Background The causes and clinical course of acute rejectionvary, and it is not possible to predict graft outcome reliablyon the basis of available clinical, pathological, and geneticmarkers. We hypothesized that previously unrecognized molecularheterogeneity might underlie some of the variability in theclinical course of acute renal allograft rejection and in itsresponse to treatment.
Methods We used DNA microarrays in a systematic study of gene-expressionpatterns in biopsy samples from normal and dysfunctional renalallografts. A combination of exploratory and supervised bioinformaticmethods was used to analyze these profiles.
Results We found consistent differences among the gene-expressionpatterns associated with acute rejection, nephrotoxic effectsof drugs, chronic allograft nephropathy, and normal kidneys.The gene-expression patterns associated with acute rejectionsuggested at least three possible distinct subtypes of acuterejection that, although indistinguishable by light microscopy,were marked by differences in immune activation and cellularproliferation. Since the gene-expression patterns pointed tosubstantial variation in the composition of immune infiltrates,we used immunohistochemical staining to define these subtypesfurther. This analysis revealed a striking association betweendense CD20+ B-cell infiltrates and both clinical glucocorticoidresistance (P=0.01) and graft loss (P<0.001).
Conclusions Systematic analysis of gene-expression patternsprovides a window on the biology and pathogenesis of renal allograftrejection. Biopsy samples from patients with acute rejectionthat are indistinguishable on conventional histologic analysisreveal extensive differences in gene expression, which are associatedwith differences in immunologic and cellular features and clinicalcourse. The presence of dense clusters of B cells in a biopsysample was strongly associated with severe graft rejection,suggesting a pivotal role of infiltrating B cells in acute rejection.
Acute rejection is a complex process of injury to the allograftcaused by infiltrating cells of the host immune system. It leadsto multiple responses within the graft and is a major risk factorfor chronic rejection and loss of the graft.1,2,3 Acute rejectiontypically develops soon after transplantation and is thoughtto be secondary to cell-mediated immune responses involvingdelayed mechanisms of hypersensitivity and cytotoxicity. Despiteefforts at systematization,4,5 clinical and pathological diagnosisand classification of acute rejection remain unreliable in predictingresponses to therapy and graft outcomes.6,7,8,9,10 Thus, thereis a great need to improve risk stratification and modes ofearly treatment. We investigated the possibility that variationsin gene-expression patterns in allograft-biopsy samples frompatients with acute rejection and related disorders would permitthe identification of molecularly distinct subtypes of acuterejection that may be related to differences in clinical behavior.
Methods
Patient Information
We analyzed 67 allograft-biopsy samples from 50 patients (1.4to 22 years of age). Immunosuppressive therapy consisted ofglucocorticoids, a calcineurin inhibitor (tacrolimus or cyclosporine),an antimetabolite (azathioprine or mycophenolate mofetil), andinduction therapy with daclizumab. Nine graft losses occurredbetween 1.5 and 8.0 years after transplantation, a mean of 10months after a biopsy was performed because of acute rejection.Written informed consent was obtained from all study patients,and the study was approved by the institutional review boardof Stanford University.
Biopsy Samples
A total of 52 biopsy samples were obtained between 1 month and10 years after transplantation during acute allograft dysfunction(defined by an increase of more than 10 percent in the serumcreatinine concentration from base line) or chronic allograftdysfunction (defined by a glomerular filtration rate11 of 50ml per minute per 1.73 m2 of body-surface area); 8 biopsy sampleswere obtained at the time of engraftment; and 7 samples wereobtained at times when graft function was stable (as definedby a glomerular filtration rate of more than 80 ml per minuteper 1.73 m2). All biopsy samples were snap-frozen. All but fivebiopsy samples were obtained before the intensification of treatmentfor rejection. Microscopical analyses were performed by investigatorswho were unaware of the clinical outcomes.5,12,13 No biopsysample contained evidence of post-transplantation lymphoproliferativedisorder or viral inclusions.
