AMR.^{25,27,28,31,32} We have recently demonstrated that the inability of graft ECs to deliver self HLA I–mediated inhibitory signals to recipient’s NK cells (i.e., MS) was sufficient to promote NK cell–mediated chronic vascular rejection.³⁵ We therefore hypothesized that MS could synergize with low-titer DSAs to accelerate graft loss in chronic AMR. To test this theory, we integrated, for each donor/recipient pair, the genetic analyses of: recipient inhibitory KIRs and recipient HLA I (in order to identify educating inhibitory KIRs, i.e., functional inhibitory KIRs susceptible to sense MS on graft ECs), with the donor HLA I genotype, so as to identify situations of MS (Supplemental Figure 1). These analyses revealed that 42.5% (n=17/40) of MVI⁺DSA⁺C3d⁺ patients and 48% (n=21/44) of MVI⁺DSA⁺C3d⁻ patients had a MS (Figure 1A). In line with our hypothesis, MVI⁺DSA⁺C3d⁻MS⁺ patients had a significantly higher risk for renal graft loss as compared with MVI⁺DSA⁺C3d⁻MS⁻ patients (P=0.02, log-rank test; Figure 2B). Of note, the additional effect of MS could not be observed in the group of MVI⁺DSA⁺C3d⁺ patients, in whom the activation of complement led to a very fast graft loss (P=0.7, log-rank test; Figure 2C).

MS Is an Independent Predictive Factor for Allograft Loss in Chronic AMR

Although univariate analysis showed that the presence of a MS correlates with a higher risk of graft loss in chronic AMR (Figure 2B), this difference could be due to many confounding factors. To rule out this possibility, the characteristics of the MVI⁺DSA⁺C3d⁻MS⁺ and MVI⁺DSA⁺C3d⁻MS⁻ patients were compared (Table 1). Baseline characteristics of donors and recipients at time of transplantation were similar between the two groups as was the treatment of AMR. However, at the time of rejection, patients with a MS had significantly more interstitial fibrosis and tubular atrophy on graft biopsy (IFTA Banff score: 1.8±0.9 versus 1.1±0.7, respectively; P=0.007; Table 1). On the other hand, MVI⁺DSA⁺C3d⁻MS⁺ patients had lower titers of circulating DSAs (MFI of immunodominant DSA obtained with the standard IgG detection assay: 2607±1444 versus 4322±2993; P=0.04; Table 1) than MVI⁺DSA⁺C3d⁻MS⁻ patients.

To test whether MS was an independent risk factor for graft loss in chronic AMR, a multivariable analysis was conducted using a Cox regression proportional hazard model. Exploratory analysis identified three risk factors statistically associated with graft failure (Table 2, left column): MS, proteinuria at time of diagnosis of AMR, and the intensity of microvascular inflammation. When integrated in a multivariable analysis (Table 2, right column), only two independent predictors for allograft failure at diagnosis of chronic AMR remained: proteinuria (hazard ratio, 7.24; 95% confidence interval, 1.51 to 34.60; P=0.01) and the presence of a MS (hazard ratio, 3.57; 95% confidence interval, 1.04 to 12.18; P=0.04). We concluded that MS is an independent mechanism that synergizes with low-titer DSAs to accelerate graft loss in chronic AMR.

Table 2. - Univariable and multivariable analyses of risk factors for death-censored allograft loss

