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Published ahead of print on May 11, 2005
J Am Soc Nephrol 16: 1542-1548, 2005
© 2005 American Society of Nephrology
doi: 10.1681/ASN.2005020210
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On the Intraoperative Molecular Status of Renal Allografts after Vascular Reperfusion and Clinical Outcomes

Yingyos Avihingsanon*, Naili Ma*, Martha Pavlakis*, W. James Chon*, Marc E. Uknis{dagger}, Anthony P. Monaco{dagger}, Christiane Ferran{dagger}, Isaac Stillman{ddagger}, Asher D. Schachter§,||, Christina Mottley*, Xin Xiao Zheng* and Terry B. Strom*,{dagger}

* Department of Medicine, {dagger} Department of Surgery, and {ddagger} Department of Pathology, Beth Israel Deaconess Medical Center and Harvard Medical School; § Department of Pediatrics, Children’s Hospital and Harvard Medical School; and || Children’s Hospital Informatics Program, Children’s Hospital Boston

Address correspondence to: Dr. Terry B. Strom, Transplant Research Center, Beth Israel Deaconess Medical Center, Harvard Institutes of Medicine-1; Room 1026, 77 Avenue Louis Pasteur, Boston MA 02115. Phone: 617-667-0850; Fax: 617-667-0923; E-mail: tstrom{at}bidmc.harvard.edu


   Abstract
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 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
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Many hypothesize that subtle inflammation and immune activity detected in the intraoperative period are linked to adverse postkidney transplant clinical outcomes. To this end, renal allografts were analyzed for expression of pro-inflammatory, inflammation-induced adhesion molecules, immune activation as well as anti-apoptotic genes expressed 15 min after vascular reperfusion (zero-hour) to determine whether this analysis can aid in predicting the occurrence of delayed graft function (DGF), acute rejection (AR), and the quality of graft function at 6 mo. Intraoperative biopsies were obtained from 75 consecutively performed renal allografts in which consent was obtained 15 min after vascular reperfusion. These biopsies were analyzed by quantitative real-time PCR for transcription of 15 select genes and by standard histopathology. Posttransplant clinical outcomes were also analyzed in respect to intraoperative transcriptional profiles and clinical parameters available at the time of transplantation. This study demonstrates that a limited and hypothesis-driven PCR-based transcriptional profile of the zero-hour kidney biopsy predicts posttransplant clinical outcomes including DGF, early AR, and the quality of renal function 6 mo posttransplantation. For some clinical endpoints, the combined use of molecular analysis and established clinical indicators available at the time of transplantation further enhances the quality of prognosis. The transcriptional profiling data provide absolutely essential data to the predictive models, particularly with respect to AR and renal function 6 mo posttransplantation.


   Introduction
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 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
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After renal transplantation, the clinical outcome is dependent upon various recipient factors and upon donor characteristics such as donor brain death, prolonged cold ischemia, age, sex, and race (1–9). Nonetheless, delayed graft function (DGF) and acute rejection (AR) are inter-related posttransplant complications that can contribute to impaired intermediate- and long-term graft function and survival (1,2,5–11). We have tested the hypothesis that molecular evidence of intragraft inflammation and active T cell immunity present intraoperatively at the zero-hour and detected via PCR-based transcriptional profiling are linked to adverse posttransplant clinical outcomes such as DGF, AR within 3 mo following transplantation, and the quality of graft function 6 mo posttransplantation. A predictive role for suboptimal expression of anti-apoptotic genes, some expressed in response to inflammation (hemoxygenase-1 and A20) (12–17) and others not triggered by inflammation, was also investigated. In short, identification of pertinent molecular markers at the zero-hour provides a keen insight into future clinical outcomes.


   Materials and Methods
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 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
Study Subjects
We studied 75 renal allografts (31 cadaver and 44 living donor) and the clinical course of transplant recipients. Transplants were performed at Beth Israel Deaconess Medical Center. The Beth Israel Deaconess Medical Center Committee on Clinical Investigations approved the study. Each patient gave informed consent. Patients with a bleeding diathesis or on anticoagulant therapy were excluded from the study.

Immunosuppressive Regimen
The intraoperative immunosuppressive regimen consisted of 1.5 mg/kg of thymoglobulin (Sangstat, Fremont, CA) given in a slow infusion begun before the transplant procedure or 20 mg of anti-CD25 mAb (Simulect; Novartis, East Hanover, NJ) and solumedrol 500 mg intravenously. Maintenance immunosuppressive regimens consisted of the calcineurin inhibitors tacrolimus (Fujisawa, Deerfield, IL) or cyclosporine (Novartis), prednisone, and mycophenolate mofetil (Roche, Nutley, NJ). Five patients received sirolimus (Wyeth-Ayerst, St. David’s, PA), prednisone, and mycophenolate mofetil.

