A universal urinary cell gene signature of acute rejection in kidney allografts.

INTRODUCTION: Acute rejection (AR) undermines the life-extending benefits of kidney transplantation and is diagnosed using the invasive biopsy procedure. T cell-mediated rejection (TCMR), antibody-mediated rejection (ABMR), or concurrent TCMR + ABMR (Mixed Rejection [MR]) are the three major types of AR. Development of noninvasive biomarkers diagnostic of AR due to any of the three types is a useful addition to the diagnostic armamentarium. METHODS: We developed customized RT-qPCR assays and measured urinary cell mRNA copy numbers in 145 biopsy-matched urine samples from 126 kidney allograft recipients. We determined whether the urinary cell three-gene signature diagnostic of TCMR (Suthanthiran et al., 2013) discriminates patients with no rejection biopsies (NR, n = 50) from those with ABMR (n = 28) or MR (n = 20) biopsies. RESULTS: The urinary cell three-gene signature discriminated all three types of rejection biopsies from NR biopsies (P < 0.0001, One-way ANOVA). Dunnett's multiple comparisons test yielded P < 0.0001 for NR vs. TCMR; P < 0.001 for NR vs. ABMR; and P < 0.0001 for NR vs. MR. By bootstrap resampling, optimism-corrected area under the receiver operating characteristic curve (AUC) was 0.749 (bias-corrected 95% confidence interval [CI], 0.638 to 0.840) for NR vs. TCMR (P < 0.0001); 0.780 (95% CI, 0.656 to 0.878) for NR vs. ABMR (P < 0.0001); and 0.857 (95% CI, 0.727 to 0.947) for NR vs. MR (P < 0.0001). All three rejection categories were distinguished from NR biopsies with similar accuracy (all AUC comparisons P > 0.05). CONCLUSION: The urinary cell three-gene signature score discriminates AR due to TCMR, ABMR or MR from NR biopsies in human kidney allograft recipients.

Urinary cell mRNA profiling of kidney allograft recipients: Development of a portable protocol for noninvasive diagnosis of T cell mediated rejection and BK virus nephropathy.

BACKGROUND: We developed urinary cell mRNA profiling for noninvasive diagnosis of acute T cell mediated rejection (TCMR) and BK virus nephropathy (BKVN), two significant post-transplant complications. Our profiling protocol for the multicenter Clinical Trial of Transplantation-04 (CTOT-04) study consisted of centrifugation of urine to prepare cell pellets, washes, addition of an RNA preservative, storage at 800C and shipment in cold containers to our Gene Expression Monitoring (GEM) Core for RNA isolation and quantification of mRNA in RT-qPCR assays. To simplify profiling, we developed a filter-based protocol (ZFBP) that eliminated the need for centrifugation, RNA preservative, storage at 800C, and shipment in cold containers for mRNA profiling. Furthermore, we trained kidney allograft recipients to perform the filtration of urine at home using the filter and post the urinary cell lysate containing the RNA at ambient temperature to our GEM Core for profiling. Here, we report our refinement of ZFBP and investigation of its diagnostic performance characteristics. METHODS: Total RNA was isolated from kidney allograft biopsy-matched urines using a filter-based protocol complemented by a silica-membrane-based cartridge for mRNA enrichment, the Weill Cornell Hybrid Protocol (WCHP). Absolute copy numbers of CD3ε mRNA, CXCL10 mRNA, and 18S rRNA, components of the CTOT-04 three-gene TCMR diagnostic signature, and urinary cell BKV VP 1 mRNA copy number were measured using RT-qPCR assays. Mann-Whitney test, Fischer exact test, and receiver operating characteristic (ROC) curve analysis were used for data analyses. RESULTS: Urinary cell three-gene TCMR diagnostic signature scores in urines processed using the WCHP discriminated kidney allograft recipients with TCMR (12 TCMR biopsies from 11 patients) from those without TCMR or BKVN (29 No TCMR/No BKVN biopsies from 29 patients). The median (25th and 75th percentiles) score of the CTOT-04 three-gene TCMR diagnostic signature was -0.448 (-1.664, 0.204) in the TCMR group and - 2.542 (-3.267, -1.365) in the No TCMR/ No BKVN group (P = 0.0005, Mann-Whitney test). ROC curve analysis discriminated the TCMR group from the No TCMR/ No BKVN group; the area under the ROC curve (AUROC) was 0.84 (95% Confidence Intervals [CI], 0.69 to 0.98) (P < 0.001), and TCMR was diagnosed with a sensitivity of 67% (95% CI, 35 to 89) at a specificity of 86% (95% CI, 67 to 95) using the CTOT-04 validated cutpoint of -1.213 (P = 0.0016, Fisher exact test). BKV VP1 mRNA copy number in urines processed using the WCHP discriminated patients with BKVN (n = 7) from patients without TCMR or BKVN (n = 29) and the AUROC was 1.0 (95% CI, 1.00 to 1.00) (P < 0.0001) and BKVN was diagnosed with a sensitivity of 86% (95% CI, 42 to 99) at a specificity of 100% (95% CI, 85 to 100) with the previously validated cutpoint of 6.5 × 108 BKV-VP1 mRNA copies per microgram of RNA (P < 0.0001, Fisher exact test). CONCLUSION: Urine processed using the WCHP predicted TCMR and BKVN in kidney allograft recipients. WCHP represents not only a significant advance toward the portability of urinary cell mRNA profiling but also improved patient management by minimizing their visits for urine collection.

