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Steroid Pretreatment of Organ Donors to Prevent Postischemic Renal Allograft Failure: A Randomized, Controlled Trial FREE

Alexander Kainz, PhD; Julia Wilflingseder, PhD; Christa Mitterbauer, MD; Maria Haller, MD; Christopher Burghuber, MD; Paul Perco, PhD; Robert M. Langer, MD, PhD; Georg Heinze, PhD; and Rainer Oberbauer, MD, MSc
[+] Article and Author Information

From Medical University of Vienna, Vienna, Austria; KH Elisabethinen, Linz, Austria; and Semmelweis University, Budapest, Hungary.


Note: Drs. Kainz and Wilflingseder contributed equally to this manuscript.

Acknowledgment: The authors thank the organ procurement organization coordinators for their valuable contribution.

Grant Support: By the Austrian Science Fund (FWF P-18325) and the Austrian Academy of Science (OELZELT EST370/04).

Potential Conflicts of Interest: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M10-0505.

Reproducible Research Statement:Study protocol: Available at www.meduniwien.ac.at/nephrogene/data/artfstudy. Statistical code and data set: Available from Dr. Kainz (e-mail, alexander.kainz@meduniwien.ac.at).

Requests for Single Reprints: Rainer Oberbauer, MD, MSc, Department of Nephrology, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria; e-mail, rainer.oberbauer@meduniwien.ac.at.

Current Author Addresses: Drs. Kainz, Wilflingseder, Mitterbauer, and Oberbauer: Medizinische Universität Wien, Nephrology and Dialysis, Währinger Gürtel 18-20, 1090 Vienna, Austria.

Drs. Haller and Perco: KH Elisabethinen, Nephrology, Fadinger Straße 1, 4010 Linz, Austria.

Dr. Burghuber: Medizinische Universität Wien, Transplant Surgery, Währinger Gürtel 18-20, 1090 Vienna, Austria.

Dr. Langer: Semmelweis University, Department of Transplantation and Surgery, Baross u. 23, H-1082 Budapest, Hungary.

Dr. Heinze: Medizinische Universität Wien, Center for Medical Statistics, Informatics and Intelligent Systems, Währinger Gürtel 18-20, 1090 Vienna, Austria.

Author Contributions: Conception and design: P. Perco, R. Oberbauer.

Analysis and interpretation of the data: A. Kainz, J. Wilflingseder, G. Heinze, P. Perco, R.M. Langer.

Drafting of the article: A. Kainz, J. Wilflingseder.

Critical revision of the article for important intellectual content: J. Wilflingseder, R. Oberbauer.

Final approval of the article: C. Mitterbauer, R. Oberbauer.

Provision of study materials or patients: C. Mitterbauer, M. Haller, C. Burghuber.

Obtaining of funding: R. Oberbauer.

Administrative, technical, or logistic support: A. Kainz, J. Wilflingseder, C. Mitterbauer, C. Burghuber, R.M. Langer, R. Oberbauer.

Collection and assembly of data: C. Mitterbauer, M. Haller, C. Burghuber, R.M. Langer.


Ann Intern Med. 2010;153(4):222-230. doi:10.7326/0003-4819-153-4-201008170-00003
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Background: Posttransplantation acute renal failure (ARF) occurs in roughly 25% of recipients of organs from deceased donors. Inflammation in the donor organ is associated with risk for ARF.

Objective: To determine whether administering corticosteroids to deceased organ donors reduces the incidence and duration of ARF in organ recipients more than placebo.

Design: Parallel, blocked randomized trial, performed between February 2006 and November 2008, with computer-generated randomization and centralized allocation. Investigators were masked to group assignment. (Controlled-trials.com registration number: ISRCTN78828338)

Setting: 3 renal transplantation centers in Austria and Hungary.

Patients: 306 deceased heart-beating donors and 455 renal transplant recipients.

Interventions: Organ donors were administered an intravenous infusion of either 1000 mg of methylprednisolone (136 donors) or placebo (0.9% saline) (133 donors) at least 3 hours before organ harvesting.

Measurements: Incidence of ARF, defined as more than 1 dialysis session in the first week after transplantation, was the primary end point. Secondary and other end points included duration of ARF and trajectories of serum creatinine level. The suppression of immune response and inflammation by the intervention was assessed in the donor organ on a genome-wide basis.

Results: 52 of 238 recipients (22%) of kidneys from steroid-treated donors and 54 of 217 recipients (25%) of kidneys from placebo-treated donors had ARF (difference, 3 percentage points [95% CI, −11 to 5 percentage points]). One graft was lost on day 1 in each group, and 1 recipient in the placebo group died of cardiac arrest on day 2. The median duration of ARF was 5 days (interquartile range, 2 days) in the steroid group and 4 days (interquartile range, 2 days) in the placebo group (P = 0.31). The groups had similar trajectories of serum creatinine level in the first week (P = 0.72). Genomic analysis showed suppressed inflammation and immune response in kidney biopsies from deceased donors who received corticosteroids.

