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Keywords:

  • B cell;
  • biopsy;
  • gene expression;
  • human;
  • immunoglobulins;
  • inflammation;
  • kidney allograft;
  • microarrays;
  • plasma cells

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information

To assess the significance of B-cell and plasma cell infiltrates in renal allografts, we compared expression of B-cell-associated transcripts (BATs) and immunoglobulin transcripts (IGTs) to histopathology and function in 177 renal allograft biopsies for clinical indications. BAT and IGT expression correlated with immunostaining for B cells and plasma cells and with expression of B-cell and plasma cell transcription factors. BATs and IGTs were increased in both T-cell-mediated and antibody-mediated rejection. BAT and IGT scores were strongly related to time posttransplant: biopsies <5 months expressed less BATs and did not express increased IGTs. In contrast, T-cell-associated transcripts were independent of time posttransplant. In biopsies ≥5 months, BAT and IGT scores correlated with interstitial inflammation, tubular atrophy and interstitial fibrosis. By regression tree analysis, the only variables independently correlated with BATs and IGTs were time and inflammation. Expression of BATs and IGTs correlated with renal function, but this relationship was due to differences in early versus late biopsies: BATs and IGTs were not related to function or future function after correcting for time.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information

Recent reports have focused interest on the significance of CD20+ B-cells and plasma cells in infiltrates in kidney allografts, suggesting that these infiltrates are associated with poor graft survival (1–5). In a study by Sarwal et al. T-cell-mediated rejection (TCMR) with dense CD20+ B-cell infiltrates and expression of rearranged immunoglobulin genes and B-cell markers was associated with steroid resistance and graft loss (1). Similarly, inferior outcomes were reported for TCMR cases with plasma cell-rich infiltrates (4,5). In protocol biopsies, B-cell-rich infiltrates are associated with interstitial fibrosis and tubular atrophy and are a negative prognostic indicator (6). However, the association of lymphoid clusters or CD20+ B-cell infiltrates with inferior graft survival in biopsies for cause was not confirmed by all studies (7–11). The ambiguous results may be explained by the variable definitions and thresholds for the assessment of B-cell and plasma cell infiltrates in immunohistochemistry-based studies. Nodular clusters in renal allograft biopsies vary in size (between 15 and ≥275 cells [1,8]) and represent mixed aggregates of CD20+ cells with T cells and macrophages (8,12) with varying B-cell content (5-90%) (8). Depending on the definition, the reported incidence of lymphoid clusters and B-cell-rich rejection varies between 15% (13) and 59% (8). In addition to the subjective and variable definitions, interpretation of the results is limited by inclusion of severely damaged kidneys such as transplant nephrectomies, selection of cases from archived biopsies from earlier eras in retrospective studies and the lack of separation between protocol biopsies versus biopsies for clinical indications (‘biopsies for cause’) in some studies. The significance of B-cell and plasma cell infiltrates remains unclear, but is relevant because of potential consequences for therapy.

The present prospective study used microarrays to evaluate B-cell and plasma cell infiltrates in unselected renal allograft biopsies for clinical indications. Transcript expression is highly altered in rejection (1,14–17) and provides an objective and quantitative measurement of events in the graft without the need for arbitrary thresholds. Expression of a pathogenesis-based transcript (PBTs) sets associated with inflammatory cells allows a robust and objective estimation of the inflammatory disturbance in allografts (18–20). Analogous to previously defined cytotoxic T-cell-associated transcripts (QCATs), which reflect the T-cell burden in the graft, the present study defined a set of B-cell-associated transcripts (BATs) and a set of immunoglobulin associated transcripts (IGTs) to assess the burden of B cells and plasma cells in human renal allograft biopsies taken for clinical indication. We analyzed expression of these transcript sets in relationship to histologic lesions, clinical diagnosis and future renal allograft function.

Material and Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information

Patient population and specimens

Every consenting patient undergoing a transplant biopsy for cause (deterioration in function, proteinuria, stable impaired function) as standard of care between January/2004 and March/2007 was included in this study, which was approved by the University of Alberta Health Research Ethics Board (Issue 5299). Normal kidney tissue from histopathologically unaffected areas of the cortex of tumor nephrectomies served as controls. In addition to biopsy cores for routine histopathological assessment, one 18-gauge biopsy core was collected for RNA extraction and gene expression analysis (15). Detailed methods are available as supplementary data.