Urinary Cell Gene Signature of Acute Rejection in Kidney Allografts.

Introduction: A kidney allograft biopsy may display acute T cell-mediated rejection (TCMR), antibody-mediated rejection (ABMR), or concurrent TCMR + ABMR (MR). Development of noninvasive biomarkers diagnostic of all three types of acute rejection is a useful addition to the diagnostic armamentarium. Methods: We developed customized RT-qPCR assays and measured urinary cell mRNA copy number in 145 biopsy-matched urine samples from 126 kidney allograft recipients and calculated urinary cell three-gene signature score from log 10 -transformed values for the 18S-normalized CD3E mRNA, 18S-normalized CXCL10 mRNA and 18S rRNA. We determined whether the signature score in biopsy-matched urine specimens discriminates biopsies without rejection (NR, n=50) from biopsies displaying TCMR (n=47), ABMR (n=28) or MR (n=20). Results: Urinary cell three-gene signature discriminated TCMR, ABMR or MR biopsies from NR biopsies (P <0.0001, One-way ANOVA). Dunnett's multiple comparisons test yielded P<0.0001 for NR vs. TCMR; P <0.001 for NR vs. ABMR; and P <0.0001 for NR vs. MR. By bootstrap resampling, optimism-corrected area under the receiver operating characteristic curve (AUC) was 0.749 (bias-corrected 95% confidence interval [CI], 0.638 to 0.840) for NR vs. TCMR (P<0.0001); 0.780 (95% CI, 0.656 to 0.878) for NR vs. ABMR (P<0.0001); and 0.857 (95% CI, 0.727 to 0.947) for NR vs. MR (P<0.0001). All three rejection biopsy categories were distinguished from NR biopsies with similar accuracy (all AUC comparisons P>0.05). Conclusion: Urinary cell three-gene signature score may serve as a universal diagnostic signature of acute rejection due to TCMR, ABMR or MR in human kidney allografts with similar performance characteristics.

Development of a Bak gene based standard curve for absolute quantification of BK virus in real time quantitative PCR assay and noninvasive diagnosis of BK virus nephropathy in kidney allograft recipients.