Microarray Hybridization and Data Analysis
Each microarray was a lymphochip14 gridded at Stanford University,and each contained 28,032 DNA spots representing approximately12,440 human genes. Total RNA was isolated from frozen biopsysamples (TRI Reagent, Molecular Research Center). A common referencepool of RNA15 was used as an internal standard. Sample or referenceRNA was subjected to two successive rounds of amplification16before undergoing hybridization to microarrays.
All 67 biopsy samples were used for initial unsupervised, hierarchicalclustering (i.e., analysis without prior knowledge of sampleidentity).17 For subsequent supervised analyses (i.e., comparativeanalyses between defined sample groups), with the use of significanceanalysis of microarrays,18 the five samples from patients withpartially treated acute rejection were excluded in order toeliminate possible bias due to the effects of drugs. The enrichmentof specific functional groups of genes was assessed in our dataset on the basis of the hypergeometric distribution,19 withthe use of 86 T-cell–specific genes,20 2610 T-cell–inducibletranscripts,21 and 874 cell-cycle–related genes.22 Kaplan–Meiersurvival analyses, based on the Cox log-rank method, were usedto determine the relation between graft survival or recoveryof graft function (defined as the return of the serum creatinineconcentration to the base-line level one month after the treatmentof acute rejection) and the density of CD20+ cells.
Immunohistochemistry
Immunohistochemical staining for CD20, CD4, CD8, and proliferating-cellnuclear antigen (PCNA) was performed on samples from patientswith untreated acute rejection. In addition, an independentset of 31 archived biopsy samples from patients with acute rejectionwas also analyzed by CD20 staining.
Entire cores were scanned in a blinded fashion by a single observerto determine the density of CD20+, CD4+, and CD8+ cells. Celldensity per high-power field and the number of high-power fieldscounted per core were documented. For each specimen, the singlehigh-power field with the highest CD20+ cell count was identified,and cell counts of more than 275 and less than 100 were chosenarbitrarily as definitions of CD20+ and CD20 – status,so that the high threshold was more than 2.5 times the low threshold.
Supplemental Information
Additional information on methods, immunohistochemical images,and analytic methods are available as supplementary appendixesat http://genome-www.stanford.edu/rejection/ or from the NationalAuxiliary Publications Service (*). Data are available at theGene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo).
Results
Clustering of Samples
The gene-expression profiles of 67 allograft-biopsy sampleswere compared by the hierarchical clustering of samples accordingto the correlation in their patterns of expression in 1340 selectedcomplementary DNA (cDNA) fragments, representing approximately912 genes (Figure 1 and supplementary appendixes). In general,biopsy samples from patients with similar clinical diagnosesclustered together on the basis of corresponding similaritiesin gene expression, irrespective of the immunosuppressive regimenthe patient was receiving. The possibility that differentialsampling of the medullary and cortical regions might accountfor the observed molecular variation was addressed through thecomparison of these data with those obtained from an examinationof variation in gene expression in distinct regions of the kidney.Patterns of gene expression in normal cortex and medulla werecharacterized in samples obtained from the gross dissectionof normal kidneys (supplementary appendixes). The exclusionof data from genes whose expression was highly correlated withthe depth of the biopsy did not change the composition of theclusters of samples (supplementary appendixes).
Figure 1. Hierarchical Clustering of 1340 Transcripts in 67 Biopsy Samples on the Basis of Similarity in Gene-Expression Patterns (Panel A) and a Dendrogram Showing the Degree of Relatedness of Samples (Panel B).