Limitation: Donors and recipients were mainly white, and all were from 3 transplantation centers in central Europe, which may limit generalizability.

Conclusion: Systemic suppression of inflammation in deceased donors by corticosteroids did not reduce the incidence or duration of posttransplantation ARF in allograft recipients.

Primary Funding Source: Austrian Science Fund and Austrian Academy of Science.

Editors' Notes
Context

  • Posttransplantation acute renal failure (ARF) that requires dialysis is common among recipients of renal allografts from deceased donors.

Contribution

  • In this multicenter randomized trial, deceased heart-beating organ donors received an intravenous infusion of saline or methylprednisolone at least 3 hours before organ harvesting. Kidney biopsies from steroid-treated donors showed suppression of immune response and inflammation, but the incidence of ARF in the first week after transplantation was similar in recipients of organs from steroid-treated and saline-treated donors.

Implication

  • Despite suppressing renal inflammation, corticosteroids given to deceased heart-beating organ donors did not reduce the incidence of posttransplantation ARF.

—The Editors

The high and rising prevalence and incidence of end-stage renal disease in all industrialized countries represent a major global public health problem (13). Because no drug treatment can reverse end-stage renal disease, patients must receive renal replacement therapy by either dialysis or kidney transplantation. Renal transplantation is the preferred treatment, even in elderly patients, because it is considerably cheaper than dialysis and allows for an almost normal life (4).

It is not fully understood why kidneys from living donors have a longer graft survival than those from deceased donors, because either should be in good condition before explantation. One major difference between the donor sources is the incidence of postischemic acute renal failure (ARF) in allograft recipients, also known as delayed graft function. About one quarter of kidneys from deceased donors do not immediately function after transplantation; recipients of such kidneys receive dialysis until the grafted kidney resumes function. Acute renal failure after live kidney transplantation is a rare exception that occurs in fewer than 5% of cases (56). A large cohort study of 122 175 patients (7) showed that ARF results in a hazard ratio (HR) for graft failure of 1.99 (95% CI, 1.91 to 2.08) compared with patients without ARF, after adjustment for many covariates. Acute rejection, a key risk factor for reduced long-term allograft survival, also occurs more frequently in grafts with ARF.

Donor kidney sources cannot be distinguished on a morphologic basis; however, on the molecular level, a discrete set of transcripts is activated in deceased donor organs, depending on the degree of injury. We have shown that the gene expression pattern of donor kidney biopsy specimens obtained before transplantation could predict the posttransplantation occurrence of ARF (8). Among the main functional groups that distinguish donor kidneys with subsequent primary function from those with consecutive ARF were inflammation, complement activation, and apoptosis induction. Older donor age, prolonged warm ischemic time during anastomosis, and early use of calcineurin inhibitor–based immunosuppression therapy are also risk factors for ARF. Schwarz and Oberbauer (9) have published a thorough discussion of donor and recipient factors that contribute to ARF.

With the exception of 2 recent papers (1011), which show that machine perfusion of donor organs and dopamine treatment in donors reduced the risk for delayed graft function, no clinical interventions for reducing posttransplantation ARF have been studied. We conducted our randomized, blinded, placebo-controlled study to determine whether pretreating deceased donors with corticosteroids before organ retrieval reduces inflammation and the subsequent rate of delayed graft function after engraftment.

Design Overview

Between February 2006 and November 2008, deceased donors from 3 transplantation centers in central Europe were randomly assigned to receive corticosteroids or placebo at least 3 hours before organ retrieval. Immediately before transplantation, wedge biopsies of the kidneys were done. Allograft recipients were then followed for 7 days to assess the incidence and duration of ARF. Neither the allograft recipients nor the clinicians who provided care and assessed outcomes knew whether transplanted organs were from donors who received corticosteroids or placebo. The study protocol was approved by the institutional review board (Ethical Committee of the Medical University of Vienna, Vienna, Austria [study protocol EK-067/2005; to be found at http://ohrp.cit.nih.gov/search]) and the Eurotransplant kidney advisory committee (study protocol 6021KAC06) at each study site. There was no informed consent for deceased donors.

Setting and Participants

Two renal transplantation centers in Austria (Linz and Vienna) and 1 in Hungary (Budapest) participated in our trial. The 3 centers combined have performed an average of 450 transplantations from deceased kidney donors per year in the past decade (60 in Linz, 170 in Vienna, and 220 in Budapest).