BACKGROUND: BK virus nephropathy (BKVN) is a frequent and serious post-transplant complication and undermines realization of the full benefits of kidney transplantation. We developed a Bak amplicon-based standard curve for absolute quantification of BKV VP1 mRNA copy number in the real time quantitative PCR (RT-qPCR) assay and investigated the performance characteristics of this novel assay. METHODS: We determined analytical specificity, sensitivity, and precision of our 73 bp mouse Bak amplicon based standard curve for absolute quantification of BKV VP1 mRNA in RT-qPCR assays. The diagnostic accuracy of the Bak standard curve in the RT-qPCR assay for the noninvasive diagnosis of BKVN in human kidney allograft recipients was investigated by quantification of BKV VP1 mRNA copy number in 192 urine samples matched to 192 kidney allograft biopsies from 155 unique kidney allograft recipients. Intraclass correlation coefficients (ICC) were calculated for the threshold cycles (Ct) and BKV VP1 mRNA copy number observed in the RT-qPCR assay with the Bak standard curve or the BKV standard curve. RESULTS: Performance characteristics of the Bak amplicon-based RT-qPCR assay were exceptional with a slope of -3.291, Y-intercept of 38.60, R2 value of 1.00, efficiency of 101% and error of 0.014. Amplification was specific for the Bak amplicon. Intra assay standard deviation (SD) was 0.08 or less and inter assay SD was 0.11 or less for 31 cycles or less of amplification of the Bak amplicon. Receiver operating characteristic (ROC) curve analysis of BKV VP1 mRNA copy number in 192 biopsy matched urines yielded an area under the ROC of 0.982 (95% CI, 0.964 to 0.999, P < 0.0001) for discriminating patients with BKVN biopsies from patients without BKVN biopsies. The striking identity in the measurement of BKV VP1 mRNA copy numbers in the Bak amplicon-based RT-qPCR assay and in the BKV amplicon-based RT-qPCR assay was shown by an ICC of 1.00 when the Cts were compared, and an ICC of 0.99 when the log10 BKV VP1 mRNA copy numbers were compared. CONCLUSIONS: Bak standard curve for absolute quantification of BKV VP1 mRNA copy number in the RT-qPCR assay demonstrated high efficiency, short and long-term precision and analytical specificity. BKVN was diagnosed with high accuracy. Our new findings, viewed in the light of our earlier demonstration that absolute quantification of a panel of mRNAs encoding immunoregulatory proteins is feasible with the Bak amplicon-based RT-qPCR assays, suggest that the Bak standard curve could serve as a universal calibrator for absolute quantification of transcripts in RT-qPCR assays and help reduce the workload, costs and eliminate contamination of genes of interest by repeated amplification of gene specific standard curves.

Detection of infiltrating fibroblasts by single-cell transcriptomics in human kidney allografts.

We tested the hypothesis that single-cell RNA-sequencing (scRNA-seq) analysis of human kidney allograft biopsies will reveal distinct cell types and states and yield insights to decipher the complex heterogeneity of alloimmune injury. We selected 3 biopsies of kidney cortex from 3 individuals for scRNA-seq and processed them fresh using an identical protocol on the 10x Chromium platform; (i) HK: native kidney biopsy from a living donor, (ii) AK1: allograft kidney with transplant glomerulopathy, tubulointerstitial fibrosis, and worsening graft function, and (iii) AK2: allograft kidney after successful treatment of active antibody-mediated rejection. We did not study T-cell-mediated rejections. We generated 7217 high-quality single cell transcriptomes. Taking advantage of the recipient-donor sex mismatches revealed by X and Y chromosome autosomal gene expression, we determined that in AK1 with fibrosis, 42 months after transplantation, more than half of the kidney allograft fibroblasts were recipient-derived and therefore likely migratory and graft infiltrative, whereas in AK2 without fibrosis, 84 months after transplantation, most fibroblasts were donor-organ-derived. Furthermore, AK1 was enriched for tubular progenitor cells overexpressing profibrotic extracellular matrix genes. AK2, eight months after successful treatment of rejection, contained plasmablast cells with high expression of immunoglobulins, endothelial cell elaboration of T cell chemoattractant cytokines, and persistent presence of cytotoxic T cells. In addition to these key findings, our analysis revealed unique cell types and states in the kidney. Altogether, single-cell transcriptomics yielded novel mechanistic insights, which could pave the way for individualizing the care of transplant recipients.