Histology

Paraffin sections were graded according to the current Banff criteria (21–23). C4d staining was performed on frozen sections using a monoclonal anti-C4d antibody (Quidel, San Diego, CA). All samples had adequate cortical tissue for analysis by Banff criteria with the exception of six biopsies with less than two large arteries (four biopsies had only one artery, two biopsies had no arteries).

CD20+ B-cell and CD138+ plasma cell infiltrates were evaluated by immunohistochemistry in the 32 biopsies with clinical overt TCMR or ABMR episodes. Deparaffinized sections were incubated with primary antibodies (mouse anti-CD20 [clone L26] and mouse anti-CD138 [clone MI15], DAKO, Canada), followed by antimouse biotinylated secondary antibody (DAKO). Staining was developed by an avidin-biotin-based detection system with peroxidase and DAB visualization (iView DAB™, Ventana Benchmark™). According to previously published data nodular infiltrates were defined as nodular shaped, dense aggregates of ≥15 lymphoid cells, which could either be localized interstitially or perivascularly in the renal cortex (8). For every immunohistochemically stained slide, we determined the number of nodules per biopsy (normalized by the biopsy size, i.e. the number of microscopic fields in 10 × magnification) and the absolute number of CD20+ and CD138+ cells within the largest nodule. In addition, the number of scatter intermixed CD20+ and CD138+ cells in five counting grids (= 0.5 mm2) of interstitial inflammation (i.e. none nodular) was assessed. Since CD138 also stains tubular epithelial cells, only those cells were counted as plasma cells, which were clearly localized in the interstitium and simultaneously showed the plasma cells typical eccentric localization and morphology of their nucleus.

Diagnostic classifications

Histopathologic diagnoses included rejection (TCMR, ABMR and mixed TCMR plus ABMR), borderline changes, BK nephropathy (biopsies showing BK virus by in situ hybridization and/or electron microscopy) and others (e.g. calcineurin inhibitor toxicity). A clinical rejection episode was defined by retrospective assessment of the clinical course, based on compatible histopathology with clinically apparent functional changes: a decrease in estimated glomerular filtration rate (GFR, Cockroft Gault) ≥25% from baseline (up to 4 months preceding biopsy) and/or response to therapy (increase in GFR ≥25% within 1month), in the absence of alternative explanations. Not all biopsies were stained for polyomavirus. However, all transplant patients being followed in our center are routinely screened for BK virus load in the urine; in the case of a positive result, this is followed by a test for BK virus in the serum and in any patient with a positive BK serum test undergoing a renal biopsy, the biopsy is stained for polyomavirus. All biopsies performed ≥3 months posttransplant at our center are routinely assessed for kappa and lambda light chains by immunofluorescence. None of the biopsies in this study showed any indication for monoclonality.

Isolation and generation of cell populations

We generated purified cell populations (CD4+ CTL, CD8+ CTL, B cells, CD14+ monocytes, NK cells) isolated from whole blood of healthy volunteers by density gradient centrifugation. A detailed description is available as supplementary data.

Microarray data pre-processing

Data files were pre-processed using robust multi-chip averaging in Bioconductor and subjected to variance-based filtering (24) as described previously (25). The following data files were included in the analysis: B cells (n = 3), CD4+ CTL (n = 3), CD8+ CTL (n = 5), NK cells (n = 3), monocytes unstimulated (n = 3), monocytes stimulated with interferon-γ (n = 3), control kidneys (n = 8), biopsies for cause (n = 177). Pre-processed data were imported into GeneSpring™ GX 7.3 (Agilent, Palo Alto, CA) for further analyses.

