Copyright C Blackwell Munksgaard 2002 American Journal of Transplantation 2002; 2: 913–925 Blackwell Munksgaard ISSN 1600-6135 Gene Expression During Acute Allograft Rejection: Novel Statistical Analysis of Microarray Data Mark Stegall a, *, Walter Park a , Dean Kim a and Walter Kremers b a Department of Surgery, Division of Transplantation, b Section of Biostatistics, Department of Health Sciences Research, Mayo Foundation and Clinic, 200 First St SW, Rochester, MN 55905, USA * Corresponding author: Mark Stegall, [email protected]High-throughput microarrays promise a comprehen- sive analysis of complex biological processes, yet their applicability is hampered by problems of reproducibil- ity and data management. The current study examines some of the major questions of microarray use in a well-described model of allograft rejection. Using the Brown Norway to Lewis heterotopic heart transplant model, highly purified RNA was isolated from cardiac tissue at postoperative days (POD) 3, 5 and 7 and hy- bridized onto Affymetrix U34A microarrays. Using the log average ratio (LAR), changes in gene expression were monitored at each timepoint and p-values gener- ated through statistical analysis. Microarray data were verified for 13 significant transcripts using RT-PCR. Of the 8800 transcripts studied, 2864 were increased on POD 3, 1418 on POD 5 and 2745 on POD 7. Verifying previous studies, many up-regulated genes appeared to be associated with the inflammatory process and graft infiltrating cells. Down-regulated transcripts in- cluded many novel molecules such as SC1 and deco- rin. LAR analysis provides a useful approach to analyze microarray data. Results were reproducible and corre- lated well with both RT-PCR and prior studies. Most importantly, these results provide new insights into the pathogenesis of acute rejection and suggest new molecules for future studies. Key words: Bioinformatics, cardiac transplantation, gene expression, immune response, microarray Received 11 April 2002, revised and accepted for publi- cation 26 June 2002 Introduction Despite improvements in immunosuppression, acute allograft rejection still occurs in 20–40% of organ allograft recipients (1). In addition to increasing patient cost and morbidity, acute rejection has been associated with the development of chronic allograft rejection, the major cause of late graft loss in heart, lung and kidney transplant recipients (2–4). 913 Previous studies have demonstrated that acute allograft rejec- tion is a progressive inflammatory process involving the ische- mia/reperfusion injury, presentation of antigen in regional lymph nodes, infiltration of the graft by a variety of cell types and terminal graft damage (5). The rodent heterotopic cardiac allograft model has been instrumental in defining critical events of allograft rejection (6–9). Despite intensive research, the molecular mechanisms and cellular pathways responsible for organ allograft rejection still have been only partially de- fined. High-throughput microarrays allow for the simultaneous as- sessment of mRNA from thousands of transcripts and promise a comprehensive analysis of the pathogenesis of complex bio- logical processes such as acute rejection (10–14). However, the study of thousands of transcripts at one time raises con- cerns regarding the accuracy of the data when each transcript is not verified by other methods of RNA analysis such as RT- PCR or Northern blot. To address this issue we investigated several critical variables specific to the use of microarray tech- nology. First, we investigated the most appropriate means of analyzing the raw expression data to determine the most use- ful algorithm while allowing for the application of thorough statistical analyses. Next, we confirmed several significant changes with other methods (RT-PCR, etc.). Finally, we ad- dressed other areas of concern including the run-to-run vari- ability of the technique and the validity of the results from somewhat heterogeneous mRNA. The current studies address these fundamental questions of microarray technology in the investigation of the pathophysiology of acute cellular rejection in a standard rat heterotopic heart transplant model. Materials and Methods Animals and procedures Rats were purchased from Harlan Sprague-Dawley (Indianapolis, IN) and maintained in accordance with guidelines from the American Association for Laboratory Animal Care and the Institutional Animal Care and Use Com- mittee of the Mayo Foundation. Using the standard heterotopic abdominal technique (15), donor hearts from male Brown Norway rats were trans- planted into either the MHC incompatible male Lewis rats (RT1 l , allografts) or inbred male Brown Norway rats (RT1 n , isografts). No immunosuppres- sion was used. Transplanted hearts were examined by daily palpation and rejection was determined by cessation of heartbeats. Cardiac isografts and allografts were explanted on postoperative day (POD) 3, 5 and 7 (n Ω 5 at each timepoint). Five test allografts rejected on POD 7.6 ∫ 0.5. Five native Brown Norway hearts served as normal controls. Tissue was either fixed in 10% neutral buffered formalin and embedded in paraffin for histopathologic examination or divided into 5 mm pieces,
Transcript
Copyright C Blackwell Munksgaard 2002American Journal of Transplantation 2002; 2: 913–925
Blackwell Munksgaard ISSN 1600-6135
Gene Expression During Acute Allograft Rejection:Novel Statistical Analysis of Microarray Data
Mark Stegalla,*, Walter Parka, Dean Kima andWalter Kremersb
a Department of Surgery, Division of Transplantation,bSection of Biostatistics, Department of Health Sciences
Research, Mayo Foundation and Clinic, 200 First St SW,
High-throughput microarrays promise a comprehen-sive analysis of complex biological processes, yet theirapplicability is hampered by problems of reproducibil-ity and data management. The current study examinessome of the major questions of microarray use in awell-described model of allograft rejection. Using theBrown Norway to Lewis heterotopic heart transplantmodel, highly purified RNA was isolated from cardiactissue at postoperative days (POD) 3, 5 and 7 and hy-bridized onto Affymetrix U34A microarrays. Using thelog average ratio (LAR), changes in gene expressionwere monitored at each timepoint and p-values gener-ated through statistical analysis. Microarray data wereverified for 13 significant transcripts using RT-PCR. Ofthe 8800 transcripts studied, 2864 were increased onPOD 3, 1418 on POD 5 and 2745 on POD 7. Verifyingprevious studies, many up-regulated genes appearedto be associated with the inflammatory process andgraft infiltrating cells. Down-regulated transcripts in-cluded many novel molecules such as SC1 and deco-rin. LAR analysis provides a useful approach to analyzemicroarray data. Results were reproducible and corre-lated well with both RT-PCR and prior studies. Mostimportantly, these results provide new insights intothe pathogenesis of acute rejection and suggest newmolecules for future studies.
Received 11 April 2002, revised and accepted for publi-cation 26 June 2002
Introduction
Despite improvements in immunosuppression, acute allograftrejection still occurs in 20–40% of organ allograft recipients(1). In addition to increasing patient cost and morbidity, acuterejection has been associated with the development ofchronic allograft rejection, the major cause of late graft lossin heart, lung and kidney transplant recipients (2–4).