Deep sequencing of DNA from urine of kidney allograft recipients to estimate donor/recipient-specific DNA fractions.

Kidney transplantation is the treatment of choice for patients with end-stage kidney failure, but transplanted allograft could be affected by viral and bacterial infections and by immune rejection. The standard test for the diagnosis of acute pathologies in kidney transplants is kidney biopsy. However, noninvasive tests would be desirable. Various methods using different techniques have been developed by the transplantation community. But these methods require improvements. We present here a cost-effective method for kidney rejection diagnosis that estimates donor/recipient-specific DNA fraction in recipient urine by sequencing urinary cell DNA. We hypothesized that in the no-pathology stage, the largest tissue types present in recipient urine are donor kidney cells, and in case of rejection, a larger number of recipient immune cells would be observed. Extensive in-silico simulation was used to tune the sequencing parameters: number of variants and depth of coverage. Sequencing of DNA mixture from 2 healthy individuals showed the method is highly predictive (maximum error < 0.04). We then demonstrated the insignificant impact of familial relationship and ethnicity using an in-house and public database. Lastly, we performed deep DNA sequencing of urinary cell pellets from 32 biopsy-matched samples representing two pathology groups: acute rejection (AR, 11 samples) and acute tubular injury (ATI, 12 samples) and 9 samples with no pathology. We found a significant association between the donor/recipient-specific DNA fraction in the two pathology groups compared to no pathology (P = 0.0064 for AR and P = 0.026 for ATI). We conclude that deep DNA sequencing of urinary cells from kidney allograft recipients offers a noninvasive means of diagnosing acute pathologies in the human kidney allograft.

Validation of a noninvasive prognostic signature for allograft failure following BK virus associated nephropathy.

Identifying kidney transplant recipients at risk for graft failure following BK virus nephropathy (BKVN) may allow personalization of therapy. We have reported that a noninvasive composite signature of urinary cell level of plasminogen activator inhibitor-1(PAI-1) mRNA and serum creatinine level, measured at the time of BKVN diagnosis, is prognostic of graft failure. In this investigation, we determined whether the composite signature is prognostic of graft failure in an independent cohort of 25 patients with BKVN. Of the 25 patients, 8 developed graft failure and 17 did not. We measured urinary cell levels of PAI-1 mRNA, 18S rRNA, and BKV VP1 mRNA at the time of BKVN diagnosis and evaluated clinical parameters including Banff pathology scores, acute rejection, and graft function. The area under the receiver operating characteristic curve for the noninvasive composite signature was 0.95 (P < .001) for prognosticating graft failure. The previously reported threshold of -0.858 predicted graft failure with a sensitivity of 75% and a specificity of 94%. Our current study validates the use of composite signature and the threshold of -0.858 to identify those at risk for graft failure following BKVN diagnosis, and supports future studies utilizing the composite signature score to personalize treatment of BKVN.

Urinary cell transcriptomics and acute rejection in human kidney allografts.

BACKGROUNDRNA sequencing (RNA-Seq) is a molecular tool to analyze global transcriptional changes, deduce pathogenic mechanisms, and discover biomarkers. We performed RNA-Seq to investigate gene expression and biological pathways in urinary cells and kidney allograft biopsies during an acute rejection episode and to determine whether urinary cell gene expression patterns are enriched for biopsy transcriptional profiles.METHODSWe performed RNA-Seq of 57 urine samples collected from 53 kidney allograft recipients (patients) with biopsies classified as acute T cell-mediated rejection (TCMR; n = 22), antibody-mediated rejection (AMR; n = 8), or normal/nonspecific changes (No Rejection; n = 27). We also performed RNA-Seq of 49 kidney allograft biopsies from 49 recipients with biopsies classified as TCMR (n = 12), AMR (n = 17), or No Rejection (n = 20). We analyzed RNA-Seq data for differential gene expression, biological pathways, and gene set enrichment across diagnoses and across biospecimens.RESULTSWe identified unique and shared gene signatures associated with biological pathways during an episode of TCMR or AMR compared with No Rejection. Gene Set Enrichment Analysis demonstrated enrichment for TCMR biopsy signature and AMR biopsy signature in TCMR urine and AMR urine, irrespective of whether the biopsy and urine were from the same or different patients. Cell type enrichment analysis revealed a diverse cellular landscape with an enrichment of immune cell types in urinary cells compared with biopsies.CONCLUSIONSRNA-Seq of urinary cells and biopsies, in addition to identifying enriched gene signatures and pathways associated with TCMR or AMR, revealed genomic changes between TCMR and AMR, as well as between allograft biopsies and urinary cells.

