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A major challenge for kidney transplantation is balancing the need for immunosuppression to prevent rejection, while minimizing drug-induced toxicities.
We used DNA microarrays (HG-U95Av2 GeneChips, Affymetrix) to determine gene expression profiles for kidney biopsies and peripheral blood lymphocytes (PBLs) in transplant patients including normal donor kidneys, well-functioning transplants without rejection, kidneys undergoing acute rejection, and transplants with renal dysfunction without rejection. We developed a data analysis schema based on expression signal determination, class comparison and prediction, hierarchical clustering, statistical power analysis and real-time quantitative PCR validation. We identified distinct gene expression signatures for both biopsies and PBLs that correlated significantly with each of the different classes of transplant patients. This is the most complete report to date using commercial arrays to identify unique expression signatures in transplant biopsies distinguishing acute rejection, acute dysfunction without rejection and well-functioning transplants with no rejection history. We demonstrate for the first time the successful application of high density DNA chip analysis of PBL as a diagnostic tool for transplantation. The significance of these results, if validated in a multicenter prospective trial, would be the establishment of a metric based on gene expression signatures for monitoring the immune status and immunosuppression of transplanted patients.
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Kidney transplantation has extended and improved the quality of life for the majority of patients with end stage renal disease. Most transplants involve genetically nonidentical donor-to-recipient combinations. As a consequence the immune response is a major impediment to successful graft survival, necessitating lifelong treatment with potent immunosuppressive drugs. These drugs suppress the host immune system in a nonspecific manner and have many side-effects including, but not limited to, increased risk of life-threatening infections and cancer. Another key point is that responses of the donor organ itself are also major contributors to post transplant events. Despite recent reductions in the incidence of acute rejection, chronic allograft nephropathy and immunosuppressive drug side-effects are still major causes of graft loss and patient morbidity. In this context, it is essential to further our understanding of the immune system and the transplanted organ to both immune and non-immune mechanisms of injury.
High-density microarray technology provides one means to measure the differential expression of hundreds to thousands of genes simultaneously. While its basic applications in gene discovery are well established, high-density microarrays also have promise as a clinical tool. For example, this technology has been used with different cancers to predict prognosis and response to therapy (1–3) and in multiple sclerosis to identify inflammatory genes in brain lesions (4). Several publications have examined gene expression in kidney transplant patients using quantitative PCR (5,6), and demonstrated that for a very small set of immunologically relevant gene transcripts good correlations with acute rejection and clinical outcomes were present. Studies in small animal transplant models using DNA microarrays supported the potential use of this technology in a clinical setting (7,8). A small study of kidney transplant patients with acute rejection demonstrated the up-regulation of four genes consistently and two transcripts down-regulated (9). Recently the experience using the Stanford Lymphochip cDNA glass slide array (10) with kidney transplant biopsies of 50 pediatric patients defined three different gene expression signatures for acute rejection that correlated with graft survival (11). Finally, a study using the Hu95Av2 Affymetrix GeneChip for kidney biopsies performed 6 months post transplant identified 10 genes for which expression correlated with the risk of developing chronic rejection defined by biopsy at 12 months post transplant (12).
In the present study we extended the work previously carried out in this field. We developed a data analysis strategy based on expression signal determination, class comparison and prediction, hierarchical clustering, statistical power analysis and real-time quantitative PCR validation. We determined gene expression profiles in biopsies obtained from normal kidneys at the time of their recovery for living donor transplantation, creating a unique control population for gene expression profiling of any renal disease including transplanted kidneys. This study includes a collection of profiles for transplant patients with normal graft function on full immunosuppression compared with transplant patients with biopsy-documented acute rejection. In addition, we provide the first gene expression profile information on patients with acute kidney transplant dysfunction who did not demonstrate evidence of histological acute rejection by biopsy. Finally, this is the first report of high-density DNA array gene expression profiles of peripheral blood lymphocytes (PBLs) from each of these classes of patients.
Hierarchical clustering of samples and statistical analysis of individual gene expression signals demonstrated significant differences in the profiles of biopsies and PBLs from patients with acute rejection and acute dysfunction without rejection as compared with normal donors and well-functioning transplant patients with no history of rejection. One implication of these results is that gene profiling of PBLs could be used as a minimally invasive surrogate marker for rejection and identify patients with acute dysfunction but without rejection. These data support the hypothesis that the gene expression profiles of PBLs can be used to dynamically monitor the state of the immune response to the transplant. Thus, it may be possible to determine the impact and adequacy of immunosuppression in individual patients at any time post transplant using DNA array technology.