913
Previous studies have demonstrated that acute allograft rejec-tion is a progressive inflammatory process involving the ische-mia/reperfusion injury, presentation of antigen in regionallymph nodes, infiltration of the graft by a variety of cell typesand terminal graft damage (5). The rodent heterotopic cardiacallograft model has been instrumental in defining criticalevents of allograft rejection (6–9). Despite intensive research,the molecular mechanisms and cellular pathways responsiblefor organ allograft rejection still have been only partially de-fined.
High-throughput microarrays allow for the simultaneous as-sessment of mRNA from thousands of transcripts and promisea comprehensive analysis of the pathogenesis of complex bio-logical processes such as acute rejection (10–14). However,the study of thousands of transcripts at one time raises con-cerns regarding the accuracy of the data when each transcriptis not verified by other methods of RNA analysis such as RT-PCR or Northern blot. To address this issue we investigatedseveral critical variables specific to the use of microarray tech-nology. First, we investigated the most appropriate means ofanalyzing the raw expression data to determine the most use-ful algorithm while allowing for the application of thoroughstatistical analyses. Next, we confirmed several significantchanges with other methods (RT-PCR, etc.). Finally, we ad-dressed other areas of concern including the run-to-run vari-ability of the technique and the validity of the results fromsomewhat heterogeneous mRNA. The current studies addressthese fundamental questions of microarray technology in theinvestigation of the pathophysiology of acute cellular rejectionin a standard rat heterotopic heart transplant model.
Materials and Methods
Animals and procedures
Rats were purchased from Harlan Sprague-Dawley (Indianapolis, IN) andmaintained in accordance with guidelines from the American Associationfor Laboratory Animal Care and the Institutional Animal Care and Use Com-mittee of the Mayo Foundation. Using the standard heterotopic abdominaltechnique (15), donor hearts from male Brown Norway rats were trans-planted into either the MHC incompatible male Lewis rats (RT1l, allografts)or inbred male Brown Norway rats (RT1n, isografts). No immunosuppres-sion was used. Transplanted hearts were examined by daily palpation andrejection was determined by cessation of heartbeats.
Cardiac isografts and allografts were explanted on postoperative day (POD)3, 5 and 7 (nΩ5 at each timepoint). Five test allografts rejected on POD7.6∫0.5. Five native Brown Norway hearts served as normal controls.Tissue was either fixed in 10% neutral buffered formalin and embedded inparaffin for histopathologic examination or divided into 5mm pieces,
Stegall et al.
placed in RNAlaterA (Ambion Inc., Austin, TX) and stored in ª80 æC forRNA extraction and GeneChipA analyses.
RNA isolation and microarray hybridization
Total RNA was extracted with TRIzolA Reagent (Invitrogen Corp, Carlsbad,CA) and further purified using the RNeasy Mini KitA (Qiagen Inc., Valencia,CA). Sample quality was assessed with an Agilent 2100 BioanalyzerA
(Agilent Technologies, Palo Alto, CA). All samples possessed 18S and 28SrRNA peaks with no signs of RNA degradation. The minimum total RNAquantity used for labeling was 8.0mg. RNA from 5 native, 15 isograft (nΩ5 at POD 3, 5 and 7) and 15 allograft (nΩ5 at POD 3, 5 and 7) sampleswere processed for microarray hybridization.
Using protocols described in the Affymetrix GeneChipA Expression AnalysisManual (Affymetrix, Inc., Santa Clara, CA) double-stranded cDNA was syn-thesized from each total RNA sample via oligo-dT-mediated reverse tran-scription. Double-stranded cDNA was cleaned using phase lock gel/phenol-chloroform precipitation. Biotin-labeled cRNA was synthesized using theEnzo BioArray High Yield KitA (Enzo Diagnostics, Inc., Farmingdale, NY),further purified using Qiagen RNeasy KitA (Qiagen, Inc), and then quantifiedby spectrophotometer. The purified cRNA was fragmented into 100–150 ntfragments. Fifteen micrograms of fragmented, biotin-labeled cRNA was hy-bridized to a U34A (Affymetrix, Inc.) microarray. Each array was washed withbuffers of varying stringencies, stained with streptavidin/phycoerythrin andscanned with the Hewlett-Packard GeneArrayA Scanner (Hewlett-Packard,Santa Clara, CA). The GeneChipA Microarray Suite v4.01 (Affymetrix, Inc)was used to generate the subsequent data used for statistical evaluation.
Reproducibility of the microarray data
Using this protocol we performed a blinded reproducibility study with twoof the POD 5 allograft samples. Total RNA from each POD 5 sample wasaliquoted into four separate reactions and prepared as illustrated in Table1.All eight total RNA aliquots were subjected to separate fragmentation,labeling and hybridization to Affymetrix U34A GeneChipsA. The RNA ali-quots in Assay II were processed for hybridization approximately 3weeksafter the Assay I samples. Variance components analyses (16) were per-formed using the log average ratio (LAR) values for the entire U34A data-set of each sample. Based upon the small number of samples, these analy-ses showed the hybridization measures across probe sets to be measur-ably different for the separate samples, although it was found that theintra-assay components (chip, sample processing, etc.) contributed abouthalf or more of the total variability to the system.
Statistical analysis of microarray data
The basic design of the GeneChipA is a comparison of perfect match (PM)oligonucleotides (25 mer sequences believed to represent portions of theactual genomic DNA sequence) to that of mismatch (MM) oligonucle-otides containing a single base mismatch at the center position. Eachmicroarray contains a probe set of approximately 20 PM/MM pairs, for aparticular transcript. The manufacturer has selected the probe pairs tomaximize sensitivity and specificity when considered as a set. A probe pairwith PM minus MM values greater than three standard deviations from themean of the probe set are considered outliers and are eliminated from the
Table1: Design of reproducibility
Allograft AllograftPOD5–1 POD5–2
Assay I: A (8mg) A (8mg)B (8mg) B (8mg)
Assay II: C (8mg) C (8mg)D (8mg) D (8mg)
914 American Journal of Transplantation 2002; 2: 913–925
analysis. After taking into account background and variability, the valuesfrom the remaining probe pairs are reduced to a single numerical value bythe GeneChipA Microarray Suite. This value, or measure of expression, canbe represented as either the average difference (AD) or the LAR.
Rationale for statistical approach
Since the statistical approach to microarray data is one of the major thrustsof the paper, a discussion of our rationale is in order. The AD is commonlyused as the measure of transcript expression and is calculated by the meanfluorescence of the PM – MM for a probe set. By using the AD values fromtwo microarrays, the GeneChipA Microarray Suite can calculate a fold-change (FC) or semi-quantitative measure of the change in expression foreach transcript. Transcripts usually are compared by FC differences with a 3-fold-change increase or decrease considered significant. While this ap-proach simplifies data analysis, it has several limitations. We believe that thisapproach is likely to underestimate the number of genes altered. For ex-ample, a transcript is only deemed significantly altered when it shows a 3-fold increase (or decrease) in every pairwise comparison performed. Giventhe combined variability of biological systems, RNA preparations andmicroarray technology, it is likely that the number of transcripts deemed al-tered would decrease to near zero as the sample size (number of pairwisecomparisons) increases, thus discarding many significant alterations in geneexpression. In addition, because of a discontinuity between ª1 and 1 andthe high influence of outliers, FC is not well suited to analysis with standardstatistical methodology. Our approach has been to use the second measureof expression, the LAR, to develop a hybridization index as described below.This measure of relative fluorescence allows for the use of more conventionalstatistical analysis and generation of p-values.