A total of 59 samples were from pediatric renal-allograft recipients, and 8 were from donors. In Panel A, the genes (rows) and samples (columns) were ordered on the basis of the overall similarity in the expression pattern by investigators who were unaware of the clinical diagnoses. We selected 1340 complementary DNA fragments after filtering (for nonflagged spots with a fluorescence intensity more than 2.5 times that of the background, genes with technically adequate measurements in more than 75 percent of all samples, and messenger RNA levels differing from the median by at least a factor of 2.9 in at least six samples). The colored bars on the right of the diagram indicate clusters (labeled A, B, C, and D) with high discrimination scores. The degree of relatedness of the expression patterns in biopsy samples is represented by the dendrogram at the top of the panel. The color in each cell reflects the level of expression of the corresponding gene in the corresponding sample, relative to its mean level of expression in the entire set of biopsy samples. Gray areas represent missing or excluded data. The scale (shown at bottom right) extends from transcript abundance ratios of 0.25 to 4 relative to the mean level for all samples. In Panel B, replicate samples from the same patient (2 and 3) clustered together, indicating that experimental noise and artifacts caused by the handling or processing of the tissue are negligible in this analysis. AR-I denotes acute rejection type I, AR-II acute rejection type II, and AR-III acute rejection type III.
Clustering of Allograft Biopsies According to Gene-Expression Profile
We identified four clusters of expression patterns in the biopsysamples, which generally corresponded closely with clinicopathologicalcategories (Figure 1 and Table 1). Biopsy samples from patientswith acute rejection were observed to have relative molecularheterogeneity. Unlike the samples from normal kidney or thosefrom patients with chronic allograft nephropathy, toxic drugeffects, or infection, the samples from patients with acuterejection were dispersed among three of the four major clusters.Although one distinct cluster (cluster A) consisted only ofsamples from patients with acute rejection, the remaining 14of the 26 samples from such patients were dispersed in clustersB and C.
Table 1. Pathological and Clinical Characteristics of 52 Biopsy Samples from Dysfunctional Kidneys.
All samples from patients with a clinicopathological diagnosisof toxic drug effects or infection were grouped in cluster B.All samples from patients with chronic allograft nephropathywere grouped in cluster C, and all the samples in this clustershowed clinicopathological evidence of chronic allograft nephropathy(in some cases, with accompanying acute rejection). All biopsysamples from normal kidneys were grouped in cluster D, and onlynormal samples were found in this cluster. The division of thesamples into these four clusters reflects only a fraction ofthe molecular variations among them: within each of these clusters,extensive residual variation in gene expression was observed(the gene-expression signatures of each cluster are availableat http://genome-www.stanford.edu/rejection/ or from the NationalAuxiliary Publications Service (*). We focused our detailedanalysis on the molecular characterization of the samples frompatients with acute rejection.
Molecular Heterogeneity of Acute Rejection
We examined the characteristic gene-expression patterns thatdistinguished the 26 biopsy samples from patients with acuterejection. There were significant differences between thesesamples and those from normal kidney in the expression of 586genes, representing 64 percent of all 912 unique genes analyzed;the median false discovery rate was 12 percent, or 68 genes(the false discovery rate, or the percentage of genes identifiedby chance, is calculated as the median number [or 90th percentile]of falsely identified genes divided by the number of genes achievingsignificance levels for differential expression18). The varyinglevels of expression of many of these genes suggests a varyingabundance of distinctive cell populations, such as T and B lymphocytes,natural killer cells, macrophages, and endothelial cells.
At least three different groups of biopsy samples from patientswith acute rejection, which were not differentiated by lightmicroscopy, could be defined by unsupervised hierarchical clusteringon the basis of pervasive differences in their gene-expressionprofiles: acute rejection type I, which we designated as AR-I(cluster A, accounting for 12 biopsy samples, with one repeatedexperiment), acute rejection type II (designated as AR-II, clusterB, accounting for 9 biopsy samples, 5 of which were from patientswho had been partially treated at the time of biopsy), and acuterejection type III (designated as AR-III, cluster C, accountingfor 5 biopsy samples). These differences in patterns of expressionmay reflect distinct mechanisms of molecular pathogenesis ofrejection (Figure 1).