Urinary cell mRNA profiles and differential diagnosis of acute kidney graft dysfunction.

Noninvasive tests to differentiate the basis for acute dysfunction of the kidney allograft are preferable to invasive allograft biopsies. We measured absolute levels of 26 prespecified mRNAs in urine samples collected from kidney graft recipients at the time of for-cause biopsy for acute allograft dysfunction and investigated whether differential diagnosis of acute graft dysfunction is feasible using urinary cell mRNA profiles. We profiled 52 urine samples from 52 patients with biopsy specimens indicating acute rejection (26 acute T cell-mediated rejection and 26 acute antibody-mediated rejection) and 32 urine samples from 32 patients with acute tubular injury without acute rejection. A stepwise quadratic discriminant analysis of mRNA measures identified a linear combination of mRNAs for CD3ε, CD105, TLR4, CD14, complement factor B, and vimentin that distinguishes acute rejection from acute tubular injury; 10-fold cross-validation of the six-gene signature yielded an estimate of the area under the curve of 0.92 (95% confidence interval, 0.86 to 0.98). In a decision analysis, the six-gene signature yielded the highest net benefit across a range of reasonable threshold probabilities for biopsy. Next, among patients diagnosed with acute rejection, a similar statistical approach identified a linear combination of mRNAs for CD3ε, CD105, CD14, CD46, and 18S rRNA that distinguishes T cell-mediated rejection from antibody-mediated rejection, with a cross-validated estimate of the area under the curve of 0.81 (95% confidence interval, 0.68 to 0.93). Incorporation of these urinary cell mRNA signatures in clinical decisions may reduce the number of biopsies in patients with acute dysfunction of the kidney allograft.

Noninvasive prognostication of polyomavirus BK virus-associated nephropathy.

BACKGROUND: BK virus-associated nephropathy (BKVN) is associated with an increased risk of graft failure. METHODS: Levels of mRNAs encoding proteins implicated in inflammation and fibrosis were measured in urine collected at the time of biopsy diagnosis of BKVN in 29 kidney graft recipients and analyzed for prognosticating graft failure using logistic regression. RESULTS: Ten of 29 BKVN patients had graft failure within 36 months of BKVN diagnosis and the remaining 19 patients did not. Serum creatinine level, BKVN biopsy stage, and urinary cell levels of mRNA for plasminogen activator inhibitor (PAI)-1, vimentin, tissue inhibitor of metalloproteinase-1, fibronectin, granzyme B, or perforin were associated with graft failure. A combination of PAI-1 mRNA level, BKVN biopsy stage, and creatinine level (P = 0.0015, by logistic regression) and a combination of PAI-1 mRNA and creatinine levels (P = 0.001) were the best-fitting models for prognosticating graft failure, and PAI-1 mRNA level was the only independent predictor (odds ratio, 2.8; 95% confidence interval [CI], 1.1-6.8; P = 0.03) by multivariable analysis. The area under the curve for the combination of PAI-1 mRNA, biopsy, and creatinine was 0.92 (95% CI, 0.80-1.0; P < 0.001) by receiver operating characteristic curve analysis, and the area under the curve was 0.92 (95% CI, 0.80-1.0; P < 0.001) for the combination of PAI-1 mRNA and creatinine. Graft outcome was correctly predicted in 27 of 29 BKVN patients by either model. CONCLUSION: Urinary cell level of PAI-1 mRNA, measured at the time of BKVN diagnosis, is an independent prognosticator of graft failure and a prediction model of serum creatinine and PAI-1 mRNA is as accurate as the model that includes the biopsy result.