Heart-beating donors older than 18 years were eligible for the study. We included 306 donors who were reported to the organ procurement organizations of the 3 study sites (Figure 1). The local transplant coordinators collected the donor and recipient demographic characteristics and follow-up data on the recipients at our study Web site.

Randomization and Intervention

After brain death was declared, the deceased donor was enrolled by the local transplant coordinator and randomly assigned to receive either 1000 mg of methylprednisolone (corticosteroids) or placebo (0.9% saline) at least 3 hours before organ harvesting. The blinded study drug was sent to the donor site with the transplant coordinator, who removed an inguinal lymph node for HLA typing. All organs were perfused with a histidine–tryptophan–ketoglutarate cold preservation solution at 4 °C during organ procurement (12). The cold ischemic time was no longer than 24 hours.

Donors were randomly assigned in a 1:1 ratio, stratified by age (>50 or ≤50 years), to receive corticosteroids or placebo. Random assignments were done centrally through our study Web site on the basis of a permuted block design, with block sizes of 4 in each clinical site and donor age category, and concealed until intervention assignment. The randomization order did not have a repeating sequence and the randomization code was not revealed to recipients or investigators.

Outcomes and Measurements

Our primary outcome was incidence of ARF, defined as more than 1 dialysis after engraftment within the first week after transplantation. We defined a urine output less than 400 mL/d, serum potassium level greater than 6 mmol/L, or blood urea nitrogen level greater than 36 mmol/L (>100 mg/dL) as mandatory indications for hemodialysis. Serum potassium and blood urea nitrogen levels were determined daily in the first week after transplantation. Creatinine level was also measured every day (every other day at 1 center) during the first week after transplantation. The principal investigator of the study center recorded follow-up data until the seventh day after transplantation. We determined the secondary end point (duration of ARF) during the first 7 days and defined it as the number of days from first to last dialysis (maximum, 7 days). Suppression of inflammation and immune response were identified by gene expression analysis.

Donor Kidney Biopsy Specimen, RNA Isolation, and RNA Amplification

Wedge biopsies of each kidney were done under sterile conditions at the end of the cold ischemic time shortly before transplantation. The biopsy specimens were immediately submerged in RNAlater (Ambion, Austin, Texas) and stored at 4 °C. The study protocol required that 10 randomly selected biopsy specimens from each treatment group (steroid or placebo) and outcome (ARF or primary function) be subjected to a thorough transcriptome analysis; this representative sample size was based on previous work (13). Analysis of gene expression in the biopsy sample thus served to test whether dose and timing of the steroid therapy was sufficient to suppress expression of the genes that contributed to inflammation in the donor organ.

Total RNA was isolated and purified by using chloroform and TRIzol reagent (Invitrogen, Carlsbad, California). We checked RNA yield and quality with the Agilent 2100 Bioanalyzer and RNA6000 LabChip kit (Agilent, Palo Alto, California) and used Stratagene Universal human reference RNA (Stratagene, La Jolla, California) as a reference.

We used the RiboAmp RNA amplification kit (Arcturus, Mountain View, California) to amplify 2 µg of isolated total RNA and then inspected the amplified RNA on an ethidium bromide–stained 1% agarose gel and the Agilent 2100 Bioanalyzer.

Microarray Hybridization and Scanning

We obtained cDNA microarrays holding 41 421 features (batch SHEO) from the Stanford University Functional Genomics core facility. All microarray experiments were performed as described elsewhere (14); detailed protocols are available at http://genome-www.stanford.edu/. Using a type 2 experimental setup, we labeled 1 µg of sample and standard Stratagene Universal human reference amplified RNA (Agilent) by using a CyScribe cDNA postlabeling kit (Amersham Pharmacia Biotech, Buckinghamshire, United Kingdom) in a 2-step procedure.

We loaded the samples onto arrays and incubated them for 18 hours in a 65 °C water bath. After 3 washing steps, we examined the fluorescence images of the hybridized microarrays by using a GenePix 4100A scanner (Axon Instruments, Union City, California) and used the GenePix Pro 6.0 software to grid images and calculate spot intensities. The arrays were numbered according to the anonymous organ donor identification number and processed in random order. Image, grid, and data files were submitted to the Stanford Microarray Database (http://genome-www5.stanford.edu/) and follow MIAME (Minimum Information About a Microarray Experiment) guidelines for array experiments (1516). Raw data files, as well as the MIAME checklist, are available at our laboratory Web site (www.meduniwien.ac.at/nephrogene/data/artfstudy/) or at the GEO Omnibus Database (www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token=hrabxkiccskiqne&acc=GSE14700).

Follow-up Procedures and Monitoring

A data safety monitoring board from the Department of Medical Statistics and Informatics of Medical University of Vienna monitored our study. The board performed 2 interim analyses, after 60 and 120 persons in the treatment group received kidneys, and applied the DeMets and Lan extension of the O'Brian–Fleming stopping rules to the incidence of ARF (17). The board would have stopped the trial if the observed P values in these analyses had been less than 0.00001 and less than 0.00305, respectively. The cumulative 2-sided P value was set to less than 0.05.