Detailed statistical methods
The LAR is also calculated by the GeneChipA Microarray Suite and as illus-trated in the Affymetrix Expression Analysis Manual is based upon the ratioof PM/MM, specifically
LARΩ10¿ (( log10 PM/MM)/. pairs tested)
The LAR, based upon the log transformation, by its very nature, transformsmultiplicative effects to additive effects. Thus the LAR also changes multi-plicative errors, which are commonly observed in bioassays, to additiveerrors, rendering data amenable to standard statistical analyses. Import-antly, LAR is not intended to be an absolute quantitative measure of theexpression level for a transcript. Instead it is a relative measure used whencomparing two groups.
The average expression of a probe set for a group is represented as theaverage of the LAR∫standard error, hereafter termed the HybridizationIndex (HI). The HI for a transcript can be compared to the HI for the sametranscript in another group using the two-sided Student’s t-test for thedifference of means where p 0.05 is considered statistically significant.We have termed this the change in HI (DHI) such that: DHIΩHIallografts ªHIisografts. For each POD the p-value and DHI were calculatedfor each transcript. A DHI of 1.0 or more suggests that there is about a26% difference or more in the mean PM/MM hybridization of allograftsvs. isografts (this measure does not account for the standard errors of thegroups). A major advantage of this method is its justification based uponfundamental statistical principles.
There are two situations where we have excluded significant DHI data.First, because a HI less than ª1 indicates that the MM probe sets had amuch higher fluorescence than the PM, we omitted transcripts from thisgroup. Second, since the lower limits of detection of the Affymetrix systemare not well known, we had concerns about data in which the HI of bothgroups is low. Therefore, transcripts with HI between ª1 and π1 for bothallografts and isografts were considered to be below the level of detection.
Gene Expression During Acute Rejection
Figure1: Histology of cardiac grafts. Hematoxylin and Eosin staining (¿100) of cardiac grafts on POD 3, 5 and 7. Isografts (panels A–C) showed normal staining patterns, with no evidence of interstitial edema or inflammation. Allografts (panels D–F) showed increasingamounts of interstitial infiltrate and myocyte necrosis.
All statistical analyses were performed using the statistical software pack-age SAS (SAS Institute Inc, Cary, NC).
Gene expression by RT-PCR
Semi-quantitative RT-PCR was performed on 13 significant transcripts forvalidation of the microarray data. Probe sequences from the U34A wereused to create primers for two-step RT-PCR. The rat transcripts investi-
Table2: Summary of the significant changes identified at each timepoint
915American Journal of Transplantation 2002; 2: 913–925
gated were: Perforin, VEGF, Decorin, Fas-ligand, Allograft InflammatoryFactor (AIF), IL-10, Fibronectin 1, Heme Oxygenase 1 (HO1), Homeobox 1,Transforming Growth Factor-b1 (TGF-b1), SC1, Fc-g receptor andEST197895. Additionally, the starting amount of cDNA was normalizedusing primers for GAPDH and the quantity of DNA contamination assessedwith primers for b-actin. Reverse transcription was performed using 1mgof total RNA with the ProSTARTM First-Strand RT-PCR kit (Stratagene, La
Stegall et al.
Table3: Most activated transcripts at each timepoint
916 American Journal of Transplantation 2002; 2: 913–925
Gene Expression During Acute Rejection
Table3: Continued
Probe set Transcript description DHIA,B p-value
18. L25387_g_at Phosphofructokinase C (PFK-C) 5.77∫0.63 ∞0.00119. M57276_at Leukocyte antigen MRC-OX44 5.75∫0.32 ∞0.00120. M36151cds_s_at MHC class II A-beta RT1.B-b-beta 5.73∫0.34 0.001
A Transcripts with isograft HI values less than 1 and allograft HI values greater than 1 are identified by plain text, B Transcripts in bold haveHI values for the isografts and allografts greater than one.
Figure2: Kinetics of the most significantly increased transcripts. AIF, HO1, Fc-gamma, TGF-b1, Thymocyte L-CA and IRF-1 mRNAexpression for isografts and allografts at POD3, 5 and 7. For each transcript, the differences in expression were statistically significant (p0.05) at all three timepoints. To assess variability, error bars representing the min/max hybridization index value for each transcript havebeen added.
917American Journal of Transplantation 2002; 2: 913–925
Stegall et al.
Jolla, CA) to a final reaction volume of 50mL. PCR was performed with2.5mL of cDNA, 1.5 units of Taq DNA polymerase (PE Biosystems, FosterCity, CA), 2.5mL PCR Buffer II (PE Biosystems), 2.0mM MgCl2, 0.2mM eachdNTP (Roche, Indianapolis, IN) and 0.4mM of each primer. PCR productswere electrophoresed through 2% agarose, stained with ethidium bromideand visualized by Gel Doc 2000A (Bio-Rad, Hercules, CA). For each primerpair the linear range of amplification was determined by densitometry.
Results
Histology
Minimal lymphocytic infiltration was noted on POD 3 in the
Figure3: Kinetics of the most significantly decreased transcripts. Lumican, Collagen IIIa1, Cytochrome C oxidase, Decorin, SC1 andGelatinase mRNA expression for isografts and allografts at POD 3, 5 and 7. Although all transcripts showed significant differences by POD7, only Lumican, Collagen IIIa1 and Cytochrome C oxidase expression were significant (p 0.05) at all three timepoints. Error bars illustratethe min/max hybridization index value for each transcript.
918 American Journal of Transplantation 2002; 2: 913–925
allografts and no infiltration was seen on any POD in theisografts by H&E stain (Figure 1). A progressive, denselymphocytic infiltrate was noted by POD 5 in all allograftswith microvascular thrombosis by POD 7, verifying that severeacute cellular rejection occurred in all of the allograft hearts.
Comparing LAR and AD to measure expression
In the current studies we used the LAR rather than the AD tomeasure transcript expression. We inspected data from 18microarrays, selected 35 probe sets hypothesized to be relatedto rejection using probability plots and observed that the LAR
Gene Expression During Acute Rejection
was more nearly normally distributed than AD. This is in accordwith what would be expected for a process where effects anderrors tend to be multiplicative rather than additive. Thereforewe feel LAR provides a more representative estimate of thelevel of transcript expression. Also, the use of LAR as ameasure of expression provides the opportunity to determinethe level of significance of a particular transcript through theuse of traditional statistical analyses and allows for the com-parison between multiple samples in each group.