Variable		Univariate			Multivariate
	No. of Patients	HR	95% CI	P Value	HR	95% CI	P Value
Clinico-biologic factors
Recipient sex
Female	15	1.00	Reference
Male	29	1.11	(0.39 to 3.17)	0.85			a
Recipient age (per 1-yr increment)	44	1.01	(0.98 to 1.05)	0.49			a
Retransplantation
No	35	1.00	Reference
Yes	8	0.55	(0.15 to 1.96)	0.35			a
Donor type
Living	3	1.00	Reference
Deceased	41	2.15	(0.26 to 17.62)	0.47			a
Donor age (per 1-yr increment)	44	1.00	(0.97 to 1.03)	0.82			a
Number of mismatches A/B/DR
≤3	40	1.00	Reference
>3	4	0.59	(0.13 to 2.61)	0.48			a
Cold-ischemia time per 1-min increment	44	1.00	(0.99 to 1.00)	0.56			a
eGFR at the time of rejection b (ml/min per 1.73 m2)
≥30	28	1.00	Reference
<30	15	2.45	(0.94 to 6.38)	0.06			a
Proteinuria at the time of rejection (grams/d)
<0.5	25	1.00	Reference
≥0.5	14	5.31	(1.67 to 16.90)	0.002	7.24	(1.51 to 34.60)	0.01
Histologic factors c
Microvascular inflammation d
2 or 3	26	1.00	Reference
≥4	18	2.92	(1.02 to 8.32)	0.04			NS
Interstitial inflammation and tubulitis
0 or 1	25	1.00	Reference
≥2	19	1.34	(0.50 to 3.55)	0.56			a
Transplant glomerulopathy
0 or 1	38	1.00	Reference
≥2	6	0.98	(0.22 to 4.36)	0.98			a
Endarteritis (vasculitis)
0	39	1.00	Reference
≥1	4	0.40	(0.05 to 3.10)	0.37			a
Arteriosclerosis
0 or 1	32	1.00
≥2	11	2.24	(0.74 to 6.72)	0.14			a
Interstitial fibrosis and tubular atrophy
0 or 1	23	1.00	Reference
≥2	20	2.15	(0.78 to 5.93)	0.13			a
Immunologic factors
MS
No	23	1.00	Reference
Yes	21	3.21	(1.12 to 9.15)	0.02	3.57	(1.04 to 12.18)	0.04
MFI of immuno-dominant DSA e
<6000	38	1.00	Reference
≥6000	6	1.64	(0.36 to 7.36)	0.52			a
MFI of immuno-dominant DSA e (per one-unit increment)	44	1.00	(0.99 to 1.00)	0.75			a
Number of DSA
1	30	1.00	Reference
≥2	14	0.94	(0.30 to 2.89)	0.91			a

HR, hazard ratio; 95% CI, confidence interval; NS, not significant; MFI, mean fluorescence intensity.

^aIndicates the variables that were not tested in the multivariable model.

^bCalculated with the Modification of Diet in Renal Disease formula.

^cBanff scores (0, no significant lesion; 1, mild; 2, moderate; 3, severe).

^dSum of the Banff scores for glomerulitis and capillaritis.

^eMFI of the standard IgG detection assay. Variables at the P level <0.05 in the univariate model were incorporated into the multivariable model.

MS Increases NK Cell Recruitment and Activation during Chronic AMR

We next wondered what is the effect of MS-induced NK cell activation on the histologic lesions of chronic AMR.

First, the intensity of chronic and acute histologic lesions, graded according to the international Banff classification, was compared between MVI⁺DSA⁺C3d⁺, MVI+DSA+C3d−MS− and MVI+DSA+C3d−MS+ groups. The two groups with the worst graft survival (MVI⁺DSA⁺C3d⁺ and MVI⁺DSA⁺C3d⁻MS⁺) had significantly more interstitial fibrosis and tubular atrophy than MVI⁺DSA⁺C3d⁻MS⁻ (1.8±0.9 and 1.7±0.8 versus 1.1±0.7, respectively; P=0.006; Figure 3A, Table 1), suggesting a more aggressive pathogenic mechanism. However, biopsy specimens of the three groups showed comparable Banff scores for acute lesions: both microvascular (glomerulitis and peritubular capillaritis) and tubulo-interstitial inflammation (Figure 3, B and C, Table 1). These data led us to hypothesize that it was the nature, rather than the mere “quantity” of inflammation, that was the main factor accounting for the differences in graft survival.