Defining Subtypes of Acute Renal Allograft Rejection
A total of 385 genes (42 percent of all unique genes analyzed)were differentially expressed in the biopsy samples in the AR-Igroup and the other samples from patients with acute rejection(median false discovery rate, 24 percent, or 94 genes). Thefunctional theme reflected in these genes suggests that thereis greater apoptosis as well as infiltration and activationof lymphocytes, driven by NF-B and interferon- in AR-I thanin the other subtypes of acute rejection (Figure 2A and supplementaryappendixes). Also prominent in this subtype are increased transcriptsfrom T cells (interleukin-2–receptor chains and T-cell–receptorchains), natural killer cells (natural-killer–cell transcript4), and macrophages (matrix metalloproteinase-7 and macrophagereceptor).
Figure 2. Expanded View of the Gene Clusters, Showing Specific Features of the Gene-Expression Patterns within the Signatures in the Various Subtypes of Acute Rejection.
Panel A shows samples in the AR-I group, Panel B the AR-II group, and Panel C the AR-III group. RANTES denotes regulated upon activation normal T-cell expressed and secreted, AR-I acute rejection type I, AR-II acute rejection type II, and AR-III acute rejection type III.
Responses occurring downstream of T-cell activation may be enhanced,as suggested by increased expression of cytotoxic T-lymphocyte–effectorgenes (granzyme A and RANTES [regulated upon activation normalT-cell expressed and secreted], which are important effectorsof acute rejection23,24,25), adhesion molecules, cytokines,cytokine receptors, and growth factors (Figure 2A). In supportof this hypothesis, we found an enrichment19 of two classesof T-cell transcripts within the gene cluster characteristicof AR-I: 15 of 23 T-cell–specific transcripts20 (P<0.001for the enrichment of T-cell–specific genes in AR-I ascompared with the rest of the gene cluster) (supplementary appendixes),and 43 of 145 T-cell–inducible transcripts21 (P<0.001for the enrichment of T-cell–inducible genes in AR-I ascompared with the rest of the gene cluster) (supplementary appendixes).Furthermore, all eight of eight genes that were noted to beboth T-cell–inducible and T-cell–specific were characteristicof the AR-I signature (P<0.001, supporting increased T-cellinfiltration and activation in AR-I as compared with the restof the gene cluster). Unexpectedly, an overriding signaturefor B cells (CD20, CD74, immunoglobulin heavy and light chains,and other molecules associated with B-cell receptors) was foundin AR-I, as compared with the other subtypes (Figure 2 and supplementaryappendixes).
Nine samples from patients with acute rejection (AR-II) sharedfeatures with biopsy samples from grafts with clinicopathologicalevidence of toxic drug effects or infection. Some similaritiesto the gene-expression profiles of the AR-I samples (Figure 1and Figure 2) may reflect common features of immune activationby pathogens and alloantigens. Many features of the expressionprogram of the innate immune response were prominent in thesesamples. Genes of the annexin family — specifically, annexinV, a potential marker of acute rejection26 — were expressedat a particularly high level in this group of biopsy samples.Expression of transforming growth factor (induced by calcineurin-inhibitordrugs)27 was relatively elevated, supporting the clusteringof samples from patients with toxic drug effects in this groupof biopsy samples.
Five AR-III samples clustered with samples from patients withchronic allograft nephropathy in cluster C, despite the factthat they met the Banff histologic criteria for acute rejection.Perhaps the most striking feature of these samples was the expressionof genes involved in cellular proliferation and cell cycling(Figure 2), suggesting active tissue repair and regeneration.Sixty of the 1340 transcripts in our data set were related tocell-cycle functions,22 and 14 of these 60 genes were amongthe genes whose expression was significantly elevated in AR-III,representing a statistically significant enrichment19 (P<0.001,for the enrichment of cell-cycle genes in AR-III as comparedwith the rest of the gene cluster). The molecular features oflymphocyte infiltration and activation were minimal in thissubtype (Figure 2 and supplementary appendixes), suggestinga relatively quiescent rejection process and ongoing recoveryfrom previous or chronic tubulointerstitial inflammation ortubular necrosis.
