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Keywords:

  • Antibody-mediated rejection;
  • Banff lesions;
  • C4d;
  • donor specific antibody;
  • HLA antibody;
  • NK cell;
  • renal allograft pathology;
  • renal allograft rejection;
  • transplantation

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Funding
  9. Disclosure
  10. References

NK cell transcripts are increased in biopsies with antibody-mediated rejection, whereas T cell transcripts are increased in T cell-mediated rejection. However, NK and T cells share many features, creating potential ambiguity. Therefore to estimate the NK- versus T cell transcript burdens separately, we defined nonoverlapping transcripts selective for NK cells (N = 4) or T cells (N = 5). We compared NK- versus T cell transcript burdens in microarrays from 403 kidney transplant biopsies (182 early, 221 late). In late biopsies, high NK-cell transcript expression was associated with antibody-mediated rejection, correlating with microvascular inflammation and donor specific HLA antibody. However, some early biopsies with T cell-mediated rejection had high NK-cell transcript expression, as well as T cell transcripts, without evidence of antibody-mediated rejection or DSA, correlating with interstitial inflammation and tubulitis. Both NK-cell and T cell transcripts were moderately increased in many kidneys with inflammation secondary to injury or atrophy scarring. These results support the distinct role of NK cells in late antibody-mediated rejection, but indicate a role for NK-transcript expressing cells (NK cells or T cells with NK features) both in T cell-mediated rejection and in inflammation associated with injury and atrophy scarring.


Abbreviations: 
ABMR

antibody-mediated rejection

AKI

acute kidney injury

DSA

donor specific antibodies

DSASTs

DSA selective transcripts

HLA

human leukocyte antigen

IFNG

interferon gamma

IFTA-NOS

interstitial fibrosis and tubular atrophy not otherwise specified

NKtb

natural killer cell transcript burden

NK cell

natural killer cell

PRA

panel reactive antibodies

TCtb

T cell transcript burden

TCMR

T cell-mediated rejection

Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Funding
  9. Disclosure
  10. References

Study of the molecules associated with allograft rejection can reveal mechanisms and be useful for diagnosis. T cell-mediated allograft rejection (TCMR) manifests as intense interstitial mononuclear cell infiltration, primarily of effector or effector-memory T cells and macrophages, with parenchymal deterioration and sometimes with endothelial arteritis (1). Transcripts associated with TCMR include cytotoxic and signaling molecules, which are highly expressed in infiltrating effector/effector-memory T cells (2,3), as well as many macrophage transcripts and transcripts representing interferon gamma (IFNG) effects (4–6). Analysis of transcript expression in antibody-mediated rejection (ABMR), primarily focused on the relatively common late ABMR, have shown endothelial (7) and NK-cell transcripts (8) and transcripts reflecting IFNG effects (5). Some of these studies used transcripts selectively expressed in biopsies from patients with donor-specific antibody (DSA)—the DSA-selective transcripts (DSAST; Ref. 8). Some DSAST were expressed in NK cells, whereas others were expressed in endothelium.

With the resulting increase in interest in the role of NK cells in ABMR, the confounding issue is that NK cells share many features with effector T cells in general, particularly NKT cells (9,10). The lack of satisfactory markers to distinguish these populations in biopsies or to purify them from biopsies has limited our understanding of the roles of this aspect of transplant immunity in humans, leaving many issues unresolved in understanding the role of NK-cell-related molecules and mechanisms and the cells that express them in organ transplant biopsies.

This study aimed to distinguish the transcripts arising from NK cells from those arising from effector T cells. Although this does not address the unresolved problem of defining and isolating the human NK-type T cells in human biopsies, we hypothesized that identification of the transcripts that are expressed in biopsies but are unique to NK versus T cells would be a step toward understanding the role of NK-cell-related mechanisms in ABMR, TCMR and in general inflammatory reactions to tissue injury. We identified nonoverlapping, highly expressed NK-cell selective and T cell selective transcripts, which were used to explore the associations of the NK-cell transcript burden (NKtb) and the T cell transcript burden (TCtb) with histologic lesions and diagnoses in the biopsy and with the presence of circulating DSA at the time of the biopsy.

Materials and Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Funding
  9. Disclosure
  10. References

Patients and sample collection

This study was approved by the institutional review board of the University of Alberta (issue number 5299), the University of Illinois (Protocol number 2006–0544), the University of Minnesota (Protocol #HSC#0606 M87646) and the Hennepin County Medical Center (Protocol #HSR#06–2670). Written informed consent was obtained from all study patients. All consenting renal transplant patients undergoing a transplant biopsy for clinical indication (deterioration in function, proteinuria or stable impaired function) as standard of care between September 2004 and November 2008 were included in the study. Biopsies were obtained and processed for microarray analysis as described previously (6).