Transcript sets

Pathogenesis-based transcript sets (PBTs) were derived by a strategy similar to that previously published (16). B-cell-associated transcripts (BATs) were defined as transcripts with signal intensity at least equal to normal human kidney tissue and signal intensity >200 in B cells and 5× higher expression in B cells compared to CD8+CTL, CD4+CTL, NK cells and monocytes (false discovery rate 0.05) (15,25,26). A ‘>200 in B cells’ refers to the signal strength of the probe set (representing one gene) on the array. A signal intensity of 200 is an arbitrary cutoff that we have chosen to ensure that this transcript is detectable above the noise of the array and thus represents a reliable measurement. Immunoglobulin transcripts were removed from BATs and formed a separate set: IGTs were defined as all immunoglobulin transcripts represented on the array, regardless of their expression values.

We compared expression of BATs and IGTs to previously defined transcript sets: cytotoxic T-cell-associated transcripts (QCATs), interferon-γ- and rejection-induced transcripts (GRITs) and kidney transcripts (KTs). QCATs (n = 25) were defined in cultured human effector T cells and robustly estimate the effector T-cell burden in the graft (19). GRITs (n = 68) were increased in rejecting mouse allografts and reflect interferon-γ effects in the graft (25). KTs (n = 64) are solute carrier transcripts expressed in epithelium that are decreased in rejecting kidneys, reflecting loss of epithelial integrity (27). Microarray CEL files and Affymetrix® probe sets in each transcript set are available on our homepage (http://transplants.med.ualberta.ca/). Transcript abbreviations use Entrez Gene nomenclature: http://ncbi.nlm.nih.gov/entrez.

Statistical analysis

Gene expression was analyzed as fold change compared to control kidneys and was summarized for each transcript set as a gene set score, representing the geometric mean of individual transcript fold change values in each set. To summarize BAT/IGT scores in diagnostic categories, results are given as the average ± standard deviation of the BAT/IGT scores in all biopsies with a histologic diagnosis of rejection. The BAT/IGT score is a summary measure (geometric mean) of the normalized expression values of all BATs in one biopsy; i.e. each biopsy has one BAT score. The normalized expression value for each BAT is defined as the fold change of the signal intensity (Affymetrix® GeneChip output) of that BAT in the biopsy of interest compared to the average signal intensity of the same BAT in the control kidneys.

For comparisons between diagnostic classes, we have limited the statistical analysis to a priori defined comparisons: for the clinical diagnostic categories (i)Controls versus all other groups (non rejection, rejection, BK) using Dunnett's t-test; (ii) rejection versus nonrejection and versus BK in an ANOVA, corrected for multiple comparisons with Tukey's HSD; for all rejecting biopsies (i) Clinical episode versus no clinical episode (t-test); (ii) within the biopsies with clinical episodes: TCMR versus ABMR (t-test). Correlation between transcript sets and histology scores was analyzed by Spearman rank correlation coefficient using SPSS. Because of the high correlations amongst both the Banff scores and the PBT scores, no Bonferroni correction was applied, since this would be overly conservative. To evaluate the relationship between BAT/IGT scores and clinical parameters, regression tree analysis was performed using binary recursive partitioning of the R language package ”rpart'. Histologic lesions, histologic diagnosis, time posttransplant, recipient and donor age, recipient and donor gender, cold ischemia time, immunosuppression and renal function were used as input variables. At each node in the tree, all explanatory variables are considered. The variable/split point combination that maximally separates the response variable into left and right branches is chosen. Once the full tree is generated, a 10-fold cross-validation is used to prune it back to an optimal set of branches.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information

Patient demographics

We analyzed BATs and IGTs compared to QCATs in 177 consecutive renal transplant biopsies from 133 patients obtained between 1 week and 32 years posttransplant (for patient demographics see supplementary Table 1). Twenty-five allograft biopsies had borderline changes, 32 were diagnosed as TCMR by histology; 23 of these biopsies were clinically confirmed as a TCMR episode. Fifteen biopsies were histologically diagnosed as ABMR with diffuse peritubular capillary C4d staining. All ABMR had circulating anti-HLA antibodies at biopsy and met clinical episode criteria. Three biopsies displayed mixed TCMR and ABMR.