Urinary cell levels of mRNA for OX40, OX40L, PD-1, PD-L1, or PD-L2 and acute rejection of human renal allografts.

BACKGROUND: The positive costimulatory proteins OX40 and OX40L and negative regulatory proteins programmed death (PD)-1, PD ligand 1, and PD ligand 2 have emerged as significant regulators of acute rejection in experimental transplantation models. METHODS: We obtained 21 urine specimens from 21 renal allograft recipients with graft dysfunction and biopsy-confirmed acute rejection and 25 specimens from 25 recipients with stable graft function and normal biopsy results (stable). Urinary cell levels of mRNAs were measured using real-time quantitative polymerase chain reaction assays, and the levels were correlated with allograft status and outcomes. RESULTS: Levels of OX40 mRNA (P<0.0001, Mann-Whitney test), OX40L mRNA (P=0.0004), and PD-1 mRNA (P=0.004), but not the mRNA levels of PD ligand 1 (P=0.08) or PD ligand 2 (P=0.20), were significantly higher in the urinary cells from the acute rejection group than the stable group. Receiver operating characteristic curve analysis demonstrated that acute rejection is predicted with a sensitivity of 95% and a specificity of 92% (area under the curve=0.98, 95% confidence interval 0.96-1.0, P<0.0001) using a combination of levels of mRNA for OX40, OX40L, PD-1, and levels of mRNA for the previously identified biomarker Foxp3. Within the acute rejection group, levels of mRNA for OX40 (P=0.0002), OX40L (P=0.0004), and Foxp3 (P=0.04) predicted acute rejection reversal, whereas only OX40 mRNA levels (P=0.04) predicted graft loss after acute rejection. CONCLUSION: A linear combination of urinary cell levels of mRNA for OX40, OX40L, PD-1, and Foxp3 was a strong predictor of acute rejection in human renal allograft biopsies. This prediction model should be validated using an independent cohort of renal allograft recipients.

Validation of noninvasive diagnosis of BK virus nephropathy and identification of prognostic biomarkers.

BACKGROUND: BK virus nephropathy (BKVN) may cause renal allograft dysfunction and failure. The gold standard test is kidney biopsy, which is invasive and costly. A noninvasive, accurate biomarker for diagnosis of BKVN and prognostication of allograft function after BKVN infection may improve allograft survival. METHODS: We tested the diagnostic accuracy of our previously reported cutoff value of 6.5x10(5) BKV viral capsid protein 1 (VP-1) mRNA/ng RNA in urinary cells (Ding et al., Transplantation 2002; 74: 987) using an independent cohort (n=89). We also examined whether urinary cell mRNA profiles obtained at the time of BKVN diagnosis identified patients at risk of subsequent decline in graft function. RESULTS: BKVN was accurately diagnosed (sensitivity of 100% and specificity of 97%) using our previously reported cutoff value. Levels of granzyme B (GB) mRNA (P=0.002) and proteinase inhibitor (PI)-9 mRNA (P=0.01) in urinary cells were higher in BKVN patients with a subsequent decline in renal function (n=8) compared with patients with stable function (n=10), and were positively associated (GB, P=0.01; PI-9, P=0.04) with rise in serum creatinine from the time of BKVN diagnosis to 12 months after diagnosis. GB levels in the BKVN patients with a decline in renal function were similar to those in the acute rejection group (n=11, P>0.05), but higher than the normal biopsy group (n=36, P<0.001); levels in BKVN patients with stable function were lower than those in the acute rejection group (P<0.01) and not significantly different from the normal biopsy group (P>0.05). CONCLUSIONS: Noninvasive diagnosis of BKVN and prognostication of renal allograft function after BKVN diagnosis are feasible by measurement of transcripts for BKV viral capsid protein 1 (VP-1), GB, and PI-9 in urine.