Criteria for determining increased and decreased ex-
pression
As previously described, we excluded some of the microar-ray data if they did not meet certain criteria. For example,transcripts were excluded if the HI of one or both groupswas less than ª1, suggesting that the MM hybridizationwas greater than the PM hybridization. Taking POD 3 asan example, the HI for both the isograft and the allograftgroups were ª1 in 80 transcripts (0.9%) and 11 werefound to be significantly decreased using the Student’s t-test (Table2). In addition, the HI of one group, but not theother was found to be ª1 in 46 transcripts (1.9%) and46 of these were found significantly altered (45 decreasedand 1 increased). Finally, we also excluded data whentranscripts in both groups showed low expression (HI be-tween ª1 and 1) because we believed that this low levelof expression might often be below the level of detectionfor the microarray system. On POD 3, 3577 (40.7%) tran-scripts were in this category, suggesting that over half ofthe transcripts tested by the microarray had little or no
Figure4: Linear range assessment of Fc-gamma (A) and Decorin (B). Semi-quantitative RT-PCR was performed to confirm trendsidentified by the GeneChipsA. For each transcript, allograft, isograft and normal RNA was used to determine the linear range of PCRamplification. During PCR amplification, samples were removed at the indicated PCR cycle, products separated via electrophoresis, stainedwith ethidium bromide and analyzed via densitometry. The center of the linear range of PCR amplification for fc-gamma was cycle 27 andcycle 24 for decorin.
919American Journal of Transplantation 2002; 2: 913–925
expression in both groups at this timepoint. Of this group,408 transcripts (320 increased and 88 decreased) weresignificantly altered but excluded due to their overall lowexpression.
Several observations support the use of these exclusion cri-teria. First, the DHI between isografts and allografts usingthese criteria was generally small – it must be less than 2by definition. Second, the p-values of these significant butexcluded transcripts generally were higher than those thatwere included for further analysis. No p-values were 0.001in the excluded group, while p-values 0.001 were commonin the included group. Finally, a relatively small percentage ofthose excluded on POD 3 were significant (12.2%, 465 of3824), while a large percentage of those included were sig-nificant (57.6%, 2868 of 4975). Similarly, we excluded sig-nificant transcripts using the same criteria on POD 5 (nΩ424) and POD 7 (nΩ754) (Table2).
After the above exclusions, 2864 transcripts were found tobe significantly increased on POD 3 in allografts comparedto isografts. In 942 of these transcripts, the HI of the isograftwas1 while the HI in allograft rejection was1. This repre-sents transcripts that were only minimally expressed in isog-rafts but significantly increased during allograft rejection. Inanother 1922 significantly increased transcripts, both the HIof the isograft and the allograft groups were 1. This repre-sents transcripts that were expressed at detectable levels inthe isografts, but were increased during allograft rejection. ByPOD 5, 1418 transcripts were significantly increased (451
Stegall et al.
Figure5: Analysis of normal, isograft and allograft mRNA expression by RT-PCR. After determining the linear range of PCR amplifi-cation for significant transcripts, a comparison between normal, isograft (POD3, 5 and 7) and allograft (POD3, 5 and 7) samples was madefor decorin, IL-10, HO1, TGF-b1 and SC1. PCR products were electrophoresed through 2% agarose and visualized with ethidium bromide. Ingeneral, trends in mRNA expression identified with the GeneChipA correlated well with RT-PCR. GAPDH primers were used as housekeepingcontrol to assess the amount of cDNA in each reaction.
with minimal expression in the isografts and 967 with moder-ate expression in the isografts). By POD 7, 2745 transcriptswere significantly increased (1188 with minimal expression inisografts and 1557 with moderate expression in isografts).
A slightly different picture emerges when transcripts with de-creased expression are studied. Only 4 transcripts were sig-nificantly decreased on POD 3 and 3 of these were ex-pressed significantly in isografts and then only minimally inallografts. By POD 5, 572 transcripts were significantly de-creased in the acute rejection group, including 211 transcriptsdecreased to minimally detectable levels and 361 transcriptsdecreased from high levels in isografts to lower levels in allo-grafts (HI of isograft and allografts both 1). By POD 7, 105transcripts were significantly decreased – 50 decreased tominimally detectable levels and 55 decreased from highlevels to lower levels in allografts.
Transcripts most increased during acute allograft re-
jection
Table3 shows the most increased transcripts at all three time-
920 American Journal of Transplantation 2002; 2: 913–925
points. The transcripts are sorted by DHI and classified bythe minimum HI criteria. Many of the most up-regulated tran-scripts on POD 3 were those commonly associated with in-flammatory processes including interferon regulatory factor 1(IRF-1), MHC Class I and II antigens and AIF. All of thesegenes remained up-regulated at POD 5 and 7 and wereamong the most significantly increased at these later time-points. Some of the most significantly up-regulated geneswere those that have never been associated with allograftrejection such as isoprenylated 67kDa protein.
The probe sets increased on POD5 and POD7 appear to con-tinue the association with inflammation. HO1, for example,was up-regulated more by POD 5 and 7 than on POD 3.Other probe sets were generally up-regulated at all threetimepoints (AIF, TGF-b1, Fc-g receptor, thymocyte L-CA andIRF-1 (Figure 2).
Transcripts most decreased during acute allograft re-
jection
Table4 shows the most decreased transcripts at all three
Gene Expression During Acute Rejection
timepoints sorted by DHI and classified by the minimum HIcriteria. The p-values of the 4 transcripts decreased on POD3 ranged from 0.006 to 0.048, while the p-values of theother 20 most-decreased transcripts on POD5 and 7 weregenerally smaller with nearly all0.001.
The kinetics of selected decreased genes are shown in Figure3. On POD 3, a group of genes not commonly associatedwith rejection were significantly down-regulated includingmRNA up-regulated during prostatic apoptosis, phosphacanmRNA, prolactin receptor and follicle stimulating hormone re-ceptor. On POD 5, the SC1 protein was the most down-regu-lated along with three genes involved in fatty acid transport.By POD7 the SC1 was still decreased along with decorin, anantagonist of TGFb1.
In contrast to many of the up-regulated genes that were in-creased at all timepoints, the genes most down-regulated byPOD 7 were not down-regulated on POD 3. These genesinclude: lumican, collagen IIIa1, cytochrome C oxidase, deco-rin, SC1 and gelatinase (Figure 3).