Renal Biopsy
An intraoperative allograft wedge biopsy was performed 15 minutes after vascular reperfusion. One half of the biopsy was subjected to standard histopathologic analysis, and the rest was immediately snap-frozen in liquid nitrogen and stored in a –80°C refrigerator before RNA isolation.

Isolation of RNA
Total RNA was isolated from tissue homogenate samples with a commercial kit (RNeasy kit; Qiagen Inc, Chatworth, CA) (17). Reverse transcription of 1 mg of RNA was performed using multiscribed reverse transcriptase enzyme (PE Applied Biosystems, CA).

Quantification of Gene Expression by Real-Time Quantitative PCR
Real-time PCR was performed using the ABI 7700 sequence detector system (Applied Biosystems, Foster City, CA). PCR amplification was performed in a total volume of 25 ml containing 5 ml of cDNA sample, 0.6 mM of forward and reverse primer, 0.2 mM of TaqMan probe and 12.5 ml of TaqMan Universal PCR mastermix (Applied Biosystems). Amplification was performed using primer and hybridization probe sets of the listed targeted mRNAs (Table 1). To quantify the levels of mRNA, we normalized expression of the target genes to 18s ribosomal RNA (1).


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  		Table 1. List of studied genes

 
Clinical Variables
The clinical data included recipient age, race, prior transplant(s), type of induction therapy, warm ischemic time (WIT), cold ischemic time (CIT, for cadaveric donor transplants only), donor type, donor age, and donor race (Table 2).


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  		Table 2. Prediction of clinical outcomes

 
Criteria for Categorizing the Clinical Outcomes
Clinical data were retrieved from computerized medical records and chart reviews (Table 2). DGF was defined as a requirement for dialysis during the first week posttransplantation in the absence of AR, vascular complications, or urinary tract obstruction. The diagnosis of AR was confirmed by pathologic examination of the graft biopsy. Poor graft function was grossly defined as a serum creatinine level ≥2 mg/dl at 6 mo posttransplantation.

Statistical Analyses
RNA expression data were first normalized (1). Next, simple logistic regression was performed for each variable on each of the three clinical outcomes of interest. For each outcome, each variable that demonstrated a P value <0.05 is listed in Table 2, along with the variable’s R value (18). Multiple logistic regression was then performed to determine combinations of time-zero intragraft gene expression patterns and clinical variables that correlate with each outcome of interest, using only those genes and clinical variables that demonstrated individual P values <0.05, as described above. For the purposes of training artificial neural nets (ANN), missing data points were imputed from the five nearest neighbors (19), as measured by Euclidean distance. Of 2700 data points, only 260 (9.6%) were missing. Missing data points were evenly distributed within each outcome (AR versus no rejection, DGF versus no DGF, and poor 6-mo outcome versus good 6-mo outcome). ANN were trained for each outcome (18), and cross-validation was performed. The area under the receiver-operator characteristic curve (ROC AUC) was then calculated to determine each ANN's performance for each outcome.


   Results
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 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
Patient Characteristics and Clinical Data
Of the 75 studied patients, 10 developed DGF, 10 experienced AR within 3 mo posttransplantation, and 10 had poor graft function at 6 mo posttransplantation. Five patients died with a functioning graft during the follow-up period. Two live kidney recipients died, one from cardiovascular disease and one from lymphoproliferative disease. Three cadaver kidney recipients died as a result of sepsis (Table 2).

Zero-Hour Intragraft Gene Expression, Clinical Variables, and Prediction of DGF
Cadaver donor type, prolonged WIT and CIT, an abundance of transcripts for each of select pro-inflammatory cytokines (TNF-{alpha}, TGF-{beta}, IL-10), the early T cell immune activation marker CD25, the pro-inflammatory cytokine-induced intercellular adhesion molecule (ICAM)-1 adhesion (e.g., TNF-{alpha}), and A20 molecules detected in zero-hour renal allograft biopsies were individually predictive for DGF (Table 2). In particular, TNF-{alpha} gene expression was highly predictive for DGF (R2 = 0.68, P < 0.001). Overall, these data support the hypothesis that evidence of intragraft inflammatory cytokines plus expression of inflammation-induced ICAM-1 and A20 molecules and early T cell immunity at the zero-hour are linked to DGF. The multiple logistic regression model that includes transcriptional events and pertinent clinical variables identified patients at very high risk for DGF (R2 = 0.98). Overall gene expression for TNF-{alpha} and other pro-inflammatory cytokines was far more abundant in cadaver than in living donor grafts at the zero-hour (data not shown). Only one recipient of a living donor graft, with a prolonged, intraoperative, warm ischemia experienced DGF. The zero-hour graft biopsy did reveal amplified gene expression of TNF-{alpha} and other pro-inflammatory cytokines that exceeded gene expression for these cytokines noted in any other recipient of a living donor graft. The presence of a cytomegalovirus carrier state or infusion of induction therapy, either thymoglobulin or anti-CD25 mAb, during the intraoperative period did not alter the pattern of gene expression in the zero-hour biopsy.