Statistical Analysis

On the basis of data from the past decade in the Eurotransplant region, we assumed an ARF incidence of 24% in recipients of kidneys from deceased donors (18). Therefore, 217 recipients were required in each group to detect a 50% reduction in the proportion of ARF for an α level of 5% (2-sided), statistical power of 80%, and 10% loss to follow-up. We also assumed that in one third of deceased organ donors, only 1 kidney would be available for transplantation at the study sites because in most cases, the recipient is not known at the time of planned donor treatment. The number of theoretically required donors was set to 264. Finally, on the basis of registry data, organs harvested from 15% of deceased organ donors will not be subsequently transplanted for various reasons. We therefore planned for 310 randomly assigned deceased kidney donors. However, we had predetermined that donor enrollment would end when both groups contained at least 217 recipients.

Baseline characteristics were compared by using t tests for continuous data and chi-square tests or, in the case of expected cell frequencies of 5 or less, Fisher exact tests for categorical data. The primary outcome variable (incidence of ARF) was analyzed by computing a 95% CI for the difference in incidences and adjusting for the correlated nature of paired donor kidneys by applying bootstrap resampling, blocked by donor, with 10 000 runs (19). Patients who died or experienced graft loss without sufficient follow-up for the assessment of ARF were counted as having not had ARF. The analysis was also adjusted for stratification criteria (donor age and clinical site) in a hierarchical logistic regression analysis by using the PROC GLIMMIX command in SAS (SAS Institute, Cary, North Carolina) to account for paired donor kidneys (20). We tested the effect modification of treatment by 13 prespecified demographic variables by including corresponding interaction terms in the analysis. The expected number of significant interactions at a level of 5% was 0.65. We compared the between-group difference in duration of ARF among patients who experienced ARF by using a time-to-event analysis, with death and graft loss as competing risks (21).

Creatinine level trajectories, stratified by treatment, were analyzed by using a linear mixed model with time, therapy, and the stratification criteria (donor age and clinical site) as the independent variables and donor and recipient as random effects. We tested unstructured, first-order autoregressive, and banded Toeplitz covariance matrices, and we determined that the unstructured type was the most appropriate by using graphical analysis and evaluating the log likelihood ratio. Postdialysis values were multiplied by 1.2 to account for creatinine removal during dialysis.

A P value less than 0.05 was considered statistically significant. Statistical analyses were performed with SAS for Windows, version 9.2, and the cmprsk package (available at http://cran.r-project.org/web/packages/cmprsk/index.html) for R (R Foundation, Vienna, Austria).

Role of the Funding Source

Our study was funded by the Austrian Science Fund and the Austrian Academy of Science. The funding sources had no role in any part of conduct of the study or preparation of the manuscript.

We excluded 22 of the 158 donors allocated to corticosteroids and 15 of the 148 donors allocated to placebo for various reasons (Figure 1). Also, we included only 1 kidney from about one third of donors because the paired kidney was allocated to a nonstudy center in the Eurotransplant region and biopsy could not be obtained. Age, sex, and clinical characteristics of donors and recipients were similar among those allocated to receive either corticosteroids or placebo (Table 1). Although we did not obtain ethnicity data, almost all donors and recipients seen at the 3 transplantation centers were white. Histologic results of biopsies were also similar for the 2 groups (Table 2).

Table Jump PlaceholderTable 1.  Demographic Characteristics of Donors and Recipients, by Treatment Assignment
Table Jump PlaceholderTable 2.  Histologic Results of Biopsies

Of the 238 participants in the steroid group, 153 required no dialysis during the first 7 days after transplantation and 32 recipients required 1 posttransplantation hemodialysis session. One patient lost his graft on day 1. In the placebo group, 136 patients required no dialysis and 27 participants required 1 dialysis session. One recipient died of cardiac arrest on day 2 without ARF, and 3 experienced graft loss on days 1, 5, and 6, respectively. The latter 2 patients had developed ARF.

Fifty-two recipients (22%) of kidneys from steroid-treated donors and 54 recipients (25%) of kidneys from placebo-treated donors developed ARF (difference, 3 percentage points [CI, −11.3 to 5.0 percentage points]; odds ratio, 0.83 [CI, 0.52 to 1.35]; P = 0.47). Figure 2 shows the incidence of ARF stratified by various prespecified donor and recipient characteristics. We observed no statistically significant interactions between treatment and any characteristics.

Grahic Jump Location
Figure 2.
ARF, by donor and recipient characteristics.

The P value is for the interaction between treatment and characteristic. ARF = acute renal failure.