Histopathology

Paraffin sections were graded according to Banff criteria (11) by a renal pathologist (BS). All biopsies were deemed suitable by Banff criteria, although 13 biopsies for clinical indications did not include arteries for assessment. C4d staining was performed on frozen sections using a monoclonal anti-C4d antibody (Quidel, San Diego, CA, USA) by indirect immunofluorescence. Diffuse linear C4d staining (>50% of biopsy area) was interpreted as positive. The criteria for the diagnosis of C4d negative ABMR or C4d negative mixed rejection was previously described (12): microvascular lesions of inflammation (glomerulitis [g] or peritubular capillaritis [ptc] > 0) and/or microvascular deterioration (transplant glomerulopathy [cg] > 0) and/or thromboses from a patient with detectable DSA at the time of biopsy. C4d positive ABMR was amalgamated with C4d negative ABMR and possible ABMR (histologic lesions plus nondonor specific HLA antibodies), to form an ABMR diagnostic label. Biopsies diagnosed as interstitial fibrosis (ci) and tubular atrophy (ct) not otherwise specified (IFTA-NOS) were defined as having a ci-score >1 and no features of specific disease. Non-rejecting diagnoses were labeled as others, including recurrent or de novo glomerulonephritis, polyoma virus nephropathy, IFTA-NOS and calcineurin inhibitor toxicity. Transplant injury (acute kidney injury (AKI)) was diagnosed in all biopsies for clinical indications before 42 days that lacked histological disease features. The diagnosis of no major abnormalities (NOMOA) was assigned to biopsies occurring after 42 days that also lacked histological disease features.

HLA antibody screening

HLA antibody testing method varied depending on the transplant center. Antibody specificities were either determined by Luminex single antigen beads, the majority by FlowPRA® single antigen I and II beads (One Lambda Canoga Park, CA, USA) after a positive HLA antibody screening test using FlowPRA® beads or as a result of a positive flow cytometry or CDC-AHG crossmatch. Specificities to Cw were not assigned as DSA. Donor HLA typings for DP or DQA1 were not performed and therefore DSA were not attributed to DP or DQA1.

Microarray analysis

One additional 18-gauge biopsy core was collected for gene expression analysis. The tissue was placed immediately in RNALater and stored at −20°C. RNA extraction, labeling and hybridization to the HG_U133_Plus_2.0 GeneChip (Affymetrix, Santa Clara, CA, USA) were carried out according to manufacturer's protocols. Microarrays were scanned using GeneArrayScanner (Affymetrix) and processed with GeneChip Operating Software Version 1.4.0 (Affymetrix). Microarray data were preprocessed by robust multiarray analysis (RMA) and implemented in Bioconductor version 2.4.

Summarized expression of transcripts in a given transcript set was calculated as the geometric mean of the fold change values versus normal nephrectomies across all transcripts in a set. Correlation of a summarized transcript set expression with biopsy lesion scores was tested by two-tailed Spearman correlation.

Pathogenesis-based transcript sets (PBTs)

Transcript sets defining distinct biological processes involved in allograft rejection are detailed on our website (http://transplants.med.ualberta.ca/). Abbreviations for all transcripts conform to the Gene names (http://www.ncbi.nlm.nih.gov/sites/entrez).

Cell isolations and treatments

Purified cell populations from peripheral blood mononuclear cells isolated from whole blood of healthy volunteers as previously described (13). All leukocyte cell cultures were maintained in complete RPMI (RPMI 1640 supplemented with 10% FBS, 2 mM L-glutamine, β-mercaptoethanol, nonessential amino acids, sodium pyruvate and antibiotic/antimycotic (Invitrogen Life Technologies, Burlington, Ontario, Canada) in 5% CO2 at 37°C. Human umbilical vein endothelial cells (ATCC; Manassas, VA, USA) and renal proximal tubule epithelial cells (Lonza, Inc., Allendale, NJ, USA) were maintained in tissue culture media as recommended by the supplier.

Statistical analysis

Data analysis was performed using GraphPad Prism 5, Bioconductor version 2.4 and R version 2.9.1. Group comparisons for the mean PBT score used a one-way ANOVA with a Dunnett's multiple comparison test. A Mann-Whitney t-test was performed for non-parametric data comparing two groups.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Funding
  9. Disclosure
  10. References