Verifying microarray gene expression data with RT-
PCR
To confirm that microarray data actually represented mRNAexpression, we performed RT-PCR on 13 transcripts. Tran-scripts with high induction (nΩ3), mild induction (nΩ4),minimal changes (nΩ3) and high down-regulation (nΩ3)were tested. First, the linear range assessment was per-formed using semi-quantitative RT-PCR with primers de-signed in our laboratory. A sample of these linear range as-sessments is shown in Figure 4. Figure5 shows that the RT-PCR results were visually correlative with what was observedin the microarray. For 10 of the transcripts, the microarraydata correctly predicted the magnitude and direction of thechange identified by RT-PCR. In three up-regulated tran-scripts (Homeobox 1, VEGF and Fas Ligand), the direction(up/down) of expression was consistent, but the magnitudeof the difference was errant. Further investigation of themicroarray data showed the HI values for these transcriptswere negative for both the allograft and isograft samples. Thisfurther supports that transcripts with negative HI values maybe below the level of sensitivity of the system and cautionshould be taken when interpreting data from these tran-scripts.
Expression of known ‘transplant-related’ genes
We selected prospectively a subset of 400 transcripts iden-tified as known to be associated with acute cellular rejection.These transcripts were primarily MHC molecules, cytokines,chemokines, adhesion molecules and ECM proteins. Signifi-cant changes in expression were identified on POD 3 (157),5 (131) and 7 (166). Table5 shows that 95 of these 400 tran-scripts were significant throughout the study of acute rejec-tion.
The entire dataset and RT-PCR sequences may be obtainedelectronically from [email protected].
921American Journal of Transplantation 2002; 2: 913–925
Discussion
The use of high-throughput microarrays promises to providedetailed measurements of gene expression on an unpre-cedented global scale. However, skeptics rightly criticize theinterpretation of microarray data obtained from complex bio-logical systems because of sampling variability, assay vari-ability and difficulties in managing the enormous amount ofdata. The current study was an initial attempt to address someof the fundamental questions of microarray technology usinga well-described rat heterotopic cardiac allograft rejectionmodel.
Given the homogeneity of the model system and the purityof mRNA isolated, run-to-run variability was found to be quitelow. Our statistical approach using the HI based on the LARallowed for the evaluation of the large amount of microarraydata. The HI also allowed for the censoring of some signifi-cant results when hybridization was very low or when thehybridization of the MM primers was greater than that of thePM ones. In addition, the HI could generally classify transcriptexpression as very low (HI1) or detectable to high (HI1).
Using these methods, thousands of transcripts were found tobe significantly altered in cardiac allografts undergoing acuterejection at various timepoints after transplantation. For ex-ample, 1418 transcripts were determined to be significantlyincreased by POD 5 during acute rejection – 451 increasingfrom minimal or no expression and 967 increasing from alower but detectable level in isografts. Also at POD 5, 572transcripts were significantly decreased – 211 decreased toundetectable levels and 361 decreased from higher levels inisografts to lower levels in the allograft group. Of note, com-parisons between HI and mean FC were at most only weaklycorrelated. Using FC would have incorrectly predicted themagnitude and direction of the change for 38% of the POD7 transcripts we verified by RT-PCR (data not shown).
Our approach resulted in the identification of many more sig-nificantly altered transcripts compared to two previous reportswhich used FC to evaluate microarray data from acute rejectionmodels (13,14). In a mouse cardiac allograft model, only 84 of10000 transcripts were reported as increased on POD 5. Tran-scripts that showed a 3-fold increase in every one of the threerejecting grafts when compared individually to the three isog-rafts were considered ‘reliable’ for consideration (13). Only 10of 7129 probe sets were reported as increased during acute re-jection of human renal allografts. Transcripts from seven renalallograft biopsies were sequentially compared to three normalrenal allograft biopsies, yielding only four transcripts that wereconsistently up-regulated (14). We believe that given the strik-ing pathologic changes of acute rejection, these results seemunlikely. Further, given the variability of biological systems andthe microarray technology, it is unlikely that any significantalterations in transcript expression would be found using theFC method as the sample size increases.
The LAR, as used in the current studies, has several ad-
Stegall et al.
Table4: Most down-regulated transcripts at each timepoint
Probe set Transcript description DHIA,B p-value
POD31. U07610_at Zinc finger protein (HF-1b) ª1.32∫0.56 0.0482. D63886_s_at Rat MT3-MMP-del ª0.94∫0.33 0.0223. rc_AI639417_at Rat cDNA clone rx02173 3 ª0.89∫0.37 0.0404. rc_AA926193_at R Rat cDNA clone UIA1evh020UI.s1 ª0.79∫0.21 0.006
POD51. U27562_at SC1 protein mRNA ª4.34∫0.27 0.0012. rc_AA893242_g_at EST197045 Rat cDNA clone ª4.09∫0.79 0.0013. AB005743_g_at Rat mRNA for fatty acid transporter ª3.95∫0.54 0.0014. AB005743_at Rat mRNA for fatty acid transporter ª3.81∫0.61 ∞0.0015. rc_AI169612_at EST215498 Rat cDNA clone ª3.79∫0.55 ∞0.0016. X67948_at Channel integral membrane protein 28 ª3.62∫0.34 ∞0.0017. rc_AA799666_g_at EST189163 Rat cDNA clone ª3.62∫1.16 0.0148. U08976_at Wistar peroxisomal enoyl hydratase-like protein ª3.29∫0.58 ∞0.0019. rc_AI639162_at Rat cDNA clone rx01122 3 ª3.26∫0.44 0.001
10. M21354_s_at Collagen type III alpha-1 ª3.03∫0.51 ∞0.00111. X70369_s_at Pro alpha 1 collagen type III ª3.03∫0.83 0.00612. D00680_at Plasma glutathione peroxidase (EC 1.11.1.9) ª2.96∫0.44 ∞0.00113. U08976_at Wistar peroxisomal enoyl hydratase-like protein ª2.88∫0.91 0.01314. rc_AA851223_at EST193991 Rat cDNA clone ª2.66∫0.52 0.00115. rc_AI012030_at EST206481 Rat cDNA clone ª2.60∫0.50 0.00116. X84039_at Lumican mRNA ª2.60∫0.70 0.00617. D89730_g_at Rat T16 mRNA ª2.54∫0.62 0.00318. Z24721_at Rat SOD gene ª2.34∫0.43 0.00119. rc_AA800190_at EST189687 Rat cDNA clone ª2.34∫0.54 0.00220. X64827cds_s_at Cytochrome c oxidase (subunit VIII-h) ª2.29∫0.40 0.001
A Transcripts with allograft HI values less than 1 and isograft HI values greater than 1 are identified by plain text, B Transcripts in bold haveHI values for the isografts and allografts greater than one.
ditional advantages over the FC. Using this measure we candetermine relative changes in expression, DHI, between twogroups. We can also determine which transcripts are signifi-cantly changing between the two groups by using the Stu-dent’s t-test to calculate the p-value. Further, we can easilyselect and remove outliers which fall outside the criteria of apredetermined minimum hybridization ratio. These calcu-lations allow for expedient analysis of microarray data.