Grahic Jump Location

When we treated death and graft loss as competing risks, the median duration of ARF was 5 days (interquartile range, 2 days) in the steroid group and 4 days (interquartile range, 2 days) in the placebo group (P = 0.31). Thirteen and 14 patients, respectively, still had ARF on day 7, which means that these patients might have had a longer, unobserved duration of ARF.

Figure 3 shows posttransplant renal function measured by the continuous variable of serum creatinine level, rather than the dichotomous outcome of ARF. Serum creatinine levels decreased over the first 7 days (P < 0.001) in a similar manner in both groups (P = 0.72).

Grahic Jump Location
Figure 3.
Trajectories of serum creatinine levels in the first week after transplantation, by therapy.

Values from the days after dialysis were multiplied by 1.2. The P value was derived from the mixed linear model for longitudinal data (treatment effect). To convert serum creatinine values to mg/dL, divide by 88.4.

Grahic Jump Location

We evaluated the gene expression profile to determine the efficacy of the treatment (Appendix Figure 1) and found that systemic administration of 1000 mg of corticosteroids to the deceased organ donor at least 3 hours before organ retrieval suppressed the molecular inflammation signature. Other molecular features belonging to gene transcription or signaling were also suppressed in the steroid-treated kidneys, which suggests that the steroid dose and the timing of the intervention were appropriate. The Appendix provides analyses of gene expression data that use a systems biology approach.

Grahic Jump Location
Appendix Figure 1.
Dendrogram derived by unsupervised hierarchical clustering of gene expression profiles that characterize the steroid group and the placebo group.

The algorithm groups the kidney biopsies according to their similarity in gene expression profiles. Red spots indicate upregulated transcripts and green spots indicate downregulated transcripts, relative to the reference RNA used. The differentially regulated genes in the steroid group could be categorized into the main biological functions of immune response, transcription, and signaling in the Gene Ontology classification system.

Grahic Jump Location

In our trial, we administered 1000 mg of corticosteroids to deceased organ donors at least 3 hours before their organs were harvested. Our novel finding was that administering corticosteroids to deceased organ donors did not reduce the incidence or duration of ARF, despite the suppression of inflammatory response in transplanted kidneys.

A MEDLINE search for English articles to April 2010 with the keywords delayed graft function, donor treatment, kidney, or renal identified few other pertinent trials. Studies in the late 1970s and early 1980s assessed whether immunosuppressive pretreatment of deceased donors would improve graft survival. Several nonrandomized studies (2224) showed short-term graft survival benefits, and a larger retrospective study (25) showed a graft survival benefit in recipients of pretreated donor organs at 5 years after transplantation. In contrast, 3 small, randomized studies (2628) found that steroids (plus additional cyclophosphamide in 2 studies) had no effect on graft survival measured between 3 and 12 months. The primary outcome in these trials was short-term graft survival, and their ability to detect important effects of treatment was limited by the low incidence of early graft failure. Finally, the small sample sizes (between 30 and 40 patients in each group) limited their ability to detect even large differences in ARF.

Previous genetic association studies (8, 13, 2931) found that genes that influence inflammation, metabolism, and immune response were highly associated with ARF, which subsequently led to a reduced long-term survival after transplantation. We found that steroid administration downregulated these genes. Many of these molecular features are reported in the literature to represent steroid targets (3243). The regulation of highly connected, differentially regulated features in the protein–protein interaction network are also well known (3233, 3539, 43).

Our study's strengths include the random assignment of deceased donors to receive corticosteroids or placebo, the blinding of investigators to treatment allocation, the use of standard criteria for dialysis indications to define the outcome of ARF, and that no patient was lost to follow-up. However, our study is limited by the inclusion of mainly white donors and recipients from 3 transplantation centers in central Europe, which may limit generalizability. Also, we did not assess clinical outcomes other than ARF, graft loss, and death in the first week after transplantation. Finally, our subgroup analyses had very limited power to detect interactions.

In summary, our study showed that steroid pretreatment of deceased organ donors suppressed inflammation in the transplanted kidney but did not reduce the incidence of ARF. Therefore, we do not recommend routinely pretreating deceased organ donors with corticosteroids.