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Patients. Patients signed Cleveland Clinic Foundation-approved IRB consent forms. Kidney biopsies were obtained from nine living donor controls, seven recipients with histologically confirmed acute rejection, five recipients with renal dysfunction without rejection on biopsy, and 10 protocol biopsies carried out more than one year post transplant in patients with good transplant function and normal histology (Table 1). Peripheral blood lymphocytes were obtained from one living kidney donor and seven healthy volunteer blood donor controls, seven recipients with biopsy-proven acute rejection, eight recipients biopsied for renal dysfunction without rejection, and from nine of the 10 recipients who had protocol biopsies carried out more than 1 year post transplant (Table 1). It is important to emphasize that all the acute rejection profiles of transplant biopsies and PBLs are matched to the same patients for all samples. For example, AR3 PBLs are from the patient of biopsy AR3. Evaluation of renal function for living donors included creatinine clearance, protein excretion and renal imaging with ultrasound and angiography. Acute rejection episodes were Banff criteria scored (13) and confirmed by response to anti-rejection therapy. Patients with clinical or laboratory evidence of CMV or other infections were excluded. Immunosuppression comprised a calcineurin inhibitor or sirolimus, with mycophenolate mofetil and steroids. Control biopsies were obtained from the cortex of diuresing kidneys during open-donor nephrectomies. Transplant biopsies were obtained under ultrasound guidance by spring-loaded 15-gauge needles (ASAP Automatic Biopsy, Microvasive, Watertown, MA). Cores went immediately into 1.5 mL of RNALater (Ambion, Austin, TX), and –80°C freezers within 4 h. Peripheral blood (20 mL) was obtained before biopsy, placed on ice and mononuclear cells were isolated within 1 h by density-gradient centrifugation and stored in RNALater at –80°C.
Table 1. Clinical and demographic data of patients entered into the study
|Patient ID||BX||PBL||Age||Sex||Immunosuppression||Histopathology||LD/CAD||Scr (mg/dL)||Days post TX|
|C1||•|| ||38||Female|| ||0.8|| |
|C2||•|| ||42||Male|| ||0.9|| |
|C3||•|| ||35||Female|| ||0.6|| |
|C4||•|| ||39||Female|| ||0.9|| |
|C5||•||•||39||Male|| ||1.2|| |
|C6||•|| ||44||Male|| ||0.8|| |
|C7||•|| ||36||Male|| ||1.2|| |
|C8||•|| ||35||Female|| ||0.8|| |
|C9||•|| ||50||Female|| ||0.6|| |
|TX1||•|| ||51||Male||CsA/MMF/P||Normal||CAD||1.5|| 932|
|TX2||•|| ||56||Male||CsA/MMF/P||Normal||LD||1.3|| 911|
|TX3||•|| ||52||Male||CsA/MMF/P||Normal||CAD||1.2|| 902|
|TX4||•|| ||31||Female||CsA/MMF/P||Normal||LD||1.1|| 651|
|TX5||•|| ||53||Female||CsA/MMF/P||Normal||LD||1.1|| 689|
|TX6||•|| ||32||Male||CsA/MMF/P||Normal||LD||1.6|| 776|
|TX7||•|| ||46||Female||CsA/MMF/P||Normal||CAD||1.2|| 713|
|TX8||•|| ||61||Male||CsA/MMF/P||Normal||CAD||0.9|| 733|
|TX9||•|| ||44||Male||CsA/MMF/P||Normal||LD||1.8|| 718|
|TX10||•|| ||21||Male||CsA/MMF/P||Normal||CAD||1.5|| 674|
|TXPBL1|| ||•||38||Male||CsA/MMF/P|| ||CAD||1.4|| 461|
|TXPBL2|| ||•||57||Female||FK/MMF/P|| ||LD||1.3|| 42|
|TXPBL3|| ||•||65||Male||CsA/MMF/P|| ||CAD||1.5|| 213|
|TXPBL4|| ||•||65||Female||FK/MMF/P|| ||CAD||0.8|| 246|
|TXPBL5|| ||•||36||Female||CsA/MMF/P|| ||CAD||1.1||1278|
|TXPBL6|| ||•||68||Male||CsA/MMF/|| ||CAD||1.7|| 376|
|TXPBL7|| ||•||39||Male||SRL/MMF/P|| ||CAD||0.9|| 36|
|TXPBL8|| ||•||61||Female||CsA/MMF/P|| ||CAD||0.9||1491|
|TXPBL9|| ||•||46||Male||SRL/MMF/P|| ||LD||1.2|| 81|
|NR1||•||•||55||Male||CsA/MMF/P||CNI toxicity||LD||1.7|| 456|
|NR2||•||•||38||Male||FK/MMF/P||CNI toxicity||LD||2.3|| 155|
|NR4||•||•||43||Male||CsA/MMF/P||CNI toxicity||CAD||3.8|| 262|
|NR6|| ||•||35||Female||FK/MMF/P||CNI toxicity||CAD||2.6|| 37|
|NR7|| ||•||44||Male||SRL/MMF/P||ATN||CAD||6.3|| 40|
|NR9|| ||•||58||Male||CsA/MMF/P||ATN||CAD||5|| 47|
Frozen biopsy specimens were thawed, poured into 2-mL tissue grinders with 1 mL of Trizol (Invitrogen, Carlsbad, CA) and manually homogenized. Frozen PBLs were thawed and disrupted in 1 mL of Trizol using a 21-gauge needle. Total RNA was isolated from homogenates using chloroform: isopropanol and further purified using an RNeasy column (Qiagen, Valencia, CA) and DNAse-treated (DNA-free, Ambion) to remove genomic DNA. RNA quality was confirmed by electropherograms using an Agilent 2100 BioAnalyzer (Palo Alto, CA). Total RNA yields from 14 consecutive 15-gauge needle biopsies were 14.9 ± 3.9 μG.