922 American Journal of Transplantation 2002; 2: 913–925
A detailed presentation and analysis of such a large amountof gene expression data is beyond the scope of this initialmanuscript. Using microarrays we identified a large cohort ofsignificantly altered genes at each timepoint. Most of thesehave not been previously reported and therefore may or maynot be related to acute rejection. Instead, these changes maybe due to downstream events and ‘side-effects’ unrelated tothe rejection process. However, a general assessment of the
Gene Expression During Acute Rejection
Table5: 95 Transplant related transcripts (significant at all timepoints)
Probe set Transcript description POD3 (DHI) POD5 (DHI) POD7 (DHI)
10. AF030358_g_at Chemokine CX3C 2.26∫0.71 1.33∫0.29 3.15∫0.4711. U77349cds_at Chemokine receptor CCR2 gene 2.84∫0.45 2.82∫0.98 1.24∫0.3512. U16025_at Class Ib RT1 mRNA 1.85∫0.34 1.61∫0.24 2.29∫0.3213. U90610_g_at CXC chemokine receptor (CXCR4) 1.18∫0.36 0.92∫0.35 3.02∫0.3714. C07012_f_at Cyclophilin C mRNA 1.19∫0.20 1.44∫0.19 2.12∫0.1715. X52815cds_f_at Cytoplasmic-gamma isoform of actin 2.07∫0.38 1.17∫0.30 2.56∫0.1916. X05834_at Fibronectin gene 2.31∫0.50 1.40∫0.58 3.86∫0.3117. D86641_at FK506-binding protein 12 1.25∫0.36 0.92∫0.24 1.65∫0.3318. L01624_at Glucocorticoid-regulated kinase (sgk) 1.72∫0.56 2.34∫0.26 4.11∫0.4919. X66693_f_at Granzyme-like protein I 1.39∫0.60 3.56∫0.37 2.41∫0.2620. J02722cds_at Heme oxygenase 2.45∫0.48 5.37∫0.42 5.92∫0.3821. X14323cds_at IgG receptor FcRn large subunit p51 0.67∫0.18 ª0.87∫0.19 ª0.66∫0.2722. E01884cds_s_at IL-1-beta 2.22∫0.55 2.64∫0.32 1.44∫0.1923. M14050_s_at Immunoglobulin heavy chain binding protein 3.15∫0.80 1.18∫0.48 2.93∫0.6024. U59801_at Integrin alpha-M (Itgam) 1.14∫0.27 0.97∫0.42 2.64∫0.4325. AF003598_at Integrin beta-7 subunit 0.97∫0.29 0.96∫0.22 1.00∫0.1726. AF017437_g_at Integrin-associated protein form 4 (IAP) 2.68∫0.44 1.81∫0.51 2.11∫0.5327. M34253_at Interferon regulatory factor 1 (IRF-1) 4.36∫0.82 4.59∫0.31 4.06∫0.3028. M34253_g_at Interferon regulatory factor 1 (IRF-1) 5.11∫0.69 5.05∫0.42 5.95∫0.4229. U77777_s_at IFN-gamma inducing factor isoform a 3.60∫0.88 4.89∫0.54 6.20∫0.2830. M98820_at IL-1-beta 3.38∫0.94 3.21∫0.34 2.70∫0.4131. M98820_g_at IL-1-beta 2.15∫0.60 2.19∫0.55 1.27∫0.4432. X69903_at IL-4 receptor 1.27∫0.27 0.68∫0.14 1.72∫0.2633. M58587_at IL-6 receptor ligand binding chain 1.67∫0.48 1.38∫0.52 2.22∫0.3334. U14647_at IL-1 beta converting enzyme 2.65∫0.57 3.39∫0.52 3.48∫0.3535. U14647_g_at IL-1 beta converting enzyme 1.96∫0.71 2.74∫0.57 3.33∫0.2536. S79676_s_at IL-1 beta-converting enzyme 3.03∫0.56 3.18∫0.36 3.82∫0.3337. M55050_at IL-2 receptor beta chain 2.46∫0.54 3.03∫0.26 1.73∫0.2838. S79263_s_at IL-3 receptor beta-subunit 1.16∫0.43 0.81∫0.29 1.50∫0.1139. M82826_i_at Leucopus neurofibromatosis protein type I 0.75∫0.17 0.49∫0.13 0.43∫0.1140. M57276_at Leukocyte antigen MRC-OX44 3.97∫0.44 4.23∫0.54 5.75∫0.3241. M25823_s_at Leukocyte-common antigen (L-CA or CD45) 1.23∫0.27 0.94∫0.23 1.32∫0.1342. S79523_at Lymphocyte membrane protein A.11 2.29∫0.72 3.09∫0.43 2.00∫0.3943. U22414_at Macrophage inflammatory protein-1alpha 1.66∫0.54 3.30∫0.56 2.41∫0.2944. U06434_at Macrophage inflammatory protein-1beta 1.98∫0.44 2.90∫0.30 2.65∫0.2545. S73424_s_at Macrophage migration inhibitory factor 1.53∫0.44 0.96∫0.19 2.22∫0.4246. AF074608mRNA_f_at MHC class I antigen (RT1.EC2) 2.27∫0.29 2.13∫0.19 3.09∫0.2247. AF074609mRNA_f_at MHC class I antigen (RT1.EC3) 2.31∫0.37 1.65∫0.14 2.57∫0.2948. M64795_f_at MHC class I antigen gene (RT1u haplotype) 2.63∫0.36 2.44∫0.24 3.24∫0.2249. M11071_f_at MHC class I cell surface antigen 1.52∫0.40 0.68∫0.23 1.72∫0.4650. L23128_g_at MHC class I mRNA 1.57∫0.36 1.92∫0.31 1.63∫0.2851. L23128_at MHC class I mRNA 1.72∫0.39 1.97∫0.23 1.71∫0.3052. M31038_at MHC class I non-RT1.A alpha-1-chain 3.32∫0.60 4.20∫0.36 4.99∫0.6053. M24026_f_at MHC class I RT1 (RT44) 2.01∫0.31 1.38∫0.36 2.39∫0.3254. M24324_f_at MHC class I RT1 (RTS) 2.15∫0.24 1.96∫0.26 2.46∫0.2755. M31018_f_at MHC class I RT1.Aa alpha-chain 1.92∫0.34 1.35∫0.28 2.03∫0.3556. L40362_f_at MHC class I RT1.C-type protein 1.72∫0.46 1.85∫0.23 2.33∫0.2857. L40364_f_at MHC class I RT1.O type ª149 processed 2.02∫0.37 1.62∫0.18 2.22∫0.3758. M10094_at MHC class I truncated cell surface antigen 1.26∫0.35 1.23∫0.40 1.19∫0.3459. M10094_g_at MHC class I truncated cell surface antigen 2.79∫0.38 2.67∫0.32 3.45∫0.4260. AF025308_f_at MHC class Ib antigen (RT1.Cl) 2.56∫0.31 2.50∫0.20 3.90∫0.31
923American Journal of Transplantation 2002; 2: 913–925
Stegall et al.