Identifying transcripts selective for T cell versus NK cell

We examined the cell populations of interest for this study; NK cells and CD8 T cells. NK cells (CD3/ CD56+) were purified from whole blood by immunomagnetic negative selection to achieve an average purity of 95.6% (N = 3; Figure 1A). The majority (average 96.1%) of NK cells showed a cytotoxic phenotype (CD56dim; Figure 1B) as expected in whole blood (14). CD8 T cells (N = 5) were allostimulated in a mixed lymphocyte reaction for four rounds before a final round of purification by negative selection (average 97.4% purity; Figure 1C). At the end of the final round, cells were restimulated and IFN-γ production was examined by intracellular cytokine staining. Nearly, 90% of CD8 T cells produced IFN-γupon restimulation as evidence of an effector phenotype (Figure 1D). Little variability was observed in the percentages of CD56dim NK cells or IFN-γ or CD8 T cells from the different donors.

image

Figure 1. NK and T cell purities, marker expression and functional characteristics. (A) NK cells were purified from PBMCs by immunomagnetic negative selection and purity assessed based on expression of CD3 and CD56 by flow cytometry. Mean purity of NK cells (CD3/ CD56+) is shown as the percent of total lymphocytes with the range of purities for the three cell isolations. (B) Bar graph represents the percent of NK cells staining with high (CD56 hi) or low (CD56 lo) levels of CD56 ±SD. (C) CD8 T cells were purified following four rounds of MLR and purity assessed based on the percent of CD3+/ CD8 T cells. Mean purity of CD8 T cells (CD3/ CD56+) is shown as the percentage of total lymphocytes with the range of purities for the five cell isolations. Dot plot shows cells within the CD3+ gate. (D) CD8 T cells were restimulated with alloantigen for 24 hours and IFNG was detected by intracellular cytokine staining by flow cytometry. Bar graph represents the mean percent of CD8 T cells staining for IFNG ±SD.

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We previously identified 25 effector T cell transcripts capable of quantifying the TCtb in renal transplant biopsies (15). However, these transcripts are not completely specific for T cells: some are expressed in NK cells. We now identified the members of this set with selective expression in cultured effector T cells (>1000 probeset signal) but not in NK cells, umbilical vein endothelial cells or renal proximal epithelial cells (Figure 2A, Table 1). Five T cell transcripts had minimal overlap with NK cells: CD3D, TCRA, CXCR6, GPR171 and NELL2. We reasoned that this transcript set, already shown to have quantitative properties and relative specificity for T cells, would estimate the TCtb in biopsies with few concerns about overlap with NK cells.

image

Figure 2. Algorithms used to define transcripts with selective expression in NK cells and T cells. Criteria applied to derive transcripts with selective expression in (A) NK cells and (B) T cells, respectively. Signal numbers represent probeset signal intensity.

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Table 1.  Detailed annotation of the transcripts making up the T cell and NK-cell transcript burden
 Affymetrix Gene     MacrophageMacrophageHUVECHUVECRPTECRPTEC+
 probeset IDGene titlesymbolControlCD4CD8NK cellB cellunstim+ IFNgunstim+ IFNgunstimIFNg
T cell transcript burden213539_atCD3D antigen, delta polypeptide (TiT3 complex)CD3D91480453366396710010664613331
 209671_x_atT cell receptor alpha locusTCRA984765523052013556601071031320
 207651_atG protein-coupled receptor 171GPR17120226919074201113161213 7 8
 206974_atchemokine (C-X-C motif) receptor 6CXCR63087313245711202220181017
 203413_atprotein kinase C-binding protein NELL2NELL251816101211331343535361626
NK cell transcript burden205898_atchemokine (C-X3-C motif) receptor 1CX3CR110141106107,712423141212 6 8
 220646_s_atkiller cell lectin-like receptor subfamily F, member 1KLRF11821167393171111 811 7 8
 1553177_atSH2 domain-containing molecule EAT2SH2D1B13101353921016161716 7 9
 213906_atv-myb myeloblastosis viral oncogene homolog (avian)-like 1MYBL110331103883129956594334

Similarly, we identified NK-cell selective transcripts not expressed in T cells, starting with six NK-cell expressed transcripts previously reported (8). The selection algorithm was similar to that used to generate the TCtb set above: probeset signal >1000 and low expression in other cell types, including CD4 and CD8 T cells (Figure 2B, Table 1). Four NK-cell selective transcripts were identified with minimal expression in T cells: KLRF1 (NKp80); the fractalkine receptor CX3CR1; SH2D1B, an SH2-domain containing signaling molecule also known as EAT2; and MYBL1, a viral oncogene homologue. We reasoned that these transcripts would estimate the NKtb in biopsies with minimal concerns about confusing signals from other cell types including T cells. (This is notwithstanding the unresolved poor definition of human NK-type T cells.)

Human population demographics

The study included all available indication biopsies from consented patients from four transplant centers, to prevent the bias introduced by selecting “typical” cases or extreme phenotypes or by excluding the ambiguous cases commonly observed clinically. The 403 consecutive indication biopsies from 315 patients, between 1 week and 35 years posttransplant, were obtained between 2004 and 2009 as part of a prospective Genome Canada study (16; Table 2). Of 403 biopsies, 123 were from DSA-positive patients. For 46 biopsies, recipient HLA antibody testing at the time of biopsy was not available and such biopsies had to be excluded from some analyses.