The ROC-AUC values for the ANN for DGF, including all significant molecular and clinical variables, the significant clinical variables alone, and the significant gene expression variables only were 1.0, 1.0, and 0.87, respectively, demonstrating that either clinical variables or transcriptional profiling can predict the occurrence of DGF with very high sensitivity and specificity (Figure 1, A, D, and G). The incidence of DGF was 14.5% during the study period. Analysis of the eight variables reported here therefore precisely identifies patients at risk for DGF.



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  		Figure 1. (A, B, C) Receiver operator characteristic (ROC) curves for artificial neural networks (ANN) for delayed graft function (DGF), acute rejection (AR), and 6-mo function, respectively, using clinical and gene variables deemed significant by simple logistic regression. (D, E, F) ROC curves for ANN for DGF, AR, and 6-mo function, respectively, using only clinical variables deemed significant by simple logistic regression. (G, H, I) ROC curves for ANN for DGF, AR, and 6-mo function, respectively, using only gene expression variables deemed significant by simple logistic regression. ROC AUC indicates receiver operator characteristic area under the curve; FPR, false positive rate = 1 – specificity.

 
Zero-Hour Intragraft Gene Expression, Clinical Variables, and Prediction of AR Episodes
Cadaver donor type, graft ischemic times, occurrence of DGF and an abundance of transcripts for pro-inflammatory cytokines inflammatory cytokines (TNF-{alpha}, TGF-{beta}, IL-6), activated T cells (CD3 and CD25), and the inflammation-induced ICAM-1 adhesion and hemoxygenase (HO)-1 molecules detected in zero-hour individually predicted increased risk of AR (Table 2). The data may suggest that patients with heretofore undetected forms of T cell anti-donor immunity (gene expression of CD3 and CD25) present at the time of transplantation are at heightened risk for rejection.

It has been hypothesized that injury to the donor kidney present at the organ harvest and heightened injury during vascular reperfusion may serve as a stimulus for rejection (20,21). Indeed, DGF and AR are inter-related complications that can lead to impaired long-term graft function and survival (1,2,5,6,8). Ischemia/reperfusion injury in several settings initiates an inflammatory response leading to increased host immunologic reactivity (20–24). It is notable that a near-identical pattern of intragraft gene expression events including amplified expression of pro-inflammatory cytokines (TNF-{alpha}, TGF- {beta}, and either IL-10 or IL-6), T cell activation/T cell (CD25/CD3), and expression of inflammation-induced (ICAM-1, HO-1, or A20) molecules at the zero-hour is linked to both DGF and AR (Table 2).

The multiple logistic model that included the gene expression events cited above and clinical variables were highly predictive of AR during the first 3 mo (R2 = 0.88). The ROC AUC values for the ANN for AR, including all significant variables, the significant clinical variables only, and the significant gene variables alone were 0.73, 0.56, and 0.77, respectively. Thus, analysis of the abundance of select immune- and inflammation-related transcripts can predict the occurrence of AR with high sensitivity and specificity (Table 2; Figure 1, B, E, and H). By comparison, the incidence of AR was only 12% during the study period.

Zero-Hour Intragraft Gene Expression, Clinical Variables, and Prediction of 6-Month Graft Function
With respect to 6-mo graft function, African-American recipient race, cadaver organ donation, increased donor age, an episode of AR during the first three transplant months, the degree of HLA mismatching, increased CD25, and decreased Bcl-Xl gene expression individually predicted risk for poor graft function (serum creatinine >2.0 mg/ dl) 6 mo posttransplantation (Table 2). Because AR during the first 3 mo is not a "time-zero" event, multiple logistic regression analyses were performed with and without AR as a predictor. The multiple logistic model, which included all five clinical variables, increased expression of the T cell activation gene CD25, and decreased expression of the cytoprotective Bcl-Xl gene, an anti-apoptotic gene that unlike HO-1 and A20 is not triggered by pro-inflammatory cytokines, predicted with modest accuracy for poor graft function at 6 mo posttransplantation (R2 = 0.48). Evidence of early T cell active immunity unopposed by cytoprotective Bcl-Xl, anti-apoptotic, cytoprotective molecules that are not elicited by pro-inflammatory cytokines predicts for compromised graft function at 6 mo.