Epidemiology of BK virus in renal allograft recipients: independent risk factors for BK virus replication.

BACKGROUND: Identification of risk factors for BK virus (BKV) replication may improve transplant outcome. We investigated the impact of immunosuppressive drugs on the prevalence of BKV replication in recipients of human renal allografts. METHODS: One hundred twenty renal allograft recipients were studied prospectively at 1, 3, and 6 months posttransplantation to identify risk factors for BKV replication. BKV replication was quantified by measurement of urinary cell BKV VP1 mRNA levels using BKV specific primers and TaqMan probe in a real-time quantitative polymerase chain reaction assay. Levels of urinary cell mRNA for granzyme B, CD103, and transforming growth factor-beta1 were measured to ascertain whether BKV replication is associated with an inflammatory signature. RESULTS: The prevalence of BKV replication increased over time and was highest at 6 months compared with 1 or 3 months posttransplantation (P<0.001). A logistic regression model analysis demonstrated that steroid maintenance therapy (odds ratio: 8.3, P=0.003) and induction with rabbit anti-human thymocyte globulin (odds ratio: 5.8, P=0.008) were independent risk factors for BKV replication. Neither mycophenolate mofetil dose nor tacrolimus dose or trough levels were different between those with or without BKV replication. The development of acute rejection or antirejection treatment with methylprednisolone did not increase the risk of BKV replication. BKV replication was associated with heightened levels of urinary cell mRNA for granzyme B (P<0.002), CD103 (P<0.005) but not for transforming growth factor-beta1 (P>0.05). CONCLUSIONS: Steroid maintenance therapy and induction with antithymocyte globulin are independent risk factors for BKV replication in renal allograft recipients treated with tacrolimus and mycophenolate mofetil.

Messenger RNA for FOXP3 in the urine of renal-allograft recipients.

BACKGROUND: The outcome of renal transplantation after an episode of acute rejection is difficult to predict, even with an allograft biopsy. METHODS: We studied urine specimens from 36 subjects with acute rejection, 18 subjects with chronic allograft nephropathy, and 29 subjects with normal biopsy results. Levels of messenger RNA (mRNA) for FOXP3, a specification and functional factor for regulatory T lymphocytes, and mRNA for CD25, CD3epsilon, perforin, and 18S ribosomal RNA (rRNA) were measured with a kinetic, quantitative polymerase-chain-reaction assay. We examined associations of mRNA levels with acute rejection, rejection reversal, and graft failure. RESULTS: The log-transformed mean (+/-SE) ratio of FOXP3 mRNA copies to 18S ribosomal RNA copies was higher in urine from the group with acute rejection (3.8+/-0.5) than in the group with chronic allograft nephropathy (1.3+/-0.7) or the group with normal biopsy results (1.6+/-0.4) (P<0.001 by the Kruskal-Wallis test). FOXP3 mRNA levels were inversely correlated with serum creatinine levels measured at the time of biopsy in the acute-rejection group (Spearman's correlation coefficient = -0.38, P=0.02) but not in the group with chronic allograft nephropathy or the group with normal biopsy results. Analyses of receiver-operating-characteristic curves demonstrated that reversal of acute rejection can be predicted with 90 percent sensitivity and 73 percent specificity with use of the optimal identified cutoff for FOXP3 mRNA of 3.46 (P=0.001). FOXP3 mRNA levels identified subjects at risk for graft failure within six months after the incident episode of acute rejection (relative risk for the lowest third of FOXP3 mRNA levels, 6; P=0.02). None of the other mRNA levels were predictive of reversal of acute rejection or graft failure. CONCLUSIONS: Measurement of FOXP3 mRNA in urine may offer a noninvasive means of improving the prediction of outcome of acute rejection of renal transplants.