Table 2.  Patient demographics and biopsy characteristics
Patient demographics [n = 315]All patients (n = 315)Patients biopsied early (n = 129)Patients biopsied late (n = 186)p-Value (early vs. late)
Mean recipient age43 (19–76)48 (14–81)40 (5–73)<0.001
Recipient gender (% male) [n = 315]196 (62%)82 (64%)114ns
Race [n = 315]
 Caucasian198 (63%)76 (59%)125 (67%)ns
 Black33 (10%)17 (13%)16 (9%)ns
 Other84 (27%)22 (17%)25 (13%)ns
 Unknown0 (0%)14 (11%)20 (11%)ns
Primary disease [n = 315]
 Diabetic nephropathy65 (21%)29 (22%)36 (19%)ns
 Hypertension /large vessel disease29 (9%)18 (14%)11 (6%)0.02
 Glomerulonephritis /vasculitis119 (38%)38 (29%)81 (44%)0.01
 Interstitial nephritis /pyelonephritis19 (6%)9 (7%)10 (5%)ns
 Polycystic kidney disease46 (15%)22 (17%)24 (13%)ns
 Others15 (5%)6 (5%)9 (5%)ns
 Unknown etiology22 (7%)7 (5%)15 (8%)ns
Mean donor age40 (2 - 70)44 (13 - 70)38 (2 - 68)0.002
Donor gender (% male)120 (46%)46 (43%)74 (47%)ns
Donor type (% deceased donor transplants)151 (49%)59 (46%)92 (51%)ns
Clinical characteristics at time of biopsy [n = 403]All biopsies (n = 403)Biopsies taken early (n = 182)Biopsies taken late (n = 221)p-Value (early vs. late)
Indication for biopsy
 Primary nonfunction10 (2%)10 (5%)0 (0%)<0.001
 Rapid deterioration of graft function96 (24%)33 (18%)63 (29%)0.01
 Slow deterioration of graft function150 (37%)63 (35%)87 (39%)ns
 Stable impaired graft function71 (18%)48 (26%)23 (10%)<0.001
 Investigate proteinuria38 (9%)13 (7%)25 (11%)ns
 Follow-up from previous biopsy14 (3%)7 (4%)7 (3%)ns
 Others9 (2%)2 (1%)7 (3%)ns
 Indication unknown15 (4%)6 (3%)9 (4%)ns
Maintenance immunosuppressive regimens at biopsy
 MMF, tacrolimus, steroid132 (33%)67 (37%)65 (29%)ns
 MMF, tacrolimus15 (4%)0 (0%)15 (7%)<0.001
 MMF, cyclosporine, steroid70 (17%)30 (16%)40 (18%)ns
 MMF, cyclosporine30 (7%)16 (9%)14 (6%)ns
 MMF, steroids20 (5%)15 (8%)5 (2%)0.01
 Steroids, tacrolimus11 (3%)0 (0%)11 (5%)0.001
 Azathioprine, cyclosporine, steroids0 (0%)0 (0%)0 (0%)ns
 Others130 (32%)54 (30%)76 (34%)ns
DSA status at biopsy
 DSA+124 (31%)19 (10%)105 (48%)<0.001
 HLA ab neg165 (41%)110 (60%)55 (25%)<0.001
 NDSA70 (17%)29 (16%)41 (19%)ns
 Not done44 (11%)24 (13%)20 (9%)ns

Correlations of NKtb and TCtb with histologic diagnoses in early and late biopsies

We compared NKtb and TCtb scores in early and late biopsies with the histological diagnoses based on lesions and DSA (Figure 3, Tables 3A and B). For this analysis, the range of transcript set scores was split into low, moderate and high. Thus low scores were defined as those within the range observed in control nephrectomies; moderate scores were defined as elevated but within the range observed in transplant biopsies with NOMOA; and high scores were defined as those above the moderate range.

image

Figure 3. Individual biopsy NKtb and TCtb scores according to histologic diagnosis. NKtb (top panels) and TCtb scores (bottom panels) were calculated for each individual biopsy. Biopsies were then separated into early or late and each subdivided into diagnostic categories based on histology and HLA antibody information. Low, moderate and high transcript burden categories were defined as: Low: range of transcript set scores observed in control nephrectomies, Moderate: range observed within the NOMOA groups in each of the early and late time periods, High: transcript set scores above those observed within the NOMOA group. Horizontal lines within each diagnostic category show the mean transcript set score for each diagnostic group.