For tissue biopsies, standard Affymetrix GeneChip (Santa Clara, CA) protocols were used [affymetrix.com (14)]. RNA extracted from PBLs underwent one additional round of RNA amplification owing to limited RNA yields in the early samples of the study. Amplification was carried out starting with 100 nG of total RNA using the Ambion MEGAscript™ aRNA Amplification Kit following the manufacturer's protocols. All labeled samples were hybridized to HG-U95Av2 GeneChip arrays. GeneChip data were analyzed using Microarray Suite 5.0 (MAS 5.0, Affymetrix) and DNA Chip Analyzer (dChip) (15,16) software using the PM only model. ‘Present’ and ‘Absent’ calls were determined with MAS 5.0. The dChip software used all the Affymetrix.CEL files generated in this study as a training set. BRB ArrayTools (http://linus.nci.nih.gov/BRB-ArrayTools.html) was used to perform hierarchical clustering and class prediction. Statistically significant changes in gene expression were measured with Significance Analysis of MicroArrays (SAM v1.3; 17). Delta values were chosen to minimize the median false discovery rate (FDR) at a level less than one false discovery per gene list. Two additional methods were used to filter the gene list. First, we applied the limit fold change model, which systematically bins genes by signal intensity; those genes within the top 10% of the highest fold changes for each bin were selected (18). Second, MAS 5.0 Present/Absent calls were used to filter the list; we required the majority of calls in the up-regulated group to be ‘Present’.
Real-time quantitative PCR (Q-PCR)
Q-PCR was performed on 15 genes selected for relatively large fold-changes from the list of 65 genes shown in Figure 3B using predesigned primer and probe sets from the Assays-on-Demand Genomic Assays (12 genes) and Assays-by-Design service (three genes) (Applied Biosystems, Foster City, CA). Each assay consisted of two unlabeled PCR primers and a FAM™ dye-labeled TaqMan® MGB probe. The endogenous control, 18S rRNA, was detected with a VIC™ dye-labeled TaqMan® MGB probe. Briefly, cDNA was transcribed from 100 nG total RNA using the ABI cDNA Archive kit (Applied Biosystems). Nine μL of the cDNA reaction was added to 11 μL of Platinum® Quantitative PCR SuperMix-UDG PCR reagent (Invitrogen, Carlsbad, CA) and PCR performed on an ABI Prism 7900HT (Applied Biosystems). All amplifications were carried out in triplicate and threshold cycle (Ct) scores were averaged for calculations of relative expression values. The Ct scores for genes of interest were normalized against Ct scores for the corresponding 18S rRNA control. Relative expression was determined by the following calculation where the amount of target is normalized to an endogenous reference (18S rRNA) and relative to an arbitrary calibrator (the reference class of patients used in the comparison):
Power calculations for application to microarray experiments has been attempted by several research groups (Simon, 2003; Zien, 2003). The basic premise is to determine the variability for measurements of gene expression by standard deviation of the results of multiple samples. While there is not general agreement on a single best method to perform these calculations, the data we had collected to date provided us with real data upon which to make estimates of variability. Variability for a measurement is described in terms of the standard deviation and is the key experimental metric for sample size calculations. In this context, the measurement is the mean signal intensity measured for each gene's probe set on the GeneChip. The variance value (s) was based on the median log2 transformed signal intensities derived from our data on more than 30 experiments using the GeneChips on either transplant biopsy or PBL samples. The next step is to set values for an acceptable alpha error (false-positive rate), beta error (false-negative) and the delta (minimal detectable change that will be confidently determined). We used values of 0.001 (alpha; a), beta (b) of 0.8 and a minimal detectable fold change of 2 (delta; d).