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Hansson AC, Fuxe K.  Time-course of immediate early gene expression in hippocampal subregions of adrenalectomized rats after acute corticosterone challenge. Brain Res. 2008; 1215:1-10. PubMed
 
Hata Y, Sassa Y, Kita T, Miura M, Kano K, Kawahara S, et al..  Vascular endothelial growth factor expression by hyalocytes and its regulation by glucocorticoid. Br J Ophthalmol. 2008; 92:1540-4. PubMed
 
Hubler TR, Scammell JG.  Intronic hormone response elements mediate regulation of FKBP5 by progestins and glucocorticoids. Cell Stress Chaperones. 2004; 9:243-52. PubMed
 
Ishmael FT, Fang X, Galdiero MR, Atasoy U, Rigby WF, Gorospe M, et al..  Role of the RNA-binding protein tristetraprolin in glucocorticoid-mediated gene regulation. J Immunol. 2008; 180:8342-53. PubMed
 
Lund T, Fosby B, Korsgren O, Scholz H, Foss A.  Glucocorticoids reduce pro-inflammatory cytokines and tissue factor in vitro and improve function of transplanted human islets in vivo. Transpl Int. 2008; 21:669-78. PubMed
 
Makrygiannakis D, afKlint E, Catrina SB, Botusan IR, Klareskog E, Klareskog L, et al..  Intraarticular corticosteroids decrease synovial RANKL expression in inflammatory arthritis. Arthritis Rheum. 2006; 54:1463-72. PubMed
 
McTiernan CF, Lemster BH, Frye C, Brooks S, Combes A, Feldman AM.  Interleukin-1 beta inhibits phospholamban gene expression in cultured cardiomyocytes. Circ Res. 1997; 81:493-503. PubMed
 
Paul C, Seiliez I, Thissen JP, LeCam A.  Regulation of expression of the rat SOCS-3 gene in hepatocytes by growth hormone, interleukin-6 and glucocorticoids mRNA analysis and promoter characterization. Eur J Biochem. 2000; 267:5849-57. PubMed
 
Woodruff PG, Boushey HA, Dolganov GM, Barker CS, Yang YH, Donnelly S, et al..  Genome-wide profiling identifies epithelial cell genes associated with asthma and with treatment response to corticosteroids. Proc Natl Acad Sci U S A. 2007; 104:15858-63. PubMed
 
Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, et al..  Missing value estimation methods for DNA microarrays. Bioinformatics. 2001; 17:520-5. PubMed
 
Tusher VG, Tibshirani R, Chu G.  Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A. 2001; 98:5116-21. PubMed
 
Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N, et al..  TM4: a free, open-source system for microarray data management and analysis. Biotechniques. 2003; 34:374-8. PubMed
 
Eisen MB, Spellman PT, Brown PO, Botstein D.  Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A. 1998; 95:14863-8. PubMed
 
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al..  Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000; 25:25-9. PubMed
 
Diehn M, Sherlock G, Binkley G, Jin H, Matese JC, Hernandez-Boussard T, et al..  SOURCE: a unified genomic resource of functional annotations, ontologies, and gene expression data. Nucleic Acids Res. 2003; 31:219-23. PubMed
 
Mi H, Lazareva-Ulitsky B, Loo R, Kejariwal A, Vandergriff J, Rabkin S, et al..  The PANTHER database of protein families, subfamilies, functions and pathways. Nucleic Acids Res. 2005; 33:D284-8. PubMed
 
Hoffmann R, Valencia A.  A gene network for navigating the literature [Letter]. Nat Genet. 2004; 36:664. PubMed
 
Brown KR, Jurisica I.  Online predicted human interaction database. Bioinformatics. 2005; 21:2076-82. PubMed
 
Chen JY, Shen C, Sivachenko AY.  Mining Alzheimer disease relevant proteins from integrated protein interactome data. Pac Symp Biocomput. 2006; 367-78. PubMed
 
Appendix: Genomics and Proteomics
Methods
Bioinformatic Workflow

Our microarray data set consisted of 41 421 cDNA features. Of these, 41 025 held a UniGene Cluster ID and 396 were expressed sequence tags not assigned to a UniGene Cluster. Mean sector and printing plate analysis of variance R2 values of the microarray experiments were 4.5 × 10−2 and 3.1 × 10−2, respectively, which suggests no dependency of results on spatial location or plate printing procedures. In a first preprocessing step, we applied a quality filter on the data set by considering only genes and expressed sequence tags with spot intensities of 1.5-fold more than background in either channel 1 or channel 2 in the array experiments, which yielded 34 599 cDNA features. We substituted the remaining missing data points by applying a k-nearest-neighbor algorithm, in which we set the number of neighbors (k) to 10 (44). No correction for a putative batch bias was necessary because we used only 1 array batch in the whole analysis for all arrays. We used the significance analysis of microarrays to determine significant differentially expressed genes (DEGs) between the steroid and placebo groups (45). The number of permutations was set to 100 and genes with a fold change greater than 2 and a Δ greater than 1.2 were assigned as DEGs, which resulted in a median false discovery rate of 0.47%. We then hierarchically clustered the DEGs and graphically represented them by using the MultiExperiment Viewer software developed at the Institute for Genomic Research (Rockville, Maryland) (46). The cosine correlation and complete linkage were used as the distance measure and linkage rule, respectively, in the hierarchical cluster algorithm (4647).