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Table 3A.  Relating biopsy diagnosis to NK or T cell transcript burden in early biopsies
 NKtbTCtb
Histologic diagnosisLowModerateHighLowModerateHigh
  1. Note: Results are shown as the number and percentage of all biopsies within each level of transcript burden that have each histologic diagnosis. Highlighted cells indicate diagnostic categories accounting for >10% of biopsies in high transcript burden category.

ABMR 33% 45% 17% 55% 24% 13%
Mixed 11% 11% 00% 11% 00% 13%
TCMR 44%1621% 747% 44% 714%1648%
Borderline 910%1824% 213%1111%1122% 721%
GN 22% 34% 17% 44% 12% 13%
Other 78% 57% 00% 77% 36% 26%
IFTA NOS 22% 34% 17% 11% 36% 26%
NOMOA2729% 68% 00%2525% 816% 00%
AKI3033%1621% 320%3636%1122% 26%
PVN 78% 34% 00% 55% 48% 13%
Total92100%75100%15100%99100%50100%33100%
Table 3B.  Relating biopsy diagnosis to NK or T cell transcript burden in late biopsies
  1. Note: Results are shown as the number and percentage of all biopsies within each level of transcript burden that have each histologic diagnosis. Highlighted cells indicate diagnostic categories accounting for >10% of biopsies in high transcript burden category.

Histologic DiagnosisNKtbTCtb
 LowModerateHighLowModerateHigh
ABMR815%3632%3056%2931%3740%824%
Mixed 00%  54%1528% 22% 910% 926%
TCMR 24%  44% 24% 00% 44% 412%
Borderline 36%  65% 12% 22% 55% 39%
GN1121% 2219% 24%1920%1213% 412%
Other 12%  44% 36% 33% 22% 39%
IFTA NOS 815% 1816% 12%1112%1314% 39%
NOMOA1936% 1816% 00%2628%1112% 00%
PVN 12%  11% 00% 22% 00% 00%
Total53100%114100%54100%94100%93100%34100%

High NKtb were uncommon in early biopsies with only 15 biopsies reaching these levels. The majority of the early biopsies with high NKtb had histologic diagnoses of TCMR, borderline or AKI (Table 3A, Figure 3; Ref. 17). Of the eight early ABMR, only one showed a high NKtb score. Moderate elevations of NKtb were seen in many early biopsies and primarily focused on the same diagnostic categories as the high NKtb: TCMR, borderline and AKI. The majority of the early biopsies showed low NKtb scores (N = 92).

High TCtb were mainly seen in early biopsies diagnosed as TCMR and borderline (Table 3A, Figure 3). Not surprisingly, almost 70% of the high TCtb scores were in early biopsies diagnosed as TCMR (48%) or borderline (21%). Most of the 182 early biopsies were categorized as TCtb low (N = 99 [54%]) but moderate scores were observed in 50 biopsies (27%) and high scores in 33 (18%).

Many late biopsies manifested high NKtb scores, almost always with ABMR and mixed rejection (Table 3B). Thus ABMR, either alone (56%) or mixed ABMR/TCMR (28%), accounted for 84% of the high NKtb scores. Mixed rejection biopsies showed overall higher NKtb scores, with no mixed cases in the low NKtb category (Figure 3).

Late biopsies with high TCtb scores were mainly diagnosed as mixed (26%) or TCMR (12%), but 24% were diagnosed as ABMR and 12% as glomerulonephritis. The late ABMR biopsies with high TCtb scores may represent undetected TCMR in these biopsies, because TCMR is difficult to diagnose in late biopsies because of atrophy and scarring.

NKtb and TCtb scores correlate differently with microvascular lesions reflecting inflammation and damage

We examined the correlation of the NKtb and TCtb with histologic lesions, particularly microcirculation inflammation (Figure 4). Mean histologic lesion scores were calculated for ptc, g, cg, interstitial inflammation (i), tubulitis (t), intimal arteritis (v), ci and ct. Spearman correlations between NKtb and TCtb scores and lesion scores, as well as correlations between the two transcript sets were calculated.

image

Figure 4. Associations between NKtb and TCtb and histologic lesions. Mean lesion scores were calculated for each histologic lesion listed (ptc, peritubular capillaritis; g, glomerulitis; cg, transplant glomerulopathy; i, interstitial inflammation; t, tubulitis; v, intimal arteritis; ci, interstitial fibrosis; ct, tubular atrophy) using all (A) early biopsies or (B) late biopsies. Corresponding Spearman correlation coefficients between NKtb or TCtb and the listed histologic lesion scores and between NKtb and TCtb were calculated. The strongest correlations (r > 0.3) are highlighted in grey cells with bold text. *p < 0.01, **p < 0.0001. The number of biopsies showing a positive (≥1) or negative (0) lesion score are indicated for each lesion.