Table5: Continued
Probe set Transcript description POD3 (DHI) POD5 (DHI) POD7 (DHI)
61. AF029240_g_at MHC class Ib RT1.S3 (RT1.S3) 2.61∫0.51 2.45∫0.24 2.40∫0.3362. AF029240_at MHC class Ib RT1.S3 (RT1.S3) 3.85∫0.79 4.49∫0.44 4.35∫0.3463. M36151cds_s_at MHC class II A-beta RT1.B-b-beta 4.34∫0.46 3.93∫0.34 5.73∫0.3464. X56596_at MHC class II antigen RT1.B-1 beta-chain 4.22∫0.43 3.32∫0.39 5.15∫0.3665. M15562_g_at MHC class II RT1.u-D-alpha chain 2.00∫0.44 1.01∫0.19 2.43∫0.4266. M15562_at MHC class II RT1.u-D-alpha chain 3.42∫0.37 2.87∫0.26 3.87∫0.5267. X14254cds_at MHC class II-associated invariant chain 3.14∫0.44 2.52∫0.33 3.36∫0.3568. X14254cds_g_at MHC class II-associated invariant chain 3.09∫0.32 2.28∫0.28 3.60∫0.3269. U31598_s_at MHC class II-like alpha chain (RT1.DMa) 2.29∫0.31 2.66∫0.29 4.24∫0.3270. U31599_at MHC class II-like beta chain (RT1.DMb) 2.58∫0.27 2.94∫0.39 4.18∫0.2171. U31599_g_at MHC class II-like beta chain (RT1.DMb) 2.22∫0.39 2.80∫0.21 4.31∫0.2972. X07551cds_s_at MHC RT1.B-alpha gene for class II antigen 3.27∫0.38 3.01∫0.30 4.20∫0.3173. K02815_s_at MHC RT1-B region class II (Ia antigen) A-a 4.21∫0.35 4.16∫0.49 5.02∫0.4374. X55986mRNA_s_at Multicatalytic proteionase subunit L ingensin 1.73∫0.51 1.00∫0.27 2.01∫0.3575. Z14120cds_s_at Platelet-derived growth factor A chain 1.20∫0.28 1.14∫0.38 1.02∫0.2276. AJ222813_s_at Precursor IL-18 2.53∫0.54 4.12∫0.41 4.47∫0.2377. C06598_at Rapamycin-binding protein FKBp-13 0.78∫0.18 0.86∫0.17 0.95∫0.1878. E13732cds_at Rat CC chemokine receptor protein 1.61∫0.69 1.43∫0.35 2.58∫0.3679. M22366_at Rat MHC class II RT1.B-alpha chain 0.77∫0.17 0.70∫0.22 1.21∫0.2080. AF075383_at Suppressor of cytokine signaling-3 1.21∫0.43 1.47∫0.23 1.12∫0.1681. U76836_g_at T cell receptor V alpha 2 chain subunit 1.33∫0.28 2.03∫0.27 0.74∫0.1582. D13555_at T cell receptor zeta chain 1.94∫0.50 2.27∫0.46 1.63∫0.2783. X14319cds_g_at T-cell receptor beta chain 3.35∫0.49 4.76∫0.26 3.32∫0.1884. S75435_i_at T-cell receptor gamma chain 0.87∫0.17 1.21∫0.23 2.07∫0.1785. M10072mRNA_s_at Thymocyte leukocyte common antigen 2.90∫0.82 4.07∫0.58 4.29∫0.2886. M18349cds.1_s_at Thymocyte leukocyte common antigen 2.54∫0.82 3.08∫0.69 3.27∫0.2687. K03039mRNA_s_at Thymocyte leukocyte common antigen 4.77∫1.14 4.85∫0.63 6.34∫0.4488. X04310_at Thymocyte mRNA for CD8 antigen 1.14∫0.31 2.22∫0.23 2.19∫0.2489. X03015_at Thymocyte mRNA for OX-8 antigen 2.31∫0.62 4.00∫0.39 4.19∫0.3190. AJ012603UTR.1_g_at TNF-alpha converting enzyme (TACE) 1.67∫0.33 2.27∫0.41 2.79∫0.3191. AJ012603UTR.1_at TNF-alpha converting enzyme (TACE) 1.54∫0.49 1.44∫0.30 2.41∫0.2392. X52498cds_at Transforming growth factor-beta 1 1.93∫0.29 1.48∫0.34 2.36∫0.2993. M63122_at Tumor necrosis factor receptor 1.97∫0.30 1.47∫0.30 1.58∫0.3894. X63722cds_s_at VCAM-1 2.57∫0.51 1.51∫0.38 1.04∫0.3795. M15768_at W3/25 antigen (homologue of human CD4) 1.28∫0.35 0.69∫0.27 2.47∫0.36
data lends further support to our approach. Many of the tran-scripts found to be significantly up-regulated by the currentmicroarray analysis were those demonstrated to be increasedin previous studies of gene expression in the same rat cardiacallograft model using RT-PCR and Northern blot analyses.This includes molecules such as the MHC molecules, Perfor-in, Fas Ligand, IFN-g, RANTES, ICAM, E-selectin and TNF-a
(6–9,17,18). In addition, we found many other ‘transplant-re-lated genes’ increased at all 3 timepoints. As expected, mostof the up-regulated genes were associated with inflam-mation. We found many seemingly non-inflammatory geneswith unknown function in transplantation were up-regulated.In addition we found many seemingly non-related non-in-flammatory ‘transplant-related genes’.
Interestingly, not all molecules believed to be associated withinflammation were significantly altered in our study. For ex-ample, we did not identify significant changes in gene ex-pression for the cytokines IL-2, IL-4 or IL-10, which have beenshown previously to be involved in various models of trans-plant rejection (19–21). The HI values for these transcripts
924 American Journal of Transplantation 2002; 2: 913–925
consistently placed them into Category C and we thereforeconcluded these molecules were expressed below the levelof detection of the GeneChipA in this model. Although notwithin the scope of our study, it would be possible to monitorchanges in these molecules on an individual basis with asimilar RT-PCR design as the one presented here.
This is the first study of acute rejection either using microar-rays or other techniques that describe genes that are down-regulated in this process. This down-regulation may repre-sent changes secondary to cytokine release by the infiltratingcells or to graft injury by ischemia or other mechanisms. Ofthe down-regulated genes, SC1 and decorin appear to beespecially attractive for further study. Decorin is a small chon-droitin-dermatan sulphate proteoglycan and was recentlyidentified as a potential antagonist of TGF-b1 (22,23). Theabsence of decorin might allow the unimpeded activity ofTGF-b1 in its role of stimulating inflammation and fibrogen-esis. SC1 is an ECM glycoprotein detected in both the devel-oping CNS and adult brain tissue (24). SC1 shares sequencehomology with SPARC, considered to be anti-adhesive be-
Gene Expression During Acute Rejection
cause it can selectively disrupt cellular contacts with the ma-trix (25). Thus, the absence of SC1 might allow for improvedadhesion with the ECM of the graft. No previous studies ofeither decorin or SC1 exist in the transplantation literature.