Figure 3.: MS increases NK cell recruitment during chronic AMR. (A–C) The intensities of interstitial fibrosis and tubular atrophy (IFTA) (A), microvascular inflammation (defined as the sum of Banff scores for glomerulitis [g, 0–3] and peritubular capillaritis [ptc, 0–3]) (B), and tubulo-interstitial inflammation (defined as the sum of Banff scores for interstitial inflammation [i, 0–3] and tubulitis [t, 0–3]) (C) were compared between MVI⁺DSA⁺C3d⁺, MVI⁺DSA⁺C3d⁻MS⁻, and MVI⁺DSA⁺C3d⁻MS⁺ patients. *P<0.05. Kruskal–Wallis test. (D–F) For CAGI analysis, paraffin-embedded sections of available graft biopsy specimens from MVI⁺DSA⁺C3d⁺ (n=61), MVI⁺DSA⁺C3d⁻MS⁻ (n=17), and MVI⁺DSA⁺C3d⁻MS⁺ (n=13) patients were stained for ECs (CD34) and NK cells (CD56) or macrophages (CD68), or neutrophils (CD66b), or T cells (CD3), or B cells (CD20). The density of the infiltration of each immune subset was measured in the microcirculation and the interstitium of the graft. (D) We applied a discriminant analysis on the CAGI dataset of the 91 patients with available data. The scatter plot of the first two canonical function is shown. Entropy r ²=0.39. (E) A representative image of NK cell (CD56⁺) infiltration of graft microcirculation (glomeruli, upper row; peritubular capillaries, middle row) and interstitium (lower row) is shown for each group. Scale bar, 50 μm. (E) The density of NK cells infiltrating microvascular and interstitial compartments of the graft was compared between the three groups. *P<0.05. One-way ANOVA. (G) The CIBERSORT algorithm was applied to the transcriptomic data of allograft biopsy samples of MVI⁻DSA⁻ (n=43, green), MVI⁺DSA⁺C3d⁻MS⁻ (n=5, blue), and MVI⁺DSA⁺C3d⁻MS⁺ (n=6, red) patients. Absolute NK cell infiltration was estimated by adjusting the relative percentages of activated and resting NK cells on the expression of the pan-leukocyte marker CD45 in the biopsy specimen. Box, interquartile range; line, median; whiskers, 10th and 90th percentiles. *P<0.05; ****P<0.0001. One-way ANOVA. A.U., arbritary unit.

To better characterize graft inflammation, we used the CAGI technique,¹⁶ which allows for a precise quantification of the innate and adaptive immune cell subset densities in the microcirculation (glomerular and peritubular capillaries) and the interstitium of renal allograft. Linear discriminant analysis conducted on the whole CAGI dataset of patients with available biopsy material (n=91) accurately identified the three groups (MVI⁺DSA⁺C3d⁺, MVI⁺DSA⁺C3d⁻MS⁺, and MVI⁺DSA⁺C3d⁻MS⁻), confirming that the nature of graft inflammation differs between the groups (Figure 3D). Interestingly, the single parameter that distinguished patients from the MVI⁺DSA⁺C3d⁻MS⁺ group from those of MVI⁺DSA⁺C3d⁻MS⁻ and MVI⁺DSA⁺C3d⁺ group was an increased graft infiltration by NK cells (36±49 versus 6±5 and 15±17 CD56^pos cells/mm², respectively; P=0.03; Figure 3E, Supplemental Figures 3 and 4). This increased density of NK cells in grafts from MVI⁺DSA⁺C3d⁻MS⁺ patients was observed in both microvascular and interstitial compartments (Figure 3, E and F).

In order to further assess the effect of MS on NK cells during AMR, we analyzed the transcriptomic data from the graft biopsies of an independent cohort of 54 renal recipients. A recently published computational method named CIBERSORT³⁷ was applied to deconvolute the dataset and evaluate the amounts of both activated and resting NK cells infiltrating the grafts. The results obtained in the 43 patients without MVI or DSA (MVI⁻DSA⁻; control group) were compared with those of patients diagnosed with chronic AMR, with and without MS (MVI⁺DSA⁺C3d⁻MS⁺, n=6; and MVI⁺DSA⁺C3d⁻MS⁻, n=5, respectively). As expected, NK cell infiltrate was more abundant in chronic AMR biopsy specimens as compared with controls. In line with the CAGI data presented (Figure 3F), the presence of a MS was associated with an increased number of NK cells infiltrating the graft. Furthermore, transcriptomic analysis also revealed that there were more activated NK cells in the grafts of MVI⁺DSA⁺C3d⁻MS⁺ than in those of MVI⁺DSA⁺C3d⁻MS⁻ patients (Figure 3G). Altogether, these data support the theory that MS synergizes with DSA to promote NK cell recruitment and activation during chronic AMR.