BATs and IGTs correlate with lymphoid clusters and diffuse plasma cell infiltration

To confirm that BATs and IGTs reflect B cells and plasma cells, we assessed CD20+ B cells and CD138+ plasma cells in a subset of 32 biopsies with clinical rejection episodes. Most CD20+ cells were associated with nodules. BAT scores correlated with the number of CD20+ B cells per nodule (r = 0.44, p < 0.05) but not with the number of nodules (r = 0.34) or CD20+ infiltrate outside of nodules. IGT scores correlated with the number of CD20+ B cells per nodule (r = 0.50, p < 0.01) and the number of nodules (r = 0.38, p < 0.05) but not with CD20+ B cells outside of nodules. CD138+ plasma cells were not associated with nodules, but diffuse CD138+ staining correlated with IGTs (r = 0.63) and BATs (r = 0.67). Thus, BATs and IGTs approximate the burden of the B cell-plasma cell series, much of which is associated with nodules.

BAT and IGT expression is increased in both TCMR and ABMR

BATs and IGTs correlated with each other across all biopsies (r = 0.72, p < 0.01). Biopsies with histologic rejection (TCMR, ABMR or mixed ABMR and TCMR) had increased BATs and IGTs (1.23 ± 0.25 and 1.55 ± 0.90 linear fold change, respectively) versus control kidneys and biopsies without rejection (1.05 ± 0.16 and 1.1 ± 0.50, respectively) (Figure 1A, B; note that the figure shows the fold change in log units). However, some transplants without rejection also had high BATs and IGTs. Biopsies with BK virus often had elevated BATs and IGTs, similar to rejecting biopsies. Rejecting biopsies with clinical episodes had higher BATs (1.25 ± 0.29) than rejecting biopsies that did not meet the criteria of a clinical episode (1.05 ± 0.14) (Figure 1C); IGTs were not significantly higher in episodes (1.52 ± 1.05 and 1.17 ± 0.56, respectively) (Figure 1D). TCMR and ABMR episodes had similar BAT (1.25 ± 0.29, 1.18 ± 0.18) and IGT (1.52 ± 1.05, 1.51 ± 0.87) expression.

image

Figure 1. Relationship between BAT and IGT scores and diagnosis. We analyzed expression of BATs and IGTs across 175 biopsies for cause in relationship to the diagnosis. BAT and IGT scores are shown for each category. (A and B) All biopsies were classified into one of the following categories: control kidneys (n = 8), rejection episodes (n = 41) (based on a histologic diagnosis of rejection and the presence of clinical criteria as outlined in materials and methods), non-rejection (n = 130), or BK virus nephropathy (n = 6). The statistical analysis was limited to a priori defined comparisons: (i) Controls versus all other groups (non-rejection, rejection, BK) using Dunnett's t-test; (ii) rejection versus non-rejection and versus BK in an ANOVA, corrected for multiple comparisons with Tukey's HSD. (C and D) All biopsies diagnosed as rejection or borderline changes based on histologic criteria were defined as clinical rejection episodes (TCMR: n = 23, ABMR: n = 15; mixed: n = 3) (as outlined in materials and methods) or no episodes. The statistical comparisons were (i) Clinical episode versus no clinical episode (t-test); (ii) within the biopsies with clinical episodes: TCMR versus ABMR (t-test).

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BAT and IGT expression is related to time posttransplant

BAT and particularly IGT scores were correlated with time posttransplant (r = 0.336, r = 0.613) (Figure 2A, B). Based on the distribution displayed in Figure 2, this relationship is best modeled as a dichotomous variable: during the first 5months posttransplant, IGTs were never elevated above the level of control kidneys. In contrast to BATs and IGTs, the previously described PBTs representing the inflammatory disturbance (increased QCATs) and loss of epithelial integrity (decreased KTs) were not time-dependent (r = 0.06 and r = 0.10, respectively) (Figure 2C, D). Thus, QCAT increase and KT loss (the rejection associated inflammatory disturbance) are similar in early and late biopsies for cause, whereas elevated BATs and particularly IGTs are more associated with late allografts.

image

Figure 2. The relationship of time posttransplant to BAT and IGT scores, compared to QCAT and KT scores. We examined the relationship between time posttransplant and expression of BAT and IGTs. The scatterplots show expression values for (A) BATs and (B) IGTs in all biopsies for cause (n = 177), ordered along the x-axis by increasing time posttransplant. The relationship between (C) time and expression of T-cell associated transcripts (QCATs, reflecting the inflammatory burden) and (D) time and expression of kidney transcripts (KT, reflecting epithelial integrity) is shown for comparison.