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In early biopsies (Figure 4A), both the NKtb and the TCtb correlated strongly with i and t, which were prevalent in these biopsies reflecting the high incidence of TCMR. v better correlated with TCtb scores than NKtb. Both showed weak correlations with ptc, which is present in some TCMR in the absence of DSA, but neither correlated with g or cg. Both showed only weak associations with atrophy scarring (ct, ci). NKtb and TCtb scores were correlated with each other (r = 0.65). (This correlation was stronger than the lesion correlations because of the continuous numbers compared to the dichotomous histopathology scores.)

In late biopsies, NKtb scores correlated mainly with microcirculation lesions (ptc, g and cg), which are common in late biopsies (12; Figure 4B). Moderate correlations were observed between the NKtb and atrophy scarring, which is more prevalent in late biopsies (see later).

In contrast, the TCtb scores in late biopsies still correlated with t. TCtb scores also showed moderate correlations with ptc (which is sometimes seen in pure TCMR) but not with either g or cg.

Correlations between NKtb and TCtb remained high in late biopsies (r = 0.6).

Thus, the associations of NK- and T cell transcripts with lesions were similar in early biopsies (TCMR lesions), largely reflecting associations of both with TCMR lesions. In late biopsies, the associations diverged, with NK-cell transcripts associating with ABMR features and the T cell transcripts still correlating with TCMR lesions.

The NKtb in late biopsies is related to DSA status

We examined the relationship between NKtb scores, HLA antibody status and microvascular lesions (Figure 5). Scores were calculated for all biopsies for which HLA antibody testing was available at/near the time of biopsy. Biopsies were divided into early (≤1 year posttransplant; N = 157) and late (>1 year posttransplant; N = 200) populations. Biopsies were grouped according to the recipient HLA antibody status at the time of biopsy: HLA antibody negative; positive for HLA antibodies that were not donor specific (NDSA); or DSA positive. Microvascular lesions included microvascular inflammation (g or ptc ± microvascular damage (double contours in glomeruli [cg]) (g/ptc ≥ 1 ± cg ≥ 1), microvascular damage without microvascular inflammation (g/ptc = 0, cg ≥ 1) or lacking all microvascular lesions (g/ptc/cg = 0).

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Figure 5. NKtb scores according to recipient HLA antibody status and microvascular lesions. Mean NKtb scores were calculated for all biopsies having accompanying HLA antibody testing results at/ near the time of biopsy. Biopsies were separated into early (<1 year posttransplant) or late (≥1 year posttransplant) and further split according to HLA antibody status. Microvascular lesions found in each biopsy were identified as either microvascular inflammation with/ without microvascular damage (g/ptc ≥ 1, ±cg ≥ 1, blue circles), microvascular damage without microvascular inflammation (g/ptc = 0, cg ≥ 1, black circles) or lacking microvascular lesions (g/ptc/cg = 0, yellow circles). *p < 0.05 by one-way ANOVA and by Dunn's multiple comparison correction for groups based on HLA antibody status.

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As previously reported (12,18), microcirculation lesions and DSA were both more common in late biopsies because of the high frequency of ABMR (Figure 5). Thus in 157 early biopsies, where DSA was infrequent and microcirculation lesions were uncommon, most biopsies with microcirculation lesions (22/30) were in DSA negative patients and did not show high NKtb scores. In 200 late biopsies, where ABMR and microcirculation lesions were common (N = 116), most biopsies with microcirculation lesions (83/116) were in DSA-positive patients (83/105).

Among late biopsies, high NKtb was a feature of biopsies with microcirculation lesions from DSA positive patients (Figure 5, right panel). The mean NKtb score was higher in late biopsies from DSA positive patients, compared to early biopsies or to late biopsies from DSA negative patients (p < 0.05 in One-way ANOVA and Dunn's multiple comparison test). Late biopsies from HLA antibody negative patients seldom had high NKtb. Interestingly, three biopsies with microvascular damage (cg ≥ 1) but lacking microvascular inflammation showed high NKtb scores, suggesting that microcirculation activity was missed in these biopsies by histopathology.

High TCtb scores was found in both early (33/182 [18%]) and late (34/221 [15%]) biopsies and showed no association with DSA or microvascular lesions in early or late biopsies (data not shown).

Understanding why some early TCMR biopsies have high NK cell transcript scores

We split early biopsies diagnosed as TCMR into the low, moderate and high NKtb categories (Table 4). These biopsies were all DSA negative, making ABMR unlikely. The main association of NKtb was with TCMR lesions (interstitial inflammation), but there was also some association with peritubular capillary inflammation, which occurs in some TCMR biopsies with no ABMR (19). Correlations with t were not significant because all biopsies had extensive t, which is required for diagnosis of TCMR. NKtb was not associated with ABMR lesions g or cg, which were mostly absent from these biopsies.