Immunohistochemical Features of Samples from Patients with Acute Rejection
Because we observed not only a robust T-cell signature, butalso a B-cell signature in the AR-I group, we used immunohistochemicalanalysis to investigate whether variation in the cellular compositionof infiltrating lymphocytes in the 20 unique biopsy samplesfrom patients with untreated acute rejection might account forsome of the differences among groups in the observed gene-expressionpatterns; one sample in AR-I was examined twice by microarrayanalysis (Table 1). We were particularly interested in furtherstudy of B cells, since B cells have not historically been reportedto be key players in acute rejection.28 We chose CD20, a markerfor B cells that is present in AR-I, to corroborate the observationof B-cell enrichment independently by immunohistochemical analysis.
On staining, we found that there was a greater abundance ofCD8+ T lymphocytes than of CD4+ T lymphocytes in biopsy samplesfrom patients with acute rejection. There were no overall quantitativedifferences in these patterns among the subtypes of acute rejection,although two biopsy samples with glucocorticoid resistance frompatients in the AR-I group had a higher density of CD8+ cells(Figure 3A and supplementary appendixes). The apparent absenceof major differences in the density of CD4+ cells and CD8+ cellsamong the subtypes of acute rejection suggests that the relativelyprominent T-cell signature in AR-I is largely attributable toan activated T-cell phenotype (evidenced by markers of earlyand late T-cell activation) rather than to increased numbersof infiltrating T cells and that, conversely, infiltrating Tcells in AR-III are relatively quiescent.
Figure 3. Immunohistochemical Staining of Tissues.
Panel A shows staining with periodic acid–Schiff (PAS) and monoclonal antibodies against CD4, CD8, and CD20 in representative samples from patients with acute rejection of each subtype (AR-I, AR-II, and AR-III). The AR-I sample shows the presence of large B-cell clusters. Panel B shows staining for proliferating-cell nuclear antigen (PCNA) in cluster C revealing the presence of PCNA in tubular and interstitial cells in a representative AR-III biopsy sample but the absence of PCNA in a representative sample from a patient with chronic allograft nephropathy (CAN). AR-I denotes acute rejection type I, AR-II acute rejection type II, and AR-III acute rejection type III.
CD20 staining revealed unexpected large aggregates of B cellswithout formation of follicles (Figure 3A and supplementaryappendixes) in 9 of 20 biopsy samples from patients with acuterejection: 7 of 11 in the AR-I group, 1 of 4 in the AR-II group,and 1 of 5 in the AR-III group (Table 1). This finding contrastswith a previous report of few B cells in samples from patientswith acute rejection.28 CD20 staining of 31 archived biopsysamples from patients with acute rejection that were not examinedby microarray also revealed a similar proportion of CD20+ lymphocyteaggregates in 9 biopsy samples (supplementary appendixes). Immunofluorescencestaining of biopsy samples for immunoglobulin and complementdeposition was negative, despite the presence of the B-cellaggregates; in situ hybridization for Epstein–Barr virusand simian virus 40 was negative, ruling out an associationbetween B-cell infiltrates and viral infection or post-transplantationlymphoproliferative disorder (data not shown).
The presence of proliferating-cell nuclear antigen, a markerof cell proliferation, was confirmed in all 5 AR-III samplesbut not in any of the samples from patients with chronic allograftnephropathy (Figure 3B), distinguishing these groups of biopsysamples with otherwise similar expression profiles.