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BAT and IGT scores are not related to renal function in biopsies after 5 months

BAT and IGT scores did not correlate with GFR at the time of biopsy, but high BAT and IGT scores were associated with less recovery in the 6 months following the biopsy (r =–0.25, r =–0.33, p < 0.01). However, early and late allografts differ in recovery of function after biopsy: early biopsies (<5 months, n = 57) showed +41.5 ± 60.3 % recovery, whereas late biopsies (≥5 months, n = 120) displayed almost no recovery 4.0 ± 32.6 % in function. When we analyzed early and late biopsies separately, we observed no correlation between BATs or IGTs and renal function at the time of biopsy or in recovery of function following the biopsy. Thus, elevated BATs and IGTs, and lack of recovery of function, are features of late biopsies, but they do not correlate with one another. Hence BATs and IGTs do not correlate with function or future function when the time of the biopsy is factored in.

BAT and IGT expression in late biopsies is related to inflammation

BAT and IGT scores as well as QCATs (as previously reported [15]) correlated with histologic lesions with the exception of intimal arteritis (v-lesion) across all biopsies (Supplementary Table 2). The strongest correlations were observed with the interstitial infiltrate (QCATs: r = 0.62; BATs: r = 0.52; IGTs: r = 0.38). The QCATs correlated with tubulointerstitial changes in early and late biopsies. In contrast, BATs and IGTs correlated with infiltration in late biopsies more than in early biopsies (Figure 3). The correlation of BAT and IGT scores with ci scores was mainly driven by the small number of cases with ci3 (Figure 3).

image

Figure 3. Relationship between BAT and IGT scores and the degree of interstitial inflammation and fibrosis. We analyzed the relationship between BAT and IGT transcript levels and histologic scores for interstitial inflammation and interstitial fibrosis. Because BAT and IGT scores are time dependent, the analysis is shown separately for early (<5 months) and late biopsies (≥5 months posttransplant). Inflammation (i-score) and fibrosis (ci-score) are scored as 0, 1, 2 or 3 according to severity of the lesion; BAT and IGT scores are illustrated for each of these categories. Symbols illustrate BAT or IGT scores in individual biopsies, lines represent the median BAT or IGT score in each category.

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Time posttransplant and inflammation are the only variables independently related to BAT and IGT scores in regression tree analysis

To identify variables independently associated with BAT and IGT expression, we performed regression tree analysis. This creates a hierarchy and identifies cutoff values for each parameter that independently influence BAT and IGT levels. Regression tree analysis identified time posttransplant and interstitial inflammation as the only independent predictors of BAT and IGT scores (Figure 4). All other clinical or histologic parameters, including fibrosis and atrophy, were not significant. Regression tree analysis of QCATs (performed as a control) identified a histologic diagnosis of TCMR as the single predictor of QCAT expression (not shown). Thus, BATs and IGTs are a time dependent feature of inflammation in late biopsies, while QCATS reflect the degree of inflammation, whether early or late.

image

Figure 4. Regression tree analysis of BAT scores and IGT scores in relationship to histology scores and clinical parameters. We used a regression tree analysis to identify histology scores and clinical parameters that are related to BAT and IGT scores across all biopsies for cause. Histologic lesions, histologic diagnosis, time posttransplant, recipient and donor age, recipient and donor gender, cold ischemia time, immunosuppression and renal function were used as input variables.

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Relationship of BATs and IGTs to other transcripts and transcript sets

BAT and IGT scores correlate with QCAT scores (r = 0.79 and r = 0.54) (Figure 5A, B). High BAT and IGT expression did not occur in the absence of QCATs. Only four biopsies had marginally higher BAT or IGT scores than expected from the level of QCAT expression: all were diagnosed as recurrent GN, transplant glomerulopathy, calcineurin inhibitor toxicity or PTLD. Twelve biopsies had low BAT/IGT scores in relationship to QCATs: all had TCMR or borderline TCMR. The relationship between BAT/IGT scores and QCAT scores was consistent in rejecting and nonrejecting cases. Thus, elevated BAT and IGT expression in late biopsies almost invariably paralleled QCAT expression.

image

Figure 5. Relationship between BAT and IGT scores and T-cell infiltration. We previously defined a set of T-cell associated transcripts (QCATs), which estimates the T-cell burden in the graft. To analyze the relationship between B-cell/plasma cell infiltrate and the T-cell infiltrate, we correlated expression of (A) BATs and (B) IGTs with expression of the QCATs in biopsies ≥5 months posttransplant. Each symbol represents one biopsy; lines represent the regression line and 85% confidence interval. Outliers are described in the text.