Calculations were performed using the following equation:
Web site data
All the.cel files for the Affymetrix GeneChips used in these studies are available to the public at our TSRI DNA Array Core in MIAME compliant format (URL: http://www.scripps.edu/services/dna_array/). We also provide at this site a series of annotated gene lists including literature references.
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The ability to measure gene expression profiles in kidney transplantation allows us to test several hypotheses that will directly impact on clinical practice. Currently, there is no objective measure for determining the adequacy of immunosuppression, and no objective way of predicting an individual patient's response to therapy. Clinical practice is based on experience with large populations of patients that are empirically individualized by transplant physicians to take into account factors identified as unique to a given patient such as an early acute rejection episode, evidence of drug toxicity, and serial measurements of renal function. There is also a constant pressure to reduce or eliminate drugs to avoid long-term toxicity and cost. Therefore, if gene expression profiling identifies a signature for acute rejection, then a patient on any given immunosuppressive regime could be monitored for that signature as a measure of the adequacy of immunosuppression. In turn, decisions to reduce or eliminate immunosuppressive drugs could be made with a strategy to safely monitor the results before clinically apparent changes in kidney function occur. It may also be possible to improve the safety of new immunosuppressive drugs, particularly in establishing dose responses, and testing the efficacy of combining new agents with existing drug regimes.
The data presented in this study reporting an acute rejection signature for both PBLs and transplant biopsies supports the hypothesis that a prospective approach to monitoring molecular changes in transplant patients could also be used to predict acute rejection. If determining the adequacy of immunosuppression and predicting rejection could be carried out with PBLs alone, then the potential for a minimally invasive monitoring strategy would be realized. Moreover, an important goal of molecular medicine is to develop tools that effectively allow physicians to individualize therapy. However, we understand that an adequately powered prospective clinical trial would be required to test this hypothesis developed with our data and validate such a diagnostic strategy.
Another hypothesis that should be tested is that gene expression signatures can be used to predict chronic allograft nephropathy early enough to alter therapy. In this context, subclinical rejection identified in early protocol biopsies supports the hypothesis that rejection can be present long before evidence of clinical kidney dysfunction emerges (20–23). The results of Scherer et al. support this hypothesis, indicating that gene expression profiles of protocol biopsies at 6 months could predict biopsy changes of chronic rejection at 12 months (12). Therefore, a major question is whether there is a continuum between subclinical acute rejection and chronic allograft nephropathy that represents the mechanistic link between the events determining rejection, tissue injury, and repair. If such a continuum can be defined in molecular terms, then the potential of therapeutic interventions can be tested.
There remain a number of problems with the present approach that must be considered. The heterogeneity of our patient populations, differences in immunosuppressive therapy, and different degrees of rejection all contribute to biological variability in gene expression profiles that will reduce the number of statistically significant genes we have identified. Thus, while our statistical power analysis demonstrates that our group sizes are sufficient to support the conclusions we have made regarding the significance of expression signatures, it does not mean that all the genes that play a significant role in transplantation have been identified. Moreover, much larger sample sizes of patients are required to draw conclusions regarding the correlations between these gene expression signatures and clinical outcomes such as response to antirejection therapy, long-term graft function and survival. In addition, a limitation of the current microarray technology is that the sensitivity and specificity of gene expression profiling is difficult to determine objectively when thousands of genes are studied simultaneously. Of course, the HG-U95Av2 GeneChip used here represents conservatively one-third of what is now considered the full human genome and the technology has already advanced to the latest version, the HG-U133 chip set. Thus, for all these reasons it is certain that many important genes are not included in our lists. One way to address these limitations would be to design the large and prospective trial discussed above and use the latest microarrays with a more complete representation of the human transcriptosome as well as other technologies such as quantitative PCR to validate and extend these studies.