Clinical Correlates of the Subtype of Acute Rejection and CD20+ Cell Density
Analysis of the recovery of graft function over time revealedthat grafts that were clustered in the AR-I group had significantlypoorer functional recovery than those classified as either AR-IIor AR-III (P=0.02) (Table 2 and supplementary appendixes). Whendata from the five samples from partially treated patients inthe AR-II group were removed from the data set, a trend towarda correlation remained, despite reduced numbers of samples (P=0.06)(Table 2 and supplementary appendixes). In addition, four offive samples from patients with glucocorticoid-resistant acuterejection (defined by the absence of a clinical response toglucocorticoid pulse treatment) clustered in the AR-I group.
Table 2. Correlations between Acute Rejection (AR) Subtype or CD20 Status and Graft Outcome.
A strong association between the density of CD20+ cells on immunostainingand the clinical phenotype of glucocorticoid resistance wasobserved among patients in the AR-I group: all four biopsy samplesfrom patients in this group who had glucocorticoid-resistantacute rejection had a high density of CD20+ cells (one patientrequired antibody therapy with muromonab-CD3 [OKT3] at the outsetfor presumed vascular rejection, and the others required suchtherapy after the failure of glucocorticoid pulse therapy) (Table 1).The density of CD20+ cells was strongly correlated withgraft loss when all samples from patients with acute rejectionwere considered together (P<0.001) (Table 2 and supplementaryappendixes). To provide an independent test of the significanceof this result, we examined the clinical correlates of the retrospectiveseries of 31 biopsy samples from patients with acute rejectionand confirmed that dense aggregates of CD20+ cells at the timeof biopsy were strongly associated with glucocorticoid resistance(P<0.001) and poor graft outcomes (Table 3).
Table 3. Clinical Correlates of CD20 Status in Renal-Biopsy Samples from Patients with Acute Rejection.
None of the other variables we studied correlated with eitherthe density of CD20+ lymphocytes or the assignment of a subtypeof acute rejection defined according to the pattern of geneexpression. These variables were the weight of the donor orthe recipient, the age of the recipient, the number of HLA mismatches,the use of a transplant from a living or cadaveric donor, whetheror not there were repeated transplantations, the presence orabsence of panel-reactive antibody before transplantation, theoccurrence or nonoccurrence of delayed graft function, the intervalsince transplantation, the type of immunosuppression, the presenceor absence of hypertension, the presence or absence of anemia,the type of immunosuppressive therapy, or the presence or absenceof humoral rejection as determined by complement C4d staining.
Discussion
We examined the global transcript profiles of kidney-biopsysamples to help us to understand and classify acute allograftrejection. Using DNA microarrays, we identified molecular variationsuggesting the existence of distinct molecular and prognosticvariants of acute rejection, which could not previously be clearlydefined on the basis of clinical or pathological criteria. Manyof the observed differences in gene-expression patterns amongsamples from patients with acute rejection appear to reflectdifferences in the composition and activation of infiltratinglymphocytes. Confounding influences of time may be involvedin the ostensible disparities in gene expression that we reporthere, since there was residual heterogeneity within the subtypesof acute rejection that we defined by cluster analysis. A prospectiveand extensive longitudinal study of more samples by a varietyof methods is needed to refine the classification of acute rejection,with clearer connections between patterns of gene expression,pathophysiology, and clinical course. Since our study is basedlargely on pediatric patients, similar analyses should be conductedin adult renal-transplant recipients.
The molecular and immunohistochemical evidence of B-cell infiltrationin a subgroup of samples from patients with acute rejectionwas the most unexpected and important finding in this study.Dense CD20 staining was observed in approximately one thirdof the 52 biopsy samples from patients with acute rejectionthat underwent immunohistochemical analysis and was significantlyassociated with glucocorticoid resistance and eventual graftfailure. Staining for CD20 may make possible a rapid clinicaltest that will permit the definition of a high-risk group ofpatients with acute rejection who may warrant more aggressiveand specific treatment. The association between CD20+ lymphocyteinfiltration and graft loss was unexpected. We speculate thatin patients who have such an infiltration, early treatment witha monoclonal antibody against CD20 (rituximab) may be beneficial.29The association of CD20 staining with glucocorticoid resistancedoes not suggest that these cases are necessarily humorallymediated despite the presence of C1s, C1r, and C4b in some biopsysamples: staining for complement C4d, which was used as a putativemarker of humoral rejection in the biopsy samples from patientswith acute rejection in our study, showed poor correlation withCD20 staining (P=1.00) (data not shown).