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To identify other transcripts related to BAT and IGT expression, we searched our dataset for transcripts with expression patterns similar to BAT or IGT scores. Expression of the B-cell-specific transcription factor Pax5 correlated with BAT scores (r = 0.66) but less with IGT scores (r = 0.47). Expression of IRF4 correlated equally well with BATs (r = 0.73) and IGTs (r = 0.77). However, in relationship to time posttransplant the expression of the plasma cell transcription factor IRF4 strikingly resembled the pattern of IGT expression (r = 0.40), while Pax5 expression was only weakly related to time posttransplant (r = 0.24).

IGTs were strongly correlated with expression of TNFRSF17 (= BCMA), which is expressed in plasma cells (see below). TNFRSF17 correlated with time, being confined to late biopsies (Figure 6A); with IGT scores (r = 0.90; Figure 6B); and with BAT scores (r = 0.73; Figure 6C). In contrast, the ligand for TNFRSF17, BAFF (TNFSF13B) was time independent (Figure 6D); correlated weakly with IGT scores (r = 0.27; Figure 6E); and correlated strongly with BATs (r = 0.71; Figure 6F) and QCATs (r = 0.85). Thus, BAFF is a feature of inflammation, independent of time, while BAFF receptor TNFRSF17 is plasma cell associated and is a feature of late biopsies correlating with IGTs.

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Figure 6. Relationship between BAT and IGT scores and the B-cell survival factor TNFSF13B (BAFF) and its receptor TNFRSF17 (BCMA). To evaluate other transcriptional events related to BAT and IGT expression in allografts, we queried our data set t for transcripts with similar expression to BAT and IGT scores. The most striking relationship was observed between IGT expression and expression of TNFRSF17 (BCMA), the receptor for the B-cell survival factor TNFSF13B (BAFF). Expression of TNFRSF17 is shown in relationship to (A) time posttransplant, (B) IGT scores, (C) BAT scores. For comparison, expression of the ligand TNFSF13B is shown in relationship to (D) time posttransplant, (E) IGT scores, (F) BAT scores. Correlation coefficients are spearman rank correlation coefficients; **p < 0.001

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Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information

We explored the significance of B-cell and plasma cell infiltrates in allografts by analyzing expression of BATs and IGTs in human kidney transplant biopsies for clinical indications. Immunostaining confirmed that BAT and IGT expression was associated with the presence of B cells and plasma cells in the graft. BAT and IGT expression was increased in rejection but was not different between TCMR and ABMR. In contrast to other inflammatory markers, BAT and IGT expression showed a dichotomous distribution in relationship to time posttransplant: In early biopsies, BAT expression was low and IGT expression was always below that of control kidneys. In late biopsies, BAT and IGT expression correlated strongly with the inflammatory burden. BATs and IGTs correlated with the degree of tubular atrophy and interstitial fibrosis. By regression tree analysis, time posttransplant and interstitial inflammation were the only independent predictors of BAT and IGT scores. IGT expression was highly correlated with expression of TNFRSF17 (the receptor for the B-cell survival factor BAFF), which showed the same dichotomous relationship to time posttransplant. This further underscores the conclusion that the inflammatory infiltrate in late allografts has characteristics distinct from those in early allografts. Although high BAT and IGT scores were associated with less recovery of renal function across the entire patient population, this relationship was lost when correcting for time posttransplant. Thus, the association of BATs and IGTs with adverse outcomes reflects the fact that BATs and IGTs are increased in late biopsies, which have less functional recovery than early biopsies.