The finding that a large number of the transcripts were eitherup-regulated or down-regulated Expressed Sequence Tags(EST) is especially interesting. ESTs are genes that show evi-dence of mRNA and protein production for which there isno known function. Without the microarray technology, it isunlikely that these transcripts would be associated with acuterejection, yet they may be found to be crucial for its patho-genesis. Similarly, genes that are associated with a knownbiological process such as mRNA up-regulated during pros-tatic apoptosis (most down-regulated on POD 3 in acute re-jection) or osteopontin (significantly up-regulated on POD 7)will likely lead to a greater understanding of their role in fun-damental biological pathways.
One potential limitation of the microarray that we were unableto address in this analysis is the issue of sequence variants.Given the small length of the probes, it is possible that atleast some of the mRNA labeled as SC1 (or other seeminglyunrelated genes) might actually be SC1-like molecules thatshare sequence homology but actually arise from separategenes or at least represent spliced variants. Further, moredetailed analysis with actual sequencing of both DNA andmRNA will be necessary to answer this question.
We believe that microarray technology as is used in the cur-rent studies of acute rejection is a powerful screening testidentifying numerous targets for further study. An exhaustive,detailed analysis of all the potential targets will take muchmore time and is beyond the scope of this article. Elucidationof the mechanisms of acute rejection will involve a more thor-ough analysis of transcripts that are involved in the fibrogenicprocess. Similarly, the localization of transcripts within theallograft will require other techniques such as in-situ PCR.Finally, verification of these data also will require the demon-stration that protein corresponding to the altered gene is al-tered in the graft.
2. Pascual M, Swinford RD, Ingelfinger JR, Williams WW, Cosimi AB, Tolk-off-Rubin N. Chronic rejection and chronic cyclosporin toxicity in renalallografts. Immunol Today 1998; 19: 514–519.
3. Gao SZ, Alderman EL, Schroeder JS, Silverman JF, Hunt SA. Acceler-ated coronary vascular disease in the heart transplant patient: coronaryangiographic findings. J Am Coll Cardiol 1988; 12: 334–340.
4. Chaparro C, Scavuzzo M, Winton T, Keshavjee S, Kesten S. Status oflung transplant recipients surviving beyond five years. J Heart LungTransplant 1997; 16: 511–516.
5. Dallman M. Immunobiology of graft rejection. In: Ginns L, Cosimi A,Morris P, eds. Transplantation. Malden: Blackwell Science; 1999. pp.23–42.
925American Journal of Transplantation 2002; 2: 913–925
6. Kageyama Y, Li XK, Suzuki S et al. Apoptosis is involved in acute cardiacallograft rejection in rats. Ann Thorac Surg 1998; 65: 1604–1609.
8. Yamazaki S, Isobe M, Suzuki J et al. Role of selectin-dependent ad-hesion in cardiac allograft rejection. J Heart Lung Transplant 1998; 17:1007–1016.
9. Mulligan MS, McDuffie JE, Shanley TP et al. Role of RANTES in experi-mental cardiac allograft rejection. Exp Mol Pathol 2000; 69: 167–174.
10. Schulze A, Downward J. Navigating gene expression using microar-rays – a technology review. Nat Cell Biol 2001; 3: E190–E195.
12. Notterman DA, Alon U, Sierk AJ, Levine AJ. Transcriptional gene expres-sion profiles of colorectal adenoma, adenocarcinoma and normal tissueexamined by oligonucleotide arrays. Cancer Res 2001; 61: 3124–3130.
13. Saiura A, Mataki C, Murakami T et al. A comparison of gene expressionin murine cardiac allografts and isografts by means DNA microarrayanalysis. Transplantation 2001; 72: 320–329.
14. Akalin E, Hendrix RC, Polavarapu G et al. Gene expression analysis inhuman renal allograft biopsy samples using high-density oligoarraytechnology. Transplantation 2001; 72: 948–953.
15. Ono K, Lindsey E. Improved technique of heart transplantation in rats. JThorac Cardiovasc Surg 1969; 57: 225–229.
17. Pascoe MD, Marshall SE, Welsh KI, Fulton LM, Hughes DA. Increasedaccuracy of renal allograft rejection diagnosis using combined perforin,granzyme B and Fas ligand fine-needle aspiration immunocytology.Transplantation 2000; 69: 2547–2553.
18. Segerer S, Cui Y, Eitner F et al. Expression of chemokines and chemo-kine receptors during human renal transplant rejection. Am J Kidney Dis2001; 37: 518–531.
19. Xu XY, Honjo K, Devore-Carter D, Bucy RP. Immunosuppression by inhi-bition of cellular adhesion mediated by leukocyte function-associatedantigen-1/intercellular adhesion molecule-1 in murine cardiac trans-plantation. Transplantation 1997; 63: 876–885.
20. Coito AJ, Korom S, Graser E, Volk HD, Van De Water L, Kupiec-WeglinskiJW. Blockage of very late antigen-4 integrin binding to fibronectin inallograft recipients: I. Treatment with connecting segment-1 peptidesprevents acute rejection by suppressing intragraft mononuclear cell ac-cumulation, endothelial activation, and cytokine expression. Transplan-tation 1998; 65: 699–706.
21. Shirwan H, Barwari L, Khan NS. Predominant expression of T helper 2cytokines and altered expression of T helper 1 cytokines in long-termallograft survival induced by intrathymic immune modulation with do-nor class I major histocompatibility complex peptides. Transplantation1998; 66: 1802–1809.
22. Yamaguchi Y, Mann DM, Ruoslahti E. Negative regulation of trans-forming growth factor-b by the proteoglycan decorin. Nature 1990;346: 281–284.
23. Fischer JW, Kinsella MG, Levkau B, Clowes AW, Wight TN. Retroviraloverexpression of decorin differentially affects the response of arterialsmooth muscle cells to growth factors. Arterioscler Thromb Vasc Biol2000; 21: 777–784.
24. Johnston IG, Paladino T, Gurd JW, Brown IR. Molecular cloning of SC1:a putative brain ECM glycoprotein show in partial similarity to osteonec-tin/BM40/SPARC. Neuron 1990; 4: 165–176.
25. Mothe AJ, Brown IR. Selective transport of SC1 mRNA, encoding aputative ECM glycoprotein, during postnatal development of the rat cer-ebellum and retina. Mol Brain Res 2000; 76: 73–84.