Table 4.  Correlation of NK cell transcript burden with histologic lesions comparing early biopsies with TCMR to late biopsies with ABMR
 Early TCMRLate ABMR
   
Histologic lesionSpearman rp-ValueSpearman rp-Value
  1. Note: Highlighted cells indicate histologic lesions with significant (p ≤ 0.05), positive Spearman correlations with NK cell transcript burden.

Peritubular capillaritis0.370.050.270.02
Glomerulitis0.030.900.48<0.0001
Transplant glomerulopathy−0.16  0.43−0.01  0.90
Interstitial inflammation0.410.030.050.70
Tubulitis0.170.400.130.11
Interstitial fibrosis−0.36  0.070.310.01
Tubular atrophy−0.19  0.330.290.01

Correlations of high NK-cell transcript scores in late ABMR biopsies

Having assessed NKtb across all late biopsies, we examined its associations within the late biopsies diagnosed as ABMR by histology (N = 74), expressed as correlations with histology scores (Table 4). g scores showed the highest correlations (r = 0.48) and ptc scores (r = 0.27) also moderately correlated with NKtb scores. NKtb also associated with atrophy-scarring lesions: ci (r = 0.31) and ct (r = 0.29).

Thus within late ABMR, NKtb correlated with both microcirculation inflammation and atrophy-scarring lesions but not with cg, i or t, further supporting the robust association of NKtb with the degree of microcirculation inflammation, even within the ABMR biopsies.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Funding
  9. Disclosure
  10. References

We defined transcripts capable of estimating the NKtb and TCtb in renal transplant biopsies. Analysis of 403 indication biopsies showed that high NKtb scores are often observed in late biopsies from DSA-positive patients correlating with microvascular lesions and those diagnosed as ABMR or mixed rejection. However, some early biopsies with TCMR without ABMR features, from patients lacking DSA, showed high NKtb scores. In such biopsies, the NKtb scores correlated with TCMR lesions, but not ABMR lesions. None of the few early biopsies diagnosed as ABMR (N = 8) had high NKtb scores. The small numbers of early biopsies with ABMR limit firm conclusions and we are investigating this in ongoing studies. However, moderate NKtb elevation occurred in many biopsies with injury or diseases, both early and late. High TCtb scores occurred in early biopsies with TCMR and late biopsies with TCMR or mixed, but also in a few late ABMR, suggesting that these biopsies have an undiagnosed TCMR component. This may reflect the well-known difficulty of histologic diagnosis of TCMR in heavily scarred biopsies (17). Thus, high NKtb are strikingly associated with late ABMR with DSA and microvascular inflammation, but can also be observed in some early biopsies with TCMR and no ABMR. The NKtb and TCtb can be moderately elevated in some biopsies with atrophy scarring or AKI, inviting caution about overinterpretation.

Despite the difficulties of shared features between effector T cells and NK cells (13), reflecting their developmental and functional relationships (9) and notwithstanding the lack of unique markers of NK-type T cells, these distinctive patterns of NKtb and TCtb in biopsies provide mechanistic clues and suggest models for experimental testing. Of note, each cell type expressed a distinctive chemokine receptor transcript: CXCR6 in T cells and CX3CR1 in NK cells. Each of these receptors has a cell bound ligand, CXCL16 and CX3CL1 respectively (20,21), suggesting a role for these receptors and ligands in the selective recruitment of T cells and NK cells to different sites in transplants. CXCL16–CXCR6 interactions are important in recruiting effector/effector memory T cells into interstitial compartments (22,23) and could play a role in TCMR. In contrast, NK cells in ABMR do not enter interstitial compartments but interact with microvascular endothelium, where CX3CL1–CX3CR1 could be important. Potential effector molecules also make up the NKtb transcript list: SH2D1B interacting with SLAMF1 which could regulate signal transduction on endothelial cells (24) and oncogene product MYBL1 is a transcriptional activator that could alter NK-cell differentiation (25). Thus the differential patterns of associations of the NK- and T cell transcripts in biopsies present research opportunities.