In this study, we found that biopsies for cause have time dependent features that must be acknowledged when evaluating the prognostic significance of lesions and transcripts. Kidney transplants that develop an indication for a biopsy late after transplantation have different characteristics than those that develop biopsy indications early. Time-dependent features in biopsies include fibrosis and atrophy, transplant glomerulopathy, BK nephropathy and (as shown here) poor functional recovery, BATS and IGTs. Late rejection has also a reputation for resistance to treatment. Unless time is acknowledged, time-dependent features such as IGTs will appear to be associated with inferior prognosis. Once time is factored in, their influence is minimal. This may explain why B-cell and plasma cell features correlate with prognosis in some studies.

There is little evidence to support independent roles for B cells and plasma cells in the graft in the progression of rejection and allograft deterioration. Although activated B cells have the potential to sustain T-cell responses by serving as antigen-presenting cells for T cells (28), this interaction is associated with secondary lymphoid organs, not in inflamed sites. The high-affinity tissue damaging antibodies that mediate ABMR are likely to be produced by long-lived plasma cells in secondary lymphoid organs or bone marrow. B-cell and plasma cell infiltrates are a common response during renal injury in native kidney disease (3,33,34,34) and a general feature of chronically inflamed and atrophic sites (29–32).

The association of B-cell and plasma cell infiltration with the inflammatory burden in allografts suggests that B cell and plasma cells are recruited/retained in inflammatory compartments in allografts as a non-specific feature of inflammation, rather than as part of the alloimmune response. BAT and IGT expression was high in rejecting biopsies, but was also observed in some non-rejecting kidneys, and was similar between ABMR and TCMR, recalling our previous findings that TCMR and ABMR share similar inflammatory disturbances (15). The mechanism of the time dependent association of BATs and particularly IGTs with the inflammatory burden remains unknown at this point; it is possible that the inflammatory compartment in tissues evolves, acquiring homing and retention strategies to support B cells and plasma cells; however, at this time no data are available to support this hypothesis. Future studies need to dissociate the effects of the B cells and plasma cells in inflammatory compartments in late allografts from the effects of the inflammatory compartment itself (1–5).

The time dependency of IGTs and plasma cell markers such as IRF4 and TNFRSF17 agrees with previous studies that show prominence of plasma cells in late biopsies (4,12), emphasizing the importance of time in analyses of transplant pathology. The lack of an association of plasma cell infiltrates with time in the study by Gartner et al. (5) may reflect inclusion of serial biopsies and separation of patients into two categories based on the infiltrate in the first biopsy, masking any effect of time. The resolution of plasma cell infiltrates in serial biopsies reported by the same group (5) is noteworthy: B-cell and plasma cell infiltrates may thus resolve as inflammation resolves. We suggest that the time dependency is best modeled as a dichotomous variable (i.e. early versus late). Protocol biopsies have shown that early renal transplants display more inflammation, whereas late biopsies reveal more atrophy and fibrosis (35,36), itself often associated with inflammation (6).

The prominence of BAT and IGT expression in late biopsies suggests that the microenvironment in the graft changes over time in a manner that attracts and/or retains such cells and may encourage B-cell to plasma cell maturation. Maturation and survival of B lymphocytes and plasma cells require interactions with specific cell-surface and soluble factors (37–40). In our study, the B-cell survival factor TNFSF13B (= BAFF, produced by antigen-presenting cells) was associated with inflammation, indicating the presence of a supportive microenvironment in inflamed allografts. The strong correlation between IGT scores and expression of TNFRSF17 (= BCMA, the receptor for TNFSF13B) suggests that this survival factor-receptor is an important feature of the inflammatory infiltrate in late biopsies and may contribute to the evolution or persistence of plasma cell infiltrates in these biopsies. Although plasma cell differentiation usually occurs in secondary lymphoid organs, it is possible that, rather than recruiting plasma cells from peripheral blood, chronically inflamed tissue supports local differentiation of B cells into plasma cells by providing a favorable microenvironment.

Acknowledgments

  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information

The authors wish to thank Anna Hutton for technical assistance, Zija Jacaj for coordination of clinical sample and data collection and Joseph Cruz and Chunyan Meng for management of the clinical database. 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 Transplant 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.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information

Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information

Table S1: Patient demographics

Table S2: Relationship between BAT and IGT scores and histologic lesions

Supplemental Methods

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