We further analyzed the DEGs with respect to their molecular functions, associated biological processes, and cellular locations by using the gene ontology terms provided by the Gene Ontology Consortium (48). We used the SOURCE tool from the Stanford Genomics Facility (49) to link the terms to the genes of interest. Functional grouping of genes was based on the gene ontology terms, PANTHER (Protein ANalysis THrough Evolutionary Relationships) ontologies, and information derived from the Information Hyperlinked over Proteins data retrieval system (5051).

Regulatory Network Analysis

To determine the interaction of DEGs providing an indication of potential functional interactions, we retrieved human protein–protein interactions from the Online Predicted Human Interaction Database (52). All DEGs with a fold change greater than 2 were considered in this network analysis. We generated a protein–protein interaction network by using the nearest-neighbor expansion method proposed by Chen and colleagues (53) and used ProteoLens (Discovery Informatics and Computing Laboratory, Indiana University School of Informatics, Indianapolis, Indiana) to graphically represent this network.

Results
Genes Differentially Expressed Between the Steroid and Placebo Groups

We identified 52 features as significantly differentially expressed (having a fold change >2) when we compared the gene expression profiles of 0-hour kidney biopsies between the steroid and placebo groups. These features represent 46 unique genes, which resulted in 39 downregulated and 7 upregulated genes in the steroid group (Appendix Figure 1). Unsupervised hierarchical clustering of the 40 samples stemming from 33 deceased donors showed a separation between steroid and placebo groups on the basis of gene expression profiles. According to the Gene Ontology classification, the upregulated transcripts are mainly involved in immunity response, transcription, and signaling, which indicates suppression of inflammation in the graft by steroid treatment (Appendix Table and Appendix Figure 2).

Table Jump PlaceholderAppendix Table.  Significant Differentially Expressed Genes Between the Steroid and Placebo Groups
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Appendix Figure 2.
Gene Ontology classification of the differentially expressed genes with a fold change greater than 2.

GTP = guanosine triphosphate.

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Interactome Analysis

Of the 46 significantly differentially expressed genes (39 downregulated and 7 upregulated), 28 (25 downregulated and 3 upregulated in the steroid group) have at least 1 interacting partner according to the Online Predicted Human Interaction Database. The initial list of 28 genes could therefore be extended, thus including all interacting proteins that formed the respective interaction network. The resulting interaction graph gave 193 nodes and 187 edges (Appendix Figure 3). In the largest subnetwork, we detected 7 of the 28 genes derived from expression analysis: 5 downregulated DEGs (FOS [v-fos FBJ murine osteosarcoma viral oncogene homolog], JUNB [jun B proto-oncogene], EGR1 [early growth response 1], HIF1A [hypoxia-inducible factor 1, α subunit; basic helix-loop-helix transcription factor], and SNAPC2 [small nuclear RNA-activating complex, polypeptide 2, 45kDa]) and 2 upregulated DEGs (FKBP5 [FK506 binding protein 5] and TSC22D3 [TSC22 domain family, member 3]). Two chemokines, CCL2 (chemokine [C-C motif] ligand 2) and CXCL1 (chemokine [C-X-C motif] ligand 1), are also downregulated by steroid treatment and are connected over only 1 interaction partner. These chemokines play important roles in the chemokine-mediated signaling pathway and thus in the inflammation cascade.

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Appendix Figure 3.
Protein–protein interaction network of significant differentially expressed genes with a fold change greater than 2.

Corticosteroids downregulated 25 genes (black nodes) and upregulated 3 genes (white nodes). Green nodes represent proteins identified by the nearest-neighbor expansion method.

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Figures

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Figure 2.
ARF, by donor and recipient characteristics.

The P value is for the interaction between treatment and characteristic. ARF = acute renal failure.

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Figure 3.
Trajectories of serum creatinine levels in the first week after transplantation, by therapy.

Values from the days after dialysis were multiplied by 1.2. The P value was derived from the mixed linear model for longitudinal data (treatment effect). To convert serum creatinine values to mg/dL, divide by 88.4.

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Appendix Figure 1.
Dendrogram derived by unsupervised hierarchical clustering of gene expression profiles that characterize the steroid group and the placebo group.

The algorithm groups the kidney biopsies according to their similarity in gene expression profiles. Red spots indicate upregulated transcripts and green spots indicate downregulated transcripts, relative to the reference RNA used. The differentially regulated genes in the steroid group could be categorized into the main biological functions of immune response, transcription, and signaling in the Gene Ontology classification system.

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Appendix Figure 2.
Gene Ontology classification of the differentially expressed genes with a fold change greater than 2.

GTP = guanosine triphosphate.

Grahic Jump Location
Grahic Jump Location
Appendix Figure 3.
Protein–protein interaction network of significant differentially expressed genes with a fold change greater than 2.