While the clinical impact of gene expression signatures that can predict rejection and monitor immunosuppression is clear, the potential contributions to our basic understanding of transplantation biology are also important to consider. Thus, the ultimate objective of gene expression profiling is to identify specific genes and associate these with specific pathways mediating cellular mechanisms of rejection, tissue injury and repair, immunosuppression and tolerance. Therefore, we have taken care to provide lists organized by both function and specific gene names for all our significant group comparisons. We also have placed all our data files in MIAME format at our web site for public access. However, a key point is that the fields of bioinformatics and systems biology are still in their infancy with respect to taking specific gene sets and reliably establishing biological pathways. Therefore, we have concentrated on establishing the validity of our first hypothesis that gene signatures can be correlated with well characterized clinical phenotypes all established by the current gold standard of a transplant biopsy. Of course, in all these sets there are genes that we recognize and can find literature regarding their biological function and correlation with immune responses and transplantation models of various types. These are provided with annotations at our web site. But there are also many genes and pathways that are presently not fully understood or characterized and some are likely to be misunderstood at the current time.
How are lymphocytes in the peripheral lymphoid compartment influenced by events that occur within the kidney transplant such as antigen recognition and the signaling events responsible for allo-immune activation? Our results demonstrate that PBL gene expression profiles in acute rejection are distinctly different from those of normal controls and from patients with well-functioning transplants. Therefore, acute rejection does influence the gene expression profile of the circulating lymphocyte pool. Moreover, despite the fact that surprisingly we found very little common gene expression between PBLs and kidney biopsies, we did identify a large number of lymphocyte-specific genes in the kidney tissue. One interpretation is that there are compartment-specific differences between the PBLs in the circulation and the subset of lymphocytes that are activated and recruited to the transplant kidney during acute rejection. The significance of these results in the context of monitoring patients after transplantation is that they may explain the failure of more than a decade of work testing PBLs for an array of activation antigens based on findings in rejecting allografts and other immune models. In other words, the activated lymphocytes infiltrating the rejecting allograft are a distinct population compared with the circulating PBL pool. It is possible that the gene expression profile of the PBLs represents the adequacy of immunosuppression such that the rejecting patients reflect the profile of inadequate immunosuppression as compared with the PBLs sampled from patients with well-functioning transplants. Perhaps future drug therapies could be advanced by targeting the genes that are up-regulated in these PBL profiles. Nonetheless, our results do demonstrate that there is a distinct gene expression profile in the PBL pool that correlates with acute rejection and immunosuppression. If these results can be confirmed in a large, prospective trial it would support the use of such profiles as a minimally invasive monitoring strategy for the immunological status of the graft and support the potential of using them to monitor the adequacy of immunosuppression.
One limitation to consider is that we purified PBLs for analysis using a density gradient and performed one round of amplification of the mRNA before the standard labeling procedure. It is known that such physical handling of PBLs can result in ex vivo cell activation and gene induction. Secondly, amplification of RNA transcripts can also bias gene expression measurements. We were consistent in using the same protocol for all PBLs samples studied, both for amplification and processing, such that there should be no class-specific bias in the expression profiles obtained. However, recently several new technologies have been developed that will eliminate this issue by allowing investigators to draw peripheral blood samples directly into preservation solutions that instantly capture the transcriptosome at that time of the draw. Finally with respect to the possibility of RNA amplification introducing bias, it is important to note that a number of studies have been carried out demonstrating consistent gene expression profiles carried out with two and in some instances three rounds of amplification (24,25).
Given that chronic allograft nephropathy is a major cause of transplant dysfunction and loss, another question is the status of the well-functioning kidney transplant. Our results demonstrate that despite good graft function in this group there is a distinct up-regulation of inflammatory/immune response genes in both biopsies and PBLs. One possibility is that there is a continuum of immune activation that defines the status of a transplant at any given time. This activation state is influenced by factors such as the adequacy of immunosuppression, genetics, and environment. We believe that the long-term function of the transplanted kidney is determined by the intersecting effects of both recipient and donor genetics. Namely, the nature of the recipient's immune response integrated with the donor organ's response to tissue injury, including the impact of nephrotoxic drugs. Theoretically, it should be possible to distinguish genes expressed by the donor organ from genes expressed by the host's infiltrating cells using techniques such as laser capture microdissection.
In conclusion, we have developed a strategy for integrating a number of gene expression profiling and supervised and unsupervised statistical tools to generate lists of genes that represent at least parts of the complex biological pathways involved in transplantation biology. In this context, we acknowledge the fact that at the present time the function of only a minority of the human genome is documented. As the knowledge base that can be accessed through bioinformatics grows to better define cellular pathways and regulatory networks, these gene lists linked to well-defined clinical events in transplantation will provide additional opportunities to advance our understanding of the basic biology of transplantation and identify new targets for therapeutics.