A preponderance of CD8+ T cells and plasma-cell infiltrateshas been associated with glucocorticoid resistance and pooroutcomes,30,31,32,33 whereas B cells have been reported to beinfrequent or absent in acute rejection. The pathophysiologicalrole of B-cell infiltrates in this study requires further investigation;their presence does not seem to result in direct allograft injury,since immunofluorescence staining of biopsy samples for immunoglobulinand complement deposition was negative in out study (data notshown). As pharmacologic suppression of T cells has improvedover the past decade (with the introduction of tacrolimus, mycophenolatemofetil, sirolimus, and monoclonal antibodies against the interleukin-2receptor), B cells may have evolved as efficient antigen-presentingcells for indirect allorecognition, and their continued presencein the graft, unaffected by current immunosuppressive management,may be resulting in a large fraction of episodes of refractoryrejection.34,35
In conclusion, molecular profiling of transplants in patientswith acute rejection identified new subtypes of acute rejectionand a correlation between CD20+ lymphoid aggregates and poorergraft outcomes; these findings may point toward improvementsin the individualization of therapy. Gene-expression profilingthus opens a new door for the study of acute rejection and mayprovide a means to a better understanding of other categoriesof graft dysfunction.
Supported by grants from the National Institutes of Health (NIH5P3-05and NIH3P3-05S1, to Dr. Sarwal), the Clinical Center for ImmunologicalStudies at Stanford University (to Dr. Sarwal), the PackardFoundation, Roche Pharmaceuticals, and the Howard Hughes MedicalInstitute.
We are indebted to Dr. Patrick O. Brown for scientific directionand support, without which this work would not have been possible;to Drs. Xin Chen, Ash A. Alizadeh, and Maximilian Diehn forinvaluable scientific assistance; to Addie Whitney for printingof the DNA microarrays and for helpful advice; to Stella Changfor assistance with RNA amplification; to Dr. John Higgins forthe cortex and medulla samples; to the Pediatric Nephrologyteam for their assistance with sample collection; to Dr. AlanKrensky for his support; to Wijan Prapong for assistance withthe supplementary material; to Dr. Robert Colvin at HarvardUniversity for conducting C4d staining; to Jennifer Boldrickfor helpful advice, discussions, and review of the manuscript;and to the staff at the Stanford Microarray Database for theirsupport and assistance with Web-site maintenance.
* See NAPS document no. 05611 for 56 pages of supplementary material.To order, contact NAPS, c/o Microfiche Publications, 248 HempsteadTpke., West Hempstead, NY 11552.
Source Information
From the Departments of Pediatrics (M.S., M.-S.C., S.-C.H., T.S., O.S.), Pathology (N.K., M.M.), and Surgery (O.S.), Stanford University, Stanford, Calif.
Address reprint requests to Dr. Sarwal at the Department of Pediatrics, G320, 300 Pasteur Dr., Stanford, CA 94305, or at msarwal{at}stanford.edu.
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B and interferon-
in AR-I than in the other subtypes of acute rejection (Figure 2A and supplementary appendixes). Also prominent in this subtype are increased transcripts from T cells (interleukin-2–receptor chains and T-cell–receptor chains), natural killer cells (natural-killer–cell transcript 4), and macrophages (matrix metalloproteinase-7 and macrophage receptor). 
(induced by calcineurin-inhibitor drugs)27 was relatively elevated, supporting the clustering of samples from patients with toxic drug effects in this group of biopsy samples. 
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