Corticosteroids downregulated 25 genes (black nodes) and upregulated 3 genes (white nodes). Green nodes represent proteins identified by the nearest-neighbor expansion method.

Grahic Jump Location

Tables

Table Jump PlaceholderTable 1.  Demographic Characteristics of Donors and Recipients, by Treatment Assignment
Table Jump PlaceholderTable 2.  Histologic Results of Biopsies
Table Jump PlaceholderAppendix Table.  Significant Differentially Expressed Genes Between the Steroid and Placebo Groups

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de Kruif MD, Lemaire LC, Giebelen IA, Struck J, Morgenthaler NG, Papassotiriou J, et al..  The influence of corticosteroids on the release of novel biomarkers in human endotoxemia. Intensive Care Med. 2008; 34:518-22. PubMed
 
Hansson AC, Fuxe K.  Time-course of immediate early gene expression in hippocampal subregions of adrenalectomized rats after acute corticosterone challenge. Brain Res. 2008; 1215:1-10. PubMed
 
Hata Y, Sassa Y, Kita T, Miura M, Kano K, Kawahara S, et al..  Vascular endothelial growth factor expression by hyalocytes and its regulation by glucocorticoid. Br J Ophthalmol. 2008; 92:1540-4. PubMed
 
Hubler TR, Scammell JG.  Intronic hormone response elements mediate regulation of FKBP5 by progestins and glucocorticoids. Cell Stress Chaperones. 2004; 9:243-52. PubMed
 
Ishmael FT, Fang X, Galdiero MR, Atasoy U, Rigby WF, Gorospe M, et al..  Role of the RNA-binding protein tristetraprolin in glucocorticoid-mediated gene regulation. J Immunol. 2008; 180:8342-53. PubMed
 
Lund T, Fosby B, Korsgren O, Scholz H, Foss A.  Glucocorticoids reduce pro-inflammatory cytokines and tissue factor in vitro and improve function of transplanted human islets in vivo. Transpl Int. 2008; 21:669-78. PubMed
 
Makrygiannakis D, afKlint E, Catrina SB, Botusan IR, Klareskog E, Klareskog L, et al..  Intraarticular corticosteroids decrease synovial RANKL expression in inflammatory arthritis. Arthritis Rheum. 2006; 54:1463-72. PubMed
 
McTiernan CF, Lemster BH, Frye C, Brooks S, Combes A, Feldman AM.  Interleukin-1 beta inhibits phospholamban gene expression in cultured cardiomyocytes. Circ Res. 1997; 81:493-503. PubMed
 
Paul C, Seiliez I, Thissen JP, LeCam A.  Regulation of expression of the rat SOCS-3 gene in hepatocytes by growth hormone, interleukin-6 and glucocorticoids mRNA analysis and promoter characterization. Eur J Biochem. 2000; 267:5849-57. PubMed
 
Woodruff PG, Boushey HA, Dolganov GM, Barker CS, Yang YH, Donnelly S, et al..  Genome-wide profiling identifies epithelial cell genes associated with asthma and with treatment response to corticosteroids. Proc Natl Acad Sci U S A. 2007; 104:15858-63. PubMed
 
Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, et al..  Missing value estimation methods for DNA microarrays. Bioinformatics. 2001; 17:520-5. PubMed
 
Tusher VG, Tibshirani R, Chu G.  Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A. 2001; 98:5116-21. PubMed
 
Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N, et al..  TM4: a free, open-source system for microarray data management and analysis. Biotechniques. 2003; 34:374-8. PubMed
 
Eisen MB, Spellman PT, Brown PO, Botstein D.  Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A. 1998; 95:14863-8. PubMed
 
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al..  Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000; 25:25-9. PubMed
 
Diehn M, Sherlock G, Binkley G, Jin H, Matese JC, Hernandez-Boussard T, et al..  SOURCE: a unified genomic resource of functional annotations, ontologies, and gene expression data. Nucleic Acids Res. 2003; 31:219-23. PubMed
 
Mi H, Lazareva-Ulitsky B, Loo R, Kejariwal A, Vandergriff J, Rabkin S, et al..  The PANTHER database of protein families, subfamilies, functions and pathways. Nucleic Acids Res. 2005; 33:D284-8. PubMed
 
Hoffmann R, Valencia A.  A gene network for navigating the literature [Letter]. Nat Genet. 2004; 36:664. PubMed
 
Brown KR, Jurisica I.  Online predicted human interaction database. Bioinformatics. 2005; 21:2076-82. PubMed
 
Chen JY, Shen C, Sivachenko AY.  Mining Alzheimer disease relevant proteins from integrated protein interactome data. Pac Symp Biocomput. 2006; 367-78. PubMed
 

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