MS Enhances EC Damage In Vitro

Having shown that MS enhances NK cell recruitment and activation in rejected grafts, we next evaluated whether this parameter also increases the cytotoxic activity of NK cells, which could account for the accelerated destruction of these grafts. To test this hypothesis, bulk NK cells purified from the PBMCs of healthy volunteers were cocultured on a monolayer of HLA A2–expressing primary allogeneic human ECs, the integrity of which was monitored by real-time impedance measurement. Because the number of NK cells able to respond to the two stimuli (i.e., NK cells expressing both the Fcγ receptor CD16 and iKIR) was highly variable in healthy volunteers (Figure 4A), the coculture conditions were adjusted to contain the same number of CD16^pos iKIR^pos (i.e., responder) NK cells. DSA-dependent NK cell activation was triggered using the serum of a transplanted patient highly sensitized against HLA A2 (MFI=18,867; Figure 4B). This very high titer of DSA induced a rapid and complete destruction of the endothelial targets but did not reflect the clinical situation of MVI⁺DSA⁺C3d⁻ patients. To better mimic chronic AMR in vitro, we tested different dilutions of the serum. Dilution by 1:2 was still sufficient to induce a rapid and total destruction of endothelial targets in the absence of MS (Figure 4C). In contrast, 1:5 dilution resulted in slower and less-complete destruction of ECs, leaving room to observe a putative additional effect of MS. Interestingly, serum diluted at 1:5 corresponded to anti-HLA A2 DSA MFI of approximately 7000 (Figure 4B), a titer comparable to what was observed in MVI⁺DSA⁺C3d⁻ patients (Figures 1C and 4D). MS was triggered by adding to the coculture a cocktail of blocking monoclonal antibodies directed against four inhibitory KIRs (KIR2DL1, KIR2DL2, KIR2DL3, KIR3DL1; Supplemental Figure 1). This allowed using the same allogeneic ECs in all of the experimental conditions, thus guaranteeing the comparability of the results between the various culture conditions. The survival of the same primary matched allogeneic ECs exposed to NK cells from the same donor was compared for four distinct situations (Figure 4E): (1) absence of both DSA and MS (i.e., control; left panel, green); (2) DSA alone (middle left panel, blue); (3) MS alone (middle right panel, orange); and (4) DSA and MS (right panel, red). This in vitro experiment, replicated with six different pairs, confirmed that MS synergizes with DSA to accelerate and aggravate EC destruction by NK cells (Figure 4F).

Figure 4.: MS enhances EC damage in vitro. (A) The expression of FcγRIIIA (CD16) and each inhibitory KIR (KIR2DL1, KIR2DL2, KIR2DL3, and KIR3DL1) was analyzed by flow cytometry on circulating NK cells from 14 healthy volunteers. The proportion of NK cells expressing all of the receptors is shown. (B) The serum of a transplanted patient, sensitized against HLA A2, was diluted and tested in solid-phase assay. The MFI of an HLA A2 bead is plotted for the various dilutions. Dashed lines indicate: in the y axis, the threshold of MFI associated with C3d assay positivity (see receiver operating characteristic [ROC] curve in D); and in the x axis, the corresponding serum dilution (1:5). (C) Purified NK cells from a healthy donor were cocultured with the same primary human ECs in absence (0: control; light blue) or presence of decreasing titers of DSA (dilution of sensitized serum: pure, 1:2, 1:5; darker shades of blue). The impedance profile of each culture conditions is shown (mean±SEM). (D) ROC curve for prediction of the ability of DSA to activate the complement according to MFI titer of the immunodominant DSA (sensitivity of 82.2%, specificity of 87.1%, and likelihood ratio at 6.4). AUC, area under the curve. (E and F) Bulk purified NK cells from healthy volunteers bearing CD16, KIR2DL1, KIR2DL2–3, and KIR3DL1 were cocultured with the same primary allogeneic HLA A2–expressing human ECs (1) in presence of human AB serum (SAB) and an isotype control mAb cocktail (control, left panel); (2) in presence of a low-titer DSA and an isotype control mAb cocktail (DSA alone, middle left panel); (3) in presence of SAB and a blocking anti-inhibitory KIR mAb cocktail (MS alone, middle right panel); or (4) in presence of a low-titer DSA and a blocking anti-inhibitory KIR mAb cocktail (DSA + MS, right panel). (E) Schematic representation of the experimental model. (F) The viability of ECs was assessed by real-time impedance measurement in each coculture and the data were normalized over control. Left panel: impedance profiles of the four coculture conditions are shown for one representative experiment (mean±SEM; left panel). Right panel: AUCs of the impedance profiles from six independent experiments are compared. *P<0.05. Friedman test. A.U., arbritary unit.

Discussion

The recipient’s humoral alloimmune response is widely accepted as a major cause of allograft loss after organ transplantation. AMR outcome is, however, highly heterogeneous at the individual level, suggesting that different patients with AMR have different immunopathologic mechanisms involved in the destruction of their graft. Understanding these various mechanisms and identifying which of them are involved in a given patient could pave the way for more efficient, personalized treatments.