The presence of high NK- and T cell transcripts in some early TCMR biopsies raises the possibility of a role for NKT cells in this condition. NKT cells are heterogeneous, with poorly understood roles in human diseases. NKT cells include at least two types: invariant NKT (iNKT) cells and NK-type T cells. The iNKT cells express NK receptors and a semivariant TCR that recognizes the glycolipid antigens presented on the nonclassical MHC class I molecule CD1d (26). Because iNKT cells produce polarizing cytokines immediately after activation, they are candidates for directing immune responses (9). Human NK-type T cells are primarily a subset of CD8 T cells that are not CD1d restricted but are mainly effector and effector-memory T cells (10). NK-type T cells make up a large fraction of circulating lymphocytes (∼15% of CD8 T cells) compared to iNKT cells (10). NK-type T cells produce more effector cytokines and have higher cytotoxic activity and apoptosis rates than non-NK-type CD8 T cells (27). Although, the transcripts selected to represent the NKtb were highly selective for NK cells and are not expressed in the effector CD8 T cells in our cell panel, the NK-cell selective molecules in the present studies could be expressed in NK-type T cells, particularly in the early TCMRs (27). Indeed, CX3CR1, KLRF1 and SH2D1B have been detected in CD8 T cell populations despite their preferential expression in NK cells at the protein level (28–30), probably reflecting T cells with NK features or NKT cells in the T cell populations used in those studies. Although CD56 (NCAM or neural cell adhesion molecule) is a commonly used NK-cell marker, the staining procedure for tissue sections is difficult and is also expressed by NKT cells, neurons and muscle cells and thus not NK-cell specific. The lack of single, truly NK-cell specific and NK-type T cell specific markers that can be used in tissue sections is a missing tool in elucidating the specific roles that these cell types play in rejection.

The failure of the eight early biopsies diagnosed as ABMR may reflect mechanistic differences between type 1 ABMR in sensitized patients and type 2 ABMR in late patients, but the custom of treating all type 1 patients heavily in the early period complicates these results and requires further analysis (Alexandre Loupy, personal communication). Early cases may suffer more direct effects from complement activation, as attested by their high frequency of C4d staining and their response to C5 neutralization (31,32).

The high NKtb in many late ABMR biopsies with DSA and microvascular inflammation, usually without T cell transcripts, supports the model we proposed previously in which true NK cells interact with and damage microcirculation endothelium, presumably through their Fc receptors. Late onset ABMR emerges as a slowly progressive stress, providing opportunity for extensive endothelial adaptation, differing from early ABMR where the rapid pace of development limits endothelial adaptation. In late cases, de novo DSA emerges and binds to graft endothelium. NK cells in the vascular lumen recognize antibody on a cell surface through their Fc-receptor CD16 (FCGR3A), leading to IFNG production (33). IFNG increases endothelial surface HLA expression, adding more targets for DSA to engage—an important feature because Fc regions of IgG antibodies need to be in close proximity to fix complement (34). A role for antibody-directed cellular cytotoxicity by NK cells on endothelium is possible, assisted by interactions through fractalkine on the endothelial surface engaging NK-cell fractalkine receptors. The process is slow and subtle, the endothelium adapts for long periods, even years. Monocytes may also participate through Fc-receptor cross-linking as well as through activation by IFNG released by NK cells and along with platelets and other cell types, add to the injury. An important molecule mediating the crosstalk between NK cells and monocytes is KLRF1, which binds to its myeloid-specific ligand AICL leading to mutual activation of NK cells and monocytes and increasing the secretion of proinflammatory cytokines by both cell types (35). The endothelium endures against this subtle force but eventually double contours in glomeruli and basement membrane multilayering in peritubular capillaries supervene. The advantage of exploring this model is that the NKtb could emerge as an important marker to guide therapy, potentially leading to targeted interventions against NK cells when the NK cell signal indicates its active involvement.

Acknowledgments

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Funding
  9. Disclosure
  10. References

The authors are grateful to Drs. Alexandre Loupy and Declan de Freitas for discussions and critical review of the manuscript. We thank Dr. Zija Jacaj for help with collection of the clinical data; and Kara Allanach, Vido Ramassar and Anna Hutton for technical support.

Funding

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Funding
  9. Disclosure
  10. References

This research has been supported by funding and/or resources from Genome Canada, Genome Alberta, the University of Alberta, the University of Alberta Hospital Foundation, Roche Molecular Systems, Hoffmann-La Roche Canada Ltd., the Alberta Ministry of Advanced Education and Technology, the Roche Organ Transplantation Research Foundation, the Kidney Foundation of Canada and Astellas Canada. Dr. Halloran also holds a Canada Research Chair in Transplant Immunology and the Muttart Chair in Clinical Immunology. Dr. Sis’ research has been supported by funding from Canadian Institutes of Health Research, Roche Organ Transplantation Research Foundation, University of Alberta Hospital Foundation and Kidney & Urology Foundation of America – Renal Pathology Society. The authors have no competing financial interests.

Disclosure

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Funding
  9. Disclosure
  10. References

The authors of this manuscript have the following conflicts of interest to disclose as described by the American Journal of Transplantation: P.F. Halloran is the chief executive officer of Transcriptome Sciences Inc, a company with an interest in molecular diagnostics and has consulted for Novartis, Astellas and Bristol-Myers Squibb in the past three years. The other authors have no conflicts of interests to disclose as described by the American Journal of Transplantation.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Funding
  9. Disclosure
  10. References