Removing AR, as AR is not a zero-hour event, from the multiple logistic regression model did not change the model’s performance for predicting graft function 6 mo posttransplantation (R2 = 0.48). The ROC-AUC values for the ANN for 6-mo graft function, including all significant variables except AR, the significant clinical variables only (except AR), and the two significant gene variables alone were 0.84, 0.73, and 0.78, respectively, demonstrating that gene expression values for only two genes can predict the quality of 6-mo function with high sensitivity and specificity (Figure 1C, 1F, 1I), exceeding the accuracy achieved with the clinical variable model.

Histologic Analysis of Zero-Hour Biopsies and Prediction of Clinical Outcomes
The histology of zero-hour biopsies was evaluated by standard histology without knowledge of clinical outcomes. At most, minor changes were noted. Rare focal infiltration of monocytes/macrophages was seen in five samples. Two of these five samples were from patients with DGF. Glomerulosclerosis involving less than 10% of glomeruli was found in three samples. One of these samples was obtained from a patient who ultimately had poor graft function at 6 mo.


   Discussion
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 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
A means to identify patients at risk to adverse clinical endpoints at the time of engraftment might enable individualized care in the critical early posttransplant period. As both early inflammation and preformed immunity may produce super-additive injury (24), we hypothesized that robust expression of immune activation and pro-inflammatory transcripts at the zero-hour (15 min postreperfusion) would aid prediction poor clinical outcomes. Thus we have targeted a panel of gene expression events for analysis in the zero-hour biopsy that prominently includes pro-inflammatory cytokines whose expression enhances activation of cytopathic tissue injuring T cells (24). Using a targeted, hypothesis-driven PCR-based transcription profile of the kidney transplant, our data provides support and molecular texture to the view that the quality of the donor kidney at the zero-hour has an impact upon later occurring clinical outcomes (25). Our data support the hypothesis that subtle evidence of intragraft inflammation and T cell immunity at the zero-hour is closely linked to poor clinical outcomes such as DGF and AR. It is interesting that expression of CD3, a T cell lineage marker, and CD25, a transcript expressed by alloactivated (26) and a small population of regulatory T cells (27), but not by resting T cells, 15 mins after reperfusion is predictive of adverse outcomes. As activation of naïve T cells does not stimulate expression of CD25 within 15 mins (26), we suspect that the expression of CD3 and CD25 may be due to the detrimental presence of preactivated alloreactive T cells in these recipients. The detrimental impact of DGR and AR, in turn, may be modified by the resilience of the graft imparted by expression of certain anti-apoptotic tissue protective genes that are not induced by inflammation (Table 2).

For some clinical endpoints, the combined use of molecular analysis and established clinical indicators further enhances the quality of prognosis. In the absence of pretransplant clinical information, the transcriptional profiling data provide absolutely essential data to the predictive models, particularly with respect to AR and renal function 6 mo posttransplantation. The data also supports the hypothesis that evidence of concurrent intraoperative intragraft inflammation plus active T cell immunity leads to the early complications of DGF and AR.

In the future, identification of renal transplant recipients at high risk for DGF, AR, or chronic allograft nephropathy by molecular analysis of the zero-hour biopsy in combination with time-zero clinical variables may facilitate individualized, optimized care. Linkage between the molecular analysis of the allograft and ischemia-reperfusion, DGF, AR, and long-term graft function has been demonstrated through clinical studies. Our study may give some insight into the molecular nature and pathogenesis of these interactions and identify potential therapeutic targets. Because of the inability to use immunostaining or proteomic methods to readily and quantitatively analyze a wide range of protein expression events in each biopsy, we utilized a transcriptional profiling approach. Although the transcriptional profile associated with adverse clinical events may be for prognosis, as concluded herein, the data in the absence of protein expression data do not fully indicate that TNF-{alpha} and other pro-inflammatory cytokines can be considered important potential therapeutic targets.


   Acknowledgments
 
This work was supported by grants: American Society of Transplantation and Chulalongkorn University Hospital, Thailand (to Y.A.); National Institutes of Health (NIH) grant K23 RR16080 (to A.D.S.); NIH grant 1 UO1 AI46134 (to T.B.S.); and support from the Immune Tolerance Network.

We would like to acknowledge Drs. David Shaffer and Marc Uknis for providing intraoperative biopsy specimens, Drs. Candace Wang and Christian Mix for helping in communicating with the patients and aiding the acquisition of clinical data, Dr. Takashi Maki for providing the HLA-typing data, Dr. S. Ananth Karumanchi for sharing his critical information, Prof. H.D. Volk and Dr. M. Suthanthiran for aid in probe design, and Christina Mottley for expert technical help with PCR analysis.


   Footnotes
 
Published online ahead of print. Publication date available at www.jasn.org.


   References
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 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

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