Analyzing a cohort of renal transplant recipients diagnosed with AMR, our translational study confirms previous publications, which reported that complement activation is a major accelerator of allograft destruction.^15,22,39,40

For patients whose DSA titer is too low to trigger complement activation, graft destruction is thought to result from the recruitment of Fcγ receptor–expressing innate effectors.^18,41 Several lines of experimental evidence point toward the central role of NK cells in this process.^25,27–29 In line with these data, a recent clinical study has reported that the intensity of NK cell infiltration within the graft is correlated with transplant survival in AMR.³⁰ Recently, our group demonstrated that in the absence of DSA, MS alone was sufficient to trigger NK cell–mediated chronic vascular rejection.³⁵ In this study, we show for the first time that the inability of the graft to deliver HLA I–mediated inhibitory signals to NK cells (i.e., MS) synergizes with DSA to increase NK cell recruitment and cytotoxicity, which in turn accelerate graft destruction. In fact, despite lower DSA titers, patients with chronic AMR in whom these two pathogenic mechanisms were identified had a graft survival equally gloomy as for patients with complement-dependent (“acute”) AMR.

Our study has, of course, several limitations, notably the fact that the clinical data were obtained retrospectively in a monocentric cohort of limited size. Nevertheless, these limits are balanced by the concordant transcriptomic data obtained in an independent cohort of patients receiving kidney transplant and the results of in vitro models. The latter indeed validated the mechanistic role of MS in potentiating NK cell recruitment and cytotoxicity in chronic AMR.

Another limitation of this study is that MS was defined only upon HLA class I/educated inhibitory KIR combinations. However, this molecular mechanism is not the only antibody-independent process by which NK cells could receive a synergistic activation signal during AMR. NK cell activation indeed depends on the integration of activating and inhibitory signals coming from several surface receptors. Besides inhibitory KIRs, which play a critical role in the acquisition of their functions,⁴² NK cells also possess other inhibitory receptors such as NKG2A which can also modulate their reactivity.⁴³ The latter interacts with HLA E, a nonclassic HLA class I molecule expressed by all nucleated cells.⁴⁴ Even if the HLA E molecule is characterized by a limited polymorphism, its stabilization on the cell surface is known to be modulated by its ability to load peptide derived from the classic HLA class I leader sequence.⁴⁵ The leader sequence derived from most HLA B haplotypes does not bind effectively to HLA E, resulting in decreased surface expression of HLA E–peptide complexes and thus a reduced capacity for the cell to deliver NKG2A-mediated inhibitory signals to NK cells.^43,45 In the context of allogeneic organ transplantation, it is tempting to speculate that, depending on the HLA B haplotypes of the donor, the inability of graft ECs to deliver enough HLA E–mediated inhibitory signals might promote recipients’ NK cell activation. Finally, in other circumstances, NK cells could be activated not by a lack of inhibitory signals but by an excess of activating signals, i.e., the recognition of “induced self.”⁴⁶ In the context of organ transplantation, ischemia/reperfusion is known to induce the expression of stress ligands on graft ECs,^47–49 which in turn bind to NK cell–activating receptors (NKG2D or activating KIRs) and trigger activation.^50,51 Future studies are needed to determine the respective importance of these molecular mechanisms in the pathophysiology of rejection, not only AMR but also TCMR.

In conclusion, our translational study demonstrates that the combination of MS- and DSA-induced activation of recipient’s NK cells is synergistically deleterious for graft survival. Identification of patients with MS at the time of diagnosis of chronic AMR could help in stratifying the risk of graft loss and to guide a personalized therapeutic approach.

Disclosures

B. Charreau reports current employment with CRTI INSERM UMR1064. V. Dubois reports current employment with EFS AURA. A. Koenig is supported by INSERM (Institut National de la Santé et de la Recherche Médicale) (poste accueil 2015/1239/BT), the Hospices Civils de Lyon, and the Fondation du Rein, outside of the submitted work. E. Morelon reports research funding and honoraria from Astellas. M. Naesens reports scientific advisor or membership as an Editorial Board member for several journals and advisor for the European Medicines Agency. O. Thaunat reports consultancy agreements with NOVARTIS; research funding from BIOMERIEUX, BMS, IMMUCOR, and NOVARTIS; and scientific advisor or membership with ESOT. All remaining authors have nothing to disclose.

Funding

This work was supported by the Agence Nationale de la Recherche (ANR-16-CE17-0007-01) and the Fondation pour la Recherche Médicale (PME20180639518).